Clustering.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# KDSH - Algorithmic trading for Bitcoin\n",
        "\n",
        "THe following jupyter notebook contains submitted for the Kharagpur Data Science Hackathon - 2024"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L495rI4u5Y1f"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from ta.trend import SMAIndicator, MACD\n",
        "from ta.volatility import BollingerBands\n",
        "from ta.momentum import RSIIndicator\n",
        "from ta.volume import ChaikinMoneyFlowIndicator\n",
        "from sklearn.cluster import KMeans\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "import ta\n",
        "import xgboost as xgb\n",
        "import matplotlib.dates as mdates\n",
        "from matplotlib.ticker import MaxNLocator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Choosing Indicators\n",
        "\n",
        "The following indicators have been chosen for the model:\n",
        "\n",
        "- **Relative Strength Index (RSI)** : The 7, 14 and 21 day RSI were calculated.\n",
        "- **Moving Average Convergence Divergence** : MACD is a trend-following momentum indicator that highlights the relationship between two moving averages, signaling potential buy or sell opportunities based on their convergence or divergence.\n",
        "- **Chaikin Money flow line (AD Line)**: CMF assesses the flow of money by combining price and volume data. Positive CMF indicates buying pressure, while negative values suggest selling pressure.\n",
        "- **William Percent Range**: Williams %R is a momentum oscillator gauging overbought and oversold levels. Readings above -20 indicate overbought conditions, while below -80 suggests oversold conditions.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "ZFNWqiqQLkwG"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2022-01-31 05:30:00\n"
          ]
        },
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>datetime</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>RSI7</th>\n",
              "      <th>RSI14</th>\n",
              "      <th>RSI21</th>\n",
              "      <th>MACD_HIST</th>\n",
              "      <th>AD</th>\n",
              "      <th>William_Percent_Range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-01 05:30:00</td>\n",
              "      <td>13715.65</td>\n",
              "      <td>13715.65</td>\n",
              "      <td>13155.38</td>\n",
              "      <td>13203.06</td>\n",
              "      <td>827.053205</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>1</th>\n",
              "      <td>2018-01-01 07:30:00</td>\n",
              "      <td>13203.00</td>\n",
              "      <td>13611.27</td>\n",
              "      <td>13200.00</td>\n",
              "      <td>13410.03</td>\n",
              "      <td>849.151602</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-01-01 09:30:00</td>\n",
              "      <td>13434.98</td>\n",
              "      <td>13699.00</td>\n",
              "      <td>13322.15</td>\n",
              "      <td>13558.99</td>\n",
              "      <td>745.036375</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-01 11:30:00</td>\n",
              "      <td>13539.00</td>\n",
              "      <td>13818.55</td>\n",
              "      <td>13510.00</td>\n",
              "      <td>13570.35</td>\n",
              "      <td>557.178461</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01-01 13:30:00</td>\n",
              "      <td>13569.98</td>\n",
              "      <td>13735.24</td>\n",
              "      <td>13400.00</td>\n",
              "      <td>13616.99</td>\n",
              "      <td>484.909382</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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17837</th>\n",
              "      <td>2022-01-30 21:30:00</td>\n",
              "      <td>37977.47</td>\n",
              "      <td>38074.76</td>\n",
              "      <td>37751.72</td>\n",
              "      <td>37991.00</td>\n",
              "      <td>1141.092080</td>\n",
              "      <td>51.668626</td>\n",
              "      <td>54.498744</td>\n",
              "      <td>54.513946</td>\n",
              "      <td>-40.502930</td>\n",
              "      <td>0.139355</td>\n",
              "      <td>-50.247194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17838</th>\n",
              "      <td>2022-01-30 23:30:00</td>\n",
              "      <td>37991.00</td>\n",
              "      <td>38020.39</td>\n",
              "      <td>37442.52</td>\n",
              "      <td>37512.37</td>\n",
              "      <td>2210.156590</td>\n",
              "      <td>34.779755</td>\n",
              "      <td>46.450866</td>\n",
              "      <td>49.610159</td>\n",
              "      <td>-74.166374</td>\n",
              "      <td>0.053838</td>\n",
              "      <td>-86.104876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17839</th>\n",
              "      <td>2022-01-31 01:30:00</td>\n",
              "      <td>37512.37</td>\n",
              "      <td>37762.60</td>\n",
              "      <td>37351.63</td>\n",
              "      <td>37729.00</td>\n",
              "      <td>1911.983080</td>\n",
              "      <td>44.379771</td>\n",
              "      <td>50.046413</td>\n",
              "      <td>51.675990</td>\n",
              "      <td>-79.685341</td>\n",
              "      <td>0.120439</td>\n",
              "      <td>-72.436839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17840</th>\n",
              "      <td>2022-01-31 03:30:00</td>\n",
              "      <td>37729.00</td>\n",
              "      <td>38230.00</td>\n",
              "      <td>37625.41</td>\n",
              "      <td>37881.76</td>\n",
              "      <td>2463.470350</td>\n",
              "      <td>50.387600</td>\n",
              "      <td>52.469986</td>\n",
              "      <td>53.099657</td>\n",
              "      <td>-71.168994</td>\n",
              "      <td>0.086099</td>\n",
              "      <td>-61.279225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17841</th>\n",
              "      <td>2022-01-31 05:30:00</td>\n",
              "      <td>37881.75</td>\n",
              "      <td>37951.07</td>\n",
              "      <td>36760.87</td>\n",
              "      <td>36821.05</td>\n",
              "      <td>4330.978400</td>\n",
              "      <td>26.873081</td>\n",
              "      <td>38.501748</td>\n",
              "      <td>43.710841</td>\n",
              "      <td>-131.580640</td>\n",
              "      <td>-0.003963</td>\n",
