{
 "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": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Back test parameter settings\n",
    "\"\"\"\n",
    "\n",
    "start_date = '2012-01-01'\n",
    "end_date = '2018-01-02'\n",
    "benchmark_code = 300\n",
    "universe_name = ['zz500', 'hs300']\n",
    "universe = Universe(universe_name, universe_name)\n",
    "frequency = '5b'\n",
    "batch = 16\n",
    "method = 'risk_neutral'\n",
    "use_rank = 100\n",
    "industry_lower = 1.\n",
    "industry_upper = 1.\n",
    "neutralize_risk = ['SIZE', 'LEVERAGE'] + industry_styles\n",
    "constraint_risk = ['SIZE', 'LEVERAGE'] + industry_styles\n",
    "size_risk_lower = 0\n",
    "size_risk_upper = 0\n",
    "turn_over_target_base = 0.25\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_keys = ['roe_q_raw',\n",
    "                       'ep_q_raw',\n",
    "                       'DivP_raw',\n",
    "                       'cfinc1_q_raw',\n",
    "                       'EBIT_raw',\n",
    "                       'EARNYILD_raw',\n",
    "                       'EPIBS_raw']\n",
    "\n",
    "const_weights = [0.2,\n",
    "                 0.2,\n",
    "                 0.1,\n",
    "                 0.15,\n",
    "                 0.10,\n",
    "                 0.10,\n",
    "                 0.10]\n",
    "\n",
    "const_weights = pd.Series(const_weights,\n",
    "                          index=const_features_keys)\n",
    "\n",
    "const_model = ConstLinearModel(features=const_features_keys,\n",
    "                               weights=const_weights)\n",
    "\n",
    "model_features = {\n",
    "    'ep_q_raw': LAST('ep_q'),\n",
    "    'roe_q_raw': LAST('roe_q'),\n",
    "    'DivP_raw': LAST('DivP'),\n",
    "    'cfinc1_q_raw': LAST('cfinc1_q'),\n",
    "    'EBIT_raw': LAST('EBIT'),\n",
    "    'EARNYILD_raw': LAST('EARNYILD'),\n",
    "    'EPIBS_raw': LAST('EPIBS')\n",
    "}\n",
    "\n",
    "total_features = {\n",
    "    'DivP_raw': LAST('DivP'),\n",
    "    'cfinc1_q_raw': LAST('cfinc1_q'),\n",
    "    'roe_q_raw': LAST('roe_q'),\n",
    "    'ep_q_raw': LAST('ep_q'),\n",
    "    'EBIT_raw': LAST('EBIT'),\n",
    "    'EARNYILD_raw': LAST('EARNYILD'),\n",
    "    'EPIBS_raw': LAST('EPIBS')\n",
    "}\n",
    "\n",
    "total_features_keys = list(total_features.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:13:17,471 - ALPHA_MIND - INFO - Starting data package fetching ...\n",
      "2018-01-08 18:14:01,830 - ALPHA_MIND - INFO - factor data loading finished\n",
      "2018-01-08 18:14:14,301 - ALPHA_MIND - INFO - return data loading finished\n",
      "2018-01-08 18:14:16,707 - ALPHA_MIND - INFO - industry data loading finished\n",
      "2018-01-08 18:14:17,294 - ALPHA_MIND - INFO - benchmark data loading finished\n",
      "2018-01-08 18:14:33,232 - ALPHA_MIND - INFO - risk data loading finished\n",
      "2018-01-08 18:14:37,795 - ALPHA_MIND - INFO - data merging finished\n",
      "2018-01-08 18:14:40,868 - ALPHA_MIND - INFO - Loading data is finished\n",
      "2018-01-08 18:14:53,651 - ALPHA_MIND - INFO - Data processing is finished\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Data phase\n",
    "\"\"\"\n",
    "\n",
    "engine = SqlEngine()\n",
    "\n",
    "model_factor_data = fetch_data_package(engine,\n",
    "                                       alpha_factors=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",
    "model_features_keys = model_factor_data['x_names']\n",
    "\n",
    "train_x = model_factor_data['train']['x']\n",
    "train_y = model_factor_data['train']['y']\n",
    "train_risk = model_factor_data['train']['risk']\n",
    "ref_dates = sorted(train_x.keys())\n",
    "\n",
    "predict_x = model_factor_data['predict']['x']\n",
    "predict_y = model_factor_data['predict']['y']\n",
    "predict_risk = model_factor_data['predict']['risk']\n",
    "predict_code = model_factor_data['predict']['code']\n",
    "settlement = model_factor_data['settlement']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for key, val in train_y.items():\n",
    "    train_y[key] = np.where(val > 0., 1, 0)\n",
    "    \n",
    "for key, val in predict_y.items():\n",
    "    predict_y[key] = np.where(val > 0., 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cross_product(x, y):\n",
    "    n, m = x.shape\n",
    "    res = []\n",
    "    \n",
    "    for j in range(m):\n",
    "        res.append(x[:, [j]] * y)\n",
    "        \n",
    "    return np.concatenate(res, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:23:44,590 - ALPHA_MIND - INFO - trade_date: 2015-08-26 00:00:00 training finished\n",
      "2018-01-08 18:23:47,383 - ALPHA_MIND - INFO - trade_date: 2015-09-02 00:00:00 training finished\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:27:39,355 - ALPHA_MIND - INFO - trade_date: 2017-06-07 00:00:00 training finished\n",
      "2018-01-08 18:27:41,681 - ALPHA_MIND - INFO - trade_date: 2017-06-14 00:00:00 training finished\n",
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      "2018-01-08 18:28:50,374 - ALPHA_MIND - INFO - trade_date: 2017-12-13 00:00:00 training finished\n",
      "2018-01-08 18:28:53,248 - ALPHA_MIND - INFO - trade_date: 2017-12-20 00:00:00 training finished\n",
      "2018-01-08 18:28:56,154 - 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]\n",
    "    risk = train_risk[ref_date][:, 1:]\n",
    "    new_x = cross_product(x, risk)\n",
    "\n",
