lecture/Pipeline built-in filters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42795695",
   "metadata": {},
   "source": [
    "<span id=\"top\"></span>\n",
    "# <font color=#57a892>Pipeline built-in filters</font>\n",
    "\n",
    "本文介紹常用的內建 `Filters`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "44e37ddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merging daily equity files:\n",
      "Currently used TEJ API key call quota 126/100000 (0.13%)\n",
      "Currently used TEJ API key data quota 872686/10000000 (8.73%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-07-09 05:27:50.154669] INFO: zipline.data.bundles.core: Ingesting tquant.\n",
      "[2024-07-09 05:27:56.108611] INFO: zipline.data.bundles.core: Ingest tquant successfully.\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",
    "\n",
    "os.environ['ticker'] = \"1101 1301 1303 1802 2002 2101 2303 2317 2330 2337 2382 2388 2451 2454 2603 2881 2885 2890 2903 3711 IR0001\"\n",
    "os.environ['mdate'] = '20180101 20220330'\n",
    "\n",
    "!zipline ingest -b tquant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c11a9d52",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zipline.data import bundles\n",
    "from zipline.pipeline import Pipeline\n",
    "from zipline.TQresearch.tej_pipeline import run_pipeline\n",
    "from zipline.pipeline.data import TWEquityPricing, TQAltDataSet, TQDataSet\n",
    "from zipline.pipeline.factors import *\n",
    "from zipline.pipeline.filters import *\n",
    "\n",
    "start = pd.Timestamp(\"2018-02-06\", tz='utc')\n",
    "end = pd.Timestamp(\"2022-02-06\", tz='utc')\n",
    "\n",
    "bundle = bundles.load('tquant')\n",
    "sids = bundle.asset_finder.equities_sids\n",
    "assets = bundle.asset_finder.retrieve_all(sids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e155ae12",
   "metadata": {},
   "source": [
    "<span id=\"menu\"></span>\n",
    "    \n",
    "### Menu\n",
    "* [All](#All)\n",
    "* [Any](#Any)\n",
    "* [AtLeastN](#AtLeastN)\n",
    "* [AllPresent](#AllPresent)\n",
    "* [StaticAssets](#StaticAssets)\n",
    "* [StaticSids](#StaticSids)\n",
    "* [SingleAsset](#SingleAsset)\n",
    "* [top/bottom](#top/bottom)\n",
    "* [percentile_between](#percentile_between)\n",
    "* [if_else](#if_else)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf86a688",
   "metadata": {},
   "source": [
    "<span id=\"All\"></span>\n",
    "\n",
    "## zipline.pipeline.filters.<font color=#57a892>All</font>\n",
    "\n",
    "在 n 日內，若一資產每日皆符合條件值，該資產為 True。\n",
    "\n",
    ">### Parameters:\n",
    ">* inputs _( zipline.pipeline.data.Dataset.Boundcolumn_ or _boolean )_ - 資產價量資訊與條件值。\n",
    ">* window_length _( int )_ - 決定 n 日。\n",
    "\n",
    "[Go to Menu](#menu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4783970",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ALL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2018-02-06 00:00:00+00:00</th>\n",
       "      <th>Equity(0 [1101])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1 [1301])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2 [1303])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(3 [1802])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(4 [2002])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2022-01-26 00:00:00+00:00</th>\n",
       "      <th>Equity(16 [2885])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(17 [2890])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(18 [2903])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(19 [3711])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(20 [IR0001])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20340 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 ALL\n",
       "2018-02-06 00:00:00+00:00 Equity(0 [1101])     False\n",
       "                          Equity(1 [1301])      True\n",
       "                          Equity(2 [1303])      True\n",
       "                          Equity(3 [1802])     False\n",
       "                          Equity(4 [2002])     False\n",
       "...                                              ...\n",
       "2022-01-26 00:00:00+00:00 Equity(16 [2885])    False\n",
       "                          Equity(17 [2890])    False\n",
       "                          Equity(18 [2903])    False\n",
       "                          Equity(19 [3711])     True\n",
       "                          Equity(20 [IR0001])   True\n",
       "\n",
       "[20340 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from zipline.pipeline.filters import All\n",
    "\n",
    "def make_pipeline():\n",
    "    return Pipeline(\n",
    "        columns = {\n",
    "            \"ALL\": All(\n",
    "                inputs = [TWEquityPricing.close.latest > 40], # 設定條件為前一日收盤價 > 40 時為 True\n",
    "                window_length = 1\n",
    "            )\n",
    "        }\n",
    "    )\n",
    "\n",
    "run_pipeline(make_pipeline(), start, end)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a41f05bb",
