mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-06 04:20:57 +08:00
init commit
This commit is contained in:
Young
355
examples/train_backtest_analyze.ipynb
Normal file
355
examples/train_backtest_analyze.ipynb
Normal file
@@ -0,0 +1,355 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sys\n",
|
||||
"from pathlib import Path\n",
|
||||
"\n",
|
||||
"import qlib\n",
|
||||
"import pandas as pd\n",
|
||||
"from qlib.config import REG_CN\n",
|
||||
"from qlib.contrib.model.gbdt import LGBModel\n",
|
||||
"from qlib.contrib.estimator.handler import QLibDataHandlerClose\n",
|
||||
"from qlib.contrib.strategy.strategy import TopkDropoutStrategy\n",
|
||||
"from qlib.contrib.evaluate import (\n",
|
||||
" backtest as normal_backtest,\n",
|
||||
" risk_analysis,\n",
|
||||
")\n",
|
||||
"from qlib.utils import exists_qlib_data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 --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(Path.cwd().parent.joinpath(\"scripts\")))\n",
|
||||
" from get_data import GetData\n",
|
||||
" GetData().qlib_data_cn(provider_uri)\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": {
|
||||
"scrolled": true,
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"###################################\n",
|
||||
"# train model\n",
|
||||
"###################################\n",
|
||||
"DATA_HANDLER_CONFIG = {\n",
|
||||
" \"dropna_label\": True,\n",
|
||||
" \"start_date\": \"2008-01-01\",\n",
|
||||
" \"end_date\": \"2020-08-01\",\n",
|
||||
" \"market\": MARKET,\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"TRAINER_CONFIG = {\n",
|
||||
" \"train_start_date\": \"2008-01-01\",\n",
|
||||
" \"train_end_date\": \"2014-12-31\",\n",
|
||||
" \"validate_start_date\": \"2015-01-01\",\n",
|
||||
" \"validate_end_date\": \"2016-12-31\",\n",
|
||||
" \"test_start_date\": \"2017-01-01\",\n",
|
||||
" \"test_end_date\": \"2020-08-01\",\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"# use default DataHandler\n",
|
||||
"# custom DataHandler, refer to: TODO: DataHandler api url\n",
|
||||
"x_train, y_train, x_validate, y_validate, x_test, y_test = QLibDataHandlerClose(**DATA_HANDLER_CONFIG).get_split_data(**TRAINER_CONFIG)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"MODEL_CONFIG = {\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",
|
||||
"# use default model\n",
|
||||
"# custom Model, refer to: TODO: Model api url\n",
|
||||
"model = LGBModel(**MODEL_CONFIG)\n",
|
||||
"model.fit(x_train, y_train, x_validate, y_validate)\n",
|
||||
"_pred = model.predict(x_test)\n",
|
||||
"_pred = pd.DataFrame(_pred, index=x_test.index, columns=y_test.columns)\n",
|
||||
"\n",
|
||||
"# backtest requires pred_score\n",
|
||||
"pred_score = pd.DataFrame(index=_pred.index)\n",
|
||||
"pred_score[\"score\"] = _pred.iloc(axis=1)[0]\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# backtest"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"###################################\n",
|
||||
"# backtest\n",
|
||||
"###################################\n",
|
||||
"STRATEGY_CONFIG = {\n",
|
||||
" \"topk\": 50,\n",
|
||||
" \"n_drop\": 5",
|
||||
"}\n",
|
||||
"BACKTEST_CONFIG = {\n",
|
||||
" \"verbose\": False,\n",
|
||||
" \"limit_threshold\": 0.095,\n",
|
||||
" \"account\": 100000000,\n",
|
||||
" \"benchmark\": BENCHMARK,\n",
|
||||
" \"deal_price\": \"close\",\n",
|
||||
" \"open_cost\": 0.0005,\n",
|
||||
" \"close_cost\": 0.0015,\n",
|
||||
" \"min_cost\": 5,\n",
|
||||
" \n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"# use default strategy\n",
|
||||
"# custom Strategy, refer to: TODO: Strategy api url\n",
|
||||
"strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)\n",
|
||||
"report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# analyze"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"###################################\n",
|
||||
"# analyze\n",
|
||||
"# If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb\n",
|
||||
"###################################\n",
|
||||
"analysis = dict()\n",
|
||||
"analysis[\"sub_bench\"] = risk_analysis(report_normal[\"return\"] - report_normal[\"bench\"])\n",
|
||||
"analysis[\"sub_cost\"] = risk_analysis(\n",
|
||||
" report_normal[\"return\"] - report_normal[\"bench\"] - report_normal[\"cost\"]\n",
|
||||
")\n",
|
||||
"analysis_df = pd.concat(analysis) # type: pd.DataFrame\n",
|
||||
"print(analysis_df)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get label data\n",
|
||||
"from qlib.data import D\n",
|
||||
"pred_df_dates = pred_score.index.get_level_values(level='datetime')\n",
|
||||
"features_df = D.features(D.instruments(MARKET), ['Ref($close, -1)/$close - 1'], pred_df_dates.min(), pred_df_dates.max())\n",
|
||||
"features_df.columns = ['label']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### score IC"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pred_label = pd.concat([features_df, pred_score], axis=1, sort=True).reindex(features_df.index)\n",
|
||||
"analysis_position.score_ic_graph(pred_label)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### cumulative return"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"analysis_position.cumulative_return_graph(positions_normal, report_normal, features_df)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### risk analysis"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"analysis_position.risk_analysis_graph(analysis_df, report_normal)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### rank label"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"analysis_position.rank_label_graph(positions_normal, features_df, pred_df_dates.min(), pred_df_dates.max())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## analysis model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### model performance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"analysis_model.model_performance_graph(pred_label)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"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
|
||||
}
|
||||
Reference in New Issue
Block a user