NasdaqTrading_backtesting.ipynb

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    "colab": {
      "name": "xgb_trading_backtesting.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/saritmaitra/Trading-strategy/blob/master/xgb_trading_backtesting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pc5il8HF_mp-"
      },
      "source": [
        "import pandas  as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "from sklearn import metrics # for the check the error and accuracy of the model\n",
        "from sklearn.metrics import confusion_matrix, classification_report, r2_score, accuracy_score, r2_score\n",
        "from math import sqrt\n",
        "from xgboost import XGBRegressor\n",
        "import xgboost as xgb\n",
        "from sklearn.model_selection import train_test_split, KFold, StratifiedKFold, cross_val_score, GridSearchCV, cross_validate\n",
        "import warnings\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "pd.set_option('display.max_rows', 500)\n",
        "pd.set_option('display.max_columns', 500)\n",
        "pd.set_option('display.width', 150)\n",
        "!pip install pyforest\n",
        "from pyforest import *\n",
        "import pandas_datareader as web\n",
        "plt.style.use('ggplot')\n",
        "!pip install pyfolio\n",
        "import pyfolio as pf\n",
        "!pip install backtrader\n",
        "import backtrader as bt\n",
        "from backtrader.feeds import PandasData\n",
        "import warnings\n",
        "\n",
        "# set the style and ignore warnings\n",
        "plt.style.use('seaborn-colorblind')\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUTNNRBx_yyY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "outputId": "768990d0-420e-4341-8ceb-ec3264891250"
      },
      "source": [
        "def dataExtraction():\n",
        "  dataset = web.DataReader('^IXIC', data_source = 'yahoo', start = '2010-01-01')\n",
        "  dataset = dataset.sort_index(ascending=True)\n",
        "  # display \n",
        "  # print(dataset.head()); print(dataset.shape)\n",
        "\n",
        "  # Plot the closing prices \n",
        "  dataset['Adj Close'].plot(grid=True, figsize=(10, 6))\n",
        "  plt.title('Nasdaq Composite close price')\n",
        "  plt.ylabel('price ($)')\n",
        "  # Show the plot\n",
        "  plt.show()\n",
        "  return dataset\n",
        "\n",
        "dataset = dataExtraction()\n",
        "print(dataset)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
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              "    "
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          "data": {
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              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from openpyxl import load_workbook\\nimport xgboost as xgb\\nimport bokeh\\nimport pandas as pd\\nimport plotly as py\\nimport numpy as np\\nimport plotly.express as px\\nimport lightgbm as lgb\\nimport pickle\\nimport pydot\\nimport re\\nimport matplotlib.pyplot as plt\\nimport altair as alt\\nfrom pathlib import Path'); }\n",
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        {
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            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from openpyxl import load_workbook\\nimport xgboost as xgb\\nimport bokeh\\nimport pandas as pd\\nimport plotly as py\\nimport numpy as np\\nimport plotly.express as px\\nimport lightgbm as lgb\\nimport pickle\\nimport pydot\\nimport re\\nimport matplotlib.pyplot as plt\\nimport altair as alt\\nfrom pathlib import Path'); }\n",
              "    "
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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "               High      Low     Open    Close      Volume  Adj Close\n",
            "Date                                                                 \n",
            "2010-01-04  2311.15  2294.41  2294.41  2308.42  1931380000    2308.42\n",
            "2010-01-05  2313.73  2295.62  2307.27  2308.71  2367860000    2308.71\n",
            "2010-01-06  2314.07  2295.68  2307.71  2301.09  2253340000    2301.09\n",
            "2010-01-07  2301.30  2285.22  2298.09  2300.05  2270050000    2300.05\n",
            "2010-01-08  2317.60  2290.61  2292.24  2317.17  2145390000    2317.17\n",
            "...             ...      ...      ...      ...         ...        ...\n",
            "2021-03-15 13460.35 13272.50 13323.47 13459.71  5933440000   13459.71\n",
            "2021-03-16 13620.71 13397.08 13523.17 13471.57  5463770000   13471.57\n",
            "2021-03-17 13595.00 13272.69 13336.91 13525.20  5465500000   13525.20\n",
            "2021-03-18 13384.46 13101.92 13349.20 13116.17  5699900000   13116.17\n",
            "2021-03-19 13252.37 13039.45 13119.90 13215.24  7591530000   13215.24\n",
            "\n",
            "[2822 rows x 6 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqRKFJngW1d1"
      },
      "source": [
        "### ***Predictors***\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qk_QJ9_yt0OL",
        "outputId": "bc113e2c-fc15-4e00-d217-81824165cbfd"
      },
      "source": [
        "def classificationData(dataset):\n",
        "   dataset['closeGap'] = dataset['Close'].pct_change()\n",
        "   dataset['highGap'] = dataset['High'].pct_change()\n",
        "   dataset['lowGap'] = dataset['Low'].pct_change() \n",
        "   dataset['volGap'] = dataset['Volume'].pct_change() \n",
        "   dataset['dailyChange'] = (dataset['Close'] - dataset['Open']) / dataset['Open']\n",
        "   dataset['nextDayDirection'] = (dataset['Volume'].shift(-1) - dataset['Volume'])\n",
        "   dataset = dataset.dropna()\n",
        "   a = dataset.copy()\n",
        "   df = dataset[['closeGap', 'highGap', 'lowGap', 'volGap', 'dailyChange', 'nextDayDirection']]\n",
        "   # Y = dataset['nextDayDirection']\n",
        "   return df,a\n",
        "\n",
        "df,a = classificationData(dataset)\n",
        "print(df.head())"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            closeGap  highGap  lowGap  volGap  dailyChange  nextDayDirection\n",
            "Date                                                                        \n",
            "2010-01-05      0.00     0.00    0.00    0.23         0.00     -114520000.00\n",
            "2010-01-06     -0.00     0.00    0.00   -0.05        -0.00       16710000.00\n",
            "2010-01-07     -0.00    -0.01   -0.00    0.01         0.00     -124660000.00\n",
            "2010-01-08      0.01     0.01    0.00   -0.05         0.01      -67500000.00\n",
            "2010-01-11     -0.00     0.00    0.01   -0.03        -0.01      290430000.00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYkh-tM1GLJ4"
      },
      "source": [
        "# def regressionTradingCondition(dataset):\n",
        "#   dataset['Open-Close'] = dataset.Open - dataset.Close\n",
        "#   dataset['High-Low'] = dataset.High - dataset.Low\n",
        "#   dataset['target'] = dataset['Close'].shift(-1) - dataset['Close']\n",
        "#   lags = 2\n",
        "#   # Create the shifted lag series of prior trading period close values\n",
        "#   for i in range(0, lags):\n",
        "#     dataset[\"Lag%s\" % str(i+1)] = dataset[\"Close\"].shift(i+1)\n",
        "#   dataset['returns'] = dataset['Close'].pct_change() \n",
        "#   dataset['volIncrement'] = dataset.Volume.diff() / dataset.Volume\n",
        "#   dataset = dataset.dropna()\n",
        "#   X = dataset[['Close','Open-Close', 'High-Low', 'Lag1', 'Lag2', 'returns', 'volIncrement']]\n",
        "#   Y = dataset['target']\n",
        "#   return (X, Y)\n",
        "\n",
        "# X, Y = regressionTradingCondition(dataset)\n",
        "# print(X)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "tI6_2hw4H3kJ",
        "outputId": "14d5c646-31c5-417e-b379-a8df1029e7c7"
      },
      "source": [
        "pd.options.display.float_format = \"{:.2f}\".format\n",
        "df.nextDayDirection.describe()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from openpyxl import load_workbook\\nimport xgboost as xgb\\nimport bokeh\\nimport pandas as pd\\nimport plotly as py\\nimport numpy as np\\nimport plotly.express as px\\nimport lightgbm as lgb\\nimport pickle\\nimport pydot\\nimport re\\nimport matplotlib.pyplot as plt\\nimport altair as alt\\nfrom pathlib import Path'); }\n",
              "    "
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          },
          "metadata": {
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count          2820.00\n",
              "mean        1852365.25\n",
              "std       449122605.38\n",
              "min     -3722700000.00\n",
              "25%      -163090000.00\n",
              "50%        -3965000.00\n",
              "75%       164785000.00\n",
              "max      4320700000.00\n",
              "Name: nextDayDirection, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "tWNxF7Eax6Nj",
        "outputId": "d6f68723-cdb4-4ccc-df59-ffd86c6af8f3"
      },
      "source": [
        "df['nextDayDirection'] = np.where((df.nextDayDirection > 100000000), 1, 0)\n",
        "df['nextDayDirection']"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from openpyxl import load_workbook\\nimport xgboost as xgb\\nimport bokeh\\nimport pandas as pd\\nimport plotly as py\\nimport numpy as np\\nimport plotly.express as px\\nimport lightgbm as lgb\\nimport pickle\\nimport pydot\\nimport re\\nimport matplotlib.pyplot as plt\\nimport altair as alt\\nfrom pathlib import Path'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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          },
          "metadata": {
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2010-01-05    0\n",
              "2010-01-06    0\n",
              "2010-01-07    0\n",
              "2010-01-08    0\n",
              "2010-01-11    1\n",
              "             ..\n",
              "2021-03-12    1\n",
              "2021-03-15    0\n",
              "2021-03-16    0\n",
              "2021-03-17    1\n",
              "2021-03-18    1\n",
              "Name: nextDayDirection, Length: 2820, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "gXiUyww2R4lD",
        "outputId": "0879363c-ac55-47d0-ca24-7c208a0630a3"
      },
      "source": [
        "b = pd.concat([a.Close, df.nextDayDirection], 1)\n",
        "b = b['2021-01-01':]\n",
        "\n",
        "print(b.nextDayDirection.value_counts())\n",
        "buys = b.loc[b[\"nextDayDirection\"] == 1];\n",
        "sells = b.loc[b[\"nextDayDirection\"] == 0];\n",
        "# Plot\n",
        "fig = plt.figure(figsize=(20, 5));\n",
        "plt.plot(b['Close'], lw=2., label='Price');\n",
        "# Plot buy and sell signals\n",
        "# up arrow when we buy one share\n",
        "plt.plot(buys.index, b.loc[buys.index]['Close'], '^', markersize=10, color='red', lw=2., label='Buy');\n",
        "# down arrow when we sell one share\n",
        "plt.plot(sells.index, b.loc[sells.index]['Close'], 'v', markersize = 10, color='green', lw=2., label='Sell');\n",
        "plt.ylabel('Price (USD)'); plt.xlabel('Date');\n",
        "plt.title('Buy and Sell signals (2021)'); plt.legend(loc='best');\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from openpyxl import load_workbook\\nimport xgboost as xgb\\nimport bokeh\\nimport pandas as pd\\nimport plotly as py\\nimport numpy as np\\nimport plotly.express as px\\nimport lightgbm as lgb\\nimport pickle\\nimport pydot\\nimport re\\nimport matplotlib.pyplot as plt\\nimport altair as alt\\nfrom pathlib import Path'); }\n",
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            "0    29\n",
            "1    23\n",
            "Name: nextDayDirection, dtype: int64\n"
          ],
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            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfZU8CorUnsT"
      },
      "source": [
        "# lags = 5\n",
        "# # Create the shifted lag series of prior trading period close values\n",
        "# for i in range(0, lags):\n",
        "#     dataset[\"Lag%s\" % str(i+1)] = dataset[\"Adj Close\"].shift(i+1).pct_change()*100.0\n",
        "# dataset['HL'] = (dataset['High'] - dataset['Close']) / dataset['Close'] * 100\n",
        "# # creating more features\n",
        "# dataset['returns'] = dataset['Adj Close'].pct_change() * 100\n",
        "# dataset['vol_increment'] = dataset.Volume.diff() / dataset.Volume\n",
        "# dataset.dropna(inplace=True)\n",
        "# print(dataset.head()); print(dataset.shape)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pIuonocSXaB8"
      },
      "source": [
        "### ***Target Variable***\n",
        "This will be a classification variable, if the average price will go either up or down the next day.  The target will be forecasting the difference between today’s price and tomorrow’s price (which is unkonwn)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7ezuUwGVVu_"
      },
      "source": [
        "# #dataset['target'] = (dataset.Lag1.shift(-1) - dataset.Lag1)\n",
        "# dataset['target'] = dataset['Close'].shift(-1)- dataset['Close']\n",
        "# dataset.dropna(inplace=True)\n",
        "# print(dataset.target); print()\n",
        "# print('Total dataset has {} samples, and {} features.'.format(dataset.shape[0], dataset.shape[1]))"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "put3MKrlk2NJ"
      },
      "source": [
        "# corr = dataset.corr()\n",
        "# ax = sns.heatmap(\n",
        "#     corr, \n",
        "#     vmin=-1, vmax=1, center=0,\n",
        "#     cmap=sns.diverging_palette(20, 220, n=200),\n",
        "#     square=True\n",
        "# )\n",
        "# ax.set_xticklabels(\n",
        "#     ax.get_xticklabels(),\n",
        "#     rotation=45,\n",
        "#     horizontalalignment='right'\n",
        "# );"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hT29QN8-UsWZ"
      },
      "source": [
        "# # Checking Correlation \n",
        "# sns.set(style='darkgrid', context='talk', palette='Dark2')\n",
        "# plt.figure(figsize=(14,5))\n",
        "# dataset.corr()['target'].sort_values(ascending = False).plot(kind='bar')\n",
        "# plt.show()"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "WXC5k5mhbn1Z",
        "outputId": "4fac9dd9-d4c0-48c0-9862-7d9aba53a00f"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "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>closeGap</th>\n",
              "      <th>highGap</th>\n",
              "      <th>lowGap</th>\n",
              "      <th>volGap</th>\n",
              "      <th>dailyChange</th>\n",
              "      <th>nextDayDirection</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-03-12</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-15</th>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-16</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-17</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-18</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.04</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            closeGap  highGap  lowGap  volGap  dailyChange  nextDayDirection\n",
              "Date                                                                        \n",
              "2021-03-12     -0.01    -0.01   -0.01   -0.08         0.01                 1\n",
              "2021-03-15      0.01     0.01    0.01    0.08         0.01                 0\n",
              "2021-03-16      0.00     0.01    0.01   -0.08        -0.00                 0\n",
              "2021-03-17      0.00    -0.00   -0.01    0.00         0.01                 1\n",
              "2021-03-18     -0.03    -0.02   -0.01    0.04        -0.02                 1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3xKITb6Pv47",
        "outputId": "6f610019-56b8-4b74-969a-d614d2a579d4"
      },
      "source": [
        "def features():\n",
        "    X = df.drop( columns = ['nextDayDirection'], axis=1)\n",
        "    y = df.nextDayDirection\n",
        "    return X,y\n",
        "X,y = features()\n",
        "print(X)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            closeGap  highGap  lowGap  volGap  dailyChange\n",
            "Date                                                      \n",
            "2010-01-05      0.00     0.00    0.00    0.23         0.00\n",
            "2010-01-06     -0.00     0.00    0.00   -0.05        -0.00\n",
            "2010-01-07     -0.00    -0.01   -0.00    0.01         0.00\n",
            "2010-01-08      0.01     0.01    0.00   -0.05         0.01\n",
            "2010-01-11     -0.00     0.00    0.01   -0.03        -0.01\n",
            "...              ...      ...     ...     ...          ...\n",
            "2021-03-12     -0.01    -0.01   -0.01   -0.08         0.01\n",
            "2021-03-15      0.01     0.01    0.01    0.08         0.01\n",
            "2021-03-16      0.00     0.01    0.01   -0.08        -0.00\n",
            "2021-03-17      0.00    -0.00   -0.01    0.00         0.01\n",
            "2021-03-18     -0.03    -0.02   -0.01    0.04        -0.02\n",
            "\n",
            "[2820 rows x 5 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hCwWZc5jbrpB",
        "outputId": "d1678f4c-9371-4286-9ca6-3f196b65afd4"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Date\n",
            "2010-01-05    0\n",
            "2010-01-06    0\n",
            "2010-01-07    0\n",
            "2010-01-08    0\n",
            "2010-01-11    1\n",
            "             ..\n",
            "2021-03-12    1\n",
            "2021-03-15    0\n",
            "2021-03-16    0\n",
            "2021-03-17    1\n",
            "2021-03-18    1\n",
            "Name: nextDayDirection, Length: 2820, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Di7OmCN9vw4s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ba1028d-9fde-4744-f830-705102fbdbe8"
