example/TQ_乖離率策略.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "70b58cb5",
   "metadata": {},
   "source": [
    "# 乖離率策略\n",
    "\n",
    "## 交易邏輯\n",
    "乖離率為當期股價與均線的垂直距離。\n",
    "* 當距離正越大時，股價出現反轉機會上升，應當賣出。\n",
    "* 當距離負越大時，股價出現反轉機會上升，應當買入。\n",
    "\n",
    "## 交易策略\n",
    "加入收盤價與過去最高低價格比較做為第二層濾網。\n",
    "* 若收盤價低於過去 7 天的最低價，同時出現負乖離時，隔日開盤買入。\n",
    "* 若收盤價高於過去 7 天最最高價，同時出現正乖離時，隔日開盤賣出。\n",
    "\n",
    "## 參考來源\n",
    "[官網文章: 乖離率策略]:https://www.tejwin.com/insight/%E4%B9%96%E9%9B%A2%E7%8E%87%E4%BA%A4%E6%98%93%E7%AD%96%E7%95%A5/\n",
    "[官網文章: 乖離率策略]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da01fe1e",
   "metadata": {},
   "source": [
    "## 導入資料與套件\n",
    "\n",
    "資料導入階段，我們使用 `os.environ` 設置環境變數，分別設定:\n",
    "\n",
    "1. TEJAPI_BASE: 設定 tej api 網域名稱。\n",
    "2. TEJAPI_KEY: 為購買 TQuant Lab 隨附的 api key，用於驗證個人身分。\n",
    "3. mdate: 所欲抓取資料的時間範圍，格式為 \"西元年份月份日期 西元年份月份日期\"。\n",
    "4. ticker: 所欲抓取資料的股價代碼。\n",
    "\n",
    "於本次案例我們抓取台積電資料，時間區間設定為 2005-07-02 到 2023-07-02 之間。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c19b81e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merging daily equity files:\n",
      "Currently used TEJ API key call quota 516/9223372036854775807 (0.0%)\n",
      "Currently used TEJ API key data quota 61121197/9223372036854775807 (0.0%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-02-07 03:35:10.490051] INFO: zipline.data.bundles.core: Ingesting tquant.\n",
      "[2024-02-07 03:35:14.476018] INFO: zipline.data.bundles.core: Ingest tquant successfully.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import tejapi\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'] = '20050702 20230702'\n",
    "os.environ['ticker'] = '2330'\n",
    "\n",
    "!zipline ingest -b tquant"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4420265e",
   "metadata": {},
   "source": [
    "## 編輯交易策略\n",
    "\n",
    "### 導入所需套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e90a0f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zipline.api import (set_slippage, \n",
    "                         set_commission, \n",
    "                         set_benchmark, \n",
    "                         attach_pipeline, \n",
    "                         symbol, \n",
    "                         pipeline_output,\n",
    "                         record, \n",
    "                         order,\n",
    "                         order_target\n",
    "                        )\n",
    "from zipline.pipeline.filters import StaticSids\n",
    "from zipline.finance import slippage, commission\n",
    "from zipline import run_algorithm\n",
    "from zipline.pipeline import CustomFactor, Pipeline\n",
    "from zipline.pipeline.data import EquityPricing\n",
    "from zipline.pipeline.factors import ExponentialWeightedMovingAverage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2237466",
   "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",
    "* 股價的 7 日指數移動平均\n",
    "* 過去 7 日的股價最高價 (自定義 factor 函式: `NdaysMaxHigh`)\n",
    "* 過去 7 日的股價最低價 (自定義 factor 函式: `NdaysMinLow`)\n",
    "* 當日收盤價\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32111f09",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_pipeline():\n",
    "    ema = ExponentialWeightedMovingAverage(inputs = [EquityPricing.close],window_length = 7,decay_rate = 1/7)\n",
    "    high = NdaysMaxHigh(inputs = [EquityPricing.close], window_length = 8) # window_length 設定為 8，因為 factor 會包含當日價格。\n",
    "    low = NdaysMinLow(inputs = [EquityPricing.close], window_length = 8)\n",
    "    close = EquityPricing.close.latest\n",
    "    return Pipeline(\n",
    "        columns = {\n",
    "            'ema':ema,\n",
    "            'highesthigh':high,\n",
    "            'lowestlow':low,\n",
    "            'latest':close\n",
    "        }\n",
    "    )\n",
    "class NdaysMaxHigh(CustomFactor):\n",
    "    def compute(self, today, assets, out, data):\n",
    "        out[:] = np.nanmax(data[:-2], axis=0)\n",
    "class NdaysMinLow(CustomFactor):\n",
    "    def compute(self, today, assets, out, data):\n",
    "        out[:] = np.nanmin(data[:-2], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b636af70",
   "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",
    "* 買入持有台積電的報酬作為基準\n",
    "* 將 Pipeline 導入交易流程中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6360fc74",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize(context):\n",
    "    set_slippage(slippage.VolumeShareSlippage())\n",
    "    set_commission(commission.PerShare(cost=0.00285))\n",
    "    set_benchmark(symbol('2330'))\n",
    "    attach_pipeline(make_pipeline(), 'mystrategy')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebdae7b1",
   "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，其中:\n",
    "\n",
    "* condition1: 當日收盤價大於過去 7 日最高價且產生正乖離時，產生賣出訊號。\n",
    "* condition2: 當日收盤價小於過去 7 日最低價且產生負乖離時，產生買入訊號。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2e5030e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_data(context, data):\n",
    "    \n",
    "    pipe = pipeline_output('mystrategy')\n",
