Combo1.ipynb

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  "metadata": {
    "colab": {
      "name": "Combo1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install yfinance"
      ],
      "metadata": {
        "id": "7kCJAOcJF3pO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas_ta"
      ],
      "metadata": {
        "id": "v6mYE30qGHoD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import pandas_ta as ta\n",
        "\n",
        "plt.style.use(\"seaborn\")\n",
        "plt.rcParams[\"figure.figsize\"] = [14, 8]"
      ],
      "metadata": {
        "id": "urN0YgP5-lar"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = yf.download(\"HDFCBANK.NS\", start=\"2021-01-01\", end=\"2021-12-31\")\n",
        "\n",
        "df[\"Adj Close\"].plot(title=\"HDFC's stock in 2021\");"
      ],
      "metadata": {
        "id": "OxJike3g-vP7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CustomStrategy = ta.Strategy(\n",
        "    name=\"Momo and Volatility\",\n",
        "    description=\"SMA 50,200, BBANDS, RSI, MACD and Volume SMA 20\",\n",
        "    ta=[\n",
        "        {\"kind\": \"sma\", \"length\": 30},\n",
        "        {\"kind\": \"sma\", \"length\": 70},\n",
        "        {\"kind\": \"bbands\", \"length\": 20},\n",
        "        {\"kind\": \"rsi\"},\n",
        "        {\"kind\": \"macd\", \"fast\": 8, \"slow\": 21},\n",
        "        {\"kind\": \"stoch\"}\n",
        "    ]\n",
        ")\n",
        "# To run your \"Custom Strategy\"\n",
        "df.ta.strategy(CustomStrategy)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "UbAnqfjuAlPp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(200)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "id": "vXs2Y6VkBuLW",
        "outputId": "073f62bd-bbbe-4446-911a-e3ad66cd6d4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Open         High          Low        Close    Adj Close  \\\n",
              "Date                                                                          \n",
              "2021-01-01  1440.000000  1443.000000  1420.599976  1425.050049  1402.600952   \n",
              "2021-01-04  1438.000000  1438.000000  1399.000000  1416.000000  1393.693481   \n",
              "2021-01-05  1419.199951  1430.750000  1409.000000  1426.699951  1404.224854   \n",
              "2021-01-06  1435.000000  1440.000000  1413.099976  1420.550049  1398.171753   \n",
              "2021-01-07  1432.500000  1432.599976  1412.550049  1416.250000  1393.939453   \n",
              "...                 ...          ...          ...          ...          ...   \n",
              "2021-10-18  1705.000000  1725.000000  1667.050049  1670.300049  1651.102661   \n",
              "2021-10-19  1675.449951  1692.449951  1671.000000  1688.699951  1669.291016   \n",
              "2021-10-20  1689.099976  1698.750000  1664.449951  1673.849976  1654.611816   \n",
              "2021-10-21  1671.800049  1681.949951  1660.849976  1676.300049  1657.033691   \n",
              "2021-10-22  1680.099976  1708.000000  1670.750000  1680.750000  1661.432495   \n",
              "\n",
              "              Volume       SMA_30       SMA_70   BBL_20_2.0   BBM_20_2.0  \\\n",
              "Date                                                                       \n",
              "2021-01-01   4405469          NaN          NaN          NaN          NaN   \n",
              "2021-01-04   7870096          NaN          NaN          NaN          NaN   \n",
              "2021-01-05   7193412          NaN          NaN          NaN          NaN   \n",
              "2021-01-06  11067025          NaN          NaN          NaN          NaN   \n",
              "2021-01-07   9947421          NaN          NaN          NaN          NaN   \n",
              "...              ...          ...          ...          ...          ...   \n",
              "2021-10-18  14428228  1591.581669  1539.306428  1531.890566  1604.922498   \n",
              "2021-10-19   4493770  1595.336666  1541.437856  1532.847064  1611.364996   \n",
              "2021-10-20   5454284  1598.941667  1543.629285  1539.413123  1617.459998   \n",
              "2021-10-21   4869095  1602.510002  1546.119285  1552.636907  1624.590002   \n",
              "2021-10-22   6087777  1605.988334  1548.887143  1558.787698  1630.127502   \n",
              "\n",
              "             BBU_20_2.0  BBB_20_2.0  BBP_20_2.0     RSI_14  MACD_8_21_9  \\\n",
              "Date                                                                      \n",
              "2021-01-01          NaN         NaN         NaN        NaN          NaN   \n",
              "2021-01-04          NaN         NaN         NaN        NaN          NaN   \n",
              "2021-01-05          NaN         NaN         NaN        NaN          NaN   \n",
              "2021-01-06          NaN         NaN         NaN        NaN          NaN   \n",
              "2021-01-07          NaN         NaN         NaN        NaN          NaN   \n",
