{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from alphamind.api import *\n",
    "from PyFin.api import *\n",
    "from PyFin.Math.Accumulators.StatefulAccumulators import MovingAverage\n",
    "from PyFin.Math.Accumulators.StatefulAccumulators import MovingSharp\n",
    "from PyFin.Math.Accumulators.StatefulAccumulators import MovingMaxDrawdown\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Back test parameter settings\n",
    "\"\"\"\n",
    "\n",
    "start_date = '2012-01-01'\n",
    "end_date = '2018-01-02'\n",
    "benchmark_code = 905\n",
    "universe_name = ['zz500', 'hs300']\n",
    "universe = Universe(universe_name, universe_name)\n",
    "frequency = '5b'\n",
    "batch = 8\n",
    "method = 'risk_neutral'\n",
    "use_rank = 100\n",
    "industry_lower = 1.\n",
    "industry_upper = 1.\n",
    "neutralize_risk = ['SIZE'] + industry_styles\n",
    "constraint_risk = ['SIZE'] + industry_styles\n",
    "size_risk_lower = 0\n",
    "size_risk_upper = 0\n",
    "turn_over_target_base = 0.25\n",
    "weight_gaps = [0.01]\n",
    "benchmark_total_lower = 0.8\n",
    "benchmark_total_upper = 1.\n",
    "horizon = map_freq(frequency)\n",
    "hedging_ratio = 0.\n",
    "\n",
    "executor = NaiveExecutor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Model phase: we need 1 constant linear model and one alpha model\n",
    "\"\"\"\n",
    "\n",
    "# const_features = [\"IVR\", \"eps_q\", \"DivP\", \"CFinc1\", \"BDTO\"]\n",
    "\n",
    "const_features = {\n",
    "    'IVR_raw': LAST('IVR'),\n",
    "    'DivP_raw': LAST('DivP'),\n",
    "    'eps_q_raw': LAST('eps_q'),\n",
    "    'CFinc1_raw': LAST('CFinc1'),\n",
    "    'BDTO_raw': LAST('BDTO')\n",
    "}\n",
    "\n",
    "const_features_keys = list(const_features.keys())\n",
    "\n",
    "const_weights = pd.Series([0.05, 0.2, 0.075, 0.15, 0.05],\n",
    "                          index=['IVR_raw',\n",
    "                                 'eps_q_raw',\n",
    "                                 'DivP_raw',\n",
    "                                 'CFinc1_raw',\n",
    "                                 'BDTO_raw'])\n",
    "\n",
    "const_weights = const_weights[const_features_keys].values\n",
    "\n",
    "const_model = ConstLinearModel(features=const_features_keys,\n",
    "                               weights=const_weights)\n",
    "\n",
    "linear_model_features = {\n",
    "    'roe_q_raw': LAST('roe_q'),\n",
    "    'BDTO_raw': LAST('BDTO'),\n",
    "    'CFinc1_raw': LAST('CFinc1'),\n",
    "    'CHV_raw': LAST('CHV'),\n",
    "    'IVR_raw': LAST('IVR'),\n",
    "    'VAL_raw': LAST('VAL'),\n",
    "    'GREV_raw': LAST('GREV')\n",
    "}\n",
    "\n",
    "total_features = {\n",
    "    'IVR_raw': LAST('IVR'),\n",
    "    'DivP_raw': LAST('DivP'),\n",
    "    'CFinc1_raw': LAST('CFinc1'),\n",
    "    'BDTO_raw': LAST('BDTO'),\n",
    "    'eps_q_raw': LAST('eps_q'),\n",
    "    'roe_q_raw': LAST('roe_q'),\n",
    "    'CHV_raw': LAST('CHV'),\n",
    "    'VAL_raw': LAST('IVR'),\n",
    "    'GREV_raw': LAST('GREV')\n",
    "}\n",
    "\n",
    "total_features_keys = list(total_features.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-03 16:22:49,660 - ALPHA_MIND - INFO - Starting data package fetching ...\n",
      "2018-01-03 16:23:38,398 - ALPHA_MIND - INFO - Loading data is finished\n",
      "2018-01-03 16:23:41,677 - ALPHA_MIND - INFO - Data processing is finished\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Data phase\n",
    "\"\"\"\n",
    "\n",
    "engine = SqlEngine()\n",
    "\n",
    "linear_model_factor_data = fetch_data_package(engine,\n",
    "                                              alpha_factors=linear_model_features,\n",
    "                                              start_date=start_date,\n",
    "                                              end_date=end_date,\n",
    "                                              frequency=frequency,\n",
    "                                              universe=universe,\n",
    "                                              benchmark=benchmark_code,\n",
    "                                              batch=batch,\n",
    "                                              neutralized_risk=neutralize_risk,\n",
