04_alpha_factor_research/04_single_factor_zipline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zipline Backtest with Single Factor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook develops and test a simple mean-reversion factor that measures how much recent performance has deviated from the historical average. Short-term reversal is a common strategy that takes advantage of the weakly predictive pattern that stock price increases are likely to mean-revert back down over horizons from less than a minute to one month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:03:39.372356Z",
     "start_time": "2021-04-15T20:03:39.366068Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:03:39.841933Z",
     "start_time": "2021-04-15T20:03:39.376523Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:03:39.844642Z",
     "start_time": "2021-04-15T20:03:39.842894Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are first going to illustrate the zipline alpha factor research workflow in an offline environment. In particular, we will develop and test a simple mean-reversion factor that measures how much recent performance has deviated from the historical average. \n",
    "\n",
    "Short-term reversal is a common strategy that takes advantage of the weakly predictive pattern that stock price increases are likely to mean-revert back down over horizons from less than a minute to one month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To this end, the factor computes the z-score for the last monthly return relative to the rolling monthly returns over the last year. At this point, we will not place any orders to simply illustrate the implementation of a CustomFactor and record the results during the simulation.\n",
    "\n",
    "After some basic settings, `MeanReversion` subclasses `CustomFactor` and defines a `compute()` method. It creates default inputs of monthly returns over an also default year-long window so that the monthly_return variable will have 252 rows and one column for each security in the Quandl dataset on a given day.\n",
    "\n",
    "The `compute_factors()` method creates a `MeanReversion` factor instance and creates long, short, and ranking pipeline columns. The former two contain Boolean values that could be used to place orders, and the latter reflects that overall ranking to evaluate the overall factor performance. Furthermore, it uses the built-in `AverageDollarVolume` factor to limit the computation to more liquid stocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result would allow us to place long and short orders. We will see in the next chapter how to build a portfolio by choosing a rebalancing period and adjusting portfolio holdings as new signals arrive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The `initialize()` method registers the compute_factors() pipeline, and the before_trading_start() method ensures the pipeline runs on a daily basis. \n",
    "- The `record()` function adds the pipeline's ranking column as well as the current asset prices to the performance DataFrame returned by the `run_algorithm()` function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run using jupyter notebook extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:03:40.490212Z",
     "start_time": "2021-04-15T20:03:39.845915Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext zipline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `quandl` bundle instead of the default `quantopian-quandl` because the latter has a bug that requires (manually) fixing the SQL database. If you have a file with benchmark returns you can provide this instead of `--no-benchmark` (see [docs](https://github.com/stefan-jansen/zipline/blob/master/docs/source/whatsnew/1.4.0.txt))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:04:32.950056Z",
     "start_time": "2021-04-15T20:03:40.491568Z"
    },
    "scrolled": true
   },
   "outputs": [
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       "      <th>2015-01-02 21:00:00+00:00</th>\n",
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       "      <th>2015-01-06 21:00:00+00:00</th>\n",
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       "Equity(2 [AAL]) ...</td>\n",
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       "      <th>2015-01-07 21:00:00+00:00</th>\n",
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       "Equity(2 [AAL]) ...</td>\n",
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       "      <th>2015-01-08 21:00:00+00:00</th>\n",
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       "      <td>2017-12-27 14:31:00+00:00</td>\n",
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       "    <tr>\n",
       "      <th>2017-12-28 21:00:00+00:00</th>\n",
       "      <td>2017-12-28 14:31:00+00:00</td>\n",
