布林通道交易策略.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "85743b0c",
   "metadata": {},
   "source": [
    "# 布林通道實戰\n",
    "\n",
    "## 交易邏輯\n",
    "\n",
    "* 根據統計學的常態分佈理論，有 95.44% 的樣本都會位於正負兩個標準差這個區間，因此當股價超出這區間時，產生反轉的機率會大幅上升。\n",
    "* 我們可以把移動平均、正二標準差、負二標準差，轉換成中軌、上軌、下軌\n",
    "\n",
    "## 交易策略\n",
    "\n",
    "* 當今天的收盤價觸碰到上軌且持有部位時，隔日賣出。\n",
    "* 當今天的收盤價觸碰到下軌且現金部位大於零時，隔日買入。\n",
    "* 當今天的收盤價觸碰到下軌、現金部位大於零且當日收盤價低於上次買入訊號收盤價時，隔日加碼一單位。\n",
    "\n",
    "## 資料來源\n",
    "\n",
    "[布林通道交易策略](https://www.tejwin.com/insight/%e5%b8%83%e6%9e%97%e9%80%9a%e9%81%93%e4%ba%a4%e6%98%93%e7%ad%96%e7%95%a5/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad4d0b7",
   "metadata": {},
   "source": [
    "## 編輯交易策略\n",
    "\n",
    "### 載入所需套件與輸入資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af2cb3dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merging daily equity files:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2023-08-01 06:43:51.357085] INFO: zipline.data.bundles.core: Ingesting tquant.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import tejapi\n",
    "import os \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "os.environ['TEJAPI_BASE'] = 'https://api.tej.com.tw'\n",
    "os.environ['TEJAPI_KEY'] = 'YOUR KEY'\n",
    "os.environ['mdate'] = '20210401 20221231'\n",
    "os.environ['ticker'] = '2409'\n",
    "\n",
    "# 使用 ingest 將股價資料導入暫存，並且命名該股票組合 (bundle) 為 tquant\n",
    "!zipline ingest -b tquant "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defc2ee8",
   "metadata": {},
   "source": [
    "### 載入所需套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a491743c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zipline.api import set_slippage, set_commission, set_benchmark, attach_pipeline, order, order_target, symbol, pipeline_output, record\n",
    "from zipline.finance import commission, slippage\n",
    "from zipline.data import bundles\n",
    "from zipline import run_algorithm\n",
    "from zipline.pipeline import Pipeline\n",
    "from zipline.pipeline.filters import StaticAssets\n",
    "from zipline.pipeline.factors import BollingerBands\n",
    "from zipline.pipeline.data import EquityPricing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "863bf5f4",
   "metadata": {},
   "source": [
    "### 建立 Pipeline 函式\n",
    "\n",
    "`Pipeline()` 提供使用者快速處理多檔標的的量化指標與價量資料的功能，於本次案例我們用以處理:\n",
    "\n",
    "* 過去 20 日布林通道上軌\n",
    "* 過去 20 日布林通道中軌\n",
    "* 過去 20 日布林通道下軌\n",
    "* 當期收盤價"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff0d96fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_pipeline():\n",
    "    \n",
    "    perf = BollingerBands(inputs=[EquityPricing.close], window_length=20, k=2)\n",
    "    upper,middle,lower = perf.upper,perf.middle, perf.lower\n",
    "    curr_price = EquityPricing.close.latest\n",
    "     \n",
    "    return Pipeline(\n",
    "        columns = {\n",
    "            'upper':  upper,\n",
    "            'middle':  middle,\n",
    "            'lower':  lower,\n",
    "            'curr_price':curr_price\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71a457f6",
   "metadata": {},
   "source": [
    "### 建立 initialize 函式\n",
    "\n",
    "`inintialize` 函式用於定義交易開始前的每日交易環境，與此例中我們設置:\n",
    "\n",
    "* 流動性滑價\n",
    "* 交易手續費\n",
    "* 買入持有友達的報酬作為基準\n",
    "* 將 Pipeline 導入交易流程中\n",
    "* 設定 __context.last_signal_price__ 紀錄上次買入訊號的收盤價"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bfe9965e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize(context):\n",
    "    context.last_buy_price = 0\n",
    "    set_slippage(slippage.VolumeShareSlippage())\n",
    "    set_commission(commission.PerShare(cost=0.00285))\n",
    "    set_benchmark(symbol('2409'))\n",
    "    attach_pipeline(make_pipeline(), 'mystrategy')\n",
    "    context.last_signal_price = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce1d1f77",
   "metadata": {},
   "source": [
    "### 建立 handle_date 函式\n",
    "\n",
    "`handle_data` 函式用於處理每天的交易策略或行動。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "28f5019c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_data(context, data):\n",
