performanceAnalysisTest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pymongo import MongoClient\n",
    "import math\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using local config file\n"
     ]
    }
   ],
   "source": [
    "def divtd(td1, td2):\n",
    "    us1 = td1.microseconds + 1000000 * (td1.seconds + 86400 * td1.days)\n",
    "    us2 = td2.microseconds + 1000000 * (td2.seconds + 86400 * td2.days)\n",
    "    return float(us1) / us2\n",
    "\n",
    "try:\n",
    "    import config\n",
    "    print('using local config file')\n",
    "    mongodb_uri = config.mongodb_uri\n",
    "except:\n",
    "    print('using environment variable')\n",
    "    mongodb_uri = os.getenv('MONGODB_URI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing Data\n",
      "                         90dayTreasury     portfolio           spy  \\\n",
      "timestamp                                                            \n",
      "2016-09-15 13:30:08.905   1.042600e-07  9.502560e-06  1.732656e-05   \n",
      "2016-09-15 13:31:08.905   1.042600e-07 -1.068206e-05  9.669740e-06   \n",
      "2016-09-15 13:32:08.905   1.042600e-07 -2.054629e-05 -1.907024e-05   \n",
      "2016-09-15 13:33:08.905   1.042600e-07  2.051233e-05 -1.202655e-06   \n",
      "2016-09-15 13:34:08.905   1.042600e-07  3.165038e-05  3.346574e-05   \n",
      "2016-09-15 13:35:08.905   1.042600e-07 -4.241881e-07  4.377680e-05   \n",
      "2016-09-15 13:36:08.905   1.042600e-07  1.150198e-05  1.056903e-05   \n",
      "2016-09-15 13:37:08.905   1.042600e-07 -2.756307e-06 -2.638356e-06   \n",
      "2016-09-15 13:38:08.905   1.042600e-07 -1.248820e-05  2.048732e-05   \n",
      "2016-09-15 13:39:08.905   1.042600e-07  1.104375e-05  1.261777e-05   \n",
      "2016-09-15 13:40:08.905   1.042600e-07  2.284670e-05  5.897027e-06   \n",
      "2016-09-15 13:41:08.905   1.042600e-07 -5.786695e-06  1.419843e-05   \n",
      "2016-09-15 13:42:08.905   1.042600e-07 -1.345121e-06 -6.389505e-06   \n",
      "2016-09-15 13:43:08.905   1.042600e-07 -2.787521e-05 -4.172200e-05   \n",
      "2016-09-15 13:44:08.905   1.042600e-07  4.596871e-06 -1.341571e-05   \n",
      "2016-09-15 13:45:08.905   1.042600e-07  1.615275e-05  2.859447e-05   \n",
      "2016-09-15 13:46:08.905   1.042600e-07  9.957465e-07 -1.193861e-05   \n",
      "2016-09-15 13:47:08.905   1.042600e-07 -7.031693e-07  1.678258e-05   \n",
      "2016-09-15 13:48:08.905   1.042600e-07 -2.799324e-05 -3.043182e-05   \n",
      "2016-09-15 13:49:08.905   1.042600e-07 -3.695282e-06 -1.491205e-05   \n",
      "2016-09-15 13:50:08.905   1.042600e-07 -4.976693e-06  3.175914e-05   \n",
      "2016-09-15 13:51:08.905   1.042600e-07  1.936439e-05  3.061050e-05   \n",
      "2016-09-15 13:52:08.905   1.042600e-07  6.595964e-07  1.172348e-05   \n",
      "2016-09-15 13:53:08.905   1.042600e-07  3.486452e-05  5.537832e-05   \n",
      "2016-09-15 13:54:08.905   1.042600e-07 -2.896507e-06  1.566402e-05   \n",
      "2016-09-15 13:55:08.905   1.042600e-07  3.167075e-05  2.794278e-05   \n",
      "2016-09-15 13:56:08.905   1.042600e-07 -6.751124e-06 -6.904177e-07   \n",