   "metadata": {},
   "source": [
    "<span id=\"Any\"></span>\n",
    "\n",
    "## zipline.pipeline.filters.<font color=#57a892>Any</font>\n",
    "\n",
    "在 n 日內，若一資產任一日符合條件值，該資產為 True。\n",
    "\n",
    ">### Parameters:\n",
    ">* inputs _( zipline.pipeline.data.Dataset.Boundcolumn_ or _boolean )_ - 資產價量資訊與條件值。\n",
    ">* window_length _( int )_ - 決定 n 日。\n",
    "\n",
    "[Go to Menu](#menu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "22d3c62f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Any</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2018-02-06 00:00:00+00:00</th>\n",
       "      <th>Equity(0 [1101])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1 [1301])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2 [1303])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(3 [1802])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(4 [2002])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2022-01-26 00:00:00+00:00</th>\n",
       "      <th>Equity(16 [2885])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(17 [2890])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(18 [2903])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(19 [3711])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(20 [IR0001])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20340 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Any\n",
       "2018-02-06 00:00:00+00:00 Equity(0 [1101])     False\n",
       "                          Equity(1 [1301])      True\n",
       "                          Equity(2 [1303])      True\n",
       "                          Equity(3 [1802])     False\n",
       "                          Equity(4 [2002])     False\n",
       "...                                              ...\n",
       "2022-01-26 00:00:00+00:00 Equity(16 [2885])    False\n",
       "                          Equity(17 [2890])    False\n",
       "                          Equity(18 [2903])    False\n",
       "                          Equity(19 [3711])     True\n",
       "                          Equity(20 [IR0001])   True\n",
       "\n",
       "[20340 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from zipline.pipeline.filters import Any\n",
    "\n",
    "def make_pipeline():\n",
    "    return Pipeline(\n",
    "        columns = {\n",
    "            \"Any\": Any(\n",
    "                inputs = [TWEquityPricing.close.latest > 40], \n",
    "                window_length = 10\n",
    "            )\n",
    "        }\n",
    "    )\n",
    "\n",
    "run_pipeline(make_pipeline(), start, end)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3828abb",
   "metadata": {},
   "source": [
    "<span id=\"AtLeastN\"></span>\n",
    "\n",
    "## zipline.pipeline.filters.<font color=#57a892>AtLeastN</font>\n",
    "\n",
    "在 m 日內，若一資產至少有 n 日符合條件值，該資產為 True。\n",
    "\n",
    ">### Parameters:\n",
    ">* inputs _( zipline.pipeline.data.Dataset.Boundcolumn_ or _boolean )_ - 資產價量資訊與條件值。\n",
    ">* window_length _( int )_ - 決定 m 日。\n",
    ">* N _( int )_ - 決定 n 日。\n",
    "\n",
    "[Go to Menu](#menu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36164da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>AtLeastN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2018-02-06 00:00:00+00:00</th>\n",
       "      <th>Equity(0 [1101])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1 [1301])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2 [1303])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(3 [1802])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(4 [2002])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2022-01-26 00:00:00+00:00</th>\n",
       "      <th>Equity(16 [2885])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(17 [2890])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(18 [2903])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(19 [3711])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(20 [IR0001])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20340 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               AtLeastN\n",
       "2018-02-06 00:00:00+00:00 Equity(0 [1101])        False\n",
       "                          Equity(1 [1301])         True\n",
       "                          Equity(2 [1303])         True\n",
       "                          Equity(3 [1802])        False\n",
       "                          Equity(4 [2002])        False\n",
       "...                                                 ...\n",
       "2022-01-26 00:00:00+00:00 Equity(16 [2885])       False\n",
       "                          Equity(17 [2890])       False\n",
       "                          Equity(18 [2903])       False\n",
       "                          Equity(19 [3711])        True\n",
       "                          Equity(20 [IR0001])      True\n",
       "\n",
       "[20340 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from zipline.pipeline.filters import AtLeastN\n",
    "\n",
