{ "cells": [ { "cell_type": "markdown", "id": "42795695", "metadata": {}, "source": [ "\n", "# Pipeline built-in filters\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": [ "\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": [ "\n", "\n", "## zipline.pipeline.filters.All\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": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ALL
2018-02-06 00:00:00+00:00Equity(0 [1101])False
Equity(1 [1301])True
Equity(2 [1303])True
Equity(3 [1802])False
Equity(4 [2002])False
.........
2022-01-26 00:00:00+00:00Equity(16 [2885])False
Equity(17 [2890])False
Equity(18 [2903])False
Equity(19 [3711])True
Equity(20 [IR0001])True
\n", "

20340 rows × 1 columns

\n", "
" ], "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": [ "\n", "\n", "## zipline.pipeline.filters.Any\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": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Any
2018-02-06 00:00:00+00:00Equity(0 [1101])False
Equity(1 [1301])True
Equity(2 [1303])True
Equity(3 [1802])False
Equity(4 [2002])False
.........
2022-01-26 00:00:00+00:00Equity(16 [2885])False
Equity(17 [2890])False
Equity(18 [2903])False
Equity(19 [3711])True
Equity(20 [IR0001])True
\n", "

20340 rows × 1 columns

\n", "
" ], "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": [ "\n", "\n", "## zipline.pipeline.filters.AtLeastN\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": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AtLeastN
2018-02-06 00:00:00+00:00Equity(0 [1101])False
Equity(1 [1301])True
Equity(2 [1303])True
Equity(3 [1802])False
Equity(4 [2002])False
.........
2022-01-26 00:00:00+00:00Equity(16 [2885])False
Equity(17 [2890])False
Equity(18 [2903])False
Equity(19 [3711])True
Equity(20 [IR0001])True
\n", "

20340 rows × 1 columns

\n", "
" ], "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": [ "\n", "\n", "## zipline.pipeline.filters.AllPresent\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": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AllPresent
Equity(0 [1101])True
Equity(1 [1301])True
Equity(2 [1303])True
Equity(3 [1802])True
Equity(4 [2002])True
Equity(5 [2101])True
Equity(6 [2303])True
Equity(7 [2317])True
Equity(8 [2330])True
Equity(9 [2337])True
Equity(10 [2382])True
Equity(11 [2388])True
Equity(12 [2451])True
Equity(13 [2454])True
Equity(14 [2603])True
Equity(15 [2881])True
Equity(16 [2885])True
Equity(17 [2890])True
Equity(18 [2903])True
Equity(19 [3711])False
Equity(20 [IR0001])True
\n", "
" ], "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": [ "\n", "\n", "## zipline.pipeline.filters.StaticAssets\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": [ "\n", "\n", "## zipline.pipeline.filters.StaticSids\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": [ "\n", "\n", "## zipline.pipeline.filters.SingleAsset\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": [ "\n", "\n", "## top/bottom\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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Average Dollar VolumeSMA QuartileSimple Betatop_beta
2022-02-07 00:00:00+00:00Equity(14 [2603])1.386923e+1031.920292True
Equity(13 [2454])5.716348e+0931.453462True
Equity(7 [2317])2.912853e+0921.142091True
Equity(2 [1303])6.476843e+0820.881953True
Equity(6 [2303])8.088602e+0911.774655True
Equity(9 [2337])7.098468e+0811.380687True
Equity(4 [2002])1.103390e+0901.134731True
Equity(16 [2885])6.144226e+0800.843807True
\n", "
" ], "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": [ "\n", "\n", "## percentile_between\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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Daily Returntop_r
2022-02-07 00:00:00+00:00Equity(9 [2337])0.062176True
Equity(14 [2603])0.027273True
Equity(2 [1303])0.010526True
Equity(18 [2903])0.009615True
Equity(11 [2388])0.007519False
Equity(4 [2002])0.005979False
Equity(1 [1301])0.004785False
Equity(12 [2451])0.004310False
Equity(16 [2885])0.003976False
Equity(17 [2890])0.003049False
\n", "
" ], "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": [ "\n", "\n", "## if_else(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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
主產業別子產業別是否符合條件回傳產業
2022-02-07 00:00:00+00:00Equity(0 [1101])M1100 水泥工業TrueM1100 水泥工業
Equity(1 [1301])M1300 塑膠工業TrueM1300 塑膠工業
Equity(2 [1303])M1300 塑膠工業TrueM1300 塑膠工業
Equity(3 [1802])M1800 玻璃陶瓷TrueM1800 玻璃陶瓷
Equity(4 [2002])M2000 鋼鐵工業TrueM2000 鋼鐵工業
Equity(5 [2101])M2100 橡膠工業TrueM2100 橡膠工業
Equity(6 [2303])M2300 電子工業M2324 半導體業FalseM2324 半導體業
Equity(7 [2317])M2300 電子工業M2331 其他電子業FalseM2331 其他電子業
Equity(8 [2330])M2300 電子工業M2324 半導體業FalseM2324 半導體業
Equity(9 [2337])M2300 電子工業M2324 半導體業FalseM2324 半導體業
\n", "
" ], "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": [] } ], "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": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 5 }