Using_EMA_30_50_100_+_RSI_+_MFI.ipynb

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      "name": "Using EMA 30/50/100 + RSI + MFI.ipynb",
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      "source": [
        "!pip install yfinance"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas_ta"
      ],
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        "id": "i_pB2Wr3uMaL"
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      "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": "BNd5E8VwuN0s"
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      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stocksymbols = ['KOTAKBANK.NS']\n",
        "startdate = date(2015,1,1)\n",
        "end_date = date(2019,12,31)\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"
      ],
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      "execution_count": 227,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2019-12-31\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = getMyPortfolio(stocksymbols)\n"
      ],
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      "execution_count": 228,
      "outputs": [
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
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    {
      "cell_type": "code",
      "source": [
        "CustomStrategy = ta.Strategy(\n",
        "    name=\"Defining Various TI to be used\",\n",
        "    description=\"MFI,RSI & EMA 30,50,100\",\n",
        "    ta=[\n",
        "        {\"kind\": \"mfi\",}, #volume\n",
        "        {\"kind\": \"rsi\"}, #momentum\n",
        "        {\"kind\": \"ema\", \"length\": 14}, #trend\n",
        "        {\"kind\": \"ema\", \"length\": 50}, #trend\n",
        "        {\"kind\": \"ema\", \"length\": 100}, #trend\n",
        "    ]\n",
        ")\n",
        "df.ta.strategy(CustomStrategy)"
      ],
      "metadata": {
        "id": "gzQxgnlnvBMR"
      },
      "execution_count": 229,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns = {'EMA_14':'Short', 'EMA_50':'Middle', 'EMA_100':'Long'}, inplace = True)"
      ],
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        "id": "erK3U7Qv0Vev"
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      "execution_count": 230,
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        "df.tail(200)"
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      "execution_count": 231,
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              "                   Open         High          Low        Close    Adj Close  \\\n",
              "Date                                                                          \n",
              "2019-03-01  1221.699951  1230.500000  1217.349976  1225.949951  1224.681274   \n",
              "2019-03-05  1223.900024  1246.300049  1217.800049  1240.449951  1239.166260   \n",
              "2019-03-06  1242.099976  1247.000000  1236.599976  1241.300049  1240.015503   \n",
              "2019-03-07  1244.000000  1246.000000  1235.199951  1237.199951  1235.919678   \n",
              "2019-03-08  1237.000000  1243.500000  1231.699951  1238.900024  1237.618042   \n",
              "...                 ...          ...          ...          ...          ...   \n",
              "2019-12-23  1699.000000  1711.199951  1695.099976  1704.900024  1704.053833   \n",
              "2019-12-24  1704.949951  1716.500000  1700.000000  1711.550049  1710.700562   \n",
              "2019-12-26  1711.050049  1715.000000  1689.699951  1694.250000  1693.409058   \n",
              "2019-12-27  1705.000000  1705.000000  1681.349976  1685.599976  1684.763306   \n",
              "2019-12-30  1690.000000  1706.800049  1685.500000  1690.400024  1689.561035   \n",
              "\n",
              "             Volume     MFI_14     RSI_14        Short       Middle  \\\n",
              "Date                                                                  \n",
              "2019-03-01  2162834   7.041826  40.121540  1249.613685  1248.209347   \n",
              "2019-03-05  3128451   6.899010  45.551142  1248.391854  1247.905057   \n",
              "2019-03-06  2306834   8.710797  45.861092  1247.446280  1247.646037   \n",
              "2019-03-07  1434561   8.792024  44.544039  1246.080103  1247.236387   \n",
              "2019-03-08  1051433   8.865546  45.246192  1245.122759  1246.909471   \n",
              "...             ...        ...        ...          ...          ...   \n",
              "2019-12-23  1745488  74.951584  60.769993  1688.668472  1631.915945   \n",
              "2019-12-24  1702704  74.356858  62.075162  1691.719349  1635.038851   \n",
              "2019-12-26  1646970  69.086722  56.782529  1692.056769  1637.360856   \n",
              "2019-12-27  1529366  62.777854  54.290064  1691.195864  1639.252587   \n",
              "2019-12-30  1868284  68.098840  55.458467  1691.089752  1641.258369   \n",
              "\n",
