{
 "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 = 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",
    "linear_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",
    "    'IVR_raw': LAST('IVR'),\n",
    "    'DivP_raw': LAST('DivP'),\n",
    "    'cfinc1_q_raw': LAST('cfinc1_q'),\n",
    "    'CFinc1_raw': LAST('CFinc1'),\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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:07:19,841 - ALPHA_MIND - INFO - Starting data package fetching ...\n",
      "2018-01-08 18:07:45,968 - ALPHA_MIND - INFO - factor data loading finished\n",
      "2018-01-08 18:07:56,680 - ALPHA_MIND - INFO - return data loading finished\n",
      "2018-01-08 18:07:58,390 - ALPHA_MIND - INFO - industry data loading finished\n",
      "2018-01-08 18:07:58,843 - ALPHA_MIND - INFO - benchmark data loading finished\n",
      "2018-01-08 18:08:10,650 - ALPHA_MIND - INFO - risk data loading finished\n",
      "2018-01-08 18:08:13,305 - ALPHA_MIND - INFO - data merging finished\n",
      "2018-01-08 18:08:15,133 - ALPHA_MIND - INFO - Loading data is finished\n",
      "2018-01-08 18:08:24,804 - 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",
    "train_risk = linear_model_factor_data['train']['risk']\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",
    "predict_risk = linear_model_factor_data['predict']['risk']\n",
    "predict_code = linear_model_factor_data['predict']['code']\n",
    "settlement = linear_model_factor_data['settlement']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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      "2018-01-08 18:09:10,032 - ALPHA_MIND - INFO - trade_date: 2015-08-19 00:00:00 training finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:09:10,099 - ALPHA_MIND - INFO - trade_date: 2015-08-26 00:00:00 training finished\n",
      "2018-01-08 18:09:10,180 - ALPHA_MIND - INFO - trade_date: 2015-09-02 00:00:00 training finished\n",
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      "2018-01-08 18:09:10,969 - ALPHA_MIND - INFO - trade_date: 2015-11-13 00:00:00 training finished\n",
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      "2018-01-08 18:09:11,805 - ALPHA_MIND - INFO - trade_date: 2016-01-04 00:00:00 training finished\n",
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      "2018-01-08 18:09:16,787 - ALPHA_MIND - INFO - trade_date: 2017-02-15 00:00:00 training finished\n",
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      "2018-01-08 18:09:18,252 - ALPHA_MIND - INFO - trade_date: 2017-05-15 00:00:00 training finished\n",
      "2018-01-08 18:09:18,407 - ALPHA_MIND - INFO - trade_date: 2017-05-22 00:00:00 training finished\n",
      "2018-01-08 18:09:18,500 - ALPHA_MIND - INFO - trade_date: 2017-05-31 00:00:00 training finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:09:18,588 - ALPHA_MIND - INFO - trade_date: 2017-06-07 00:00:00 training finished\n",
      "2018-01-08 18:09:18,680 - ALPHA_MIND - INFO - trade_date: 2017-06-14 00:00:00 training finished\n",
      "2018-01-08 18:09:18,783 - ALPHA_MIND - INFO - trade_date: 2017-06-21 00:00:00 training finished\n",
      "2018-01-08 18:09:18,876 - ALPHA_MIND - INFO - trade_date: 2017-06-28 00:00:00 training finished\n",
      "2018-01-08 18:09:18,957 - ALPHA_MIND - INFO - trade_date: 2017-07-05 00:00:00 training finished\n",
      "2018-01-08 18:09:19,040 - ALPHA_MIND - INFO - trade_date: 2017-07-12 00:00:00 training finished\n",
      "2018-01-08 18:09:19,154 - ALPHA_MIND - INFO - trade_date: 2017-07-19 00:00:00 training finished\n",
      "2018-01-08 18:09:19,254 - ALPHA_MIND - INFO - trade_date: 2017-07-26 00:00:00 training finished\n",
      "2018-01-08 18:09:19,370 - ALPHA_MIND - INFO - trade_date: 2017-08-02 00:00:00 training finished\n",
      "2018-01-08 18:09:19,476 - ALPHA_MIND - INFO - trade_date: 2017-08-09 00:00:00 training finished\n",
      "2018-01-08 18:09:19,572 - ALPHA_MIND - INFO - trade_date: 2017-08-16 00:00:00 training finished\n",
      "2018-01-08 18:09:19,667 - ALPHA_MIND - INFO - trade_date: 2017-08-23 00:00:00 training finished\n",
      "2018-01-08 18:09:19,779 - ALPHA_MIND - INFO - trade_date: 2017-08-30 00:00:00 training finished\n",
      "2018-01-08 18:09:19,884 - ALPHA_MIND - INFO - trade_date: 2017-09-06 00:00:00 training finished\n",
      "2018-01-08 18:09:19,987 - ALPHA_MIND - INFO - trade_date: 2017-09-13 00:00:00 training finished\n",
      "2018-01-08 18:09:20,056 - ALPHA_MIND - INFO - trade_date: 2017-09-20 00:00:00 training finished\n",
      "2018-01-08 18:09:20,125 - ALPHA_MIND - INFO - trade_date: 2017-09-27 00:00:00 training finished\n",
      "2018-01-08 18:09:20,215 - ALPHA_MIND - INFO - trade_date: 2017-10-11 00:00:00 training finished\n",
      "2018-01-08 18:09:20,351 - ALPHA_MIND - INFO - trade_date: 2017-10-18 00:00:00 training finished\n",
      "2018-01-08 18:09:20,471 - ALPHA_MIND - INFO - trade_date: 2017-10-25 00:00:00 training finished\n",
      "2018-01-08 18:09:20,603 - ALPHA_MIND - INFO - trade_date: 2017-11-01 00:00:00 training finished\n",
      "2018-01-08 18:09:20,737 - ALPHA_MIND - INFO - trade_date: 2017-11-08 00:00:00 training finished\n",
      "2018-01-08 18:09:20,917 - ALPHA_MIND - INFO - trade_date: 2017-11-15 00:00:00 training finished\n",
      "2018-01-08 18:09:21,105 - ALPHA_MIND - INFO - trade_date: 2017-11-22 00:00:00 training finished\n",
      "2018-01-08 18:09:21,326 - ALPHA_MIND - INFO - trade_date: 2017-11-29 00:00:00 training finished\n",
      "2018-01-08 18:09:21,475 - ALPHA_MIND - INFO - trade_date: 2017-12-06 00:00:00 training finished\n",
      "2018-01-08 18:09:21,598 - ALPHA_MIND - INFO - trade_date: 2017-12-13 00:00:00 training finished\n",
      "2018-01-08 18:09:21,678 - ALPHA_MIND - INFO - trade_date: 2017-12-20 00:00:00 training finished\n",
      "2018-01-08 18:09:21,770 - 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 = LinearRegression(features=linear_model_features_keys, fit_intercept=True) # n_jobs=8, min_samples_split=20)\n",
    "    model = LassoRegression(alpha=0.01, features=linear_model_features_keys, fit_intercept=True) # n_jobs=8, min_samples_split=20)\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",
