{
 "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": "3.7.9-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import copy\n",
    "from pathlib import Path\n",
    "\n",
    "import qlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from qlib.config import REG_CN\n",
    "from qlib.contrib.model.gbdt import LGBModel\n",
    "from qlib.contrib.data.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, init_instance_by_config\n",
    "from qlib.workflow import R\n",
    "from qlib.workflow.record_temp import SignalRecord, PortAnaRecord\n",
    "from qlib.utils import flatten_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[20768:MainThread](2020-11-27 08:15:01,096) INFO - qlib.Initialization - [__init__.py:41] - default_conf: client.\n",
      "[20768:MainThread](2020-11-27 08:15:03,120) WARNING - qlib.Initialization - [__init__.py:57] - redis connection failed(host=127.0.0.1 port=6379), cache will not be used!\n",
      "[20768:MainThread](2020-11-27 08:15:03,121) INFO - qlib.Initialization - [__init__.py:76] - qlib successfully initialized based on client settings.\n",
      "[20768:MainThread](2020-11-27 08:15:03,122) INFO - qlib.Initialization - [__init__.py:79] - data_path=C:\\Users\\v-donzh\\.qlib\\qlib_data\\cn_data\n"
     ]
    }
   ],
   "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(target_dir=provider_uri, region=REG_CN)\n",
    "qlib.init(provider_uri=provider_uri, region=REG_CN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "market = \"csi300\"\n",
    "benchmark = \"SH000300\""
   ]
  },
  {
   "source": [
    "## Model Training"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[20768:MainThread](2020-11-27 08:15:55,319) INFO - qlib.timer - [log.py:81] - Time cost: 52.158s | Loading data Done\n",
      "[20768:MainThread](2020-11-27 08:15:56,107) INFO - qlib.timer - [log.py:81] - Time cost: 0.669s | DropnaLabel Done\n",
      "[20768:MainThread](2020-11-27 08:15:59,716) INFO - qlib.timer - [log.py:81] - Time cost: 3.608s | CSZScoreNorm Done\n",
      "[20768:MainThread](2020-11-27 08:15:59,717) INFO - qlib.timer - [log.py:81] - Time cost: 4.397s | fit & process data Done\n",
      "[20768:MainThread](2020-11-27 08:15:59,717) INFO - qlib.timer - [log.py:81] - Time cost: 56.556s | Init data Done\n",
      "[20768:MainThread](2020-11-27 08:15:59,722) INFO - qlib.workflow - [exp.py:180] - Experiment 1 starts running ...\n",
      "[20768:MainThread](2020-11-27 08:16:00,133) INFO - qlib.workflow - [recorder.py:234] - Recorder 46e50379b45a4a7684c683cd423535a9 starts running under Experiment 1 ...\n",
      "[20768:MainThread](2020-11-27 08:16:00,134) INFO - qlib.workflow - [expm.py:251] - No tracking URI is provided. The default tracking URI is set as `mlruns` under the working directory.\n",
      "Training until validation scores don't improve for 50 rounds\n",
      "[20]\ttrain's l2: 0.990556\tvalid's l2: 0.994299\n",
      "[40]\ttrain's l2: 0.986919\tvalid's l2: 0.993683\n",
      "[60]\ttrain's l2: 0.984485\tvalid's l2: 0.993495\n",
      "[80]\ttrain's l2: 0.982363\tvalid's l2: 0.993365\n",
      "[100]\ttrain's l2: 0.980538\tvalid's l2: 0.993251\n",
      "[120]\ttrain's l2: 0.978755\tvalid's l2: 0.993265\n",
      "[140]\ttrain's l2: 0.977079\tvalid's l2: 0.993324\n",
      "[160]\ttrain's l2: 0.97535\tvalid's l2: 0.99336\n",
      "Early stopping, best iteration is:\n",
      "[118]\ttrain's l2: 0.978921\tvalid's l2: 0.993248\n"
     ]
    }
   ],
   "source": [
    "###################################\n",
    "# train model\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",
    "\n",
    "# model initiaiton\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"
   ]
  },
  {
   "source": [
    "## Optimization Based Strategy"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qlib.contrib.strategy.strategy import BaseStrategy\n",
    "\n",
    "\n",
    "class OptBasedStrategy(BaseStrategy):\n",
    "    \"\"\"Optimization Based Strategy\"\"\"\n",
    "\n",
    "    def __init__(self, data_handler, cov_estimator, optimizer):\n",
    "        self.data_handler = data_handler\n",
    "        self.cov_estimator = cov_estimator\n",
    "        self.optimizer = optimizer\n",
    "\n",
    "    def generate_order_list(self, score_series, current, trade_exchange, pred_date, trade_date):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        -----------\n",
    "        score_series : pd.Seires\n",
    "            stock_id , score.\n",
    "        current : Position()\n",
    "            current of account.\n",
    "        trade_exchange : Exchange()\n",
    "            exchange.\n",
    "        trade_date : pd.Timestamp\n",
    "            date.\n",
    "        \"\"\"\n",