              "      <td>-96.354605</td>\n",
              "    </tr>\n",
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              "<p>17842 rows × 12 columns</p>\n",
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            ],
            "text/plain": [
              "                  datetime      open      high       low     close   \n",
              "0      2018-01-01 05:30:00  13715.65  13715.65  13155.38  13203.06  \\\n",
              "1      2018-01-01 07:30:00  13203.00  13611.27  13200.00  13410.03   \n",
              "2      2018-01-01 09:30:00  13434.98  13699.00  13322.15  13558.99   \n",
              "3      2018-01-01 11:30:00  13539.00  13818.55  13510.00  13570.35   \n",
              "4      2018-01-01 13:30:00  13569.98  13735.24  13400.00  13616.99   \n",
              "...                    ...       ...       ...       ...       ...   \n",
              "17837  2022-01-30 21:30:00  37977.47  38074.76  37751.72  37991.00   \n",
              "17838  2022-01-30 23:30:00  37991.00  38020.39  37442.52  37512.37   \n",
              "17839  2022-01-31 01:30:00  37512.37  37762.60  37351.63  37729.00   \n",
              "17840  2022-01-31 03:30:00  37729.00  38230.00  37625.41  37881.76   \n",
              "17841  2022-01-31 05:30:00  37881.75  37951.07  36760.87  36821.05   \n",
              "\n",
              "            volume       RSI7      RSI14      RSI21   MACD_HIST        AD   \n",
              "0       827.053205        NaN        NaN        NaN         NaN       NaN  \\\n",
              "1       849.151602        NaN        NaN        NaN         NaN       NaN   \n",
              "2       745.036375        NaN        NaN        NaN         NaN       NaN   \n",
              "3       557.178461        NaN        NaN        NaN         NaN       NaN   \n",
              "4       484.909382        NaN        NaN        NaN         NaN       NaN   \n",
              "...            ...        ...        ...        ...         ...       ...   \n",
              "17837  1141.092080  51.668626  54.498744  54.513946  -40.502930  0.139355   \n",
              "17838  2210.156590  34.779755  46.450866  49.610159  -74.166374  0.053838   \n",
              "17839  1911.983080  44.379771  50.046413  51.675990  -79.685341  0.120439   \n",
              "17840  2463.470350  50.387600  52.469986  53.099657  -71.168994  0.086099   \n",
              "17841  4330.978400  26.873081  38.501748  43.710841 -131.580640 -0.003963   \n",
              "\n",
              "       William_Percent_Range  \n",
              "0                        NaN  \n",
              "1                        NaN  \n",
              "2                        NaN  \n",
              "3                        NaN  \n",
              "4                        NaN  \n",
              "...                      ...  \n",
              "17837             -50.247194  \n",
              "17838             -86.104876  \n",
              "17839             -72.436839  \n",
              "17840             -61.279225  \n",
              "17841             -96.354605  \n",
              "\n",
              "[17842 rows x 12 columns]"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Assuming df is your DataFrame with 'open', 'high', 'low', 'close', and 'volume' columns\n",
        "\n",
        "df = pd.read_csv('btc_2h.csv')\n",
        "print(df['datetime'][len(df) - 1])\n",
        "# Calculating Relative Strength Index (RSI)\n",
        "df['RSI7'] = RSIIndicator(df['close'], window=7).rsi()\n",
        "df['RSI14'] = RSIIndicator(df['close'], window=14).rsi()\n",
        "df['RSI21'] = RSIIndicator(df['close'], window=21).rsi()\n",
        "\n",
        "# Calculating MACD\n",
        "macd = MACD(df['close'], window_slow=26, window_fast=12, window_sign=9)\n",
        "df['MACD_HIST'] = macd.macd_diff()\n",
        "\n",
        "# Calculating Chaikin Money Flow (AD line)\n",
        "df['AD'] = ChaikinMoneyFlowIndicator(df['high'], df['low'], df['close'], df['volume']).chaikin_money_flow()\n",
        "df['William_Percent_Range'] = ta.momentum.williams_r(df['high'], df['low'], df['close'], lbp=14)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {},
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(pd.to_datetime(df['datetime']), df['close'])\n",
        "plt.ylabel('Close Price')\n",
        "plt.xlabel('Date')\n",
        "plt.title('Close Price of BTC')\n",
        "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
        "plt.gca().xaxis.set_major_locator(MaxNLocator(8))  # Adjust '5' based on your desired number of ticks\n",
        "# Optional: Adjust tick interval\n",
        "\n",
        "# Rotating x-axis tick labels for better readability\n",
        "plt.gcf().autofmt_xdate()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Price of buy and sell is decided by the next day's open price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "MaJxCwXBSIvw"
      },
      "outputs": [],
      "source": [
        "df = df.dropna()\n",
        "df['price']=df.open.shift(-1)\n",
        "df = df.drop(['datetime'], axis = 1)\n",
        "df = df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "kcP7X4lxYvVI",
        "outputId": "05668457-9d25-497f-d65b-ea6901cc6ccf"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>RSI7</th>\n",
              "      <th>RSI14</th>\n",
              "      <th>RSI21</th>\n",
              "      <th>MACD_HIST</th>\n",
              "      <th>AD</th>\n",
              "      <th>William_Percent_Range</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>14800.00</td>\n",
              "      <td>14899.86</td>\n",
              "      <td>14671.00</td>\n",
              "      <td>14700.01</td>\n",
              "      <td>792.750301</td>\n",
              "      <td>52.426274</td>\n",
              "      <td>58.614210</td>\n",
              "      <td>60.010717</td>\n",
              "      <td>-28.660792</td>\n",
              "      <td>0.073790</td>\n",
              "      <td>-39.253181</td>\n",
              "      <td>14700.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>14700.01</td>\n",
              "      <td>14990.00</td>\n",
              "      <td>14683.00</td>\n",
              "      <td>14926.96</td>\n",
              "      <td>535.314197</td>\n",
              "      <td>59.947740</td>\n",
              "      <td>62.132386</td>\n",
              "      <td>62.595713</td>\n",
              "      <td>-28.392790</td>\n",
              "      <td>0.089636</td>\n",
              "      <td>-31.810880</td>\n",
              "      <td>14926.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>14926.96</td>\n",
