BitcoinPredictionRNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bitcoin Prediction Using ARIMA and RNN-LSTM\n",
    "## Please refer to the project report for details on results and observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.5.11\n",
      "  latest version: 4.5.12\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/ec2-user/anaconda3/envs/python3\n",
      "\n",
      "  added / updated specs: \n",
      "    - pytorch-nightly-cpu\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    pytorch-nightly-cpu-1.0.0.dev20181220|      py3.6_cpu_0        44.3 MB  pytorch\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "    ninja:               1.8.2-py36h6bb024c_1                     \n",
      "    pytorch-nightly-cpu: 1.0.0.dev20181220-py3.6_cpu_0 pytorch    \n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    certifi:             2018.8.24-py36_1              conda-forge --> 2018.11.29-py36_0\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "pytorch-nightly-cpu- | 44.3 MB   | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "Solving environment: failed\n",
      "\n",
      "PackagesNotFoundError: The following packages are not available from current channels:\n",
      "\n",
      "  - torchvision-nightly-cpu\n",
      "\n",
      "Current channels:\n",
      "\n",
      "  - https://conda.anaconda.org/fastai/linux-64\n",
      "  - https://conda.anaconda.org/fastai/noarch\n",
      "  - https://repo.anaconda.com/pkgs/main/linux-64\n",
      "  - https://repo.anaconda.com/pkgs/main/noarch\n",
      "  - https://repo.anaconda.com/pkgs/free/linux-64\n",
      "  - https://repo.anaconda.com/pkgs/free/noarch\n",
      "  - https://repo.anaconda.com/pkgs/r/linux-64\n",
      "  - https://repo.anaconda.com/pkgs/r/noarch\n",
      "  - https://repo.anaconda.com/pkgs/pro/linux-64\n",
      "  - https://repo.anaconda.com/pkgs/pro/noarch\n",
      "\n",
      "To search for alternate channels that may provide the conda package you're\n",
      "looking for, navigate to\n",
      "\n",
      "    https://anaconda.org\n",
      "\n",
      "and use the search bar at the top of the page.\n",
      "\n",
      "\n",
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.5.11\n",
      "  latest version: 4.5.12\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/ec2-user/anaconda3/envs/python3\n",
      "\n",
      "  added / updated specs: \n",
      "    - fastai\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    preshed-2.0.1              |   py36he6710b0_0          84 KB\n",
      "    fastai-1.0.34              |             py_1         110 KB  fastai\n",
      "    tqdm-4.28.1                |   py36h28b3542_0          61 KB\n",
      "    regex-2018.08.29           |   py36h7b6447c_0         348 KB\n",
      "    spacy-2.0.16               |   py36h962f231_0        47.7 MB\n",
      "    msgpack-numpy-0.4.3.2      |           py36_0          14 KB\n",
      "    ujson-1.35                 |   py36h14c3975_0          26 KB\n",
      "    cymem-2.0.2                |   py36hfd86e86_0          39 KB\n",
      "    thinc-6.12.0               |   py36h4989274_0         1.6 MB\n",
      "    dill-0.2.8.2               |           py36_0         112 KB\n",
      "    plac-0.9.6                 |           py36_0          36 KB\n",
      "    dataclasses-0.6            |             py_0          19 KB  fastai\n",
      "    murmurhash-1.0.1           |   py36he6710b0_0          18 KB\n",
      "    fastprogress-0.1.18        |             py_0          13 KB  fastai\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        50.2 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "    cymem:         2.0.2-py36hfd86e86_0            \n",
      "    dataclasses:   0.6-py_0                  fastai\n",
      "    dill:          0.2.8.2-py36_0                  \n",
      "    fastai:        1.0.34-py_1               fastai\n",
      "    fastprogress:  0.1.18-py_0               fastai\n",
      "    msgpack-numpy: 0.4.3.2-py36_0                  \n",
      "    murmurhash:    1.0.1-py36he6710b0_0            \n",
      "    plac:          0.9.6-py36_0                    \n",
      "    preshed:       2.0.1-py36he6710b0_0            \n",
      "    regex:         2018.08.29-py36h7b6447c_0       \n",
      "    spacy:         2.0.16-py36h962f231_0           \n",
      "    thinc:         6.12.0-py36h4989274_0           \n",
      "    tqdm:          4.28.1-py36h28b3542_0           \n",
      "    ujson:         1.35-py36h14c3975_0             \n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    libstdcxx-ng:  7.2.0-hdf63c60_3                 --> 8.2.0-hdf63c60_1\n",
      "    mkl:           2018.0.2-1                       --> 2018.0.3-1      \n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "preshed-2.0.1        | 84 KB     | ##################################### | 100% \n",
      "fastai-1.0.34        | 110 KB    | ##################################### | 100% \n",
      "tqdm-4.28.1          | 61 KB     | ##################################### | 100% \n",
      "regex-2018.08.29     | 348 KB    | ##################################### | 100% \n",
      "spacy-2.0.16         | 47.7 MB   | ##################################### | 100% \n",
      "msgpack-numpy-0.4.3. | 14 KB     | ##################################### | 100% \n",
      "ujson-1.35           | 26 KB     | ##################################### | 100% \n",
      "cymem-2.0.2          | 39 KB     | ##################################### | 100% \n",
      "thinc-6.12.0         | 1.6 MB    | ##################################### | 100% \n",
      "dill-0.2.8.2         | 112 KB    | ##################################### | 100% \n",
      "plac-0.9.6           | 36 KB     | ##################################### | 100% \n",
      "dataclasses-0.6      | 19 KB     | ##################################### | 100% \n",
      "murmurhash-1.0.1     | 18 KB     | ##################################### | 100% \n",
      "fastprogress-0.1.18  | 13 KB     | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "Collecting tensorflow\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/22/cc/ca70b78087015d21c5f3f93694107f34ebccb3be9624385a911d4b52ecef/tensorflow-1.12.0-cp36-cp36m-manylinux1_x86_64.whl (83.1MB)\n",
      "\u001b[K    100% |████████████████████████████████| 83.1MB 624kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.13.3 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow) (1.14.5)\n",
      "Collecting grpcio>=1.8.6 (from tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3b/bb/701d879849c938028c09fdb5405dbde7c86644bbbb90098094002db23ded/grpcio-1.17.1-cp36-cp36m-manylinux1_x86_64.whl (10.1MB)\n",
      "\u001b[K    100% |████████████████████████████████| 10.1MB 5.8MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting tensorboard<1.13.0,>=1.12.0 (from tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bd/7e/528c868bb8a0542c8a5686ff3a08502d2691bd50499c6e55f8989fa8e5a0/tensorboard-1.12.1-py3-none-any.whl (3.0MB)\n",
      "\u001b[K    100% |████████████████████████████████| 3.1MB 20.7MB/s ta 0:00:01\n",
      "\u001b[?25hCollecting astor>=0.6.0 (from tensorflow)\n",
      "  Downloading https://files.pythonhosted.org/packages/35/6b/11530768cac581a12952a2aad00e1526b89d242d0b9f59534ef6e6a1752f/astor-0.7.1-py2.py3-none-any.whl\n",
      "Collecting keras-applications>=1.0.6 (from tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3f/c4/2ff40221029f7098d58f8d7fb99b97e8100f3293f9856f0fb5834bef100b/Keras_Applications-1.0.6-py2.py3-none-any.whl (44kB)\n",
      "\u001b[K    100% |████████████████████████████████| 51kB 31.9MB/s ta 0:00:01\n",
      "\u001b[?25hCollecting gast>=0.2.0 (from tensorflow)\n",
      "  Downloading https://files.pythonhosted.org/packages/5c/78/ff794fcae2ce8aa6323e789d1f8b3b7765f601e7702726f430e814822b96/gast-0.2.0.tar.gz\n",
      "Requirement already satisfied: six>=1.10.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow) (1.11.0)\n",
      "Requirement already satisfied: wheel>=0.26 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow) (0.31.1)\n",
      "Collecting absl-py>=0.1.6 (from tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0c/63/f505d2d4c21db849cf80bad517f0065a30be6b006b0a5637f1b95584a305/absl-py-0.6.1.tar.gz (94kB)\n",
      "\u001b[K    100% |████████████████████████████████| 102kB 39.3MB/s a 0:00:01\n",
      "\u001b[?25hCollecting protobuf>=3.6.1 (from tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c2/f9/28787754923612ca9bfdffc588daa05580ed70698add063a5629d1a4209d/protobuf-3.6.1-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)\n",
      "\u001b[K    100% |████████████████████████████████| 1.1MB 28.0MB/s ta 0:00:01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[?25hCollecting termcolor>=1.1.0 (from tensorflow)\n",
      "  Downloading https://files.pythonhosted.org/packages/8a/48/a76be51647d0eb9f10e2a4511bf3ffb8cc1e6b14e9e4fab46173aa79f981/termcolor-1.1.0.tar.gz\n",
      "Collecting keras-preprocessing>=1.0.5 (from tensorflow)\n",
      "  Downloading https://files.pythonhosted.org/packages/fc/94/74e0fa783d3fc07e41715973435dd051ca89c550881b3454233c39c73e69/Keras_Preprocessing-1.0.5-py2.py3-none-any.whl\n",
      "Collecting markdown>=2.6.8 (from tensorboard<1.13.0,>=1.12.0->tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7a/6b/5600647404ba15545ec37d2f7f58844d690baf2f81f3a60b862e48f29287/Markdown-3.0.1-py2.py3-none-any.whl (89kB)\n",
      "\u001b[K    100% |████████████████████████████████| 92kB 37.4MB/s ta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: werkzeug>=0.11.10 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow) (0.14.1)\n",
      "Requirement already satisfied: h5py in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras-applications>=1.0.6->tensorflow) (2.8.0)\n",
      "Requirement already satisfied: setuptools in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from protobuf>=3.6.1->tensorflow) (39.1.0)\n",
      "Building wheels for collected packages: gast, absl-py, termcolor\n",
      "  Running setup.py bdist_wheel for gast ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/ec2-user/.cache/pip/wheels/9a/1f/0e/3cde98113222b853e98fc0a8e9924480a3e25f1b4008cedb4f\n",
      "  Running setup.py bdist_wheel for absl-py ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/ec2-user/.cache/pip/wheels/18/ea/5e/e36e1b8739e78cd2eba0a08fdc602c2b16a4b263912af8cb64\n",
      "  Running setup.py bdist_wheel for termcolor ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/ec2-user/.cache/pip/wheels/7c/06/54/bc84598ba1daf8f970247f550b175aaaee85f68b4b0c5ab2c6\n",
      "Successfully built gast absl-py termcolor\n",
      "\u001b[31mfastai 1.0.34 requires torchvision-nightly, which is not installed.\u001b[0m\n",
      "\u001b[31mspacy 2.0.16 has requirement numpy>=1.15.0, but you'll have numpy 1.14.5 which is incompatible.\u001b[0m\n",
      "\u001b[31mspacy 2.0.16 has requirement regex==2018.01.10, but you'll have regex 2018.8.29 which is incompatible.\u001b[0m\n",
      "Installing collected packages: grpcio, protobuf, markdown, tensorboard, astor, keras-applications, gast, absl-py, termcolor, keras-preprocessing, tensorflow\n",
      "  Found existing installation: protobuf 3.6.0\n",
      "    Uninstalling protobuf-3.6.0:\n",
      "      Successfully uninstalled protobuf-3.6.0\n",
      "Successfully installed absl-py-0.6.1 astor-0.7.1 gast-0.2.0 grpcio-1.17.1 keras-applications-1.0.6 keras-preprocessing-1.0.5 markdown-3.0.1 protobuf-3.6.1 tensorboard-1.12.1 tensorflow-1.12.0 termcolor-1.1.0\n",
      "\u001b[33mYou are using pip version 10.0.1, however version 18.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: fastai in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (1.0.34)\n",
      "Requirement already satisfied: fastprogress>=0.1.18 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (0.1.18)\n",
      "Requirement already satisfied: matplotlib in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (2.2.2)\n",
      "Requirement already satisfied: numpy>=1.12 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (1.14.5)\n",
      "Requirement already satisfied: pandas in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (0.22.0)\n",
      "Requirement already satisfied: bottleneck in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (1.2.1)\n",
      "Requirement already satisfied: numexpr in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (2.6.5)\n",
      "Requirement already satisfied: Pillow in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (5.2.0)\n",
      "Requirement already satisfied: requests in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (2.20.1)\n",
      "Requirement already satisfied: scipy in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (1.1.0)\n",
      "Requirement already satisfied: spacy==2.0.16 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (2.0.16)\n",
      "Requirement already satisfied: regex in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (2018.8.29)\n",
      "Requirement already satisfied: thinc==6.12.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (6.12.0)\n",
      "Requirement already satisfied: cymem==2.0.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (2.0.2)\n",
      "Collecting torchvision-nightly (from fastai)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/bd/d0f9a33c81c79710eb7ee428b66869b49a8be16c7f1e446c211a7fbfb7be/torchvision_nightly-0.2.1-py2.py3-none-any.whl (54kB)\n",
      "\u001b[K    100% |████████████████████████████████| 61kB 21.4MB/s ta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: typing in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (3.6.4)\n",
      "Requirement already satisfied: pyyaml in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (3.12)\n",
      "Requirement already satisfied: dataclasses in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai) (0.6)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib->fastai) (2.7.3)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib->fastai) (2.2.0)\n",
      "Requirement already satisfied: pytz in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib->fastai) (2018.4)\n",
      "Requirement already satisfied: six>=1.10 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib->fastai) (1.11.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib->fastai) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib->fastai) (1.0.1)\n",
      "Requirement already satisfied: idna<2.8,>=2.5 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests->fastai) (2.6)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests->fastai) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests->fastai) (2018.11.29)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests->fastai) (1.22)\n",
      "Requirement already satisfied: msgpack-numpy<0.4.4 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from spacy==2.0.16->fastai) (0.4.3.2)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from spacy==2.0.16->fastai) (1.0.1)\n",
      "Requirement already satisfied: preshed<2.1.0,>=2.0.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from spacy==2.0.16->fastai) (2.0.1)\n",
      "Requirement already satisfied: plac<1.0.0,>=0.9.6 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from spacy==2.0.16->fastai) (0.9.6)\n",
      "Requirement already satisfied: ujson>=1.35 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from spacy==2.0.16->fastai) (1.35)\n",
      "Requirement already satisfied: dill<0.3,>=0.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from spacy==2.0.16->fastai) (0.2.8.2)\n",
      "Requirement already satisfied: msgpack<1.0.0,>=0.5.6 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from thinc==6.12.0->fastai) (0.5.6)\n",
      "Requirement already satisfied: cytoolz<0.10,>=0.9.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from thinc==6.12.0->fastai) (0.9.0.1)\n",
      "Requirement already satisfied: wrapt<1.11.0,>=1.10.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from thinc==6.12.0->fastai) (1.10.11)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.10.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from thinc==6.12.0->fastai) (4.28.1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: setuptools in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib->fastai) (39.1.0)\n",
      "Requirement already satisfied: toolz>=0.8.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from cytoolz<0.10,>=0.9.0->thinc==6.12.0->fastai) (0.9.0)\n",
      "\u001b[31mspacy 2.0.16 has requirement numpy>=1.15.0, but you'll have numpy 1.14.5 which is incompatible.\u001b[0m\n",
      "\u001b[31mspacy 2.0.16 has requirement regex==2018.01.10, but you'll have regex 2018.8.29 which is incompatible.\u001b[0m\n",
      "Installing collected packages: torchvision-nightly\n",
      "Successfully installed torchvision-nightly-0.2.1\n",
      "\u001b[33mYou are using pip version 10.0.1, however version 18.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.5.11\n",
      "  latest version: 4.5.12\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/ec2-user/anaconda3/envs/python3\n",
      "\n",
      "  added / updated specs: \n",
      "    - nb_conda\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    nb_conda-2.2.1             |           py36_0          33 KB\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    nb_conda:        2.2.1-py36_0         conda-forge --> 2.2.1-py36_0     \n",
      "    openssl:         1.0.2p-h470a237_0    conda-forge --> 1.0.2p-h14c3975_0\n",
      "\n",
      "The following packages will be DOWNGRADED:\n",
      "\n",
      "    ca-certificates: 2018.8.24-ha4d7672_0 conda-forge --> 2018.03.07-0     \n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "nb_conda-2.2.1       | 33 KB     | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: \\ + /home/ec2-user/anaconda3/envs/python3/bin/jupyter-nbextension disable nb_conda --py --sys-prefix\n",
      "Disabling notebook extension nb_conda/main...\n",
      "      - Validating: \u001b[32mOK\u001b[0m\n",
      "Disabling tree extension nb_conda/tree...\n",
      "      - Validating: \u001b[32mOK\u001b[0m\n",
      "+ /home/ec2-user/anaconda3/envs/python3/bin/jupyter-serverextension disable nb_conda --py --sys-prefix\n",
      "Disabling: nb_conda\n",
      "- Writing config: /home/ec2-user/anaconda3/envs/python3/etc/jupyter\n",
      "\n",
      "- + /home/ec2-user/anaconda3/envs/python3/bin/jupyter-nbextension enable nb_conda --py --sys-prefix\n",
      "Enabling notebook extension nb_conda/main...\n",
      "      - Validating: \u001b[32mOK\u001b[0m\n",
      "Enabling tree extension nb_conda/tree...\n",
      "      - Validating: \u001b[32mOK\u001b[0m\n",
      "+ /home/ec2-user/anaconda3/envs/python3/bin/jupyter-serverextension enable nb_conda --py --sys-prefix\n",
      "Enabling: nb_conda\n",
      "- Writing config: /home/ec2-user/anaconda3/envs/python3/etc/jupyter\n",
      "    - Validating...\n",
      "      nb_conda 2.2.1 \u001b[32mOK\u001b[0m\n",
      "\n",
      "done\n",
      "Requirement already satisfied: dataclasses in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (0.6)\n",
      "\u001b[31mspacy 2.0.16 has requirement numpy>=1.15.0, but you'll have numpy 1.14.5 which is incompatible.\u001b[0m\n",
      "\u001b[31mspacy 2.0.16 has requirement regex==2018.01.10, but you'll have regex 2018.8.29 which is incompatible.\u001b[0m\n",
      "\u001b[33mYou are using pip version 10.0.1, however version 18.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Collecting fastai==0.7.0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/50/6d/9d0d6e17a78b0598d5e8c49a0d03ffc7ff265ae62eca3e2345fab14edb9b/fastai-0.7.0-py3-none-any.whl (112kB)\n",
      "\u001b[K    100% |████████████████████████████████| 122kB 21.8MB/s ta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: ipykernel in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (4.8.2)\n",
      "Requirement already satisfied: ptyprocess in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (0.5.2)\n",
      "Requirement already satisfied: tqdm in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (4.28.1)\n",
      "Collecting sklearn-pandas (from fastai==0.7.0)\n",
      "  Downloading https://files.pythonhosted.org/packages/1f/48/4e1461d828baf41d609efaa720d20090ac6ec346b5daad3c88e243e2207e/sklearn_pandas-1.8.0-py2.py3-none-any.whl\n",
      "Requirement already satisfied: numpy in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (1.14.5)\n",
      "Collecting isoweek (from fastai==0.7.0)\n",
      "  Downloading https://files.pythonhosted.org/packages/c2/d4/fe7e2637975c476734fcbf53776e650a29680194eb0dd21dbdc020ca92de/isoweek-1.3.3-py2.py3-none-any.whl\n",
      "Collecting torchtext (from fastai==0.7.0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c6/bc/b28b9efb4653c03e597ed207264eea45862b5260f48e9f010b5068d64db1/torchtext-0.3.1-py3-none-any.whl (62kB)\n",
      "\u001b[K    100% |████████████████████████████████| 71kB 35.3MB/s ta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: seaborn in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (0.8.1)\n",
      "Requirement already satisfied: cycler in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (0.10.0)\n",
      "Requirement already satisfied: ipywidgets in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (7.4.0)\n",
      "Requirement already satisfied: simplegeneric in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (0.8.1)\n",
      "Requirement already satisfied: bleach in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (2.1.3)\n",
      "Requirement already satisfied: Pillow in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (5.2.0)\n",
      "Collecting torchvision (from fastai==0.7.0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/0d/f00b2885711e08bd71242ebe7b96561e6f6d01fdb4b9dcf4d37e2e13c5e1/torchvision-0.2.1-py2.py3-none-any.whl (54kB)\n",
      "\u001b[K    100% |████████████████████████████████| 61kB 32.6MB/s ta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: decorator in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (4.3.0)\n",
      "Collecting bcolz (from fastai==0.7.0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5c/4e/23942de9d5c0fb16f10335fa83e52b431bcb8c0d4a8419c9ac206268c279/bcolz-1.2.1.tar.gz (1.5MB)\n",
      "\u001b[K    100% |████████████████████████████████| 1.5MB 23.2MB/s ta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: jsonschema in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (2.6.0)\n",
      "Requirement already satisfied: certifi in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (2018.11.29)\n",
      "Collecting opencv-python (from fastai==0.7.0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/85/16/446110984e7a814a9e12862bd6da8d09501d274867b55223e48ee10cd752/opencv_python-3.4.4.19-cp36-cp36m-manylinux1_x86_64.whl (25.4MB)\n",
      "\u001b[K    100% |████████████████████████████████| 25.4MB 2.1MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pandas in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (0.22.0)\n",
      "Requirement already satisfied: matplotlib in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (2.2.2)\n",
      "Requirement already satisfied: ipython-genutils in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (0.2.0)\n",
      "Collecting graphviz (from fastai==0.7.0)\n",
      "  Downloading https://files.pythonhosted.org/packages/1f/e2/ef2581b5b86625657afd32030f90cf2717456c1d2b711ba074bf007c0f1a/graphviz-0.10.1-py2.py3-none-any.whl\n",
      "Requirement already satisfied: PyYAML in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (3.12)\n",
      "Requirement already satisfied: entrypoints in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (0.2.3)\n",
      "Collecting torch<0.4 (from fastai==0.7.0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5b/a5/e8b50b55b1abac9f1e3346c4242f1e42a82d368a8442cbd50c532922f6c4/torch-0.3.1-cp36-cp36m-manylinux1_x86_64.whl (496.4MB)\n",
      "\u001b[K    100% |████████████████████████████████| 496.4MB 106kB/s eta 0:00:01\n",
      "\u001b[?25hCollecting pandas-summary (from fastai==0.7.0)\n",
      "  Downloading https://files.pythonhosted.org/packages/97/55/ea54109a4e7a8e7342bdf23e9382c858224263d984b0d95610568e564f59/pandas_summary-0.0.5-py2.py3-none-any.whl\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: jedi in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (0.12.0)\n",
      "Requirement already satisfied: pickleshare in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (0.7.4)\n",
      "Requirement already satisfied: pytz in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (2018.4)\n",
      "Requirement already satisfied: wcwidth in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (0.1.7)\n",
      "Requirement already satisfied: Pygments in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (2.2.0)\n",
      "Requirement already satisfied: pyparsing in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (2.2.0)\n",
      "Requirement already satisfied: ipython in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (6.4.0)\n",
      "Requirement already satisfied: traitlets in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (4.3.2)\n",
      "Requirement already satisfied: pyzmq in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (17.0.0)\n",
      "Requirement already satisfied: testpath in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (0.3.1)\n",
      "Requirement already satisfied: webencodings in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (0.5.1)\n",
      "Collecting feather-format (from fastai==0.7.0)\n",
      "  Downloading https://files.pythonhosted.org/packages/08/55/940b97cc6f19a19f5dab9efef2f68a0ce43a7632f858b272391f0b851a7e/feather-format-0.4.0.tar.gz\n",
      "Requirement already satisfied: scipy in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (1.1.0)\n",
      "Requirement already satisfied: widgetsnbextension in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (3.4.2)\n",
      "Requirement already satisfied: html5lib in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (1.0.1)\n",
      "Collecting plotnine (from fastai==0.7.0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/84/02/b171c828560aea3a5da1efda464230dac3ef4f4834b88e0bd52ad14a08f0/plotnine-0.5.1-py2.py3-none-any.whl (3.6MB)\n",
      "\u001b[K    100% |████████████████████████████████| 3.6MB 14.7MB/s ta 0:00:016kB 60.2MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: Jinja2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (2.10)\n",
      "Requirement already satisfied: jupyter in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (1.0.0)\n",
      "Requirement already satisfied: tornado in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (5.0.2)\n",
      "Requirement already satisfied: python-dateutil in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (2.7.3)\n",
      "Requirement already satisfied: MarkupSafe in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from fastai==0.7.0) (1.0)\n",
      "Requirement already satisfied: jupyter_client in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from ipykernel->fastai==0.7.0) (5.2.3)\n",
      "Requirement already satisfied: scikit-learn>=0.15.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from sklearn-pandas->fastai==0.7.0) (0.19.1)\n",
      "Requirement already satisfied: requests in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from torchtext->fastai==0.7.0) (2.20.1)\n",
      "Requirement already satisfied: six in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from cycler->fastai==0.7.0) (1.11.0)\n",
      "Requirement already satisfied: nbformat>=4.2.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from ipywidgets->fastai==0.7.0) (4.4.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib->fastai==0.7.0) (1.0.1)\n",
      "Requirement already satisfied: parso>=0.2.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from jedi->fastai==0.7.0) (0.2.0)\n",
      "Requirement already satisfied: backcall in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from ipython->fastai==0.7.0) (0.1.0)\n",
      "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from ipython->fastai==0.7.0) (4.5.0)\n",
      "Requirement already satisfied: setuptools>=18.5 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from ipython->fastai==0.7.0) (39.1.0)\n",
      "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.15 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from ipython->fastai==0.7.0) (1.0.15)\n",
      "Collecting pyarrow>=0.4.0 (from feather-format->fastai==0.7.0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/36/94/23135312f97b20d6457294606fb70fad43ef93b7bffe567088ebe3623703/pyarrow-0.11.1-cp36-cp36m-manylinux1_x86_64.whl (11.6MB)\n",
      "\u001b[K    100% |████████████████████████████████| 11.6MB 5.3MB/s eta 0:00:01   43% |█████████████▉                  | 5.0MB 51.2MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: notebook>=4.4.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from widgetsnbextension->fastai==0.7.0) (5.5.0)\n",
      "Collecting mizani>=0.5.2 (from plotnine->fastai==0.7.0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/10/3a/1d1c5563b6aeb5fffda694b70d649a0f728a112b79a66b85a6af4814a643/mizani-0.5.2-py2.py3-none-any.whl (58kB)\n",
      "\u001b[K    100% |████████████████████████████████| 61kB 32.9MB/s ta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: statsmodels>=0.8.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from plotnine->fastai==0.7.0) (0.9.0)\n",
      "Requirement already satisfied: patsy>=0.4.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from plotnine->fastai==0.7.0) (0.5.0)\n",
      "Collecting descartes>=1.1.0 (from plotnine->fastai==0.7.0)\n",
      "  Downloading https://files.pythonhosted.org/packages/e5/b6/1ed2eb03989ae574584664985367ba70cd9cf8b32ee8cad0e8aaeac819f3/descartes-1.1.0-py3-none-any.whl\n",
      "Requirement already satisfied: qtconsole in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from jupyter->fastai==0.7.0) (4.3.1)\n",
      "Requirement already satisfied: jupyter-console in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from jupyter->fastai==0.7.0) (5.2.0)\n",
      "Requirement already satisfied: nbconvert in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from jupyter->fastai==0.7.0) (5.3.1)\n",
      "Requirement already satisfied: jupyter_core in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from jupyter_client->ipykernel->fastai==0.7.0) (4.4.0)\n",
      "Requirement already satisfied: idna<2.8,>=2.5 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests->torchtext->fastai==0.7.0) (2.6)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests->torchtext->fastai==0.7.0) (1.22)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests->torchtext->fastai==0.7.0) (3.0.4)\n",
      "Requirement already satisfied: Send2Trash in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from notebook>=4.4.1->widgetsnbextension->fastai==0.7.0) (1.5.0)\n",
      "Requirement already satisfied: terminado>=0.8.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from notebook>=4.4.1->widgetsnbextension->fastai==0.7.0) (0.8.1)\n",
      "Collecting palettable (from mizani>=0.5.2->plotnine->fastai==0.7.0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/56/8a/84537c0354f0d1f03bf644b71bf8e0a50db9c1294181905721a5f3efbf66/palettable-3.1.1-py2.py3-none-any.whl (77kB)\n",
      "\u001b[K    100% |████████████████████████████████| 81kB 35.6MB/s ta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: mistune>=0.7.4 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from nbconvert->jupyter->fastai==0.7.0) (0.8.3)\n",
      "Requirement already satisfied: pandocfilters>=1.4.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from nbconvert->jupyter->fastai==0.7.0) (1.4.2)\n",
      "Building wheels for collected packages: bcolz, feather-format\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Running setup.py bdist_wheel for bcolz ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/ec2-user/.cache/pip/wheels/9f/78/26/fb8c0acb91a100dc8914bf236c4eaa4b207cb876893c40b745\n",
      "  Running setup.py bdist_wheel for feather-format ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/ec2-user/.cache/pip/wheels/85/7d/12/2dfa5c0195f921ac935f5e8f27deada74972edc0ae9988a9c1\n",
      "Successfully built bcolz feather-format\n",
      "\u001b[31mspacy 2.0.16 has requirement numpy>=1.15.0, but you'll have numpy 1.14.5 which is incompatible.\u001b[0m\n",
      "\u001b[31mspacy 2.0.16 has requirement regex==2018.01.10, but you'll have regex 2018.8.29 which is incompatible.\u001b[0m\n",
      "\u001b[31mmizani 0.5.2 has requirement pandas>=0.23.4, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mplotnine 0.5.1 has requirement matplotlib>=3.0.0, but you'll have matplotlib 2.2.2 which is incompatible.\u001b[0m\n",
      "\u001b[31mplotnine 0.5.1 has requirement pandas>=0.23.4, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
      "Installing collected packages: sklearn-pandas, isoweek, torch, torchtext, torchvision, bcolz, opencv-python, graphviz, pandas-summary, pyarrow, feather-format, palettable, mizani, descartes, plotnine, fastai\n",
      "  Found existing installation: torch 1.0.0.dev20181220\n",
      "    Uninstalling torch-1.0.0.dev20181220:\n",
      "      Successfully uninstalled torch-1.0.0.dev20181220\n",
      "  Found existing installation: fastai 1.0.34\n",
      "    Uninstalling fastai-1.0.34:\n",
      "      Successfully uninstalled fastai-1.0.34\n",
      "Successfully installed bcolz-1.2.1 descartes-1.1.0 fastai-0.7.0 feather-format-0.4.0 graphviz-0.10.1 isoweek-1.3.3 mizani-0.5.2 opencv-python-3.4.4.19 palettable-3.1.1 pandas-summary-0.0.5 plotnine-0.5.1 pyarrow-0.11.1 sklearn-pandas-1.8.0 torch-0.3.1 torchtext-0.3.1 torchvision-0.2.1\n",
      "\u001b[33mYou are using pip version 10.0.1, however version 18.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Collecting keras\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5e/10/aa32dad071ce52b5502266b5c659451cfd6ffcbf14e6c8c4f16c0ff5aaab/Keras-2.2.4-py2.py3-none-any.whl (312kB)\n",
      "\u001b[K    100% |████████████████████████████████| 317kB 22.6MB/s ta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: h5py in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (2.8.0)\n",
      "Requirement already satisfied: six>=1.9.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (1.11.0)\n",
      "Requirement already satisfied: scipy>=0.14 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (1.1.0)\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (1.0.5)\n",
      "Requirement already satisfied: keras-applications>=1.0.6 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (1.0.6)\n",
      "Requirement already satisfied: pyyaml in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (3.12)\n",
      "Requirement already satisfied: numpy>=1.9.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from keras) (1.14.5)\n",
      "\u001b[31mspacy 2.0.16 has requirement numpy>=1.15.0, but you'll have numpy 1.14.5 which is incompatible.\u001b[0m\n",
      "\u001b[31mspacy 2.0.16 has requirement regex==2018.01.10, but you'll have regex 2018.8.29 which is incompatible.\u001b[0m\n",
      "\u001b[31mplotnine 0.5.1 has requirement matplotlib>=3.0.0, but you'll have matplotlib 2.2.2 which is incompatible.\u001b[0m\n",
      "\u001b[31mplotnine 0.5.1 has requirement pandas>=0.23.4, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mmizani 0.5.2 has requirement pandas>=0.23.4, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
      "Installing collected packages: keras\n",
      "Successfully installed keras-2.2.4\n",
      "\u001b[33mYou are using pip version 10.0.1, however version 18.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Collecting pip\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c2/d7/90f34cb0d83a6c5631cf71dfe64cc1054598c843a92b400e55675cc2ac37/pip-18.1-py2.py3-none-any.whl (1.3MB)\n",
      "\u001b[K    100% |████████████████████████████████| 1.3MB 21.4MB/s ta 0:00:01\n",
      "\u001b[31mspacy 2.0.16 has requirement numpy>=1.15.0, but you'll have numpy 1.14.5 which is incompatible.\u001b[0m\n",
      "\u001b[31mspacy 2.0.16 has requirement regex==2018.01.10, but you'll have regex 2018.8.29 which is incompatible.\u001b[0m\n",
      "\u001b[31mplotnine 0.5.1 has requirement matplotlib>=3.0.0, but you'll have matplotlib 2.2.2 which is incompatible.\u001b[0m\n",
      "\u001b[31mplotnine 0.5.1 has requirement pandas>=0.23.4, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mmizani 0.5.2 has requirement pandas>=0.23.4, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: pip\n",
      "  Found existing installation: pip 10.0.1\n",
      "    Uninstalling pip-10.0.1:\n",
      "      Successfully uninstalled pip-10.0.1\n",
      "Successfully installed pip-18.1\n",
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.5.11\n",
      "  latest version: 4.5.12\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/ec2-user/anaconda3/envs/python3\n",
      "\n",
      "  removed specs: \n",
      "    - seaborn\n",
      "\n",
      "\n",
      "The following packages will be REMOVED:\n",
      "\n",
      "    seaborn: 0.8.1-py36hfad7ec4_0\n",
      "\n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "\u001b[33mSkipping seaborn as it is not installed.\u001b[0m\n",
      "Collecting seaborn\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/76/220ba4420459d9c4c9c9587c6ce607bf56c25b3d3d2de62056efe482dadc/seaborn-0.9.0-py3-none-any.whl (208kB)\n",
      "\u001b[K    100% |████████████████████████████████| 215kB 25.3MB/s ta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: scipy>=0.14.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from seaborn) (1.1.0)\n",
      "Requirement already satisfied: matplotlib>=1.4.3 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from seaborn) (2.2.2)\n",
      "Requirement already satisfied: pandas>=0.15.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from seaborn) (0.22.0)\n",
      "Requirement already satisfied: numpy>=1.9.3 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from seaborn) (1.14.5)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (2.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (1.0.1)\n",
      "Requirement already satisfied: pytz in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (2018.4)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (2.7.3)\n",
      "Requirement already satisfied: six>=1.10 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (1.11.0)\n",
      "Requirement already satisfied: setuptools in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.3->seaborn) (39.1.0)\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.9.0\n"
     ]
    }
   ],
   "source": [
    "!conda install -c pytorch pytorch-nightly-cpu -y \n",
    "!conda install -c fastai torchvision-nightly-cpu -y \n",
    "!conda install -c fastai fastai -y \n",
    "!pip install tensorflow\n",
    "!pip install fastai\n",
    "!conda install nb_conda -y\n",
    "!pip install dataclasses\n",
    "!pip install fastai==0.7.0\n",
    "!pip install keras\n",
    "!pip install --upgrade pip\n",
    "!conda remove seaborn -y\n",
    "!pip uninstall seaborn \n",
    "!pip3 --no-cache-dir install seaborn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn; seaborn.set()\n",
