LongOnly_3SMA_6TP_ETH1H.ipynb

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  "metadata": {
    "colab": {
      "name": "LongOnly_3SMA_6TP_ETH1H.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EptkI5u-Eaiy"
      },
      "source": [
        "Long Positions based on 3SMA \n",
        "\n",
        "Confirm when fourth SMA break the third one\n",
        "\n",
        "Take profit always 6percent above entering price\n",
        "\n",
        "Results with ETH H1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "udvJzBMfEFm2"
      },
      "source": [
        "!git clone https://github.com/mohammadataei93/Algorithmic-Trading-.git"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXHVB7IEGObr"
      },
      "source": [
        "!wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz \n",
        "!tar xvzf ta-lib-0.4.0-src.tar.gz\n",
        "import os\n",
        "os.chdir('ta-lib') # Can't use !cd in co-lab\n",
        "!./configure --prefix=/usr\n",
        "!make\n",
        "!make install\n",
        "os.chdir('../')\n",
        "!pip install TA-Lib\n",
        "!pip install zigzag\n",
        "!pip install --upgrade mplfinance\n",
        "!pip install yfinance\n",
        "!pip install git+https://github.com/yhilpisch/tpqoa.git"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hpfSQwVDFmRY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fae8fa0-51cf-4674-c90d-655038855f71"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pylab import mpl, plt\n",
        "import datetime\n",
        "from zigzag import *\n",
        "import talib as ta\n",
        "import mplfinance as mpf\n",
        "from scipy.signal import argrelextrema\n",
        "import pywt\n",
        "from statsmodels.robust import mad\n",
        "import tpqoa\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "plt.style.use('seaborn')\n",
        "mpl.rcParams['font.family'] = 'serif'"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lq56mg2Zby_F"
      },
      "source": [
        "## Ethereum H1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "FSZraZ5lb6jO",
        "outputId": "6477c729-0c8b-41a3-ed3f-02a3e41a15a3"
      },
      "source": [
        "# get h1 eth data from google drive and preprocess it\n",
        "pd_data = pd.read_csv('/content/Algorithmic-Trading-/Data/ETHUSDT(H1).csv')\n",
        "pd_data = pd_data.drop(['Open','Close'] , axis = 1)\n",
        "pd_data = pd_data.rename(columns={'open':'Open','high':'High','low':'Low','close':'Close'})\n",
        "pd_data['Date'] = pd_data['time'].apply(lambda x : datetime.datetime.fromtimestamp(x))\n",
        "pd_data = pd_data[['Date','Open','High','Low','Close','Volume']]\n",
        "data = pd_data.copy()\n",
        "data\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01 00:00:00</td>\n",
              "      <td>131.45</td>\n",
              "      <td>132.22</td>\n",
              "      <td>131.01</td>\n",
              "      <td>131.82</td>\n",
              "      <td>12677.96176</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-01 01:00:00</td>\n",
              "      <td>131.83</td>\n",
              "      <td>131.88</td>\n",
              "      <td>131.00</td>\n",
              "      <td>131.14</td>\n",
              "      <td>9541.84565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-01 02:00:00</td>\n",
              "      <td>131.19</td>\n",
              "      <td>131.34</td>\n",
              "      <td>130.00</td>\n",
              "      <td>130.72</td>\n",
              "      <td>12039.04136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-01 03:00:00</td>\n",
              "      <td>130.72</td>\n",
              "      <td>131.47</td>\n",
              "      <td>130.71</td>\n",
              "      <td>131.32</td>\n",
              "      <td>6320.30743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-01 04:00:00</td>\n",
              "      <td>131.31</td>\n",
              "      <td>134.00</td>\n",
              "      <td>131.30</td>\n",
              "      <td>132.35</td>\n",
              "      <td>29214.64195</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>23736</th>\n",
              "      <td>2021-09-18 11:00:00</td>\n",
              "      <td>3523.24</td>\n",
              "      <td>3536.41</td>\n",
              "      <td>3489.71</td>\n",
              "      <td>3496.50</td>\n",
              "      <td>11867.58800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23737</th>\n",
              "      <td>2021-09-18 12:00:00</td>\n",
              "      <td>3496.51</td>\n",
              "      <td>3518.23</td>\n",
              "      <td>3483.54</td>\n",
              "      <td>3488.48</td>\n",
              "      <td>17310.14510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23738</th>\n",
              "      <td>2021-09-18 13:00:00</td>\n",
              "      <td>3488.25</td>\n",
              "      <td>3507.00</td>\n",
              "      <td>3464.44</td>\n",
              "      <td>3481.50</td>\n",
              "      <td>12975.42900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23739</th>\n",
              "      <td>2021-09-18 14:00:00</td>\n",
              "      <td>3481.33</td>\n",
              "      <td>3492.13</td>\n",
              "      <td>3470.35</td>\n",
              "      <td>3481.20</td>\n",
              "      <td>10199.64770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23740</th>\n",
              "      <td>2021-09-18 15:00:00</td>\n",
              "      <td>3481.00</td>\n",
              "      <td>3485.97</td>\n",
              "      <td>3472.01</td>\n",
              "      <td>3475.39</td>\n",
              "      <td>2054.06860</td>\n",
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              "<p>23741 rows × 6 columns</p>\n",
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            "text/plain": [
              "                     Date     Open     High      Low    Close       Volume\n",
              "0     2019-01-01 00:00:00   131.45   132.22   131.01   131.82  12677.96176\n",
              "1     2019-01-01 01:00:00   131.83   131.88   131.00   131.14   9541.84565\n",
              "2     2019-01-01 02:00:00   131.19   131.34   130.00   130.72  12039.04136\n",
              "3     2019-01-01 03:00:00   130.72   131.47   130.71   131.32   6320.30743\n",
              "4     2019-01-01 04:00:00   131.31   134.00   131.30   132.35  29214.64195\n",
              "...                   ...      ...      ...      ...      ...          ...\n",
              "23736 2021-09-18 11:00:00  3523.24  3536.41  3489.71  3496.50  11867.58800\n",
              "23737 2021-09-18 12:00:00  3496.51  3518.23  3483.54  3488.48  17310.14510\n",
              "23738 2021-09-18 13:00:00  3488.25  3507.00  3464.44  3481.50  12975.42900\n",
              "23739 2021-09-18 14:00:00  3481.33  3492.13  3470.35  3481.20  10199.64770\n",
              "23740 2021-09-18 15:00:00  3481.00  3485.97  3472.01  3475.39   2054.06860\n",
              "\n",
              "[23741 rows x 6 columns]"
            ]
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gQtv7AgZxux"
      },
      "source": [
        "Backtesting Class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVORi8r-xRWR"
      },
      "source": [
        "class Backtest():\n",
        "\n",
        "    def __init__(self,\n",
        "                 amount = 10000 ,\n",
        "                 ftc=0.0,\n",
        "                 ptc=0.0,\n",
        "                 verbose=True ,\n",
        "                 argrelextrema_order = 5 ,\n",
        "                 divergence = 'RSI',\n",
        "                 sma = None):\n",
        "\n",
        "        self.initial_amount = amount\n",
        "        self.amount = amount\n",
        "        self.ftc = ftc\n",
        "        self.ptc = ptc\n",
        "        self.units = 0\n",
        "        self.position = 0\n",
        "        self.trades = 0\n",
        "        self.verbose = verbose\n",
        "        self.argrelextrema_order = argrelextrema_order \n",
        "        self.divergence = divergence\n",
        "        self.sma = sma\n",
        "        self.data = None\n",
        "        self.PnL = pd.DataFrame(columns=['Date','pnl'])\n",
        "        self.open_position = None\n",
        "        self.positions_info = []\n",
        "\n",
        "\n",
        "    def add_data(self , data):\n",
        "\n",
        "      self.data = data.copy()\n",
        "      self.data.index = self.data['Date']\n",
        "      self.data['RSI'] = ta.RSI(self.data['Close'])\n",
        "      self.data['MACD'],self.data['MACD_Signal'],self.data['MACD_Hist'] = ta.MACD(self.data['Close'])\n",
        "      self.data['ATR'] = ta.ATR(self.data['High'],self.data['Low'],self.data['Close'])\n",
        "      self.data['slowk'], self.data['slowd'] = ta.STOCH(self.data['High'],self.data['Low'],\n",
        "                                                        self.data['Close'],fastk_period=5,slowk_period=3,slowd_period=3)\n",
        "      # if self.sma:\n",
        "      #   self.sma_key = f'SMA{self.sma}'\n",
        "      #   self.data[self.sma_key] = ta.SMA(self.data['Close'],self.sma)\n",
        "      self.data['SMA10'] = ta.SMA(self.data['Close'],10)\n",
        "      self.data['SMA20'] = ta.SMA(self.data['Close'],20)\n",
        "      self.data['SMA50'] = ta.SMA(self.data['Close'],50)\n",
        "      self.data['SMA100'] = ta.SMA(self.data['Close'],100)\n",
        "      self.data.dropna(inplace = True)\n",
        "\n",
        "\n",
        "    def plot_data(self, cols='Close'):\n",
        "        ''' Plots the closing prices for symbol.\n",
        "        '''\n",
        "        self.data[cols].plot(figsize=(10, 6))\n",
        "\n",
        "    def get_date_price(self, bar , priceindex = 'Close'):\n",
        "        ''' Return date and price for bar.\n",
        "        '''\n",