    "    \n",
    "    for i in pipe.index:\n",
    "        ema = pipe.loc[i, 'ema']\n",
    "        highesthigh = pipe.loc[i, 'highesthigh']\n",
    "        lowestlow = pipe.loc[i, 'lowestlow']\n",
    "        close = pipe.loc[i, 'latest']\n",
    "        bias = close - ema\n",
    "        residual_position = context.portfolio.positions[i].amount # 當日該資產的股數\n",
    "        condition1 = (close > highesthigh) and (bias > 0) and (residual_position > 0) # 賣出訊號\n",
    "        condition2 = (close < lowestlow) and (bias < 0) # 買入訊號\n",
    "        \n",
    "        record( # 用以紀錄以下資訊至最終產出的 result 表格中\n",
    "            con1 = condition1,\n",
    "            con2 = condition2,\n",
    "            price = close,\n",
    "            ema = ema,\n",
    "            bias = bias,\n",
    "            highesthigh = highesthigh,\n",
    "            lowestlow = lowestlow\n",
    "        )\n",
    "    \n",
    "        if condition1:\n",
    "            order_target(i, 0)\n",
    "        elif condition2:\n",
    "            order(i, 10)\n",
    "        else:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422c15b3",
   "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": "983b9ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "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",
    "    ax2.plot( # 繪製買入訊號\n",
    "        perf.index[perf.con2],\n",
    "        perf.loc[perf.con2, 'price'],\n",
    "        '^',\n",
    "        markersize=5,\n",
    "        color='red'\n",
    "    )\n",
    "    ax2.plot( # 繪製賣出訊號\n",
    "        perf.index[perf.con1],\n",
    "        perf.loc[perf.con1, '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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9870b25e",
   "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 2015-01-05 到 2022-07-02，所使用資料集為 *tquant*，初始資金為 10,000 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": "a5c2ffad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = run_algorithm(start = pd.Timestamp('20150106', tz='UTC'),\n",
    "                       end = pd.Timestamp('20221125', tz='UTC'),\n",
    "                       initialize=initialize,\n",
    "                       bundle='tquant',\n",
    "                       analyze=analyze,\n",
    "                       capital_base=1e4,\n",
    "                       handle_data = handle_data\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be13d238",
   "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>period_open</th>\n",
       "      <th>period_close</th>\n",
       "      <th>starting_cash</th>\n",
       "      <th>ending_cash</th>\n",
       "      <th>capital_used</th>\n",
       "      <th>portfolio_value</th>\n",
       "      <th>longs_count</th>\n",
       "      <th>pnl</th>\n",
       "      <th>positions</th>\n",
       "      <th>shorts_count</th>\n",
       "      <th>...</th>\n",
       "      <th>max_drawdown</th>\n",
       "      <th>max_leverage</th>\n",
       "      <th>trading_days</th>\n",
       "      <th>period_label</th>\n",
       "      <th>benchmark_period_return</th>\n",
       "      <th>excess_return</th>\n",
       "      <th>benchmark_volatility</th>\n",
       "      <th>algo_volatility</th>\n",
       "      <th>treasury_period_return</th>\n",
       "      <th>algorithm_period_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-06 13:30:00+08:00</th>\n",
       "      <td>2015-01-06 09:01:00+08:00</td>\n",
       "      <td>2015-01-06 13:30:00+08:00</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>-0.043009</td>\n",
       "      <td>0.043009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07 13:30:00+08:00</th>\n",
       "      <td>2015-01-07 09:01:00+08:00</td>\n",
       "      <td>2015-01-07 13:30:00+08:00</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>-0.039429</td>\n",
       "      <td>0.039429</td>\n",
       "      <td>0.524768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 13:30:00+08:00</th>\n",
       "      <td>2015-01-08 09:01:00+08:00</td>\n",
       "      <td>2015-01-08 13:30:00+08:00</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>8619.971500</td>\n",
       "      <td>-1380.0285</td>\n",
       "      <td>9999.971500</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0285</td>\n",
       "      <td>[{'sid': Equity(0 [2330]), 'amount': 10, 'cost...</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>0.138000</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>-0.010750</td>\n",
       "      <td>0.010747</td>\n",
       "      <td>0.586026</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09 13:30:00+08:00</th>\n",
       "      <td>2015-01-09 09:01:00+08:00</td>\n",
       "      <td>2015-01-09 13:30:00+08:00</td>\n",
       "      <td>8619.971500</td>\n",
       "      <td>7279.943000</td>\n",
       "      <td>-1340.0285</td>\n",
       "      <td>9959.943000</td>\n",
       "      <td>1</td>\n",
       "      <td>-40.0285</td>\n",
       "      <td>[{'sid': Equity(0 [2330]), 'amount': 20, 'cost...</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004006</td>\n",