      },
      "source": [
        "pip install tscv"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tscv in /usr/local/lib/python3.7/dist-packages (0.0.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjNleEIxQtJo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b9875de-73e3-4e85-f3ab-626892b01be1"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score, KFold, cross_validate, train_test_split, TimeSeriesSplit\n",
        "\n",
        "from tscv import GapKFold\n",
        "gkcv = GapKFold(n_splits=3, gap_before=2, gap_after=1)\n",
        "#tscv = TimeSeriesSplit(max_train_size=None, n_splits=5)\n",
        "for train_samples, test_samples in gkcv.split(X):\n",
        "  #print(\"TRAIN:\", train_samples, \"TEST:\", test_samples)\n",
        "  xTrain, xTest = X.values[train_samples], X.values[test_samples]\n",
        "  yTrain, yTest = y[train_samples], y[test_samples]\n",
        "\n",
        "# feature_names = ['Close','Open-Close', 'High-Low', 'Lag1', 'Lag2', 'returns', 'volIncrement']\n",
        "# xTrain = pd.DataFrame(data=xTrain, columns=feature_names)\n",
        "# xTest = pd.DataFrame(data=xTest, columns=feature_names)\n",
        "\n",
        "print(xTrain.shape, yTrain.shape)\n",
        "print(xTest.shape, yTest.shape)\n",
        "\n",
        "# yTrain = pd.DataFrame(yTrain)\n",
        "# yTrain.rename(columns = {0: 'target'}, inplace=True)\n",
        "\n",
        "# yTest = pd.DataFrame(yTest)\n",
        "# yTest.rename(columns = {0: 'target'}, inplace=True)\n",
        "\n",
        "# def getBinary(val):\n",
        "#     if val>0:\n",
        "#         return 1\n",
        "#     else:\n",
        "#         return -1\n",
        "# yTest_binary = pd.DataFrame(yTest[\"target\"].apply(getBinary))\n",
        "# print(yTest_binary)\n"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1878, 5) (1878,)\n",
            "(940, 5) (940,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGaA-wAk7qbj",
        "outputId": "6a6fbf4d-be81-444f-8e75-b9bf3db47b32"
      },
      "source": [
        "len(xTrain)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1878"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23QrJkVEbznn",
        "outputId": "6401c7e0-5169-4749-b160-2300f70ca737"
      },
      "source": [
        "yTest"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2017-06-23    0\n",
              "2017-06-26    0\n",
              "2017-06-27    0\n",
              "2017-06-28    1\n",
              "2017-06-29    0\n",
              "             ..\n",
              "2021-03-12    1\n",
              "2021-03-15    0\n",
              "2021-03-16    0\n",
              "2021-03-17    1\n",
              "2021-03-18    1\n",
              "Name: nextDayDirection, Length: 940, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FcmMQJOjUqzy"
      },
      "source": [
        "The target variables will be transformed for binary classification. A positive change in the value of prices will be classified as 1 and a non-positive change as 0."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54G7DTl0X14y"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import model_selection"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "H_x-cEjlaAMT",
        "outputId": "7e468247-43df-41c7-82c9-943460491e9f"
      },
      "source": [
        "def MLModel():\n",
        "  modelDT = DecisionTreeClassifier().fit(xTrain,yTrain)\n",
        "  scoring = 'accuracy'\n",
        "  cvResults = model_selection.cross_val_score(modelDT, xTrain, yTrain, cv=gkcv, scoring=scoring)\n",
        "  accuracy =  (cvResults.mean(), cvResults.std())\n",
        "  print(accuracy)\n",
        "  return modelDT\n",
        "\n",
        "modelDT = MLModel()\n",
        "predict = pd.DataFrame(modelDT.predict(xTest))\n",
        "predict.rename(columns ={0: 'predictedSignal'}, inplace=True)\n",
        "print(predict.value_counts())\n",
        "print(predict)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0.5857294994675186, 0.0061639174136262865)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from openpyxl import load_workbook\\nimport xgboost as xgb\\nimport bokeh\\nimport pandas as pd\\nimport plotly as py\\nimport numpy as np\\nimport plotly.express as px\\nimport lightgbm as lgb\\nimport pickle\\nimport pydot\\nimport re\\nimport matplotlib.pyplot as plt\\nimport altair as alt\\nfrom pathlib import Path'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "predictedSignal\n",
            "0                  642\n",
            "1                  298\n",
            "dtype: int64\n",
            "     predictedSignal\n",
            "0                  1\n",
            "1                  1\n",
            "2                  0\n",
            "3                  0\n",
            "4                  0\n",
            "..               ...\n",
            "935                1\n",
            "936                0\n",
            "937                0\n",
            "938                0\n",
            "939                0\n",
            "\n",
            "[940 rows x 1 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "2QbdTBe55Nmk",
        "outputId": "8777f09c-e842-412d-c6be-2f5e741aa4ed"
      },
      "source": [
        "z = dataset[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
        "z = z.tail(len(xTest))\n",
        "# print(z)\n",
        "\n",
        "predict.index = z.index\n",
        "# print(predict.tail())\n",
        "\n",
        "pricesDf = pd.concat([z, predict],1)\n",
        "pricesDf['predictedSignal'] = pricesDf['predictedSignal'].diff()\n",
        "print(pricesDf.tail(15))"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from openpyxl import load_workbook\\nimport xgboost as xgb\\nimport bokeh\\nimport pandas as pd\\nimport plotly as py\\nimport numpy as np\\nimport plotly.express as px\\nimport lightgbm as lgb\\nimport pickle\\nimport pydot\\nimport re\\nimport matplotlib.pyplot as plt\\nimport altair as alt\\nfrom pathlib import Path'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "               Open     High      Low    Close      Volume  predictedSignal\n",
            "Date                                                                       \n",
            "2021-03-01 13406.16 13596.59 13362.66 13588.83  5047710000             0.00\n",
            "2021-03-02 13599.45 13601.33 13352.00 13358.79  4920320000             1.00\n",
            "2021-03-03 13336.25 13372.52 12995.07 12997.75  5493960000            -1.00\n",
            "2021-03-04 12953.99 13068.71 12553.96 12723.47  7799710000             0.00\n",
            "2021-03-05 12860.04 12941.21 12397.05 12920.15  7696900000             0.00\n",
            "2021-03-08 12904.26 13001.00 12599.23 12609.16  5971260000             1.00\n",
            "2021-03-09 12923.07 13151.54 12882.49 13073.82  6294910000             0.00\n",
            "2021-03-10 13234.73 13277.11 13035.44 13068.83  5991730000            -1.00\n",
            "2021-03-11 13273.31 13433.62 13246.33 13398.67  5956260000             1.00\n",
            "2021-03-12 13222.81 13324.69 13158.72 13319.86  5472340000            -1.00\n",
            "2021-03-15 13323.47 13460.35 13272.50 13459.71  5933440000             1.00\n",
            "2021-03-16 13523.17 13620.71 13397.08 13471.57  5463770000            -1.00\n",
            "2021-03-17 13336.91 13595.00 13272.69 13525.20  5465500000             0.00\n",
            "2021-03-18 13349.20 13384.46 13101.92 13116.17  5699900000             0.00\n",
            "2021-03-19 13119.90 13252.37 13039.45 13215.24  7591530000             0.00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVyNI38pUP11"
      },
      "source": [
        "# regressor = xgb.XGBRegressor(objective ='reg:squarederror',\n",
        "#                              gamma=0.0,\n",
        "#                              n_estimators=200,\n",
        "#                              base_score=0.7,\n",
        "#                              colsample_bytree=1,\n",
        "#                              learning_rate=0.01).fit(xTrain, yTrain)\n",
        "# scores = cross_val_score(regressor, xTrain.values, yTrain.values, cv=gkcv)\n",
        "# print(\"Training Accuracy (cross validated): %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
        "# scores = cross_val_score(regressor, xTest.values, yTest.values, cv=gkcv)\n",
        "# print(\"Test Accuracy (cross validated): %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHi-U1De4HG3"
      },
      "source": [
        "# yPredicted = regressor.predict(xTest)\n",
        "# yPredicted_binary = [1 if yp > 0 else -1 for yp in yPredicted]\n",
        "# from sklearn.metrics import mean_squared_error\n",
        "# print('RMSE: XGBoost', round(sqrt(mean_squared_error(yTest, yPredicted)),4))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KVZoFYI9L1n"
      },
      "source": [
        "# model =  xgb.XGBRegressor(objective ='reg:squarederror',\n",
        "#                              gamma=0.0,\n",
        "#                              n_estimators=200,\n",
        "#                              base_score=0.7,\n",
        "#                              colsample_bytree=1,\n",
        "#                              learning_rate=0.01).fit(X, y)\n",
        "# Predict = model.predict(X)\n",
        "# Predict_Binary = [1 if yp > 0 else -1 for yp in Predict]\n",
        "# BinaryPredict = pd.DataFrame(Predict_Binary)\n",
        "# BinaryPredict.rename({0 : 'Prediction'}, axis=1, inplace=True)\n",
        "# BinaryPredict.index = dataset.index\n",
        "# print(BinaryPredict) ; print()\n",
        "# # number of trades over time for highest and second highest return strategy\n",
        "# print('Number of trades = ', (BinaryPredict.Prediction.diff()!=0).sum())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Bc2DIGU_jHI"
      },
      "source": [
        "# db = dataset[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
        "# prices = pd.concat([db, BinaryPredict],1)\n",
        "# prices.rename(columns ={\n",
        "#     'Prediction': 'predicted',\n",
        "#     'Open':'open',\n",
        "#     'High': 'high',\n",
        "#     'Low': 'low',\n",
        "#     'Close': 'close',\n",
        "#     'Volume': 'volume'}, inplace=True)\n",
        "# prices"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "ha249Ber5h95",
        "outputId": "e5837d15-c2db-40d7-c040-acc23f32200a"
      },
      "source": [
        "dd = pricesDf['2021-01-01':]\n",
        "# Buy/Sell signals plot\n",
        "sells = dd.loc[dd['predictedSignal'] == -1]; \n",
        "buys = dd.loc[dd['predictedSignal'] == 1];\n",
        "# Plot\n",
        "fig = plt.figure(figsize=(20, 5));\n",
        "plt.plot(dd.index, dd['Close'], lw=.5, label='Price');\n",
        "# Plot buy and sell signals\n",
        "\n",
        "# up arrow when we buy one share\n",
        "plt.plot(buys.index, dd.loc[buys.index]['Close'], 'v', markersize=10, color='red', lw=2., label='Buy');\n",
        "# down arrow when we sell one share\n",
        "plt.plot(sells.index, dd.loc[sells.index]['Close'], '^', markersize = 10, color='green', lw=2., label='Sell');\n",
        "plt.ylabel('Price (USD)'); plt.xlabel('Date');\n",
        "plt.title('Buy and Sell signals (year 2021)'); plt.legend(loc='best');\n",
        "plt.show()"
      ],
      "execution_count": 83,
      "outputs": [
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          "output_type": "display_data",
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              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from openpyxl import load_workbook\\nimport xgboost as xgb\\nimport bokeh\\nimport pandas as pd\\nimport plotly as py\\nimport numpy as np\\nimport plotly.express as px\\nimport lightgbm as lgb\\nimport pickle\\nimport pydot\\nimport re\\nimport matplotlib.pyplot as plt\\nimport altair as alt\\nfrom pathlib import Path'); }\n",
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              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from openpyxl import load_workbook\\nimport xgboost as xgb\\nimport bokeh\\nimport pandas as pd\\nimport plotly as py\\nimport numpy as np\\nimport plotly.express as px\\nimport lightgbm as lgb\\nimport pickle\\nimport pydot\\nimport re\\nimport matplotlib.pyplot as plt\\nimport altair as alt\\nfrom pathlib import Path'); }\n",
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              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from openpyxl import load_workbook\\nimport xgboost as xgb\\nimport bokeh\\nimport pandas as pd\\nimport plotly as py\\nimport numpy as np\\nimport plotly.express as px\\nimport lightgbm as lgb\\nimport pickle\\nimport pydot\\nimport re\\nimport matplotlib.pyplot as plt\\nimport altair as alt\\nfrom pathlib import Path'); }\n",
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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvNG6zhkL364"
      },
      "source": [
        "import sys, logging, json, pprint, requests"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rufNW8M4Elzo"
      },
      "source": [
        "\n",
        "OHLCV = ['open', 'high', 'low', 'close', 'volume']\n",
        "\n",
        "# class to define the columns we will provide\n",
        "class SignalData(PandasData):\n",
        "    \"\"\"\n",
        "    Define pandas DataFrame structure\n",
        "    \"\"\"\n",
        "    cols = OHLCV + ['predictedSignal']\n",
        "\n",
        "    # create lines\n",
        "    lines = tuple(cols)\n",
        "\n",
        "    # define parameters\n",
        "    params = {c: -1 for c in cols}\n",
        "    params.update({'Date': None})\n",
        "    params = tuple(params.items())"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Zd0yIU2SErOw",
        "outputId": "e167b869-c24d-4bd2-c86a-d4134344136d"
      },
      "source": [
        "# define backtesting strategy class\n",
        "class businessStrategy(bt.Strategy):\n",
        "    params = dict(\n",
        "    )\n",
        "    \n",
        "    def __init__(self):\n",
        "        # keep track of open, close prices and predicted value in the series\n",
        "        self.data_predicted = self.datas[0].predictedSignal\n",
        "        self.data_open = self.datas[0].open\n",
        "        self.data_close = self.datas[0].close\n",
        "        self.data_volume = self.datas[0].volume\n",
        "        \n",
        "        # # keep track of pending orders/buy price/buy commission\n",
        "        # self.order = None\n",
        "        # self.price = None\n",
        "        # self.comm = None\n",
        "\n",
        "    # logging function\n",
        "    def log(self, txt):\n",
        "        '''Logging function'''\n",
        "        dt = self.datas[0].datetime.datetime(0).isoformat()\n",
        "        print(f'{dt}, {txt}')\n",
        "\n",
        "    def notify_order(self, order):\n",
        "        if order.status in [order.Submitted, order.Accepted]:\n",
        "            return\n",
        "\n",
        "        # report executed order\n",
        "        if order.status in [order.Completed]:\n",
        "            if order.isbuy():\n",
        "                self.log(f'BUY EXECUTED @ Price: {order.executed.price:.2f}, Cost: {order.executed.value:.2f},Commission: {order.executed.comm:.2f}')\n",
        "                self.price = order.executed.price\n",
        "                self.comm = order.executed.comm\n",
        "            else:\n",
        "                self.log(f'SELL EXECUTED @ Price: {order.executed.price:.2f}, Cost: {order.executed.value:.2f},Commission: {order.executed.comm:.2f}')\n",
        "\n",
        "        # report failed order\n",
        "        elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n",
        "            self.log('Order Failed')\n",
        "\n",
        "        # set no pending order\n",
        "        self.order = None\n",
        "\n",
        "    def notify_trade(self, trade):\n",
        "        if not trade.isclosed:\n",
        "            return\n",
        "        \n",
        "    def next_open(self):\n",
        "        if not self.position:\n",
        "            if self.data_predicted == 1:\n",
        "                size = int(self.broker.getcash() / self.datas[0].open)\n",
        "                self.buy(size=size)\n",
        "        else:\n",
        "            if self.data_predicted == -1:\n",
        "                self.sell(size=self.position.size)\n",
        "\n",
        "# instantiate SignalData class\n",
        "data = SignalData(dataname=pricesDf)\n",
        "\n",
        "# instantiate Cerebro, add strategy, data, initial cash, commission and pyfolio for performance analysis\n",
        "strategyEvaluation = bt.Cerebro(stdstats = False, cheat_on_open=True)\n",
        "strategyEvaluation.addstrategy(businessStrategy)\n",
        "strategyEvaluation.adddata(data)\n",
        "strategyEvaluation.broker.setcash(15000.0)\n",
        "strategyEvaluation.broker.setcommission(commission=0.001)\n",
        "strategyEvaluation.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')\n",
        "\n",
        "# run the backtest\n",
        "print('Starting Portfolio Value: %.2f' % strategyEvaluation.broker.getvalue())\n",
        "backtest_result = strategyEvaluation.run()\n",
        "print('Final Portfolio Value: %.2f' % strategyEvaluation.broker.getvalue())\n",
        "strategyEvaluation.broker.set_coc(True)\n",
        "strategyEvaluation.plot(style='candlestick', barup='green', bardown='red',\n",
        "                subtxtsize=8)[0][0].savefig('samplefigure.png', dpi=300)"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting Portfolio Value: 15000.00\n",
            "2017-07-06T00:00:00, BUY EXECUTED @ Price: 6109.60, Cost: 12219.20,Commission: 12.22\n",