        "        date = str(self.data.index[bar])\n",
        "        price = self.data[priceindex].iloc[bar]\n",
        "        # self.data['positions'] = np.nan\n",
        "        return date, price\n",
        "\n",
        "    def print_balance(self, bar):\n",
        "        ''' Print out current cash balance info.\n",
        "        '''\n",
        "        date, price = self.get_date_price(bar)\n",
        "        print(f'{date} | current balance {self.amount:.2f}')\n",
        "\n",
        "    def calculate_asset(self,bar):\n",
        "        date, price = self.get_date_price(bar)\n",
        "        self.asset = self.units * price + self.amount\n",
        "        return self.asset\n",
        "\n",
        "    def calculate_PnL(self,dates,pnl,show = True):\n",
        "        self.PnL['Date'] = dates\n",
        "        self.PnL['pnl'] = pnl\n",
        "        self.PnL.index = self.PnL['Date']\n",
        "        self.PnL['pnl'].plot(figsize=(10, 6))\n",
        "\n",
        "    def print_net_wealth(self, bar):\n",
        "        ''' Print out current cash balance info.\n",
        "        '''\n",
        "        date, price = self.get_date_price(bar)\n",
        "        net_wealth = self.units * price + self.amount\n",
        "        print(f'{date} | current net wealth {net_wealth:.2f}')\n",
        "\n",
        "    def place_buy_order(self, bar, units=None, amount=None):\n",
        "        ''' Place a buy order.\n",
        "        '''\n",
        "        date, price = self.get_date_price(bar)\n",
        "        if units is None:\n",
        "            units = np.round(amount / price ,4)\n",
        "        self.amount -= ( (units * price ) + (units * price * self.ptc) )\n",
        "        self.units += units\n",
        "        self.trades += 1\n",
        "        if self.verbose:\n",
        "            print(f'{date} | buying {units} units at {price:.2f}')\n",
        "            self.print_balance(bar)\n",
        "            self.print_net_wealth(bar)\n",
        "\n",
        "    def place_sell_order(self, bar, units=None, amount=None):\n",
        "        ''' Place a sell order.\n",
        "        '''\n",
        "        date, price = self.get_date_price(bar)\n",
        "        if units is None:\n",
        "            units = np.round(amount / price ,4)\n",
        "        self.amount += ( (units * price ) - (units * price * self.ptc) ) \n",
        "        self.units -= units\n",
        "        self.trades += 1\n",
        "        if self.verbose:\n",
        "            print(f'{date} | selling {units} units at {price:.2f}')\n",
        "            self.print_balance(bar)\n",
        "            self.print_net_wealth(bar)\n",
        "\n",
        "    def close_out(self, bar):\n",
        "        ''' Closing out a long or short position.\n",
        "        '''\n",
        "        date, price = self.get_date_price(bar)\n",
        "        self.amount += self.units * price\n",
        "        self.units = 0\n",
        "        self.trades += 1\n",
        "        if self.verbose:\n",
        "            print(f'{date} | inventory {self.units} units at {price:.2f}')\n",
        "            print('=' * 55)\n",
        "        print('Final balance   [$] {:.2f}'.format(self.amount))\n",
        "        perf = ((self.amount - self.initial_amount) /\n",
        "                self.initial_amount * 100)\n",
        "        count ,avg_win , avg_lose = 0,0,0\n",
        "        for pos in self.positions_info:\n",
        "          if pos['Profit'] > 0 :\n",
        "            count += 1\n",
        "            avg_win += pos['Profit']\n",
        "          if pos['Profit'] < 0 :\n",
        "            avg_lose += pos['Profit']\n",
        "\n",
        "        self.accuracy = count / len(self.positions_info)\n",
        "        print('Net Performance [%] {:.2f}'.format(perf))\n",
        "        print('Trades Executed [#] {}'.format(len(self.positions_info)))\n",
        "        print('Accuracy        [%] {:.2f}'.format(self.accuracy * 100))\n",
        "        print('Avg Win/Avg Lose   {:.2f}'.format( np.abs(avg_win / avg_lose)) )\n",
        "        print('=' * 55)\n",
        "\n",
        "\n",
        "    def go_long(self, bar, units=None, amount=None):\n",
        "        if units:\n",
        "            self.place_buy_order(bar, units=units)\n",
        "        elif amount:\n",
        "            self.place_buy_order(bar, amount=amount)\n",
        "\n",
        "    def go_short(self, bar, units=None, amount=None):\n",
        "        if units:\n",
        "            self.place_sell_order(bar, units=units)\n",
        "        elif amount:\n",
        "            self.place_sell_order(bar, amount=amount)\n",
        "\n",
        "\n",
        "    def divergence_detector(self, data):\n",
        "      divergence = None\n",
        "      peak = None\n",
        "      # price_data_h = data['High'].values\n",
        "      # price_data_l = data['Low'].values\n",
        "      price_data = data['Close'].values\n",
        "      highs = argrelextrema(price_data , np.greater_equal, order=self.argrelextrema_order)[0]\n",
        "      lows = argrelextrema(price_data , np.less_equal, order=self.argrelextrema_order)[0]\n",
        "      if highs[-1] > lows[-1]:\n",
        "        peak = 'High'\n",
        "        current_peak_idx = highs[-1]\n",
        "        previous_peak_idx = highs[-2]\n",
        "        reverse_peak_idx = lows[-1]\n",
        "      else :\n",
        "        peak = 'Low'\n",
        "        current_peak_idx = lows[-1]\n",
        "        previous_peak_idx = lows[-2]\n",
        "        reverse_peak_idx = highs[-1]\n",
        "\n",
        "      peak1 = data['Date'][current_peak_idx]\n",
        "      peak2 = data['Date'][previous_peak_idx]\n",
        "      peak_reverse = data['Date'][reverse_peak_idx]\n",
        "\n",
        "\n",
        "      # price_h = data['High'].values\n",
        "      # price_l = data['Low'].values\n",
        "      price = data['Close'].values\n",
        "      indicator = data[self.divergence].values\n",
        "\n",
        "      if peak == 'High':\n",
        "        if price[current_peak_idx] > price[previous_peak_idx] and indicator[current_peak_idx] < indicator[previous_peak_idx] and \\\n",
        "                  data['MACD_Hist'][current_peak_idx] > 0 and data['MACD_Hist'][previous_peak_idx] > 0: \n",
        "          divergence = 'Bearish'\n",
        "      if peak == 'Low':\n",
        "        if price[current_peak_idx] < price[previous_peak_idx] and indicator[current_peak_idx] > indicator[previous_peak_idx] and \\\n",
        "                  data['MACD_Hist'][current_peak_idx] < 0 and data['MACD_Hist'][previous_peak_idx] < 0:\n",
        "          divergence = 'Bullish'\n",
        "\n",
        "      return divergence , peak1 , peak2 , peak_reverse \n",
        "\n",
        "\n",
        "    def smasignal(self, data):\n",
        "      sma20,sma50,sma100 = data['SMA20'],data['SMA50'],data['SMA100']\n",
        "      condition1 = sma20 > sma50\n",
        "      condition2 = sma50 > sma100\n",
        "      if condition1[0] == True and all(condition1) and condition2[0]== True and all(condition2):\n",
        "        return 'Bullish'\n",
        "      if condition1[0] == False and all(condition1) and condition2[0]== False and all(condition2):\n",
        "        return 'Bearish'\n",
        "      return None\n",
        "\n",
        "\n",
        "    def run(self , window , found_risk = 0.02):\n",
        "        msg = f'\\n\\nRunning MACD Divergence strategy'\n",
        "        msg += f'\\nfixed costs {self.ftc} | '\n",
        "        msg += f'proportional costs {self.ptc}'\n",
        "        print(msg)\n",
        "        print('=' * 55)\n",
        "        self.position = 0\n",
        "        self.trades = 0 \n",
        "        self.amount = self.initial_amount \n",
        "        self.window = window\n",
        "        self.found_risk = found_risk\n",
        "        self.state = 'Wait For Signal'\n",
        "        self.observed_divergence = None\n",
        "        self.data['long_position'] = np.nan\n",
        "        self.data['short_position'] = np.nan\n",
        "        self.peak1 ,self.peak2 = None,None\n",
        "        dates,pnl = [],[]\n",
        "        position_id = 0\n",
        "        counter = 0\n",
        "        for bar in range(self.window , len(self.data)):\n",
        "\n",
        "          data = self.data[bar - self.window : bar]\n",
        "          \n",
        "          if self.position != 0: self.follow_positions(bar)\n",
        "          else:\n",
        "\n",
        "            if self.state == 'Wait For Signal':\n",
        "                counter +=1\n",
        "                self.observed_smasignal = self.smasignal(data)\n",
        "                self.state = 'Wait For Confirmation'\n",
        "\n",
        "            if self.state == 'Wait For Confirmation':\n",
        "\n",
        "              if self.data['SMA10'][bar-2] < self.data['SMA20'][bar-2] and self.data['SMA10'][bar-1] >= self.data['SMA20'][bar-1]and self.observed_smasignal  == 'Bullish': \n",
        "\n",
        "                self.position = 'Long'\n",
        "                tp = self.place_tp(bar)\n",
        "                if 1:\n",
        "                  self.data['long_position'][bar] = 1 \n",
        "                  date , entering_price = self.get_date_price(bar)\n",
        "                  position_id += 1\n",
        "                  print(f'{date} | Take Profit : {tp:.02f}')\n",
        "                  self.open_position = {'position_id':position_id,'Index':bar,'Date':date,\n",
        "                                              'Position':'Long','EnteringPrice':entering_price,\n",
        "                                              'TakeProfit':tp,\n",