    "    out_dir = pipeline_output('mystrategy')\n",
    "    for i in out_dir.index: \n",
    "        upper = out_dir.loc[i, 'upper']\n",
    "        middle = out_dir.loc[i, 'middle']\n",
    "        lower = out_dir.loc[i, 'lower']\n",
    "        curr_price = out_dir.loc[i, 'curr_price']\n",
    "        cash_position = context.portfolio.cash\n",
    "        stock_position = context.portfolio.positions[i].amount\n",
    "        \n",
    "        buy, sell = False, False\n",
    "        record(price = curr_price, upper = upper, lower = lower, buy = buy, sell = sell)\n",
    "        \n",
    "        if stock_position == 0:\n",
    "            if (curr_price <= lower) and (cash_position >= curr_price * 1000):\n",
    "                order(i, 1000)\n",
    "                context.last_signal_price = curr_price\n",
    "                buy = True\n",
    "                record(buy = buy)\n",
    "        elif stock_position > 0:\n",
    "            if (curr_price <= lower) and (curr_price <= context.last_signal_price) and (cash_position >= curr_price * 1000):\n",
    "                order(i, 1000)\n",
    "                context.last_signal_price = curr_price\n",
    "                buy = True\n",
    "                record(buy = buy)\n",
    "            elif (curr_price >= upper):\n",
    "                order_target(i, 0)\n",
    "                context.last_signal_price = 0\n",
    "                sell = True\n",
    "                record(sell = sell)\n",
    "            else:\n",
    "                pass\n",
    "        else:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "538c14e2",
   "metadata": {},
   "source": [
    "### 建立 analyze 函式\n",
    "\n",
    "多半用於繪製績效圖表，於本案例使用 matplotlib 將視覺化買賣點與投組價值變化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "82a399a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze(context, perf):\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(211)\n",
    "    perf.portfolio_value.plot(ax=ax1)\n",
    "    ax1.set_ylabel(\"Portfolio value (NTD)\")\n",
    "    ax2 = fig.add_subplot(212)\n",
    "    ax2.set_ylabel(\"Price (NTD)\")\n",
    "    perf.price.plot(ax=ax2)\n",
    "    perf.upper.plot(ax=ax2)\n",
    "    perf.lower.plot(ax=ax2)\n",
    "    ax2.plot( # 繪製買入訊號\n",
    "        perf.index[perf.buy],\n",
    "        perf.loc[perf.buy, 'price'],\n",
    "        '^',\n",
    "        markersize=5,\n",
    "        color='red'\n",
    "    )\n",
    "    ax2.plot( # 繪製賣出訊號\n",
    "        perf.index[perf.sell],\n",
    "        perf.loc[perf.sell, 'price'],\n",
    "        'v',\n",
    "        markersize=5,\n",
    "        color='green'\n",
    "    )\n",
    "    plt.legend(loc=0)\n",
    "    plt.gcf().set_size_inches(18,8)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36336fc8",
   "metadata": {},
   "source": [
    "## 執行交易策略\n",
    "\n",
    "使用 `run_algorithm` 執行上述所編撰的交易策略，設置交易期間為 2021-06-01 到 2022-12-31，所使用資料集為 *tquant*，初始資金為 500,000 元。其中輸出的 __results__ 就是每日績效與交易的明細表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4afa5e8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\envs\\zipline-tej\\lib\\site-packages\\empyrical\\stats.py:706: RuntimeWarning: invalid value encountered in true_divide\n",
      "  np.divide(\n",
      "C:\\Users\\Admin\\anaconda3\\envs\\zipline-tej\\lib\\site-packages\\empyrical\\stats.py:797: RuntimeWarning: invalid value encountered in true_divide\n",
      "  np.divide(average_annual_return, annualized_downside_risk, out=out)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>period_open</th>\n",
       "      <th>period_close</th>\n",
       "      <th>longs_count</th>\n",
       "      <th>shorts_count</th>\n",
       "      <th>long_value</th>\n",
       "      <th>short_value</th>\n",
       "      <th>benchmark_return</th>\n",
       "      <th>long_exposure</th>\n",
       "      <th>treasury_return</th>\n",
       "      <th>pnl</th>\n",
       "      <th>...</th>\n",
       "      <th>treasury_period_return</th>\n",
