MACD_RSI_STOCHASTIC_strategy_(ccxt).ipynb

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      "name": "MACD_RSI_STOCHASTIC_strategy_(ccxt).ipynb",
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      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chemicoPy/MACD-RSI-STOCHASTIC-strategy/blob/ccxt/MACD_RSI_STOCHASTIC_strategy_(ccxt).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVUr_Q6v_926",
        "outputId": "69d39ae7-09fe-45b0-e393-30f25db286b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: ccxt in /usr/local/lib/python3.7/dist-packages (1.68.16)\n",
            "Requirement already satisfied: cryptography>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from ccxt) (36.0.1)\n",
            "Requirement already satisfied: yarl==1.7.2 in /usr/local/lib/python3.7/dist-packages (from ccxt) (1.7.2)\n",
            "Requirement already satisfied: aiohttp>=3.8 in /usr/local/lib/python3.7/dist-packages (from ccxt) (3.8.1)\n",
            "Requirement already satisfied: certifi>=2018.1.18 in /usr/local/lib/python3.7/dist-packages (from ccxt) (2021.10.8)\n",
            "Requirement already satisfied: requests>=2.18.4 in /usr/local/lib/python3.7/dist-packages (from ccxt) (2.23.0)\n",
            "Requirement already satisfied: aiodns>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from ccxt) (3.0.0)\n",
            "Requirement already satisfied: setuptools>=38.5.1 in /usr/local/lib/python3.7/dist-packages (from ccxt) (57.4.0)\n",
            "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.7/dist-packages (from yarl==1.7.2->ccxt) (2.10)\n",
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            "Requirement already satisfied: multidict>=4.0 in /usr/local/lib/python3.7/dist-packages (from yarl==1.7.2->ccxt) (5.2.0)\n",
            "Requirement already satisfied: pycares>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from aiodns>=1.1.1->ccxt) (4.1.2)\n",
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            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp>=3.8->ccxt) (2.0.10)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.7/dist-packages (from cryptography>=2.6.1->ccxt) (1.15.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.12->cryptography>=2.6.1->ccxt) (2.21)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.18.4->ccxt) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.18.4->ccxt) (3.0.4)\n",
            "Requirement already satisfied: pandas_ta in /usr/local/lib/python3.7/dist-packages (0.3.14b0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from pandas_ta) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas->pandas_ta) (1.19.5)\n",
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            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->pandas_ta) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->pandas_ta) (1.15.0)\n",
            "Requirement already satisfied: schedule in /usr/local/lib/python3.7/dist-packages (1.1.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install ccxt\n",
        "!pip install pandas_ta\n",
        "!pip install schedule"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import ccxt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from numpy import *\n",
        "import pandas as pd\n",
        "from pandas import DataFrame, Series\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime, timedelta, date\n",
        "from IPython.display import clear_output\n",
        "import schedule\n"
      ],
      "metadata": {
        "id": "3AQ-rbQiAcH4"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "API_KEY = \"\"\n",
        "API_SECRET = \"\""
      ],
      "metadata": {
        "id": "Hf1_spBMAcOR"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "XhNqM4i7AcYC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" Examples_of_coins_are = [\"BNBUSDT\", \"ETHUSDT\", \"ADAUSDT\", \"DOTUSDT\", \"BTCUSDT\", \"FTMUSDT\", \"DOGEUSDT\", \"LINKUSDT\", \"SUSHIUSDT\", \"SOLUSDT\",\n",
        "                 \"IOSTUSDT\", \"LTCUSDT\", \"XRPUSDT\", \"SXPUSDT\", \"MATICUSDT\", \"BCHUSDT\"] \"\"\"\n",
        "\n",
        "coin = str(input(\"Enter coin name: \").upper())\n",
        "interval = str(input(\"Enter time interval name e.g 5m for 5-minute: \").lower())\n",
        "\n",
        "\"\"\" For example:\n",
        "coin = 'BTCUSDT'\n",
        "interval = '1m' \"\"\"\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "WGXhfED0Acv6",
        "outputId": "908eabe1-675d-4086-86a7-695eb0899318"
      },
      "execution_count": 9,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter coin name: btcusdt\n",
            "Enter time interval name e.g 5m for 5-minute: 1h\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\" For example:\\ncoin = 'BTCUSDT'\\ninterval = '1m' \""
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" Code line to see all columns of that data grabbed \"\"\"\n",
        "exchangepub = ccxt.binanceus()   # You can choose/enter the ID you prefer here e.g binance, coinbase, coinmate, coinmetro e.t.c\n",
        "\n",
        "exchange_id = 'binanceus'\n",
        "exchange_class = getattr(ccxt, exchange_id)\n",
        "exchange = exchange_class({\n",
        "    'apiKey': API_KEY,\n",
        "    'secret': API_SECRET,\n",
        "    'timeout': 3333,\n",
        "    'enableRateLimit': True,\n",
        "})\n",
        "\n",
        "index = coin.find('USDT')\n",
        "symbol = coin[:index] + '/' + coin[index:]\n",
        "\n",
        "instrument = exchange.fetchTickers(str(symbol))\n",
        "print((instrument))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rR1vwjbLAc0s",