            "2017-07-10T00:00:00, SELL EXECUTED @ Price: 6156.03, Cost: 12219.20,Commission: 12.31\n",
            "2017-07-11T00:00:00, BUY EXECUTED @ Price: 6171.25, Cost: 12342.50,Commission: 12.34\n",
            "2017-07-12T00:00:00, SELL EXECUTED @ Price: 6238.90, Cost: 12342.50,Commission: 12.48\n",
            "2017-07-14T00:00:00, BUY EXECUTED @ Price: 6289.08, Cost: 12578.16,Commission: 12.58\n",
            "2017-07-17T00:00:00, SELL EXECUTED @ Price: 6320.35, Cost: 12578.16,Commission: 12.64\n",
            "2017-07-24T00:00:00, BUY EXECUTED @ Price: 6387.82, Cost: 12775.64,Commission: 12.78\n",
            "2017-07-26T00:00:00, SELL EXECUTED @ Price: 6425.93, Cost: 12775.64,Commission: 12.85\n",
            "2017-07-31T00:00:00, BUY EXECUTED @ Price: 6394.69, Cost: 12789.38,Commission: 12.79\n",
            "2017-08-01T00:00:00, SELL EXECUTED @ Price: 6372.16, Cost: 12789.38,Commission: 12.74\n",
            "2017-08-04T00:00:00, BUY EXECUTED @ Price: 6350.79, Cost: 12701.58,Commission: 12.70\n",
            "2017-08-08T00:00:00, SELL EXECUTED @ Price: 6373.33, Cost: 12701.58,Commission: 12.75\n",
            "2017-08-14T00:00:00, BUY EXECUTED @ Price: 6306.11, Cost: 12612.22,Commission: 12.61\n",
            "2017-08-15T00:00:00, SELL EXECUTED @ Price: 6350.51, Cost: 12612.22,Commission: 12.70\n",
            "2017-08-21T00:00:00, BUY EXECUTED @ Price: 6216.32, Cost: 12432.64,Commission: 12.43\n",
            "2017-08-22T00:00:00, SELL EXECUTED @ Price: 6241.21, Cost: 12432.64,Commission: 12.48\n",
            "2017-08-23T00:00:00, BUY EXECUTED @ Price: 6263.47, Cost: 12526.94,Commission: 12.53\n",
            "2017-08-24T00:00:00, SELL EXECUTED @ Price: 6294.82, Cost: 12526.94,Commission: 12.59\n",
            "2017-08-25T00:00:00, BUY EXECUTED @ Price: 6293.81, Cost: 12587.62,Commission: 12.59\n",
            "2017-08-30T00:00:00, SELL EXECUTED @ Price: 6308.68, Cost: 12587.62,Commission: 12.62\n",
            "2017-09-05T00:00:00, BUY EXECUTED @ Price: 6414.82, Cost: 12829.64,Commission: 12.83\n",
            "2017-09-06T00:00:00, SELL EXECUTED @ Price: 6394.35, Cost: 12829.64,Commission: 12.79\n",
            "2017-09-07T00:00:00, BUY EXECUTED @ Price: 6402.94, Cost: 12805.88,Commission: 12.81\n",
            "2017-09-13T00:00:00, SELL EXECUTED @ Price: 6440.72, Cost: 12805.88,Commission: 12.88\n",
            "2017-09-19T00:00:00, BUY EXECUTED @ Price: 6465.57, Cost: 12931.14,Commission: 12.93\n",
            "2017-09-20T00:00:00, SELL EXECUTED @ Price: 6459.74, Cost: 12931.14,Commission: 12.92\n",
            "2017-09-27T00:00:00, BUY EXECUTED @ Price: 6414.37, Cost: 12828.74,Commission: 12.83\n",
            "2017-09-28T00:00:00, SELL EXECUTED @ Price: 6437.96, Cost: 12828.74,Commission: 12.88\n",
            "2017-10-03T00:00:00, BUY EXECUTED @ Price: 6523.74, Cost: 13047.48,Commission: 13.05\n",
            "2017-10-04T00:00:00, SELL EXECUTED @ Price: 6521.96, Cost: 13047.48,Commission: 13.04\n",
            "2017-10-09T00:00:00, BUY EXECUTED @ Price: 6597.37, Cost: 13194.74,Commission: 13.19\n",
            "2017-10-10T00:00:00, SELL EXECUTED @ Price: 6602.49, Cost: 13194.74,Commission: 13.20\n",
            "2017-10-12T00:00:00, BUY EXECUTED @ Price: 6594.76, Cost: 13189.52,Commission: 13.19\n",
            "2017-10-17T00:00:00, SELL EXECUTED @ Price: 6621.42, Cost: 13189.52,Commission: 13.24\n",
            "2017-10-18T00:00:00, BUY EXECUTED @ Price: 6634.26, Cost: 13268.52,Commission: 13.27\n",
            "2017-10-20T00:00:00, SELL EXECUTED @ Price: 6633.37, Cost: 13268.52,Commission: 13.27\n",
            "2017-10-25T00:00:00, BUY EXECUTED @ Price: 6587.22, Cost: 13174.44,Commission: 13.17\n",
            "2017-10-26T00:00:00, SELL EXECUTED @ Price: 6567.59, Cost: 13174.44,Commission: 13.14\n",
            "2017-10-27T00:00:00, BUY EXECUTED @ Price: 6635.03, Cost: 13270.06,Commission: 13.27\n",
            "2017-10-31T00:00:00, SELL EXECUTED @ Price: 6713.71, Cost: 13270.06,Commission: 13.43\n",
            "2017-11-07T00:00:00, BUY EXECUTED @ Price: 6785.44, Cost: 13570.88,Commission: 13.57\n",
            "2017-11-09T00:00:00, SELL EXECUTED @ Price: 6737.45, Cost: 13570.88,Commission: 13.47\n",
            "2017-11-13T00:00:00, BUY EXECUTED @ Price: 6727.39, Cost: 13454.78,Commission: 13.45\n",
            "2017-11-15T00:00:00, SELL EXECUTED @ Price: 6700.68, Cost: 13454.78,Commission: 13.40\n",
            "2017-11-16T00:00:00, BUY EXECUTED @ Price: 6742.34, Cost: 13484.68,Commission: 13.48\n",
            "2017-11-17T00:00:00, SELL EXECUTED @ Price: 6794.45, Cost: 13484.68,Commission: 13.59\n",
            "2017-11-21T00:00:00, BUY EXECUTED @ Price: 6820.55, Cost: 13641.10,Commission: 13.64\n",
            "2017-11-22T00:00:00, SELL EXECUTED @ Price: 6869.53, Cost: 13641.10,Commission: 13.74\n",
            "2017-11-24T00:00:00, BUY EXECUTED @ Price: 6878.11, Cost: 13756.22,Commission: 13.76\n",
            "2017-11-27T00:00:00, SELL EXECUTED @ Price: 6889.91, Cost: 13756.22,Commission: 13.78\n",
            "2017-11-29T00:00:00, BUY EXECUTED @ Price: 6907.28, Cost: 13814.56,Commission: 13.81\n",
            "2017-11-30T00:00:00, SELL EXECUTED @ Price: 6852.80, Cost: 13814.56,Commission: 13.71\n",
            "2017-12-07T00:00:00, BUY EXECUTED @ Price: 6785.74, Cost: 13571.48,Commission: 13.57\n",
            "2017-12-08T00:00:00, SELL EXECUTED @ Price: 6859.95, Cost: 13571.48,Commission: 13.72\n",
            "2017-12-18T00:00:00, BUY EXECUTED @ Price: 6980.40, Cost: 13960.80,Commission: 13.96\n",
            "2017-12-20T00:00:00, SELL EXECUTED @ Price: 6991.25, Cost: 13960.80,Commission: 13.98\n",
            "2017-12-28T00:00:00, BUY EXECUTED @ Price: 6953.38, Cost: 13906.76,Commission: 13.91\n",
            "2017-12-29T00:00:00, SELL EXECUTED @ Price: 6952.61, Cost: 13906.76,Commission: 13.91\n",
            "2018-01-02T00:00:00, BUY EXECUTED @ Price: 6937.65, Cost: 13875.30,Commission: 13.88\n",
            "2018-01-03T00:00:00, SELL EXECUTED @ Price: 7017.07, Cost: 13875.30,Commission: 14.03\n",
            "2018-01-05T00:00:00, BUY EXECUTED @ Price: 7105.74, Cost: 14211.48,Commission: 14.21\n",
            "2018-01-08T00:00:00, SELL EXECUTED @ Price: 7135.38, Cost: 14211.48,Commission: 14.27\n",
            "2018-01-12T00:00:00, BUY EXECUTED @ Price: 7208.17, Cost: 14416.34,Commission: 14.42\n",
            "2018-01-17T00:00:00, SELL EXECUTED @ Price: 7257.77, Cost: 14416.34,Commission: 14.52\n",
            "2018-01-19T00:00:00, BUY EXECUTED @ Price: 7312.00, Cost: 14624.00,Commission: 14.62\n",
            "2018-01-23T00:00:00, SELL EXECUTED @ Price: 7424.95, Cost: 14624.00,Commission: 14.85\n",
            "2018-01-26T00:00:00, BUY EXECUTED @ Price: 7448.33, Cost: 14896.66,Commission: 14.90\n",
            "2018-01-29T00:00:00, SELL EXECUTED @ Price: 7484.47, Cost: 14896.66,Commission: 14.97\n",
            "2018-01-30T00:00:00, BUY EXECUTED @ Price: 7388.89, Cost: 14777.78,Commission: 14.78\n",
            "2018-01-31T00:00:00, SELL EXECUTED @ Price: 7443.25, Cost: 14777.78,Commission: 14.89\n",
            "2018-02-02T00:00:00, BUY EXECUTED @ Price: 7347.59, Cost: 14695.18,Commission: 14.70\n",
            "2018-02-06T00:00:00, SELL EXECUTED @ Price: 6837.56, Cost: 14695.18,Commission: 13.68\n",
            "2018-02-07T00:00:00, BUY EXECUTED @ Price: 7086.20, Cost: 14172.40,Commission: 14.17\n",
            "2018-02-08T00:00:00, SELL EXECUTED @ Price: 7067.30, Cost: 14172.40,Commission: 14.13\n",
            "2018-02-14T00:00:00, BUY EXECUTED @ Price: 6979.24, Cost: 13958.48,Commission: 13.96\n",
            "2018-02-15T00:00:00, SELL EXECUTED @ Price: 7200.75, Cost: 13958.48,Commission: 14.40\n",
            "2018-02-21T00:00:00, BUY EXECUTED @ Price: 7258.48, Cost: 14516.96,Commission: 14.52\n",
            "2018-02-22T00:00:00, SELL EXECUTED @ Price: 7252.46, Cost: 14516.96,Commission: 14.50\n",
            "2018-02-28T00:00:00, BUY EXECUTED @ Price: 7371.41, Cost: 14742.82,Commission: 14.74\n",
            "2018-03-02T00:00:00, SELL EXECUTED @ Price: 7099.54, Cost: 14742.82,Commission: 14.20\n",
            "2018-03-07T00:00:00, BUY EXECUTED @ Price: 7311.74, Cost: 7311.74,Commission: 7.31\n",
            "2018-03-08T00:00:00, SELL EXECUTED @ Price: 7422.77, Cost: 7311.74,Commission: 7.42\n",
            "2018-03-09T00:00:00, BUY EXECUTED @ Price: 7475.98, Cost: 7475.98,Commission: 7.48\n",
            "2018-03-14T00:00:00, SELL EXECUTED @ Price: 7539.78, Cost: 7475.98,Commission: 7.54\n",
            "2018-03-16T00:00:00, BUY EXECUTED @ Price: 7504.37, Cost: 7504.37,Commission: 7.50\n",
            "2018-03-19T00:00:00, SELL EXECUTED @ Price: 7419.20, Cost: 7504.37,Commission: 7.42\n",
            "2018-03-21T00:00:00, BUY EXECUTED @ Price: 7347.50, Cost: 7347.50,Commission: 7.35\n",
            "2018-03-23T00:00:00, SELL EXECUTED @ Price: 7170.68, Cost: 7347.50,Commission: 7.17\n",
            "2018-03-29T00:00:00, BUY EXECUTED @ Price: 6984.66, Cost: 13969.32,Commission: 13.97\n",
            "2018-04-03T00:00:00, SELL EXECUTED @ Price: 6924.35, Cost: 13969.32,Commission: 13.85\n",
            "2018-04-04T00:00:00, BUY EXECUTED @ Price: 6811.77, Cost: 13623.54,Commission: 13.62\n",
            "2018-04-05T00:00:00, SELL EXECUTED @ Price: 7099.25, Cost: 13623.54,Commission: 14.20\n",
            "2018-04-10T00:00:00, BUY EXECUTED @ Price: 7060.99, Cost: 14121.98,Commission: 14.12\n",
            "2018-04-11T00:00:00, SELL EXECUTED @ Price: 7055.00, Cost: 14121.98,Commission: 14.11\n",
            "2018-04-12T00:00:00, BUY EXECUTED @ Price: 7112.02, Cost: 14224.04,Commission: 14.22\n",
            "2018-04-13T00:00:00, SELL EXECUTED @ Price: 7179.62, Cost: 14224.04,Commission: 14.36\n",
            "2018-04-17T00:00:00, BUY EXECUTED @ Price: 7215.12, Cost: 14430.24,Commission: 14.43\n",
            "2018-04-18T00:00:00, SELL EXECUTED @ Price: 7292.38, Cost: 14430.24,Commission: 14.58\n",
            "2018-04-19T00:00:00, BUY EXECUTED @ Price: 7258.61, Cost: 14517.22,Commission: 14.52\n",
            "2018-04-20T00:00:00, SELL EXECUTED @ Price: 7220.64, Cost: 14517.22,Commission: 14.44\n",
            "2018-04-23T00:00:00, BUY EXECUTED @ Price: 7173.99, Cost: 14347.98,Commission: 14.35\n",
            "2018-04-25T00:00:00, SELL EXECUTED @ Price: 7009.99, Cost: 14347.98,Commission: 14.02\n",
            "2018-05-01T00:00:00, BUY EXECUTED @ Price: 7053.65, Cost: 14107.30,Commission: 14.11\n",
            "2018-05-03T00:00:00, SELL EXECUTED @ Price: 7065.03, Cost: 14107.30,Commission: 14.13\n",
            "2018-05-08T00:00:00, BUY EXECUTED @ Price: 7255.34, Cost: 7255.34,Commission: 7.26\n",
            "2018-05-10T00:00:00, SELL EXECUTED @ Price: 7355.90, Cost: 7255.34,Commission: 7.36\n",
            "2018-05-15T00:00:00, BUY EXECUTED @ Price: 7361.30, Cost: 7361.30,Commission: 7.36\n",
            "2018-05-23T00:00:00, SELL EXECUTED @ Price: 7335.03, Cost: 7361.30,Commission: 7.34\n",
            "2018-05-29T00:00:00, BUY EXECUTED @ Price: 7398.51, Cost: 7398.51,Commission: 7.40\n",
            "2018-05-30T00:00:00, SELL EXECUTED @ Price: 7428.41, Cost: 7398.51,Commission: 7.43\n",
            "2018-06-11T00:00:00, BUY EXECUTED @ Price: 7647.24, Cost: 7647.24,Commission: 7.65\n",
            "2018-06-12T00:00:00, SELL EXECUTED @ Price: 7673.87, Cost: 7647.24,Commission: 7.67\n",
            "2018-06-13T00:00:00, BUY EXECUTED @ Price: 7713.90, Cost: 7713.90,Commission: 7.71\n",
            "2018-06-14T00:00:00, SELL EXECUTED @ Price: 7723.53, Cost: 7713.90,Commission: 7.72\n",
            "2018-06-20T00:00:00, BUY EXECUTED @ Price: 7764.15, Cost: 7764.15,Commission: 7.76\n",
            "2018-06-21T00:00:00, SELL EXECUTED @ Price: 7800.30, Cost: 7764.15,Commission: 7.80\n",
            "2018-06-25T00:00:00, BUY EXECUTED @ Price: 7631.12, Cost: 7631.12,Commission: 7.63\n",
            "2018-06-26T00:00:00, SELL EXECUTED @ Price: 7553.74, Cost: 7631.12,Commission: 7.55\n",
            "2018-06-28T00:00:00, BUY EXECUTED @ Price: 7438.11, Cost: 7438.11,Commission: 7.44\n",
            "2018-07-03T00:00:00, SELL EXECUTED @ Price: 7593.63, Cost: 7438.11,Commission: 7.59\n",
            "2018-07-05T00:00:00, BUY EXECUTED @ Price: 7550.66, Cost: 7550.66,Commission: 7.55\n",
            "2018-07-06T00:00:00, SELL EXECUTED @ Price: 7595.93, Cost: 7550.66,Commission: 7.60\n",
            "2018-07-09T00:00:00, BUY EXECUTED @ Price: 7731.74, Cost: 7731.74,Commission: 7.73\n",
            "2018-07-10T00:00:00, SELL EXECUTED @ Price: 7770.63, Cost: 7731.74,Commission: 7.77\n",
            "2018-07-12T00:00:00, BUY EXECUTED @ Price: 7752.71, Cost: 7752.71,Commission: 7.75\n",
            "2018-07-13T00:00:00, SELL EXECUTED @ Price: 7827.62, Cost: 7752.71,Commission: 7.83\n",
            "2018-07-16T00:00:00, BUY EXECUTED @ Price: 7831.74, Cost: 7831.74,Commission: 7.83\n",
            "2018-07-17T00:00:00, SELL EXECUTED @ Price: 7751.97, Cost: 7831.74,Commission: 7.75\n",
            "2018-07-18T00:00:00, BUY EXECUTED @ Price: 7859.43, Cost: 7859.43,Commission: 7.86\n",
            "2018-07-20T00:00:00, SELL EXECUTED @ Price: 7843.08, Cost: 7859.43,Commission: 7.84\n",
            "2018-07-27T00:00:00, BUY EXECUTED @ Price: 7889.75, Cost: 7889.75,Commission: 7.89\n",
            "2018-07-30T00:00:00, SELL EXECUTED @ Price: 7735.27, Cost: 7889.75,Commission: 7.74\n",
            "2018-08-01T00:00:00, BUY EXECUTED @ Price: 7701.82, Cost: 7701.82,Commission: 7.70\n",
            "2018-08-02T00:00:00, SELL EXECUTED @ Price: 7659.52, Cost: 7701.82,Commission: 7.66\n",
            "2018-08-10T00:00:00, BUY EXECUTED @ Price: 7834.71, Cost: 7834.71,Commission: 7.83\n",
            "2018-08-13T00:00:00, SELL EXECUTED @ Price: 7848.00, Cost: 7834.71,Commission: 7.85\n",
            "2018-08-17T00:00:00, BUY EXECUTED @ Price: 7786.64, Cost: 7786.64,Commission: 7.79\n",
            "2018-08-20T00:00:00, SELL EXECUTED @ Price: 7834.37, Cost: 7786.64,Commission: 7.83\n",
            "2018-08-21T00:00:00, BUY EXECUTED @ Price: 7840.09, Cost: 7840.09,Commission: 7.84\n",
            "2018-08-22T00:00:00, SELL EXECUTED @ Price: 7844.04, Cost: 7840.09,Commission: 7.84\n",
            "2018-08-29T00:00:00, BUY EXECUTED @ Price: 8044.34, Cost: 8044.34,Commission: 8.04\n",
            "2018-08-30T00:00:00, SELL EXECUTED @ Price: 8094.20, Cost: 8044.34,Commission: 8.09\n",
            "2018-09-11T00:00:00, BUY EXECUTED @ Price: 7894.87, Cost: 7894.87,Commission: 7.89\n",
            "2018-09-12T00:00:00, SELL EXECUTED @ Price: 7958.87, Cost: 7894.87,Commission: 7.96\n",
            "2018-09-17T00:00:00, BUY EXECUTED @ Price: 7992.21, Cost: 7992.21,Commission: 7.99\n",
            "2018-09-18T00:00:00, SELL EXECUTED @ Price: 7903.57, Cost: 7992.21,Commission: 7.90\n",
            "2018-09-19T00:00:00, BUY EXECUTED @ Price: 7962.55, Cost: 7962.55,Commission: 7.96\n",
            "2018-09-21T00:00:00, SELL EXECUTED @ Price: 8041.69, Cost: 7962.55,Commission: 8.04\n",
            "2018-09-25T00:00:00, BUY EXECUTED @ Price: 8001.59, Cost: 8001.59,Commission: 8.00\n",
            "2018-09-26T00:00:00, SELL EXECUTED @ Price: 8011.68, Cost: 8001.59,Commission: 8.01\n",
            "2018-09-28T00:00:00, BUY EXECUTED @ Price: 8024.50, Cost: 8024.50,Commission: 8.02\n",
            "2018-10-01T00:00:00, SELL EXECUTED @ Price: 8091.50, Cost: 8024.50,Commission: 8.09\n",
            "2018-10-16T00:00:00, BUY EXECUTED @ Price: 7501.78, Cost: 7501.78,Commission: 7.50\n",
            "2018-10-17T00:00:00, SELL EXECUTED @ Price: 7669.26, Cost: 7501.78,Commission: 7.67\n",
            "2018-10-29T00:00:00, BUY EXECUTED @ Price: 7272.42, Cost: 14544.84,Commission: 14.54\n",
            "2018-10-30T00:00:00, SELL EXECUTED @ Price: 7017.87, Cost: 14544.84,Commission: 14.04\n",
            "2018-10-31T00:00:00, BUY EXECUTED @ Price: 7276.62, Cost: 7276.62,Commission: 7.28\n",
            "2018-11-01T00:00:00, SELL EXECUTED @ Price: 7327.82, Cost: 7276.62,Commission: 7.33\n",
            "2018-11-06T00:00:00, BUY EXECUTED @ Price: 7326.07, Cost: 7326.07,Commission: 7.33\n",
            "2018-11-08T00:00:00, SELL EXECUTED @ Price: 7544.17, Cost: 7326.07,Commission: 7.54\n",
            "2018-11-09T00:00:00, BUY EXECUTED @ Price: 7468.51, Cost: 7468.51,Commission: 7.47\n",
            "2018-11-12T00:00:00, SELL EXECUTED @ Price: 7364.05, Cost: 7468.51,Commission: 7.36\n",
            "2018-11-13T00:00:00, BUY EXECUTED @ Price: 7230.69, Cost: 7230.69,Commission: 7.23\n",
            "2018-11-14T00:00:00, SELL EXECUTED @ Price: 7265.39, Cost: 7230.69,Commission: 7.27\n",
            "2018-11-15T00:00:00, BUY EXECUTED @ Price: 7112.83, Cost: 7112.83,Commission: 7.11\n",
            "2018-11-16T00:00:00, SELL EXECUTED @ Price: 7193.60, Cost: 7112.83,Commission: 7.19\n",
            "2018-11-20T00:00:00, BUY EXECUTED @ Price: 6867.43, Cost: 13734.86,Commission: 13.73\n",
            "2018-11-26T00:00:00, SELL EXECUTED @ Price: 7026.50, Cost: 13734.86,Commission: 14.05\n",
            "2018-11-30T00:00:00, BUY EXECUTED @ Price: 7279.30, Cost: 7279.30,Commission: 7.28\n",
            "2018-12-03T00:00:00, SELL EXECUTED @ Price: 7486.13, Cost: 7279.30,Commission: 7.49\n",
            "2018-12-06T00:00:00, BUY EXECUTED @ Price: 7017.05, Cost: 14034.10,Commission: 14.03\n",
            "2018-12-10T00:00:00, SELL EXECUTED @ Price: 6959.63, Cost: 14034.10,Commission: 13.92\n",
            "2018-12-24T00:00:00, BUY EXECUTED @ Price: 6278.49, Cost: 12556.98,Commission: 12.56\n",
            "2018-12-27T00:00:00, SELL EXECUTED @ Price: 6457.19, Cost: 12556.98,Commission: 12.91\n",
            "2019-01-02T00:00:00, BUY EXECUTED @ Price: 6506.91, Cost: 13013.82,Commission: 13.01\n",
            "2019-01-07T00:00:00, SELL EXECUTED @ Price: 6757.53, Cost: 13013.82,Commission: 13.52\n",
            "2019-01-10T00:00:00, BUY EXECUTED @ Price: 6908.65, Cost: 13817.30,Commission: 13.82\n",
            "2019-01-11T00:00:00, SELL EXECUTED @ Price: 6947.46, Cost: 13817.30,Commission: 13.89\n",