       "      <th>algorithm_period_return</th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>sortino</th>\n",
       "      <th>max_drawdown</th>\n",
       "      <th>max_leverage</th>\n",
       "      <th>trading_days</th>\n",
       "      <th>period_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-06-01 13:30:00+08:00</th>\n",
       "      <td>2021-06-01 09:01:00+08:00</td>\n",
       "      <td>2021-06-01 13:30:00+08:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.005791</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-02 13:30:00+08:00</th>\n",
       "      <td>2021-06-02 09:01:00+08:00</td>\n",
       "      <td>2021-06-02 13:30:00+08:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.029201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-03 13:30:00+08:00</th>\n",
       "      <td>2021-06-03 09:01:00+08:00</td>\n",
       "      <td>2021-06-03 13:30:00+08:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-04 13:30:00+08:00</th>\n",
       "      <td>2021-06-04 09:01:00+08:00</td>\n",
       "      <td>2021-06-04 13:30:00+08:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.018112</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-07 13:30:00+08:00</th>\n",
       "      <td>2021-06-07 09:01:00+08:00</td>\n",
       "      <td>2021-06-07 13:30:00+08:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.036892</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>2021-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-26 13:30:00+08:00</th>\n",
       "      <td>2022-12-26 09:01:00+08:00</td>\n",
       "      <td>2022-12-26 13:30:00+08:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>93000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006494</td>\n",
       "      <td>93000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027703</td>\n",
       "      <td>0.036098</td>\n",
       "      <td>0.069894</td>\n",
       "      <td>0.420172</td>\n",
       "      <td>0.647045</td>\n",
       "      <td>-0.056005</td>\n",
       "      <td>0.270017</td>\n",
       "      <td>391</td>\n",
       "      <td>2022-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27 13:30:00+08:00</th>\n",
       "      <td>2022-12-27 09:01:00+08:00</td>\n",
       "      <td>2022-12-27 13:30:00+08:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>93000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>93000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027703</td>\n",
       "      <td>0.036004</td>\n",
       "      <td>0.069893</td>\n",
       "      <td>0.419636</td>\n",
       "      <td>0.646220</td>\n",
       "      <td>-0.056005</td>\n",
       "      <td>0.270017</td>\n",
       "      <td>392</td>\n",
       "      <td>2022-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28 13:30:00+08:00</th>\n",
       "      <td>2022-12-28 09:01:00+08:00</td>\n",
       "      <td>2022-12-28 13:30:00+08:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>90900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.022581</td>\n",
       "      <td>90900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023503</td>\n",
       "      <td>0.034303</td>\n",
       "      <td>0.070108</td>\n",
       "      <td>0.358691</td>\n",
       "      <td>0.550359</td>\n",
       "      <td>-0.056005</td>\n",
       "      <td>0.270017</td>\n",
       "      <td>393</td>\n",
       "      <td>2022-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29 13:30:00+08:00</th>\n",
       "      <td>2022-12-29 09:01:00+08:00</td>\n",
       "      <td>2022-12-29 13:30:00+08:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>90600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003300</td>\n",
       "      <td>90600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022903</td>\n",
       "      <td>0.033981</td>\n",
       "      <td>0.070113</td>\n",
       "      <td>0.349728</td>\n",
       "      <td>0.536571</td>\n",
       "      <td>-0.056005</td>\n",
       "      <td>0.270017</td>\n",
       "      <td>394</td>\n",
       "      <td>2022-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30 13:30:00+08:00</th>\n",
       "      <td>2022-12-30 09:01:00+08:00</td>\n",
