Complete_Future_Backtest.ipynb

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    "colab": {
      "name": "Complete Future Backtest.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CryptoRobotFr/General-code-trading-bot/blob/main/Complete_Future_Backtest.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4AYk2bKelB6x"
      },
      "source": [
        "pip install python-binance"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcJvdxyRlEUY"
      },
      "source": [
        "pip install ta"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vL2r-6X3k-PU"
      },
      "source": [
        "<h1>Load yours data</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wf_ab2Fjk-PY",
        "outputId": "07f05b84-72c6-45bc-d2f0-6f9b22cc7074"
      },
      "source": [
        "# -- Import --\n",
        "import pandas as pd\n",
        "from binance.client import Client\n",
        "import ta\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# -- Define Binance Client --\n",
        "client = Client()\n",
        "\n",
        "# -- You can change the crypto pair ,the start date and the time interval below --\n",
        "pairName = \"BTCUSDT\"\n",
        "startDate = \"01 january 2017\"\n",
        "timeInterval = Client.KLINE_INTERVAL_1HOUR\n",
        "\n",
        "# -- Load all price data from binance API --\n",
        "klinesT = client.get_historical_klines(pairName, timeInterval, startDate)\n",
        "\n",
        "# -- Define your dataset --\n",
        "df = pd.DataFrame(klinesT, columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_av', 'trades', 'tb_base_av', 'tb_quote_av', 'ignore' ])\n",
        "df['close'] = pd.to_numeric(df['close'])\n",
        "df['high'] = pd.to_numeric(df['high'])\n",
        "df['low'] = pd.to_numeric(df['low'])\n",
        "df['open'] = pd.to_numeric(df['open'])\n",
        "\n",
        "# -- Set the date to index --\n",
        "df = df.set_index(df['timestamp'])\n",
        "df.index = pd.to_datetime(df.index, unit='ms')\n",
        "del df['timestamp']\n",
        "\n",
        "print(\"Data loaded 100%\")\n",
        "\n",
        "# -- Uncomment the line below if you want to check your price dataset --\n",
        "# df"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data loaded 100%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R6_21o53k-Pb"
      },
      "source": [
        "<h1>Define your indicators</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "dSZNGfBgk-Pc",
        "outputId": "925d1836-6d90-4e77-d3a7-6532633f2895"
      },
      "source": [
        "# -- Drop all columns we do not need --\n",
        "df.drop(df.columns.difference(['open','high','low','close','volume']), 1, inplace=True)\n",
        "\n",
        "# -- Indicators, you can edit every value --\n",
        "# -- Exponential Moving Average --\n",
        "df['EMA1']= ta.trend.ema_indicator(close=df['close'], window=7)\n",
        "df['EMA2']= ta.trend.ema_indicator(close=df['close'], window=30)\n",
        "df['EMA3']= ta.trend.ema_indicator(close=df['close'], window=50)\n",
        "df['EMA4']= ta.trend.ema_indicator(close=df['close'], window=100)\n",
        "df['EMA5']= ta.trend.ema_indicator(close=df['close'], window=121)\n",
        "df['EMA6']= ta.trend.ema_indicator(close=df['close'], window=200)\n",
        "# -- Stochasitc RSI --\n",
        "df['STOCH_RSI'] = ta.momentum.stochrsi(close=df['close'], window=14, smooth1=3, smooth2=3)\n",
        "\n",
        "print(\"Indicators loaded 100%\")\n",
        "\n",
        "# -- Uncomment the line below if you want to check your dataset with indicators --\n",
        "df"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Indicators loaded 100%\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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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>volume</th>\n",
              "      <th>EMA1</th>\n",
              "      <th>EMA2</th>\n",
              "      <th>EMA3</th>\n",
              "      <th>EMA4</th>\n",
              "      <th>EMA5</th>\n",
              "      <th>EMA6</th>\n",
              "      <th>STOCH_RSI</th>\n",
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              "    <tr>\n",
              "      <th>timestamp</th>\n",
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              "    <tr>\n",
              "      <th>2017-08-17 04:00:00</th>\n",
              "      <td>4261.48</td>\n",
              "      <td>4313.62</td>\n",
              "      <td>4261.32</td>\n",
              "      <td>4308.83</td>\n",
              "      <td>47.18100900</td>\n",