              "                   Long  \n",
              "Date                     \n",
              "2019-03-01  1235.877598  \n",
              "2019-03-05  1235.968139  \n",
              "2019-03-06  1236.073722  \n",
              "2019-03-07  1236.096023  \n",
              "2019-03-08  1236.151548  \n",
              "...                 ...  \n",
              "2019-12-23  1581.597356  \n",
              "2019-12-24  1584.170677  \n",
              "2019-12-26  1586.350466  \n",
              "2019-12-27  1588.315803  \n",
              "2019-12-30  1590.337272  \n",
              "\n",
              "[200 rows x 11 columns]"
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              "      <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>MFI_14</th>\n",
              "      <th>RSI_14</th>\n",
              "      <th>Short</th>\n",
              "      <th>Middle</th>\n",
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              "      <th>Date</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>2019-03-01</th>\n",
              "      <td>1221.699951</td>\n",
              "      <td>1230.500000</td>\n",
              "      <td>1217.349976</td>\n",
              "      <td>1225.949951</td>\n",
              "      <td>1224.681274</td>\n",
              "      <td>2162834</td>\n",
              "      <td>7.041826</td>\n",
              "      <td>40.121540</td>\n",
              "      <td>1249.613685</td>\n",
              "      <td>1248.209347</td>\n",
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              "      <th>2019-03-05</th>\n",
              "      <td>1223.900024</td>\n",
              "      <td>1246.300049</td>\n",
              "      <td>1217.800049</td>\n",
              "      <td>1240.449951</td>\n",
              "      <td>1239.166260</td>\n",
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              "      <td>1247.905057</td>\n",
              "      <td>1235.968139</td>\n",
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              "      <th>2019-03-06</th>\n",
              "      <td>1242.099976</td>\n",
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              "      <td>1236.599976</td>\n",
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              "      <th>2019-03-07</th>\n",
              "      <td>1244.000000</td>\n",
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              "      <td>1235.199951</td>\n",
              "      <td>1237.199951</td>\n",
              "      <td>1235.919678</td>\n",
              "      <td>1434561</td>\n",
              "      <td>8.792024</td>\n",
              "      <td>44.544039</td>\n",
              "      <td>1246.080103</td>\n",
              "      <td>1247.236387</td>\n",
              "      <td>1236.096023</td>\n",
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              "      <th>2019-03-08</th>\n",
              "      <td>1237.000000</td>\n",
              "      <td>1243.500000</td>\n",
              "      <td>1231.699951</td>\n",
              "      <td>1238.900024</td>\n",
              "      <td>1237.618042</td>\n",
              "      <td>1051433</td>\n",
              "      <td>8.865546</td>\n",
              "      <td>45.246192</td>\n",
              "      <td>1245.122759</td>\n",
              "      <td>1246.909471</td>\n",
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              "      <th>2019-12-23</th>\n",
              "      <td>1699.000000</td>\n",
              "      <td>1711.199951</td>\n",
              "      <td>1695.099976</td>\n",
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              "      <td>60.769993</td>\n",
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              "      <td>1631.915945</td>\n",
              "      <td>1581.597356</td>\n",
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              "    <tr>\n",
              "      <th>2019-12-24</th>\n",
              "      <td>1704.949951</td>\n",
              "      <td>1716.500000</td>\n",
              "      <td>1700.000000</td>\n",
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              "      <td>1702704</td>\n",
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              "      <td>62.075162</td>\n",
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              "      <th>2019-12-26</th>\n",
              "      <td>1711.050049</td>\n",
              "      <td>1715.000000</td>\n",
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              "      <th>2019-12-27</th>\n",
              "      <td>1705.000000</td>\n",
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              "      <th>2019-12-30</th>\n",
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          "metadata": {},
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      "cell_type": "code",
      "source": [