       "      <td>2022-12-30 13:30:00+08:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.006623</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021703</td>\n",
       "      <td>0.033430</td>\n",
       "      <td>0.070130</td>\n",
       "      <td>0.332247</td>\n",
       "      <td>0.509606</td>\n",
       "      <td>-0.056005</td>\n",
       "      <td>0.270017</td>\n",
       "      <td>395</td>\n",
       "      <td>2022-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>395 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        period_open              period_close  \\\n",
       "2021-06-01 13:30:00+08:00 2021-06-01 09:01:00+08:00 2021-06-01 13:30:00+08:00   \n",
       "2021-06-02 13:30:00+08:00 2021-06-02 09:01:00+08:00 2021-06-02 13:30:00+08:00   \n",
       "2021-06-03 13:30:00+08:00 2021-06-03 09:01:00+08:00 2021-06-03 13:30:00+08:00   \n",
       "2021-06-04 13:30:00+08:00 2021-06-04 09:01:00+08:00 2021-06-04 13:30:00+08:00   \n",
       "2021-06-07 13:30:00+08:00 2021-06-07 09:01:00+08:00 2021-06-07 13:30:00+08:00   \n",
       "...                                             ...                       ...   \n",
       "2022-12-26 13:30:00+08:00 2022-12-26 09:01:00+08:00 2022-12-26 13:30:00+08:00   \n",
       "2022-12-27 13:30:00+08:00 2022-12-27 09:01:00+08:00 2022-12-27 13:30:00+08:00   \n",
       "2022-12-28 13:30:00+08:00 2022-12-28 09:01:00+08:00 2022-12-28 13:30:00+08:00   \n",
       "2022-12-29 13:30:00+08:00 2022-12-29 09:01:00+08:00 2022-12-29 13:30:00+08:00   \n",
       "2022-12-30 13:30:00+08:00 2022-12-30 09:01:00+08:00 2022-12-30 13:30:00+08:00   \n",
       "\n",
       "                           longs_count  shorts_count  long_value  short_value  \\\n",
       "2021-06-01 13:30:00+08:00            0             0         0.0          0.0   \n",
       "2021-06-02 13:30:00+08:00            0             0         0.0          0.0   \n",
       "2021-06-03 13:30:00+08:00            0             0         0.0          0.0   \n",
       "2021-06-04 13:30:00+08:00            0             0         0.0          0.0   \n",
       "2021-06-07 13:30:00+08:00            0             0         0.0          0.0   \n",
       "...                                ...           ...         ...          ...   \n",
       "2022-12-26 13:30:00+08:00            1             0     93000.0          0.0   \n",
       "2022-12-27 13:30:00+08:00            1             0     93000.0          0.0   \n",
       "2022-12-28 13:30:00+08:00            1             0     90900.0          0.0   \n",
       "2022-12-29 13:30:00+08:00            1             0     90600.0          0.0   \n",
       "2022-12-30 13:30:00+08:00            1             0     90000.0          0.0   \n",
       "\n",
       "                           benchmark_return  long_exposure  treasury_return  \\\n",
       "2021-06-01 13:30:00+08:00         -0.005791            0.0              0.0   \n",
       "2021-06-02 13:30:00+08:00         -0.029201            0.0              0.0   \n",
       "2021-06-03 13:30:00+08:00         -0.003986            0.0              0.0   \n",
       "2021-06-04 13:30:00+08:00         -0.018112            0.0              0.0   \n",
       "2021-06-07 13:30:00+08:00          0.036892            0.0              0.0   \n",
       "...                                     ...            ...              ...   \n",
       "2022-12-26 13:30:00+08:00          0.006494        93000.0              0.0   \n",
       "2022-12-27 13:30:00+08:00          0.000000        93000.0              0.0   \n",
       "2022-12-28 13:30:00+08:00         -0.022581        90900.0              0.0   \n",
       "2022-12-29 13:30:00+08:00         -0.003300        90600.0              0.0   \n",
       "2022-12-30 13:30:00+08:00         -0.006623        90000.0              0.0   \n",
       "\n",
       "                              pnl  ...  treasury_period_return  \\\n",
       "2021-06-01 13:30:00+08:00     0.0  ...                     0.0   \n",
       "2021-06-02 13:30:00+08:00     0.0  ...                     0.0   \n",