    "\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:11:01,091 - ALPHA_MIND - INFO - 2012-01-04 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start 0.01 weight gap simulation ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:11:01,097 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:11:01,103 - ALPHA_MIND - INFO - 2012-01-11 00:00:00: 800\n",
      "2018-01-08 18:11:01,107 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:11:01,116 - ALPHA_MIND - INFO - 2012-01-18 00:00:00: 800\n",
      "2018-01-08 18:11:01,122 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:11:01,128 - ALPHA_MIND - INFO - 2012-02-01 00:00:00: 800\n",
      "2018-01-08 18:11:01,137 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:11:01,144 - ALPHA_MIND - INFO - 2012-02-08 00:00:00: 800\n",
      "2018-01-08 18:11:01,149 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:11:01,157 - ALPHA_MIND - INFO - 2012-02-15 00:00:00: 800\n",
      "2018-01-08 18:11:01,312 - ALPHA_MIND - INFO - 2012-02-15 00:00:00 is finished\n",
      "2018-01-08 18:11:01,321 - ALPHA_MIND - INFO - 2012-02-22 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 18:11:01,589 - ALPHA_MIND - INFO - 2012-02-22 00:00:00 is finished\n",
      "2018-01-08 18:11:01,595 - ALPHA_MIND - INFO - 2012-02-29 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 18:11:01,857 - ALPHA_MIND - INFO - 2012-02-29 00:00:00 is finished\n",
      "2018-01-08 18:11:01,865 - ALPHA_MIND - INFO - 2012-03-07 00:00:00: 799\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:11:02,224 - ALPHA_MIND - INFO - 2012-03-07 00:00:00 is finished\n",
      "2018-01-08 18:11:02,231 - ALPHA_MIND - INFO - 2012-03-14 00:00:00: 799\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:11:02,583 - ALPHA_MIND - INFO - 2012-03-14 00:00:00 is finished\n",
      "2018-01-08 18:11:02,595 - ALPHA_MIND - INFO - 2012-03-21 00:00:00: 798\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:11:02,942 - ALPHA_MIND - INFO - 2012-03-21 00:00:00 is finished\n",
      "2018-01-08 18:11:02,952 - ALPHA_MIND - INFO - 2012-03-28 00:00:00: 798\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:11:03,273 - ALPHA_MIND - INFO - 2012-03-28 00:00:00 is finished\n",
      "2018-01-08 18:11:03,280 - ALPHA_MIND - INFO - 2012-04-09 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 18:11:03,605 - ALPHA_MIND - INFO - 2012-04-09 00:00:00 is finished\n",
      "2018-01-08 18:11:03,615 - ALPHA_MIND - INFO - 2012-04-16 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 18:11:03,886 - ALPHA_MIND - INFO - 2012-04-16 00:00:00 is finished\n",
      "2018-01-08 18:11:03,892 - ALPHA_MIND - INFO - 2012-04-23 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 18:11:04,142 - ALPHA_MIND - INFO - 2012-04-23 00:00:00 is finished\n",
      "2018-01-08 18:11:04,147 - ALPHA_MIND - INFO - 2012-05-02 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 18:11:04,376 - ALPHA_MIND - INFO - 2012-05-02 00:00:00 is finished\n",
      "2018-01-08 18:11:04,381 - ALPHA_MIND - INFO - 2012-05-09 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 18:11:04,598 - ALPHA_MIND - INFO - 2012-05-09 00:00:00 is finished\n",
      "2018-01-08 18:11:04,605 - ALPHA_MIND - INFO - 2012-05-16 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 18:11:04,836 - ALPHA_MIND - INFO - 2012-05-16 00:00:00 is finished\n",
      "2018-01-08 18:11:04,842 - ALPHA_MIND - INFO - 2012-05-23 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 18:11:05,125 - ALPHA_MIND - INFO - 2012-05-23 00:00:00 is finished\n",
      "2018-01-08 18:11:05,136 - ALPHA_MIND - INFO - 2012-05-30 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 18:11:05,472 - ALPHA_MIND - INFO - 2012-05-30 00:00:00 is finished\n",
      "2018-01-08 18:11:05,481 - ALPHA_MIND - INFO - 2012-06-06 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 18:11:05,787 - ALPHA_MIND - INFO - 2012-06-06 00:00:00 is finished\n",
      "2018-01-08 18:11:05,794 - ALPHA_MIND - INFO - 2012-06-13 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 18:11:06,165 - ALPHA_MIND - INFO - 2012-06-13 00:00:00 is finished\n",
      "2018-01-08 18:11:06,174 - ALPHA_MIND - INFO - 2012-06-20 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 18:11:06,520 - ALPHA_MIND - INFO - 2012-06-20 00:00:00 is finished\n",
      "2018-01-08 18:11:06,527 - ALPHA_MIND - INFO - 2012-06-28 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 18:11:06,859 - ALPHA_MIND - INFO - 2012-06-28 00:00:00 is finished\n",
      "2018-01-08 18:11:06,863 - ALPHA_MIND - INFO - 2012-07-05 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 18:11:07,129 - ALPHA_MIND - INFO - 2012-07-05 00:00:00 is finished\n",
      "2018-01-08 18:11:07,136 - ALPHA_MIND - INFO - 2012-07-12 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 18:11:07,569 - ALPHA_MIND - INFO - 2012-07-12 00:00:00 is finished\n",
      "2018-01-08 18:11:07,574 - ALPHA_MIND - INFO - 2012-07-19 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 18:11:07,826 - ALPHA_MIND - INFO - 2012-07-19 00:00:00 is finished\n",
      "2018-01-08 18:11:07,834 - ALPHA_MIND - INFO - 2012-07-26 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 18:11:08,099 - ALPHA_MIND - INFO - 2012-07-26 00:00:00 is finished\n",
      "2018-01-08 18:11:08,105 - ALPHA_MIND - INFO - 2012-08-02 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 18:11:08,534 - ALPHA_MIND - INFO - 2012-08-02 00:00:00 is finished\n",
      "2018-01-08 18:11:08,544 - ALPHA_MIND - INFO - 2012-08-09 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 18:11:08,892 - ALPHA_MIND - INFO - 2012-08-09 00:00:00 is finished\n",
      "2018-01-08 18:11:08,899 - ALPHA_MIND - INFO - 2012-08-16 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 18:11:09,224 - ALPHA_MIND - INFO - 2012-08-16 00:00:00 is finished\n",
      "2018-01-08 18:11:09,233 - ALPHA_MIND - INFO - 2012-08-23 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 18:11:09,586 - ALPHA_MIND - INFO - 2012-08-23 00:00:00 is finished\n",
      "2018-01-08 18:11:09,593 - ALPHA_MIND - INFO - 2012-08-30 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 18:11:09,870 - ALPHA_MIND - INFO - 2012-08-30 00:00:00 is finished\n",