    "def make_pipeline():\n",
    "    return Pipeline(\n",
    "        columns = {\n",
    "            \"AtLeastN\": AtLeastN(\n",
    "                inputs = [TWEquityPricing.close.latest > 40],\n",
    "                window_length = 10,\n",
    "                N = 2\n",
    "            )\n",
    "        }\n",
    "    )\n",
    "\n",
    "run_pipeline(make_pipeline(), start, end)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dffc1dbc",
   "metadata": {},
   "source": [
    "<span id=\"AllPresent\"></span>\n",
    "\n",
    "## zipline.pipeline.filters.<font color=#57a892>AllPresent</font>\n",
    "\n",
    "在 n 日內，若每日皆有指定資料，該資產為 True。\n",
    "\n",
    ">### Parameters:\n",
    ">* inputs _( zipline.pipeline.data.Dataset.Boundcolumn_ or _boolean )_ - 資產價量資訊。\n",
    ">* window_length _( int )_ - 決定 n 日。\n",
    "\n",
    "[Go to Menu](#menu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "95c01104",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>AllPresent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Equity(0 [1101])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1 [1301])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2 [1303])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(3 [1802])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(4 [2002])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(5 [2101])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(6 [2303])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(7 [2317])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(8 [2330])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(9 [2337])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(10 [2382])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(11 [2388])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(12 [2451])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(13 [2454])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(14 [2603])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(15 [2881])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(16 [2885])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(17 [2890])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(18 [2903])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(19 [3711])</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(20 [IR0001])</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     AllPresent\n",
       "Equity(0 [1101])           True\n",
       "Equity(1 [1301])           True\n",
       "Equity(2 [1303])           True\n",
       "Equity(3 [1802])           True\n",
       "Equity(4 [2002])           True\n",
       "Equity(5 [2101])           True\n",
       "Equity(6 [2303])           True\n",
       "Equity(7 [2317])           True\n",
       "Equity(8 [2330])           True\n",
       "Equity(9 [2337])           True\n",
       "Equity(10 [2382])          True\n",
       "Equity(11 [2388])          True\n",
       "Equity(12 [2451])          True\n",
       "Equity(13 [2454])          True\n",
       "Equity(14 [2603])          True\n",
       "Equity(15 [2881])          True\n",
       "Equity(16 [2885])          True\n",
       "Equity(17 [2890])          True\n",
       "Equity(18 [2903])          True\n",
       "Equity(19 [3711])         False\n",
       "Equity(20 [IR0001])        True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from zipline.pipeline.filters import AllPresent\n",
    "\n",
    "def make_pipeline():\n",
    "    return Pipeline(\n",
    "        columns = {\n",
    "            \"AllPresent\": AllPresent(\n",
    "                inputs = [TWEquityPricing.close], \n",
    "                window_length = 10\n",
    "            )\n",
    "        }\n",
    "    )\n",
    "\n",
    "run_pipeline(make_pipeline(), start, end).loc[\"2018-05-04\"]\n",
    "# 可注意到 3711 在 2018-04-30 才上市，因此 2018-05-04 為 False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b590e42c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Equity(0 [1101]),\n",
       " Equity(1 [1301]),\n",
       " Equity(2 [1303]),\n",
       " Equity(3 [1802]),\n",
       " Equity(4 [2002]),\n",
       " Equity(5 [2101]),\n",
       " Equity(6 [2303]),\n",
       " Equity(7 [2317]),\n",
       " Equity(8 [2330]),\n",
       " Equity(9 [2337]),\n",
       " Equity(10 [2382]),\n",
       " Equity(11 [2388]),\n",
       " Equity(12 [2451]),\n",
       " Equity(13 [2454]),\n",
       " Equity(14 [2603]),\n",
       " Equity(15 [2881]),\n",
       " Equity(16 [2885]),\n",
       " Equity(17 [2890]),\n",
       " Equity(18 [2903]),\n",
       " Equity(19 [3711]),\n",
       " Equity(20 [IR0001])]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 首先抓出所有 bundle 中的股價\n",
    "from zipline.data import bundles\n",
    "\n",
    "bundle = bundles.load('tquant')\n",
    "sids = bundle.asset_finder.equities_sids\n",
    "assets = bundle.asset_finder.retrieve_all(sids)\n",
    "assets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17bcacc3",
   "metadata": {},
   "source": [