       "      <td>2017-12-28 21:00:00+00:00</td>\n",
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       "      <th>2017-12-29 21:00:00+00:00</th>\n",
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       "                                        period_open              period_close  shorts_count   long_value  short_value  long_exposure           pnl  capital_used  short_exposure                                             orders  ...     sortino max_drawdown  max_leverage  excess_return  treasury_period_return  trading_days  period_label  algorithm_period_return                                        factor_data                                             prices\n",
       "2015-01-02 21:00:00+00:00 2015-01-02 14:31:00+00:00 2015-01-02 21:00:00+00:00             0        0.000        0.000          0.000      0.000000  0.000000e+00           0.000                                                 []  ...         NaN     0.000000      0.000000            0.0                     0.0             1       2015-01                 0.000000                                                NaN                                                NaN\n",
       "2015-01-05 21:00:00+00:00 2015-01-05 14:31:00+00:00 2015-01-05 21:00:00+00:00             0        0.000        0.000          0.000      0.000000  0.000000e+00           0.000  [{'id': '4ef8337dc8614f9183a8e8a7a0df3c18', 'd...  ...         NaN     0.000000      0.000000            0.0                     0.0             2       2015-01                 0.000000  Equity(0 [A])          2707.0\n",
       "Equity(2 [AAL]) ...  Equity(0 [A])           39.800\n",
       "Equity(2 [AAL])...\n",
       "2015-01-06 21:00:00+00:00 2015-01-06 14:31:00+00:00 2015-01-06 21:00:00+00:00             4  4731525.565 -1617262.705    4731525.565  -3799.475085 -3.118062e+06    -1617262.705  [{'id': '4ef8337dc8614f9183a8e8a7a0df3c18', 'd...  ...   -9.165151    -0.000380      0.635120            0.0                     0.0             3       2015-01                -0.000380  Equity(0 [A])          2707.0\n",
       "Equity(2 [AAL]) ...  Equity(0 [A])           39.800\n",
       "Equity(2 [AAL])...\n",
       "2015-01-07 21:00:00+00:00 2015-01-07 14:31:00+00:00 2015-01-07 21:00:00+00:00             4  4757100.850 -1629987.410    4757100.850  12850.580000  0.000000e+00    -1629987.410                                                 []  ...   18.918320    -0.000380      0.638131            0.0                     0.0             4       2015-01                 0.000905  Equity(0 [A])          2707.0\n",
       "Equity(2 [AAL]) ...  Equity(0 [A])           39.800\n",
       "Equity(2 [AAL])...\n",
       "2015-01-08 21:00:00+00:00 2015-01-08 14:31:00+00:00 2015-01-08 21:00:00+00:00             4  4835941.280 -1645106.080    4835941.280  63721.760000  0.000000e+00    -1645106.080                                                 []  ...  135.877107    -0.000380      0.643422            0.0                     0.0             5       2015-01                 0.007277  Equity(0 [A])          2707.0\n",
       "Equity(2 [AAL]) ...  Equity(0 [A])           39.800\n",
       "Equity(2 [AAL])...\n",
       "...                                             ...                       ...           ...          ...          ...            ...           ...           ...             ...                                                ...  ...         ...          ...           ...            ...                     ...           ...           ...                      ...                                                ...                                                ...\n",
       "2017-12-22 21:00:00+00:00 2017-12-22 14:31:00+00:00 2017-12-22 21:00:00+00:00            10  5266915.040 -4598653.320    5266915.040 -16960.030000  0.000000e+00    -4598653.320                                                 []  ...    0.609931    -0.142578      1.259576            0.0                     0.0           751       2017-12                 0.102076  Equity(0 [A])          2393.0\n",
       "Equity(1 [AA])  ...  Equity(0 [A])          67.66\n",
       "Equity(1 [AA])   ...\n",
       "2017-12-26 21:00:00+00:00 2017-12-26 14:31:00+00:00 2017-12-26 21:00:00+00:00            10  5217746.950 -4592983.200    5217746.950 -43497.970000  0.000000e+00    -4592983.200  [{'id': 'fb0fdf179c8641beaeec4f1bff3d1ca4', 'd...  ...    0.587086    -0.142578      1.259576            0.0                     0.0           752       2017-12                 0.097727  Equity(0 [A])          2363.0\n",
       "Equity(1 [AA])  ...  Equity(0 [A])          67.25\n",
       "Equity(1 [AA])   ...\n",