    "import datetime\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "from fastai.imports import *\n",
    "from fastai.structured import *\n",
    "from pandas_summary import DataFrameSummary\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from IPython.display import display\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download all the files and Read all the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sc!wget 'https://s3.amazonaws.com/sagemakermathan/bitstamp.csv'\n",
    "%sc !wget 'https://s3.amazonaws.com/sagemakermathan/coinbase.csv'\n",
    "%sc !wget 'https://s3.amazonaws.com/sagemakermathan/cryptonewskaggle.csv'\n",
    "%sc !wget 'https://s3.amazonaws.com/sagemakermathan/news.csv'\n",
    "%sc !wget 'https://s3.amazonaws.com/sagemakermathan/cryptonewskagglecleaned.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "coinbase=pd.read_csv('coinbase.csv')\n",
    "cryptonewsonly=pd.read_csv('cryptonewscleanedallmagic.csv',encoding='iso-8859-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing Please look more for util Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coinbasedata=coinbase[['Open','Close','High','Low','Volume_(BTC)','Volume_(Currency)','Weighted_Price']]\n",
    "# coinbasedata=coinbasedata.fillna(method='ffill')\n",
    "# gold=pd.DataFrame()\n",
    "# gold['Open']=coinbasedata['Open'].resample('D').first().fillna(method='ffill')\n",
    "# gold['Close']=coinbasedata['Close'].resample('D').last().fillna(method='ffill')\n",
    "# gold['High']=coinbasedata['High'].resample('D').max().fillna(method='ffill')\n",
    "# gold['Low']=coinbasedata['Low'].resample('D').min().fillna(method='ffill')\n",
    "# gold['Volume_(BTC)']=coinbasedata['Volume_(BTC)'].resample('D').sum().fillna(method='ffill')\n",
    "# gold['Volume_(Currency)']=coinbasedata['Volume_(Currency)'].resample('D').sum().fillna(method='ffill')\n",
    "# gold['Weighted_Price']=coinbasedata['Weighted_Price'].resample('D').mean().fillna(method='ffill')\n",
    "# gold=gold['2015':]\n",
    "# gold.to_csv('Bitcoin2015Daily.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1417411980</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1417412040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1417412100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1417412160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1417412220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Timestamp   Open   High    Low  Close  Volume_(BTC)  Volume_(Currency)  \\\n",
       "0  1417411980  300.0  300.0  300.0  300.0          0.01                3.0   \n",
       "1  1417412040    NaN    NaN    NaN    NaN           NaN                NaN   \n",
       "2  1417412100    NaN    NaN    NaN    NaN           NaN                NaN   \n",
       "3  1417412160    NaN    NaN    NaN    NaN           NaN                NaN   \n",
       "4  1417412220    NaN    NaN    NaN    NaN           NaN                NaN   \n",
       "\n",
       "   Weighted_Price  \n",
       "0           300.0  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016349</th>\n",
       "      <td>1541894160</td>\n",
       "      <td>6347.41</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>6347.41</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>0.484960</td>\n",
       "      <td>3078.241490</td>\n",
       "      <td>6347.417225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016350</th>\n",
       "      <td>1541894220</td>\n",
       "      <td>6347.41</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>6347.41</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>0.770757</td>\n",
       "      <td>4892.318043</td>\n",
       "      <td>6347.419294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016351</th>\n",
       "      <td>1541894280</td>\n",
       "      <td>6347.41</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>6347.41</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>1.213145</td>\n",
       "      <td>7700.341614</td>\n",
       "      <td>6347.418653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016352</th>\n",
       "      <td>1541894340</td>\n",
       "      <td>6347.41</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>6347.41</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>1.714091</td>\n",
       "      <td>10880.053958</td>\n",
       "      <td>6347.418881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016353</th>\n",
       "      <td>1541894400</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>6347.41</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>0.489324</td>\n",
       "      <td>3105.944897</td>\n",
       "      <td>6347.417439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Timestamp     Open     High      Low    Close  Volume_(BTC)  \\\n",
       "2016349  1541894160  6347.41  6347.42  6347.41  6347.42      0.484960   \n",
       "2016350  1541894220  6347.41  6347.42  6347.41  6347.42      0.770757   \n",
       "2016351  1541894280  6347.41  6347.42  6347.41  6347.42      1.213145   \n",
       "2016352  1541894340  6347.41  6347.42  6347.41  6347.42      1.714091   \n",
       "2016353  1541894400  6347.42  6347.42  6347.41  6347.42      0.489324   \n",
       "\n",
       "         Volume_(Currency)  Weighted_Price  \n",
       "2016349        3078.241490     6347.417225  \n",
       "2016350        4892.318043     6347.419294  \n",
       "2016351        7700.341614     6347.418653  \n",
       "2016352       10880.053958     6347.418881  \n",
       "2016353        3105.944897     6347.417439  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.016354e+06</td>\n",
       "      <td>1.907397e+06</td>\n",
       "      <td>1.907397e+06</td>\n",
       "      <td>1.907397e+06</td>\n",
       "      <td>1.907397e+06</td>\n",
       "      <td>1.907397e+06</td>\n",
       "      <td>1.907397e+06</td>\n",
       "      <td>1.907397e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.481396e+09</td>\n",
       "      <td>3.208583e+03</td>\n",
       "      <td>3.209990e+03</td>\n",
       "      <td>3.207058e+03</td>\n",
       "      <td>3.208584e+03</td>\n",
       "      <td>7.684054e+00</td>\n",
       "      <td>3.551276e+04</td>\n",
       "      <td>3.208523e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.493807e+07</td>\n",
       "      <td>3.873144e+03</td>\n",
       "      <td>3.875432e+03</td>\n",
       "      <td>3.870698e+03</td>\n",
       "      <td>3.873140e+03</td>\n",
       "      <td>1.820454e+01</td>\n",
       "      <td>1.411277e+05</td>\n",
       "      <td>3.873066e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.417412e+09</td>\n",
       "      <td>6.000000e-02</td>\n",
       "      <td>6.000000e-02</td>\n",
       "      <td>6.000000e-02</td>\n",
       "      <td>6.000000e-02</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>2.641700e-06</td>\n",
       "      <td>6.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.451159e+09</td>\n",
       "      <td>4.156000e+02</td>\n",
       "      <td>4.156800e+02</td>\n",
       "      <td>4.155000e+02</td>\n",
       "      <td>4.155900e+02</td>\n",
       "      <td>8.776600e-01</td>\n",
       "      <td>5.962574e+02</td>\n",
       "      <td>4.155902e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.481404e+09</td>\n",
       "      <td>9.099600e+02</td>\n",
       "      <td>9.101000e+02</td>\n",
       "      <td>9.095700e+02</td>\n",
       "      <td>9.099800e+02</td>\n",
       "      <td>2.641103e+00</td>\n",
       "      <td>3.313449e+03</td>\n",
       "      <td>9.099194e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.511649e+09</td>\n",
       "      <td>6.374660e+03</td>\n",
       "      <td>6.375150e+03</td>\n",
       "      <td>6.373310e+03</td>\n",
       "      <td>6.374670e+03</td>\n",
       "      <td>7.478157e+00</td>\n",
       "      <td>1.855195e+04</td>\n",
       "      <td>6.374404e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.541894e+09</td>\n",
       "      <td>1.989199e+04</td>\n",
       "      <td>1.989199e+04</td>\n",
       "      <td>1.989198e+04</td>\n",
       "      <td>1.989199e+04</td>\n",
       "      <td>1.563267e+03</td>\n",
       "      <td>1.997076e+07</td>\n",
       "      <td>1.989199e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Timestamp          Open          High           Low         Close  \\\n",
       "count  2.016354e+06  1.907397e+06  1.907397e+06  1.907397e+06  1.907397e+06   \n",
       "mean   1.481396e+09  3.208583e+03  3.209990e+03  3.207058e+03  3.208584e+03   \n",
       "std    3.493807e+07  3.873144e+03  3.875432e+03  3.870698e+03  3.873140e+03   \n",
       "min    1.417412e+09  6.000000e-02  6.000000e-02  6.000000e-02  6.000000e-02   \n",
       "25%    1.451159e+09  4.156000e+02  4.156800e+02  4.155000e+02  4.155900e+02   \n",
       "50%    1.481404e+09  9.099600e+02  9.101000e+02  9.095700e+02  9.099800e+02   \n",
       "75%    1.511649e+09  6.374660e+03  6.375150e+03  6.373310e+03  6.374670e+03   \n",
       "max    1.541894e+09  1.989199e+04  1.989199e+04  1.989198e+04  1.989199e+04   \n",
       "\n",
       "       Volume_(BTC)  Volume_(Currency)  Weighted_Price  \n",
       "count  1.907397e+06       1.907397e+06    1.907397e+06  \n",
       "mean   7.684054e+00       3.551276e+04    3.208523e+03  \n",
       "std    1.820454e+01       1.411277e+05    3.873066e+03  \n",
       "min    1.000000e-08       2.641700e-06    6.000000e-02  \n",
       "25%    8.776600e-01       5.962574e+02    4.155902e+02  \n",
       "50%    2.641103e+00       3.313449e+03    9.099194e+02  \n",
       "75%    7.478157e+00       1.855195e+04    6.374404e+03  \n",
       "max    1.563267e+03       1.997076e+07    1.989199e+04  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16606.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8302.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4793.88362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4151.25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8302.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12453.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16605.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0\n",
       "count  16606.00000\n",
       "mean    8302.50000\n",
       "std     4793.88362\n",
       "min        0.00000\n",
       "25%     4151.25000\n",
       "50%     8302.50000\n",
       "75%    12453.75000\n",
       "max    16605.00000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cryptonewsonly.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp            0.000000\n",
       "Open                 0.057123\n",
       "High                 0.057123\n",
       "Low                  0.057123\n",
       "Close                0.057123\n",
       "Volume_(BTC)         0.057123\n",
       "Volume_(Currency)    0.057123\n",
       "Weighted_Price       0.057123\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.isna().sum()/coinbase.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relatively there are fewer NAN Values in Coinbase dataset and we wanted to select one of them so we go with Coinbase dataset for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of First, I feel timestamp values are not humanly readable so let's convert it into a normal date first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def timestampToDateTime(timestamp):\n",
    "    from datetime import datetime\n",
    "    return datetime.fromtimestamp(timestamp)\n",
    "coinbase['Date']=coinbase['Timestamp'].apply(timestampToDateTime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1417411980</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2014-12-01 05:33:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1417412040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-12-01 05:34:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1417412100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-12-01 05:35:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1417412160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-12-01 05:36:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1417412220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-12-01 05:37:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Timestamp   Open   High    Low  Close  Volume_(BTC)  Volume_(Currency)  \\\n",
       "0  1417411980  300.0  300.0  300.0  300.0          0.01                3.0   \n",
       "1  1417412040    NaN    NaN    NaN    NaN           NaN                NaN   \n",
       "2  1417412100    NaN    NaN    NaN    NaN           NaN                NaN   \n",
       "3  1417412160    NaN    NaN    NaN    NaN           NaN                NaN   \n",
       "4  1417412220    NaN    NaN    NaN    NaN           NaN                NaN   \n",
       "\n",
       "   Weighted_Price                Date  \n",
       "0           300.0 2014-12-01 05:33:00  \n",
       "1             NaN 2014-12-01 05:34:00  \n",
       "2             NaN 2014-12-01 05:35:00  \n",
       "3             NaN 2014-12-01 05:36:00  \n",
       "4             NaN 2014-12-01 05:37:00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get rid of the timestamp Now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "coinbase.drop('Timestamp',axis=1,inplace=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's verify if everything looks good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2014-12-01 05:33:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-12-01 05:34:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-12-01 05:35:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-12-01 05:36:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-12-01 05:37:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Open   High    Low  Close  Volume_(BTC)  Volume_(Currency)  \\\n",
       "0  300.0  300.0  300.0  300.0          0.01                3.0   \n",
       "1    NaN    NaN    NaN    NaN           NaN                NaN   \n",
       "2    NaN    NaN    NaN    NaN           NaN                NaN   \n",
       "3    NaN    NaN    NaN    NaN           NaN                NaN   \n",
       "4    NaN    NaN    NaN    NaN           NaN                NaN   \n",
       "\n",
       "   Weighted_Price                Date  \n",
       "0           300.0 2014-12-01 05:33:00  \n",
       "1             NaN 2014-12-01 05:34:00  \n",
       "2             NaN 2014-12-01 05:35:00  \n",
       "3             NaN 2014-12-01 05:36:00  \n",
       "4             NaN 2014-12-01 05:37:00  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open                 0.057123\n",
       "High                 0.057123\n",
       "Low                  0.057123\n",
       "Close                0.057123\n",
       "Volume_(BTC)         0.057123\n",
       "Volume_(Currency)    0.057123\n",
       "Weighted_Price       0.057123\n",
       "Date                 0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.isna().sum()/coinbase.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>511553</th>\n",
       "      <td>430.35</td>\n",
       "      <td>430.39</td>\n",
       "      <td>430.35</td>\n",
       "      <td>430.39</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>31.288653</td>\n",
       "      <td>430.380371</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Open    High     Low   Close  Volume_(BTC)  Volume_(Currency)  \\\n",
       "511553  430.35  430.39  430.35  430.39        0.0727          31.288653   \n",
       "\n",
       "        Weighted_Price       Date  \n",
       "511553      430.380371 2016-01-01  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase[coinbase['Date']=='2016']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fetch all the data for a year, we need to first index the dataframe by date and then we can query intresting things and explore the data more. This is pretty common in time series analysis and useful for data exploration too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeindex=pd.DatetimeIndex(coinbase['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "coinbase.set_index(timeindex,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see now we can fetch data with respect to a particular date. Let's do some exploration now! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-21 00:00:00</th>\n",
       "      <td>418.89</td>\n",
       "      <td>419.22</td>\n",
       "      <td>418.72</td>\n",
       "      <td>418.72</td>\n",
       "      <td>7.564173</td>\n",
       "      <td>3169.440356</td>\n",
       "      <td>419.006862</td>\n",
       "      <td>2016-01-21 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21 00:01:00</th>\n",
       "      <td>418.90</td>\n",
       "      <td>418.97</td>\n",
       "      <td>418.76</td>\n",
       "      <td>418.78</td>\n",
       "      <td>16.812530</td>\n",
       "      <td>7040.824523</td>\n",
       "      <td>418.784354</td>\n",
       "      <td>2016-01-21 00:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21 00:02:00</th>\n",
       "      <td>418.94</td>\n",
       "      <td>418.99</td>\n",
       "      <td>418.94</td>\n",
       "      <td>418.96</td>\n",
       "      <td>31.659400</td>\n",
       "      <td>13264.092180</td>\n",
       "      <td>418.962210</td>\n",
       "      <td>2016-01-21 00:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21 00:03:00</th>\n",
       "      <td>419.01</td>\n",
       "      <td>419.01</td>\n",
       "      <td>418.96</td>\n",
       "      <td>418.96</td>\n",
       "      <td>12.206630</td>\n",
       "      <td>5114.149465</td>\n",
       "      <td>418.964896</td>\n",
       "      <td>2016-01-21 00:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21 00:04:00</th>\n",
       "      <td>418.99</td>\n",
       "      <td>419.00</td>\n",
       "      <td>418.99</td>\n",
       "      <td>418.99</td>\n",
       "      <td>10.416198</td>\n",
       "      <td>4364.289083</td>\n",
       "      <td>418.990585</td>\n",
       "      <td>2016-01-21 00:04:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Open    High     Low   Close  Volume_(BTC)  \\\n",
       "Date                                                                \n",
       "2016-01-21 00:00:00  418.89  419.22  418.72  418.72      7.564173   \n",
       "2016-01-21 00:01:00  418.90  418.97  418.76  418.78     16.812530   \n",
       "2016-01-21 00:02:00  418.94  418.99  418.94  418.96     31.659400   \n",
       "2016-01-21 00:03:00  419.01  419.01  418.96  418.96     12.206630   \n",
       "2016-01-21 00:04:00  418.99  419.00  418.99  418.99     10.416198   \n",
       "\n",
       "                     Volume_(Currency)  Weighted_Price                Date  \n",
       "Date                                                                        \n",
       "2016-01-21 00:00:00        3169.440356      419.006862 2016-01-21 00:00:00  \n",
       "2016-01-21 00:01:00        7040.824523      418.784354 2016-01-21 00:01:00  \n",
       "2016-01-21 00:02:00       13264.092180      418.962210 2016-01-21 00:02:00  \n",
       "2016-01-21 00:03:00        5114.149465      418.964896 2016-01-21 00:03:00  \n",
       "2016-01-21 00:04:00        4364.289083      418.990585 2016-01-21 00:04:00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase['2016-01-21'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to confirm that the dataset is a legitimate one, we plot the bitcoin values for year 2017-18 and as we know that the spike in the market we can confirm that the dataset is legitimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/ipykernel/__main__.py:5: FutureWarning: \n",
      ".resample() is now a deferred operation\n",
      "You called plot(...) on this deferred object which materialized it into a series\n",
      "by implicitly taking the mean.  Use .resample(...).mean() instead\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcafdb5e278>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.set(style=\"darkgrid\")\n",
    "import seaborn as sns; sns.set()\n",
    "#sns.lineplot(x=\"Date\", y=\"Close\",\n",
    "#             data=coinbase)\n",
    "coinbase['2017':'2018']['High'].resample(rule='T').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to learn new things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/ipykernel/__main__.py:3: FutureWarning: \n",
      ".resample() is now a deferred operation\n",
      "You called plot(...) on this deferred object which materialized it into a series\n",
      "by implicitly taking the mean.  Use .resample(...).mean() instead\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcaf9db2898>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=coinbase['2017-07-02']\n",
    "data['High'].plot(style=\"-\")\n",
    "data['High'].resample('BM').plot(style=\":\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Notice the difference: at each point, resample reports the average of the previous year, while asfreq reports the value at the end of the year. You can resample by hour, minutes, years, days etc. This indeed is a convenient tool.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(dataframe):\n",
    "    data=data.fillna(method='ffill')\n",
    "    data=add_datepart(data, 'Date')\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we noticed there are few NAN values and we dont want to drop those values. Since it is a time series data, we need to be extremely careful how we handle these values.\n",
    "There are two options:\n",
    "1. Fill the missing values with previous values (forward fill)\n",
    "2. Fill the missing values with future values (backward fill)\n",
    "\n",
    "We choose forward Fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "coinbase=coinbase.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most basic form of analysis: Let's find the co-relation and see if we can find anything useful. Nothing intresting as all the features are computed from each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Open</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.161845</td>\n",
       "      <td>0.408597</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>0.999998</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.162431</td>\n",
       "      <td>0.409438</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.161182</td>\n",
       "      <td>0.407686</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Close</th>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.161820</td>\n",
       "      <td>0.408588</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <td>0.161845</td>\n",
       "      <td>0.162431</td>\n",
       "      <td>0.161182</td>\n",
       "      <td>0.161820</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700766</td>\n",
       "      <td>0.161792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <td>0.408597</td>\n",
       "      <td>0.409438</td>\n",
       "      <td>0.407686</td>\n",
       "      <td>0.408588</td>\n",
       "      <td>0.700766</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.408544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weighted_Price</th>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.161792</td>\n",
       "      <td>0.408544</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Open      High       Low     Close  Volume_(BTC)  \\\n",
       "Open               1.000000  0.999998  0.999998  0.999997      0.161845   \n",
       "High               0.999998  1.000000  0.999996  0.999998      0.162431   \n",
       "Low                0.999998  0.999996  1.000000  0.999998      0.161182   \n",
       "Close              0.999997  0.999998  0.999998  1.000000      0.161820   \n",
       "Volume_(BTC)       0.161845  0.162431  0.161182  0.161820      1.000000   \n",
       "Volume_(Currency)  0.408597  0.409438  0.407686  0.408588      0.700766   \n",
       "Weighted_Price     0.999999  0.999999  0.999999  0.999999      0.161792   \n",
       "\n",
       "                   Volume_(Currency)  Weighted_Price  \n",
       "Open                        0.408597        0.999999  \n",
       "High                        0.409438        0.999999  \n",
       "Low                         0.407686        0.999999  \n",
       "Close                       0.408588        0.999999  \n",
       "Volume_(BTC)                0.700766        0.161792  \n",
       "Volume_(Currency)           1.000000        0.408544  \n",
       "Weighted_Price              0.408544        1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We predict the price for the bitcoin after 2 days in this case. Therefore we need to prepare the dataframe with input row as todays price and predictor as price after 2 days. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "coinbase['PriceClose2D']=coinbase['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "shift=24 # 24 hours = 2days \n",
    "coinbase['PriceClose2D']=coinbase['PriceClose2D'].shift(-shift)\n",
    "coinbase=coinbase[:-shift]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "      <th>Date</th>\n",
       "      <th>PriceClose2D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:46:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.70000</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:46:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:47:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.70000</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:47:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:48:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.70000</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:48:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:49:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.70000</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:49:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:50:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:50:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:51:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:51:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:52:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:52:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:53:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:53:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:54:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:54:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:55:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:55:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:56:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:56:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:57:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:57:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:58:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:58:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:59:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 06:59:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 07:00:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 07:00:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 07:01:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 07:01:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 07:02:00</th>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.026556</td>\n",
       "      <td>9.82555</td>\n",
       "      <td>370.0</td>\n",
       "      <td>2014-12-01 07:02:00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Open   High    Low  Close  Volume_(BTC)  \\\n",
       "Date                                                            \n",
       "2014-12-01 06:46:00  370.0  370.0  370.0  370.0      0.010000   \n",
       "2014-12-01 06:47:00  370.0  370.0  370.0  370.0      0.010000   \n",
       "2014-12-01 06:48:00  370.0  370.0  370.0  370.0      0.010000   \n",
       "2014-12-01 06:49:00  370.0  370.0  370.0  370.0      0.010000   \n",
       "2014-12-01 06:50:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "2014-12-01 06:51:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "2014-12-01 06:52:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "2014-12-01 06:53:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "2014-12-01 06:54:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "2014-12-01 06:55:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "2014-12-01 06:56:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "2014-12-01 06:57:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "2014-12-01 06:58:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "2014-12-01 06:59:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "2014-12-01 07:00:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "2014-12-01 07:01:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "2014-12-01 07:02:00  370.0  370.0  370.0  370.0      0.026556   \n",
       "\n",
       "                     Volume_(Currency)  Weighted_Price                Date  \\\n",
       "Date                                                                         \n",
       "2014-12-01 06:46:00            3.70000           370.0 2014-12-01 06:46:00   \n",
       "2014-12-01 06:47:00            3.70000           370.0 2014-12-01 06:47:00   \n",
       "2014-12-01 06:48:00            3.70000           370.0 2014-12-01 06:48:00   \n",
       "2014-12-01 06:49:00            3.70000           370.0 2014-12-01 06:49:00   \n",
       "2014-12-01 06:50:00            9.82555           370.0 2014-12-01 06:50:00   \n",
       "2014-12-01 06:51:00            9.82555           370.0 2014-12-01 06:51:00   \n",
       "2014-12-01 06:52:00            9.82555           370.0 2014-12-01 06:52:00   \n",
       "2014-12-01 06:53:00            9.82555           370.0 2014-12-01 06:53:00   \n",
       "2014-12-01 06:54:00            9.82555           370.0 2014-12-01 06:54:00   \n",
       "2014-12-01 06:55:00            9.82555           370.0 2014-12-01 06:55:00   \n",
       "2014-12-01 06:56:00            9.82555           370.0 2014-12-01 06:56:00   \n",
       "2014-12-01 06:57:00            9.82555           370.0 2014-12-01 06:57:00   \n",
       "2014-12-01 06:58:00            9.82555           370.0 2014-12-01 06:58:00   \n",
       "2014-12-01 06:59:00            9.82555           370.0 2014-12-01 06:59:00   \n",
       "2014-12-01 07:00:00            9.82555           370.0 2014-12-01 07:00:00   \n",
       "2014-12-01 07:01:00            9.82555           370.0 2014-12-01 07:01:00   \n",
       "2014-12-01 07:02:00            9.82555           370.0 2014-12-01 07:02:00   \n",
       "\n",
       "                     PriceClose2D  \n",
       "Date                               \n",
       "2014-12-01 06:46:00         370.0  \n",
       "2014-12-01 06:47:00         370.0  \n",
       "2014-12-01 06:48:00         370.0  \n",
       "2014-12-01 06:49:00         370.0  \n",
       "2014-12-01 06:50:00         370.0  \n",
       "2014-12-01 06:51:00         370.0  \n",
       "2014-12-01 06:52:00         370.0  \n",
       "2014-12-01 06:53:00         370.0  \n",
       "2014-12-01 06:54:00         370.0  \n",
       "2014-12-01 06:55:00         370.0  \n",
       "2014-12-01 06:56:00         370.0  \n",
       "2014-12-01 06:57:00         370.0  \n",
       "2014-12-01 06:58:00         370.0  \n",
       "2014-12-01 06:59:00         370.0  \n",
       "2014-12-01 07:00:00         370.0  \n",
       "2014-12-01 07:01:00         370.0  \n",
       "2014-12-01 07:02:00         370.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase[73:90]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now inorder to capture temporal features for very basic algorithms we add date time features dervied from datetime like isweekday,\n",
    "is weekday, is public holiday etc to capture seasonal trends which we will cover in detail in a minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_datepart(coinbase,'Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "      <th>PriceClose2D</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>...</th>\n",
       "      <th>Day</th>\n",
       "      <th>Dayofweek</th>\n",
       "      <th>Dayofyear</th>\n",
       "      <th>Is_month_end</th>\n",
       "      <th>Is_month_start</th>\n",
       "      <th>Is_quarter_end</th>\n",
       "      <th>Is_quarter_start</th>\n",
       "      <th>Is_year_end</th>\n",
       "      <th>Is_year_start</th>\n",
       "      <th>Elapsed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:24:00</th>\n",
       "      <td>360.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>317.98</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:25:00</th>\n",
       "      <td>360.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>301.99</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:26:00</th>\n",
       "      <td>360.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>333.28</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:27:00</th>\n",
       "      <td>271.84</td>\n",
       "      <td>276.34</td>\n",
       "      <td>271.84</td>\n",
       "      <td>276.34</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>5.481800</td>\n",
       "      <td>274.090000</td>\n",
       "      <td>329.03</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:28:00</th>\n",
       "      <td>295.19</td>\n",
       "      <td>319.84</td>\n",
       "      <td>271.60</td>\n",
       "      <td>271.60</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>8.866300</td>\n",
       "      <td>295.543333</td>\n",
       "      <td>318.88</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:29:00</th>\n",
       "      <td>344.67</td>\n",
       "      <td>344.67</td>\n",
       "      <td>344.67</td>\n",
       "      <td>344.67</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.446700</td>\n",
       "      <td>344.670000</td>\n",
       "      <td>326.47</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:30:00</th>\n",
       "      <td>346.41</td>\n",
       "      <td>346.41</td>\n",
       "      <td>305.38</td>\n",
       "      <td>305.38</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>6.517900</td>\n",
       "      <td>325.895000</td>\n",
       "      <td>331.94</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:31:00</th>\n",
       "      <td>345.11</td>\n",
       "      <td>345.11</td>\n",
       "      <td>270.35</td>\n",
       "      <td>270.35</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>8.901400</td>\n",
       "      <td>296.713333</td>\n",
       "      <td>312.07</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:32:00</th>\n",
       "      <td>333.68</td>\n",
       "      <td>345.44</td>\n",
       "      <td>333.68</td>\n",
       "      <td>345.44</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>10.140900</td>\n",
       "      <td>338.030000</td>\n",
       "      <td>285.69</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:33:00</th>\n",
       "      <td>280.16</td>\n",
       "      <td>295.38</td>\n",
       "      <td>280.16</td>\n",
       "      <td>295.38</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>5.755400</td>\n",
       "      <td>287.770000</td>\n",
       "      <td>317.96</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:34:00</th>\n",
       "      <td>333.29</td>\n",
       "      <td>333.29</td>\n",
       "      <td>270.56</td>\n",
       "      <td>321.98</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>9.258300</td>\n",
       "      <td>308.610000</td>\n",
       "      <td>290.91</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:35:00</th>\n",
       "      <td>302.25</td>\n",
       "      <td>349.67</td>\n",
       "      <td>302.25</td>\n",
       "      <td>349.67</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>6.519200</td>\n",
       "      <td>325.960000</td>\n",
       "      <td>283.25</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:36:00</th>\n",
       "      <td>302.12</td>\n",
       "      <td>302.12</td>\n",
       "      <td>287.34</td>\n",
       "      <td>287.34</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>5.894600</td>\n",
       "      <td>294.730000</td>\n",
       "      <td>336.87</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420680960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:37:00</th>\n",
       "      <td>330.17</td>\n",
       "      <td>330.17</td>\n",
       "      <td>281.36</td>\n",
       "      <td>281.36</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>6.115300</td>\n",
       "      <td>305.765000</td>\n",
       "      <td>293.59</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:38:00</th>\n",
       "      <td>336.38</td>\n",
       "      <td>347.37</td>\n",
       "      <td>336.38</td>\n",
       "      <td>347.37</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>6.837500</td>\n",
       "      <td>341.875000</td>\n",
       "      <td>279.55</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:39:00</th>\n",
       "      <td>326.10</td>\n",
       "      <td>341.35</td>\n",
       "      <td>324.84</td>\n",
       "      <td>341.35</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>9.922900</td>\n",
       "      <td>330.763333</td>\n",
       "      <td>324.92</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:40:00</th>\n",
       "      <td>348.77</td>\n",
       "      <td>348.77</td>\n",
       "      <td>340.62</td>\n",
       "      <td>340.62</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>10.340600</td>\n",
       "      <td>344.686667</td>\n",
       "      <td>333.68</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:41:00</th>\n",
       "      <td>333.73</td>\n",
       "      <td>333.73</td>\n",
       "      <td>333.73</td>\n",
       "      <td>333.73</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.337300</td>\n",
       "      <td>333.730000</td>\n",
       "      <td>301.60</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:42:00</th>\n",
       "      <td>329.84</td>\n",
       "      <td>333.28</td>\n",
       "      <td>313.42</td>\n",
       "      <td>333.28</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>9.765400</td>\n",
       "      <td>325.513333</td>\n",
       "      <td>311.66</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:43:00</th>\n",
       "      <td>273.20</td>\n",
       "      <td>316.19</td>\n",
       "      <td>273.20</td>\n",
       "      <td>316.19</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>5.893900</td>\n",
       "      <td>294.695000</td>\n",
       "      <td>269.57</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:44:00</th>\n",
       "      <td>323.27</td>\n",
       "      <td>351.38</td>\n",
       "      <td>323.27</td>\n",
       "      <td>351.38</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>6.746500</td>\n",
       "      <td>337.325000</td>\n",
       "      <td>285.68</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:45:00</th>\n",
       "      <td>307.38</td>\n",
       "      <td>328.70</td>\n",
       "      <td>284.78</td>\n",
       "      <td>328.70</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>12.267900</td>\n",
       "      <td>306.697500</td>\n",
       "      <td>344.94</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:46:00</th>\n",
       "      <td>314.65</td>\n",
       "      <td>337.90</td>\n",
       "      <td>279.37</td>\n",
       "      <td>337.90</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>9.319200</td>\n",
       "      <td>310.640000</td>\n",