    "<span id=\"StaticAssets\"></span>\n",
    "\n",
    "## zipline.pipeline.filters.<font color=#57a892>StaticAssets</font>\n",
    "\n",
    "指定特定資產為 True。\n",
    "\n",
    ">### Parameters:\n",
    ">* assets _( zipline.assets.Asset, iterable )_ - 指定資產。\n",
    "\n",
    "[Go to Menu](#menu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "894c82f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================================================================\n",
      "                     StaticAssets\n",
      "Equity(0 [1101])            False\n",
      "Equity(1 [1301])            False\n",
      "Equity(2 [1303])            False\n",
      "Equity(3 [1802])            False\n",
      "Equity(4 [2002])             True\n",
      "Equity(5 [2101])             True\n",
      "Equity(6 [2303])             True\n",
      "Equity(7 [2317])             True\n",
      "Equity(8 [2330])            False\n",
      "Equity(9 [2337])            False\n",
      "Equity(10 [2382])           False\n",
      "Equity(11 [2388])           False\n",
      "Equity(12 [2451])           False\n",
      "Equity(13 [2454])           False\n",
      "Equity(14 [2603])           False\n",
      "Equity(15 [2881])           False\n",
      "Equity(16 [2885])           False\n",
      "Equity(17 [2890])           False\n",
      "Equity(18 [2903])           False\n",
      "Equity(19 [3711])           False\n",
      "Equity(20 [IR0001])         False\n"
     ]
    }
   ],
   "source": [
    "from zipline.pipeline.filters import StaticAssets\n",
    "from zipline import run_algorithm\n",
    "from zipline.api import symbol, attach_pipeline, pipeline_output\n",
    "\n",
    "def make_pipeline():\n",
    "    return Pipeline(\n",
    "        columns = {\n",
    "            \"StaticAssets\": StaticAssets(\n",
    "                assets = assets[4:8]\n",
    "            )\n",
    "        }\n",
    "    )\n",
    "\n",
    "def initialize(context):\n",
    "    my_pipe = attach_pipeline(make_pipeline(), 'my_pipe')\n",
    "    \n",
    "def handle_data(context, data):\n",
    "    pipe = pipeline_output('my_pipe')\n",
    "    print(\"=\" * 100)\n",
    "    print(pipe)\n",
    "\n",
    "def analyze(context, perf):\n",
    "    pass\n",
    "\n",
    "results = run_algorithm(\n",
    "    start = pd.Timestamp('2019-01-02', tz='utc'),\n",
    "    end = pd.Timestamp('2019-01-02', tz='utc'),\n",
    "    initialize = initialize,\n",
    "    capital_base = 1e6,\n",
    "    handle_data = handle_data,\n",
    "    analyze = analyze, \n",
    "    bundle = 'tquant'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42d04544",
   "metadata": {},
   "source": [
    "<span id=\"StaticSids\"></span>\n",
    "\n",
    "## zipline.pipeline.filters.<font color=#57a892>StaticSids</font>\n",
    "\n",
    "指定特定資產為 True。\n",
    "\n",
    ">### Parameters:\n",
    ">* sids _( int, iterable )_ - 指定資產的 sid。\n",
    "\n",
    "[Go to Menu](#menu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "262ae3f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================================================================\n",
      "                     StaticSids\n",
      "Equity(0 [1101])          False\n",
      "Equity(1 [1301])          False\n",
      "Equity(2 [1303])          False\n",
      "Equity(3 [1802])          False\n",
      "Equity(4 [2002])           True\n",
      "Equity(5 [2101])           True\n",
      "Equity(6 [2303])           True\n",
      "Equity(7 [2317])           True\n",
      "Equity(8 [2330])          False\n",
      "Equity(9 [2337])          False\n",
      "Equity(10 [2382])         False\n",
      "Equity(11 [2388])         False\n",
      "Equity(12 [2451])         False\n",
      "Equity(13 [2454])         False\n",
      "Equity(14 [2603])         False\n",
      "Equity(15 [2881])         False\n",
      "Equity(16 [2885])         False\n",
      "Equity(17 [2890])         False\n",
      "Equity(18 [2903])         False\n",
      "Equity(19 [3711])         False\n",
      "Equity(20 [IR0001])       False\n"
     ]
    }
   ],
   "source": [
    "from zipline.pipeline.filters import StaticSids\n",
    "from zipline import run_algorithm\n",
    "from zipline.api import symbol, attach_pipeline, pipeline_output\n",
    "\n",
    "def make_pipeline():\n",
    "    return Pipeline(\n",
    "        columns = {\n",
    "            \"StaticSids\": StaticSids(\n",
    "                sids = range(4,8)\n",
    "            )\n",
    "        }\n",
    "    )\n",
    "\n",
    "def initialize(context):\n",
    "    my_pipe = attach_pipeline(make_pipeline(), 'my_pipe')\n",
    "    \n",
    "def handle_data(context, data):\n",
    "    pipe = pipeline_output('my_pipe')\n",
    "    print(\"=\" * 100)\n",
    "    print(pipe)\n",
    "\n",
    "def analyze(context, perf):\n",
    "    pass\n",
    "\n",
    "results = run_algorithm(\n",
    "    start = pd.Timestamp('2019-01-01', tz='utc'),\n",
    "    end = pd.Timestamp('2019-01-02', tz='utc'),\n",