              "      <td>14949.00</td>\n",
              "      <td>14710.00</td>\n",
              "      <td>14919.51</td>\n",
              "      <td>909.583365</td>\n",
              "      <td>59.586946</td>\n",
              "      <td>61.946224</td>\n",
              "      <td>62.456555</td>\n",
              "      <td>-30.774180</td>\n",
              "      <td>0.072928</td>\n",
              "      <td>-33.523100</td>\n",
              "      <td>14919.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>14919.51</td>\n",
              "      <td>15179.00</td>\n",
              "      <td>14649.99</td>\n",
              "      <td>15172.12</td>\n",
              "      <td>2002.474003</td>\n",
              "      <td>67.358353</td>\n",
              "      <td>65.699054</td>\n",
              "      <td>65.210157</td>\n",
              "      <td>-18.024761</td>\n",
              "      <td>0.119206</td>\n",
              "      <td>-11.700473</td>\n",
              "      <td>15175.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>15175.00</td>\n",
              "      <td>15280.00</td>\n",
              "      <td>15059.00</td>\n",
              "      <td>15119.97</td>\n",
              "      <td>1060.061643</td>\n",
              "      <td>64.376699</td>\n",
              "      <td>64.289475</td>\n",
              "      <td>64.189629</td>\n",
              "      <td>-15.867977</td>\n",
              "      <td>0.112630</td>\n",
              "      <td>-16.205639</td>\n",
              "      <td>15119.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17836</th>\n",
              "      <td>37963.21</td>\n",
              "      <td>38041.32</td>\n",
              "      <td>37712.05</td>\n",
              "      <td>37980.09</td>\n",
              "      <td>1665.327300</td>\n",
              "      <td>51.205689</td>\n",
              "      <td>54.331271</td>\n",
              "      <td>54.416130</td>\n",
              "      <td>-35.811541</td>\n",
              "      <td>0.076311</td>\n",
              "      <td>-50.998416</td>\n",
              "      <td>37977.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17837</th>\n",
              "      <td>37977.47</td>\n",
              "      <td>38074.76</td>\n",
              "      <td>37751.72</td>\n",
              "      <td>37991.00</td>\n",
              "      <td>1141.092080</td>\n",
              "      <td>51.668626</td>\n",
              "      <td>54.498744</td>\n",
              "      <td>54.513946</td>\n",
              "      <td>-40.502930</td>\n",
              "      <td>0.139355</td>\n",
              "      <td>-50.247194</td>\n",
              "      <td>37991.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17838</th>\n",
              "      <td>37991.00</td>\n",
              "      <td>38020.39</td>\n",
              "      <td>37442.52</td>\n",
              "      <td>37512.37</td>\n",
              "      <td>2210.156590</td>\n",
              "      <td>34.779755</td>\n",
              "      <td>46.450866</td>\n",
              "      <td>49.610159</td>\n",
              "      <td>-74.166374</td>\n",
              "      <td>0.053838</td>\n",
              "      <td>-86.104876</td>\n",
              "      <td>37512.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17839</th>\n",
              "      <td>37512.37</td>\n",
              "      <td>37762.60</td>\n",
              "      <td>37351.63</td>\n",
              "      <td>37729.00</td>\n",
              "      <td>1911.983080</td>\n",
              "      <td>44.379771</td>\n",
              "      <td>50.046413</td>\n",
              "      <td>51.675990</td>\n",
              "      <td>-79.685341</td>\n",
              "      <td>0.120439</td>\n",
              "      <td>-72.436839</td>\n",
              "      <td>37729.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17840</th>\n",
              "      <td>37729.00</td>\n",
              "      <td>38230.00</td>\n",
              "      <td>37625.41</td>\n",
              "      <td>37881.76</td>\n",
              "      <td>2463.470350</td>\n",
              "      <td>50.387600</td>\n",
              "      <td>52.469986</td>\n",
              "      <td>53.099657</td>\n",
              "      <td>-71.168994</td>\n",
              "      <td>0.086099</td>\n",
              "      <td>-61.279225</td>\n",
              "      <td>37881.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17808 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           open      high       low     close       volume       RSI7   \n",
              "33     14800.00  14899.86  14671.00  14700.01   792.750301  52.426274  \\\n",
              "34     14700.01  14990.00  14683.00  14926.96   535.314197  59.947740   \n",
              "35     14926.96  14949.00  14710.00  14919.51   909.583365  59.586946   \n",
              "36     14919.51  15179.00  14649.99  15172.12  2002.474003  67.358353   \n",
              "37     15175.00  15280.00  15059.00  15119.97  1060.061643  64.376699   \n",
              "...         ...       ...       ...       ...          ...        ...   \n",
              "17836  37963.21  38041.32  37712.05  37980.09  1665.327300  51.205689   \n",
              "17837  37977.47  38074.76  37751.72  37991.00  1141.092080  51.668626   \n",
              "17838  37991.00  38020.39  37442.52  37512.37  2210.156590  34.779755   \n",
              "17839  37512.37  37762.60  37351.63  37729.00  1911.983080  44.379771   \n",
              "17840  37729.00  38230.00  37625.41  37881.76  2463.470350  50.387600   \n",
              "\n",
              "           RSI14      RSI21  MACD_HIST        AD  William_Percent_Range   \n",
              "33     58.614210  60.010717 -28.660792  0.073790             -39.253181  \\\n",
              "34     62.132386  62.595713 -28.392790  0.089636             -31.810880   \n",
              "35     61.946224  62.456555 -30.774180  0.072928             -33.523100   \n",
              "36     65.699054  65.210157 -18.024761  0.119206             -11.700473   \n",
              "37     64.289475  64.189629 -15.867977  0.112630             -16.205639   \n",
              "...          ...        ...        ...       ...                    ...   \n",
              "17836  54.331271  54.416130 -35.811541  0.076311             -50.998416   \n",
              "17837  54.498744  54.513946 -40.502930  0.139355             -50.247194   \n",
              "17838  46.450866  49.610159 -74.166374  0.053838             -86.104876   \n",
              "17839  50.046413  51.675990 -79.685341  0.120439             -72.436839   \n",