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              "    <tr>\n",
              "      <th>2017-08-17 05:00:00</th>\n",
              "      <td>4308.83</td>\n",
              "      <td>4328.69</td>\n",
              "      <td>4291.37</td>\n",
              "      <td>4315.32</td>\n",
              "      <td>23.23491600</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>2017-08-17 06:00:00</th>\n",
              "      <td>4330.29</td>\n",
              "      <td>4345.45</td>\n",
              "      <td>4309.37</td>\n",
              "      <td>4324.35</td>\n",
              "      <td>7.22969100</td>\n",
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              "    <tr>\n",
              "      <th>2017-08-17 07:00:00</th>\n",
              "      <td>4316.62</td>\n",
              "      <td>4349.99</td>\n",
              "      <td>4287.41</td>\n",
              "      <td>4349.99</td>\n",
              "      <td>4.44324900</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2017-08-17 08:00:00</th>\n",
              "      <td>4333.32</td>\n",
              "      <td>4377.85</td>\n",
              "      <td>4333.32</td>\n",
              "      <td>4360.69</td>\n",
              "      <td>0.97280700</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>2021-09-16 03:00:00</th>\n",
              "      <td>47915.98</td>\n",
              "      <td>47941.05</td>\n",
              "      <td>47337.58</td>\n",
              "      <td>47615.08</td>\n",
              "      <td>2620.73891000</td>\n",
              "      <td>47897.225428</td>\n",
              "      <td>47521.747895</td>\n",
              "      <td>47106.468406</td>\n",
              "      <td>46623.085765</td>\n",
              "      <td>46584.160558</td>\n",
              "      <td>46740.857989</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>2021-09-16 04:00:00</th>\n",
              "      <td>47615.07</td>\n",
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              "      <td>47583.11</td>\n",
              "      <td>48286.99</td>\n",
              "      <td>3264.26820000</td>\n",
              "      <td>47994.666571</td>\n",
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              "      <td>0.486275</td>\n",
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              "      <th>2021-09-16 05:00:00</th>\n",
              "      <td>48287.00</td>\n",
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              "      <td>48261.89</td>\n",
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              "      <td>46771.223955</td>\n",
              "      <td>0.615602</td>\n",
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              "      <th>2021-09-16 06:00:00</th>\n",
              "      <td>48261.90</td>\n",
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              "      <td>48135.279321</td>\n",
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              "      <td>0.694394</td>\n",
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              "      <th>2021-09-16 07:00:00</th>\n",
              "      <td>48356.69</td>\n",
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              "      <td>46803.001032</td>\n",
              "      <td>0.727007</td>\n",
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              "<p>35664 rows × 12 columns</p>\n",
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            ],
            "text/plain": [
              "                         open      high  ...          EMA6  STOCH_RSI\n",
              "timestamp                                ...                         \n",
              "2017-08-17 04:00:00   4261.48   4313.62  ...           NaN        NaN\n",
              "2017-08-17 05:00:00   4308.83   4328.69  ...           NaN        NaN\n",
              "2017-08-17 06:00:00   4330.29   4345.45  ...           NaN        NaN\n",
              "2017-08-17 07:00:00   4316.62   4349.99  ...           NaN        NaN\n",
              "2017-08-17 08:00:00   4333.32   4377.85  ...           NaN        NaN\n",
              "...                       ...       ...  ...           ...        ...\n",
              "2021-09-16 03:00:00  47915.98  47941.05  ...  46740.857989   0.000000\n",
              "2021-09-16 04:00:00  47615.07  48557.00  ...  46756.242387   0.486275\n",
              "2021-09-16 05:00:00  48287.00  48374.08  ...  46771.223955   0.615602\n",
              "2021-09-16 06:00:00  48261.90  48444.00  ...  46786.999836   0.694394\n",
              "2021-09-16 07:00:00  48356.69  48500.00  ...  46803.001032   0.727007\n",
              "\n",
