lecture/Filters.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d230d4c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merging daily equity files:\n",
      "Currently used TEJ API key call quota 1803/9223372036854775807 (0.0%)\n",
      "Currently used TEJ API key data quota 49629380/9223372036854775807 (0.0%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-03-04 05:03:06.531022] INFO: zipline.data.bundles.core: Ingesting tquant.\n",
      "[2024-03-04 05:03:38.696492] INFO: zipline.data.bundles.core: Ingest tquant successfully.\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "\n",
    "StockList1 = ['1227', '1234', '1304', '1314', '1434', '1440', '1476', '1504', '1507', '1590', '1605', '1704', '1710', '1717', '1723', '1789',\n",
    " '1802', '1907', '2006', '2015', '2049', '2101', '2103', '2106', '2204', '2227', '2327', '2337', '2344', '2356', '2360', '2362',\n",
    " '2379', '2384', '2385', '2392', '2395', '2448', '2449', '2450', '2451', '2489', '2501', '2504', '2511', '2542', '2545', '2548',\n",
    " '2603', '2606', '2607', '2608', '2609', '2610', '2615', '2618', '2707', '2723', '2727', '2809', '2812', '2823', '2834', '2845',\n",
    " '2847', '2855', '2884', '2887', '2888', '2889', '2903', '2915', '3034', '3037', '3044', '3149', '3189', '3406', '3702', '4938',\n",
    " '4958', '5522', '5871', '6005', '6176', '6239', '6269', '6286', '8008', '8046', '8078', '8422', '9904', '9907', '9914', '9917', '9921',\n",
    " '9933', '9940', '9945', '2458', '5264', '2206', '1201', '2347', '3231', '5534', '6116', '9910', '1477', '2353', '6271', '1319',\n",
    " '1722', '2059', '3060', '3474', '3673', '2393', '2376', '2439', '3682', '1262', '2201', '2377', '3576', '2352', '2838', '8150',\n",
    " '2324', '2231', '8454', '2833', '6285', '6409', '1536', '1702', '2313', '2498', '2867', '6415', '6456', '9938', '2383', '4137', '6452',\n",
    " '1707', '1589', '2849', '6414', '8464', '2355', '2345', '3706', '2023', '2371', '1909', '2633', '3532', '9941', '2492', '3019',\n",
    " '3443', '4915', '4943', '1229', '2441', '2027', '3026', '1210', '2104', '2456', '5269', '8341', '2354', '3005', '3481', '6669',\n",
    " '2409', '3023', '6213', '2404', '3533', '6278', '6592', '3653', '3661', '3665', '2301', '3714', '2883', '2890', '6531', '1904',\n",
    " '2014', '2105', '2108', '2474', '2637', '6781', '1102', '4919', '1402', '3035', '3036', '4961', '6719', '6770', '2368', '1795',\n",
    " '6550', '6789', '3017', '1101', '1216', '1301', '1303', '1326', '2002', '2207', '2303', '2308', '2311', '2317', '2325', '2330',\n",
    " '2357', '2382', '2412', '2454', '2801', '2880', '2881', '2882', '2885', '2886', '2891', '2892', '2912', '3008', '3045', '3697',\n",
    " '4904', '5880', '6505', '2408', '3711', '5876']\n",
    "\n",
    "tickers = \" \".join(StockList1)\n",
    "\n",
    "os.environ['TEJAPI_BASE'] = 'https://api.tej.com.tw'\n",
    "os.environ['TEJAPI_KEY'] = 'your_key'\n",
    "\n",
    "os.environ['mdate'] = '20170101 20230726'\n",
    "os.environ['ticker'] = tickers\n",
    "\n",
    "!zipline ingest -b tquant\n",
    "\n",
    "from zipline.pipeline import Pipeline\n",
    "from zipline.TQresearch.tej_pipeline import run_pipeline\n",
    "from zipline.pipeline.data import TWEquityPricing\n",
    "from zipline.pipeline.factors import SimpleMovingAverage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0297dc5e",
   "metadata": {},
   "source": [
    "## Filters\n",
    "Filters 是一個將資產在某個時間點的特徵轉換為**布林值**的函數 ：\n",
    "\n",
    "```\n",
    "F(asset, timestamp) -> boolean\n",
    "```\n",
    "**在 Pipeline 中，Filters 用於縮小股票池的大小**，這裡有兩種 `Filter` 常見的創建方式：使用 `Factor`／ `Classifier` 方法或使用比較運算子（comparison operators，`<`, `<=`, `!=`, `eq`, `>`, `>=`）。\n",
    "\n",
    "### Comparison Operators\n",
    "對 `Factors` 和 `Classifier` 使用比較運算子就可以創建 Filters。\n",
    "\n",
    "有鑒於我們尚未介紹到 `Classifier`，我們僅使用 `Factors` 舉例，下方的範例建立了一個當最近一期收盤價大於 $20 時會返回 `True` 的 filter 。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b4b27b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "last_close_price = TWEquityPricing.close.latest\n",
    "close_price_filter = last_close_price > 20"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "169f2d58",
   "metadata": {},
   "source": [
    "而以下這個範例建立一個新的 filter，只要近 10 天收盤價平均值低於近 30 天收盤價平均值，該過濾器就會返回 True。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8256aafe",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_close_10 = SimpleMovingAverage(inputs=[TWEquityPricing.close], window_length=10)\n",
    "mean_close_30 = SimpleMovingAverage(inputs=[TWEquityPricing.close], window_length=30)\n",
    "mean_crossover_filter = mean_close_10 < mean_close_30"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ee0055",