       "      <td>315.83</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:47:00</th>\n",
       "      <td>328.24</td>\n",
       "      <td>328.24</td>\n",
       "      <td>318.31</td>\n",
       "      <td>327.61</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>9.741600</td>\n",
       "      <td>324.720000</td>\n",
       "      <td>298.77</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:48:00</th>\n",
       "      <td>307.25</td>\n",
       "      <td>317.98</td>\n",
       "      <td>307.25</td>\n",
       "      <td>317.98</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>6.252300</td>\n",
       "      <td>312.615000</td>\n",
       "      <td>323.58</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:49:00</th>\n",
       "      <td>359.39</td>\n",
       "      <td>359.39</td>\n",
       "      <td>301.99</td>\n",
       "      <td>301.99</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>6.613800</td>\n",
       "      <td>330.690000</td>\n",
       "      <td>322.57</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:50:00</th>\n",
       "      <td>303.53</td>\n",
       "      <td>333.28</td>\n",
       "      <td>290.18</td>\n",
       "      <td>333.28</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>9.269900</td>\n",
       "      <td>308.996667</td>\n",
       "      <td>333.11</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:51:00</th>\n",
       "      <td>278.49</td>\n",
       "      <td>329.03</td>\n",
       "      <td>278.49</td>\n",
       "      <td>329.03</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>6.075200</td>\n",
       "      <td>303.760000</td>\n",
       "      <td>304.57</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:52:00</th>\n",
       "      <td>333.45</td>\n",
       "      <td>333.45</td>\n",
       "      <td>318.88</td>\n",
       "      <td>318.88</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>6.523300</td>\n",
       "      <td>326.165000</td>\n",
       "      <td>279.95</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:53:00</th>\n",
       "      <td>328.39</td>\n",
       "      <td>328.39</td>\n",
       "      <td>326.47</td>\n",
       "      <td>326.47</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>6.548600</td>\n",
       "      <td>327.430000</td>\n",
       "      <td>317.43</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1420681980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:30:00</th>\n",
       "      <td>430.11</td>\n",
       "      <td>430.12</td>\n",
       "      <td>430.11</td>\n",
       "      <td>430.12</td>\n",
       "      <td>0.383600</td>\n",
       "      <td>164.992522</td>\n",
       "      <td>430.116064</td>\n",
       "      <td>430.00</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451604600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:31:00</th>\n",
       "      <td>430.12</td>\n",
       "      <td>430.12</td>\n",
       "      <td>430.12</td>\n",
       "      <td>430.12</td>\n",
       "      <td>0.252100</td>\n",
       "      <td>108.433252</td>\n",
       "      <td>430.120000</td>\n",
       "      <td>430.00</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451604660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:32:00</th>\n",
       "      <td>430.11</td>\n",
       "      <td>430.12</td>\n",
       "      <td>430.11</td>\n",
       "      <td>430.12</td>\n",
       "      <td>1.556400</td>\n",
       "      <td>669.438337</td>\n",
       "      <td>430.119723</td>\n",
       "      <td>430.05</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451604720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:33:00</th>\n",
       "      <td>430.12</td>\n",
       "      <td>430.12</td>\n",
       "      <td>430.11</td>\n",
       "      <td>430.11</td>\n",
       "      <td>3.524700</td>\n",
       "      <td>1516.025964</td>\n",
       "      <td>430.114893</td>\n",
       "      <td>430.34</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451604780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:34:00</th>\n",
       "      <td>430.12</td>\n",
       "      <td>430.12</td>\n",
       "      <td>429.57</td>\n",
       "      <td>429.57</td>\n",
       "      <td>15.768648</td>\n",
       "      <td>6777.215609</td>\n",
       "      <td>429.790527</td>\n",
       "      <td>430.26</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451604840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:35:00</th>\n",
       "      <td>429.58</td>\n",
       "      <td>429.58</td>\n",
       "      <td>429.58</td>\n",
       "      <td>429.58</td>\n",
       "      <td>0.046700</td>\n",
       "      <td>20.061386</td>\n",
       "      <td>429.580000</td>\n",
       "      <td>430.35</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451604900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:36:00</th>\n",
       "      <td>429.58</td>\n",
       "      <td>429.58</td>\n",
       "      <td>429.58</td>\n",
       "      <td>429.58</td>\n",
       "      <td>2.369000</td>\n",
       "      <td>1017.675020</td>\n",
       "      <td>429.580000</td>\n",
       "      <td>430.39</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451604960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:37:00</th>\n",
       "      <td>429.57</td>\n",
       "      <td>429.57</td>\n",
       "      <td>429.00</td>\n",
       "      <td>429.31</td>\n",
       "      <td>79.919528</td>\n",
       "      <td>34304.828173</td>\n",
       "      <td>429.242126</td>\n",
       "      <td>430.40</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:38:00</th>\n",
       "      <td>429.30</td>\n",
       "      <td>429.34</td>\n",
       "      <td>429.19</td>\n",
       "      <td>429.34</td>\n",
       "      <td>1.019700</td>\n",
       "      <td>437.732069</td>\n",
       "      <td>429.275345</td>\n",
       "      <td>430.40</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:39:00</th>\n",
       "      <td>429.34</td>\n",
       "      <td>429.36</td>\n",
       "      <td>429.14</td>\n",
       "      <td>429.14</td>\n",
       "      <td>0.318900</td>\n",
       "      <td>136.906232</td>\n",
       "      <td>429.307720</td>\n",
       "      <td>430.36</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:40:00</th>\n",
       "      <td>429.14</td>\n",
       "      <td>429.14</td>\n",
       "      <td>429.14</td>\n",
       "      <td>429.14</td>\n",
       "      <td>0.084300</td>\n",
       "      <td>36.176502</td>\n",
       "      <td>429.140000</td>\n",
       "      <td>430.39</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:41:00</th>\n",
       "      <td>429.14</td>\n",
       "      <td>429.29</td>\n",
       "      <td>429.14</td>\n",
       "      <td>429.29</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>268.224611</td>\n",
       "      <td>429.159378</td>\n",
       "      <td>430.96</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:42:00</th>\n",
       "      <td>429.24</td>\n",
       "      <td>429.42</td>\n",
       "      <td>429.24</td>\n",
       "      <td>429.27</td>\n",
       "      <td>1.588500</td>\n",
       "      <td>681.916559</td>\n",
       "      <td>429.283323</td>\n",
       "      <td>431.18</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:43:00</th>\n",
       "      <td>429.29</td>\n",
       "      <td>429.30</td>\n",
       "      <td>429.28</td>\n",
       "      <td>429.30</td>\n",
       "      <td>1.103500</td>\n",
       "      <td>473.729112</td>\n",
       "      <td>429.296884</td>\n",
       "      <td>431.82</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:44:00</th>\n",
       "      <td>429.35</td>\n",
       "      <td>429.54</td>\n",
       "      <td>429.35</td>\n",
       "      <td>429.47</td>\n",
       "      <td>3.557600</td>\n",
       "      <td>1527.927504</td>\n",
       "      <td>429.482658</td>\n",
       "      <td>431.52</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:45:00</th>\n",
       "      <td>429.52</td>\n",
       "      <td>429.72</td>\n",
       "      <td>429.52</td>\n",
       "      <td>429.53</td>\n",
       "      <td>8.347200</td>\n",
       "      <td>3585.650048</td>\n",
       "      <td>429.563213</td>\n",
       "      <td>431.59</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:46:00</th>\n",
       "      <td>429.56</td>\n",
       "      <td>430.02</td>\n",
       "      <td>429.56</td>\n",
       "      <td>429.92</td>\n",
       "      <td>6.754700</td>\n",
       "      <td>2903.303022</td>\n",
       "      <td>429.819684</td>\n",
       "      <td>431.58</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:47:00</th>\n",
       "      <td>429.88</td>\n",
       "      <td>429.88</td>\n",
       "      <td>429.88</td>\n",
       "      <td>429.88</td>\n",
       "      <td>2.579700</td>\n",
       "      <td>1108.961436</td>\n",
       "      <td>429.880000</td>\n",
       "      <td>431.57</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:48:00</th>\n",
       "      <td>429.89</td>\n",
       "      <td>430.01</td>\n",
       "      <td>429.80</td>\n",
       "      <td>430.01</td>\n",
       "      <td>2.561700</td>\n",
       "      <td>1101.334879</td>\n",
       "      <td>429.923441</td>\n",
       "      <td>431.57</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:49:00</th>\n",
       "      <td>429.94</td>\n",
       "      <td>429.94</td>\n",
       "      <td>429.94</td>\n",
       "      <td>429.94</td>\n",
       "      <td>0.115800</td>\n",
       "      <td>49.787052</td>\n",
       "      <td>429.940000</td>\n",
       "      <td>431.19</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:50:00</th>\n",
       "      <td>429.95</td>\n",
       "      <td>430.01</td>\n",
       "      <td>429.95</td>\n",
       "      <td>430.01</td>\n",
       "      <td>1.357100</td>\n",
       "      <td>583.547715</td>\n",
       "      <td>429.996106</td>\n",
       "      <td>431.23</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:51:00</th>\n",
       "      <td>430.01</td>\n",
       "      <td>430.01</td>\n",
       "      <td>430.01</td>\n",
       "      <td>430.01</td>\n",
       "      <td>0.152500</td>\n",
       "      <td>65.576525</td>\n",
       "      <td>430.010000</td>\n",
       "      <td>431.33</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:52:00</th>\n",
       "      <td>430.01</td>\n",
       "      <td>430.02</td>\n",
       "      <td>430.01</td>\n",
       "      <td>430.02</td>\n",
       "      <td>0.521300</td>\n",
       "      <td>224.167015</td>\n",
       "      <td>430.015375</td>\n",
       "      <td>431.40</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:53:00</th>\n",
       "      <td>430.02</td>\n",
       "      <td>430.02</td>\n",
       "      <td>429.82</td>\n",
       "      <td>429.93</td>\n",
       "      <td>3.138726</td>\n",
       "      <td>1349.442266</td>\n",
       "      <td>429.933172</td>\n",
       "      <td>431.47</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451605980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:54:00</th>\n",
       "      <td>429.96</td>\n",
       "      <td>430.09</td>\n",
       "      <td>429.96</td>\n",
       "      <td>430.00</td>\n",
       "      <td>0.806000</td>\n",
       "      <td>346.587440</td>\n",
       "      <td>430.009230</td>\n",
       "      <td>431.50</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451606040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:55:00</th>\n",
       "      <td>430.03</td>\n",
       "      <td>430.03</td>\n",
       "      <td>430.00</td>\n",
       "      <td>430.00</td>\n",
       "      <td>1.889600</td>\n",
       "      <td>812.553978</td>\n",
       "      <td>430.013748</td>\n",
       "      <td>431.50</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451606100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:56:00</th>\n",
       "      <td>430.01</td>\n",
       "      <td>430.05</td>\n",
       "      <td>430.01</td>\n",
       "      <td>430.05</td>\n",
       "      <td>0.352400</td>\n",
       "      <td>151.545120</td>\n",
       "      <td>430.037230</td>\n",
       "      <td>431.50</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451606160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:57:00</th>\n",
       "      <td>430.05</td>\n",
       "      <td>430.34</td>\n",
       "      <td>430.05</td>\n",
       "      <td>430.34</td>\n",
       "      <td>1.614500</td>\n",
       "      <td>694.696979</td>\n",
       "      <td>430.286144</td>\n",
       "      <td>431.55</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451606220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:58:00</th>\n",
       "      <td>430.20</td>\n",
       "      <td>430.26</td>\n",
       "      <td>430.09</td>\n",
       "      <td>430.26</td>\n",
       "      <td>3.639500</td>\n",
       "      <td>1565.533519</td>\n",
       "      <td>430.150713</td>\n",
       "      <td>431.55</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451606280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:59:00</th>\n",
       "      <td>430.30</td>\n",
       "      <td>430.35</td>\n",
       "      <td>430.30</td>\n",
       "      <td>430.35</td>\n",
       "      <td>2.216600</td>\n",
       "      <td>953.895860</td>\n",
       "      <td>430.341902</td>\n",
       "      <td>431.67</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1451606340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>507435 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Open    High     Low   Close  Volume_(BTC)  \\\n",
       "Date                                                                \n",
       "2015-01-08 01:24:00  360.00  360.00  360.00  360.00      0.010000   \n",
       "2015-01-08 01:25:00  360.00  360.00  360.00  360.00      0.010000   \n",
       "2015-01-08 01:26:00  360.00  360.00  360.00  360.00      0.010000   \n",
       "2015-01-08 01:27:00  271.84  276.34  271.84  276.34      0.020000   \n",
       "2015-01-08 01:28:00  295.19  319.84  271.60  271.60      0.030000   \n",
       "2015-01-08 01:29:00  344.67  344.67  344.67  344.67      0.010000   \n",
       "2015-01-08 01:30:00  346.41  346.41  305.38  305.38      0.020000   \n",
       "2015-01-08 01:31:00  345.11  345.11  270.35  270.35      0.030000   \n",
       "2015-01-08 01:32:00  333.68  345.44  333.68  345.44      0.030000   \n",
       "2015-01-08 01:33:00  280.16  295.38  280.16  295.38      0.020000   \n",
       "2015-01-08 01:34:00  333.29  333.29  270.56  321.98      0.030000   \n",
       "2015-01-08 01:35:00  302.25  349.67  302.25  349.67      0.020000   \n",
       "2015-01-08 01:36:00  302.12  302.12  287.34  287.34      0.020000   \n",
       "2015-01-08 01:37:00  330.17  330.17  281.36  281.36      0.020000   \n",
       "2015-01-08 01:38:00  336.38  347.37  336.38  347.37      0.020000   \n",
       "2015-01-08 01:39:00  326.10  341.35  324.84  341.35      0.030000   \n",
       "2015-01-08 01:40:00  348.77  348.77  340.62  340.62      0.030000   \n",
       "2015-01-08 01:41:00  333.73  333.73  333.73  333.73      0.010000   \n",
       "2015-01-08 01:42:00  329.84  333.28  313.42  333.28      0.030000   \n",
       "2015-01-08 01:43:00  273.20  316.19  273.20  316.19      0.020000   \n",
       "2015-01-08 01:44:00  323.27  351.38  323.27  351.38      0.020000   \n",
       "2015-01-08 01:45:00  307.38  328.70  284.78  328.70      0.040000   \n",
       "2015-01-08 01:46:00  314.65  337.90  279.37  337.90      0.030000   \n",
       "2015-01-08 01:47:00  328.24  328.24  318.31  327.61      0.030000   \n",
       "2015-01-08 01:48:00  307.25  317.98  307.25  317.98      0.020000   \n",
       "2015-01-08 01:49:00  359.39  359.39  301.99  301.99      0.020000   \n",
       "2015-01-08 01:50:00  303.53  333.28  290.18  333.28      0.030000   \n",
       "2015-01-08 01:51:00  278.49  329.03  278.49  329.03      0.020000   \n",
       "2015-01-08 01:52:00  333.45  333.45  318.88  318.88      0.020000   \n",
       "2015-01-08 01:53:00  328.39  328.39  326.47  326.47      0.020000   \n",
       "...                     ...     ...     ...     ...           ...   \n",
       "2015-12-31 23:30:00  430.11  430.12  430.11  430.12      0.383600   \n",
       "2015-12-31 23:31:00  430.12  430.12  430.12  430.12      0.252100   \n",
       "2015-12-31 23:32:00  430.11  430.12  430.11  430.12      1.556400   \n",
       "2015-12-31 23:33:00  430.12  430.12  430.11  430.11      3.524700   \n",
       "2015-12-31 23:34:00  430.12  430.12  429.57  429.57     15.768648   \n",
       "2015-12-31 23:35:00  429.58  429.58  429.58  429.58      0.046700   \n",
       "2015-12-31 23:36:00  429.58  429.58  429.58  429.58      2.369000   \n",
       "2015-12-31 23:37:00  429.57  429.57  429.00  429.31     79.919528   \n",
       "2015-12-31 23:38:00  429.30  429.34  429.19  429.34      1.019700   \n",
       "2015-12-31 23:39:00  429.34  429.36  429.14  429.14      0.318900   \n",
       "2015-12-31 23:40:00  429.14  429.14  429.14  429.14      0.084300   \n",
       "2015-12-31 23:41:00  429.14  429.29  429.14  429.29      0.625000   \n",
       "2015-12-31 23:42:00  429.24  429.42  429.24  429.27      1.588500   \n",
       "2015-12-31 23:43:00  429.29  429.30  429.28  429.30      1.103500   \n",
       "2015-12-31 23:44:00  429.35  429.54  429.35  429.47      3.557600   \n",
       "2015-12-31 23:45:00  429.52  429.72  429.52  429.53      8.347200   \n",
       "2015-12-31 23:46:00  429.56  430.02  429.56  429.92      6.754700   \n",
       "2015-12-31 23:47:00  429.88  429.88  429.88  429.88      2.579700   \n",
       "2015-12-31 23:48:00  429.89  430.01  429.80  430.01      2.561700   \n",
       "2015-12-31 23:49:00  429.94  429.94  429.94  429.94      0.115800   \n",
       "2015-12-31 23:50:00  429.95  430.01  429.95  430.01      1.357100   \n",
       "2015-12-31 23:51:00  430.01  430.01  430.01  430.01      0.152500   \n",
       "2015-12-31 23:52:00  430.01  430.02  430.01  430.02      0.521300   \n",
       "2015-12-31 23:53:00  430.02  430.02  429.82  429.93      3.138726   \n",
       "2015-12-31 23:54:00  429.96  430.09  429.96  430.00      0.806000   \n",
       "2015-12-31 23:55:00  430.03  430.03  430.00  430.00      1.889600   \n",
       "2015-12-31 23:56:00  430.01  430.05  430.01  430.05      0.352400   \n",
       "2015-12-31 23:57:00  430.05  430.34  430.05  430.34      1.614500   \n",
       "2015-12-31 23:58:00  430.20  430.26  430.09  430.26      3.639500   \n",
       "2015-12-31 23:59:00  430.30  430.35  430.30  430.35      2.216600   \n",
       "\n",
       "                     Volume_(Currency)  Weighted_Price  PriceClose2D  Year  \\\n",
       "Date                                                                         \n",
       "2015-01-08 01:24:00           3.600000      360.000000        317.98  2015   \n",
       "2015-01-08 01:25:00           3.600000      360.000000        301.99  2015   \n",
       "2015-01-08 01:26:00           3.600000      360.000000        333.28  2015   \n",
       "2015-01-08 01:27:00           5.481800      274.090000        329.03  2015   \n",
       "2015-01-08 01:28:00           8.866300      295.543333        318.88  2015   \n",
       "2015-01-08 01:29:00           3.446700      344.670000        326.47  2015   \n",
       "2015-01-08 01:30:00           6.517900      325.895000        331.94  2015   \n",
       "2015-01-08 01:31:00           8.901400      296.713333        312.07  2015   \n",
       "2015-01-08 01:32:00          10.140900      338.030000        285.69  2015   \n",
       "2015-01-08 01:33:00           5.755400      287.770000        317.96  2015   \n",
       "2015-01-08 01:34:00           9.258300      308.610000        290.91  2015   \n",
       "2015-01-08 01:35:00           6.519200      325.960000        283.25  2015   \n",
       "2015-01-08 01:36:00           5.894600      294.730000        336.87  2015   \n",
       "2015-01-08 01:37:00           6.115300      305.765000        293.59  2015   \n",
       "2015-01-08 01:38:00           6.837500      341.875000        279.55  2015   \n",
       "2015-01-08 01:39:00           9.922900      330.763333        324.92  2015   \n",
       "2015-01-08 01:40:00          10.340600      344.686667        333.68  2015   \n",
       "2015-01-08 01:41:00           3.337300      333.730000        301.60  2015   \n",
       "2015-01-08 01:42:00           9.765400      325.513333        311.66  2015   \n",
       "2015-01-08 01:43:00           5.893900      294.695000        269.57  2015   \n",
       "2015-01-08 01:44:00           6.746500      337.325000        285.68  2015   \n",
       "2015-01-08 01:45:00          12.267900      306.697500        344.94  2015   \n",
       "2015-01-08 01:46:00           9.319200      310.640000        315.83  2015   \n",
       "2015-01-08 01:47:00           9.741600      324.720000        298.77  2015   \n",
       "2015-01-08 01:48:00           6.252300      312.615000        323.58  2015   \n",
       "2015-01-08 01:49:00           6.613800      330.690000        322.57  2015   \n",
       "2015-01-08 01:50:00           9.269900      308.996667        333.11  2015   \n",
       "2015-01-08 01:51:00           6.075200      303.760000        304.57  2015   \n",
       "2015-01-08 01:52:00           6.523300      326.165000        279.95  2015   \n",
       "2015-01-08 01:53:00           6.548600      327.430000        317.43  2015   \n",
       "...                                ...             ...           ...   ...   \n",
       "2015-12-31 23:30:00         164.992522      430.116064        430.00  2015   \n",
       "2015-12-31 23:31:00         108.433252      430.120000        430.00  2015   \n",
       "2015-12-31 23:32:00         669.438337      430.119723        430.05  2015   \n",
       "2015-12-31 23:33:00        1516.025964      430.114893        430.34  2015   \n",
       "2015-12-31 23:34:00        6777.215609      429.790527        430.26  2015   \n",
       "2015-12-31 23:35:00          20.061386      429.580000        430.35  2015   \n",
       "2015-12-31 23:36:00        1017.675020      429.580000        430.39  2015   \n",
       "2015-12-31 23:37:00       34304.828173      429.242126        430.40  2015   \n",
       "2015-12-31 23:38:00         437.732069      429.275345        430.40  2015   \n",
       "2015-12-31 23:39:00         136.906232      429.307720        430.36  2015   \n",
       "2015-12-31 23:40:00          36.176502      429.140000        430.39  2015   \n",
       "2015-12-31 23:41:00         268.224611      429.159378        430.96  2015   \n",
       "2015-12-31 23:42:00         681.916559      429.283323        431.18  2015   \n",
       "2015-12-31 23:43:00         473.729112      429.296884        431.82  2015   \n",
       "2015-12-31 23:44:00        1527.927504      429.482658        431.52  2015   \n",
       "2015-12-31 23:45:00        3585.650048      429.563213        431.59  2015   \n",
       "2015-12-31 23:46:00        2903.303022      429.819684        431.58  2015   \n",
       "2015-12-31 23:47:00        1108.961436      429.880000        431.57  2015   \n",
       "2015-12-31 23:48:00        1101.334879      429.923441        431.57  2015   \n",
       "2015-12-31 23:49:00          49.787052      429.940000        431.19  2015   \n",
       "2015-12-31 23:50:00         583.547715      429.996106        431.23  2015   \n",
       "2015-12-31 23:51:00          65.576525      430.010000        431.33  2015   \n",
       "2015-12-31 23:52:00         224.167015      430.015375        431.40  2015   \n",
       "2015-12-31 23:53:00        1349.442266      429.933172        431.47  2015   \n",
       "2015-12-31 23:54:00         346.587440      430.009230        431.50  2015   \n",
       "2015-12-31 23:55:00         812.553978      430.013748        431.50  2015   \n",
       "2015-12-31 23:56:00         151.545120      430.037230        431.50  2015   \n",
       "2015-12-31 23:57:00         694.696979      430.286144        431.55  2015   \n",
       "2015-12-31 23:58:00        1565.533519      430.150713        431.55  2015   \n",
       "2015-12-31 23:59:00         953.895860      430.341902        431.67  2015   \n",
       "\n",
       "                     Month     ...      Day  Dayofweek  Dayofyear  \\\n",
       "Date                           ...                                  \n",
       "2015-01-08 01:24:00      1     ...        8          3          8   \n",
       "2015-01-08 01:25:00      1     ...        8          3          8   \n",
       "2015-01-08 01:26:00      1     ...        8          3          8   \n",
       "2015-01-08 01:27:00      1     ...        8          3          8   \n",
       "2015-01-08 01:28:00      1     ...        8          3          8   \n",
       "2015-01-08 01:29:00      1     ...        8          3          8   \n",
       "2015-01-08 01:30:00      1     ...        8          3          8   \n",
       "2015-01-08 01:31:00      1     ...        8          3          8   \n",
       "2015-01-08 01:32:00      1     ...        8          3          8   \n",
       "2015-01-08 01:33:00      1     ...        8          3          8   \n",
       "2015-01-08 01:34:00      1     ...        8          3          8   \n",
       "2015-01-08 01:35:00      1     ...        8          3          8   \n",
       "2015-01-08 01:36:00      1     ...        8          3          8   \n",
       "2015-01-08 01:37:00      1     ...        8          3          8   \n",
       "2015-01-08 01:38:00      1     ...        8          3          8   \n",
       "2015-01-08 01:39:00      1     ...        8          3          8   \n",
       "2015-01-08 01:40:00      1     ...        8          3          8   \n",
       "2015-01-08 01:41:00      1     ...        8          3          8   \n",
       "2015-01-08 01:42:00      1     ...        8          3          8   \n",
       "2015-01-08 01:43:00      1     ...        8          3          8   \n",
       "2015-01-08 01:44:00      1     ...        8          3          8   \n",
       "2015-01-08 01:45:00      1     ...        8          3          8   \n",
       "2015-01-08 01:46:00      1     ...        8          3          8   \n",
       "2015-01-08 01:47:00      1     ...        8          3          8   \n",
       "2015-01-08 01:48:00      1     ...        8          3          8   \n",
       "2015-01-08 01:49:00      1     ...        8          3          8   \n",
       "2015-01-08 01:50:00      1     ...        8          3          8   \n",
       "2015-01-08 01:51:00      1     ...        8          3          8   \n",
       "2015-01-08 01:52:00      1     ...        8          3          8   \n",
       "2015-01-08 01:53:00      1     ...        8          3          8   \n",
       "...                    ...     ...      ...        ...        ...   \n",
       "2015-12-31 23:30:00     12     ...       31          3        365   \n",
       "2015-12-31 23:31:00     12     ...       31          3        365   \n",
       "2015-12-31 23:32:00     12     ...       31          3        365   \n",
       "2015-12-31 23:33:00     12     ...       31          3        365   \n",
       "2015-12-31 23:34:00     12     ...       31          3        365   \n",
       "2015-12-31 23:35:00     12     ...       31          3        365   \n",
       "2015-12-31 23:36:00     12     ...       31          3        365   \n",
       "2015-12-31 23:37:00     12     ...       31          3        365   \n",
       "2015-12-31 23:38:00     12     ...       31          3        365   \n",
       "2015-12-31 23:39:00     12     ...       31          3        365   \n",
       "2015-12-31 23:40:00     12     ...       31          3        365   \n",
       "2015-12-31 23:41:00     12     ...       31          3        365   \n",
       "2015-12-31 23:42:00     12     ...       31          3        365   \n",
       "2015-12-31 23:43:00     12     ...       31          3        365   \n",
       "2015-12-31 23:44:00     12     ...       31          3        365   \n",
       "2015-12-31 23:45:00     12     ...       31          3        365   \n",
       "2015-12-31 23:46:00     12     ...       31          3        365   \n",
       "2015-12-31 23:47:00     12     ...       31          3        365   \n",
       "2015-12-31 23:48:00     12     ...       31          3        365   \n",
       "2015-12-31 23:49:00     12     ...       31          3        365   \n",
       "2015-12-31 23:50:00     12     ...       31          3        365   \n",
       "2015-12-31 23:51:00     12     ...       31          3        365   \n",
       "2015-12-31 23:52:00     12     ...       31          3        365   \n",
       "2015-12-31 23:53:00     12     ...       31          3        365   \n",
       "2015-12-31 23:54:00     12     ...       31          3        365   \n",
       "2015-12-31 23:55:00     12     ...       31          3        365   \n",
       "2015-12-31 23:56:00     12     ...       31          3        365   \n",
       "2015-12-31 23:57:00     12     ...       31          3        365   \n",
       "2015-12-31 23:58:00     12     ...       31          3        365   \n",
       "2015-12-31 23:59:00     12     ...       31          3        365   \n",
       "\n",
       "                     Is_month_end  Is_month_start  Is_quarter_end  \\\n",
       "Date                                                                \n",
       "2015-01-08 01:24:00         False           False           False   \n",
       "2015-01-08 01:25:00         False           False           False   \n",
       "2015-01-08 01:26:00         False           False           False   \n",
       "2015-01-08 01:27:00         False           False           False   \n",
       "2015-01-08 01:28:00         False           False           False   \n",
       "2015-01-08 01:29:00         False           False           False   \n",
       "2015-01-08 01:30:00         False           False           False   \n",
       "2015-01-08 01:31:00         False           False           False   \n",
       "2015-01-08 01:32:00         False           False           False   \n",
       "2015-01-08 01:33:00         False           False           False   \n",
       "2015-01-08 01:34:00         False           False           False   \n",
       "2015-01-08 01:35:00         False           False           False   \n",
       "2015-01-08 01:36:00         False           False           False   \n",
       "2015-01-08 01:37:00         False           False           False   \n",
       "2015-01-08 01:38:00         False           False           False   \n",
       "2015-01-08 01:39:00         False           False           False   \n",
       "2015-01-08 01:40:00         False           False           False   \n",
       "2015-01-08 01:41:00         False           False           False   \n",
       "2015-01-08 01:42:00         False           False           False   \n",
       "2015-01-08 01:43:00         False           False           False   \n",
       "2015-01-08 01:44:00         False           False           False   \n",
       "2015-01-08 01:45:00         False           False           False   \n",
       "2015-01-08 01:46:00         False           False           False   \n",
       "2015-01-08 01:47:00         False           False           False   \n",
       "2015-01-08 01:48:00         False           False           False   \n",
       "2015-01-08 01:49:00         False           False           False   \n",
       "2015-01-08 01:50:00         False           False           False   \n",
       "2015-01-08 01:51:00         False           False           False   \n",
       "2015-01-08 01:52:00         False           False           False   \n",
       "2015-01-08 01:53:00         False           False           False   \n",
       "...                           ...             ...             ...   \n",
       "2015-12-31 23:30:00          True           False            True   \n",
       "2015-12-31 23:31:00          True           False            True   \n",
       "2015-12-31 23:32:00          True           False            True   \n",
       "2015-12-31 23:33:00          True           False            True   \n",
       "2015-12-31 23:34:00          True           False            True   \n",
       "2015-12-31 23:35:00          True           False            True   \n",
       "2015-12-31 23:36:00          True           False            True   \n",
       "2015-12-31 23:37:00          True           False            True   \n",
       "2015-12-31 23:38:00          True           False            True   \n",
       "2015-12-31 23:39:00          True           False            True   \n",
       "2015-12-31 23:40:00          True           False            True   \n",
       "2015-12-31 23:41:00          True           False            True   \n",
       "2015-12-31 23:42:00          True           False            True   \n",
       "2015-12-31 23:43:00          True           False            True   \n",
       "2015-12-31 23:44:00          True           False            True   \n",
       "2015-12-31 23:45:00          True           False            True   \n",
       "2015-12-31 23:46:00          True           False            True   \n",
       "2015-12-31 23:47:00          True           False            True   \n",
       "2015-12-31 23:48:00          True           False            True   \n",
       "2015-12-31 23:49:00          True           False            True   \n",
       "2015-12-31 23:50:00          True           False            True   \n",
       "2015-12-31 23:51:00          True           False            True   \n",
       "2015-12-31 23:52:00          True           False            True   \n",
       "2015-12-31 23:53:00          True           False            True   \n",
       "2015-12-31 23:54:00          True           False            True   \n",
       "2015-12-31 23:55:00          True           False            True   \n",
       "2015-12-31 23:56:00          True           False            True   \n",
       "2015-12-31 23:57:00          True           False            True   \n",
       "2015-12-31 23:58:00          True           False            True   \n",
       "2015-12-31 23:59:00          True           False            True   \n",
       "\n",
       "                     Is_quarter_start  Is_year_end  Is_year_start     Elapsed  \n",
       "Date                                                                           \n",
       "2015-01-08 01:24:00             False        False          False  1420680240  \n",
       "2015-01-08 01:25:00             False        False          False  1420680300  \n",
       "2015-01-08 01:26:00             False        False          False  1420680360  \n",
       "2015-01-08 01:27:00             False        False          False  1420680420  \n",
       "2015-01-08 01:28:00             False        False          False  1420680480  \n",
       "2015-01-08 01:29:00             False        False          False  1420680540  \n",
       "2015-01-08 01:30:00             False        False          False  1420680600  \n",
       "2015-01-08 01:31:00             False        False          False  1420680660  \n",
       "2015-01-08 01:32:00             False        False          False  1420680720  \n",
       "2015-01-08 01:33:00             False        False          False  1420680780  \n",
       "2015-01-08 01:34:00             False        False          False  1420680840  \n",
       "2015-01-08 01:35:00             False        False          False  1420680900  \n",
       "2015-01-08 01:36:00             False        False          False  1420680960  \n",
       "2015-01-08 01:37:00             False        False          False  1420681020  \n",
       "2015-01-08 01:38:00             False        False          False  1420681080  \n",
       "2015-01-08 01:39:00             False        False          False  1420681140  \n",
       "2015-01-08 01:40:00             False        False          False  1420681200  \n",
       "2015-01-08 01:41:00             False        False          False  1420681260  \n",
       "2015-01-08 01:42:00             False        False          False  1420681320  \n",
       "2015-01-08 01:43:00             False        False          False  1420681380  \n",
       "2015-01-08 01:44:00             False        False          False  1420681440  \n",
       "2015-01-08 01:45:00             False        False          False  1420681500  \n",
       "2015-01-08 01:46:00             False        False          False  1420681560  \n",
       "2015-01-08 01:47:00             False        False          False  1420681620  \n",
       "2015-01-08 01:48:00             False        False          False  1420681680  \n",
       "2015-01-08 01:49:00             False        False          False  1420681740  \n",
       "2015-01-08 01:50:00             False        False          False  1420681800  \n",
       "2015-01-08 01:51:00             False        False          False  1420681860  \n",
       "2015-01-08 01:52:00             False        False          False  1420681920  \n",
       "2015-01-08 01:53:00             False        False          False  1420681980  \n",
       "...                               ...          ...            ...         ...  \n",
       "2015-12-31 23:30:00             False         True          False  1451604600  \n",
       "2015-12-31 23:31:00             False         True          False  1451604660  \n",
       "2015-12-31 23:32:00             False         True          False  1451604720  \n",
       "2015-12-31 23:33:00             False         True          False  1451604780  \n",
       "2015-12-31 23:34:00             False         True          False  1451604840  \n",
       "2015-12-31 23:35:00             False         True          False  1451604900  \n",
       "2015-12-31 23:36:00             False         True          False  1451604960  \n",
       "2015-12-31 23:37:00             False         True          False  1451605020  \n",
       "2015-12-31 23:38:00             False         True          False  1451605080  \n",
       "2015-12-31 23:39:00             False         True          False  1451605140  \n",
       "2015-12-31 23:40:00             False         True          False  1451605200  \n",
       "2015-12-31 23:41:00             False         True          False  1451605260  \n",
       "2015-12-31 23:42:00             False         True          False  1451605320  \n",
       "2015-12-31 23:43:00             False         True          False  1451605380  \n",
       "2015-12-31 23:44:00             False         True          False  1451605440  \n",
       "2015-12-31 23:45:00             False         True          False  1451605500  \n",
       "2015-12-31 23:46:00             False         True          False  1451605560  \n",
       "2015-12-31 23:47:00             False         True          False  1451605620  \n",
       "2015-12-31 23:48:00             False         True          False  1451605680  \n",
       "2015-12-31 23:49:00             False         True          False  1451605740  \n",
       "2015-12-31 23:50:00             False         True          False  1451605800  \n",
       "2015-12-31 23:51:00             False         True          False  1451605860  \n",
