example/TQ_MACD交易策略.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "81f35904",
   "metadata": {},
   "source": [
    "# MACD 交易策略\n",
    "\n",
    "## 交易邏輯\n",
    "* 利用兩條不同速度的快慢EMA(指數移動平均線)交錯來判斷股價走勢。\n",
    "* 比較兩條長短天數的指數移動平均線(EMA)，相減算出 差離值(DIF)後，對差離值再進行一次指數移動平均線的計算。\n",
    "* EMA(n)=(前一日EMA(n) × (n-1)+今日收盤價 × 2) ÷ (n+1)\n",
    "* EMA(m)=(前一日EMA(m) × (m-1)+今日收盤價 × 2) ÷ (m+1)\n",
    "* DIF=EMA(n)－EMA(m)\n",
    "* MACD(x)=(前一日xMACD × (x-1)+DIF × 2) ÷ (x+1)\n",
    "* 其中n, m, x分別代表快線天數、慢線天數與DIF的x日指數移動平均線\n",
    "* ※短期EMA常使用12日、長期EMA常使用26日、DIF常使用9日來比較。\n",
    "\n",
    "## 交易策略\n",
    "* 當快線往上突破慢線，且MACD柱由負轉正，被視為黃金交叉(預測市場會上漲)，表示後續可能會有一波漲幅，視為買入信號。\n",
    "* 當快線向下跌破慢線，且MACD柱由正轉負，被視死亡交叉(預測市場會下跌)，表示後續可能會有一波跌幅可能，視為賣出信號。\n",
    "\n",
    "## 參考來源\n",
    "https://www.tejwin.com/insight/%e3%80%90%e9%87%8f%e5%8c%96%e5%88%86%e6%9e%90%e3%80%91macd%e6%8c%87%e6%a8%99%e5%9b%9e%e6%b8%ac%e5%af%a6%e6%88%b0/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "588a4c32",
   "metadata": {},
   "source": [
    "## 導入資料與套件\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: 為欲連結的網域名稱\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股價資料，時間區間設定為 2018-12-30 到 2023-05-26 之間。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "35d75d5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2023-08-23 05:37:21.335994] INFO: zipline.data.bundles.core: Ingesting tquant.\n",
      "\u001b[?25lMerging daily equity files:  [####################################]   \u001b[?25h\n"
     ]
    }
   ],
   "source": [
    "import os    \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "os.environ['TEJAPI_BASE'] = 'https://api.tej.com.tw'\n",
    "os.environ['TEJAPI_KEY'] = 'your key'\n",
    "os.environ['mdate'] = '20181230 20230526'\n",
    "os.environ['ticker'] = '2330'\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 \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27284b1d",
   "metadata": {},
   "source": [
    "## 編輯交易策略\n",
    "### 導入所需套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "4dfff004",
   "metadata": {},
   "outputs": [],
   "source": [
    "import talib\n",
    "from zipline.api import order_target, record, symbol\n",
    "from zipline.finance import commission, slippage\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "086b0c6a",
   "metadata": {},
   "source": [
    "## 建立Algorithm\n",
    "每個 zipline 程序都必須先定義兩個的`function`，`initialize()` 與 `handle_data()`。\n",
    "\n",
    "在執行程序前必須先呼叫`initialize()`定義 __context__ 變數。\n",
    "__context__ 是一個讓您能儲存回測資訊的 persistent namespace ，它提供了高度的自由讓使用者可以客製化自己的回測情境。\n",
    "\n",
    "初始化程序後，zipline 會在為每次事件調用 `handle_data()`函數。每次調用時，它都會傳遞相同的 __context__ 變數和一個叫做 __data__ 的 event-frame， 其中包含當前交易欄的開盤價、最高價、最低價和收盤價 (OHLC) 以及您的投資組合中每隻股票的交易量。\n",
    "\n",
    "最後，我們可以定義函式`analyze()`來對交易策略進行績效評估與視覺化。\n",
    "請注意，`analyze()`在執行程式時是一個 optional 的功能。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "173b8538",
   "metadata": {},
   "source": [
    "### 建立 initialize 函式\n",
    "\n",
    "`inintialize` 函式用於定義交易開始前的交易環境，與此例中我們設置:\n",
    "\n",
    "* 股票代號\n",
    "* 持有股數\n",
    "* 是否持有部位\n",
    "* 交易手續費\n",
    "* 流動性滑價\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c054ffa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize(context):\n",
    "    context.sym = symbol('2330')\n",
    "    context.i = 0\n",
    "    context.invested = False\n",
    "    context.set_commission(commission.PerDollar(cost=0.00285))\n",
    "    context.set_slippage(slippage.VolumeShareSlippage())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f116e0",
   "metadata": {},
   "source": [
    "## 建立 handle_data 函式\n",
    "`handle_data` 函式用於處理每天的交易策略或行動，其中:\n",
    "\n",
    "* condition1: 當快線往上突破慢線，且持有部位為0，視為買入信號。\n",
    "* condition2: 當快線向下跌破慢線，且持有部位不為0，視為賣出信號。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ae157a18",
