alpha/strategies/optimization_1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": "\n# Import Data\n\nImporting AXP data."
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "outputs": [
        {
          "data": {
            "text/plain": "            close   high    low  p_change   open  pre_close   volume  \\\n                                                                       \n2017-06-12  80.17  80.73  79.95    -0.174  80.35      80.31  3352279   \n2017-06-13  80.59  80.74  80.07     0.524  80.20      80.17  3174361   \n2017-06-14  80.84  80.92  79.62     0.310  80.11      80.59  4013089   \n2017-06-15  80.70  81.24  80.23    -0.173  80.38      80.84  2773369   \n2017-06-16  81.45  81.48  80.77     0.929  80.86      80.70  5914676   \n\n                date  date_week     atr21     atr14  key  \n                                                          \n2017-06-12  20170612          0  0.780000  0.780000    0  \n2017-06-13  20170613          1  0.722381  0.721071    1  \n2017-06-14  20170614          2  0.933535  0.942224    2  \n2017-06-15  20170615          3  0.955464  0.962959    3  \n2017-06-16  20170616          4  0.896598  0.896962    4  ",
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          },
          "metadata": {},
          "output_type": "execute_result",
          "execution_count": 6
        }
      ],
      "source": "\nimport pandas as pd\n\namex_df \u003d pd.read_csv(\u0027../../data/gen/usAXP_df.csv\u0027,\n                           parse_dates\u003dTrue,\n                           index_col\u003d0)\n# Strip whitespace from column names\namex_df.columns \u003d amex_df.columns.str.strip()\n# Print\namex_df.head()\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "\n# First Approach: Using TradeStrategy Object\n\n## Processing Data\n\nAdding slope \u0026 velocity to each day.",
      "metadata": {
        "pycharm": {
          "metadata": false
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "outputs": [
        {
          "data": {
            "text/plain": "            close   high    low  p_change   open  pre_close   volume  \\\n                                                                       \n2017-06-12  80.17  80.73  79.95    -0.174  80.35      80.31  3352279   \n2017-06-13  80.59  80.74  80.07     0.524  80.20      80.17  3174361   \n2017-06-14  80.84  80.92  79.62     0.310  80.11      80.59  4013089   \n2017-06-15  80.70  81.24  80.23    -0.173  80.38      80.84  2773369   \n2017-06-16  81.45  81.48  80.77     0.929  80.86      80.70  5914676   \n\n                date  date_week     atr21     atr14  key  slope  velocity  \n                                                                           \n2017-06-12  20170612          0  0.780000  0.780000    0  0.420   -0.0850  \n2017-06-13  20170613          1  0.722381  0.721071    1  0.335   -0.1825  \n2017-06-14  20170614          2  0.933535  0.942224    2  0.055   -0.0150  \n2017-06-15  20170615          3  0.955464  0.962959    3  0.305    0.2675  \n2017-06-16  20170616          4  0.896598  0.896962    4  0.590    0.1125  ",
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          },
          "metadata": {},
          "output_type": "execute_result",
          "execution_count": 7
        }
      ],
      "source": "\nimport numpy as np\n\nextra_df \u003d amex_df.copy()\nextra_df[\u0027slope\u0027] \u003d pd.Series(np.gradient(extra_df.close), extra_df.index, name\u003d\u0027slope\u0027)\nextra_df[\u0027velocity\u0027] \u003d pd.Series(np.gradient(extra_df.slope), extra_df.index, name\u003d\u0027velocity\u0027)\n# Print\nextra_df.head()\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "\n## Performing Backtests\n\n1. Prepare Parameters\n2. Define Backtest Tasks\n3. Defining a DataFrame to handle results\n4. Running BackTest on Multi Threads\n\n### Preparing Parameters\n",
      "metadata": {
        "pycharm": {
          "metadata": false
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "outputs": [
        {
          "name": "stdout",
          "text": [
            "Params Ready, 1024 tasks to run, will take approximately 3.41 minutes or 0.06 hours to complete. \n"
          ],
          "output_type": "stream"
        }
      ],
      "source": "\nimport itertools\n\nsm_buy_slope_threshold_list \u003d np.arange(-0.2, 0.2, 0.1)\nsm_buy_velocity_threshold_list \u003d np.arange(-0.2, 0.2, 0.1)\nsm_close_slope_threshold_list \u003d np.arange(-0.1, 0.1, 0.1)\nsm_close_velocity_threshold_list \u003d np.arange(-0.1, 0.1, 0.1)\nsm_sell_slope_threshold_list \u003d np.arange(-0.2, 0.2, 0.1)\nsm_sell_velocity_threshold_list \u003d np.arange(-0.2, 0.2, 0.1)\n\ntask_list \u003d list(itertools.product(\n    sm_buy_slope_threshold_list, \n    sm_buy_velocity_threshold_list, \n    sm_close_slope_threshold_list, \n    sm_close_velocity_threshold_list, \n    sm_sell_slope_threshold_list, \n    sm_sell_velocity_threshold_list\n))\nprint(\"Params Ready, {} tasks to run, will take approximately {} minutes or {} hours to complete. \".format(len(task_list), round(len(task_list)/(5*60), 2), round(len(task_list)/(5*60*60), 2)))\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "\n### Defining Backtest Task\n",