      "2016-09-15 13:57:08.905   1.042600e-07  1.264379e-05 -4.278551e-06   \n",
      "2016-09-15 13:58:08.905   1.042600e-07  2.403249e-05  1.323484e-05   \n",
      "2016-09-15 13:59:08.905   1.042600e-07  1.445548e-05  3.282187e-06   \n",
      "...                                ...           ...           ...   \n",
      "2017-09-14 19:30:08.905   1.042600e-07  1.931961e-05  1.947669e-05   \n",
      "2017-09-14 19:31:08.905   1.042600e-07  2.132474e-05  2.336068e-05   \n",
      "2017-09-14 19:32:08.905   1.042600e-07  2.178105e-05  2.220791e-05   \n",
      "2017-09-14 19:33:08.905   1.042600e-07 -1.139265e-05  3.892450e-06   \n",
      "2017-09-14 19:34:08.905   1.042600e-07  1.211062e-05  1.359886e-05   \n",
      "2017-09-14 19:35:08.905   1.042600e-07  8.854727e-06  2.566905e-05   \n",
      "2017-09-14 19:36:08.905   1.042600e-07 -2.967100e-05 -5.001316e-05   \n",
      "2017-09-14 19:37:08.905   1.042600e-07  4.140727e-05  4.262602e-05   \n",
      "2017-09-14 19:38:08.905   1.042600e-07  1.725389e-05  5.396904e-06   \n",
      "2017-09-14 19:39:08.905   1.042600e-07 -3.082603e-05 -3.509411e-05   \n",
      "2017-09-14 19:40:08.905   1.042600e-07 -5.511431e-06 -5.417480e-06   \n",
      "2017-09-14 19:41:08.905   1.042600e-07 -2.923344e-05  3.101948e-05   \n",
      "2017-09-14 19:42:08.905   1.042600e-07 -2.979902e-05 -3.287521e-05   \n",
      "2017-09-14 19:43:08.905   1.042600e-07  5.247618e-06 -1.062989e-05   \n",
      "2017-09-14 19:44:08.905   1.042600e-07  6.906723e-06  1.217298e-05   \n",
      "2017-09-14 19:45:08.905   1.042600e-07  1.299221e-05  1.921505e-05   \n",
      "2017-09-14 19:46:08.905   1.042600e-07 -5.955946e-06 -3.036878e-06   \n",
      "2017-09-14 19:47:08.905   1.042600e-07  1.443935e-06 -4.496851e-05   \n",
      "2017-09-14 19:48:08.905   1.042600e-07  2.639770e-05  3.641148e-05   \n",
      "2017-09-14 19:49:08.905   1.042600e-07  2.012513e-06 -1.933888e-06   \n",
      "2017-09-14 19:50:08.905   1.042600e-07 -1.321174e-05 -8.126649e-06   \n",
      "2017-09-14 19:51:08.905   1.042600e-07 -1.122756e-05 -1.247180e-05   \n",
      "2017-09-14 19:52:08.905   1.042600e-07  4.278435e-06  5.786775e-06   \n",
      "2017-09-14 19:53:08.905   1.042600e-07  2.491826e-05  2.443111e-05   \n",
      "2017-09-14 19:54:08.905   1.042600e-07  2.072085e-05  3.381196e-05   \n",
      "2017-09-14 19:55:08.905   1.042600e-07 -7.420221e-06 -6.834554e-06   \n",
      "2017-09-14 19:56:08.905   1.042600e-07  2.027617e-05 -3.039314e-05   \n",
      "2017-09-14 19:57:08.905   1.042600e-07 -1.711009e-05 -3.122751e-05   \n",
      "2017-09-14 19:58:08.905   1.042600e-07 -2.404214e-05  1.154739e-05   \n",
      "2017-09-14 19:59:08.905   1.042600e-07 -2.957851e-05 -3.618723e-05   \n",
      "\n",
      "                                  tlt  \n",
      "timestamp                              \n",
      "2016-09-15 13:30:08.905 -2.796710e-05  \n",
      "2016-09-15 13:31:08.905 -1.931863e-05  \n",
      "2016-09-15 13:32:08.905 -2.222772e-05  \n",
      "2016-09-15 13:33:08.905  2.168834e-05  \n",