    "    initialize = initialize,\n",
    "    capital_base = 1e6,\n",
    "    handle_data = handle_data,\n",
    "    analyze = analyze, \n",
    "    bundle = 'tquant'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae06f54f",
   "metadata": {},
   "source": [
    "<span id=\"SingleAsset\"></span>\n",
    "\n",
    "## zipline.pipeline.filters.<font color=#57a892>SingleAsset</font>\n",
    "\n",
    "指定單一特定資產為 True。\n",
    "\n",
    ">### Parameters:\n",
    ">* assets _( zipline.assets.Asset )_ - 指定資產。\n",
    "\n",
    "[Go to Menu](#menu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4c4aebe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================================================================\n",
      "                     SingleAsset\n",
      "Equity(0 [1101])           False\n",
      "Equity(1 [1301])           False\n",
      "Equity(2 [1303])           False\n",
      "Equity(3 [1802])           False\n",
      "Equity(4 [2002])            True\n",
      "Equity(5 [2101])           False\n",
      "Equity(6 [2303])           False\n",
      "Equity(7 [2317])           False\n",
      "Equity(8 [2330])           False\n",
      "Equity(9 [2337])           False\n",
      "Equity(10 [2382])          False\n",
      "Equity(11 [2388])          False\n",
      "Equity(12 [2451])          False\n",
      "Equity(13 [2454])          False\n",
      "Equity(14 [2603])          False\n",
      "Equity(15 [2881])          False\n",
      "Equity(16 [2885])          False\n",
      "Equity(17 [2890])          False\n",
      "Equity(18 [2903])          False\n",
      "Equity(19 [3711])          False\n",
      "Equity(20 [IR0001])        False\n"
     ]
    }
   ],
   "source": [
    "from zipline.pipeline.filters import SingleAsset\n",
    "from zipline import run_algorithm\n",
    "from zipline.api import symbol, attach_pipeline, pipeline_output\n",
    "\n",
    "def make_pipeline():\n",
    "    return Pipeline(\n",
    "        columns = {\n",
    "            \"SingleAsset\": SingleAsset(\n",
    "                asset = assets[4]\n",
    "            )\n",
    "        }\n",
    "    )\n",
    "\n",
    "def initialize(context):\n",
    "    my_pipe = attach_pipeline(make_pipeline(), 'my_pipe')\n",
    "    \n",
    "def handle_data(context, data):\n",
    "    pipe = pipeline_output('my_pipe')\n",
    "    print(\"=\" * 100)\n",
    "    print(pipe)\n",
    "\n",
    "def analyze(context, perf):\n",
    "    pass\n",
    "\n",
    "results = run_algorithm(\n",
    "    start = pd.Timestamp('2019-01-02', tz='utc'),\n",
    "    end = pd.Timestamp('2019-01-02', tz='utc'),\n",
    "    initialize = initialize,\n",
    "    capital_base = 1e6,\n",
    "    handle_data = handle_data,\n",
    "    analyze = analyze, \n",
    "    bundle = 'tquant'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02dcc34f-17de-4783-99fb-054b1894b9c6",
   "metadata": {},
   "source": [
    "<span id=\"top/bottom\"></span>\n",
    "\n",
    "## <font color=#57a892>top/bottom</font>\n",
    "\n",
    "將最大 / 最小的 N 項標為 True，其餘為 False。\n",
    "\n",
    ">### Parameters:\n",
    ">* N _( int )_ - 數量。\n",
    ">* mask _( zipline.pipeline.Filter, optional )_ - 預設為無，若加上 mask，僅排名 mask = True 的項目。\n",
    ">* groupby _( zipline.pipeline.Classifier, optional )_ -\n",
    ">   * 預設為無。\n",
    ">   * 必須為 `Classifier`，若給定 `Classifier` 則是每個分類取最大／最小的 N 項。\n",
    "\n",
    "[Go to Menu](#menu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e8aa6e-6e63-4bb2-8175-1d20e51ea308",
   "metadata": {},
   "source": [
    "### Examples－top\n",
    "\n",
    "在以下範例中： \n",
    "* *sma_quartiles* 將股票依據 SMA 由低至高分成四個級距 (0, 1, 2, 3)\n",
    "* *top_beta* 會先篩出平均成交額超過 5 億的股票，再從 4 個 SMA 等級中，各挑 beta 最高的 2 支股票。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ca2f4c30-0700-4dc5-9035-005d2fc677d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "assets_ex_IR0001 = [i for i in assets if i!= bundle.asset_finder.lookup_symbol('IR0001', as_of_date=None)]\n",
    "\n",
    "def make_pipeline():\n",
    "\n",
    "#     quartiles\n",
    "    sma = SimpleMovingAverage(inputs = [TWEquityPricing.close], window_length = 30)\n",
    "    sma_quartiles = sma.quartiles(mask = StaticAssets(assets_ex_IR0001))\n",
    "    \n",
    "#     top  \n",
    "    sbeta = SimpleBeta(target = bundle.asset_finder.lookup_symbol('IR0001', as_of_date=None),\n",
    "                       regression_length = 300,\n",
    "                       allowed_missing_percentage = 0.25)\n",
    "    \n",
    "    adv = AverageDollarVolume(window_length = 10)\n",
    "    top_dollar = adv > 500000000\n",
    "    top_beta = sbeta.top(N = 2, mask = top_dollar & StaticAssets(assets_ex_IR0001), groupby = sma_quartiles)\n",
    "    \n",
    "    return Pipeline(\n",
    "        columns={\n",
    "            'SMA': sma,\n",
    "            'SMA Quartile': sma_quartiles,\n",
    "            'Average Dollar Volume':adv,\n",
    "            'Simple Beta': sbeta,\n",
