Complete_backtest.ipynb

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    "colab": {
      "name": "Complete backtest.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPMptQwCtRuaXatIZy1ztSK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CryptoRobotFr/1-create-personnal-trading-bot/blob/main/Complete_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": "Yfi2qr-OmHJb"
      },
      "source": [
        "pip install python-binance"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GSVP99iMmTt4"
      },
      "source": [
        "pip install ta"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVPXdKzhDgUi"
      },
      "source": [
        "pip install pandas_ta"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XwM1VV4d2V-R"
      },
      "source": [
        "<h1>Import Dataset</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1F0pok5BmY3X",
        "outputId": "45f3978e-6400-42fe-c982-e96ec75267e6"
      },
      "source": [
        "import pandas as pd\n",
        "from binance.client import Client\n",
        "import ta\n",
        "import pandas_ta as pda\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from termcolor import colored\n",
        "\n",
        "client = Client()\n",
        "\n",
        "klinesT = client.get_historical_klines(\"ETHUSDT\", Client.KLINE_INTERVAL_1HOUR, \"01 january 2021\")\n",
        "\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",
        "df = df.set_index(df['timestamp'])\n",
        "df.index = pd.to_datetime(df.index, unit='ms')\n",
        "del df['timestamp']\n",
        "print(df)\n",
        "\n",
        "# ----- Functions Definition -------\n",
        "def get_chop(high, low, close, window):\n",
        "    tr1 = pd.DataFrame(high - low).rename(columns = {0:'tr1'})\n",
        "    tr2 = pd.DataFrame(abs(high - close.shift(1))).rename(columns = {0:'tr2'})\n",
        "    tr3 = pd.DataFrame(abs(low - close.shift(1))).rename(columns = {0:'tr3'})\n",
        "    frames = [tr1, tr2, tr3]\n",
        "    tr = pd.concat(frames, axis = 1, join = 'inner').dropna().max(axis = 1)\n",
        "    atr = tr.rolling(1).mean()\n",
        "    highh = high.rolling(window).max()\n",
        "    lowl = low.rolling(window).min()\n",
        "    ci = 100 * np.log10((atr.rolling(window).sum()) / (highh - lowl)) / np.log10(window)\n",
        "    return ci"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                        open     high  ...        tb_quote_av  ignore\n",
            "timestamp                              ...                           \n",
            "2021-01-01 00:00:00   736.42   739.00  ...  11015918.97221470       0\n",
            "2021-01-01 01:00:00   734.08   749.00  ...  20362373.93350290       0\n",
            "2021-01-01 02:00:00   748.27   749.00  ...  13207123.35208410       0\n",
            "2021-01-01 03:00:00   744.06   747.23  ...   7079926.31087480       0\n",
            "2021-01-01 04:00:00   744.87   747.09  ...   7539500.28687210       0\n",
            "...                      ...      ...  ...                ...     ...\n",
            "2021-08-23 11:00:00  3331.06  3352.03  ...  22554223.67444570       0\n",
            "2021-08-23 12:00:00  3342.98  3352.02  ...  22025621.57092540       0\n",
            "2021-08-23 13:00:00  3339.79  3349.31  ...  28574299.24442080       0\n",
            "2021-08-23 14:00:00  3344.20  3377.89  ...  89358262.45546750       0\n",
            "2021-08-23 15:00:00  3310.73  3332.70  ...  15452006.94532830       0\n",
            "\n",
            "[5621 rows x 11 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1iFHBre2Suk"
      },
      "source": [
        "<h1>Backtest Execution</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "VznGtRXG_g_s",
        "outputId": "7638df38-d6b7-4324-bbb4-c8af426f6197"
      },
      "source": [