    "                                              pre_process=[winsorize_normal, standardize],\n",
    "                                              post_process=[winsorize_normal, standardize],\n",
    "                                              warm_start=batch)\n",
    "\n",
    "linear_model_features_keys = linear_model_factor_data['x_names']\n",
    "\n",
    "train_x = linear_model_factor_data['train']['x']\n",
    "train_y = linear_model_factor_data['train']['y']\n",
    "ref_dates = sorted(train_x.keys())\n",
    "\n",
    "predict_x = linear_model_factor_data['predict']['x']\n",
    "predict_y = linear_model_factor_data['predict']['y']\n",
    "settlement = linear_model_factor_data['settlement']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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    },
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-03 16:23:42,515 - ALPHA_MIND - INFO - trade_date: 2015-08-05 00:00:00 training finished\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-03 16:23:42,871 - ALPHA_MIND - INFO - trade_date: 2017-05-15 00:00:00 training finished\n",
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      "2018-01-03 16:23:42,994 - ALPHA_MIND - INFO - trade_date: 2017-12-13 00:00:00 training finished\n",
      "2018-01-03 16:23:42,999 - ALPHA_MIND - INFO - trade_date: 2017-12-20 00:00:00 training finished\n",
      "2018-01-03 16:23:43,006 - ALPHA_MIND - INFO - trade_date: 2017-12-27 00:00:00 training finished\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Training phase\n",
    "\"\"\"\n",
    "\n",
    "models_series = pd.Series()\n",
    "\n",
    "for ref_date in ref_dates:\n",
    "    x = train_x[ref_date]\n",
    "    y = train_y[ref_date].flatten()\n",
    "\n",
    "    model = LinearRegression(features=linear_model_features_keys, fit_intercept=False)\n",
    "    model.fit(x, y)\n",
    "    models_series.loc[ref_date] = model\n",
    "    alpha_logger.info('trade_date: {0} training finished'.format(ref_date))\n",
    "\n",
    "\n",
    "frequency = '5b'\n",
    "ref_dates = makeSchedule(start_date, end_date, frequency, 'china.sse')\n",
    "\n",
    "const_model_factor_data = engine.fetch_data_range(universe,\n",
    "                                                  total_features,\n",
    "                                                  dates=ref_dates,\n",
    "                                                  benchmark=benchmark_code)['factor']\n",
    "\n",
    "horizon = map_freq(frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-03 16:24:22,101 - ALPHA_MIND - INFO - 2012-01-04 00:00:00: 800\n",
      "2018-01-03 16:24:22,119 - ALPHA_MIND - INFO - 2012-01-11 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start 0.01 weight gap simulation ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-03 16:24:22,128 - ALPHA_MIND - INFO - 2012-01-18 00:00:00: 800\n",
      "2018-01-03 16:24:22,139 - ALPHA_MIND - INFO - 2012-02-01 00:00:00: 800\n",
      "2018-01-03 16:24:22,148 - ALPHA_MIND - INFO - 2012-02-08 00:00:00: 800\n",
      "2018-01-03 16:24:22,274 - ALPHA_MIND - INFO - 2012-02-08 00:00:00 is finished\n",
      "2018-01-03 16:24:22,281 - ALPHA_MIND - INFO - 2012-02-15 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using alpha model\n",
      "using alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-03 16:24:22,529 - ALPHA_MIND - INFO - 2012-02-15 00:00:00 is finished\n",
      "2018-01-03 16:24:22,533 - ALPHA_MIND - INFO - 2012-02-22 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-03 16:24:22,752 - ALPHA_MIND - INFO - 2012-02-22 00:00:00 is finished\n",
      "2018-01-03 16:24:22,759 - ALPHA_MIND - INFO - 2012-02-29 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using alpha model\n"
     ]
    },
    {
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     "text": [
      "2018-01-03 16:24:22,975 - ALPHA_MIND - INFO - 2012-02-29 00:00:00 is finished\n",
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     ]
    },
    {
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     ]
    },
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     "output_type": "stream",
     "text": [
      "2018-01-03 16:24:23,199 - ALPHA_MIND - INFO - 2012-03-14 00:00:00: 799\n"
     ]
    },
    {