    "            'top_beta': top_beta\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "315f0a96-3be6-463b-aad3-d482b86ba0ac",
   "metadata": {},
   "source": [
    "可以看到在 *top_beta* 欄位中，4 個 SMA 級距各有兩檔股票被標為 True，且平均成交額皆大於 5 億 ( 5e+08 )。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19e75435-b7a1-4fc4-a8a4-df2a1d47e0b3",
   "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></th>\n",
       "      <th>Average Dollar Volume</th>\n",
       "      <th>SMA Quartile</th>\n",
       "      <th>Simple Beta</th>\n",
       "      <th>top_beta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">2022-02-07 00:00:00+00:00</th>\n",
       "      <th>Equity(14 [2603])</th>\n",
       "      <td>1.386923e+10</td>\n",
       "      <td>3</td>\n",
       "      <td>1.920292</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(13 [2454])</th>\n",
       "      <td>5.716348e+09</td>\n",
       "      <td>3</td>\n",
       "      <td>1.453462</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(7 [2317])</th>\n",
       "      <td>2.912853e+09</td>\n",
       "      <td>2</td>\n",
       "      <td>1.142091</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2 [1303])</th>\n",
       "      <td>6.476843e+08</td>\n",
       "      <td>2</td>\n",
       "      <td>0.881953</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(6 [2303])</th>\n",
       "      <td>8.088602e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>1.774655</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(9 [2337])</th>\n",
       "      <td>7.098468e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>1.380687</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(4 [2002])</th>\n",
       "      <td>1.103390e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>1.134731</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(16 [2885])</th>\n",
       "      <td>6.144226e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>0.843807</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Average Dollar Volume  \\\n",
       "2022-02-07 00:00:00+00:00 Equity(14 [2603])           1.386923e+10   \n",
       "                          Equity(13 [2454])           5.716348e+09   \n",
       "                          Equity(7 [2317])            2.912853e+09   \n",
       "                          Equity(2 [1303])            6.476843e+08   \n",
       "                          Equity(6 [2303])            8.088602e+09   \n",
       "                          Equity(9 [2337])            7.098468e+08   \n",
       "                          Equity(4 [2002])            1.103390e+09   \n",
       "                          Equity(16 [2885])           6.144226e+08   \n",
       "\n",
       "                                             SMA Quartile  Simple Beta  \\\n",
       "2022-02-07 00:00:00+00:00 Equity(14 [2603])             3     1.920292   \n",
       "                          Equity(13 [2454])             3     1.453462   \n",
       "                          Equity(7 [2317])              2     1.142091   \n",
       "                          Equity(2 [1303])              2     0.881953   \n",
       "                          Equity(6 [2303])              1     1.774655   \n",
       "                          Equity(9 [2337])              1     1.380687   \n",
       "                          Equity(4 [2002])              0     1.134731   \n",
       "                          Equity(16 [2885])             0     0.843807   \n",
       "\n",
       "                                             top_beta  \n",
       "2022-02-07 00:00:00+00:00 Equity(14 [2603])      True  \n",
       "                          Equity(13 [2454])      True  \n",
       "                          Equity(7 [2317])       True  \n",
       "                          Equity(2 [1303])       True  \n",
       "                          Equity(6 [2303])       True  \n",
       "                          Equity(9 [2337])       True  \n",
       "                          Equity(4 [2002])       True  \n",
       "                          Equity(16 [2885])      True  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = run_pipeline(make_pipeline(), end, end)\n",
    "result.loc[:,['Average Dollar Volume', 'SMA Quartile', 'Simple Beta', 'top_beta']]\\\n",
    "            [result.top_beta == True].sort_values(['SMA Quartile', 'Simple Beta'], ascending=[False, False])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c1c9fe-662c-4c8d-8d53-32b04f416a90",
   "metadata": {},
   "source": [
    "<span id=\"percentile_between\"></span>\n",
    "\n",
    "## <font color=#57a892>percentile_between</font>\n",
    "\n",
    "將數值大小介於兩個百分位數（含）之間的資料標為 True，其餘為 False。\n",
    "\n",
    ">### Parameters:\n",
    ">* min_percentile _( float )_ - 下限，介於 [0.0, 100.0]。\n",
    ">* max_percentile _( float )_ - 上限，介於 [0.0, 100.0]。\n",
    ">* mask _( zipline.pipeline.Filter, optional )_ - 預設為無，若加上 mask，僅排名 mask = True 的項目。\n",
    "\n",
    "[Go to Menu](#menu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "733ce9e5-c767-4b7e-878a-3a740cfccf51",