              "[35664 rows x 12 columns]"
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          "metadata": {},
          "execution_count": 5
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUt8RHMok-Pc"
      },
      "source": [
        "<h1>RUN the BackTest</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "qA6zFOOok-Pd",
        "outputId": "4dd14a55-231e-421e-9cf4-a2b1794b00cb"
      },
      "source": [
        "dfTest = df.copy()\n",
        "\n",
        "# -- If you want to run your BackTest on a specific period, uncomment the line below --\n",
        "# dfTest = df['2021-03-01':'2021-09-01']\n",
        "\n",
        "# -- Definition of dt, that will be the dataset to do your trades analyses --\n",
        "dt = None\n",
        "dt = pd.DataFrame(columns=['date', 'position', 'reason',\n",
        "                           'price', 'frais', 'wallet', 'drawBack'])\n",
        "\n",
        "# -- You can change variables below --\n",
        "leverage = 3\n",
        "wallet = 1000\n",
        "makerFee = 0.0002\n",
        "takerFee = 0.0007\n",
        "\n",
        "# -- Do not touch these values --\n",
        "initalWallet = wallet\n",
        "lastAth = wallet\n",
        "previousRow = dfTest.iloc[0]\n",
        "stopLoss = 0\n",
        "takeProfit = 500000\n",
        "orderInProgress = ''\n",
        "longIniPrice = 0\n",
        "shortIniPrice = 0\n",
        "longLiquidationPrice = 500000\n",
        "shortLiquidationPrice = 0\n",
        "\n",
        "# -- Condition to open Market LONG --\n",
        "def openLongCondition(row, previousRow):\n",
        "    if (row['EMA1'] > row['EMA2'] \n",
        "    and row['EMA2'] > row['EMA3'] \n",
        "    and row['EMA3'] > row['EMA4'] \n",
        "    and row['EMA4'] > row['EMA5'] \n",
        "    and row['EMA5'] > row['EMA6'] \n",
        "    and row['STOCH_RSI'] < 0.82):\n",
        "        return True\n",
        "    else:\n",
        "        return False\n",
        "\n",
        "# -- Condition to close Market LONG --\n",
        "def closeLongCondition(row, previousRow):\n",
        "    if row['EMA6'] > row['EMA1']:\n",
        "        return True\n",
        "    else:\n",
        "        return False\n",
        "\n",
        "# -- Condition to open Market SHORT --\n",
        "def openShortCondition(row, previousRow):\n",
        "    if ( row['EMA6'] > row['EMA5'] \n",
        "    and row['EMA5'] > row['EMA4'] \n",
        "    and row['EMA4'] > row['EMA3'] \n",
        "    and row['EMA3'] > row['EMA2'] \n",
        "    and row['EMA2'] > row['EMA1'] \n",
        "    and row['STOCH_RSI'] > 0.2 ):\n",
        "        return True\n",
        "    else:\n",
        "        return False\n",
        "\n",
        "# -- Condition to close Market SHORT --\n",
        "def closeShortCondition(row, previousRow):\n",
        "    if row['EMA1'] > row['EMA6']:\n",
        "        return True\n",
        "    else:\n",
        "        return False\n",
        "\n",
        "# -- Iteration on all your price dataset (df) --\n",
        "for index, row in dfTest.iterrows():\n",
        "\n",
        "    # -- If there is an order in progress --\n",
        "    if orderInProgress != '':\n",
        "        # -- Check if there is a LONG order in progress --\n",
        "        if orderInProgress == 'LONG':\n",
        "            # -- Check Liquidation --\n",
        "            if row['low'] < longLiquidationPrice:\n",
        "                print('/!\\ YOUR LONG HAVE BEEN LIQUIDATED the',index)\n",
        "                break\n",
        "            \n",
        "            # -- Check If you have to close the LONG --\n",
        "            if closeLongCondition(row, previousRow) == True:\n",
        "                orderInProgress = ''\n",
        "                closePrice = row['close']\n",
        "                closePriceWithFee = row['close'] - takerFee * row['close']\n",
        "                pr_change = (closePriceWithFee - longIniPrice) / longIniPrice\n",
        "                wallet = wallet + wallet*pr_change*leverage\n",
        "\n",
        "                # -- You can uncomment the line below if you want to see logs --\n",
        "                # print('Close LONG at',closePrice,\"the\", index, '| wallet :', wallet,\n",
        "                #       '| result :', pr_change*100*leverage)\n",
        "\n",
        "\n",
        "                # -- Check if your wallet hit a new ATH to know the drawBack --\n",
        "                if wallet > lastAth:\n",
        "                    lastAth = wallet\n",
        "                \n",