        "outputId": "c3f8e065-4ca7-472b-decf-3005510858ae"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'BTC/USD': {'symbol': 'BTC/USD', 'timestamp': 1642352724269, 'datetime': '2022-01-16T17:05:24.269Z', 'high': 43828.92, 'low': 42602.14, 'bid': 43318.21, 'bidVolume': 0.162, 'ask': 43330.74, 'askVolume': 0.567, 'vwap': 43238.9806, 'open': 43371.04, 'close': 43325.15, 'last': 43325.15, 'previousClose': 43373.3, 'change': -45.89, 'percentage': -0.106, 'average': 43348.095, 'baseVolume': 479.650263, 'quoteVolume': 20739588.419, 'info': {'symbol': 'BTCUSD', 'priceChange': '-45.8900', 'priceChangePercent': '-0.106', 'weightedAvgPrice': '43238.9806', 'prevClosePrice': '43373.3000', 'lastPrice': '43325.1500', 'lastQty': '0.00045900', 'bidPrice': '43318.2100', 'bidQty': '0.16200000', 'askPrice': '43330.7400', 'askQty': '0.56700000', 'openPrice': '43371.0400', 'highPrice': '43828.9200', 'lowPrice': '42602.1400', 'volume': '479.65026300', 'quoteVolume': '20739588.4190', 'openTime': '1642266324269', 'closeTime': '1642352724269', 'firstId': '28515292', 'lastId': '28537793', 'count': '22502'}}, 'BTC/USDT': {'symbol': 'BTC/USDT', 'timestamp': 1642352722673, 'datetime': '2022-01-16T17:05:22.673Z', 'high': 43789.47, 'low': 42606.27, 'bid': 43289.03, 'bidVolume': 0.287, 'ask': 43303.94, 'askVolume': 0.287, 'vwap': 43181.05658731, 'open': 43353.45, 'close': 43298.61, 'last': 43298.61, 'previousClose': 43353.45, 'change': -54.84, 'percentage': -0.126, 'average': 43326.03, 'baseVolume': 200.552617, 'quoteVolume': 8660073.90341107, 'info': {'symbol': 'BTCUSDT', 'priceChange': '-54.84000000', 'priceChangePercent': '-0.126', 'weightedAvgPrice': '43181.05658731', 'prevClosePrice': '43353.45000000', 'lastPrice': '43298.61000000', 'lastQty': '0.00168900', 'bidPrice': '43289.03000000', 'bidQty': '0.28700000', 'askPrice': '43303.94000000', 'askQty': '0.28700000', 'openPrice': '43353.45000000', 'highPrice': '43789.47000000', 'lowPrice': '42606.27000000', 'volume': '200.55261700', 'quoteVolume': '8660073.90341107', 'openTime': '1642266322673', 'closeTime': '1642352722673', 'firstId': '9483896', 'lastId': '9489003', 'count': '5108'}}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "CZrGdDv3BRlH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import ccxt\n",
        "\n",
        "exchangepub = ccxt.binanceus()   # You can choose/enter the ID you prefer here e.g binanceus, coinbase, coinmate, coinmetro e.t.c\n",
        "\n",
        "exchange_id = 'binanceus'\n",
        "exchange_class = getattr(ccxt, exchange_id)\n",
        "exchange = exchange_class({\n",
        "    'apiKey': API_KEY,\n",
        "    'secret': API_SECRET,\n",
        "    'timeout': 3333,\n",
        "    'enableRateLimit': True,\n",
        "})\n",
        "\n",
        "def orderSignal():\n",
        "  markets = exchange.fetch_ohlcv(coin, timeframe=interval, limit = 1000)\n",
        "  df = pd.DataFrame(markets, columns=[\"time\", \"open\", \"high\", \"low\", \"close\", \"volume\"])\n",
        "  df.time = pd.to_datetime(df.time, unit='ms')\n",
        "  df.rename(columns = {'time':'dateTime'}, inplace = True)\n",
        "  df.set_index('dateTime', inplace=True)\n",
        "  df['open'] = pd.to_numeric(df['open'], errors='coerce')\n",
        "  df['high'] = pd.to_numeric(df['high'], errors='coerce')\n",
        "  df['low'] = pd.to_numeric(df['low'], errors='coerce')\n",
        "  df['close'] = pd.to_numeric(df['close'], errors='coerce')\n",
        "  df['volume'] = pd.to_numeric(df['volume'], errors='coerce')\n",
        "\n",
        "  return df\n",
        "\n",
        "\n",
        "df = orderSignal()\n",
        "print(df.tail())\n",
        "\n",
        "\n",
        "print(\"\")\n",
        "print(\"Size of data:\", len(df))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Crc7ZdoXBRye",
        "outputId": "a1917565-3b90-4a97-9894-a3377a293b07"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                         open      high       low     close    volume\n",
            "dateTime                                                             \n",
            "2022-01-16 13:00:00  43003.75  43186.39  42606.27  43145.83  9.801632\n",
            "2022-01-16 14:00:00  43167.40  43416.07  43046.40  43297.33  7.155894\n",
            "2022-01-16 15:00:00  43315.11  43350.64  43155.81  43221.98  6.256289\n",
            "2022-01-16 16:00:00  43236.60  43478.57  43178.57  43380.28  4.421617\n",
            "2022-01-16 17:00:00  43384.69  43409.24  43288.70  43316.53  0.072478\n",
            "\n",
            "Size of data: 1000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "51mAp_4tBXwM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MACD - Moving Average Convergence Divergence"
      ],
      "metadata": {
        "id": "3g2XLN0_OQZg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_macd(price, slow, fast, smooth):\n",
        "    exp1 = price.ewm(span = fast, adjust = False).mean()\n",
        "    exp2 = price.ewm(span = slow, adjust = False).mean()\n",
        "    macd = pd.DataFrame(exp1 - exp2).rename(columns = {'close':'macd'})\n",
        "    signal = pd.DataFrame(macd.ewm(span = smooth, adjust = False).mean()).rename(columns = {'macd':'signal'})\n",
        "    hist = pd.DataFrame(macd['macd'] - signal['signal']).rename(columns = {0:'hist'})\n",
        "    frames =  [macd, signal, hist]\n",
        "    df = pd.concat(frames, join = 'inner', axis = 1)\n",
        "    return df\n",
        "    \n",
        "macd = get_macd(df['close'], 26, 12, 9)\n",
        "df = pd.concat([df, macd], join = 'inner', axis = 1)\n",
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "nl8xc4ygBX2J",
        "outputId": "11315d65-a306-41f2-acb9-3973ee906377"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-eea195e1-d937-407f-bd2c-f664619476d7\">\n",
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              "    <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</th>\n",
              "      <th>macd</th>\n",
              "      <th>signal</th>\n",
              "      <th>hist</th>\n",
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              "      <th>dateTime</th>\n",
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              "      <th>2022-01-16 13:00:00</th>\n",
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              "    <tr>\n",
              "      <th>2022-01-16 14:00:00</th>\n",
              "      <td>43167.40</td>\n",
              "      <td>43416.07</td>\n",
              "      <td>43046.40</td>\n",
              "      <td>43297.33</td>\n",
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              "      <th>2022-01-16 15:00:00</th>\n",
              "      <td>43315.11</td>\n",