       "2021-06-03 13:30:00+08:00     0.0  ...                     0.0   \n",
       "2021-06-04 13:30:00+08:00     0.0  ...                     0.0   \n",
       "2021-06-07 13:30:00+08:00     0.0  ...                     0.0   \n",
       "...                           ...  ...                     ...   \n",
       "2022-12-26 13:30:00+08:00   600.0  ...                     0.0   \n",
       "2022-12-27 13:30:00+08:00     0.0  ...                     0.0   \n",
       "2022-12-28 13:30:00+08:00 -2100.0  ...                     0.0   \n",
       "2022-12-29 13:30:00+08:00  -300.0  ...                     0.0   \n",
       "2022-12-30 13:30:00+08:00  -600.0  ...                     0.0   \n",
       "\n",
       "                           algorithm_period_return     alpha      beta  \\\n",
       "2021-06-01 13:30:00+08:00                 0.000000       NaN       NaN   \n",
       "2021-06-02 13:30:00+08:00                 0.000000  0.000000  0.000000   \n",
       "2021-06-03 13:30:00+08:00                 0.000000  0.000000  0.000000   \n",
       "2021-06-04 13:30:00+08:00                 0.000000  0.000000  0.000000   \n",
       "2021-06-07 13:30:00+08:00                 0.000000  0.000000  0.000000   \n",
       "...                                            ...       ...       ...   \n",
       "2022-12-26 13:30:00+08:00                 0.027703  0.036098  0.069894   \n",
       "2022-12-27 13:30:00+08:00                 0.027703  0.036004  0.069893   \n",
       "2022-12-28 13:30:00+08:00                 0.023503  0.034303  0.070108   \n",
       "2022-12-29 13:30:00+08:00                 0.022903  0.033981  0.070113   \n",
       "2022-12-30 13:30:00+08:00                 0.021703  0.033430  0.070130   \n",
       "\n",
       "                             sharpe   sortino  max_drawdown  max_leverage  \\\n",
       "2021-06-01 13:30:00+08:00       NaN       NaN      0.000000      0.000000   \n",
       "2021-06-02 13:30:00+08:00       NaN       NaN      0.000000      0.000000   \n",
       "2021-06-03 13:30:00+08:00       NaN       NaN      0.000000      0.000000   \n",
       "2021-06-04 13:30:00+08:00       NaN       NaN      0.000000      0.000000   \n",
       "2021-06-07 13:30:00+08:00       NaN       NaN      0.000000      0.000000   \n",
       "...                             ...       ...           ...           ...   \n",
       "2022-12-26 13:30:00+08:00  0.420172  0.647045     -0.056005      0.270017   \n",
       "2022-12-27 13:30:00+08:00  0.419636  0.646220     -0.056005      0.270017   \n",
       "2022-12-28 13:30:00+08:00  0.358691  0.550359     -0.056005      0.270017   \n",
       "2022-12-29 13:30:00+08:00  0.349728  0.536571     -0.056005      0.270017   \n",
       "2022-12-30 13:30:00+08:00  0.332247  0.509606     -0.056005      0.270017   \n",
       "\n",
       "                           trading_days  period_label  \n",
       "2021-06-01 13:30:00+08:00             1       2021-06  \n",
       "2021-06-02 13:30:00+08:00             2       2021-06  \n",
       "2021-06-03 13:30:00+08:00             3       2021-06  \n",
       "2021-06-04 13:30:00+08:00             4       2021-06  \n",
       "2021-06-07 13:30:00+08:00             5       2021-06  \n",
       "...                                 ...           ...  \n",
       "2022-12-26 13:30:00+08:00           391       2022-12  \n",
       "2022-12-27 13:30:00+08:00           392       2022-12  \n",
       "2022-12-28 13:30:00+08:00           393       2022-12  \n",
       "2022-12-29 13:30:00+08:00           394       2022-12  \n",
       "2022-12-30 13:30:00+08:00           395       2022-12  \n",
       "\n",
       "[395 rows x 44 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = run_algorithm(\n",
    "    start = pd.Timestamp('2021-06-01', tz='UTC'),\n",
    "    end = pd.Timestamp('2022-12-31', tz ='UTC'),\n",
    "    initialize=initialize,\n",
    "    bundle='tquant',\n",
    "    analyze=analyze,\n",
    "    capital_base=5e5,\n",
    "    handle_data = handle_data\n",
    ")\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a2f011",