      "2018-01-08 18:11:09,877 - ALPHA_MIND - INFO - 2012-09-06 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 18:11:10,116 - ALPHA_MIND - INFO - 2012-09-06 00:00:00 is finished\n",
      "2018-01-08 18:11:10,121 - ALPHA_MIND - INFO - 2012-09-13 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 18:11:10,358 - ALPHA_MIND - INFO - 2012-09-13 00:00:00 is finished\n",
      "2018-01-08 18:11:10,363 - ALPHA_MIND - INFO - 2012-09-20 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 18:11:10,602 - ALPHA_MIND - INFO - 2012-09-20 00:00:00 is finished\n",
      "2018-01-08 18:11:10,606 - ALPHA_MIND - INFO - 2012-09-27 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 18:11:10,817 - ALPHA_MIND - INFO - 2012-09-27 00:00:00 is finished\n",
      "2018-01-08 18:11:10,824 - ALPHA_MIND - INFO - 2012-10-11 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 18:11:11,066 - ALPHA_MIND - INFO - 2012-10-11 00:00:00 is finished\n",
      "2018-01-08 18:11:11,074 - ALPHA_MIND - INFO - 2012-10-18 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 18:11:11,431 - ALPHA_MIND - INFO - 2012-10-18 00:00:00 is finished\n",
      "2018-01-08 18:11:11,439 - ALPHA_MIND - INFO - 2012-10-25 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 18:11:11,747 - ALPHA_MIND - INFO - 2012-10-25 00:00:00 is finished\n",
      "2018-01-08 18:11:11,757 - ALPHA_MIND - INFO - 2012-11-01 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 18:11:12,108 - ALPHA_MIND - INFO - 2012-11-01 00:00:00 is finished\n",
      "2018-01-08 18:11:12,116 - ALPHA_MIND - INFO - 2012-11-08 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 18:11:12,567 - ALPHA_MIND - INFO - 2012-11-08 00:00:00 is finished\n",
      "2018-01-08 18:11:12,573 - ALPHA_MIND - INFO - 2012-11-15 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 18:11:12,826 - ALPHA_MIND - INFO - 2012-11-15 00:00:00 is finished\n",
      "2018-01-08 18:11:12,831 - ALPHA_MIND - INFO - 2012-11-22 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 18:11:13,064 - ALPHA_MIND - INFO - 2012-11-22 00:00:00 is finished\n",
      "2018-01-08 18:11:13,069 - ALPHA_MIND - INFO - 2012-11-29 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 18:11:13,300 - ALPHA_MIND - INFO - 2012-11-29 00:00:00 is finished\n",
      "2018-01-08 18:11:13,306 - ALPHA_MIND - INFO - 2012-12-06 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 18:11:13,532 - ALPHA_MIND - INFO - 2012-12-06 00:00:00 is finished\n",
      "2018-01-08 18:11:13,538 - ALPHA_MIND - INFO - 2012-12-13 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 18:11:13,772 - ALPHA_MIND - INFO - 2012-12-13 00:00:00 is finished\n",
      "2018-01-08 18:11:13,778 - ALPHA_MIND - INFO - 2012-12-20 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 18:11:14,023 - ALPHA_MIND - INFO - 2012-12-20 00:00:00 is finished\n",
      "2018-01-08 18:11:14,031 - ALPHA_MIND - INFO - 2012-12-27 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 18:11:14,360 - ALPHA_MIND - INFO - 2012-12-27 00:00:00 is finished\n",
      "2018-01-08 18:11:14,369 - ALPHA_MIND - INFO - 2013-01-08 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 18:11:14,728 - ALPHA_MIND - INFO - 2013-01-08 00:00:00 is finished\n",
      "2018-01-08 18:11:14,737 - ALPHA_MIND - INFO - 2013-01-15 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 18:11:15,118 - ALPHA_MIND - INFO - 2013-01-15 00:00:00 is finished\n",
      "2018-01-08 18:11:15,125 - ALPHA_MIND - INFO - 2013-01-22 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
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     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:12:29,090 - ALPHA_MIND - INFO - 2017-12-13 00:00:00 is finished\n",
      "2018-01-08 18:12:29,098 - ALPHA_MIND - INFO - 2017-12-20 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 18:12:29,441 - ALPHA_MIND - INFO - 2017-12-20 00:00:00 is finished\n",
      "2018-01-08 18:12:29,448 - ALPHA_MIND - INFO - 2017-12-27 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 18:12:29,838 - ALPHA_MIND - INFO - 2017-12-27 00:00:00 is finished\n",
      "2018-01-08 18:12:30,941 - ALPHA_MIND - INFO - 2012-01-04 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start 0.02 weight gap simulation ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:12:30,950 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:12:30,956 - ALPHA_MIND - INFO - 2012-01-11 00:00:00: 800\n",
      "2018-01-08 18:12:30,963 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:12:30,972 - ALPHA_MIND - INFO - 2012-01-18 00:00:00: 800\n",
      "2018-01-08 18:12:30,981 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:12:30,989 - ALPHA_MIND - INFO - 2012-02-01 00:00:00: 800\n",
      "2018-01-08 18:12:30,999 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:12:31,005 - ALPHA_MIND - INFO - 2012-02-08 00:00:00: 800\n",
      "2018-01-08 18:12:31,015 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:12:31,022 - ALPHA_MIND - INFO - 2012-02-15 00:00:00: 800\n",
      "2018-01-08 18:12:31,195 - ALPHA_MIND - INFO - 2012-02-15 00:00:00 is finished\n",
      "2018-01-08 18:12:31,205 - ALPHA_MIND - INFO - 2012-02-22 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 18:12:31,458 - ALPHA_MIND - INFO - 2012-02-22 00:00:00 is finished\n",
      "2018-01-08 18:12:31,464 - ALPHA_MIND - INFO - 2012-02-29 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 18:12:31,874 - ALPHA_MIND - INFO - 2012-02-29 00:00:00 is finished\n",
      "2018-01-08 18:12:31,880 - ALPHA_MIND - INFO - 2012-03-07 00:00:00: 799\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:12:32,185 - ALPHA_MIND - INFO - 2012-03-07 00:00:00 is finished\n",
      "2018-01-08 18:12:32,193 - ALPHA_MIND - INFO - 2012-03-14 00:00:00: 799\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:12:32,536 - ALPHA_MIND - INFO - 2012-03-14 00:00:00 is finished\n",
      "2018-01-08 18:12:32,543 - ALPHA_MIND - INFO - 2012-03-21 00:00:00: 798\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:12:32,874 - ALPHA_MIND - INFO - 2012-03-21 00:00:00 is finished\n",
      "2018-01-08 18:12:32,882 - ALPHA_MIND - INFO - 2012-03-28 00:00:00: 798\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:12:33,196 - ALPHA_MIND - INFO - 2012-03-28 00:00:00 is finished\n",