      "2016-09-15 13:34:08.905  1.363946e-05  \n",
      "2016-09-15 13:35:08.905 -9.288270e-06  \n",
      "2016-09-15 13:36:08.905  1.304531e-05  \n",
      "2016-09-15 13:37:08.905 -2.822077e-06  \n",
      "2016-09-15 13:38:08.905 -1.847448e-05  \n",
      "2016-09-15 13:39:08.905 -1.545896e-05  \n",
      "2016-09-15 13:40:08.905  2.497085e-05  \n",
      "2016-09-15 13:41:08.905 -3.306520e-05  \n",
      "2016-09-15 13:42:08.905  3.336095e-07  \n",
      "2016-09-15 13:43:08.905 -5.025378e-06  \n",
      "2016-09-15 13:44:08.905  2.980608e-05  \n",
      "2016-09-15 13:45:08.905 -1.589134e-05  \n",
      "2016-09-15 13:46:08.905  1.122720e-05  \n",
      "2016-09-15 13:47:08.905 -6.880386e-06  \n",
      "2016-09-15 13:48:08.905 -1.815870e-05  \n",
      "2016-09-15 13:49:08.905  1.186126e-05  \n",
      "2016-09-15 13:50:08.905 -1.364502e-05  \n",
      "2016-09-15 13:51:08.905 -1.432597e-05  \n",
      "2016-09-15 13:52:08.905 -1.969025e-05  \n",
      "2016-09-15 13:53:08.905  2.160674e-05  \n",
      "2016-09-15 13:54:08.905 -5.408087e-06  \n",
      "2016-09-15 13:55:08.905  3.559376e-05  \n",
      "2016-09-15 13:56:08.905 -1.213115e-05  \n",
      "2016-09-15 13:57:08.905  1.350705e-05  \n",
      "2016-09-15 13:58:08.905  2.942075e-05  \n",
      "2016-09-15 13:59:08.905  1.721048e-05  \n",
      "...                               ...  \n",
      "2017-09-14 19:30:08.905 -2.513459e-05  \n",
      "2017-09-14 19:31:08.905  1.017176e-05  \n",
      "2017-09-14 19:32:08.905  1.695404e-05  \n",
      "2017-09-14 19:33:08.905 -1.601398e-05  \n",
      "2017-09-14 19:34:08.905  9.403792e-06  \n",
      "2017-09-14 19:35:08.905 -4.159162e-05  \n",
      "2017-09-14 19:36:08.905 -2.596819e-05  \n",
      "2017-09-14 19:37:08.905  4.018505e-05  \n",
      "2017-09-14 19:38:08.905  2.142425e-05  \n",
      "2017-09-14 19:39:08.905  1.369246e-05  \n",
      "2017-09-14 19:40:08.905 -8.781106e-06  \n",
      "2017-09-14 19:41:08.905 -2.986020e-05  \n",
      "2017-09-14 19:42:08.905 -2.847323e-05  \n",
      "2017-09-14 19:43:08.905  1.038342e-05  \n",
      "2017-09-14 19:44:08.905  3.473821e-06  \n",
      "2017-09-14 19:45:08.905 -1.091255e-05  \n",
      "2017-09-14 19:46:08.905 -1.818585e-05  \n",
      "2017-09-14 19:47:08.905  1.765203e-06  \n",
      "2017-09-14 19:48:08.905 -9.528152e-06  \n",
      "2017-09-14 19:49:08.905  4.142693e-06  \n",
      "2017-09-14 19:50:08.905 -3.407593e-05  \n",
      "2017-09-14 19:51:08.905  8.790382e-06  \n",
      "2017-09-14 19:52:08.905  2.760328e-06  \n",
      "2017-09-14 19:53:08.905  2.498226e-05  \n",
      "2017-09-14 19:54:08.905  2.679045e-06  \n",
      "2017-09-14 19:55:08.905 -1.292683e-05  \n",
      "2017-09-14 19:56:08.905  3.392285e-05  \n",
      "2017-09-14 19:57:08.905 -1.166599e-05  \n",
      "2017-09-14 19:58:08.905 -2.602549e-05  \n",
      "2017-09-14 19:59:08.905  1.952647e-05  \n",
      "\n",
      "[101790 rows x 4 columns]\n",
      "                         90dayTreasury     portfolio       spy       tlt\n",
      "timestamp                                                               \n",