       "      <td>0.269078</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>-0.039429</td>\n",
       "      <td>0.035423</td>\n",
       "      <td>0.520633</td>\n",
       "      <td>0.031764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.004006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12 13:30:00+08:00</th>\n",
       "      <td>2015-01-12 09:01:00+08:00</td>\n",
       "      <td>2015-01-12 13:30:00+08:00</td>\n",
       "      <td>7279.943000</td>\n",
       "      <td>7279.943000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9919.943000</td>\n",
       "      <td>1</td>\n",
       "      <td>-40.0000</td>\n",
       "      <td>[{'sid': Equity(0 [2330]), 'amount': 20, 'cost...</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008006</td>\n",
       "      <td>0.269078</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>-0.053759</td>\n",
       "      <td>0.045753</td>\n",
       "      <td>0.452459</td>\n",
       "      <td>0.034854</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.008006</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>2022-11-21 13:30:00+08:00</th>\n",
       "      <td>2022-11-21 09:01:00+08:00</td>\n",
       "      <td>2022-11-21 13:30:00+08:00</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.487303</td>\n",
       "      <td>4.620656</td>\n",
       "      <td>1928</td>\n",
       "      <td>2022-11</td>\n",
       "      <td>3.424819</td>\n",
       "      <td>-3.004240</td>\n",
       "      <td>0.252670</td>\n",
       "      <td>0.298093</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.420579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-22 13:30:00+08:00</th>\n",
       "      <td>2022-11-22 09:01:00+08:00</td>\n",
       "      <td>2022-11-22 13:30:00+08:00</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.487303</td>\n",
       "      <td>4.620656</td>\n",
       "      <td>1929</td>\n",
       "      <td>2022-11</td>\n",
       "      <td>3.507440</td>\n",
       "      <td>-3.086861</td>\n",
       "      <td>0.252686</td>\n",
       "      <td>0.298016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.420579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-23 13:30:00+08:00</th>\n",
       "      <td>2022-11-23 09:01:00+08:00</td>\n",
       "      <td>2022-11-23 13:30:00+08:00</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.487303</td>\n",
       "      <td>4.620656</td>\n",
       "      <td>1930</td>\n",
       "      <td>2022-11</td>\n",
       "      <td>3.516621</td>\n",
       "      <td>-3.096042</td>\n",
       "      <td>0.252621</td>\n",
       "      <td>0.297939</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.420579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-24 13:30:00+08:00</th>\n",
       "      <td>2022-11-24 09:01:00+08:00</td>\n",
       "      <td>2022-11-24 13:30:00+08:00</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.487303</td>\n",
       "      <td>4.620656</td>\n",
       "      <td>1931</td>\n",
       "      <td>2022-11</td>\n",
       "      <td>3.553341</td>\n",
       "      <td>-3.132762</td>\n",
       "      <td>0.252569</td>\n",
       "      <td>0.297861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.420579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-25 13:30:00+08:00</th>\n",
       "      <td>2022-11-25 09:01:00+08:00</td>\n",
       "      <td>2022-11-25 13:30:00+08:00</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>14205.790999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.487303</td>\n",
       "      <td>4.620656</td>\n",
       "      <td>1932</td>\n",
       "      <td>2022-11</td>\n",
       "      <td>3.571701</td>\n",
       "      <td>-3.151122</td>\n",
       "      <td>0.252506</td>\n",
       "      <td>0.297784</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.420579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1932 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        period_open              period_close  \\\n",
       "2015-01-06 13:30:00+08:00 2015-01-06 09:01:00+08:00 2015-01-06 13:30:00+08:00   \n",
       "2015-01-07 13:30:00+08:00 2015-01-07 09:01:00+08:00 2015-01-07 13:30:00+08:00   \n",
       "2015-01-08 13:30:00+08:00 2015-01-08 09:01:00+08:00 2015-01-08 13:30:00+08:00   \n",
       "2015-01-09 13:30:00+08:00 2015-01-09 09:01:00+08:00 2015-01-09 13:30:00+08:00   \n",
       "2015-01-12 13:30:00+08:00 2015-01-12 09:01:00+08:00 2015-01-12 13:30:00+08:00   \n",
       "...                                             ...                       ...   \n",
       "2022-11-21 13:30:00+08:00 2022-11-21 09:01:00+08:00 2022-11-21 13:30:00+08:00   \n",
       "2022-11-22 13:30:00+08:00 2022-11-22 09:01:00+08:00 2022-11-22 13:30:00+08:00   \n",
       "2022-11-23 13:30:00+08:00 2022-11-23 09:01:00+08:00 2022-11-23 13:30:00+08:00   \n",
       "2022-11-24 13:30:00+08:00 2022-11-24 09:01:00+08:00 2022-11-24 13:30:00+08:00   \n",
       "2022-11-25 13:30:00+08:00 2022-11-25 09:01:00+08:00 2022-11-25 13:30:00+08:00   \n",
       "\n",