      "2018-01-08 18:12:33,201 - ALPHA_MIND - INFO - 2012-04-09 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 18:12:33,456 - ALPHA_MIND - INFO - 2012-04-09 00:00:00 is finished\n",
      "2018-01-08 18:12:33,463 - ALPHA_MIND - INFO - 2012-04-16 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 18:12:33,716 - ALPHA_MIND - INFO - 2012-04-16 00:00:00 is finished\n",
      "2018-01-08 18:12:33,721 - ALPHA_MIND - INFO - 2012-04-23 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 18:12:33,963 - ALPHA_MIND - INFO - 2012-04-23 00:00:00 is finished\n",
      "2018-01-08 18:12:33,969 - ALPHA_MIND - INFO - 2012-05-02 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 18:12:34,220 - ALPHA_MIND - INFO - 2012-05-02 00:00:00 is finished\n",
      "2018-01-08 18:12:34,224 - ALPHA_MIND - INFO - 2012-05-09 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 18:12:34,453 - ALPHA_MIND - INFO - 2012-05-09 00:00:00 is finished\n",
      "2018-01-08 18:12:34,459 - ALPHA_MIND - INFO - 2012-05-16 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 18:12:34,703 - ALPHA_MIND - INFO - 2012-05-16 00:00:00 is finished\n",
      "2018-01-08 18:12:34,709 - ALPHA_MIND - INFO - 2012-05-23 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 18:12:34,951 - ALPHA_MIND - INFO - 2012-05-23 00:00:00 is finished\n",
      "2018-01-08 18:12:34,958 - ALPHA_MIND - INFO - 2012-05-30 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 18:12:35,295 - ALPHA_MIND - INFO - 2012-05-30 00:00:00 is finished\n",
      "2018-01-08 18:12:35,302 - ALPHA_MIND - INFO - 2012-06-06 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 18:12:35,650 - ALPHA_MIND - INFO - 2012-06-06 00:00:00 is finished\n",
      "2018-01-08 18:12:35,657 - ALPHA_MIND - INFO - 2012-06-13 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 18:12:36,002 - ALPHA_MIND - INFO - 2012-06-13 00:00:00 is finished\n",
      "2018-01-08 18:12:36,009 - ALPHA_MIND - INFO - 2012-06-20 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 18:12:36,292 - ALPHA_MIND - INFO - 2012-06-20 00:00:00 is finished\n",
      "2018-01-08 18:12:36,299 - ALPHA_MIND - INFO - 2012-06-28 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 18:12:36,643 - ALPHA_MIND - INFO - 2012-06-28 00:00:00 is finished\n",
      "2018-01-08 18:12:36,649 - ALPHA_MIND - INFO - 2012-07-05 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 18:12:36,893 - ALPHA_MIND - INFO - 2012-07-05 00:00:00 is finished\n",
      "2018-01-08 18:12:36,898 - ALPHA_MIND - INFO - 2012-07-12 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 18:12:37,150 - ALPHA_MIND - INFO - 2012-07-12 00:00:00 is finished\n",
      "2018-01-08 18:12:37,156 - ALPHA_MIND - INFO - 2012-07-19 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 18:12:37,387 - ALPHA_MIND - INFO - 2012-07-19 00:00:00 is finished\n",
      "2018-01-08 18:12:37,393 - ALPHA_MIND - INFO - 2012-07-26 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 18:12:37,623 - ALPHA_MIND - INFO - 2012-07-26 00:00:00 is finished\n",
      "2018-01-08 18:12:37,629 - ALPHA_MIND - INFO - 2012-08-02 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 18:12:37,871 - ALPHA_MIND - INFO - 2012-08-02 00:00:00 is finished\n",
      "2018-01-08 18:12:37,878 - ALPHA_MIND - INFO - 2012-08-09 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 18:12:38,198 - ALPHA_MIND - INFO - 2012-08-09 00:00:00 is finished\n",
      "2018-01-08 18:12:38,207 - ALPHA_MIND - INFO - 2012-08-16 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 18:12:38,549 - ALPHA_MIND - INFO - 2012-08-16 00:00:00 is finished\n",
      "2018-01-08 18:12:38,556 - ALPHA_MIND - INFO - 2012-08-23 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 18:12:38,993 - ALPHA_MIND - INFO - 2012-08-23 00:00:00 is finished\n",
      "2018-01-08 18:12:39,000 - ALPHA_MIND - INFO - 2012-08-30 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 18:12:39,392 - ALPHA_MIND - INFO - 2012-08-30 00:00:00 is finished\n",
      "2018-01-08 18:12:39,398 - ALPHA_MIND - INFO - 2012-09-06 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 18:12:39,673 - ALPHA_MIND - INFO - 2012-09-06 00:00:00 is finished\n",
      "2018-01-08 18:12:39,678 - ALPHA_MIND - INFO - 2012-09-13 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 18:12:39,942 - ALPHA_MIND - INFO - 2012-09-13 00:00:00 is finished\n",
      "2018-01-08 18:12:39,948 - ALPHA_MIND - INFO - 2012-09-20 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 18:12:40,207 - ALPHA_MIND - INFO - 2012-09-20 00:00:00 is finished\n",
      "2018-01-08 18:12:40,212 - ALPHA_MIND - INFO - 2012-09-27 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 18:12:40,464 - ALPHA_MIND - INFO - 2012-09-27 00:00:00 is finished\n",
      "2018-01-08 18:12:40,470 - ALPHA_MIND - INFO - 2012-10-11 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 18:12:40,718 - ALPHA_MIND - INFO - 2012-10-11 00:00:00 is finished\n",
      "2018-01-08 18:12:40,723 - ALPHA_MIND - INFO - 2012-10-18 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 18:12:40,977 - ALPHA_MIND - INFO - 2012-10-18 00:00:00 is finished\n",
      "2018-01-08 18:12:40,984 - ALPHA_MIND - INFO - 2012-10-25 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 18:12:41,414 - ALPHA_MIND - INFO - 2012-10-25 00:00:00 is finished\n",
      "2018-01-08 18:12:41,422 - ALPHA_MIND - INFO - 2012-11-01 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 18:12:41,928 - ALPHA_MIND - INFO - 2012-11-01 00:00:00 is finished\n",
      "2018-01-08 18:12:41,936 - ALPHA_MIND - INFO - 2012-11-08 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 18:12:42,297 - ALPHA_MIND - INFO - 2012-11-08 00:00:00 is finished\n",
      "2018-01-08 18:12:42,302 - ALPHA_MIND - INFO - 2012-11-15 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 18:12:42,600 - ALPHA_MIND - INFO - 2012-11-15 00:00:00 is finished\n",
      "2018-01-08 18:12:42,606 - ALPHA_MIND - INFO - 2012-11-22 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 18:12:42,853 - ALPHA_MIND - INFO - 2012-11-22 00:00:00 is finished\n",