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              "      <td>6.256289</td>\n",
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              "      <td>12.032519</td>\n",
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              "    <tr>\n",
              "      <th>2022-01-16 16:00:00</th>\n",
              "      <td>43236.60</td>\n",
              "      <td>43478.57</td>\n",
              "      <td>43178.57</td>\n",
              "      <td>43380.28</td>\n",
              "      <td>4.421617</td>\n",
              "      <td>33.367692</td>\n",
              "      <td>8.482089</td>\n",
              "      <td>24.885603</td>\n",
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              "    <tr>\n",
              "      <th>2022-01-16 17:00:00</th>\n",
              "      <td>43384.69</td>\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eea195e1-d937-407f-bd2c-f664619476d7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-eea195e1-d937-407f-bd2c-f664619476d7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                         open      high  ...     signal       hist\n",
              "dateTime                                 ...                      \n",
              "2022-01-16 13:00:00  43003.75  43186.39  ...  -2.398589 -10.268944\n",
              "2022-01-16 14:00:00  43167.40  43416.07  ...  -0.747442   6.604587\n",
              "2022-01-16 15:00:00  43315.11  43350.64  ...   2.260688  12.032519\n",
              "2022-01-16 16:00:00  43236.60  43478.57  ...   8.482089  24.885603\n",
              "2022-01-16 17:00:00  43384.69  43409.24  ...  15.354943  27.491417\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_macd(prices, macd, signal, hist):\n",
        "    ax1 = plt.subplot2grid((8,1), (0,0), rowspan = 5, colspan = 1)\n",
        "    ax2 = plt.subplot2grid((8,1), (5,0), rowspan = 3, colspan = 1)\n",
        "\n",
        "    ax1.plot(prices)\n",
        "    ax2.plot(macd, color = 'grey', linewidth = 1.5, label = 'MACD')\n",
        "    ax2.plot(signal, color = 'skyblue', linewidth = 1.5, label = 'SIGNAL')\n",
        "\n",
        "    for i in range(len(prices)):\n",
        "        if str(hist[i])[0] == '-':\n",
        "            ax2.bar(prices.index[i], hist[i], color = '#ef5350')\n",
        "        else:\n",
        "            ax2.bar(prices.index[i], hist[i], color = '#26a69a')\n",
        "\n",
        "    plt.legend(loc = 'lower right')\n",
        "\n",
        "plot_macd(df['close'], df['macd'], df['signal'], df['hist'])\n"
      ],
      "metadata": {
        "id": "st6ZwsLfLG_P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "59f8aa39-4bbf-4078-edb5-bf3b75806eea"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def implement_macd_strategy(prices, data):    \n",
        "    buy_price = []\n",
        "    sell_price = []\n",
        "    macd_signal = []\n",
        "    signal = 0\n",
        "\n",
        "    for i in range(len(data)):\n",
        "        if data['macd'][i] > data['signal'][i]:\n",
        "            if signal != 1:\n",
        "                buy_price.append(prices[i])\n",
        "                sell_price.append(np.nan)\n",
        "                signal = 1\n",
        "                macd_signal.append(signal)\n",
        "            else:\n",
        "                buy_price.append(np.nan)\n",
        "                sell_price.append(np.nan)\n",
        "                macd_signal.append(0)\n",
        "        elif data['macd'][i] < data['signal'][i]:\n",
        "            if signal != -1:\n",
        "                buy_price.append(np.nan)\n",
        "                sell_price.append(prices[i])\n",
        "                signal = -1\n",
        "                macd_signal.append(signal)\n",
        "            else:\n",
        "                buy_price.append(np.nan)\n",
        "                sell_price.append(np.nan)\n",
        "                macd_signal.append(0)\n",
        "        else:\n",
        "            buy_price.append(np.nan)\n",
        "            sell_price.append(np.nan)\n",
        "            macd_signal.append(0)\n",
        "            \n",
        "    return buy_price, sell_price, macd_signal\n",
        "            \n",
        "buy_price, sell_price, macd_signal = implement_macd_strategy(df['close'], df)"
      ],
      "metadata": {
        "id": "78_F8btoLHL2"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax1 = plt.subplot2grid((8,1), (0,0), rowspan = 5, colspan = 1)\n",
        "ax2 = plt.subplot2grid((8,1), (5,0), rowspan = 3, colspan = 1)\n",
        "\n",
        "ax1.plot(df['close'], color = 'skyblue', linewidth = 2, label = str(coin))\n",
        "ax1.plot(df.index, buy_price, marker = '^', color = 'green', markersize = 10, label = 'BUY SIGNAL', linewidth = 0)\n",
        "ax1.plot(df.index, sell_price, marker = 'v', color = 'r', markersize = 10, label = 'SELL SIGNAL', linewidth = 0)\n",
        "ax1.legend()\n",
        "ax1.set_title(str(coin+\" MACD SIGNALS\"))\n",
        "ax2.plot(df['macd'], color = 'grey', linewidth = 1.5, label = 'MACD')\n",
        "ax2.plot(df['signal'], color = 'skyblue', linewidth = 1.5, label = 'SIGNAL')\n",
        "\n",
        "for i in range(len(df)):\n",
        "    if str(df['hist'][i])[0] == '-':\n",
        "        ax2.bar(df.index[i], df['hist'][i], color = '#ef5350')\n",
        "    else:\n",
        "        ax2.bar(df.index[i], df['hist'][i], color = '#26a69a')\n",
        "        \n",
        "plt.legend(loc = 'lower right')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "hFIC2gt6LHVv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "ba13201c-b240-4937-b817-599ea6c4dc34"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "macd_signal"
      ],
      "metadata": {
        "id": "Dwh-cZ-ndEJa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "position = []\n",
        "for i in range(len(macd_signal)):\n",
        "    if macd_signal[i] > 1:\n",
        "        position.append(0)\n",
        "    else:\n",
        "        position.append(1)\n",
        "        \n",
        "for i in range(len(df['close'])):\n",