       "2015-12-31 23:52:00             False         True          False  1451605920  \n",
       "2015-12-31 23:53:00             False         True          False  1451605980  \n",
       "2015-12-31 23:54:00             False         True          False  1451606040  \n",
       "2015-12-31 23:55:00             False         True          False  1451606100  \n",
       "2015-12-31 23:56:00             False         True          False  1451606160  \n",
       "2015-12-31 23:57:00             False         True          False  1451606220  \n",
       "2015-12-31 23:58:00             False         True          False  1451606280  \n",
       "2015-12-31 23:59:00             False         True          False  1451606340  \n",
       "\n",
       "[507435 rows x 21 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase['2015']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "      <th>PriceClose2D</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>...</th>\n",
       "      <th>Day</th>\n",
       "      <th>Dayofweek</th>\n",
       "      <th>Dayofyear</th>\n",
       "      <th>Is_month_end</th>\n",
       "      <th>Is_month_start</th>\n",
       "      <th>Is_quarter_end</th>\n",
       "      <th>Is_quarter_start</th>\n",
       "      <th>Is_year_end</th>\n",
       "      <th>Is_year_start</th>\n",
       "      <th>Elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Open</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.161848</td>\n",
       "      <td>0.408601</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999939</td>\n",
       "      <td>0.759325</td>\n",
       "      <td>0.067457</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004283</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.065973</td>\n",
       "      <td>-0.001450</td>\n",
       "      <td>-0.003857</td>\n",
       "      <td>-0.001774</td>\n",
       "      <td>-0.001980</td>\n",
       "      <td>0.022534</td>\n",
       "      <td>0.022791</td>\n",
       "      <td>0.772079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>0.999998</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.162435</td>\n",
       "      <td>0.409441</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999940</td>\n",
       "      <td>0.759188</td>\n",
       "      <td>0.067436</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004278</td>\n",
       "      <td>0.002731</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>-0.001454</td>\n",
       "      <td>-0.003867</td>\n",
       "      <td>-0.001776</td>\n",
       "      <td>-0.001986</td>\n",
       "      <td>0.022547</td>\n",
       "      <td>0.022796</td>\n",
       "      <td>0.771938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.161186</td>\n",
       "      <td>0.407689</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999940</td>\n",
       "      <td>0.759466</td>\n",
       "      <td>0.067483</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004288</td>\n",
       "      <td>0.002736</td>\n",
       "      <td>0.065998</td>\n",
       "      <td>-0.001448</td>\n",
       "      <td>-0.003846</td>\n",
       "      <td>-0.001771</td>\n",
       "      <td>-0.001974</td>\n",
       "      <td>0.022519</td>\n",
       "      <td>0.022782</td>\n",
       "      <td>0.772226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Close</th>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.161824</td>\n",
       "      <td>0.408592</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999942</td>\n",
       "      <td>0.759322</td>\n",
       "      <td>0.067465</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004285</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.065981</td>\n",
       "      <td>-0.001450</td>\n",
       "      <td>-0.003856</td>\n",
       "      <td>-0.001772</td>\n",
       "      <td>-0.001980</td>\n",
       "      <td>0.022536</td>\n",
       "      <td>0.022790</td>\n",
       "      <td>0.772078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <td>0.161848</td>\n",
       "      <td>0.162435</td>\n",
       "      <td>0.161186</td>\n",
       "      <td>0.161824</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700766</td>\n",
       "      <td>0.161795</td>\n",
       "      <td>0.162087</td>\n",
       "      <td>0.088634</td>\n",
       "      <td>0.007600</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015111</td>\n",
       "      <td>-0.041325</td>\n",
       "      <td>0.006059</td>\n",
       "      <td>-0.005342</td>\n",
       "      <td>-0.006074</td>\n",
       "      <td>-0.008781</td>\n",
       "      <td>-0.013923</td>\n",
       "      <td>-0.001985</td>\n",
       "      <td>-0.006797</td>\n",
       "      <td>0.089792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <td>0.408601</td>\n",
       "      <td>0.409441</td>\n",
       "      <td>0.407689</td>\n",
       "      <td>0.408592</td>\n",
       "      <td>0.700766</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.408548</td>\n",
       "      <td>0.408886</td>\n",
       "      <td>0.223065</td>\n",
       "      <td>0.037395</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010549</td>\n",
       "      <td>-0.016482</td>\n",
       "      <td>0.036368</td>\n",
       "      <td>-0.006403</td>\n",
       "      <td>-0.006600</td>\n",
       "      <td>-0.007769</td>\n",
       "      <td>-0.011206</td>\n",
       "      <td>0.008317</td>\n",
       "      <td>0.001724</td>\n",
       "      <td>0.231154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weighted_Price</th>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.161795</td>\n",
       "      <td>0.408548</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999941</td>\n",
       "      <td>0.759327</td>\n",
       "      <td>0.067460</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004284</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.065976</td>\n",
       "      <td>-0.001451</td>\n",
       "      <td>-0.003857</td>\n",
       "      <td>-0.001774</td>\n",
       "      <td>-0.001980</td>\n",
       "      <td>0.022533</td>\n",
       "      <td>0.022788</td>\n",
       "      <td>0.772082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PriceClose2D</th>\n",
       "      <td>0.999939</td>\n",
       "      <td>0.999940</td>\n",
       "      <td>0.999940</td>\n",
       "      <td>0.999942</td>\n",
       "      <td>0.162087</td>\n",
       "      <td>0.408886</td>\n",
       "      <td>0.999941</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.759307</td>\n",
       "      <td>0.067533</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004309</td>\n",
       "      <td>0.002744</td>\n",
       "      <td>0.066046</td>\n",
       "      <td>-0.001424</td>\n",
       "      <td>-0.003847</td>\n",
       "      <td>-0.001730</td>\n",
       "      <td>-0.001995</td>\n",
       "      <td>0.022599</td>\n",
       "      <td>0.022780</td>\n",
       "      <td>0.772080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>0.759325</td>\n",
       "      <td>0.759188</td>\n",
       "      <td>0.759466</td>\n",
       "      <td>0.759322</td>\n",
       "      <td>0.088634</td>\n",
       "      <td>0.223065</td>\n",
       "      <td>0.759327</td>\n",
       "      <td>0.759307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.104269</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016244</td>\n",
       "      <td>0.005658</td>\n",
       "      <td>-0.106359</td>\n",
       "      <td>-0.003117</td>\n",
       "      <td>0.000930</td>\n",
       "      <td>-0.005048</td>\n",
       "      <td>0.013858</td>\n",
       "      <td>-0.019084</td>\n",
       "      <td>0.023007</td>\n",
       "      <td>0.967883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>0.067457</td>\n",
       "      <td>0.067436</td>\n",
       "      <td>0.067483</td>\n",
       "      <td>0.067465</td>\n",
       "      <td>0.007600</td>\n",
       "      <td>0.037395</td>\n",
       "      <td>0.067460</td>\n",
       "      <td>0.067533</td>\n",
       "      <td>-0.104269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019061</td>\n",
       "      <td>0.003713</td>\n",
       "      <td>0.996257</td>\n",
       "      <td>-0.006380</td>\n",
       "      <td>0.010639</td>\n",
       "      <td>0.024885</td>\n",
       "      <td>-0.018588</td>\n",
       "      <td>0.077452</td>\n",
       "      <td>-0.074648</td>\n",
       "      <td>0.148165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week</th>\n",
       "      <td>0.058106</td>\n",
       "      <td>0.058087</td>\n",
       "      <td>0.058131</td>\n",
       "      <td>0.058115</td>\n",
       "      <td>0.004549</td>\n",
       "      <td>0.033793</td>\n",
       "      <td>0.058109</td>\n",
       "      <td>0.058179</td>\n",
       "      <td>-0.116247</td>\n",
       "      <td>0.979069</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050894</td>\n",
       "      <td>-0.004313</td>\n",
       "      <td>0.981463</td>\n",
       "      <td>0.018339</td>\n",
       "      <td>0.010461</td>\n",
       "      <td>0.037818</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.082985</td>\n",
       "      <td>0.029097</td>\n",
       "      <td>0.132510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>-0.004283</td>\n",
       "      <td>-0.004278</td>\n",
       "      <td>-0.004288</td>\n",
       "      <td>-0.004285</td>\n",
       "      <td>-0.015111</td>\n",
       "      <td>-0.010549</td>\n",
       "      <td>-0.004284</td>\n",
       "      <td>-0.004309</td>\n",
       "      <td>-0.016244</td>\n",
       "      <td>-0.019061</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>0.067122</td>\n",
       "      <td>0.307857</td>\n",
       "      <td>-0.309819</td>\n",
       "      <td>0.174196</td>\n",
       "      <td>-0.173435</td>\n",
       "      <td>0.080370</td>\n",
       "      <td>-0.077229</td>\n",
       "      <td>0.000810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dayofweek</th>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.002731</td>\n",
       "      <td>0.002736</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>-0.041325</td>\n",
       "      <td>-0.016482</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.002744</td>\n",
       "      <td>0.005658</td>\n",
       "      <td>0.003713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003727</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.003930</td>\n",
       "      <td>0.042003</td>\n",
       "      <td>0.045478</td>\n",
       "      <td>0.038815</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.006568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dayofyear</th>\n",
       "      <td>0.065973</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>0.065998</td>\n",
       "      <td>0.065981</td>\n",
       "      <td>0.006059</td>\n",
       "      <td>0.036368</td>\n",
       "      <td>0.065976</td>\n",
       "      <td>0.066046</td>\n",
       "      <td>-0.106359</td>\n",
       "      <td>0.996257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067122</td>\n",
       "      <td>0.003727</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020224</td>\n",
       "      <td>-0.016107</td>\n",
       "      <td>0.039728</td>\n",
       "      <td>-0.033629</td>\n",
       "      <td>0.084320</td>\n",
       "      <td>-0.080819</td>\n",
       "      <td>0.147031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_month_end</th>\n",
       "      <td>-0.001450</td>\n",
       "      <td>-0.001454</td>\n",
       "      <td>-0.001448</td>\n",
       "      <td>-0.001450</td>\n",
       "      <td>-0.005342</td>\n",
       "      <td>-0.006403</td>\n",
       "      <td>-0.001451</td>\n",
       "      <td>-0.001424</td>\n",
       "      <td>-0.003117</td>\n",
       "      <td>-0.006380</td>\n",
       "      <td>...</td>\n",
       "      <td>0.307857</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.020224</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.034260</td>\n",
       "      <td>0.564614</td>\n",
       "      <td>-0.019179</td>\n",
       "      <td>0.251417</td>\n",
       "      <td>-0.008540</td>\n",
       "      <td>0.002013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_month_start</th>\n",
       "      <td>-0.003857</td>\n",
       "      <td>-0.003867</td>\n",
       "      <td>-0.003846</td>\n",
       "      <td>-0.003856</td>\n",
       "      <td>-0.006074</td>\n",
       "      <td>-0.006600</td>\n",
       "      <td>-0.003857</td>\n",
       "      <td>-0.003847</td>\n",
       "      <td>0.000930</td>\n",
       "      <td>0.010639</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.309819</td>\n",
       "      <td>0.003930</td>\n",
       "      <td>-0.016107</td>\n",
       "      <td>-0.034260</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019344</td>\n",
       "      <td>0.559796</td>\n",
       "      <td>-0.008614</td>\n",
       "      <td>0.249271</td>\n",
       "      <td>-0.003144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_quarter_end</th>\n",
       "      <td>-0.001774</td>\n",
       "      <td>-0.001776</td>\n",
       "      <td>-0.001771</td>\n",
       "      <td>-0.001772</td>\n",
       "      <td>-0.008781</td>\n",
       "      <td>-0.007769</td>\n",
       "      <td>-0.001774</td>\n",
       "      <td>-0.001730</td>\n",
       "      <td>-0.005048</td>\n",
       "      <td>0.024885</td>\n",
       "      <td>...</td>\n",
       "      <td>0.174196</td>\n",
       "      <td>0.042003</td>\n",
       "      <td>0.039728</td>\n",
       "      <td>0.564614</td>\n",
       "      <td>-0.019344</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010829</td>\n",
       "      <td>0.445289</td>\n",
       "      <td>-0.004822</td>\n",
       "      <td>0.005023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_quarter_start</th>\n",
       "      <td>-0.001980</td>\n",
       "      <td>-0.001986</td>\n",
       "      <td>-0.001974</td>\n",
       "      <td>-0.001980</td>\n",
       "      <td>-0.013923</td>\n",
       "      <td>-0.011206</td>\n",
       "      <td>-0.001980</td>\n",
       "      <td>-0.001995</td>\n",
       "      <td>0.013858</td>\n",
       "      <td>-0.018588</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.173435</td>\n",
       "      <td>0.045478</td>\n",
       "      <td>-0.033629</td>\n",
       "      <td>-0.019179</td>\n",
       "      <td>0.559796</td>\n",
       "      <td>-0.010829</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004822</td>\n",
       "      <td>0.445289</td>\n",
       "      <td>0.005281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_year_end</th>\n",
       "      <td>0.022534</td>\n",
       "      <td>0.022547</td>\n",
       "      <td>0.022519</td>\n",
       "      <td>0.022536</td>\n",
       "      <td>-0.001985</td>\n",
       "      <td>0.008317</td>\n",
       "      <td>0.022533</td>\n",
       "      <td>0.022599</td>\n",
       "      <td>-0.019084</td>\n",
       "      <td>0.077452</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080370</td>\n",
       "      <td>0.038815</td>\n",
       "      <td>0.084320</td>\n",
       "      <td>0.251417</td>\n",
       "      <td>-0.008614</td>\n",
       "      <td>0.445289</td>\n",
       "      <td>-0.004822</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002147</td>\n",
       "      <td>0.002336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_year_start</th>\n",
       "      <td>0.022791</td>\n",
       "      <td>0.022796</td>\n",
       "      <td>0.022782</td>\n",
       "      <td>0.022790</td>\n",
       "      <td>-0.006797</td>\n",
       "      <td>0.001724</td>\n",
       "      <td>0.022788</td>\n",
       "      <td>0.022780</td>\n",
       "      <td>0.023007</td>\n",
       "      <td>-0.074648</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.077229</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>-0.080819</td>\n",
       "      <td>-0.008540</td>\n",
       "      <td>0.249271</td>\n",
       "      <td>-0.004822</td>\n",
       "      <td>0.445289</td>\n",
       "      <td>-0.002147</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elapsed</th>\n",
       "      <td>0.772079</td>\n",
       "      <td>0.771938</td>\n",
       "      <td>0.772226</td>\n",
       "      <td>0.772078</td>\n",
       "      <td>0.089792</td>\n",
       "      <td>0.231154</td>\n",
       "      <td>0.772082</td>\n",
       "      <td>0.772080</td>\n",
       "      <td>0.967883</td>\n",
       "      <td>0.148165</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000810</td>\n",
       "      <td>0.006568</td>\n",
       "      <td>0.147031</td>\n",
       "      <td>0.002013</td>\n",
       "      <td>-0.003144</td>\n",
       "      <td>0.005023</td>\n",
       "      <td>0.005281</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>0.002451</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Open      High       Low     Close  Volume_(BTC)  \\\n",
       "Open               1.000000  0.999998  0.999998  0.999997      0.161848   \n",
       "High               0.999998  1.000000  0.999996  0.999998      0.162435   \n",
       "Low                0.999998  0.999996  1.000000  0.999998      0.161186   \n",
       "Close              0.999997  0.999998  0.999998  1.000000      0.161824   \n",
       "Volume_(BTC)       0.161848  0.162435  0.161186  0.161824      1.000000   \n",
       "Volume_(Currency)  0.408601  0.409441  0.407689  0.408592      0.700766   \n",
       "Weighted_Price     0.999999  0.999999  0.999999  0.999999      0.161795   \n",
       "PriceClose2D       0.999939  0.999940  0.999940  0.999942      0.162087   \n",
       "Year               0.759325  0.759188  0.759466  0.759322      0.088634   \n",
       "Month              0.067457  0.067436  0.067483  0.067465      0.007600   \n",
       "Week               0.058106  0.058087  0.058131  0.058115      0.004549   \n",
       "Day               -0.004283 -0.004278 -0.004288 -0.004285     -0.015111   \n",
       "Dayofweek          0.002734  0.002731  0.002736  0.002734     -0.041325   \n",
       "Dayofyear          0.065973  0.065953  0.065998  0.065981      0.006059   \n",
       "Is_month_end      -0.001450 -0.001454 -0.001448 -0.001450     -0.005342   \n",
       "Is_month_start    -0.003857 -0.003867 -0.003846 -0.003856     -0.006074   \n",
       "Is_quarter_end    -0.001774 -0.001776 -0.001771 -0.001772     -0.008781   \n",
       "Is_quarter_start  -0.001980 -0.001986 -0.001974 -0.001980     -0.013923   \n",
       "Is_year_end        0.022534  0.022547  0.022519  0.022536     -0.001985   \n",
       "Is_year_start      0.022791  0.022796  0.022782  0.022790     -0.006797   \n",
       "Elapsed            0.772079  0.771938  0.772226  0.772078      0.089792   \n",
       "\n",
       "                   Volume_(Currency)  Weighted_Price  PriceClose2D      Year  \\\n",
       "Open                        0.408601        0.999999      0.999939  0.759325   \n",
       "High                        0.409441        0.999999      0.999940  0.759188   \n",
       "Low                         0.407689        0.999999      0.999940  0.759466   \n",
       "Close                       0.408592        0.999999      0.999942  0.759322   \n",
       "Volume_(BTC)                0.700766        0.161795      0.162087  0.088634   \n",
       "Volume_(Currency)           1.000000        0.408548      0.408886  0.223065   \n",
       "Weighted_Price              0.408548        1.000000      0.999941  0.759327   \n",
       "PriceClose2D                0.408886        0.999941      1.000000  0.759307   \n",
       "Year                        0.223065        0.759327      0.759307  1.000000   \n",
       "Month                       0.037395        0.067460      0.067533 -0.104269   \n",
       "Week                        0.033793        0.058109      0.058179 -0.116247   \n",
       "Day                        -0.010549       -0.004284     -0.004309 -0.016244   \n",
       "Dayofweek                  -0.016482        0.002734      0.002744  0.005658   \n",
       "Dayofyear                   0.036368        0.065976      0.066046 -0.106359   \n",
       "Is_month_end               -0.006403       -0.001451     -0.001424 -0.003117   \n",
       "Is_month_start             -0.006600       -0.003857     -0.003847  0.000930   \n",
       "Is_quarter_end             -0.007769       -0.001774     -0.001730 -0.005048   \n",
       "Is_quarter_start           -0.011206       -0.001980     -0.001995  0.013858   \n",
       "Is_year_end                 0.008317        0.022533      0.022599 -0.019084   \n",
       "Is_year_start               0.001724        0.022788      0.022780  0.023007   \n",
       "Elapsed                     0.231154        0.772082      0.772080  0.967883   \n",
       "\n",
       "                      Month    ...          Day  Dayofweek  Dayofyear  \\\n",
       "Open               0.067457    ...    -0.004283   0.002734   0.065973   \n",
       "High               0.067436    ...    -0.004278   0.002731   0.065953   \n",
       "Low                0.067483    ...    -0.004288   0.002736   0.065998   \n",
       "Close              0.067465    ...    -0.004285   0.002734   0.065981   \n",
       "Volume_(BTC)       0.007600    ...    -0.015111  -0.041325   0.006059   \n",
       "Volume_(Currency)  0.037395    ...    -0.010549  -0.016482   0.036368   \n",
       "Weighted_Price     0.067460    ...    -0.004284   0.002734   0.065976   \n",
       "PriceClose2D       0.067533    ...    -0.004309   0.002744   0.066046   \n",
       "Year              -0.104269    ...    -0.016244   0.005658  -0.106359   \n",
       "Month              1.000000    ...    -0.019061   0.003713   0.996257   \n",
       "Week               0.979069    ...     0.050894  -0.004313   0.981463   \n",
       "Day               -0.019061    ...     1.000000   0.000450   0.067122   \n",
       "Dayofweek          0.003713    ...     0.000450   1.000000   0.003727   \n",
       "Dayofyear          0.996257    ...     0.067122   0.003727   1.000000   \n",
       "Is_month_end      -0.006380    ...     0.307857   0.000554   0.020224   \n",
       "Is_month_start     0.010639    ...    -0.309819   0.003930  -0.016107   \n",
       "Is_quarter_end     0.024885    ...     0.174196   0.042003   0.039728   \n",
       "Is_quarter_start  -0.018588    ...    -0.173435   0.045478  -0.033629   \n",
       "Is_year_end        0.077452    ...     0.080370   0.038815   0.084320   \n",
       "Is_year_start     -0.074648    ...    -0.077229   0.007874  -0.080819   \n",
       "Elapsed            0.148165    ...     0.000810   0.006568   0.147031   \n",
       "\n",
       "                   Is_month_end  Is_month_start  Is_quarter_end  \\\n",
       "Open                  -0.001450       -0.003857       -0.001774   \n",
       "High                  -0.001454       -0.003867       -0.001776   \n",
       "Low                   -0.001448       -0.003846       -0.001771   \n",
       "Close                 -0.001450       -0.003856       -0.001772   \n",
       "Volume_(BTC)          -0.005342       -0.006074       -0.008781   \n",
       "Volume_(Currency)     -0.006403       -0.006600       -0.007769   \n",
       "Weighted_Price        -0.001451       -0.003857       -0.001774   \n",
       "PriceClose2D          -0.001424       -0.003847       -0.001730   \n",
       "Year                  -0.003117        0.000930       -0.005048   \n",
       "Month                 -0.006380        0.010639        0.024885   \n",
       "Week                   0.018339        0.010461        0.037818   \n",
       "Day                    0.307857       -0.309819        0.174196   \n",
       "Dayofweek              0.000554        0.003930        0.042003   \n",
       "Dayofyear              0.020224       -0.016107        0.039728   \n",
       "Is_month_end           1.000000       -0.034260        0.564614   \n",
       "Is_month_start        -0.034260        1.000000       -0.019344   \n",
       "Is_quarter_end         0.564614       -0.019344        1.000000   \n",
       "Is_quarter_start      -0.019179        0.559796       -0.010829   \n",
       "Is_year_end            0.251417       -0.008614        0.445289   \n",
       "Is_year_start         -0.008540        0.249271       -0.004822   \n",
       "Elapsed                0.002013       -0.003144        0.005023   \n",
       "\n",
       "                   Is_quarter_start  Is_year_end  Is_year_start   Elapsed  \n",
       "Open                      -0.001980     0.022534       0.022791  0.772079  \n",
       "High                      -0.001986     0.022547       0.022796  0.771938  \n",
       "Low                       -0.001974     0.022519       0.022782  0.772226  \n",
       "Close                     -0.001980     0.022536       0.022790  0.772078  \n",
       "Volume_(BTC)              -0.013923    -0.001985      -0.006797  0.089792  \n",
       "Volume_(Currency)         -0.011206     0.008317       0.001724  0.231154  \n",
       "Weighted_Price            -0.001980     0.022533       0.022788  0.772082  \n",
       "PriceClose2D              -0.001995     0.022599       0.022780  0.772080  \n",
       "Year                       0.013858    -0.019084       0.023007  0.967883  \n",
       "Month                     -0.018588     0.077452      -0.074648  0.148165  \n",
       "Week                       0.014089     0.082985       0.029097  0.132510  \n",
       "Day                       -0.173435     0.080370      -0.077229  0.000810  \n",
       "Dayofweek                  0.045478     0.038815       0.007874  0.006568  \n",
       "Dayofyear                 -0.033629     0.084320      -0.080819  0.147031  \n",
       "Is_month_end              -0.019179     0.251417      -0.008540  0.002013  \n",
       "Is_month_start             0.559796    -0.008614       0.249271 -0.003144  \n",
       "Is_quarter_end            -0.010829     0.445289      -0.004822  0.005023  \n",
       "Is_quarter_start           1.000000    -0.004822       0.445289  0.005281  \n",
       "Is_year_end               -0.004822     1.000000      -0.002147  0.002336  \n",
       "Is_year_start              0.445289    -0.002147       1.000000  0.002451  \n",
       "Elapsed                    0.005281     0.002336       0.002451  1.000000  \n",
       "\n",
       "[21 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optional Step if you want to convert problem into classification problem and use a Deep ANN\n",
    "# #coinbase['increaseAfterX']=coinbase['Close']-coinbase['PriceClose2H']\n",
    "# #coinbase['increaseAfterX']=coinbase['increaseAfterX']>0\n",
    "# coinbase['increaseAfterX']=coinbase.apply(lambda row:row['Close']-row['PriceClose2H']<0, axis=1).astype(int)b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Financial Stock Market Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Financial Markets & companies all around the globe often use variety of mathematical models to predict and analyze stock market. One such measure is autocorrelation. Intutively, it is very similar to correlation but dependent on  lagged variables. The wikipedia article is a good read. Though simple it is a powerful tool used by many stock market brokers to mint money.\n",
    "https://en.wikipedia.org/wiki/Autocorrelation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one problem with the prices of bitcoin and time series domain as a whole that it has alot of variability. Intuitvely, on a higher level there are many hidden patterns in the time series data but are subject to price changes in a particular year. For example the prices of bitcoin increase in particular year as compared to other, some models are very senstive to the range of values. (aka 'Seasonality'). The best way to avoid this is by making the signal stationary. (stationary?? - subtract the previous value from the current value (Technical jargon: First order difference))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arima2015day=coinbase['2015':].resample('D').mean().fillna(method='ffill')['Close']\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "plot_acf(arima2015day.diff().dropna(), lags= 48, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what does this graph represent? If you observe the autocorrelation value for the present value with itself is 1.0(since current value is totally dependent on current value) and then the correlation goes on decreasing. Intutively, this plot gives you a good guess of the window size to consider for predicting todays bitcoin value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training & Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1: RANDOM FOREST :|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Though many financial analysts and domain experts would argue why random forests for a time series data?? We were performing this project as a part of Machine Learning course and wanted to evaluate its performance over other tradtional models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "      <th>PriceClose2D</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>...</th>\n",
       "      <th>Day</th>\n",
       "      <th>Dayofweek</th>\n",
       "      <th>Dayofyear</th>\n",
       "      <th>Is_month_end</th>\n",
       "      <th>Is_month_start</th>\n",
       "      <th>Is_quarter_end</th>\n",
       "      <th>Is_quarter_start</th>\n",
       "      <th>Is_year_end</th>\n",
       "      <th>Is_year_start</th>\n",
       "      <th>Elapsed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:33:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417411980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:34:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:35:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:36:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:37:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:38:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:39:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:40:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:41:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:42:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:43:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:44:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:45:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:46:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:47:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:48:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:49:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417412940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:50:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:51:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:52:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:53:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:54:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:55:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:56:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:57:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:58:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 05:59:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:00:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>370.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:01:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>370.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01 06:02:00</th>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>370.00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1417413720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:07:00</th>\n",
       "      <td>6364.00</td>\n",
       "      <td>6364.99</td>\n",
       "      <td>6364.00</td>\n",
       "      <td>6364.00</td>\n",
       "      <td>0.809671</td>\n",
       "      <td>5153.138003</td>\n",
       "      <td>6364.486129</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:08:00</th>\n",
       "      <td>6364.37</td>\n",
       "      <td>6364.37</td>\n",
       "      <td>6364.00</td>\n",
       "      <td>6364.01</td>\n",
       "      <td>0.778106</td>\n",
       "      <td>4951.897641</td>\n",
       "      <td>6364.039422</td>\n",
       "      <td>6357.01</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:09:00</th>\n",
       "      <td>6364.01</td>\n",
       "      <td>6364.01</td>\n",
       "      <td>6364.00</td>\n",
       "      <td>6364.01</td>\n",
       "      <td>0.628995</td>\n",
       "      <td>4002.928373</td>\n",
       "      <td>6364.003631</td>\n",
       "      <td>6355.78</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:10:00</th>\n",
       "      <td>6364.01</td>\n",
       "      <td>6364.01</td>\n",
       "      <td>6364.00</td>\n",
       "      <td>6364.01</td>\n",
       "      <td>0.579078</td>\n",
       "      <td>3685.258844</td>\n",
       "      <td>6364.006966</td>\n",
       "      <td>6355.77</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:11:00</th>\n",
       "      <td>6364.00</td>\n",
       "      <td>6364.01</td>\n",
       "      <td>6364.00</td>\n",
       "      <td>6364.01</td>\n",
       "      <td>1.363758</td>\n",
       "      <td>8678.963994</td>\n",
       "      <td>6364.006347</td>\n",
       "      <td>6353.93</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:12:00</th>\n",
       "      <td>6364.01</td>\n",
       "      <td>6364.01</td>\n",
       "      <td>6364.00</td>\n",
       "      <td>6364.01</td>\n",
       "      <td>1.519286</td>\n",
       "      <td>9668.738221</td>\n",
       "      <td>6364.002566</td>\n",
       "      <td>6350.12</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:13:00</th>\n",
       "      <td>6364.01</td>\n",
       "      <td>6364.01</td>\n",
       "      <td>6364.00</td>\n",
       "      <td>6364.00</td>\n",
       "      <td>0.661806</td>\n",
       "      <td>4211.740553</td>\n",
       "      <td>6364.007274</td>\n",
       "      <td>6351.99</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:14:00</th>\n",
       "      <td>6364.01</td>\n",
       "      <td>6364.01</td>\n",
       "      <td>6361.56</td>\n",
       "      <td>6361.56</td>\n",
       "      <td>1.611373</td>\n",
       "      <td>10254.448697</td>\n",
       "      <td>6363.796728</td>\n",
       "      <td>6352.45</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:15:00</th>\n",
       "      <td>6361.56</td>\n",
       "      <td>6362.67</td>\n",
       "      <td>6361.55</td>\n",
       "      <td>6361.56</td>\n",
       "      <td>0.965239</td>\n",
       "      <td>6140.506139</td>\n",
       "      <td>6361.644406</td>\n",
       "      <td>6352.48</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:16:00</th>\n",
       "      <td>6361.55</td>\n",
       "      <td>6361.56</td>\n",
       "      <td>6361.55</td>\n",
       "      <td>6361.56</td>\n",
       "      <td>1.546673</td>\n",
       "      <td>9839.253631</td>\n",
       "      <td>6361.559157</td>\n",
       "      <td>6352.98</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:17:00</th>\n",
       "      <td>6361.56</td>\n",
       "      <td>6361.56</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>2.504971</td>\n",
       "      <td>15934.994301</td>\n",
       "      <td>6361.348408</td>\n",
       "      <td>6355.00</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:18:00</th>\n",
       "      <td>6361.20</td>\n",
       "      <td>6362.42</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6362.42</td>\n",
       "      <td>0.710242</td>\n",
       "      <td>4518.286567</td>\n",
       "      <td>6361.612885</td>\n",
       "      <td>6355.78</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:19:00</th>\n",
       "      <td>6362.41</td>\n",
       "      <td>6362.42</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>1.240855</td>\n",
       "      <td>7893.462491</td>\n",
       "      <td>6361.307538</td>\n",
       "      <td>6355.78</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541891940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:20:00</th>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>0.988902</td>\n",
       "      <td>6290.605420</td>\n",
       "      <td>6361.199145</td>\n",
       "      <td>6355.78</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:21:00</th>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>1.101818</td>\n",
       "      <td>7008.877845</td>\n",
       "      <td>6361.192139</td>\n",
       "      <td>6355.00</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:22:00</th>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>0.747242</td>\n",
       "      <td>4753.349741</td>\n",
       "      <td>6361.193240</td>\n",
       "      <td>6352.19</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:23:00</th>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>0.432201</td>\n",
       "      <td>2749.314706</td>\n",
       "      <td>6361.199548</td>\n",
       "      <td>6351.30</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:24:00</th>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>2.003098</td>\n",
       "      <td>12742.092693</td>\n",
       "      <td>6361.193589</td>\n",
       "      <td>6350.00</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:25:00</th>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>0.774424</td>\n",
       "      <td>4926.258271</td>\n",
       "      <td>6361.193865</td>\n",
       "      <td>6347.41</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:26:00</th>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>0.885321</td>\n",
       "      <td>5631.695713</td>\n",
       "      <td>6361.192210</td>\n",
       "      <td>6347.41</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:27:00</th>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>1.259064</td>\n",
       "      <td>8009.157270</td>\n",
       "      <td>6361.199688</td>\n",
       "      <td>6347.41</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:28:00</th>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>1.296138</td>\n",
       "      <td>8244.986414</td>\n",
       "      <td>6361.196552</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:29:00</th>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>0.352906</td>\n",
       "      <td>2244.904596</td>\n",
       "      <td>6361.197743</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:30:00</th>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>1.765413</td>\n",
       "      <td>11230.145549</td>\n",
       "      <td>6361.198734</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:31:00</th>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.19</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>1.522197</td>\n",
       "      <td>9682.999649</td>\n",
       "      <td>6361.199726</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:32:00</th>\n",
       "      <td>6361.20</td>\n",
       "      <td>6361.20</td>\n",
       "      <td>6357.00</td>\n",
       "      <td>6357.01</td>\n",
       "      <td>4.325664</td>\n",
       "      <td>27512.756078</td>\n",
       "      <td>6360.355111</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:33:00</th>\n",
       "      <td>6357.01</td>\n",
       "      <td>6357.01</td>\n",
       "      <td>6355.77</td>\n",