        "                # -- Add the trade to DT to analyse it later --\n",
        "                myrow = {'date': index, 'position': \"LONG\", 'reason': 'Close Long Market', 'price': closePrice,\n",
        "                        'frais': takerFee * wallet * leverage, 'wallet': wallet, 'drawBack': (wallet-lastAth)/lastAth}\n",
        "                dt = dt.append(myrow, ignore_index=True)\n",
        "\n",
        "        # -- Check if there is a SHORT order in progress --\n",
        "        elif orderInProgress == 'SHORT':\n",
        "            # -- Check Liquidation --\n",
        "            if row['high'] > shortLiquidationPrice:\n",
        "                print('/!\\ YOUR SHORT HAVE BEEN LIQUIDATED the',index)\n",
        "                break\n",
        "\n",
        "            # -- Check If you have to close the SHORT --\n",
        "            if closeShortCondition(row, previousRow) == True:\n",
        "                orderInProgress = ''\n",
        "                closePrice = row['close']\n",
        "                closePriceWithFee = row['close'] + takerFee * row['close']\n",
        "                pr_change = -(closePriceWithFee - shortIniPrice) / shortIniPrice\n",
        "                wallet = wallet + wallet*pr_change*leverage\n",
        "\n",
        "                # -- You can uncomment the line below if you want to see logs --\n",
        "                # print('Close SHORT at',closePrice,\"the\", index, '| wallet :', wallet,\n",
        "                #       '| result :', pr_change*100*leverage)\n",
        "\n",
        "                # -- Check if your wallet hit a new ATH to know the drawBack --\n",
        "                if wallet > lastAth:\n",
        "                    lastAth = wallet\n",
        "\n",
        "                # -- Add the trade to DT to analyse it later --\n",
        "                myrow = {'date': index, 'position': \"SHORT\", 'reason': 'Close Short Market', 'price': closePrice,\n",
        "                        'frais': takerFee * wallet * leverage, 'wallet': wallet, 'drawBack': (wallet-lastAth)/lastAth}\n",
        "                dt = dt.append(myrow, ignore_index=True)\n",
        "\n",
        "    # -- If there is NO order in progress --\n",
        "    if orderInProgress == '':\n",
        "        # -- Check If you have to open a LONG --\n",
        "        if openLongCondition(row, previousRow) == True:\n",
        "            orderInProgress = 'LONG'\n",
        "            closePrice = row['close']\n",
        "            longIniPrice = row['close'] + takerFee * row['close']\n",
        "            tokenAmount = (wallet * leverage) / row['close']\n",
        "            longLiquidationPrice = longIniPrice - (wallet/tokenAmount)\n",
        "\n",
        "            # -- You can uncomment the line below if you want to see logs --\n",
        "            # print('Open LONG at', closePrice, '$ the', index, '| Liquidation price :', longLiquidationPrice)\n",
        "\n",
        "            # -- Add the trade to DT to analyse it later --\n",
        "            myrow = {'date': index, 'position': \"Open Long\", 'reason': 'Open Long Market', 'price': closePrice,\n",
        "                     'frais': takerFee * wallet * leverage, 'wallet': wallet, 'drawBack': (wallet-lastAth)/lastAth}\n",
        "            dt = dt.append(myrow, ignore_index=True)\n",
        "        \n",
        "        # -- Check If you have to open a SHORT --\n",
        "        if openShortCondition(row, previousRow) == True:\n",
        "            orderInProgress = 'SHORT'\n",
        "            closePrice = row['close']\n",
        "            shortIniPrice = row['close'] - takerFee * row['close']\n",
        "            tokenAmount = (wallet * leverage) / row['close']\n",
        "            shortLiquidationPrice = shortIniPrice + (wallet/tokenAmount)\n",
        "            # -- You can uncomment the line below if you want to see logs --\n",
        "            # print('Open SHORT', closePrice, '$ the', index, '| Liquidation price :', shortLiquidationPrice)\n",
        "\n",
        "            # -- Add the trade to DT to analyse it later --\n",
        "            myrow = {'date': index, 'position': \"Open Short\", 'reason': 'Open Short Market', 'price': closePrice,\n",
        "                     'frais': takerFee * wallet * leverage, 'wallet': wallet, 'drawBack': (wallet-lastAth)/lastAth}\n",
        "            dt = dt.append(myrow, ignore_index=True)\n",
        "\n",
        "\n",
        "# -- BackTest Analyses --\n",
        "dt = dt.set_index(dt['date'])\n",