        "    if macd_signal[i] == 1:\n",
        "        position[i] = 1\n",
        "    elif macd_signal[i] == -1:\n",
        "        position[i] = 0\n",
        "    else:\n",
        "        position[i] = position[i-1]\n",
        "        \n",
        "macd = df['macd']\n",
        "signal = df['signal']\n",
        "close_price = df['close']\n",
        "macd_signal = pd.DataFrame(macd_signal).rename(columns = {0:'macd_signal'}).set_index(df.index)\n",
        "position = pd.DataFrame(position).rename(columns = {0:'macd_position'}).set_index(df.index)\n",
        "\n",
        "frames = [close_price, macd, signal, macd_signal, position]\n",
        "strategy = pd.concat(frames, join = 'inner', axis = 1)\n",
        "\n",
        "strategy"
      ],
      "metadata": {
        "id": "0alTtxiZLHu2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "87fdf400-1b86-4664-e94a-ba2b1aa6e793"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-fe225d95-5fb2-4676-97a2-f0c944fa9ce7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>close</th>\n",
              "      <th>macd</th>\n",
              "      <th>signal</th>\n",
              "      <th>macd_signal</th>\n",
              "      <th>macd_position</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dateTime</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>2021-12-06 02:00:00</th>\n",
              "      <td>48873.08</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 03:00:00</th>\n",
              "      <td>49013.72</td>\n",
              "      <td>11.219145</td>\n",
              "      <td>2.243829</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 04:00:00</th>\n",
              "      <td>48999.78</td>\n",
              "      <td>18.769198</td>\n",
              "      <td>5.548903</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 05:00:00</th>\n",
              "      <td>48688.55</td>\n",
              "      <td>-0.356878</td>\n",
              "      <td>4.367747</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 06:00:00</th>\n",
              "      <td>48167.18</td>\n",
              "      <td>-56.928397</td>\n",
              "      <td>-7.891482</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 13:00:00</th>\n",
              "      <td>43145.83</td>\n",
              "      <td>-12.667532</td>\n",
              "      <td>-2.398589</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 14:00:00</th>\n",
              "      <td>43297.33</td>\n",
              "      <td>5.857145</td>\n",
              "      <td>-0.747442</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 15:00:00</th>\n",
              "      <td>43221.98</td>\n",
              "      <td>14.293207</td>\n",
              "      <td>2.260688</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 16:00:00</th>\n",
              "      <td>43380.28</td>\n",
              "      <td>33.367692</td>\n",
              "      <td>8.482089</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 17:00:00</th>\n",
              "      <td>43316.53</td>\n",
              "      <td>42.846361</td>\n",
              "      <td>15.354943</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe225d95-5fb2-4676-97a2-f0c944fa9ce7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fe225d95-5fb2-4676-97a2-f0c944fa9ce7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fe225d95-5fb2-4676-97a2-f0c944fa9ce7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                        close       macd     signal  macd_signal  macd_position\n",
              "dateTime                                                                       \n",
              "2021-12-06 02:00:00  48873.08   0.000000   0.000000            0              1\n",
              "2021-12-06 03:00:00  49013.72  11.219145   2.243829            1              1\n",
              "2021-12-06 04:00:00  48999.78  18.769198   5.548903            0              1\n",
              "2021-12-06 05:00:00  48688.55  -0.356878   4.367747           -1              0\n",
              "2021-12-06 06:00:00  48167.18 -56.928397  -7.891482            0              0\n",
              "...                       ...        ...        ...          ...            ...\n",
              "2022-01-16 13:00:00  43145.83 -12.667532  -2.398589            0              0\n",
              "2022-01-16 14:00:00  43297.33   5.857145  -0.747442            1              1\n",
              "2022-01-16 15:00:00  43221.98  14.293207   2.260688            0              1\n",
              "2022-01-16 16:00:00  43380.28  33.367692   8.482089            0              1\n",
              "2022-01-16 17:00:00  43316.53  42.846361  15.354943            0              1\n",
              "\n",
              "[1000 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "mpAma70eVadY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RSI - Relative Strength Index"
      ],
      "metadata": {
        "id": "oo_3nGNyd6KA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_rsi(close, lookback):\n",
        "    ret = close.diff()\n",
        "    up = []\n",
        "    down = []\n",
        "    for i in range(len(ret)):\n",
        "        if ret[i] < 0:\n",
        "            up.append(0)\n",
        "            down.append(ret[i])\n",
        "        else:\n",
        "            up.append(ret[i])\n",
        "            down.append(0)\n",
        "    up_series = pd.Series(up)\n",
        "    down_series = pd.Series(down).abs()\n",
        "    up_ewm = up_series.ewm(com = lookback - 1, adjust = False).mean()\n",
        "    down_ewm = down_series.ewm(com = lookback - 1, adjust = False).mean()\n",
        "    rs = up_ewm/down_ewm\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "    rsi_df = pd.DataFrame(rsi).rename(columns = {0:'rsi'}).set_index(close.index)\n",
        "    rsi_df = rsi_df.dropna()\n",
        "    return rsi_df[3:]\n",
        "\n",
        "df['rsi_14'] = get_rsi(df['close'], 14)\n",
        "df = df.dropna()\n"
      ],
      "metadata": {
        "id": "ZxXDeqc_VakC"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "JegujVEreTom",