       "      <td>6355.78</td>\n",
       "      <td>0.749786</td>\n",
       "      <td>4765.588930</td>\n",
       "      <td>6355.935270</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:34:00</th>\n",
       "      <td>6355.78</td>\n",
       "      <td>6355.78</td>\n",
       "      <td>6355.77</td>\n",
       "      <td>6355.77</td>\n",
       "      <td>1.360339</td>\n",
       "      <td>8646.003296</td>\n",
       "      <td>6355.772404</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:35:00</th>\n",
       "      <td>6355.78</td>\n",
       "      <td>6355.78</td>\n",
       "      <td>6353.92</td>\n",
       "      <td>6353.93</td>\n",
       "      <td>5.583475</td>\n",
       "      <td>35483.281042</td>\n",
       "      <td>6355.052698</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 23:36:00</th>\n",
       "      <td>6353.93</td>\n",
       "      <td>6353.93</td>\n",
       "      <td>6350.11</td>\n",
       "      <td>6350.12</td>\n",
       "      <td>0.983608</td>\n",
       "      <td>6249.117576</td>\n",
       "      <td>6353.260670</td>\n",
       "      <td>6347.42</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1541892960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2016330 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close  Volume_(BTC)  \\\n",
       "Date                                                                    \n",
       "2014-12-01 05:33:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:34:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:35:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:36:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:37:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:38:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:39:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:40:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:41:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:42:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:43:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:44:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:45:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:46:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:47:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:48:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:49:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:50:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:51:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:52:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:53:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:54:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:55:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:56:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:57:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:58:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 05:59:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 06:00:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 06:01:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "2014-12-01 06:02:00   300.00   300.00   300.00   300.00      0.010000   \n",
       "...                      ...      ...      ...      ...           ...   \n",
       "2018-11-10 23:07:00  6364.00  6364.99  6364.00  6364.00      0.809671   \n",
       "2018-11-10 23:08:00  6364.37  6364.37  6364.00  6364.01      0.778106   \n",
       "2018-11-10 23:09:00  6364.01  6364.01  6364.00  6364.01      0.628995   \n",
       "2018-11-10 23:10:00  6364.01  6364.01  6364.00  6364.01      0.579078   \n",
       "2018-11-10 23:11:00  6364.00  6364.01  6364.00  6364.01      1.363758   \n",
       "2018-11-10 23:12:00  6364.01  6364.01  6364.00  6364.01      1.519286   \n",
       "2018-11-10 23:13:00  6364.01  6364.01  6364.00  6364.00      0.661806   \n",
       "2018-11-10 23:14:00  6364.01  6364.01  6361.56  6361.56      1.611373   \n",
       "2018-11-10 23:15:00  6361.56  6362.67  6361.55  6361.56      0.965239   \n",
       "2018-11-10 23:16:00  6361.55  6361.56  6361.55  6361.56      1.546673   \n",
       "2018-11-10 23:17:00  6361.56  6361.56  6361.19  6361.20      2.504971   \n",
       "2018-11-10 23:18:00  6361.20  6362.42  6361.19  6362.42      0.710242   \n",
       "2018-11-10 23:19:00  6362.41  6362.42  6361.19  6361.20      1.240855   \n",
       "2018-11-10 23:20:00  6361.20  6361.20  6361.19  6361.19      0.988902   \n",
       "2018-11-10 23:21:00  6361.19  6361.20  6361.19  6361.20      1.101818   \n",
       "2018-11-10 23:22:00  6361.19  6361.20  6361.19  6361.20      0.747242   \n",
       "2018-11-10 23:23:00  6361.19  6361.20  6361.19  6361.20      0.432201   \n",
       "2018-11-10 23:24:00  6361.19  6361.20  6361.19  6361.20      2.003098   \n",
       "2018-11-10 23:25:00  6361.19  6361.20  6361.19  6361.20      0.774424   \n",
       "2018-11-10 23:26:00  6361.19  6361.20  6361.19  6361.20      0.885321   \n",
       "2018-11-10 23:27:00  6361.19  6361.20  6361.19  6361.20      1.259064   \n",
       "2018-11-10 23:28:00  6361.20  6361.20  6361.19  6361.20      1.296138   \n",
       "2018-11-10 23:29:00  6361.20  6361.20  6361.19  6361.20      0.352906   \n",
       "2018-11-10 23:30:00  6361.20  6361.20  6361.19  6361.20      1.765413   \n",
       "2018-11-10 23:31:00  6361.19  6361.20  6361.19  6361.20      1.522197   \n",
       "2018-11-10 23:32:00  6361.20  6361.20  6357.00  6357.01      4.325664   \n",
       "2018-11-10 23:33:00  6357.01  6357.01  6355.77  6355.78      0.749786   \n",
       "2018-11-10 23:34:00  6355.78  6355.78  6355.77  6355.77      1.360339   \n",
       "2018-11-10 23:35:00  6355.78  6355.78  6353.92  6353.93      5.583475   \n",
       "2018-11-10 23:36:00  6353.93  6353.93  6350.11  6350.12      0.983608   \n",
       "\n",
       "                     Volume_(Currency)  Weighted_Price  PriceClose2D  Year  \\\n",
       "Date                                                                         \n",
       "2014-12-01 05:33:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:34:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:35:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:36:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:37:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:38:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:39:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:40:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:41:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:42:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:43:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:44:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:45:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:46:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:47:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:48:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:49:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:50:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:51:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:52:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:53:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:54:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:55:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:56:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:57:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:58:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 05:59:00           3.000000      300.000000        300.00  2014   \n",
       "2014-12-01 06:00:00           3.000000      300.000000        370.00  2014   \n",
       "2014-12-01 06:01:00           3.000000      300.000000        370.00  2014   \n",
       "2014-12-01 06:02:00           3.000000      300.000000        370.00  2014   \n",
       "...                                ...             ...           ...   ...   \n",
       "2018-11-10 23:07:00        5153.138003     6364.486129       6361.20  2018   \n",
       "2018-11-10 23:08:00        4951.897641     6364.039422       6357.01  2018   \n",
       "2018-11-10 23:09:00        4002.928373     6364.003631       6355.78  2018   \n",
       "2018-11-10 23:10:00        3685.258844     6364.006966       6355.77  2018   \n",
       "2018-11-10 23:11:00        8678.963994     6364.006347       6353.93  2018   \n",
       "2018-11-10 23:12:00        9668.738221     6364.002566       6350.12  2018   \n",
       "2018-11-10 23:13:00        4211.740553     6364.007274       6351.99  2018   \n",
       "2018-11-10 23:14:00       10254.448697     6363.796728       6352.45  2018   \n",
       "2018-11-10 23:15:00        6140.506139     6361.644406       6352.48  2018   \n",
       "2018-11-10 23:16:00        9839.253631     6361.559157       6352.98  2018   \n",
       "2018-11-10 23:17:00       15934.994301     6361.348408       6355.00  2018   \n",
       "2018-11-10 23:18:00        4518.286567     6361.612885       6355.78  2018   \n",
       "2018-11-10 23:19:00        7893.462491     6361.307538       6355.78  2018   \n",
       "2018-11-10 23:20:00        6290.605420     6361.199145       6355.78  2018   \n",
       "2018-11-10 23:21:00        7008.877845     6361.192139       6355.00  2018   \n",
       "2018-11-10 23:22:00        4753.349741     6361.193240       6352.19  2018   \n",
       "2018-11-10 23:23:00        2749.314706     6361.199548       6351.30  2018   \n",
       "2018-11-10 23:24:00       12742.092693     6361.193589       6350.00  2018   \n",
       "2018-11-10 23:25:00        4926.258271     6361.193865       6347.41  2018   \n",
       "2018-11-10 23:26:00        5631.695713     6361.192210       6347.41  2018   \n",
       "2018-11-10 23:27:00        8009.157270     6361.199688       6347.41  2018   \n",
       "2018-11-10 23:28:00        8244.986414     6361.196552       6347.42  2018   \n",
       "2018-11-10 23:29:00        2244.904596     6361.197743       6347.42  2018   \n",
       "2018-11-10 23:30:00       11230.145549     6361.198734       6347.42  2018   \n",
       "2018-11-10 23:31:00        9682.999649     6361.199726       6347.42  2018   \n",
       "2018-11-10 23:32:00       27512.756078     6360.355111       6347.42  2018   \n",
       "2018-11-10 23:33:00        4765.588930     6355.935270       6347.42  2018   \n",
       "2018-11-10 23:34:00        8646.003296     6355.772404       6347.42  2018   \n",
       "2018-11-10 23:35:00       35483.281042     6355.052698       6347.42  2018   \n",
       "2018-11-10 23:36:00        6249.117576     6353.260670       6347.42  2018   \n",
       "\n",
       "                     Month     ...      Day  Dayofweek  Dayofyear  \\\n",
       "Date                           ...                                  \n",
       "2014-12-01 05:33:00     12     ...        1          0        335   \n",
       "2014-12-01 05:34:00     12     ...        1          0        335   \n",
       "2014-12-01 05:35:00     12     ...        1          0        335   \n",
       "2014-12-01 05:36:00     12     ...        1          0        335   \n",
       "2014-12-01 05:37:00     12     ...        1          0        335   \n",
       "2014-12-01 05:38:00     12     ...        1          0        335   \n",
       "2014-12-01 05:39:00     12     ...        1          0        335   \n",
       "2014-12-01 05:40:00     12     ...        1          0        335   \n",
       "2014-12-01 05:41:00     12     ...        1          0        335   \n",
       "2014-12-01 05:42:00     12     ...        1          0        335   \n",
       "2014-12-01 05:43:00     12     ...        1          0        335   \n",
       "2014-12-01 05:44:00     12     ...        1          0        335   \n",
       "2014-12-01 05:45:00     12     ...        1          0        335   \n",
       "2014-12-01 05:46:00     12     ...        1          0        335   \n",
       "2014-12-01 05:47:00     12     ...        1          0        335   \n",
       "2014-12-01 05:48:00     12     ...        1          0        335   \n",
       "2014-12-01 05:49:00     12     ...        1          0        335   \n",
       "2014-12-01 05:50:00     12     ...        1          0        335   \n",
       "2014-12-01 05:51:00     12     ...        1          0        335   \n",
       "2014-12-01 05:52:00     12     ...        1          0        335   \n",
       "2014-12-01 05:53:00     12     ...        1          0        335   \n",
       "2014-12-01 05:54:00     12     ...        1          0        335   \n",
       "2014-12-01 05:55:00     12     ...        1          0        335   \n",
       "2014-12-01 05:56:00     12     ...        1          0        335   \n",
       "2014-12-01 05:57:00     12     ...        1          0        335   \n",
       "2014-12-01 05:58:00     12     ...        1          0        335   \n",
       "2014-12-01 05:59:00     12     ...        1          0        335   \n",
       "2014-12-01 06:00:00     12     ...        1          0        335   \n",
       "2014-12-01 06:01:00     12     ...        1          0        335   \n",
       "2014-12-01 06:02:00     12     ...        1          0        335   \n",
       "...                    ...     ...      ...        ...        ...   \n",
       "2018-11-10 23:07:00     11     ...       10          5        314   \n",
       "2018-11-10 23:08:00     11     ...       10          5        314   \n",
       "2018-11-10 23:09:00     11     ...       10          5        314   \n",
       "2018-11-10 23:10:00     11     ...       10          5        314   \n",
       "2018-11-10 23:11:00     11     ...       10          5        314   \n",
       "2018-11-10 23:12:00     11     ...       10          5        314   \n",
       "2018-11-10 23:13:00     11     ...       10          5        314   \n",
       "2018-11-10 23:14:00     11     ...       10          5        314   \n",
       "2018-11-10 23:15:00     11     ...       10          5        314   \n",
       "2018-11-10 23:16:00     11     ...       10          5        314   \n",
       "2018-11-10 23:17:00     11     ...       10          5        314   \n",
       "2018-11-10 23:18:00     11     ...       10          5        314   \n",
       "2018-11-10 23:19:00     11     ...       10          5        314   \n",
       "2018-11-10 23:20:00     11     ...       10          5        314   \n",
       "2018-11-10 23:21:00     11     ...       10          5        314   \n",
       "2018-11-10 23:22:00     11     ...       10          5        314   \n",
       "2018-11-10 23:23:00     11     ...       10          5        314   \n",
       "2018-11-10 23:24:00     11     ...       10          5        314   \n",
       "2018-11-10 23:25:00     11     ...       10          5        314   \n",
       "2018-11-10 23:26:00     11     ...       10          5        314   \n",
       "2018-11-10 23:27:00     11     ...       10          5        314   \n",
       "2018-11-10 23:28:00     11     ...       10          5        314   \n",
       "2018-11-10 23:29:00     11     ...       10          5        314   \n",
       "2018-11-10 23:30:00     11     ...       10          5        314   \n",
       "2018-11-10 23:31:00     11     ...       10          5        314   \n",
       "2018-11-10 23:32:00     11     ...       10          5        314   \n",
       "2018-11-10 23:33:00     11     ...       10          5        314   \n",
       "2018-11-10 23:34:00     11     ...       10          5        314   \n",
       "2018-11-10 23:35:00     11     ...       10          5        314   \n",
       "2018-11-10 23:36:00     11     ...       10          5        314   \n",
       "\n",
       "                     Is_month_end  Is_month_start  Is_quarter_end  \\\n",
       "Date                                                                \n",
       "2014-12-01 05:33:00         False            True           False   \n",
       "2014-12-01 05:34:00         False            True           False   \n",
       "2014-12-01 05:35:00         False            True           False   \n",
       "2014-12-01 05:36:00         False            True           False   \n",
       "2014-12-01 05:37:00         False            True           False   \n",
       "2014-12-01 05:38:00         False            True           False   \n",
       "2014-12-01 05:39:00         False            True           False   \n",
       "2014-12-01 05:40:00         False            True           False   \n",
       "2014-12-01 05:41:00         False            True           False   \n",
       "2014-12-01 05:42:00         False            True           False   \n",
       "2014-12-01 05:43:00         False            True           False   \n",
       "2014-12-01 05:44:00         False            True           False   \n",
       "2014-12-01 05:45:00         False            True           False   \n",
       "2014-12-01 05:46:00         False            True           False   \n",
       "2014-12-01 05:47:00         False            True           False   \n",
       "2014-12-01 05:48:00         False            True           False   \n",
       "2014-12-01 05:49:00         False            True           False   \n",
       "2014-12-01 05:50:00         False            True           False   \n",
       "2014-12-01 05:51:00         False            True           False   \n",
       "2014-12-01 05:52:00         False            True           False   \n",
       "2014-12-01 05:53:00         False            True           False   \n",
       "2014-12-01 05:54:00         False            True           False   \n",
       "2014-12-01 05:55:00         False            True           False   \n",
       "2014-12-01 05:56:00         False            True           False   \n",
       "2014-12-01 05:57:00         False            True           False   \n",
       "2014-12-01 05:58:00         False            True           False   \n",
       "2014-12-01 05:59:00         False            True           False   \n",
       "2014-12-01 06:00:00         False            True           False   \n",
       "2014-12-01 06:01:00         False            True           False   \n",
       "2014-12-01 06:02:00         False            True           False   \n",
       "...                           ...             ...             ...   \n",
       "2018-11-10 23:07:00         False           False           False   \n",
       "2018-11-10 23:08:00         False           False           False   \n",
       "2018-11-10 23:09:00         False           False           False   \n",
       "2018-11-10 23:10:00         False           False           False   \n",
       "2018-11-10 23:11:00         False           False           False   \n",
       "2018-11-10 23:12:00         False           False           False   \n",
       "2018-11-10 23:13:00         False           False           False   \n",
       "2018-11-10 23:14:00         False           False           False   \n",
       "2018-11-10 23:15:00         False           False           False   \n",
       "2018-11-10 23:16:00         False           False           False   \n",
       "2018-11-10 23:17:00         False           False           False   \n",
       "2018-11-10 23:18:00         False           False           False   \n",
       "2018-11-10 23:19:00         False           False           False   \n",
       "2018-11-10 23:20:00         False           False           False   \n",
       "2018-11-10 23:21:00         False           False           False   \n",
       "2018-11-10 23:22:00         False           False           False   \n",
       "2018-11-10 23:23:00         False           False           False   \n",
       "2018-11-10 23:24:00         False           False           False   \n",
       "2018-11-10 23:25:00         False           False           False   \n",
       "2018-11-10 23:26:00         False           False           False   \n",
       "2018-11-10 23:27:00         False           False           False   \n",
       "2018-11-10 23:28:00         False           False           False   \n",
       "2018-11-10 23:29:00         False           False           False   \n",
       "2018-11-10 23:30:00         False           False           False   \n",
       "2018-11-10 23:31:00         False           False           False   \n",
       "2018-11-10 23:32:00         False           False           False   \n",
       "2018-11-10 23:33:00         False           False           False   \n",
       "2018-11-10 23:34:00         False           False           False   \n",
       "2018-11-10 23:35:00         False           False           False   \n",
       "2018-11-10 23:36:00         False           False           False   \n",
       "\n",
       "                     Is_quarter_start  Is_year_end  Is_year_start     Elapsed  \n",
       "Date                                                                           \n",
       "2014-12-01 05:33:00             False        False          False  1417411980  \n",
       "2014-12-01 05:34:00             False        False          False  1417412040  \n",
       "2014-12-01 05:35:00             False        False          False  1417412100  \n",
       "2014-12-01 05:36:00             False        False          False  1417412160  \n",
       "2014-12-01 05:37:00             False        False          False  1417412220  \n",
       "2014-12-01 05:38:00             False        False          False  1417412280  \n",
       "2014-12-01 05:39:00             False        False          False  1417412340  \n",
       "2014-12-01 05:40:00             False        False          False  1417412400  \n",
       "2014-12-01 05:41:00             False        False          False  1417412460  \n",
       "2014-12-01 05:42:00             False        False          False  1417412520  \n",
       "2014-12-01 05:43:00             False        False          False  1417412580  \n",
       "2014-12-01 05:44:00             False        False          False  1417412640  \n",
       "2014-12-01 05:45:00             False        False          False  1417412700  \n",
       "2014-12-01 05:46:00             False        False          False  1417412760  \n",
       "2014-12-01 05:47:00             False        False          False  1417412820  \n",
       "2014-12-01 05:48:00             False        False          False  1417412880  \n",
       "2014-12-01 05:49:00             False        False          False  1417412940  \n",
       "2014-12-01 05:50:00             False        False          False  1417413000  \n",
       "2014-12-01 05:51:00             False        False          False  1417413060  \n",
       "2014-12-01 05:52:00             False        False          False  1417413120  \n",
       "2014-12-01 05:53:00             False        False          False  1417413180  \n",
       "2014-12-01 05:54:00             False        False          False  1417413240  \n",
       "2014-12-01 05:55:00             False        False          False  1417413300  \n",
       "2014-12-01 05:56:00             False        False          False  1417413360  \n",
       "2014-12-01 05:57:00             False        False          False  1417413420  \n",
       "2014-12-01 05:58:00             False        False          False  1417413480  \n",
       "2014-12-01 05:59:00             False        False          False  1417413540  \n",
       "2014-12-01 06:00:00             False        False          False  1417413600  \n",
       "2014-12-01 06:01:00             False        False          False  1417413660  \n",
       "2014-12-01 06:02:00             False        False          False  1417413720  \n",
       "...                               ...          ...            ...         ...  \n",
       "2018-11-10 23:07:00             False        False          False  1541891220  \n",
       "2018-11-10 23:08:00             False        False          False  1541891280  \n",
       "2018-11-10 23:09:00             False        False          False  1541891340  \n",
       "2018-11-10 23:10:00             False        False          False  1541891400  \n",
       "2018-11-10 23:11:00             False        False          False  1541891460  \n",
       "2018-11-10 23:12:00             False        False          False  1541891520  \n",
       "2018-11-10 23:13:00             False        False          False  1541891580  \n",
       "2018-11-10 23:14:00             False        False          False  1541891640  \n",
       "2018-11-10 23:15:00             False        False          False  1541891700  \n",
       "2018-11-10 23:16:00             False        False          False  1541891760  \n",
       "2018-11-10 23:17:00             False        False          False  1541891820  \n",
       "2018-11-10 23:18:00             False        False          False  1541891880  \n",
       "2018-11-10 23:19:00             False        False          False  1541891940  \n",
       "2018-11-10 23:20:00             False        False          False  1541892000  \n",
       "2018-11-10 23:21:00             False        False          False  1541892060  \n",
       "2018-11-10 23:22:00             False        False          False  1541892120  \n",
       "2018-11-10 23:23:00             False        False          False  1541892180  \n",
       "2018-11-10 23:24:00             False        False          False  1541892240  \n",
       "2018-11-10 23:25:00             False        False          False  1541892300  \n",
       "2018-11-10 23:26:00             False        False          False  1541892360  \n",
       "2018-11-10 23:27:00             False        False          False  1541892420  \n",
       "2018-11-10 23:28:00             False        False          False  1541892480  \n",
       "2018-11-10 23:29:00             False        False          False  1541892540  \n",
       "2018-11-10 23:30:00             False        False          False  1541892600  \n",
       "2018-11-10 23:31:00             False        False          False  1541892660  \n",
       "2018-11-10 23:32:00             False        False          False  1541892720  \n",
       "2018-11-10 23:33:00             False        False          False  1541892780  \n",
       "2018-11-10 23:34:00             False        False          False  1541892840  \n",
       "2018-11-10 23:35:00             False        False          False  1541892900  \n",
       "2018-11-10 23:36:00             False        False          False  1541892960  \n",
       "\n",
       "[2016330 rows x 21 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "coinbase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The important thing to note for time series data is you # CANNOT shuffle the data around as you will loose all the temporal patterns. Make sure to shuffle=false"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "trainColumns=['Open', 'High', 'Low', 'Close', 'Volume_(BTC)', 'Volume_(Currency)',\n",
    "       'Weighted_Price', 'Month', 'Week', 'Day',\n",
    "       'Dayofweek', 'Dayofyear', 'Is_month_end', 'Is_month_start',\n",
    "       'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start']\n",
    "predictColumn='PriceClose2D'\n",
    "X=coinbase[trainColumns]\n",
    "y=coinbase[predictColumn]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   17.9s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 2 of 5\n",
      "building tree 3 of 5\n",
      "building tree 4 of 5\n",
      "building tree 5 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:  1.5min finished\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 23.904274914135275 44.1832973947234\n",
      "building tree 1 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   20.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 2 of 5\n",
      "building tree 3 of 5\n",
      "building tree 4 of 5\n",
      "building tree 5 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:  1.7min finished\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 19.819067696712516 46.60960602046178\n",
      "building tree 1 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   21.8s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 2 of 5\n",
      "building tree 3 of 5\n",
      "building tree 4 of 5\n",
      "building tree 5 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:  1.8min finished\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.7s finished\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 18.518996363251322 47.566669716941796\n",
      "building tree 1 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   22.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 2 of 5\n",
      "building tree 3 of 5\n",
      "building tree 4 of 5\n",
      "building tree 5 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:  1.9min finished\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35 18.320901629169043 48.39190001714336\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "train_error=[]\n",
    "test_error=[]\n",
    "minDepth=20\n",
    "maxDepth=40\n",
    "models=[]\n",
    "for depth in range(minDepth,maxDepth,5):\n",
    "    regr=RandomForestRegressor(max_depth=depth, random_state=0,n_estimators=5,verbose=2)\n",
    "    regr.fit(X_train, y_train)\n",
    "    models.append(regr)\n",
    "    tr_error=math.sqrt(mean_squared_error(regr.predict(X_train),y_train))\n",
    "    te_error=math.sqrt(mean_squared_error(regr.predict(X_test),y_test))\n",
    "    test_error.append(tr_error)\n",
    "    train_error.append(te_error)\n",
    "    print (depth,tr_error,te_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[44.1832973947234, 46.60960602046178, 47.566669716941796, 48.39190001714336]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the curve and see the confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3629394"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "# print(confusion_matrix(models[2].predict(X_test), y_test))\n",
    "train_plot=pd.DataFrame(train_error,index=range(20,40,5),columns=[\"test_Data\"])\n",
    "test_plot=pd.DataFrame(test_error,index=range(20,40,5),columns=[\"train_Data\"])\n",
    "plotdata=pd.concat([train_plot,test_plot],axis=1)\n",
    "plotdata.plot()\n",
    "X_test.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the training error and test error are apart and as we increase the depth the model tends to overfit. (Expected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2018-06-23 23:04:00    6178.60\n",
       "2018-06-23 23:05:00    6178.25\n",
       "2018-06-23 23:06:00    6178.24\n",
       "2018-06-23 23:07:00    6178.25\n",
       "2018-06-23 23:08:00    6178.25\n",
       "Name: PriceClose2D, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.metrics import mean_squared_error\n",
    "# print('testing error',mean_squared_error(regr.predict(X_test),y_test))\n",
    "# print('training error',mean_squared_error(regr.predict(X_train),y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 2: Recurrent Neural Networks with LSTM and GRU (Seq to Seq) :0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bitcoin2015Daily => Contains all the prices aggregated by day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "940/940 [==============================] - 1s 2ms/step - loss: 3.8474e-04\n",
      "Epoch 2/50\n",
      "940/940 [==============================] - 0s 256us/step - loss: 0.0024\n",
      "Epoch 3/50\n",
      "940/940 [==============================] - 0s 255us/step - loss: 6.4764e-04\n",
      "Epoch 4/50\n",
      "940/940 [==============================] - 0s 251us/step - loss: 7.7444e-04\n",
      "Epoch 5/50\n",
      "940/940 [==============================] - 0s 256us/step - loss: 3.0776e-04\n",
      "Epoch 6/50\n",
      "940/940 [==============================] - 0s 253us/step - loss: 8.4690e-05\n",
      "Epoch 7/50\n",
      "940/940 [==============================] - 0s 262us/step - loss: 1.8288e-05\n",
      "Epoch 8/50\n",
      "940/940 [==============================] - 0s 249us/step - loss: 4.3297e-05\n",
      "Epoch 9/50\n",
      "940/940 [==============================] - 0s 251us/step - loss: 1.0647e-05\n",
      "Epoch 10/50\n",
      "940/940 [==============================] - 0s 249us/step - loss: 8.9587e-06\n",
      "Epoch 11/50\n",
      "940/940 [==============================] - 0s 272us/step - loss: 8.4198e-06\n",
      "Epoch 12/50\n",
      "940/940 [==============================] - 0s 249us/step - loss: 7.0573e-06\n",
      "Epoch 13/50\n",
      "940/940 [==============================] - 0s 242us/step - loss: 7.0267e-06\n",
      "Epoch 14/50\n",
      "940/940 [==============================] - 0s 257us/step - loss: 7.0851e-06\n",
      "Epoch 15/50\n",
      "940/940 [==============================] - 0s 255us/step - loss: 7.1006e-06\n",
      "Epoch 16/50\n",
      "940/940 [==============================] - 0s 266us/step - loss: 7.1673e-06\n",
      "Epoch 17/50\n",
      "940/940 [==============================] - 0s 252us/step - loss: 7.2371e-06\n",
      "Epoch 18/50\n",
      "940/940 [==============================] - 0s 247us/step - loss: 7.3196e-06\n",
      "Epoch 19/50\n",
      "940/940 [==============================] - 0s 250us/step - loss: 7.4047e-06\n",
      "Epoch 20/50\n",
      "940/940 [==============================] - 0s 254us/step - loss: 7.4835e-06\n",
      "Epoch 21/50\n",
      "940/940 [==============================] - 0s 254us/step - loss: 7.5447e-06\n",
      "Epoch 22/50\n",
      "940/940 [==============================] - 0s 246us/step - loss: 7.5770e-06\n",
      "Epoch 23/50\n",
      "940/940 [==============================] - 0s 262us/step - loss: 7.5746e-06\n",
      "Epoch 24/50\n",
      "940/940 [==============================] - 0s 269us/step - loss: 7.5354e-06\n",
      "Epoch 25/50\n",
      "940/940 [==============================] - 0s 261us/step - loss: 7.4680e-06\n",
      "Epoch 26/50\n",
      "940/940 [==============================] - 0s 250us/step - loss: 7.3805e-06\n",
      "Epoch 27/50\n",
      "940/940 [==============================] - 0s 251us/step - loss: 7.2891e-06\n",
      "Epoch 28/50\n",
      "940/940 [==============================] - 0s 247us/step - loss: 7.2035e-06\n",
      "Epoch 29/50\n",
      "940/940 [==============================] - 0s 267us/step - loss: 7.1317e-06\n",
      "Epoch 30/50\n",
      "940/940 [==============================] - 0s 252us/step - loss: 7.0747e-06\n",
      "Epoch 31/50\n",
      "940/940 [==============================] - 0s 251us/step - loss: 7.0324e-06\n",
      "Epoch 32/50\n",
      "940/940 [==============================] - 0s 250us/step - loss: 7.0002e-06\n",
      "Epoch 33/50\n",
      "940/940 [==============================] - 0s 247us/step - loss: 6.9764e-06\n",
      "Epoch 34/50\n",
      "940/940 [==============================] - 0s 254us/step - loss: 6.9574e-06\n",
      "Epoch 35/50\n",
      "940/940 [==============================] - 0s 255us/step - loss: 6.9415e-06\n",
      "Epoch 36/50\n",
      "940/940 [==============================] - 0s 254us/step - loss: 6.9284e-06\n",
      "Epoch 37/50\n",
      "940/940 [==============================] - 0s 258us/step - loss: 6.9165e-06\n",
      "Epoch 38/50\n",
      "940/940 [==============================] - 0s 249us/step - loss: 6.9066e-06\n",
      "Epoch 39/50\n",
      "940/940 [==============================] - 0s 260us/step - loss: 6.8977e-06\n",
      "Epoch 40/50\n",
      "940/940 [==============================] - 0s 249us/step - loss: 6.8900e-06\n",
      "Epoch 41/50\n",
      "940/940 [==============================] - 0s 250us/step - loss: 6.8829e-06\n",
      "Epoch 42/50\n",
      "940/940 [==============================] - 0s 254us/step - loss: 6.8770e-06\n",
      "Epoch 43/50\n",
      "940/940 [==============================] - 0s 253us/step - loss: 6.8713e-06\n",
      "Epoch 44/50\n",
      "940/940 [==============================] - 0s 250us/step - loss: 6.8668e-06\n",
      "Epoch 45/50\n",
      "940/940 [==============================] - 0s 258us/step - loss: 6.8625e-06\n",
      "Epoch 46/50\n",
      "940/940 [==============================] - 0s 268us/step - loss: 6.8590e-06\n",
      "Epoch 47/50\n",
      "940/940 [==============================] - 0s 264us/step - loss: 6.8564e-06\n",
      "Epoch 48/50\n",
      "940/940 [==============================] - 0s 251us/step - loss: 6.8539e-06\n",
      "Epoch 49/50\n",
      "940/940 [==============================] - 0s 248us/step - loss: 6.8523e-06\n",
      "Epoch 50/50\n",
      "940/940 [==============================] - 0s 264us/step - loss: 6.8520e-06\n",
      "Train Score: 51.89 RMSE\n",
      "Test Score: 586.17 RMSE\n",
      "              Close    predicted\n",
      "Date                            \n",
      "2015-01-01   340.00          NaN\n",
      "2015-01-02   340.00          NaN\n",
      "2015-01-03   340.00          NaN\n",
      "2015-01-04   340.00   355.961578\n",
      "2015-01-05   340.00   355.961578\n",
      "2015-01-06   340.00   355.961578\n",
      "2015-01-07   340.00   355.961578\n",
      "2015-01-08   288.99   355.961578\n",
      "2015-01-09   288.99   335.972778\n",
      "2015-01-10   288.99   320.242828\n",
      "2015-01-11   288.99   304.697144\n",
      "2015-01-12   288.99   304.697144\n",
      "2015-01-13   260.00   304.697144\n",
      "2015-01-14   120.00   293.338196\n",
      "2015-01-15   204.22   229.549271\n",
      "2015-01-16   199.46   210.550186\n",
      "2015-01-17   184.00   191.990540\n",
      "2015-01-18   184.00   210.128174\n",
      "2015-01-19   225.51   203.911911\n",
      "2015-01-20   218.00   215.462860\n",
      "2015-01-21   225.51   225.317596\n",
      "2015-01-22   226.32   238.593094\n",
      "2015-01-23   235.00   238.937332\n",
      "2015-01-24   240.00   244.876129\n",
      "2015-01-25   254.53   249.757950\n",
      "2015-01-26   274.48   259.637207\n",
      "2015-01-27   263.65   273.457123\n",
      "2015-01-28   236.09   279.792877\n",
      "2015-01-29   235.03   271.735016\n",
      "2015-01-30   229.07   259.522217\n",
      "...             ...          ...\n",
      "2018-10-12  6188.01  6464.911621\n",
      "2018-10-13  6196.00  6343.548340\n",
      "2018-10-14  6183.49  6244.177734\n",
      "2018-10-15  6440.42  6251.907227\n",
      "2018-10-16  6457.11  6351.374512\n",
      "2018-10-17  6443.70  6433.752930\n",
      "2018-10-18  6394.96  6512.024414\n",
      "2018-10-19  6382.99  6493.821777\n",
      "2018-10-20  6414.00  6469.924316\n",
      "2018-10-21  6415.88  6463.525391\n",
      "2018-10-22  6407.65  6470.223633\n",
      "2018-10-23  6395.14  6477.033203\n",
      "2018-10-24  6415.98  6470.144531\n",
      "2018-10-25  6395.58  6471.939453\n",
      "2018-10-26  6404.87  6466.572754\n",
      "2018-10-27  6409.12  6470.253906\n",
      "2018-10-28  6403.62  6468.579590\n",
      "2018-10-29  6266.00  6470.570312\n",
      "2018-10-30  6267.63  6416.123047\n",
      "2018-10-31  6304.18  6372.427734\n",
      "2018-11-01  6344.00  6345.306152\n",
      "2018-11-02  6349.80  6372.770020\n",
      "2018-11-03  6331.96  6398.540039\n",
      "2018-11-04  6423.28  6405.479004\n",
      "2018-11-05  6404.00  6437.578125\n",
      "2018-11-06  6448.50  6452.871582\n",
      "2018-11-07  6503.12  6492.221191\n",
      "2018-11-08  6406.24  6521.579102\n",
      "2018-11-09  6334.89  6514.041504\n",
      "2018-11-10  6350.12          NaN\n",