        "dt.index = pd.to_datetime(dt.index)\n",
        "dt['resultat%'] = dt['wallet'].pct_change()*100\n",
        "\n",
        "dt['tradeIs'] = ''\n",
        "dt.loc[dt['resultat%'] > 0, 'tradeIs'] = 'Good'\n",
        "dt.loc[dt['resultat%'] < 0, 'tradeIs'] = 'Bad'\n",
        "\n",
        "iniClose = dfTest.iloc[0]['close']\n",
        "lastClose = dfTest.iloc[len(dfTest)-1]['close']\n",
        "holdPercentage = ((lastClose - iniClose)/iniClose)\n",
        "holdWallet = holdPercentage * leverage * initalWallet\n",
        "algoPercentage = ((wallet - initalWallet)/initalWallet)\n",
        "vsHoldPercentage = ((wallet - holdWallet)/holdWallet) * 100\n",
        "\n",
        "try:\n",
        "    tradesPerformance = round(dt.loc[(dt['tradeIs'] == 'Good') | (dt['tradeIs'] == 'Bad'), 'resultat%'].sum()\n",
        "            / dt.loc[(dt['tradeIs'] == 'Good') | (dt['tradeIs'] == 'Bad'), 'resultat%'].count(), 2)\n",
        "except:\n",
        "    tradesPerformance = 0\n",
        "    print(\"/!\\ There is no Good or Bad Trades in your BackTest, maybe a problem...\")\n",
        "\n",
        "try:\n",
        "    TotalGoodTrades = dt.groupby('tradeIs')['date'].nunique()['Good']\n",
        "    AveragePercentagePositivTrades = round(dt.loc[dt['tradeIs'] == 'Good', 'resultat%'].sum()\n",
        "                                           / dt.loc[dt['tradeIs'] == 'Good', 'resultat%'].count(), 2)\n",
        "    idbest = dt.loc[dt['tradeIs'] == 'Good', 'resultat%'].idxmax()\n",
        "    bestTrade = str(\n",
        "        round(dt.loc[dt['tradeIs'] == 'Good', 'resultat%'].max(), 2))\n",
        "except:\n",
        "    TotalGoodTrades = 0\n",
        "    AveragePercentagePositivTrades = 0\n",
        "    idbest = ''\n",
        "    bestTrade = 0\n",
        "    print(\"/!\\ There is no Good Trades in your BackTest, maybe a problem...\")\n",
        "\n",
        "try:\n",
        "    TotalBadTrades = dt.groupby('tradeIs')['date'].nunique()['Bad']\n",
        "    AveragePercentageNegativTrades = round(dt.loc[dt['tradeIs'] == 'Bad', 'resultat%'].sum()\n",
        "                                           / dt.loc[dt['tradeIs'] == 'Bad', 'resultat%'].count(), 2)\n",
        "    idworst = dt.loc[dt['tradeIs'] == 'Bad', 'resultat%'].idxmin()\n",
        "    worstTrade = round(dt.loc[dt['tradeIs'] == 'Bad', 'resultat%'].min(), 2)\n",
        "except:\n",
        "    TotalBadTrades = 0\n",
        "    AveragePercentageNegativTrades = 0\n",
        "    idworst = ''\n",
        "    worstTrade = 0\n",
        "    print(\"/!\\ There is no Bad Trades in your BackTest, maybe a problem...\")\n",
        "\n",
        "totalTrades = TotalBadTrades + TotalGoodTrades\n",
        "\n",
        "try:\n",
        "    TotalLongTrades = dt.groupby('position')['date'].nunique()['LONG']\n",
        "    AverageLongTrades = round(dt.loc[dt['position'] == 'LONG', 'resultat%'].sum()\n",
        "                              / dt.loc[dt['position'] == 'LONG', 'resultat%'].count(), 2)\n",
        "    idBestLong = dt.loc[dt['position'] == 'LONG', 'resultat%'].idxmax()\n",
        "    bestLongTrade = str(\n",
        "        round(dt.loc[dt['position'] == 'LONG', 'resultat%'].max(), 2))\n",
        "    idWorstLong = dt.loc[dt['position'] == 'LONG', 'resultat%'].idxmin()\n",
        "    worstLongTrade = str(\n",
        "        round(dt.loc[dt['position'] == 'LONG', 'resultat%'].min(), 2))\n",
        "except:\n",
        "    AverageLongTrades = 0\n",
        "    TotalLongTrades = 0\n",
        "    bestLongTrade = ''\n",
        "    idBestLong = ''\n",
        "    idWorstLong = ''\n",
        "    worstLongTrade = ''\n",
        "    print(\"/!\\ There is no LONG Trades in your BackTest, maybe a problem...\")\n",
        "\n",
        "try:\n",
        "    TotalShortTrades = dt.groupby('position')['date'].nunique()['SHORT']\n",
        "    AverageShortTrades = round(dt.loc[dt['position'] == 'SHORT', 'resultat%'].sum()\n",
        "                               / dt.loc[dt['position'] == 'SHORT', 'resultat%'].count(), 2)\n",
        "    idBestShort = dt.loc[dt['position'] == 'SHORT', 'resultat%'].idxmax()\n",
        "    bestShortTrade = str(\n",
        "        round(dt.loc[dt['position'] == 'SHORT', 'resultat%'].max(), 2))\n",
        "    idWorstShort = dt.loc[dt['position'] == 'SHORT', 'resultat%'].idxmin()\n",
        "    worstShortTrade = str(\n",
        "        round(dt.loc[dt['position'] == 'SHORT', 'resultat%'].min(), 2))\n",
        "except:\n",
        "    AverageShortTrades = 0\n",
        "    TotalShortTrades = 0\n",
        "    bestShortTrade = ''\n",