       "                           starting_cash   ending_cash  capital_used  \\\n",
       "2015-01-06 13:30:00+08:00   10000.000000  10000.000000        0.0000   \n",
       "2015-01-07 13:30:00+08:00   10000.000000  10000.000000        0.0000   \n",
       "2015-01-08 13:30:00+08:00   10000.000000   8619.971500    -1380.0285   \n",
       "2015-01-09 13:30:00+08:00    8619.971500   7279.943000    -1340.0285   \n",
       "2015-01-12 13:30:00+08:00    7279.943000   7279.943000        0.0000   \n",
       "...                                  ...           ...           ...   \n",
       "2022-11-21 13:30:00+08:00   14205.790999  14205.790999        0.0000   \n",
       "2022-11-22 13:30:00+08:00   14205.790999  14205.790999        0.0000   \n",
       "2022-11-23 13:30:00+08:00   14205.790999  14205.790999        0.0000   \n",
       "2022-11-24 13:30:00+08:00   14205.790999  14205.790999        0.0000   \n",
       "2022-11-25 13:30:00+08:00   14205.790999  14205.790999        0.0000   \n",
       "\n",
       "                           portfolio_value  longs_count      pnl  \\\n",
       "2015-01-06 13:30:00+08:00     10000.000000            0   0.0000   \n",
       "2015-01-07 13:30:00+08:00     10000.000000            0   0.0000   \n",
       "2015-01-08 13:30:00+08:00      9999.971500            1  -0.0285   \n",
       "2015-01-09 13:30:00+08:00      9959.943000            1 -40.0285   \n",
       "2015-01-12 13:30:00+08:00      9919.943000            1 -40.0000   \n",
       "...                                    ...          ...      ...   \n",
       "2022-11-21 13:30:00+08:00     14205.790999            0   0.0000   \n",
       "2022-11-22 13:30:00+08:00     14205.790999            0   0.0000   \n",
       "2022-11-23 13:30:00+08:00     14205.790999            0   0.0000   \n",
       "2022-11-24 13:30:00+08:00     14205.790999            0   0.0000   \n",
       "2022-11-25 13:30:00+08:00     14205.790999            0   0.0000   \n",
       "\n",
       "                                                                   positions  \\\n",
       "2015-01-06 13:30:00+08:00                                                 []   \n",
       "2015-01-07 13:30:00+08:00                                                 []   \n",
       "2015-01-08 13:30:00+08:00  [{'sid': Equity(0 [2330]), 'amount': 10, 'cost...   \n",
       "2015-01-09 13:30:00+08:00  [{'sid': Equity(0 [2330]), 'amount': 20, 'cost...   \n",
       "2015-01-12 13:30:00+08:00  [{'sid': Equity(0 [2330]), 'amount': 20, 'cost...   \n",
       "...                                                                      ...   \n",
       "2022-11-21 13:30:00+08:00                                                 []   \n",
       "2022-11-22 13:30:00+08:00                                                 []   \n",
       "2022-11-23 13:30:00+08:00                                                 []   \n",
       "2022-11-24 13:30:00+08:00                                                 []   \n",
       "2022-11-25 13:30:00+08:00                                                 []   \n",
       "\n",
       "                           shorts_count  ...  max_drawdown  max_leverage  \\\n",
       "2015-01-06 13:30:00+08:00             0  ...      0.000000      0.000000   \n",
       "2015-01-07 13:30:00+08:00             0  ...      0.000000      0.000000   \n",
       "2015-01-08 13:30:00+08:00             0  ...     -0.000003      0.138000   \n",
       "2015-01-09 13:30:00+08:00             0  ...     -0.004006      0.269078   \n",
       "2015-01-12 13:30:00+08:00             0  ...     -0.008006      0.269078   \n",
       "...                                 ...  ...           ...           ...   \n",
       "2022-11-21 13:30:00+08:00             0  ...     -0.487303      4.620656   \n",
       "2022-11-22 13:30:00+08:00             0  ...     -0.487303      4.620656   \n",
       "2022-11-23 13:30:00+08:00             0  ...     -0.487303      4.620656   \n",
       "2022-11-24 13:30:00+08:00             0  ...     -0.487303      4.620656   \n",
       "2022-11-25 13:30:00+08:00             0  ...     -0.487303      4.620656   \n",
       "\n",
       "                           trading_days  period_label  \\\n",
       "2015-01-06 13:30:00+08:00             1       2015-01   \n",
       "2015-01-07 13:30:00+08:00             2       2015-01   \n",
       "2015-01-08 13:30:00+08:00             3       2015-01   \n",
       "2015-01-09 13:30:00+08:00             4       2015-01   \n",
       "2015-01-12 13:30:00+08:00             5       2015-01   \n",
       "...                                 ...           ...   \n",
       "2022-11-21 13:30:00+08:00          1928       2022-11   \n",
       "2022-11-22 13:30:00+08:00          1929       2022-11   \n",
       "2022-11-23 13:30:00+08:00          1930       2022-11   \n",
       "2022-11-24 13:30:00+08:00          1931       2022-11   \n",
       "2022-11-25 13:30:00+08:00          1932       2022-11   \n",
       "\n",
       "                           benchmark_period_return  excess_return  \\\n",
       "2015-01-06 13:30:00+08:00                -0.043009       0.043009   \n",