        "df.drop(df.columns.difference(['open','high','low','close','volume']), 1, inplace=True)\n",
        "\n",
        "#Simple Moving Average\n",
        "# df['SMA']=ta.trend.sma_indicator(df['close'], window=12)\n",
        "\n",
        "#Exponential Moving Average\n",
        "df['EMA1']=ta.trend.ema_indicator(close=df['close'], window=13)\n",
        "df['EMA2']=ta.trend.ema_indicator(close=df['close'], window=38)\n",
        "\n",
        "# #Relative Strength Index (RSI)\n",
        "# df['RSI'] =ta.momentum.rsi(close=df['close'], window=14)\n",
        "\n",
        "# #MACD\n",
        "# MACD = ta.trend.MACD(close=df['close'], window_fast=12, window_slow=26, window_sign=9)\n",
        "# df['MACD'] = MACD.macd()\n",
        "# df['MACD_SIGNAL'] = MACD.macd_signal()\n",
        "# df['MACD_DIFF'] = MACD.macd_diff() #Histogramme MACD\n",
        "\n",
        "# #Stochastic RSI\n",
        "# df['STOCH_RSI'] = ta.momentum.stochrsi(close=df['close'], window=14, smooth1=3, smooth2=3) #Non moyenné \n",
        "# df['STOCH_RSI_D'] = ta.momentum.stochrsi_d(close=df['close'], window=14, smooth1=3, smooth2=3) #Orange sur TradingView\n",
        "# df['STOCH_RSI_K'] =ta.momentum.stochrsi_k(close=df['close'], window=14, smooth1=3, smooth2=3) #Bleu sur TradingView\n",
        "\n",
        "# #Ichimoku\n",
        "# df['KIJUN'] = ta.trend.ichimoku_base_line(high=df['high'], low=df['low'], window1=9, window2=26)\n",
        "# df['TENKAN'] = ta.trend.ichimoku_conversion_line(high=df['high'], low=df['low'], window1=9, window2=26)\n",
        "# df['SSA'] = ta.trend.ichimoku_a(high=df['high'], low=df['low'], window1=9, window2=26)\n",
        "# df['SSB'] = ta.trend.ichimoku_b(high=df['high'], low=df['low'], window2=26, window3=52)\n",
        "\n",
        "# #Bollinger Bands\n",
        "# BOL_BAND = ta.volatility.BollingerBands(close=df['close'], window=20, window_dev=2)\n",
        "# df['BOL_H_BAND'] = BOL_BAND.bollinger_hband() #Bande Supérieur\n",
        "# df['BOL_L_BAND'] = BOL_BAND.bollinger_lband() #Bande inférieur\n",
        "# df['BOL_MAVG_BAND'] = BOL_BAND.bollinger_mavg() #Bande moyenne\n",
        "\n",
        "# #Average True Range (ATR)\n",
        "# df['ATR'] = ta.volatility.average_true_range(high=df['high'], low=df['low'], close=df['close'], window=14)\n",
        "\n",
        "# #Super Trend\n",
        "# ST_length = 10\n",
        "# ST_multiplier = 3.0\n",
        "# superTrend = pda.supertrend(high=df['high'], low=df['low'], close=df['close'], length=ST_length, multiplier=ST_multiplier)\n",
        "# df['SUPER_TREND'] = superTrend['SUPERT_'+str(ST_length)+\"_\"+str(ST_multiplier)] #Valeur de la super trend\n",
        "# df['SUPER_TREND_DIRECTION'] = superTrend['SUPERTd_'+str(ST_length)+\"_\"+str(ST_multiplier)] #Retourne 1 si vert et -1 si rouge\n",
        "\n",
        "# #Awesome Oscillator\n",
        "# df['AWESOME_OSCILLATOR'] = ta.momentum.awesome_oscillator(high=df['high'], low=df['low'], window1=5, window2=34)\n",
        "\n",
        "# # Kaufman’s Adaptive Moving Average (KAMA)\n",
        "# df['KAMA'] = ta.momentum.kama(close=df['close'], window=10, pow1=2, pow2=30)\n",
        "\n",
        "# #Choppiness index\n",
        "# df['CHOP'] = get_chop(high=df['high'], low=df['low'], close=df['close'], window=14)  \n",
        "\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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              "    <tr>\n",
              "      <th>timestamp</th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>2021-01-01 00:00:00</th>\n",
              "      <td>736.42</td>\n",
              "      <td>739.00</td>\n",
              "      <td>729.33</td>\n",
              "      <td>734.07</td>\n",
              "      <td>27932.69884000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>2021-01-01 01:00:00</th>\n",
              "      <td>734.08</td>\n",
              "      <td>749.00</td>\n",
