{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from alphamind.api import *\n",
    "from PyFin.api import *\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameter Setting\n",
    "----------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _map_freq(freq):\n",
    "\n",
    "    if freq == '1m':\n",
    "        horizon = 21\n",
    "    elif freq == '1w':\n",
    "        horizon = 4\n",
    "    elif freq == '2w':\n",
    "        horizon = 8\n",
    "    elif freq == '3w':\n",
    "        horizon = 12\n",
    "    elif freq == '1d':\n",
    "        horizon = 0\n",
    "    else:\n",
    "        raise ValueError(\"Unrecognized freq: {0}\".format(freq))\n",
    "    return horizon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha_factors = {\n",
    "    'eps': LAST('eps_q'),\n",
    "    'roe': LAST('roe_q'),\n",
    "    'bdto': LAST('BDTO'),\n",
    "    'cfinc1': LAST('CFinc1'),\n",
    "    'chv': LAST('CHV'),\n",
    "    'rvol': LAST('RVOL'),\n",
    "    'val': LAST('VAL'),\n",
    "    'grev': LAST('GREV'),\n",
    "    'droeafternonorecurring': LAST('DROEAfterNonRecurring')\n",
    "}\n",
    "engine = SqlEngine()\n",
    "universe = Universe('custom', ['zz500'])\n",
    "benchmark_code = 905\n",
    "neutralize_risk = ['SIZE'] + industry_styles\n",
    "constraint_risk = ['SIZE'] + industry_styles\n",
    "start_date = '2012-01-01'\n",
    "end_date = '2017-11-02'\n",
    "industry_lower = 1.\n",
    "industry_upper = 1.\n",
    "\n",
    "freq = '2w'\n",
    "batch = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-06 15:50:00,321 - ALPHA_MIND - INFO - Starting data package fetching ...\n",
      "2017-11-06 15:50:14,655 - ALPHA_MIND - INFO - Loading data is finished\n",
      "2017-11-06 15:50:15,783 - ALPHA_MIND - INFO - Data processing is finished\n"
     ]
    }
   ],
   "source": [
    "data_package = fetch_data_package(engine,\n",
    "                                  alpha_factors=alpha_factors,\n",
    "                                  start_date=start_date,\n",
    "                                  end_date=end_date,\n",
    "                                  frequency=freq,\n",
    "                                  universe=universe,\n",
    "                                  benchmark=benchmark_code,\n",
    "                                  batch=batch,\n",
    "                                  neutralized_risk=neutralize_risk,\n",
    "                                  pre_process=[winsorize_normal],\n",
    "                                  post_process=[winsorize_normal],\n",
    "                                  warm_start=batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = data_package['train']['x']\n",
    "train_y = data_package['train']['y']\n",
    "\n",
    "predict_x = data_package['predict']['x']\n",
    "predict_y = data_package['predict']['y']\n",
    "\n",
    "settlement = data_package['settlement']\n",
    "features = data_package['x_names']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Executor Strategy\n",
    "---------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-02-02 00:00:00 is finished\n",
      "2012-02-16 00:00:00 is finished\n",
      "2012-03-01 00:00:00 is finished\n",
      "2012-03-15 00:00:00 is finished\n",
      "2012-03-29 00:00:00 is finished\n",
      "2012-04-12 00:00:00 is finished\n",
      "2012-04-26 00:00:00 is finished\n",
      "2012-05-10 00:00:00 is finished\n",
      "2012-05-24 00:00:00 is finished\n",
      "2012-06-07 00:00:00 is finished\n",
      "2012-06-21 00:00:00 is finished\n",
      "2012-07-05 00:00:00 is finished\n",
      "2012-07-19 00:00:00 is finished\n",
      "2012-08-02 00:00:00 is finished\n",
      "2012-08-16 00:00:00 is finished\n",
      "2012-08-30 00:00:00 is finished\n",
      "2012-09-13 00:00:00 is finished\n",
      "2012-09-27 00:00:00 is finished\n",
      "2012-10-11 00:00:00 is finished\n",
      "2012-10-25 00:00:00 is finished\n",
      "2012-11-08 00:00:00 is finished\n",
      "2012-11-22 00:00:00 is finished\n",
      "2012-12-06 00:00:00 is finished\n",
      "2012-12-20 00:00:00 is finished\n",
      "2013-01-04 00:00:00 is finished\n",
      "2013-01-17 00:00:00 is finished\n",
      "2013-01-31 00:00:00 is finished\n",
      "2013-02-18 00:00:00 is finished\n",
      "2013-02-28 00:00:00 is finished\n",
      "2013-03-14 00:00:00 is finished\n",
      "2013-03-28 00:00:00 is finished\n",
      "2013-04-11 00:00:00 is finished\n",
      "2013-04-25 00:00:00 is finished\n",
      "2013-05-09 00:00:00 is finished\n",
      "2013-05-23 00:00:00 is finished\n",
      "2013-06-06 00:00:00 is finished\n",
      "2013-06-20 00:00:00 is finished\n",
      "2013-07-04 00:00:00 is finished\n",
      "2013-07-18 00:00:00 is finished\n",
      "2013-08-01 00:00:00 is finished\n",
      "2013-08-15 00:00:00 is finished\n",
      "2013-08-29 00:00:00 is finished\n",
      "2013-09-12 00:00:00 is finished\n",
      "2013-09-26 00:00:00 is finished\n",
      "2013-10-10 00:00:00 is finished\n",
      "2013-10-24 00:00:00 is finished\n",
      "2013-11-07 00:00:00 is finished\n",
      "2013-11-21 00:00:00 is finished\n",
      "2013-12-05 00:00:00 is finished\n",
      "2013-12-19 00:00:00 is finished\n",
      "2014-01-02 00:00:00 is finished\n",
      "2014-01-16 00:00:00 is finished\n",
      "2014-01-30 00:00:00 is finished\n",
      "2014-02-13 00:00:00 is finished\n",
      "2014-02-27 00:00:00 is finished\n",
      "2014-03-13 00:00:00 is finished\n",
      "2014-03-27 00:00:00 is finished\n",
      "2014-04-10 00:00:00 is finished\n",
      "2014-04-24 00:00:00 is finished\n",
      "2014-05-08 00:00:00 is finished\n",
      "2014-05-22 00:00:00 is finished\n",
      "2014-06-05 00:00:00 is finished\n",
      "2014-06-19 00:00:00 is finished\n",
      "2014-07-03 00:00:00 is finished\n",
      "2014-07-17 00:00:00 is finished\n",
      "2014-07-31 00:00:00 is finished\n",
      "2014-08-14 00:00:00 is finished\n",
      "2014-08-28 00:00:00 is finished\n",
      "2014-09-11 00:00:00 is finished\n",
      "2014-09-25 00:00:00 is finished\n",
      "2014-10-09 00:00:00 is finished\n",
      "2014-10-23 00:00:00 is finished\n",
      "2014-11-06 00:00:00 is finished\n",
      "2014-11-20 00:00:00 is finished\n",
      "2014-12-04 00:00:00 is finished\n",
      "2014-12-18 00:00:00 is finished\n",
      "2015-01-05 00:00:00 is finished\n",
      "2015-01-15 00:00:00 is finished\n",
      "2015-01-29 00:00:00 is finished\n",
      "2015-02-12 00:00:00 is finished\n",
      "2015-02-26 00:00:00 is finished\n",
      "2015-03-12 00:00:00 is finished\n",
      "2015-03-26 00:00:00 is finished\n",
      "2015-04-09 00:00:00 is finished\n",
      "2015-04-23 00:00:00 is finished\n",
      "2015-05-07 00:00:00 is finished\n",
      "2015-05-21 00:00:00 is finished\n",
      "2015-06-04 00:00:00 is finished\n",
      "2015-06-18 00:00:00 is finished\n",
      "2015-07-02 00:00:00 is finished\n",
      "2015-07-16 00:00:00 is finished\n",
      "2015-07-30 00:00:00 is finished\n",
      "2015-08-13 00:00:00 is finished\n",
      "2015-08-27 00:00:00 is finished\n",
      "2015-09-10 00:00:00 is finished\n",
      "2015-09-24 00:00:00 is finished\n",
      "2015-10-08 00:00:00 is finished\n",
      "2015-10-22 00:00:00 is finished\n",
      "2015-11-05 00:00:00 is finished\n",
      "2015-11-19 00:00:00 is finished\n",
      "2015-12-03 00:00:00 is finished\n",
      "2015-12-17 00:00:00 is finished\n",
      "2015-12-31 00:00:00 is finished\n",
      "2016-01-14 00:00:00 is finished\n",
      "2016-01-28 00:00:00 is finished\n",
      "2016-02-15 00:00:00 is finished\n",