   "metadata": {},
   "source": [
    "記住，在每天中每檔證券都會擁有自己的 `True` 或 `False` 值。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51622e08",
   "metadata": {},
   "source": [
    "### Factor／Classifier Methods\n",
    "很多種 `Factor` 和 `Classifier` 都會回傳 `Filters`。\n",
    "\n",
    "但同樣地，由於我們尚未討論到 `Classifier`，目前我們僅使用 `Factor`（稍後我們會討論 `Classifier`）。\n",
    "\n",
    "`Factor.top(n)`會建立一個 `Filter` ，當排名在指定 `Factor` 前 `n` 名的證券，會標記為 `True` ，其餘標記為 `False`。\n",
    "\n",
    "下面的範例建立了一個會每天將最近一期收盤價排名前30名的證券標示為`True` 的filter。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "503afe3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "last_close_price = TWEquityPricing.close.latest\n",
    "top_close_price_filter = last_close_price.top(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec445dd",
   "metadata": {},
   "source": [
    "## Dollar Volume Filter\n",
    "作為第一個範例，我們創建一個 Filter 。如果該證券過去 30 日平均交易量大於 $1, 000,000, 000 則回傳 `True` 值。\n",
    "\n",
    "為了實現這個效果，我們首先需要創建一個 `AverageDollarVolume` factor 以計算過去 30 日平均交易量。讓我們匯入 __built-in factor__ `AverageDollarVolume`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0942b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zipline.pipeline.factors import AverageDollarVolume"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1e168e",
   "metadata": {},
   "source": [
    "接著，讓我們實例化 `AverageDollarVolume` factor。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "23a2db1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dollar_volume = AverageDollarVolume(window_length=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0480e041",
   "metadata": {},
   "source": [
    "在預設情況下， `AverageDollarVolume` 會使用 `TWEquityPricing.close` 與 `TWEquityPricing.volume` 作為 `inputs`，所以我們不須去定義他們。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f84dba52",
   "metadata": {},
   "source": [
    "現在我們有了平均交易量 factor ，我們可以以布林值表達式創建 filter。下方程式創建了一個當證券的平均成交量 `dollar_volume` 大於 $1, 000,000, 000 時會回傳 `True` 的 filter。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bc44caf",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_dollar_volume = (dollar_volume > 1000000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4eac677",
   "metadata": {},
   "source": [
    "為了知道 fitler 的實際結果，我們將它作為一個欄位加入先前課程中創立的 pipeline 中。此外，如同在 [lecture/Factors](https://github.com/tejtw/TQuant-Lab/blob/main/lecture/Factors.ipynb) 中所介紹的，我們額外加入了`percent_difference`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "918d80d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_pipeline():\n",
    "\n",
    "    mean_close_10 = SimpleMovingAverage(inputs=[TWEquityPricing.close], window_length=10)\n",
    "    mean_close_30 = SimpleMovingAverage(inputs=[TWEquityPricing.close], window_length=30)\n",
    "\n",
    "    percent_difference = (mean_close_10 - mean_close_30) / mean_close_30\n",
    "    \n",
    "    dollar_volume = AverageDollarVolume(window_length=30)\n",
    "    high_dollar_volume = (dollar_volume > 1000000000)\n",
    "\n",
    "    return Pipeline(\n",
    "        columns={\n",
    "            'percent_difference': percent_difference,            \n",
    "            'high_dollar_volume': high_dollar_volume\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57cdfbb8",
   "metadata": {},
   "source": [
    "如果我們執行 pipeline ，就會產出 `high_dollar_volume` 欄位，當 filter 條件成立時會回傳布林值 `True`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ad36bbd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percent_difference</th>\n",
       "      <th>high_dollar_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Equity(0 [1101])</th>\n",
       "      <td>0.037521</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1 [1102])</th>\n",
       "      <td>0.023348</td>\n",
       "      <td>False</td>\n",
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       "      <th>Equity(2 [1201])</th>\n",
       "      <td>0.033230</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>Equity(3 [1210])</th>\n",
       "      <td>0.034966</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(4 [1216])</th>\n",
       "      <td>-0.002188</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>Equity(235 [9933])</th>\n",
       "      <td>0.015381</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(236 [9938])</th>\n",
       "      <td>0.027505</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(237 [9940])</th>\n",
       "      <td>0.021334</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(238 [9941])</th>\n",
       "      <td>0.013461</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(239 [9945])</th>\n",
