mirror of
https://github.com/microsoft/qlib.git
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305 lines
9.9 KiB
Plaintext
305 lines
9.9 KiB
Plaintext
{
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"metadata": {
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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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"version": "3.8.8"
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},
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"orig_nbformat": 2,
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"kernelspec": {
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"name": "pythonjvsc74a57bd0fcc004278713aaede7c629a6a43738a929cb09abb52817d4f72eb70db44cd87b",
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"display_name": "Python 3.8.8 ('qlib_backtest': conda)"
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},
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"metadata": {
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"interpreter": {
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"hash": "fcc004278713aaede7c629a6a43738a929cb09abb52817d4f72eb70db44cd87b"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2,
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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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"# Copyright (c) Microsoft Corporation.\n",
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"# Licensed under the MIT License."
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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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"import sys, site\n",
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"from pathlib import Path\n",
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"\n",
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"################################# NOTE #################################\n",
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"# Please be aware that if colab installs the latest numpy and pyqlib #\n",
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"# in this cell, users should RESTART the runtime in order to run the #\n",
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"# following cells successfully. #\n",
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"########################################################################\n",
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"\n",
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"try:\n",
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" import qlib\n",
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"except ImportError:\n",
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" # install qlib\n",
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" ! pip install --upgrade numpy\n",
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" ! pip install pyqlib\n",
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" # reload\n",
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" site.main()\n",
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"\n",
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"scripts_dir = Path.cwd().parent.joinpath(\"scripts\")\n",
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"if not scripts_dir.joinpath(\"get_data.py\").exists():\n",
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" # download get_data.py script\n",
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" scripts_dir = Path(\"~/tmp/qlib_code/scripts\").expanduser().resolve()\n",
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" scripts_dir.mkdir(parents=True, exist_ok=True)\n",
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" import requests\n",
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" with requests.get(\"https://raw.githubusercontent.com/microsoft/qlib/main/scripts/get_data.py\") as resp:\n",
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" with open(scripts_dir.joinpath(\"get_data.py\"), \"wb\") as fp:\n",
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" fp.write(resp.content)"
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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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"\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.utils import exists_qlib_data, init_instance_by_config, flatten_dict\n",
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"from qlib.workflow import R\n",
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"from qlib.workflow.record_temp import SignalRecord, PortAnaRecord\n",
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"from qlib.tests.data import GetData"
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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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"# use default 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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" GetData().qlib_data(target_dir=provider_uri, region=REG_CN)\n",
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"\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\"\n",
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"\n",
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"###################################\n",
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"# train model\n",
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"###################################\n",
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"\n",
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"data_handler_config = {\n",
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" \"start_time\": \"2008-01-01\",\n",
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" \"end_time\": \"2020-08-01\",\n",
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" \"fit_start_time\": \"2008-01-01\",\n",
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" \"fit_end_time\": \"2014-12-31\",\n",
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" \"instruments\": market,\n",
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"}\n",
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"\n",
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"task = {\n",
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" \"model\": {\n",
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" \"class\": \"LGBModel\",\n",
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" \"module_path\": \"qlib.contrib.model.gbdt\",\n",
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" \"kwargs\": {\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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" },\n",
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" \"dataset\": {\n",
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" \"class\": \"DatasetH\",\n",
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" \"module_path\": \"qlib.data.dataset\",\n",
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" \"kwargs\": {\n",
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" \"handler\": {\n",
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" \"class\": \"Alpha158\",\n",
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" \"module_path\": \"qlib.contrib.data.handler\",\n",
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" \"kwargs\": data_handler_config,\n",
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" },\n",
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" \"segments\": {\n",
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" \"train\": (\"2008-01-01\", \"2014-12-31\"),\n",
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" \"valid\": (\"2015-01-01\", \"2016-12-31\"),\n",
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" \"test\": (\"2017-01-01\", \"2020-08-01\"),\n",
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" },\n",
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" },\n",
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" },\n",
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"}\n",
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"# model initialization\n",