      "2016-02-25 00:00:00 is finished\n",
      "2016-03-10 00:00:00 is finished\n",
      "2016-03-24 00:00:00 is finished\n",
      "2016-04-07 00:00:00 is finished\n",
      "2016-04-21 00:00:00 is finished\n",
      "2016-05-05 00:00:00 is finished\n",
      "2016-05-19 00:00:00 is finished\n",
      "2016-06-02 00:00:00 is finished\n",
      "2016-06-16 00:00:00 is finished\n",
      "2016-06-30 00:00:00 is finished\n",
      "2016-07-14 00:00:00 is finished\n",
      "2016-07-28 00:00:00 is finished\n",
      "2016-08-11 00:00:00 is finished\n",
      "2016-08-25 00:00:00 is finished\n",
      "2016-09-08 00:00:00 is finished\n",
      "2016-09-22 00:00:00 is finished\n",
      "2016-10-10 00:00:00 is finished\n",
      "2016-10-20 00:00:00 is finished\n",
      "2016-11-03 00:00:00 is finished\n",
      "2016-11-17 00:00:00 is finished\n",
      "2016-12-01 00:00:00 is finished\n",
      "2016-12-15 00:00:00 is finished\n",
      "2016-12-29 00:00:00 is finished\n",
      "2017-01-12 00:00:00 is finished\n",
      "2017-01-26 00:00:00 is finished\n",
      "2017-02-09 00:00:00 is finished\n",
      "2017-02-23 00:00:00 is finished\n",
      "2017-03-09 00:00:00 is finished\n",
      "2017-03-23 00:00:00 is finished\n",
      "2017-04-06 00:00:00 is finished\n",
      "2017-04-20 00:00:00 is finished\n",
      "2017-05-04 00:00:00 is finished\n",
      "2017-05-18 00:00:00 is finished\n",
      "2017-06-01 00:00:00 is finished\n",
      "2017-06-15 00:00:00 is finished\n",
      "2017-06-29 00:00:00 is finished\n",
      "2017-07-13 00:00:00 is finished\n",
      "2017-07-27 00:00:00 is finished\n",
      "2017-08-10 00:00:00 is finished\n",
      "2017-08-24 00:00:00 is finished\n",
      "2017-09-07 00:00:00 is finished\n",
      "2017-09-21 00:00:00 is finished\n",
      "2017-10-09 00:00:00 is finished\n",
      "2017-10-19 00:00:00 is finished\n",
      "2017-11-02 00:00:00 is finished\n"
     ]
    }
   ],
   "source": [
    "dates = sorted(train_x.keys())\n",
    "model_df = pd.Series()\n",
    "horizon = _map_freq(freq)\n",
    "\n",
    "rets = []\n",
    "turn_overs = []\n",
    "executor = NaiveExecutor()\n",
    "leverags = []\n",
    "\n",
    "for i, ref_date in enumerate(dates):\n",
    "    \n",
    "    # Model Training and Prediction\n",
    "    \n",
    "    sample_train_x = train_x[ref_date]\n",
    "    sample_train_y = train_y[ref_date].flatten()\n",
    "    \n",
    "    model = LinearRegression()\n",
    "    model.fit(sample_train_x, sample_train_y)\n",
    "    \n",
    "    sample_test_x = predict_x[ref_date]\n",
    "    sample_test_y = predict_y[ref_date].flatten()\n",
    "    \n",
    "    er = model.predict(sample_test_x)\n",
    "    model_df.loc[ref_date] = model\n",
    "    \n",
    "    # Constraints Building #\n",
    "    \n",
    "    today_settlement = settlement[settlement.trade_date == ref_date]\n",
    "    codes = today_settlement.code.tolist()\n",
    "    \n",
    "    dx_return = None\n",
    "    risk_exp = today_settlement[neutralize_risk].values.astype(float)\n",
    "    industry = today_settlement.industry.values\n",
    "    benchmark_w = today_settlement.weight.values\n",
    "    \n",
    "    constraint_exp = today_settlement[constraint_risk].values\n",
    "    risk_exp_expand = np.concatenate((constraint_exp, np.ones((len(risk_exp), 1))), axis=1).astype(float)\n",
    "    risk_names = constraint_risk + ['total']\n",
    "    risk_target = risk_exp_expand.T @ benchmark_w\n",
    "    lbound = np.zeros(len(today_settlement))\n",
    "    ubound = 0.01 + benchmark_w\n",
    "\n",
    "    constraint = Constraints(risk_exp_expand, risk_names)\n",
    "    for i, name in enumerate(risk_names):\n",
    "        if name == 'total' or name == 'SIZE':\n",
    "            constraint.set_constraints(name, lower_bound=risk_target[i], upper_bound=risk_target[i])\n",
    "        else:\n",
    "            constraint.set_constraints(name, lower_bound=risk_target[i]*industry_lower, upper_bound=risk_target[i]*industry_upper)\n",
    "    \n",
    "    target_pos, _ = er_portfolio_analysis(er,\n",
    "                                          industry,\n",
    "                                          dx_return,\n",
    "                                          constraint,\n",
    "                                          False,\n",
    "                                          benchmark_w)\n",
    "    \n",
    "    target_pos['code'] = today_settlement['code'].values\n",
    "    \n",
    "    turn_over, executed_pos = executor.execute(target_pos=target_pos)\n",
    "    \n",
    "    executed_codes = executed_pos.code.tolist()\n",
    "    dx_retuns = engine.fetch_dx_return(ref_date.strftime('%Y-%m-%d'), executed_codes, horizon=horizon)\n",
    "    \n",
    "    result = pd.merge(executed_pos, today_settlement[['code', 'weight']], on=['code'], how='inner')\n",
    "    result = pd.merge(result, dx_retuns, on=['code'])\n",
    "    \n",
    "    leverage = result.weight_x.abs().sum()\n",
    "    \n",
    "    ret = (result.weight_x - result.weight_y * leverage / result.weight_y.sum()).values @ result.dx.values\n",
    "    rets.append(ret)\n",
    "    executor.set_current(executed_pos)\n",
    "    turn_overs.append(turn_over)\n",
    "    leverags.append(leverage)\n",
    "    print('{0} is finished'.format(ref_date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret_df1 = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverage}, index=dates)\n",
    "ret_df1.loc[advanceDateByCalendar('china.sse', dates[-1], freq)] = 0.\n",
    "ret_df1 = ret_df1.shift(1)\n",
    "ret_df1.iloc[0] = 0.\n",
    "ret_df1['tc_cost'] = ret_df1.turn_over * 0.002"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x193002a0400>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Qxo4dy/LlywkMDCQsLAyAr776iqqqKstKHRciLS2t1nZpaWmDUyuud5mZmcyY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\nREREpOcA7vF4qK6u7r5dXV1NfHx8nxY1FBnj6+9e9Wk1nx5sIS7MxU1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rr71GUlIS55xz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19309ce4940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_df1[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6), title='Fixed frequency rebalanced: {0}'.format(freq), secondary_y='tc_cost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sharp:  2.72204351054\n"
     ]
    }
   ],
   "source": [
    "ret_atfer_tc = ret_df1.returns - ret_df1.tc_cost\n",
    "print(\"sharp: \", ret_atfer_tc.mean() / ret_atfer_tc.std() * np.sqrt(52))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19309ce4e80>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BqH494hjHwRk2AnJ2Yj+c7XU4Ip5Qwi4ivhVwUA27VAlrbbitYwMl7JHIdDgL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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x193009a17f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_df1[['returns', 'leverage']].rolling(window=60).std().plot(figsize=(12, 6), title='rolling std', secondary_y='leverage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Threshold Turn Over + Strategy\n",
    "------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "freq = '1d'\n",
    "horizon = _map_freq(freq)\n",
    "dates = makeSchedule(start_date, end_date, tenor=freq, calendar='china.sse', dateGenerationRule=DateGeneration.Backward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_data = engine.fetch_data_range(universe, alpha_factors, dates=dates, benchmark=905)\n",
    "factor_all_data = all_data['factor']\n",
    "factor_groups = factor_all_data.groupby('trade_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-02-02 00:00:00 is finished: 0.9763\n",