              "17840  52.469986  53.099657 -71.168994  0.086099             -61.279225   \n",
              "\n",
              "          price  \n",
              "33     14700.01  \n",
              "34     14926.96  \n",
              "35     14919.51  \n",
              "36     15175.00  \n",
              "37     15119.97  \n",
              "...         ...  \n",
              "17836  37977.47  \n",
              "17837  37991.00  \n",
              "17838  37512.37  \n",
              "17839  37729.00  \n",
              "17840  37881.75  \n",
              "\n",
              "[17808 rows x 12 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Clustering\n",
        "\n",
        "The entries are clustered in to identify market trends based on the indicators generated above.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "8EmDhWm2YvVJ",
        "outputId": "0e6a9a23-5af3-4c9e-aadd-a1a750520bfa"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RSI7</th>\n",
              "      <th>RSI14</th>\n",
              "      <th>RSI21</th>\n",
              "      <th>MACD_HIST</th>\n",
              "      <th>AD</th>\n",
              "      <th>William_Percent_Range</th>\n",
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              "      <th>33</th>\n",
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              "      <td>0.073790</td>\n",
              "      <td>-39.253181</td>\n",
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              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>59.947740</td>\n",
              "      <td>62.132386</td>\n",
              "      <td>62.595713</td>\n",
              "      <td>-28.392790</td>\n",
              "      <td>0.089636</td>\n",
              "      <td>-31.810880</td>\n",
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              "      <td>0.072928</td>\n",
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              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>67.358353</td>\n",
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              "      <td>65.210157</td>\n",
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              "      <td>0.119206</td>\n",
              "      <td>-11.700473</td>\n",
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              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>64.376699</td>\n",
              "      <td>64.289475</td>\n",
              "      <td>64.189629</td>\n",
              "      <td>-15.867977</td>\n",
              "      <td>0.112630</td>\n",
              "      <td>-16.205639</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",
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              "    <tr>\n",
              "      <th>17836</th>\n",
              "      <td>51.205689</td>\n",
              "      <td>54.331271</td>\n",
              "      <td>54.416130</td>\n",
              "      <td>-35.811541</td>\n",
              "      <td>0.076311</td>\n",
              "      <td>-50.998416</td>\n",
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              "    <tr>\n",
              "      <th>17837</th>\n",
              "      <td>51.668626</td>\n",
              "      <td>54.498744</td>\n",
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              "      <td>-50.247194</td>\n",
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              "    <tr>\n",
              "      <th>17838</th>\n",
              "      <td>34.779755</td>\n",
              "      <td>46.450866</td>\n",
              "      <td>49.610159</td>\n",
              "      <td>-74.166374</td>\n",
              "      <td>0.053838</td>\n",
              "      <td>-86.104876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17839</th>\n",
              "      <td>44.379771</td>\n",
              "      <td>50.046413</td>\n",
              "      <td>51.675990</td>\n",
              "      <td>-79.685341</td>\n",
              "      <td>0.120439</td>\n",
              "      <td>-72.436839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17840</th>\n",
              "      <td>50.387600</td>\n",
              "      <td>52.469986</td>\n",
              "      <td>53.099657</td>\n",
              "      <td>-71.168994</td>\n",
              "      <td>0.086099</td>\n",
              "      <td>-61.279225</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17808 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            RSI7      RSI14      RSI21  MACD_HIST        AD   \n",
              "33     52.426274  58.614210  60.010717 -28.660792  0.073790  \\\n",
              "34     59.947740  62.132386  62.595713 -28.392790  0.089636   \n",
              "35     59.586946  61.946224  62.456555 -30.774180  0.072928   \n",
              "36     67.358353  65.699054  65.210157 -18.024761  0.119206   \n",
              "37     64.376699  64.289475  64.189629 -15.867977  0.112630   \n",
              "...          ...        ...        ...        ...       ...   \n",
              "17836  51.205689  54.331271  54.416130 -35.811541  0.076311   \n",
              "17837  51.668626  54.498744  54.513946 -40.502930  0.139355   \n",
              "17838  34.779755  46.450866  49.610159 -74.166374  0.053838   \n",
              "17839  44.379771  50.046413  51.675990 -79.685341  0.120439   \n",
              "17840  50.387600  52.469986  53.099657 -71.168994  0.086099   \n",
              "\n",
              "       William_Percent_Range  \n",
              "33                -39.253181  \n",
              "34                -31.810880  \n",
              "35                -33.523100  \n",
              "36                -11.700473  \n",
              "37                -16.205639  \n",
              "...                      ...  \n",
              "17836             -50.998416  \n",
              "17837             -50.247194  \n",
              "17838             -86.104876  \n",
              "17839             -72.436839  \n",
              "17840             -61.279225  \n",
              "\n",
              "[17808 rows x 6 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cluster = df.iloc[:, 5:len(df.columns)-1]\n",
        "df_cluster"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6-vjFFj-Swta",
        "outputId": "5f2390ea-653b-4adc-b58f-76dc951fe6ee"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RSI7</th>\n",
              "      <th>RSI14</th>\n",
              "      <th>RSI21</th>\n",
              "      <th>MACD_HIST</th>\n",
              "      <th>AD</th>\n",
              "      <th>William_Percent_Range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>52.426274</td>\n",