      "\n",
      "[1410 rows x 2 columns] [3, 51.8927451597765, 586.1710704755135]\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import read_csv\n",
    "import math\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM,GRU\n",
    "from sklearn.preprocessing import MinMaxScaler,RobustScaler,StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from pandas import Series\n",
    "\n",
    "data=pd.read_csv('Bitcoin2015Daily.csv')\n",
    "\n",
    "\n",
    "data=data.set_index(pd.DatetimeIndex(data['Date']))['Close']\n",
    "def difference(dataset, interval=1):\n",
    "\tdiff = list()\n",
    "\tfor i in range(interval, len(dataset)):\n",
    "\t\tvalue = dataset[i] - dataset[i - interval]\n",
    "\t\tdiff.append(value)\n",
    "\treturn Series(diff)\n",
    "\n",
    "look_back=3\n",
    "#data=difference(data,look_back)\n",
    "\n",
    "\n",
    "#def project(data,look_back):\n",
    "    #convert an array of values into a dataset matrix\n",
    "    \n",
    "\n",
    "def create_dataset(dataset, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        #takes \n",
    "        a = dataset[i:(i+look_back), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i+look_back, 0])\n",
    "    return numpy.array(dataX), numpy.array(dataY)\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "numpy.random.seed(0)\n",
    "\n",
    "# load the dataset\n",
    "dataframe = data\n",
    "dataset = dataframe.values\n",
    "dataset = dataset.astype('float64').reshape(-1, 1)\n",
    "\n",
    "# normalize the dataset\n",
    "scaler = MinMaxScaler()\n",
    "#scaler=RobustScaler()\n",
    "#scaler=StandardScaler()\n",
    "dataset = scaler.fit_transform(dataset)\n",
    "\n",
    "# split into train and test sets\n",
    "train_size = int(len(dataset) * 0.67)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\n",
    "\n",
    "# reshape into X=t and Y=t+1\n",
    "#look_back = 10\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)\n",
    "\n",
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
    "testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n",
    "# create and fit the LSTM network\n",
    "from keras.layers import Activation, Dense,Dropout\n",
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(256, return_sequences=True,input_shape=(1, look_back)))\n",
    "#model.add(LSTM(256, return_sequences=True,input_shape=(1, look_back)))\n",
    "model.add(LSTM(256))\n",
    "#model.add(LSTM(100, input_shape=(1, look_back)))\n",
    "\n",
    "model.add(Dense(1))\n",
    "import keras\n",
    "from keras import optimizers\n",
    "\n",
    "#keras.optimizers.Adam(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False, clipnorm=1)\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.fit(trainX, trainY, epochs=50, verbose=1,shuffle=False,batch_size=50)\n",
    "# make predictions\n",
    "trainPredict = model.predict(trainX)\n",
    "testPredict = model.predict(testX)\n",
    "# invert predictions\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "trainY = scaler.inverse_transform([trainY])\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "testY = scaler.inverse_transform([testY])\n",
    "# calculate root mean squared error\n",
    "trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))\n",
    "predictions = numpy.empty_like(dataset)\n",
    "predictions[:, :] = numpy.nan\n",
    "predictions[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
    "predictions[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n",
    "#data=pd.DataFrame(numpy.concatenate((trainPredict[0:len(trainPredict)-look_back-1],testPredict[0:len(testPredict)-look_back-1])),columns=[\"predicted\"])\n",
    "#print('one',data.count())\n",
    "#print('two',dataframe.count())\n",
    "predictionsDF=pd.DataFrame(predictions,columns=[\"predicted\"],index=dataframe.index)\n",
    "ans=pd.concat([dataframe,predictionsDF],axis=1)\n",
    "print( ans,[look_back,trainScore,testScore])\n",
    "\n",
    "#return ans,[look_back,trainScore,testScore]\n",
    "\n",
    "#     # shift train predictions for plotting\n",
    "#     trainPredictPlot = numpy.empty_like(dataset)\n",
    "#     trainPredictPlot[:, :] = numpy.nan\n",
    "#     trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
    "#     # shift test predictions for plotting\n",
    "#     testPredictPlot = numpy.empty_like(dataset)\n",
    "#     testPredictPlot[:, :] = numpy.nan\n",
    "#     testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n",
    "    \n",
    "#     # plot baseline and predictions\n",
    "#     plt.plot(scaler.inverse_transform(dataset))\n",
    "#     plt.plot(trainPredictPlot)\n",
    "#     plt.plot(testPredictPlot)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot and compare the prices predicted and actual price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcac0588f28>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAESCAYAAAA48DgcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xl8VPW98PHPOWf2yTJJCDBhFw1GUbYoWkVscMFWpa2PiruiXi9Vr8sFH1sVLMJFkMfH+hSLrdutpdLrghREEUtVXKqiUkEERHYSyB4ymcx2znn+mDBJWLJMJpkh+b5fr77KnN/vnPnNz7zmO79dMU3TRAghhOgANdkFEEIIcfyTYCKEEKLDJJgIIYToMAkmQgghOkyCiRBCiA6TYCKEEKLDJJgIIYToMAkmQgghOkyCiRBCiA6TYCKEEKLDJJgIIYToMAkmQgghOkyCiRBCiA6zJLsAnaGqqg7DaP9myDk5aVRU+DqhRN2f1F38pO7iJ3UXv6Z1l5OT1uHndctgYhhmXMHk0L0iPlJ38ZO6i5/UXfwSWXetdnNVVVVx++23c/HFF3PZZZdx1113UVlZCcD69eu5/PLLufjii5kyZQoVFRWx+zojTQghRGpqNZgoisJtt93GqlWrWL58OQMGDGDBggUYhsH06dOZMWMGq1atorCwkAULFgB0SpoQQojU1Wo3l8fjYezYsbHXI0eO5JVXXmHjxo3Y7XYKCwsBmDx5MhMmTGDu3LmdktYRpmlSVVVGKBQAjt2sKy1VMQyjQ+/VUzXWnYLN5iArKxdFUZJdLCFEF2nXmIlhGLzyyisUFRVRUlJCXl5eLC07OxvDMKiuru6UNI/H0+ZyHj6YVFpaisWikps7EEWRCWydyTQNKivLgQC5ub2TXZzjRm5uerKLcNySuotfIuuuXcHksccew+Vycf3117N69eqEFSLRKip8zQaWysoqyM7ug64DHLvlYbGoRCLSMolH07pzuzMpKzuAojiTXKrjQ25uOmVltckuxnFJ6i5+TesuEUGlzcFk3rx57Nq1i0WLFqGqKl6vl+Li4lh6ZWUlqqri8Xg6Ja0jDENH07rlxLWUpGkWDENPdjGEEF2oTX0+Tz75JBs3bmThwoXYbDYAhg8fTiAQYN26dQAsWbKEiRMndlpaR0n/fdeRuhadYfknO7l13ppkF0McQ6s/17///nueffZZBg8ezOTJkwHo378/CxcuZP78+cycOZNgMEi/fv144oknAFBVNeFp3U0kEuGll57jvffexW63oaoqo0efwaBBg/n880+ZPXt+sosoREpZ+uF2ACK6gUWTsc9U02owOemkk9iyZctR00aPHs3y5cu7LK07+a//+g3BYIAXXngZl8tNJBLhrbf+RjgcSnbRhEhJmqqgGyb+QIQMty3ZxRGHkYGEJNizZzcffvgP3nhjJS6XGwCLxcKkSb9g5crmgfTPf36JVatWAlBQcCr33jsdl8vF2rXv88c//h5V1dD1CPfd9wCjRxdSXl7OU0/N58CB/QSDQS644GJuvHFKl39GIRLNabfgqw9TFwhLMElBPTKYfLyhhI++KTniuqKA2cHdBc493cs5p3lbzLN16xb69x9IRkZGi/k+/fRjVq1ayaJFL+ByuZk9eyYvvfQcv/zlf/Dcc8/ywAMPMXz46ei6TiBQD8Ds2TO4+ebbGDlyNOFwmHvumUpBwSmcccZZHftgQiSZy27BGazAX1MNOe5kF0ccpkcGk+PFunWfM2HCRbjd0XUzl1/+C3772+iOAGPGFPL0009y/vlFnHXWjzjhhBOpr6/n66+/pLq6OvYMv7+OnTt3SjARxz2n3cL9njeJfPh3OOH/Jbs44jA9Mpicc9rRWw9dtc4kP38Ye/fu5uDBg622To7lP/7jP/nhh218+eUXPPLIg1x99XVccMFFKIrCc8/9CYulR/6nFd2Y065BGCwhWVeSimRKRBIMGDCQc845jyee+C/8/joAdF1n+fI3qa/3x/IVFp7JmjWr8fvrME2TFSve5Iwzolvb7N69k6FDT+Sqq67hoosu4bvvNuFyuRkxYhR//vNLsWccOLCfioryLv18QnQGmyYLilOZ/HxNkocf/g0vvPAHpky5AavVgmmanHXWOQwcOCiW5+yzz+GHH77njjtuAeDkk0/hpptuBeD3v/8de/fuRtMspKWl8atfzQBgxozHePrpJ7nxxqsBcLnc/OpXM8jJ6dXFn1CIxHIQTnYRRAsU0+zokHPqOXw7lf37d9G376AW7oiS7VTid3jdtbXOhWwJ0lbPL/mQqw6+AED6v70ESN11RKK3U5FuLiHEcUFTut3v3m5FgokQ4rigIfu9pTIJJkKI44KmSBd0KpNgIoQ4LqhGJNlFEC2QYCKEOC4opnRzpTIJJkKI44IEk9QmwUQIcVxQ5MC1lCbBpBt5/vln+d3vngLgzTdf469/Xdxi/pKSYpYteyPu9zv33EL8fn/rGYVIANWUMZNUJivgU1gkEol7j62f/ex/tZqnpKSYv/1tKZMm/SKu9xCiKylm42wu0zTlRM8U06Zvqnnz5rFq1Sr27dvH8uXLyc/PZ+/evdx5552xPLW1tfh8Pj7//HMAioqKsNls2O12AKZNm8a4ceMAWL9+PTNmzGh2mmJOTk6raYkS3vox4S0fHnFdURQ6uiGAddh5WPPPaTHPuecWcsstt7N27QcEgwHuuONOzj9/QrO0Tz/9mLFjz+b226fy5z+/xAcfrEHXdXr16s3//t8PkZPTC5/Px+OPz2L79h/Izs6hT58+ZGVF6+r555+lvr6eu+66F4CXX36R1avfQVFUnE4nzzzzHE8+OZ+Skn3cfPO19O/fn9mz57N7905++9snqampJhwOc9VV1/DTn14OwAcfrOHZZxdis9k5//yiDtWTEO1x0B9if/lBSGu4YBqgaEktk2iuTcFkwoQJ3HjjjVx33XWxa/3792fZsmWx13PmzEHXm/dpPv300+Tn5ze7ZhgG06dPZ+7cuRQWFvLMM8+wYMEC5s6d22Jad6OqKi+99Bd2797Jv//7rYwYMYqsrGwA7HY7zz33JwBWrVrJvn37ePbZl1BVlaVLX+N3v3uKmTNn8+KLf8TlcvOXv7xOdXU1U6ZcR1HRhUe819tvr+Cjjz6MnYtSU1ONqqrcf/8DLFz4W55//mUg2hJ69NGHmTlzNoMGDcbvr+PWW29g+PDTSU9PZ968OSxa9DwDBw5m8eL/7rrKEj3eH5dvIp0m60yMCKgSTFJJm4JJYWFhi+mhUIjly5fz/PPPt/qsjRs3YrfbY8+cPHkyEyZMYO7cuS2mJZI1/5yjth66cm+uSy+dBMDAgYPJzx/Gt99u4NxzxwNwySWXxvJ99NGHbN78HVOmXA+ArkdIS4v+PPv663Xce+90ADweD+PHH7218PHHa/nZz66IneqYmek5ar49e3aza9cOZs78dexaOBxm584daJpKfv4wBg4cDETPVvn97+VMCdE1av0hspQmP1b1CFjsySuQOEJCxkzWrFlDnz59OPXUU5tdnzZtGqZpMmbMGO6//34yMjIoKSkhLy8vlic7OxvDMKiurm4xzeM5+hdgd+R0umL/Nk2Tm26aEgs+nck0TTIzPbz00l+OSPvoow86/f2FOBrTNNF1E61Jy8TUI8iISWpJSDB5/fXXueKKK5pdW7x4MV6vl1AoxJw5c5g1axYLFixIxNu1Kicnrdnr0lIVi6VtE9famq+j3n57BVOm3Mbu3bv5/vutnH76iNh7WyyN5R0/fjx//esrFBVNICMjg1AoxK5dOznppHwKC8/k7bdXMHr0aGpqqlm79h8UFV2IxaKiqgqqqmCxqIwbdx5Ll75GUdEE3O5oN1dmpoeMjHTq6nyx9zrhhCE4nU5Wr14Zax3t3LmD3NxcTj99BHPnPkZx8V4GDhzIypXLjihr07pTVTUhO5H2FFJXx/b83zayr7yOYY7GYJKT5cCSEa0zqbv4JbLuOhxMDhw4wBdffMH8+fObXfd6oycZ2mw2rr32WqZOnRq7XlxcHMtXWVmJqqp4PJ4W09rj8C3oDcNoU/dVV3ZzhcNhbrhhMoFAgOnTf0VGhif23pFIY3kvvPAnVFZWMXXqbUD0s/z851cyZMiJ3Hjjrcyd+xuuuurnZGfnMGLEKAzDJBIxMAwz9u+LLvoJBw4c4NZbb8JiseB0Olm48I8MHjyUgQMHcc01/4tBgwYze/Z8Hn/8SZ5++v/w5z//CV03yM7OZtasx/F4PDzwwK+ZNu0e7HZ7rEvtUFkPrzvDMGRr8DaSbdRb9vYnOwGwNGmZVJTVoAbtUncdkOgt6DscTJYuXcr48ePJysqKXfP7/ei6Tnp6OqZpsnLlSgoKCgAYPnw4gUCAdevWUVhYyJIlS5g4cWKrad3NNdfcwK233nHE9Y8+WnfEtauvvo6rr77uiOtpaWnMmfPEUZ/f9NmKonDjjVO48cYpzfKoqsr8+U81uzZgwECeeOK3R33m+PFFzcZlbr75tqPmEyKR1IYGb9ONHk09uubEVx9GNww0VZbMJVubgsns2bN59913KS8v55ZbbsHj8fDWW28B0WDy0EMPNctfUVHB3Xffja7rGIbB0KFDmTlzJnDoC2w+M2fObDb9t7U0IUTPpDasJym0bW+8aESI6AbXPLySotH9uP6iYUkqnThETlpsQk5ajJ+ctBg/6app2T1Pr8XnD/FU9suxa66fzaBU68NDf/yMrHQ7/+fOltd2iSPJSYtx6oYxM2VJXYtEUhUFuxI9/71Kj850NI0IdfXRri6XQzbySAU9Ipioqoauy74+XUXXI6iyoEwkiKoqOJUQAN9H+kYv6hGMhh8tTXshRPL0iGDidKZRW1uNaUoXVmczTYPa2iqczrTWMwvRBqpCLJjUGo7oRSOCYZicadvG5ebqJJZOHNIj2odpaZlUVZVx4MBe4Ni/YlRVxTAk4MSjse4UbDYHaWmZyS6S6CZUVcHacP67J8sDAUDXMTC5Lu2T5BZOxPSIYKIoCtnZvVvNJwOh8ZO6E51FVRQsDdOCTasdAtExE0OR7q1U0iO6uYQQxy9VVbA0tEx0tWE/Lj1M004EU86HTzoJJkKIlKYqCmPsOwDQtYZgYuixAXgAIuEklEw0JcFECJHSVFVhrP0HAAzVBkRXwJtNZnGZkVBSyiYaSTARQqQ0VW3cH9jQmszmatoy0SWYJJsEEyFESjs0kwtAP3SGiRGh6fISU5durmSTYCKESGk2tckGjw0tE1OPNF+sKGMmSSfBRAiR0pruFhyxRk8LXb52G776JgFExkySToKJECKlWZocqahYbeimgmrq7NhXFbsu3VzJJ8FECJHSmoy/oyoKETQ0DMKhJq0RaZkknQQTIURKs6jRsZE36grRNJWIqaIpzYOJtEyST4KJECKlaQ3bptSbNiyqgo6KBZ1IKNiYSVomSdemYDJv3jyKiooYNmwYW7dujV0vKipi4sSJTJo0iUmTJrF27dpY2vr167n88su5+OKLmTJlChUVFR1OE0L0PJaGbymnw4aqKuimikUxMJrM4JKWSfK1KZhMmDCBxYsX069fvyPSnn76aZYtW8ayZcsYN24cAIZhMH36dGbMmMGqVasoLCxkwYIFHUoTQvRMtoYR+PGj+qGp0TGTsfYfGGLsbMwkLZOka1MwKSwsxOv1tvmhGzduxG63U1hYCMDkyZN55513OpQmhOihzOiixX69M6PBxIx+bV3Ex02ySDBJtg5vQT9t2jRM02TMmDHcf//9ZGRkUFJSQl5eXixPdnY2hmFQXV0dd5rH4+loUYUQx6ND26YoCuGIgaIcJUtYurmSrUPBZPHixXi9XkKhEHPmzGHWrFkp0S2VkxP/KX+5uekJLEnPInUXP6m7YzvUzZXpcZMWdMS2o2/KNCNSh3FIZJ11KJgc6vqy2Wxce+21TJ06NXa9uLg4lq+yshJVVfF4PHGntUdFhS+uc6HlgKf4Sd3FT+quZeFgtNVxsDZEbW2A/hx5GurXm/biPFPqsD2a/t0lIqjEPTXY7/dTWxstiGmarFy5koKCAgCGDx9OIBBg3bp1ACxZsoSJEyd2KE0I0UOZDcFDUTFNYqcuNqXJ4VhJ16aWyezZs3n33XcpLy/nlltuwePxsGjRIu6++250XccwDIYOHcrMmTOB6Hng8+fPZ+bMmQSDQfr168cTTzzRoTQhRM+kHBozUVVMTLTDWiYhU8OCBJNkU0zT7HYHKUs3V9eTuouf1F3LlvzlLX7qexXnTx/g3T1uCr5+gl6aL5ZebbiotOUxYsrDSSzl8SdlurmEEKIrKByazaVimibP1k4gZGqx9DrDjsd5lCleoktJMBFCpDTz0JiJqmKYUGpkstR/Riw9YFrRTOnmSjYJJkKIlKY0BBNFUXHZo8O8utn41RVGo6raRzfssT+uSDARQqQ0pclsrvNH5eF2WIg0+eqqM+ykKfWEI0fO8hJdR4KJECLFNc7m0lSV/3fveQzKa1x7VmmkkaXWEQxJV1cySTARQqQ0pWFvLpTGryujyQC8z3CgKSb6d3/v6qKJJiSYCCFSW2xvrsYA8vnexi6tnXqvaPJ373ZpsURzEkyEECktNmbS5Pzecr1x/72dkd6U6ulEMgd2ddFEExJMhBAprelsrkNCh23eUW/amp8JL7qcBBMhRIprnM3VqPkiRd1UCTc9xld0OQkmQojUFtubSzsiyZ8xGIAIGopx5Nb0outIMBFCpDTVPLJloihwf+V15N0wC4CIqaHKKvikkmAihEhtTU5abHpJR8NitXJ10YlEUBunEIukkGAihEhpyqExk6N0c1k0hQsLBxAxVRQ50ySpJJgIIVLbUWZzHWLRVBQlOmaiSsskqSSYCCFSmnrU2VxRmqaiKAq6qcqYSZK1KZjMmzePoqIihg0bxtatWwGoqqri9ttv5+KLL+ayyy7jrrvuorKyMnbPsGHDuOyyy5g0aRKTJk1iy5YtsbQ1a9YwceJELrzwQu69917q6+vblCaE6HmanmdySP9cNwBaw0JGXdFAjxAMSeskWdoUTCZMmMDixYvp169f7JqiKNx2222sWrWK5cuXM2DAABYsWNDsviVLlrBs2TKWLVvGsGHDAKirq+ORRx5h0aJFrF69GrfbzfPPP99qmhCih2pynskhD1w7ml9fPyb2OoIF04jw1Kv/6urSiQZtCiaFhYV4vd5m1zweD2PHjo29HjlyJMXFxa0+68MPP2T48OEMHjwYgMmTJ/P222+3miaE6JnUo7RM0pxWTuyfGXttoGLBYMue6q4unmhgaT1L6wzD4JVXXqGoqKjZ9RtuuAFd1znvvPO4++67sdlslJSUkJeXF8uTl5dHSUkJQItpQoieSTV1TIWjjpkcEkHFohiNXWKiyyUkmDz22GO4XC6uv/762LX3338fr9eLz+dj+vTpLFy4kPvuuy8Rb9eqnJy01jMdQ25uegJL0rNI3cVP6u7YNMXEQKN374yjpufmphNp2JJew5C6bIdE1lWHg8m8efPYtWsXixYtQm3Sp3moWywtLY0rr7ySF198MXb9s88+i+UrLi6O5W0prT0qKnwYRvt/oeTmplNWVtvu+4TUXUdI3bXCiGBo6lHr6FDdhRuO8bUoutRlGzX9u0tEUOnQ1OAnn3ySjRs3snDhQmw2W+x6TU0NgUAAgEgkwqpVqygoKABg3LhxbNiwgZ07dwLRQfpLLrmk1TQhRM+kYmBw5ILFppq2TERytKllMnv2bN59913Ky8u55ZZb8Hg8PPXUUzz77LMMHjyYyZMnA9C/f38WLlzI9u3bmTFjBoqiEIlEGDVqFPfccw8QbanMmjWLO+64A8MwKCgo4KGHHmo1TQjRM2kYmC2MlwBEDrVMkKnBydKmYPLwww/z8MMPH3G96dqRpkaNGsXy5cuP+bwLLriACy64oN1pQoieR0XHaGMwGW3f2fkFEkclK+CFEClNNQ3MVrq5wg1fZT9zfdkVRRJHIcFECJHSVAwMpW1jJgBmRE5cTAYJJkKIlKa2YcxEb/JVZgbrOrtI4igkmAghUlatP4SpR1C0lod3m7VMJJgkhQQTIUTKqvaFcChhNJuzxXyRJl9lv3/tSyoPBo6arz4QxPftB5iGTCFONAkmQoiUVR+M4FRCYHe1mK9py2R06Au+XvfNUfMtf+FFzI9fJLx1bULLKSSYCCFSmL8hmKh2d4v59uuZVOjRbZROs+1l9I4XjprPEakBQK/3JbagQoKJECJ11QciuJQQmqPlYBLCynO+82OvNT0Y+3dpdT0LlnyNrz6MpWGFvG4qhz9CdJAEEyFEyvIHQjjVMJqz5c1bLzpjAGGzcZDe52rcffzBRZ+yaWcV3/xQjk2Jnsaoh4NHPEN0jAQTIUTKCvuj3VE2V8stk8kTTsLlyYqthFf1xrUmKgZXuD7D6S8hTYkOzBshCSaJJsFECJGyIg1jGxZn67va+g0rv666mi+CJ6AajcFkqOUA5zm20Gfzq2So0WPADWmZJFxCzjMRQojOUF5WCYDSymwuAN0wCWIlYFrRmgSTNDXaGlFCdWSq0etm+OhTh0X8pGUihEhZZtAf/Yet9WCS1yvaFRY0LbFgYhgmTiXc8DAj1s1lhmXLlUSTlokQImVZzYZWRStTgwFu++kplFXX89UbG9BMHdOIoBsKLiXapeUw/agNk7jMiHRzJZq0TIQQKctmtD2YZLhtDO2XSYSGg/rCQSK6yUBLBUBsWjAAEkwSToKJECJlxYJJG7q5DgmrVgDMcBDdMHErzQNHnWED2Vk44SSYCCFSls0MYqCCxdZ65gYRpSGYRAJEdCO2tuSQciMdRZdgkmitBpN58+ZRVFTEsGHD2Lp1a+z6jh07uPrqq7n44ou5+uqrY+e2d1aaEKLnsZtBQqoDRWn7ivWI0rSb68hgUmmkSTDpBK0GkwkTJrB48WL69evX7PrMmTO59tprWbVqFddeey0zZszo1DQhRM/jIEhYc7TrntpQ9GvNDAcIRwzshJulBwyrBJNO0GowKSwsxOv1NrtWUVHBpk2buPTSSwG49NJL2bRpE5WVlZ2SJoTomRwEiajtCyYHQw2tmHCQipoANiVCjdG4hX0IC6oRPsbdIl5xTQ0uKSmhT58+aFp022dN0+jduzclJSWYppnwtOzs7HaVLyen5X18WpKb2/pKW3F0Unfxk7o70g97KslTK6jRhrRYP4enBc3omEm6U0GvU7EpEUr1DDIbVr+HGtahSJ0n9u+uW64zqajwYRhmu+/LzU2nrKy2E0rU/UndxU/q7ug+/uRbzlWD7PcMPWb9HK3uTj85D/ZDTWU1Vf5sTlAMdkd6McRSTp0rj5DfgmIalB6oQlFb/wo0TZP9n6+mz+lnoTozEvLZUkHTuktEUIlrNpfX6+XAgQPoug6AruuUlpbi9Xo7JU0I0fMEa6LrQ4YPP7Fd9zmcDWtSwoHY7sBVhptna4vYnn8DEaUhgLRxf64/vfEpaf/6C9VL57arHD1NXMEkJyeHgoICVqxYAcCKFSsoKCggOzu7U9KEED2PEYx2S7VlX65mrNHZXGY4iBmKrlMZ4M1mU7g/PtxNpg63PAj/5ZYy1ny1l907dkcf6ytpXzl6mFbbeLNnz+bdd9+lvLycW265BY/Hw1tvvcWjjz7Kgw8+yDPPPENGRgbz5s2L3dMZaUKInsWMNCxYtLZvAF7VLIRNFWs4gNGw0n1gvxzYATmZTvY0BJP6Oj9ud9Yxn7Nw6QYAznLLppBt0Wowefjhh3n44YePuD506FBeffXVo97TGWlCiJ5FaQgE7Q0mmqoQNK04QoFYV9aAvBzmTx1OToaDtSsUsMO7n37Pzyf1a+VpMNhaEfu3aUTaNM7SE8kKeCFESlIOdUNZ7e26T9NUgqYlus18w+7AitVOr0wniqIQavgNfUL1P1t5ksl17o84W90Qu2JU7G1XWXoSCSZCiJSkGvG3TEKmlbrausbdgS2NASlsRpcfnBDcjGkee9bnydZizrRvb3atfv/2Y+QWEkyEECkpdsBVO/blgoZuLizs3FMG+qGA1BhMstxNuqlaOCTrPPvm2L9fqzuTiKkSqtzfrrL0JBJMhBApSdWDhBUbitK+rymT6AFZdiUc6ypTmrRM9moD2K9nRvOG6o/5nHKjce3F2uDJ1Jl2In5ZD3QsEkyEECnJYobR1fa1SgBMw6TetOFUwo17cDUJJpqm8k796QDs2b6DXR+/c0R3l2GYZKl1AMyo+l/cf/UI6gw7RkCCybFIMBFCpBzDMLGZIYw4gonNqhEwbTjVEMX7q4Dm3VyaohAwo8/N/uf/I/vbJRjVxc2e8fr72zjRcoB1wSEUjs4nw2XDZzpQgr4OfKruTYKJECLlhCI6NiWCobU/mPTyOKg3bDiVUOP2801aJkP7ZVLfsH/XIUZNabPXpaXluNQQeyI5XHdhPjarhs+wo4WkZXIsEkyEECknGDawKxFMS/umBQOcOjibABbsSgRPQ1eVojUOul9zwUm40pvvRRU+LJh43dFtna766RgAbBYVi2JgD1RQt255u8vUE0gwEUKknFBYx66E4womiqLgcEa3nD/XsfWIdIumkuft1exa5LBgooT80byuaNCxWlQ2h/MACGxYTThiIJqTYCKESDnBsI5diTTrnmoPXbG2mB6xNj+mIljVPJho4YYWjT2az2bR+DiYz7Zwb2zhg3z21+fiKld3JsFECJFyQmEDtxIEmzuu+3W1MZj8q//VR6SrWvMtUYzaxi1TNu+qoqQ4GlwUR/T9rVYVUNir5wAwou4TTKP5ccAtMUyT2+f/g9Xr9rT5nuONBBMhREqJ6AZ//+tfSFcDEOf5IU1bJgedR+6/VVXbuP18he5GCzfO0pr/ytdka81bJqqiMGvKmRRHGjeGNOuq21yecMTAMAwy//l7av82t8WV98crCSZCiJSyZU81k93RfbOUtJy4nmE0aZkEOLKrzDSJLVzcHumDNVIX+4IfZilmovObaMYmW7n0753GutAQgma0VWMG69pcnnDEYIJjIwW2Yti/BbMbrleRYCKESCl6RCdkalTpLlynnBfXM5oGE7Qjx09uvbSAxb5zWFJ3FqUcSXMcAAAgAElEQVTkoJo6hKOr4U+wRru46tU0FEVpXjY0nq2dAIAZaPuak3DEoL9WGXtt+ipbyH18kmAihEgp9VUV2BSd9wLDyUhr3yaPhzQNJpeMHXhEutthZbfei0+D+dTo0ZaLWV+LaZp4VD8hU+OdPlOOuO+micPwNyx4bF/LRG9c8wIYvvI233u8kGAihEgp277bAsDkX4xDPaxl0FZNV8477S2fP3IomBj1NWwvPohTCVGhp+POyDwi7/iR/Rh+cjQ4me1YDR+OGLjUIHsi0ZNjzSYD/t1Fh0552bt3L3feeWfsdW1tLT6fj88//5yioiJsNht2e/Q/1LRp0xg3bhwA69evZ8aMGQSDQfr168cTTzxBTk5Oq2lCiO4tohsEK/aBGxy9+sf9HPMoXVuHGz8yjyHeDN57N/rFHqk7SB3ZOJUQmsPF5ecMPup9h2Z4mQEfobCOWrMX1eVBdR0ZfA4JRaKz08rU3gTNg1i7YcukQ8Gkf//+LFu2LPZ6zpw56Loee/3000+Tn5/f7B7DMJg+fTpz586lsLCQZ555hgULFjB37twW04QQ3V8orJOr1RI0LaS5PHE/Z29lCLSW89w08WQAdu0YAKWg+2vQ7SYOJYwnuw9Wy9EfYLPbqTesqL4qNv/hIfKt+9GdWXhu+L8tfq5MJcgBq5vKgBvHwe4XTBLWzRUKhVi+fDlXXHFFi/k2btyI3W6nsLAQgMmTJ/POO++0miaE6P5CEQOXEsSwuY8Y/G6PvTVtn3qbl9cHAKO+lohh4lRCYHMeu4xhgwojjYO7vyffGj3fRKuvanHdib8+iFMJ4fZ4qDLchKrL2ly+40XCgsmaNWvo06cPp556auzatGnTuOyyy3j00Uc5ePAgACUlJeTl5cXyZGdnYxgG1dXVLaYJIbovwzBZ+69iPvqmBIcSBkt8A++HhGi9m+sQzWqj3rDiq6wgohs4lDBKC8Gkpi7Erkgv0vzRI3w/Cw4FwAz6j5o/ohuUr34BVYHsvnlUGWlQ1/1mc3Wom6up119/vVmrZPHixXi9XkKhEHPmzGHWrFksWLAgUW/XopyctNYzHUNubnrrmcRRSd3Fr6fX3Z9f+5gfbXmSl3zj6GsPo9qdba6TY+VbUncWN19T1OpzsjwufKaD3d/vYb2vlJOVEM6MzGPe95/XFzJr5hec4/gegG1mf8byA1kuE1uvI+/5cvMBzrJF9wg7ofBMvvzXD1j1reRkWlFtzYPmZ6/+idwMOydcfOSq/c6QyL+7hASTAwcO8MUXXzB//vzYNa/XC4DNZuPaa69l6tSpsevFxY1nB1RWVqKqKh6Pp8W09qio8GEY7V9hmpubTllZ91tM1BWk7uIndQcb163nR+lwc9paag0HujKwTXXSUt19GsznyrRBrT6n3h8Ew0G6GmDbrlIs6QYBw9Liffv06KysYvcwctNzoBYqSg5gMY8chF+/qZgfAyv9I7jalk3AmgkGlO7chZbV2BOzcXsZg7ZGx6DLRv+ktY/eYU3rLhFBJSHdXEuXLmX8+PFkZUW3GvD7/dTWRgtpmiYrV66koKAAgOHDhxMIBFi3bh0AS5YsYeLEia2mCSG6L6cSiv07XQ00W3neERat9a84i6biMx3kW/dzru07AFS7q8V7BgwZxJzqSdQVTiFiacgbOPq6k3BdtIt/0gXR0x2D1oYjg2ubD8Lv3PRdq2VNZQlpmSxdupSHHnoo9rqiooK7774bXdcxDIOhQ4cyc+ZMAFRVZf78+cycObPZ9N/W0oQQ3VeGeth4Q4KCibWNwSRAdLD/p/YvgdaDSf/cNL75IROnw4ZhjeY91roTJRz9bJoz2v1eZ82CIBgHD8Ty7CutZey+P9FQDExDR1FbmY6WYhISTFatWtXs9YABA3jzzTePmX/06NEsX370A2ZaShNCdD/1wQgZSqDZNcWWoJaJpfUZYU67RkgJN7umtRJMfjZuCEPzMjh5oIcvNzasOznWIsZQdJsWxRZ95s4ahXrFilG2l0NLK//xwZdMarJC3gz4UFpYt5KKZAW8ECKp6urDZKh+KvTG7ebVFmZTtUVORnSxtKa2/hWX4baxM9y72TVLenaL91g0lVH5uSiKQk1QQTcVwnVHH2NRwoeCSfQzXXbOEEqNDALljePDXqMEgLf8IwEw6w+2Wu5UI8FECJFUdYEIGWo9YVsmhhltSRw+y6m9fn1DIfdeOaJNebPTHbwbOI1/Nkzx/TI4GGvvIW1+ry+3luM37ZTv2UnlC78ksGE1AHv3V1G85hU8oehaFBpaO8OH5FCqZ2LxNR7IZanYTj0OAp7o+5r1B9ENgy0fvkv9Z6+2uSzJlLCpwUIIEY+6QJh0NUDY1hci0V/oWgdbJlnpdrLS23ZKo92mMf+X5/Kb3wfxGQ56nXdVu97rivEnUPO1k/7VmwCo+9dqHKddyDev/YFxtu8Y1fCb/VA3V4bbSqmegS28HTMcZHdFiP7KfvbSl5DFDSEwAwf5ZlsFJ23+CxGgYsc3ZJ53HUbVPqxDx6I44l/+0FmkZSKESKq6QIQMxQ+ODA5N6NccHQsm7ZWd4cCekcXy+jGMGzWoXfeOPaUPFUbj1NqQ6uC/nnmLcQ0zwzSi58Uf6ubSVJVSPXro10cffc137/wPvbVack8ajtqwhYxeuY/0rStiz7Qd3EP9iscJfvwy/i+PHFM2w8F2nfzYGaRlIoRIKn+dH5capjotCxoWhtudLQ+Ad4aHbyqk6mCw9YyHSXNa+SHchxG23QBoYT/DwpvBAnWGDbcaQkcDrXEn41IjOrietukNRjacn9J32OlkblaIlKmwfgV9m7zHrkgOhqkyxFqG8e0qarZ+iNU7DDUtm/q8MdS/9wxaXgF9Lr0r/groIAkmQoikiviqAEjLykHbE22bKPb4zn7viAyXjQyXrfWMh7FbNT4K5nOidT/Zqo++ai1ezUFxxMO/wgO5xPkNYdXRbK+xMj3akjnR2mTcpPdgeh0ow2c68CiNU6VnV/+MjL790DSNsyuXMdq+CzVcT+XOLWSq9Vg2rSEdeHlHFr+M/+N3mHRzCSGSSvdF995zZ/eKHYmr9WpfV1MyKYqCjsbzvh/zZWgIFiPIMGsJQUcO28LR9oVx2Jb44Sa/40simcyruQzFYiM30xE7TvijQD5rwqN48N8n8tCNZ/AfV5xOmRHtHvsmNIAZ1Vfyln8kEVPl+drzWVfnTerZ8tIyEUIkVV1V9DwRe3oWC2sn4FHruDMJLZOOuPPnw8nOcPDBkug4iU3R8Q4ewrbSvrziO5tzJ5xDnyb57TaN3x68mB87NvE3/xhuvip6PHFOpoP5vh9xsrWEz0NDuWHiyWRlRGe2OWwaP4T7gHMDA8aM49kzzufRF134e4/nlgmngKJ0aKfljpJgIoRICl2PEP727wTK94ELbBnZ/BDp0/qNKWjMsOg6lQWhE7jC/QUAVu9J/O7eMXy36zQK8ns1y3/LJSezaJmOu/cpPH7l6bEg0CfLxYjTT2TCmB8zJduF1dLYeaSqCgccg5njn8zM0UVYLSpzbj+riz5h6ySYCCGS4u0332V8xf9wqSva7aO6MpJdpA4Lq05+XzuB69wfkzvoVGwOC2OG5R6R74yTe3PqkGzcjubdX6qqcPMlBcd8/hO//BFqklsgxyJjJkKIpKjYvSP2b7/Li6Ic/19Hf5h+PpvD/Xik+ipsrmOvBVEU5YhA0haaqqZkIAFpmQghuliNL8hf//AS16R9Ebt20B49suKnZw9iUJ/j+2yX/P6ZbN1bk+xidDkJJkKILrW71Mc1aZ/GXn8b6of91MsBuGL80GQVK2GmXTMqqbOqkkWCiRCiS/nqw5Tq6fTWannZdw7rQkN5vqBfsouVMG05Q6U76pmfWgiRNJUHA7iVIF8Yp7AuNJQ+Wc6UHQcQbSctEyFEl6qprcethjhnzDD65oykX27qbVoo2q/DwaSoqAibzYbdHt2hc9q0aYwbN47169czY8aMZicm5uTkAMSdJoQ4/imB6FkdiiuTUwa3fG6IOH4kpJvr6aefZtmyZSxbtoxx48ZhGAbTp09nxowZrFq1isLCQhYsWAAQd5oQonvQwtETCRXn8b+uRDTqlDGTjRs3YrfbKSwsBGDy5Mm88847HUoTQnQPllD0REJVgkm3kpAxk2nTpmGaJmPGjOH++++npKSEvLy8WHp2djaGYVBdXR13msfjSURRhRCdyDBN6urDpLew+64tUgdw3J1xLlrW4WCyePFivF4voVCIOXPmMGvWLC688MJElC1uOTnxD+jl5h7fC6aSSeouft2l7l5c/i1vvL+NJbN/gtt59BXeTjO6vXrugH6o1radhtiS7lJ3yZDIuutwMPF6oytXbTYb1157LVOnTuXGG2+kuLg4lqeyshJVVfF4PHi93rjS2qOiwodhtH/RUG5uOmVlte2+T0jddUR3qrvPP9/IzMy32POxRs6I8UekG6ZJsLqcoMNGRXUICHXo/bpT3XW1pnWXiKDSoTETv99PbW20MKZpsnLlSgoKChg+fDiBQIB169YBsGTJEiZOnAgQd5oQIvUNMPaRrdVh+fp/jpq+bW8NJ1hKqbR5u7hkorN1qGVSUVHB3Xffja7rGIbB0KFDmTlzJqqqMn/+fGbOnNlsii8Qd5oQIrWZpkk/tRwANVRHybp/4C38cbM8ZRW1nKpVExl8RjKKKDqRYnbDTWSkm6vrSd3Fr7vUXVl1PQf+8jCDLBWxa+n/9lKzPO+t/oSxO/6A9fx/w5H/ow6/Z3epu2RIqW4uIYQoLq/jYF2Qvdu3M8hSwd/rT+GTwEkAmMG6WL4vV7/D2B1/AMDW98SklFV0HtlORQgRt9IqP18sXki+tQS/kYZhVTjlxz/htXe/4UeO79EPbMMycAQVNQEytr0NGoQVG0r6kQdGieObtEyEEHF7/YPtFDk30d9Sxem2PexxnUz+6cM5ZcwodFOhbnf0TPRVH28mVz1IteFi16lTZGPHbkiCiRAibjaLQsRs/BoZcMrpAJw9YhB79WwObNnAxh0VVGz+Ek0x+TDjMgoadrgQ3YsEEyFE3DKUeiyKwbZwb/ZEskk/+SwAcrOcFOtZpIUr+OrNVyh07CaAnWsnX4zDJr3r3ZH8VxVCxE2vjU4FLu9/Pnrf4ZzizgJAVRQiqoN0NcDP3dF1YxXWvuSq8vu1u5JgIoSIm1ZfCUDReSPQspqflqhbnM1eB+1ylER3Jj8ThBBxswaqAFDTeh2RVuZvPsiee9rZXVImkRwSTIQQcTFNE2ekhqDmQjnKho17Io0tkbWBYbhPGNmVxRNdTLq5hBBxqQtEyFJrCduzjpq+S+/F2/4RhPqcgnfMqdjdcjxvdybBRAgRlzWfbOZHljLq04/V4lB4JzCCF64p6tJyieSQbi4hRFwCG1djJYJaMCHZRREpQIKJEKLd9lf6GahVUKxnk3fSyckujkgB0s0lhGi34vI6stU6HL28x9waZe4dZ1Hj69jhV+L4IS0TIQSmafL5dwcIhvU25Q8Ew2RrPtJzj33IVZ8sF/kD2ndKqjh+STARQvDxNyX0eX8Wm//20jHzBEM69cEIAGF/LXYlgpYpu/+KKOnmEqKH27K7iq1r3mSEu47sirXArUfN98sn3+eejLeh76ms3e5gdAbYMnt3bWFFyupQMKmqquKBBx5g9+7d2Gw2Bg0axKxZs8jOzmbYsGHk5+ejNuzFM3/+fIYNGwbAmjVrmD9/Prquc+qppzJ37lycTmeraUKIxPt+bw0/dmyKvX51yQrO6u2n//m/QFEbvyKyVR9DLOVQ/gH3ZMCWcF/G9BuWjCKLFNShbi5FUbjttttYtWoVy5cvZ8CAASxYsCCWvmTJEpYtW8ayZctigaSuro5HHnmERYsWsXr1atxuN88//3yraUKIzmEN1tBL87EvEl18OPHga3i2raT2szdZt+J1Spf/FjMSwqM2nppYrqdRPOIOFIcsRBRRHQomHo+HsWPHxl6PHDmS4uLiFu/58MMPGT58OIMHDwZg8uTJvP32262mCSE6R/H6jwD4p35qs+vKhhUMK16Os+RrKj96lRMcNQDsjPTCfu5NXH7ukC4vq0hdCRszMQyDV155haKixtWuN9xwA7quc95553H33Xdjs9koKSkhLy8vlicvL4+SkhKAFtPaIycn/l9Lubnpcd/b00ndxa+r684wTF5440tGDnYxxraTEiML5aRzYfdH+Aw7qwOn8XNXdOv4A3oG2ds+xGs7kYBp45+DbuWB8YVYtNSYvyN/d/FLZN0lLJg89thjuFwurr/+egDef/99vF4vPp+P6dOns3DhQu67775EvV2LKip8GIbZ7vtyc9MpK6vthBJ1f1J38UtG3VUeDOD+5n/I3vID2VbwD72AM4b25ve7r+Hmn41k49If+KDyZK4vGsL6tWu5Lf19xvAtBy0ebv9pAVWVda2/SReQv7v4Na27RASVhASTefPmsWvXLhYtWhQbcPd6o/PP09LSuPLKK3nxxRdj1z/77LPYvcXFxbG8LaUJIRKntKqesfYfYq89BWfSJy+bU/7tYgDmT+0TS/tsSxVlvi/J1WqpDDvod8TThEjAOpMnn3ySjRs3snDhQmw2GwA1NTUEAgEAIpEIq1atoqCgAIBx48axYcMGdu7cCUQH6S+55JJW04QQiVNZeiD27zf9Y7Dn5R8z7wPXjeH7cF8Acoec1OllE8enDrVMvv/+e5599lkGDx7M5MmTAejfvz+33XYbM2bMQFEUIpEIo0aN4p577gGiLZVZs2Zxxx13YBgGBQUFPPTQQ62mCSESZ+fnH3KaFf6r+nL+49aLW8yrqgo+0wFAZt7AriieOA4ppmm2f3AhxcmYSdeTuotfV9dd5cEAO1+eSW9HhD43zcdq0Vq95/75yznPvplJd96DYrF1QSnbRv7u4peSYyZCiOPD+x9+RWDDe5xjPUDgxIltCiQAI04/ia/29eFnKRRIRGqRYCJED1FeU8/Ab18k11pLxFTJGjG+zffeNFG2mRctS42J4kKITrfu48/J1Wr5OJBP+QWPYfHITEmRONIyEaIH2Lh5L8N2vUpAs3POTXeSnpmZ7CKJbkZaJkJ0c6ZpcmDt6/TSfJjnSyARnUOCiRDHGX99iKptGzDqD7Yp//tvLuNM82vKe42md/7pnVw60VNJN5cQxwnTNCnbv5/vl73A6er31JoK9YoDNf88ep9/DQCGafLd+m+w7v6C9Jxcard9TWFoJ6UWLwMvvSPJn0B0ZxJMhEiiUFhn6T++56yC3q1unPjWsvc4Y/+rnK6G2B7O5QRrGW7qiWx9j4rhl+BOT+P1P/yRn9i+wKIYcAA008p3OT9m1E+vwGqzd9GnEj2RBBMhksQ0Td79+zpG73yRHTvP4qRLb26W/t1X6wl9+Sbpih+fvRfj/N9RbqZx8Mxf8m2pk882vk+O5uMi5wZ44158ppXL7WE2GwMJj7kR/OWcOvxkzsyWc9hF55NgIkQS7NpbzuaVixlnrkdVTdKL38e3fRRf7dXJKP4UVQ+S59tEyLSgoePRK9ibOZKTLp+CzZXOSYD/vBP43V+/YFddMYMsFdiVMDu9F1F46WQURQVOTPKnFD2JBBPRLZmmiaIoyS5GjGnoYOiY/hpKi0uo+PtifmQ9wPdGHn/3n8K/p7+H+d7/ZVRDft1UKFH70u9n97KvzkpebhqnupuvPnc5rNx0+Ug++9bL3zdv5tLT0zjtzHFd/+GEQIKJ6GZCYZ1lf3qF8ZEP0UZejqfwEmqKd+PplYPqzMAMBzBD9SiuzIZf74lRvHMXe754H5cSwOFy4/fX46rZjl3V0VU7rmAZLqI7abuBoRbYN/ASRl50JaOsVn41181t6f9gS9hLr3OuwJuXS36fTDRVJSP32O/bJ8sVPfFQTj0USSYbPTYhm8bFLxXqLlR3kL8vXca5/r8fNT1ialgUPfpvNPxaOq6C8zB7nYDLacXqcGGqFlSLlTp/kH0bv8RadwBLejaYgKGDqYNhUB8IovrKsRp+jHCIXnop6mENoeKIh4NKOjYzRMSeSb2jF1paNqork/7DTqHfwOjJILm56WzdXg5AVroMkrdHKvzdHa9ko0chDrNl42YCXyxlYOh7zlUMSs0s1mZeRn75GiqMdOoMOzbNJN0OrgwPdWEVZ6SG3vXbydj4NwDCDf9rqh8QNC3YSyOxa4YJBipuVA4aTmpwAxb2pZ2NPugs+uT1xul0oCoK+d7MNne1SRARxzsJJqKZuvoggd2byOo3EDUtJ3bdNE30QB1ffLIO197PyQntQ1WgHjtGWm+2hXzYwrVgT0MbcgbWdA/u9AycvbwYKJimwv4ftuLf8S8soVp0VJxuF7qrF7quoyqgAESC6CaYegQiYYxwAAIHyQiUoGEQUuzYzCAAumIhqDrJjtSgYrDHdSqhvqdyypljucaZQSB0Dplpx/6SfuWdDWwt30DewP5U1YZQwgEcFpNwMIjFopEz+CRyBgyh1FeHqmqgatH/B5x2jcEeJ2oKjcsIkUzSzdVgw9YS7OtfwYZO3jmXYs07uU2/Kk1Dx19ejBqoxZaRhdaJm+eFAvUc2PkDfXPcqI506mqqsSsRrG4PijMdbK64Bp1N08QM1bNvyyZ2fvQ2Iy3R41z3OU5EdWcRDBukH/yBHKoB8BtW9qn9qAsZ5Kq1uNUA1YabWsOJV6uil+Y79mcwNeoMOwYKDiWMWw01S9dNBQXQUQiZFkKmhXrFQYXaiwgaViJgdWICejiEy/Sj2N2cdNHVpOcNbvdnTwXSVRM/qbv49Yhurh07dvDggw9SXV2Nx+Nh3rx5DB48uFPf89sdlQworeJEywGCb81jX68x1Jgu3NXbUO1udHdvTFXFX1NDOtEvSzNYR45RgVXRMYAI0V/qQUsGpqJiNYKELW4imf0I66CaESJYMPQIih5GNSMomJiKBqoFi9WKoVowDYNIwI8SCWAnjBKpx6X7cJp+PAoNw7jRX/Khhv8B6Kj41Az86YNRbA40I0QoFEYxDSwWFQUFVa9HjQRRIwFUPYDVCGIzQ6iKiQc4TVP4xsynNmhyir4Pi38vmRjUWLL53D6OLG9/Tj57HKPdLgBq/SHcDisjc9OpqPDxp+XrsRzYRHbfvlhVE81XiqYqWDTw9O1PXsEIPDY7dqvGdzsrSbca2B02whEDFAWrxYrVomKxaLgsKr0cllYX8wkhki8lWyY33ngjV1xxBZMmTWLZsmW8/vrr/OlPf2rz/fEOwKdnOHnpfz7htO3/jddSjWEq7NWzSVfqcaqhaDeLaaHCzARMDM1OwO3FyMijVknDrNqHvb4Ml+FDwSSIDbfho49aBRD7Za2jEUEjjAUTUDHQMLCgY8HABIJYiSg2AqaViGonYElHS8vBZ++NzaJghvzY3enUGxYMfy16/UHM+oNkBffRWzuIXQkTMK0YqOhm9MtYwaTetBEwbYQVG4bFgWl1oNhcaHYnWnY/8k4ZibdvLw76Q9gsKhE9Wo9pTmuLdSe/EOMndRc/qbv4dfuWSUVFBZs2beLFF18E4NJLL+Wxxx6jsrKS7OzsTn1vh93C5Reczn3/52JG2XZy+dWTKPB68fnDBMI6hgK90uz0bccv5YhuUFUbxGm3YNVUDNPEaT+y2k3TpC4QHei1WlSsmop6+PSgNjhYFyIY1rFoKpmagtthQVUUQhED0zTRVKVNp+tluOREPSFE26VcMCkpKaFPnz5oWvQLT9M0evfuTUlJSacHEwCrRWP+PRfisGuxwdUMt42MOJ9n0VRyPc5W8ymK0uqv/7bIcB89CNitbTueVQgh4pFywSQRcnLS4r43Ec29nkrqLn5Sd/GTuotfIusu5YKJ1+vlwIED6LqOpmnouk5paSleb9tnScmixa4ndRc/qbv4Sd3FL9FjJik3TSYnJ4eCggJWrFgBwIoVKygoKOiSLi4hhBDxSbmWCcCjjz7Kgw8+yDPPPENGRgbz5s1LdpGEEEK0ICWDydChQ3n11VeTXQwhhBBtlHLdXEIIIY4/EkyEEEJ0WEp2c3VUPIv9EnFvTyd1Fz+pu/hJ3cUvkXWXktupCCGEOL5IN5cQQogOk2AihBCiwySYCCGE6DAJJkIIITpMgokQQogOk2AihBCiwySYCCGE6DAJJkIIITpMgokQQogO65bbqQBUVVXxwAMPsHv3bmw2G4MGDWLWrFlkZ2ezfv16ZsyYQTAYpF+/fjzxxBPk5OQA8J//+Z989tlnlJWV8dVXX+F2u2PPHDZsGPn5+ahqNAbPnz+fYcOGJeXzdabOqLvq6mpmzZrFt99+i8Vi4ZJLLuGuu+5K1kfsNImuu6+++orf/OY3sedXVFSQm5vL0qVLk/L5OlNn/N299tpr/Pd//zeqqqJpGr/+9a8pLCxM1kfsFJ1Rb6+//jovvfQShmEwYMAAHn/8cTweT8sFMbupqqoq85///Gfs9eOPP27+6le/MnVdNy+44ALziy++ME3TNBcuXGg++OCDsXyffPKJWV5ebubn55s+n6/ZM492rTvqjLq74447zBdffDH2urS0tHM/RJJ0Rt01NXXqVPO5557rvA+QRImuu8rKSnPUqFFmWVmZaZqm+d5775mXXHJJF32arpPoetu2bZt57rnnmhUVFbH7HnnkkVbL0W27uTweD2PHjo29HjlyJMXFxWzcuBG73R77dTJ58mTeeeedWL6zzz47Frl7qkTX3c6dO9m6dSs33XRT7Fpubm4nfoLk6cy/u4qKCj7++GMmTZrUOYVPskTXnWmamKZJXV0dALW1tfTt27eTP0XXS3S9bd26tdnptuPHj2f58uWtlqPbdnM1ZRgGr7zyCkVFRZSUlJCXlxdLy87OxjAMqqurW2/GATfccAO6rnPeeedx9913Y7PZOrPoSZeIutu2bRt9+vThoYce4rvvvqNXr1488MADnHTSSV3xEfO5BqwAAAWaSURBVJImkX93AG+++SbnnHMOvXr16qwip4xE1F12djazZs3i5z//ORkZGRiGwcsvv9wVxU+aRNTbySefzIYNG9izZw/9+/dnxYoV+P3+Vu/rti2Tph577DFcLhfXX399h57z/vvv88Ybb7B48WK2bdvGwoULE1TC1JWIujMMg3/961/84he/YOnSpVx55ZVMnTo1gaVMTYn6uzvkjTfe4IorrkjIs1JdIurO5/OxePFiXnvtNd5//30efPBB7rrrLsxuvFF6IuptyJAhPPzww9x3331cddVVZGZmAmCxtNz26PbBZN68eezatYunnnoKVVXxer0UFxfH0isrK1FVtU2/Dr1eLwBpaWlceeWVfPXVV51W7lSQqLrzer14vd5Yc/uiiy6irKyMysrKTi1/MiXy7w5g/fr11NTUMH78+M4qcspIVN199NFHpKenc8IJJwDwk5/8hN27d1NVVdWp5U+WRP7N/fT/t3c/IU2/ARzH326VGcrQgyl2CBRNEHGKCfMQ6KU0prc8eCoRQUHQiygoHjQGIv6FFMVLoJhRB0EFQW+FDodLEtxRJwwVshVMHfQ7iOMXP0p+fjeH4/M6fvk+43keHvhsz54/lZXMzc3x7t07bDYb9+/fJzEx8a9lYjpM+vv72draYnR0NDQdlZeXRyAQwOl0AjAzM8PTp08v/azj42MCgQAAwWCQpaUlcnNzI1f5KAtn3+Xl5XHv3j08Hg8A6+vrWCwWkpOTI9eAKApn3114//49drv90m+HN104++7Bgwd8/fqVo6MjAD5//kxiYmJMjrtwj7mDgwMATk5OGBoa4uXLl5eWidnLsTweD8+fP+fhw4fcvXsXOB9co6OjbGxs0NXV9dtyuYt56KamJtxuNz6fj9TUVLKzs5mcnMTlctHZ2UlcXBzBYBCr1Up7e/tvy+liRbj7DuDLly90d3dzenpKQkICHR0d5OfnR62NkRKJvgsEApSWljI7O0tmZmbU2hZpkei7qakpZmdnuX37Nnfu3KGtrS3mlgZHot/q6urY39/n7OyMiooKmpubQ1si/iRmw0RERK5PTE9ziYjI9VCYiIiIYQoTERExTGEiIiKGKUxERMQwhYmIiBgW2zugRCKkrKyMw8NDzGYzZrOZrKwsqqqqePHixaXr8ff29igvLw8dxy8SCzSSRa7ozZs32Gw2/H4/a2tr9PT04Ha7ef36dbSrJnLtNM0lYlBSUhLl5eUMDAzw4cMHdnZ2WF1dpbq6msLCQp48ecLw8HDo/YtD+IqLi7FarbhcLuD8Iqdnz55RXFzMq1ev8Hq9UWmPyFUoTETCJD8/n7S0NJxOJwkJCTgcDpxOJ2NjY0xPT7O8vAzA27dvgfMzylwuF1arleXlZcbGxhgZGeHTp08UFRXR2toazeaI/C8KE5EwSk1N5fj4mJKSEnJycjCZTDx69IjKykrW1tb+WG5mZob6+noyMzO5desWDQ0NbG9v69eJ3Bj6z0QkjHw+HxaLhc3NTfr6+vB4PJydnXF6evrXE1v39/fp7e3F4XCEnv369Qufz0dGRsZ1VF3EEIWJSJhcnMBaVFREY2MjtbW1TExMEB8fT09PT+gejbi4uP+UTU9Pp6GhAbvdft3VFgkLTXOJGPTjxw9WVlZoaWnBbreTk5PDz58/sVgsxMfH43a7mZ+fD72fkpKCyWRid3c39Kympobx8fHQnS9+v5+FhYVrb4vIVekIepEr+Pc+E5PJRFZWFna7nZqaGsxmM4uLizgcDr59+8bjx4/JyMjg+/fv9PX1ATA4OMj09DTBYJCJiQkKCgr4+PEjk5OTeL1ekpKSsNlsWmYsN4bCREREDNM0l4iIGKYwERERwxQmIiJimMJEREQMU5iIiIhhChMRETFMYSIiIoYpTERExDCFiYiIGPYPTUod3YAUdyEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ans.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcab50579e8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ans['2017-12'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph is bitcoin prediction during the great bitcoin fall in 2017 December. As you can see its pretty close. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcab1b3da90>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ans['2017-11'].plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcab1b27b00>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ans['2017-12'].plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcab19ef320>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ans['2018-01'].plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcab1c28fd0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ans['2018-02'].plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcab190a828>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAE1CAYAAAAfy2z0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xd8VvW9wPHPOeeZ2XuQkEVICGET9hABxSqKG6Xu2tbacdvbW7WtBW/rrRd7a8fV1lateq11L8QBKiJDmbLDDCFhZIfsZz/n/hFBUEae5zlPSML3/XrlJeSc53t+TyTn+5zf+P4UXdd1hBBCiACo57oBQggheh9JHkIIIQImyUMIIUTAJHkIIYQImCQPIYQQAZPkIYQQImCSPIQQQgRMkocQQoiASfIQQggRMEkeQgghAibJQwghRMAkeQghhAiYJA8hhBABM53rBoTD0aPt+P2BFwtOTIyioaEtDC0SQoie4VT3OVVViI+PDChOn0wefr8eVPI49lohhOjLjLjPSbeVEEKIgEnyEEIIETBJHkIIIQLWJ8c8hBC9g67rtLU143C04ff7znVz+jyTyUJcnM2YWIZEEUKIIBw9WoeiKCQkpKJpJhRFOddN6rN0Xae9vYWDBw8SHZ0ccjzpthJCnDNut5O4uERMJrMkjjBTFIXIyBgcDqch8SR59CG6z4Nnzyo63vkd3sOl57o5QnSBjqLIbai7GJmgpdvqHHC5fVQ3dlDV0E5VQwcOl5fRhckU9I8L6n+u39GCZ+fHeHZ8hO5oAc2EY+l+Iubcj5aQEYZ3IETf5fV6eeaZJ/nww6VYrRZUVWXUqDFkZ+ewbt1nPPjgw+e6iT2CJI8wampzUdXQQfUXSaLqi4TR2OI6fo6igFlT+XDjIVLj7Uwels6koenERVnPGt939DCebUvx7P0UfB60/sOwDL0YNS6djjd+jeP9R4i4cj5qRGw436YQfcpvf/ufuFxO/vGP54iIiMTr9fLOO4vweNznumk9iiSPMHl7dTlvrCw//nerRSM9IYLC/nGkJUaSnhBBemIEKfER+HWdDbtqWbm1itc+2c8bK8oZNiCRKcPSGTogEZP25WO9ruv4Du/AvfV9fIe2g2bGPHAS5qEXo8X3O36e/ZIf07HoIRxL/0TE7PtQTJZuff9C9EYHD1ayYsXHvP76u0REdJbrMJlMzJlzNe+++/ZJ5/7zn8+wZMm7ABQVFfPjH/+MiIgIVq5czhNP/BVV1fD5vPzkJ/cwalQJ9fX1/PGPD1NTU43L5WLmzFnccssd3f4ejSLJIwzqmx28/WkFwwYkctGY/qQnRBAfbT1jl9SkoZ1PHDWNHazcWsXqbVVs3ldPbKSFiUPSmDIsjYS6jXi2LcV/9DCKPRZLydWYB1+Iaov+WjwtORfb9O/i/OBRnMufwDbje9K3LHq81duqWLW1Kiyxjz3Vn8mePbvJzMwiJibmjOd99tlqlix5l8cf/wcREZE8+OACnnnmSe6++0c8+eTfuOeeXzJkyDB8Ph9OpwOABx+cz2233cmIEaPweDz82799j6KiwYwZM96w99idJHmEwesr9qMocMusQhJiAptTnZoQwbXTBnDV1Fy2lTWyYssRPlu/mwGlTxBproH4TGzTvo1pwFgUzXzGWObc0ejjrsO19mXcMalYx14bytsSQnxhw4Z1zJhxMZGRUQBcccXV/OlP/wPA6NEl/PnPjzBt2nTGj59IXl4+DoeDTZs20tTUdDxGR0c7Bw4ckOQhOh2obmHNjhoum5AdcOI4kaaqjBiYxBB7NR0d7+N3OXm+dSI5w2ZxSUF2l+OYh30Df3MN7s2LUWNTMRdOCbpNQoTbsSfwc6WgoJBDhyppaWk569PH6fzoRz+lrGwfGzeu51e/uo+5c7/JzJkXoygKTz75f5hMfeO2K/0YBtJ1nZeX7SPKbuYb47p+gz9lLL8P1/rXcLz7ezR7DNHXPkBd4ijW7KwJKI6iKFgn34yWMRjnymfwHtkZUruE6Mv6989i0qSp/O53v6Wjox0An8/H22+/icPRcfy8kpKxLFv2AR0d7ei6zuLFbzJmzDgAKisPMGBAPtdffyMXX/wNdu4sJSIikuHDR/LPfz5zPEZNTTUNDfXd+v6M1DdSYA+xtayBXZVNfPOiAiJswf9o/e1HcS57HF/VbsyFU7BOugnFZGX8YD8vfLSXI/Xt9Evqeu19RTVhn/l9Ot76LxwfPErknPtR487dpzsherL77/9P/vGPv3PHHTdjNpvQdZ3x4yeRlfXlB8IJEyZRVraX7373dgAGDRrMrbd+C4C//vVRDh2qRNNMREVF8fOfzwdg/vzf8Oc/P8Itt8wFICIikp//fD6JiUnd/A6Noei63uc2sGhoaAuqXn1ycjR1da1BXdPn9zP/qXX4/Tq/uXPcSTOkAuE9uA3nx39H97qxTb4Fc8Gk48ea2lz89LHVXDYhh6un5gUc299SR8ebvwaLnYgrf3XKgXYhulN1dQVpaaE9pYvA1NYeJCWl/0nfU1WFxMSogOJIt5VBVm6toqqhg2un5QeVOHS/D9e6V3C893sUeywRVy84KXEAxEVZKcqOZ21pNcHkfDUmGfusf0Nvb8S59H/RfZ6AYwghBEjyMITT7eXNleXkZ8YyqiDwR1B/WyOOxQtxb34H86ALiLhqPlpcv1OeO25wKnVNTvZXtQTVVi01H9u0b+Or3oPzk38ElYSEEEKShwHeX1tJS7ubuRfmB1xexFd/gI7X5uNrqMQ2/bvYpt5+xgV9owtSMGkqa3cENnB+IvOAcVhKrsa77zM8Oz4MOo4Q4vwlySNER1tdvL+ukpJBKQzICKwMiO734vz4STCZibxqAeb8CWd9TYTNxPD8RNbtqsXn9wfbbCwjL0dLL8S95V10vzfoOEKI85MkjxC9tWo/Pp/OtRcEPoDt3vI+/qOHsE26JaDZT+MHp9LS7mZXRdPZTz4NRVGwDP8GevtRvPvXBx1HCHF+kuQRgsN1bazcWsX0UZmkxEcE9Fp/czXuz9/ElFuCKWdkQK8dNiARu1VjzY7qgF73VVr/Yaixabi3vi9jH0KIgEjyCMEry8uwWUxcPiknoNfpuo5z5bOgmrFO/GbA1zWbNEYXpLBxTx1uT/BbdyqKinnoLPz1Ffiq9wQdRwhx/pHkEaTSA41sLWtg9sRsouxnrjH1Vd69q/Ed2Yl13HWokfFBXX9ccSpOt4+tZQ1Bvf4Yc8FEFGsUnq3vhxRHCHFmTz31Nx599I8AvPnmq7z00vNnPL+q6ghvvfV60NebPLmEjo6Os58YpLMmj4ULFzJ9+nQKCwvZs2fPWb8PUF5ezty5c5k1axZz587lwIEDIR/rSfy6zssf7yMxxsbM0ZmBvdbRgvOzF1BT8zEXTQu6DUVZ8cRGWlhTGvysKwDFZMU8+EK8FZvxN4cWS4jzidcb/ESTK6+8lrlzz9zrUFV1hEWL3gj6GuF21uQxY8YMnn/+eTIyMrr0fYAFCxYwb948lixZwrx585g/f37Ix3qSNTuqqaxp45oL8jCbtIBe6/rsBfA4sU25PaQS6aqqMLYola1l9XQ4Q1vsZy6eAaqKe/vSkOII0RdMnlzCU0/9jdtum8eNN17N8uUffe3YnXfewtNPPwF07uvx7W/fwh13fJN77vnJ8XpVbW1t3H//Pcybdw0/+MF3OHLk0PE4Jz6FADz33NPccstcbr31Ru666w78fj+PPPIwBw7s57bb5nH//fcAnXWzfvrTH3Hnnbdw66038s47i47H+OSTZcybdw233TaPZ555Mqw/I+hCbauSkpKAvt/Q0EBpaSlPP/00ALNnz+Y3v/kNjY2N6Loe1LGEhISg3lw4uD0+Xl+xn+y0aMYOTg3otd5D2/Hu+wzLqCsM2R52fHEqH2w4yIbddUwdfupFhV2hRsRhyh+PZ/dKrCVXo1i7XjdLCCN59qzGs3tFWGKbC6d+rWrD6aiqyjPP/IvKygPcdde3GD58JPHxnfchq9XKk0/+HwBLlrzL4cOH+dvfnkFVVd5441UeffSPLFjwIE8//QQREZH861+v0dTUxB13fJPp0y/62rXee28xq1atOL43SHNzE6qq8u//fg+PPfYnnnrqOaDzSeeBB+5nwYIHyc7OoaOjnW9962aGDBlGdHQ0Cxf+F48//hRZWTk8//yzBv3UTs/wwohVVVWkpqaiaZ2fyDVNIyUlhaqqKnRdD+pYT0kerR1uXl+xn8YWF3deNhg1gAWButeFc+WzKLFpWEbMNqQ9OWnRpMTbWVtaE1LyALAMnYV3z2rcO5djHXGZIe0ToreaPXsOAFlZORQUFLJjxzYmT74AgG9848vf31WrVrBr107uuOMmAHw+L1FRnTWiNm3awI9//DMA4uLiuOCC6ae81urVK7nyymuO71wYGxt3yvMOHqykoqKcBQt+cfx7Ho+HAwfK0TSVgoJCsrJygM79Rf761/8N9u13SZ+sqhtoga8TJSd/vVhgRVULi1buZ/nGg7i9fi6ZkMOUkqyA4jYsexO9tY70m36NPT0x6PZ91fSSLF76cDeqxURirD34QMnFVOUMxb3zI5KmX4ui9cl/GqKHqa1VMZm+7L41DZ6CffC533NG075sl6IoqOqXf4+OjjrhGNxxx7e4/PIrTxFFOSmOqnZ2N5tMKqqqnPTnE887sQ2KwvHva5pCXFw8//zni1+70ooVn6AoyvFzT/zvV+PCqe9zgTL8DpGenk5NTQ0+nw9N0/D5fNTW1pKeno6u60EdC5QRVXX9us7WsgY+WH+QnRVHsZhUJg5JY0ZJfzKSIgOqvuurr6BjzSLMg6bSFpFFW5CVe09laE4cL+rw/qr9XDw2sIT2NYNm4nv/D1StW9al1e5ChMrv9+P1Bl8pIVwWLXqT2267k4MHK9mzZxdFRcXH2+n1ftnmiROn8MorLzJp0jRiYmJwu91UVBxg4MACRo0q4e2336K4eBjNzU0sX/4xF144E6/Xj9+v4/freL1+JkyYzGuvvcLkyRcc77aKjY3DZougra3t+LX69euP1Wpl8eK3ueSSzt6BiooDJCUlUVRUzJ49uygvP0D//lm88cbrX2vrib56/wqmqq7hySMxMZGioiIWL17MnDlzWLx4MUVFRce7noI91l0cLi+rt1Xx4cZD1B51EB9t5ZoL8rhgREbAU3IBdL8f58pnUGxRWMfNNby96YmRZKdFs6a0JuTkofUfihqXjnvr+5gGjA+4TpcQfYXP5+P22+fhdDr52c9+cXy846suueQympub+OEPvwN0JsOrrrqOgQMLuO22O3noof9k3rxrSEhIZMSIUy8GvuSSy6irq+U737kdk8mE3W7nsceeYMCAfLKysrn55uvJzs7hwQcfZuHCP/DnP/+eF154Dp/PT0JCAr/+9X8TH5/APff8knvv/QlWq/W0XWRGOut+Hg8++CBLly6lvr6e+Ph44uLieOedd077fYCysjLuu+++41s5Lly4kLy8vJCOBSKYJ4+jrS5Wbq9myZoDOFw+BvSL4aIx/RlVkBz03hwA7m1LcX32L2wzvod5wLig45zJknWVvLRsH7/9znjSEgJb6f5V7tKPca16FvvlP8eUXmhQC4U4tZ64n8fkySUsXbqCiIjQfpd6KqP285DNoL7w51e3sm1/AyWDUphZksmAfoEVOTwVf1sD7S//Ai29EPslPwnbJ/mjrS7+47HVXD4phyunBJ5sT6R7XbQ//1O0tIHYZ/2bQS0U4tQkeXQ/o5KHjIp+4bZvDCI+IRKfy5gNknRdx7nq/wAd2+RbwtoFFB9tpTArjrWlNcyZnBvStY4tGnRvWoy/uRo1Ns3AlgrR861ateFcN6FXkPIkX4iJtJAQYzMklu7349n6Hr7KLVhLrkGNDv8exeOL06g56uBAdeiD8Z2LBjXc2z4woGVCiL5IkofBfLVldLz5a1xrX0brPwzzkJndct3RhcmYNIW1IZYrgRMWDe5Zie5qN6B1QpyOgq73vNlWfZWRoxSSPAzid7biXPEPOt78DXpHE7bpd3WOc6iBlS8JVqTNzNC8RNburAlqmvJXWYbOAq8b987loTdOiNOwWGw0NdXj9XpkW4Aw03Wd9vYW7HZjelhkzCNEut+PZ9dyXOtfA7cT87BLsI6ag2IJYcFekMYXp7Fpbz27K49SlBPaFGctsT9aRjGe7R9gGTpLFg2KsIiPT6atrZnGxhr8/uC3FxBdYzJZyM/PpanJGXosA9pz3vLVluFc9Rz++gNo/YqwTroJLT70mlXBGj4gEatF47PSmpCTB4Bl6MU43v8D3v3rMA+caEALhTiZoihER8cRHX3qkhzCeGazGQg9eUi3VRBO2UV12T3nNHEAWMwaowuS2bi7Do839E9xxxcNblsiXQpCiJNI8giQZ98a2l+6D8/u1ZiHXULk9Q9hzu85q7HHF6ficHnZWtYYcizZaVAIcTqSPALg3vEhzmWPo8X1I+LaX2Mbf8M5Gds4k6LseCJtJjbtrTMknnngRBRbNJ7tMm1XCPElGfPoIvfW93CteQktawT2mXejmCznukmnpKkqwwYksWVfPT6/H00N7fOBYrJ0Ttvd+TG6uwPF0jdX3QohAiNPHmeh6zquz9/CteYlTHljsF/8gx6bOI4ZOTCJdqeXfYeaDYlnzh8PPi/e8o2GxBNC9H6SPM5A13Xc61/DveENTAMnYZt+F4ra8x/WinMTMGkKm/bWGxJPTc5DiU7GU7bWkHhCiN5Pksdp6LqO67N/4d68GHPRNGzTvtVtC/5CZbeaKMpOYNPeOkNmSSmKgnnAOHyHd+DvMOZpRgjRu0nyOAVd9+Na+Sye7R9gHnIR1sm3oii960c1cmASdU1ODtcbU17ElD8BdB3v/vWGxBNC9G69647YDXS/D+fyJ/HsWo5lxGysE+b1mGm4gRie31mMcbNBXVdaQgZqQiaesjWGxBNC9G6SPE6g+zw4P/or3r2fYim5GuvYa3tl4oDOMu256dGGjXsAmAaMx1+zD3+rMdOAhRC9lySPL+heNzWv/Q/e8g1Yx9+AddQV57pJIRsxMJnyqhaOtroMiXdsJ0QZOBdCSPL4guvT5+nYuwHr5FuwDLvkXDfHECMHdnZdbdln0KyrmGTU1Hy8+yR5CHG+k+TxBa1fEanX3otlcPg3ju8uGUmRJMfZDO26Mg8Yh7/xIL7Gw4bFFEL0PpI8vmDOH09k4dhz3QxDKYrCyIHJ7KxoxOn2GhLTlDcWFAWvDJwLcV6T5NHHjRyYhNens31/6IUSAdSIWLR+g/HsWyOVdoU4j0ny6OPyM2O/KJRoYNdV/nj01jr8deWGxRRC9C6SPPq4Y4USt5Z1Fko0gil3NGgmPPs+MySeEKL3keRxHjhWKHHvQWNKiyiWCEz9h+MtW4duUEISQvQukjzOA0PyEjBpqrELBvPHoTua8VXtMiymEKL3kORxHrBZTAzOiTesUCKAKWsEmG1498msKyHOR5I8zhMj8pOobzauUKJismDKGYWnfAO6z2NITCFE7yHJ4zxxrFCisQsGx4O7A9/B7YbFFEL0DpI8zhOdhRJj2GzQ3uYAWubgzv3NZcGgEOcdSR7nkZEDkyivajWsUKKimjDljcF7YBO6x2lITCFE7yDJ4zxidKFEANOAceBz463YZFhMIUTPJ8njPNIvKZKUOLuh4x5a2kCUyAQ8MutKiPOKJI/ziKIojBiYxM6KRhwuYwolKoqKacA4fAe3ozvbDIkphOj5JHmcZ44VStxRbkyhROisdYXuw1O+wbCYQoie7azJY+HChUyfPp3CwkL27Nlz/Pvl5eXMnTuXWbNmMXfuXA4cOBDWY8IYXxZKNG7WlZqYhRqXLgsGhTiPnDV5zJgxg+eff56MjIyTvr9gwQLmzZvHkiVLmDdvHvPnzw/rMWEMTVUZnp/E1rIGvD5j6lIpioJpwHh8Vbvxtx81JKYQomc7a/IoKSkhPT39pO81NDRQWlrK7NmzAZg9ezalpaU0NjaG5Zgw1rFCifsOGVMoEcCcPw7Q8cr+5kKcF4Ia86iqqiI1NRVN0wDQNI2UlBSqqqrCckwYqzjX+EKJamwaanIunj2r0HWptCtEX2c61w0Ih8TEqKBfm5wcbWBLeq4RBcls3d/AD5NGoiiKITFtEy6nbtGfiajbRlTxZENiCiGMZ8R9LqjkkZ6eTk1NDT6fD03T8Pl81NbWkp6ejq7rhh8LVENDG35/4NVjk5OjqatrDfh1vdHg7Dg27Kxhc2k1mSnBJ9sT6akjUBP6U7fseToSi1G0PvnZRIhe7VT3OVVVAv7QHVS3VWJiIkVFRSxevBiAxYsXU1RUREJCQliOCeON+KJQ4ud7jJt1pSgq1rHXobfU4tm13LC4QoieR9HPssHDgw8+yNKlS6mvryc+Pp64uDjeeecdysrKuO+++2hpaSEmJoaFCxeSl5cHEJZjgZAnj655+F+f09ji4rffHY9qUNeVrus4Fv83/qYqIm94GMVsMySuEMIYRj15nDV59EaSPLrm0+1VPLl4J/fOG0lhVrxhcX01++h460EsJVdhHTXHsLhCiNCd024r0TeMLkzBbtVYscXYGW1aaj6mnNG4t7yH39FiaGwhRM8gyeM8ZjVrjCtKZePuWjqcxtS6OsYy9hrwunBvetvQuEKInkGSx3luyvB+uL1+1u2sMTSuFtcPc+EUPKXL8LcYNygvhOgZJHmc53LSoslMjmTlVuMXY1pGXwWKimvD64bHFkKcW5I8znOKojB5WD/Kq1o4VGdsSXU1Mh7LkIvw7luDr+GgobGFEOeWJA/BhOJUNFVhVTiePkZcBhY7rnWvGB5bCHHuSPIQREdYGDkwiU+3VxtWafcYxRqJdeRsfAe34j2yy9DYQohzR5KHADoHztscHjYbWCzxGHPxTJTIBFzrXqYPLisS4rwkyUMAUJyTQHy0NSwD54rJgmX0HPy1+/Ee+Nzw+EKI7ifJQwCdK0wnDU1ne3kDjS1Ow+ObCyajxvXDve4VdL/P8PhCiO4lyUMcN3lYOroOq7dXGx5bUTUsY6/B31yNZ88qw+MLIbqXJA9xXEqcnUFZcazaegR/GMYmTNmjUFPzcW98E93rMjy+EKL7SPIQJ5kyrB91TU72VDYZHltRlM6S7e1HcW//0PD4QojuI8lDnGR0YTJ2q4mVW4+EJb4pvRAtazjuzYvxd5x5D3Wvz095lRRWFKInkuQhTmIxa4wbnMqG3XWGF0s8xjb+BvB6cK154YznfbzpML95dgPb9jeEpR1CiOBJ8hBfM2VYOh6vn7UGF0s8Ro1LxzLiMrz71uA9tP20523YVQvAix/tNXzxohAiNJI8xNd0FkuMYlWYuq6gs2yJEpuGc9X/oXvdXzve3OZi36FmCjJjqWro4OPPD4etLUKIwEnyEF+jKApThqVTXtXKoVpjiyUev4bJgm3Kregttafc8+PzPXXowM2zCinOieetVeW0dnw9yQghzg1JHuKUJgxJw6QpYVlxfoypXxGmgZNwb3kX39GTnyw27K4jLSGCfkmR3DBjIE63jzdXloetLUKIwEjyEKcUZTczYmAyn+2oxuMN33iDdfxcMNtwrXwWXe+8TpvDw+7KJkYXJqMoChnJUVw4KoPlmw9zMExPQkKIwEjyEKc1dVg6bQ4PW/YZXyzxGNUeg23cXHzVe/DsXgnApr11+HWd0YXJx8+bMzmXCKuJFz7cI8UVhegBJHmI0xqck0BCjJUVYRw4BzAVTkFLL8S19mX8jhY27q4jKdZGdmr08XOi7GaunJLHrsomPt8TvmQmhOgaSR7itFRVYdKQdHbsbwxLscRjFEXBOvlW8DjpWP0CpQcaGVXQ2WV1omkj+5GRFMnLH+/F4w1PcUXd78ezfz0d7/0ez741YbmGEH2BJA9xRpOGpaMDLy7bx/b9DbQ5PGG5jhbfD8uIy9D3f0aecoSSwpSvn6Oq3DBzIHVNTpau/3JbW93dgWfvp/gaDwfdpaW7Hbi3LaH9pXtxfvgYvkPbcX32wimnEQshwHSuGyB6tpQ4