        "outputId": "fe54138b-048a-44ca-f555-2dc028f88b07"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-746dbc12-9985-4ac8-8db8-8f282290667f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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</th>\n",
              "      <th>macd</th>\n",
              "      <th>signal</th>\n",
              "      <th>hist</th>\n",
              "      <th>rsi_14</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dateTime</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>2021-12-06 06:00:00</th>\n",
              "      <td>48672.09</td>\n",
              "      <td>48747.40</td>\n",
              "      <td>48064.26</td>\n",
              "      <td>48167.18</td>\n",
              "      <td>13.007586</td>\n",
              "      <td>-56.928397</td>\n",
              "      <td>-7.891482</td>\n",
              "      <td>-49.036915</td>\n",
              "      <td>65.717345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 07:00:00</th>\n",
              "      <td>48140.90</td>\n",
              "      <td>48426.21</td>\n",
              "      <td>47751.82</td>\n",
              "      <td>48426.21</td>\n",
              "      <td>9.360184</td>\n",
              "      <td>-79.938693</td>\n",
              "      <td>-22.300924</td>\n",
              "      <td>-57.637769</td>\n",
              "      <td>69.288679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 08:00:00</th>\n",
              "      <td>48416.67</td>\n",
              "      <td>49410.09</td>\n",
              "      <td>47842.48</td>\n",
              "      <td>48249.86</td>\n",
              "      <td>47.714943</td>\n",
              "      <td>-111.123527</td>\n",
              "      <td>-40.065445</td>\n",
              "      <td>-71.058083</td>\n",
              "      <td>64.372092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 09:00:00</th>\n",
              "      <td>48111.68</td>\n",
              "      <td>48320.27</td>\n",
              "      <td>47146.36</td>\n",
              "      <td>47439.18</td>\n",
              "      <td>29.496123</td>\n",
              "      <td>-198.959355</td>\n",
              "      <td>-71.844227</td>\n",
              "      <td>-127.115128</td>\n",
              "      <td>47.637674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 10:00:00</th>\n",
              "      <td>47415.75</td>\n",
              "      <td>47794.07</td>\n",
              "      <td>47294.34</td>\n",
              "      <td>47495.63</td>\n",
              "      <td>11.810367</td>\n",
              "      <td>-261.006121</td>\n",
              "      <td>-109.676606</td>\n",
              "      <td>-151.329515</td>\n",
              "      <td>48.638932</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 13:00:00</th>\n",
              "      <td>43003.75</td>\n",
              "      <td>43186.39</td>\n",
              "      <td>42606.27</td>\n",
              "      <td>43145.83</td>\n",
              "      <td>9.801632</td>\n",
              "      <td>-12.667532</td>\n",
              "      <td>-2.398589</td>\n",
              "      <td>-10.268944</td>\n",
              "      <td>52.226521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 14:00:00</th>\n",
              "      <td>43167.40</td>\n",
              "      <td>43416.07</td>\n",
              "      <td>43046.40</td>\n",
              "      <td>43297.33</td>\n",
              "      <td>7.155894</td>\n",
              "      <td>5.857145</td>\n",
              "      <td>-0.747442</td>\n",
              "      <td>6.604587</td>\n",
              "      <td>57.309333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 15:00:00</th>\n",
              "      <td>43315.11</td>\n",
              "      <td>43350.64</td>\n",
              "      <td>43155.81</td>\n",
              "      <td>43221.98</td>\n",
              "      <td>6.256289</td>\n",
              "      <td>14.293207</td>\n",
              "      <td>2.260688</td>\n",
              "      <td>12.032519</td>\n",
              "      <td>54.219548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 16:00:00</th>\n",
              "      <td>43236.60</td>\n",
              "      <td>43478.57</td>\n",
              "      <td>43178.57</td>\n",
              "      <td>43380.28</td>\n",
              "      <td>4.421617</td>\n",
              "      <td>33.367692</td>\n",
              "      <td>8.482089</td>\n",
              "      <td>24.885603</td>\n",
              "      <td>59.196697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 17:00:00</th>\n",
              "      <td>43384.69</td>\n",
              "      <td>43409.24</td>\n",
              "      <td>43288.70</td>\n",
              "      <td>43316.53</td>\n",
              "      <td>0.072478</td>\n",
              "      <td>42.846361</td>\n",
              "      <td>15.354943</td>\n",
              "      <td>27.491417</td>\n",
              "      <td>56.531232</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>996 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-746dbc12-9985-4ac8-8db8-8f282290667f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-746dbc12-9985-4ac8-8db8-8f282290667f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-746dbc12-9985-4ac8-8db8-8f282290667f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                         open      high  ...        hist     rsi_14\n",
              "dateTime                                 ...                       \n",
              "2021-12-06 06:00:00  48672.09  48747.40  ...  -49.036915  65.717345\n",
              "2021-12-06 07:00:00  48140.90  48426.21  ...  -57.637769  69.288679\n",
              "2021-12-06 08:00:00  48416.67  49410.09  ...  -71.058083  64.372092\n",
              "2021-12-06 09:00:00  48111.68  48320.27  ... -127.115128  47.637674\n",
              "2021-12-06 10:00:00  47415.75  47794.07  ... -151.329515  48.638932\n",
              "...                       ...       ...  ...         ...        ...\n",
              "2022-01-16 13:00:00  43003.75  43186.39  ...  -10.268944  52.226521\n",
              "2022-01-16 14:00:00  43167.40  43416.07  ...    6.604587  57.309333\n",