              "      <td>58.614210</td>\n",
              "      <td>60.010717</td>\n",
              "      <td>-28.660792</td>\n",
              "      <td>0.073790</td>\n",
              "      <td>-39.253181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>59.947740</td>\n",
              "      <td>62.132386</td>\n",
              "      <td>62.595713</td>\n",
              "      <td>-28.392790</td>\n",
              "      <td>0.089636</td>\n",
              "      <td>-31.810880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>59.586946</td>\n",
              "      <td>61.946224</td>\n",
              "      <td>62.456555</td>\n",
              "      <td>-30.774180</td>\n",
              "      <td>0.072928</td>\n",
              "      <td>-33.523100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>67.358353</td>\n",
              "      <td>65.699054</td>\n",
              "      <td>65.210157</td>\n",
              "      <td>-18.024761</td>\n",
              "      <td>0.119206</td>\n",
              "      <td>-11.700473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>64.376699</td>\n",
              "      <td>64.289475</td>\n",
              "      <td>64.189629</td>\n",
              "      <td>-15.867977</td>\n",
              "      <td>0.112630</td>\n",
              "      <td>-16.205639</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12028</th>\n",
              "      <td>51.274169</td>\n",
              "      <td>46.813901</td>\n",
              "      <td>46.215536</td>\n",
              "      <td>8.170355</td>\n",
              "      <td>-0.229177</td>\n",
              "      <td>-38.673273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12029</th>\n",
              "      <td>48.727798</td>\n",
              "      <td>45.792783</td>\n",
              "      <td>45.540351</td>\n",
              "      <td>9.271544</td>\n",
              "      <td>-0.219222</td>\n",
              "      <td>-43.909992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12030</th>\n",
              "      <td>47.066873</td>\n",
              "      <td>45.146864</td>\n",
              "      <td>45.118804</td>\n",
              "      <td>9.402547</td>\n",
              "      <td>-0.154434</td>\n",
              "      <td>-47.099505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12031</th>\n",
              "      <td>47.044673</td>\n",
              "      <td>45.138727</td>\n",
              "      <td>45.113601</td>\n",
              "      <td>9.481728</td>\n",
              "      <td>-0.024403</td>\n",
              "      <td>-32.017699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12032</th>\n",
              "      <td>49.328539</td>\n",
              "      <td>45.997214</td>\n",
              "      <td>45.652618</td>\n",
              "      <td>10.084685</td>\n",
              "      <td>-0.107765</td>\n",
              "      <td>-28.030973</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12000 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            RSI7      RSI14      RSI21  MACD_HIST        AD   \n",
              "33     52.426274  58.614210  60.010717 -28.660792  0.073790  \\\n",
              "34     59.947740  62.132386  62.595713 -28.392790  0.089636   \n",
              "35     59.586946  61.946224  62.456555 -30.774180  0.072928   \n",
              "36     67.358353  65.699054  65.210157 -18.024761  0.119206   \n",
              "37     64.376699  64.289475  64.189629 -15.867977  0.112630   \n",
              "...          ...        ...        ...        ...       ...   \n",
              "12028  51.274169  46.813901  46.215536   8.170355 -0.229177   \n",
              "12029  48.727798  45.792783  45.540351   9.271544 -0.219222   \n",
              "12030  47.066873  45.146864  45.118804   9.402547 -0.154434   \n",
              "12031  47.044673  45.138727  45.113601   9.481728 -0.024403   \n",
              "12032  49.328539  45.997214  45.652618  10.084685 -0.107765   \n",
              "\n",
              "       William_Percent_Range  \n",
              "33                -39.253181  \n",
              "34                -31.810880  \n",
              "35                -33.523100  \n",
              "36                -11.700473  \n",
              "37                -16.205639  \n",
              "...                      ...  \n",
              "12028             -38.673273  \n",
              "12029             -43.909992  \n",
              "12030             -47.099505  \n",
              "12031             -32.017699  \n",
              "12032             -28.030973  \n",
              "\n",
              "[12000 rows x 6 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X = df_cluster.to_numpy()\n",
        "train_length = 12000\n",
        "X_train = X[:train_length]\n",
        "kmeans = KMeans(n_clusters = 3, n_init = \"auto\").fit(X_train)\n",
        "labels = kmeans.labels_\n",
        "df_train = df_cluster.iloc[:len(X_train)]\n",
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "2_mic7mbWewy",
        "outputId": "0c19a329-b888-4753-a941-93274c16fb5b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0 0 0 ... 0 0 0]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\ACER\\AppData\\Local\\Temp\\ipykernel_8508\\2248231060.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_train['label'] = labels\n"
          ]
        },
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RSI7</th>\n",
              "      <th>RSI14</th>\n",
              "      <th>RSI21</th>\n",
              "      <th>MACD_HIST</th>\n",
              "      <th>AD</th>\n",
              "      <th>William_Percent_Range</th>\n",
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              "    <tr>\n",
              "      <th>12030</th>\n",
              "      <td>47.066873</td>\n",
              "      <td>45.146864</td>\n",
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              "      <td>-47.099505</td>\n",
              "      <td>0</td>\n",
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              "</table>\n",
              "<p>12000 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            RSI7      RSI14      RSI21  MACD_HIST        AD   \n",
              "33     52.426274  58.614210  60.010717 -28.660792  0.073790  \\\n",
              "34     59.947740  62.132386  62.595713 -28.392790  0.089636   \n",
              "35     59.586946  61.946224  62.456555 -30.774180  0.072928   \n",
              "36     67.358353  65.699054  65.210157 -18.024761  0.119206   \n",
              "37     64.376699  64.289475  64.189629 -15.867977  0.112630   \n",
