TQ_量能回測實戰.ipynb

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  {
   "cell_type": "markdown",
   "id": "f554dd27",
   "metadata": {},
   "source": [
    "# 量能回測實戰\n",
    "\n",
    "## 交易邏輯\n",
    "\n",
    "* 成交量增長象徵該檔股票受到投資者青睞，未來預期會有價格大幅波動情形\n",
    "\n",
    "## 交易策略\n",
    "\n",
    "* 當日成交量為前四日簡單移動平均的 2.5 倍時，視為買入訊號，於隔日買入。\n",
    "* 當日成交量為前五日簡單移動平均的 0.75 倍時，視為賣出訊號，於隔日賣出。\n",
    "\n",
    "## 資料來源\n",
    "\n",
    "[官網文章:量能回測實戰]:https://www.tejwin.com/?s=%E9%87%8F%E8%83%BD%E5%9B%9E%E6%B8%AC%E5%AF%A6%E6%88%B0\n",
    "[官網文章:量能回測實戰]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772d81df",
   "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_KEY: 為購買 Tquant Lab 隨附的 api key，用於驗證個人身分。\n",
    "2. 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",
    "3. ticker: 所欲抓取資料的股價代碼，其中的 _IR0001_ 為大盤指數代碼。\n",
    "\n",
    "於本次案例我們抓取台積電、創意、旺宏與台股大盤資料，時間區間設定為 2012-07-02 到 2022-07-02 之間。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "758ce99b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merging daily equity files:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2023-07-26 02:01:00.179343] 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",
    "\n",
    "os.environ['TEJAPI_BASE'] = 'https://api.tej.com.tw'\n",
    "os.environ['TEJAPI_KEY'] = 'IB4733e8TFbM780cNl5ezOKY2kNaBG'\n",
    "os.environ['mdate'] = '20120702 20220702'\n",
    "os.environ['ticker'] = 'IR0001 2330 3443 2337'\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": "0e258d7f",
   "metadata": {},
   "source": [
    "## 編輯交易策略\n",
    "\n",
    "### 導入所需套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2bbfb2f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zipline.api import set_slippage, set_commission, set_benchmark, attach_pipeline, order, order_target, symbol, pipeline_output\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 SimpleMovingAverage\n",
    "from zipline.pipeline.data import EquityPricing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cf596bf",
   "metadata": {},
   "source": [
    "### 建立 Pipeline 函式\n",
    "\n",
    "`Pipeline()` 提供使用者同時處理不同標的相關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",
    "* 各股成交量的四日簡單移動平均\n",
    "* 各股成交量的五日簡單移動平均\n",
    "* 各股的當日成交量\n",
    "\n",
    "此外搭配 `screen` 與 `StaticAssets` 讓我們在每日計算上述指標時，過濾掉大盤資料 (*IR0001*)。讓之後在計算每個股票的成交量的四日簡單移動平均、成交量的五日簡單移動平均與當日成交量時，能跳過計算大盤指數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba61d7b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "bundle = bundles.load('tquant')\n",
    "ir0001_asset = bundle.asset_finder.lookup_symbol('IR0001',as_of_date = None)\n",
    "\n",
    "def make_pipeline():\n",
    "    sma_vol_win_4 = SimpleMovingAverage(inputs=[EquityPricing.volume], window_length=4)\n",
    "    sma_vol_win_5 = SimpleMovingAverage(inputs=[EquityPricing.volume], window_length=5)\n",
    "    curr_vol = EquityPricing.volume.latest\n",
    "    \n",
    "    return Pipeline(\n",
    "        columns = {\n",
    "            'sma_4':sma_vol_win_4,\n",
    "            'sma_5':sma_vol_win_5,\n",
    "            'curr_vol':curr_vol\n",
    "        },\n",
    "        screen = ~StaticAssets([ir0001_asset])\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0712de69",
   "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",
    "* 大盤報酬作為基準\n",
    "* 將 Pipeline 導入交易流程中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0489b408",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize(context):\n",
    "    set_slippage(slippage.VolumeShareSlippage())\n",
    "    set_commission(commission.PerShare(cost=0.00285))\n",
    "    set_benchmark(symbol('IR0001'))\n",
    "    attach_pipeline(make_pipeline(), 'mystrategy')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3fbdd68",
   "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: 當日成交量大於四日簡單移動平均之 2.5 倍且現金部位大於 0 ，產生買入訊號。\n",
    "* condition2: 當日成交量小於五日簡單移動平均之 0.75 倍，產生賣出訊號。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b243ea0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_data(context, data):\n",
    "    out_dir = pipeline_output('mystrategy')\n",
    "    for i in out_dir.index: # 遍歷過每檔股票\n",