            "2019-01-14T00:00:00, BUY EXECUTED @ Price: 6908.03, Cost: 13816.06,Commission: 13.82\n",
            "2019-01-15T00:00:00, SELL EXECUTED @ Price: 6931.39, Cost: 13816.06,Commission: 13.86\n",
            "2019-01-17T00:00:00, BUY EXECUTED @ Price: 7010.13, Cost: 14020.26,Commission: 14.02\n",
            "2019-01-18T00:00:00, SELL EXECUTED @ Price: 7134.10, Cost: 14020.26,Commission: 14.27\n",
            "2019-02-04T00:00:00, BUY EXECUTED @ Price: 7266.28, Cost: 14532.56,Commission: 14.53\n",
            "2019-02-05T00:00:00, SELL EXECUTED @ Price: 7356.34, Cost: 14532.56,Commission: 14.71\n",
            "2019-02-08T00:00:00, BUY EXECUTED @ Price: 7232.30, Cost: 14464.60,Commission: 14.46\n",
            "2019-02-11T00:00:00, SELL EXECUTED @ Price: 7327.37, Cost: 14464.60,Commission: 14.65\n",
            "2019-02-22T00:00:00, BUY EXECUTED @ Price: 7481.63, Cost: 14963.26,Commission: 14.96\n",
            "2019-02-25T00:00:00, SELL EXECUTED @ Price: 7585.30, Cost: 14963.26,Commission: 15.17\n",
            "2019-03-01T00:00:00, BUY EXECUTED @ Price: 7587.45, Cost: 15174.90,Commission: 15.17\n",
            "2019-03-04T00:00:00, SELL EXECUTED @ Price: 7636.62, Cost: 15174.90,Commission: 15.27\n",
            "2019-03-15T00:00:00, BUY EXECUTED @ Price: 7658.41, Cost: 15316.82,Commission: 15.32\n",
            "2019-03-20T00:00:00, SELL EXECUTED @ Price: 7721.95, Cost: 15316.82,Commission: 15.44\n",
            "2019-03-21T00:00:00, BUY EXECUTED @ Price: 7705.43, Cost: 15410.86,Commission: 15.41\n",
            "2019-03-22T00:00:00, SELL EXECUTED @ Price: 7800.25, Cost: 15410.86,Commission: 15.60\n",
            "2019-03-25T00:00:00, BUY EXECUTED @ Price: 7618.98, Cost: 15237.96,Commission: 15.24\n",
            "2019-03-27T00:00:00, SELL EXECUTED @ Price: 7702.05, Cost: 15237.96,Commission: 15.40\n",
            "2019-03-28T00:00:00, BUY EXECUTED @ Price: 7660.07, Cost: 15320.14,Commission: 15.32\n",
            "2019-04-01T00:00:00, SELL EXECUTED @ Price: 7800.24, Cost: 15320.14,Commission: 15.60\n",
            "2019-04-03T00:00:00, BUY EXECUTED @ Price: 7891.18, Cost: 15782.36,Commission: 15.78\n",
            "2019-04-04T00:00:00, SELL EXECUTED @ Price: 7894.26, Cost: 15782.36,Commission: 15.79\n",
            "2019-04-08T00:00:00, BUY EXECUTED @ Price: 7924.89, Cost: 15849.78,Commission: 15.85\n",
            "2019-04-10T00:00:00, SELL EXECUTED @ Price: 7922.73, Cost: 15849.78,Commission: 15.85\n",
            "2019-04-12T00:00:00, BUY EXECUTED @ Price: 7984.15, Cost: 15968.30,Commission: 15.97\n",
            "2019-04-15T00:00:00, SELL EXECUTED @ Price: 7987.16, Cost: 15968.30,Commission: 15.97\n",
            "2019-04-23T00:00:00, BUY EXECUTED @ Price: 8026.75, Cost: 16053.50,Commission: 16.05\n",
            "2019-04-24T00:00:00, SELL EXECUTED @ Price: 8122.88, Cost: 16053.50,Commission: 16.25\n",
            "2019-05-01T00:00:00, BUY EXECUTED @ Price: 8132.93, Cost: 16265.86,Commission: 16.27\n",
            "2019-05-02T00:00:00, SELL EXECUTED @ Price: 8046.48, Cost: 16265.86,Commission: 16.09\n",
            "2019-05-03T00:00:00, BUY EXECUTED @ Price: 8092.88, Cost: 16185.76,Commission: 16.19\n",
            "2019-05-06T00:00:00, SELL EXECUTED @ Price: 7981.85, Cost: 16185.76,Commission: 15.96\n",
            "2019-05-15T00:00:00, BUY EXECUTED @ Price: 7682.80, Cost: 15365.60,Commission: 15.37\n",
            "2019-05-16T00:00:00, SELL EXECUTED @ Price: 7832.58, Cost: 15365.60,Commission: 15.67\n",
            "2019-05-17T00:00:00, BUY EXECUTED @ Price: 7829.03, Cost: 15658.06,Commission: 15.66\n",
            "2019-05-20T00:00:00, SELL EXECUTED @ Price: 7714.06, Cost: 15658.06,Commission: 15.43\n",
            "2019-05-23T00:00:00, BUY EXECUTED @ Price: 7660.72, Cost: 15321.44,Commission: 15.32\n",
            "2019-05-29T00:00:00, SELL EXECUTED @ Price: 7553.02, Cost: 15321.44,Commission: 15.11\n",
            "2019-05-31T00:00:00, BUY EXECUTED @ Price: 7470.95, Cost: 14941.90,Commission: 14.94\n",
            "2019-06-04T00:00:00, SELL EXECUTED @ Price: 7413.94, Cost: 14941.90,Commission: 14.83\n",
            "2019-06-06T00:00:00, BUY EXECUTED @ Price: 7582.24, Cost: 15164.48,Commission: 15.16\n",
            "2019-06-07T00:00:00, SELL EXECUTED @ Price: 7652.97, Cost: 15164.48,Commission: 15.31\n",
            "2019-06-12T00:00:00, BUY EXECUTED @ Price: 7803.13, Cost: 15606.26,Commission: 15.61\n",
            "2019-06-13T00:00:00, SELL EXECUTED @ Price: 7822.56, Cost: 15606.26,Commission: 15.65\n",
            "2019-06-18T00:00:00, BUY EXECUTED @ Price: 7920.98, Cost: 15841.96,Commission: 15.84\n",
            "2019-06-19T00:00:00, SELL EXECUTED @ Price: 7970.26, Cost: 15841.96,Commission: 15.94\n",
            "2019-06-25T00:00:00, BUY EXECUTED @ Price: 8005.27, Cost: 16010.54,Commission: 16.01\n",
            "2019-06-26T00:00:00, SELL EXECUTED @ Price: 7933.92, Cost: 16010.54,Commission: 15.87\n",
            "2019-06-27T00:00:00, BUY EXECUTED @ Price: 7939.36, Cost: 15878.72,Commission: 15.88\n",
            "2019-06-28T00:00:00, SELL EXECUTED @ Price: 7988.76, Cost: 15878.72,Commission: 15.98\n",
            "2019-07-05T00:00:00, BUY EXECUTED @ Price: 8123.28, Cost: 8123.28,Commission: 8.12\n",
            "2019-07-08T00:00:00, SELL EXECUTED @ Price: 8112.91, Cost: 8123.28,Commission: 8.11\n",
            "2019-07-09T00:00:00, BUY EXECUTED @ Price: 8061.41, Cost: 16122.82,Commission: 16.12\n",
            "2019-07-11T00:00:00, SELL EXECUTED @ Price: 8219.28, Cost: 16122.82,Commission: 16.44\n",
            "2019-07-15T00:00:00, BUY EXECUTED @ Price: 8263.18, Cost: 8263.18,Commission: 8.26\n",
            "2019-07-16T00:00:00, SELL EXECUTED @ Price: 8251.66, Cost: 8263.18,Commission: 8.25\n",
            "2019-07-18T00:00:00, BUY EXECUTED @ Price: 8151.76, Cost: 16303.52,Commission: 16.30\n",
            "2019-07-19T00:00:00, SELL EXECUTED @ Price: 8241.34, Cost: 16303.52,Commission: 16.48\n",
            "2019-07-22T00:00:00, BUY EXECUTED @ Price: 8171.99, Cost: 16343.98,Commission: 16.34\n",
            "2019-07-23T00:00:00, SELL EXECUTED @ Price: 8242.50, Cost: 16343.98,Commission: 16.48\n",
            "2019-08-07T00:00:00, BUY EXECUTED @ Price: 7747.27, Cost: 15494.54,Commission: 15.49\n",
            "2019-08-08T00:00:00, SELL EXECUTED @ Price: 7921.59, Cost: 15494.54,Commission: 15.84\n",
            "2019-08-12T00:00:00, BUY EXECUTED @ Price: 7907.49, Cost: 15814.98,Commission: 15.81\n",
            "2019-08-13T00:00:00, SELL EXECUTED @ Price: 7852.37, Cost: 15814.98,Commission: 15.70\n",
            "2019-09-12T00:00:00, BUY EXECUTED @ Price: 8206.58, Cost: 16413.16,Commission: 16.41\n",
            "2019-09-17T00:00:00, SELL EXECUTED @ Price: 8148.65, Cost: 16413.16,Commission: 16.30\n",
            "2019-09-19T00:00:00, BUY EXECUTED @ Price: 8193.59, Cost: 16387.18,Commission: 16.39\n",
            "2019-09-23T00:00:00, SELL EXECUTED @ Price: 8106.49, Cost: 16387.18,Commission: 16.21\n",
            "2019-09-30T00:00:00, BUY EXECUTED @ Price: 7964.09, Cost: 15928.18,Commission: 15.93\n",
            "2019-10-01T00:00:00, SELL EXECUTED @ Price: 8026.83, Cost: 15928.18,Commission: 16.05\n",
            "2019-10-09T00:00:00, BUY EXECUTED @ Price: 7895.96, Cost: 15791.92,Commission: 15.79\n",
            "2019-10-11T00:00:00, SELL EXECUTED @ Price: 8047.34, Cost: 15791.92,Commission: 16.09\n",
            "2019-10-14T00:00:00, BUY EXECUTED @ Price: 8044.35, Cost: 16088.70,Commission: 16.09\n",
            "2019-10-15T00:00:00, SELL EXECUTED @ Price: 8074.85, Cost: 16088.70,Commission: 16.15\n",
            "2019-10-17T00:00:00, BUY EXECUTED @ Price: 8176.91, Cost: 16353.82,Commission: 16.35\n",
            "2019-10-18T00:00:00, SELL EXECUTED @ Price: 8149.85, Cost: 16353.82,Commission: 16.30\n",
            "2019-10-22T00:00:00, BUY EXECUTED @ Price: 8188.12, Cost: 16376.24,Commission: 16.38\n",
            "2019-10-23T00:00:00, SELL EXECUTED @ Price: 8090.24, Cost: 16376.24,Commission: 16.18\n",
            "2019-10-24T00:00:00, BUY EXECUTED @ Price: 8180.04, Cost: 16360.08,Commission: 16.36\n",
            "2019-10-25T00:00:00, SELL EXECUTED @ Price: 8150.59, Cost: 16360.08,Commission: 16.30\n",
            "2019-11-04T00:00:00, BUY EXECUTED @ Price: 8445.50, Cost: 8445.50,Commission: 8.45\n",
            "2019-11-05T00:00:00, SELL EXECUTED @ Price: 8446.62, Cost: 8445.50,Commission: 8.45\n",
            "2019-11-06T00:00:00, BUY EXECUTED @ Price: 8426.57, Cost: 8426.57,Commission: 8.43\n",
            "2019-11-07T00:00:00, SELL EXECUTED @ Price: 8455.11, Cost: 8426.57,Commission: 8.46\n",
            "2019-11-08T00:00:00, BUY EXECUTED @ Price: 8422.67, Cost: 8422.67,Commission: 8.42\n",
            "2019-11-13T00:00:00, SELL EXECUTED @ Price: 8455.02, Cost: 8422.67,Commission: 8.46\n",
            "2019-11-14T00:00:00, BUY EXECUTED @ Price: 8461.06, Cost: 8461.06,Commission: 8.46\n",
            "2019-11-15T00:00:00, SELL EXECUTED @ Price: 8524.48, Cost: 8461.06,Commission: 8.52\n",
            "2019-11-22T00:00:00, BUY EXECUTED @ Price: 8530.54, Cost: 8530.54,Commission: 8.53\n",
            "2019-11-25T00:00:00, SELL EXECUTED @ Price: 8559.65, Cost: 8530.54,Commission: 8.56\n",
            "2019-11-29T00:00:00, BUY EXECUTED @ Price: 8682.01, Cost: 8682.01,Commission: 8.68\n",
            "2019-12-02T00:00:00, SELL EXECUTED @ Price: 8672.84, Cost: 8682.01,Commission: 8.67\n",
            "2019-12-05T00:00:00, BUY EXECUTED @ Price: 8587.93, Cost: 8587.93,Commission: 8.59\n",
            "2019-12-06T00:00:00, SELL EXECUTED @ Price: 8634.25, Cost: 8587.93,Commission: 8.63\n",
            "2019-12-11T00:00:00, BUY EXECUTED @ Price: 8631.12, Cost: 8631.12,Commission: 8.63\n",
            "2019-12-12T00:00:00, SELL EXECUTED @ Price: 8645.36, Cost: 8631.12,Commission: 8.65\n",
            "2019-12-18T00:00:00, BUY EXECUTED @ Price: 8834.65, Cost: 8834.65,Commission: 8.83\n",
            "2019-12-20T00:00:00, SELL EXECUTED @ Price: 8911.84, Cost: 8834.65,Commission: 8.91\n",
            "2019-12-24T00:00:00, BUY EXECUTED @ Price: 8955.01, Cost: 8955.01,Commission: 8.96\n",
            "2019-12-26T00:00:00, SELL EXECUTED @ Price: 8970.21, Cost: 8955.01,Commission: 8.97\n",
            "2019-12-27T00:00:00, BUY EXECUTED @ Price: 9049.47, Cost: 9049.47,Commission: 9.05\n",
            "2019-12-30T00:00:00, SELL EXECUTED @ Price: 9004.45, Cost: 9049.47,Commission: 9.00\n",
            "2020-01-06T00:00:00, BUY EXECUTED @ Price: 8943.50, Cost: 8943.50,Commission: 8.94\n",
            "2020-01-07T00:00:00, SELL EXECUTED @ Price: 9076.64, Cost: 8943.50,Commission: 9.08\n",
            "2020-01-28T00:00:00, BUY EXECUTED @ Price: 9201.82, Cost: 9201.82,Commission: 9.20\n",
            "2020-01-30T00:00:00, SELL EXECUTED @ Price: 9211.15, Cost: 9201.82,Commission: 9.21\n",
            "2020-02-03T00:00:00, BUY EXECUTED @ Price: 9190.72, Cost: 9190.72,Commission: 9.19\n",
            "2020-02-07T00:00:00, SELL EXECUTED @ Price: 9526.64, Cost: 9190.72,Commission: 9.53\n",
            "2020-02-12T00:00:00, BUY EXECUTED @ Price: 9688.60, Cost: 9688.60,Commission: 9.69\n",
            "2020-02-13T00:00:00, SELL EXECUTED @ Price: 9657.04, Cost: 9688.60,Commission: 9.66\n",
            "2020-02-25T00:00:00, BUY EXECUTED @ Price: 9301.20, Cost: 9301.20,Commission: 9.30\n",
            "2020-02-26T00:00:00, SELL EXECUTED @ Price: 9011.55, Cost: 9301.20,Commission: 9.01\n",
            "2020-02-27T00:00:00, BUY EXECUTED @ Price: 8744.03, Cost: 8744.03,Commission: 8.74\n",
            "2020-02-28T00:00:00, SELL EXECUTED @ Price: 8269.74, Cost: 8744.03,Commission: 8.27\n",
            "2020-03-02T00:00:00, BUY EXECUTED @ Price: 8667.14, Cost: 8667.14,Commission: 8.67\n",
            "2020-03-05T00:00:00, SELL EXECUTED @ Price: 8790.09, Cost: 8667.14,Commission: 8.79\n",
            "2020-03-06T00:00:00, BUY EXECUTED @ Price: 8469.02, Cost: 8469.02,Commission: 8.47\n",
            "2020-03-10T00:00:00, SELL EXECUTED @ Price: 8219.76, Cost: 8469.02,Commission: 8.22\n",
            "2020-03-17T00:00:00, BUY EXECUTED @ Price: 7072.00, Cost: 14144.00,Commission: 14.14\n",
            "2020-03-18T00:00:00, SELL EXECUTED @ Price: 6902.32, Cost: 14144.00,Commission: 13.80\n",
            "2020-03-24T00:00:00, BUY EXECUTED @ Price: 7196.15, Cost: 14392.30,Commission: 14.39\n",
            "2020-03-25T00:00:00, SELL EXECUTED @ Price: 7421.36, Cost: 14392.30,Commission: 14.84\n",
            "2020-03-26T00:00:00, BUY EXECUTED @ Price: 7462.21, Cost: 14924.42,Commission: 14.92\n",
            "2020-03-27T00:00:00, SELL EXECUTED @ Price: 7554.25, Cost: 14924.42,Commission: 15.11\n",
            "2020-04-01T00:00:00, BUY EXECUTED @ Price: 7459.50, Cost: 14919.00,Commission: 14.92\n",
            "2020-04-02T00:00:00, SELL EXECUTED @ Price: 7317.45, Cost: 14919.00,Commission: 14.63\n",
            "2020-04-06T00:00:00, BUY EXECUTED @ Price: 7660.17, Cost: 15320.34,Commission: 15.32\n",
            "2020-04-08T00:00:00, SELL EXECUTED @ Price: 7975.72, Cost: 15320.34,Commission: 15.95\n",
            "2020-04-13T00:00:00, BUY EXECUTED @ Price: 8127.69, Cost: 16255.38,Commission: 16.26\n",
            "2020-04-15T00:00:00, SELL EXECUTED @ Price: 8355.96, Cost: 16255.38,Commission: 16.71\n",
            "2020-04-20T00:00:00, BUY EXECUTED @ Price: 8553.38, Cost: 8553.38,Commission: 8.55\n",
            "2020-04-21T00:00:00, SELL EXECUTED @ Price: 8460.69, Cost: 8553.38,Commission: 8.46\n",
            "2020-04-22T00:00:00, BUY EXECUTED @ Price: 8434.55, Cost: 8434.55,Commission: 8.43\n",
            "2020-04-23T00:00:00, SELL EXECUTED @ Price: 8528.84, Cost: 8434.55,Commission: 8.53\n",
            "2020-05-01T00:00:00, BUY EXECUTED @ Price: 8681.29, Cost: 8681.29,Commission: 8.68\n",
            "2020-05-04T00:00:00, SELL EXECUTED @ Price: 8555.32, Cost: 8681.29,Commission: 8.56\n",
            "2020-05-06T00:00:00, BUY EXECUTED @ Price: 8874.70, Cost: 8874.70,Commission: 8.87\n",
            "2020-05-07T00:00:00, SELL EXECUTED @ Price: 8973.78, Cost: 8874.70,Commission: 8.97\n",
            "2020-05-11T00:00:00, BUY EXECUTED @ Price: 9054.91, Cost: 9054.91,Commission: 9.05\n",
            "2020-05-12T00:00:00, SELL EXECUTED @ Price: 9225.15, Cost: 9054.91,Commission: 9.23\n",
            "2020-05-15T00:00:00, BUY EXECUTED @ Price: 8839.99, Cost: 8839.99,Commission: 8.84\n",
            "2020-05-19T00:00:00, SELL EXECUTED @ Price: 9227.46, Cost: 8839.99,Commission: 9.23\n",
            "2020-05-20T00:00:00, BUY EXECUTED @ Price: 9305.62, Cost: 9305.62,Commission: 9.31\n",
            "2020-05-21T00:00:00, SELL EXECUTED @ Price: 9375.19, Cost: 9305.62,Commission: 9.38\n",
            "2020-05-26T00:00:00, BUY EXECUTED @ Price: 9501.21, Cost: 9501.21,Commission: 9.50\n",
            "2020-05-29T00:00:00, SELL EXECUTED @ Price: 9382.35, Cost: 9501.21,Commission: 9.38\n",
            "2020-06-03T00:00:00, BUY EXECUTED @ Price: 9651.86, Cost: 9651.86,Commission: 9.65\n",
            "2020-06-04T00:00:00, SELL EXECUTED @ Price: 9649.65, Cost: 9651.86,Commission: 9.65\n",
            "2020-06-05T00:00:00, BUY EXECUTED @ Price: 9703.54, Cost: 9703.54,Commission: 9.70\n",
            "2020-06-09T00:00:00, SELL EXECUTED @ Price: 9867.19, Cost: 9703.54,Commission: 9.87\n",
            "2020-06-24T00:00:00, BUY EXECUTED @ Price: 10092.92, Cost: 10092.92,Commission: 10.09\n",
            "2020-06-29T00:00:00, SELL EXECUTED @ Price: 9771.72, Cost: 10092.92,Commission: 9.77\n",
            "2020-07-10T00:00:00, BUY EXECUTED @ Price: 10545.91, Cost: 10545.91,Commission: 10.55\n",
            "2020-07-13T00:00:00, SELL EXECUTED @ Price: 10729.92, Cost: 10545.91,Commission: 10.73\n",
            "2020-07-14T00:00:00, BUY EXECUTED @ Price: 10310.25, Cost: 10310.25,Commission: 10.31\n",
            "2020-07-20T00:00:00, SELL EXECUTED @ Price: 10526.02, Cost: 10310.25,Commission: 10.53\n",
            "2020-07-27T00:00:00, BUY EXECUTED @ Price: 10421.70, Cost: 10421.70,Commission: 10.42\n",
            "2020-07-28T00:00:00, SELL EXECUTED @ Price: 10509.20, Cost: 10421.70,Commission: 10.51\n",
            "2020-08-03T00:00:00, BUY EXECUTED @ Price: 10848.64, Cost: 10848.64,Commission: 10.85\n",
            "2020-08-04T00:00:00, SELL EXECUTED @ Price: 10897.89, Cost: 10848.64,Commission: 10.90\n",
            "2020-08-07T00:00:00, BUY EXECUTED @ Price: 11072.53, Cost: 11072.53,Commission: 11.07\n",
            "2020-08-10T00:00:00, SELL EXECUTED @ Price: 11033.73, Cost: 11072.53,Commission: 11.03\n",
            "2020-08-11T00:00:00, BUY EXECUTED @ Price: 10942.66, Cost: 10942.66,Commission: 10.94\n",
            "2020-08-12T00:00:00, SELL EXECUTED @ Price: 10878.12, Cost: 10942.66,Commission: 10.88\n",
            "2020-08-14T00:00:00, BUY EXECUTED @ Price: 11042.24, Cost: 11042.24,Commission: 11.04\n",
            "2020-08-17T00:00:00, SELL EXECUTED @ Price: 11083.25, Cost: 11042.24,Commission: 11.08\n",
            "2020-08-21T00:00:00, BUY EXECUTED @ Price: 11258.44, Cost: 11258.44,Commission: 11.26\n",
            "2020-08-24T00:00:00, SELL EXECUTED @ Price: 11449.25, Cost: 11258.44,Commission: 11.45\n",
            "2020-08-26T00:00:00, BUY EXECUTED @ Price: 11516.62, Cost: 11516.62,Commission: 11.52\n",
            "2020-08-27T00:00:00, SELL EXECUTED @ Price: 11688.19, Cost: 11516.62,Commission: 11.69\n",
            "2020-08-28T00:00:00, BUY EXECUTED @ Price: 11689.28, Cost: 11689.28,Commission: 11.69\n",
            "2020-08-31T00:00:00, SELL EXECUTED @ Price: 11718.81, Cost: 11689.28,Commission: 11.72\n",
            "2020-09-01T00:00:00, BUY EXECUTED @ Price: 11850.96, Cost: 11850.96,Commission: 11.85\n",
            "2020-09-02T00:00:00, SELL EXECUTED @ Price: 12047.26, Cost: 11850.96,Commission: 12.05\n",
            "2020-09-11T00:00:00, BUY EXECUTED @ Price: 11010.07, Cost: 11010.07,Commission: 11.01\n",
            "2020-09-15T00:00:00, SELL EXECUTED @ Price: 11193.96, Cost: 11010.07,Commission: 11.19\n",
            "2020-09-18T00:00:00, BUY EXECUTED @ Price: 10973.45, Cost: 10973.45,Commission: 10.97\n",