              "...                 ...         ...         ...        ...          ...   \n",
              "2021-10-18  1677.954429    9.100992    0.947596  66.921590    30.505174   \n",
              "2021-10-19  1689.882929    9.745518    0.992467  69.671047    33.895987   \n",
              "2021-10-20  1695.506872    9.650548    0.861257  64.976905    33.658767   \n",
              "2021-10-21  1696.543098    8.858001    0.859332  65.391218    33.132788   \n",
              "2021-10-22  1701.467307    8.752666    0.854798  66.173902    32.675871   \n",
              "\n",
              "            MACDh_8_21_9  MACDs_8_21_9  STOCHk_14_3_3  STOCHd_14_3_3  \n",
              "Date                                                                  \n",
              "2021-01-01           NaN           NaN            NaN            NaN  \n",
              "2021-01-04           NaN           NaN            NaN            NaN  \n",
              "2021-01-05           NaN           NaN            NaN            NaN  \n",
              "2021-01-06           NaN           NaN            NaN            NaN  \n",
              "2021-01-07           NaN           NaN            NaN            NaN  \n",
              "...                  ...           ...            ...            ...  \n",
              "2021-10-18      8.917783     21.587391      84.427387      87.863392  \n",
              "2021-10-19      9.846877     24.049110      80.317282      85.200629  \n",
              "2021-10-20      7.687726     25.971041      70.339066      78.361245  \n",
              "2021-10-21      5.729397     27.403391      71.591023      74.082457  \n",
              "2021-10-22      4.217984     28.457887      69.249401      70.393163  \n",
              "\n",
              "[200 rows x 19 columns]"
            ],
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              "<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>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>SMA_30</th>\n",
              "      <th>SMA_70</th>\n",
              "      <th>BBL_20_2.0</th>\n",
              "      <th>BBM_20_2.0</th>\n",
              "      <th>BBU_20_2.0</th>\n",
              "      <th>BBB_20_2.0</th>\n",
              "      <th>BBP_20_2.0</th>\n",
              "      <th>RSI_14</th>\n",
              "      <th>MACD_8_21_9</th>\n",
              "      <th>MACDh_8_21_9</th>\n",
              "      <th>MACDs_8_21_9</th>\n",
              "      <th>STOCHk_14_3_3</th>\n",
              "      <th>STOCHd_14_3_3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-01-01</th>\n",
              "      <td>1440.000000</td>\n",
              "      <td>1443.000000</td>\n",
              "      <td>1420.599976</td>\n",
              "      <td>1425.050049</td>\n",
              "      <td>1402.600952</td>\n",
              "      <td>4405469</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-04</th>\n",
              "      <td>1438.000000</td>\n",
              "      <td>1438.000000</td>\n",
              "      <td>1399.000000</td>\n",
              "      <td>1416.000000</td>\n",
              "      <td>1393.693481</td>\n",
              "      <td>7870096</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-05</th>\n",
              "      <td>1419.199951</td>\n",
              "      <td>1430.750000</td>\n",
              "      <td>1409.000000</td>\n",
              "      <td>1426.699951</td>\n",
              "      <td>1404.224854</td>\n",
              "      <td>7193412</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-06</th>\n",
              "      <td>1435.000000</td>\n",
              "      <td>1440.000000</td>\n",
              "      <td>1413.099976</td>\n",
              "      <td>1420.550049</td>\n",
              "      <td>1398.171753</td>\n",
              "      <td>11067025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-07</th>\n",
              "      <td>1432.500000</td>\n",
              "      <td>1432.599976</td>\n",
              "      <td>1412.550049</td>\n",
              "      <td>1416.250000</td>\n",
              "      <td>1393.939453</td>\n",
              "      <td>9947421</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2021-10-18</th>\n",
              "      <td>1705.000000</td>\n",
              "      <td>1725.000000</td>\n",
              "      <td>1667.050049</td>\n",
              "      <td>1670.300049</td>\n",
              "      <td>1651.102661</td>\n",
              "      <td>14428228</td>\n",
              "      <td>1591.581669</td>\n",
              "      <td>1539.306428</td>\n",
              "      <td>1531.890566</td>\n",
              "      <td>1604.922498</td>\n",
              "      <td>1677.954429</td>\n",
              "      <td>9.100992</td>\n",
              "      <td>0.947596</td>\n",
              "      <td>66.921590</td>\n",
              "      <td>30.505174</td>\n",
              "      <td>8.917783</td>\n",
              "      <td>21.587391</td>\n",