              "2022-01-16 15:00:00  43315.11  43350.64  ...   12.032519  54.219548\n",
              "2022-01-16 16:00:00  43236.60  43478.57  ...   24.885603  59.196697\n",
              "2022-01-16 17:00:00  43384.69  43409.24  ...   27.491417  56.531232\n",
              "\n",
              "[996 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "wEuLm6oyeV4V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax3 = plt.subplot2grid((10,1), (0,0), rowspan = 4, colspan = 1)\n",
        "ax4 = plt.subplot2grid((10,1), (5,0), rowspan = 4, colspan = 1)\n",
        "ax3.plot(df['close'], linewidth = 2.5)\n",
        "ax3.set_title(str(coin+\" CLOSE PRICE\"))\n",
        "ax4.plot(df['rsi_14'], color = 'orange', linewidth = 2.5)\n",
        "ax4.axhline(30, linestyle = '--', linewidth = 1.5, color = 'grey')\n",
        "ax4.axhline(70, linestyle = '--', linewidth = 1.5, color = 'grey')\n",
        "ax4.set_title(str(coin+\" RELATIVE STRENGTH INDEX\"))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "id": "am0tiX6xeaC9",
        "outputId": "90a73df1-f703-4897-dc1a-ec3e88b45336"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def implement_rsi_strategy(prices, rsi):    \n",
        "    buy_price = []\n",
        "    sell_price = []\n",
        "    rsi_signal = []\n",
        "    signal = 0\n",
        "\n",
        "    for i in range(len(rsi)):\n",
        "        if rsi[i-1] > 30 and rsi[i] < 30:\n",
        "            if signal != 1:\n",
        "                buy_price.append(prices[i])\n",
        "                sell_price.append(np.nan)\n",
        "                signal = 1\n",
        "                rsi_signal.append(signal)\n",
        "            else:\n",
        "                buy_price.append(np.nan)\n",
        "                sell_price.append(np.nan)\n",
        "                rsi_signal.append(0)\n",
        "        elif rsi[i-1] < 70 and rsi[i] > 70:\n",
        "            if signal != -1:\n",
        "                buy_price.append(np.nan)\n",
        "                sell_price.append(prices[i])\n",
        "                signal = -1\n",
        "                rsi_signal.append(signal)\n",
        "            else:\n",
        "                buy_price.append(np.nan)\n",
        "                sell_price.append(np.nan)\n",
        "                rsi_signal.append(0)\n",
        "        else:\n",
        "            buy_price.append(np.nan)\n",
        "            sell_price.append(np.nan)\n",
        "            rsi_signal.append(0)\n",
        "            \n",
        "    return buy_price, sell_price, rsi_signal\n",
        "            \n",
        "\n",
        "rsi_buy_price, rsi_sell_price, rsi_signal = implement_rsi_strategy(df['close'], df['rsi_14'])"
      ],
      "metadata": {
        "id": "dm22GLBseTt9"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "RvVmjpCyiVwh",
        "outputId": "fbfabcf8-2ffe-4af7-ed8c-39bf79eb4bd3"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f8226295-8730-452d-b5d4-99ff3e67a26b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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</th>\n",
              "      <th>macd</th>\n",
              "      <th>signal</th>\n",
              "      <th>hist</th>\n",
              "      <th>rsi_14</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dateTime</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>2021-12-06 06:00:00</th>\n",
              "      <td>48672.09</td>\n",
              "      <td>48747.40</td>\n",
              "      <td>48064.26</td>\n",
              "      <td>48167.18</td>\n",
              "      <td>13.007586</td>\n",
              "      <td>-56.928397</td>\n",
              "      <td>-7.891482</td>\n",
              "      <td>-49.036915</td>\n",
              "      <td>65.717345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 07:00:00</th>\n",
              "      <td>48140.90</td>\n",
              "      <td>48426.21</td>\n",
              "      <td>47751.82</td>\n",
              "      <td>48426.21</td>\n",
              "      <td>9.360184</td>\n",
              "      <td>-79.938693</td>\n",
              "      <td>-22.300924</td>\n",
              "      <td>-57.637769</td>\n",
              "      <td>69.288679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 08:00:00</th>\n",
              "      <td>48416.67</td>\n",
              "      <td>49410.09</td>\n",
              "      <td>47842.48</td>\n",
              "      <td>48249.86</td>\n",
              "      <td>47.714943</td>\n",
              "      <td>-111.123527</td>\n",
              "      <td>-40.065445</td>\n",
              "      <td>-71.058083</td>\n",
              "      <td>64.372092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 09:00:00</th>\n",
              "      <td>48111.68</td>\n",
              "      <td>48320.27</td>\n",
              "      <td>47146.36</td>\n",
              "      <td>47439.18</td>\n",
              "      <td>29.496123</td>\n",
              "      <td>-198.959355</td>\n",
              "      <td>-71.844227</td>\n",
              "      <td>-127.115128</td>\n",
              "      <td>47.637674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 10:00:00</th>\n",
              "      <td>47415.75</td>\n",
              "      <td>47794.07</td>\n",
              "      <td>47294.34</td>\n",
              "      <td>47495.63</td>\n",
              "      <td>11.810367</td>\n",
              "      <td>-261.006121</td>\n",
              "      <td>-109.676606</td>\n",
              "      <td>-151.329515</td>\n",
              "      <td>48.638932</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 13:00:00</th>\n",
              "      <td>43003.75</td>\n",
              "      <td>43186.39</td>\n",
              "      <td>42606.27</td>\n",
              "      <td>43145.83</td>\n",
              "      <td>9.801632</td>\n",
              "      <td>-12.667532</td>\n",
              "      <td>-2.398589</td>\n",
              "      <td>-10.268944</td>\n",
              "      <td>52.226521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 14:00:00</th>\n",