      "metadata": {
        "pycharm": {
          "metadata": false
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "outputs": [
        {
          "name": "stdout",
          "text": [
            "importing Jupyter notebook from strategy_base.ipynb\n"
          ],
          "output_type": "stream"
        }
      ],
      "source": "\nimport import_ipynb\nfrom strategy_base import TradeStrategyBase\n\ndef backtest(tradedays: pd.DataFrame, \n             strategy: TradeStrategyBase):\n    \n    # Iterate through each data and trade\n    for date, trade_day in extra_df.iterrows():\n        strategy.trade(date\u003ddate,\n                       tradeday\u003dtrade_day)\n    # Return resulting profit\n    return strategy\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "\n### Defining a DataFrame to handle results\n",
      "metadata": {
        "pycharm": {
          "metadata": false
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "outputs": [
        {
          "data": {
            "text/plain": "Empty DataFrame\nColumns: [buy_slope_threshold, buy_velocity_threshold, close_slope_threshold, close_velocity_threshold, sell_slope_threshold, sell_velocity_threshold, profit_percentage]\nIndex: []",
            "text/html": "\u003cdiv\u003e\n\u003cstyle scoped\u003e\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\u003c/style\u003e\n\u003ctable border\u003d\"1\" class\u003d\"dataframe\"\u003e\n  \u003cthead\u003e\n    \u003ctr style\u003d\"text-align: right;\"\u003e\n      \u003cth\u003e\u003c/th\u003e\n      \u003cth\u003ebuy_slope_threshold\u003c/th\u003e\n      \u003cth\u003ebuy_velocity_threshold\u003c/th\u003e\n      \u003cth\u003eclose_slope_threshold\u003c/th\u003e\n      \u003cth\u003eclose_velocity_threshold\u003c/th\u003e\n      \u003cth\u003esell_slope_threshold\u003c/th\u003e\n      \u003cth\u003esell_velocity_threshold\u003c/th\u003e\n      \u003cth\u003eprofit_percentage\u003c/th\u003e\n    \u003c/tr\u003e\n  \u003c/thead\u003e\n  \u003ctbody\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"
          },
          "metadata": {},
          "output_type": "execute_result",
          "execution_count": 10
        }
      ],
      "source": "\nfinal_result \u003d pd.DataFrame(columns\u003d[\u0027buy_slope_threshold\u0027, \u0027buy_velocity_threshold\u0027, \u0027close_slope_threshold\u0027, \u0027close_velocity_threshold\u0027, \u0027sell_slope_threshold\u0027, \u0027sell_velocity_threshold\u0027, \u0027profit_percentage\u0027])\nfinal_result\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "\n### Diagnosing Environment\n    ",
      "metadata": {
        "pycharm": {
          "metadata": false
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "outputs": [
        {
          "name": "stdout",
          "text": [
            "System is 8 cpus\n"
          ],
          "output_type": "stream"
        }
      ],
      "source": "\nimport multiprocessing\n\nprint(\"System is {} cpus\".format(multiprocessing.cpu_count()))\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "\n### Running Backtest with Multi-Threading\n",
      "metadata": {
        "pycharm": {
          "metadata": false
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "outputs": [
        {
          "name": "stdout",
          "text": [
            "importing Jupyter notebook from strategy_4.ipynb\n",
            "Starting 1024 tasks\n"
          ],
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": [
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            "\r779tests [02:21, 6.39tests/s]",
            "\r781tests [02:22, 7.90tests/s]",
            "\r783tests [02:23, 4.00tests/s]",
            "\r785tests [02:23, 5.23tests/s]",
            "\r788tests [02:23, 6.81tests/s]",
            "\r790tests [02:24, 3.70tests/s]",
            "\r792tests [02:24, 4.80tests/s]",
            "\r795tests [02:24, 6.20tests/s]",
            "\r798tests [02:25, 4.44tests/s]",
            "\r800tests [02:26, 5.67tests/s]",
            "\r804tests [02:26, 7.39tests/s]",
            "\r806tests [02:27, 3.60tests/s]",
            "\r808tests [02:27, 4.65tests/s]",
            "\r810tests [02:27, 5.88tests/s]",
            "\r813tests [02:27, 7.57tests/s]",
            "\r815tests [02:28, 3.71tests/s]",
            "\r817tests [02:29, 4.85tests/s]",
            "\r820tests [02:29, 6.47tests/s]",
            "\r822tests [02:30, 3.07tests/s]",
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            "\r826tests [02:30, 5.19tests/s]",
            "\r830tests [02:32, 4.21tests/s]",
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            "\r838tests [02:34, 3.76tests/s]",
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            "\r847tests [02:35, 3.47tests/s]",
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            "\r862tests [02:38, 4.02tests/s]",
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            "\r868tests [02:39, 6.60tests/s]",
            "\r870tests [02:40, 3.64tests/s]",
            "\r872tests [02:40, 4.44tests/s]",