    "        score_series = score_series.dropna()\n",
    "\n",
    "        # check stock holdings, if\n",
    "        # 1. doesn't have score: target amount = 0 (force sell)\n",
    "        # 2. stock not tradable: target amount = current amount\n",
    "        current_position = current.get_stock_amount_dict()\n",
    "        target_position = {}\n",
    "        for stock_id in current_position:\n",
    "            if not trade_exchange.is_stock_tradable(stock_id=stock_id, trade_date=trade_date):\n",
    "                target_position[stock_id] = current_position[stock_id]\n",
    "            elif stock_id not in score_series.index:\n",
    "                target_position[stock_id] = 0\n",
    "            else:\n",
    "                # need to be solved by optimizer\n",
    "                pass\n",
    "\n",
    "        # filter scores, if\n",
    "        # 1. kept in `amount_dict` by previous rules\n",
    "        # 2. not tradable\n",
    "        skipped = []\n",
    "        for stock_id in score_series.index:\n",
    "            if stock_id in target_position:\n",
    "                skipped.append(stock_id)\n",
    "            elif not trade_exchange.is_stock_tradable(stock_id=stock_id, trade_date=trade_date):\n",
    "                skipped.append(stock_id)\n",
    "        score_series = score_series[~score_series.index.isin(skipped)]\n",
    "\n",
    "        # calc remaining value\n",
    "        current_value = pd.Series({\n",
    "            stock_id: trade_exchange.get_close(stock_id, pred_date) * amount\n",
    "            for stock_id, amount in current_position.items()\n",
    "        })\n",
    "        risk_total_value = self.get_risk_degree(trade_date) * current.calculate_value()\n",
    "        traded_value = risk_total_value - current_value.loc[list(target_position)].sum()\n",
    "\n",
    "        # portfolio init weight\n",
    "        init_weight = current_value.reindex(score_series.index, fill_value=0)\n",
    "        init_weight /= init_weight.sum() + 1e-12\n",
    "\n",
    "        # covariance estimation\n",
    "        selector = (self.data_handler.get_range_selector(pred_date, 252), score_series.index)\n",
    "        price = self.data_handler.fetch(selector, level=None, squeeze=True)\n",
    "        cov = self.cov_estimator(price)\n",
    "        cov = cov.reindex(\n",
    "            index=score_series.index, \n",
    "            columns=score_series.index, \n",
    "            #fill_value=cov.max().max()\n",
    "        )\n",
    "\n",
    "        # optimize target portfolio\n",
    "        target_weight = self.optimizer(cov, score_series, init_weight)\n",
    "        for stock_id, weight in target_weight.items():\n",
    "            try:\n",
    "                target_position[stock_id] = traded_value * weight / trade_exchange.get_close(stock_id, pred_date)\n",
    "            except Exception as e:\n",
    "                print(e)\n",
    "                target_position[stock_id] = 0\n",
    "        print(target_weight[target_weight>1e-4])\n",
    "\n",
    "        # generate order list\n",
    "        order_list = trade_exchange.generate_order_for_target_amount_position(\n",
    "            target_position=target_position,\n",
    "            current_position=current_position,\n",
    "            trade_date=trade_date,\n",
    "        )\n",
    "\n",
    "        return order_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qlib.data.dataset.loader import QlibDataLoader\n",
    "from qlib.data.dataset.handler import DataHandler\n",
    "from qlib.model.riskmodel import ShrinkCovEstimator\n",
    "from qlib.portfolio.optimizer import PortfolioOptimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[20768:MainThread](2020-11-27 08:45:28,512) INFO - qlib.timer - [log.py:81] - Time cost: 14.502s | Loading data Done\n",
      "[20768:MainThread](2020-11-27 08:45:28,513) INFO - qlib.timer - [log.py:81] - Time cost: 14.503s | Init data Done\n"
     ]
    }
   ],
   "source": [
    "data_loader = QlibDataLoader([\"$close\"])\n",
    "data_handler = DataHandler(\"all\", \"2015-01-01\", \"2020-08-01\", data_loader)\n",
    "cov_estimator = ShrinkCovEstimator(nan_option=\"mask\")\n",
    "optimizer = PortfolioOptimizer(\"mvo\", lamb=1.0, delta=0.2)\n",
    "strategy = OptBasedStrategy(data_handler, cov_estimator, optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[20768:MainThread](2020-11-27 08:45:28,543) INFO - qlib.workflow - [exp.py:180] - Experiment 2 starts running ...\n",
      "[20768:MainThread](2020-11-27 08:45:28,581) INFO - qlib.workflow - [recorder.py:234] - Recorder d9bd45391cf5431bb339531baf5fb6f2 starts running under Experiment 2 ...\n",