              "      <td>84.427387</td>\n",
              "      <td>87.863392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-19</th>\n",
              "      <td>1675.449951</td>\n",
              "      <td>1692.449951</td>\n",
              "      <td>1671.000000</td>\n",
              "      <td>1688.699951</td>\n",
              "      <td>1669.291016</td>\n",
              "      <td>4493770</td>\n",
              "      <td>1595.336666</td>\n",
              "      <td>1541.437856</td>\n",
              "      <td>1532.847064</td>\n",
              "      <td>1611.364996</td>\n",
              "      <td>1689.882929</td>\n",
              "      <td>9.745518</td>\n",
              "      <td>0.992467</td>\n",
              "      <td>69.671047</td>\n",
              "      <td>33.895987</td>\n",
              "      <td>9.846877</td>\n",
              "      <td>24.049110</td>\n",
              "      <td>80.317282</td>\n",
              "      <td>85.200629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-20</th>\n",
              "      <td>1689.099976</td>\n",
              "      <td>1698.750000</td>\n",
              "      <td>1664.449951</td>\n",
              "      <td>1673.849976</td>\n",
              "      <td>1654.611816</td>\n",
              "      <td>5454284</td>\n",
              "      <td>1598.941667</td>\n",
              "      <td>1543.629285</td>\n",
              "      <td>1539.413123</td>\n",
              "      <td>1617.459998</td>\n",
              "      <td>1695.506872</td>\n",
              "      <td>9.650548</td>\n",
              "      <td>0.861257</td>\n",
              "      <td>64.976905</td>\n",
              "      <td>33.658767</td>\n",
              "      <td>7.687726</td>\n",
              "      <td>25.971041</td>\n",
              "      <td>70.339066</td>\n",
              "      <td>78.361245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-21</th>\n",
              "      <td>1671.800049</td>\n",
              "      <td>1681.949951</td>\n",
              "      <td>1660.849976</td>\n",
              "      <td>1676.300049</td>\n",
              "      <td>1657.033691</td>\n",
              "      <td>4869095</td>\n",
              "      <td>1602.510002</td>\n",
              "      <td>1546.119285</td>\n",
              "      <td>1552.636907</td>\n",
              "      <td>1624.590002</td>\n",
              "      <td>1696.543098</td>\n",
              "      <td>8.858001</td>\n",
              "      <td>0.859332</td>\n",
              "      <td>65.391218</td>\n",
              "      <td>33.132788</td>\n",
              "      <td>5.729397</td>\n",
              "      <td>27.403391</td>\n",
              "      <td>71.591023</td>\n",
              "      <td>74.082457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-22</th>\n",
              "      <td>1680.099976</td>\n",
              "      <td>1708.000000</td>\n",
              "      <td>1670.750000</td>\n",
              "      <td>1680.750000</td>\n",
              "      <td>1661.432495</td>\n",
              "      <td>6087777</td>\n",
              "      <td>1605.988334</td>\n",
              "      <td>1548.887143</td>\n",
              "      <td>1558.787698</td>\n",
              "      <td>1630.127502</td>\n",
              "      <td>1701.467307</td>\n",
              "      <td>8.752666</td>\n",
              "      <td>0.854798</td>\n",
              "      <td>66.173902</td>\n",
              "      <td>32.675871</td>\n",
              "      <td>4.217984</td>\n",
              "      <td>28.457887</td>\n",
              "      <td>69.249401</td>\n",
              "      <td>70.393163</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 19 columns</p>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df['SMA_50'], label='SMA', color='green')\n",
        "plt.plot(df['RSI_14'], label='RSI', color='red')\n",
        "plt.plot(df['STOCHk_14_3_3'], label='STOCHASTIC', color='purple')\n",
        "plt.plot(df['Open'], label='SHARE PRICE', color='black')\n",
        "\n",
        "#add legend\n",
        "plt.legend(title='Group')\n",
        "\n",
        "#add axes labels and a title\n",
        "plt.ylabel('SHARE PRICE', fontsize=14)\n",
        "plt.xlabel('Time', fontsize=14)\n",
        "plt.title('TA', fontsize=16)\n",
        "\n",
        "#display plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "O86NrT4ADDTR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df['RSI_14'], label='RSI', color='red')\n",
        "plt.plot(df['STOCHk_14_3_3'], label='STOCHASTIC', color='purple')\n",
        "\n",
        "#add legend\n",
        "plt.legend(title='Group')\n",
        "\n",
        "#add axes labels and a title\n",
        "plt.ylabel('SHARE PRICE', fontsize=14)\n",
        "plt.xlabel('Time', fontsize=14)\n",
        "plt.title('TA', fontsize=16)\n",
        "\n",
        "#display plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "83W_G4qwG-8n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def buy_sell(data):\n",