      "2012-02-03 00:00:00 is finished: 0.0\n",
      "2012-02-06 00:00:00 is finished: 0.0\n",
      "2012-02-07 00:00:00 is finished: 0.0\n",
      "2012-02-08 00:00:00 is finished: 0.0\n",
      "2012-02-09 00:00:00 is finished: 0.0\n",
      "2012-02-10 00:00:00 is finished: 0.0\n",
      "2012-02-13 00:00:00 is finished: 0.9309219332698693\n",
      "2012-02-14 00:00:00 is finished: 0.0\n",
      "2012-02-15 00:00:00 is finished: 0.0\n",
      "2012-02-16 00:00:00 is finished: 0.0\n",
      "2012-02-17 00:00:00 is finished: 0.0\n",
      "2012-02-20 00:00:00 is finished: 0.0\n",
      "2012-02-21 00:00:00 is finished: 0.0\n",
      "2012-02-22 00:00:00 is finished: 0.9004128902190728\n",
      "2012-02-23 00:00:00 is finished: 0.0\n",
      "2012-02-24 00:00:00 is finished: 0.0\n",
      "2012-02-27 00:00:00 is finished: 0.0\n",
      "2012-02-28 00:00:00 is finished: 0.0\n",
      "2012-02-29 00:00:00 is finished: 0.9316366577185158\n",
      "2012-03-01 00:00:00 is finished: 0.0\n",
      "2012-03-02 00:00:00 is finished: 0.0\n",
      "2012-03-05 00:00:00 is finished: 0.0\n",
      "2012-03-06 00:00:00 is finished: 0.0\n",
      "2012-03-07 00:00:00 is finished: 0.0\n",
      "2012-03-08 00:00:00 is finished: 0.0\n",
      "2012-03-09 00:00:00 is finished: 0.0\n",
      "2012-03-12 00:00:00 is finished: 1.0391587155963293\n",
      "2012-03-13 00:00:00 is finished: 0.0\n",
      "2012-03-14 00:00:00 is finished: 0.0\n",
      "2012-03-15 00:00:00 is finished: 0.0\n",
      "2012-03-16 00:00:00 is finished: 0.8894429317527197\n",
      "2012-03-19 00:00:00 is finished: 0.0\n",
      "2012-03-20 00:00:00 is finished: 0.0\n",
      "2012-03-21 00:00:00 is finished: 0.0\n",
      "2012-03-22 00:00:00 is finished: 0.0\n",
      "2012-03-23 00:00:00 is finished: 0.0\n",
      "2012-03-26 00:00:00 is finished: 0.0\n",
      "2012-03-27 00:00:00 is finished: 0.8839901754385964\n",
      "2012-03-28 00:00:00 is finished: 0.0\n",
      "2012-03-29 00:00:00 is finished: 0.0\n",
      "2012-03-30 00:00:00 is finished: 0.953276791808874\n",
      "2012-04-05 00:00:00 is finished: 0.0\n",
      "2012-04-06 00:00:00 is finished: 0.0\n",
      "2012-04-09 00:00:00 is finished: 0.0\n",
      "2012-04-10 00:00:00 is finished: 0.0\n",
      "2012-04-11 00:00:00 is finished: 0.9957288793103426\n",
      "2012-04-12 00:00:00 is finished: 0.0\n",
      "2012-04-13 00:00:00 is finished: 0.0\n",
      "2012-04-16 00:00:00 is finished: 0.9850711206896573\n",
      "2012-04-17 00:00:00 is finished: 0.0\n",
      "2012-04-18 00:00:00 is finished: 0.0\n",
      "2012-04-19 00:00:00 is finished: 0.9388806015037596\n",
      "2012-04-20 00:00:00 is finished: 0.0\n",
      "2012-04-23 00:00:00 is finished: 0.0\n",
      "2012-04-24 00:00:00 is finished: 1.097\n",
      "2012-04-25 00:00:00 is finished: 0.0\n",
      "2012-04-26 00:00:00 is finished: 0.9373999999999999\n",
      "2012-04-27 00:00:00 is finished: 0.0\n",
      "2012-05-02 00:00:00 is finished: 0.0\n",
      "2012-05-03 00:00:00 is finished: 0.0\n",
      "2012-05-04 00:00:00 is finished: 0.9971836065573778\n",
      "2012-05-07 00:00:00 is finished: 0.0\n",
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      "2017-11-02 00:00:00 is finished: 0.0\n"
     ]
    }
   ],
   "source": [
    "rets = []\n",
    "turn_overs = []\n",
    "turn_over_threshold = 0.90\n",
    "executor = ThresholdExecutor(turn_over_threshold=turn_over_threshold)\n",
    "execution_pipeline = ExecutionPipeline(executors=[executor])\n",
    "leverags = []\n",
    "execution_dates = []\n",
    "horizon = _map_freq(freq)\n",
    "\n",
    "for i, value in enumerate(factor_groups):\n",
    "    \n",
    "    date = value[0]\n",
    "    data = value[1]\n",
    "    \n",
    "    # get the latest model\n",
    "    models = model_df[model_df.index <= date]\n",
    "    \n",
    "    if models.empty:\n",
    "        continue\n",
    "    \n",
    "    execution_dates.append(date)\n",
    "    model = models[-1]\n",
    "    \n",
    "    codes = data.code.tolist()\n",
    "    ref_date = date.strftime('%Y-%m-%d')\n",
    "    total_data = data.dropna()\n",
    "    dx_return = None\n",
    "    risk_exp = total_data[neutralize_risk].values.astype(float)\n",
    "    industry = total_data.industry.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",
    "    risk_names = constraint_risk + ['total']\n",
    "    risk_target = risk_exp_expand.T @ benchmark_w\n",
    "    lbound = np.zeros(len(total_data))\n",
    "    ubound = 0.01 + benchmark_w\n",
    "\n",
    "    constraint = Constraints(risk_exp_expand, risk_names)\n",
    "    for i, name in enumerate(risk_names):\n",
    "        if name == 'total' or name == 'SIZE':\n",
    "            constraint.set_constraints(name, lower_bound=risk_target[i], upper_bound=risk_target[i])\n",
    "        else:\n",
    "            constraint.set_constraints(name, lower_bound=risk_target[i]*industry_lower, upper_bound=risk_target[i]*industry_upper)\n",
    "            \n",
    "    \n",
    "    factors_values = factor_processing(total_data[features].values,\n",
    "                                       pre_process=[winsorize_normal],\n",
    "                                       post_process=[winsorize_normal])\n",
    "\n",
    "    er = model.predict(factors_values)\n",
    "    \n",
    "    target_pos, _ = er_portfolio_analysis(er,\n",
    "                                          industry,\n",
    "                                          dx_return,\n",
    "                                          constraint,\n",
    "                                          False,\n",
    "                                          benchmark_w)\n",
    "    \n",
    "    target_pos['code'] = total_data['code'].values\n",
    "    \n",
    "    turn_over, executed_pos = execution_pipeline.execute(target_pos=target_pos)\n",
    "    \n",
    "    executed_codes = executed_pos.code.tolist()\n",
    "    dx_retuns = engine.fetch_dx_return(date, executed_codes, horizon=horizon)\n",
    "    \n",
    "    result = pd.merge(executed_pos, total_data, on=['code'], how='inner')\n",
    "    result = pd.merge(result, dx_retuns, on=['code'])\n",
    "    leverage = result.weight_x.abs().sum()\n",
    "    \n",
    "    ret = (result.weight_x - result.weight_y * leverage / result.weight_y.sum()).values @ result.dx.values\n",
    "    rets.append(ret)\n",
    "    leverags.append(executed_pos.weight.abs().sum())\n",
    "    turn_overs.append(turn_over)\n",
    "    print('{0} is finished: {1}'.format(date, turn_over))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret_df2 = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverags}, index=execution_dates)\n",
    "ret_df2.loc[advanceDateByCalendar('china.sse', dates[-1], freq)] = 0.\n",
    "ret_df2 = ret_df2.shift(1)\n",
    "ret_df2.iloc[0] = 0.\n",