              "...          ...        ...        ...        ...       ...   \n",
              "12028  51.274169  46.813901  46.215536   8.170355 -0.229177   \n",
              "12029  48.727798  45.792783  45.540351   9.271544 -0.219222   \n",
              "12030  47.066873  45.146864  45.118804   9.402547 -0.154434   \n",
              "12031  47.044673  45.138727  45.113601   9.481728 -0.024403   \n",
              "12032  49.328539  45.997214  45.652618  10.084685 -0.107765   \n",
              "\n",
              "       William_Percent_Range  label  \n",
              "33                -39.253181      0  \n",
              "34                -31.810880      0  \n",
              "35                -33.523100      0  \n",
              "36                -11.700473      1  \n",
              "37                -16.205639      1  \n",
              "...                      ...    ...  \n",
              "12028             -38.673273      0  \n",
              "12029             -43.909992      0  \n",
              "12030             -47.099505      0  \n",
              "12031             -32.017699      0  \n",
              "12032             -28.030973      0  \n",
              "\n",
              "[12000 rows x 7 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(labels)\n",
        "unique, counts = np.unique(labels, return_counts=True)\n",
        "dict(zip(unique, counts))\n",
        "df_train['label'] = labels\n",
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tgcjj5S3YvVL",
        "outputId": "ff297d01-a941-4aca-c6c4-a27d7b7e6940"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0: 5264 times\n",
            "1: 4294 times\n",
            "2: 2442 times\n"
          ]
        }
      ],
      "source": [
        "unique_elements, counts = np.unique(df_train['label'], return_counts=True)\n",
        "\n",
        "# Print the results\n",
        "for element, count in zip(unique_elements, counts):\n",
        "    print(f\"{element}: {count} times\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Target label for each day\n",
        "\n",
        "Two values *a* and *b* are calculated. The mean and standard deviation of the difference between adjacent days are calculated. Then a and b are set 0.2 and 1 standard deviation away from the mean. Only when the target is between and b, a trade is executed. This acts as a risk prevention method as trades are not executed for very small or very large price fluctuations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5X87XwGHYvVM",
        "outputId": "5160b29e-c054-47a3-bd48-dc56c6489ed2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "20.891537364334937\n",
            "105.84558914686845\n"
          ]
        }
      ],
      "source": [
        "avg_diff=df.iloc[:len(df_train), 3].diff().mean()\n",
        "std_dev=df.iloc[:len(df_train), 3].diff().std()\n",
        "a = avg_diff + 0.2*std_dev\n",
        "b = avg_diff + std_dev\n",
        "print(a)\n",
        "print(b)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y8K2--QzZ3Tx",
        "outputId": "334683fb-8f43-4b08-a460-cd7d5b99569e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\ACER\\AppData\\Local\\Temp\\ipykernel_8508\\3014275574.py:24: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_train['target']=l\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "# 0 buy 1 sell 2 hold\n",
        "\n",
        "l=[]\n",
        "\n",
        "# for i in range(len(df_train) - 1):\n",
        "#   if(df_train.iloc[i]['price'] > df_train.iloc[i+1]['price']):\n",
        "#     l.append(0)\n",
        "#   else:\n",
        "#     l.append(1)\n",
        "\n",
        "close = list(df.iloc[:len(df_train), 3])\n",
        "for i in range(len(df_train)-1):\n",
        "    if close[i] > close[i+1]:\n",
        "      if (close[i] - close[i+1])>a and (close[i] - close[i+1])<b:\n",
        "        l.append(1)\n",
        "      else:\n",
        "        l.append(2)\n",
        "    else:\n",
        "      if (close[i+1] - close[i])>a and (close[i+1] - close[i])<b:\n",
        "        l.append(0)\n",
        "      else:\n",
        "        l.append(2)\n",
        "l.append(1)\n",
        "df_train['target']=l\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hCa1Qw8KZ1rm",
        "outputId": "d91975ff-17e5-424f-f9a8-969605b0b32f"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "    }\n",
              "\n",
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              "        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>RSI7</th>\n",
              "      <th>RSI14</th>\n",
              "      <th>RSI21</th>\n",
              "      <th>MACD_HIST</th>\n",
              "      <th>AD</th>\n",
              "      <th>William_Percent_Range</th>\n",
              "      <th>label</th>\n",
              "      <th>target</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",
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              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-03 23:30:00</th>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2018-01-04 05:30:00</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2018-01-04 07:30:00</th>\n",
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              "    <tr>\n",
              "      <th>2020-10-03 07:30:00</th>\n",
              "      <td>51.274169</td>\n",
              "      <td>46.813901</td>\n",
              "      <td>46.215536</td>\n",
              "      <td>8.170355</td>\n",
              "      <td>-0.229177</td>\n",
              "      <td>-38.673273</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2020-10-03 09:30:00</th>\n",
              "      <td>48.727798</td>\n",
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              "      <td>45.540351</td>\n",
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              "      <td>-43.909992</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2020-10-03 11:30:00</th>\n",
              "      <td>47.066873</td>\n",
              "      <td>45.146864</td>\n",
              "      <td>45.118804</td>\n",
              "      <td>9.402547</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2020-10-03 13:30:00</th>\n",
              "      <td>47.044673</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2020-10-03 15:30:00</th>\n",
              "      <td>49.328539</td>\n",
              "      <td>45.997214</td>\n",
              "      <td>45.652618</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12000 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                          RSI7      RSI14      RSI21  MACD_HIST        AD   \n",