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    },
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    },
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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     "output_type": "stream",
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    },
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    },
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    },
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    },
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    },
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    },
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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     "output_type": "stream",
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     "output_type": "stream",
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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     "output_type": "stream",
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     "output_type": "stream",
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    },
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    },
    {
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    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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    },
    {
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    },
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    },
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    },
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    },
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    {
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    },
    {
     "data": {
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1NgF3dnYmL++3+Ul33nknn3/+OYWFhQAVTkFxdXWlQYMGrF27FoDi4mIKCwsJ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\nmHi3iMwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1029f54a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Predicting and re-balance phase\n",
    "\"\"\"\n",
    "\n",
    "factor_groups = const_model_factor_data.groupby('trade_date')\n",
    "\n",
    "for weight_gap in weight_gaps:\n",
    "    print(\"start {0} weight gap simulation ...\".format(weight_gap))\n",
    "\n",
    "    rets = []\n",
    "    turn_overs = []\n",
    "    leverags = []\n",
    "    previous_pos = pd.DataFrame()\n",
    "\n",
    "    index_dates = []\n",
    "\n",
    "    for i, value in enumerate(factor_groups):\n",
    "        date = value[0]\n",
    "        data = value[1]\n",
    "        ref_date = date.strftime('%Y-%m-%d')\n",
    "\n",
    "        total_data = data.fillna(data[total_features_keys].median())\n",
    "        alpha_logger.info('{0}: {1}'.format(date, len(total_data)))\n",
    "        risk_exp = total_data[neutralize_risk].values.astype(float)\n",
    "        industry = total_data.industry_code.values\n",
    "        benchmark_w = total_data.weight.values\n",
    "\n",
    "        constraint_exp = total_data[constraint_risk].values\n",
    "        risk_exp_expand = np.concatenate((constraint_exp, np.ones((len(risk_exp), 1))), axis=1).astype(float)\n",
    "\n",
    "        risk_names = constraint_risk + ['total']\n",
    "        risk_target = risk_exp_expand.T @ benchmark_w\n",
    "\n",
    "        lbound = np.maximum(0., benchmark_w - weight_gap)  # np.zeros(len(total_data))\n",
    "        ubound = weight_gap + benchmark_w\n",
    "\n",
    "        is_in_benchmark = (benchmark_w > 0.).astype(float)\n",
    "\n",
    "        risk_exp_expand = np.concatenate((risk_exp_expand, is_in_benchmark.reshape((-1, 1))), axis=1).astype(float)\n",
    "        risk_names.append('benchmark_total')\n",
    "\n",
    "        constraint = Constraints(risk_exp_expand, risk_names)\n",
    "\n",
    "        for i, name in enumerate(risk_names):\n",
    "            if name == 'total':\n",
    "                constraint.set_constraints(name,\n",
    "                                           lower_bound=risk_target[i],\n",
    "                                           upper_bound=risk_target[i])\n",
    "            elif name == 'SIZE':\n",
    "                base_target = abs(risk_target[i])\n",
    "                constraint.set_constraints(name,\n",