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_data(context, data):\n",
    "    trailing_window = data.history(context.sym, 'price', 35, '1d')#35 = 26 + 9\n",
    "    if trailing_window.isnull().values.any():\n",
    "        return\n",
    "    \n",
    "    short_ema = talib.EMA(trailing_window.values, timeperiod = 12)\n",
    "    long_ema = talib.EMA(trailing_window.values, timeperiod = 26)\n",
    "    dif = short_ema - long_ema\n",
    "    MACD = talib.EMA(dif, timeperiod = 9)\n",
    "    bar = dif - MACD\n",
    "    \n",
    "    buy = False\n",
    "    sell = False\n",
    "\n",
    "    # Trading logic\n",
    "    #condition1     \n",
    "    if (dif[-2] < MACD[-2]) and (dif[-1] > MACD[-1]) and (bar[-2] < 0) and (bar[-1] > 0):\n",
    "\n",
    "        order_target(context.sym, 1000)\n",
    "        context.invested = True\n",
    "        buy = True\n",
    "        \n",
    "    #condition2\n",
    "    elif (dif[-2] > MACD[-2]) and (dif[-1] < MACD[-1]) and (bar[-2] > 0) and (bar[-1] < 0) and context.invested:\n",
    "        order_target(context.sym, 0)\n",
    "        context.invested = False\n",
    "        sell = True\n",
    "\n",
    "    # Save values for later inspection\n",
    "    record(TSMC = data.current(symbol('2330'), 'close'),         \n",
    "           dif = dif[-1],\n",
    "           MACD = MACD[-1],\n",
    "           bar = bar[-1],\n",
    "           buy = buy,\n",
    "           sell = sell)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea76f844",
   "metadata": {},
   "source": [
    "## 建立 analyze 函式\n",
    "\n",
    "在`analyze`中使用 __matplotlib.pyplot__ 繪製 投資組合價值折線圖 與 MACD 指標圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "5aca5aee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: this function can be removed if running\n",
    "# this algorithm on quantopian.com\n",
    "def analyze(context=None, results=None):\n",
    "    import matplotlib.pyplot as plt\n",
    "    import logbook\n",
    "    logbook.StderrHandler().push_application()\n",
    "    log = logbook.Logger('Algorithm')\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(211)\n",
    "    results.portfolio_value.plot(ax=ax1)\n",
    "    ax1.set_ylabel('Portfolio value (TWD)')\n",
    "\n",
    "    ax2 = fig.add_subplot(212, sharex = ax1)\n",
    "    ax2.set_ylabel('MACD')\n",
    "\n",
    "    # If data has been record()ed, then plot it.\n",
    "    # Otherwise, log the fact that no data has been recorded.\n",
    "    if 'dif' in results and 'MACD' in results:\n",
    "        results[['dif', 'MACD']].plot(ax=ax2)\n",
    "        ax2.plot(\n",
    "            results.index[results[\"buy\"] == True],\n",
    "            results.loc[results[\"buy\"] == True, 'MACD'],\n",
    "            '^',\n",
    "            markersize=10,\n",
    "            color='m',\n",
    "        )\n",
    "        ax2.plot(\n",
    "            results.index[results[\"sell\"] == True],\n",
    "            results.loc[results[\"sell\"] == True, 'MACD'],\n",
    "            'v',\n",
    "            markersize=10,\n",
    "            color='k',\n",
    "        )\n",
    "\n",
    "        ax3 = ax2.twinx()\n",
    "        colors = [\"red\" if i > 0 else \"green\" for i in results['bar']]\n",
    "        ax3.bar(results.index, results['bar'], color=colors, alpha=0.5, width=0.4, label='MACD Bar')\n",
    "        \n",
    "        lines, labels = ax2.get_legend_handles_labels()\n",
    "        bars, bar_labels = ax3.get_legend_handles_labels()\n",
    "        ax2.legend(lines + bars, labels + bar_labels, loc='upper right')\n",
    "        \n",
    "        plt.gcf().set_size_inches(18, 8)\n",
    "\n",
    "    else:\n",
    "        msg = 'TSMC - dif and MACD data not captured using record().'\n",
    "        ax2.annotate(msg, xy=(0.1, 0.5))\n",