            "\r874tests [02:40, 5.56tests/s]",
            "\r876tests [02:40, 6.88tests/s]",
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            "\r879tests [02:42, 3.53tests/s]",
            "\r880tests [02:42, 4.31tests/s]",
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            "\r886tests [02:44, 4.10tests/s]",
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            "\r973tests [03:01, 8.84tests/s]",
            "\r975tests [03:03, 4.06tests/s]",
            "\r977tests [03:03, 5.26tests/s]",
            "\r979tests [03:03, 6.55tests/s]",
            "\r981tests [03:03, 8.20tests/s]",
            "\r983tests [03:04, 4.00tests/s]",
            "\r985tests [03:04, 5.12tests/s]",
            "\r987tests [03:04, 6.33tests/s]",
            "\r990tests [03:05, 4.44tests/s]",
            "\r993tests [03:06, 5.72tests/s]",
            "\r998tests [03:07, 5.07tests/s]",
            "\r999tests [03:07, 5.66tests/s]",
            "\r1.00ktests [03:07, 7.46tests/s]",
            "\r1.00ktests [03:07, 9.33tests/s]",
            "\r1.01ktests [03:09, 3.49tests/s]",
            "\r1.01ktests [03:09, 4.70tests/s]",
            "\r1.01ktests [03:10, 3.92tests/s]",
            "\r1.02ktests [03:10, 5.02tests/s]",
            "\r1.02ktests [03:10, 6.73tests/s]",
            "\r1.02ktests [03:11, 4.41tests/s]",
            "\r1.02ktests [03:11, 5.73tests/s]",
            "\n"
          ],
          "output_type": "stream"
        }
      ],
      "source": "\nimport concurrent.futures\nfrom concurrent.futures import ThreadPoolExecutor\nfrom concurrent.futures.process import ProcessPoolExecutor\nfrom tqdm import tqdm\nfrom strategy_4 import TradeStrategy4\n\nwith ProcessPoolExecutor() as executor:\n    \"\"\"\n    By default, ProcessPool uses maximum available number of cores to process.\n    \"\"\"\n    \n    \"\"\"\n    Equivalent to executor.map(fn, *iterables),\n    but displays a tqdm-based progress bar.\n    \n    Does not support timeout or chunksize as executor.submit is used internally\n    \n    **kwargs is passed to tqdm.\n    \"\"\"\n    futures_list \u003d []\n    kwargs \u003d {\n        \u0027total\u0027: len(futures_list),\n        \u0027unit\u0027: \u0027tests\u0027,\n        \u0027unit_scale\u0027: True,\n        \u0027leave\u0027: True\n    }\n        \n    futures_list \u003d [executor.submit(backtest, \n                                    extra_df,\n                                    TradeStrategy4(buy_slope_threshold\u003dbuy_slope_threshold,\n                                                   buy_velocity_threshold\u003dbuy_velocity_threshold,\n                                                   close_slope_threshold\u003dclose_slope_threshold,\n                                                   close_velocity_threshold\u003dclose_velocity_threshold,\n                                                   sell_slope_threshold\u003dsell_slope_threshold,\n                                                   sell_velocity_threshold\u003dsell_velocity_threshold,\n                                                   should_log\u003dFalse, \n                                                   should_plot\u003dFalse)) for buy_slope_threshold, \\\n                                                                           buy_velocity_threshold, \\\n                                                                           close_slope_threshold, \\\n                                                                           close_velocity_threshold, \\\n                                                                           sell_slope_threshold, \\\n                                                                           sell_velocity_threshold in task_list]\n    print(\"Starting {} tasks\".format(len(futures_list)))\n    \n    for f in tqdm(concurrent.futures.as_completed(futures_list), **kwargs):\n        # done_callback runs on the main process\n        strategy \u003d f.result()\n        \n        result_row \u003d [strategy.buy_slope_threshold, \n                      strategy.buy_velocity_threshold, \n                      strategy.close_slope_threshold, \n                      strategy.close_velocity_threshold, \n                      strategy.sell_slope_threshold, \n                      strategy.sell_velocity_threshold, \n                      strategy.trade_profit * 100]\n        \n        final_result \u003d final_result.append(pd.Series(result_row, index\u003dfinal_result.columns), ignore_index\u003dTrue)\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "\n## Analysing Results\n",
      "metadata": {
        "pycharm": {
          "metadata": false
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "outputs": [
        {
          "data": {