    "                                           lower_bound=risk_target[i] + base_target * size_risk_lower,\n",
    "                                           upper_bound=risk_target[i] + base_target * size_risk_upper)\n",
    "            elif name == 'benchmark_total':\n",
    "                base_target = benchmark_w.sum()\n",
    "                constraint.set_constraints(name,\n",
    "                                           lower_bound=benchmark_total_lower * base_target,\n",
    "                                           upper_bound=benchmark_total_upper * base_target)\n",
    "            else:\n",
    "                constraint.set_constraints(name,\n",
    "                                           lower_bound=risk_target[i] * industry_lower,\n",
    "                                           upper_bound=risk_target[i] * industry_upper)\n",
    "\n",
    "        factor_values = factor_processing(total_data[const_features_keys].values,\n",
    "                                          pre_process=[winsorize_normal, standardize],\n",
    "                                          risk_factors=risk_exp,\n",
    "                                          post_process=[winsorize_normal, standardize])\n",
    "\n",
    "        # const linear model\n",
    "        er1 = const_model.predict(factor_values)\n",
    "\n",
    "        # linear regression model\n",
    "        models = models_series[models_series.index <= date]\n",
    "        if models.empty:\n",
    "            continue\n",
    "\n",
    "        index_dates.append(date)\n",
    "        model = models[-1]\n",
    "\n",
    "        # x = predict_x[date]\n",
    "        x = factor_processing(total_data[linear_model_features_keys].values,\n",
    "                              pre_process=[winsorize_normal, standardize],\n",
    "                              risk_factors=risk_exp,\n",
    "                              post_process=[winsorize_normal, standardize])\n",
    "        er2 = model.predict(x)\n",
    "\n",
    "        # combine model\n",
    "        er1_table = pd.DataFrame({'er1': er1, 'code': total_data.code.values})\n",
    "        er2_table = pd.DataFrame({'er2': er2, 'code': total_data.code.values})\n",
    "        er_table = pd.merge(er1_table, er2_table, on=['code'], how='left').fillna(0)\n",
    "\n",
    "        er1 = er_table.er1.values\n",
    "        er2 = er_table.er2.values\n",
    "\n",
    "        if er2.std() > 0.:\n",
    "            print(\"using alpha model\")\n",
    "\n",
    "        er = er1 + er2\n",
    "\n",
    "        codes = total_data['code'].values\n",
    "\n",
    "        if previous_pos.empty:\n",
    "            current_position = None\n",
    "            turn_over_target = None\n",
    "        else:\n",
    "            previous_pos.set_index('code', inplace=True)\n",
    "            remained_pos = previous_pos.loc[codes]\n",
    "\n",
    "            remained_pos.fillna(0., inplace=True)\n",
    "            turn_over_target = turn_over_target_base\n",
    "            current_position = remained_pos.weight.values\n",
    "\n",
    "        try:\n",
    "            target_pos, _ = er_portfolio_analysis(er,\n",
    "                                                  industry,\n",
    "                                                  None,\n",
    "                                                  constraint,\n",
    "                                                  False,\n",
    "                                                  benchmark_w,\n",
    "                                                  method=method,\n",
    "                                                  use_rank=use_rank,\n",
    "                                                  turn_over_target=turn_over_target,\n",
    "                                                  current_position=current_position,\n",
    "                                                  lbound=lbound,\n",
    "                                                  ubound=ubound)\n",
    "        except ValueError:\n",
    "            alpha_logger.info('{0} full re-balance'.format(date))\n",
    "            target_pos, _ = er_portfolio_analysis(er,\n",