        "    signalBuy = []\n",
        "    signalSell = []\n",
        "    position = False \n",
        "\n",
        "    for i in range(len(data)):\n",
        "        if data['SMA_30'][i] > data['SMA_70'][i]:\n",
        "            if position == False :\n",
        "                signalBuy.append(data['Adj Close'][i])\n",
        "                signalSell.append(np.nan)\n",
        "                position = True\n",
        "            else:\n",
        "                signalBuy.append(np.nan)\n",
        "                signalSell.append(np.nan)\n",
        "        elif data['SMA_30'][i] < data['SMA_70'][i]:\n",
        "            if position == True:\n",
        "                signalBuy.append(np.nan)\n",
        "                signalSell.append(data['Adj Close'][i])\n",
        "                position = False\n",
        "            else:\n",
        "                signalBuy.append(np.nan)\n",
        "                signalSell.append(np.nan)\n",
        "        else:\n",
        "            signalBuy.append(np.nan)\n",
        "            signalSell.append(np.nan)\n",
        "    return pd.Series([signalBuy, signalSell])"
      ],
      "metadata": {
        "id": "GKRbiXMyRxIB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Buy_Signal_price'], df['Sell_Signal_price'] = buy_sell(df)\n"
      ],
      "metadata": {
        "id": "-y6UoQGOR-PT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bs=df['Buy_Signal_price']\n",
        "ss=df['Sell_Signal_price']\n",
        "b = bs[np.logical_not(np.isnan(bs))]\n",
        "s = ss[np.logical_not(np.isnan(ss))]\n",
        "print(b)\n",
        "print(s)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rvvO4mHCSDWr",
        "outputId": "d399daf1-61a7-4676-a02d-be60d9ca4983"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date\n",
            "2021-06-17    1443.004150\n",
            "2021-09-02    1570.737061\n",
            "Name: Buy_Signal_price, dtype: float64\n",
            "Date\n",
            "2021-08-18    1495.610474\n",
            "2021-12-01    1487.356445\n",
            "Name: Sell_Signal_price, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tp=0\n",
        "fl=0\n",
        "tot=len(s)+len(b)\n",
        "for i in range(tot):\n",
        "  t = int(i/2)\n",
        "  if(fl==0):\n",
        "    tp = tp + (-1*b[t])\n",
        "    fl=1\n",
        "  else:\n",
        "    tp = tp + s[t]\n",
        "    fl=0\n",
        "print(tp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ipBtn3fnSHF_",
        "outputId": "9a2c7595-9f4b-4a87-8a38-8999104276d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-30.7742919921875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "import pandas_datareader.data as web\n",
        "import pandas_ta as ta\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import date\n",
        "plt.style.use('fivethirtyeight')\n",
        "yf.pdr_override()"
      ],
      "metadata": {
        "id": "cdNlBA88MlCr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stocksymbols = ['TATAMOTORS.NS']\n",
        "startdate = date(2017,8,4)\n",
        "end_date = date.today()\n",
        "print(end_date)\n",
        "def getMyPortfolio(stocks = stocksymbols ,start = startdate , end = end_date):\n",
        "    data = web.get_data_yahoo(stocks , data_source='yahoo' , start = start ,end= end )\n",
        "    return data\n"
      ],
      "metadata": {
        "id": "H25_0m5QMdi0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = getMyPortfolio(stocksymbols)\n"
      ],
      "metadata": {
        "id": "JoaApzmpMfgt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['SMA 30'] = ta.sma(data['Close'],30)\n",
        "data['SMA 100'] = ta.sma(data['Close'],100)\n",
        "#SMA BUY SELL\n",
        "#Function for buy and sell signal"
      ],
      "metadata": {
        "id": "MpdYw4XXK1Sr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Buy_Signal_price'], data['Sell_Signal_price'] = buy_sell(data)\n"
      ],
      "metadata": {
        "id": "ZAkpwjByMw9I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bs=data['Buy_Signal_price']\n",
        "ss=data['Sell_Signal_price']\n",
        "b = bs[np.logical_not(np.isnan(bs))]\n",
        "s = ss[np.logical_not(np.isnan(ss))]\n",
        "print(b)\n",
        "print(s)\n"
      ],
      "metadata": {
        "id": "IIqu6gh4M6VV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tp=0\n",
        "fl=0\n",
        "tot=len(s)+len(b)\n",
        "for i in range(tot):\n",
        "  t = int(i/2)\n",
        "  if(fl==0):\n",
        "    tp = tp + (-1*b[t])\n",
        "    fl=1\n",
        "  else:\n",
        "    tp = tp + s[t]\n",
        "    fl=0\n",
        "print(tp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3oNFnQaN6-U",
        "outputId": "00c4754a-dfc3-43e9-d733-03afaaa2e149"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "87.59999084472656\n"
          ]
        }
      ]
    }
  ]
}