      "2016-09-15 13:30:08.905   0.000000e+00  0.000000e+00  0.000000  0.000000\n",
      "2016-09-15 13:31:08.905   1.042600e-07 -1.068216e-05  0.000010 -0.000019\n",
      "2016-09-15 13:32:08.905   2.085200e-07 -3.122843e-05 -0.000009 -0.000042\n",
      "2016-09-15 13:33:08.905   3.127801e-07 -1.071654e-05 -0.000011 -0.000020\n",
      "2016-09-15 13:34:08.905   4.170401e-07  2.093380e-05  0.000023 -0.000006\n",
      "2016-09-15 13:35:08.905   5.213002e-07  2.050960e-05  0.000067 -0.000016\n",
      "2016-09-15 13:36:08.905   6.255602e-07  3.201192e-05  0.000077 -0.000002\n",
      "2016-09-15 13:37:08.905   7.298203e-07  2.925550e-05  0.000075 -0.000005\n",
      "2016-09-15 13:38:08.905   8.340804e-07  1.676681e-05  0.000095 -0.000024\n",
      "2016-09-15 13:39:08.905   9.383405e-07  2.781085e-05  0.000108 -0.000039\n",
      "2016-09-15 13:40:08.905   1.042601e-06  5.065840e-05  0.000114 -0.000014\n",
      "2016-09-15 13:41:08.905   1.146861e-06  4.487136e-05  0.000128 -0.000047\n",
      "2016-09-15 13:42:08.905   1.251121e-06  4.352616e-05  0.000121 -0.000047\n",
      "2016-09-15 13:43:08.905   1.355381e-06  1.564948e-05  0.000080 -0.000052\n",
      "2016-09-15 13:44:08.905   1.459641e-06  2.024646e-05  0.000066 -0.000022\n",
      "2016-09-15 13:45:08.905   1.563901e-06  3.639969e-05  0.000095 -0.000038\n",
      "2016-09-15 13:46:08.905   1.668161e-06  3.739548e-05  0.000083 -0.000027\n",
      "2016-09-15 13:47:08.905   1.772422e-06  3.669228e-05  0.000100 -0.000034\n",
      "2016-09-15 13:48:08.905   1.876682e-06  8.697746e-06  0.000069 -0.000052\n",
      "2016-09-15 13:49:08.905   1.980942e-06  5.002397e-06  0.000054 -0.000040\n",
      "2016-09-15 13:50:08.905   2.085202e-06  2.563122e-08  0.000086 -0.000054\n",
      "2016-09-15 13:51:08.905   2.189463e-06  1.939020e-05  0.000117 -0.000068\n",
      "2016-09-15 13:52:08.905   2.293723e-06  2.004982e-05  0.000128 -0.000088\n",
      "2016-09-15 13:53:08.905   2.397983e-06  5.491537e-05  0.000184 -0.000066\n",
      "2016-09-15 13:54:08.905   2.502243e-06  5.201868e-05  0.000199 -0.000072\n",
      "2016-09-15 13:55:08.905   2.606504e-06  8.369137e-05  0.000227 -0.000036\n",
      "2016-09-15 13:56:08.905   2.710764e-06  7.693962e-05  0.000227 -0.000048\n",
      "2016-09-15 13:57:08.905   2.815024e-06  8.958450e-05  0.000222 -0.000035\n",
      "2016-09-15 13:58:08.905   2.919284e-06  1.136194e-04  0.000236 -0.000005\n",
      "2016-09-15 13:59:08.905   3.023545e-06  1.280766e-04  0.000239  0.000012\n",
      "...                                ...           ...       ...       ...\n",
      "2017-09-14 19:30:08.905   1.066598e-02  6.790914e-02  0.073095  0.058803\n",
      "2017-09-14 19:31:08.905   1.066608e-02  6.793192e-02  0.073120  0.058814\n",
      "2017-09-14 19:32:08.905   1.066619e-02  6.795518e-02  0.073143  0.058832\n",
      "2017-09-14 19:33:08.905   1.066629e-02  6.794301e-02  0.073148  0.058815\n",
      "2017-09-14 19:34:08.905   1.066640e-02  6.795594e-02  0.073162  0.058825\n",
      "2017-09-14 19:35:08.905   1.066651e-02  6.796540e-02  0.073190  0.058781\n",