      "2018-01-08 18:12:42,859 - ALPHA_MIND - INFO - 2012-11-29 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 18:12:43,097 - ALPHA_MIND - INFO - 2012-11-29 00:00:00 is finished\n",
      "2018-01-08 18:12:43,103 - ALPHA_MIND - INFO - 2012-12-06 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 18:12:43,360 - ALPHA_MIND - INFO - 2012-12-06 00:00:00 is finished\n",
      "2018-01-08 18:12:43,366 - ALPHA_MIND - INFO - 2012-12-13 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 18:12:43,612 - ALPHA_MIND - INFO - 2012-12-13 00:00:00 is finished\n",
      "2018-01-08 18:12:43,618 - ALPHA_MIND - INFO - 2012-12-20 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:14:07,311 - ALPHA_MIND - INFO - 2017-11-15 00:00:00 is finished\n",
      "2018-01-08 18:14:07,318 - 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 18:14:07,599 - ALPHA_MIND - INFO - 2017-11-22 00:00:00 is finished\n",
      "2018-01-08 18:14:07,604 - 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 18:14:07,853 - ALPHA_MIND - INFO - 2017-11-29 00:00:00 is finished\n",
      "2018-01-08 18:14:07,858 - 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 18:14:08,206 - ALPHA_MIND - INFO - 2017-12-06 00:00:00 is finished\n",
      "2018-01-08 18:14:08,212 - 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 18:14:08,458 - ALPHA_MIND - INFO - 2017-12-13 00:00:00 is finished\n",
      "2018-01-08 18:14:08,462 - ALPHA_MIND - INFO - 2017-12-20 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 18:14:08,719 - ALPHA_MIND - INFO - 2017-12-20 00:00:00 is finished\n",
      "2018-01-08 18:14:08,728 - ALPHA_MIND - INFO - 2017-12-27 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 18:14:09,186 - ALPHA_MIND - INFO - 2017-12-27 00:00:00 is finished\n",
      "2018-01-08 18:14:10,670 - ALPHA_MIND - INFO - 2012-01-04 00:00:00: 800\n",
      "2018-01-08 18:14:10,675 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:14:10,681 - ALPHA_MIND - INFO - 2012-01-11 00:00:00: 800\n",
      "2018-01-08 18:14:10,687 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:14:10,694 - ALPHA_MIND - INFO - 2012-01-18 00:00:00: 800\n",
      "2018-01-08 18:14:10,700 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:14:10,708 - ALPHA_MIND - INFO - 2012-02-01 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start 0.03 weight gap simulation ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:14:10,716 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:14:10,723 - ALPHA_MIND - INFO - 2012-02-08 00:00:00: 800\n",
      "2018-01-08 18:14:10,729 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:14:10,738 - ALPHA_MIND - INFO - 2012-02-15 00:00:00: 800\n",
      "2018-01-08 18:14:10,887 - ALPHA_MIND - INFO - 2012-02-15 00:00:00 is finished\n",
      "2018-01-08 18:14:10,894 - ALPHA_MIND - INFO - 2012-02-22 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 18:14:11,147 - ALPHA_MIND - INFO - 2012-02-22 00:00:00 is finished\n",
      "2018-01-08 18:14:11,154 - ALPHA_MIND - INFO - 2012-02-29 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 18:14:11,409 - ALPHA_MIND - INFO - 2012-02-29 00:00:00 is finished\n",
      "2018-01-08 18:14:11,414 - ALPHA_MIND - INFO - 2012-03-07 00:00:00: 799\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:14:11,664 - ALPHA_MIND - INFO - 2012-03-07 00:00:00 is finished\n",
      "2018-01-08 18:14:11,669 - ALPHA_MIND - INFO - 2012-03-14 00:00:00: 799\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:14:11,943 - ALPHA_MIND - INFO - 2012-03-14 00:00:00 is finished\n",
      "2018-01-08 18:14:11,950 - ALPHA_MIND - INFO - 2012-03-21 00:00:00: 798\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:14:12,406 - ALPHA_MIND - INFO - 2012-03-21 00:00:00 is finished\n",
      "2018-01-08 18:14:12,414 - ALPHA_MIND - INFO - 2012-03-28 00:00:00: 798\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:14:12,896 - ALPHA_MIND - INFO - 2012-03-28 00:00:00 is finished\n",
      "2018-01-08 18:14:12,903 - ALPHA_MIND - INFO - 2012-04-09 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 18:14:13,398 - ALPHA_MIND - INFO - 2012-04-09 00:00:00 is finished\n",
      "2018-01-08 18:14:13,403 - ALPHA_MIND - INFO - 2012-04-16 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 18:14:13,679 - ALPHA_MIND - INFO - 2012-04-16 00:00:00 is finished\n",
      "2018-01-08 18:14:13,688 - ALPHA_MIND - INFO - 2012-04-23 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 18:14:13,987 - ALPHA_MIND - INFO - 2012-04-23 00:00:00 is finished\n",
      "2018-01-08 18:14:13,993 - ALPHA_MIND - INFO - 2012-05-02 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 18:14:14,408 - ALPHA_MIND - INFO - 2012-05-02 00:00:00 is finished\n",
      "2018-01-08 18:14:14,413 - ALPHA_MIND - INFO - 2012-05-09 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 18:14:14,651 - ALPHA_MIND - INFO - 2012-05-09 00:00:00 is finished\n",
      "2018-01-08 18:14:14,658 - ALPHA_MIND - INFO - 2012-05-16 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 18:14:14,892 - ALPHA_MIND - INFO - 2012-05-16 00:00:00 is finished\n",
      "2018-01-08 18:14:14,898 - ALPHA_MIND - INFO - 2012-05-23 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 18:14:15,159 - ALPHA_MIND - INFO - 2012-05-23 00:00:00 is finished\n",
      "2018-01-08 18:14:15,169 - ALPHA_MIND - INFO - 2012-05-30 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 18:14:15,678 - ALPHA_MIND - INFO - 2012-05-30 00:00:00 is finished\n",
      "2018-01-08 18:14:15,686 - ALPHA_MIND - INFO - 2012-06-06 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 18:14:16,159 - ALPHA_MIND - INFO - 2012-06-06 00:00:00 is finished\n",
      "2018-01-08 18:14:16,169 - ALPHA_MIND - INFO - 2012-06-13 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 18:14:16,678 - ALPHA_MIND - INFO - 2012-06-13 00:00:00 is finished\n",
      "2018-01-08 18:14:16,683 - ALPHA_MIND - INFO - 2012-06-20 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 18:14:16,952 - ALPHA_MIND - INFO - 2012-06-20 00:00:00 is finished\n",
      "2018-01-08 18:14:16,958 - ALPHA_MIND - INFO - 2012-06-28 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 18:14:17,240 - ALPHA_MIND - INFO - 2012-06-28 00:00:00 is finished\n",