    "        log.info(msg)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aaae68d",
   "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 2018-12-30 到 2023-05-26，初始資金為 100,000 元，所使用資料集為 *tquant*，其中輸出的變數 __results__ 就是每日績效與交易的明細表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "fe6a7080",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2023-08-23 07:01:06.107587] INFO: Performance: Equity(0 [2330]), cash_dividend amount: 8.0, pay_date: 2019-07-18, div_owed: 8000.0\n",
      "[2023-08-23 07:01:06.237962] INFO: Performance: Equity(0 [2330]), cash_dividend amount: 2.0, pay_date: 2019-10-17, div_owed: 2000.0\n",
      "[2023-08-23 07:01:06.368012] INFO: Performance: Equity(0 [2330]), cash_dividend amount: 2.5, pay_date: 2020-01-16, div_owed: 2500.0\n",
      "[2023-08-23 07:01:06.613176] INFO: Performance: Equity(0 [2330]), cash_dividend amount: 2.5, pay_date: 2020-07-16, div_owed: 2500.0\n",
      "[2023-08-23 07:01:06.746925] INFO: Performance: Equity(0 [2330]), cash_dividend amount: 2.5, pay_date: 2020-10-15, div_owed: 2500.0\n",
      "[2023-08-23 07:01:07.137171] INFO: Performance: Equity(0 [2330]), cash_dividend amount: 2.5, pay_date: 2021-07-15, div_owed: 2500.0\n",
      "[2023-08-23 07:01:07.287506] INFO: Performance: Equity(0 [2330]), cash_dividend amount: 2.75, pay_date: 2021-10-14, div_owed: 2750.0\n",
      "[2023-08-23 07:01:07.658584] INFO: Performance: Equity(0 [2330]), cash_dividend amount: 2.75, pay_date: 2022-07-14, div_owed: 2750.0\n",
      "[2023-08-23 07:01:07.933783] INFO: Performance: Equity(0 [2330]), cash_dividend amount: 2.75, pay_date: 2023-01-12, div_owed: 2750.0\n",
      "[2023-08-23 07:01:08.158949] INFO: zipline.finance.metrics.tracker: Simulated 1067 trading days\n",
      "first open: 2019-01-02 01:01:00+00:00\n",
      "last close: 2023-05-26 05:30:00+00:00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from zipline import run_algorithm\n",
    "\n",
    "start_date = pd.Timestamp('2018-12-30',tz='utc')\n",
    "end_date = pd.Timestamp('2023-05-26',tz='utc')\n",
    "\n",
    "results = run_algorithm(start= start_date,  \n",
    "                       end=end_date,\n",
    "                       initialize=initialize,                       \n",
    "                       capital_base=1e6,\n",
    "                       analyze=analyze,\n",
    "                       handle_data=handle_data,\n",
    "                       data_frequency='daily',\n",
    "                       bundle='tquant'\n",
    "                       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "e97e8e97",
   "metadata": {
    "scrolled": false
   },
   "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>benchmark_return</th>\n",
       "      <th>portfolio_value</th>\n",
       "      <th>positions</th>\n",
       "      <th>longs_count</th>\n",
       "      <th>gross_leverage</th>\n",
       "      <th>starting_exposure</th>\n",
       "      <th>short_exposure</th>\n",
       "      <th>pnl</th>\n",
       "      <th>...</th>\n",
       "      <th>algo_volatility</th>\n",
       "      <th>max_drawdown</th>\n",
       "      <th>max_leverage</th>\n",
       "      <th>treasury_period_return</th>\n",
       "      <th>TSMC</th>\n",
       "      <th>dif</th>\n",
       "      <th>MACD</th>\n",
       "      <th>bar</th>\n",
       "      <th>buy</th>\n",
       "      <th>sell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02 13:30:00+08:00</th>\n",
       "      <td>2019-01-02 09:01:00+08:00</td>\n",
       "      <td>2019-01-02 13:30:00+08:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03 13:30:00+08:00</th>\n",
       "      <td>2019-01-03 09:01:00+08:00</td>\n",
       "      <td>2019-01-03 13:30:00+08:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 13:30:00+08:00</th>\n",
       "      <td>2019-01-04 09:01:00+08:00</td>\n",