      "2017-09-14 19:36:08.905   1.066661e-02  6.793371e-02  0.073136  0.058753\n",
      "2017-09-14 19:37:08.905   1.066672e-02  6.797793e-02  0.073182  0.058796\n",
      "2017-09-14 19:38:08.905   1.066682e-02  6.799636e-02  0.073188  0.058818\n",
      "2017-09-14 19:39:08.905   1.066693e-02  6.796344e-02  0.073150  0.058833\n",
      "2017-09-14 19:40:08.905   1.066703e-02  6.795755e-02  0.073144  0.058824\n",
      "2017-09-14 19:41:08.905   1.066714e-02  6.792633e-02  0.073177  0.058792\n",
      "2017-09-14 19:42:08.905   1.066724e-02  6.789451e-02  0.073142  0.058762\n",
      "2017-09-14 19:43:08.905   1.066735e-02  6.790011e-02  0.073131  0.058773\n",
      "2017-09-14 19:44:08.905   1.066745e-02  6.790749e-02  0.073144  0.058777\n",
      "2017-09-14 19:45:08.905   1.066756e-02  6.792136e-02  0.073164  0.058765\n",
      "2017-09-14 19:46:08.905   1.066766e-02  6.791500e-02  0.073161  0.058746\n",
      "2017-09-14 19:47:08.905   1.066777e-02  6.791654e-02  0.073113  0.058748\n",
      "2017-09-14 19:48:08.905   1.066788e-02  6.794473e-02  0.073152  0.058738\n",
      "2017-09-14 19:49:08.905   1.066798e-02  6.794688e-02  0.073150  0.058742\n",
      "2017-09-14 19:50:08.905   1.066809e-02  6.793277e-02  0.073141  0.058706\n",
      "2017-09-14 19:51:08.905   1.066819e-02  6.792078e-02  0.073128  0.058715\n",
      "2017-09-14 19:52:08.905   1.066830e-02  6.792535e-02  0.073134  0.058718\n",
      "2017-09-14 19:53:08.905   1.066840e-02  6.795196e-02  0.073160  0.058745\n",
      "2017-09-14 19:54:08.905   1.066851e-02  6.797409e-02  0.073197  0.058747\n",
      "2017-09-14 19:55:08.905   1.066861e-02  6.796617e-02  0.073189  0.058734\n",
      "2017-09-14 19:56:08.905   1.066872e-02  6.798782e-02  0.073157  0.058770\n",
      "2017-09-14 19:57:08.905   1.066882e-02  6.796955e-02  0.073123  0.058757\n",
      "2017-09-14 19:58:08.905   1.066893e-02  6.794387e-02  0.073135  0.058730\n",
      "2017-09-14 19:59:08.905   1.066903e-02  6.791228e-02  0.073097  0.058750\n",
      "\n",
      "[101790 rows x 4 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'       \\nif success:\\n    try:\\n        dataCursor = db.tracking.find(projection={\\'_id\\': False},sort=[(\"timestamp\", 1)])\\n    except Exception as e:\\n        print(\\'mongo data cursor error \\', str(e))\\n        success = False\\n        \\nif success:\\n    try:\\n        tracking =  pd.DataFrame(list(dataCursor)).set_index([\\'timestamp\\'])\\n        print(tracking)\\n    except Exception as e:\\n        print(\\'mongo data cursor error \\', str(e))\\n        success = False \\n'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Analyzing Data\")\n",
    "success = True\n",
    "\n",
    "if success:\n",
    "    try:\n",
    "        client = MongoClient(mongodb_uri)\n",
    "        db = client.get_database()\n",
    "    except Exception as e:\n",
    "        print('mongo login error ', str(e))\n",
    "        success = False\n",
    "if success:\n",
    "    try:\n",
    "        dataCursor = db.percentageMoveTest.find(projection={'_id': False},sort=[(\"timestamp\", 1)])\n",
    "    except Exception as e:\n",