       "      <td>-0.001735</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    percent_difference  high_dollar_volume\n",
       "Equity(0 [1101])              0.037521               False\n",
       "Equity(1 [1102])              0.023348               False\n",
       "Equity(2 [1201])              0.033230               False\n",
       "Equity(3 [1210])              0.034966               False\n",
       "Equity(4 [1216])             -0.002188               False\n",
       "...                                ...                 ...\n",
       "Equity(235 [9933])            0.015381               False\n",
       "Equity(236 [9938])            0.027505               False\n",
       "Equity(237 [9940])            0.021334               False\n",
       "Equity(238 [9941])            0.013461               False\n",
       "Equity(239 [9945])           -0.001735               False\n",
       "\n",
       "[230 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = run_pipeline(make_pipeline(), '2023-03-02', '2023-06-01')\n",
    "result.loc['2023-03-03']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cc296ef",
   "metadata": {},
   "source": [
    "## Applying a Screen\n",
    "在預設情況下，pipeline 每日會針對每個在 bundle 中的資產生成一筆資料。\n",
    "\n",
    "但很多時候，我們只關注那些**符合特定條件的證券**（例如，為了能快速成交，我們可能只會關注那些每日交易量足夠大的證券）。我們可以透過 `screen` 告訴 pipeline 去忽略那些 filter 回傳的布林值為 `False` 的證券。  \n",
    "  \n",
    "為了讓 pipeline 過濾出那些**30日平均交易量**大於 $1, 000,000, 000 的證券，我們可以簡單的將 `high_dollar_volume` filter 作為 `screen` 的參數。\n",
    "\n",
    "現在我們的 pipeline 應該會長這樣："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34e40c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_pipeline():\n",
    "\n",
    "    mean_close_10 = SimpleMovingAverage(inputs=[TWEquityPricing.close], window_length=10)\n",
    "    mean_close_30 = SimpleMovingAverage(inputs=[TWEquityPricing.close], window_length=30)\n",
    "\n",
    "    percent_difference = (mean_close_10 - mean_close_30) / mean_close_30\n",
    "    \n",
    "    dollar_volume = AverageDollarVolume(window_length=30)\n",
    "    high_dollar_volume = (dollar_volume > 1000000000)\n",
    "\n",
    "    return Pipeline(\n",
    "        columns={\n",
    "            'percent_difference': percent_difference\n",
    "        },\n",
    "        screen=high_dollar_volume\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d977e566",
   "metadata": {},
   "source": [
    "當我們執行程式時，pipeline 的輸出僅僅只會包含那些在特定日子滿足 `high_dollar_volume` filter 的證券。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "77058008",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "通過 filter 的證券數: 35\n"
     ]
    },
    {
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       "      <th>Equity(25 [1605])</th>\n",
       "      <td>0.060100</td>\n",
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       "      <th>Equity(34 [1795])</th>\n",
       "      <td>0.074224</td>\n",
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       "      <th>Equity(53 [2201])</th>\n",
       "      <td>0.049502</td>\n",
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       "      <th>Equity(60 [2303])</th>\n",
       "      <td>0.034036</td>\n",
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       "      <th>Equity(61 [2308])</th>\n",
       "      <td>0.007811</td>\n",
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       "      <th>Equity(64 [2317])</th>\n",
       "      <td>0.018607</td>\n",
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       "      <th>Equity(67 [2327])</th>\n",
       "      <td>0.030150</td>\n",
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       "      <th>Equity(68 [2330])</th>\n",
       "      <td>0.001321</td>\n",
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       "    <tr>\n",
       "      <th>Equity(85 [2379])</th>\n",
       "      <td>0.070902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(95 [2412])</th>\n",
       "      <td>0.007754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(102 [2454])</th>\n",
       "      <td>0.014696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(108 [2498])</th>\n",
       "      <td>0.036708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(115 [2603])</th>\n",
       "      <td>0.001640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(119 [2609])</th>\n",
       "      <td>0.004344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(120 [2610])</th>\n",
       "      <td>0.043368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(121 [2615])</th>\n",
       "      <td>0.004935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(122 [2618])</th>\n",
       "      <td>-0.000347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(127 [2727])</th>\n",