    "ret_df2['tc_cost'] = ret_df2.turn_over * 0.002"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19302f8c5f8>"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EOP7HsJWIKhhNW5X2bZUqDcCVUkoppdRVWZR4mkkbT5BnFwbdEUmjyILLFAd4\nurkE3wDme69C8lHHQc1bYN8u2LoRatfD0utJjCrVkcyzmM/3Rn79BULCMVp3hqxMjEf+huHrVwZ3\nV3ouKwDftGkTkydPxjRN2rdvT7du3VzOp6Sk8PHHH5OZmYlpmvTq1Yvbb7+9VBpcms6cOcPs2bN5\n/PHHy6X+8ePH89xzzxV6TkT461//yqRJk/D39y9wvk+fPnz00UcEBhZcuuecv/zlL4wcOZJGjVyX\n5Nm6dSvJycm0b98egEWLFrF582ZefPHFa7gbpZRSSt2ITBFmbk3lVI6Njccy8Xa3cF/tINrGBOBh\nPb9RjuTmIKt/hrwcTIsVko8iqSewtLkHko86erEbNcNo1hoO7IG8XIxbGjivN3z9Me5sh2xai2X0\nhAq9qsmVKjYAN02TL774gldeeYWQkBCGDRtGfHw80dHnZ53OmjWLO++8k06dOnHkyBHGjBlzXQbg\n6enpfP311+UWgH/44YdFBuBLlizh1ltvLRB8iwgiwpQpU6663m3btrFlyxZnAN6hQwfeffddBg0a\nhLe391WXq5RSSqkby+lsGztOZvN/W1LwcbdgtRh0rxdC17rBLvlkcwLmzMnnh41cwNy6EQDj3r9g\nRMc4EmNqF1qf8fhgDJEbKviGywjAExMTiYyMJCIiAoAWLVqQkJDgEoAbhkFWVhbg2CUxKCjomhv2\n+fpk9p/KueZyLhQT5MWA+Igiz7/11lscPHiQjh070rp1a0aOHMknn3zCrFmzMAyDu+++m+HDhxd6\n7f79+xk6dCipqalYrVYmTJhA9erVGTVqFEuXLsUwDJ577jm6du1KcnIyTz/9NGfPnsVutzNmzBiW\nLFlCTk4OHTt25JZbbuGjjz5yKX/27Nn07t0bgMOHD/PII4/QokULNmzYwKRJk3jooYf43//+R3Bw\nMOPGjWP27NlERUURHBxMw4YNeeqppwD48ccfGT58OGfOnGHs2LHcdttt/Pvf/yYnJ4d169bxzDPP\n0LVrV+68804WLVrEn/70pxJ6+koppZS6nuXaTJ76fh/ZNhMDGH9fDKGeICsWIpXvBL8ADDc3JCcL\n86M3HRfVaYgRHIbRqRv4BcD+XcieHVCl2vng+xIMy6W3nr9eFRuAp6WlERIS4jwOCQlhz549Lnm6\nd+/OqFGjWLBgAbm5uYwcObLQshYvXszixYsBePvtt6+l3aVi+PDh7Nq1i0WLHLNvf/75ZxYsWMCP\nP/6It7cA4DHsAAAgAElEQVQ3p06dKvLaZ599lkGDBtGlSxdycnIQEebPn8+2bdtYtGgRaWlp3Hvv\nvTRv3pzZs2fTpk0bBg8ejN1uJzs7mzvuuIPJkyc7675YQkIC77zzjvN47969vPfee4wZM8Yl3+bN\nm5k/fz4//fQTdrudzp0707BhQ+d5m83GvHnzWLJkCe+99x7Tpk3jxRdfZMuWLYwePdqZr1GjRqxb\nt04DcKWUUuomt+NkFvN3nSYz3062zaR7vRBuj/IlzNcdc/kC5JsJyDcTADDuvBtZ87PjwupxWB4f\njBESdr6wxs0xGjcvh7uoWIoNwEUKzmi9eCD9qlWraNu2LQ888AC7d+/mww8/ZOzYsVgu+tbSoUMH\nOnTocFkNu1RPdVlZsWIFPXr0cA7DKKpnPyMjg6SkJLp06QKAl5cXAOvWraNbt25YrVbCwsJo3rw5\nmzdvpnHjxvz973/HZrPRuXNn6tevX2xbTp8+jZ/f+QkH0dHRNGnSpEC+devW0blzZ2ebO3bs6HL+\n3nvvBaBhw4YcOVLw10LnhIaGkpycXGy7lFJKKXX9sZvCwdO52MzCVy650Le/p7ItOYswX3dqh3jR\nrW4wfp5WzISVyNRPXfI6g++qMVhGjC0QMyqHYgPwkJAQUlNTncepqakFAtGff/7ZOTSjdu3a5Ofn\nc/bs2UtOCLweiMhlvXEK+5JyqfTmzZsza9YslixZwuDBg3nqqafo3r37Jetwc3PDNE3nlxofn4Kz\njC9V5zkeHh4AWK1WbDZbkflycnKcXySUUkopdWNZuv8MH/56/LLzt4sJ4PkWUc5jMe3I7K8hujqW\nf4wBN3fYvxt8fOHMKahaQ4PvSyg2AI+NjSUpKYkTJ04QHBzM6tWrC0wUDA0NZevWrbRt25YjR46Q\nn59PQEBAqTW6tPj6+pKRkeE8btOmDePGjePBBx90DkEprBfc39+fypUrs2DBAu655x5yc3MxTZPm\nzZszdepUunfvzunTp1m7di0jR47kyJEjREZG0rt3b7Kysvj999/p3r077u7u5Ofn4+7uXqCOmjVr\ncvDgQWJiLj1eqlmzZrz88ss888wz2O12lixZ4hw7XhQ/Pz+X+wbYt28ft9xyyyWvU0oppdT1Y9XB\ndObuTEMETmbmE+hl5bnmlS/r2jqhFy3KsHENnDyO5amhGN5/dArWruf4O7pGyTX6BlVsAG61WunX\nrx+jR4/GNE3atWtH1apVmTZtGrGxscTHx/Poo48yYcIE5s2bB8Df/va36/JbT3BwME2bNuXuu++m\nXbt2jBw5km3bttGlSxfc3d25++67GTZsWKHXjh8/npdffpl///vfuLm5MWHCBLp06cKGDRvo2LEj\nhmEwYsQIwsPDmT59Op9++ilubm74+vrywQcfANC7d286dOhAgwYNCkzCbN++PWvWrCk2AG/cuDGd\nOnWiY8eOREdH06hRo0KXLbxQixYt+Pjjj+nYsaNzEubq1auLvFellFJKVXwbj2WQnOHYndJmCl9v\nOkmojzsRfu74elhpWsWP+CoF19OWzLMYvgVjB9mzHcSEWvUwF3wH4VFw2x2lfh83IkOKG7NQio4d\nO+ZynJWVVeTQiptdcnIygwcP5ttvvy02b2ZmJr6+vmRnZ/PnP/+Zf/3rXzRo0KDY6845efIkgwYN\nYvr06YWe19dJKaWUqpjsf4zpXnvkLO+scI2zQrzd+HeHKgQHnO/NlsP7kQ2rMP70MBgWZPUS5Mvx\nWF7/CMIrw+lUCI3A/M8Y+O3XPwoKh9QTGH3+hqX1PWV2b9ciKiqq+ExlSAPw68j3339Pu3btiu3R\nHjRoELt37yY3N5fu3bvz7LPPXlE9mzZtws3NrcjJofo6KaWUUhXPkfRchsw/4NwSPjbYkxFtorH8\nMSrB+7tJuC+fj+XVDxw7TaYkYw574nwB9W+HU6lw9KDj2LA4ery9vCEnG6N5WyQ7CzLPYgSFYvQd\njOHuUda3eVU0AL/A9RiADx8+nISEBJe0AQMG0KNHj3JqUdm7Hl4npZRS6mZxJD2XuTvSOJqex7YT\n2XSvF4KXu4V2MQGE+DjmlcmmXzE/fst5jXF/T+THC36r7usPmWddC653G2z7DWrditGgKUaHP2EU\nMk/teqAB+AWuxwBc6euklFJKVRS5NpO3lh1hS3IWQd5u1Azy5JW2VQvksw8fCJ7eGJWjkYQVznTL\nkDegVj3Iz8f86E2M2LoYD/a54TbA0QD8AhcH4OfGLquKTV8npZRSqvzl2U3+seAgB07n0jDShzfb\nVyuQR0wTWbcM+WIcxl/7Y+nYFbHb4fhRcHfHCL+8VVCudxUtAC92FZSyZLFYsNlsuLlVqGapC9hs\ntgIbLCmllFKq7HyWcJzEtBwy80yOpOfRv0k4d1UrZNWSzeswPxrlOHBzw2jSAgDDaoUqBYN1VXYq\nVKTr5eVFTk4Oubm51+Uyhjc6EcFisegGPUoppVQZSc+xkZ5ndx7n2YR5u08THeBBqK87neIq8ac6\nwYVeK+dWLQmPwvLCmxjBYYXmU2WvQgXghmE4t1BXSimllLqZ5dhMnpi7jxybWeBc/ybh3B5VcA3v\nc8S0Q34+BFTC8vqH1+3kyRtVhQrAlVJKKaVuFkv2nub7naeKPJ9nF3JsJj0ahBAd4OlM93QzaFy5\n6LlYkp+HOWwgnEmD+k00+K6ANABXSimllColaw+f5XB6XqHnFiWeJtcu1A4pemjnreHe/KVeCB7W\n4udfiQhsXIO5fIEj+AYsnbpdXcNvMps2bWLy5MmYpkn79u3p1s31uS1cuJCffvrJORT3ySefJDo6\nGoDZs2fz888/Y7FY6Nu3L40bNy62Pg3AlVJKKaVKweK9p/nw1+OXzNPv9nC61i18DPflEhHYknB+\nwmVgMMYDPTHu73nDLSdYGkzT5IsvvuCVV14hJCSEYcOGER8f7wywAVq2bEmnTp0AWL9+PV999RUj\nRozgyJEjrF69mvfee49Tp07x5ptv8sEHHxS7YIUG4EoppZRS10hEGP/rcVYfSnem5dqExpE+DGsT\njaWQtSUMwP0yeraLrDM/H1mzBJn7DaSfdqZb3vnCsdKJuiyJiYlERkYSEREBQIsWLUhISHAJwC/c\n/yQnJ8e5WEhCQgItWrTA3d2d8PBwIiMjSUxMpHbt2pesUwNwpZRSSqkrYDOF/25JIfOC1UnO5NpZ\nfegsLav7E/rH7pPe7hbuvyUIL7eS7YWWE8eQfbuQBd85to3/Y1t4cnMxKkdr8H2F0tLSCAkJcR6H\nhISwZ8+eAvkWLFjAvHnzsNlsvPrqq85ra9Wq5cwTHBxMWlpasXWWawAeGhpantUrpZRSShUr12Zy\n6FSW83h3SiYzt6Xi7+mG9YKu7XvqhDGiU20sl7uUck42WN2gsEmSYkJW5vk8AZUgOwtys8HXBxrc\nBo3iISgEfPxAh5oUa+jQoc6fO3ToQIcOHYA/hvBcpLDlsO+55x7uueceVq5cyaxZs3jmmWcKvfZy\nlGsAnpKSUp7VK6WUUkoV65O1x/kp8bRLmtWATx+Iwc/Ttbc5LTW12PLkVCqybjkyc/L5RC9v8PKB\nwCCwWmHfrsIv9vCA4DAs3ftB7XoYOXmQU3yP680uKiqKt99+u9BzISEhpF7wuqWmphIUFFRkWS1a\ntGDixImFXpuWlkZwcPFj+nUIilJKKaVUIUSEqZtT+CnxNHXDvF0mS4b6uBUIvostLzcXmTMFWfy9\nI8HNDaNjN3Bzg+ws5ORxOLwf0k4CYPR6CnKzMcIrYy6cgxEWidF3MIZFh5iUpNjYWJKSkjhx4gTB\nwcGsXr2a5557ziVPUlISlStXBmDjxo3On+Pj4xk/fjz3338/p06dIikpibi4uGLr1ABcKaWUUuoP\neXaT35IysZvC/lO5zNyWSmywF93rhdCkStEb31wOc9I42LgaAKPfEIzmbQsd6iCnUiE1GSPuVmea\n5bY7HdfpTuElzmq10q9fP0aPHo1pmrRr146qVasybdo0YmNjiY+PZ8GCBfz+++9YrVb8/PwYNGgQ\nAFWrVuXOO+/khRdewGKx0L9//2JXQAEw5GoHr5SAY8eOlVfVSimllFIF/LAzjc83nHAeN4r04fW7\nq17+uO6LiGnHsFiRzLOY/+iLEd8So1NXjOiYkmqyugxRUVHl3QQX2gOulFJKqZvW4r2n+XLjCc71\nRubYhDAfN15p61iCrmqgZ6HBt5h25Kc5GDVvgeqx4OHpsua2mbAS+eZTyEiHsEjHZMr8PIxmrTT4\nVhqAK6WUUurGtvxAOrtTsgs9t+FYBu5WC3dW83em3RbpS42gonenFLsdc+K7sGG1M3A37u+J0bWX\nY23udcuRbz+D0EjHKiUnkuCWBljueQij1q1FlqtuHhqAK6WUUuqGkZFrJy3b5jw2Rfh4bRJ2Ezys\nhQ8j+Uu9EP5cL6TQcxcyVyxE5kwFN3fHRMk6DcHDE06nIT9+i/3wPti/27EpTngUlkHDMUIjEBEd\nu61caACulFJKqRtCckYeQ/53gMw8s8C5l1pGcVf1gCsqTzauxlz0PUblaKgWi/zffxwnDAPjT70w\n7u+BYRjIwUTMCf+Czescp/s+j3FnO2fQrcG3uphOwlRKKaXUdWlPajbfbknBZjpCmeMZ+ZzOsfN0\nswjcL9ggx91q0CTKz2XTnKKICPLrL7B/N7J0XoHzlkEjoFosRnDBzQRlcwK4u2HcetvV35QqFToJ\nUymllFLqKpzJsbH5uGNHShFhyqaT5NqFyv4eAAR5u9H39nCaV/W/VDGFkqxMZOVCZN8u2PDHUoFN\n7sLo/TTy61Jwc0PWr4Ta9TF8fAstw2jU9CrvTN1stAdcKaWUUteFD39NYvHeM85jd4vB6I7VuCXU\n+5rKlfTTmH9/9HxCdAyWoe9geBY9EVNdX7QHXCmllFKqGOuPZvDRr0nk2c/3E2bbTBpH+vBE0wgA\nAjysBHgVH8qICJgmhtV1B0nJycL8cjwcPwqA0bYLRqNmULOOBt+qVGkArpRSSqkK4Zf9Z9h50rFc\n4MpDZwn0tNKy8gXDPQxoFxNIdIDnZZcppon53kjYuwPqN8Fo2soRZLu5I7O+Pj/cpFM3LN37lej9\nKFUUDcCVUkopVW5OZuaTmmVDEP6z7jgi4OlmIdDTyrDWVYgOvPxg+2KSnwd7d8Ku36F2fdi/B9m0\nFnFzB7sdxHRsB//Ysxhu7iV4V0pdmgbgSimllCoXiak5vLzwALYLVg18uVUULapd2XKBF5L9e5C1\nvyCnU2HTWkeg7emF5dmR4OEBe3Zgfv5vOJ2G8adeWB7oWQJ3otSV0UmYSimllCozZ3JsjFudRI7N\nJOlsHhbD4Jk7IjEM8LBaqBvmfVnLBZ4jmRnInClgGMivyyA703EiNMKxWU7NW7D8pS9GbJ3z16Qk\nw+k0iK2ja3TfJHQSplJKKaVuOqeybWTm2Vl/LIPfkjK5NcybmCAv/lo/hFvDfS6rDLHlw+6tyPZN\nyOEDGEEhyKrFLnmMlh0dkymrxxVZjhEa4QjQlSonGoArpZRSqlSlZdt4Yk6ic6iJl5uFUR2qXd7G\nOHY7HEzE/HEabN0Icn68ioBjDHd8K/DzhyrVMLwuL5hXqjxpAK6UUkqpEpGVb+edFcfIzLNflG5i\nM6Hv7WEEe7sT5e9RbPAtIsiyBee3fweo3wQMA0u7+yC8MlgsGGGRpXErSpUqDcCVUkopdVVSsvLZ\n8sfOlACHTuey6Y/hJd7uFmd6gKeVW8O8eeCW4Mse3y3fTkR+/tFxUCkE47Y7MLr3w3D3KNF7UKo8\naACulFJKqavy1W8nWX4g3SXN18PC63dXxdPNUsRVxZMdmx3Bd2QVjLvvx2h7r06WVDcUDcCVUkop\nddkSU3PYk+rYLGfnySwaRvrwzB3nh4H4eVivKfgGkL07ALCMGKtjutUNSQNwpZRSSl22f686StLZ\nfOdxp7hKRPiV7LAQOXIAwiI1+FY3LA3AlVJKKVWs6b+nsGTfGY5n5PPX+iHcWzsIAwj0spZ8ZYcP\nQHSNki9XqQpCA3CllFJKFZCckcfKg2c5t1vfj7tO4ethpUNsIJ3iKhHkXbIhhOzeimxOwLitOZxM\nwrijTYmWr1RFogG4UkoppQDIs5vk5DvW2f6/zSksu2iCZf8m4bSJCSzxes1Fc5HpXwAgC2cDYFSN\nKfF6lKooNABXSimlFMfS83hp4UHO5p5fw7tZtB//aOnYwtvAwN1a8iuRyG+/OoLvkHBIPeGoK74l\n1GlY4nUpVVFoAK6UUkrdhLafyGLOjjTMP8aYHD6Ti4gwoEk4lj+W/GsS5YuH9dpWNLmYpKVgfvIW\nnE7FuKsDsu03CA7DMvJ98PSC3BwMX78SrVOpikYDcKWUUuomcDrbxs4Ux/KBpgifJiSDQIiPIxTw\n97QyMD6CJlVKJ/g1f12KfDcFzqSBaULcrcj8GQAY7R84H3S7afCtbnwagCullFI3gU/WHWftkQzn\nsYfV4N3O1akR5FXqdUtONjLlE0fgHRSK0eQuLN37Yq5chGxcg9Gsdam3QamKxBARKT5b6Th27Fh5\nVa2UUkrdFEb9cpjtJ7LJyje5s5o/3euFABDs40Ylr9Lth5PsLMhIx5z8PuzZjuVvwx2rnChVxqKi\nosq7CS60B1wppZS6QZzOtjFzWyq2PwZ2mwIJRzNpGOFDjSBPOsdVIjrQ85rqkP27Mb/7GkuPAY6t\n4t3ci8xrTvw3/L4eAKPfEA2+lfqDBuBKKaXUdS7PbnLwdC4rDqTzw65TBHqe3xwnzMeNJ5pGUO0a\nA28AsdsxJ70Px