        "                                              'Profit':None}\n",
        "                  \n",
        "                  self.go_long(bar, amount= self.amount * self.found_risk)\n",
        "                  self.state = 'Wait For Signal'\n",
        "                  if self.verbose:\n",
        "                    print(f'{date} | @@@@@ Long Order Executed @@@@@')\n",
        "                else: self.position = 0\n",
        "\n",
        "              else :\n",
        "                counter += 1\n",
        "\n",
        "              if counter > 4 :\n",
        "                counter = 0\n",
        "                self.state= 'Wait For Signal'\n",
        "\n",
        "\n",
        "          date , price = self.get_date_price(bar)\n",
        "          dates.append(date)\n",
        "          pnl.append(self.calculate_asset(bar))\n",
        "\n",
        "        self.close_out(bar)\n",
        "        self.calculate_PnL(dates , pnl)\n",
        "\n",
        "\n",
        "\n",
        "    def place_tp(self , bar):\n",
        "      tp = None\n",
        "      price = self.data['Close'][bar] \n",
        "      if self.position == 'Long':\n",
        "        tp = (price + 0.06*price)\n",
        "\n",
        "      return tp\n",
        "\n",
        "\n",
        "    def follow_positions(self , bar):\n",
        "      date , highprice = self.get_date_price(bar,priceindex ='High')\n",
        "      date , lowprice = self.get_date_price(bar,priceindex ='Low')\n",
        "\n",
        "      if self.open_position['Position'] == 'Long':\n",
        "\n",
        "        # if lowprice < self.open_position['StopLoss']:\n",
        "        #   self.place_sell_order(bar, units=self.units)\n",
        "        #   loss = self.open_position['EnteringPrice'] - self.open_position['StopLoss']\n",
        "        #   self.open_position['Profit'] = -loss\n",
        "        #   self.open_position['CloseDate'] = date\n",
        "        #   price = self.open_position['StopLoss']\n",
        "        #   self.positions_info.append(self.open_position)\n",
        "        #   self.open_position = None\n",
        "        #   self.position = 0 \n",
        "        #   if self.verbose:\n",
        "        #     print(f'{date} | !!! StopLoss Activated at {price:.2f} !!!')       \n",
        "        #     print(f'{date} | Long Position Closed with {loss:.2f} Loss :((( ')\n",
        "        #     print('=' * 55)  \n",
        "        #     return  \n",
        "\n",
        "        if highprice > self.open_position['TakeProfit']:\n",
        "          self.place_sell_order(bar, units=self.units)\n",
        "          profit = self.open_position['TakeProfit'] - self.open_position['EnteringPrice']\n",
        "          self.open_position['Profit'] = profit\n",
        "          self.open_position['CloseDate'] = date\n",
        "          price = self.open_position['TakeProfit']\n",
        "          self.positions_info.append(self.open_position)\n",
        "          self.open_position = None\n",
        "          self.position = 0\n",
        "          if self.verbose:\n",
        "            print(f'{date} | $$$ Take Profit Activated at {price:.2f} $$$')       \n",
        "            print(f'{date} | Long Position Closed with {profit:.2f} Profit :)))')  \n",
        "            print('=' * 55)\n",
        "          return\n",
        "\n",
        "      # if self.open_position['Position'] == 'Short':\n",
        "\n",
        "      #   if highprice > self.open_position['StopLoss']:\n",
        "      #     self.place_buy_order(bar, units=-self.units)\n",
        "      #     loss = self.open_position['StopLoss'] - self.open_position['EnteringPrice']\n",
        "      #     self.open_position['Profit'] = -loss\n",
        "      #     self.open_position['CloseDate'] = date\n",
        "      #     price = self.open_position['StopLoss']\n",
        "      #     self.positions_info.append(self.open_position)\n",
        "      #     self.open_position = None\n",
        "      #     self.position = 0\n",
        "      #     if self.verbose:\n",
        "      #       print(f'{date} | !!! StopLoss Activated at {price:.2f} !!!')       \n",
        "      #       print(f'{date} | Short Position Closed  with {loss:.2f} Loss :((( ')\n",
        "      #       print('=' * 55)  \n",
        "      #     return\n",
        "\n",
        "      #   if lowprice < self.open_position['TakeProfit']:\n",
        "      #     self.place_buy_order(bar, units=-self.units)  \n",
        "      #     profit = self.open_position['EnteringPrice'] - self.open_position['TakeProfit']\n",
        "      #     self.open_position['Profit'] = profit\n",
        "      #     self.open_position['CloseDate'] = date\n",
        "      #     price = self.open_position['TakeProfit']\n",
        "      #     self.positions_info.append(self.open_position)\n",
        "      #     self.open_position = None\n",
        "      #     self.position = 0\n",
        "      #     if self.verbose:\n",
        "      #       print(f'{date} | $$$ take profit activated at {price:.2f} $$$')       \n",
        "      #       print(f'{date} | Short Position Closed  with {profit:.2f} Profit :)))') \n",
        "      #       print('=' * 55)\n",
        "      #     return\n",
        "      \n",
        "\n",
        "    def illustrate_position(self,idx,forward_candles = 100):\n",
        "      time_idx = self.positions_info[idx]['Index']\n",
        "      position = self.positions_info[idx]['Position']\n",
        "      date = self.positions_info[idx]['Date']\n",
        "      takeprofit = self.positions_info[idx]['TakeProfit']\n",
        "      profit = self.positions_info[idx]['Profit']\n",
        "      id = self.positions_info[idx]['position_id']\n",
        "      closedate = self.positions_info[idx]['CloseDate']\n",
        "\n",
        "      print('=' * 55) \n",
        "      print(f'{date} |{position} Order Executed | ID:{id}')\n",
        "      print(f'{closedate} |Profit : {profit:0.2f} ')\n",
        "      print('=' * 55)\n",
        "\n",
        "      data1 = self.data[time_idx-self.window:time_idx]\n",
        "      data2 = self.data[time_idx:time_idx+forward_candles]\n",
        "      data = pd.DataFrame()\n",
        "      data1['takeprofit'] = np.nan \n",
        "      data2['takeprofit'] = takeprofit\n",
        "      data = pd.concat([data1,data2])\n",
        "      data['signal'] = np.nan\n",
        "      data['signal'][self.window] = data['Close'][self.window] \n",
        "\n",
        "      \n",
        "\n",
        "      # macd panel\n",
        "      ticker_name = 'Instrument'\n",
        "      colors = ['g' if v >= 0 else 'r' for v in data[\"MACD_Hist\"]]\n",
        "      macd_plot = mpf.make_addplot(data[\"MACD\"], panel=1, color='fuchsia', title=\"MACD\" )\n",
        "      macd_hist_plot = mpf.make_addplot(data[\"MACD_Hist\"], type='bar', panel=1, color=colors) # color='dimgray'\n",
        "      macd_signal_plot = mpf.make_addplot(data[\"MACD_Signal\"], panel=1, color='b')\n",
        "      if position == 'Long':\n",
        "        signal_plot = mpf.make_addplot(data['signal'],type='scatter',markersize=150,marker='^' , panel=0)\n",
        "      if position == 'Short':\n",
        "        signal_plot = mpf.make_addplot(data['signal'],type='scatter',markersize=150,marker='v' , panel=0)\n",
        "      # pivot_plot = mpf.make_addplot(data['pivots'],type='scatter',markersize=10, marker='o' , panel=0 , color='black')\n",
        "      # rsi_plot =  mpf.make_addplot(data['RSI'], panel=2, color='blue', title=\"RSI\" )\n",
        "      sma10_plot =  mpf.make_addplot(data['SMA10'], panel=0, color='yellow', title=\"SMA\" )\n",
        "      sma20_plot =  mpf.make_addplot(data['SMA20'], panel=0, color='blue', title=\"SMA\" )\n",
        "      sma50_plot =  mpf.make_addplot(data['SMA50'], panel=0, color='red', title=\"SMA\" )\n",
        "      sma100_plot =  mpf.make_addplot(data['SMA100'], panel=0, color='pink', title=\"SMA\" )\n",
        "\n",
        "      # SL = mpf.make_addplot(data['stoploss'] ,panel=0,color='red')\n",
        "      TP = mpf.make_addplot(data['takeprofit'] ,panel=0,color='green')\n",
        "\n",
        "      plots = [signal_plot,TP,macd_plot,macd_hist_plot,macd_signal_plot,sma10_plot,sma20_plot,sma50_plot,sma100_plot]\n",
        "      mpf.plot(data ,\n",
        "               type='candle',\n",
        "               style='yahoo',\n",
        "               figsize = (15,10) ,\n",
        "               volume=False,\n",
        "               volume_panel=3,\n",
        "               addplot=plots, \n",
        "               title=f\"\\n{ticker_name}\",\n",
        "               ylabel='',\n",
        "               ylabel_lower='')\n",
        "      print(' ')\n",
        "      print(' ')\n",
        "\n",
        "    def plot(self):\n",
        "      long_position_plot = mpf.make_addplot(self.data['long_position'],type='scatter',markersize=50,marker='o' , panel=0 , color = 'green' )\n",
        "      # short_position_plot = mpf.make_addplot(self.data['short_position'],type='scatter',markersize=50,marker='o' , panel=0 , color = 'red')\n",
        "      plots = [long_position_plot]\n",
        "      \n",
        "      mpf.plot(self.data ,\n",
        "                type='candle',\n",
        "                style='yahoo',\n",
        "                figsize = (30,20) ,\n",
        "                addplot=plots, \n",
        "                title=f\"\\nAll Data\",\n",
        "                ylabel='',\n",
        "                ylabel_lower='')\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZT693ic26G2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cb87a95a-cff6-4f81-a726-174c55d00f55"
      },
      "source": [
        "bt = Backtest(verbose=True,\n",
        "              amount=10000,\n",
        "              sma=200,\n",