        "    idBestShort = ''\n",
        "    idWorstShort = ''\n",
        "    worstShortTrade = ''\n",
        "    print(\"/!\\ There is no SHORT Trades in your BackTest, maybe a problem...\")\n",
        "\n",
        "try:\n",
        "    totalGoodLongTrade = dt.groupby(['position', 'tradeIs']).size()['LONG']['Good']\n",
        "except:\n",
        "    totalGoodLongTrade = 0\n",
        "    print(\"/!\\ There is no good LONG Trades in your BackTest, maybe a problem...\")\n",
        "\n",
        "try:\n",
        "    totalBadLongTrade = dt.groupby(['position', 'tradeIs']).size()['LONG']['Bad']\n",
        "except:\n",
        "    totalBadLongTrade = 0\n",
        "    print(\"/!\\ There is no bad LONG Trades in your BackTest, maybe a problem...\")\n",
        "\n",
        "try:\n",
        "    totalGoodShortTrade = dt.groupby(['position', 'tradeIs']).size()['SHORT']['Good']\n",
        "except:\n",
        "    totalGoodShortTrade = 0\n",
        "    print(\"/!\\ There is no good SHORT Trades in your BackTest, maybe a problem...\")\n",
        "\n",
        "try:\n",
        "    totalBadShortTrade = dt.groupby(['position', 'tradeIs']).size()['SHORT']['Bad']\n",
        "except:\n",
        "    totalBadShortTrade = 0\n",
        "    print(\"/!\\ There is no bad SHORT Trades in your BackTest, maybe a problem...\")\n",
        "\n",
        "TotalTrades = TotalGoodTrades + TotalBadTrades\n",
        "winRateRatio = (TotalGoodTrades/TotalTrades) * 100\n",
        "\n",
        "reasons = dt['reason'].unique()\n",
        "\n",
        "print(\"BackTest finished, final wallet :\",wallet,\"$\")\n",
        "dt\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BackTest finished, final wallet : 179636.00921007246 $\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>date</th>\n",
              "      <th>position</th>\n",
              "      <th>reason</th>\n",
              "      <th>price</th>\n",
              "      <th>frais</th>\n",
              "      <th>wallet</th>\n",
              "      <th>drawBack</th>\n",
              "      <th>resultat%</th>\n",
              "      <th>tradeIs</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-08-25 14:00:00</th>\n",
              "      <td>2017-08-25 14:00:00</td>\n",
              "      <td>Open Long</td>\n",
              "      <td>Open Long Market</td>\n",
              "      <td>4305.00</td>\n",
              "      <td>2.100000</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-09-02 20:00:00</th>\n",
              "      <td>2017-09-02 20:00:00</td>\n",
              "      <td>LONG</td>\n",
              "      <td>Close Long Market</td>\n",
              "      <td>4351.19</td>\n",
              "      <td>2.158687</td>\n",
              "      <td>1027.95</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.794606</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-09-04 23:00:00</th>\n",
              "      <td>2017-09-04 23:00:00</td>\n",
              "      <td>Open Short</td>\n",
              "      <td>Open Short Market</td>\n",
              "      <td>4100.11</td>\n",
              "      <td>2.158687</td>\n",
              "      <td>1027.95</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-09-06 01:00:00</th>\n",
              "      <td>2017-09-06 01:00:00</td>\n",
              "      <td>SHORT</td>\n",
              "      <td>Close Short Market</td>\n",
              "      <td>4502.83</td>\n",
              "      <td>1.512633</td>\n",
              "      <td>720.301</td>\n",
              "      <td>-0.299281</td>\n",
              "      <td>-29.928103</td>\n",
              "      <td>Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-09-07 14:00:00</th>\n",
              "      <td>2017-09-07 14:00:00</td>\n",
              "      <td>Open Long</td>\n",
              "      <td>Open Long Market</td>\n",
              "      <td>4669.81</td>\n",
              "      <td>1.512633</td>\n",
              "      <td>720.301</td>\n",
              "      <td>-0.299281</td>\n",
              "      <td>0.000000</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-01 17:00:00</th>\n",
              "      <td>2021-09-01 17:00:00</td>\n",
              "      <td>SHORT</td>\n",
              "      <td>Close Short Market</td>\n",
              "      <td>48730.01</td>\n",
              "      <td>484.521847</td>\n",
              "      <td>230725</td>\n",
              "      <td>-0.579739</td>\n",
              "      <td>-10.890732</td>\n",
              "      <td>Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-02 07:00:00</th>\n",
              "      <td>2021-09-02 07:00:00</td>\n",