    "        sma_vol_4 = out_dir.loc[i, 'sma_4']\n",
    "        sma_vol_5 = out_dir.loc[i, 'sma_5']\n",
    "        curr_vol = out_dir.loc[i, 'curr_vol']\n",
    "        \n",
    "        condition1 = (curr_vol > 2.5 * sma_vol_4) and (context.portfolio.cash > 0)\n",
    "        condition2 = (curr_vol < 0.75 * sma_vol_5)\n",
    "        \n",
    "        if condition1:\n",
    "            order(i, 10)\n",
    "        elif condition2:\n",
    "            order_target(i, 0)\n",
    "        else:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf98cea",
   "metadata": {},
   "source": [
    "### 建立 analyze 函式\n",
    "\n",
    "多半用於繪製績效圖表，於本次案例將使用 pyfolio 繪製，故直接略過。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e84a710",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze(context, perf):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e985b5a2",
   "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 2012-07-02 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": "e4a07dfe",
   "metadata": {
    "scrolled": false
   },
   "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"
     ]
    },
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       "      <td>2451</td>\n",
       "      <td>2022-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30 13:30:00+08:00</th>\n",
       "      <td>2022-06-30 09:01:00+08:00</td>\n",
       "      <td>2022-06-30 13:30:00+08:00</td>\n",
       "      <td>-0.025612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041733</td>\n",
       "      <td>0.003942</td>\n",
       "      <td>0.003032</td>\n",
       "      <td>0.303638</td>\n",
       "      <td>0.700934</td>\n",
       "      <td>-0.025339</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>2452</td>\n",
       "      <td>2022-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01 13:30:00+08:00</th>\n",
       "      <td>2022-07-01 09:01:00+08:00</td>\n",
       "      <td>2022-07-01 13:30:00+08:00</td>\n",
       "      <td>-0.032422</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041733</td>\n",
       "      <td>0.003952</td>\n",
       "      <td>0.003018</td>\n",
       "      <td>0.303576</td>\n",
       "      <td>0.700792</td>\n",
       "      <td>-0.025339</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>2453</td>\n",
       "      <td>2022-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2453 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        period_open              period_close  \\\n",
       "2012-07-02 13:30:00+08:00 2012-07-02 09:01:00+08:00 2012-07-02 13:30:00+08:00   \n",
       "2012-07-03 13:30:00+08:00 2012-07-03 09:01:00+08:00 2012-07-03 13:30:00+08:00   \n",
       "2012-07-04 13:30:00+08:00 2012-07-04 09:01:00+08:00 2012-07-04 13:30:00+08:00   \n",
       "2012-07-05 13:30:00+08:00 2012-07-05 09:01:00+08:00 2012-07-05 13:30:00+08:00   \n",
       "2012-07-06 13:30:00+08:00 2012-07-06 09:01:00+08:00 2012-07-06 13:30:00+08:00   \n",
       "...                                             ...                       ...   \n",
       "2022-06-27 13:30:00+08:00 2022-06-27 09:01:00+08:00 2022-06-27 13:30:00+08:00   \n",
       "2022-06-28 13:30:00+08:00 2022-06-28 09:01:00+08:00 2022-06-28 13:30:00+08:00   \n",
       "2022-06-29 13:30:00+08:00 2022-06-29 09:01:00+08:00 2022-06-29 13:30:00+08:00   \n",
       "2022-06-30 13:30:00+08:00 2022-06-30 09:01:00+08:00 2022-06-30 13:30:00+08:00   \n",
       "2022-07-01 13:30:00+08:00 2022-07-01 09:01:00+08:00 2022-07-01 13:30:00+08:00   \n",
       "\n",
       "                           benchmark_return  long_exposure  treasury_return  \\\n",
       "2012-07-02 13:30:00+08:00               NaN            0.0              0.0   \n",
       "2012-07-03 13:30:00+08:00          0.010450            0.0              0.0   \n",
       "2012-07-04 13:30:00+08:00          0.006789         1947.4              0.0   \n",
       "2012-07-05 13:30:00+08:00         -0.002474         1955.1              0.0   \n",
       "2012-07-06 13:30:00+08:00         -0.002480         1948.0              0.0   \n",
       "...                                     ...            ...              ...   \n",