        "              ptc = 0,\n",
        "              )\n",
        "bt.add_data(data)\n",
        "bt.run(window = 50 , found_risk = 1 )"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Running MACD Divergence strategy\n",
            "fixed costs 0.0 | proportional costs 0\n",
            "=======================================================\n",
            "2019-02-19 20:00:00 | Take Profit : 154.95\n",
            "2019-02-19 20:00:00 | buying 68.4088 units at 146.18\n",
            "2019-02-19 20:00:00 | current balance 0.00\n",
            "2019-02-19 20:00:00 | current net wealth 10000.00\n",
            "2019-02-19 20:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2019-02-23 17:00:00 | selling 68.4088 units at 156.12\n",
            "2019-02-23 17:00:00 | current balance 10679.98\n",
            "2019-02-23 17:00:00 | current net wealth 10679.98\n",
            "2019-02-23 17:00:00 | $$$ Take Profit Activated at 154.95 $$$\n",
            "2019-02-23 17:00:00 | Long Position Closed with 8.77 Profit :)))\n",
            "=======================================================\n",
            "2019-04-20 03:00:00 | Take Profit : 183.59\n",
            "2019-04-20 03:00:00 | buying 61.6627 units at 173.20\n",
            "2019-04-20 03:00:00 | current balance 0.00\n",
            "2019-04-20 03:00:00 | current net wealth 10679.98\n",
            "2019-04-20 03:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2019-05-11 08:00:00 | selling 61.6627 units at 184.89\n",
            "2019-05-11 08:00:00 | current balance 11400.82\n",
            "2019-05-11 08:00:00 | current net wealth 11400.82\n",
            "2019-05-11 08:00:00 | $$$ Take Profit Activated at 183.59 $$$\n",
            "2019-05-11 08:00:00 | Long Position Closed with 10.39 Profit :)))\n",
            "=======================================================\n",
            "2019-05-16 22:00:00 | Take Profit : 275.05\n",
            "2019-05-16 22:00:00 | buying 43.9372 units at 259.48\n",
            "2019-05-16 22:00:00 | current balance -0.00\n",
            "2019-05-16 22:00:00 | current net wealth 11400.82\n",
            "2019-05-16 22:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2019-05-27 16:00:00 | selling 43.9372 units at 266.36\n",
            "2019-05-27 16:00:00 | current balance 11703.11\n",
            "2019-05-27 16:00:00 | current net wealth 11703.11\n",
            "2019-05-27 16:00:00 | $$$ Take Profit Activated at 275.05 $$$\n",
            "2019-05-27 16:00:00 | Long Position Closed with 15.57 Profit :)))\n",
            "=======================================================\n",
            "2019-06-17 12:00:00 | Take Profit : 287.28\n",
            "2019-06-17 12:00:00 | buying 43.1817 units at 271.02\n",
            "2019-06-17 12:00:00 | current balance 0.00\n",
            "2019-06-17 12:00:00 | current net wealth 11703.11\n",
            "2019-06-17 12:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2019-06-21 06:00:00 | selling 43.1817 units at 284.04\n",
            "2019-06-21 06:00:00 | current balance 12265.33\n",
            "2019-06-21 06:00:00 | current net wealth 12265.33\n",
            "2019-06-21 06:00:00 | $$$ Take Profit Activated at 287.28 $$$\n",
            "2019-06-21 06:00:00 | Long Position Closed with 16.26 Profit :)))\n",
            "=======================================================\n",
            "2019-06-23 22:00:00 | Take Profit : 329.33\n",
            "2019-06-23 22:00:00 | buying 39.4777 units at 310.69\n",
            "2019-06-23 22:00:00 | current balance 0.01\n",
            "2019-06-23 22:00:00 | current net wealth 12265.33\n",
            "2019-06-23 22:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2019-06-26 03:00:00 | selling 39.4777 units at 332.03\n",
            "2019-06-26 03:00:00 | current balance 13107.79\n",
            "2019-06-26 03:00:00 | current net wealth 13107.79\n",
            "2019-06-26 03:00:00 | $$$ Take Profit Activated at 329.33 $$$\n",
            "2019-06-26 03:00:00 | Long Position Closed with 18.64 Profit :)))\n",
            "=======================================================\n",
            "2019-08-03 06:00:00 | Take Profit : 236.38\n",
            "2019-08-03 06:00:00 | buying 58.7793 units at 223.00\n",
            "2019-08-03 06:00:00 | current balance 0.00\n",
            "2019-08-03 06:00:00 | current net wealth 13107.79\n",
            "2019-08-03 06:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2019-08-06 10:00:00 | selling 58.7793 units at 230.22\n",
            "2019-08-06 10:00:00 | current balance 13532.17\n",
            "2019-08-06 10:00:00 | current net wealth 13532.17\n",
            "2019-08-06 10:00:00 | $$$ Take Profit Activated at 236.38 $$$\n",
            "2019-08-06 10:00:00 | Long Position Closed with 13.38 Profit :)))\n",
            "=======================================================\n",
            "2019-09-17 00:00:00 | Take Profit : 210.13\n",
            "2019-09-17 00:00:00 | buying 68.2616 units at 198.24\n",
            "2019-09-17 00:00:00 | current balance -0.00\n",
            "2019-09-17 00:00:00 | current net wealth 13532.17\n",
            "2019-09-17 00:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2019-09-17 16:00:00 | selling 68.2616 units at 209.07\n",
            "2019-09-17 16:00:00 | current balance 14271.45\n",
            "2019-09-17 16:00:00 | current net wealth 14271.45\n",
            "2019-09-17 16:00:00 | $$$ Take Profit Activated at 210.13 $$$\n",
            "2019-09-17 16:00:00 | Long Position Closed with 11.89 Profit :)))\n",
            "=======================================================\n",
            "2019-10-10 23:00:00 | Take Profit : 202.61\n",
            "2019-10-10 23:00:00 | buying 74.6649 units at 191.14\n",
            "2019-10-10 23:00:00 | current balance -0.00\n",
            "2019-10-10 23:00:00 | current net wealth 14271.45\n",
            "2019-10-10 23:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2020-02-05 18:00:00 | selling 74.6649 units at 203.31\n",
            "2020-02-05 18:00:00 | current balance 15180.12\n",
            "2020-02-05 18:00:00 | current net wealth 15180.12\n",
            "2020-02-05 18:00:00 | $$$ Take Profit Activated at 202.61 $$$\n",
            "2020-02-05 18:00:00 | Long Position Closed with 11.47 Profit :)))\n",
            "=======================================================\n",
            "2020-02-08 12:00:00 | Take Profit : 239.23\n",
            "2020-02-08 12:00:00 | buying 67.2609 units at 225.69\n",
            "2020-02-08 12:00:00 | current balance 0.01\n",
            "2020-02-08 12:00:00 | current net wealth 15180.12\n",
            "2020-02-08 12:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2020-02-12 00:00:00 | selling 67.2609 units at 247.07\n",
            "2020-02-12 00:00:00 | current balance 16618.16\n",
            "2020-02-12 00:00:00 | current net wealth 16618.16\n",
            "2020-02-12 00:00:00 | $$$ Take Profit Activated at 239.23 $$$\n",
            "2020-02-12 00:00:00 | Long Position Closed with 13.54 Profit :)))\n",
            "=======================================================\n",
            "2020-02-14 14:00:00 | Take Profit : 288.42\n",
            "2020-02-14 14:00:00 | buying 61.076 units at 272.09\n",
            "2020-02-14 14:00:00 | current balance -0.01\n",
            "2020-02-14 14:00:00 | current net wealth 16618.16\n",
            "2020-02-14 14:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2020-07-25 16:00:00 | selling 61.076 units at 288.93\n",
            "2020-07-25 16:00:00 | current balance 17646.68\n",
            "2020-07-25 16:00:00 | current net wealth 17646.68\n",
            "2020-07-25 16:00:00 | $$$ Take Profit Activated at 288.42 $$$\n",
            "2020-07-25 16:00:00 | Long Position Closed with 16.33 Profit :)))\n",
            "=======================================================\n",
            "2020-07-28 02:00:00 | Take Profit : 337.39\n",
            "2020-07-28 02:00:00 | buying 55.4421 units at 318.29\n",
            "2020-07-28 02:00:00 | current balance 0.01\n",
            "2020-07-28 02:00:00 | current net wealth 17646.68\n",
            "2020-07-28 02:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2020-07-30 20:00:00 | selling 55.4421 units at 338.22\n",
            "2020-07-30 20:00:00 | current balance 18751.64\n",
            "2020-07-30 20:00:00 | current net wealth 18751.64\n",
            "2020-07-30 20:00:00 | $$$ Take Profit Activated at 337.39 $$$\n",
            "2020-07-30 20:00:00 | Long Position Closed with 19.10 Profit :)))\n",
            "=======================================================\n",
            "2020-08-02 23:00:00 | Take Profit : 394.77\n",
            "2020-08-02 23:00:00 | buying 50.3508 units at 372.42\n",
            "2020-08-02 23:00:00 | current balance -0.01\n",
            "2020-08-02 23:00:00 | current net wealth 18751.64\n",
            "2020-08-02 23:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2020-08-03 13:00:00 | selling 50.3508 units at 393.22\n",
            "2020-08-03 13:00:00 | current balance 19798.94\n",
            "2020-08-03 13:00:00 | current net wealth 19798.94\n",
            "2020-08-03 13:00:00 | $$$ Take Profit Activated at 394.77 $$$\n",
            "2020-08-03 13:00:00 | Long Position Closed with 22.35 Profit :)))\n",
            "=======================================================\n",
            "2020-08-04 08:00:00 | Take Profit : 414.97\n",
            "2020-08-04 08:00:00 | buying 50.5746 units at 391.48\n",
            "2020-08-04 08:00:00 | current balance -0.01\n",
            "2020-08-04 08:00:00 | current net wealth 19798.94\n",
            "2020-08-04 08:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2020-08-13 21:00:00 | selling 50.5746 units at 418.67\n",
            "2020-08-13 21:00:00 | current balance 21174.06\n",
            "2020-08-13 21:00:00 | current net wealth 21174.06\n",