    "        print('mongo data cursor error ', str(e))\n",
    "        success = False\n",
    "        \n",
    "if success:\n",
    "    try:\n",
    "        df =  pd.DataFrame(list(dataCursor)).set_index(['timestamp'])\n",
    "        print(df)\n",
    "        increase = df+1\n",
    "        cumulative = increase.cumprod(axis=0)\n",
    "        cumulative = cumulative - cumulative.iloc[0]\n",
    "        print(cumulative)\n",
    "    except Exception as e:\n",
    "        print('mongo data cursor error ', str(e))\n",
    "        success = False  \n",
    "        \n",
    "\"\"\"       \n",
    "if success:\n",
    "    try:\n",
    "        dataCursor = db.tracking.find(projection={'_id': False},sort=[(\"timestamp\", 1)])\n",
    "    except Exception as e:\n",
    "        print('mongo data cursor error ', str(e))\n",
    "        success = False\n",
    "        \n",
    "if success:\n",
    "    try:\n",
    "        tracking =  pd.DataFrame(list(dataCursor)).set_index(['timestamp'])\n",
    "        print(tracking)\n",
    "    except Exception as e:\n",
    "        print('mongo data cursor error ', str(e))\n",
    "        success = False \n",
    "\"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stephan/anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\n    plt.figure()\\n    plt.plot(tracking['spyTarget'])\\n    plt.plot(tracking['tltTarget'])\\n    plt.plot(tracking['spyActual'])\\n    plt.plot(tracking['tltActual'])\\n    plt.legend()\\n\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KVKWE0+fCvtqDMXG4+s8Py/Dw1n+qCv8Wrw0V9irmY4k3NpVvjZuQTOeB/XDs24uK/zwX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f5b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Cd7yDZXUxQL8m8/cL5GQSU40srg/TZhs+h1dRFOYVu+joCeEPxXSKUIj0IMlb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\n46I7USoUJ4wAdf46lh0MM7U1BYBtylTcCxZSdvc9w34Mo2U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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111c5d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lv+2ls0hwFkIIUSxzvkWTXjKy11VPferSoiZdWtR0ZrXsZB8/VmRgBgh/e16Z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\ne+ROwBEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1169b7390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if success:\n",
    "    plt.figure()\n",
    "    plt.plot(df['portfolio'])\n",
    "    plt.plot(df['spy'])\n",
    "    plt.plot(df['tlt'])\n",
    "    plt.plot(df['90dayTreasury'])\n",
    "    plt.legend()\n",
    "    plt.figure()\n",
    "    sns.distplot(df['portfolio'],label=\"portfolio\")\n",
    "    sns.distplot(df['spy'],label=\"spy\")\n",
    "    sns.distplot(df['tlt'],label=\"tlt\")\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(cumulative['portfolio'])\n",
    "    plt.plot(cumulative['spy'])\n",
    "    plt.plot(cumulative['tlt'])\n",
    "    plt.plot(cumulative['90dayTreasury'])\n",
    "    plt.legend()\n",
    "\"\"\"\n",
    "    plt.figure()\n",
    "    plt.plot(tracking['spyTarget'])\n",
    "    plt.plot(tracking['tltTarget'])\n",
    "    plt.plot(tracking['spyActual'])\n",