       "2017-12-27 21:00:00+00:00 2017-12-27 14:31:00+00:00 2017-12-27 21:00:00+00:00            13  3966687.510 -5240774.395    3966687.510  41248.841483  1.940099e+06    -5240774.395  [{'id': 'fb0fdf179c8641beaeec4f1bff3d1ca4', 'd...  ...    0.607626    -0.142578      1.259576            0.0                     0.0           753       2017-12                 0.101852  Equity(0 [A])          2363.0\n",
       "Equity(1 [AA])  ...  Equity(0 [A])          67.25\n",
       "Equity(1 [AA])   ...\n",
       "2017-12-28 21:00:00+00:00 2017-12-28 14:31:00+00:00 2017-12-28 21:00:00+00:00            12  3967663.630 -5158764.315    3967663.630  25543.807669 -5.744239e+04    -5158764.315  [{'id': '5e21283eb12b47749c70fb46d0525f10', 'd...  ...    0.620128    -0.142578      1.259576            0.0                     0.0           754       2017-12                 0.104406  Equity(0 [A])          2363.0\n",
       "Equity(1 [AA])  ...  Equity(0 [A])          67.25\n",
       "Equity(1 [AA])   ...\n",
       "2017-12-29 21:00:00+00:00 2017-12-29 14:31:00+00:00 2017-12-29 21:00:00+00:00            12  3953902.420 -5097704.930    3953902.420  47298.175000  0.000000e+00    -5097704.930                                                 []  ...    0.643540    -0.142578      1.259576            0.0                     0.0           755       2017-12                 0.109136  Equity(0 [A])          2363.0\n",
       "Equity(1 [AA])  ...  Equity(0 [A])          67.25\n",
       "Equity(1 [AA])   ...\n",
       "\n",
       "[755 rows x 39 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%zipline --start 2015-1-1 --end 2018-1-1 --output single_factor.pickle --no-benchmark --bundle quandl\n",
    "\n",
    "from zipline.api import (\n",
    "    attach_pipeline,\n",
    "    date_rules,\n",
    "    time_rules,\n",
    "    order_target_percent,\n",
    "    pipeline_output,\n",
    "    record,\n",
    "    schedule_function,\n",
    "    get_open_orders,\n",
    "    calendars\n",
    ")\n",
    "from zipline.finance import commission, slippage\n",
    "from zipline.pipeline import Pipeline, CustomFactor\n",
    "from zipline.pipeline.factors import Returns, AverageDollarVolume\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "MONTH = 21\n",
    "YEAR = 12 * MONTH\n",
    "N_LONGS = N_SHORTS = 25\n",
    "VOL_SCREEN = 1000\n",
    "\n",
    "\n",
    "class MeanReversion(CustomFactor):\n",
    "    \"\"\"Compute ratio of latest monthly return to 12m average,\n",
    "       normalized by std dev of monthly returns\"\"\"\n",
    "    inputs = [Returns(window_length=MONTH)]\n",
    "    window_length = YEAR\n",
    "\n",
    "    def compute(self, today, assets, out, monthly_returns):\n",
    "        df = pd.DataFrame(monthly_returns)\n",
    "        out[:] = df.iloc[-1].sub(df.mean()).div(df.std())\n",
    "\n",
    "\n",
    "def compute_factors():\n",
    "    \"\"\"Create factor pipeline incl. mean reversion,\n",
    "        filtered by 30d Dollar Volume; capture factor ranks\"\"\"\n",
    "    mean_reversion = MeanReversion()\n",
    "    dollar_volume = AverageDollarVolume(window_length=30)\n",
    "    return Pipeline(columns={'longs': mean_reversion.bottom(N_LONGS),\n",
    "                             'shorts': mean_reversion.top(N_SHORTS),\n",
    "                             'ranking': mean_reversion.rank(ascending=False)},\n",
    "                    screen=dollar_volume.top(VOL_SCREEN))\n",
    "\n",
    "\n",
    "def exec_trades(data, assets, target_percent):\n",
    "    \"\"\"Place orders for assets using target portfolio percentage\"\"\"\n",
    "    for asset in assets:\n",
    "        if data.can_trade(asset) and not get_open_orders(asset):\n",
    "            order_target_percent(asset, target_percent)\n",
    "\n",
    "\n",
    "def rebalance(context, data):\n",
    "    \"\"\"Compute long, short and obsolete holdings; place trade orders\"\"\"\n",
    "    factor_data = context.factor_data\n",
    "    record(factor_data=factor_data.ranking)\n",
    "\n",
    "    assets = factor_data.index\n",
    "    record(prices=data.current(assets, 'price'))\n",
    "\n",
    "    longs = assets[factor_data.longs]\n",
    "    shorts = assets[factor_data.shorts]\n",
    "    divest = set(context.portfolio.positions.keys()) - set(longs.union(shorts))\n",
    "\n",
    "    exec_trades(data, assets=divest, target_percent=0)\n",
    "    exec_trades(data, assets=longs, target_percent=1 / N_LONGS)\n",
    "    exec_trades(data, assets=shorts, target_percent=-1 / N_SHORTS)\n",
    "\n",
    "\n",
    "def initialize(context):\n",
    "    \"\"\"Setup: register pipeline, schedule rebalancing,\n",
    "        and set trading params\"\"\"\n",
    "    attach_pipeline(compute_factors(), 'factor_pipeline')\n",
    "    schedule_function(rebalance,\n",
    "                      date_rules.week_start(),\n",
    "                      time_rules.market_open(),\n",