       "2015-01-07 13:30:00+08:00                -0.039429       0.039429   \n",
       "2015-01-08 13:30:00+08:00                -0.010750       0.010747   \n",
       "2015-01-09 13:30:00+08:00                -0.039429       0.035423   \n",
       "2015-01-12 13:30:00+08:00                -0.053759       0.045753   \n",
       "...                                            ...            ...   \n",
       "2022-11-21 13:30:00+08:00                 3.424819      -3.004240   \n",
       "2022-11-22 13:30:00+08:00                 3.507440      -3.086861   \n",
       "2022-11-23 13:30:00+08:00                 3.516621      -3.096042   \n",
       "2022-11-24 13:30:00+08:00                 3.553341      -3.132762   \n",
       "2022-11-25 13:30:00+08:00                 3.571701      -3.151122   \n",
       "\n",
       "                           benchmark_volatility  algo_volatility  \\\n",
       "2015-01-06 13:30:00+08:00                   NaN              NaN   \n",
       "2015-01-07 13:30:00+08:00              0.524768         0.000000   \n",
       "2015-01-08 13:30:00+08:00              0.586026         0.000026   \n",
       "2015-01-09 13:30:00+08:00              0.520633         0.031764   \n",
       "2015-01-12 13:30:00+08:00              0.452459         0.034854   \n",
       "...                                         ...              ...   \n",
       "2022-11-21 13:30:00+08:00              0.252670         0.298093   \n",
       "2022-11-22 13:30:00+08:00              0.252686         0.298016   \n",
       "2022-11-23 13:30:00+08:00              0.252621         0.297939   \n",
       "2022-11-24 13:30:00+08:00              0.252569         0.297861   \n",
       "2022-11-25 13:30:00+08:00              0.252506         0.297784   \n",
       "\n",
       "                           treasury_period_return  algorithm_period_return  \n",
       "2015-01-06 13:30:00+08:00                     0.0                 0.000000  \n",
       "2015-01-07 13:30:00+08:00                     0.0                 0.000000  \n",
       "2015-01-08 13:30:00+08:00                     0.0                -0.000003  \n",
       "2015-01-09 13:30:00+08:00                     0.0                -0.004006  \n",
       "2015-01-12 13:30:00+08:00                     0.0                -0.008006  \n",
       "...                                           ...                      ...  \n",
       "2022-11-21 13:30:00+08:00                     0.0                 0.420579  \n",
       "2022-11-22 13:30:00+08:00                     0.0                 0.420579  \n",
       "2022-11-23 13:30:00+08:00                     0.0                 0.420579  \n",
       "2022-11-24 13:30:00+08:00                     0.0                 0.420579  \n",
       "2022-11-25 13:30:00+08:00                     0.0                 0.420579  \n",
       "\n",
       "[1932 rows x 46 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results # 績效與交易明細"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411e2903",
   "metadata": {},
   "source": [
    "# 績效評估與視覺化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0e32166e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyfolio as pf \n",
    "returns, positions, transactions = pf.utils.extract_rets_pos_txn_from_zipline(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72cc9ddd",
   "metadata": {},
   "source": [
    "### 交易策略投組報酬率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32c45177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-06 00:00:00+00:00    0.000000\n",
       "2015-01-07 00:00:00+00:00    0.000000\n",
       "2015-01-08 00:00:00+00:00   -0.000003\n",
       "2015-01-09 00:00:00+00:00   -0.004003\n",
       "2015-01-12 00:00:00+00:00   -0.004016\n",
       "                               ...   \n",
       "2022-11-21 00:00:00+00:00    0.000000\n",
       "2022-11-22 00:00:00+00:00    0.000000\n",
       "2022-11-23 00:00:00+00:00    0.000000\n",
       "2022-11-24 00:00:00+00:00    0.000000\n",
       "2022-11-25 00:00:00+00:00    0.000000\n",
       "Name: returns, Length: 1932, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d89f7663",
   "metadata": {},
   "source": [
    "### 交易策略部位數量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a39f03bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sid</th>\n",
       "      <th>Equity(0 [2330])</th>\n",
       "      <th>cash</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-08 00:00:00+00:00</th>\n",
       "      <td>1380.0</td>\n",
       "      <td>8619.971500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09 00:00:00+00:00</th>\n",
       "      <td>2680.0</td>\n",
       "      <td>7279.943000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12 00:00:00+00:00</th>\n",
       "      <td>2640.0</td>\n",
       "      <td>7279.943000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-13 00:00:00+00:00</th>\n",
       "      <td>2650.0</td>\n",
       "      <td>7279.943000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14 00:00:00+00:00</th>\n",
       "      <td>3900.0</td>\n",