      "2018-01-08 18:14:17,246 - ALPHA_MIND - INFO - 2012-07-05 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 18:14:17,522 - ALPHA_MIND - INFO - 2012-07-05 00:00:00 is finished\n",
      "2018-01-08 18:14:17,529 - ALPHA_MIND - INFO - 2012-07-12 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 18:14:17,801 - ALPHA_MIND - INFO - 2012-07-12 00:00:00 is finished\n",
      "2018-01-08 18:14:17,808 - ALPHA_MIND - INFO - 2012-07-19 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 18:14:18,058 - ALPHA_MIND - INFO - 2012-07-19 00:00:00 is finished\n",
      "2018-01-08 18:14:18,064 - ALPHA_MIND - INFO - 2012-07-26 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 18:14:18,311 - ALPHA_MIND - INFO - 2012-07-26 00:00:00 is finished\n",
      "2018-01-08 18:14:18,316 - ALPHA_MIND - INFO - 2012-08-02 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 18:14:18,748 - ALPHA_MIND - INFO - 2012-08-02 00:00:00 is finished\n",
      "2018-01-08 18:14:18,755 - ALPHA_MIND - INFO - 2012-08-09 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 18:14:19,292 - ALPHA_MIND - INFO - 2012-08-09 00:00:00 is finished\n",
      "2018-01-08 18:14:19,299 - ALPHA_MIND - INFO - 2012-08-16 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 18:14:19,674 - ALPHA_MIND - INFO - 2012-08-16 00:00:00 is finished\n",
      "2018-01-08 18:14:19,681 - ALPHA_MIND - INFO - 2012-08-23 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 18:14:19,992 - ALPHA_MIND - INFO - 2012-08-23 00:00:00 is finished\n",
      "2018-01-08 18:14:19,998 - ALPHA_MIND - INFO - 2012-08-30 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 18:14:20,358 - ALPHA_MIND - INFO - 2012-08-30 00:00:00 is finished\n",
      "2018-01-08 18:14:20,363 - ALPHA_MIND - INFO - 2012-09-06 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 18:14:20,604 - ALPHA_MIND - INFO - 2012-09-06 00:00:00 is finished\n",
      "2018-01-08 18:14:20,609 - ALPHA_MIND - INFO - 2012-09-13 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 18:14:20,849 - ALPHA_MIND - INFO - 2012-09-13 00:00:00 is finished\n",
      "2018-01-08 18:14:20,855 - ALPHA_MIND - INFO - 2012-09-20 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 18:14:21,097 - ALPHA_MIND - INFO - 2012-09-20 00:00:00 is finished\n",
      "2018-01-08 18:14:21,103 - ALPHA_MIND - INFO - 2012-09-27 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 18:14:21,343 - ALPHA_MIND - INFO - 2012-09-27 00:00:00 is finished\n",
      "2018-01-08 18:14:21,350 - ALPHA_MIND - INFO - 2012-10-11 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 18:14:21,716 - ALPHA_MIND - INFO - 2012-10-11 00:00:00 is finished\n",
      "2018-01-08 18:14:21,723 - ALPHA_MIND - INFO - 2012-10-18 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 18:14:22,184 - ALPHA_MIND - INFO - 2012-10-18 00:00:00 is finished\n",
      "2018-01-08 18:14:22,192 - ALPHA_MIND - INFO - 2012-10-25 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 18:14:22,667 - ALPHA_MIND - INFO - 2012-10-25 00:00:00 is finished\n",
      "2018-01-08 18:14:22,675 - ALPHA_MIND - INFO - 2012-11-01 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 18:14:23,121 - ALPHA_MIND - INFO - 2012-11-01 00:00:00 is finished\n",
      "2018-01-08 18:14:23,129 - ALPHA_MIND - INFO - 2012-11-08 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 18:14:23,445 - ALPHA_MIND - INFO - 2012-11-08 00:00:00 is finished\n",
      "2018-01-08 18:14:23,453 - ALPHA_MIND - INFO - 2012-11-15 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 18:14:23,704 - ALPHA_MIND - INFO - 2012-11-15 00:00:00 is finished\n",
      "2018-01-08 18:14:23,710 - ALPHA_MIND - INFO - 2012-11-22 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:16:14,712 - ALPHA_MIND - INFO - 2017-10-18 00:00:00 is finished\n",
      "2018-01-08 18:16:14,717 - ALPHA_MIND - INFO - 2017-10-25 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 18:16:14,986 - ALPHA_MIND - INFO - 2017-10-25 00:00:00 is finished\n",
      "2018-01-08 18:16:14,993 - 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 18:16:15,455 - ALPHA_MIND - INFO - 2017-11-01 00:00:00 is finished\n",
      "2018-01-08 18:16:15,463 - 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 18:16:15,897 - ALPHA_MIND - INFO - 2017-11-08 00:00:00 is finished\n",
      "2018-01-08 18:16:15,905 - 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 18:16:16,883 - ALPHA_MIND - INFO - 2017-11-15 00:00:00 is finished\n",
      "2018-01-08 18:16:16,891 - 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 18:16:17,483 - ALPHA_MIND - INFO - 2017-11-22 00:00:00 is finished\n",
      "2018-01-08 18:16:17,491 - 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 18:16:18,019 - ALPHA_MIND - INFO - 2017-11-29 00:00:00 is finished\n",
      "2018-01-08 18:16:18,031 - 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 18:16:18,338 - ALPHA_MIND - INFO - 2017-12-06 00:00:00 is finished\n",
      "2018-01-08 18:16:18,345 - 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 18:16:18,695 - ALPHA_MIND - INFO - 2017-12-13 00:00:00 is finished\n",
      "2018-01-08 18:16:18,703 - ALPHA_MIND - INFO - 2017-12-20 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 18:16:19,219 - ALPHA_MIND - INFO - 2017-12-20 00:00:00 is finished\n",
      "2018-01-08 18:16:19,229 - ALPHA_MIND - INFO - 2017-12-27 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 18:16:19,852 - ALPHA_MIND - INFO - 2017-12-27 00:00:00 is finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start 0.04 weight gap simulation ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:16:21,568 - ALPHA_MIND - INFO - 2012-01-04 00:00:00: 800\n",
      "2018-01-08 18:16:21,579 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:16:21,589 - ALPHA_MIND - INFO - 2012-01-11 00:00:00: 800\n",
      "2018-01-08 18:16:21,598 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:16:21,606 - ALPHA_MIND - INFO - 2012-01-18 00:00:00: 800\n",
      "2018-01-08 18:16:21,623 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:16:21,632 - ALPHA_MIND - INFO - 2012-02-01 00:00:00: 800\n",
      "2018-01-08 18:16:21,646 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:16:21,655 - ALPHA_MIND - INFO - 2012-02-08 00:00:00: 800\n",
      "2018-01-08 18:16:21,675 - ALPHA_MIND - INFO - model is not available. Omit this date\n",