            "text/plain": "     buy_slope_threshold  buy_velocity_threshold  close_slope_threshold  \\\n772                  0.1                    -0.2                   -0.1   \n767                  0.1                    -0.2                   -0.1   \n774                  0.1                    -0.2                   -0.1   \n775                  0.1                    -0.2                   -0.1   \n776                  0.1                    -0.2                   -0.1   \n777                  0.1                    -0.2                   -0.1   \n778                  0.1                    -0.2                   -0.1   \n780                  0.1                    -0.2                   -0.1   \n781                  0.1                    -0.2                   -0.1   \n768                  0.1                    -0.2                   -0.1   \n\n     close_velocity_threshold  sell_slope_threshold  sell_velocity_threshold  \\\n772                      -0.1                  -0.1                     -0.2   \n767                      -0.1                  -0.2                     -0.1   \n774                      -0.1                  -0.1                      0.0   \n775                      -0.1                   0.0                     -0.2   \n776                      -0.1                   0.0                     -0.1   \n777                      -0.1                  -0.1                      0.1   \n778                      -0.1                   0.0                      0.0   \n780                      -0.1                   0.0                      0.1   \n781                       0.0                  -0.2                     -0.2   \n768                      -0.1                  -0.2                      0.0   \n\n     profit_percentage  \n772         210.019729  \n767         210.019729  \n774         210.019729  \n775         210.019729  \n776         210.019729  \n777         210.019729  \n778         210.019729  \n780         210.019729  \n781         210.019729  \n768         210.019729  ",
            "text/html": "\u003cdiv\u003e\n\u003cstyle scoped\u003e\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\u003c/style\u003e\n\u003ctable border\u003d\"1\" class\u003d\"dataframe\"\u003e\n  \u003cthead\u003e\n    \u003ctr style\u003d\"text-align: right;\"\u003e\n      \u003cth\u003e\u003c/th\u003e\n      \u003cth\u003ebuy_slope_threshold\u003c/th\u003e\n      \u003cth\u003ebuy_velocity_threshold\u003c/th\u003e\n      \u003cth\u003eclose_slope_threshold\u003c/th\u003e\n      \u003cth\u003eclose_velocity_threshold\u003c/th\u003e\n      \u003cth\u003esell_slope_threshold\u003c/th\u003e\n      \u003cth\u003esell_velocity_threshold\u003c/th\u003e\n      \u003cth\u003eprofit_percentage\u003c/th\u003e\n    \u003c/tr\u003e\n  \u003c/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n      \u003cth\u003e772\u003c/th\u003e\n      \u003ctd\u003e0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e210.019729\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e767\u003c/th\u003e\n      \u003ctd\u003e0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e210.019729\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e774\u003c/th\u003e\n      \u003ctd\u003e0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e0.0\u003c/td\u003e\n      \u003ctd\u003e210.019729\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e775\u003c/th\u003e\n      \u003ctd\u003e0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e0.0\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e210.019729\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e776\u003c/th\u003e\n      \u003ctd\u003e0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e0.0\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e210.019729\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e777\u003c/th\u003e\n      \u003ctd\u003e0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e0.1\u003c/td\u003e\n      \u003ctd\u003e210.019729\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e778\u003c/th\u003e\n      \u003ctd\u003e0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e0.0\u003c/td\u003e\n      \u003ctd\u003e0.0\u003c/td\u003e\n      \u003ctd\u003e210.019729\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e780\u003c/th\u003e\n      \u003ctd\u003e0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e0.0\u003c/td\u003e\n      \u003ctd\u003e0.1\u003c/td\u003e\n      \u003ctd\u003e210.019729\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e781\u003c/th\u003e\n      \u003ctd\u003e0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e0.0\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e210.019729\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e768\u003c/th\u003e\n      \u003ctd\u003e0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.1\u003c/td\u003e\n      \u003ctd\u003e-0.2\u003c/td\u003e\n      \u003ctd\u003e0.0\u003c/td\u003e\n      \u003ctd\u003e210.019729\u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"
          },
          "metadata": {},
          "output_type": "execute_result",
          "execution_count": 15
        }
      ],
      "source": "        \nfinal_result \u003d final_result.sort_values(by\u003d[\u0027profit_percentage\u0027],\n                         ascending\u003dFalse)\nfinal_result.head(10)\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": "",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%"
        }
      }
    }
  ],
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}