       "2022-06-27 13:30:00+08:00          0.017703            0.0              0.0   \n",
       "2022-06-28 13:30:00+08:00         -0.005615            0.0              0.0   \n",
       "2022-06-29 13:30:00+08:00         -0.009731            0.0              0.0   \n",
       "2022-06-30 13:30:00+08:00         -0.025612            0.0              0.0   \n",
       "2022-07-01 13:30:00+08:00         -0.032422            0.0              0.0   \n",
       "\n",
       "                              pnl  capital_used  short_exposure  \\\n",
       "2012-07-02 13:30:00+08:00  0.0000        0.0000             0.0   \n",
       "2012-07-03 13:30:00+08:00  0.0000        0.0000             0.0   \n",
       "2012-07-04 13:30:00+08:00 -0.0855    -1947.4855             0.0   \n",
       "2012-07-05 13:30:00+08:00  7.7000        0.0000             0.0   \n",
       "2012-07-06 13:30:00+08:00 -7.1000        0.0000             0.0   \n",
       "...                           ...           ...             ...   \n",
       "2022-06-27 13:30:00+08:00  0.0000        0.0000             0.0   \n",
       "2022-06-28 13:30:00+08:00  0.0000        0.0000             0.0   \n",
       "2022-06-29 13:30:00+08:00  0.0000        0.0000             0.0   \n",
       "2022-06-30 13:30:00+08:00  0.0000        0.0000             0.0   \n",
       "2022-07-01 13:30:00+08:00  0.0000        0.0000             0.0   \n",
       "\n",
       "                                                                      orders  \\\n",
       "2012-07-02 13:30:00+08:00                                                 []   \n",
       "2012-07-03 13:30:00+08:00  [{'id': '9d17e2fd712644c686d322e24c40fc18', 'd...   \n",
       "2012-07-04 13:30:00+08:00  [{'id': '9d17e2fd712644c686d322e24c40fc18', 'd...   \n",
       "2012-07-05 13:30:00+08:00                                                 []   \n",
       "2012-07-06 13:30:00+08:00  [{'id': '43ec939b255d4efaaab36163955a90f1', 'd...   \n",
       "...                                                                      ...   \n",
       "2022-06-27 13:30:00+08:00                                                 []   \n",
       "2022-06-28 13:30:00+08:00                                                 []   \n",
       "2022-06-29 13:30:00+08:00                                                 []   \n",
       "2022-06-30 13:30:00+08:00                                                 []   \n",
       "2022-07-01 13:30:00+08:00                                                 []   \n",
       "\n",
       "                            returns  ... treasury_period_return  \\\n",
       "2012-07-02 13:30:00+08:00  0.000000  ...                    0.0   \n",
       "2012-07-03 13:30:00+08:00  0.000000  ...                    0.0   \n",
       "2012-07-04 13:30:00+08:00 -0.000009  ...                    0.0   \n",
       "2012-07-05 13:30:00+08:00  0.000770  ...                    0.0   \n",
       "2012-07-06 13:30:00+08:00 -0.000709  ...                    0.0   \n",
       "...                             ...  ...                    ...   \n",
       "2022-06-27 13:30:00+08:00  0.000000  ...                    0.0   \n",
       "2022-06-28 13:30:00+08:00  0.000000  ...                    0.0   \n",
       "2022-06-29 13:30:00+08:00  0.000000  ...                    0.0   \n",
       "2022-06-30 13:30:00+08:00  0.000000  ...                    0.0   \n",
       "2022-07-01 13:30:00+08:00  0.000000  ...                    0.0   \n",
       "\n",
       "                          algorithm_period_return     alpha      beta  \\\n",
       "2012-07-02 13:30:00+08:00                0.000000       NaN       NaN   \n",
       "2012-07-03 13:30:00+08:00                0.000000       NaN       NaN   \n",
       "2012-07-04 13:30:00+08:00               -0.000009 -0.006131  0.002335   \n",
       "2012-07-05 13:30:00+08:00                0.000761  0.154572 -0.064349   \n",
       "2012-07-06 13:30:00+08:00                0.000051  0.005455 -0.002797   \n",
       "...                                           ...       ...       ...   \n",