49gvvEcAMZjz2Jp2bFg3o1rzgffjz2L5c5211y/UjcKDcCVUkqp69yXv51k3q5T\nAEQHePDxAzVLpR5ZvgCOH8Ho8hdk2QLIykC++hDT3QOjaSsMi2PFFEk9iTnlI6gWi2XYvy7ZS67U\nzUjHgCullFIV1PStKfx6OKPYfEfT86heyYO/1g+laqAHEX4eJd4Wyc3FfLkfVI3B8sKbkJsDWRmY\nn4yBg4mOTKERcDYdcrPB3QPLyPcxKkeXeFuUulLFjQHftGkTkydPxjRN2rdvT7du3VzO//jjjyxZ\nsgSr1UpAQABPP/00YWFhAPTo0YNq1aoBEBoayssvv1xse7QHXCmllKog8uwmKw+eJfuP3Sjn7EjD\n38NKdMClA+pgb2/urR3E7VElv4SfnDwOGWeRTWsh8yyWjl0xDAO8vMHLG8vg1zFfeMSROSXZeZ3R\nc4AG3+q6YJomX3zxBa+88gohISEMGzaM+Ph4oqPPv39r1KjB22+/jaenJwsXLmTq1KkMGTIEAA8P\nD959990rqlMDcKWUUqoCEBE+XHOc5Qddt39/5o5IWlQLKLc2ma8OAlu+I8EwIO5WlzyGfwCWf34C\n+XlgtyP7d2HUaQiVq5ZDi5W6comJiURGRhIREQFAixYtSEhIcAnA69ev7/y5Vq1arFix4prq1ABc\nKaWUKgffbUtl0d7TzmObCScy83m4YShdalUCwGoY+F0wobIsSVYmsmapM/g27u+J0fFPGD6+BfJe\n2NNtxNQqszYqVRLS0tIICQlxHoeEhLBnz54i8//88880btzYeZyfn8/QoUOxWq107dqVZs2aFVtn\nuQbgoaGh5Vm9UkopVaZSMvNYsz8NAeYnnsHdaqVehL/zfM1QH/rERzuGeJS3pGxo3hJatoOoao7e\nb6WuY0OHDnX+3KFDBzp06AA4ftNzsaI+g8uXL2ffvn28/vrrzrRPPvmE4OBgkpOT+ec//0m1atWI\njIy8ZFvKNQBPSUkpz+qVUkqpEpFvN8mzF7+mwYSEZJYdOD/E5MmmEdxbO8glT2pqaom370rJprWY\nH4/GuPevGF3+jFEB2qTUtYiKiuLtt98u9FxISIjL5y41NZWgoKAC+bZs2cLs2bN5/fXXcXc/v7JP\ncHAwABEREdx6660cOHCgYgfgSiml1PUuI9fOwLl7yfxj4mRxmlbx46lmEVgMgyCv8hleUhRzxUJk\n+heQkw1ubhgd/oTh5VPezVKqVMXGxpKUlMSJEycIDg5m9erVPPfccy559u/fz8SJExk+fDiBgYHO\n9IyMDDw9PXF3dyc9PZ1du3bRtWvXYuu8rAC8uKVZAFavXs2MGTMwDIPq1aszePDgyylaKaWUuu7s\nTslmyb4ziMDpHBuZ+SZ/qhNEqE/x6103i/a7rHxlRU4ex5z2OWxedz7xlgZYegzA8C+fyZ9KlSWr\n1Uq/fv0YPXo0pmnSrl07qlatyrRp04iNjSU+Pp6pU6eSk5PDe++9B5xfbvDo0aN89tlnWCwWTNOk\nW7duLpM3i1LsOuCmaTJ48GCXpVkGDx7sUnhSUhLjxo3j1Vdfxc/PjzNnzrh8OyiKrgOulFKqojt+\nNo9T2TbncZ4p/HvlMXJtJt7ujo1ngrzdeKdTdTzdLOXVzEuSvTuRJT9AaDhGt0cwLI6ed/uoF86v\n4Q0YTVuBfyBG+wcwwiuXV3OVKnHFrQNe1ortAb+cpVmWLFlC586d8fNzrD96OcG3UkopVdFl55s8\nO29/gfHdHlaDsV1qlMj27ldL7HYMa/FDWMQ0MT94A7IzATBi60KjZpjLFziDb8uoT+HoQWh8h3M3\nS6VU6Sk2AL+cpVnO9WSPHDkS0zTp3r27y/Is5yxevJjFixcDFDkQXimllCovc3akknA003mca3NM\nrnyscRg1g72c6ZX93Utlt8miyOZ1yOF9GA2bYlSLxVw0F5kxGRo1w/LwExAUiiSsQJYtwAiohDHw\nH+dXcUhJhuxMjN5PIzMnI2uXYa5Y6BxyYnnzE4yIKIioWD2ESt3Iig3AL2dpFtM0SUpK4rXXXiMt\nLY1XX32VsWPH4uvrulbohUu+KKWUUmUpO99kzeGz2M2iR15O35qKt5uFCD/HGG0Pq8Ed0X50qR3k\nHG5S1uRgIuZHoxw//28Wllc/QL77CsIiYPtGzFefAXs+2BzDZAQw7u0OVWMcx+tXAmDE1UHqNEQS\nzm8gYhn7NUZApbK9IaVU8QH45SzNEhwcTO3atXFzcyM8PJyoqCiSkpKIi4sr+RYrpZRSV+GnxFNM\n3niy2HyD7ojkrnLaefJiYsvH/O9nABidH0R+mo35ylMAWJ4eBp5emN9MgK0bHHnueQhZMAv5+Ufo\n3hesbsjCOVD/dozoGCz9nofEHWDaoX4TDLeKMxlUqZtJsQH45SzN0qxZM1auXEnbtm1JT08nKSnJ\nOWZcKaWUKiuZeXae+XE/p3NsBc6ZApF+7ozqUK3I690tBpW8y3eFXhFBZn0JaSnI3p2QdhKjz98w\n4lshP80GwBj4EkZ0DQAsz70Ku36HiCoYQSHYF8xCVi5CVi6CajUh8yxGozsc1/n4QcOm5XRnSqlz\niv1X5nKWZmnUqBGbN29myJAhWCwWHnnkEfz9/YsrWimllCpWZp6dr347SY6t+HW203PtpGXb6Bgb\nSFAhgXSDCB/CfCt2r6/5r6GOXmoAvwCMPoOwtO7sOPbwgLw8LE1bOvMbhgF1Gp4//svjyKa1jjIO\n7QN3D4xbC87LUkqVn2KXISxNugyhUkqp4szffYoJCcmE+7phuYzt0IO93Xj97qoVdknAokh2Fpgm\n5vO9ALAMexdiarvMu5KMdMjOwgi79C57AHJoH+akcVge7IPRqFmptVup60FFW4ZQA3CllFIV2sPT\nd5NnN5ne4xasluID8OuR2GyYIwbCmdNgt2G0uw9LryfLu1lK3TAqWgCuW9ErpZQqN/l2k6mbU8jI\nsxd63hTIyjfpFBd4wwTfcioV+eG/EBmN7NyCUT3WcaNpKY4MEVUcq5gopW5YGoArpZQqc/l2YWdK\nFompOczZkUaglxW3IoaXVPZ3p2ud4DJuYcmSzevAaoWAIMx/D4fsrPPnfl8PgNGyI5bHni2vJiql\nypAG4Eoppcrc4r2n+TQhGQBPq8FnXWPxus7GbINj3LasX4kR3xLD26fwPKbpXMf7HKPnQGTbRoxm\nrZFvJkDtehgPDyyLJiulKgANwJVSSpWp/245ybe/pxLi48Y/7ooiyNvtugy+AWTGJGTFQmTdciz9\nhmAEhRTMdPL4+Z9va47RIB5Lq07Q/n5HGXe0KbDBnVLqxqYBuFJKqTJxbrz3nB1pNIjw4d7alagb\nXniv8fVAjh50rLXt7QO7fsd8qa9j1ZKat4CPH0b7BzB8/eCUY2y35e+jMC5YLvAcDb6VuvloAK6U\nUqpU2U0hO99k7ZGzzNmRRqSfOyPbRl93ywReTLasBxEsoz6FnGzMt1+C/buR/bsd5xfOxmjZEXx8\nHRdUKqR3XCl1U9IAXCmlVKl6/efDbEl2TDp0s8BH99fE3Xr99/rKvp0QHoURUAkCKmF58z/ItxMh\nKATZkgDHDiO/zAe73RGEB4eWd5OVUhWEBuBKKaVK3IFTOfz39xTy7cLWE1k0i/ajYYQP1St53hjB\ntwjs3YlR/3ZnmuHrh9F/iOPgz4868m37DfOH/2Lp9SSGh2d5NFUpVQFpAK6UUqpEmCKkZNrIsZmM\n+uUI2TaTyv4e1An1pk/jMKoF3kABaEoynD0DNetcMptR7zas9W4ro0Yppa4XGoArpZQqEd/+nsK0\n31MB8LAajOlYnbgQr3JuVcmTrAzHRjqAcUv9cm6NUup6pAG4Ukqpq7b2yFnWH80AYOOxTKIDPPjz\nrcHEBntRI+jGC74B5Pv/Ir/+gtHmHozKVcu7OUqp65AG4Eoppa5Ydr7JoTO5TNpwgrRsG74eVgDu\nrR1E+9hK5dy6kiciGIaB2PKR336FWvWwPPK38m6WUuo6pQG4UkqpK/bpuuP8ciAdgL63h9Gt7o27\nxJ7s3II54V8YrTrCyWRIO4lRv0l5N0spdR3TAFwppdRlW7z3NLtTctiQlEmDCB/+Ui+EeuHe5d2s\nUiO5uZgfjYbcbOR/s86fKGzHS6WUukwagCullLosuTaTD389jpebBW93C53jKtG4sm95N6tUyaZf\nITmtuaEAACAASURBVDcb49FnkPUrMWLrgJs7Rseu5d00pdR1TANwpZRSxRIR3llxFIC/31WZZtH+\n5dyi0iWmHflhGjJvGlSpzv+zd+fxUVV3H8c/5072PZmQhLATNgHZjIhBBSRalxYpLvXR1ro8tepT\nFVoXsLaltip1bdXaWotI1VqsolaqrSLihmxCQNmRsAQSQhKW7Mvc8/wxGogEEiTJJMP3/XrxYu69\n5879DQOTL2fOPceMPAvnzHMDXZaIBAkFcBERadKfl+3m013lfKd/IpldYgJdTqtzn3oAVnyCyRqP\nueLHmPDgnNFFRAJDAVxERI5od1kN6/ZU8n7uAVJjQrlmRAqO6fgrWR6J9fn8vd4rPsF8+3s4F10Z\n6JJEJAgpgIuIyBE9vriAz3ZXAHDzsDQ8TvCGbwD7xovYf7+EGTUWc8GlgS5HRIKUAriIiBymzrU8\nucQfvsf1iuPKoZ1IjgruHxm2uhr775fg5Eyc634a6HJEJIg5gS5ARETaF9da/rikgHe37GdoWhQX\n9k+kU3QoJsiGntjqamzJHv/julrc390BgBk8IpBlicgJILi7M0RE5Jh9urOcBVv2M6pbDNPO6hro\nclqNe88tUJiP85fXsfPmwI5czLe/hznzW4EuTUSCnAK4iIjU21xcxW/fzwPgp1npAa6m9VhroTDf\nv1GYj/3PK5hRY3XTpcgJKicnh1mzZuG6LuPHj2fixIkNjs+bN493330Xj8dDXFwcN954I506dQJg\n4cKFzJ07F4BJkyYxduzYJq+nAC4iIgAszSvlsS8X2rlxZCrhIcE5StF9+mFs7ob6bfvxO+DzYU4/\nO4BViUiguK7LzJkzufvuu/F6vUybNo3MzEy6dj34DWDPnj2ZMWMG4eHhvP322zz//PNMmTKFsrIy\nXn75ZWbMmAHA1KlTyczMJCbm6NO1Buenq4iINFutz7K7rIYnlxRQWu3jrjFdGNsrPtBltRq7dSPs\nKzm4/cl7/gdpXQJUkYgE0ubNm0lLSyM1NZWQkBCysrJYtmxZgzaDBw8mPDwcgL59+1JS4v8MycnJ\nYciQIcTExBATE8OQIUPIyclp8poK4CIiJ7iHP97J9a9vYW+VjxtOTWVoWnAvL09VJeb0cTiPz4GT\nhh4M4zFxga1LRAKipKQEr9dbv+31eusDdmMWLFjAsGHDGj03KSnpqOd+JaBDUJKTkwN5eRGRE87z\ny/NYum1vg32f5VdwWo8Ezj8plbMykggP8QSoulZgLZTuh9g4MF/2Of3mcYhNgEQv3P0g7CnwH0tX\nD7hIMJs6dWr94+zsbLKzs4Ev7wn5miPN+vTBBx+wZcsWpk+ffsTrNGfGqIAG8KKiokBeXkQk6NX6\nLEt3llLr8/+AmbV0NzFhDinRofVt+nnDuXhAPP29htJ9eykNVLEtzFaUYV9+Fvvh25jR2dB3IPbF\np6G6EjPhCpzvXO6ffvAXN0NYOJ4Zfw10ySLSStLT0+vHaX+d1+uluLi4fru4uJjExMTD2q1evZpX\nX32V6dOnExrq/wxNSkpi7dq19W1KSkoYOHBgk/XoJkwRkSDlcy0fbN3PY4sLGuyfktWZUd1iA1RV\n67PFhbj3TIaKsoP79u+Fj96B6sovd/j/Q2JCQjEXXw37m/7KWESCU0ZGBvn5+RQWFpKUlMSiRYu4\n5ZZbGrTJzc3l6aef5q677iI+/uA9MsOGDePFF1+krMz/ebNq1SquuOKKJq+pAC4iEoReW1fMrBX+\nRWZiwz387tweGCDUY4J6RUtrrX9O70PCNwA11VBeBh4P+HxQV1t/yBk9vo2rFJH2xOPxcO2113Lv\nvffiui7jxo2jW7duzJkzh4yMDDIzM3n++eepqqrikUceAfzDqO+8805iYmK4+OKLmTZtGgCXXHJJ\nkzOgABjb2MCXNrJr165AXVpEJCgUlNbwzzXFVNe5DfavKawkzGMY3zuefsmRDOsc5DdWfsld9hH2\nLw/ASUNxJn4fUrvgPvpLKN0HNTWYEVnQdyBm6EhMZFSgyxWRNpKe3r7WNQjebhARkSBQVedS87Vw\n/ZWSyjruWZhHeY2PpMjQBsciQhwmDUzinD4JbVFmu2CtxS59HwDn2imYhCQAzBnZ2Bf+7G8UF48z\namyAKhQR8VMAFxFpp1bsKuP+D3ZS4zvyF5Vx4R5mnNuDXokRbVhZ+2Tf+zfkLIGEpPrwDWCGnHow\ngMcE7/zmItJxKICLiLRD6woruPf9PDrHhnF+38Pvxv/KKenRpMWGtWFl7Zd9798AOD/4vwb7TVKn\ng2O/4xTARSTwFMBFRNqRWp/L54WV/GtdCXUu3HFmF7rHhwe6rHbNWgs7t0LBTszl12OGnHp4o5R0\nyN+B0WI7ItIOKICLiLQj/928j6eXFwIwunuswvcRuMs+hI2fYxe+hTnzXOyHbwNgTj2j8RM6d4P8\nHRCqbwtEJPAUwEVE2oFX1xbzytoSqmpdOkWFcOdZXegap/D9de6787Bvvdxg3u6vwjcp6Zi4xm86\nda76CTajP2QMaIsyRUSOStMQiogEiGst72zeT3mtj/9s2ocBRqRHM7xzNCO7Bu9COd+U3bUd91c/\nObhj2GmYsHDs0g8AcP74T0yY/tMiIofTNIQiIie4Wp9LUUUdm4qreHLpwVUqrx2RwkUnJR3lzBOb\nXbcaAOfW6dj1qzGTrsI4DjZrPLZ0v8K3iHQYCuAiIm3sgY92sTTPv1Kjx8DM7/YhKtQhPMQJcGXt\nl62txf7jLxAbjxk8AjN4RP0xM2g4JoC1iYgcKwVwEZE2MmtFITv2V7O6oIJT0qM5q2ccqdGhJEbq\no7gpdsE8/4PS/YEtRESkBehTX0SklWzfX01ptQ+AylqX19aVkBoTSu+kCC4bnMyATpEBrrADKdoN\ngLn02gAXIiJy/BTARURawe6yGm6Zl8vX73K/bXQ6/ZIVvI+V3bEF+gzEOXdioEsRETluCuAiIsfh\nkx2lPLW0APdrSbvOtVhg8umdSYryf9RGhjgK38egfpIunw+2b8GMOT+wBYmItBAFcBGRb8i1lieX\nFGCBM7ofPm2gNyqEsb3iMEa3CB4rW16GO+1H0L23/4bL2hro2SfQZYmItAgFcBGRb6C6zmXtnkoO\nVPs4q0ccN4xMC3RJQcMu/wj36YfAdWHDZ9gNnwFg0rsHuDIRkZahAC4icoyq61yue3UzpTUuAFeP\n6BTgioKL+/ZrkOCFbr0wvfphX3vef8CbEtjCRERaiAK4iMgxmru2mNIalwkDEjk5NQpvVGigSwoK\n7n9egdAw2LkVk5WNc+UNAPjWrYINn2GiogNcoYhIy1AAFxFpJmst+aW1LPlyEZ0rh3YiQovntAhb\nU419ZfbBHd171z90bvklVFUEoCoRkdahAC4i0kxrCyu5a/52AC4Z5FX4bkmFu/y/ZwzApHfHjBpb\nf8iEhYOWmReRIKIALiLShPV7KpnzWRGF5bUY4I4z0xnWWcMhWlTBTgCcK27AHNL7LSISjBTARUSO\n4EBVHdv31/DauhI+L6ygV2I4F/ZPJKt7XKBLCzp21w7/g9QugS1ERKQNNCuA5+TkMGvWLFzXZfz4\n8Uyc2PhKZIsXL+aRRx7h/vvvJyMjo0ULFRFpaw8vyicnvxyAUd1imHZW1wBXFJzszu3Y+a9Dr36Y\ncA01EZHg12QAd12XmTNncvfdd+P1epk2bRqZmZl07drwB1FlZSVvvfUWffv2bbViRUTawqwVhWzf\nV82a3RVkdY/l/L4J9EqMCHRZQclWVuA+fg+EheP8+M5AlyMi0iaavINo8+bNpKWlkZqaSkhICFlZ\nWSxbtuywdnPmzGHChAmEhmo6LhHpmLburWL+F/t4bV0Ju0pryEiK4KIBSQxJiyY23BPo8oKOranG\nvedWKC7E+dHtGK/mUxeRE0OTPeAlJSV4vd76ba/Xy6ZNmxq0yc3NpaioiFNOOYU33nij5asUEWll\n1lp+uWAH+6t8ANx5Zhd6J6nXu1UV5EHRbug3GPoNCnQ1IiJtpskAbq09bJ8xpv6x67rMnj2bm266\nqcmLzZ8/n/nz5wMwY8aMY6lTRKRV/Wv9XvZX+fjBsE6M7RVHshbXaX379wLgTLqqwc8VEZFg12QA\n93q9FBcX128XFxeTmJhYv11VVcWOHTv49a9/DcC+fft44IEHuOOOOw67ETM7O5vs7OyWql1E5Lj5\nXMuzKwtZvMO/uM63+ydqfu82Yot2+x/EJx69oYhIkGkygGdkZJCfn09hYSFJSUksWrSIW265pf54\nVFQUM2fOrN+ePn06P/jBDzQLioh0CFv3VfOv9XvxRobwv6ekKHy3Ebvxc+ycmZCcCgnepk8QEQki\nTQZwj8fDtddey7333ovruowbN45u3boxZ84cMjIyyMzMbIs6RURa1IaiSqYv2EFVnQvAb7O7kx4X\nFuCqThx2yQcQGopz18OYEC1JISInFmMbG+TdRnbt2hWoS4vICcy1lnvey2NlfjmTBiaRHBXKBf0S\nNA65Dfnuuw3CI/D87LeBLkVETgDp6emBLqEBdTuIyAlnWV4ZK/PLSYsJ5YfDUwJdzolpTwFmxOmB\nrkJEBGh60cm1a9cye/Zstm3bxuTJkxk1alT9se9973t0794dgOTkZO68s+k1DRTAReSE8qsFO1hd\n4F/d8r5zuge4mhOTrayAsgOQnBboUkREmrXoZHJyMjfddFOj022HhYXx4IMPHtM1FcBFJOhZa5m3\nYS/7qnzk5JczLC2Kc/sm4NVUg4Hx5ewnJkUBXEQC79BFJ4H6RScPDeApKf5vS1tqqKICuIgEvRW7\nyvnrp4U4BiJCHH44PEWL7ATSnnz/7+oBF5F2oDmLTh5NbW0tU6dOxePxcNFFFzFy5MgmzwloAE9O\nTg7k5UXkBLBxTxn3LFwPwNxrT6VTTHiAKxJOOwP6/w269wajaR9FpG1MnTq1/vGha9M0tehkU558\n8kmSkpLYvXs399xzD927dyct7egdDAEN4EVFRYG8vIgEuSU7Svn9J/lEhzrcfmYXTFUpRVWlgS7r\nhOd7+G4oKcJz758DXYqInCDS09OPuAp7U4tONiUpKQmA1NRUBg4cyNatW5sM4Op6EJGg41rL0rxS\n/pazh4pal9vP7MLwztGBLksA968Pw/rVmDPOCXQpIiJAw0Un6+rqWLRoUbPXuSkrK6O2thaAAwcO\nsGHDhgZjx49EY8BFJOjk5Jdz7/s7AbhyaLLCdzthy0uxS94HwIy7IMDViIj4NWfRyc2bN/PQQw9R\nXl7Op59+yksvvcQjjzzCzp07+ctf/oLjOLiuy8SJE5sVwLUQj4gEleo6lx+//gV7q3z8/oKedI8P\nx+NogZ32wK5aivvEbzHX345z6pmBLkdETiBaiEdEpBWtLqhgb5WPvt4IeiVqppP2xK7NAU8IZmjT\nMwSIiAQzBXAR6fBe+qyId77YD0BlrQ+Ae8Z3C2RJ8jW2aDf2g/9iMkdjwjQTjYic2BTARaRDqnMt\n723ZT7XP5c2Ne4kMdeifHAlA9/hwokI9Aa5QvmJdF3fOX8ExmElXBbocEZGAUwAXkXbP5x5+q8qS\nHaU8saSgfvv7wzqRnZHQlmVJM9mFb0LOEsyl12CSOgW6HBGRgFMAF5F27bmcPby8prjRY2Eew9MX\nZRDiMcSEqce7vbHWQnkpdsUn0LUX5pyJgS5JRKRdUAAXkYB76bMiduyvafTYyoJyeiaEk9U99rBj\nPRPDSYjUx1h7ZRe+if37UwCYsRcc08pyIiLBTD+5RCRgan2WoopaXlhdRHyEh+jQw9cGiwv38L2T\nvWR1jwtAhfJN2Zpq7KvPH9yR2jlwxYiItDMK4CISMLf/dyu5e6sBuG10OkPStGBOMLAlRdhVS6Gy\nHHP1rbC/BDNyTKDLEhFpNxTARaTVVde5zPy0kPyyg8NMrIXcvdWc1SOO4enRDE6NCmCF0pLcZx6F\nDZ8BYHr1xaR3D3BFIiLtiwK4iLSKbfuq+aKkCoAPtx5gZX45/ZIjOXRRyqFpUVw1vBOdokMDVKW0\nNFtR7g/f/QZhxpwPnTUfu4jI1ymAi0irmPFBHrtKawEwwI9PTeX8fomBLUpalfv2a9i1KwFwzr8U\nM3hEgCsSEWmfFMBF5Jgt31nG/R/sbHR+7q9Y4LLBXrIz4gkPcUiI0MdNa7A+H2xaA/1PbpNZRmz+\nDtxHfgGOB+ey6zCnZPn3Fxdi//mMv1F4BPQb1Oq1iIh0VPqJKCINbN9XTWF57VHbzP9iHx4DkwZ7\nj9gm1DGc3y+R2HDNz92a7CvPYt95HWfyr2HQ8Na9lrW4Mx+F0gMQE4f7/lt4vgrgSz8EwLn5F5CU\nrOXmRUSOQgFcROpV17n87D9bqfEduWf7KyenRnHlUK1qGEh2bzH23TcAcP/1d5z+J2NCWvFjfeUn\nsG0zDB2JiY3HfvQO7sxHMVf8GHbnQUISZsiprXd9EZEgoQAucgLZtq+a25oRsK/PTKWvN+KobdJj\nw1qyNGkmu2UD7ow7YehIyFkMgPn25dh5/8Au/QCTdXbrXLd0P+6LT0NoGM41k7GrlsBH72AXv4dd\n/J6/UZcerXJtEZFgowAucoKoqnP57cId1Pgslw7y4jl8zRsAokI9nNc3AY+jVQvbI/evD4N1D4bv\n7/wP5sLLsKuXYv/2OO6mNZgrbsCEHt/MMnbD5/4HGf2hugp3+s1QXoYz7UFMdAycfjam7yDsv1/C\nfjzfX0vv/sd1TRGRE4UCuEgQWbengooat9Fjm4qrKCyv46ROkXx/mIaOdES2uBD2FABgrvgxZsx5\nGMc/xt752b24T96H/egdSOuK+dZ3v/l1tn2B+9Bd/o34RHBdKN2PueBSTI8M//WNgU5pkHkG9uP5\nmKt+gjlNi+2IiDSHsdY2PdizlezatStQlxYJOpuKK7ntP9uO2iYmzGH2xX0JUe92h2IrK2DdKtw/\n3Q+A87uZmKTD/xNla2twb7oE0rvj/OoxjHOErzma4P7zGezbr2FGjsEufR8AM+kqzNnfxoQfPjTJ\nVlZgIrWQkoi0X+np6YEuoQH1gIt0UHPXFvPq2pL67a/GdU8/uxtRoY0HL29UiMJ3B2ML8nB/cVP9\ntrnk6kbDN4AJDcP878+wf30YVi6GL2coafa1amv9s6q8+wbmlNGYcy/yB/C0rjjnX3LE8xS+RUSO\njQK4SDtUWFbLJztKsRz5C6q3Nu4jKtRheOfo+n1d4sIabEvHZq3F/eO9AJjLrsOMGoeJjTvqOebU\nM7AzH8H98wwYOAxn4vehsgJOGnrEecLtti/AVwc11fWzqpjR2dCtNyZ7AmbUuJZ9YSIiJzgNQRFp\nhx5fnM/8L/Y32e66U1KYMCCpDSqSQHAXLcDO+j3mvItxLv5hs8/z/fE+/02aISFQVweAOf9izCmj\ncZ96APYU4Ey5BzNwmL/9jyY0ON+Z/GtMK88pLiLSltrbEBQFcJFW9vbmfWzfX31M5yzaXkrn2DB+\nPqbLEdsYDJFHGGoiHZu1FvvfudhXZkO/wTi3/LLRsddHPL+yAop3Q2E+7p9mHLlhbDx06wVrcxrs\ndp567RuPHxcRaY/aWwDXEBSRFlRW42uwPHtVncuTSwoI9ZhjHns9qmsMUaFaRfJEY/cW4858BDZ8\nBoBz/e3HFL7hyzHZXXthu/TEXH8HJioa9/e/8h+78DLsts3w+Qoo3Q8V5f6l46ur/MevvlXhW0Sk\nlSmAi7SQJXml3Pf+zkaP3TWmq8ZmS7PY/86FL9ZhRo3FjDwLE5/4jZ/LGIM59Qz/47O/jV0wD3PO\nRTjRsf4pDWPiMeH+JeNtcSF29TJNJSgi0gY0BEXkOMxdW8z6PZUA7Nhfw97KOn7wtTm2I0MdxvSM\n08I20ii7pwBCQrGrlmISk3GffhAyBuCZck/LXsdaqCzHRMW06POKiHQEGoIi0kHtraxjQ1Fl/ba1\n8MKqPcSGhxAf7iHMY/jOgEQu7P/NeyzlxOL+5UHssg/rt7/qDTFde7b4tYwxoPAtItIuKICLNNOf\nlhawJK/ssP23jEpjRLqCjTSfrauDirL68G3OPBe7fjXmtLHgq8WMOT+wBYqISKtSABf5muKKWqYv\n2MGe8roG+6vqXLK6x3LpIG/9vrAQQ5fYsLYuUToIu38vRMdiQg5+1NryUtxpP/LPze04OLffj+lz\nUgCrFBGRtqYALiekFbvKWLbz8N5sgPV7KtldVsu5fRM4dNS2Ywzn9Imna1x42xQpHZrdV4I79ToI\nDcOccxHm3O9CWDh27nP+8G0Mzi//gOnSI9CliohIG1MAl6DkWkvegRrqfI3fYzzz00IKymobnUc7\nxMBNp6Uxtld8a5cpwWzHFvD5wFeJfeMf2KUfYkaNxX7wH8y538W59JpAVygiIgGiAC5B6cOtB3hk\nUf5R2/xgaCcuGew9ahuR5rCVFdh3Xvcv515Xh83L9a9A6Tg4D8yCLRtw//Y49vUXIMGLOYZVLUVE\nJPgogEuHVudanlxSwP6qhuO18w7UEB3qcMvpnRs9z2MMQ9Ki2qJECXK2rg73z7+DdTlgDLhu/TEz\n7kL/PN7DR+GkdMZ99TmcCy7VQjciIu1MTk4Os2bNwnVdxo8fz8SJExscX7t2LbNnz2bbtm1MnjyZ\nUaNG1R9buHAhc+fOBWDSpEmMHTu2yespgEuHVOdaispryd1Xzbtb9tMlLoyIkIOhJjrMw7he8Yzq\nFhvAKiWYWWth5zbcuX+DtSsxP7wZMzobamqgsgzCIjBRBxdfMl164PnJ3QGsWEREGuO6LjNnzuTu\nu+/G6/Uybdo0MjMz6dq1a32b5ORkbrrpJt54440G55aVlfHyyy8zY8YMAKZOnUpmZiYxMUefHU0B\nXDqcrXurePCjXeQdqKnf95vx3fBGhQawKjmR2HWrcP94H1T754U3Z56Lc8Y5/oPh4f5fIiLSIWze\nvJm0tDRSU1MByMrKYtmyZQ0CeEpKCvDlmgqHyMnJYciQIfWBe8iQIeTk5HDGGWcc9ZoBDeDJycmB\nvLx0QH/9ZBsvfJpHbHgIt43LICLUoVN0OP27JwS6NDkRuC7sK4HuPeB3T0F8EoSFaYEbEZEOYOrU\nqfWPs7Ozyc7OBqCkpASv9+A9YV6vl02bNjXrOb9+blJSEiUlJU2eF9AAXlRUFMjLSwewbk8FxRX+\n8d2uhWeX7iIhwsMj5/UgIfKrv751+rskrcJdMA+KCzEXXgZVldh338C+/RoA5luTcC65GuqqoKIq\nsIWKiMhRpaen1w8T+TprD58x7es93ceiOedqCIq0Wweq6rjrne24X/t38dPR6YeEb5GWZ+tqYV8J\n9sW/+Le/DN0ADBqOc+WNkKAZdEREgoHX66W4uLh+u7i4mMTExGadm5SUxNq1a+u3S0pKGDhwYJPn\nKcVIu1Be4+Pu+ds5UO2r31fnWlwLP83qTK+kCADCPYaUaI31ltZhfT7sK89i33sTuvXy7+w7EDat\nhcGnYLr1xIzIwnRKC2yhIiLSYjIyMsjPz6ewsJCkpCQWLVrELbfc0qxzhw0bxosvvkhZmX9xv1Wr\nVnHFFVc0eZ6xjfW7t5Fdu3YF6tISQLU+l/lf7Kes5mDY3l1Wyztf7Oe0rjHEhHnq98eGe/jBsE6E\nON/8qyCRQ9nqKthfAp06w45c7JqVEBqKOW0s7kN3wa7tBxsPHoHn1unYA/swcbrPQESko0pPTz/q\n8RUrVjB79mxc12XcuHFMmjSJOXPmkJGRQWZmJps3b+ahhx6ivLyc0NBQEhISeOSRRwBYsGABr776\nKuCfhnDcuHFN1qMALq2qstY9bN+Lq/fw+vq9h+1PigzhzxN6Ex6iOZKl9bgzH8Uufg+SkqHk8HsH\nzKSrMOO/A2WlkJCkObtFRIJAUwG8rWkIirSaF1bt4aXPixs99q0+CfwoM7XBPo8DznHc9CByJLbs\nAO7MR+HzTw/uDIvAZI3HnH0hdssG7ItPQ0ws5pyJmJAQSNJUgiIi0joUwKVF7C6r4dW1JdT4Dn6h\nsmJXGd3jwzi7d3yDtpGhDuN6xRPqUdgOZrYwH7t2JWbM+d/4bnK7ehl2Ry7OhZcdXy0L/t0gfDuP\nPo+JiavfNj36YLP801GZEH0siohI69JPGjkurrVsKq7iqWW72b6vmviIg+O3QxzDpIFexn0tgEvb\ns1WVUFOFiTv6Xd22uBCqqzDp3Y//mh/8F/vfuV/2NJ999LY7ciEqBuPthP1sOXbFJ5Dgxc77h//4\nWd+C/DzsR+9gqypgywbo1R8z8izM4BHY5/+EGT0eomMhKRkTG48tO1Afsu2OXEjtgjP1d1Cws0H4\n/orR4jkiItJGFMDluCzbWcZ97+8EYOpZXThdS7+3G9Za2FMA5WW49/2sfr+ZdBXO+ZccbLd/L3bV\nEuxzT37ZwOA89GyDsG4P7IXwqGMLqcWF/nNn/R7fkoWY5FRM9kWYzl0P1vfZcuwn72GXf+Q/J7UL\n7N55+Gt5ZTZ2/Wr/cyanQteekLMYm7MYm9IZCvOxS98/eEKfgbB5LZyciXP2t2HnVkz3DH/w7nN4\n+BYREWlLuglTjkl5jY9p7xycLrCq1qWyzuX3F/SkV2JEgKuTr1jXxf7zGez8fx3c2SnNH8gB878/\nw5x6JhiDO/3mhjN/fMmceS4mewImvTu+26+GykqcB57BREU3ff26WtybLoXUdDAGfD4o/PLfe69+\nmNQu4O2E/fdL/n1DTsXEJ2I/fBsAZ9qD2D0FmMGn4D78c9iR66/phzfXL/lua2twH7wLcjfCyZmY\n9G7+15sxADauOfz1TPz+cQ9lERGRjqm93YTZrACek5PDrFmzcF2X8ePHM3HixAbH582bx7vvvovH\n4yEuLo4bb7yRTp06NXlxBfCO5b0t+/m8sIL5X+zn9G6xxIb7Z4fonxxJdoamaGsPbG0N9v23sHNm\n1u8z512MOWcCJi4Rm7cVd8YdUF3l70XGQF4u5nvXYQadAmldsE89gN25FYr3QG1Ng+BOaBjOox2+\niAAAIABJREFU9bdhho1qeN31q7Frc8BazKhx2AXzsB/8B/Pt7+FcdCUA7jO/x36y4LCazY9uwxl5\nlr/NWy9DbQ3mO/9TP27c1tb6e76jYzGxh/de29paTOjBueGttbByMVgX98+/8+8cOAzn+tsx0fqG\nRkTkRNThArjrutx6663cfffdeL1epk2bxq233krXrl3r23z++ef07duX8PBw3n77bdasWcOUKVOa\nvLgCeMdRXFHLta9+AWi6wPbKfvox7qzHoLrSP5vHhCswYy847AZI6/pwfzMF8rbW73N+/8Jh4dSW\nHsB+/A7243dhT76/F/srXXtBamfY9gVm6Ejsu28cPOYJAV8dRETiPDQbE+7/ZsT6fFC8G5OSjs3L\nxc57CQYOxTnrvBb/s2jwOlwfxvE03VBERIJWewvgTY4B37x5M2lpaaSm+qeMy8rKYtmyZQ0C+ODB\ng+sf9+3blw8//LAVSpVA2VJSxdS3twFwb3Z3BqZEarrAdsbuK8F96kGwLs6Ue+CkoUececQ4Hpxb\np2NzlvhvtvR4Gu0ZNrFxmPMuxp4zEcpLcf/2BKxaijn1TOyyDyHPPyzEvvsG9D8ZkzUeO+v3/vAN\nOA/9rcGYcePxQIr/A9B07YW54c6W/mNolMK3iIi0N00G8JKSErxeb/221+tl06ZNR2y/YMEChg0b\n1jLVScDsKa9l54EaAP6WU0hEiEN2nzgGdFL4bo/s/NfBuphrJmMGNv3vzyQkYcae36znNh4PxCXg\nXH87VFdDTKz/Zsm4BOy//o45bQzO5T/y1xGXgPuH6ZhvfVezioiIiBxBkwG8sREqR+pZ++CDD9iy\nZQvTp09v9Pj8+fOZP38+ADNmzDiGMqWt/fq9HezYX1O/fdvodM7sqdkj2iu7ZiX0Pxmnien+jocJ\nC4cwf6g2F13h3znugoZtBo/AeXAWxOqeABERkSNpMoB7vV6Kiw+uZlhcXExi4uFzCa9evZpXX32V\n6dOnE3rIDVGHys7OJjs7+zjKldY2e2Uh72zeR2mNy3f6JzK6eywx4R66xas3s72y+0ogbytm0g8D\nXQoAJsHbdCMREZETWJN30WVkZJCfn09hYSF1dXUsWrSIzMzMBm1yc3N5+umnueOOO4iP16IrHU2d\naymr8VFW4+Pj7aXER4Rw0YBELhns5aSUKIXvds6uWQGAGTQ8wJWIiIhIczTZA+7xeLj22mu59957\ncV2XcePG0a1bN+bMmUNGRgaZmZk8//zzVFVV8cgjjwCQnJzMnXe2zQ1WcnystUx5M5fthww3uXJI\nMpednBzAquRIbHkZFO7C9Orn3966CfvsYxAVDd16Bbg6ERERaQ4txHMC+2jbAVYVlPP25v2c2SOW\nfsmReIzhzJ5xxIVr5oj2yPfrW/zTB3brBcaBndvAV4e54DKc734/0OWJiIi0Sx1uGkIJPgeq6iit\ncfnz0gKqfZZOUSFcObQTnWPDAl2afI3N34F985/QZyB25ScH5+7+cmVIuvbE+fblMOy0gNUoIiIi\nx0YB/ARTVu3jute+oMbn/+Lj5lFpWsWyHXNffwE+XQSLFwJgzr8Yc8FlsKcAm78DM+J0TEjjNz2L\niIhI+6QAfgJwreW5nD1s2VtNRY2PGp/lf05OpltCGCO7xAS6PDkCW1frD99deviHmqR1wflqppNu\nvTAa8y0iItIhKYAHsZX55eyvqmNLSRWvr99Lr8RwwjyGkV1j+O7AJC0l347ZqkrsGy8CYE4bA/FJ\nmMGa5URERCQYKIAHqbz91UxfsKN++7SuMUw7q8sRF1GS42ethY2fQ/cM2LoJ+g7ChHyzf2LuXx6E\nz5ZDz76Y8y7W+yYiIhJEFMCD0AdbD/Dwx/4ZZn4zvhudokNJjQlViGshtq4O+68XMBkDMUNPxdZU\n495/B9TVQkFeg7bOL36P6d776M+3pwD7xouYS67BxCVgKyvg8xWYcRdgLv+R3jcREZEgowAeJDYU\nVfKfTfsAy6LtZWQkRXBORjxD0qIDXVqHYq1tOvCuWYF96xUsr+DcOBX3P3MhL7fRpu5vJuPc+xQm\npfPBa2z7AvvhfyGlM9TWYl973r//k/cwYy/ADBgC1sUMG4VxNB2kiIhIsFEADxKvrSthaV4ZSZEe\nuseHcceZXegUrdkxmsPmbsRu+wKTmo77h+mYq27GyTr78Haui33rZX9gjoiE8EjcP82oP+7cOBUS\nvJCYDMZgF7+HfWU2dvO6BgHcffOfsGJR47UsfBO78E1wHOjdv+VfrIiIiAScAngHV+da7n8/j+W7\nysnqHsudZ3YJdEkdivvvlw72QIdHgs8Hq5dBYwH800X+thkDMOMuhK2bsfNfh6EjcW6Yevh47+wJ\n2LnP+acMzNuK3fA57C2CFYsw507EXHgZhIUDBqyLO/kKqPlyRdLoWExEZCu/ehEREQkEBfAO6t8b\n9rKnvJb8shqW7yonOyOeC/slBrqsDsUW7KwP3wBUV/r3f/oxNmcxDByOCQv376sow77wJ4iOxblj\nBsZxsAOHYYt341x2XaM3W5qQUEjrgn37Vew7r9c/P53SMBOuxISHN2jvTHsIrMWuWqopBkVERIKY\nAngHYK2l1rX120Xldfxl+W5CHHCM4cL+iVyfmRrACjsmu/xDAJyHZ2PX5sCalZgx5+HOfBT3j/dB\nt144dz8KKxfjPvsHqKrEXHMrxvFP32hi4/HcdNdRr2FGnol9/e/QKQ3nhvugpAjSuhwWvgFM157+\n3xW+RUREgpqx1tqmm7WOXbt2BerSHcqvF+xgRX75YfsfPq8nfbwRAagoOLgzH8FuXIPndzMb7LdV\nFbi/+D/YV9zwhG69/LOaHMOsJNbng1VLoc8ATJy+oRAREQmE9PT0QJfQgHrA26GX1xSzY191/XZO\nQTkjOkczKDWqfl9cuIeMpMN7UaV5rOtit2yAzl0PO2YionB+8yTujDv8K1AC5qqfYEaPP+YpAY3H\nAyNOb5GaRUREJDgogLcju8tqKKms47mcPcSHe4gM9Q91SI8N4/vDOpGRpN7ulmB35MLunVCYj5lw\nRaNtTEQknumPY79Yj922GZM1XlMCioiISItQAG8ntu2r5pZ/H5xLeupZXRiYEnWUM+SbsHlbce+5\n1b8Rn4g5Jeuo7U3GAEzGgDaoTERERE4UCuABtKe8lpc+L6LOtewuqwXgJ6el0Sk6lJM6aQq6I7G1\ntbB3D/hcTCNDSGxdHWBh/14AjDfFv790P+7MR+vbmZFn+WcqEREREWlDCuABUl3n8u6W/by9eT8p\n0f63YUTnaLIz4rX0+FG481/HvvQMfHXvcFQ0pHbxL4yzexdmwv/ApjXYxQv9c3pHReP5w4vYgjzc\nx+6BkiLMlTeAJwQzbFRAX4uIiIi0Dzk5OcyaNQvXdRk/fjwTJ05scLy2tpYnnniCLVu2EBsby+TJ\nk0lJSaGwsJApU6bU3+TZt29frr/++iavpwAeAC+s2sNLn/tn2EiPDeVPEzICXFHHYT9ZCJ27Yb71\nXdjwOXbRu5C78eDxZx9reEJFOb5f/h/k74DYeJw77sdohUkRERH5kuu6zJw5k7vvvhuv18u0adPI\nzMyka9eD37IvWLCA6OhoHn/8cT7++GNeeOEFpkyZAkBaWhoPPvjgMV1TAbwNfbztAOv2VLJoeynd\n4sMY1yuegRpq0iR32YeY0DA4ORN2bsOccxFO1njIGo+95Brsyk9gy3rMty/HnfV72LgG57b7ICYW\nd/rN/vANOFMfaLAkvIiIiMjmzZtJS0sjNdW/pkpWVhbLli1rEMCXL1/OpZdeCsCoUaN45plnOJ6Z\nvAMawJOTkwN5+VZXWl3H/kr/2G5r4U/LNlFd5xIe4nDdKd256OS0AFfYARQXwsCT/Y+jo+DhZ8Cb\nAjFx/n3JydCr98H29z8FvjoIDfNv/30+lB2A5FTQ0B4REZET1tSpU+sfZ2dnk52dDUBJSQler7f+\nmNfrZdOmTQ3OPbSNx+MhKiqK0tJSAAoLC7njjjuIjIzk8ssv56STTmqyloAG8KKiokBevlXVuZar\nX9lEaY3bYP+tp3fm7N7xQHC/fuu6sLfIP9NIM290tK7rn3e7shxS0iFvK+4fpvvDdG1N/e/OjL/W\n31jZLCYEioubbiciIiJBKT09nRkzZjR6rLGe7K/fj3ekNomJiTz55JPExsayZcsWHnzwQR5++GGi\noo4+k52GoLSwTcWVvLF+L5V1LqU1LhMGJNbP3x3qGEZ2jQ1wha3H+nzYf72IXb/KH6Srq/wHTsnC\nmXQVJuXoq1DZZx/DfrLgsP3OXQ/ivvQMFO3GfGvSsYVvERERkaPwer0UH9JRV1xcTGJiYqNtvF4v\nPp+PiooKYmJiMMYQGurvaOzduzepqank5+eTkXH0+/sUwFtInWvZUlLFPz4rYlVBBZ2iQ8hIiuCS\nQV7iI77ZH7P97FPsts1Qsgcz4QpMQlILV93CPluOffMl6N0fc8Y52E/eg4oy+OxT3E8XYc6/BPPd\nH0BJESR6MY5Tf6rdvsUfvvsNxow5D/aVwL5izKlnYbr2wvPT3wTwhYmIiEiwysjIID8/n8LCQpKS\nkli0aBG33HJLgzannHIKCxcupF+/fixevJhBgwZhjOHAgQPExMTgOA67d+8mPz+/fiz50Rh7PCPI\nj9OuXbsCdekW96/1Jcz8tBCAs3rE8bMzjt7bC2CLdmPn/g0cB3r2xYw5HyrK/NPsDRyGffYPBxsb\nAwOGYGITILUzZuwFsGs7tnQ/5uRMTETgb+b0/el+2LQW54FZmJAQ/3zdNdVQfgD35zcAYDLPwC7/\nCJJTcW6/DxKTsS8/i337VQCcX/4B061XIF+GiIiIBJmvpgk8khUrVjB79mxc12XcuHFMmjSJOXPm\nkJGRQWZmJjU1NTzxxBPk5uYSExPD5MmTSU1NZfHixbz00kt4PB4cx+HSSy8lMzOzyXoUwL+Bshof\nf1m2m6q6g+O7c/dW4Vr4yajO9E2KICb86MuWW2tx77+9wRR6AHRKgz0F9Zvm6lth7UpsdRUc2Of/\nVVzY8JzYeJwrb8CcMvq4X9s3ZcsO4N52NWbchTjfu+7w4yVFuL+40R/ID32N3pT61+P8312am1tE\nRERaXFMBvK1pCEoz5B2opvDLlSoB1u2p5P2tB+gWH0aI4x+kHx3mYWyvOIZ3jm7Wc9olCyF3I+bc\niZgJV2BfewH78fz6YGp+eDNm2GmYmDgYPb7huWtWYnOWQJfuEBaBXTAP98+/8y9G07s/zoQroEcf\nKC7EpB78C2erKiEsDOM08Z+D6mrs7McgMqp+MRvCwv3BuewAxMThjM4+2H7XdtyHfg6+OkzW2Y0+\np0lKxpx6Jvbj+Tg/ug33j/f6V6qsqcZ8+3uYc7+LiTz6DQsiIiIiwUA94E3YUFTJ1Le34X7tTyk2\n3MPsSX3wOMc+tZ2tqcb97U8hJATn7kfrx0Jb14W1OdCtFyY+sYlnOeT5dm7D/e0U/1R7lRXg+TJg\nlxRhrp2CXfQuJiUd+8F/MBdchpl4JcYYbNkBCA33L+sen4SJjMLuK8G+/x/svH/4nyMhCSrK/T3X\nhzA/uAkzZCQmIQn3b0/4g/Wtv8IMHH7kOg/shc3rMSNOx1ZXYT96B3PWeZhQLQcvIiIirae99YAr\ngB/Ffe/nkZNfTly4h5+OTm8Qtr1RISRHHXtwtAf24j5xL2zdhPPjO1ps2Ij1+TAeDzZ3I+4ffg3l\npUc/YdgoyFl8cNubgnPH/bjPPgbrVkFcAs79T2PCwuuf3y58C6orsUs/8M9yEhWNufJG7NMPwcDh\neKb8ukVei4iIiEhLUgA/RHsM4K61vLF+L/uq6pi7toTECA+/OrsbvRIjjul57M7tuL+7E3PhZZgx\n38K+8BR2+xewazuEheP8788ww1tnvLMtL4MtG6BbT38vc7fekJoOYeHYpR9gX5nd8IShI2HVUgiP\nhOpKzKix/iEwR5i/27ou9m+PYz9+t36f85snMWldG20vIiIiEkgK4IdobwG8tNrHZ7vL+d2HuzBA\neIjDg+f1oHt8+DE9j7UW95c3QcHOww+mdsG5/nZM996HH2sjtqrSP/OK69bPnmI3rsH9y4OwvwRz\n9S0Nxng3+hzlpbiTrwTAeWj2MQ2ZEREREWlLCuCHaE8BvLzGxzVzN1PtszgGZk/qQ9w3mL/b5m7E\nvvM6dtmH/mkDTxqKffU5SOmM88s/QGhYg/mv2xO7txj7+aeY0eObvFETwOblQmyCwreIiIi0a+0t\ngGsWFGBJXimrCyqo9lkuGeRlWOeobxS+3Tf/6Q/bAENH4tw0DeN4sMNHQXJau7/Z0CR6MWee2/z2\nXTVft4iIiMixOuEDeGWty4wPduJaiAp1uOikJOKamMO7Mba6GvvvOdC1p3+mkczRGOO/adN07tbS\nZYuIiIhIB3XCBvCSyjp+9e52SmtcXAt3npnOqV1iCfUc27SCNmcJtrgQ+4+nAXAuuw5z0tDWKFlE\nREREgsAJGcBXF5SzfGcZ2/fXMLp7LImRIZySHtOs8G2t9c+JHRYOq5f7F5QB6JSG6XMS9BvcytWL\niIiISEd2wgXwXQdq+MW7OwCIC/fws6/N7300dtNa3AemHn4gNh7n109gQsNaslQRERERCUInVAD3\nh+/tANw1pgsndYpqVvi2rutfHfLFpxrsN+O/g7n4hxASWj/eW0RERETkaII+gLvWUlJZh7Xw6KJd\nlNX4GNE5msz0mCbDt7UW9+G7YcNn/h3eFJyrb8EMGNIGlYuIiIhIMAr6AP7CqiJeXlNcvz0lqzNj\ne8U37+SCPH/4/nKFSOfnD2Nim3muiIiIiEgjgjqAV9a6vLymmB4J4XynfyJJkSGMSI9u8jy7r8R/\nc+Vef3B3fvEoJrV9TeAuIiIiIh1TUAfwVQXlAIzuHss5fRKabG+txf7rRey8fxzc6U2BlM6tVaKI\niIiInGCCNoD/4ZNdfLStFANcdFJSs86xS973h+8+J2HSu2MGDYc+A3WDpYiIiIi0mKAM4Gt2V7Bg\nywH6eSM4p08CESFOs86zOYshMRnnjhkK3SIiIiLSKpqXTDuYJ5cWAHDl0E6c++XQE1uQh/vMo9ii\n3fXtrOsefLw2Bz5dhBl2msK3iIiIiLSaoOoBX7enghnv57Gv2uWSbe8yZHEctv9g3H+9CF+sB/xB\n27nnjxAZjTvjDoiKxvTuj503BxKSMBddEeBXISIiIiLBzFhrbaAuvmvXruM63+ZtxX6ygKKeg1hd\nGcHSzXtYHtqZC3Z+zIQdH+CtOeC/gbK8DDNqLHhTsC/NBE8IRERCeenBJxs2CufSazC64VJEREQk\nqKSnt6/Z7Dp0D7j976