       "      <td>2019-01-04 13:30:00+08:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07 13:30:00+08:00</th>\n",
       "      <td>2019-01-07 09:01:00+08:00</td>\n",
       "      <td>2019-01-07 13:30:00+08:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08 13:30:00+08:00</th>\n",
       "      <td>2019-01-08 09:01:00+08:00</td>\n",
       "      <td>2019-01-08 13:30:00+08:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2023-05-22 13:30:00+08:00</th>\n",
       "      <td>2023-05-22 09:01:00+08:00</td>\n",
       "      <td>2023-05-22 13:30:00+08:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.294057e+06</td>\n",
       "      <td>[{'sid': Equity(0 [2330]), 'amount': 1000, 'co...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.410337</td>\n",
       "      <td>532000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084263</td>\n",
       "      <td>-0.177213</td>\n",
       "      <td>0.494637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>1.145142</td>\n",
       "      <td>-4.532980</td>\n",
       "      <td>5.678121</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-23 13:30:00+08:00</th>\n",
       "      <td>2023-05-23 09:01:00+08:00</td>\n",
       "      <td>2023-05-23 13:30:00+08:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.293057e+06</td>\n",
       "      <td>[{'sid': Equity(0 [2330]), 'amount': 1000, 'co...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.409881</td>\n",
       "      <td>531000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084225</td>\n",
       "      <td>-0.177213</td>\n",
       "      <td>0.494637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2.439485</td>\n",
       "      <td>-3.212420</td>\n",
       "      <td>5.651905</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-24 13:30:00+08:00</th>\n",
       "      <td>2023-05-24 09:01:00+08:00</td>\n",
       "      <td>2023-05-24 13:30:00+08:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.288057e+06</td>\n",
       "      <td>[{'sid': Equity(0 [2330]), 'amount': 1000, 'co...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.407591</td>\n",
       "      <td>530000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084209</td>\n",
       "      <td>-0.177213</td>\n",
       "      <td>0.494637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>3.097344</td>\n",
       "      <td>-1.945854</td>\n",
       "      <td>5.043198</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-25 13:30:00+08:00</th>\n",
       "      <td>2023-05-25 09:01:00+08:00</td>\n",
       "      <td>2023-05-25 13:30:00+08:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.306057e+06</td>\n",
       "      <td>[{'sid': Equity(0 [2330]), 'amount': 1000, 'co...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.415755</td>\n",
       "      <td>525000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084434</td>\n",
       "      <td>-0.177213</td>\n",
       "      <td>0.494637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>5.007625</td>\n",
       "      <td>-0.467820</td>\n",
       "      <td>5.475445</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-26 13:30:00+08:00</th>\n",
       "      <td>2023-05-26 09:01:00+08:00</td>\n",
       "      <td>2023-05-26 13:30:00+08:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.329057e+06</td>\n",
       "      <td>[{'sid': Equity(0 [2330]), 'amount': 1000, 'co...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.425866</td>\n",
       "      <td>543000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084814</td>\n",
       "      <td>-0.177213</td>\n",
       "      <td>0.494637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>566.0</td>\n",
       "      <td>8.301565</td>\n",
       "      <td>1.427145</td>\n",
       "      <td>6.874421</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1067 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        period_open              period_close  \\\n",