              "datetime                                                                    \n",
              "2018-01-03 23:30:00  52.426274  58.614210  60.010717 -28.660792  0.073790  \\\n",
              "2018-01-04 01:30:00  59.947740  62.132386  62.595713 -28.392790  0.089636   \n",
              "2018-01-04 03:30:00  59.586946  61.946224  62.456555 -30.774180  0.072928   \n",
              "2018-01-04 05:30:00  67.358353  65.699054  65.210157 -18.024761  0.119206   \n",
              "2018-01-04 07:30:00  64.376699  64.289475  64.189629 -15.867977  0.112630   \n",
              "...                        ...        ...        ...        ...       ...   \n",
              "2020-10-03 07:30:00  51.274169  46.813901  46.215536   8.170355 -0.229177   \n",
              "2020-10-03 09:30:00  48.727798  45.792783  45.540351   9.271544 -0.219222   \n",
              "2020-10-03 11:30:00  47.066873  45.146864  45.118804   9.402547 -0.154434   \n",
              "2020-10-03 13:30:00  47.044673  45.138727  45.113601   9.481728 -0.024403   \n",
              "2020-10-03 15:30:00  49.328539  45.997214  45.652618  10.084685 -0.107765   \n",
              "\n",
              "                     William_Percent_Range  label  target  \n",
              "datetime                                                   \n",
              "2018-01-03 23:30:00             -39.253181      2       2  \n",
              "2018-01-04 01:30:00             -31.810880      2       2  \n",
              "2018-01-04 03:30:00             -33.523100      2       2  \n",
              "2018-01-04 05:30:00             -11.700473      1       1  \n",
              "2018-01-04 07:30:00             -16.205639      1       2  \n",
              "...                                    ...    ...     ...  \n",
              "2020-10-03 07:30:00             -38.673273      2       2  \n",
              "2020-10-03 09:30:00             -43.909992      2       2  \n",
              "2020-10-03 11:30:00             -47.099505      2       2  \n",
              "2020-10-03 13:30:00             -32.017699      2       2  \n",
              "2020-10-03 15:30:00             -28.030973      2       1  \n",
              "\n",
              "[12000 rows x 8 columns]"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 52.42627418  58.61420998  60.01071742 -28.66079225   0.07378959\n",
            " -39.25318068]\n"
          ]
        }
      ],
      "source": [
        "print(X_train[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 5.24262742e+01  5.86142100e+01  6.00107174e+01 -2.86607923e+01\n",
            "   7.37895905e-02 -3.92531807e+01]\n",
            " [ 5.99477403e+01  6.21323860e+01  6.25957135e+01 -2.83927896e+01\n",
            "   8.96363101e-02 -3.18108797e+01]\n",
            " [ 5.95869463e+01  6.19462237e+01  6.24565550e+01 -3.07741803e+01\n",
            "   7.29279537e-02 -3.35231003e+01]\n",
            " ...\n",
            " [ 4.70668726e+01  4.51468637e+01  4.51188039e+01  9.40254671e+00\n",
            "  -1.54433773e-01 -4.70995045e+01]\n",
            " [ 4.70446731e+01  4.51387274e+01  4.51136009e+01  9.48172827e+00\n",
            "  -2.44030831e-02 -3.20176991e+01]\n",
            " [ 4.93285390e+01  4.59972144e+01  4.56526178e+01  1.00846847e+01\n",
            "  -1.07764884e-01 -2.80309735e+01]]\n"
          ]
        }
      ],
      "source": [
        "print(X_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Training the Model\n",
        "\n",
        "Now for each trading instance, the cluster it belongs to is identified. Now the previous 300 entries(i.e. one quarter) belonging to the same cluster as the test instance are gathered and a XGBoost model is trained on them. This predicts the label as 0 (buy), 1 (sell) or 2 (hold).\n",
        "Based on this, trades are executed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Lybr53brYwAr"
      },
      "outputs": [],
      "source": [
        "def predict(x):\n",
        "  label = kmeans.predict(x)[0]\n",
        "\n",
        "  i = len(X_train) - 1\n",
        "  m = len(df_train.columns)\n",
        "  window = np.zeros((300, m - 2))\n",
        "  y = []\n",
        "  cnt = 0\n",
        "  while(i>0 and cnt<300):\n",
        "    if(df_train['label'][i] == label):\n",
        "      window[cnt] = X_train[i]\n",
        "      y.append(df_train['target'][i])\n",
        "      cnt = cnt + 1\n",
        "    i = i - 1\n",
        "  if(len(np.unique(y)) == 1):\n",
        "    return [y[0]]\n",
        "  unique_elements, counts = np.unique(y, return_counts=True)\n",
        "\n",
        "  X_train_clf = window\n",
        "  \n",
        "  dtrain = xgb.DMatrix(X_train_clf, label = y)\n",
        "  params = {\n",
        "    'objective': 'multi:softmax',\n",
        "    'num_class': len(set(y)),\n",
        "    'eta': 0.3,\n",
        "    'max_depth': 3,\n",
        "    'eval_metric': 'merror'\n",
        "}\n",
        "  num_round = 100\n",
        "  model = xgb.train(params, dtrain, num_round)\n",
        "  \n",
        "  d_test = xgb.DMatrix(x)\n",
        "  y_pred = model.predict(d_test)\n",
        "\n",
        "  return y_pred,label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shS7NGPyi4A6",
        "outputId": "eff6217f-4cae-4d88-9846-6abcc62cf2d7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0: 2726 times\n",
            "1: 2604 times\n",
            "2: 6670 times\n"
          ]
        }
      ],
      "source": [
        "unique_elements, counts = np.unique(df_train['target'], return_counts=True)\n",
        "\n",
        "# Print the results\n",
        "for element, count in zip(unique_elements, counts):\n",
        "    print(f\"{element}: {count} times\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Testing the model\n",
        "\n",
        "The first 12000 instances were utilized for the training part. For the remaining part the model is tested. Based on the buy or signal received the model executes trades."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "K-8_4cvdlCBA"
      },
      "outputs": [],
      "source": [
        "warnings.simplefilter('ignore')\n",
        "\n",
        "initial_amount = 1000\n",
        "\n",
        "amt = initial_amount\n",
        "buy_signal = None\n",
        "buy_datetime = None\n",
        "sell_datetime = None\n",
        "\n",
        "trades=[]\n",
        "l=[]\n",
        "pred=[]\n",
        "\n",
        "buy=True\n",
        "sell=False\n",
        "trades=[]\n",
        "\n",
        "for i in range(len(df_train), len(df)-1):\n",
        "  x = df.iloc[i, 5:len(df.columns)-1]\n",
        "  prediction,label = predict(x.to_numpy().reshape(1,-1))\n",
