examples/train_backtest_analyze.ipynb

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 "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 Alpha158\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_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(Path.cwd().parent.joinpath(\"scripts\")))\n",
    "    from get_data import GetData\n",
    "    GetData().qlib_data_cn(target_dir=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": {
    "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 = Alpha158(**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[\"excess_return_without_cost\"] = risk_analysis(report_normal[\"return\"] - report_normal[\"bench\"])\n",
    "analysis[\"excess_return_with_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\n",
    "from qlib.data import D\n",
    "pred_df_dates = pred_score.index.get_level_values(level='datetime')\n",
    "report_normal_df = report_normal\n",
    "positions = positions_normal\n",
    "pred_df = pred_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## analysis position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_ret = D.features(D.instruments(MARKET), ['Ref($close, -1)/$close - 1'], pred_df_dates.min(), pred_df_dates.max())\n",
    "stock_ret.columns = ['label']"
   ]
  },
  {
   "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 = D.features(D.instruments(MARKET), ['Ref($close, -2)/Ref($close, -1) - 1'], pred_df_dates.min(), pred_df_dates.max())\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)"
   ]
  }
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