       "2022-06-27 13:30:00+08:00                0.041733  0.003933  0.003040   \n",
       "2022-06-28 13:30:00+08:00                0.041733  0.003933  0.003040   \n",
       "2022-06-29 13:30:00+08:00                0.041733  0.003935  0.003040   \n",
       "2022-06-30 13:30:00+08:00                0.041733  0.003942  0.003032   \n",
       "2022-07-01 13:30:00+08:00                0.041733  0.003952  0.003018   \n",
       "\n",
       "                             sharpe     sortino  max_drawdown  max_leverage  \\\n",
       "2012-07-02 13:30:00+08:00       NaN         NaN      0.000000      0.000000   \n",
       "2012-07-03 13:30:00+08:00       NaN         NaN      0.000000      0.000000   \n",
       "2012-07-04 13:30:00+08:00 -9.165151   -9.165151     -0.000009      0.194742   \n",
       "2012-07-05 13:30:00+08:00  7.819750  706.885842     -0.000009      0.195361   \n",
       "2012-07-06 13:30:00+08:00  0.315410    0.520275     -0.000709      0.195361   \n",
       "...                             ...         ...           ...           ...   \n",
       "2022-06-27 13:30:00+08:00  0.303824    0.701364     -0.025339      0.420975   \n",
       "2022-06-28 13:30:00+08:00  0.303762    0.701220     -0.025339      0.420975   \n",
       "2022-06-29 13:30:00+08:00  0.303700    0.701077     -0.025339      0.420975   \n",
       "2022-06-30 13:30:00+08:00  0.303638    0.700934     -0.025339      0.420975   \n",
       "2022-07-01 13:30:00+08:00  0.303576    0.700792     -0.025339      0.420975   \n",
       "\n",
       "                           trading_days  period_label  \n",
       "2012-07-02 13:30:00+08:00             1       2012-07  \n",
       "2012-07-03 13:30:00+08:00             2       2012-07  \n",
       "2012-07-04 13:30:00+08:00             3       2012-07  \n",
       "2012-07-05 13:30:00+08:00             4       2012-07  \n",
       "2012-07-06 13:30:00+08:00             5       2012-07  \n",
       "...                                 ...           ...  \n",
       "2022-06-27 13:30:00+08:00          2449       2022-06  \n",
       "2022-06-28 13:30:00+08:00          2450       2022-06  \n",
       "2022-06-29 13:30:00+08:00          2451       2022-06  \n",
       "2022-06-30 13:30:00+08:00          2452       2022-06  \n",
       "2022-07-01 13:30:00+08:00          2453       2022-07  \n",
       "\n",
       "[2453 rows x 39 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = run_algorithm(\n",
    "    start = pd.Timestamp('2012-07-02', tz='UTC'),\n",
    "    end = pd.Timestamp('2022-07-02', tz ='UTC'),\n",
    "    initialize=initialize,\n",
    "    bundle='tquant',\n",
    "    analyze=analyze,\n",
    "    capital_base=1e4,\n",
    "    handle_data = handle_data\n",
    ")\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39f635f5",
   "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": 8,
   "id": "0daca63f",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 9,
   "id": "1dd1b0d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-07-02 00:00:00+08:00    0.000000\n",
       "2012-07-03 00:00:00+08:00    0.000000\n",
       "2012-07-04 00:00:00+08:00   -0.000009\n",
       "2012-07-05 00:00:00+08:00    0.000770\n",
       "2012-07-06 00:00:00+08:00   -0.000709\n",
       "Name: returns, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "463fa8f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sid</th>\n",
       "      <th>Equity(0 [2330])</th>\n",
       "      <th>Equity(1 [2337])</th>\n",
       "      <th>Equity(2 [3443])</th>\n",
       "      <th>cash</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-07-04 00:00:00+08:00</th>\n",
       "      <td>812.0</td>\n",
       "      <td>95.4</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>8052.5145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-05 00:00:00+08:00</th>\n",
       "      <td>810.0</td>\n",
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       "      <td>1050.0</td>\n",
       "      <td>8052.5145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-06 00:00:00+08:00</th>\n",
       "      <td>809.0</td>\n",