       "      <td>5979.914500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-27 00:00:00+00:00</th>\n",
       "      <td>23130.0</td>\n",
       "      <td>-8834.038001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-28 00:00:00+00:00</th>\n",
       "      <td>22770.0</td>\n",
       "      <td>-8834.038001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-31 00:00:00+00:00</th>\n",
       "      <td>23400.0</td>\n",
       "      <td>-8834.038001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01 00:00:00+00:00</th>\n",
       "      <td>23490.0</td>\n",
       "      <td>-8834.038001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-02 00:00:00+00:00</th>\n",
       "      <td>23700.0</td>\n",
       "      <td>-8834.038001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>814 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sid                        Equity(0 [2330])         cash\n",
       "index                                                   \n",
       "2015-01-08 00:00:00+00:00            1380.0  8619.971500\n",
       "2015-01-09 00:00:00+00:00            2680.0  7279.943000\n",
       "2015-01-12 00:00:00+00:00            2640.0  7279.943000\n",
       "2015-01-13 00:00:00+00:00            2650.0  7279.943000\n",
       "2015-01-14 00:00:00+00:00            3900.0  5979.914500\n",
       "...                                     ...          ...\n",
       "2022-10-27 00:00:00+00:00           23130.0 -8834.038001\n",
       "2022-10-28 00:00:00+00:00           22770.0 -8834.038001\n",
       "2022-10-31 00:00:00+00:00           23400.0 -8834.038001\n",
       "2022-11-01 00:00:00+00:00           23490.0 -8834.038001\n",
       "2022-11-02 00:00:00+00:00           23700.0 -8834.038001\n",
       "\n",
       "[814 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105d5203",
   "metadata": {},
   "source": [
    "### 交易策略交易紀錄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "390ac3cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2015-01-08 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>138.0</td>\n",
       "      <td>80c170abd9f6465695574fcaafc35397</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>2015-01-08 13:30:00+08:00</td>\n",
       "      <td>-1380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>134.0</td>\n",
       "      <td>6ffac82b7aa94fd7ad2daca3de2537c8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>2015-01-09 13:30:00+08:00</td>\n",
       "      <td>-1340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>130.0</td>\n",
       "      <td>6c5a7adeca254c7e9564f65d3a78cca5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>2015-01-14 13:30:00+08:00</td>\n",
       "      <td>-1300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>137.0</td>\n",
       "      <td>80e996fb0d65445eaa67bcdc08583f0b</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>2015-01-16 13:30:00+08:00</td>\n",
       "      <td>-1370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-21 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0529b0a8abea448f8e42116dd271afc4</td>\n",
       "      <td>-40</td>\n",
       "      <td>None</td>\n",
       "      <td>2015-01-21 13:30:00+08:00</td>\n",
       "      <td>5640.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-17 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>397.0</td>\n",
       "      <td>a735d20be6d64140af141b49a38e017d</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-10-17 13:30:00+08:00</td>\n",
       "      <td>-3970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-25 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>371.0</td>\n",
       "      <td>33c145e7f29b4473a30011f2705cb967</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-10-25 13:30:00+08:00</td>\n",
       "      <td>-3710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-26 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>376.0</td>\n",
       "      <td>1cfd9c299ebd402aaf96030fb6ecfa02</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-10-26 13:30:00+08:00</td>\n",
       "      <td>-3760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-27 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>385.5</td>\n",
       "      <td>9828c96c67b941ada9b762c57c1cc9bb</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-10-27 13:30:00+08:00</td>\n",
       "      <td>-3855.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-03 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>384.0</td>\n",
       "      <td>4c6e3aa7fcd44bb08e8afc6bafdca9a6</td>\n",
       "      <td>-60</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-11-03 13:30:00+08:00</td>\n",
       "      <td>23040.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>417 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        sid            symbol  price  \\\n",
       "2015-01-08 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  138.0   \n",
       "2015-01-09 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  134.0   \n",
       "2015-01-14 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  130.0   \n",