              "      <td>733.37</td>\n",
              "      <td>748.28</td>\n",
              "      <td>52336.18779000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2021-01-01 02:00:00</th>\n",
              "      <td>748.27</td>\n",
              "      <td>749.00</td>\n",
              "      <td>742.27</td>\n",
              "      <td>744.06</td>\n",
              "      <td>33019.50100000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2021-01-01 03:00:00</th>\n",
              "      <td>744.06</td>\n",
              "      <td>747.23</td>\n",
              "      <td>743.10</td>\n",
              "      <td>744.82</td>\n",
              "      <td>17604.80859000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2021-01-01 04:00:00</th>\n",
              "      <td>744.87</td>\n",
              "      <td>747.09</td>\n",
              "      <td>739.30</td>\n",
              "      <td>742.29</td>\n",
              "      <td>18794.15424000</td>\n",
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              "    <tr>\n",
              "      <th>2021-08-23 11:00:00</th>\n",
              "      <td>3331.06</td>\n",
              "      <td>3352.03</td>\n",
              "      <td>3321.93</td>\n",
              "      <td>3342.98</td>\n",
              "      <td>13260.55855000</td>\n",
              "      <td>3309.162270</td>\n",
              "      <td>3268.018021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-23 12:00:00</th>\n",
              "      <td>3342.98</td>\n",
              "      <td>3352.02</td>\n",
              "      <td>3334.03</td>\n",
              "      <td>3339.79</td>\n",
              "      <td>14219.32111000</td>\n",
              "      <td>3313.537660</td>\n",
              "      <td>3271.698635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-23 13:00:00</th>\n",
              "      <td>3339.79</td>\n",
              "      <td>3349.31</td>\n",
              "      <td>3328.33</td>\n",
              "      <td>3344.21</td>\n",
              "      <td>17902.16398000</td>\n",
              "      <td>3317.919423</td>\n",
              "      <td>3275.417167</td>\n",
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              "    <tr>\n",
              "      <th>2021-08-23 14:00:00</th>\n",
              "      <td>3344.20</td>\n",
              "      <td>3377.89</td>\n",
              "      <td>3296.58</td>\n",
              "      <td>3310.61</td>\n",
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              "      <td>3316.875220</td>\n",
              "      <td>3277.221927</td>\n",
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              "    <tr>\n",
              "      <th>2021-08-23 15:00:00</th>\n",
              "      <td>3310.73</td>\n",
              "      <td>3332.70</td>\n",
              "      <td>3304.52</td>\n",
              "      <td>3332.54</td>\n",
              "      <td>9133.59453000</td>\n",
              "      <td>3319.113045</td>\n",
              "      <td>3280.058752</td>\n",
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              "<p>5621 rows × 7 columns</p>\n",
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            ],
            "text/plain": [
              "                        open     high  ...         EMA1         EMA2\n",
              "timestamp                              ...                          \n",
              "2021-01-01 00:00:00   736.42   739.00  ...          NaN          NaN\n",
              "2021-01-01 01:00:00   734.08   749.00  ...          NaN          NaN\n",
              "2021-01-01 02:00:00   748.27   749.00  ...          NaN          NaN\n",
              "2021-01-01 03:00:00   744.06   747.23  ...          NaN          NaN\n",
              "2021-01-01 04:00:00   744.87   747.09  ...          NaN          NaN\n",
              "...                      ...      ...  ...          ...          ...\n",
