{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/qlib/blob/main/examples/workflow_by_code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Copyright (c) Microsoft Corporation.\n",
"# Licensed under the MIT License."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys, site\n",
"from pathlib import Path\n",
"\n",
"################################# NOTE #################################\n",
"# Please be aware that if colab installs the latest numpy and pyqlib #\n",
"# in this cell, users should RESTART the runtime in order to run the #\n",
"# following cells successfully. #\n",
"########################################################################\n",
"\n",
"try:\n",
" import qlib\n",
"except ImportError:\n",
" # install qlib\n",
" ! pip install --upgrade numpy\n",
" ! pip install pyqlib\n",
" if \"google.colab\" in sys.modules:\n",
" # The Google colab environment is a little outdated. We have to downgrade the pyyaml to make it compatible with other packages\n",
" ! pip install pyyaml==5.4.1\n",
" # reload\n",
" site.main()\n",
"\n",
"scripts_dir = Path.cwd().parent.joinpath(\"scripts\")\n",
"if not scripts_dir.joinpath(\"get_data.py\").exists():\n",
" # download get_data.py script\n",
" scripts_dir = Path(\"~/tmp/qlib_code/scripts\").expanduser().resolve()\n",
" scripts_dir.mkdir(parents=True, exist_ok=True)\n",
" import requests\n",
"\n",
" with requests.get(\"https://raw.githubusercontent.com/microsoft/qlib/main/scripts/get_data.py\", timeout=10) as resp:\n",
" with open(scripts_dir.joinpath(\"get_data.py\"), \"wb\") as fp:\n",
" fp.write(resp.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import qlib\n",
"import pandas as pd\n",
"from qlib.constant import REG_CN\n",
"from qlib.utils import exists_qlib_data, init_instance_by_config\n",
"from qlib.workflow import R\n",
"from qlib.workflow.record_temp import SignalRecord, PortAnaRecord\n",
"from qlib.utils import flatten_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# use default data\n",
"# NOTE: need to download data from remote: python scripts/get_data.py qlib_data_cn --target_dir ~/.qlib/qlib_data/cn_data\n",
"provider_uri = \"~/.qlib/qlib_data/cn_data\" # target_dir\n",
"if not exists_qlib_data(provider_uri):\n",
" print(f\"Qlib data is not found in {provider_uri}\")\n",
" sys.path.append(str(scripts_dir))\n",
" from get_data import GetData\n",
"\n",
" GetData().qlib_data(target_dir=provider_uri, region=REG_CN)\n",
"qlib.init(provider_uri=provider_uri, region=REG_CN)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"market = \"csi300\"\n",
"benchmark = \"SH000300\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# train model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"###################################\n",
"# train model\n",
"###################################\n",
"data_handler_config = {\n",
" \"start_time\": \"2008-01-01\",\n",
" \"end_time\": \"2020-08-01\",\n",
" \"fit_start_time\": \"2008-01-01\",\n",
" \"fit_end_time\": \"2014-12-31\",\n",
" \"instruments\": market,\n",
"}\n",
"\n",
"task = {\n",
" \"model\": {\n",
" \"class\": \"LGBModel\",\n",
" \"module_path\": \"qlib.contrib.model.gbdt\",\n",
" \"kwargs\": {\n",
" \"loss\": \"mse\",\n",
" \"colsample_bytree\": 0.8879,\n",
" \"learning_rate\": 0.0421,\n",
" \"subsample\": 0.8789,\n",
" \"lambda_l1\": 205.6999,\n",
" \"lambda_l2\": 580.9768,\n",
" \"max_depth\": 8,\n",
" \"num_leaves\": 210,\n",
" \"num_threads\": 20,\n",
" },\n",
" },\n",
" \"dataset\": {\n",
" \"class\": \"DatasetH\",\n",
" \"module_path\": \"qlib.data.dataset\",\n",
" \"kwargs\": {\n",
" \"handler\": {\n",
" \"class\": \"Alpha158\",\n",
" \"module_path\": \"qlib.contrib.data.handler\",\n",
" \"kwargs\": data_handler_config,\n",
" },\n",
" \"segments\": {\n",
" \"train\": (\"2008-01-01\", \"2014-12-31\"),\n",
" \"valid\": (\"2015-01-01\", \"2016-12-31\"),\n",
" \"test\": (\"2017-01-01\", \"2020-08-01\"),\n",