       "2019-01-02 13:30:00+08:00 2019-01-02 09:01:00+08:00 2019-01-02 13:30:00+08:00   \n",
       "2019-01-03 13:30:00+08:00 2019-01-03 09:01:00+08:00 2019-01-03 13:30:00+08:00   \n",
       "2019-01-04 13:30:00+08:00 2019-01-04 09:01:00+08:00 2019-01-04 13:30:00+08:00   \n",
       "2019-01-07 13:30:00+08:00 2019-01-07 09:01:00+08:00 2019-01-07 13:30:00+08:00   \n",
       "2019-01-08 13:30:00+08:00 2019-01-08 09:01:00+08:00 2019-01-08 13:30:00+08:00   \n",
       "...                                             ...                       ...   \n",
       "2023-05-22 13:30:00+08:00 2023-05-22 09:01:00+08:00 2023-05-22 13:30:00+08:00   \n",
       "2023-05-23 13:30:00+08:00 2023-05-23 09:01:00+08:00 2023-05-23 13:30:00+08:00   \n",
       "2023-05-24 13:30:00+08:00 2023-05-24 09:01:00+08:00 2023-05-24 13:30:00+08:00   \n",
       "2023-05-25 13:30:00+08:00 2023-05-25 09:01:00+08:00 2023-05-25 13:30:00+08:00   \n",
       "2023-05-26 13:30:00+08:00 2023-05-26 09:01:00+08:00 2023-05-26 13:30:00+08:00   \n",
       "\n",
       "                           benchmark_return  portfolio_value  \\\n",
       "2019-01-02 13:30:00+08:00               0.0     1.000000e+06   \n",
       "2019-01-03 13:30:00+08:00               0.0     1.000000e+06   \n",
       "2019-01-04 13:30:00+08:00               0.0     1.000000e+06   \n",
       "2019-01-07 13:30:00+08:00               0.0     1.000000e+06   \n",
       "2019-01-08 13:30:00+08:00               0.0     1.000000e+06   \n",
       "...                                     ...              ...   \n",
       "2023-05-22 13:30:00+08:00               0.0     1.294057e+06   \n",
       "2023-05-23 13:30:00+08:00               0.0     1.293057e+06   \n",
       "2023-05-24 13:30:00+08:00               0.0     1.288057e+06   \n",
       "2023-05-25 13:30:00+08:00               0.0     1.306057e+06   \n",
       "2023-05-26 13:30:00+08:00               0.0     1.329057e+06   \n",
       "\n",
       "                                                                   positions  \\\n",
       "2019-01-02 13:30:00+08:00                                                 []   \n",
       "2019-01-03 13:30:00+08:00                                                 []   \n",
       "2019-01-04 13:30:00+08:00                                                 []   \n",
       "2019-01-07 13:30:00+08:00                                                 []   \n",
       "2019-01-08 13:30:00+08:00                                                 []   \n",
       "...                                                                      ...   \n",
       "2023-05-22 13:30:00+08:00  [{'sid': Equity(0 [2330]), 'amount': 1000, 'co...   \n",
       "2023-05-23 13:30:00+08:00  [{'sid': Equity(0 [2330]), 'amount': 1000, 'co...   \n",
       "2023-05-24 13:30:00+08:00  [{'sid': Equity(0 [2330]), 'amount': 1000, 'co...   \n",
       "2023-05-25 13:30:00+08:00  [{'sid': Equity(0 [2330]), 'amount': 1000, 'co...   \n",
       "2023-05-26 13:30:00+08:00  [{'sid': Equity(0 [2330]), 'amount': 1000, 'co...   \n",
       "\n",
       "                           longs_count  gross_leverage  starting_exposure  \\\n",
       "2019-01-02 13:30:00+08:00            0        0.000000                0.0   \n",
       "2019-01-03 13:30:00+08:00            0        0.000000                0.0   \n",
       "2019-01-04 13:30:00+08:00            0        0.000000                0.0   \n",
       "2019-01-07 13:30:00+08:00            0        0.000000                0.0   \n",
       "2019-01-08 13:30:00+08:00            0        0.000000                0.0   \n",
       "...                                ...             ...                ...   \n",
       "2023-05-22 13:30:00+08:00            1        0.410337           532000.0   \n",