              "2021-08-23 11:00:00  3331.06  3352.03  ...  3309.162270  3268.018021\n",
              "2021-08-23 12:00:00  3342.98  3352.02  ...  3313.537660  3271.698635\n",
              "2021-08-23 13:00:00  3339.79  3349.31  ...  3317.919423  3275.417167\n",
              "2021-08-23 14:00:00  3344.20  3377.89  ...  3316.875220  3277.221927\n",
              "2021-08-23 15:00:00  3310.73  3332.70  ...  3319.113045  3280.058752\n",
              "\n",
              "[5621 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsmuqT5Vgu0R"
      },
      "source": [
        "<h1>Spot Backtest</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vGY9ySpQf4C0",
        "outputId": "672b8279-df78-499a-96f8-c28668965adb"
      },
      "source": [
        "dfTest = df.copy()\n",
        "# dfTest = df['2021-01-01':]\n",
        "dt = None\n",
        "dt = pd.DataFrame(columns = ['date','position', 'reason', 'price', 'frais' ,'fiat', 'coins', 'wallet', 'drawBack'])\n",
        "\n",
        "usdt = 1000\n",
        "initalWallet = usdt\n",
        "coin = 0\n",
        "wallet = 1000\n",
        "lastAth = 0\n",
        "previousRow = dfTest.iloc[0]\n",
        "makerFee = 0.0005\n",
        "takerFee = 0.0007\n",
        "stopLoss = 0\n",
        "takeProfit = 500000\n",
        "buyReady = True\n",
        "sellReady = True\n",
        "\n",
        "def buyCondition(row, previousRow):\n",
        "  if row['EMA1'] > row['EMA2']:\n",
        "    return True\n",
        "  else:\n",
        "    return False\n",
        "\n",
        "  \n",
        "def sellCondition(row, previousRow):\n",
        "  if row['EMA2'] > row['EMA1']:\n",
        "    return True\n",
        "  else:\n",
        "    return False\n",
        "\n",
        "\n",
        "for index, row in dfTest.iterrows():\n",
        "  #Buy market order\n",
        "  if buyCondition(row, previousRow) == True and usdt > 0 and buyReady == True:\n",
        "    #You can define here at what price you buy\n",
        "    buyPrice = row['close']\n",
        "\n",
        "    #Define the price of you SL and TP or comment it if you don't want a SL or TP\n",
        "    stopLoss = buyPrice - 0.02 * buyPrice\n",
        "    takeProfit = buyPrice + 0.1 * buyPrice\n",
        "\n",
        "    coin = usdt / buyPrice\n",
        "    fee = takerFee * coin\n",
        "    coin = coin - fee\n",
        "    usdt = 0\n",
        "    wallet = coin * row['close']\n",
        "    if wallet > lastAth:\n",
        "      lastAth = wallet\n",
        "\n",
        "    # print(\"Buy COIN at\",buyPrice,'$ the', index)\n",
        "    myrow = {'date': index,'position': \"Buy\", 'reason': 'Buy Market','price': buyPrice,'frais': fee*row['close'],'fiat': usdt,'coins': coin,'wallet': wallet,'drawBack':(wallet-lastAth)/lastAth}\n",
        "    dt = dt.append(myrow,ignore_index=True)\n",
        "  \n",
        "  #Stop Loss\n",
        "  elif row['low'] < stopLoss and coin > 0:\n",
        "\n",
        "    sellPrice = stopLoss\n",
        "\n",
        "    usdt = coin * sellPrice\n",
        "    fee = makerFee * usdt\n",
        "    usdt = usdt - fee\n",
        "    coin = 0\n",
        "    buyReady = False\n",
        "    wallet = usdt\n",
        "    if wallet > lastAth:\n",
        "      lastAth = wallet\n",
        "    # print(\"Sell COIN at Stop Loss\",sellPrice,'$ the', index)\n",
        "    myrow = {'date': index,'position': \"Sell\", 'reason': 'Sell Stop Loss', 'price': sellPrice, 'frais': fee, 'fiat': usdt, 'coins': coin, 'wallet': wallet, 'drawBack':(wallet-lastAth)/lastAth}\n",
        "    dt = dt.append(myrow,ignore_index=True)\n",
        "\n",
        "  #Take Profit\n",
        "  elif row['high'] > takeProfit and coin > 0:\n",
        "\n",
        "    sellPrice = takeProfit\n",
        "\n",
        "    usdt = coin * sellPrice\n",