            "2020-09-21T00:00:00, SELL EXECUTED @ Price: 10610.14, Cost: 10973.45,Commission: 10.61\n",
            "2020-10-05T00:00:00, BUY EXECUTED @ Price: 11169.11, Cost: 11169.11,Commission: 11.17\n",
            "2020-10-06T00:00:00, SELL EXECUTED @ Price: 11314.53, Cost: 11169.11,Commission: 11.31\n",
            "2020-10-07T00:00:00, BUY EXECUTED @ Price: 11271.68, Cost: 11271.68,Commission: 11.27\n",
            "2020-10-09T00:00:00, SELL EXECUTED @ Price: 11487.60, Cost: 11271.68,Commission: 11.49\n",
            "2020-10-13T00:00:00, BUY EXECUTED @ Price: 11901.76, Cost: 11901.76,Commission: 11.90\n",
            "2020-10-15T00:00:00, SELL EXECUTED @ Price: 11559.88, Cost: 11901.76,Commission: 11.56\n",
            "2020-10-21T00:00:00, BUY EXECUTED @ Price: 11530.39, Cost: 11530.39,Commission: 11.53\n",
            "2020-10-22T00:00:00, SELL EXECUTED @ Price: 11526.97, Cost: 11530.39,Commission: 11.53\n",
            "2020-10-26T00:00:00, BUY EXECUTED @ Price: 11440.64, Cost: 11440.64,Commission: 11.44\n",
            "2020-10-29T00:00:00, SELL EXECUTED @ Price: 11064.47, Cost: 11440.64,Commission: 11.06\n",
            "2020-11-10T00:00:00, BUY EXECUTED @ Price: 11622.44, Cost: 11622.44,Commission: 11.62\n",
            "2020-11-11T00:00:00, SELL EXECUTED @ Price: 11656.65, Cost: 11622.44,Commission: 11.66\n",
            "2020-11-13T00:00:00, BUY EXECUTED @ Price: 11794.94, Cost: 11794.94,Commission: 11.79\n",
            "2020-11-16T00:00:00, SELL EXECUTED @ Price: 11847.11, Cost: 11794.94,Commission: 11.85\n",
            "2020-11-23T00:00:00, BUY EXECUTED @ Price: 11916.76, Cost: 11916.76,Commission: 11.92\n",
            "2020-11-24T00:00:00, SELL EXECUTED @ Price: 11939.33, Cost: 11916.76,Commission: 11.94\n",
            "2020-11-27T00:00:00, BUY EXECUTED @ Price: 12159.18, Cost: 12159.18,Commission: 12.16\n",
            "2020-11-30T00:00:00, SELL EXECUTED @ Price: 12224.25, Cost: 12159.18,Commission: 12.22\n",
            "2020-12-01T00:00:00, BUY EXECUTED @ Price: 12313.36, Cost: 12313.36,Commission: 12.31\n",
            "2020-12-07T00:00:00, SELL EXECUTED @ Price: 12461.00, Cost: 12313.36,Commission: 12.46\n",
            "2020-12-09T00:00:00, BUY EXECUTED @ Price: 12591.69, Cost: 12591.69,Commission: 12.59\n",
            "2020-12-10T00:00:00, SELL EXECUTED @ Price: 12247.55, Cost: 12591.69,Commission: 12.25\n",
            "2020-12-14T00:00:00, BUY EXECUTED @ Price: 12447.44, Cost: 12447.44,Commission: 12.45\n",
            "2020-12-16T00:00:00, SELL EXECUTED @ Price: 12611.04, Cost: 12447.44,Commission: 12.61\n",
            "2020-12-18T00:00:00, BUY EXECUTED @ Price: 12804.93, Cost: 12804.93,Commission: 12.80\n",
            "2020-12-21T00:00:00, SELL EXECUTED @ Price: 12596.14, Cost: 12804.93,Commission: 12.60\n",
            "2020-12-24T00:00:00, BUY EXECUTED @ Price: 12791.54, Cost: 12791.54,Commission: 12.79\n",
            "2020-12-28T00:00:00, SELL EXECUTED @ Price: 12914.64, Cost: 12791.54,Commission: 12.91\n",
            "2020-12-29T00:00:00, BUY EXECUTED @ Price: 12965.39, Cost: 12965.39,Commission: 12.97\n",
            "2020-12-31T00:00:00, SELL EXECUTED @ Price: 12877.09, Cost: 12965.39,Commission: 12.88\n",
            "2021-01-04T00:00:00, BUY EXECUTED @ Price: 12958.52, Cost: 12958.52,Commission: 12.96\n",
            "2021-01-05T00:00:00, SELL EXECUTED @ Price: 12665.65, Cost: 12958.52,Commission: 12.67\n",
            "2021-01-19T00:00:00, BUY EXECUTED @ Price: 13132.73, Cost: 13132.73,Commission: 13.13\n",
            "2021-01-20T00:00:00, SELL EXECUTED @ Price: 13342.55, Cost: 13132.73,Commission: 13.34\n",
            "2021-01-25T00:00:00, BUY EXECUTED @ Price: 13681.21, Cost: 13681.21,Commission: 13.68\n",
            "2021-01-27T00:00:00, SELL EXECUTED @ Price: 13486.58, Cost: 13681.21,Commission: 13.49\n",
            "2021-01-28T00:00:00, BUY EXECUTED @ Price: 13323.29, Cost: 13323.29,Commission: 13.32\n",
            "2021-01-29T00:00:00, SELL EXECUTED @ Price: 13284.72, Cost: 13323.29,Commission: 13.28\n",
            "2021-02-01T00:00:00, BUY EXECUTED @ Price: 13226.18, Cost: 13226.18,Commission: 13.23\n",
            "2021-02-03T00:00:00, SELL EXECUTED @ Price: 13718.31, Cost: 13226.18,Commission: 13.72\n",
            "2021-02-09T00:00:00, BUY EXECUTED @ Price: 13966.82, Cost: 13966.82,Commission: 13.97\n",
            "2021-02-10T00:00:00, SELL EXECUTED @ Price: 14093.35, Cost: 13966.82,Commission: 14.09\n",
            "2021-02-12T00:00:00, BUY EXECUTED @ Price: 13979.21, Cost: 13979.21,Commission: 13.98\n",
            "2021-02-17T00:00:00, SELL EXECUTED @ Price: 13911.65, Cost: 13979.21,Commission: 13.91\n",
            "2021-02-18T00:00:00, BUY EXECUTED @ Price: 13814.67, Cost: 13814.67,Commission: 13.81\n",
            "2021-02-19T00:00:00, SELL EXECUTED @ Price: 13929.20, Cost: 13814.67,Commission: 13.93\n",
            "2021-02-22T00:00:00, BUY EXECUTED @ Price: 13714.20, Cost: 13714.20,Commission: 13.71\n",
            "2021-02-23T00:00:00, SELL EXECUTED @ Price: 13262.61, Cost: 13714.20,Commission: 13.26\n",
            "2021-03-02T00:00:00, BUY EXECUTED @ Price: 13599.45, Cost: 13599.45,Commission: 13.60\n",
            "2021-03-03T00:00:00, SELL EXECUTED @ Price: 13336.25, Cost: 13599.45,Commission: 13.34\n",
            "2021-03-08T00:00:00, BUY EXECUTED @ Price: 12904.26, Cost: 12904.26,Commission: 12.90\n",
            "2021-03-10T00:00:00, SELL EXECUTED @ Price: 13234.73, Cost: 12904.26,Commission: 13.23\n",
            "2021-03-11T00:00:00, BUY EXECUTED @ Price: 13273.31, Cost: 13273.31,Commission: 13.27\n",
            "2021-03-12T00:00:00, SELL EXECUTED @ Price: 13222.81, Cost: 13273.31,Commission: 13.22\n",
            "2021-03-15T00:00:00, BUY EXECUTED @ Price: 13323.47, Cost: 13323.47,Commission: 13.32\n",
            "2021-03-16T00:00:00, SELL EXECUTED @ Price: 13523.17, Cost: 13323.47,Commission: 13.52\n",
            "Final Portfolio Value: 16602.29\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "/* Put everything inside the global mpl namespace */\n",
              "window.mpl = {};\n",
              "\n",
              "\n",
              "mpl.get_websocket_type = function() {\n",
              "    if (typeof(WebSocket) !== 'undefined') {\n",
              "        return WebSocket;\n",
              "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
              "        return MozWebSocket;\n",
              "    } else {\n",
              "        alert('Your browser does not have WebSocket support. ' +\n",
              "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
              "              'Firefox 4 and 5 are also supported but you ' +\n",
              "              'have to enable WebSockets in about:config.');\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
              "    this.id = figure_id;\n",
              "\n",
              "    this.ws = websocket;\n",
              "\n",
              "    this.supports_binary = (this.ws.binaryType != undefined);\n",
              "\n",
              "    if (!this.supports_binary) {\n",
              "        var warnings = document.getElementById(\"mpl-warnings\");\n",
              "        if (warnings) {\n",
              "            warnings.style.display = 'block';\n",
              "            warnings.textContent = (\n",
              "                \"This browser does not support binary websocket messages. \" +\n",
              "                    \"Performance may be slow.\");\n",
              "        }\n",
              "    }\n",
              "\n",
              "    this.imageObj = new Image();\n",
              "\n",
              "    this.context = undefined;\n",
              "    this.message = undefined;\n",
              "    this.canvas = undefined;\n",
              "    this.rubberband_canvas = undefined;\n",
              "    this.rubberband_context = undefined;\n",
              "    this.format_dropdown = undefined;\n",
              "\n",
              "    this.image_mode = 'full';\n",
              "\n",
              "    this.root = $('<div/>');\n",
              "    this._root_extra_style(this.root)\n",
              "    this.root.attr('style', 'display: inline-block');\n",
              "\n",
              "    $(parent_element).append(this.root);\n",
              "\n",
              "    this._init_header(this);\n",
              "    this._init_canvas(this);\n",
              "    this._init_toolbar(this);\n",
              "\n",
              "    var fig = this;\n",
              "\n",
              "    this.waiting = false;\n",
              "\n",
              "    this.ws.onopen =  function () {\n",
              "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
              "            fig.send_message(\"send_image_mode\", {});\n",
              "            if (mpl.ratio != 1) {\n",
              "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
              "            }\n",
              "            fig.send_message(\"refresh\", {});\n",
              "        }\n",
              "\n",
              "    this.imageObj.onload = function() {\n",
              "            if (fig.image_mode == 'full') {\n",
              "                // Full images could contain transparency (where diff images\n",
              "                // almost always do), so we need to clear the canvas so that\n",
              "                // there is no ghosting.\n",
              "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
              "            }\n",
              "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
              "        };\n",
              "\n",
              "    this.imageObj.onunload = function() {\n",
              "        fig.ws.close();\n",
              "    }\n",
              "\n",
              "    this.ws.onmessage = this._make_on_message_function(this);\n",
              "\n",
              "    this.ondownload = ondownload;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_header = function() {\n",
              "    var titlebar = $(\n",
              "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
              "        'ui-helper-clearfix\"/>');\n",
              "    var titletext = $(\n",
              "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
              "        'text-align: center; padding: 3px;\"/>');\n",
              "    titlebar.append(titletext)\n",
              "    this.root.append(titlebar);\n",
              "    this.header = titletext[0];\n",
              "}\n",
              "\n",
              "\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_canvas = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var canvas_div = $('<div/>');\n",
              "\n",
              "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
              "\n",
              "    function canvas_keyboard_event(event) {\n",
              "        return fig.key_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
              "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
              "    this.canvas_div = canvas_div\n",
              "    this._canvas_extra_style(canvas_div)\n",
              "    this.root.append(canvas_div);\n",
              "\n",
              "    var canvas = $('<canvas/>');\n",
              "    canvas.addClass('mpl-canvas');\n",
              "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
              "\n",
              "    this.canvas = canvas[0];\n",
              "    this.context = canvas[0].getContext(\"2d\");\n",
              "\n",
              "    var backingStore = this.context.backingStorePixelRatio ||\n",
              "\tthis.context.webkitBackingStorePixelRatio ||\n",
              "\tthis.context.mozBackingStorePixelRatio ||\n",
              "\tthis.context.msBackingStorePixelRatio ||\n",
              "\tthis.context.oBackingStorePixelRatio ||\n",
              "\tthis.context.backingStorePixelRatio || 1;\n",
              "\n",
              "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
              "\n",
              "    var rubberband = $('<canvas/>');\n",
              "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
              "\n",
              "    var pass_mouse_events = true;\n",
              "\n",
              "    canvas_div.resizable({\n",
              "        start: function(event, ui) {\n",
              "            pass_mouse_events = false;\n",
              "        },\n",
              "        resize: function(event, ui) {\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "        stop: function(event, ui) {\n",
              "            pass_mouse_events = true;\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "    });\n",
              "\n",
              "    function mouse_event_fn(event) {\n",
              "        if (pass_mouse_events)\n",
              "            return fig.mouse_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    rubberband.mousedown('button_press', mouse_event_fn);\n",
              "    rubberband.mouseup('button_release', mouse_event_fn);\n",
              "    // Throttle sequential mouse events to 1 every 20ms.\n",
              "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
              "\n",
              "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
              "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
              "\n",
              "    canvas_div.on(\"wheel\", function (event) {\n",
              "        event = event.originalEvent;\n",
              "        event['data'] = 'scroll'\n",
              "        if (event.deltaY < 0) {\n",
              "            event.step = 1;\n",
              "        } else {\n",
              "            event.step = -1;\n",
              "        }\n",
              "        mouse_event_fn(event);\n",
              "    });\n",
              "\n",
              "    canvas_div.append(canvas);\n",
              "    canvas_div.append(rubberband);\n",
              "\n",
              "    this.rubberband = rubberband;\n",
              "    this.rubberband_canvas = rubberband[0];\n",
              "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
              "    this.rubberband_context.strokeStyle = \"#000000\";\n",
              "\n",
              "    this._resize_canvas = function(width, height) {\n",
              "        // Keep the size of the canvas, canvas container, and rubber band\n",
              "        // canvas in synch.\n",
              "        canvas_div.css('width', width)\n",
              "        canvas_div.css('height', height)\n",
              "\n",
              "        canvas.attr('width', width * mpl.ratio);\n",
              "        canvas.attr('height', height * mpl.ratio);\n",
              "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
              "\n",
              "        rubberband.attr('width', width);\n",
              "        rubberband.attr('height', height);\n",
              "    }\n",
              "\n",
              "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
              "    // upon first draw.\n",
              "    this._resize_canvas(600, 600);\n",
              "\n",
              "    // Disable right mouse context menu.\n",
              "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
              "        return false;\n",
              "    });\n",
              "\n",
              "    function set_focus () {\n",
              "        canvas.focus();\n",
              "        canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    window.setTimeout(set_focus, 100);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items) {\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) {\n",
              "            // put a spacer in here.\n",
              "            continue;\n",
              "        }\n",
              "        var button = $('<button/>');\n",
              "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
              "                        'ui-button-icon-only');\n",
              "        button.attr('role', 'button');\n",
              "        button.attr('aria-disabled', 'false');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "\n",
              "        var icon_img = $('<span/>');\n",
              "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
              "        icon_img.addClass(image);\n",
              "        icon_img.addClass('ui-corner-all');\n",
              "\n",
              "        var tooltip_span = $('<span/>');\n",
              "        tooltip_span.addClass('ui-button-text');\n",
              "        tooltip_span.html(tooltip);\n",
              "\n",
              "        button.append(icon_img);\n",
              "        button.append(tooltip_span);\n",
              "\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    var fmt_picker_span = $('<span/>');\n",
              "\n",
              "    var fmt_picker = $('<select/>');\n",
              "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
              "    fmt_picker_span.append(fmt_picker);\n",
              "    nav_element.append(fmt_picker_span);\n",
              "    this.format_dropdown = fmt_picker[0];\n",
              "\n",
              "    for (var ind in mpl.extensions) {\n",
              "        var fmt = mpl.extensions[ind];\n",
              "        var option = $(\n",
              "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
              "        fmt_picker.append(option);\n",
              "    }\n",
              "\n",
              "    // Add hover states to the ui-buttons\n",
              "    $( \".ui-button\" ).hover(\n",
              "        function() { $(this).addClass(\"ui-state-hover\");},\n",
              "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
              "    );\n",
              "\n",
