Support Highfreq Backtest with the Model/Rule/RL Strategy

examples/multi_level_trading/README.md

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+# Multi-level Trading
+
+This worflow is an example for multi-level trading.
+
+## Introduction
+
+Qlib supports backtesting of various strategies, including portfolio management strategies, order split strategies, model-based strategies (such as deep learning models), rule-based strategies, and RL-based strategies.
+
+And, Qlib also supports multi-level trading and backtesting. It means that users can use different strategies to trade at different frequencies.
+
+This example uses a DropoutTopkStrategy (a strategy based on the daily frequency Lightgbm model) in weekly frequency for portfolio generation. And, at the daily frequency level, this example uses SBBStrategyEMA (a rule-based strategy that uses EMA for decision-making) to split orders. 
+
+## Usage
+
+Start backtesting by running the following command:
+```bash
+    python workflow.py
+```
+
+Also, reports is shown in workflow.ipynb
+

examples/multi_level_trading/workflow.ipynb

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+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "pythonjvsc74a57bd0fcc004278713aaede7c629a6a43738a929cb09abb52817d4f72eb70db44cd87b",
+   "display_name": "Python 3.8.8  ('qlib_backtest': conda)"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "fcc004278713aaede7c629a6a43738a929cb09abb52817d4f72eb70db44cd87b"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "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",
+    "    # 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",
+    "    with requests.get(\"https://raw.githubusercontent.com/microsoft/qlib/main/scripts/get_data.py\") 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": [
+    "\n",
+    "import pandas as pd\n",
+    "from qlib.config import REG_CN\n",
+    "from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict\n",
+    "from qlib.workflow import R\n",
+    "from qlib.workflow.record_temp import SignalRecord, PortAnaRecord\n",
+    "from qlib.tests.data import GetData"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use default 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",
+    "    GetData().qlib_data(target_dir=provider_uri, region=REG_CN)\n",
+    "\n",
+    "qlib.init(provider_uri=provider_uri, region=REG_CN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "market = \"csi300\"\n",
+    "benchmark = \"SH000300\"\n",
+    "\n",
+    "###################################\n",
+    "# train model\n",
+    "###################################\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",
+    "# 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\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "outputPrepend"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "trade_start_time = \"2017-01-01\"\n",
+    "trade_end_time = \"2020-08-01\"\n",
+    "\n",
+    "port_analysis_config = {\n",
+    "    \"strategy\": {\n",
+    "        \"class\": \"TopkDropoutStrategy\",\n",
+    "        \"module_path\": \"qlib.contrib.strategy.model_strategy\",\n",
+    "        \"kwargs\": {\n",
+    "            \"step_bar\": \"week\",\n",
+    "            \"model\": model,\n",
+    "            \"dataset\": dataset,\n",
+    "            \"topk\": 50,\n",
+    "            \"n_drop\": 5,\n",
+    "        },\n",
+    "    },\n",
+    "    \"env\": {\n",
+    "        \"class\": \"SplitExecutor\",\n",
+    "        \"module_path\": \"qlib.contrib.backtest.executor\",\n",
+    "        \"kwargs\": {\n",
+    "            \"step_bar\": \"week\",\n",
+    "            \"generate_report\": True,\n",
+    "            \"sub_env\": {\n",
+    "                \"class\": \"SimulatorExecutor\",\n",
+    "                \"module_path\": \"qlib.contrib.backtest.executor\",\n",
+    "                \"kwargs\": {\n",
+    "                    \"step_bar\": \"day\",\n",
+    "                    \"verbose\": True,\n",
+    "                    \"generate_report\": True,\n",
+    "                },\n",
+    "            },\n",
+    "            \"sub_strategy\": {\n",
+    "                \"class\": \"SBBStrategyEMA\",\n",
+    "                \"module_path\": \"qlib.contrib.strategy.rule_strategy\",\n",
+    "                \"kwargs\": {\n",
+    "                    \"step_bar\": \"day\",\n",
+    "                    \"freq\": \"day\",\n",
+    "                    \"instruments\": market,\n",
+    "                },\n",
+    "            },\n",
+    "        },\n",
+    "    },\n",
+    "    \"backtest\": {\n",
+    "        \"start_time\": trade_start_time,\n",
+    "        \"end_time\": trade_end_time,\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",
+    "# backtest and analysis\n",
+    "with R.start(experiment_name=\"backtest_analysis\"):\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": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qlib.contrib.report import analysis_model, analysis_position\n",
+    "from qlib.data import D\n",
+    "recorder = R.get_recorder(ba_rid, experiment_name=\"backtest_analysis\")\n",
+    "pred_df = recorder.load_object(\"pred.pkl\")\n",
+    "pred_df_dates = pred_df.index.get_level_values(level='datetime')\n",
+    "report_normal_df_1d = recorder.load_object(\"portfolio_analysis/report_normal_1day.pkl\")\n",
+    "positions_1d = recorder.load_object(\"portfolio_analysis/positions_normal_1day.pkl\")\n",
+    "analysis_df_1d = recorder.load_object(\"portfolio_analysis/port_analysis_1day.pkl\")\n",
+    "report_normal_df_1w = recorder.load_object(\"portfolio_analysis/report_normal_1week.pkl\")\n",
+    "positions_1w = recorder.load_object(\"portfolio_analysis/positions_normal_1week.pkl\")\n",
+    "analysis_df_1w = recorder.load_object(\"portfolio_analysis/port_analysis_1week.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analysis_position.report_graph(report_normal_df_1d)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analysis_position.report_graph(report_normal_df_1w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analysis_position.risk_analysis_graph(analysis_df_1d, report_normal_df_1d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analysis_position.risk_analysis_graph(analysis_df_1w, report_normal_df_1w)"
+   ]
+  }
+ ]
+}