       "      <td>0.241944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(140 [2882])</th>\n",
       "      <td>0.015239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(155 [3008])</th>\n",
       "      <td>0.008678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(160 [3034])</th>\n",
       "      <td>0.084553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(161 [3035])</th>\n",
       "      <td>0.056630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(163 [3037])</th>\n",
       "      <td>0.019296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(171 [3443])</th>\n",
       "      <td>0.200979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(172 [3481])</th>\n",
       "      <td>0.116559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(177 [3661])</th>\n",
       "      <td>0.039343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(183 [3711])</th>\n",
       "      <td>0.009755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(188 [4919])</th>\n",
       "      <td>0.046445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(192 [4961])</th>\n",
       "      <td>0.116186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(197 [5871])</th>\n",
       "      <td>-0.003119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(211 [6415])</th>\n",
       "      <td>0.022885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(215 [6531])</th>\n",
       "      <td>0.153928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(218 [6669])</th>\n",
       "      <td>0.061960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(219 [6719])</th>\n",
       "      <td>-0.004284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(223 [8046])</th>\n",
       "      <td>0.009388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    percent_difference\n",
       "Equity(25 [1605])             0.060100\n",
       "Equity(34 [1795])             0.074224\n",
       "Equity(53 [2201])             0.049502\n",
       "Equity(60 [2303])             0.034036\n",
       "Equity(61 [2308])             0.007811\n",
       "Equity(64 [2317])             0.018607\n",
       "Equity(67 [2327])             0.030150\n",
       "Equity(68 [2330])             0.001321\n",
       "Equity(85 [2379])             0.070902\n",
       "Equity(95 [2412])             0.007754\n",
       "Equity(102 [2454])            0.014696\n",
       "Equity(108 [2498])            0.036708\n",
       "Equity(115 [2603])            0.001640\n",
       "Equity(119 [2609])            0.004344\n",
       "Equity(120 [2610])            0.043368\n",
       "Equity(121 [2615])            0.004935\n",
       "Equity(122 [2618])           -0.000347\n",
       "Equity(127 [2727])            0.241944\n",
       "Equity(140 [2882])            0.015239\n",
       "Equity(155 [3008])            0.008678\n",
       "Equity(160 [3034])            0.084553\n",
       "Equity(161 [3035])            0.056630\n",
       "Equity(163 [3037])            0.019296\n",
       "Equity(171 [3443])            0.200979\n",
       "Equity(172 [3481])            0.116559\n",
       "Equity(177 [3661])            0.039343\n",
       "Equity(183 [3711])            0.009755\n",
       "Equity(188 [4919])            0.046445\n",
       "Equity(192 [4961])            0.116186\n",
       "Equity(197 [5871])           -0.003119\n",
       "Equity(211 [6415])            0.022885\n",
       "Equity(215 [6531])            0.153928\n",
       "Equity(218 [6669])            0.061960\n",
       "Equity(219 [6719])           -0.004284\n",
       "Equity(223 [8046])            0.009388"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = run_pipeline(make_pipeline(), '2023-03-02', '2023-03-03')\n",
    "print('通過 filter 的證券數: %d' % len(result))\n",
    "result.loc['2023-03-03']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a23525e",
   "metadata": {},
   "source": [
    "## Inverting a Filter\n",
    "`~` 運算符是用於反轉 filter 的，將所有 `True` 轉變為 `False`，反之亦然。例如，我們可以撰寫下列的 filter 去過濾出低交易量的證券。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10989dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "low_dollar_volume = ~high_dollar_volume"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c83e42f",
   "metadata": {},
   "source": [
    "這會對過去 30 天內所有平均交易量低於或等於 $1,000,000,000 的證券回傳 `True`。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "585e0af4",
   "metadata": {},
   "source": [
    "其餘 __built-in filters__ 請參考：[Pipeline built-in filters.ipynb](https://github.com/tejtw/TQuant-Lab/blob/main/lecture/Pipeline%20built-in%20filters.ipynb)"
   ]
  }
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