              "      <td>43167.40</td>\n",
              "      <td>43416.07</td>\n",
              "      <td>43046.40</td>\n",
              "      <td>43297.33</td>\n",
              "      <td>7.155894</td>\n",
              "      <td>5.857145</td>\n",
              "      <td>-0.747442</td>\n",
              "      <td>6.604587</td>\n",
              "      <td>57.309333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 15:00:00</th>\n",
              "      <td>43315.11</td>\n",
              "      <td>43350.64</td>\n",
              "      <td>43155.81</td>\n",
              "      <td>43221.98</td>\n",
              "      <td>6.256289</td>\n",
              "      <td>14.293207</td>\n",
              "      <td>2.260688</td>\n",
              "      <td>12.032519</td>\n",
              "      <td>54.219548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 16:00:00</th>\n",
              "      <td>43236.60</td>\n",
              "      <td>43478.57</td>\n",
              "      <td>43178.57</td>\n",
              "      <td>43380.28</td>\n",
              "      <td>4.421617</td>\n",
              "      <td>33.367692</td>\n",
              "      <td>8.482089</td>\n",
              "      <td>24.885603</td>\n",
              "      <td>59.196697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-16 17:00:00</th>\n",
              "      <td>43384.69</td>\n",
              "      <td>43409.24</td>\n",
              "      <td>43288.70</td>\n",
              "      <td>43316.53</td>\n",
              "      <td>0.072478</td>\n",
              "      <td>42.846361</td>\n",
              "      <td>15.354943</td>\n",
              "      <td>27.491417</td>\n",
              "      <td>56.531232</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>996 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8226295-8730-452d-b5d4-99ff3e67a26b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f8226295-8730-452d-b5d4-99ff3e67a26b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f8226295-8730-452d-b5d4-99ff3e67a26b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                         open      high  ...        hist     rsi_14\n",
              "dateTime                                 ...                       \n",
              "2021-12-06 06:00:00  48672.09  48747.40  ...  -49.036915  65.717345\n",
              "2021-12-06 07:00:00  48140.90  48426.21  ...  -57.637769  69.288679\n",
              "2021-12-06 08:00:00  48416.67  49410.09  ...  -71.058083  64.372092\n",
              "2021-12-06 09:00:00  48111.68  48320.27  ... -127.115128  47.637674\n",
              "2021-12-06 10:00:00  47415.75  47794.07  ... -151.329515  48.638932\n",
              "...                       ...       ...  ...         ...        ...\n",
              "2022-01-16 13:00:00  43003.75  43186.39  ...  -10.268944  52.226521\n",
              "2022-01-16 14:00:00  43167.40  43416.07  ...    6.604587  57.309333\n",
              "2022-01-16 15:00:00  43315.11  43350.64  ...   12.032519  54.219548\n",
              "2022-01-16 16:00:00  43236.60  43478.57  ...   24.885603  59.196697\n",
              "2022-01-16 17:00:00  43384.69  43409.24  ...   27.491417  56.531232\n",
              "\n",
              "[996 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(df.index))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ytyxBKJak5jN",
        "outputId": "4874c3e3-2f8e-4da0-bddb-95aa18a67ab6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "996\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax5 = plt.subplot2grid((10,1), (0,0), rowspan = 4, colspan = 1)\n",
        "ax6 = plt.subplot2grid((10,1), (5,0), rowspan = 4, colspan = 1)\n",
        "ax5.plot(df['close'], linewidth = 2.5, color = 'skyblue', label = 'RSI')\n",
        "ax5.plot(df.index, rsi_buy_price, marker = '^', markersize = 10, color = 'green', label = 'BUY SIGNAL')\n",
        "ax5.plot(df.index, rsi_sell_price, marker = 'v', markersize = 10, color = 'r', label = 'SELL SIGNAL')\n",
        "ax5.set_title(str(coin+\" RSI TRADE SIGNALS\"))\n",
        "ax6.plot(df['rsi_14'], color = 'orange', linewidth = 2.5)\n",
        "ax6.axhline(30, linestyle = '--', linewidth = 1.5, color = 'grey')\n",
        "ax6.axhline(70, linestyle = '--', linewidth = 1.5, color = 'grey')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "id": "fDmir9h8Vapz",
        "outputId": "efc2f78a-a0a8-4c3c-e8bd-6ebf3f6c11bc"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "v5OjDPlpmmwQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "position = []\n",
        "for i in range(len(rsi_signal)):\n",
        "    if rsi_signal[i] > 1:\n",
        "        position.append(0)\n",
        "    else:\n",
        "        position.append(1)\n",
        "        \n",
        "for i in range(len(df['close'])):\n",
        "    if rsi_signal[i] == 1:\n",
        "        position[i] = 1\n",
        "    elif rsi_signal[i] == -1:\n",
        "        position[i] = 0\n",
        "    else:\n",
        "        position[i] = position[i-1]\n",
        "        \n",
        "rsi = df['rsi_14']\n",
        "close_price = df['close']\n",
        "rsi_signal = pd.DataFrame(rsi_signal).rename(columns = {0:'rsi_signal'}).set_index(df.index)\n",
        "position = pd.DataFrame(position).rename(columns = {0:'rsi_position'}).set_index(df.index)\n",
        "\n",
        "frames = [close_price, rsi, rsi_signal, position]\n",
        "strategy = pd.concat(frames, join = 'inner', axis = 1)\n",
        "\n",
        "strategy.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "r-Q8XKTOg9--",
        "outputId": "bd7b3a92-0eea-4fed-93fb-f83542f5388b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9b8d05f1-a659-4f3f-a434-2a556e7f396c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>close</th>\n",
              "      <th>rsi_14</th>\n",
              "      <th>rsi_signal</th>\n",
              "      <th>rsi_position</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dateTime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-12-06 06:00:00</th>\n",
              "      <td>48167.18</td>\n",
              "      <td>65.717345</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 07:00:00</th>\n",