    "    model = RandomForestClassifier(n_estimators=1000, max_features='sqrt', max_depth=3, n_jobs=-1, features=model_features_keys)\n",
    "    model.fit(new_x, y)\n",
    "    models_series.loc[ref_date] = model\n",
    "    alpha_logger.info('trade_date: {0} training finished'.format(ref_date))\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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:30:31,765 - ALPHA_MIND - INFO - 2012-02-15 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start 0.01 weight gap simulation ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\IntelPython3_2018\\lib\\site-packages\\ipykernel_launcher.py:76: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "2018-01-08 18:30:32,219 - ALPHA_MIND - INFO - 2012-02-15 00:00:00 is finished\n",
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      "start 0.02 weight gap simulation ...\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:39:05,239 - ALPHA_MIND - INFO - 2017-10-25 00:00:00 is finished\n",
      "2018-01-08 18:39:05,245 - ALPHA_MIND - INFO - 2017-11-01 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:39:05,667 - ALPHA_MIND - INFO - 2017-11-01 00:00:00 is finished\n",
      "2018-01-08 18:39:05,673 - ALPHA_MIND - INFO - 2017-11-08 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:39:06,107 - ALPHA_MIND - INFO - 2017-11-08 00:00:00 is finished\n",
      "2018-01-08 18:39:06,111 - ALPHA_MIND - INFO - 2017-11-15 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:39:06,534 - ALPHA_MIND - INFO - 2017-11-15 00:00:00 is finished\n",
      "2018-01-08 18:39:06,539 - ALPHA_MIND - INFO - 2017-11-22 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:39:06,966 - ALPHA_MIND - INFO - 2017-11-22 00:00:00 is finished\n",
      "2018-01-08 18:39:06,970 - ALPHA_MIND - INFO - 2017-11-29 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:39:07,386 - ALPHA_MIND - INFO - 2017-11-29 00:00:00 is finished\n",
      "2018-01-08 18:39:07,391 - ALPHA_MIND - INFO - 2017-12-06 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:39:07,811 - ALPHA_MIND - INFO - 2017-12-06 00:00:00 is finished\n",
      "2018-01-08 18:39:07,816 - ALPHA_MIND - INFO - 2017-12-13 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:39:08,244 - ALPHA_MIND - INFO - 2017-12-13 00:00:00 is finished\n",
      "2018-01-08 18:39:08,249 - ALPHA_MIND - INFO - 2017-12-20 00:00:00: 800\n",
      "2018-01-08 18:39:08,759 - ALPHA_MIND - INFO - 2017-12-20 00:00:00 is finished\n",
      "2018-01-08 18:39:08,763 - ALPHA_MIND - INFO - 2017-12-27 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n",
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:39:09,198 - ALPHA_MIND - INFO - 2017-12-27 00:00:00 is finished\n"
     ]
    },
    {
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yMj94mxNmI4f6juLHk6W4Oxu4oamJAHcnWvq50TPcdE1NI1jf7oBLABeXTD4n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\nKrIJaayFwjxUzPUYIy/+yfbMLz9CL/rnjw8kdsO484+ouMSftBUdh96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atWsZOHDgEXvA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x268b6882da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YaN68uaNtYWEhnp6etGjRotL5s7KycHV1JSgoCKvVyuHDhyt92Dlw4ADt2rXD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\nxBr6H80HNefAdHnbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x268e5195d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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A2NraUlRUVGYfTZo04dSpUxVeQ8eOHVm3bh35+fnk5ORYzW5SHhcXF6vrBjhx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      "text/plain": [
       "<matplotlib.figure.Figure at 0x268adc25fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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WHTYTf64AJ6NGnzBvBvyZbeEtFvT3X4VSC9rg0aiUhLJVSzb9hHJ1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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x268b6828a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicting and re-balance phase\n",
    "\n",
    "weight_gaps = [0.01, 0.02, 0.03, 0.04]\n",
    "\n",
    "model1_weight = 1.0\n",
    "model2_weight = 0.5\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",
    "        try:\n",
    "            model = models_series.ix[date]\n",
    "        except KeyError:\n",
    "            alpha_logger.info(\"model is not available. Omit this date\")\n",
    "            continue\n",
    "            \n",
    "        index_dates.append(date)\n",
    "\n",
    "        # x = predict_x[date]\n",
    "        x = predict_x[date]\n",
    "        risk = predict_risk[date][:, 1:]\n",
    "        this_predict_code = predict_code[date]\n",
    "        \n",
    "        new_x = cross_product(x, risk)\n",
    "        er2 = model.predict(new_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': this_predict_code})\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 backbone model and alpha model')\n",
    "            er = model1_weight * er1 / er1.std() +  model2_weight * er2 / er2.std()\n",
    "        else:\n",
    "            print('using only backbone modedl')\n",
    "            er = model1_weight * er1\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': leverags}, 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",
    "    \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_rf_{0}.csv'.format(int(weight_gap * 100)))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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