              "      <td>Open Long</td>\n",
              "      <td>Open Long Market</td>\n",
              "      <td>49846.27</td>\n",
              "      <td>484.521847</td>\n",
              "      <td>230725</td>\n",
              "      <td>-0.579739</td>\n",
              "      <td>0.000000</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-07 15:00:00</th>\n",
              "      <td>2021-09-07 15:00:00</td>\n",
              "      <td>LONG</td>\n",
              "      <td>Close Long Market</td>\n",
              "      <td>46656.07</td>\n",
              "      <td>389.589105</td>\n",
              "      <td>185519</td>\n",
              "      <td>-0.662081</td>\n",
              "      <td>-19.593078</td>\n",
              "      <td>Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-08 13:00:00</th>\n",
              "      <td>2021-09-08 13:00:00</td>\n",
              "      <td>Open Short</td>\n",
              "      <td>Open Short Market</td>\n",
              "      <td>46315.68</td>\n",
              "      <td>389.589105</td>\n",
              "      <td>185519</td>\n",
              "      <td>-0.662081</td>\n",
              "      <td>0.000000</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-14 17:00:00</th>\n",
              "      <td>2021-09-14 17:00:00</td>\n",
              "      <td>SHORT</td>\n",
              "      <td>Close Short Market</td>\n",
              "      <td>46739.74</td>\n",
              "      <td>377.235619</td>\n",
              "      <td>179636</td>\n",
              "      <td>-0.672796</td>\n",
              "      <td>-3.170901</td>\n",
              "      <td>Bad</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>394 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   date    position  ...  resultat%  tradeIs\n",
              "date                                                 ...                    \n",
              "2017-08-25 14:00:00 2017-08-25 14:00:00   Open Long  ...        NaN         \n",
              "2017-09-02 20:00:00 2017-09-02 20:00:00        LONG  ...   2.794606     Good\n",
              "2017-09-04 23:00:00 2017-09-04 23:00:00  Open Short  ...   0.000000         \n",
              "2017-09-06 01:00:00 2017-09-06 01:00:00       SHORT  ... -29.928103      Bad\n",
              "2017-09-07 14:00:00 2017-09-07 14:00:00   Open Long  ...   0.000000         \n",
              "...                                 ...         ...  ...        ...      ...\n",
              "2021-09-01 17:00:00 2021-09-01 17:00:00       SHORT  ... -10.890732      Bad\n",
              "2021-09-02 07:00:00 2021-09-02 07:00:00   Open Long  ...   0.000000         \n",
              "2021-09-07 15:00:00 2021-09-07 15:00:00        LONG  ... -19.593078      Bad\n",
              "2021-09-08 13:00:00 2021-09-08 13:00:00  Open Short  ...   0.000000         \n",
              "2021-09-14 17:00:00 2021-09-14 17:00:00       SHORT  ...  -3.170901      Bad\n",
              "\n",
              "[394 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jSO-P5zDk-Pl"
      },
      "source": [
        "<h1>Print Complete BackTest Analyses</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nj2MK1PPk-Pn",
        "outputId": "21b2ae70-352d-4e63-ee0a-9ecf236f68ae"
      },
      "source": [
        "print(\"Pair Symbol :\",pairName,)\n",
        "print(\"Period : [\" + str(dfTest.index[0]) + \"] -> [\" +\n",
        "      str(dfTest.index[len(dfTest)-1]) + \"]\")\n",
        "print(\"Starting balance :\", initalWallet, \"$\")\n",
        "\n",
        "print(\"\\n----- General Informations -----\")\n",
        "print(\"Final balance :\", round(wallet, 2), \"$\")\n",
        "print(\"Performance vs US Dollar :\", round(algoPercentage*100, 2), \"%\")\n",
        "print(\"Buy and Hold Performence :\", round(holdPercentage*100, 2),\n",
        "      \"% | with Leverage :\", round(holdPercentage*100, 2)*leverage, \"%\")\n",
        "print(\"Performance vs Buy and Hold :\", round(vsHoldPercentage, 2), \"%\")\n",
        "print(\"Best trade : +\"+bestTrade, \"%, the \", idbest)\n",
        "print(\"Worst trade :\", worstTrade, \"%, the \", idworst)\n",
        "print(\"Worst drawBack :\", str(100*round(dt['drawBack'].min(), 2)), \"%\")\n",
        "print(\"Total fees : \", round(dt['frais'].sum(), 2), \"$\")\n",
        "\n",
        "print(\"\\n----- Trades Informations -----\")\n",
        "print(\"Total trades on period :\",totalTrades)\n",
        "print(\"Number of positive trades :\", TotalGoodTrades)\n",
        "print(\"Number of negative trades : \", TotalBadTrades)\n",
        "print(\"Trades win rate ratio :\", round(winRateRatio, 2), '%')\n",
        "print(\"Average trades performance :\",tradesPerformance,\"%\")\n",
        "print(\"Average positive trades :\", AveragePercentagePositivTrades, \"%\")\n",
        "print(\"Average negative trades :\", AveragePercentageNegativTrades, \"%\")\n",
        "\n",
        "print(\"\\n----- LONG Trades Informations -----\")\n",
        "print(\"Number of LONG trades :\",TotalLongTrades)\n",
        "print(\"Average LONG trades performance :\",AverageLongTrades, \"%\")\n",
        "print(\"Best  LONG trade +\"+bestLongTrade, \"%, the \", idBestLong)\n",
        "print(\"Worst LONG trade\", worstLongTrade, \"%, the \", idWorstLong)\n",
        "print(\"Number of positive LONG trades :\",totalGoodLongTrade)\n",
        "print(\"Number of negative LONG trades :\",totalBadLongTrade)\n",
        "print(\"LONG trade win rate ratio :\", round(totalGoodLongTrade/TotalLongTrades*100, 2), '%')\n",
        "\n",
        "print(\"\\n----- SHORT Trades Informations -----\")\n",
        "print(\"Number of SHORT trades :\",TotalShortTrades)\n",
        "print(\"Average SHORT trades performance :\",AverageShortTrades, \"%\")\n",
        "print(\"Best  SHORT trade +\"+bestShortTrade, \"%, the \", idBestShort)\n",
        "print(\"Worst SHORT trade\", worstShortTrade, \"%, the \", idWorstShort)\n",
        "print(\"Number of positive SHORT trades :\",totalGoodShortTrade)\n",
        "print(\"Number of negative SHORT trades :\",totalBadShortTrade)\n",
        "print(\"SHORT trade win rate ratio :\", round(totalGoodShortTrade/TotalShortTrades*100, 2), '%')\n",
        "\n",
        "print(\"\\n----- Trades Reasons -----\")\n",
        "reasons = dt['reason'].unique()\n",
        "for r in reasons:\n",
        "    print(r+\" number :\", dt.groupby('reason')['date'].nunique()[r])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pair Symbol : BTCUSDT\n",
            "Period : [2017-08-17 04:00:00] -> [2021-09-16 07:00:00]\n",
            "Starting balance : 1000 $\n",
            "\n",
            "----- General Informations -----\n",
            "Final balance : 179636.01 $\n",
            "Performance vs US Dollar : 17863.6 %\n",
            "Buy and Hold Performence : 1023.16 % | with Leverage : 3069.48 %\n",
            "Performance vs Buy and Hold : 485.23 %\n",
            "Best trade : +385.94 %, the  2017-12-19 21:00:00\n",
            "Worst trade : -37.19 %, the  2019-07-03 03:00:00\n",
            "Worst drawBack : -77.0 %\n",
            "Total fees :  76845.49 $\n",
            "\n",
            "----- Trades Informations -----\n",
            "Total trades on period : 197\n",
            "Number of positive trades : 62\n",
            "Number of negative trades :  135\n",
            "Trades win rate ratio : 31.47 %\n",
            "Average trades performance : 7.41 %\n",
            "Average positive trades : 43.64 %\n",
            "Average negative trades : -9.22 %\n",
            "\n",
            "----- LONG Trades Informations -----\n",
            "Number of LONG trades : 99\n",
            "Average LONG trades performance : 14.09 %\n",
            "Best  LONG trade +385.94 %, the  2017-12-19 21:00:00\n",
            "Worst LONG trade -29.89 %, the  2021-01-15 18:00:00\n",
            "Number of positive LONG trades : 37\n",
            "Number of negative LONG trades : 62\n",
            "LONG trade win rate ratio : 37.37 %\n",
            "\n",
            "----- SHORT Trades Informations -----\n",
            "Number of SHORT trades : 98\n",
            "Average SHORT trades performance : 0.67 %\n",
            "Best  SHORT trade +122.74 %, the  2018-12-17 16:00:00\n",
            "Worst SHORT trade -37.19 %, the  2019-07-03 03:00:00\n",
            "Number of positive SHORT trades : 25\n",
            "Number of negative SHORT trades : 73\n",
            "SHORT trade win rate ratio : 25.51 %\n",
            "\n",
            "----- Trades Reasons -----\n",
            "Open Long Market number : 99\n",
            "Close Long Market number : 99\n",
            "Open Short Market number : 98\n",
            "Close Short Market number : 98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3WYP8irk-Po"
      },
      "source": [
        "<h1>Plot to improve ...</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "biPtfHJMk-Po",
        "outputId": "8505db8b-6192-41b7-a823-bcc182c1fd95"
      },
      "source": [
        "dt[['wallet', 'price']].plot(subplots=True, figsize=(20, 10))\n",
        "print(\"\\n----- Plot -----\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "----- Plot -----\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}