              "      <td>48426.21</td>\n",
              "      <td>69.288679</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 08:00:00</th>\n",
              "      <td>48249.86</td>\n",
              "      <td>64.372092</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 09:00:00</th>\n",
              "      <td>47439.18</td>\n",
              "      <td>47.637674</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06 10:00:00</th>\n",
              "      <td>47495.63</td>\n",
              "      <td>48.638932</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b8d05f1-a659-4f3f-a434-2a556e7f396c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9b8d05f1-a659-4f3f-a434-2a556e7f396c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9b8d05f1-a659-4f3f-a434-2a556e7f396c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                        close     rsi_14  rsi_signal  rsi_position\n",
              "dateTime                                                          \n",
              "2021-12-06 06:00:00  48167.18  65.717345           0             1\n",
              "2021-12-06 07:00:00  48426.21  69.288679           0             1\n",
              "2021-12-06 08:00:00  48249.86  64.372092           0             1\n",
              "2021-12-06 09:00:00  47439.18  47.637674           0             1\n",
              "2021-12-06 10:00:00  47495.63  48.638932           0             1"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "fG_RMhhVg-HC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# STOCHASTIC OSCILLATOR"
      ],
      "metadata": {
        "id": "fk3CFuramqlW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "stoch_data = df.copy()\n",
        "\n",
        "stoch_data['high'] = stoch_data['high'].rolling(14).max()\n",
        "stoch_data['low'] = stoch_data['low'].rolling(14).min()\n",
        "stoch_data['%k'] = (stoch_data[\"close\"] - stoch_data['low'])*100/(stoch_data['high'] - stoch_data['low'])\n",
        "stoch_data['%d'] = stoch_data['%k'].rolling(3).mean()\n",
        "\n",
        "def plot_stoch(price, k, d):\n",
        "    ax7 = plt.subplot2grid((9, 1), (0,0), rowspan = 5, colspan = 1)\n",
        "    ax8 = plt.subplot2grid((9, 1), (6,0), rowspan = 3, colspan = 1)\n",
        "    ax7.plot(price)\n",
        "    ax7.set_title(f'{coin} STOCK PRICE')\n",
        "    ax8.plot(k, color = 'deepskyblue', linewidth = 1.5, label = '%K')\n",
        "    ax8.plot(d, color = 'orange', linewidth = 1.5, label = '%D')\n",
        "    ax8.axhline(80, color = 'black', linewidth = 1, linestyle = '--')\n",
        "    ax8.axhline(20, color = 'black', linewidth = 1, linestyle = '--')\n",
        "    ax8.set_title(f'{coin} STOCH')\n",
        "    ax8.legend()\n",
        "    plt.show()\n",
        "    \n",
        "plot_stoch(df['close'], stoch_data['%k'], stoch_data['%d'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "bIXYspceg-NE",
        "outputId": "30368d8b-64c3-409a-e79b-e4c1f540ab43"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def implement_stoch_strategy(prices, k, d):    \n",
        "    buy_price = []\n",
        "    sell_price = []\n",
        "    stoch_signal = []\n",
        "    signal = 0\n",
        "\n",
        "    for i in range(len(prices)):\n",
        "        if k[i] < 20 and d[i] < 20 and k[i] < d[i]:\n",
        "            if signal != 1:\n",
        "                buy_price.append(prices[i])\n",
        "                sell_price.append(np.nan)\n",
        "                signal = 1\n",
        "                stoch_signal.append(signal)\n",
        "            else:\n",
        "                buy_price.append(np.nan)\n",
        "                sell_price.append(np.nan)\n",
        "                stoch_signal.append(0)\n",
        "        elif k[i] > 80 and d[i] > 80 and k[i] > d[i]:\n",
        "            if signal != -1:\n",
        "                buy_price.append(np.nan)\n",
        "                sell_price.append(prices[i])\n",
        "                signal = -1\n",
        "                stoch_signal.append(signal)\n",
        "            else:\n",
        "                buy_price.append(np.nan)\n",
        "                sell_price.append(np.nan)\n",
        "                stoch_signal.append(0)\n",
        "        else:\n",
        "            buy_price.append(np.nan)\n",
        "            sell_price.append(np.nan)\n",
        "            stoch_signal.append(0)\n",
        "            \n",
        "    return buy_price, sell_price, stoch_signal\n",
        "            \n",
        "stoch_buy_price, stoch_sell_price, stoch_signal = implement_stoch_strategy(stoch_data['close'], stoch_data['%k'], stoch_data['%d'])"
      ],
      "metadata": {
        "id": "pvS43JWSmpTD"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "position = []\n",
        "for i in range(len(stoch_signal)):\n",
        "    if stoch_signal[i] > 1:\n",
        "        position.append(0)\n",
        "    else:\n",
        "        position.append(1)\n",
        "        \n",
        "for i in range(len(stoch_data['close'])):\n",
        "    if stoch_signal[i] == 1:\n",
        "        position[i] = 1\n",
        "    elif stoch_signal[i] == -1:\n",
        "        position[i] = 0\n",
        "    else:\n",
        "        position[i] = position[i-1]\n",
        "        \n",
        "k = stoch_data['%k']\n",
        "d = stoch_data['%d']\n",
        "close_price = stoch_data['close']\n",
        "stoch_signal = pd.DataFrame(stoch_signal).rename(columns = {0:'stoch_signal'}).set_index(stoch_data.index)\n",
        "position = pd.DataFrame(position).rename(columns = {0:'stoch_position'}).set_index(stoch_data.index)\n",
        "\n",
        "frames = [close_price, k, d, stoch_signal, position]\n",
        "strategy = pd.concat(frames, join = 'inner', axis = 1)\n",
        "\n",
        "strategy.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "f0XMCaCFmpbs",
        "outputId": "f35eb71f-46d0-43ba-e68c-68872f547116"
      },
      "execution_count": 26,
      "outputs": [
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