       "2023-05-23 13:30:00+08:00            1        0.409881           531000.0   \n",
       "2023-05-24 13:30:00+08:00            1        0.407591           530000.0   \n",
       "2023-05-25 13:30:00+08:00            1        0.415755           525000.0   \n",
       "2023-05-26 13:30:00+08:00            1        0.425866           543000.0   \n",
       "\n",
       "                           short_exposure      pnl  ... algo_volatility  \\\n",
       "2019-01-02 13:30:00+08:00             0.0      0.0  ...             NaN   \n",
       "2019-01-03 13:30:00+08:00             0.0      0.0  ...        0.000000   \n",
       "2019-01-04 13:30:00+08:00             0.0      0.0  ...        0.000000   \n",
       "2019-01-07 13:30:00+08:00             0.0      0.0  ...        0.000000   \n",
       "2019-01-08 13:30:00+08:00             0.0      0.0  ...        0.000000   \n",
       "...                                   ...      ...  ...             ...   \n",
       "2023-05-22 13:30:00+08:00             0.0  -1000.0  ...        0.084263   \n",
       "2023-05-23 13:30:00+08:00             0.0  -1000.0  ...        0.084225   \n",
       "2023-05-24 13:30:00+08:00             0.0  -5000.0  ...        0.084209   \n",
       "2023-05-25 13:30:00+08:00             0.0  18000.0  ...        0.084434   \n",
       "2023-05-26 13:30:00+08:00             0.0  23000.0  ...        0.084814   \n",
       "\n",
       "                           max_drawdown  max_leverage  treasury_period_return  \\\n",
       "2019-01-02 13:30:00+08:00      0.000000      0.000000                     0.0   \n",
       "2019-01-03 13:30:00+08:00      0.000000      0.000000                     0.0   \n",
       "2019-01-04 13:30:00+08:00      0.000000      0.000000                     0.0   \n",
       "2019-01-07 13:30:00+08:00      0.000000      0.000000                     0.0   \n",
       "2019-01-08 13:30:00+08:00      0.000000      0.000000                     0.0   \n",
       "...                                 ...           ...                     ...   \n",
       "2023-05-22 13:30:00+08:00     -0.177213      0.494637                     0.0   \n",
       "2023-05-23 13:30:00+08:00     -0.177213      0.494637                     0.0   \n",
       "2023-05-24 13:30:00+08:00     -0.177213      0.494637                     0.0   \n",
       "2023-05-25 13:30:00+08:00     -0.177213      0.494637                     0.0   \n",
       "2023-05-26 13:30:00+08:00     -0.177213      0.494637                     0.0   \n",
       "\n",
       "                            TSMC       dif      MACD       bar    buy   sell  \n",
       "2019-01-02 13:30:00+08:00    NaN       NaN       NaN       NaN    NaN    NaN  \n",
       "2019-01-03 13:30:00+08:00    NaN       NaN       NaN       NaN    NaN    NaN  \n",
       "2019-01-04 13:30:00+08:00    NaN       NaN       NaN       NaN    NaN    NaN  \n",
       "2019-01-07 13:30:00+08:00    NaN       NaN       NaN       NaN    NaN    NaN  \n",
       "2019-01-08 13:30:00+08:00    NaN       NaN       NaN       NaN    NaN    NaN  \n",
       "...                          ...       ...       ...       ...    ...    ...  \n",
       "2023-05-22 13:30:00+08:00  531.0  1.145142 -4.532980  5.678121  False  False  \n",
       "2023-05-23 13:30:00+08:00  530.0  2.439485 -3.212420  5.651905  False  False  \n",
       "2023-05-24 13:30:00+08:00  525.0  3.097344 -1.945854  5.043198  False  False  \n",
       "2023-05-25 13:30:00+08:00  543.0  5.007625 -0.467820  5.475445  False  False  \n",
       "2023-05-26 13:30:00+08:00  566.0  8.301565  1.427145  6.874421  False  False  \n",
       "\n",
       "[1067 rows x 45 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d42a026f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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