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"model = init_instance_by_config(task[\"model\"])\n",
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"dataset = init_instance_by_config(task[\"dataset\"])\n",
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"\n",
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"# start exp to train model\n",
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"with R.start(experiment_name=\"train_model\"):\n",
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" R.log_params(**flatten_dict(task))\n",
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" model.fit(dataset)\n",
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" R.save_objects(trained_model=model)\n",
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" rid = R.get_recorder().id\n"
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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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"outputPrepend"
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]
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},
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"outputs": [],
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"source": [
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"trade_start_time = \"2017-01-01\"\n",
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"trade_end_time = \"2020-08-01\"\n",
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"\n",
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"port_analysis_config = {\n",
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" \"strategy\": {\n",
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" \"class\": \"TopkDropoutStrategy\",\n",
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" \"module_path\": \"qlib.contrib.strategy.model_strategy\",\n",
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" \"kwargs\": {\n",
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" \"step_bar\": \"week\",\n",
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" \"model\": model,\n",
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" \"dataset\": dataset,\n",
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" \"topk\": 50,\n",
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" \"n_drop\": 5,\n",
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" },\n",
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" },\n",
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" \"env\": {\n",
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" \"class\": \"SplitExecutor\",\n",
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" \"module_path\": \"qlib.contrib.backtest.executor\",\n",
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" \"kwargs\": {\n",
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" \"step_bar\": \"week\",\n",
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" \"generate_report\": True,\n",
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" \"sub_env\": {\n",
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" \"class\": \"SimulatorExecutor\",\n",
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" \"module_path\": \"qlib.contrib.backtest.executor\",\n",
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" \"kwargs\": {\n",
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" \"step_bar\": \"day\",\n",
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" \"verbose\": True,\n",
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" \"generate_report\": True,\n",
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" },\n",
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" },\n",
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" \"sub_strategy\": {\n",
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" \"class\": \"SBBStrategyEMA\",\n",
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" \"module_path\": \"qlib.contrib.strategy.rule_strategy\",\n",
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" \"kwargs\": {\n",
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" \"step_bar\": \"day\",\n",
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" \"freq\": \"day\",\n",
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" \"instruments\": market,\n",
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" },\n",
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" },\n",
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" },\n",
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" },\n",
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" \"backtest\": {\n",
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" \"start_time\": trade_start_time,\n",
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" \"end_time\": trade_end_time,\n",
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" \"account\": 100000000,\n",
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" \"benchmark\": benchmark,\n",
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" \"exchange_kwargs\": {\n",
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" \"freq\": \"day\",\n",
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" \"limit_threshold\": 0.095,\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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"# backtest and analysis\n",
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"with R.start(experiment_name=\"backtest_analysis\"):\n",
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" # prediction\n",
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" recorder = R.get_recorder()\n",
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" ba_rid = recorder.id\n",
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" sr = SignalRecord(model, dataset, recorder)\n",
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" sr.generate()\n",
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"\n",
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" # backtest & analysis\n",
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" par = PortAnaRecord(recorder, port_analysis_config, \"day\")\n",
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" par.generate()"
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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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"recorder = R.get_recorder(ba_rid, experiment_name=\"backtest_analysis\")\n",
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"pred_df = recorder.load_object(\"pred.pkl\")\n",
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"pred_df_dates = pred_df.index.get_level_values(level='datetime')\n",
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"report_normal_df_1d = recorder.load_object(\"portfolio_analysis/report_normal_1day.pkl\")\n",
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"positions_1d = recorder.load_object(\"portfolio_analysis/positions_normal_1day.pkl\")\n",
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"analysis_df_1d = recorder.load_object(\"portfolio_analysis/port_analysis_1day.pkl\")\n",
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"report_normal_df_1w = recorder.load_object(\"portfolio_analysis/report_normal_1week.pkl\")\n",
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"positions_1w = recorder.load_object(\"portfolio_analysis/positions_normal_1week.pkl\")\n",
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"analysis_df_1w = recorder.load_object(\"portfolio_analysis/port_analysis_1week.pkl\")"
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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_1d)\n"
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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_1w)"
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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_1d, report_normal_df_1d)"
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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_1w, report_normal_df_1w)"
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]
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}
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]
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} |