        "plt.plot(df['MFI_14'], label='MFI', color='green')\n",
        "plt.plot(df['RSI_14'], label='RSI', color='blue')\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": "Upu17QGCvfsD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "f9219e0b-75e6-462a-bd80-d28fc527b11e"
      },
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def buy_sell_function(data):\n",
        "  buysig = []\n",
        "  buyprice = []\n",
        "  flag_long = False\n",
        "  flag_short = False\n",
        "  for i in range(0,len(data)):\n",
        "    if data['Middle'][i] < data['Long'][i] and data['Middle'][i] > data['Short'][i] and flag_long == False and flag_short == False:\n",
        "        buysig.append(1)\n",
        "        buyprice.append(data['Close'][i])\n",
        "        flag_short = True\n",
        "    elif data['Middle'][i] > data['Long'][i] and data['Middle'][i] < data['Short'][i] and flag_short == False and flag_long == False:\n",
        "        buysig.append(1)\n",
        "        buyprice.append(data['Close'][i])\n",
        "        flag_long = True\n",
        "    elif flag_short == True and data['Short'][i] > data['Middle'][i]:\n",
        "        buyprice.append(data['Close'][i])\n",
        "        buysig.append(-1)\n",
        "        flag_short = False\n",
        "    elif flag_long == True and data['Short'][i] < data['Middle'][i]:\n",
        "        buyprice.append(data['Close'][i])\n",
        "        buysig.append(-1)\n",
        "        flag_long = False\n",
        "    else:\n",
        "        buysig.append(0)\n",
        "        buyprice.append(data['Close'][i])\n",
        "  return (buysig, buyprice)\n",
        "\n",
        "df['buysignal'] = buy_sell_function(df)[0]\n",
        "df['buyprice'] =buy_sell_function(df)[1]"
      ],
      "metadata": {
        "id": "lGaKlgh7v2Oy"
      },
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mfi=df['MFI_14']\n",
        "rsi=df['RSI_14']\n",
        "mfisig=[]\n",
        "for i in range(len(mfi)):\n",
        "  if mfi[i]>80:\n",
        "    mfisig.append(-1)\n",
        "  elif mfi[i]<20:\n",
        "    mfisig.append(1)\n",
        "  else:\n",
        "    mfisig.append(0)"
      ],
      "metadata": {
        "id": "W96cxFXILhAI"
      },
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rsi=df['RSI_14']\n",
        "rsisig=[]\n",
        "for i in range(len(rsi)):\n",
        "  if rsi[i]>70:\n",
        "    rsisig.append(-1)\n",
        "  elif rsi[i]<30:\n",
        "    rsisig.append(1)\n",
        "  else:\n",
        "    rsisig.append(0)"
      ],
      "metadata": {
        "id": "ohVoOCeEMuYM"
      },
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bs=df['buysignal']\n",
        "ss=df['buyprice']\n",
        "ct=0\n",
        "for i in range(len(bs)):\n",
        "  if(bs[i]==1 and mfisig[i]==1):\n",
        "    ct+=1\n",
        "print(ct)"
      ],
      "metadata": {
        "id": "gKKKLh16HNnR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da2e9d07-92c5-4a2c-be19-7dcb5f55be4b"
      },
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tp=0\n",
        "fl=0\n",
        "bp=0\n",
        "for i in range(len(bs)):\n",
        "  val=bs[i]*50 + mfisig[i]*50 + rsisig[i]*50\n",
        "  if val>=50 and fl==0:\n",
        "    tp-=ss[i]\n",
        "    fl=1\n",
        "    bp=ss[i]\n",
        "    print('Buy :'+ str(ss[i]))\n",
        "  elif val<=-50 and fl==1:\n",
        "    tp+=ss[i]\n",
        "    fl=0\n",
        "    print('Sell :'+ str(ss[i]))    \n",
        "\n",
        "if(fl==1):\n",
        "    tp+=bp\n",
        "\n",
        "print(tp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5--4BiMwJFH",
        "outputId": "ac2b4b73-bdcf-4148-fd48-ecff6cdf21a0"
      },
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Buy :699.6500244140625\n",
            "Sell :667.2999877929688\n",
            "Buy :644.8499755859375\n",
            "Sell :714.4249877929688\n",
            "Buy :627.25\n",
            "Sell :687.6500244140625\n",
            "Buy :686.5999755859375\n",
            "Sell :701.3499755859375\n",
            "Buy :667.4000244140625\n",
            "Sell :671.5\n",
            "Buy :700.8499755859375\n",
            "Sell :726.75\n",
            "Buy :772.5999755859375\n",
            "Sell :769.5999755859375\n",
            "Buy :727.7999877929688\n",
            "Sell :795.2000122070312\n",
            "Buy :784.2999877929688\n",
            "Sell :810.9500122070312\n",
            "Buy :1009.75\n",
            "Sell :1005.1500244140625\n",
            "Buy :1038.3499755859375\n",
            "Sell :1060.550048828125\n",
            "Buy :1096.550048828125\n",
            "Sell :1172.0999755859375\n",
            "Buy :1148.699951171875\n",
            "Sell :1233.949951171875\n",
            "Buy :1256.5\n",
            "Sell :1325.1500244140625\n",
            "480.47509765625\n"
          ]
        }
      ]
    }
  ]
}