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       "      <td>1045.0</td>\n",
       "      <td>8052.5145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-09 00:00:00+08:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>8945.3575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-10 00:00:00+08:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>8945.3575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "sid                        Equity(0 [2330])  Equity(1 [2337])  \\\n",
       "index                                                           \n",
       "2012-07-04 00:00:00+08:00             812.0              95.4   \n",
       "2012-07-05 00:00:00+08:00             810.0              95.1   \n",
       "2012-07-06 00:00:00+08:00             809.0              94.0   \n",
       "2012-07-09 00:00:00+08:00               0.0               0.0   \n",
       "2012-07-10 00:00:00+08:00               0.0               0.0   \n",
       "\n",
       "sid                        Equity(2 [3443])       cash  \n",
       "index                                                   \n",
       "2012-07-04 00:00:00+08:00            1040.0  8052.5145  \n",
       "2012-07-05 00:00:00+08:00            1050.0  8052.5145  \n",
       "2012-07-06 00:00:00+08:00            1045.0  8052.5145  \n",
       "2012-07-09 00:00:00+08:00            1015.0  8945.3575  \n",
       "2012-07-10 00:00:00+08:00             980.0  8945.3575  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d9257edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>80.00</td>\n",
       "      <td>43ec939b255d4efaaab36163955a90f1</td>\n",
       "      <td>-10</td>\n",
       "      <td>None</td>\n",
       "      <td>2012-07-09 13:30:00+08:00</td>\n",
       "      <td>800.0</td>\n",
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       "      <th>2012-07-09 05:30:00+00:00</th>\n",
       "      <td>Equity(1 [2337])</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                        sid            symbol   price  \\\n",
       "2012-07-04 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])   81.20   \n",
       "2012-07-04 05:30:00+00:00  Equity(1 [2337])  Equity(1 [2337])    9.54   \n",
       "2012-07-04 05:30:00+00:00  Equity(2 [3443])  Equity(2 [3443])  104.00   \n",
       "2012-07-09 05:30:00+00:00  Equity(0 [2330])  Equity(0 [2330])   80.00   \n",
       "2012-07-09 05:30:00+00:00  Equity(1 [2337])  Equity(1 [2337])    9.29   \n",
       "\n",
       "                                                   order_id  amount  \\\n",
       "2012-07-04 05:30:00+00:00  9d17e2fd712644c686d322e24c40fc18      10   \n",
       "2012-07-04 05:30:00+00:00  fcb751a7e39845db94df1d06b7ecab26      10   \n",
       "2012-07-04 05:30:00+00:00  76a9901f4c994acc9c295e33c85ddf92      10   \n",
       "2012-07-09 05:30:00+00:00  43ec939b255d4efaaab36163955a90f1     -10   \n",
       "2012-07-09 05:30:00+00:00  18b86f4b87c74756a1f760cab4fe0d5b     -10   \n",
       "\n",
       "                          commission                        dt  txn_dollars  \n",
       "2012-07-04 05:30:00+00:00       None 2012-07-04 13:30:00+08:00       -812.0  \n",
       "2012-07-04 05:30:00+00:00       None 2012-07-04 13:30:00+08:00        -95.4  \n",
       "2012-07-04 05:30:00+00:00       None 2012-07-04 13:30:00+08:00      -1040.0  \n",
       "2012-07-09 05:30:00+00:00       None 2012-07-09 13:30:00+08:00        800.0  \n",
       "2012-07-09 05:30:00+00:00       None 2012-07-09 13:30:00+08:00         92.9  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45cd5a82",
   "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": 12,