      "2018-01-08 18:16:21,683 - ALPHA_MIND - INFO - 2012-02-15 00:00:00: 800\n",
      "2018-01-08 18:16:21,895 - ALPHA_MIND - INFO - 2012-02-15 00:00:00 is finished\n",
      "2018-01-08 18:16:21,903 - ALPHA_MIND - INFO - 2012-02-22 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 18:16:22,334 - ALPHA_MIND - INFO - 2012-02-22 00:00:00 is finished\n",
      "2018-01-08 18:16:22,342 - ALPHA_MIND - INFO - 2012-02-29 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 18:16:23,284 - ALPHA_MIND - INFO - 2012-02-29 00:00:00 is finished\n",
      "2018-01-08 18:16:23,293 - ALPHA_MIND - INFO - 2012-03-07 00:00:00: 799\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:16:23,858 - ALPHA_MIND - INFO - 2012-03-07 00:00:00 is finished\n",
      "2018-01-08 18:16:23,867 - ALPHA_MIND - INFO - 2012-03-14 00:00:00: 799\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:16:24,286 - ALPHA_MIND - INFO - 2012-03-14 00:00:00 is finished\n",
      "2018-01-08 18:16:24,293 - ALPHA_MIND - INFO - 2012-03-21 00:00:00: 798\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:16:24,547 - ALPHA_MIND - INFO - 2012-03-21 00:00:00 is finished\n",
      "2018-01-08 18:16:24,554 - ALPHA_MIND - INFO - 2012-03-28 00:00:00: 798\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:16:24,812 - ALPHA_MIND - INFO - 2012-03-28 00:00:00 is finished\n",
      "2018-01-08 18:16:24,819 - ALPHA_MIND - INFO - 2012-04-09 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 18:16:25,179 - ALPHA_MIND - INFO - 2012-04-09 00:00:00 is finished\n",
      "2018-01-08 18:16:25,189 - ALPHA_MIND - INFO - 2012-04-16 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 18:16:25,907 - ALPHA_MIND - INFO - 2012-04-16 00:00:00 is finished\n",
      "2018-01-08 18:16:25,918 - ALPHA_MIND - INFO - 2012-04-23 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 18:16:27,846 - ALPHA_MIND - INFO - 2012-04-23 00:00:00 is finished\n",
      "2018-01-08 18:16:27,853 - ALPHA_MIND - INFO - 2012-05-02 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 18:16:28,196 - ALPHA_MIND - INFO - 2012-05-02 00:00:00 is finished\n",
      "2018-01-08 18:16:28,204 - ALPHA_MIND - INFO - 2012-05-09 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 18:16:28,484 - ALPHA_MIND - INFO - 2012-05-09 00:00:00 is finished\n",
      "2018-01-08 18:16:28,493 - ALPHA_MIND - INFO - 2012-05-16 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 18:16:28,751 - ALPHA_MIND - INFO - 2012-05-16 00:00:00 is finished\n",
      "2018-01-08 18:16:28,756 - ALPHA_MIND - INFO - 2012-05-23 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 18:16:29,017 - ALPHA_MIND - INFO - 2012-05-23 00:00:00 is finished\n",
      "2018-01-08 18:16:29,025 - ALPHA_MIND - INFO - 2012-05-30 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 18:16:29,536 - ALPHA_MIND - INFO - 2012-05-30 00:00:00 is finished\n",
      "2018-01-08 18:16:29,548 - ALPHA_MIND - INFO - 2012-06-06 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 18:16:30,777 - ALPHA_MIND - INFO - 2012-06-06 00:00:00 is finished\n",
      "2018-01-08 18:16:30,786 - ALPHA_MIND - INFO - 2012-06-13 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 18:16:31,561 - ALPHA_MIND - INFO - 2012-06-13 00:00:00 is finished\n",
      "2018-01-08 18:16:31,569 - ALPHA_MIND - INFO - 2012-06-20 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 18:16:31,944 - ALPHA_MIND - INFO - 2012-06-20 00:00:00 is finished\n",
      "2018-01-08 18:16:31,951 - ALPHA_MIND - INFO - 2012-06-28 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 18:16:32,255 - ALPHA_MIND - INFO - 2012-06-28 00:00:00 is finished\n",
      "2018-01-08 18:16:32,262 - ALPHA_MIND - INFO - 2012-07-05 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 18:16:32,540 - ALPHA_MIND - INFO - 2012-07-05 00:00:00 is finished\n",
      "2018-01-08 18:16:32,546 - ALPHA_MIND - INFO - 2012-07-12 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 18:16:32,843 - ALPHA_MIND - INFO - 2012-07-12 00:00:00 is finished\n",
      "2018-01-08 18:16:32,851 - ALPHA_MIND - INFO - 2012-07-19 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 18:16:33,290 - ALPHA_MIND - INFO - 2012-07-19 00:00:00 is finished\n",
      "2018-01-08 18:16:33,300 - ALPHA_MIND - INFO - 2012-07-26 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 18:16:33,768 - ALPHA_MIND - INFO - 2012-07-26 00:00:00 is finished\n",
      "2018-01-08 18:16:33,776 - ALPHA_MIND - INFO - 2012-08-02 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 18:16:34,157 - ALPHA_MIND - INFO - 2012-08-02 00:00:00 is finished\n",
      "2018-01-08 18:16:34,163 - ALPHA_MIND - INFO - 2012-08-09 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 18:16:34,485 - ALPHA_MIND - INFO - 2012-08-09 00:00:00 is finished\n",
      "2018-01-08 18:16:34,492 - ALPHA_MIND - INFO - 2012-08-16 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 18:16:34,994 - ALPHA_MIND - INFO - 2012-08-16 00:00:00 is finished\n",
      "2018-01-08 18:16:35,003 - ALPHA_MIND - INFO - 2012-08-23 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 18:16:35,582 - ALPHA_MIND - INFO - 2012-08-23 00:00:00 is finished\n",
      "2018-01-08 18:16:35,591 - ALPHA_MIND - INFO - 2012-08-30 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 18:16:36,180 - ALPHA_MIND - INFO - 2012-08-30 00:00:00 is finished\n",
      "2018-01-08 18:16:36,188 - ALPHA_MIND - INFO - 2012-09-06 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 18:16:36,834 - ALPHA_MIND - INFO - 2012-09-06 00:00:00 is finished\n",
      "2018-01-08 18:16:36,841 - ALPHA_MIND - INFO - 2012-09-13 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 18:16:37,221 - ALPHA_MIND - INFO - 2012-09-13 00:00:00 is finished\n",
      "2018-01-08 18:16:37,229 - ALPHA_MIND - INFO - 2012-09-20 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 18:16:37,660 - ALPHA_MIND - INFO - 2012-09-20 00:00:00 is finished\n",
      "2018-01-08 18:16:37,668 - ALPHA_MIND - INFO - 2012-09-27 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 18:16:37,931 - ALPHA_MIND - INFO - 2012-09-27 00:00:00 is finished\n",