   "metadata": {},
   "source": [
    "### Examples－percentile_between\n",
    "\n",
    "在以下範例中：\n",
    "```python\n",
    "daily_r = DailyReturns()\n",
    "top_r = daily_r.percentile_between(min_percentile = 80, max_percentile = 100, mask=StaticAssets(assets_ex_IR0001))\n",
    "```\n",
    "篩選出日報酬率前 20% 的股票。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d8c21c24-905e-48a3-bc78-4a9b37041620",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_pipeline():\n",
    "\n",
    "#     percentile_between  \n",
    "    daily_r = DailyReturns(inputs = [TWEquityPricing.close])\n",
    "    top_r = daily_r.percentile_between(min_percentile = 80, max_percentile = 100, mask=StaticAssets(assets_ex_IR0001))\n",
    "    \n",
    "    return Pipeline(\n",
    "        columns={\n",
    "            'Daily Return': daily_r,\n",
    "            'top_r': top_r\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65941ae6-91da-471d-9565-d29273dc48ae",
   "metadata": {},
   "source": [
    "共有 20 x ( 100% - 80% ) = 4 檔股票被標為 True。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e0434a41-471e-4670-8790-3ac473685a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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></th>\n",
       "      <th>Daily Return</th>\n",
       "      <th>top_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">2022-02-07 00:00:00+00:00</th>\n",
       "      <th>Equity(9 [2337])</th>\n",
       "      <td>0.062176</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(14 [2603])</th>\n",
       "      <td>0.027273</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2 [1303])</th>\n",
       "      <td>0.010526</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(18 [2903])</th>\n",
       "      <td>0.009615</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(11 [2388])</th>\n",
       "      <td>0.007519</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(4 [2002])</th>\n",
       "      <td>0.005979</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1 [1301])</th>\n",
       "      <td>0.004785</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(12 [2451])</th>\n",
       "      <td>0.004310</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(16 [2885])</th>\n",
       "      <td>0.003976</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(17 [2890])</th>\n",
       "      <td>0.003049</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Daily Return  top_r\n",
       "2022-02-07 00:00:00+00:00 Equity(9 [2337])       0.062176   True\n",
       "                          Equity(14 [2603])      0.027273   True\n",
       "                          Equity(2 [1303])       0.010526   True\n",
       "                          Equity(18 [2903])      0.009615   True\n",
       "                          Equity(11 [2388])      0.007519  False\n",
       "                          Equity(4 [2002])       0.005979  False\n",
       "                          Equity(1 [1301])       0.004785  False\n",
       "                          Equity(12 [2451])      0.004310  False\n",
       "                          Equity(16 [2885])      0.003976  False\n",
       "                          Equity(17 [2890])      0.003049  False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = run_pipeline(make_pipeline(), end, end)\n",
    "result.loc[:,['Daily Return','top_r']].sort_values(by = 'Daily Return', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be733d8-d9fd-4e91-ad26-5f8898166277",
   "metadata": {},
   "source": [
    "<span id=\"if_else\"></span>\n",
    "\n",
    "## <font color=#57a892>if_else</font>(if_true, if_false)\n",
    "\n",
    "在 `if_else` 函數前會先給定一個條件，若符合條件則回傳 *if_true* 的值，不符合條件則回傳 *if_false* 的值。\n",
    "\n",
    ">### Parameters:\n",
    ">* if_true _( zipline.pipeline.term.ComputableTerm )_ - 符合條件回傳的值。\n",
    ">* if_false _( zipline.pipeline.term.ComputableTerm )_ - 不符合條件回傳的值。\n",
    "\n",
    "[Go to Menu](#menu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b43deb8b-1d36-4403-9f22-8c0ea8e8edc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently used TEJ API key call quota 138/100000 (0.14%)\n",
      "Currently used TEJ API key data quota 992682/10000000 (9.93%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-07-09 05:28:16.975164] INFO: zipline.data.bundles.core: Ingesting fundamentals.\n",
      "[2024-07-09 05:28:34.900705] INFO: zipline.data.bundles.core: Ingest fundamentals successfully.\n"
     ]
    }
   ],
   "source": [
    "columns = ['Industry', 'Sub_Industry']\n",
    "\n",
    "fields = ' '.join(columns)\n",
    "os.environ['fields'] = fields\n",
    "\n",
    "!zipline ingest -b fundamentals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35156211-5bb4-42a8-aa71-9b152ae730cb",
   "metadata": {},
   "source": [
    "### Examples - if_else\n",
    "\n",
    "```python\n",
    "ind = TQAltDataSet.Sub_Industry.latest.eq('').if_else(TQAltDataSet.Industry.latest, TQAltDataSet.Sub_Industry.latest)\n",
    "```\n",