   "metadata": {},
   "source": [
    "## 績效評估\n",
    "\n",
    "使用 `extract_rets_pos_txn_from_zipline()` 計算報酬、部位與交易紀錄。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aa8a20fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyfolio as pf \n",
    "returns, positions, transactions = pf.utils.extract_rets_pos_txn_from_zipline(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85468ce9",
   "metadata": {},
   "source": [
    "### 報酬紀錄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0b3362a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022-12-26 00:00:00+08:00    0.001169\n",
       "2022-12-27 00:00:00+08:00    0.000000\n",
       "2022-12-28 00:00:00+08:00   -0.004087\n",
       "2022-12-29 00:00:00+08:00   -0.000586\n",
       "2022-12-30 00:00:00+08:00   -0.001173\n",
       "Name: returns, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75de1559",
   "metadata": {},
   "source": [
    "### 部位紀錄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "76af3c51",
   "metadata": {},
   "outputs": [
    {
     "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>sid</th>\n",
       "      <th>Equity(0 [2409])</th>\n",
       "      <th>cash</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-07-16 00:00:00+08:00</th>\n",
       "      <td>21350.0</td>\n",
       "      <td>478647.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-19 00:00:00+08:00</th>\n",
       "      <td>20900.0</td>\n",
       "      <td>478647.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-20 00:00:00+08:00</th>\n",
       "      <td>20100.0</td>\n",
       "      <td>478647.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-21 00:00:00+08:00</th>\n",
       "      <td>19150.0</td>\n",
       "      <td>478647.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-22 00:00:00+08:00</th>\n",
       "      <td>39400.0</td>\n",
       "      <td>458944.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sid                        Equity(0 [2409])       cash\n",
       "index                                                 \n",
       "2021-07-16 00:00:00+08:00           21350.0  478647.15\n",
       "2021-07-19 00:00:00+08:00           20900.0  478647.15\n",
       "2021-07-20 00:00:00+08:00           20100.0  478647.15\n",
       "2021-07-21 00:00:00+08:00           19150.0  478647.15\n",
       "2021-07-22 00:00:00+08:00           39400.0  458944.30"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b938283",
   "metadata": {},
   "source": [
    "### 交易紀錄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "db0908ff",
   "metadata": {},
   "outputs": [
    {
     "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>sid</th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "      <th>order_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>commission</th>\n",
       "      <th>dt</th>\n",
       "      <th>txn_dollars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-07-16 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2409])</td>\n",
       "      <td>Equity(0 [2409])</td>\n",
       "      <td>21.35</td>\n",
       "      <td>aae2682f78ce4dd693812403358cc2a6</td>\n",
       "      <td>1000</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-07-16 13:30:00+08:00</td>\n",
       "      <td>-21350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-22 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2409])</td>\n",
       "      <td>Equity(0 [2409])</td>\n",
       "      <td>19.70</td>\n",
       "      <td>8c8204dce99d4d10837d8ffb6de9da59</td>\n",
       "      <td>1000</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-07-22 13:30:00+08:00</td>\n",
       "      <td>-19700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-23 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2409])</td>\n",
       "      <td>Equity(0 [2409])</td>\n",
       "      <td>20.70</td>\n",
       "      <td>6d32fd87cf56413d91055dae2e449b29</td>\n",
       "      <td>1000</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-07-23 13:30:00+08:00</td>\n",