            "2020-08-13 21:00:00 | $$$ Take Profit Activated at 414.97 $$$\n",
            "2020-08-13 21:00:00 | Long Position Closed with 23.49 Profit :)))\n",
            "=======================================================\n",
            "2020-08-31 13:00:00 | Take Profit : 460.09\n",
            "2020-08-31 13:00:00 | buying 48.7825 units at 434.05\n",
            "2020-08-31 13:00:00 | current balance 0.01\n",
            "2020-08-31 13:00:00 | current net wealth 21174.06\n",
            "2020-08-31 13:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2020-09-01 06:00:00 | selling 48.7825 units at 463.52\n",
            "2020-09-01 06:00:00 | current balance 22611.68\n",
            "2020-09-01 06:00:00 | current net wealth 22611.68\n",
            "2020-09-01 06:00:00 | $$$ Take Profit Activated at 460.09 $$$\n",
            "2020-09-01 06:00:00 | Long Position Closed with 26.04 Profit :)))\n",
            "=======================================================\n",
            "2020-10-12 08:00:00 | Take Profit : 396.06\n",
            "2020-10-12 08:00:00 | buying 60.5173 units at 373.64\n",
            "2020-10-12 08:00:00 | current balance -0.01\n",
            "2020-10-12 08:00:00 | current net wealth 22611.68\n",
            "2020-10-12 08:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2020-10-21 16:00:00 | selling 60.5173 units at 396.13\n",
            "2020-10-21 16:00:00 | current balance 23972.71\n",
            "2020-10-21 16:00:00 | current net wealth 23972.71\n",
            "2020-10-21 16:00:00 | $$$ Take Profit Activated at 396.06 $$$\n",
            "2020-10-21 16:00:00 | Long Position Closed with 22.42 Profit :)))\n",
            "=======================================================\n",
            "2020-10-24 08:00:00 | Take Profit : 433.90\n",
            "2020-10-24 08:00:00 | buying 58.5643 units at 409.34\n",
            "2020-10-24 08:00:00 | current balance 0.00\n",
            "2020-10-24 08:00:00 | current net wealth 23972.71\n",
            "2020-10-24 08:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2020-11-06 01:00:00 | selling 58.5643 units at 435.48\n",
            "2020-11-06 01:00:00 | current balance 25503.58\n",
            "2020-11-06 01:00:00 | current net wealth 25503.58\n",
            "2020-11-06 01:00:00 | $$$ Take Profit Activated at 433.90 $$$\n",
            "2020-11-06 01:00:00 | Long Position Closed with 24.56 Profit :)))\n",
            "=======================================================\n",
            "2020-11-22 21:00:00 | Take Profit : 607.10\n",
            "2020-11-22 21:00:00 | buying 44.5291 units at 572.74\n",
            "2020-11-22 21:00:00 | current balance -0.01\n",
            "2020-11-22 21:00:00 | current net wealth 25503.58\n",
            "2020-11-22 21:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2020-11-23 21:00:00 | selling 44.5291 units at 605.65\n",
            "2020-11-23 21:00:00 | current balance 26969.04\n",
            "2020-11-23 21:00:00 | current net wealth 26969.04\n",
            "2020-11-23 21:00:00 | $$$ Take Profit Activated at 607.10 $$$\n",
            "2020-11-23 21:00:00 | Long Position Closed with 34.36 Profit :)))\n",
            "=======================================================\n",
            "2020-12-18 12:00:00 | Take Profit : 689.46\n",
            "2020-12-18 12:00:00 | buying 41.4634 units at 650.43\n",
            "2020-12-18 12:00:00 | current balance -0.00\n",
            "2020-12-18 12:00:00 | current net wealth 26969.04\n",
            "2020-12-18 12:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2020-12-27 14:00:00 | selling 41.4634 units at 685.15\n",
            "2020-12-27 14:00:00 | current balance 28408.65\n",
            "2020-12-27 14:00:00 | current net wealth 28408.65\n",
            "2020-12-27 14:00:00 | $$$ Take Profit Activated at 689.46 $$$\n",
            "2020-12-27 14:00:00 | Long Position Closed with 39.03 Profit :)))\n",
            "=======================================================\n",
            "2020-12-29 16:00:00 | Take Profit : 757.62\n",
            "2020-12-29 16:00:00 | buying 39.7468 units at 714.74\n",
            "2020-12-29 16:00:00 | current balance 0.02\n",
            "2020-12-29 16:00:00 | current net wealth 28408.65\n",
            "2020-12-29 16:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2020-12-30 19:00:00 | selling 39.7468 units at 752.97\n",
            "2020-12-30 19:00:00 | current balance 29928.17\n",
            "2020-12-30 19:00:00 | current net wealth 29928.17\n",
            "2020-12-30 19:00:00 | $$$ Take Profit Activated at 757.62 $$$\n",
            "2020-12-30 19:00:00 | Long Position Closed with 42.88 Profit :)))\n",
            "=======================================================\n",
            "2021-01-04 22:00:00 | Take Profit : 1073.48\n",
            "2021-01-04 22:00:00 | buying 29.5523 units at 1012.72\n",
            "2021-01-04 22:00:00 | current balance -0.04\n",
            "2021-01-04 22:00:00 | current net wealth 29928.17\n",
            "2021-01-04 22:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-01-05 00:00:00 | selling 29.5523 units at 1095.03\n",
            "2021-01-05 00:00:00 | current balance 32360.62\n",
            "2021-01-05 00:00:00 | current net wealth 32360.62\n",
            "2021-01-05 00:00:00 | $$$ Take Profit Activated at 1073.48 $$$\n",
            "2021-01-05 00:00:00 | Long Position Closed with 60.76 Profit :)))\n",
            "=======================================================\n",
            "2021-01-05 18:00:00 | Take Profit : 1138.44\n",
            "2021-01-05 18:00:00 | buying 30.1309 units at 1074.00\n",
            "2021-01-05 18:00:00 | current balance 0.03\n",
            "2021-01-05 18:00:00 | current net wealth 32360.62\n",
            "2021-01-05 18:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-01-06 11:00:00 | selling 30.1309 units at 1151.88\n",
            "2021-01-06 11:00:00 | current balance 34707.21\n",
            "2021-01-06 11:00:00 | current net wealth 34707.21\n",
            "2021-01-06 11:00:00 | $$$ Take Profit Activated at 1138.44 $$$\n",
            "2021-01-06 11:00:00 | Long Position Closed with 64.44 Profit :)))\n",
            "=======================================================\n",
            "2021-01-08 14:00:00 | Take Profit : 1332.42\n",
            "2021-01-08 14:00:00 | buying 27.6111 units at 1257.00\n",
            "2021-01-08 14:00:00 | current balance 0.06\n",
            "2021-01-08 14:00:00 | current net wealth 34707.21\n",
            "2021-01-08 14:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-01-10 01:00:00 | selling 27.6111 units at 1323.44\n",
            "2021-01-10 01:00:00 | current balance 36541.69\n",
            "2021-01-10 01:00:00 | current net wealth 36541.69\n",
            "2021-01-10 01:00:00 | $$$ Take Profit Activated at 1332.42 $$$\n",
            "2021-01-10 01:00:00 | Long Position Closed with 75.42 Profit :)))\n",
            "=======================================================\n",
            "2021-02-05 03:00:00 | Take Profit : 1738.39\n",
            "2021-02-05 03:00:00 | buying 22.2817 units at 1639.99\n",
            "2021-02-05 03:00:00 | current balance -0.07\n",
            "2021-02-05 03:00:00 | current net wealth 36541.69\n",
            "2021-02-05 03:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-02-05 14:00:00 | selling 22.2817 units at 1737.72\n",
            "2021-02-05 14:00:00 | current balance 38719.28\n",
            "2021-02-05 14:00:00 | current net wealth 38719.28\n",
            "2021-02-05 14:00:00 | $$$ Take Profit Activated at 1738.39 $$$\n",
            "2021-02-05 14:00:00 | Long Position Closed with 98.40 Profit :)))\n",
            "=======================================================\n",
            "2021-02-06 18:00:00 | Take Profit : 1809.58\n",
            "2021-02-06 18:00:00 | buying 22.6807 units at 1707.15\n",
            "2021-02-06 18:00:00 | current balance -0.07\n",
            "2021-02-06 18:00:00 | current net wealth 38719.28\n",
            "2021-02-06 18:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-02-09 08:00:00 | selling 22.6807 units at 1803.00\n",
            "2021-02-09 08:00:00 | current balance 40893.23\n",
            "2021-02-09 08:00:00 | current net wealth 40893.23\n",
            "2021-02-09 08:00:00 | $$$ Take Profit Activated at 1809.58 $$$\n",
            "2021-02-09 08:00:00 | Long Position Closed with 102.43 Profit :)))\n",
            "=======================================================\n",
            "2021-02-14 02:00:00 | Take Profit : 1933.43\n",
            "2021-02-14 02:00:00 | buying 22.4197 units at 1823.99\n",
            "2021-02-14 02:00:00 | current balance -0.08\n",
            "2021-02-14 02:00:00 | current net wealth 40893.23\n",
            "2021-02-14 02:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-02-18 18:00:00 | selling 22.4197 units at 1926.17\n",
            "2021-02-18 18:00:00 | current balance 43184.07\n",
            "2021-02-18 18:00:00 | current net wealth 43184.07\n",
            "2021-02-18 18:00:00 | $$$ Take Profit Activated at 1933.43 $$$\n",
            "2021-02-18 18:00:00 | Long Position Closed with 109.44 Profit :)))\n",
            "=======================================================\n",
            "2021-03-09 23:00:00 | Take Profit : 1982.74\n",
            "2021-03-09 23:00:00 | buying 23.0868 units at 1870.51\n",
            "2021-03-09 23:00:00 | current balance -0.02\n",
            "2021-03-09 23:00:00 | current net wealth 43184.07\n",
            "2021-03-09 23:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-04-01 19:00:00 | selling 23.0868 units at 1974.12\n",
            "2021-04-01 19:00:00 | current balance 45576.10\n",
            "2021-04-01 19:00:00 | current net wealth 45576.10\n",
            "2021-04-01 19:00:00 | $$$ Take Profit Activated at 1982.74 $$$\n",
            "2021-04-01 19:00:00 | Long Position Closed with 112.23 Profit :)))\n",