      "2018-01-08 18:16:37,938 - ALPHA_MIND - INFO - 2012-10-11 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 18:16:38,196 - ALPHA_MIND - INFO - 2012-10-11 00:00:00 is finished\n",
      "2018-01-08 18:16:38,202 - ALPHA_MIND - INFO - 2012-10-18 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 18:16:38,543 - ALPHA_MIND - INFO - 2012-10-18 00:00:00 is finished\n",
      "2018-01-08 18:16:38,553 - ALPHA_MIND - INFO - 2012-10-25 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "2018-01-08 18:18:26,806 - ALPHA_MIND - INFO - 2017-09-13 00:00:00 is finished\n",
      "2018-01-08 18:18:26,815 - ALPHA_MIND - INFO - 2017-09-20 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 18:18:27,191 - ALPHA_MIND - INFO - 2017-09-20 00:00:00 is finished\n",
      "2018-01-08 18:18:27,199 - ALPHA_MIND - INFO - 2017-09-27 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 18:18:27,754 - ALPHA_MIND - INFO - 2017-09-27 00:00:00 is finished\n",
      "2018-01-08 18:18:27,759 - ALPHA_MIND - INFO - 2017-10-11 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:18:28,028 - ALPHA_MIND - INFO - 2017-10-11 00:00:00 is finished\n",
      "2018-01-08 18:18:28,035 - ALPHA_MIND - INFO - 2017-10-18 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 18:18:28,488 - ALPHA_MIND - INFO - 2017-10-18 00:00:00 is finished\n",
      "2018-01-08 18:18:28,495 - ALPHA_MIND - INFO - 2017-10-25 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 18:18:28,914 - ALPHA_MIND - INFO - 2017-10-25 00:00:00 is finished\n",
      "2018-01-08 18:18:28,921 - ALPHA_MIND - INFO - 2017-11-01 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:18:29,398 - ALPHA_MIND - INFO - 2017-11-01 00:00:00 is finished\n",
      "2018-01-08 18:18:29,406 - ALPHA_MIND - INFO - 2017-11-08 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:18:29,728 - ALPHA_MIND - INFO - 2017-11-08 00:00:00 is finished\n",
      "2018-01-08 18:18:29,735 - 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 18:18:30,040 - ALPHA_MIND - INFO - 2017-11-15 00:00:00 is finished\n",
      "2018-01-08 18:18:30,050 - ALPHA_MIND - INFO - 2017-11-22 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:18:30,512 - ALPHA_MIND - INFO - 2017-11-22 00:00:00 is finished\n",
      "2018-01-08 18:18:30,521 - 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 18:18:30,925 - ALPHA_MIND - INFO - 2017-11-29 00:00:00 is finished\n",
      "2018-01-08 18:18:30,934 - 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 18:18:31,347 - ALPHA_MIND - INFO - 2017-12-06 00:00:00 is finished\n",
      "2018-01-08 18:18:31,354 - ALPHA_MIND - INFO - 2017-12-13 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:18:31,608 - ALPHA_MIND - INFO - 2017-12-13 00:00:00 is finished\n",
      "2018-01-08 18:18:31,612 - ALPHA_MIND - INFO - 2017-12-20 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:18:31,987 - ALPHA_MIND - INFO - 2017-12-20 00:00:00 is finished\n",
      "2018-01-08 18:18:31,995 - ALPHA_MIND - INFO - 2017-12-27 00:00:00: 800\n"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "using backbone model and alpha model\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-01-08 18:18:32,531 - ALPHA_MIND - INFO - 2017-12-27 00:00:00 is finished\n"
     ]
    },
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N418//8E9X6fy5pZMMovKK4P34EgGt/WT4P031NshKHXBz8+P66+/nptuuok+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\nADz6Ak5tfu2V8Q+yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24aa7118048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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44AMcHR3x8PDgrbfeAmD06NFER0fToUOHSg9h9u/fn507d1abgHfu3JmBAwcy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24aa154d4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RI0do3rx5lecKIYQQ4tqiCvLISjjExh5jWDToSabc9BTTWt7DxkIvbm4SxOuD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\nhhlDEjkvqUewS+uw3L8sxubnYIaOwIy7HNN3YPN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U191d2I3PQX0d5oqZwS5F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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24aa85b9da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YFi1aZP0cGgwGli9fjru7O927d2fQoEHce++9tG/f3qaf8+fPk5iYeNVYSktLeemll+jU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\nXEREREQkh/4DUk5v28msk1wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24aa4ab9cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Predicting and re-balance phase\n",
    "\"\"\"\n",
    "weight_gaps = [0.01, 0.02, 0.03, 0.04]\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",
    "    leverages = []\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",
    "        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",
    "        leverages.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': leverages}, 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_{0}.csv'.format(int(weight_gap * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## combine\n",
    "-------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import ExcelWriter\n",
    "writer = ExcelWriter('d:/多因子500+300合并.xlsx')\n",
    "\n",
    "df1 = pd.read_csv('d:/hs300.csv', index_col=0)\n",
    "df2 = pd.read_csv('d:/zz500.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weights_secnario = {\n",
    "    '3-1': (3., 1.),\n",
    "    '2-1': (2., 1.),\n",
    "    '1-1': (1., 1.),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for key, weights in weights_secnario.items():\n",
    "    weight300, weight500 = weights\n",
    "    ret_series = (weight300* df1['ret_after_tc'] + weight500 * df2['ret_after_tc']) /  (weight300 + weight500)\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'])\n",
    "\n",
    "    total_returns = 0.\n",
    "\n",
    "    for i, ret in enumerate(ret_series):\n",
    "        date = ret_series.index[i]\n",
    "        sharp_calc.push({'ret': ret, 'riskFree': 0.})\n",
    "        drawdown_calc.push({'ret': ret})\n",
    "        max_drawdown_calc.push({'ret': ret})\n",
    "        total_returns += ret\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",
    "\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_excel(writer, key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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