        "    fee = makerFee * usdt\n",
        "    usdt = usdt - fee\n",
        "    coin = 0\n",
        "    buyReady = False\n",
        "    wallet = usdt\n",
        "    if wallet > lastAth:\n",
        "      lastAth = wallet\n",
        "    # print(\"Sell COIN at Take Profit Loss\",sellPrice,'$ the', index)\n",
        "    myrow = {'date': index,'position': \"Sell\", 'reason': 'Sell Take Profit', 'price': sellPrice, 'frais': fee, 'fiat': usdt, 'coins': coin, 'wallet': wallet, 'drawBack':(wallet-lastAth)/lastAth}\n",
        "    dt = dt.append(myrow,ignore_index=True)     \n",
        "\n",
        "  # Sell Market\n",
        "  elif sellCondition(row, previousRow) == True:\n",
        "    buyReady = True\n",
        "    if coin > 0 and sellReady == True:\n",
        "      sellPrice = row['close']\n",
        "      usdt = coin * sellPrice\n",
        "      frais = takerFee * usdt\n",
        "      usdt = usdt - frais\n",
        "      coin = 0\n",
        "      wallet = usdt\n",
        "      if wallet > lastAth:\n",
        "        lastAth = wallet\n",
        "      # print(\"Sell COIN at\",sellPrice,'$ the', index)\n",
        "      myrow = {'date': index,'position': \"Sell\", 'reason': 'Sell Market', 'price': sellPrice, 'frais': frais, 'fiat': usdt, 'coins': coin, 'wallet': wallet, 'drawBack':(wallet-lastAth)/lastAth}\n",
        "      dt = dt.append(myrow,ignore_index=True)\n",
        "  \n",
        "  previousRow = row\n",
        "\n",
        "#///////////////////////////////////////\n",
        "print(\"Period : [\" + str(dfTest.index[0]) + \"] -> [\" +str(dfTest.index[len(dfTest)-1]) + \"]\")\n",
        "dt = dt.set_index(dt['date'])\n",
        "dt.index = pd.to_datetime(dt.index)\n",
        "dt['resultat'] = dt['wallet'].diff()\n",
        "dt['resultat%'] = dt['wallet'].pct_change()*100\n",
        "dt.loc[dt['position']=='Buy','resultat'] = None\n",
        "dt.loc[dt['position']=='Buy','resultat%'] = None\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",
        "holdPorcentage = ((lastClose - iniClose)/iniClose) * 100\n",
        "algoPorcentage = ((wallet - initalWallet)/initalWallet) * 100\n",
        "vsHoldPorcentage = ((algoPorcentage - holdPorcentage)/holdPorcentage) * 100\n",
        "\n",
        "print(\"Starting balance : 1000 $\")\n",
        "print(\"Final balance :\",round(wallet,2),\"$\")\n",
        "print(\"Performance vs US Dollar :\",round(algoPorcentage,2),\"%\")\n",
        "print(\"Buy and Hold Performence :\",round(holdPorcentage,2),\"%\")\n",
        "print(\"Performance vs Buy and Hold :\",round(vsHoldPorcentage,2),\"%\")\n",
        "print(\"Number of negative trades : \",dt.groupby('tradeIs')['date'].nunique()['Bad'])\n",
        "print(\"Number of positive trades : \",dt.groupby('tradeIs')['date'].nunique()['Good'])\n",
        "print(\"Average Positive Trades : \",round(dt.loc[dt['tradeIs'] == 'Good', 'resultat%'].sum()/dt.loc[dt['tradeIs'] == 'Good', 'resultat%'].count(),2),\"%\")\n",
        "print(\"Average Negative Trades : \",round(dt.loc[dt['tradeIs'] == 'Bad', 'resultat%'].sum()/dt.loc[dt['tradeIs'] == 'Bad', 'resultat%'].count(),2),\"%\")\n",
        "idbest = dt.loc[dt['tradeIs'] == 'Good', 'resultat%'].idxmax()\n",
        "idworst = dt.loc[dt['tradeIs'] == 'Bad', 'resultat%'].idxmin()\n",
        "print(\"Best trade +\"+str(round(dt.loc[dt['tradeIs'] == 'Good', 'resultat%'].max(),2)),\"%, the \",dt['date'][idbest])\n",
        "print(\"Worst trade\",round(dt.loc[dt['tradeIs'] == 'Bad', 'resultat%'].min(),2),\"%, the \",dt['date'][idworst])\n",
        "print(\"Worst drawBack\", str(100*round(dt['drawBack'].min(),2)),\"%\")\n",
        "print(\"Total fee : \",round(dt['frais'].sum(),2),\"$\")\n",
        "reasons = dt['reason'].unique()\n",
        "for r in reasons:\n",
        "  print(r+\" number :\",dt.groupby('reason')['date'].nunique()[r])\n",
        "\n",