O1OH92PFliPHF+2lxNnJSY8mLz2GnPQYstOisZq1kK9lGTGb+i0ruCF6Lf1TrzvlOcU5CYwcmMTiTyuYNDiZiIOf4t74FrqrcyBdiUnBlD0SU/YItLQCFPXM7fK31uPe8SGenZ+Ax4GWVoB5/FwUsx3Hu7/Ds2sFliEzQ35vQvQ1it4HRx8bGtrw+wN/W8nJ0dTVtYahRb1fh9NLRXUL5dWtlB9poby6hcaWzsq4qqLQLymSvH7RTChOozArPqhruDw+Hn3sJb4TsRTLqCuwllx9yvNqGtt5+dmXuSZmC9G+JrSMwVhGXo6/qRpvxSZ8h0vB7wVrJKb+wzDljMSUORTFYj8ew1dbhnvrErzlGwAw5Y3BMnQWWkoeALqu43j7IfytdUTe8DCKZg7qPQnR05zqPqeqComJUQHFkeRxAkkegWluc1Fe1Up5VQvlVS3sP9KCz6+z8K4JxERaAo63cXctj72xnd8W7yCyZjMR1/4GLa7fSef4qvfiXPsS/pp9HPHGETnxBjKGjztpfET3OPEe2o73wCZ8lVs6n0pUE1q/QWj9BuGt2Iy/Zh9Y7JgHTcMyZCZqVOLX2uM9tAPHu7/DOvkWLIOnB/4DEqIHMip5SLeVCFpslJURA62MGJgEQHVjB/c/sZZ3PqvgxpkDA463cU8dUXYzSdNvxvHqLlwrn8U++z4URcHfXI1r7St4D2xEiYhDnXgrj3+skbTNxC+GnxxHMdsw55Zgzi1B9/vw1ezDW7Gp86lk3XaU6GSsE7+JuWDySU8jX6VlDO7cf2TTYsyFU1E0+XUR4hj5bRCGSUuIYOLQND7edJhZY/uTEGPr8ms9Xj9b9tUzujAFU2Qc1nHX41rxNJ5t7+NvrcdTuhxMZiwlV2EZegmK2crVviM8/d4u1pbWML447ZRxFVXDlF6IKb0Qfdxc9I4mFHssinr2uSKKomAddQWO9x7Bs2cVlqJpXX4/QvR1MttKGOqKSTnous7bnx4I6HU7KxpxuHyUfLEw0Fw4BS2tANeal/CUfox50FQi5y7EOmoOitkKdM4Ey06L5pXlZbjcZ5+Y+S31AAAgAElEQVS6qygKamR8lxLHMVrmUNTkXNybF6P7w1OiXojeSJ48hKGSYu1MG9FZSuQb47JIiY/o0us27K7DbtUoyk4AQFFUbNPuxL39A8yDL/za2Ad0DtTfOGMg//3857y3toIrp3QOdjtcXpraXBxtPeGrzUVTq4tWh4cbpg8kr19Ml9qlKArW0XNwvP9HvHs+xTxoahd/EkL0bZI8hOEum5jNyq1HeGtVOd++vPis5/v8fjbvrWf4gCTMpi+fCtSYFGwTv3nG1xb0j2NsUQrvrqlk/a5ajra6cJ7iKSTSZiI+2kp1Ywefbq/qcvIA0PoPR03KxrXpbUwFk846/VeI84EkD2G4uCgr00dnsmRtJZeOzyYj+cyzOPZUNtHm8JxUyyoQ11+YT4fTi9WiUZyTQHy09fhXXLSVuCjr8XUoj7y0mV0B7s+uKAqWUXNwLv0z3n1rMBdMCqqdQvQlISeP5cuX86c//Qmv10tsbCwPPfQQ/fv3p7y8nPvuu4+mpibi4uJYuHAhOTk5AEEfE73HpeOzWb7pMG+uKuf7Vw0947kb9tRhMakMyfv6dNmuSIix8e9zR3Tp3EHZ8by6vIzmdjexAUwnNmWPRE3sj2vTIkz5EwIaNxGiLwrpN6C5uZl7772XRx55hLfffpvrrruOBx54AIAFCxYwb948lixZwrx585g/f/7x1wV7TPQeUXYzF4/pz8bddVRUn37tjF/X+XxPHUPzEg1ZpX42RdmdCxh3VRwN6HXHnj705hq8+9eGo2lC9CohJY+KigqSkpLIzc0F4IILLmDVqlU0NDRQWlrK7NmzAZg9ezalpaU0NjYGfUz0PrPGZhFpM/H6iv2nPWf/4Raa29xBd1kFKis1CrtVY1dlYMkDwJQzCjU+E/fni9D9sqe6OL+FlDxyc3Opr69n69atALz9dud2olVVVaSmpqJpnZ8kNU0jJSWFqqqqoI+J3sduNXHp+Gy27W9g76FTjzNs2F2LSVMYnp/ULW3SVJWCzLiAnzygcwaYZdQV+Juq8JavD0PrhOg9QhrziI6O5g9/+AMPPfQQLpeLqVOnEhMTQ0dHh1HtC0qgy+xPlJwcffaTRJddP2sQH248xNufVfDb72WeXEZE19m8r54RBSlkZQZXDysYJcVpPLVoB6rFRGLs6VeYn4qeOI1DWxbh27qY9HHTURQZ+xC9jxH3uZAHzCdOnMjEiRMBqK+v56mnniIjI4Oamhp8Ph+apuHz+aitrSU9PR1d14M6FgipbdWzXDo+m+c/2MMn6yspzk04/v2K6lZqjzq4bHx2t/7cMxM6156s/vwQE4acemX6mWjDLsO57G9UrVuOOW+M0c0TIqyMqm0V8semuro6APx+P4888gg33HADGRkZFBUVsXjxYgAWL15MUVERCQkJJCYmBnVM9F5Th/cjMcbK6yvKTtpvY8PuWlRFYWRB94x3HNM/NYpIm4mdQYx7AJjyxqHEpnWOfegy9hGqsiPNbN578u6Qut+Hv60BX80+PPvX4962BOeaF3GtexW/Uz7g9QQhV9X95S9/yeeff47H42HSpEn84he/wGq1UlZWxn333UdLSwsxMTEsXLiQvLzOFcDBHusqefLoeVZu6axD9cOrhzKyIBld1/nlE2uJj7bysxtHdnt7/ve1rRysbePh700M6vWePatxLn8C28U/xJwz2uDWnRu7Ko7S1O7CYtKwmFTMJhXzsT+bVcyaisXc+XdLkDPjdL8f3dmK7mhG72hi375KNm3dT4zSwYRcK1ZvC3r7UXRHM3z11qSZwe8Dix3rmGswD5omU6aDICXZz0CSR8/j8/u5/8l1mDWFB+4YS1V9O796ah03XVzA9FGZ3d6eDzYc5IUP9/Lw9yaQFOC4B3R+Mm5/+ecoZjsRVz/wtS1ze5tPt1fx5OKdXT5/RH4St186iOiI06+V8exfh+/gNvwdzegdzZ0Jw9ECp3hac+pm2tVoUvqlo0YloEQmoETGd9Yii0xAjYwHayT+o0dwrX4OX9Uu1KRsbJNuRkvND+o9n6+kJLvoVTRV5crJufxt0Q7W7ayh9qgDBRjVzV1WxxRlHVvv0cTkYYEnD0XVsI68HOcnT+Gr3IIpu2uLFHuiQ3Vt/N/7uynoH8ctswrxeP1ffPlwf/Fn9wl/bmpz8cH6gzzw9Hq+c/ngr23+pes67o1v4v78LRR7zBeJIA4tKRslIhbssaze18GqfR3k5mYy97LRbNp9lGfe28WdE4qYOOT0Y5xaQgb22ffiLVuLa82LdLz1IKaCKVjHXYdq73rJGRE6SR6i24wpSuGdzw7w1spyzCaVAZmxxEVZz0lb+iVHEmU3s6vyKJOHBTYh4xjTwAkony/Cufo5IpKyOz8d9zIOl5fH3tiOzWrirjnFXf7/MXZQKo+/tZ2HX9jEnMm5zJ6Qg6oq6H4frlXP4tm1AlPBFGxTb0VRv7zNeLw+/v52KRt313HxmCKun56PqihMHmZjxZYjvLxsHyPyk4iwnX7nRkVRMOePx5Q1HNfni/BsW4r3wEasY67GXDRdurK6ifbAsSXhfYjD4f5ad2lXREZa6ehwG98gAXT+0sdFWVm26TAtHR4uLunPgIzYc9aW8upW9h5q4qKS/kF1OymKipY2EM/Oj/FVbMGcPw7FFPgOiueKrus8uXgnew818eNrh5F5lhpkJ4qLsjJpaDoNLU4+3HCIvYeaKe4fif7J43jL1mEZeTnWifNOKiLZ5vDwp1e2sL28kbnT87lySt7xn7uiKGSnRvPB+oO4PH6GDTh7qRpFM2PKHIIpbwz+xko8Oz7CW7EJLSHzlDtDik6nus8pikLEGbogT0VStOhWIwYmkZve2b1wrrqsjinKiqOxxUVtkyPoGFpyLvaLfoi/uQrHkj+he3vPh48PNx5i/a5arrlgQFD7ztutJr49ezC3XzqIqqoajrz4G7yVW7FOvgXrmGtOSsgNzU4e+udG9le1cNecYmaNzfpavOy0aKaNymDZ54eorOn62KMW3w/7pT/DNvNudGcrHYv+C8fHf8dXd4A+OKTbY8iTxwnkySP8FEUhPzOWrJQoBuec2ynYNovGss8Pk5kcSU5a8P3lakwKamw6nm1L8DUcxJQ3pscvHtx3uJm/L9rB8AFJzLuoIOgBf0VR6G93Mv7I80R6Gnm6dSrVscMo6B+HqnbGrKxp5eEXNtHu9PLja4efsZpAfkYsK7dWUXakmcnD0rvcLkVR0OIzMBdNA92PZ/cKPKXL8JZvAK8LJSYZxdz1nS37MqOePCR5nECSR/eIibCEdLM2SpTdzCebj4CiUFKYElIsLSEDxRaNZ/tS/G2NmLJH9tgZWC0dbv7nxc1E2Ez8ZO7wkApS+uorcCxeiOJzYZ31E8r0TD7ceIidFUcZnJPAgeoWHnl5CxaTxs9uHElevzN3U1pMGtF2C8s+P0xirI3s1MBWQiuaCVNmMZbiGSjRSfibq/HuXtmZ2GvLQFFRY1LO6z1ZjEoeMmAuzluKojAoO55dFUfRdT3km72leAa6owX352/htsdgHXe9QS01jt+v88TbpbR2ePjlzaOJPMPA9Nl4D23H8cGjKNZIImbfixbfj9syOysXP/v+Lhb8Yx0uj4+0xAh+ct3wLu9pP3FoGiu2HOGVj8sYVZAcVBsVaySWwdOxDJ6Ov6kaz97VnWtzlj0OFjvmvLGYCiajpeb32CTf08mTxwnkyeP80+7wsHZnLeMGp55xzUJXaemD0J2teLYvRTFb0VIHGtBK4yxafYCVW6u4eVZBSMUoPXs/xfnhX1BjU4mYfR9azJdPbpnJUZQMSmHvoSbSEiP5yXXDiYns+qw6RVHITovmgw0Hcbh8IRfNVGxRmDIGYx56EVp6Ifi8ePevxbtzOZ6yNWipA1Ej40K6Rm8iTx5CGGDQCft7pCdGhhxPURSsE29Cd7TgWvMSii2mx+w8uH1/A4tWlTNpSBpTh399T/iucm9dgmvNC2jpg7Bf/EMU69d/bqnxEfzy5pKgr5GVGs2MUZl8tPEQU4anG9LNqSgqpozBmDIGo0++GW/5Blwb3sDx3u+JmHM/amxqyNc4n/TsUT0hwiwlzk58tJWdAW5NeyaKqmKb/l20fkU4P3kKb+Vmw2IHq7HFyd/fLiUjOZKbZhUG3VXjPVyKa80LmHJLsF/601MmDqNcOSWP6EgLzy3Zg9/gWVOK2Ya5YDIRl/4H6Dod7/4P/o5mQ6/R10nyEOc1RVEYlBXP7sqjhk7rVDQz9ot/hJqYheODv+Cr3mtY7EB5fX7+8uZ2vD4/d181NOgBct3VjnP5kyixadgu/DaKFvx4SVdE2EzMvTCf8qoWVm45EpZrqHHp2L/xE3RHM473H0F3Bz9t+3wjyUOc9wZlx9Ha4eFwfbuhcRWLHfs3/h0lKp6O9/+Ar/GQofG76uVl+9h/pIU7Li0i7Yty9MFwfvov9I4m7Bd+B8XUPZUBxhenUtA/jleXl9Hm8ITlGlrKAOwzv4+/4SCODx5F93nDcp2+RpKHOO99WecquBLtZ6LaY4i49D9QTBYc7/4P/tb6s7/IQNv3N/DhxkNcVNKfkkHBT0f2lG/Au3c1lpGXo6UEVuU6FIqicNPFBThcPl77pCxs1zFlDcc29XZ8h3fgXP6klNrvAkke4ryXFGcnKdbGLgPHPU6kRidjv/Sn6F5XZwLppv0oPF4///xgD6kJEVw7bUDQcfwdTbhWPIOalINl1OUGtrBrMpOjmFmSyYrNR9h/pCVs1zEXTsEy9lq8ZWtwrXlJVqefhSQPIeicdbW78qjhA7PHaAn9sc/6Mf62ehzv/QHd4wrLdU70/rpKao86uOmiAsym4H7VdV3HueJpdK8L24XfOanIYXeaMzmX2CgLT71TyvpdtTjd4elasgy/DPOQi/BsW4Jn63thuUZfIclDCDq7rtqdXg7WtIXtGqb0Qmwz7sZfX47jw0fR/eHrW69vcvDOpwcoKUw+aevfQHl2r8BXuQXr2OvQ4oOf3hsqu9XE7ZcW0e708tc3t/PjP6/isde3saa0GofLuJ+joihYJ9yIKW8srrUv49mz2rDYfY2s8xCCE9Z7VB4lOy2wkhiBMOeMQp9yG64VT+Nc/lTnrKUw1MF64aO9oMANM4JfpOhvqcX12Qto/YowD5lpYOuCMzQvkUe+P4m9h5rYsLuOjbtr2binDpOmMiQ3gZJByWct594ViqJiu/DbOJytOD/5B4o9GlP/YQa9i75DVpifQFaYn7/sVhNrdlTjdPsYX5wW1mtpSTmgani2L0X3ONEyhxhaImNrWT1vriznqql5DBsQ3Ops3e/HufTP6I4WIi77D9QwrucIhKIoJMXaGTYgkYvG9Kc4NwGrWWNnxVE+3V7NknUHKTvcAjpkJkcGX/BR1TDljMZ7cDOe0uWYMop75X4tpyIl2YUw2KDsePYcasLnD/9MG8uI2ZiLZxret+7x+vjXB3tJS4jg4jH9g47j3vo+vuo92Cbd1GP3xlAVhYGZcdw4cyC/u3siv7xlNBeV9KeqoZ0nFpeyZN3BkOJ3TrX+KYo9Gsf7f8DfVG1Qy/sGSR5CfGFQVjwOl4/KMI57HNNZxmTeCX3rqwyJ+97aSmqbHHzz4gJMWnC/3r6Gg7g3vI4pZzSmgRMNaVe4qYrCgH6xXD89n4V3TWB0QTKvLi9jd2Vo06/ViLjOVehAx7u/w9/WaERz+wRJHkJ8YVBWZ3G8cKz3OJVjfetaRjHOT/4RchmTuiYH73xWwZhBKRQHuVeK7vPg/PjvKNYIrFNu7ZUVZxVF4Y7LikiOt/PXt3bQ1BbazDY1Nq1zqrWrHce7v+u2qdY9nSQPIb4QG2UlPTGCnSF+Wg2EopmxX/QDQ8qYvPDhXlRFYe70/KBjuDe8gb/xILapd6Daz/2eK8GyW018/6ohON2ds7O8vtC6IrWknM6p1q31ON6TMiYgyUOIkwzKjmfvweaQbzaBOKmMyZI/4jt6OOAYm/fVs3lfPVdMyunyvhlf5a3eg3vLe5gHTcWUPSKoGD1JZnIUt14yiL2Hmg1ZnW7qN6izjEl9Ra/bcjgcJHkIcYKirHhcHh8Hqru3a0K1xxDxjf9AUU04Fi/Es29Nl1c4uz0+/vXBHtITI7goyEFyb9VunMv+hhKdhHX8jUHF6IkmFKcxfVQGS9YdZMOu2pDjmbJHYJt2J76qXTg/+iu632dAK3snSR5CnKCwm8c9TqTGJGO/7B6UiHicyx7H8c7D+BrP/hTy3tpK6pudfPOiwAfJfU1HcCz5E463HwLdj33GXSgWe7BvoUe6YcZA8vrF8NS7O6lqCL34pXngRKwTb8JbsQnnJ/84b+tgySJBIU4QHWEhMzmKnRVHmT0x56znt3S4aWxx0uH0dn65jv3Xc/LfnV5y0qKZd1HBGeNpCRlEXLUAz67luNa/Rsdr8zEPvQjrqDmnvKnXfjFIPrYohcEBDJL7HS24N76JZ+dyMFmwjLkGy9CLu61abncyaSp3XzmEB55ez2NvbOf+W0Zjs4R267MMmYnubse94Q1c1gisE+b1yskFoZDkIcRXDMqO45PNR/B4/SfVhPL5/RyqbWff4WbKjjRTdriZuibnKWMoCkRYTUTYTERYzei6zocbDzFsQCJD8s68bkJRVSyDp2PKLcG9/lU8W5fg3bcG6/i5mAaMP+km9eKHe9FUhbnTu7aSXPe6cG9binvzO+B1Yy66EMvoOb16cLwrEmJs3DWnmN+/tJln39/Ndy4fHPqe9SOvQHe2d245bI3EOvpKg1rbO0jyEOIrirLi+XDDIbaW1aMqCvuONFN2uIUD1S24PZ1dFLFRFvL7xTJtZAZpCRFE2szHk4XdasJm0U66OXl9fu5/ci0vfLSXX+fEo6ln715S7THYpt6BedA0nKufw7nsb2g7l2OddBNaQn827+0cJL/+wnzio8/8xKDrfrx7P8W1/nX09kZM2SOxjrseNS49tB9WLzI4J4GrpuTx+or95GfEMmN0ZkjxOutg3YDu7sC98U0UaySWIRcZ1NqeT5KHEF9RkBWHAjz2xnYANFUhKzWaqcP6MSAjlgEZMSTG2AL65GrSVOZemM//vr6N5ZuOBHTj0lLyiJjzKzy7V+Ba9wodry1AK5rBa9sySE+MYGZJZyzd7wOfF3yezqKLvs4vf0s1rvWv42+oRE3OxTb9u5jSCwP6mfQVl07IZv+RFl78aC/ZadHkZ8SGFE9RVGxTb8fp7sD16fMologes2d9uCl6Hyxa39DQht8f+NtKTo6mrk4WAAn4ZPNhHC4fAzJiyE6NxhLk1q0n0nWd/3lxM5U1rTz03QlE2QMv4Kc723Ctfw33zo9x6ybMZjOq7gO/hzMVdFOik7COuRbTgLFhKcTYm7Q7Pfz6mfV4fToLbh9DTIA1nU5F97pxvP8HfFW7sU25DVPhlB47BnKq+5yqKiQmRgUUR5LHCSR5iHA7WNvGA0+vY8boTObNPPPg+elsL2/g5VeXcXVmNYOyE0DVOvcT10ygmlE0E2im499TzLbO4oth3nO8N6msaeW/nttIfkYsP507AlUN/Uavux2dCaR6D1p6IdZJt6AlZBjQWmMZlTyk20qIbtQ/JYoLhvfj488Pc+HIDNITA6tW29rh5qnFO4mI60/RtVdjNeCJ6HyUlRrNTRcX8PS7u3h3TUWXZtadjWKxY5993wndi2eeKdfbnd/Pr0KcA1dOycNiVnlp2b6AXqfrOk+/u4t2p4fvXlEsiSNEU4b1Y2xRCm+tKudgrTHFMBVVxVI0jci5/425cBKere/T/vLP8ZSt7XPb2kryEKKbxURauHxiLlvLGti2v6HLr1u++Qib99Vz7QUDyEoN34ZV55ObLi4k0m7mycWlhpakUW3R2KbeQcSc+1EiYnF+9Fcc7/4OX9MRw65xroWcPD7++GOuvPJK5syZwxVXXMHSpUsBKC8vZ+7cucyaNYu5c+dy4MCB468J9pgQfcXMkkxS4u28+NHeLt20jtS389JHeynOTWBmCPt0iJNF2c3cekkhB2vbWLT6gOHxtdR8Iq5cgHXSzfjqDtDx6q9wrX25W/awD7eQkoeu69xzzz08/PDDvPXWWzz88MPce++9+P1+FixYwLx581iyZAnz5s1j/vz5x18X7DEh+opjU3erGjr4ZPOZP416vH7+vmgHFrPGty4rQu2hs3h6q5EDk5k0NI13P6tg/5EWw+MrqoqleAaRc/8bU/4E3Fvepf3ln+M9tN3wa3WnkJ88VFWltbVz5L61tZWUlBSOHj1KaWkps2fPBmD27NmUlpbS2NhIQ0NDUMeE6GtGDEyiKDueN1fup83hOe15r68oo7K2jdsvHURcVN8rH9IT3DijgLhoC0+9U4rbE55ih6o9Bvu0O7Ff8UsUsw3Hh3/B7zA+WXWXkJKHoij88Y9/5O677+bCCy/k+9//PgsXLqSqqorU1FQ0rXNAT9M0UlJSqKqqCvqYEH2NoijcOGMgHS4vi1aVn/KcHQcaWbLuIBeOzGDkwORubuH5I8Jm4vZLi6hq6OD1FfvDei1T2kBsF/0APC7c614J67XCKaSpul6vl7/97W/85S9/YfTo0WzcuJEf//jHPPzww0a1LyiBzlc+UXKyDESK7pOcHM0l43NYsraCq2cU0P+EgfDmNhdPv7uT/qlR3H39iJCL+Ykzm5YcTWllE+9/doALx2QxZEBS+C6WXEjD+Mtp/uxNkidcii0juDU/QV/egPtcSP8ad+7cSW1tLaNHjwZg9OjR2O12rFYrNTU1+Hw+NE3D5/NRW1tLeno6uq4HdSwQskhQ9CazxmSy/POD/PXVLfzk+uFA53jio69vo6XdzY+uGUZrswP5lxl+l4/PYkNpNb9/fiO//tbYgBJ2u9PDknWVFOckUJgVf9bz9UGXoGz9hOrFjxNx5QKULtQ7M4JRiwRDam1aWhrV1dXs39/5mFdWVkZDQwPZ2dkUFRWxePFiABYvXkxRUREJCQkkJiYGdUyIviomonPq7rb9DWwt65y6+8mWI2zaW881Mi23W9ksJr512WAamp288nHXdh/UdZ01pdX88om1LP60osuzthSzDev4G/DXV+DZtTz4Rp8jIZcnWbRoEU888cTxOi4/+tGPmDlzJmVlZdx33320tLQQExPDwoULycvLAwj6WFfJk4fobY5V3dVUhe9dOYQHn93AwMxYfjJ3hMyuOgdeWraXJesO8u9zhzMk9/Ql9GuPdvDc0j3sKG8kJy2axBgbW8rq+d9/m4rVcvZFnLqud2761VBJ5Nz/RrWF/4OC1LY6A0keojfatLeO/31tG3arhqaq/OcdY89aal2Eh8fr44Gn1+N0+/jNt8YSYTu5LpjX52fJukoWrT6ApipcPTWP6aMy2Vl5lN+/uJl/u3YYw/O7NmbiO3qYjlfnYy6chG3qHeF4OyfpEd1WQgjjjMjvnLrrcPm4/dJBkjjOIbNJ487Zg2luc/PCh3tPOrb3UBP/+fR6XvtkP8PyEnnwznHMLOmPqioUZMZiMalsL+/68gItPgPz0Ivw7FqBr7ZrXWU9gUzfEKKHUBSF784pprKm9YxdJaJ75KbHcOmEbBZ/eoBRBckUZMXxysdlrNhyhMQYKz+6ZhgjBp78dGE2aRRmxbMjgOQBYB01B+++NThXPUfElfO7bfA8FJI8hOhBYiIskjh6kCsm5bBlXz1Pv7cLVYE2h5dZY/szZ3LuaWdiFecm8OJHe6lvdpAU27VquorFjnX8DTiXPY5n1ydYBl9o5NsIi56f3oQQ4hwxaSp3zh6My+MjMdbGr24tYe70gWecwjskt3N2aCBdVwCmAePQ0gtxrX8V3WlMld9wkuQhhBBn0D8likd+MIlf3lJCdtrZZ0OlJ0aQEGNlx/7AkoeiKFgn3QxuB651rwbb3G4jyUMIIc4i0mbu8pRpRVEYkptAacVRfP7AyrxrCZmYh1yEZ9cn+GrDWyYlVJI8hBDCYENyE3G4vEFV6bWOvhLFHoNz9XPounF7jBhNkocQQhisKCceRSHgWVdwbPB8Lv66cjy7VoShdcaQ5CGEEAaLtJnJS48JeND8GFP+BLT0Qtzreu7guSQPIYQIg+LcBMqrWs64V8vpdA6e34Tu7uixg+eSPIQQIgyG5CWi61B6ILinDy2hP+ahF+PZtRzPgY0Gty50kjyEECIMctOjibCagu66ArCOuQY1ORfn8ifxt9Qa2LrQSfIQQogw0FSVwTmdpUqCrT+raGbsM+8GRcXxwWPoXrfBrQyeJA8hhAiT4twEjra6ONLQEXQMNToZ+7Rv42+owPXpvwxsXWgkeQghRJgcq1O2Y39DSHFM2SOwjLisc/xjz2ojmhYySR5CCBEmibE20hMjQhr3OMZScjVaeiHOVc/iazxkQOtCI8lDCCHCqDg3gd0Hm3B7fCHFUVQN24zvoZhtOD94FN3tMKiFwZHkIYQQYTQkNxGP18+eQ00hx1Ij4rDN+B7+lhqcK58JeiDeCJI8hBAijAqz4jBpSlClSk7F1K8IS8k1eMvW4ildZkjMYEjyEEKIMLKaNQZmxhky7nGMZcSlaP2H4frsX+es+q4kDyGECLMheQkcrmvnaKvLkHiKomK/8DsoEXE4PnzsnNS/kuQhhBBhdmzK7vby0KbsnkixRWGf+X30jiYcy5/o9vLtkjyEECLMMpMjiY2yGDbucYyWkod1wo34Krfg3vKuobHPRpKHEEKEmaIoDMlJYEd5I36/sTOkzINnYBowDs+2DwyNezan38VdCCGEYYrzEli9vZqKmlZy02MMi6soCrbp30V3BL5rYSjkyUMIIbrB4JwEFGB7iKVKTkVRVNSIOMPjnokkDyGE6AYxERay0qINnbJ7LknyEEKIbjIkN4Gywy10OL3nuikhk+QhhBDdZEhuAn5dZ2fF0XPdlJBJ8hBCiG4yICMWm0Vjh4HrPc4VSbayKM4AAA/hSURBVB5CCNFNTJpKUXY820PYXbCnkOQhhBDdqDg3gfpmJ7VHz21J9VBJ8hBCiG40JDcBoNfPugppkeChQ4f4/ve/f/zvra2ttLW1sW7dOsrLy7nvvvtoamoiLi6OhQsXkpOTAxD0MSGE6O1S4iNIibOzcusRJg5Jw27tnWu1Q3ryyMzM5K233jr+NWPGDGbPng3AggULmDdvHkuWLGHevHnMnz//+OuCPSaEEH3BdRcO4FBtO4+8tJkOp+dcNycohnVbud1u3n77ba655hoaGhooLS09nkhmz55NaWkpjY2NQR8TQoi+YnRhCt+7cggHqlv53YubaXP0vgRiWPJYtmwZqampFBcXU1VVRWpqKpqmAaBpGikpKVRVVQV9TAgh+pLRhcn84OqhHK5r53cvbKKlw32umxQQwzrbXnvtNa655hqjwoUkMTEq6NcmJ0cb2BIhhDi9mcnRJCRE8l//WMvvX9rCf901kfgYW9iva8R9zpDkUVNTw/r16/+/vXsPiqr+/zj+3F1clOK2IkpoXgMpI01QI79ROKOReJ38ybfRaSanssn6jZmK0q8UL7RlhoM6WTpOk4o1ApZo5ujkzM9GU8PASymaIYk/7jd1BGT394fTjn6/5tdDyC7wevy357Lnfc58dl97Puecz/LBBx8AEBISQklJCU1NTVgsFpqamigtLSUkJASn09mseUZUVFxu1rDH3br5UlZWZ3g9EZHm6mXrwn9PeYxV2/KYm/6/zPvnEAJ9ve/Z9m73PWc2mwz/6G6Rbqvs7GxiY2MJDAwEoGvXrkRERJCTkwNATk4OERER2Gy2Zs8TEWmvInoH8tZ/Dabmcj3vb/6J8pq7fwakvqGJw7+U8MPx1u3eNzlb4DHHMWPGkJyczFNPPeWadu7cOZKSkqitrcXPzw+73U6/fv3+1ry7pTMPEWmLzhXXsPLLPHy8Lcx94XGCA7rcdrn6xiaOn6vgyK+l5J0rp6HRQe8evrz7YhQmk+mO22ipM48WCQ9Po/AQkbaq8P/qWLH1GNZOFub+cwg9bD4ANF5v4vhvlRz+pYS8sxXUNzbh69OJqPBgogcGE9YrALP5zsEBCo87UniISFv2R+llPtx6DJPJxPOx/TlVWMnPBeVca2ji/i6dGBrejeiBwYQ/GIDFbOzqg8LjDhQeItLWXSy/woqMY9RcaeC+zl48HtaN6IhgBj4YiJel+ZerWyo82uZz8SIi7Vxo0H38z4tRXKq8SnivgL8VGPeCwkNExEPZ/Dpja4XnPprDs6JMRETaBIWHiIgYpvAQERHDFB4iImKYwkNERAxTeIiIiGEKDxERMaxdPudxN+O73It1RUTagn/9nmvO9167HJ5ERETuLXVbiYiIYQoPERExTOEhIiKGKTxERMQwhYeIiBim8BAREcPa5XMe7cH58+dJSkqiurqagIAA7HY7ffr0IS4uDqvVire3NwBvv/02//jHP9xcbeuy2+189913XLx4kR07dhAWFkZVVRXz5s3jwoULWK1WevfuTUpKCjabzd3ltqrbHRuA/fv3s2rVKq5fv46/vz+pqan06tXLzdW2nju1j+rqalJSUjh58iReXl7Ex8cza9Ysd5fs+ZzikaZPn+7cvn270+l0Ordv3+6cPn260+l0Op955hnn6dOn3Vma2x05csRZXFx8y7GoqqpyHjp0yLXM+++/71ywYIG7SnSb2x2b6upq57Bhw5y//fab0+m80Z5eeukld5bZ6u7UPl599VXnxo0bXfNKS0tbu7w2Sd1WHqiiooJTp06RkJAAQEJCAqdOnaKystLNlXmGqKgoQkJCbpkWEBDA8OHDXa8HDx5McXFxa5fmdrc7NoWFhQQFBdG3b18AYmNjOXDgQIdqT3/VPn7//XfOnDnDiy++6JrXrVs3d5TY5ig8PNClS5fo3r07FosFAIvFQnBwMJcuXQJudFWNGzeORYsWUVtb685SPZLD4SAjI4O4uDh3l+IR+vbtS3l5Ofn5+QDs2LEDwNWeOpqb28fZs2fp3r07ycnJTJo0iZdffpmCggJ3l9gmKDzamM2bN/PNN9+QmZmJ0+kkJSXF3SV5nCVLluDj48O0adPcXYpH8PX15eOPPyY1NZXJkydTUVGBn5+f68dJR3Nz+3A4HOTl5TF58mSys7OZMmUKr732mrtLbBMUHh4oJCSEkpISmpqaAGhqaqK0tJSQkBBXl4TVauWFF14gNzfXnaV6HLvdTmFhIWlpaZjNat5/iomJISMjg6ysLKZNm8a1a9d48MEH3V1Wq/vX9vHnZyoqKgqA0aNHU1ZW1qG69JpLny4P1LVrVyIiIsjJyQEgJyeHiIgIOnfuTF1dHQBOp5Ndu3YRERHhzlI9ysqVKzlx4gRr1qzBarW6uxyPUlZWBtzoslm5ciWJiYn4+Pi4uarWdbv2MWjQIHx8fFxdVUeOHMHf35/AwEB3ltomaFRdD3Xu3DmSkpKora3Fz88Pu91Op06deOONN2hqasLhcNC/f3/eeecdgoOD3V1uq1q6dCl79uyhvLycwMBAAgICSEtLIyEhgT59+tC5c2cAevbsyZo1a9xcbeu63bHZuXMnycnJ5Obm0tjYyJNPPsnChQtdt3t3BAUFBX/ZPo4fP87ixYtpaGigS5cuJCcnExkZ6eaKPZ/CQ0REDFO3lYiIGKbwEBERwxQeIiJimMJDREQMU3iIiIhhCg8RETFM4SEiIoYpPERExDCFh4iIGKbwEBERwxQeIiJimMJDREQMU3iIiIhhCg8RETFM4SEiIoYpPERExLAOEx5xcXGMHDnS9b/gAFlZWYSHh7Np0yY3ViYi0rJqamqIjIxk6dKl92wbHSY8AIKDgzlw4IDrdXZ2No888oih97h+/XpLlyUi0qJycnJ47LHH2LlzJw0NDYbWdTgc3M0fzHo1t7i2aNKkSWRlZREbG0tRURFXr14lLCwMgIMHD5KWlkZ9fT1NTU3MnDmTsWPHAjB9+nQGDhxIXl4e/v7+fPbZZ+7cDRGRO8rMzGTu3LmsW7eOffv2ER8fT3p6OmfPnqWqqorS0lIeeughli9fjq+vL+np6RQUFHD58mWKi4v58ssv8ff3v+M2OlR4DBs2jC1btlBTU0N2djYTJ07k5MmTADz88MNs2bIFi8VCeXk5kydPZuTIka4DWFRUxJYtW/Dy6lCHTETamF9//ZXq6mpGjBhBWVkZmZmZxMfHA/DTTz+xfft2goKCWLBgAWvXrmX+/PkA5Ofnk5WVhc1mu6vtdKhuK5PJRHx8PDt37mTnzp0kJCS45lVWVvLmm2+SkJDAjBkzqKmp4fz5867548aNU3CIiMfbtm0bEyZMwGQyMXr0aPLz8ykpKQHg6aefJigoCIDnn3+eQ4cOudZ76qmn7jo4oIOdecCNrqspU6YQHR1NYGCga/qiRYuIi4tj9erVmEwmxowZQ319vWu+j4+PO8oVEblrDQ0N5OTkYLVa+frrrwFobGwkKyvrP6573333GdpWhwuPXr16MXv2bCIjI2+ZXldXR2hoKCaTiR9++IHCwkI3VSgi0jz79u2jb9++ZGRkuKYdO3aM+fPnM27cOPbv309lZSU2m42srCxGjBjR7G11uPAAmDp16r9NmzNnDosXLyY9PZ1HH32U8PBwN1QmItJ8mZmZjBs37pZpQ4YMweFwcPjwYaKiopg9ezYlJSUMGDCApKSkZm/L5Lybe7JERKRNS09P5+rVq64L5H9Xh7pgLiIiLUNnHiIiYpjOPERExLB2e8G8qqqKefPmceHCBaxWK7179yYlJQWbzcbPP//Mu+++S319PaGhoXz44Yd07doVuHHh/Mcff6SsrIzc3Nxbbl/btm0bn3/+OWazGYvFwsKFC4mKinLXLoqIuE277baqrq7m9OnTDB8+HAC73U5NTQ1Lly5lzJgxpKamEhUVxdq1aykqKiI1NRW4MUxJWFgYMTExt4RHVVUVo0aNYs+ePQQFBbFv3z4++ugjdu3a5bZ9FBFxl3bbbRUQEOAKDoDBgwdTXFzMiRMn8Pb2dp0xJCYmsnv3btdyTzzxhOss5GZOpxOn08mVK1eAG8+F9OjR4x7vhYiIZ2q33VY3czgcZGRkEBcXx6VLl3jggQdc82w2Gw6Hg+rqagICAv7yPWw2GykpKUyaNAk/Pz8cDgdffPFFa5QvIuJx2u2Zx82WLFmCj48P06ZNa/Z7XL58mc2bN7Nt2zb2799PUlISs2bNuquhi0VE2pt2Hx52u53CwkLS0tIwm82EhIRQXFzsml9ZWYnZbL7jWQfAgQMH8PX1pV+/fgA899xzXLhwgaqqqntav4iIJ2rX4bFy5UpOnDjBmjVrsFqtAAwaNIhr165x9OhRALZu3cqzzz77H9+rZ8+enDp1ioqKCgAOHTrE/ffff8vgiiIiHUW7vduqoKCAhIQE+vTpQ+fOnYEbAbBmzRpyc3N57733brlV989himfNmuUawjg4OJiwsDA2bNgAwMaNG/nqq6/o1KkTVquVpKQk3aorIh1Suw0PERG5d9p1t5WIiNwbCg8RETFM4SEiIoYpPERExDCFh4iIGKbwEBERwzrE2FYiLS0uLo7y8nIsFgsWi4UBAwYwYcIEpk6ditl8599kf/zxB6NGjeLkyZN4eekjKG2TWq5IM33yySfExMRQV1fH4cOHWbZsGfn5+a7h/UXaM3VbifxNvr6+jBo1irS0NLKzszlz5gz79+9n4sSJPP7448TGxpKenu5a/s8BOqOjoxkyZAjHjh0DbvzZWHx8PNHR0cyYMYOLFy+6ZX9E7obCQ6SFREZG0qNHD44ePUqXLl2w2+0cPXqUdevWkZGRwd69ewHYtGkTAEeOHOHYsWMMGTKEvXv3sm7dOlavXs3BgwcZOnQoc+bMcefuiNyRwkOkBQUHB1NTU8Pw4cMJDw/HbDYzcOBAxo4dy+HDh/9yva1bt/LKK6/Qv39/vLy8mDlzJr/88ovOPsRj6ZqHSAsqKSnB39+fvLw8VqxYQUFBAY2NjTQ0NNxx9Obi4mKWL1+O3W53TXM6nZSUlBAaGtoapYsYovAQaSF/jsY8dOhQXn/9daZNm8b69evx9vZm2bJlrv9+MZlM/7ZuSEgIM2fOZPz48a1dtkizqNtK5G+6fPky33//PW+99Rbjx48nPDycK1eu4O/vj7e3N/n5+eTk5LiWt9lsmM1mioqKXNMSExP59NNPKSgoAKCuro5vv/221fdF5G5pSHaRZrj5OQ+z2cyAAQMYP348iYmJWCwWdu/ejd1up7q6mmHDhhEaGkptbS0rVqwAYNWqVWRkZHD9+nXWr1/P4MGD2b59Oxs2bODixYv4+voSExOj237FYyk8RETEMHVbiYiIYQoPERExTOEhIiKGKTxERMQwhYeIiBim8BAREcMUHiIiYpjCQ0REDFN4iIiIYf8P2Lx1JbTgLrAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ans['2018-03'].plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcab1753438>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ans['2017-08'].plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Lookup analysis\n",
    "# rmse=[]\n",
    "# datas=[]\n",
    "# for i in range(1,100,5):\n",
    "#     data,error=project(i)\n",
    "#     rmse.append(error)\n",
    "#     datas.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaler.inverse_transform(trainX[31])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 3: ARIMA MODEL :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Autoregressive Integrated Moving Average Model. An ARIMA model is a class of statistical models for analyzing and forecasting time series data. It explicitly caters to a suite of standard structures in time series data, and as such provides a simple yet powerful method for making skillful time series forecasts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The accuracy of Arima Model here is better as we are training for 50% of data and then while testing we are training the whole data set again + the any new instance. So on a high level the model predicts price of next day given last few days price. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "arima2015hour=coinbase['2015':].resample('D').mean().fillna(method='ffill')['Close']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we are intrested in is not the price but the trend and finding if there is any hidden pattern in ups and down of bitcoin price. We therefore first take the difference of each Xt with its previous price to signify if the price has increased or decreased and see if there is any autocorrelation and find the approximate lagging. We find this graph to be indicating a good lag to be around 30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "#hourarim=arima2015hour.resample('H').mean()['Close']\n",
    "plot_acf(arima2015hour.diff().dropna(), lags= 48, alpha=0.05)\n",
    "#arima2015hour['Close'].pct_change().autocorr()\n",
    "#dayarima.diff()\n",
    "#arima2015hour.dropna().diff().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often financial stock market analysts use some statistical measure to see if the prices are predictable or not. (Technically speaking if the prices are random walk or even if its white noise.) Test TO find if the data is random walk. Which is Not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47447987655531804"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "adfuller(arima2015hour)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16297.183073536282  [12732.03070138887, 15493.172388888888, 16144.979236111114, 15574.154416666643] 15574.154416666643 0 \n",
      "15397.150049336873  [15493.172388888888, 16144.979236111114, 15574.154416666643, 14779.160097222219] 14779.160097222219 1 \n",
      "14715.47336773422  [16144.979236111114, 15574.154416666643, 14779.160097222219, 16693.400402777803] 16693.400402777803 2 \n",
      "16922.993409830247  [15574.154416666643, 14779.160097222219, 16693.400402777803, 17133.031465277738] 17133.031465277738 3 \n",
      "17224.307847133943  [14779.160097222219, 16693.400402777803, 17133.031465277738, 17139.606777777775] 17139.606777777775 4 \n",
      "16927.070929825597  [16693.400402777803, 17133.031465277738, 17139.606777777775, 16791.305166666643] 16791.305166666643 5 \n",
      "16869.566469836966  [17133.031465277738, 17139.606777777775, 16791.305166666643, 17548.66850694447] 17548.66850694447 6 \n",
      "17573.745069917255  [17139.606777777775, 16791.305166666643, 17548.66850694447, 18467.820166666614] 18467.820166666614 7 \n",
      "18798.465302749846  [16791.305166666643, 17548.66850694447, 18467.820166666614, 19537.65331944445] 19537.65331944445 8 \n",
      "19606.177920365844  [17548.66850694447, 18467.820166666614, 19537.65331944445, 18883.086381944446] 18883.086381944446 9 \n",
      "18864.81797807257  [18467.820166666614, 19537.65331944445, 18883.086381944446, 18400.047861111172] 18400.047861111172 10 \n",
      "18125.299910187394  [19537.65331944445, 18883.086381944446, 18400.047861111172, 16866.442631944436] 16866.442631944436 11 \n",
      "16625.408228198386  [18883.086381944446, 18400.047861111172, 16866.442631944436, 16345.586659722212] 16345.586659722212 12 \n",
      "15969.64049854873  [18400.047861111172, 16866.442631944436, 16345.586659722212, 13914.177416666662] 13914.177416666662 13 \n",
      "13490.797883983478  [16866.442631944436, 16345.586659722212, 13914.177416666662, 15103.153215277773] 15103.153215277773 14 \n",
      "15058.494077198708  [16345.586659722212, 13914.177416666662, 15103.153215277773, 14166.849215277754] 14166.849215277754 15 \n",
      "14224.669913400683  [13914.177416666662, 15103.153215277773, 14166.849215277754, 14188.495895833328] 14188.495895833328 16 \n",
      "13947.821530687783  [15103.153215277773, 14166.849215277754, 14188.495895833328, 15365.250250000003] 15365.250250000003 17 \n",
      "15991.122819640706  [14166.849215277754, 14188.495895833328, 15365.250250000003, 15654.341256944434] 15654.341256944434 18 \n",
      "15480.378125849526  [14188.495895833328, 15365.250250000003, 15654.341256944434, 14293.096395833325] 14293.096395833325 19 \n",
      "14241.050623884285  [15365.250250000003, 15654.341256944434, 14293.096395833325, 14556.957694444438] 14556.957694444438 20 \n",
      "14310.870844097308  [15654.341256944434, 14293.096395833325, 14556.957694444438, 13583.643736111093] 13583.643736111093 21 \n",
      "13461.08731828479  [14293.096395833325, 14556.957694444438, 13583.643736111093, 13387.558423611126] 13387.558423611126 22 \n",
      "12967.386410049936  [14556.957694444438, 13583.643736111093, 13387.558423611126, 13425.692805555564] 13425.692805555564 23 \n",
      "13603.343146935698  [13583.643736111093, 13387.558423611126, 13425.692805555564, 13898.003201388912] 13898.003201388912 24 \n",
      "13804.481411065102  [13387.558423611126, 13425.692805555564, 13898.003201388912, 14990.05915972224] 14990.05915972224 25 \n",
      "15508.383690489774  [13425.692805555564, 13898.003201388912, 14990.05915972224, 14851.726770833275] 14851.726770833275 26 \n",
      "14786.09349802288  [13898.003201388912, 14990.05915972224, 14851.726770833275, 15797.65659722219] 15797.65659722219 27 \n",
      "16151.261932355874  [14990.05915972224, 14851.726770833275, 15797.65659722219, 16662.248527777756] 16662.248527777756 28 \n",
      "16698.604985306378  [14851.726770833275, 15797.65659722219, 16662.248527777756, 16526.662881944445] 16526.662881944445 29 \n",
      "16392.702423978084  [15797.65659722219, 16662.248527777756, 16526.662881944445, 15328.254527777806] 15328.254527777806 30 \n",
      "14896.395460639429  [16662.248527777756, 16526.662881944445, 15328.254527777806, 14894.29425694449] 14894.29425694449 31 \n",
      "14729.395732573299  [16526.662881944445, 15328.254527777806, 14894.29425694449, 14221.492513888858] 14221.492513888858 32 \n",
      "14044.865090204194  [15328.254527777806, 14894.29425694449, 14221.492513888858, 13712.780770833328] 13712.780770833328 33 \n",
      "13637.199834123536  [14894.29425694449, 14221.492513888858, 13712.780770833328, 13658.756041666646] 13658.756041666646 34 \n",
      "13736.220113554571  [14221.492513888858, 13712.780770833328, 13658.756041666646, 14213.238381944448] 14213.238381944448 35 \n",
      "14356.18419652543  [13712.780770833328, 13658.756041666646, 14213.238381944448, 13694.376409722185] 13694.376409722185 36 \n",
      "13606.296202931575  [13658.756041666646, 14213.238381944448, 13694.376409722185, 13786.224743055602] 13786.224743055602 37 \n",
      "13699.371336146067  [14213.238381944448, 13694.376409722185, 13786.224743055602, 12183.001250000021] 12183.001250000021 38 \n",
      "11825.729168756405  [13694.376409722185, 13786.224743055602, 12183.001250000021, 10690.532229166683] 10690.532229166683 39 \n",
      "10198.41567792705  [13786.224743055602, 12183.001250000021, 10690.532229166683, 11482.588215277774] 11482.588215277774 40 \n",
      "11637.997793606302  [12183.001250000021, 10690.532229166683, 11482.588215277774, 11495.365576388867] 11495.365576388867 41 \n",
      "11405.423798473023  [10690.532229166683, 11482.588215277774, 11495.365576388867, 12423.704548611116] 12423.704548611116 42 \n",
      "12672.032298910164  [11482.588215277774, 11495.365576388867, 12423.704548611116, 11876.49790277778] 11876.49790277778 43 \n",
      "11917.316219253351  [11495.365576388867, 12423.704548611116, 11876.49790277778, 11093.031055555564] 11093.031055555564 44 \n",
      "10983.009305562307  [12423.704548611116, 11876.49790277778, 11093.031055555564, 10694.852736111117] 10694.852736111117 45 \n",
      "10674.996969319704  [11876.49790277778, 11093.031055555564, 10694.852736111117, 10972.448472222226] 10972.448472222226 46 \n",
      "10827.598703818398  [11093.031055555564, 10694.852736111117, 10972.448472222226, 11259.235854166673] 11259.235854166673 47 \n",
      "11176.362725492116  [10694.852736111117, 10972.448472222226, 11259.235854166673, 10992.569951388889] 10992.569951388889 48 \n",
      "10758.637165103399  [10972.448472222226, 11259.235854166673, 10992.569951388889, 11177.383819444445] 11177.383819444445 49 \n",
      "11254.155775513524  [11259.235854166673, 10992.569951388889, 11177.383819444445, 11426.11227083337] 11426.11227083337 50 \n",
      "11567.244116702264  [10992.569951388889, 11177.383819444445, 11426.11227083337, 11137.279298611098] 11137.279298611098 51 \n",
      "11246.247921268827  [11177.383819444445, 11426.11227083337, 11137.279298611098, 10620.31311805554] 10620.31311805554 52 \n",
      "10592.50252635407  [11426.11227083337, 11137.279298611098, 10620.31311805554, 9978.950659722226] 9978.950659722226 53 \n",
      "9828.615728482057  [11137.279298611098, 10620.31311805554, 9978.950659722226, 9497.57781250002] 9497.57781250002 54 \n",
      "9211.887546623868  [10620.31311805554, 9978.950659722226, 9497.57781250002, 8547.647486111111] 8547.647486111111 55 \n",
      "8166.606354227224  [9978.950659722226, 9497.57781250002, 8547.647486111111, 8909.813083333349] 8909.813083333349 56 \n",
      "8802.845887499614  [9497.57781250002, 8547.647486111111, 8909.813083333349, 8736.636180555557] 8736.636180555557 57 \n",
      "8753.459327189063  [8547.647486111111, 8909.813083333349, 8736.636180555557, 7613.970402777774] 7613.970402777774 58 \n",
      "7475.931662399147  [8909.813083333349, 8736.636180555557, 7613.970402777774, 6807.2735972222235] 6807.2735972222235 59 \n",
      "6901.419490964499  [8736.636180555557, 7613.970402777774, 6807.2735972222235, 7905.416027777783] 7905.416027777783 60 \n",
      "8174.073908961769  [7613.970402777774, 6807.2735972222235, 7905.416027777783, 8203.784236111107] 8203.784236111107 61 \n",
      "8260.456075418382  [6807.2735972222235, 7905.416027777783, 8203.784236111107, 8243.71535416667] 8243.71535416667 62 \n",
      "8012.804406386434  [7905.416027777783, 8203.784236111107, 8243.71535416667, 8649.373326388879] 8649.373326388879 63 \n",
      "8645.654655261395  [8203.784236111107, 8243.71535416667, 8649.373326388879, 8195.261715277766] 8195.261715277766 64 \n",
      "7953.769029004089  [8243.71535416667, 8649.373326388879, 8195.261715277766, 8619.035437500017] 8619.035437500017 65 \n",
      "8769.167605844646  [8649.373326388879, 8195.261715277766, 8619.035437500017, 8615.99859722222] 8615.99859722222 66 \n",
      "8729.270206384746  [8195.261715277766, 8619.035437500017, 8615.99859722222, 9019.139416666687] 9019.139416666687 67 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9217.626107094366  [8619.035437500017, 8615.99859722222, 9019.139416666687, 9798.3624375] 9798.3624375 68 \n",
      "10042.214968090648  [8615.99859722222, 9019.139416666687, 9798.3624375, 10017.390673611098] 10017.390673611098 69 \n",
      "9995.66662902428  [9019.139416666687, 9798.3624375, 10017.390673611098, 10684.652395833347] 10684.652395833347 70 \n",
      "10698.470727199914  [9798.3624375, 10017.390673611098, 10684.652395833347, 10729.090354166656] 10729.090354166656 71 \n",
      "10606.371493199094  [10017.390673611098, 10684.652395833347, 10729.090354166656, 10852.905000000013] 10852.905000000013 72 \n",
      "10770.869215163068  [10684.652395833347, 10729.090354166656, 10852.905000000013, 11493.998937499999] 11493.998937499999 73 \n",
      "11659.134011195314  [10729.090354166656, 10852.905000000013, 11493.998937499999, 10780.311527777787] 10780.311527777787 74 \n",
      "10712.251106310658  [10852.905000000013, 11493.998937499999, 10780.311527777787, 10267.79890277777] 10267.79890277777 75 \n",
      "10263.895633349257  [11493.998937499999, 10780.311527777787, 10267.79890277777, 9999.474298611101] 9999.474298611101 76 \n",
      "10007.117960593583  [10780.311527777787, 10267.79890277777, 9999.474298611101, 9932.505222222213] 9932.505222222213 77 \n",
      "9811.014037101999  [10267.79890277777, 9999.474298611101, 9932.505222222213, 9593.621930555533] 9593.621930555533 78 \n",
      "9406.852809170176  [9999.474298611101, 9932.505222222213, 9593.621930555533, 9898.05745833333] 9898.05745833333 79 \n",
      "9787.970946927751  [9932.505222222213, 9593.621930555533, 9898.05745833333, 10537.109187499993] 10537.109187499993 80 \n",
      "10644.046110303843  [9593.621930555533, 9898.05745833333, 10537.109187499993, 10612.798402777767] 10612.798402777767 81 \n",
      "10650.797476211232  [9898.05745833333, 10537.109187499993, 10612.798402777767, 10635.522798611108] 10635.522798611108 82 \n",
      "10801.20356219339  [10537.109187499993, 10612.798402777767, 10635.522798611108, 10973.365826388886] 10973.365826388886 83 \n",
      "11146.500737866812  [10612.798402777767, 10635.522798611108, 10973.365826388886, 11331.807881944447] 11331.807881944447 84 \n",
      "11435.099339376524  [10635.522798611108, 10973.365826388886, 11331.807881944447, 11239.93793750004] 11239.93793750004 85 \n",
      "11110.581889607318  [10973.365826388886, 11331.807881944447, 11239.93793750004, 11506.99118055554] 11506.99118055554 86 \n",
      "11400.769602782459  [11331.807881944447, 11239.93793750004, 11506.99118055554, 11019.041895833327] 11019.041895833327 87 \n",
      "10783.333789061962  [11239.93793750004, 11506.99118055554, 11019.041895833327, 10402.282909722197] 10402.282909722197 88 \n",
      "10202.084120359099  [11506.99118055554, 11019.041895833327, 10402.282909722197, 9704.847076388873] 9704.847076388873 89 \n",
      "9641.579455952626  [11019.041895833327, 10402.282909722197, 9704.847076388873, 8904.254840277774] 8904.254840277774 90 \n",
      "8818.78216704437  [10402.282909722197, 9704.847076388873, 8904.254840277774, 9240.678923611098] 9240.678923611098 91 \n",
      "9399.239241530364  [9704.847076388873, 8904.254840277774, 9240.678923611098, 9060.166291666668] 9060.166291666668 92 \n",
      "9026.074918208335  [8904.254840277774, 9240.678923611098, 9060.166291666668, 9435.44851388889] 9435.44851388889 93 \n",
      "9412.286165528072  [9240.678923611098, 9060.166291666668, 9435.44851388889, 9153.619479166664] 9153.619479166664 94 \n",
      "9035.642288890209  [9060.166291666668, 9435.44851388889, 9153.619479166664, 8761.228229166654] 8761.228229166654 95 \n",
      "8548.79314245985  [9435.44851388889, 9153.619479166664, 8761.228229166654, 8102.989847222255] 8102.989847222255 96 \n",
      "8003.395976196513  [9153.619479166664, 8761.228229166654, 8102.989847222255, 8310.097187499994] 8310.097187499994 97 \n",
      "8317.702956238063  [8761.228229166654, 8102.989847222255, 8310.097187499994, 8075.865895833326] 8075.865895833326 98 \n",
      "8146.956751611982  [8102.989847222255, 8310.097187499994, 8075.865895833326, 7671.829256944445] 7671.829256944445 99 \n",
      "7567.734985698038  [8310.097187499994, 8075.865895833326, 7671.829256944445, 8346.892236111114] 8346.892236111114 100 \n",
      "8551.219409418649  [8075.865895833326, 7671.829256944445, 8346.892236111114, 8617.348722222228] 8617.348722222228 101 \n",
      "8581.611359392322  [7671.829256944445, 8346.892236111114, 8617.348722222228, 8980.54859722224] 8980.54859722224 102 \n",
      "9003.89702180366  [8346.892236111114, 8617.348722222228, 8980.54859722224, 8788.90138888886] 8788.90138888886 103 \n",
      "8702.636301444272  [8617.348722222228, 8980.54859722224, 8788.90138888886, 8526.124659722209] 8526.124659722209 104 \n",
      "8456.059288120985  [8980.54859722224, 8788.90138888886, 8526.124659722209, 8853.862854166684] 8853.862854166684 105 \n",
      "8942.236592803954  [8788.90138888886, 8526.124659722209, 8853.862854166684, 8531.024597222195] 8531.024597222195 106 \n",
      "8488.40297300609  [8526.124659722209, 8853.862854166684, 8531.024597222195, 8208.62628472221] 8208.62628472221 107 \n",
      "8143.581077219485  [8853.862854166684, 8531.024597222195, 8208.62628472221, 7963.3353611111] 7963.3353611111 108 \n",
      "7922.211593065676  [8531.024597222195, 8208.62628472221, 7963.3353611111, 7912.117666666679] 7912.117666666679 109 \n",
      "7829.999846109678  [8208.62628472221, 7963.3353611111, 7912.117666666679, 7515.393368055544] 7515.393368055544 110 \n",
      "7400.712754233033  [7963.3353611111, 7912.117666666679, 7515.393368055544, 6889.112041666665] 6889.112041666665 111 \n",
      "6700.883200072118  [7912.117666666679, 7515.393368055544, 6889.112041666665, 7007.942895833332] 7007.942895833332 112 \n",
      "7040.0318507107995  [7515.393368055544, 6889.112041666665, 7007.942895833332, 6804.685479166641] 6804.685479166641 113 \n",
      "6771.871360683759  [6889.112041666665, 7007.942895833332, 6804.685479166641, 6978.3859652777755] 6978.3859652777755 114 \n",
      "7032.569143536601  [7007.942895833332, 6804.685479166641, 6978.3859652777755, 7328.582937500013] 7328.582937500013 115 \n",
      "7443.464541066949  [6804.685479166641, 6978.3859652777755, 7328.582937500013, 7075.8206805555465] 7075.8206805555465 116 \n",
      "7014.842010342361  [6978.3859652777755, 7328.582937500013, 7075.8206805555465, 6754.247277777777] 6754.247277777777 117 \n",
      "6680.495340232175  [7328.582937500013, 7075.8206805555465, 6754.247277777777, 6648.538312499972] 6648.538312499972 118 \n",
      "6578.903882673809  [7075.8206805555465, 6754.247277777777, 6648.538312499972, 6887.246152777776] 6887.246152777776 119 \n",
      "6874.619117712722  [6754.247277777777, 6648.538312499972, 6887.246152777776, 6985.434340277794] 6985.434340277794 120 \n",
      "6968.461882797332  [6648.538312499972, 6887.246152777776, 6985.434340277794, 6892.153583333344] 6892.153583333344 121 \n",
      "6876.5776752749625  [6887.246152777776, 6985.434340277794, 6892.153583333344, 6777.5806805555785] 6777.5806805555785 122 \n",
      "6804.88213696718  [6985.434340277794, 6892.153583333344, 6777.5806805555785, 6875.148388888895] 6875.148388888895 123 \n",
      "6933.432642046862  [6892.153583333344, 6777.5806805555785, 6875.148388888895, 7331.74750694445] 7331.74750694445 124 \n",
      "7436.329811024969  [6777.5806805555785, 6875.148388888895, 7331.74750694445, 7990.35156249999] 7990.35156249999 125 \n",
      "8088.115658091314  [6875.148388888895, 7331.74750694445, 7990.35156249999, 8005.498493055562] 8005.498493055562 126 \n",
      "7968.135016504884  [7331.74750694445, 7990.35156249999, 8005.498493055562, 8217.286916666671] 8217.286916666671 127 \n",
      "8209.338685437378  [7990.35156249999, 8005.498493055562, 8217.286916666671, 8073.869444444465] 8073.869444444465 128 \n",
      "8052.938067959714  [8005.498493055562, 8217.286916666671, 8073.869444444465, 8037.364465277784] 8037.364465277784 129 \n",
      "8000.557062522664  [8217.286916666671, 8073.869444444465, 8037.364465277784, 8054.522715277807] 8054.522715277807 130 \n",
      "8100.302788773831  [8073.869444444465, 8037.364465277784, 8054.522715277807, 8212.14790277778] 8212.14790277778 131 \n",
      "8214.913032458513  [8037.364465277784, 8054.522715277807, 8212.14790277778, 8446.483131944442] 8446.483131944442 132 \n",
      "8512.684237837278  [8054.522715277807, 8212.14790277778, 8446.483131944442, 8804.268749999996] 8804.268749999996 133 \n",
      "8829.225775277642  [8212.14790277778, 8446.483131944442, 8804.268749999996, 8889.286215277789] 8889.286215277789 134 \n",
      "8910.301597899535  [8446.483131944442, 8804.268749999996, 8889.286215277789, 8892.973604166687] 8892.973604166687 135 \n",
      "8852.598265549364  [8804.268749999996, 8889.286215277789, 8892.973604166687, 9312.592652777788] 9312.592652777788 136 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9406.998434118308  [8889.286215277789, 8892.973604166687, 9312.592652777788, 9207.445152777775] 9207.445152777775 137 \n",
      "9165.155985777072  [8892.973604166687, 9312.592652777788, 9207.445152777775, 8882.526166666667] 8882.526166666667 138 \n",
      "8815.080733986506  [9312.592652777788, 9207.445152777775, 8882.526166666667, 9212.623145833331] 9212.623145833331 139 \n",
      "9266.81001956079  [9207.445152777775, 8882.526166666667, 9212.623145833331, 9236.83329861113] 9236.83329861113 140 \n",
      "9224.420301185606  [8882.526166666667, 9212.623145833331, 9236.83329861113, 9344.712749999973] 9344.712749999973 141 \n",
      "9321.06400107887  [9212.623145833331, 9236.83329861113, 9344.712749999973, 9283.216534722218] 9283.216534722218 142 \n",
      "9280.610210585646  [9236.83329861113, 9344.712749999973, 9283.216534722218, 8987.233124999993] 8987.233124999993 143 \n",
      "8896.113592166206  [9344.712749999973, 9283.216534722218, 8987.233124999993, 9109.845611111117] 9109.845611111117 144 \n",
      "9146.407593735052  [9283.216534722218, 8987.233124999993, 9109.845611111117, 9361.614590277799] 9361.614590277799 145 \n",
      "9385.612351442727  [8987.233124999993, 9109.845611111117, 9361.614590277799, 9648.745534722215] 9648.745534722215 146 \n",
      "9693.331557362066  [9109.845611111117, 9361.614590277799, 9648.745534722215, 9819.568777777771] 9819.568777777771 147 \n",
      "9839.838434547495  [9361.614590277799, 9648.745534722215, 9819.568777777771, 9608.540472222207] 9608.540472222207 148 \n",