            "=======================================================\n",
            "2021-04-15 00:00:00 | Take Profit : 2560.88\n",
            "2021-04-15 00:00:00 | buying 18.8649 units at 2415.92\n",
            "2021-04-15 00:00:00 | current balance 0.01\n",
            "2021-04-15 00:00:00 | current net wealth 45576.10\n",
            "2021-04-15 00:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-04-22 11:00:00 | selling 18.8649 units at 2544.70\n",
            "2021-04-22 11:00:00 | current balance 48005.52\n",
            "2021-04-22 11:00:00 | current net wealth 48005.52\n",
            "2021-04-22 11:00:00 | $$$ Take Profit Activated at 2560.88 $$$\n",
            "2021-04-22 11:00:00 | Long Position Closed with 144.96 Profit :)))\n",
            "=======================================================\n",
            "2021-04-30 07:00:00 | Take Profit : 2940.95\n",
            "2021-04-30 07:00:00 | buying 17.3025 units at 2774.48\n",
            "2021-04-30 07:00:00 | current balance 0.08\n",
            "2021-04-30 07:00:00 | current net wealth 48005.52\n",
            "2021-04-30 07:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-05-01 18:00:00 | selling 17.3025 units at 2940.04\n",
            "2021-05-01 18:00:00 | current balance 50870.12\n",
            "2021-05-01 18:00:00 | current net wealth 50870.12\n",
            "2021-05-01 18:00:00 | $$$ Take Profit Activated at 2940.95 $$$\n",
            "2021-05-01 18:00:00 | Long Position Closed with 166.47 Profit :)))\n",
            "=======================================================\n",
            "2021-05-02 19:00:00 | Take Profit : 3141.37\n",
            "2021-05-02 19:00:00 | buying 17.1652 units at 2963.56\n",
            "2021-05-02 19:00:00 | current balance 0.02\n",
            "2021-05-02 19:00:00 | current net wealth 50870.12\n",
            "2021-05-02 19:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-05-03 07:00:00 | selling 17.1652 units at 3146.95\n",
            "2021-05-03 07:00:00 | current balance 54018.05\n",
            "2021-05-03 07:00:00 | current net wealth 54018.05\n",
            "2021-05-03 07:00:00 | $$$ Take Profit Activated at 3141.37 $$$\n",
            "2021-05-03 07:00:00 | Long Position Closed with 177.81 Profit :)))\n",
            "=======================================================\n",
            "2021-05-05 11:00:00 | Take Profit : 3553.81\n",
            "2021-05-05 11:00:00 | buying 16.112 units at 3352.65\n",
            "2021-05-05 11:00:00 | current balance 0.15\n",
            "2021-05-05 11:00:00 | current net wealth 54018.05\n",
            "2021-05-05 11:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-05-06 15:00:00 | selling 16.112 units at 3551.25\n",
            "2021-05-06 15:00:00 | current balance 57217.89\n",
            "2021-05-06 15:00:00 | current net wealth 57217.89\n",
            "2021-05-06 15:00:00 | $$$ Take Profit Activated at 3553.81 $$$\n",
            "2021-05-06 15:00:00 | Long Position Closed with 201.16 Profit :)))\n",
            "=======================================================\n",
            "2021-07-26 01:00:00 | Take Profit : 2453.49\n",
            "2021-07-26 01:00:00 | buying 24.7203 units at 2314.61\n",
            "2021-07-26 01:00:00 | current balance 0.04\n",
            "2021-07-26 01:00:00 | current net wealth 57217.89\n",
            "2021-07-26 01:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-07-30 21:00:00 | selling 24.7203 units at 2441.62\n",
            "2021-07-30 21:00:00 | current balance 60357.61\n",
            "2021-07-30 21:00:00 | current net wealth 60357.61\n",
            "2021-07-30 21:00:00 | $$$ Take Profit Activated at 2453.49 $$$\n",
            "2021-07-30 21:00:00 | Long Position Closed with 138.88 Profit :)))\n",
            "=======================================================\n",
            "2021-07-31 00:00:00 | Take Profit : 2603.86\n",
            "2021-07-31 00:00:00 | buying 24.5709 units at 2456.47\n",
            "2021-07-31 00:00:00 | current balance -0.06\n",
            "2021-07-31 00:00:00 | current net wealth 60357.61\n",
            "2021-07-31 00:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-08-01 03:00:00 | selling 24.5709 units at 2616.50\n",
            "2021-08-01 03:00:00 | current balance 64289.70\n",
            "2021-08-01 03:00:00 | current net wealth 64289.70\n",
            "2021-08-01 03:00:00 | $$$ Take Profit Activated at 2603.86 $$$\n",
            "2021-08-01 03:00:00 | Long Position Closed with 147.39 Profit :)))\n",
            "=======================================================\n",
            "2021-08-01 21:00:00 | Take Profit : 2812.87\n",
            "2021-08-01 21:00:00 | buying 24.2269 units at 2653.65\n",
            "2021-08-01 21:00:00 | current balance -0.02\n",
            "2021-08-01 21:00:00 | current net wealth 64289.70\n",
            "2021-08-01 21:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-08-05 14:00:00 | selling 24.2269 units at 2802.95\n",
            "2021-08-05 14:00:00 | current balance 67906.77\n",
            "2021-08-05 14:00:00 | current net wealth 67906.77\n",
            "2021-08-05 14:00:00 | $$$ Take Profit Activated at 2812.87 $$$\n",
            "2021-08-05 14:00:00 | Long Position Closed with 159.22 Profit :)))\n",
            "=======================================================\n",
            "2021-09-02 18:00:00 | Take Profit : 4004.68\n",
            "2021-09-02 18:00:00 | buying 17.9743 units at 3778.00\n",
            "2021-09-02 18:00:00 | current balance -0.13\n",
            "2021-09-02 18:00:00 | current net wealth 67906.77\n",
            "2021-09-02 18:00:00 | @@@@@ Long Order Executed @@@@@\n",
            "2021-09-03 14:00:00 | selling 17.9743 units at 3979.38\n",
            "2021-09-03 14:00:00 | current balance 71526.44\n",
            "2021-09-03 14:00:00 | current net wealth 71526.44\n",
            "2021-09-03 14:00:00 | $$$ Take Profit Activated at 4004.68 $$$\n",
            "2021-09-03 14:00:00 | Long Position Closed with 226.68 Profit :)))\n",
            "=======================================================\n",
            "2021-09-18 15:00:00 | inventory 0 units at 3475.39\n",
            "=======================================================\n",
            "Final balance   [$] 71526.44\n",
            "Net Performance [%] 615.26\n",
            "Trades Executed [#] 34\n",
            "Accuracy        [%] 100.00\n",
            "Avg Win/Avg Lose   inf\n",
            "=======================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FvHnzsGnTJsTGxmLhwoUoLS3FihUr8MEHH7jkDRMREQ0URmNbGLti4hBEhARKXBvqiUOXKY8ePYq33noLr7zyCvLy8gAA+fn5SEtLAwCkp6cjPz8fALBjxw6kpqYCABQKBRITE7Fv3z5UVFSgpaUFarXaqkxeXp54rKSkJJSUlECn0znvXRIREQ1AHdNaKDithUdzqGds4cKFSElJgclkwm9/+1solUpoNBoolUoAgEqlQl1dHYxGI7RaLRITE8WyKpUKWq3WYv+O7RpN22DCnl5TqVQ91ikiYhAUCr/evds+UqtD3HIe6j+2lXdhe3kPtpV36WivRmNbGAtRBbENPZhDYSwlJQUA4Ofnh8mTJ6OgoABRUVFobGxEaGgodDodwsLCoFAoEBkZicbGRrGsTqdDZGSkuH/X7VFRUQBg87We1NQ0Of4u+0GtDkF1dYNbzkX9w7byLmwv78G28i5d2+vI8WoAwLHyGrahxGyFYbuXKU+cOIGPPvpIfH769GkkJCQgMzMThYWFAID9+/cjMzMTAJCVlYWioiIAgMFgwMmTJzFlyhQkJCQgKCgI1dXV3ZbpOFZpaSmSk5Nt9ooRERGRfa9/dggAcLSiVuKakC12e8ZUKhXy8/Nx4cIF6HQ6DBkyBDfeeCMyMzOxZs0alJWVoaKiAtnZ2QCA1NRUTJ06Fbm5uairq0N2djZCQ0MBAKtXr8ZLL72EuLg4mEwm3HrrrQCAe++9Fzk5OVi/fj3Ky8vx/PPPu/AtExEREXkOmSAIXjklr7u6W9k97z3YVt6F7eU92FbepWt7rdiwF1WaJgyPCcHK+6dIXLOBrV+XKYmIiMg7zUyJAwDcOGOEtBUhmxjGiIiIfJTJ3DbPmL+Cf+49mUN3UxIREZHnM5rMqLrYCG37jAMf558EAHCWMc/GMEZEROQjXv7oAA6X1Vht/+WkBhMTbU8ZRdJhvyUREZGXMpnN2PjlEZSWtwWw8zXNCAzww5UpsbgyJVbcTy5j35gnYxgjIiLyUgdPaLH70DnkfNA2V6fJLCBcFYgFc8dhwdxx4n4xEcFSVZEcwDBGRETkpVqNJovnJpO523UoJycPdleVqA84ZoyIiMhLmc2WU4WazAIUfp39LA/eMA6VFxsRMijA3VWjXmAYIyIi8lKtRrP4uErTCKNZgF+XMHbFxNjuipGH4WVKIiIiL/Xe1hLx8amqephMQreXKcmzMYwRERH5ABlk7WPG+Kfd27DFiIiIfIDeYIIA63Fk5PkYxoiIiHzA9wfOAgBKTltP+kqejQP4iYiIPNzF2maLwfrdOVPd6KbakLMxjBEREXmwfcXn8cbnh+3uNyjQD/VNtgMbeSaGMSIiIg92oaYZAHDZqChEhAZZvHbqbD1On28AAIspLci7MIwRERF5sI5Z9q+fNhxjE8KtXl+wajsAoKZB79Z6kfMwRhMREXmwL3efBgD4K/gn21exZYmIiCRQ06DHH9/d5/Ddj5yywncxjBEREUlg248VKD+vw7qPDji0Pydz9V1sWSIiIgn4tS9bZG/Kig6RoYEO7XfTzMQ+14mkwTBGREQkgbMXezcvWMiggG63jxseYfFcGezf5zqRNBjGiIiIJDBqaJjD+9oavH9pGGtsMfS5TiQNhjEiIiIJhCk7e7r0BlO3+wiCABmAkUNCejxOxrjBFs///f1Jp9SP3IfzjBERETnJLycuoqnFaHc/hZ8czfrO/Y6UaZE2Rm21n1kQIMD2hK4ymaxPdSXPwTBGRETkBD+VXMD6zw45vH9MRLD4WIbuA5XJ1DadRcdg/+4wi3k/hjEiIiIn+PcPp8TH985J6nG/0vJaFBw5j/PtyxwBbYHqh4NVqNI04Y6sUeL2P7y9DwBw6KS2x+PJL0ljaWOte9jIszGMEREROcGZ6s67I7NSh/a4X2zkIBQcOW+xTddswNtfFQMAbs9MFC89Xqhttip/qUsvU469ZEA/eT4O4CciInKiwAA/m693N8arI4gBgNHk2LxjPbnrmrH9Kk/uxzBGRETkBHHRSgDATVeMsLmfINhe1khvaAtjF+vs94oBgPySbOevsB0GyfMwjBERETlBx3xfE0ZG2tzPYKfny9A+I7/JwbUoeTel92MYIyIicoKO8GRrGgoAMBpth6xtP1UAAPwdXYuSWczrMYwRERE5gam9x0tx6XXDSzS32p6H7JuCcqttChtTW1x6NyV5H4YxIiKifjhaUYsFq7ajpkEPAPCzE8bg2NVHmLtcpvz9/0zpcT9mMe/HqS2IiIj6oGMg/qot+wEAh061zQUmtxPGzHYG8ANtQazjsueooaFIGKzqT1XJwzGMERER9cGb/z6MfcUXrLbbGzM2JiHc7rFbWo0wtoexhME9r0sJAA5kO/JwvExJRETUB0crauGvsP4zau8y5eDwYJuvA4BZ6LxM6WfnOqS9qTLI8zncM9bS0oJ58+bhyiuvRHZ2NvR6PXJychATE4OysjIsWrQII0eOBAB8/vnnKC4uhlwux7BhwzB//nwAwJkzZ7B+/XoMHz4clZWVyM7OhlKphNlsRm5uLpRKJSorK3HHHXcgNTXVNe+YiIjICYwmAdFhQajSNFlsVwb1/6KTIAgwmdtuCLC1LiUABAXwIpe3c7hnbN26dRg/frz4fNOmTYiNjcXixYtx3333YcWKFQCAc+fO4Z133kF2djaeeeYZfPTRRygrKwMArFy5EvPnz8fixYsxZswYbNiwAQCwdetW6HQ6PPzww3j66aeRnZ0Nk8nkxLdJRETkXLpmA6prmzEnY5jF9r7O+/XO8qvFucrMQpdFwu30tHXXO0fexaEW/Oyzz5Ceno74+HhxW15eHtLS0gAASUlJKCkpgU6nw86dOzFhwgTxy5iWlobvv/8eBoMBBQUFmDRpEgAgPT0d+fn54rE6esLCw8MREBCAY8eOOe9dEhEROVGroa3DwGgScG1GQq/Lj+1h3FjIIH8AQM6W/eL6lfZ6xsj72e3bPH78OE6ePImlS5eitLRU3K7RaKBUKsXnKpUKGo0GWq3WYrtSqYRGo0FNTQ2CgoLEkNaxPwBotVqoVCqLY2m1Pa9QDwAREYOgcNOSD2q17cGT5DnYVt6F7eU92FaWahpaxMfR0ZZ3OjryWf3loStwrKIWw4aE4MX3fsSNMxOhVocgKLAtjJ3TNuGctu3yZ6gqqNefP9vLu9gNY9u2bUNAQADeeust/PzzzzAYDHjvvfcQFRWFxsbOFep1Oh2ioqIQGRmJ06dPi9sbGxsxbNgwREREoKWlBYIgQCaTifsDQGRkJHQ6ncWxIiNtLydRU9Nk83VnUatDUF3d4JZzUf+wrbwL28t7sK2sdfRaAYBW02jxmqOfVVx4EIwtBiybnyqWKymz7ohobjH0+vNne3keWwHZbhh7+OGHxcd6vR5NTU2477770NraisLCQkyePBmlpaVITk6GSqXCzJkz8fe//10MXYWFhbj77rvh7++PqVOn4uDBg0hJScH+/fuRmZkJAMjKysKPP/6IW265BbW1tWhtbcWYMWOc8NaJiIicr6prAHPiVcQLtdaLg1/QuqfzgaTj8C0Y3377LX788UcYDAZ8+eWXuPfee5GTk4P169ejvLwczz//PABgyJAhWLBgAV544QX4+flh3rx5GDFiBADgT3/6E1577TXs2rULVVVVWL58OQDg+uuvx5EjR/Dqq6/i7NmzyMnJgZ8fV50nIiLPtOtglfjY1SO6fjh0Dg/cMN7+juS1HA5j1113Ha677jqLbStXrux235tvvhk333yz1fb4+Hi8+OKLVtvlcjmWLVvmaFWIiIgk09JqhLZeLz7v692TrvD8wqlSV4H6gPfDEhER9cIjud9bPO866epfH57u9PPdkTXK4X1jo5T2dyKPw5niiIiI+kHRvvxRoL8fosPsz67fW5GhgXb3eeSWiZwCw4sxjBEREfXR7ZmJCA5U4I/3T0FEiP3QZE90WBAu1nVOm5GZGoeM5Bi75SYnD+73uUk6vExJRETUR3HtlwWHxYQgZFBAv4/3u9tTLJ7fOWs05HZm4CfvxzBGRETUR84OSvGDVbjqsjjxecclUPJtbGUiIqI+csU4ra4Bj+PABgaGMSIioj7yc8G0Ficq68THcg+aNoNch2GMiIioj1wxnqvigs7+TuRTGMaIiIj6yE/OP6PUf5zagoiIBqRmvRGHT2lh7jJpqz1ms+W+dY36HvYkchzDGBERDUhf7C7DNwXl/TrG0Yo6XJ7EOb6ofxjGiIhoQGpsNgAAbr5yJEIH+TtURlOvx9d7T4vPJ4yMdEndAEDBOykHDIYxIiIakDouT14xcQjU4Y4vYzRtQgz+8PY+AK4JTAo/GYwmAddOGeb0Y5Nn4shDIiIakDrGf/V2+oh4tUp87OeCuymnjm9b/mj00DCnH5s8E3vGiIhoQOoYi9+f6SlUTlgC6VL/MycZ11yegGExKvs7k09gGCMiogGps2es78cYGq10Um06KfzkGD4kxOnHJc/Fy5RERDQgdYwZk3EhbpIYwxgREQ04BqMJeoMJgGvGfRH1Bi9TEhHRgHLybD1e2Pyz2DPWl/Uf50wdBqPJ7Oyq0QDFMEZERAPK2YuNFrPu9yWM3TlrtDOrRAMcL1MSEdGAcmmPFpeXJKnxK0hERAOKdRjjmDGSFsMYERENKGXnGiye9+UyJZEzMYwREdGAsvvQOYvnMoYxkhjDGBEREZGEGMaIiIiIJMQwRkRERCQhhjEiIiIiCTGMEREREUmIYYyIiAaMxhaD1FUgssIwRkREA0argetJkudhGCMiogHDZGYYI8/DMEZERAOG2SxYPI+NGiRRTYg6KaSuABERkbuY2sPYlSmxiItSImPcYIlrRMQwRkREA0hHz5i/Qo45U4dJXBuiNrxMSUREA0ZHz5gf16MkD8IwRkREA4ZZaAtjcjnDGHkOu5cpzWYzHnroIaSkpMBgMKCiogIvvPACWlpasHbtWiQkJKCsrAxLly5FdHQ0AGDjxo3Q6XSor6/HjBkzMHv2bABAcXExtmzZgvj4eGg0GmRnZ0OhUECv1yMnJwcxMTEoKyvDokWLMHLkSNe+cyIiGnDEnjGGMfIgDvWMpaam4rHHHsOTTz6J5uZmfPfdd8jNzcX06dOxaNEiXHPNNcjJyQEAHDhwAAUFBViyZAmeffZZ5OTkoKGhAYIgYNmyZXjiiSfw0EMPwc/PD59++ikAYNOmTYiNjcXixYtx3333YcWKFa57x0RENGB1jBljzxh5ErthTC6X45FHHgEAGI1GnD9/HiNHjkR+fj7S0tIAAOnp6cjPzwcA7NixA6mpqQAAhUKBxMRE7Nu3DxUVFWhpaYFarbYqk5eXJx4rKSkJJSUl0Ol0Tn6rREQ0UJ3TNmFrwWnsOXQOAHvGyLM4fDflzp078d577yErKwuTJk2CRqOBUqkEAKhUKtTV1cFoNEKr1SIxMVEsp1KpoNVqLfbv2K7RaACgx9dUKlWP9YmIGASFws/xd9oPanWIW85D/ce28i5sL+/h7W31xr+PYN+Rc+LzIeoQr39Ptvjye/NFDoexmTNnYubMmXjmmWewZcsWREVFobGxEaGhodDpdAgLC4NCoUBkZCQaGxvFcjqdDpGRkeL+XbdHRUUBgM3XelJT0+Twm+wPtToE1dUNbjkX9Q/byruwvbyHt7eVyWwWg9iSeSlQ+MkxNiHcq9+TLd7eXr7KVkC2e5ny+PHjyMvLE5/Hx8fjzJkzyMzMRGFhIQBg//79yMzMBABkZWWhqKgIAGAwGHDy5ElMmTIFCQkJCAoKQnV1dbdlOo5VWlqK5ORkm71iREREjsovOis+ThkVjfEjIqHw42QC5Dns9owFBATgX//6F44cOQKj0YgTJ07gueeeg7+/P9asWYOysjJUVFQgOzsbQNtg/6lTpyI3Nxd1dXXIzs5GaGgoAGD16tV46aWXEBcXB5PJhFtvvRUAcO+99yInJwfr169HeXk5nn/+eRe+ZSIiGkhOVdVLXQUim2SCIAj2d/M87uqCZXev92BbeRe2l/fw9rZ69ZOD2H+07arMO8uvlrg2ruft7eWr+nWZkoiIyJuFKQOkrgKRTQxjRETks2oa9MgrrAQAzE6Pl7g2RN1jGKLePN8AACAASURBVCMiIp+1r/g8OsbiZKUPlbQuRD1hGCMiIp/VajQDAB68YRyGRivt7E0kDYYxIiLyKfpWk/jYZGoLYxEhQVJVh8guhjEiIvIZn+86hYdz81FaXgOAC4OTd2AYIyIin/H5rlMAgJwP2iYSNzOMkRdweDkkIiIidzILAhqaDP06hqa+BQDg58cwRp6LYYyIiDzSm58fxo8lF/p1jH3F/StP5A4MY0RE5HEEQRCD2JTkwQ6VadIbcfiUVnzerDeKj2vq9RgxxLl1JHIWhjEiIvI4ekPnHZEP3zLRoTJVmkas2FAgPu+6QLgfFwYnD8ZvJxEReZyu01M4KjosCP6Kzj9rH+44Lj4OCvBzSr2IXIFhjIiIPE7HZK294a/wwxtPZXb72pj4sP5WichlGMaIiMjjGNrDWFZqXK/KyWQyTEqM6nY7kafimDEiInK7oxW1OHamtsfXj5+pA2A5dsxRB09q+lwvIikwjBERkdtt+OIwNPV6u/vtOXweC2+c0K9z/Xr68H6VJ3I1hjEiInK7xhYjBocH47fXju329Q+3H0flxUYonHAX5C0zR/b7GESuxDBGRERuZzCaEaoM6HZ8FwAcPqVF5cVGpyxj5Cfn8GjybPyGEhGRWxlNZpjMgsU0FD3qQxabOj6m94WIJMQwRkRELrPn8Dlo29eH7NBqaLtTMtC/57m/koaFAwCunBTb63PeOWt0r8sQSYmXKYmIyCU+zj+Br/acBgC8s/xqcXvlRR0AoOj4xR7Lpo6OxguLpmFweHCvzxsREtjrMkRSYs8YERE53YnKOjGIAUBeYaX4eOvecrvlZTIZhkQOgtwJY8aIPB3DGBER9UtNgx5f7SkTJ2oFgI/yTljs8/63pdA1GwAAl41uG7Sf3H4pkmigYxgjIqJ++esH+/Fx/kmL3q+Gplar/aprm/H370qx6ZtSAK5dvHvJvMsAANMnDHHZOYichWGMiIj65XxNMwDgH/89BqPJDEEQ0NhiREzkINxwxQhxv7MXG7F9f2dgO32uwWV1ShkVhbezZ2HhjeNddg4iZ+EAfiIicppjFbVY/c8iAEBIsD9Cgv3F11paLZc2uvlK107GyvUoyVuwZ4yIiPplcETnHY8dQQwAKi824l/5nWPHtmw7alHuqst6P20FkS9izxgR0QByXtuE4tM1vSqjCgmCrqGlx9cvtF+m7E5ctLLHy5G8U5KoDcMYEdEA8u7XxTh6ps4t55qSPBhD1TbCGC8jEgFgGCMiGlDqmwwIDlTgnuu6X6C7O6Ehwahv6Ln3661/H+l2+48lF7DwxiyEKgPwfvsdlF1xTBdRG4YxIqIBRG8wISTYH9PGOz7lg1odgurqnu98fPvLYpjMgtX2ZfNTofCTIyt1KA6d1GL/0eo+1ZnI1zGMERENIC2tRqjCer/EkC1rHp2BOp0ef/2gEE16IxbeON5qfq87Z41C6CB/HDypgaZej+unDnNqHYi8GcMYEdEAIQgCWlpNCAzoeYHuvghTBiBMGYB1v7sSLa0mqLpMZ9FhcMQg3DsnGXU6PfYfrUZW2lCn1oHImzGMERENEK0GMwQBaNGb7O/cBwo/OVTBtmdMClMFYlZ6vEvOT+StGMaIiHzAmQs6vPrJQbQYeg5a9Y1tSxSdqda5q1pE5ACGMSIiH3DibB0u1DYjTBWA4IDuf7V3hDEi8iwMY0REPsBoarub8TezxyBjXEyP+/335zNIGxPtrmoRkQPshrHy8nKsW7cO48ePx7lz5xAeHo7HHnsMtbW1WLt2LRISElBWVoalS5ciOrrtB3zjxo3Q6XSor6/HjBkzMHv2bABAcXExtmzZgvj4eGg0GmRnZ0OhUECv1yMnJwcxMTEoKyvDokWLMHKka9csIyLyZoXHqrFjfyUevz0F/gq5OLWEws/2mK3Zl3O8FpGnsRvGamtrMXfuXFxzzTUAgLlz5yIrKwsffvghpk+fjrlz52L79u3IycnB6tWrceDAARQUFGDDhg0wGo2YO3cuMjIyoFKpsGzZMrz77rtQq9VYtWoVPv30U8ybNw+bNm1CbGwsFi5ciNLSUqxYsQIffPCBy988EZG3euXjgwCAPYfP4YqJQ2AymQEACj9OpErkbewuFJ6SkiIGMQAwm80IDg5Gfn4+0tLSAADp6enIz88HAOzYsQOpqakAAIVCgcTEROzbtw8VFRVoaWmBWq22KpOXlyceKykpCSUlJdDpOMCUiMie97aWYNHqPHyU17Ygt5+dnjEi8jy9GjO2bds2XHnllRg1ahQ0Gg2USiUAQKVSoa6uDkajEVqtFomJiWIZlUoFrVZrsX/Hdo1GAwA9vqZSqXqsS0TEICgUzp0rpydqdYhbzkP9x7byLmyvvtm6p6zH11SqIJd8rmwr78L28i4Oh7G9e/eioKAAzz77LAAgKioKjY2NCA0NhU6nQ1hYGBQKBSIjI9HY2CiW0+l0iIyMFPfvuj0qKsriWN291pOamiZHq94v9pYBIc/BtvIubK++W/+vAz2+pvSXOf1zZVt5F7aXZ7IVkB3qz87Ly8OuXbuwYsUKVFdXo7CwEJmZmSgsLAQA7N+/H5mZmQCArKwsFBUVAQAMBgNOnjyJKVOmICEhAUFBQaiuru62TMexSktLkZycbLNXjIiIuhemDJS6CkTUSzJBEKxXd+3i0KFDuOeeezBx4kQAQFNTE37729/i6quvxpo1axAXF4eKigo89dRTFndT1tfXo66uDldddZXF3ZSbN29GXFwc6urqxLspW1pakJOTA7VajfLycixevNju3ZTuSv38F4b3YFt5F7ZX3wiCgAdydvT4+tvZsyCTOXcQP9vKu7C9PJOtnjG7YcxTMYzRpdhW3oXt1TetBhMeWpvf4+vvLL/a6edkW3kXtpdnshXGOOkrEZGX+P7AWfzjP8cAAJcnqdHQZMDRilqJa0VE/cUwRkTkJQ6e1EBvMGFkbAiumDgEkxKjUKdrRa1Oj+c3/4zf3Z4idRWJqA8YxoiIvERjswEAsOKeyZDL28aFRYUFISosyCVjxYjIPTg7IBGRF6hvbEVJedslyY4g1hWDGJH3YhgjIvJwgiBgySu7pK4GEbkIwxgROZ1ZEGAwmqSuhk84U62zOZUFEXk/jhkjIqd77+sS7DpYhTtnjUbBkfNYce/lUHDNxD75w9v7pK4CEbkYfzsSkdPtOlgFAPhwx3GcPt+ARavzpK2Ql9K3WvcuvrhomgQ1ISJXYs8YEbnF2YuNUA3yR+igAKmr4jX+lXdCfJyVGoeEmBDERA6SsEZE5AoMY0TkVJXVum63P7exAIBrZoj3VQdOXBQf3zsnWcKaEJEr8TIlETmN2Szgfzf/LHU1iIi8CsMYETmN3mDqdpxTV7zL0nGRIYFSV4GI3IBhjIicpllvtLvP+9+WuqEmvuHomToAwD3XjpW4JkTkSgxjROQ0u36psrvPDwfPuaEmvmVSYpTUVSAiF2IYIyKnGRUfBgAYHhMicU1czywILj/H8CFtn2N0eLDLz0VE0mEYIyKn2FFYibX/LAIATE5WW73+21+1XWqLDgtya71cYfU/CvFIbr7LzyMDEKDgr2kiX8efciLql2a9EX/9YD82dxkL1t1s+1GhbSHsYl2LVfmjFbWuraQTNbUYUXy6Bq0GM85pm2AWBOw9fA4LVm1H0fGLVvufPFuPt786gpZW6/F0ZrOABau2i9N+XErXbOh2UXAi8i2cZ4yI+uVkVT1Kyi3D1N4j5y2e/3lBRls3Tzcefel7AMDT81MxfkSkS+roLM16Ix5b9734/Nm39lq8/kn+CaSOjhaf61tN+N/3fwIAjIwNxdXp8Rb7l5TXAGibEDe/qBJHK2rxwA3jIZfJ8N+fz1gFVyLyTewZI3Iyg9EMo8ksdTXcpl7XCgAY0z5eDABOn2vAb64ZAwB4/PZJiB+sQrxaZfM4J87Wu66STtLYYrD5erPectqO//xcIT42mS3HmAmCgDXtl3UBYNM3pdhz+DwezNkBQRCwZdtRJ9SYiLwBwxiRky1ek4dl63d3+5q+1YR3vi7GmQuWs9Q3tRjxw8EqmM2uHxTuTHqDCRdqmwEAczKGidsfvmUifjU5Ae8svxppY6zHj12saytTdq4zgO097Pl3Wbbobc+RpqlvwYJV23G8sg6CICAooPPiw39/OmOxr63w+XCX8Wg3XDGib5UlIq/By5REdnx/4Cx+KrnQqzJ1ja3dbs8vqsSuX6qw65cqRIUGYt6s0cgYF4PnN/+EKk0Ttv1YgVnpQzFuRCQGe8EddA+v7QwNoaoAbHxmFgQI8JPb/nfeM6/vwQuPzMB/ugSUy5MGu6yezrK9sLLb7cGBfha9Yi+0r0IQpupch/NCbTPMgoD6xlbU6Vq7HUPWodXQ2bN668yR/a02EXk4hjEiO774oQya+t6P3alp0GNQoAKBAX7iNr2x84+spl6PNz4/jIxxMajSNAEAyi/osOmbzoHwrz15FYIDPfPHtFant3geoQpsH2zu2IDzZ9f/YPH8y91luO2qRGdVzyVGDLGcsuP1pzJRWl6LodFKLHvduje0TmcZyh/M2WG1z9j4MCy/+3IAQPHpGqz+R6H42r3XJUEm4wB+Il/nmb/lifpg+Wu7IBMELL0r1WnHNAsCanV6jIwNxfLfpjlUZvGatt6ip15rCxtdF8Y+p2m02r9jv+50DG5/46lMBPj79bifu+0/Wo1XPzlosS3CS5bu2Xv4HAYF+SNlVO8nUjW0h+mh0Ur8aUEG5HIZUkZF9WuJp5mXxYmPxyZ0jrvb+Mws3klJNEAwjJFPuFDbjMMnNQCA7/aVIzjIOV/t+sZWmMwCggL84K/oWxi6WNeM6LC2S46jhoZhz2HLOw1rGvTdFbPw2LqdeGtZVp/O7wo/HLScaf+GK4b3uwdnwoiIfpV31FtfHAFgGZId1TGoft6sURZBya99Kg+FnwxGk+W4v0mJUXj01ol4aG3385J1vbHBTy7vU72IyLsxjJFP+G5fufj4n9uPO/34xadr+lx2R2El5mWNBgAE2ujdmjo+BgXtU0I8eedleOnDA+JrUt6dKQiCVdAqOmY5n9ZtV41yZ5WcorS8BmMTwh0OkV0XQK+utbxsLZfJsPGZWSi/0IA/v9c2lYWtUDVhRAQOl9Vg2vgYcZZ9Ihq4GMZIEscr63C0ohZClyVlOh4KXTZ0Pu78X3dltu/vHFj9wK/HOa2e57RN+GrPadw7J8nhMjkPTUf2G3vE51v3lmPr3raweOes0T2We/CGcVh80wTx+eVJavxcWi0+X7Bqu9t7TXI/LMKhk1osmXeZeFmv4Mh5dO37eeKOFIePF6YKsBpH9ecHMrDy7X3iJUBX0hs6A1XOB4W459qxaNIbseuXKjy/aBrk7cHs/W9LkVdYibhoJf7yQAZkMpnFtBbjhlv34snlMowYEoq/PDgVg8OtVxmYMDISh09p8b8PTkVctNIF746IvBXDGEli4xdHxCkRnOn+65MxY1KsU495e2bven3U4cF4Z/nVOFFZh+fb76rr8OGOzl67t7NnQRAAmQzd9s48euskmM0CHvxr56Dv7nqpXOnQSS0AYN1HB8Qg+I//HhNfv+vq0bisyySn9jw9Pw1bvisVJ4n9nzlJbZfpZJY3N7hKWZXldBJ5RWdR0T7NyIM5O8T3mNd+1+TZi41Y99EvuOfasSjvMh2JrTA1tIfX7r8+GZr6FgYxIrLCMEaSaGhuhTo8CHdf29bjJMYLGSDreGb5v7b/twcRcZsMaNIb8crHbYPJu1uGRyq2Jjl9en4qZDIZ7OUquVyGORnD8E37ZVi9wWQxd5WrtLQakdvlMikArPlnIZ6en4b6LtN2XDM5/tKiNg2NVuKZ/5eO0+casK+0WgzOgtA2UayrtbRaDrSvuGS+t+4cPKnBM116Ovv6HYsMDUJkqPevy0lEzscwRm5lMpvR1GJEs96EZr0JkxJ7f0dbd264YjgOl9Vg+sQhTjmeMwQG+OHFxdPw/7251/q1XtwZeefVo6FtaMG+4gto1rsnjP1+YwE09ZY3FhwpqxF7jABgybzL7M4n1pPhQ0IweVIcqqstA9hfP9iPZ/5fep+O6YhTVT1PtBqgkKO0vAY5HxT2uA8g7fg9IvJNDGPkNv/5qQIf/OeY/R374LarRmHx7SFWf9ylFhMxCG9nz0L5eR3+9N6P4vbehDEA4rQWrf2YQqE3Lg1iHd7vshh4X6aGsOfSNS6dqdVgwqAg/x5fD1UG2A1iAPDH+6c4s1pERFwOidzHVUHM08lkMgwfEoLBEZ0z6gcE9C6MHTtTBwAoPHrRzp7usfSuy1x27Iam7lcv6A9BEPDQ2nz8s3282+O3TbLa59K1I3syLIZ3PxKRczGMUZ/UN7Xi4dx87Cs+j4u1zaiubcaF2mZcqGnC+ZomnNc24Zy2CVWaRhytqBUnL+3q9acyJai5dC7UdN6wEDYowMae1s5r22bo/zj/BIC2cGFrOR1nWnrnZfjV5ASLbeNHRLrsfP/KO+H0Y36285TF80FBCgyLaRvTd9OMEQhXBVhMXQG0rX7Q4d45SVAF++MpJ04oTETUgZcpya5DJzU4eqYOc6cNQ6C/H2QyGZb8bRcA4I3PD/f6eC8smoYwZUCvL9V5u9szE/Fx/kmEqQIslkhyxK8mJ2DbTxWYPmEIBEHAmn8Wofh0DR65ZSImJzt/TcfEuFCcPFuPh26egImJUZiYGIVtP1WIr8udfEfntAkx2Ns+Ge7OX6pw/1znTU9iNJnxxe4yi21JwyLwx/szxOf//sHy9YTBKotlqLJShyIrdajT6kRE1BXDGHXLYDTj3a+LUdOgR2lF2zieL9v/oE2fYDlI/oqJQ9rvdGy/E7Ltv/Y7BWX4ufQCGls6e3EGhwcPyGVefj19BK6fOrxP7z1j/GBs+6kCuw5WQRXsL05Cu/6zQ9jwTBb85HLoDSY0Nhuccseepq5tUtOMcTHitvuvT8aR0zVYdOP4fh//UotunCCGMWf5fNcp/FxajYTBnVNJBCjkyHlout2yf1rQFtT+cN9kNLe4pweSiAYuhjGysP9oNd7+qhi/u30S9h7p/o/jnsPnxMd/eXBqj/Mqdbjv+mQsWLUdQNtdjwMxiHXo63sP6LIU0zddVhsAgIV/zbNYhufJOy/r912qdY3W47ZmXhZnsY6is8kAODZqyzGf72q7NHmmunP6itWPXIEQO5eI33w6S3w8YkioE2tERNQ9jhkbYLrOXt+dVz85iGa9Ubyr7H/mJGHa+Jge9w9UOPYVevl3V+LFRdO8ctkcT6Dwsx3iuq6H+NKHB3Dg+EW7bd1VU4sRD+Rsx79/OIXsN3b3uZ798citEwEAM1OcO2lvh9ioQT0Gsa5zh/k7+J0mInIW9oxJqLJah5+PVju3O8CGczVN4qWgyUlqLLxxgt0/PKGDAmz25tjrZei6n6P7krXejtF6+V+/AGi7azBtrNru/k++uguCYD3Q3Z0GRwwCYNkL6AhdswGFR6sxbcIQ8fts6GYKEFvj0FbePwU/HKzCbVcl9urcRETOwDAmoY/zT6LouDRTFfxUWo0Zk7TiUjYGoxm5/1dktd+QqEHYfeic1XYASBsT3euB6NQ36ohgXD9tmLjGJQD85YEMhIcE4vF1OwEAbzyVidc/O4QDJzTiPq98clAcU2aLO9aFtKej98/Qy0lVf/dy2/t/d2sJNj4zC3K5DIvX5FvtN3poWI/HGBqttLluKBGRK9kNY9XV1Vi3bh1KSkrw8ccfAwD0ej1ycnIQExODsrIyLFq0CCNHjgQAfP755yguLoZcLsewYcMwf/58AMCZM2ewfv16DB8+HJWVlcjOzoZSqYTZbEZubi6USiUqKytxxx13IDXV928f1xtM0LUYIJMBT7vxdvnV/+wMXO9/W4q17WHsox3HxYH6XcVG9Twe7PHbHV8gmvpHLpNhXtZoMYxdP20YhrYvt9R18fBHbp1oFUR0TQaEqQJ7PHb5+e4nyn11yVXdbncV//ZLhf2Z4f7Bv+5w+2LqRET9ZTeM/fzzz5g9ezaKi4vFbZs2bUJsbCwWLlyI0tJSrFixAh988AHOnTuHd955B5999hlkMhluv/12TJs2DSNGjMDKlSvxxBNPICUlBZs3b8aGDRuwZMkSbN26FTqdDk8//TRqa2tx11134euvv4afn7Q9LiazGfuOXIDcvxo6XfezkffVlm1HLZ6Pc+GcTZeKDgvCxfY75Woa9Dh4UgNlkD/+8/MZq31X3HO51ba7rx2Lv393FA/dPMHldaWezZ02vNvt/go/vLP8agiCgDc+P4wfSy7gwAkNpiQPxssfHcAjt01C6KAALFq9A0aTgNeXZuKP7/5odRwpAk3nJca+hzF/hdxiwH6HR26Z2OdjEhG5mt0wNmfOHBQUFFhsy8vLw9KlSwEASUlJKCkpgU6nw86dOzFhwgTI2se3pKWl4fvvv8fQoUNRUFCASZPaZr1OT0/Hc889hyVLliAvLw8zZswAAISHhyMgIADHjh1DcnKyzXpFRAyCopdjS3rjaHkNNnx5xGXH70qtdt+M3u/+4Tpo61vwP3/6FkDbYO/ufLH2ZvHx1ZMTsP2nCsxIicNd143Dndcmi23sadz5WUppaGy43fF+P5ZcAAC8t7UE720tAQAs+dsufLH2ZnHA/8O5nb1ot2WNxid5x5E8PMJtn2PX8yjalyoKDFQ4fP5Lx4YZjGb84e19AIC0sWo8ffdkNLUYMMRGDy85ZqD8bPkKtpd36dOYMY1GA6Wy85ebSqWCRqOBVqu12K5UKqHRaFBTU4OgoCDxD3jH/gCg1WqhUqksjqXVau3WoaamqS9Vd1hYkB+emp8K/wB/1Nc32y/QC+s/OyQ+fumxGR63nuKEkZEWdbr7mjH47ezRkMlkHlfXrtRqz1ub0tluz0xE+Xkdamsa+3yMG5/6vNvtN0wbhhumDQMAt3yOl7ZXx3QaLXqjw+evs9FrXXi0GvomPfzgnvfjywbCz5YvYXt5JlsBuU9hLCoqCo2NnX8MdDodoqKiEBkZidOnT4vbGxsbMWzYMERERKClpQWCIEAmk4n7A0BkZCR0Op3FsSIj3XfZridymQwTRkS65Et91WWx+P5AFV578iqLWb7dqeNyY1dvZ8/qscfLU3vCBppfTx/h8L5/vH9Kt5cgu5P72Iw+1sh5+vIN0zUbAABTx8egoId58YiIPF2fJtTJyspCYWHbPFSlpaVITk6GSqXCzJkzcfjwYXF+o8LCQlx11VXw9/fH1KlTcfDgQQDA/v37kZmZKR6rqKhtUHltbS1aW1sxZsyYfr8xT3bf9ePwzvKrJQtiADArbSge+PU4rLxvCqJCA/HSYzMYuHyMve/X2kdn4Or0tiV+QpUeNO1Il/nRvikox54e7uYFOsNYdFj/Vx0gIpKKTLAzM+S+ffvw2WefYefOnfjNb36DBQsWAABycnKgVqtRXl6OxYsXW9xNeejQIfj5+WHEiBEWd1O+9tprSEhIQFVVFZYvXy7eTbl27VoEBwfj7NmzuPPOOx26m9JdXbDs7vUebCtLDU2teKJ9DdHXnrwKQQF+eCBnBwDghitGSD6n1qXtVd/UiiV/24XJSWo8cmvb+NKOlRu6u6Hgvz+fwfcHzqLigg7zrx6N9LFq/Hy0Gv+3/ThmTByC66YOQ7xaZVWOeo8/W96F7eWZ+nWZMiMjAxkZGVbbV65c2e3+N998M26++War7fHx8XjxxRettsvlcixbtsxeNYiol7pOshsU0LbAuzdM+1B50Xo8nCAIENA5+W19Y6vFXclyuQzR4cG4LmMYrssY5q6qEhE5BSd9JfJh3hC+OnRcJK/StN2cozd03inZ0aM3VK3Ec/dMRpPecvHuwRHBbqkjEZErMIwRkUfoOmDCbBa6XfmhsroRD+fmW1x+zBg3GCmjot1RRSIil+CKuETkEbpO9vrgX3fg+Jm6HvftmNh1THwYHrqZE7oSkXdjGCMijxAZarlk057DPd9F2eGmK0e6qjpERG7DMEZEHsHe1CoTR1rOP5gyKgpJCeGurBIRkVtwzBgReYVDpzpX5kgeFo4l8y6TsDZERM7DMEZEHi3AX45Wgxl/eSADQ9UqtBpM8PPjBMVE5DsYxojIoz11VyrGxHdejgzw95OwNkREzscxY0Tk0boGMSIiX8QwRkQea1baUKmrQETkcgxjROSx7r52rNRVICJyOYYxIvJY9qa7ICLyBRzAT0Qe55FbJnK9SSIaMBjGiMhj3H3tWJw6W4/JyYOlrgoRkdswjBGRx7g6PR5Il7oWRETuxTFjRERERBJiGCMiIiKSEMMYERERkYQYxoiIiIgkxDBGREREJCGGMSIiIiIJMYwRERERSYhhjIiIiEhCDGNEREREEmIYIyIiIpIQwxgRERGRhBjGiIiIiCTEMEZEREQkIZkgCILUlSAiIiIaqNgzRkRERCQhhjEiIiIiCTGMEREREUmIYYyIiIhIQgxjRERERBJiGCMiIiKSkMKZBysvL8e6deswfvx4nDt3DuHh4XjssccAALW1tVi7di0SEhJQVlaGpUuXIjo6GgBw6NAhrFq1CpMmTUJ2drZ4vN27d+Orr77C8OHDcerUKSxfvhxhYWFW5929eze+++47REVFQSaTiec0m8348MMP8fLLL2PTpk0YO3Zst/U+c+YM1q9fj+HDh6OyshLZ2dlQKpV45ZVX8MMPP8Df3x8AcOrUKaxduxZTp061KF9cXIwtW7YgPj4eGo0G2dnZUCgU+OSTT/DPf/4TgYGBAIDbb78dt9xyi8PnN5vNyM3NhVKpRGVlJe644w6kpqZalbf12W7cuBE6nQ719fWYMWMGZs+e7ZFtdejQIbz11luYOHEifvnlFzzwwANIS0uzKt/TZ93h3//+N5YtW4b9+/dDqVQ6XF4QBGzevBkAUFlZifr6erz44osOl9frrx3BTQAAEFBJREFU9cjJyUFMTAzKysqwaNEijBw50mlt3Zf2ys/PxzfffIPRo0ejtLQU1157La655hqHPscOn3/+OYqLiyGXyzFs2DDMnz8fgiDgmWeewYgRIyAIAsrLy/HHP/4RgwYNsihrq85HjhzB+++/j1GjRuH48eN44oknEBcX59D5AWDnzp347LPPkJSUhKKiIixbtqzbz7un75ut73h/29uT2qrjM7D3s2mrzh1ef/11vPfeeygoKLA6t63yL7zwAoKDgzFo0CCUlJTg2WefhVqtdspn3VVffrac3VYNDQ1444038Nlnn+GHH36wqmOHnr6XjnzW9n7+du/ejZycHMybNw933313t+dvbW3F+++/j7/97W/Ys2eP+Lvyk08+wd///nfx+dmzZ/Hoo4/itttusyhvq016+h52ZatN7H02PktwogMHDgjbtm0Tn19//fXCwYMHBUEQhN///vfCV199JQiCIPz3v/8Vnn76aXG/jz/+WFi3bp2watUqcZvRaBSmTp0qaLVaQRAE4f333xf+8pe/WJ2zqalJuOaaawS9Xi8IgiA89thjwu7duwVBEITDhw8LR44cEWbNmiWUlpb2WO8FCxYIBw4cEM/z0ksvCYIgCNu3bxcaGxsFQRAEk8kkPPDAA4LZbLYoazabhV//+tfChQsXBEEQhBdffFH48MMPxfdVUVFh+0Ozcf4vv/xSWLlypSAIglBTUyNce+21gtFotCrf02dbVFQkPPjgg4IgCILBYBB+9atfCfX19YIgeF5bPfDAA8J3330nCIIgfPfdd8J9991nVd7WZy0IgnD8+HEhNzdXGDt2rKDT6XpV/tNPPxU+/fRTcd/i4uJelX/zzTeFt956SxAEQSgpKRF+85vfWJUXhL63dV/a6x//+Idw9uxZQRAEobq6WkhPTxdMJpPdz7FDVVWVcNNNN4nf+dtuu004deqUYDQahZdfflnc7w9/+IPw9ttvW5W3VeebbrpJOHz4sFjnhx56yOHzC4IgzJkzRyz/7rvvCitWrLAqb+v7Zus73qGv7e1pbeXIz6atOguCIOzdu1d48cUXhYyMDKuy9srn5uaK2998803hz3/+s1V5qX62nNlWgiAIO3bsEA4cONDj5yQItr+XgmD/s7b189fQ0CBs3bpVyM7OFjZv3txjHQoKCoTy8nKr35X79+8XKisrxeePPPJIt79Le2oTWz+zXfXUJvY+G1/m1MuUKSkp4r8QgLb0GxwcDADIz88XezrS09ORn58v7nfbbbdBLresSl1dHfR6PSIiIgAACQkJ2Lt3r9U5i4qKEBcXh4CAAPHYeXl5AIDx48dj3LhxNutsMBhQUFCASZMmWdVt1qxZ4r82tm/fjqysLMhkMovyFRUVaGlpEf+ld+l727JlC95++228+uqrqK2t7dX58/LyxH8thIeHIyAgAMeOHbM6Rk+f7Y4dO8TyCoUCiYmJ2LdvHwDPa6vo6GhotVoAgFarxYQJE6zK2/qsm5ubsXHjRjz66KNW5Rwp/8UXX6C2thbvv/+++C+23pTPy8sTP7OkpCSUlJRAp9NZlO9PW/elvebPn4/Y2FiL/eVyud3vbIedO3diwoQJ4nc+LS0N33//Pfz8/PC73/1O3E8QBKteMXt1Pn36tFi3nr4vPZ0fcOz7Yuv7Zus73qGv7e1JbeXoz6atOl+8eBFff/11j70s9so/+eST4vaevitS/Ww5s60AICsrC5GRkT1+ToDt76Ujn7Wtnz+VSoU5c+bYPD8AZGRkICEhwWp7Wlqa2ENdXFyMYcOGdfu7sKc2sfUze2n57trE1mfj61w2Zmzbtm248sorMWrUKACARqMRG1WlUqGurg5Go7HH8pGRkYiJiRF/aH755RerH8BLj9txbM3/3979x0Rd/wEcfzogSFkZ7iBO5MdG2pokTF20RkdOpDECUVaay8kkSgbScDXTRfOWDFMyK9IKQ4kmMuYZq5krXWu6hMqrsNKshhw7finewQHewcn3D758BnL3EYU6wtfjP7h73/vH6/3+fF6fH3efK1fG3M6rV6/i5+enTB535Q0GA2lpabdU/+LFi3n++edZv349UVFR5OXlAeB0OtHpdLS2tqrW39HRgb+//4jPHtoBrVy5kl9++WVUG4aPbUdHx6i2DZUfbjLE6qWXXuLo0aPs2LEDg8HgcoOiVn737t1kZ2cri9gVtfJmsxmbzcbatWtJS0sjMzMTp9NJe3s7cXFx9PX1qZZ399pExPpGtxOv0tJSXnvttZuOw3A3zp8ZM2aMel9TUxMmk2nUZYybtXnhwoX8/PPPwOB86e3tHdVmtfq3bt1KSUkJRUVFGI1GHnvsMWDwcvfQOh1rvIaP2XjjfbN+/9uxGuvadNfmoctJ+fn5qmXU+jyks7OTU6dOsX79eoBJt7bGG6uxctef2xnrsa6/21FRUTEiKczJyeHLL78E3PdBbc0WFhZSVlYGuI/JePfn/2UTes/YkDNnzlBbW8uWLVuU/82aNYvu7m7uuecebDYb9957r8v7Hob76KOPqKqqQqPR4O/vrxyNbNy4ke7ubhISEggLC6O7u1spY7PZmDVrlurnvvfeexiNRkJDQ9myZQvXrl1jYGCAadOmuSyvdoQw1C9X9Q8/8oiNjWXDhg04nU68vLyoqqoiKCiIvr4+t/UHBASM2HDabDblqGvv3r3KPS7uxjYgIGBU2248apsssdqwYQMFBQXExMRw4cIFMjIyOH36NHq9nsbGRmJiYkhJSXFZvrm5mc7OTo4dO6a8VlZWhk6nU46UbxYrf39/FixYAEBERAQ2m43m5mZCQkKorq7Gx8dHtby71yYi1uON1/79+5k7dy6JiYmqbe3p6SE3NxeA1atXExAQwKVLl5T3dXd3Exoaqvzd0tLCW2+9xe7du5UkeHi8h+4VcdXmN998k/Lyci5duoS/vz+BgYGj5pi7+h0OB1lZWVRVVXH//fdz8uRJ8vPzqa6u5qGHHuKDDz5Q7afamGk0mnHFezLGaixr012sfv31V7y9vTl8+LBylu3DDz9k2bJlhIeHcyNXfYbBe6m2bdtGYWEhM2fOBBj3WE/k2pqIWKkZy3ZQbaw///xzZZ/1+uuvA67X30S5fPkydrud2bNnK//T6/VKAuUuJmrzMCcnRxk/dzGZNm3aLe/Pp4oJT8a++eYbfvjhB7Zu3UpbWxtms5mYmBh0Oh1Go5Hg4GDOnj2LTqe76WfZbDY2bdoEwOHDh0lNTQXgnXfeUd7T29uL2WzG4XBw1113cfbsWZ599lnVz73xhsBHHnmE+vp6Hn74YZdtq6ioIDs72+VnzZkzBz8/P9rb29FoNCPKFxcXk5eXh7e3Nw0NDcyePRsvLy9g8LQ6gI+Pj9v64+Pj+f7771m+fDkWiwWHw8EDDzwAQH9/PwP/f6you7GNj4+npKREqe/vv/9m8eLFStsnU6yam5uVSxQajQaHwwGgbHhg8HS8q7EODg6mqKhIeV9xcTEZGRmjkme1WD366KOYTCalL06nU2nPUKzUysfHx2M0Glm0aBEXLlzgwQcfVDZc4431eOJVUlJCREQESUlJ1NbWMnfuXLf9mD59Ovv371fKtrS0UFFRoezgjEajcqTc2NjIu+++q2ygjx8/TmJi4oh4q7W5vb1duXx16tQpZb4MFxcX57J+h8OB1WpVdqgajQa73a6UGzp7ER0d7Xa+qY3ZeOM92WI1lrWp1uahA5qmpiaqq6vJysoaFSu18h0dHRQWFvLKK68QFBSkzJWJGOuJWFsTFauhS8GujGU7GBUV5Xasb9xnuVt/E+XQoUOjbrwfflbQXUzcrVkYvCI0/HKuq5g4HI5b3p9PFRP6oPBz587x3HPPMX/+fAB6enpYs2YNK1aswGKxsGvXLrRaLSaTiU2bNilndo4ePcqRI0fo6+tj+fLlPPPMM8Dgjtjb2xutVkt/fz+ZmZlKMjPc6dOnOX78OPfddx8+Pj7KxLVarXz66aeUlZWRmppKcnKyy28jNjU1UVJSwpw5c2hubmbz5s3Kjvzy5csUFRWxa9cut/3+/fff+eSTT9BqtVitVuVbQAcPHuTixYuEhITwxx9/sHbtWqKjo3E6nSxZskQ5qnNX//Xr1ykuLubuu+/GbDbz9NNPK+1fuXIlBQUFLFiwQHVsS0tL6ezsxGq18vjjjyvfppxssfrqq684duwY8+bN488//2TZsmUkJCSMeaxh8NR3ZWUle/bsITs7m1WrVhEUFDSm8l1dXezcuROtVktjYyOJiYnodDra2tpIT0/nxIkT+Pj4uC1/7do1duzYgUajobGxkRdeeIGIiIgJifXtxqu8vJy9e/cSGRkJQGtrKx9//DEhISGq4zjcZ599xrlz5/Dy8iI8PJxVq1Zht9tZunQpQUFByv01YWFhvPHGGyPKqrX5/fffx2QyERkZSVdXFy+++CJ+fn5jqh+gsrKSn376iYiICM6fP8+aNWtYtGgR9fX1FBQUYDAYVOebuzEbb7wnW6xgbGtTrc0weI9fZWUlhw4dIisri3Xr1o2490utfFpaGv39/coZsRkzZrBv375Jsbb+iVjV1NQo45Senk5gYOCoWLmbl2MZ65utv/LycgwGA0FBQaSkpJCUlDSq/qamJmpqapRtZXJysnJ51uFwkJubq5xhHpKTk0NycjJPPvmk6vx3Nw+3b9+OVqslIyNDNSZqYzOVTWgyJoQQQgghbo386KsQQgghhAdJMiaEEEII4UGSjAkhhBBCeJAkY0IIIYQQHiTJmBBCCCGEB/0jP/oqhBCTxY8//sjbb7/NX3/9xdKlS+ns7KSnp4cVK1bc9NExR44coa6ubsTv2AkhxESTZEwIMaUtXLiQtLQ0Kisr0ev1ALS1tZGZmUlLSwvr1q3zbAOFEHc8ScaEEHecwMBAXn75ZTZu3EhCQgJ6vZ7IyEgsFgvz589n9erVNDQ0UFNTQ2trK3q9nieeeIK4uDjKy8tpaGjA19eXrq4uXn31VZePShNCiLGSZEwIcUeKioqip6eHK1eukJGRQWxsLABPPfUUS5YsITw8nJSUFOrq6igoKADgu+++4+TJkxw4cAAYfEB9aWkpeXl5nuqGEGIKkGRMCHFHGxgYoK6uji+++AI/Pz+sVismk2nUo7QAvv32W65evaokZxaLRXmGqRBC3C5JxoQQd6T6+nqmT5/OmTNn+O2339i3bx8A58+fx+l0ui0XHR3Ntm3bgMFErre3919prxBi6pKfthBC3HHa29vZuXMnubm5WCwW5SHW169fp6WlRXmfr68vTqeTgYEBDAYDcXFx1NbW0t/fD8DXX3/NwYMHPdIHIcTUIQ8KF0JMaUajkT179nDx4kUSExOxWq3YbDZSU1NJSkrCbDaTn59PWFgYM2fO5MSJE8ybN4/t27djt9vJzc0lNDSU2NhY0tPTOXDgAEajkeDgYOx2O5s3b8bX19fT3RRC/IdJMiaEEEII4UFymVIIIYQQwoMkGRNCCCGE8CBJxoQQQgghPEiSMSGEEEIID5JkTAghhBDCgyQZE0IIIYTwIEnGhBBCCCE8SJIxIYQQQggP+h/aE4MZ7Nnm2wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xq3UMoX0Pi71"
      },
      "source": [
        "bt.plot()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cUXoT394-VOe",
        "outputId": "6d302c0a-61a0-46bb-d3d8-18f134c2833c"
      },
      "source": [
        "def show_positions(num_sample = 1):\n",
        "  for i in range(num_sample):\n",
        "    index = np.random.randint(0,len(bt.positions_info))\n",
        "    bt.illustrate_position(index)\n",
        "\n",
        "show_positions()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=======================================================\n",
            "2020-08-31 13:00:00 |Long Order Executed | ID:14\n",
            "2020-09-01 06:00:00 |Profit : 26.04 \n",
            "=======================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " \n",
            " \n"
          ]
        }
      ]
    }
  ]
}