              "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
              "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
              "    // which will in turn request a refresh of the image.\n",
              "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_message = function(type, properties) {\n",
              "    properties['type'] = type;\n",
              "    properties['figure_id'] = this.id;\n",
              "    this.ws.send(JSON.stringify(properties));\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_draw_message = function() {\n",
              "    if (!this.waiting) {\n",
              "        this.waiting = true;\n",
              "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
              "    }\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    var format_dropdown = fig.format_dropdown;\n",
              "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
              "    fig.ondownload(fig, format);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
              "    var size = msg['size'];\n",
              "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
              "        fig._resize_canvas(size[0], size[1]);\n",
              "        fig.send_message(\"refresh\", {});\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
              "    var x0 = msg['x0'] / mpl.ratio;\n",
              "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
              "    var x1 = msg['x1'] / mpl.ratio;\n",
              "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
              "    x0 = Math.floor(x0) + 0.5;\n",
              "    y0 = Math.floor(y0) + 0.5;\n",
              "    x1 = Math.floor(x1) + 0.5;\n",
              "    y1 = Math.floor(y1) + 0.5;\n",
              "    var min_x = Math.min(x0, x1);\n",
              "    var min_y = Math.min(y0, y1);\n",
              "    var width = Math.abs(x1 - x0);\n",
              "    var height = Math.abs(y1 - y0);\n",
              "\n",
              "    fig.rubberband_context.clearRect(\n",
              "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
              "\n",
              "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
              "    // Updates the figure title.\n",
              "    fig.header.textContent = msg['label'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
              "    var cursor = msg['cursor'];\n",
              "    switch(cursor)\n",
              "    {\n",
              "    case 0:\n",
              "        cursor = 'pointer';\n",
              "        break;\n",
              "    case 1:\n",
              "        cursor = 'default';\n",
              "        break;\n",
              "    case 2:\n",
              "        cursor = 'crosshair';\n",
              "        break;\n",
              "    case 3:\n",
              "        cursor = 'move';\n",
              "        break;\n",
              "    }\n",
              "    fig.rubberband_canvas.style.cursor = cursor;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
              "    fig.message.textContent = msg['message'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
              "    // Request the server to send over a new figure.\n",
              "    fig.send_draw_message();\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
              "    fig.image_mode = msg['mode'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Called whenever the canvas gets updated.\n",
              "    this.send_message(\"ack\", {});\n",
              "}\n",
              "\n",
              "// A function to construct a web socket function for onmessage handling.\n",
              "// Called in the figure constructor.\n",
              "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
              "    return function socket_on_message(evt) {\n",
              "        if (evt.data instanceof Blob) {\n",
              "            /* FIXME: We get \"Resource interpreted as Image but\n",
              "             * transferred with MIME type text/plain:\" errors on\n",
              "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
              "             * to be part of the websocket stream */\n",
              "            evt.data.type = \"image/png\";\n",
              "\n",
              "            /* Free the memory for the previous frames */\n",
              "            if (fig.imageObj.src) {\n",
              "                (window.URL || window.webkitURL).revokeObjectURL(\n",
              "                    fig.imageObj.src);\n",
              "            }\n",
              "\n",
              "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
              "                evt.data);\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
              "            fig.imageObj.src = evt.data;\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        var msg = JSON.parse(evt.data);\n",
              "        var msg_type = msg['type'];\n",
              "\n",
              "        // Call the  \"handle_{type}\" callback, which takes\n",
              "        // the figure and JSON message as its only arguments.\n",
              "        try {\n",
              "            var callback = fig[\"handle_\" + msg_type];\n",
              "        } catch (e) {\n",
              "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        if (callback) {\n",
              "            try {\n",
              "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
              "                callback(fig, msg);\n",
              "            } catch (e) {\n",
              "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
              "            }\n",
              "        }\n",
              "    };\n",
              "}\n",
              "\n",
              "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
              "mpl.findpos = function(e) {\n",
              "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
              "    var targ;\n",
              "    if (!e)\n",
              "        e = window.event;\n",
              "    if (e.target)\n",
              "        targ = e.target;\n",
              "    else if (e.srcElement)\n",
              "        targ = e.srcElement;\n",
              "    if (targ.nodeType == 3) // defeat Safari bug\n",
              "        targ = targ.parentNode;\n",
              "\n",
              "    // jQuery normalizes the pageX and pageY\n",
              "    // pageX,Y are the mouse positions relative to the document\n",
              "    // offset() returns the position of the element relative to the document\n",
              "    var x = e.pageX - $(targ).offset().left;\n",
              "    var y = e.pageY - $(targ).offset().top;\n",
              "\n",
              "    return {\"x\": x, \"y\": y};\n",
              "};\n",
              "\n",
              "/*\n",
              " * return a copy of an object with only non-object keys\n",
              " * we need this to avoid circular references\n",
              " * http://stackoverflow.com/a/24161582/3208463\n",
              " */\n",
              "function simpleKeys (original) {\n",
              "  return Object.keys(original).reduce(function (obj, key) {\n",
              "    if (typeof original[key] !== 'object')\n",
              "        obj[key] = original[key]\n",
              "    return obj;\n",
              "  }, {});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.mouse_event = function(event, name) {\n",
              "    var canvas_pos = mpl.findpos(event)\n",
              "\n",
              "    if (name === 'button_press')\n",
              "    {\n",
              "        this.canvas.focus();\n",
              "        this.canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    var x = canvas_pos.x * mpl.ratio;\n",
              "    var y = canvas_pos.y * mpl.ratio;\n",
              "\n",
              "    this.send_message(name, {x: x, y: y, button: event.button,\n",
              "                             step: event.step,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "\n",
              "    /* This prevents the web browser from automatically changing to\n",
              "     * the text insertion cursor when the button is pressed.  We want\n",
              "     * to control all of the cursor setting manually through the\n",
              "     * 'cursor' event from matplotlib */\n",
              "    event.preventDefault();\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    // Handle any extra behaviour associated with a key event\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.key_event = function(event, name) {\n",
              "\n",
              "    // Prevent repeat events\n",
              "    if (name == 'key_press')\n",
              "    {\n",
              "        if (event.which === this._key)\n",
              "            return;\n",
              "        else\n",
              "            this._key = event.which;\n",
              "    }\n",
              "    if (name == 'key_release')\n",
              "        this._key = null;\n",
              "\n",
              "    var value = '';\n",
              "    if (event.ctrlKey && event.which != 17)\n",
              "        value += \"ctrl+\";\n",
              "    if (event.altKey && event.which != 18)\n",
              "        value += \"alt+\";\n",
              "    if (event.shiftKey && event.which != 16)\n",
              "        value += \"shift+\";\n",
              "\n",
              "    value += 'k';\n",
              "    value += event.which.toString();\n",
              "\n",
              "    this._key_event_extra(event, name);\n",
              "\n",
              "    this.send_message(name, {key: value,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
              "    if (name == 'download') {\n",
              "        this.handle_save(this, null);\n",
              "    } else {\n",
              "        this.send_message(\"toolbar_button\", {name: name});\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
              "    this.message.textContent = tooltip;\n",
              "};\n",
              "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
              "\n",
              "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
              "\n",
              "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
              "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
              "    // object with the appropriate methods. Currently this is a non binary\n",
              "    // socket, so there is still some room for performance tuning.\n",
              "    var ws = {};\n",
              "\n",
              "    ws.close = function() {\n",
              "        comm.close()\n",
              "    };\n",
              "    ws.send = function(m) {\n",
              "        //console.log('sending', m);\n",
              "        comm.send(m);\n",
              "    };\n",
              "    // Register the callback with on_msg.\n",
              "    comm.on_msg(function(msg) {\n",
              "        //console.log('receiving', msg['content']['data'], msg);\n",
              "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
              "        ws.onmessage(msg['content']['data'])\n",
              "    });\n",
              "    return ws;\n",
              "}\n",
              "\n",
              "mpl.mpl_figure_comm = function(comm, msg) {\n",
              "    // This is the function which gets called when the mpl process\n",
              "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
              "\n",
              "    var id = msg.content.data.id;\n",
              "    // Get hold of the div created by the display call when the Comm\n",
              "    // socket was opened in Python.\n",
              "    var element = $(\"#\" + id);\n",
              "    var ws_proxy = comm_websocket_adapter(comm)\n",
              "\n",
              "    function ondownload(figure, format) {\n",
              "        window.open(figure.imageObj.src);\n",
              "    }\n",
              "\n",
              "    var fig = new mpl.figure(id, ws_proxy,\n",
              "                           ondownload,\n",
              "                           element.get(0));\n",
              "\n",
              "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
              "    // web socket which is closed, not our websocket->open comm proxy.\n",
              "    ws_proxy.onopen();\n",
              "\n",
              "    fig.parent_element = element.get(0);\n",
              "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
              "    if (!fig.cell_info) {\n",
              "        console.error(\"Failed to find cell for figure\", id, fig);\n",
              "        return;\n",
              "    }\n",
              "\n",
              "    var output_index = fig.cell_info[2]\n",
              "    var cell = fig.cell_info[0];\n",
              "\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
              "    var width = fig.canvas.width/mpl.ratio\n",
              "    fig.root.unbind('remove')\n",
              "\n",
              "    // Update the output cell to use the data from the current canvas.\n",
              "    fig.push_to_output();\n",
              "    var dataURL = fig.canvas.toDataURL();\n",
              "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
              "    // the notebook keyboard shortcuts fail.\n",
              "    IPython.keyboard_manager.enable()\n",
              "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
              "    fig.close_ws(fig, msg);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.close_ws = function(fig, msg){\n",
              "    fig.send_message('closing', msg);\n",
              "    // fig.ws.close()\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
              "    // Turn the data on the canvas into data in the output cell.\n",
              "    var width = this.canvas.width/mpl.ratio\n",
              "    var dataURL = this.canvas.toDataURL();\n",
              "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Tell IPython that the notebook contents must change.\n",
              "    IPython.notebook.set_dirty(true);\n",
              "    this.send_message(\"ack\", {});\n",
              "    var fig = this;\n",
              "    // Wait a second, then push the new image to the DOM so\n",
              "    // that it is saved nicely (might be nice to debounce this).\n",
              "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items){\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) { continue; };\n",
              "\n",
              "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    // Add the status bar.\n",
              "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "\n",
              "    // Add the close button to the window.\n",
              "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
              "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
              "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
              "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
              "    buttongrp.append(button);\n",
              "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
              "    titlebar.prepend(buttongrp);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(el){\n",
              "    var fig = this\n",
              "    el.on(\"remove\", function(){\n",
              "\tfig.close_ws(fig, {});\n",
              "    });\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(el){\n",
              "    // this is important to make the div 'focusable\n",
              "    el.attr('tabindex', 0)\n",
              "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
              "    // off when our div gets focus\n",
              "\n",
              "    // location in version 3\n",
              "    if (IPython.notebook.keyboard_manager) {\n",
              "        IPython.notebook.keyboard_manager.register_events(el);\n",
              "    }\n",
              "    else {\n",
              "        // location in version 2\n",
              "        IPython.keyboard_manager.register_events(el);\n",
              "    }\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    var manager = IPython.notebook.keyboard_manager;\n",
              "    if (!manager)\n",
              "        manager = IPython.keyboard_manager;\n",
              "\n",
              "    // Check for shift+enter\n",
              "    if (event.shiftKey && event.which == 13) {\n",
              "        this.canvas_div.blur();\n",
              "        // select the cell after this one\n",
              "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
              "        IPython.notebook.select(index + 1);\n",
              "    }\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    fig.ondownload(fig, null);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.find_output_cell = function(html_output) {\n",
              "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
              "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
              "    // IPython event is triggered only after the cells have been serialised, which for\n",
              "    // our purposes (turning an active figure into a static one), is too late.\n",
              "    var cells = IPython.notebook.get_cells();\n",
              "    var ncells = cells.length;\n",
              "    for (var i=0; i<ncells; i++) {\n",
              "        var cell = cells[i];\n",
              "        if (cell.cell_type === 'code'){\n",
              "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