       "2015-01-16 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  137.0   \n",
       "2015-01-21 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  141.0   \n",
       "...                                     ...               ...    ...   \n",
       "2022-10-17 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  397.0   \n",
       "2022-10-25 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  371.0   \n",
       "2022-10-26 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  376.0   \n",
       "2022-10-27 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  385.5   \n",
       "2022-11-03 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  384.0   \n",
       "\n",
       "                                                   order_id  amount  \\\n",
       "2015-01-08 05:30:00+00:00  80c170abd9f6465695574fcaafc35397      10   \n",
       "2015-01-09 05:30:00+00:00  6ffac82b7aa94fd7ad2daca3de2537c8      10   \n",
       "2015-01-14 05:30:00+00:00  6c5a7adeca254c7e9564f65d3a78cca5      10   \n",
       "2015-01-16 05:30:00+00:00  80e996fb0d65445eaa67bcdc08583f0b      10   \n",
       "2015-01-21 05:30:00+00:00  0529b0a8abea448f8e42116dd271afc4     -40   \n",
       "...                                                     ...     ...   \n",
       "2022-10-17 05:30:00+00:00  a735d20be6d64140af141b49a38e017d      10   \n",
       "2022-10-25 05:30:00+00:00  33c145e7f29b4473a30011f2705cb967      10   \n",
       "2022-10-26 05:30:00+00:00  1cfd9c299ebd402aaf96030fb6ecfa02      10   \n",
       "2022-10-27 05:30:00+00:00  9828c96c67b941ada9b762c57c1cc9bb      10   \n",
       "2022-11-03 05:30:00+00:00  4c6e3aa7fcd44bb08e8afc6bafdca9a6     -60   \n",
       "\n",
       "                          commission                        dt  txn_dollars  \n",
       "2015-01-08 05:30:00+00:00       None 2015-01-08 13:30:00+08:00      -1380.0  \n",
       "2015-01-09 05:30:00+00:00       None 2015-01-09 13:30:00+08:00      -1340.0  \n",
       "2015-01-14 05:30:00+00:00       None 2015-01-14 13:30:00+08:00      -1300.0  \n",
       "2015-01-16 05:30:00+00:00       None 2015-01-16 13:30:00+08:00      -1370.0  \n",
       "2015-01-21 05:30:00+00:00       None 2015-01-21 13:30:00+08:00       5640.0  \n",
       "...                              ...                       ...          ...  \n",
       "2022-10-17 05:30:00+00:00       None 2022-10-17 13:30:00+08:00      -3970.0  \n",
       "2022-10-25 05:30:00+00:00       None 2022-10-25 13:30:00+08:00      -3710.0  \n",
       "2022-10-26 05:30:00+00:00       None 2022-10-26 13:30:00+08:00      -3760.0  \n",
       "2022-10-27 05:30:00+00:00       None 2022-10-27 13:30:00+08:00      -3855.0  \n",
       "2022-11-03 05:30:00+00:00       None 2022-11-03 13:30:00+08:00      23040.0  \n",
       "\n",
       "[417 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06075b74",
   "metadata": {},
   "source": [
    "## 視覺化與績效評估\n",
    "\n",
    "### 生成 pyfolio 所需資料表\n",
    "\n",
    "之後我們使用 pyfolio 進行績效視覺化與評估，首先使用 `extract_rets_pos_txn_from_zipline()` 先將上述的 __results__ 資料表細分成以下部分:\n",
    "\n",
    "* return: 投組每日報酬\n",
    "* positions: 持有部位資料表\n",
    "* transactions: 交易明細資料表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9dddb7c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "benchmark_rets = results['benchmark_return'] \n",
    "from pyfolio.utils import extract_rets_pos_txn_from_zipline\n",
    "returns, positions, transactions = extract_rets_pos_txn_from_zipline(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f8515e8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-06 00:00:00+00:00    0.000000\n",
       "2015-01-07 00:00:00+00:00    0.000000\n",
       "2015-01-08 00:00:00+00:00   -0.000003\n",
       "2015-01-09 00:00:00+00:00   -0.004003\n",
       "2015-01-12 00:00:00+00:00   -0.004016\n",
       "Name: returns, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0fd3b05",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sid</th>\n",
       "      <th>Equity(0 [2330])</th>\n",
       "      <th>cash</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-08 00:00:00+00:00</th>\n",
       "      <td>1380.0</td>\n",
       "      <td>8619.9715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09 00:00:00+00:00</th>\n",
       "      <td>2680.0</td>\n",
       "      <td>7279.9430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12 00:00:00+00:00</th>\n",
       "      <td>2640.0</td>\n",
       "      <td>7279.9430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-13 00:00:00+00:00</th>\n",
       "      <td>2650.0</td>\n",
       "      <td>7279.9430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14 00:00:00+00:00</th>\n",
       "      <td>3900.0</td>\n",
       "      <td>5979.9145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sid                        Equity(0 [2330])       cash\n",
       "index                                                 \n",
       "2015-01-08 00:00:00+00:00            1380.0  8619.9715\n",
       "2015-01-09 00:00:00+00:00            2680.0  7279.9430\n",