      "[20768:MainThread](2020-11-27 08:45:28,582) INFO - qlib.workflow - [expm.py:251] - No tracking URI is provided. The default tracking URI is set as `mlruns` under the working directory.\n",
      "[20768:MainThread](2020-11-27 08:45:29,433) INFO - qlib.workflow - [record_temp.py:127] - Signal record 'pred.pkl' has been saved as the artifact of the Experiment 2\n",
      "[20768:MainThread](2020-11-27 08:45:29,525) INFO - qlib.Evaluate - [evaluate.py:161] - Create new exchange\n",
      "'The following are prediction results of the LGBModel model.'\n",
      "                          score\n",
      "datetime   instrument          \n",
      "2017-01-03 SH600000   -0.033506\n",
      "           SH600008    0.002120\n",
      "           SH600009    0.032941\n",
      "           SH600010   -0.012371\n",
      "           SH600015   -0.140312\n",
      "C:\\Users\\v-donzh\\AppData\\Local\\Continuum\\miniconda3\\envs\\qlib\\lib\\site-packages\\ipykernel_launcher.py:55: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "instrument\n",
      "SH600000    2.868027e-12\n",
      "SH600008    1.341080e-12\n",
      "SH600009    5.131225e-12\n",
      "SH600010    3.890177e-12\n",
      "SH600015    1.781055e-11\n",
      "                ...     \n",
      "SZ300146    2.841964e-12\n",
      "SZ300168    4.603490e-12\n",
      "SZ300182    9.855511e-12\n",
      "SZ300251    1.495177e-12\n",
      "SZ300315    4.054219e-12\n",
      "Length: 290, dtype: float64\n",
      "instrument\n",
      "SH600000    5.253178e-12\n",
      "SH600008    1.901077e-14\n",
      "SH600009    5.573006e-12\n",
      "SH600010    6.129089e-14\n",
      "SH600015    7.236246e-14\n",
      "                ...     \n",
      "SZ300146    4.604396e-12\n",
      "SZ300168    4.705359e-12\n",
      "SZ300182    6.358140e-14\n",
      "SZ300251    5.347927e-12\n",
      "SZ300315    1.288077e-13\n",
      "Length: 289, dtype: float64\n",
      "instrument\n",
      "SH600000    1.181534e-14\n",
      "SH600008    3.480454e-14\n",
      "SH600009    1.902741e-13\n",
      "SH600010    8.388080e-12\n",
      "SH600015    1.490974e-13\n",
      "                ...     \n",
      "SZ300146    9.838926e-13\n",
      "SZ300168    1.790169e-11\n",
      "SZ300182    1.002664e-11\n",
      "SZ300251    2.097283e-12\n",
      "SZ300315    1.180997e-11\n",
      "Length: 288, dtype: float64\n",
      "instrument\n",
      "SH600000    4.027211e-14\n",
      "SH600008    1.067081e-14\n",
      "SH600009    1.010989e-13\n",
      "SH600010    1.605190e-12\n",
      "SH600015    6.075465e-14\n",
      "                ...     \n",
      "SZ300146    7.338274e-12\n",
      "SZ300168    1.990990e-11\n",
      "SZ300182    4.891503e-12\n",
      "SZ300251    1.067670e-11\n",
      "SZ300315    1.009767e-11\n",
      "Length: 288, dtype: float64\n",
      "instrument\n",
      "SH600000    2.518140e-12\n",
      "SH600008    5.504712e-13\n",
      "SH600009    3.185232e-12\n",
      "SH600010    3.641971e-13\n",
      "SH600015    1.902283e-14\n",
      "                ...     \n",
      "SZ300146    5.427494e-14\n",
      "SZ300168    7.620411e-13\n",
      "SZ300182    2.484372e-14\n",
      "SZ300251    5.261806e-13\n",
      "SZ300315    1.412130e-13\n",
      "Length: 288, dtype: float64\n",
      "('SH600666', Timestamp('2017-01-10 00:00:00'))\n",
      "instrument\n",
      "SH600000    7.466386e-12\n",
      "SH600008    1.348026e-15\n",
      "SH600009    1.111042e-11\n",
      "SH600010    6.740600e-14\n",
      "SH600015    1.049665e-11\n",
      "                ...     \n",
      "SZ300146    6.842294e-14\n",
      "SZ300168    1.750970e-13\n",
      "SZ300182    6.916267e-14\n",
      "SZ300251    9.068748e-14\n",
      "SZ300315    1.193522e-13\n",
      "Length: 289, dtype: float64\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "only have -0.104491644538939 SZ002475, require 1448416.2584415162",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-2e7986244749>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     30\u001b[0m     \u001b[1;31m# backtest & analysis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m     \u001b[0mpar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPortAnaRecord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrecorder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport_analysis_config\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m     \u001b[0mpar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32md:\\qlib\\qlib\\workflow\\record_temp.