        "dt[['wallet','price']].plot(subplots=True, figsize=(20,10))\n",
        "print('PLOT')\n",
        "dt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Period : [2021-01-01 00:00:00] -> [2021-08-23 15:00:00]\n",
            "Starting balance : 1000 $\n",
            "Final balance : 2617.28 $\n",
            "Performance vs US Dollar : 161.73 %\n",
            "Buy and Hold Performence : 353.98 %\n",
            "Performance vs Buy and Hold : -54.31 %\n",
            "Number of negative trades :  45\n",
            "Number of positive trades :  23\n",
            "Average Positive Trades :  8.55 %\n",
            "Average Negative Trades :  -1.91 %\n",
            "Best trade +9.95 %, the  2021-05-24 15:00:00\n",
            "Worst trade -2.05 %, the  2021-05-30 22:00:00\n",
            "Worst drawBack -11.0 %\n",
            "Total fee :  152.89 $\n",
            "Buy Market number : 69\n",
            "Sell Stop Loss number : 38\n",
            "Sell Take Profit number : 19\n",
            "Sell Market number : 11\n",
            "PLOT\n"
          ],
          "name": "stdout"
        },
        {
          "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>fiat</th>\n",
              "      <th>coins</th>\n",
              "      <th>wallet</th>\n",
              "      <th>drawBack</th>\n",
              "      <th>resultat</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-01-02 13:00:00</th>\n",
              "      <td>2021-01-02 13:00:00</td>\n",
              "      <td>Buy</td>\n",
              "      <td>Buy Market</td>\n",
              "      <td>770.7700</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>0</td>\n",
              "      <td>1.296496</td>\n",
              "      <td>999.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-02 14:00:00</th>\n",
              "      <td>2021-01-02 14:00:00</td>\n",
              "      <td>Sell</td>\n",
              "      <td>Sell Stop Loss</td>\n",
              "      <td>755.3546</td>\n",
              "      <td>0.489657</td>\n",
              "      <td>978.824</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>978.824343</td>\n",
              "      <td>-0.020490</td>\n",
              "      <td>-20.475657</td>\n",
              "      <td>-2.049000</td>\n",
              "      <td>Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-09 08:00:00</th>\n",
              "      <td>2021-01-09 08:00:00</td>\n",
              "      <td>Buy</td>\n",
              "      <td>Buy Market</td>\n",
              "      <td>1208.0100</td>\n",
              "      <td>0.685177</td>\n",
              "      <td>0</td>\n",
              "      <td>0.809711</td>\n",
              "      <td>978.139166</td>\n",
              "      <td>-0.021176</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-10 01:00:00</th>\n",
              "      <td>2021-01-10 01:00:00</td>\n",
              "      <td>Sell</td>\n",
              "      <td>Sell Take Profit</td>\n",
              "      <td>1328.8110</td>\n",
              "      <td>0.537977</td>\n",
              "      <td>1075.42</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1075.415106</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>97.275940</td>\n",
              "      <td>9.945000</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-13 19:00:00</th>\n",
              "      <td>2021-01-13 19:00:00</td>\n",
              "      <td>Buy</td>\n",
              "      <td>Buy Market</td>\n",
              "      <td>1102.0800</td>\n",
              "      <td>0.752791</td>\n",
              "      <td>0</td>\n",
              "      <td>0.975122</td>\n",
              "      <td>1074.662315</td>\n",
              "      <td>-0.000700</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-15 23:00:00</th>\n",
              "      <td>2021-08-15 23:00:00</td>\n",
              "      <td>Buy</td>\n",