   "id": "75696d7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\envs\\zipline-tej\\lib\\site-packages\\empyrical\\stats.py:1529: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return np.abs(np.percentile(returns, 95)) / np.abs(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2012-07-02</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2022-07-01</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>116</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>0.421%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>4.173%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>1.416%</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>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-2.534%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>15.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>412.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-0.177%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross leverage</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily turnover</th>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>0.00</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",
    "benchmark_rets = results['benchmark_return']\n",
    "pf.plotting.show_perf_stats(\n",
    "        returns,\n",
    "        benchmark_rets,\n",
    "        positions=positions,\n",
    "        transactions=transactions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03ec120",
   "metadata": {},
   "source": [
    "### 製作且繪製投組成份表格\n",
    "\n",
    "使用 `show_and_plot_top_positions()` 與 `get_percent_alloc()` 製作且繪製各股於交易期間的成分佔比。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1a12aa30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Top 10 long positions of all time</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sid</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3443</th>\n",
       "      <td>42.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2337</th>\n",
       "      <td>13.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>8.12%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Top 10 short positions of all time</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sid</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Top 10 positions of all time</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sid</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3443</th>\n",
       "      <td>42.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2337</th>\n",
       "      <td>13.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>8.12%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Portfolio allocation over time, only top 10 holdings'}, xlabel='index', ylabel='Exposure by holding'>"
      ]
     },
     "execution_count": 13,
     "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": [
    "pf.plotting.show_and_plot_top_positions(returns, positions_alloc=pf.pos.get_percent_alloc(positions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d617b29",
   "metadata": {},
   "source": [
    "### 繪製基準 (benchmark) 與投組累計報酬率\n",
    "\n",
    "使用 `plot_rolling_returns()` 繪製，本案例的基準為大盤報酬率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c0be116f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Cumulative returns'>"
      ]
     },
     "execution_count": 14,
     "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": [
    "pf.plotting.plot_rolling_returns(returns, factor_returns=benchmark_rets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8faca845",
   "metadata": {},
   "source": [
    "### 繪製六個月的滾動波動度\n",
    "\n",
    "使用 `plot_rolling_volatility()` 繪製。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aad84800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Rolling volatility (6-month)'}, ylabel='Volatility'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "pf.plotting.plot_rolling_volatility(returns, factor_returns=benchmark_rets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8128227",
   "metadata": {},
   "source": [
    "### 繪製六個月的滾動夏普值\n",
    "\n",
    "使用 `plot_rolling_sharpe()` 繪製。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "565125f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Rolling Sharpe ratio (6-month)'}, ylabel='Sharpe ratio'>"
      ]
     },
     "execution_count": 16,
     "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": [
    "pf.plotting.plot_rolling_sharpe(returns)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:zipline-tej] *",
   "language": "python",
   "name": "conda-env-zipline-tej-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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}