    "                                                  industry,\n",
    "                                                  None,\n",
    "                                                  constraint,\n",
    "                                                  False,\n",
    "                                                  benchmark_w,\n",
    "                                                  method=method,\n",
    "                                                  use_rank=use_rank,\n",
    "                                                  lbound=lbound,\n",
    "                                                  ubound=ubound)\n",
    "\n",
    "        target_pos['code'] = total_data['code'].values\n",
    "\n",
    "        turn_over, executed_pos = executor.execute(target_pos=target_pos)\n",
    "\n",
    "        executed_codes = executed_pos.code.tolist()\n",
    "        dx_returns = engine.fetch_dx_return(date, executed_codes, horizon=horizon, offset=1)\n",
    "\n",
    "        result = pd.merge(executed_pos, total_data[['code', 'weight']], on=['code'], how='inner')\n",
    "        result = pd.merge(result, dx_returns, on=['code'])\n",
    "\n",
    "        leverage = result.weight_x.abs().sum()\n",
    "\n",
    "        ret = result.weight_x.values @ (np.exp(result.dx.values) - 1.)\n",
    "        rets.append(np.log(1. + ret))\n",
    "        executor.set_current(executed_pos)\n",
    "        turn_overs.append(turn_over)\n",
    "        leverags.append(leverage)\n",
    "\n",
    "        previous_pos = executed_pos\n",
    "        alpha_logger.info('{0} is finished'.format(date))\n",
    "\n",
    "    ret_df = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverage}, index=index_dates)\n",
    "\n",
    "    # index return\n",
    "    index_return = engine.fetch_dx_return_index_range(benchmark_code, start_date, end_date, horizon=horizon,\n",
    "                                                      offset=1).set_index('trade_date')\n",
    "    ret_df['index'] = index_return['dx']\n",
    "\n",
    "    ret_df.loc[advanceDateByCalendar('china.sse', ref_dates[-1], frequency)] = 0.\n",
    "    ret_df = ret_df.shift(1)\n",
    "    ret_df.iloc[0] = 0.\n",
    "    ret_df['tc_cost'] = ret_df.turn_over * 0.002\n",
    "    ret_df['returns'] = ret_df['leverage'] * (ret_df['returns'] - ret_df['index'])\n",
    "\n",
    "    ret_df[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6),\n",
    "                                                 title='Fixed frequency rebalanced: {0}'.format(frequency),\n",
    "                                                 secondary_y='tc_cost')\n",
    "\n",
    "    ret_df['ret_after_tc'] = ret_df['returns'] - ret_df['tc_cost']\n",
    "    ret_df.to_csv('d:/zz500.csv')\n",
    "\n",
    "#     sharp_calc = MovingSharp(49)\n",
    "#     drawdown_calc = MovingMaxDrawdown(49)\n",
    "#     max_drawdown_calc = MovingMaxDrawdown(len(ret_df))\n",
    "\n",
    "#     res_df = pd.DataFrame(columns=['daily_return', 'cum_ret', 'sharp', 'drawdown', 'max_drawn', 'leverage'])\n",
    "\n",
    "#     total_returns = 0.\n",
    "\n",
    "#     for i, ret in enumerate(ret_df['ret_after_tc']):\n",
    "#         date = ret_df.index[i]\n",
    "#         total_returns += ret\n",
    "#         sharp_calc.push({'ret': ret, 'riskFree': 0.})\n",
    "#         drawdown_calc.push({'ret': ret})\n",
    "#         max_drawdown_calc.push({'ret': ret})\n",
    "\n",
    "#         res_df.loc[date, 'daily_return'] = ret\n",
    "#         res_df.loc[date, 'cum_ret'] = total_returns\n",
    "#         res_df.loc[date, 'drawdown'] = drawdown_calc.result()[0]\n",
    "#         res_df.loc[date, 'max_drawn'] = max_drawdown_calc.result()[0]\n",
    "#         res_df.loc[date, 'leverage'] = ret_df.loc[date, 'leverage']\n",
    "\n",
    "#         if i < 10:\n",
    "#             res_df.loc[date, 'sharp'] = 0.\n",
    "#         else:\n",
    "#             res_df.loc[date, 'sharp'] = sharp_calc.result() * np.sqrt(49)\n",
    "\n",
    "#     res_df.to_csv('hs300_{0}.csv'.format(int(weight_gap * 100)))"
   ]
  },
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   "cell_type": "code",
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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