    "    plt.plot(tracking['tltActual'])\n",
    "    plt.legend()\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rawSpySharpe =  0.023714994301015098\n",
      "spySharpe =  7.573737144914237\n",
      "\n",
      "rawTltSharpe =  0.018223688564120502\n",
      "tltSharpe =  5.8200067536815885\n",
      "\n",
      "rawPortfolioSharpe =  0.026639517942380415\n",
      "portfolioSharpe =  8.507727389762842\n",
      "\n",
      "rawSpyInfo =  -0.0023447851919255955\n",
      "spyInfo =  -0.7488421240806115\n",
      "\n",
      "rawTltInfo =  0.00418716963886792\n",
      "tltInfo =  1.337235076821995\n"
     ]
    }
   ],
   "source": [
    "if success:\n",
    "    df['elapsed'] = df.index-df.index[0]\n",
    "    year = datetime.timedelta(days=365)\n",
    "    percentageOfYear = divtd((df['elapsed'])[-1], year)\n",
    "    \n",
    "    df['spyExcess'] = df['spy']-df['90dayTreasury']\n",
    "    df['tltExcess'] = df['tlt']-df['90dayTreasury']\n",
    "    df['portfolioExcess'] = df['portfolio']-df['90dayTreasury']\n",
    "    df['spyInfoExcess'] = df['portfolio']-df['spy']\n",
    "    df['tltInfoExcess'] = df['portfolio']-df['tlt']\n",
    "    \n",
    "    \n",
    "    meanSpyExcess = df['spyExcess'].mean()\n",
    "    meanTltExcess = df['tltExcess'].mean()\n",
    "    meanPortfolioExcess = df['portfolioExcess'].mean()\n",
    "    meanSpyInfoExcess = df['spyInfoExcess'].mean()\n",
    "    meanTltInfoExcess = df['tltInfoExcess'].mean()\n",
    "    \n",
    "    stdSpyExcess = df['spyExcess'].std()\n",
    "    stdTltExcess = df['tltExcess'].std()\n",
    "    stdPortfolioExcess = df['portfolioExcess'].std()\n",
    "    stdSpyInfoExcess = df['spyInfoExcess'].std()\n",
    "    stdTltInfoExcess = df['tltInfoExcess'].std()\n",
    "    \n",
    "    rawSpySharpe = meanSpyExcess/stdSpyExcess\n",
    "    spySharpe = math.sqrt(len(df.index)/percentageOfYear)*rawSpySharpe\n",
    "    print('rawSpySharpe = ',rawSpySharpe)\n",
    "    print('spySharpe = ',spySharpe)\n",
    "    \n",
    "    rawTltSharpe = meanTltExcess/stdTltExcess\n",
    "    tltSharpe = math.sqrt(len(df.index)/percentageOfYear)*rawTltSharpe\n",
    "    print('\\nrawTltSharpe = ',rawTltSharpe)\n",
    "    print('tltSharpe = ',tltSharpe)\n",
    "    \n",
    "    rawPortfolioSharpe = meanPortfolioExcess/stdPortfolioExcess\n",
    "    portfolioSharpe = math.sqrt(len(df.index)/percentageOfYear)*rawPortfolioSharpe\n",
    "    print('\\nrawPortfolioSharpe = ',rawPortfolioSharpe)\n",
    "    print('portfolioSharpe = ',portfolioSharpe)\n",
    "    \n",
    "    rawSpyInfo = meanSpyInfoExcess/stdSpyInfoExcess\n",
    "    spyInfo = math.sqrt(len(df.index)/percentageOfYear)*rawSpyInfo\n",
    "    print('\\nrawSpyInfo = ',rawSpyInfo)\n",
    "    print('spyInfo = ',spyInfo)\n",
    "    \n",
    "    rawTltInfo = meanTltInfoExcess/stdTltInfoExcess\n",
    "    tltInfo = math.sqrt(len(df.index)/percentageOfYear)*rawTltInfo\n",
    "    print('\\nrawTltInfo = ',rawTltInfo)\n",
    "    print('tltInfo = ',tltInfo)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}