" },\n",
" },\n",
" },\n",
"}\n",
"\n",
"# model initialization\n",
"model = init_instance_by_config(task[\"model\"])\n",
"dataset = init_instance_by_config(task[\"dataset\"])\n",
"\n",
"# start exp to train model\n",
"with R.start(experiment_name=\"train_model\"):\n",
" R.log_params(**flatten_dict(task))\n",
" model.fit(dataset)\n",
" R.save_objects(trained_model=model)\n",
" rid = R.get_recorder().id"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# prediction, backtest & analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"###################################\n",
"# prediction, backtest & analysis\n",
"###################################\n",
"port_analysis_config = {\n",
" \"executor\": {\n",
" \"class\": \"SimulatorExecutor\",\n",
" \"module_path\": \"qlib.backtest.executor\",\n",
" \"kwargs\": {\n",
" \"time_per_step\": \"day\",\n",
" \"generate_portfolio_metrics\": True,\n",
" },\n",
" },\n",
" \"strategy\": {\n",
" \"class\": \"TopkDropoutStrategy\",\n",
" \"module_path\": \"qlib.contrib.strategy.signal_strategy\",\n",
" \"kwargs\": {\n",
" \"model\": model,\n",
" \"dataset\": dataset,\n",
" \"topk\": 50,\n",
" \"n_drop\": 5,\n",
" },\n",
" },\n",
" \"backtest\": {\n",
" \"start_time\": \"2017-01-01\",\n",
" \"end_time\": \"2020-08-01\",\n",
" \"account\": 100000000,\n",
" \"benchmark\": benchmark,\n",
" \"exchange_kwargs\": {\n",
" \"freq\": \"day\",\n",
" \"limit_threshold\": 0.095,\n",
" \"deal_price\": \"close\",\n",
" \"open_cost\": 0.0005,\n",
" \"close_cost\": 0.0015,\n",
" \"min_cost\": 5,\n",
" },\n",
" },\n",
"}\n",
"\n",
"# backtest and analysis\n",
"with R.start(experiment_name=\"backtest_analysis\"):\n",
" recorder = R.get_recorder(recorder_id=rid, experiment_name=\"train_model\")\n",
" model = recorder.load_object(\"trained_model\")\n",
"\n",
" # prediction\n",
" recorder = R.get_recorder()\n",
" ba_rid = recorder.id\n",
" sr = SignalRecord(model, dataset, recorder)\n",
" sr.generate()\n",
"\n",
" # backtest & analysis\n",
" par = PortAnaRecord(recorder, port_analysis_config, \"day\")\n",
" par.generate()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# analyze graphs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from qlib.contrib.report import analysis_model, analysis_position\n",
"from qlib.data import D\n",
"\n",
"recorder = R.get_recorder(recorder_id=ba_rid, experiment_name=\"backtest_analysis\")\n",
"print(recorder)\n",
"pred_df = recorder.load_object(\"pred.pkl\")\n",
"report_normal_df = recorder.load_object(\"portfolio_analysis/report_normal_1day.pkl\")\n",
"positions = recorder.load_object(\"portfolio_analysis/positions_normal_1day.pkl\")\n",
"analysis_df = recorder.load_object(\"portfolio_analysis/port_analysis_1day.pkl\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## analysis position"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### report"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"analysis_position.report_graph(report_normal_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### risk analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"analysis_position.risk_analysis_graph(analysis_df, report_normal_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## analysis model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"label_df = dataset.prepare(\"test\", col_set=\"label\")\n",
"label_df.columns = [\"label\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### score IC"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pred_label = pd.concat([label_df, pred_df], axis=1, sort=True).reindex(label_df.index)\n",
"analysis_position.score_ic_graph(pred_label)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### model performance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"analysis_model.model_performance_graph(pred_label)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
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