              "                var data = cell.output_area.outputs[j];\n",
              "                if (data.data) {\n",
              "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
              "                    data = data.data;\n",
              "                }\n",
              "                if (data['text/html'] == html_output) {\n",
              "                    return [cell, data, j];\n",
              "                }\n",
              "            }\n",
              "        }\n",
              "    }\n",
              "}\n",
              "\n",
              "// Register the function which deals with the matplotlib target/channel.\n",
              "// The kernel may be null if the page has been refreshed.\n",
              "if (IPython.notebook.kernel != null) {\n",
              "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
              "}\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div id='d2764b9d-afc4-4bb6-87d3-d2b95ceb4d16'></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48xTB4ABV4zE"
      },
      "source": [
        "dataset['returns'] = dataset['Adj Close'].pct_change()"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "Q1-rP9URFszz",
        "outputId": "9a22f04d-cbc2-4f65-eb0e-ec77cfa2a9ef"
      },
      "source": [
        "dataset['returns'] = dataset['Adj Close'].pct_change()\n",
        "\n",
        "# Extract inputs for pyfolio\n",
        "strat = backtest_result[0]\n",
        "pyfoliozer = strat.analyzers.getbyname('pyfolio')\n",
        "returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()\n",
        "returns.name = 'Strategy'\n",
        "#returns.tail()\n",
        "\n",
        "# benchmark returns\n",
        "benchmark_rets = dataset['returns']\n",
        "benchmark_rets.index = benchmark_rets.index.tz_localize('UTC') \n",
        "benchmark_rets = benchmark_rets.filter(returns.index)\n",
        "benchmark_rets.name = 'Nasdaq Composite'\n",
        "#benchmark_rets.tail()\n",
        "\n",
        "# get performance statistics for strategy\n",
        "pf.show_perf_stats(returns)"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2017-06-26</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-03-19</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>44</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>2.8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>10.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>10.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-11.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>0.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>17.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-1.3%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "BanZRbgxUCtD",
        "outputId": "87ab5281-c147-458d-d8b4-03a683827f35"
      },
      "source": [
        "# get performance statistics for benchmark\n",
        "pf.show_perf_stats(benchmark_rets)"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2017-06-26</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-03-19</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>44</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>22.2%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>110.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>23.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>0.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-30.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>-0.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>10.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-2.9%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "5tc0S6rrGHTU",
        "outputId": "d99d196a-7182-43de-d5ac-1c5676632980"
      },
      "source": [
        "# plot performance for strategy vs benchmark\n",
        "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(16, 9),constrained_layout=True)\n",
        "axes = ax.flatten()\n",
        "pf.plot_rolling_beta(returns=returns, factor_returns=benchmark_rets, ax=axes[0])\n",
        "axes[0].grid(True)\n",
        "pf.plot_rolling_volatility(returns=returns, factor_returns=benchmark_rets,ax=axes[1])\n",
        "axes[1].grid(True)\n",
        "pf.plot_annual_returns(returns=returns, ax=axes[2])\n",
        "axes[2].grid(True)\n",
        "pf.plot_monthly_returns_heatmap(returns=returns, ax=axes[3],)\n",
        "plt.tight_layout()"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import sklearn\\nimport xgboost as xgb\\nimport spacy\\nimport seaborn as sns\\nimport pickle\\nimport bokeh\\nimport plotly.express as px\\nimport lightgbm as lgb\\nimport pydot\\nimport re\\nimport matplotlib.pyplot as plt\\nimport altair as alt\\nfrom pathlib import Path\\nfrom sklearn import svm\\nfrom openpyxl import load_workbook\\nimport pandas as pd\\nimport numpy as np\\nimport sys\\nimport statistics\\nimport plotly as py'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "/* Put everything inside the global mpl namespace */\n",
              "window.mpl = {};\n",
              "\n",
              "\n",
              "mpl.get_websocket_type = function() {\n",
              "    if (typeof(WebSocket) !== 'undefined') {\n",
              "        return WebSocket;\n",
              "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
              "        return MozWebSocket;\n",
              "    } else {\n",
              "        alert('Your browser does not have WebSocket support. ' +\n",
              "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
              "              'Firefox 4 and 5 are also supported but you ' +\n",
              "              'have to enable WebSockets in about:config.');\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
              "    this.id = figure_id;\n",
              "\n",
              "    this.ws = websocket;\n",
              "\n",
              "    this.supports_binary = (this.ws.binaryType != undefined);\n",
              "\n",
              "    if (!this.supports_binary) {\n",
              "        var warnings = document.getElementById(\"mpl-warnings\");\n",
              "        if (warnings) {\n",
              "            warnings.style.display = 'block';\n",
              "            warnings.textContent = (\n",
              "                \"This browser does not support binary websocket messages. \" +\n",
              "                    \"Performance may be slow.\");\n",
              "        }\n",
              "    }\n",
              "\n",
              "    this.imageObj = new Image();\n",
              "\n",
              "    this.context = undefined;\n",
              "    this.message = undefined;\n",
              "    this.canvas = undefined;\n",
              "    this.rubberband_canvas = undefined;\n",
              "    this.rubberband_context = undefined;\n",
              "    this.format_dropdown = undefined;\n",
              "\n",
              "    this.image_mode = 'full';\n",
              "\n",
              "    this.root = $('<div/>');\n",
              "    this._root_extra_style(this.root)\n",
              "    this.root.attr('style', 'display: inline-block');\n",
              "\n",
              "    $(parent_element).append(this.root);\n",
              "\n",
              "    this._init_header(this);\n",
              "    this._init_canvas(this);\n",
              "    this._init_toolbar(this);\n",
              "\n",
              "    var fig = this;\n",
              "\n",
              "    this.waiting = false;\n",
              "\n",
              "    this.ws.onopen =  function () {\n",
              "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
              "            fig.send_message(\"send_image_mode\", {});\n",
              "            if (mpl.ratio != 1) {\n",
              "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
              "            }\n",
              "            fig.send_message(\"refresh\", {});\n",
              "        }\n",
              "\n",
              "    this.imageObj.onload = function() {\n",
              "            if (fig.image_mode == 'full') {\n",
              "                // Full images could contain transparency (where diff images\n",
              "                // almost always do), so we need to clear the canvas so that\n",
              "                // there is no ghosting.\n",
              "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
              "            }\n",
              "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
              "        };\n",
              "\n",
              "    this.imageObj.onunload = function() {\n",
              "        fig.ws.close();\n",
              "    }\n",
              "\n",
              "    this.ws.onmessage = this._make_on_message_function(this);\n",
              "\n",
              "    this.ondownload = ondownload;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_header = function() {\n",
              "    var titlebar = $(\n",
              "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
              "        'ui-helper-clearfix\"/>');\n",
              "    var titletext = $(\n",
              "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
              "        'text-align: center; padding: 3px;\"/>');\n",
              "    titlebar.append(titletext)\n",
              "    this.root.append(titlebar);\n",
              "    this.header = titletext[0];\n",
              "}\n",
              "\n",
              "\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_canvas = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var canvas_div = $('<div/>');\n",
              "\n",
              "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
              "\n",
              "    function canvas_keyboard_event(event) {\n",
              "        return fig.key_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
              "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
              "    this.canvas_div = canvas_div\n",
              "    this._canvas_extra_style(canvas_div)\n",
              "    this.root.append(canvas_div);\n",
              "\n",
              "    var canvas = $('<canvas/>');\n",
              "    canvas.addClass('mpl-canvas');\n",
              "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
              "\n",
              "    this.canvas = canvas[0];\n",
              "    this.context = canvas[0].getContext(\"2d\");\n",
              "\n",
              "    var backingStore = this.context.backingStorePixelRatio ||\n",
              "\tthis.context.webkitBackingStorePixelRatio ||\n",
              "\tthis.context.mozBackingStorePixelRatio ||\n",
              "\tthis.context.msBackingStorePixelRatio ||\n",
              "\tthis.context.oBackingStorePixelRatio ||\n",
              "\tthis.context.backingStorePixelRatio || 1;\n",
              "\n",
              "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
              "\n",
              "    var rubberband = $('<canvas/>');\n",
              "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
              "\n",
              "    var pass_mouse_events = true;\n",
              "\n",
              "    canvas_div.resizable({\n",
              "        start: function(event, ui) {\n",
              "            pass_mouse_events = false;\n",
              "        },\n",
              "        resize: function(event, ui) {\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "        stop: function(event, ui) {\n",
              "            pass_mouse_events = true;\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "    });\n",
              "\n",
              "    function mouse_event_fn(event) {\n",
              "        if (pass_mouse_events)\n",
              "            return fig.mouse_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    rubberband.mousedown('button_press', mouse_event_fn);\n",
              "    rubberband.mouseup('button_release', mouse_event_fn);\n",
              "    // Throttle sequential mouse events to 1 every 20ms.\n",
              "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
              "\n",
              "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
              "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
              "\n",
              "    canvas_div.on(\"wheel\", function (event) {\n",
              "        event = event.originalEvent;\n",
              "        event['data'] = 'scroll'\n",
              "        if (event.deltaY < 0) {\n",
              "            event.step = 1;\n",
              "        } else {\n",
              "            event.step = -1;\n",
              "        }\n",
              "        mouse_event_fn(event);\n",
              "    });\n",
              "\n",
              "    canvas_div.append(canvas);\n",
              "    canvas_div.append(rubberband);\n",
              "\n",
              "    this.rubberband = rubberband;\n",
              "    this.rubberband_canvas = rubberband[0];\n",
              "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
              "    this.rubberband_context.strokeStyle = \"#000000\";\n",
              "\n",
              "    this._resize_canvas = function(width, height) {\n",
              "        // Keep the size of the canvas, canvas container, and rubber band\n",
              "        // canvas in synch.\n",
              "        canvas_div.css('width', width)\n",
              "        canvas_div.css('height', height)\n",
              "\n",
              "        canvas.attr('width', width * mpl.ratio);\n",
              "        canvas.attr('height', height * mpl.ratio);\n",
              "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
              "\n",
              "        rubberband.attr('width', width);\n",
              "        rubberband.attr('height', height);\n",
              "    }\n",
              "\n",
              "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
              "    // upon first draw.\n",
              "    this._resize_canvas(600, 600);\n",
              "\n",
              "    // Disable right mouse context menu.\n",
              "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
              "        return false;\n",
              "    });\n",
              "\n",
              "    function set_focus () {\n",
              "        canvas.focus();\n",
              "        canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    window.setTimeout(set_focus, 100);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items) {\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) {\n",
              "            // put a spacer in here.\n",
              "            continue;\n",
              "        }\n",
              "        var button = $('<button/>');\n",
              "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
              "                        'ui-button-icon-only');\n",
              "        button.attr('role', 'button');\n",
              "        button.attr('aria-disabled', 'false');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "\n",
              "        var icon_img = $('<span/>');\n",
              "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
              "        icon_img.addClass(image);\n",
              "        icon_img.addClass('ui-corner-all');\n",
              "\n",
              "        var tooltip_span = $('<span/>');\n",
              "        tooltip_span.addClass('ui-button-text');\n",
              "        tooltip_span.html(tooltip);\n",
              "\n",
              "        button.append(icon_img);\n",
              "        button.append(tooltip_span);\n",
              "\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    var fmt_picker_span = $('<span/>');\n",
              "\n",
              "    var fmt_picker = $('<select/>');\n",
              "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
              "    fmt_picker_span.append(fmt_picker);\n",
              "    nav_element.append(fmt_picker_span);\n",
              "    this.format_dropdown = fmt_picker[0];\n",
              "\n",
              "    for (var ind in mpl.extensions) {\n",
              "        var fmt = mpl.extensions[ind];\n",
              "        var option = $(\n",
              "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
              "        fmt_picker.append(option);\n",
              "    }\n",
              "\n",
              "    // Add hover states to the ui-buttons\n",
              "    $( \".ui-button\" ).hover(\n",
              "        function() { $(this).addClass(\"ui-state-hover\");},\n",
              "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
              "    );\n",
              "\n",
              "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
              "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
              "    // which will in turn request a refresh of the image.\n",
              "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_message = function(type, properties) {\n",
              "    properties['type'] = type;\n",
              "    properties['figure_id'] = this.id;\n",
              "    this.ws.send(JSON.stringify(properties));\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_draw_message = function() {\n",
              "    if (!this.waiting) {\n",
              "        this.waiting = true;\n",
              "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
              "    }\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    var format_dropdown = fig.format_dropdown;\n",
              "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
              "    fig.ondownload(fig, format);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
              "    var size = msg['size'];\n",