    "此範例的條件為子產業別 ( Sub_Industry ) 是否沒有值，若符合條件則回傳主產業別 ( Industry )，否則回傳子產業別 ( Sub_Industry )。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "180a6d22-465c-4d72-a07b-54533b82dba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>主產業別</th>\n",
       "      <th>子產業別</th>\n",
       "      <th>是否符合條件</th>\n",
       "      <th>回傳產業</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">2022-02-07 00:00:00+00:00</th>\n",
       "      <th>Equity(0 [1101])</th>\n",
       "      <td>M1100 水泥工業</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "      <td>M1100 水泥工業</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1 [1301])</th>\n",
       "      <td>M1300 塑膠工業</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "      <td>M1300 塑膠工業</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2 [1303])</th>\n",
       "      <td>M1300 塑膠工業</td>\n",
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       "      <td>True</td>\n",
       "      <td>M1300 塑膠工業</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(3 [1802])</th>\n",
       "      <td>M1800 玻璃陶瓷</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "      <td>M1800 玻璃陶瓷</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(4 [2002])</th>\n",
       "      <td>M2000 鋼鐵工業</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "      <td>M2000 鋼鐵工業</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(5 [2101])</th>\n",
       "      <td>M2100 橡膠工業</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "      <td>M2100 橡膠工業</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(6 [2303])</th>\n",
       "      <td>M2300 電子工業</td>\n",
       "      <td>M2324 半導體業</td>\n",
       "      <td>False</td>\n",
       "      <td>M2324 半導體業</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(7 [2317])</th>\n",
       "      <td>M2300 電子工業</td>\n",
       "      <td>M2331 其他電子業</td>\n",
       "      <td>False</td>\n",
       "      <td>M2331 其他電子業</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(8 [2330])</th>\n",
       "      <td>M2300 電子工業</td>\n",
       "      <td>M2324 半導體業</td>\n",
       "      <td>False</td>\n",
       "      <td>M2324 半導體業</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(9 [2337])</th>\n",
       "      <td>M2300 電子工業</td>\n",
       "      <td>M2324 半導體業</td>\n",
       "      <td>False</td>\n",
       "      <td>M2324 半導體業</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  主產業別         子產業別  是否符合條件  \\\n",
       "2022-02-07 00:00:00+00:00 Equity(0 [1101])  M1100 水泥工業                 True   \n",
       "                          Equity(1 [1301])  M1300 塑膠工業                 True   \n",
       "                          Equity(2 [1303])  M1300 塑膠工業                 True   \n",
       "                          Equity(3 [1802])  M1800 玻璃陶瓷                 True   \n",
       "                          Equity(4 [2002])  M2000 鋼鐵工業                 True   \n",
       "                          Equity(5 [2101])  M2100 橡膠工業                 True   \n",
       "                          Equity(6 [2303])  M2300 電子工業   M2324 半導體業   False   \n",
       "                          Equity(7 [2317])  M2300 電子工業  M2331 其他電子業   False   \n",
       "                          Equity(8 [2330])  M2300 電子工業   M2324 半導體業   False   \n",
       "                          Equity(9 [2337])  M2300 電子工業   M2324 半導體業   False   \n",
       "\n",
       "                                                   回傳產業  \n",
       "2022-02-07 00:00:00+00:00 Equity(0 [1101])   M1100 水泥工業  \n",
       "                          Equity(1 [1301])   M1300 塑膠工業  \n",
       "                          Equity(2 [1303])   M1300 塑膠工業  \n",
       "                          Equity(3 [1802])   M1800 玻璃陶瓷  \n",
       "                          Equity(4 [2002])   M2000 鋼鐵工業  \n",
       "                          Equity(5 [2101])   M2100 橡膠工業  \n",
       "                          Equity(6 [2303])   M2324 半導體業  \n",
       "                          Equity(7 [2317])  M2331 其他電子業  \n",
       "                          Equity(8 [2330])   M2324 半導體業  \n",
       "                          Equity(9 [2337])   M2324 半導體業  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_pipeline():\n",
    "\n",
    "    Industry = TQAltDataSet.Industry.latest\n",
    "    Sub_Industry = TQAltDataSet.Sub_Industry.latest\n",
    "    check = TQAltDataSet.Sub_Industry.latest.eq('')\n",
    "    ind = TQAltDataSet.Sub_Industry.latest.eq('').if_else(TQAltDataSet.Industry.latest, TQAltDataSet.Sub_Industry.latest)\n",
    "    \n",
    "    return Pipeline(\n",
    "        columns={\n",
    "            '主產業別': Industry,\n",
    "            '子產業別': Sub_Industry,\n",
    "            '是否符合條件': check,\n",
    "            '回傳產業': ind\n",
    "        }\n",
    "    )\n",
    "\n",
    "run_pipeline(make_pipeline(), end, end).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e276fffd-eb56-4623-bedb-867fb68548b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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