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    230\u001b[0m         \u001b[1;31m# custom strategy and get backtest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m         \u001b[0mpred_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 232\u001b[1;33m         \u001b[0mreport_normal\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpositions_normal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnormal_backtest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpred_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstrategy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrategy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbacktest_config\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    233\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecorder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_objects\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"report_normal.pkl\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mreport_normal\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0martifact_path\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mPortAnaRecord\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    234\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecorder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_objects\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"positions_normal.pkl\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mpositions_normal\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0martifact_path\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mPortAnaRecord\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\qlib\\qlib\\contrib\\evaluate.py\u001b[0m in \u001b[0;36mbacktest\u001b[1;34m(pred, account, shift, benchmark, verbose, **kwargs)\u001b[0m\n\u001b[0;32m    269\u001b[0m         \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    270\u001b[0m         \u001b[0maccount\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccount\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 271\u001b[1;33m         \u001b[0mbenchmark\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbenchmark\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    272\u001b[0m     )\n\u001b[0;32m    273\u001b[0m     \u001b[1;31m# for  compatibility of the old API. return the dict positions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\qlib\\qlib\\contrib\\backtest\\backtest.py\u001b[0m in \u001b[0;36mbacktest\u001b[1;34m(pred, strategy, trade_exchange, shift, verbose, account, benchmark)\u001b[0m\n\u001b[0;32m    107\u001b[0m         \u001b[1;31m# NOTE: The following operation will modify order.amount.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    108\u001b[0m         \u001b[1;31m# NOTE: If it is buy and the cash is insufficient, the tradable amount will be recalculated\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 109\u001b[1;33m         \u001b[0mtrade_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecutor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrade_account\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_date\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    110\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    111\u001b[0m         \u001b[1;31m# 5. Update account information according to transaction\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\qlib\\qlib\\contrib\\online\\executor.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, trade_account, order_list, trade_date)\u001b[0m\n\u001b[0;32m    145\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrade_exchange\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_order\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    146\u001b[0m                 \u001b[1;31m# execute the order\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m                 \u001b[0mtrade_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_cost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_price\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrade_exchange\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdeal_order\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_account\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccount\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    148\u001b[0m                 \u001b[0mtrade_info\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_cost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_price\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\qlib\\qlib\\contrib\\backtest\\exchange.py\u001b[0m in \u001b[0;36mdeal_order\u001b[1;34m(self, order, trade_account, position)\u001b[0m\n\u001b[0;32m    209\u001b[0m             \u001b[1;31m# Otherwise, it will result some stock with 0 amount in the position\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mtrade_account\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 211\u001b[1;33m                 \u001b[0mtrade_account\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_order\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_val\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrade_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcost\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrade_cost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_price\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrade_price\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    212\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mposition\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    213\u001b[0m                 \u001b[0mposition\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_order\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_val\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrade_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcost\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrade_cost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_price\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrade_price\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\qlib\\qlib\\contrib\\backtest\\account.py\u001b[0m in \u001b[0;36mupdate_order\u001b[1;34m(self, order, trade_val, cost, trade_price)\u001b[0m\n\u001b[0;32m     77\u001b[0m             \u001b[1;31m# update current position\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m             \u001b[1;31m# for may sell all of stock_id\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurrent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_order\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_price\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     80\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     81\u001b[0m             \u001b[1;31m# buy stock\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\qlib\\qlib\\contrib\\backtest\\position.py\u001b[0m in \u001b[0;36mupdate_order\u001b[1;34m(self, order, trade_val, cost, trade_price)\u001b[0m\n\u001b[0;32m     81\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdirection\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mOrder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSELL\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m             \u001b[1;31m# SELL\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msell_stock\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstock_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_price\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"do not suppotr order direction {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdirection\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\qlib\\qlib\\contrib\\backtest\\position.py\u001b[0m in \u001b[0;36msell_stock\u001b[1;34m(self, stock_id, trade_val, cost, trade_price)\u001b[0m\n\u001b[0;32m     64\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mposition\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstock_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"amount\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1e-5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m                 raise ValueError(\n\u001b[1;32m---> 66\u001b[1;33m                     \u001b[1;34m\"only have {} {}, require {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mposition\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstock_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"amount\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstock_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrade_amount\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     67\u001b[0m                 )\n\u001b[0;32m     68\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mposition\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstock_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"amount\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m1e-5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: only have -0.104491644538939 SZ002475, require 1448416.2584415162"
     ]
    }
   ],
   "source": [
    "###################################\n",
    "# prediction, backtest & analysis\n",
    "###################################\n",
    "port_analysis_config = {\n",
    "    \"strategy\": strategy,\n",
    "    \"backtest\": {\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",
    "\n",
    "# backtest and analysis\n",
    "with R.start(experiment_name=\"backtest_analysis\"):\n",
    "    recorder = R.get_recorder(rid, experiment_name=\"train_model\")\n",
    "    model = recorder.load_object(\"trained_model\")\n",
    "\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)\n",
    "    par.generate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}