        "  prediction = prediction[0]\n",
        "  pred.append(prediction)\n",
        "  l.append(label)\n",
        "  if(prediction == 0 and buy):\n",
        "    bp=df['price'][i]\n",
        "    buy=False\n",
        "    sell=True\n",
        "  elif(prediction == 1 and sell):\n",
        "    sp=df['price'][i]\n",
        "    trades.append({'buy':bp,'sell':sp})\n",
        "    buy=True\n",
        "    sell=False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {},
      "outputs": [],
      "source": [
        "def trading(init_amt, trades):\n",
        "  amt = init_amt\n",
        "  am=[]\n",
        "  profitable = []\n",
        "  loss_trade = []\n",
        "  transaction_cost = 0\n",
        "  diff = []\n",
        "  for i in trades:\n",
        "    if(i['sell'] - i['buy'] > 0):\n",
        "      profitable.append((amt/i['buy'])*(i['sell']-i['buy']))\n",
        "    else:\n",
        "      loss_trade.append((amt/i['buy'])*(i['sell']-i['buy']))\n",
        "    diff.append((amt/i['buy'])*(i['sell']-i['buy']))\n",
        "    transaction_cost += (amt/i['buy'])*0.0015*(i['buy'] + i['sell'])\n",
        "    amt+=(amt/i['buy'])*(i['sell']-i['buy'])\n",
        "    \n",
        "    am.append(amt)\n",
        "  plt.plot(am)\n",
        "  plt.xlabel('Trade')\n",
        "  plt.ylabel('Portfolio amount in USD')\n",
        "  plt.title('Portfolio vs Trade number')\n",
        "  drawdowns = []\n",
        "  diff = np.array(diff)\n",
        "  profitable = np.array(profitable)\n",
        "  loss_trade = np.array(loss_trade)\n",
        "  for i in range(1, len(am)):\n",
        "      peak_value = max(am[:i + 1])\n",
        "      drawdown = (am[i] - peak_value) / peak_value * 100\n",
        "      drawdowns.append(drawdown)\n",
        "  \n",
        "  max_drawdown = min(drawdowns)\n",
        "  max_profit = np.max(diff)\n",
        "  max_loss = np.min(diff)\n",
        "  avg_win = np.mean(profitable)\n",
        "  avg_loss = np.mean(loss_trade)\n",
        "  sharpe=np.mean(diff)/np.std(diff)\n",
        "  \n",
        "  average_return = np.mean(diff)\n",
        "  downside_returns = [r for r in diff if r < 0]\n",
        "  downside_deviation = np.std(downside_returns) if len(downside_returns) > 0 else 0\n",
        "  sortino_ratio = average_return / downside_deviation if downside_deviation > 0 else 0\n",
        "  \n",
        "  buy_and_hold = (initial_amount/df['price'][len(df_train)]*(df['price'][len(df) - 1] - df['price'][len(df_train)])) - init_amt\n",
        "  \n",
        "  \n",
        "  print(f'Gross Profit: {am[-1] - init_amt}')\n",
        "  print(f'Net Profit: {am[-1] - init_amt - transaction_cost}')\n",
        "  print(f'Drawdown: {-1*max_drawdown}%')\n",
        "  print(f'Number of Trades: {len(trades)}')\n",
        "  print(f'Win rate: {100*len(profitable)/len(trades)}%')\n",
        "  print(f'Maximum Profit: {max_profit}')\n",
        "  print(f'Average Winning Trade: {avg_win}')\n",
        "  print(f'Average Losing Trade: {avg_loss}')\n",
        "  print(f'Maximum Loss: {max_loss}')\n",
        "  print(f'Sharpe Ratio: {sharpe}')\n",
        "  print(f'Sortino Ratio: {sortino_ratio}')\n",
        "  print(f'Buy and Hold return: {100 * buy_and_hold/ init_amt}%')\n",
        "  print(f'risk-reward', len(profitable)/len(loss_trade))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Gross Profit: 7823.715179536193\n",
            "Net Profit: 2636.119353946776\n",
            "Drawdown: 36.92283068387092%\n",
            "Number of Trades: 313\n",
            "Win rate: 58.466453674121404%\n",
            "Maximum Profit: 1289.2104153426226\n",
            "Average Winning Trade: 140.3899002144245\n",
            "Average Losing Trade: -137.4433581515653\n",
            "Maximum Loss: -1414.7242219244627\n",
            "Sharpe Ratio: 0.10623663507875687\n",
            "Sortino Ratio: 0.12564159994368873\n",
            "Buy and Hold return: 147.5107598389013%\n",
            "risk-reward 1.4076923076923078\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "trading(1000, trades)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The XGBoost model is also trained on the full training data as a control for our clustering model. This was a catastrophic failure, resulting in an overall loss. This further strengthens the reliability of the clustering model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [],
      "source": [
        "y = df_train['target']   \n",
        "dtrain_full = xgb.DMatrix(X_train, label = y)\n",
        "params = {\n",
        "'objective': 'multi:softmax',\n",
        "'num_class': len(set(y)),\n",
        "'eta': 0.3,\n",
        "'max_depth': 3,\n",
        "'eval_metric': 'merror'\n",
        "}\n",
        "num_round = 100\n",
        "model = xgb.train(params, dtrain_full, num_round)\n",
        "def predict_full(x):\n",
        "    d_test = xgb.DMatrix(x)\n",
        "    y_pred = model.predict(d_test)\n",
        "    return y_pred, label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "SA3te9eduVOo"
      },
      "outputs": [],
      "source": [
        "warnings.simplefilter('ignore')\n",
        "\n",
        "initial_amount = 1000\n",
        "amt = initial_amount\n",
        "buy=True\n",
        "sell=False\n",
        "trades_full=[]\n",
        "l_full=[]\n",
        "pred_full=[]\n",
        "for i in range(len(df_train), len(df)-1):\n",
        "  x = df.iloc[i, 5:len(df.columns)-1]\n",
        "  prediction,label = predict_full(x.to_numpy().reshape(1,-1))\n",
        "  prediction = prediction[0]\n",
        "  pred_full.append(prediction)\n",
        "  l_full.append(label)\n",
        "  if(prediction == 0 and buy):\n",
        "    bp=df['price'][i]\n",
        "    buy=False\n",
        "    sell=True\n",
        "  elif(prediction == 1 and sell):\n",
        "    sp=df['price'][i]\n",
        "    trades_full.append({'buy':bp,'sell':sp})\n",
        "    buy=True\n",
        "    sell=False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Gross Profit: -234.97399163270836\n",
            "Net Profit: -469.9467547563579\n",
            "Drawdown: 57.3169674136299%\n",
            "Number of Trades: 60\n",
            "Win rate: 58.333333333333336%\n",
            "Maximum Profit: 129.28715503901086\n",
            "Maximum Loss: -539.0572808451795\n",
            "Sharpe Ratio: -0.0429102383562824\n",
            "Sortino Ratio: -0.037805394954402986\n",
            "Buy and Hold return: 247.5107598389013%\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "trading(1000, trades_full)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}