      "9566.205141450846  [9648.745534722215, 9819.568777777771, 9608.540472222207, 9337.48618055555] 9337.48618055555 149 \n",
      "9272.199677911307  [9819.568777777771, 9608.540472222207, 9337.48618055555, 9260.343868055541] 9260.343868055541 150 \n",
      "9229.99805002797  [9608.540472222207, 9337.48618055555, 9260.343868055541, 9185.281125000005] 9185.281125000005 151 \n",
      "9128.55339587884  [9337.48618055555, 9260.343868055541, 9185.281125000005, 9261.87117361111] 9261.87117361111 152 \n",
      "9240.363305272342  [9260.343868055541, 9185.281125000005, 9261.87117361111, 8702.524944444443] 8702.524944444443 153 \n",
      "8576.61000480542  [9185.281125000005, 9261.87117361111, 8702.524944444443, 8424.72342361112] 8424.72342361112 154 \n",
      "8352.423505930441  [9261.87117361111, 8702.524944444443, 8424.72342361112, 8551.452187499988] 8551.452187499988 155 \n",
      "8598.595652111202  [8702.524944444443, 8424.72342361112, 8551.452187499988, 8578.31629166665] 8578.31629166665 156 \n",
      "8556.813217228751  [8424.72342361112, 8551.452187499988, 8578.31629166665, 8641.140166666659] 8641.140166666659 157 \n",
      "8658.146953403497  [8551.452187499988, 8578.31629166665, 8641.140166666659, 8279.812722222221] 8279.812722222221 158 \n",
      "8192.535306893933  [8578.31629166665, 8641.140166666659, 8279.812722222221, 8276.545159722233] 8276.545159722233 159 \n",
      "8256.347205188275  [8641.140166666659, 8279.812722222221, 8276.545159722233, 8104.7232291666505] 8104.7232291666505 160 \n",
      "8057.1263984957495  [8279.812722222221, 8276.545159722233, 8104.7232291666505, 8255.730305555548] 8255.730305555548 161 \n",
      "8237.642594731245  [8276.545159722233, 8104.7232291666505, 8255.730305555548, 8360.830076388876] 8360.830076388876 162 \n",
      "8389.407195510772  [8104.7232291666505, 8255.730305555548, 8360.830076388876, 8451.981013888895] 8451.981013888895 163 \n",
      "8441.311110335173  [8255.730305555548, 8360.830076388876, 8451.981013888895, 8231.380784722249] 8231.380784722249 164 \n",
      "8226.364663785393  [8360.830076388876, 8451.981013888895, 8231.380784722249, 7783.750951388888] 7783.750951388888 165 \n",
      "7677.748295828394  [8451.981013888895, 8231.380784722249, 7783.750951388888, 7535.288048611108] 7535.288048611108 166 \n",
      "7495.336937985217  [8231.380784722249, 7783.750951388888, 7535.288048611108, 7486.206930555544] 7486.206930555544 167 \n",
      "7417.874323174415  [7783.750951388888, 7535.288048611108, 7486.206930555544, 7487.810263888883] 7487.810263888883 168 \n",
      "7451.3127457657165  [7535.288048611108, 7486.206930555544, 7487.810263888883, 7309.520423611126] 7309.520423611126 169 \n",
      "7231.27539636657  [7486.206930555544, 7487.810263888883, 7309.520423611126, 7250.144638888887] 7250.144638888887 170 \n",
      "7247.110713666509  [7487.810263888883, 7309.520423611126, 7250.144638888887, 7290.33091666668] 7290.33091666668 171 \n",
      "7318.2841159493855  [7309.520423611126, 7250.144638888887, 7290.33091666668, 7430.602486111126] 7430.602486111126 172 \n",
      "7486.1727336119375  [7250.144638888887, 7290.33091666668, 7430.602486111126, 7499.505652777801] 7499.505652777801 173 \n",
      "7523.670961429188  [7290.33091666668, 7430.602486111126, 7499.505652777801, 7478.113493055553] 7478.113493055553 174 \n",
      "7458.9940781851565  [7430.602486111126, 7499.505652777801, 7478.113493055553, 7596.8808333333145] 7596.8808333333145 175 \n",
      "7583.117391106703  [7499.505652777801, 7478.113493055553, 7596.8808333333145, 7690.760125000006] 7690.760125000006 176 \n",
      "7666.016598715879  [7478.113493055553, 7596.8808333333145, 7690.760125000006, 7572.27513194446] 7572.27513194446 177 \n",
      "7514.796768424564  [7596.8808333333145, 7690.760125000006, 7572.27513194446, 7488.084506944463] 7488.084506944463 178 \n",
      "7468.385341635897  [7690.760125000006, 7572.27513194446, 7488.084506944463, 7602.136375000005] 7602.136375000005 179 \n",
      "7640.224135597951  [7572.27513194446, 7488.084506944463, 7602.136375000005, 7690.5705486111265] 7690.5705486111265 180 \n",
      "7726.674913021562  [7488.084506944463, 7602.136375000005, 7690.5705486111265, 7628.482895833334] 7628.482895833334 181 \n",
      "7622.6713657290575  [7602.136375000005, 7690.5705486111265, 7628.482895833334, 7627.248944444437] 7627.248944444437 182 \n",
      "7616.1562918758245  [7690.5705486111265, 7628.482895833334, 7627.248944444437, 7133.832388888875] 7133.832388888875 183 \n",
      "7008.166986725855  [7628.482895833334, 7627.248944444437, 7133.832388888875, 6762.050520833334] 6762.050520833334 184 \n",
      "6633.704583047414  [7627.248944444437, 7133.832388888875, 6762.050520833334, 6752.690819444447] 6752.690819444447 185 \n",
      "6722.748430886975  [7133.832388888875, 6762.050520833334, 6752.690819444447, 6439.297784722232] 6439.297784722232 186 \n",
      "6341.374274550439  [6762.050520833334, 6752.690819444447, 6439.297784722232, 6500.001791666673] 6500.001791666673 187 \n",
      "6519.049416635686  [6752.690819444447, 6439.297784722232, 6500.001791666673, 6548.064770833324] 6548.064770833324 188 \n",
      "6594.501096514185  [6439.297784722232, 6500.001791666673, 6548.064770833324, 6459.249048611106] 6459.249048611106 189 \n",
      "6459.502305658858  [6500.001791666673, 6548.064770833324, 6459.249048611106, 6505.5930347222275] 6505.5930347222275 190 \n",
      "6538.04043709042  [6548.064770833324, 6459.249048611106, 6505.5930347222275, 6521.054145833328] 6521.054145833328 191 \n",
      "6498.372517874342  [6459.249048611106, 6505.5930347222275, 6521.054145833328, 6717.418138888882] 6717.418138888882 192 \n",
      "6713.98722829231  [6505.5930347222275, 6521.054145833328, 6717.418138888882, 6679.930611111106] 6679.930611111106 193 \n",
      "6629.99409503463  [6521.054145833328, 6717.418138888882, 6679.930611111106, 6733.5991180555675] 6733.5991180555675 194 \n",
      "6713.893974637974  [6717.418138888882, 6679.930611111106, 6733.5991180555675, 6357.828638888881] 6357.828638888881 195 \n",
      "6304.863463882421  [6679.930611111106, 6733.5991180555675, 6357.828638888881, 6133.276513888877] 6133.276513888877 196 \n",
      "6094.329500962441  [6733.5991180555675, 6357.828638888881, 6133.276513888877, 6019.523187499998] 6019.523187499998 197 \n",
      "6037.490166161999  [6357.828638888881, 6133.276513888877, 6019.523187499998, 6200.701888888887] 6200.701888888887 198 \n",
      "6203.264764715002  [6133.276513888877, 6019.523187499998, 6200.701888888887, 6202.094701388894] 6202.094701388894 199 \n",
      "6198.970482107368  [6019.523187499998, 6200.701888888887, 6202.094701388894, 6094.434923611115] 6094.434923611115 200 \n",
      "6015.65729685559  [6200.701888888887, 6202.094701388894, 6094.434923611115, 6082.242451388905] 6082.242451388905 201 \n",
      "6083.947104579814  [6202.094701388894, 6094.434923611115, 6082.242451388905, 5903.992354166681] 5903.992354166681 202 \n",
      "5844.835590396597  [6094.434923611115, 6082.242451388905, 5903.992354166681, 6363.078583333331] 6363.078583333331 203 \n",
      "6470.7367423896585  [6082.242451388905, 5903.992354166681, 6363.078583333331, 6347.295979166675] 6347.295979166675 204 \n",
      "6361.543875464147  [5903.992354166681, 6363.078583333331, 6347.295979166675, 6453.177125000006] 6453.177125000006 205 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6470.346172224774  [6363.078583333331, 6347.295979166675, 6453.177125000006, 6597.636812499984] 6597.636812499984 206 \n",
      "6652.281864522685  [6347.295979166675, 6453.177125000006, 6597.636812499984, 6571.794708333331] 6571.794708333331 207 \n",
      "6524.4965874005175  [6453.177125000006, 6597.636812499984, 6571.794708333331, 6580.700847222231] 6580.700847222231 208 \n",
      "6571.506721812298  [6597.636812499984, 6571.794708333331, 6580.700847222231, 6541.403215277766] 6541.403215277766 209 \n",
      "6489.537052094271  [6571.794708333331, 6580.700847222231, 6541.403215277766, 6571.94011111109] 6571.94011111109 210 \n",
      "6568.9497947071095  [6580.700847222231, 6541.403215277766, 6571.94011111109, 6735.545048611109] 6735.545048611109 211 \n",
      "6758.57196870997  [6541.403215277766, 6571.94011111109, 6735.545048611109, 6703.552736111111] 6703.552736111111 212 \n",
      "6719.2112997363165  [6571.94011111109, 6735.545048611109, 6703.552736111111, 6480.439673611077] 6480.439673611077 213 \n",
      "6445.11761986936  [6735.545048611109, 6703.552736111111, 6480.439673611077, 6355.53125694444] 6355.53125694444 214 \n",
      "6343.535213588473  [6703.552736111111, 6480.439673611077, 6355.53125694444, 6221.788388888888] 6221.788388888888 215 \n",
      "6168.574903995459  [6480.439673611077, 6355.53125694444, 6221.788388888888, 6237.5012430555325] 6237.5012430555325 216 \n",
      "6201.137917325893  [6355.53125694444, 6221.788388888888, 6237.5012430555325, 6238.751444444442] 6238.751444444442 217 \n",
      "6201.183987410178  [6221.788388888888, 6237.5012430555325, 6238.751444444442, 6319.781729166635] 6319.781729166635 218 \n",
      "6315.40892178293  [6237.5012430555325, 6238.751444444442, 6319.781729166635, 6520.26120138888] 6520.26120138888 219 \n",
      "6575.631543791402  [6238.751444444442, 6319.781729166635, 6520.26120138888, 6878.935791666668] 6878.935791666668 220 \n",
      "6976.045344311755  [6319.781729166635, 6520.26120138888, 6878.935791666668, 7405.114715277772] 7405.114715277772 221 \n",
      "7542.67866695397  [6520.26120138888, 6878.935791666668, 7405.114715277772, 7388.681173611114] 7388.681173611114 222 \n",
      "7400.185612254078  [6878.935791666668, 7405.114715277772, 7388.681173611114, 7420.117347222228] 7420.117347222228 223 \n",
      "7405.848259254401  [7405.114715277772, 7388.681173611114, 7420.117347222228, 7346.679638888885] 7346.679638888885 224 \n",
      "7304.897672791492  [7388.681173611114, 7420.117347222228, 7346.679638888885, 7447.634395833327] 7447.634395833327 225 \n",
      "7391.354764073178  [7420.117347222228, 7346.679638888885, 7447.634395833327, 7678.603000000003] 7678.603000000003 226 \n",
      "7698.091873544138  [7346.679638888885, 7447.634395833327, 7678.603000000003, 8071.668569444438] 8071.668569444438 227 \n",
      "8121.968569095404  [7447.634395833327, 7678.603000000003, 8071.668569444438, 8251.180270833345] 8251.180270833345 228 \n",
      "8336.432605868651  [7678.603000000003, 8071.668569444438, 8251.180270833345, 8189.744131944443] 8189.744131944443 229 \n",
      "8210.522473783258  [8071.668569444438, 8251.180270833345, 8189.744131944443, 8015.374062500015] 8015.374062500015 230 \n",
      "8029.929066232701  [8251.180270833345, 8189.744131944443, 8015.374062500015, 8176.5811111110625] 8176.5811111110625 231 \n",
      "8172.9636938001195  [8189.744131944443, 8015.374062500015, 8176.5811111110625, 8201.830652777762] 8201.830652777762 232 \n",
      "8157.6193975792  [8015.374062500015, 8176.5811111110625, 8201.830652777762, 8132.646659722212] 8132.646659722212 233 \n",
      "8018.311029228677  [8176.5811111110625, 8201.830652777762, 8132.646659722212, 7913.079506944454] 7913.079506944454 234 \n",
      "7842.125813607889  [8201.830652777762, 8132.646659722212, 7913.079506944454, 7568.083624999995] 7568.083624999995 235 \n",
      "7479.20972493684  [8132.646659722212, 7913.079506944454, 7568.083624999995, 7588.619069444427] 7588.619069444427 236 \n",
      "7642.611333855795  [7913.079506944454, 7568.083624999995, 7588.619069444427, 7394.58175694444] 7394.58175694444 237 \n",
      "7391.244453219152  [7568.083624999995, 7588.619069444427, 7394.58175694444, 7249.933354166639] 7249.933354166639 238 \n",
      "7239.9915342223885  [7588.619069444427, 7394.58175694444, 7249.933354166639, 6997.011430555538] 6997.011430555538 239 \n",
      "6934.064905427023  [7394.58175694444, 7249.933354166639, 6997.011430555538, 6989.845576388888] 6989.845576388888 240 \n",
      "6921.520011866261  [7249.933354166639, 6997.011430555538, 6989.845576388888, 6987.278486111115] 6987.278486111115 241 \n",
      "6932.715619996746  [6997.011430555538, 6989.845576388888, 6987.278486111115, 6445.965041666649] 6445.965041666649 242 \n",
      "6287.499749480612  [6989.845576388888, 6987.278486111115, 6445.965041666649, 6396.508798611092] 6396.508798611092 243 \n",
      "6392.680108403207  [6987.278486111115, 6445.965041666649, 6396.508798611092, 6468.362388888889] 6468.362388888889 244 \n",
      "6523.2251249439705  [6445.965041666649, 6396.508798611092, 6468.362388888889, 6191.477715277774] 6191.477715277774 245 \n",
      "6163.336066780759  [6396.508798611092, 6468.362388888889, 6191.477715277774, 6307.761402777774] 6307.761402777774 246 \n",
      "6359.017739822824  [6468.362388888889, 6191.477715277774, 6307.761402777774, 6346.969861111117] 6346.969861111117 247 \n",
      "6344.043036674069  [6191.477715277774, 6307.761402777774, 6346.969861111117, 6059.220902777776] 6059.220902777776 248 \n",
      "5948.419316418516  [6307.761402777774, 6346.969861111117, 6059.220902777776, 6371.146569444434] 6371.146569444434 249 \n",
      "6383.077509464859  [6346.969861111117, 6059.220902777776, 6371.146569444434, 6345.397770833332] 6345.397770833332 250 \n",
      "6312.332124557147  [6059.220902777776, 6371.146569444434, 6345.397770833332, 6468.428284722237] 6468.428284722237 251 \n",
      "6473.149145712165  [6371.146569444434, 6345.397770833332, 6468.428284722237, 6454.486097222228] 6454.486097222228 252 \n",
      "6514.495938638652  [6345.397770833332, 6468.428284722237, 6454.486097222228, 6401.410159722228] 6401.410159722228 253 \n",
      "6405.612856312091  [6468.428284722237, 6454.486097222228, 6401.410159722228, 6444.72503472222] 6444.72503472222 254 \n",
      "6498.772693540439  [6454.486097222228, 6401.410159722228, 6444.72503472222, 6399.308055555555] 6399.308055555555 255 \n",
      "6347.805845062592  [6401.410159722228, 6444.72503472222, 6399.308055555555, 6578.963597222206] 6578.963597222206 256 \n",
      "6576.097463330261  [6444.72503472222, 6399.308055555555, 6578.963597222206, 6434.075458333335] 6434.075458333335 257 \n",
      "6341.026959323905  [6399.308055555555, 6578.963597222206, 6434.075458333335, 6550.569958333339] 6550.569958333339 258 \n",
      "6540.699115328705  [6578.963597222206, 6434.075458333335, 6550.569958333339, 6716.011270833321] 6716.011270833321 259 \n",
      "6764.498998293702  [6434.075458333335, 6550.569958333339, 6716.011270833321, 6671.399736111099] 6671.399736111099 260 \n",
      "6689.258229542276  [6550.569958333339, 6716.011270833321, 6671.399736111099, 6713.76649305555] 6713.76649305555 261 \n",
      "6772.944839834723  [6716.011270833321, 6671.399736111099, 6713.76649305555, 6987.391631944448] 6987.391631944448 262 \n",
      "7053.885121545161  [6671.399736111099, 6713.76649305555, 6987.391631944448, 7049.325111111114] 7049.325111111114 263 \n",
      "7042.868905129353  [6713.76649305555, 6987.391631944448, 7049.325111111114, 6928.433583333325] 6928.433583333325 264 \n",
      "6845.667226677864  [6987.391631944448, 7049.325111111114, 6928.433583333325, 6980.841138888865] 6980.841138888865 265 \n",
      "6944.467683394534  [7049.325111111114, 6928.433583333325, 6980.841138888865, 7104.966756944427] 7104.966756944427 266 \n",
      "7090.090285028435  [6928.433583333325, 6980.841138888865, 7104.966756944427, 7238.3362847222115] 7238.3362847222115 267 \n",
      "7266.491608505455  [6980.841138888865, 7104.966756944427, 7238.3362847222115, 7257.905347222202] 7257.905347222202 268 \n",
      "7295.189650087035  [7104.966756944427, 7238.3362847222115, 7257.905347222202, 7318.272694444449] 7318.272694444449 269 \n",
      "7371.567045936623  [7238.3362847222115, 7257.905347222202, 7318.272694444449, 7127.17163888889] 7127.17163888889 270 \n",
      "7112.625477984695  [7257.905347222202, 7318.272694444449, 7127.17163888889, 6422.3792777777635] 6422.3792777777635 271 \n",
      "6257.442161546342  [7318.272694444449, 7127.17163888889, 6422.3792777777635, 6445.112874999991] 6445.112874999991 272 \n",
      "6384.2840723308145  [7127.17163888889, 6422.3792777777635, 6445.112874999991, 6361.866361111103] 6361.866361111103 273 \n",
      "6281.216476345028  [6422.3792777777635, 6445.112874999991, 6361.866361111103, 6294.207402777775] 6294.207402777775 274 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6204.527574257176  [6445.112874999991, 6361.866361111103, 6294.207402777775, 6288.220666666666] 6288.220666666666 275 \n",
      "6322.725527887089  [6361.866361111103, 6294.207402777775, 6288.220666666666, 6291.030597222219] 6291.030597222219 276 \n",
      "6324.441604318401  [6294.207402777775, 6288.220666666666, 6291.030597222219, 6268.0327361110985] 6268.0327361110985 277 \n",
      "6350.543266553539  [6288.220666666666, 6291.030597222219, 6268.0327361110985, 6440.470868055535] 6440.470868055535 278 \n",
      "6489.112241775156  [6291.030597222219, 6268.0327361110985, 6440.470868055535, 6499.649277777766] 6499.649277777766 279 \n",
      "6507.681939807664  [6268.0327361110985, 6440.470868055535, 6499.649277777766, 6517.507756944436] 6517.507756944436 280 \n",
      "6433.172150770478  [6440.470868055535, 6499.649277777766, 6517.507756944436, 6483.263145833307] 6483.263145833307 281 \n",
      "6420.159609290988  [6499.649277777766, 6517.507756944436, 6483.263145833307, 6397.593888888873] 6397.593888888873 282 \n",
      "6317.132837002529  [6517.507756944436, 6483.263145833307, 6397.593888888873, 6294.544368055563] 6294.544368055563 283 \n",
      "6294.184215955709  [6483.263145833307, 6397.593888888873, 6294.544368055563, 6335.3697777777725] 6335.3697777777725 284 \n",
      "6375.721564117917  [6397.593888888873, 6294.544368055563, 6335.3697777777725, 6410.556388888879] 6410.556388888879 285 \n",
      "6493.73610387335  [6294.544368055563, 6335.3697777777725, 6410.556388888879, 6658.497277777779] 6658.497277777779 286 \n",
      "6722.19176538721  [6335.3697777777725, 6410.556388888879, 6658.497277777779, 6707.015444444434] 6707.015444444434 287 \n",
      "6716.429917183443  [6410.556388888879, 6658.497277777779, 6707.015444444434, 6705.795743055522] 6705.795743055522 288 \n",
      "6635.999847758683  [6658.497277777779, 6707.015444444434, 6705.795743055522, 6638.700284722219] 6638.700284722219 289 \n",
      "6579.321904719438  [6707.015444444434, 6705.795743055522, 6638.700284722219, 6414.967645833348] 6414.967645833348 290 \n",
      "6309.773398723093  [6705.795743055522, 6638.700284722219, 6414.967645833348, 6462.58413888888] 6462.58413888888 291 \n",
      "6475.362162510968  [6638.700284722219, 6414.967645833348, 6462.58413888888, 6519.35620833333] 6519.35620833333 292 \n",
      "6552.75832604825  [6414.967645833348, 6462.58413888888, 6519.35620833333, 6664.429173611098] 6664.429173611098 293 \n",
      "6736.836298229133  [6462.58413888888, 6519.35620833333, 6664.429173611098, 6543.41473611109] 6543.41473611109 294 \n",
      "6556.102048103101  [6519.35620833333, 6664.429173611098, 6543.41473611109, 6583.257374999992] 6583.257374999992 295 \n",
      "6581.558832100032  [6664.429173611098, 6543.41473611109, 6583.257374999992, 6580.807069444441] 6580.807069444441 296 \n",
      "6550.294267961754  [6543.41473611109, 6583.257374999992, 6580.807069444441, 6544.955875000006] 6544.955875000006 297 \n",
      "6460.628126064146  [6583.257374999992, 6580.807069444441, 6544.955875000006, 6447.973736111093] 6447.973736111093 298 \n",
      "6392.344425274325  [6580.807069444441, 6544.955875000006, 6447.973736111093, 6547.833138888892] 6547.833138888892 299 \n",
      "6539.822534518918  [6544.955875000006, 6447.973736111093, 6547.833138888892, 6544.640805555551] 6544.640805555551 300 \n",
      "6583.762029217549  [6447.973736111093, 6547.833138888892, 6544.640805555551, 6558.360423611109] 6558.360423611109 301 \n",
      "6590.226553329097  [6547.833138888892, 6544.640805555551, 6558.360423611109, 6535.844319444428] 6535.844319444428 302 \n",
      "6584.468530322099  [6544.640805555551, 6558.360423611109, 6535.844319444428, 6587.728340277778] 6587.728340277778 303 \n",
      "6580.276242914019  [6558.360423611109, 6535.844319444428, 6587.728340277778, 6587.260458333326] 6587.260458333326 304 \n",
      "6561.027266226065  [6535.844319444428, 6587.728340277778, 6587.260458333326, 6518.070152777784] 6518.070152777784 305 \n",
      "6427.446544578634  [6587.728340277778, 6587.260458333326, 6518.070152777784, 6214.271979166666] 6214.271979166666 306 \n",
      "6119.091968446728  [6587.260458333326, 6518.070152777784, 6214.271979166666, 6202.633958333335] 6202.633958333335 307 \n",
      "6167.160911907528  [6518.070152777784, 6214.271979166666, 6202.633958333335, 6198.606270833343] 6198.606270833343 308 \n",
      "6235.409935632759  [6214.271979166666, 6202.633958333335, 6198.606270833343, 6227.439506944452] 6227.439506944452 309 \n",
      "6250.2044521501175  [6202.633958333335, 6198.606270833343, 6227.439506944452, 6369.0915347222135] 6369.0915347222135 310 \n",
      "6452.384342435316  [6198.606270833343, 6227.439506944452, 6369.0915347222135, 6438.753493055543] 6438.753493055543 311 \n",
      "6444.084175616147  [6227.439506944452, 6369.0915347222135, 6438.753493055543, 6437.953847222239] 6437.953847222239 312 \n",
      "6423.632957179787  [6369.0915347222135, 6438.753493055543, 6437.953847222239, 6427.557229166675] 6427.557229166675 313 \n",
      "6365.691244464657  [6438.753493055543, 6437.953847222239, 6427.557229166675, 6385.112388888904] 6385.112388888904 314 \n",
      "6336.83128507217  [6437.953847222239, 6427.557229166675, 6385.112388888904, 6396.521159722233] 6396.521159722233 315 \n",
      "6364.21287066546  [6427.557229166675, 6385.112388888904, 6396.521159722233, 6433.896937500014] 6433.896937500014 316 \n",
      "6458.094446278723  [6385.112388888904, 6396.521159722233, 6433.896937500014, 6404.0707986111265] 6404.0707986111265 317 \n",
      "6423.683224758233  [6396.521159722233, 6433.896937500014, 6404.0707986111265, 6393.867666666687] 6393.867666666687 318 \n",
      "6431.850709364382  [6433.896937500014, 6404.0707986111265, 6393.867666666687, 6423.714243055521] 6423.714243055521 319 \n",
      "6435.426157319402  [6404.0707986111265, 6393.867666666687, 6423.714243055521, 6400.49537499999] 6400.49537499999 320 \n",
      "6374.714198797255  [6393.867666666687, 6423.714243055521, 6400.49537499999, 6400.008624999987] 6400.008624999987 321 \n",
      "6346.79074838772  [6423.714243055521, 6400.49537499999, 6400.008624999987, 6407.095680555522] 6407.095680555522 322 \n",
      "6364.524198602259  [6400.49537499999, 6400.008624999987, 6407.095680555522, 6403.705812499994] 6403.705812499994 323 \n",
      "6373.939616213536  [6400.008624999987, 6407.095680555522, 6403.705812499994, 6330.382076388887] 6330.382076388887 324 \n",
      "6329.78585985799  [6407.095680555522, 6403.705812499994, 6330.382076388887, 6264.165847222213] 6264.165847222213 325 \n",
      "6279.754093662366  [6403.705812499994, 6330.382076388887, 6264.165847222213, 6275.5548333333145] 6275.5548333333145 326 \n",
      "6312.6029957595865  [6330.382076388887, 6264.165847222213, 6275.5548333333145, 6310.077888888885] 6310.077888888885 327 \n",
      "6318.910843072828  [6264.165847222213, 6275.5548333333145, 6310.077888888885, 6353.589631944447] 6353.589631944447 328 \n",
      "6338.233742761686  [6275.5548333333145, 6310.077888888885, 6353.589631944447, 6335.468812499992] 6335.468812499992 329 \n",
      "6285.371224556657  [6310.077888888885, 6353.589631944447, 6335.468812499992, 6365.459451388902] 6365.459451388902 330 \n",
      "6329.80405805476  [6353.589631944447, 6335.468812499992, 6365.459451388902, 6405.173513888874] 6405.173513888874 331 \n",
      "6395.076710337695  [6335.468812499992, 6365.459451388902, 6405.173513888874, 6413.242763888902] 6413.242763888902 332 \n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "train,test = train_test_split(arima2015hour,test_size=0.24,shuffle=False)\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "mod = ARMA(arima2015hour, order=(4,4))\n",
    "result = mod.fit()\n",
    "\n",
    "history = [x for x in train]\n",
    "predictions = list()\n",
    "for i in range(len(test)):\n",
    "    # predict\n",
    "    model = ARMA(history,order=(5,1))\n",
    "    model_fit = mod.fit()\n",
    "    yhat = model_fit.forecast()\n",
    "    yhat_p = model_fit.predict(start=len(history), end=len(history))[0]\n",
    "    predictions.append(yhat_p)\n",
    "    # observation\n",
    "    obs = test[i] \n",
    "    history.append(obs)\n",
    "    print(str(yhat_p)+' '+' '+ str(history[-4:])+' '+str(obs)+' '+str(i)+' ')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predictions</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-09</th>\n",
       "      <td>16297.183074</td>\n",
       "      <td>15574.154417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-10</th>\n",
       "      <td>15397.150049</td>\n",
       "      <td>14779.160097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11</th>\n",
       "      <td>14715.473368</td>\n",
       "      <td>16693.400403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-12</th>\n",
       "      <td>16922.993410</td>\n",
       "      <td>17133.031465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-13</th>\n",
       "      <td>17224.307847</td>\n",
       "      <td>17139.606778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-14</th>\n",
       "      <td>16927.070930</td>\n",
       "      <td>16791.305167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-15</th>\n",
       "      <td>16869.566470</td>\n",
       "      <td>17548.668507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-16</th>\n",
       "      <td>17573.745070</td>\n",
       "      <td>18467.820167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-17</th>\n",
       "      <td>18798.465303</td>\n",
       "      <td>19537.653319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-18</th>\n",
       "      <td>19606.177920</td>\n",
       "      <td>18883.086382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-19</th>\n",
       "      <td>18864.817978</td>\n",
       "      <td>18400.047861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-20</th>\n",
       "      <td>18125.299910</td>\n",
       "      <td>16866.442632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-21</th>\n",
       "      <td>16625.408228</td>\n",
       "      <td>16345.586660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-22</th>\n",
       "      <td>15969.640499</td>\n",
       "      <td>13914.177417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-23</th>\n",
       "      <td>13490.797884</td>\n",
       "      <td>15103.153215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-24</th>\n",
       "      <td>15058.494077</td>\n",
       "      <td>14166.849215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-25</th>\n",
       "      <td>14224.669913</td>\n",
       "      <td>14188.495896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26</th>\n",
       "      <td>13947.821531</td>\n",
       "      <td>15365.250250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27</th>\n",
       "      <td>15991.122820</td>\n",
       "      <td>15654.341257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>15480.378126</td>\n",
       "      <td>14293.096396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>14241.050624</td>\n",
       "      <td>14556.957694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-30</th>\n",
       "      <td>14310.870844</td>\n",
       "      <td>13583.643736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>13461.087318</td>\n",
       "      <td>13387.558424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>12967.386410</td>\n",
       "      <td>13425.692806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>13603.343147</td>\n",
       "      <td>13898.003201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>13804.481411</td>\n",
       "      <td>14990.059160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>15508.383690</td>\n",
       "      <td>14851.726771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>14786.093498</td>\n",
       "      <td>15797.656597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>16151.261932</td>\n",
       "      <td>16662.248528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-07</th>\n",
       "      <td>16698.604985</td>\n",
       "      <td>16526.662882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-12</th>\n",
       "      <td>6119.091968</td>\n",
       "      <td>6202.633958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-13</th>\n",
       "      <td>6167.160912</td>\n",
       "      <td>6198.606271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-14</th>\n",
       "      <td>6235.409936</td>\n",
       "      <td>6227.439507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-15</th>\n",
       "      <td>6250.204452</td>\n",
       "      <td>6369.091535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-16</th>\n",
       "      <td>6452.384342</td>\n",
       "      <td>6438.753493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-17</th>\n",
       "      <td>6444.084176</td>\n",
       "      <td>6437.953847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-18</th>\n",
       "      <td>6423.632957</td>\n",
       "      <td>6427.557229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-19</th>\n",
       "      <td>6365.691244</td>\n",
       "      <td>6385.112389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-20</th>\n",
       "      <td>6336.831285</td>\n",
       "      <td>6396.521160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-21</th>\n",
       "      <td>6364.212871</td>\n",
       "      <td>6433.896938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-22</th>\n",
       "      <td>6458.094446</td>\n",
       "      <td>6404.070799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-23</th>\n",
       "      <td>6423.683225</td>\n",
       "      <td>6393.867667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-24</th>\n",
       "      <td>6431.850709</td>\n",
       "      <td>6423.714243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-25</th>\n",
       "      <td>6435.426157</td>\n",
       "      <td>6400.495375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-26</th>\n",
       "      <td>6374.714199</td>\n",
       "      <td>6400.008625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27</th>\n",
       "      <td>6346.790748</td>\n",
       "      <td>6407.095681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-28</th>\n",
       "      <td>6364.524199</td>\n",
       "      <td>6403.705812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-29</th>\n",
       "      <td>6373.939616</td>\n",
       "      <td>6330.382076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-30</th>\n",
       "      <td>6329.785860</td>\n",
       "      <td>6264.165847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>6279.754094</td>\n",
       "      <td>6275.554833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>6312.602996</td>\n",
       "      <td>6310.077889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>6318.910843</td>\n",
       "      <td>6353.589632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>6338.233743</td>\n",
       "      <td>6335.468812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>6285.371225</td>\n",
       "      <td>6365.459451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>6329.804058</td>\n",
       "      <td>6405.173514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>6395.076710</td>\n",
       "      <td>6413.242764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-07</th>\n",
       "      <td>6424.148161</td>\n",
       "      <td>6506.260521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08</th>\n",
       "      <td>6555.109265</td>\n",
       "      <td>6450.996382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-09</th>\n",
       "      <td>6468.887419</td>\n",
       "      <td>6366.783681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10</th>\n",
       "      <td>6350.361464</td>\n",
       "      <td>6355.763663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>337 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             predictions         Close\n",
       "Date                                  \n",
       "2017-12-09  16297.183074  15574.154417\n",
       "2017-12-10  15397.150049  14779.160097\n",
       "2017-12-11  14715.473368  16693.400403\n",
       "2017-12-12  16922.993410  17133.031465\n",
       "2017-12-13  17224.307847  17139.606778\n",
       "2017-12-14  16927.070930  16791.305167\n",
       "2017-12-15  16869.566470  17548.668507\n",
       "2017-12-16  17573.745070  18467.820167\n",
       "2017-12-17  18798.465303  19537.653319\n",
       "2017-12-18  19606.177920  18883.086382\n",
       "2017-12-19  18864.817978  18400.047861\n",
       "2017-12-20  18125.299910  16866.442632\n",
       "2017-12-21  16625.408228  16345.586660\n",
       "2017-12-22  15969.640499  13914.177417\n",
       "2017-12-23  13490.797884  15103.153215\n",
       "2017-12-24  15058.494077  14166.849215\n",
       "2017-12-25  14224.669913  14188.495896\n",
       "2017-12-26  13947.821531  15365.250250\n",
       "2017-12-27  15991.122820  15654.341257\n",
       "2017-12-28  15480.378126  14293.096396\n",
       "2017-12-29  14241.050624  14556.957694\n",
       "2017-12-30  14310.870844  13583.643736\n",
       "2017-12-31  13461.087318  13387.558424\n",
       "2018-01-01  12967.386410  13425.692806\n",
       "2018-01-02  13603.343147  13898.003201\n",
       "2018-01-03  13804.481411  14990.059160\n",
       "2018-01-04  15508.383690  14851.726771\n",
       "2018-01-05  14786.093498  15797.656597\n",
       "2018-01-06  16151.261932  16662.248528\n",
       "2018-01-07  16698.604985  16526.662882\n",
       "...                  ...           ...\n",
       "2018-10-12   6119.091968   6202.633958\n",
       "2018-10-13   6167.160912   6198.606271\n",
       "2018-10-14   6235.409936   6227.439507\n",
       "2018-10-15   6250.204452   6369.091535\n",
       "2018-10-16   6452.384342   6438.753493\n",
       "2018-10-17   6444.084176   6437.953847\n",
       "2018-10-18   6423.632957   6427.557229\n",
       "2018-10-19   6365.691244   6385.112389\n",
       "2018-10-20   6336.831285   6396.521160\n",
       "2018-10-21   6364.212871   6433.896938\n",
       "2018-10-22   6458.094446   6404.070799\n",
       "2018-10-23   6423.683225   6393.867667\n",
       "2018-10-24   6431.850709   6423.714243\n",
       "2018-10-25   6435.426157   6400.495375\n",
       "2018-10-26   6374.714199   6400.008625\n",
       "2018-10-27   6346.790748   6407.095681\n",
       "2018-10-28   6364.524199   6403.705812\n",
       "2018-10-29   6373.939616   6330.382076\n",
       "2018-10-30   6329.785860   6264.165847\n",
       "2018-10-31   6279.754094   6275.554833\n",
       "2018-11-01   6312.602996   6310.077889\n",
       "2018-11-02   6318.910843   6353.589632\n",
       "2018-11-03   6338.233743   6335.468812\n",
       "2018-11-04   6285.371225   6365.459451\n",
       "2018-11-05   6329.804058   6405.173514\n",
       "2018-11-06   6395.076710   6413.242764\n",
       "2018-11-07   6424.148161   6506.260521\n",
       "2018-11-08   6555.109265   6450.996382\n",
       "2018-11-09   6468.887419   6366.783681\n",
       "2018-11-10   6350.361464   6355.763663\n",
       "\n",
       "[337 rows x 2 columns]"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([pd.DataFrame(predictions,index=test.index,columns=['predictions']),test],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "405.2444219277155"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error=math.sqrt(mean_squared_error(test,predictions))\n",
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARMA\n",
    "mod = ARMA(arima2015hour, order=(4,4))\n",
    "result = mod.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot_predict(start=1080,end=1090)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              ARMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                  Close   No. Observations:                33671\n",
      "Model:                     ARMA(4, 4)   Log Likelihood             -180313.267\n",
      "Method:                       css-mle   S.D. of innovations             51.214\n",
      "Date:                Thu, 13 Dec 2018   AIC                         360646.535\n",
      "Time:                        07:53:13   BIC                         360730.779\n",
      "Sample:                    01-08-2015   HQIC                        360673.418\n",
      "                         - 11-10-2018                                         \n",
      "===============================================================================\n",
      "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const        3059.5062        nan        nan        nan         nan         nan\n",
      "ar.L1.Close     1.4837        nan        nan        nan         nan         nan\n",
      "ar.L2.Close     0.1348        nan        nan        nan         nan         nan\n",
      "ar.L3.Close    -1.4794   1.62e-05  -9.13e+04      0.000      -1.479      -1.479\n",
      "ar.L4.Close     0.8609   1.03e-05   8.34e+04      0.000       0.861       0.861\n",
      "ma.L1.Close    -0.3206      0.005    -59.043      0.000      -0.331      -0.310\n",
      "ma.L2.Close    -0.7281      0.004   -182.966      0.000      -0.736      -0.720\n",
      "ma.L3.Close     0.7427      0.004    186.921      0.000       0.735       0.751\n",
      "ma.L4.Close     0.1607      0.005     29.483      0.000       0.150       0.171\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1           -1.0013           -0.0000j            1.0013           -0.5000\n",
      "AR.2            0.8599           -0.6486j            1.0771           -0.1029\n",
      "AR.3            0.8599           +0.6486j            1.0771            0.1029\n",
      "AR.4            1.0000           -0.0000j            1.0000           -0.0000\n",
      "MA.1            0.8698           -0.6329j            1.0757           -0.1001\n",
      "MA.2            0.8698           +0.6329j            1.0757            0.1001\n",
      "MA.3           -1.0038           -0.0000j            1.0038           -0.5000\n",
      "MA.4           -5.3565           -0.0000j            5.3565           -0.5000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(result.summary())\n",
    "result.plot_predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot_predict(start=1060, end=1090)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot_predict(start=1060, end=1090)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ARIMA performs well for next days predictions but performs poor for longer terms like given last few days price predict next 5-7 days prices. RNN perform consistently upto 6 days. Random Forest usually perform poorly on validation set and unseen patterns. RNN and ARIMA model perform well but fail to predict the hype or the unusal spike caused by rumors or fake news. One can use sentiments from twitter to incorporate these changes."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_python3",
   "language": "python",
   "name": "conda_python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}