{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 建立 Pipeline\n",
    "\n",
    "Pipeline 幫助使用者於每個交易日整理數據，並計算各種交易所需的指標。在編寫交易策略時，會在 `initialize` 函式中使用 `attach_pipeline` 函數將 Pipeline 導入策略當中，協助我們產出交易所需的指標。\n",
    "\n",
    "### zipline.pipeline.Pipeline\n",
    "\n",
    "#### Parameters:\n",
    "* columns: _dict_, optional\n",
    "        columns 為一個將欄位名稱映射到 factors、 filters 或 classifiers 的字典，讓 pipeline 知道要輸出哪些資料\n",
    "* screen: _zipline.pipeline.Filter_, optional\n",
    "        用來設定過濾標的條件。\n",
    "\n",
    "於此例中，我們首先 ingest 台積電與旺宏股價。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tejapi\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import os\n",
    "os.environ['TEJAPI_BASE'] = 'https://api.tej.com.tw'\n",
    "os.environ['TEJAPI_KEY'] = 'your key'\n",
    "\n",
    "os.environ['ticker'] = '2330 2337'\n",
    "os.environ['mdate'] = '20170101 20230701'\n",
    "\n",
    "from zipline.pipeline import Pipeline\n",
    "from zipline.pipeline.data import TWEquityPricing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merging daily equity files:\n",
      "Currently used TEJ API key call quota 971/9223372036854775807 (0.0%)\n",
      "Currently used TEJ API key data quota 55470214/9223372036854775807 (0.0%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-08-06 09:25:26.772739] INFO: zipline.data.bundles.core: Ingesting tquant.\n",
      "[2024-08-06 09:25:32.110433] INFO: zipline.data.bundles.core: Ingest tquant successfully.\n"
     ]
    }
   ],
   "source": [
    "!zipline ingest -b tquant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接著宣告一個 `make_pipeline`，並設定 Pipeline 的 columns 參數。\n",
    "\n",
    "在這邊，我們設定 `columns` 為 `TWEquityPricing` 中的最新收盤價格。`TWEquityPricing` 為價量資料的資料集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_pipeline():\n",
    "    return Pipeline(columns={'close':TWEquityPricing.close.latest})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_pipe = make_pipeline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 運行 Pipeline\n",
    "\n",
    "接著我們使用 `run_pipeline`，`run_pipeline` 僅用於研究，並不用於編寫交易策略。\n",
    "\n",
    "觀察 Pipeline 輸出資料，可以發現 Pipeline 會提供每間公司每日的收盤價。由於我們這邊沒有設定 `screen` 與 `mask`，所以總共就是我們前面 ingest 的兩家公司（2330 與 2327）。\n",
    "\n",
    "### zipline.TQresearch.tej_pipeline.run_pipeline\n",
    "\n",
    "#### Parameters:\n",
    "* pipeline: _zipline.pipeline.Pipeline_\n",
    "        已經定義的 pipeline。\n",
    "* start_date: _str_ or _pd.Timestamp_\n",
    "        起始日期。\n",
    "* end_date: _str_ or _pd.Timestamp_\n",
    "        結束日期。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from zipline.TQresearch.tej_pipeline import run_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = run_pipeline(my_pipe, '2018-01-03', '2022-12-30')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-01-03 00:00:00+00:00</th>\n",
       "      <th>Equity(0 [2330])</th>\n",
       "      <td>232.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1 [2337])</th>\n",
       "      <td>43.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-01-04 00:00:00+00:00</th>\n",
       "      <th>Equity(0 [2330])</th>\n",
       "      <td>237.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1 [2337])</th>\n",
       "      <td>44.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05 00:00:00+00:00</th>\n",
       "      <th>Equity(0 [2330])</th>\n",
       "      <td>239.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             close\n",
       "2018-01-03 00:00:00+00:00 Equity(0 [2330])  232.50\n",
       "                          Equity(1 [2337])   43.60\n",
       "2018-01-04 00:00:00+00:00 Equity(0 [2330])  237.00\n",
       "                          Equity(1 [2337])   44.35\n",
       "2018-01-05 00:00:00+00:00 Equity(0 [2330])  239.50"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  }
 ],
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