       "2015-01-12 00:00:00+00:00            2640.0  7279.9430\n",
       "2015-01-13 00:00:00+00:00            2650.0  7279.9430\n",
       "2015-01-14 00:00:00+00:00            3900.0  5979.9145"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4e81caa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2015-01-08 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>138.0</td>\n",
       "      <td>80c170abd9f6465695574fcaafc35397</td>\n",
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       "      <td>2015-01-08 13:30:00+08:00</td>\n",
       "      <td>-1380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>134.0</td>\n",
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       "      <td>-1340.0</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-14 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>130.0</td>\n",
       "      <td>6c5a7adeca254c7e9564f65d3a78cca5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>2015-01-14 13:30:00+08:00</td>\n",
       "      <td>-1300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>137.0</td>\n",
       "      <td>80e996fb0d65445eaa67bcdc08583f0b</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>2015-01-16 13:30:00+08:00</td>\n",
       "      <td>-1370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-21 05:30:00+00:00</th>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>Equity(0 [2330])</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0529b0a8abea448f8e42116dd271afc4</td>\n",
       "      <td>-40</td>\n",
       "      <td>None</td>\n",
       "      <td>2015-01-21 13:30:00+08:00</td>\n",
       "      <td>5640.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        sid            symbol  price  \\\n",
       "2015-01-08 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  138.0   \n",
       "2015-01-09 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  134.0   \n",
       "2015-01-14 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  130.0   \n",
       "2015-01-16 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  137.0   \n",
       "2015-01-21 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])  141.0   \n",
       "\n",
       "                                                   order_id  amount  \\\n",
       "2015-01-08 05:30:00+00:00  80c170abd9f6465695574fcaafc35397      10   \n",
       "2015-01-09 05:30:00+00:00  6ffac82b7aa94fd7ad2daca3de2537c8      10   \n",
       "2015-01-14 05:30:00+00:00  6c5a7adeca254c7e9564f65d3a78cca5      10   \n",
       "2015-01-16 05:30:00+00:00  80e996fb0d65445eaa67bcdc08583f0b      10   \n",
       "2015-01-21 05:30:00+00:00  0529b0a8abea448f8e42116dd271afc4     -40   \n",
       "\n",
       "                          commission                        dt  txn_dollars  \n",
       "2015-01-08 05:30:00+00:00       None 2015-01-08 13:30:00+08:00      -1380.0  \n",
       "2015-01-09 05:30:00+00:00       None 2015-01-09 13:30:00+08:00      -1340.0  \n",
       "2015-01-14 05:30:00+00:00       None 2015-01-14 13:30:00+08:00      -1300.0  \n",
       "2015-01-16 05:30:00+00:00       None 2015-01-16 13:30:00+08:00      -1370.0  \n",
       "2015-01-21 05:30:00+00:00       None 2015-01-21 13:30:00+08:00       5640.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ae576d",
   "metadata": {},
   "source": [
    "### 製作投資績效表\n",
    "\n",
    "使用 `show_perf_stats()` 製作績效表，可以快速計算投資常用績效與風險指標。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "00638d16",
   "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>2015-01-06</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2022-11-25</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>92</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>4.686%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>42.058%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>29.778%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-48.73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>2.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>45.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-3.716%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross leverage</th>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily turnover</th>\n",
       "      <td>14.987%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pyfolio as pf \n",
    "perf_stats = pf.plotting.show_perf_stats(\n",
    "        returns,\n",
    "        benchmark_rets,\n",
    "        positions=positions,\n",
    "        transactions=transactions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731e0eb0",
   "metadata": {},
   "source": [
    "### 繪製基準 (benchmark) 與投組累積報酬率\n",
    "\n",
    "使用 `plot_rolling_returns()` 繪製，本案例的基準為台積電買入持有。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aff7a27a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Cumulative returns'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pf.plotting.plot_rolling_returns(returns, factor_returns=benchmark_rets)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:zipline-tej_v11]",
   "language": "python",
   "name": "conda-env-zipline-tej_v11-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}