       "      <td>-20700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-06 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2409])</td>\n",
       "      <td>Equity(0 [2409])</td>\n",
       "      <td>21.60</td>\n",
       "      <td>2229b782dd154ec880943e71557f1b98</td>\n",
       "      <td>-3000</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-08-06 13:30:00+08:00</td>\n",
       "      <td>64799.999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-24 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2409])</td>\n",
       "      <td>Equity(0 [2409])</td>\n",
       "      <td>17.80</td>\n",
       "      <td>8d0b1c57e8f44fa8860219a762788876</td>\n",
       "      <td>1000</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-08-24 13:30:00+08:00</td>\n",
       "      <td>-17800.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        sid            symbol  price  \\\n",
       "2021-07-16 05:30:00+00:00  Equity(0 [2409])  Equity(0 [2409])  21.35   \n",
       "2021-07-22 05:30:00+00:00  Equity(0 [2409])  Equity(0 [2409])  19.70   \n",
       "2021-07-23 05:30:00+00:00  Equity(0 [2409])  Equity(0 [2409])  20.70   \n",
       "2021-08-06 05:30:00+00:00  Equity(0 [2409])  Equity(0 [2409])  21.60   \n",
       "2021-08-24 05:30:00+00:00  Equity(0 [2409])  Equity(0 [2409])  17.80   \n",
       "\n",
       "                                                   order_id  amount  \\\n",
       "2021-07-16 05:30:00+00:00  aae2682f78ce4dd693812403358cc2a6    1000   \n",
       "2021-07-22 05:30:00+00:00  8c8204dce99d4d10837d8ffb6de9da59    1000   \n",
       "2021-07-23 05:30:00+00:00  6d32fd87cf56413d91055dae2e449b29    1000   \n",
       "2021-08-06 05:30:00+00:00  2229b782dd154ec880943e71557f1b98   -3000   \n",
       "2021-08-24 05:30:00+00:00  8d0b1c57e8f44fa8860219a762788876    1000   \n",
       "\n",
       "                          commission                        dt   txn_dollars  \n",
       "2021-07-16 05:30:00+00:00       None 2021-07-16 13:30:00+08:00 -21350.000000  \n",
       "2021-07-22 05:30:00+00:00       None 2021-07-22 13:30:00+08:00 -19700.000000  \n",
       "2021-07-23 05:30:00+00:00       None 2021-07-23 13:30:00+08:00 -20700.000000  \n",
       "2021-08-06 05:30:00+00:00       None 2021-08-06 13:30:00+08:00  64799.999998  \n",
       "2021-08-24 05:30:00+00:00       None 2021-08-24 13:30:00+08:00 -17800.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16d05987",
   "metadata": {},
   "source": [
    "### 繪製基準 (benchmark) 與投組累積報酬率\n",
    "\n",
    "使用 `plot_rolling_returns()` 繪製，本案例的基準為友達買入持有。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0d612a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Cumulative returns'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "benchmark_rets = results['benchmark_return'] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e27b36b",
   "metadata": {},
   "source": [
    "### 製作績效表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c2ecd3a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2021-06-01</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2022-12-30</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>18</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>1.379%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>2.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>4.415%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-5.6%</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>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>9.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-0.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross leverage</th>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily turnover</th>\n",
       "      <td>5.361%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pf.plotting.show_perf_stats(\n",
    "        returns,\n",
    "        benchmark_rets,\n",
    "        positions=positions,\n",
    "        transactions=transactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7be01f21",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f71b90e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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