mirror of
https://github.com/microsoft/qlib.git
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339 lines
8.4 KiB
Plaintext
339 lines
8.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"from pathlib import Path\n",
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"\n",
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"import qlib\n",
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"import pandas as pd\n",
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"from qlib.config import REG_CN\n",
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"from qlib.contrib.model.gbdt import LGBModel\n",
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"from qlib.contrib.estimator.handler import QLibDataHandlerClose\n",
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"from qlib.contrib.strategy.strategy import TopkDropoutStrategy\n",
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"from qlib.contrib.evaluate import (\n",
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" backtest as normal_backtest,\n",
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" risk_analysis,\n",
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")\n",
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"from qlib.utils import exists_qlib_data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# use default data\n",
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"# NOTE: need to download data from remote: python scripts/get_data.py qlib_data_cn --target_dir ~/.qlib/qlib_data/cn_data\n",
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"provider_uri = \"~/.qlib/qlib_data/cn_data\" # target_dir\n",
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"if not exists_qlib_data(provider_uri):\n",
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" print(f\"Qlib data is not found in {provider_uri}\")\n",
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" sys.path.append(str(Path.cwd().parent.joinpath(\"scripts\")))\n",
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" from get_data import GetData\n",
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" GetData().qlib_data_cn(provider_uri)\n",
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"qlib.init(provider_uri=provider_uri, region=REG_CN)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"MARKET = \"csi300\"\n",
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"BENCHMARK = \"SH000300\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# train model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"###################################\n",
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"# train model\n",
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"###################################\n",
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"DATA_HANDLER_CONFIG = {\n",
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" \"dropna_label\": True,\n",
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" \"start_date\": \"2008-01-01\",\n",
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" \"end_date\": \"2020-08-01\",\n",
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" \"market\": MARKET,\n",
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"}\n",
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"\n",
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"TRAINER_CONFIG = {\n",
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" \"train_start_date\": \"2008-01-01\",\n",
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" \"train_end_date\": \"2014-12-31\",\n",
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" \"validate_start_date\": \"2015-01-01\",\n",
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" \"validate_end_date\": \"2016-12-31\",\n",
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" \"test_start_date\": \"2017-01-01\",\n",
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" \"test_end_date\": \"2020-08-01\",\n",
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"}\n",
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"\n",
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"# use default DataHandler\n",
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"# custom DataHandler, refer to: TODO: DataHandler api url\n",
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"x_train, y_train, x_validate, y_validate, x_test, y_test = QLibDataHandlerClose(**DATA_HANDLER_CONFIG).get_split_data(**TRAINER_CONFIG)\n",
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"\n",
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"\n",
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"MODEL_CONFIG = {\n",
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" \"loss\": \"mse\",\n",
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" \"colsample_bytree\": 0.8879,\n",
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" \"learning_rate\": 0.0421,\n",
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" \"subsample\": 0.8789,\n",
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" \"lambda_l1\": 205.6999,\n",
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" \"lambda_l2\": 580.9768,\n",
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" \"max_depth\": 8,\n",
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" \"num_leaves\": 210,\n",
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" \"num_threads\": 20,\n",
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"}\n",
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"# use default model\n",
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"# custom Model, refer to: TODO: Model api url\n",
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"model = LGBModel(**MODEL_CONFIG)\n",
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"model.fit(x_train, y_train, x_validate, y_validate)\n",
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"_pred = model.predict(x_test)\n",
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"_pred = pd.DataFrame(_pred, index=x_test.index, columns=y_test.columns)\n",
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"\n",
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"# backtest requires pred_score\n",
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"pred_score = pd.DataFrame(index=_pred.index)\n",
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"pred_score[\"score\"] = _pred.iloc(axis=1)[0]\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# backtest"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"###################################\n",
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"# backtest\n",
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"###################################\n",
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"STRATEGY_CONFIG = {\n",
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" \"topk\": 50,\n",
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" \"n_drop\": 5}\n",
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"BACKTEST_CONFIG = {\n",
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" \"verbose\": False,\n",
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" \"limit_threshold\": 0.095,\n",
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" \"account\": 100000000,\n",
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" \"benchmark\": BENCHMARK,\n",
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" \"deal_price\": \"close\",\n",
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" \"open_cost\": 0.0005,\n",
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" \"close_cost\": 0.0015,\n",
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" \"min_cost\": 5,\n",
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" \n",
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"}\n",
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"\n",
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"# use default strategy\n",
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"# custom Strategy, refer to: TODO: Strategy api url\n",
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"strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)\n",
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"report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# analyze"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"###################################\n",
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"# analyze\n",
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"# If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb\n",
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"###################################\n",
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"analysis = dict()\n",
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"analysis[\"excess_return_without_cost\"] = risk_analysis(report_normal[\"return\"] - report_normal[\"bench\"])\n",
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"analysis[\"excess_return_with_cost\"] = risk_analysis(\n",
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" report_normal[\"return\"] - report_normal[\"bench\"] - report_normal[\"cost\"]\n",
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")\n",
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"analysis_df = pd.concat(analysis) # type: pd.DataFrame\n",
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"print(analysis_df)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# analyze graphs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from qlib.contrib.report import analysis_model, analysis_position\n",
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"from qlib.data import D\n",
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"pred_df_dates = pred_score.index.get_level_values(level='datetime')\n",
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"report_normal_df = report_normal\n",
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"positions = positions_normal\n",
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"pred_df = pred_score"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## analysis position"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stock_ret = D.features(D.instruments(MARKET), ['Ref($close, -1)/$close - 1'], pred_df_dates.min(), pred_df_dates.max())\n",
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"stock_ret.columns = ['label']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### report"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"analysis_position.report_graph(report_normal_df)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### risk analysis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"analysis_position.risk_analysis_graph(analysis_df, report_normal_df)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## analysis model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"label_df = D.features(D.instruments(MARKET), ['Ref($close, -2)/Ref($close, -1) - 1'], pred_df_dates.min(), pred_df_dates.max())\n",
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"label_df.columns = ['label']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### score IC"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pred_label = pd.concat([label_df, pred_df], axis=1, sort=True).reindex(label_df.index)\n",
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"analysis_position.score_ic_graph(pred_label)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### model performance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"analysis_model.model_performance_graph(pred_label)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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