    "                      calendar=calendars.US_EQUITIES)\n",
    "    context.set_commission(commission.PerShare(cost=.01, min_trade_cost=0))\n",
    "    context.set_slippage(slippage.VolumeShareSlippage())\n",
    "\n",
    "\n",
    "def before_trading_start(context, data):\n",
    "    \"\"\"Run factor pipeline\"\"\"\n",
    "    context.factor_data = pipeline_output('factor_pipeline')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the result `DataFrame` using `_` which captures the last cell output (only works when you run it right after the above cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:04:32.952603Z",
     "start_time": "2021-04-15T20:04:32.950968Z"
    }
   },
   "outputs": [],
   "source": [
    "result = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:04:32.970108Z",
     "start_time": "2021-04-15T20:04:32.953503Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 755 entries, 2015-01-02 21:00:00+00:00 to 2017-12-29 21:00:00+00:00\n",
      "Data columns (total 39 columns):\n",
      " #   Column                   Non-Null Count  Dtype              \n",
      "---  ------                   --------------  -----              \n",
      " 0   period_open              755 non-null    datetime64[ns, UTC]\n",
      " 1   period_close             755 non-null    datetime64[ns, UTC]\n",
      " 2   shorts_count             755 non-null    int64              \n",
      " 3   long_value               755 non-null    float64            \n",
      " 4   short_value              755 non-null    float64            \n",
      " 5   long_exposure            755 non-null    float64            \n",
      " 6   pnl                      755 non-null    float64            \n",
      " 7   capital_used             755 non-null    float64            \n",
      " 8   short_exposure           755 non-null    float64            \n",
      " 9   orders                   755 non-null    object             \n",
      " 10  transactions             755 non-null    object             \n",
      " 11  positions                755 non-null    object             \n",
      " 12  gross_leverage           755 non-null    float64            \n",
      " 13  starting_exposure        755 non-null    float64            \n",
      " 14  net_leverage             755 non-null    float64            \n",
      " 15  ending_exposure          755 non-null    float64            \n",
      " 16  starting_value           755 non-null    float64            \n",
      " 17  ending_value             755 non-null    float64            \n",
      " 18  starting_cash            755 non-null    float64            \n",
      " 19  ending_cash              755 non-null    float64            \n",
      " 20  portfolio_value          755 non-null    float64            \n",
      " 21  returns                  755 non-null    float64            \n",
      " 22  longs_count              755 non-null    int64              \n",
      " 23  algo_volatility          754 non-null    float64            \n",
      " 24  benchmark_period_return  755 non-null    float64            \n",
      " 25  benchmark_volatility     754 non-null    float64            \n",
      " 26  alpha                    0 non-null      object             \n",
      " 27  beta                     0 non-null      object             \n",
      " 28  sharpe                   753 non-null    float64            \n",
      " 29  sortino                  753 non-null    float64            \n",
      " 30  max_drawdown             755 non-null    float64            \n",
      " 31  max_leverage             755 non-null    float64            \n",
      " 32  excess_return            755 non-null    float64            \n",
      " 33  treasury_period_return   755 non-null    float64            \n",
      " 34  trading_days             755 non-null    int64              \n",
      " 35  period_label             755 non-null    object             \n",
      " 36  algorithm_period_return  755 non-null    float64            \n",
      " 37  factor_data              754 non-null    object             \n",
      " 38  prices                   754 non-null    object             \n",
      "dtypes: datetime64[ns, UTC](2), float64(26), int64(3), object(8)\n",
      "memory usage: 235.9+ KB\n"
     ]
    }
   ],
   "source": [
    "result.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:04:33.445657Z",
     "start_time": "2021-04-15T20:04:32.970905Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, figsize=(14,6))\n",
    "result.algorithm_period_return.plot(ax=axes[0], title='Cumulative Return')\n",
    "result.algo_volatility.plot(ax=axes[1], title='Volatility')\n",
    "sns.despine()\n",
    "fig.tight_layout();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "512px"
   },
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}