              "      <td>Buy Market</td>\n",
              "      <td>3307.0900</td>\n",
              "      <td>1.864218</td>\n",
              "      <td>0</td>\n",
              "      <td>0.804727</td>\n",
              "      <td>2661.304846</td>\n",
              "      <td>-0.074171</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-16 13:00:00</th>\n",
              "      <td>2021-08-16 13:00:00</td>\n",
              "      <td>Sell</td>\n",
              "      <td>Sell Stop Loss</td>\n",
              "      <td>3240.9482</td>\n",
              "      <td>1.304039</td>\n",
              "      <td>2606.77</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2606.774710</td>\n",
              "      <td>-0.093141</td>\n",
              "      <td>-54.530136</td>\n",
              "      <td>-2.049000</td>\n",
              "      <td>Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-19 19:00:00</th>\n",
              "      <td>2021-08-19 19:00:00</td>\n",
              "      <td>Buy</td>\n",
              "      <td>Buy Market</td>\n",
              "      <td>3147.5800</td>\n",
              "      <td>1.824742</td>\n",
              "      <td>0</td>\n",
              "      <td>0.827604</td>\n",
              "      <td>2604.949967</td>\n",
              "      <td>-0.093776</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-22 14:00:00</th>\n",
              "      <td>2021-08-22 14:00:00</td>\n",
              "      <td>Sell</td>\n",
              "      <td>Sell Market</td>\n",
              "      <td>3166.5300</td>\n",
              "      <td>1.519256</td>\n",
              "      <td>2619.11</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2619.113808</td>\n",
              "      <td>-0.088848</td>\n",
              "      <td>14.163841</td>\n",
              "      <td>0.543728</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-23 03:00:00</th>\n",
              "      <td>2021-08-23 03:00:00</td>\n",
              "      <td>Buy</td>\n",
              "      <td>Buy Market</td>\n",
              "      <td>3340.2200</td>\n",
              "      <td>1.833380</td>\n",
              "      <td>0</td>\n",
              "      <td>0.783565</td>\n",
              "      <td>2617.280428</td>\n",
              "      <td>-0.089486</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>137 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   date position  ... resultat%  tradeIs\n",
              "date                                              ...                   \n",
              "2021-01-02 13:00:00 2021-01-02 13:00:00      Buy  ...       NaN         \n",
              "2021-01-02 14:00:00 2021-01-02 14:00:00     Sell  ... -2.049000      Bad\n",
              "2021-01-09 08:00:00 2021-01-09 08:00:00      Buy  ...       NaN         \n",
              "2021-01-10 01:00:00 2021-01-10 01:00:00     Sell  ...  9.945000     Good\n",
              "2021-01-13 19:00:00 2021-01-13 19:00:00      Buy  ...       NaN         \n",
              "...                                 ...      ...  ...       ...      ...\n",
              "2021-08-15 23:00:00 2021-08-15 23:00:00      Buy  ...       NaN         \n",
              "2021-08-16 13:00:00 2021-08-16 13:00:00     Sell  ... -2.049000      Bad\n",
              "2021-08-19 19:00:00 2021-08-19 19:00:00      Buy  ...       NaN         \n",
              "2021-08-22 14:00:00 2021-08-22 14:00:00     Sell  ...  0.543728     Good\n",
              "2021-08-23 03:00:00 2021-08-23 03:00:00      Buy  ...       NaN         \n",
              "\n",
              "[137 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZ9whQr7US2h"
      },
      "source": [
        "<h1>Future BackTest</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b98G8Ri4e2DN"
      },
      "source": [
        "Comming Soon...."
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}