              "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
              "        fig._resize_canvas(size[0], size[1]);\n",
              "        fig.send_message(\"refresh\", {});\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
              "    var x0 = msg['x0'] / mpl.ratio;\n",
              "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
              "    var x1 = msg['x1'] / mpl.ratio;\n",
              "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
              "    x0 = Math.floor(x0) + 0.5;\n",
              "    y0 = Math.floor(y0) + 0.5;\n",
              "    x1 = Math.floor(x1) + 0.5;\n",
              "    y1 = Math.floor(y1) + 0.5;\n",
              "    var min_x = Math.min(x0, x1);\n",
              "    var min_y = Math.min(y0, y1);\n",
              "    var width = Math.abs(x1 - x0);\n",
              "    var height = Math.abs(y1 - y0);\n",
              "\n",
              "    fig.rubberband_context.clearRect(\n",
              "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
              "\n",
              "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
              "    // Updates the figure title.\n",
              "    fig.header.textContent = msg['label'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
              "    var cursor = msg['cursor'];\n",
              "    switch(cursor)\n",
              "    {\n",
              "    case 0:\n",
              "        cursor = 'pointer';\n",
              "        break;\n",
              "    case 1:\n",
              "        cursor = 'default';\n",
              "        break;\n",
              "    case 2:\n",
              "        cursor = 'crosshair';\n",
              "        break;\n",
              "    case 3:\n",
              "        cursor = 'move';\n",
              "        break;\n",
              "    }\n",
              "    fig.rubberband_canvas.style.cursor = cursor;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
              "    fig.message.textContent = msg['message'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
              "    // Request the server to send over a new figure.\n",
              "    fig.send_draw_message();\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
              "    fig.image_mode = msg['mode'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Called whenever the canvas gets updated.\n",
              "    this.send_message(\"ack\", {});\n",
              "}\n",
              "\n",
              "// A function to construct a web socket function for onmessage handling.\n",
              "// Called in the figure constructor.\n",
              "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
              "    return function socket_on_message(evt) {\n",
              "        if (evt.data instanceof Blob) {\n",
              "            /* FIXME: We get \"Resource interpreted as Image but\n",
              "             * transferred with MIME type text/plain:\" errors on\n",
              "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
              "             * to be part of the websocket stream */\n",
              "            evt.data.type = \"image/png\";\n",
              "\n",
              "            /* Free the memory for the previous frames */\n",
              "            if (fig.imageObj.src) {\n",
              "                (window.URL || window.webkitURL).revokeObjectURL(\n",
              "                    fig.imageObj.src);\n",
              "            }\n",
              "\n",
              "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
              "                evt.data);\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
              "            fig.imageObj.src = evt.data;\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        var msg = JSON.parse(evt.data);\n",
              "        var msg_type = msg['type'];\n",
              "\n",
              "        // Call the  \"handle_{type}\" callback, which takes\n",
              "        // the figure and JSON message as its only arguments.\n",
              "        try {\n",
              "            var callback = fig[\"handle_\" + msg_type];\n",
              "        } catch (e) {\n",
              "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        if (callback) {\n",
              "            try {\n",
              "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
              "                callback(fig, msg);\n",
              "            } catch (e) {\n",
              "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
              "            }\n",
              "        }\n",
              "    };\n",
              "}\n",
              "\n",
              "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
              "mpl.findpos = function(e) {\n",
              "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
              "    var targ;\n",
              "    if (!e)\n",
              "        e = window.event;\n",
              "    if (e.target)\n",
              "        targ = e.target;\n",
              "    else if (e.srcElement)\n",
              "        targ = e.srcElement;\n",
              "    if (targ.nodeType == 3) // defeat Safari bug\n",
              "        targ = targ.parentNode;\n",
              "\n",
              "    // jQuery normalizes the pageX and pageY\n",
              "    // pageX,Y are the mouse positions relative to the document\n",
              "    // offset() returns the position of the element relative to the document\n",
              "    var x = e.pageX - $(targ).offset().left;\n",
              "    var y = e.pageY - $(targ).offset().top;\n",
              "\n",
              "    return {\"x\": x, \"y\": y};\n",
              "};\n",
              "\n",
              "/*\n",
              " * return a copy of an object with only non-object keys\n",
              " * we need this to avoid circular references\n",
              " * http://stackoverflow.com/a/24161582/3208463\n",
              " */\n",
              "function simpleKeys (original) {\n",
              "  return Object.keys(original).reduce(function (obj, key) {\n",
              "    if (typeof original[key] !== 'object')\n",
              "        obj[key] = original[key]\n",
              "    return obj;\n",
              "  }, {});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.mouse_event = function(event, name) {\n",
              "    var canvas_pos = mpl.findpos(event)\n",
              "\n",
              "    if (name === 'button_press')\n",
              "    {\n",
              "        this.canvas.focus();\n",
              "        this.canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    var x = canvas_pos.x * mpl.ratio;\n",
              "    var y = canvas_pos.y * mpl.ratio;\n",
              "\n",
              "    this.send_message(name, {x: x, y: y, button: event.button,\n",
              "                             step: event.step,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "\n",
              "    /* This prevents the web browser from automatically changing to\n",
              "     * the text insertion cursor when the button is pressed.  We want\n",
              "     * to control all of the cursor setting manually through the\n",
              "     * 'cursor' event from matplotlib */\n",
              "    event.preventDefault();\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    // Handle any extra behaviour associated with a key event\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.key_event = function(event, name) {\n",
              "\n",
              "    // Prevent repeat events\n",
              "    if (name == 'key_press')\n",
              "    {\n",
              "        if (event.which === this._key)\n",
              "            return;\n",
              "        else\n",
              "            this._key = event.which;\n",
              "    }\n",
              "    if (name == 'key_release')\n",
              "        this._key = null;\n",
              "\n",
              "    var value = '';\n",
              "    if (event.ctrlKey && event.which != 17)\n",
              "        value += \"ctrl+\";\n",
              "    if (event.altKey && event.which != 18)\n",
              "        value += \"alt+\";\n",
              "    if (event.shiftKey && event.which != 16)\n",
              "        value += \"shift+\";\n",
              "\n",
              "    value += 'k';\n",
              "    value += event.which.toString();\n",
              "\n",
              "    this._key_event_extra(event, name);\n",
              "\n",
              "    this.send_message(name, {key: value,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
              "    if (name == 'download') {\n",
              "        this.handle_save(this, null);\n",
              "    } else {\n",
              "        this.send_message(\"toolbar_button\", {name: name});\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
              "    this.message.textContent = tooltip;\n",
              "};\n",
              "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
              "\n",
              "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
              "\n",
              "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
              "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
              "    // object with the appropriate methods. Currently this is a non binary\n",
              "    // socket, so there is still some room for performance tuning.\n",
              "    var ws = {};\n",
              "\n",
              "    ws.close = function() {\n",
              "        comm.close()\n",
              "    };\n",
              "    ws.send = function(m) {\n",
              "        //console.log('sending', m);\n",
              "        comm.send(m);\n",
              "    };\n",
              "    // Register the callback with on_msg.\n",
              "    comm.on_msg(function(msg) {\n",
              "        //console.log('receiving', msg['content']['data'], msg);\n",
              "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
              "        ws.onmessage(msg['content']['data'])\n",
              "    });\n",
              "    return ws;\n",
              "}\n",
              "\n",
              "mpl.mpl_figure_comm = function(comm, msg) {\n",
              "    // This is the function which gets called when the mpl process\n",
              "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
              "\n",
              "    var id = msg.content.data.id;\n",
              "    // Get hold of the div created by the display call when the Comm\n",
              "    // socket was opened in Python.\n",
              "    var element = $(\"#\" + id);\n",
              "    var ws_proxy = comm_websocket_adapter(comm)\n",
              "\n",
              "    function ondownload(figure, format) {\n",
              "        window.open(figure.imageObj.src);\n",
              "    }\n",
              "\n",
              "    var fig = new mpl.figure(id, ws_proxy,\n",
              "                           ondownload,\n",
              "                           element.get(0));\n",
              "\n",
              "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
              "    // web socket which is closed, not our websocket->open comm proxy.\n",
              "    ws_proxy.onopen();\n",
              "\n",
              "    fig.parent_element = element.get(0);\n",
              "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
              "    if (!fig.cell_info) {\n",
              "        console.error(\"Failed to find cell for figure\", id, fig);\n",
              "        return;\n",
              "    }\n",
              "\n",
              "    var output_index = fig.cell_info[2]\n",
              "    var cell = fig.cell_info[0];\n",
              "\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
              "    var width = fig.canvas.width/mpl.ratio\n",
              "    fig.root.unbind('remove')\n",
              "\n",
              "    // Update the output cell to use the data from the current canvas.\n",
              "    fig.push_to_output();\n",
              "    var dataURL = fig.canvas.toDataURL();\n",
              "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
              "    // the notebook keyboard shortcuts fail.\n",
              "    IPython.keyboard_manager.enable()\n",
              "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
              "    fig.close_ws(fig, msg);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.close_ws = function(fig, msg){\n",
              "    fig.send_message('closing', msg);\n",
              "    // fig.ws.close()\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
              "    // Turn the data on the canvas into data in the output cell.\n",
              "    var width = this.canvas.width/mpl.ratio\n",
              "    var dataURL = this.canvas.toDataURL();\n",
              "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Tell IPython that the notebook contents must change.\n",
              "    IPython.notebook.set_dirty(true);\n",
              "    this.send_message(\"ack\", {});\n",
              "    var fig = this;\n",
              "    // Wait a second, then push the new image to the DOM so\n",
              "    // that it is saved nicely (might be nice to debounce this).\n",
              "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items){\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) { continue; };\n",
              "\n",
              "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    // Add the status bar.\n",
              "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "\n",
              "    // Add the close button to the window.\n",
              "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
              "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
              "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
              "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
              "    buttongrp.append(button);\n",
              "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
              "    titlebar.prepend(buttongrp);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(el){\n",
              "    var fig = this\n",
              "    el.on(\"remove\", function(){\n",
              "\tfig.close_ws(fig, {});\n",
              "    });\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(el){\n",
              "    // this is important to make the div 'focusable\n",
              "    el.attr('tabindex', 0)\n",
              "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
              "    // off when our div gets focus\n",
              "\n",
              "    // location in version 3\n",
              "    if (IPython.notebook.keyboard_manager) {\n",
              "        IPython.notebook.keyboard_manager.register_events(el);\n",
              "    }\n",
              "    else {\n",
              "        // location in version 2\n",
              "        IPython.keyboard_manager.register_events(el);\n",
              "    }\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    var manager = IPython.notebook.keyboard_manager;\n",
              "    if (!manager)\n",
              "        manager = IPython.keyboard_manager;\n",
              "\n",
              "    // Check for shift+enter\n",
              "    if (event.shiftKey && event.which == 13) {\n",
              "        this.canvas_div.blur();\n",
              "        // select the cell after this one\n",
              "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
              "        IPython.notebook.select(index + 1);\n",
              "    }\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    fig.ondownload(fig, null);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.find_output_cell = function(html_output) {\n",
              "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
              "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
              "    // IPython event is triggered only after the cells have been serialised, which for\n",
              "    // our purposes (turning an active figure into a static one), is too late.\n",
              "    var cells = IPython.notebook.get_cells();\n",
              "    var ncells = cells.length;\n",
              "    for (var i=0; i<ncells; i++) {\n",
              "        var cell = cells[i];\n",
              "        if (cell.cell_type === 'code'){\n",
              "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
              "                var data = cell.output_area.outputs[j];\n",
              "                if (data.data) {\n",
              "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
              "                    data = data.data;\n",
              "                }\n",
              "                if (data['text/html'] == html_output) {\n",
              "                    return [cell, data, j];\n",
              "                }\n",
              "            }\n",
              "        }\n",
              "    }\n",
              "}\n",
              "\n",
              "// Register the function which deals with the matplotlib target/channel.\n",
              "// The kernel may be null if the page has been refreshed.\n",
              "if (IPython.notebook.kernel != null) {\n",
              "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
              "}\n"
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\n",
            "text/plain": [
              "<Figure size 1152x648 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}