ss21TA7yuGUxESDuFdOWlfLtd8MQ9z6pnYFZ9AYT7m9HE4l/8IAN8nC2BH\nrj98x8T5V7IsO+Bf1VJLyYuIiIhIK+uwAby0uo67bCbbT+5E7yjL/8XsInr1IhK2rcNccytm6EgI\nj8B+9A5m1Lj685zb7sWuycF06+kfchIRFbgXISIiIiInnA41BMXN28bC91dQ0G0Qu2sMC3f7OD2y\nnCkThhMe4mBra8F1MeHhANiKctiTj+nRpzXKFxEREZEOQENQvqGqsjIWzprDn3pPgB3+2UtSKkv4\n6QUDCPtymkETGtrgHBMVDQrfIiIiItKOdIgAbq3llje3sbv3BAbYffxm89/x7NwKI07Hk5gV6PJE\nRERERJqt3QbwdXsqWFVQAUBNncvuWg8D9+Uy7aoxhMWOwlZXQ0i7LV9EREREpFHtLsH6XEuda/nT\nkt1s219dvz/MV8uPq3OIiz0foH6ct4iIiIhIR9KuArjPtdz4xhZ2l9UC8D8nJ3PpwATsv17EvvUy\nnuumBLhCEREREZHj06wAnpOTw6xZs3Bdl/HjxzNx4sQGx2tra3niiSfYsmULsbGxTJ48mZSUlGYX\n8c7mfawprKCqzrK7rJZxPWLoUZbP2UkxmNeeg/++ihk2CjP89GN7dSIiIiIi7UyTAdx1XWbOnMnd\nd9+N1+tl2rRpZGZm0rVr1/o2CxYsIDo6mscff5yPP/6YF154gSlTmu6t3rq3CoCZnxbiGIgOc+hR\nU8wP/n4PCbVl8ApYgIHDcW6ahjHmG79QEREREZHGHE9n86uvvsqCBQtwHIdrrrmGYcOGNXk9p6kG\nmzdvJi0tjdTUVEJCQsjKymLZsmUN2ixfvpyxY8cCMGrUKD7//HOaM734rW9u5dY3t1JZ53J9XCFP\npe7g0UW/IyHUQkpnzMgxAJissxW+RURERKTFfdXZfNddd/Hoo4/y8ccfk5eX16DNoZ3NF154IS+8\n8AIAeXl5LFq0iEceeYSf//znzJw5E9d1m7xmkz3gJSUleL3e+m2v18umTZuO2Mbj8RAVFUVpaSlx\ncXFHfe7b1jyHsZZQ62NYyQas9Rfs3PsUJsZ/rv3O5ZDSuckXIiIiIiJyrA7tbAbqO5sPHe2xfPly\nLr30UsDf2fzMM89grWXZsmVkZWURGhpKSkoKaWlpbN68mX79+h31mk0G8MZ6sr/eG92cNgDz589n\n/vz5AMyYMYPv3ncPhIX7f1VXQVUlhIZBUvLBk5KTD3seEREREZFjMXXq1PrH2dnZZGdnA8fX2VxS\nUkLfvn3r2yUlJVFSUtJkLU0GcK/XS3Fxcf12cXExiYmJjbbxer34fD4qKiqIiYk57LkOfbEARXig\nps7/C8ATBi5QVNRk4SIiIiIizZGens6MGTMaPXY8nc3NGXLdmCbHgGdkZJCfn09hYSF1dXUsWrSI\nzMzMBm1OOeUUFi5cCMDixYsZNGiQxmyLiIiISLt3LJ3NQIPO5q+fW1JSQlJSUpPXbDKAezwerr32\nWu69916mTJnC6aefTrdu3ZgzZw7Lly8H4Oyzz6asrIybb76ZefPmceWVVzbvFYuIiIiIBNDxdDZn\nZmayaNEiamtrKSwsJD8/nz59+jR5TWO/ad95C9i1a1egLi0iIiIiJ4j09PSjHl+xYgWzZ8/GdV3G\njRvHpEmTmDNnDhkZGWRmZlJTU8MTTzxBbm4uMTExTJ48uf6mzblz5/Lee+/hOA5XX301w4cPb7Ie\nBXARERERCWpNBfC21uQQFBERERERaTkK4CIiIiIibUgBXERERESkDSmAi4iIiIi0IQVwEREREZE2\npAAuIiIiItKGFMBFRERERNpQQOcBFxERERE50agHXJo0derUQJcgLUjvZ3DR+xl89J4GF72f0hgF\ncBERERGRNqQALiIiIiLShhTApUnZ2dmBLkFakN7P4KL3M/joPQ0uej+lMboJU0RERESkDakHXERE\nRESkDSmAi4iIiIi0IQVwEREREZE2pAAuuK4b6BKkhVVUVAB6b4PFvn37ANAtO8Fhx44d1NTUBLoM\naUHr16+noKAg0GVIBxIS6AIkcL744gvefPNNUlNTOeuss0hLSwt0SXIcXNelqqqKxx57jJiYGH7y\nk5/gOPo/dkeWm5vLc889R0pKCjfccAPGmECXJMdh27Zt/PWvfyUuLo7rrruOpKSkQJckx2nLli38\n/e9/Z+3atfz2t78NdDnSgSiAn4Bc12XWrFls3LiR888/nw0bNvDPf/6T66+/nvDw8ECXJ9+Q4zhE\nRkbi8/nYu3cvixYtIisrC9d1FcQ7GGsts2fP5rPPPmPChAmMGTMm0CVJC3jllVcYNWoUF154Yf0+\na63+Y9UB1dXV8cwzz7BlyxYuvfRSQkNDWbt2Lb1799ZnrjSLAvgJyHEcBg8ezOWXX050dDQDBgzg\n5ZdfxuPxBLo0OU47d+4kNjaW0aNHM3/+fIYPH05kZKR+yHcwxhiqqqro1atXffguKCggJSVFP9g7\nINd1KSwsJCIioj58r169moyMDCIiIvB4PPo32sHU1dUxcOBArr76asLCwigtLWX9+vX4fD79LJVm\n8UyfPn16oIuQ1rdx40aqq6uJjY0FoGvXroSFhbF69Wp+85vfEBkZyY4dO/B6vcTFxQW4WmmOQ9/T\nr354R0VFsXLlSkaMGMHu3bspLi4mISGB6OjoQJcrTfj6v9GBAwfy0ksvUV5ezosvvsjGjRtZuXIl\nycnJJCYmBrhaacqh76cxBo/Hwz/+8Q9SU1N57rnnWLduHevWrSM/P5+TTjpJ4bsDOPQ99Xg89OjR\noz5sb9myhb1793Lqqafiuq7eT2mSAniQKy8v5+GHH2bu3LlER0fTu3dvQkJC6gNbWVkZw4YN4/LL\nL2f9+vXk5ubSvXt3IiIiAl26HEFj72loaCgAmzdvJj8/nzFjxlBcXMycOXPYunUrp59+OoB+KLRD\nR/o3+v/t3ctrE90fx/F3LhPbNGqIsVirRCOmWjHiJcUWCrXEhdKVCK2LLhTdeFuKezdSRPQ/cCFC\ncS2SpRqkKogIpvXSlBLjjSaNpWqlaeZZ+Gt4Fj/789c+ZCbzfF6rUhg4hw+n8+2Z75wxDAPTNEmn\n0wwODnL06FHGx8fJ5/NEIhG1i9nUUnn++PGDVCpFX18fAwMDBAIBRkZGWL9+PevWrbN66PIbv/ub\na5pm9V7a2NjI7du36e3t1dqUP6IC3OFmZ2cpl8t0dHRUT1JoaWmpFmKhUIiWlhYAfD4fjx8/pru7\nu1rQif38LlP4VWC/ePGCdDrNyMgImzdvZuPGjezbt0/Ft00tlef27dvp6uqitbUVj8dDQ0MD6XSa\n7u5uvF51ENrRUnn6/X7u3btHe3s7W7ZsYc2aNYyNjRGLxfRUw8aWuo+6XC4qlQp+v59cLofP56O1\ntdXiEUs9UDOhAz148IBMJsP3798JhUIkk0k6OzsxDIO3b99SLBb/63XZbJZgMKj+NRv600xnZ2eZ\nmZkhGAwyNDTEmTNn+PjxI+/fv7d4BvJ3/88aDQQC1Z+z2SyhUEh94Dbzp3lGIhEGBwdJpVLMzMzw\n6NEjcrlcte1I7ONPMzVNE7fbzfz8PPBrI2vx9yJL0Q64Q5imSalUYmhoiMnJSQqFAs/3MeH5AAAD\nj0lEQVSePWPnzp34/X68Xi8ej4dsNku5XCYSiQC/zoseHR3lxo0blEolBgYGCAaDFs9GYHmZrl27\nlng8zsGDBzEMA4/HQyKRIBwOWz2df73lrtH5+XnGxsa4fv06pVKJ/v5+rVEbWG6eW7duZW5ujqdP\nn/L69WtOnz6tI2BtYjmZLu6AG4bBkydP+PnzJ7t27dITR/mfVIA7wOKRR9PT00xMTHDp0iX27t3L\n6Ogo6XSarq4uAMLhMPl8nkKhQDQaxTRNGhoa+Pz5M7FYjP7+fu3E2MRyM11YWKCpqYlKpYJpmhiG\nUd2REeusZI36fD6mp6eJRqNaozax3DzL5TKGYRCLxYjH4/T09Oild5tYbqaLxTfA/v372b17t5XT\nkDqi55h1bGFhgTt37lQ/AvDhw4fqo2mPx8PJkyd58+YNmUymek0ymWRubo4rV65w7tw5isUi8Xic\nRCJh1TTkb1aa6YULFygWi7jdbrUp2MA/tUbb2tro6OiwahryHyvN8+LFi9XWBfXw28NKMz1//rwy\nlWXRHbpOZTIZLl++zLdv39iwYQPDw8N4vV5evXrFu3fvgF8v5B0/fpy7d+9Wr3v+/DmpVIpIJMK1\na9f0JTYbUabOojydRXk6jzIVK6kFpU5NTU2xadMmjh07RjQaZXx8HK/Xy549exgeHubw4cNUKhXC\n4TCZTIZt27bR1NTEp0+fSCaTHDlyREcN2owydRbl6SzK03mUqVhJO+B1KhqN0tnZSaVSAaCtrY2p\nqSl6enqoVCrcv38ft9tNoVDA7XbT3NwMQCKRoL293cqhy28oU2dRns6iPJ1HmYqVVIDXqVWrVmEY\nRrVX7eXLl9WXec6ePUs+n+fq1avcvHmTaDQK6Fgku1OmzqI8nUV5Oo8yFSvpjYE6t/if+9evXzlw\n4AAAjY2NnDhxglwuR3Nzc7U/Tcci1Qdl6izK01mUp/MoU7GCCvA653K5KJfLrF69msnJSW7dukUg\nEODUqVPs2LHD6uHJMihTZ1GezqI8nUeZihVUgNc5l8vFxMQE6XSaL1++cOjQIXp7e60elqyAMnUW\n5eksytN5lKlYwWWqoanuFQoFHj58SF9fX/WDAFLflKmzKE9nUZ7Oo0yl1lSAi4iIiIjUkE5BERER\nERGpIRXgIiIiIiI1pAJcRERERKSGVICLiIiIiNSQCnARERERkRpSAS4iIiIiUkMqwEVEREREaugv\nLLoNHsx9M1cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1933a1d61d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_df2[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6),\n",
    "                                             title='Threshold tc rebalanced: Monitored freq {0}, {1} tc'.format(freq,\n",
    "                                                                                                                turn_over_threshold),\n",
    "                                             secondary_y='tc_cost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sharp:  2.17360434401\n"
     ]
    }
   ],
   "source": [
    "ret_atfer_tc = ret_df2.returns - ret_df2.tc_cost\n",
    "print(\"sharp: \", ret_atfer_tc.mean() / ret_atfer_tc.std() * np.sqrt(252))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930182fcc0>"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Qmy5dunDTTTdx+umnB+3Py8tj2bJlPPjggzz44IMsW7aMvLy8WvtTwF4DLZwk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3AAAc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19300938d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_df2[['returns', 'leverage']].rolling(window=60).std().plot(figsize=(12, 6), title='rolling std', secondary_y='leverage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Target Vol + Threshold Turn Over + Strategy\n",
    "------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-02-02 00:00:00 is finished: turn_over: 0.9763, levegare: 0.9763\n",
      "2012-02-03 00:00:00 is finished: turn_over: 0.0, levegare: 0.9763\n",
      "2012-02-06 00:00:00 is finished: turn_over: 0.7380375568528426, levegare: 0.9763000000000001\n",
      "2012-02-07 00:00:00 is finished: turn_over: 0.0, levegare: 0.9763000000000001\n",
      "2012-02-08 00:00:00 is finished: turn_over: 0.0, levegare: 0.9763000000000001\n",
      "2012-02-09 00:00:00 is finished: turn_over: 0.0, levegare: 0.9763000000000001\n",
      "2012-02-10 00:00:00 is finished: turn_over: 0.0, levegare: 0.9763000000000001\n",
      "2012-02-13 00:00:00 is finished: turn_over: 0.0, levegare: 0.9763000000000001\n",
      "2012-02-14 00:00:00 is finished: turn_over: 0.7806408155822981, levegare: 0.9773999999999999\n",
      "2012-02-15 00:00:00 is finished: turn_over: 0.0, levegare: 0.9773999999999999\n",
      "2012-02-16 00:00:00 is finished: turn_over: 0.0, levegare: 0.9773999999999999\n",
      "2012-02-17 00:00:00 is finished: turn_over: 0.0, levegare: 0.9773999999999999\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-07-05 00:00:00 is finished: turn_over: 0.8137669817520413, levegare: 0.8791030343662123\n",
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "2017-07-13 00:00:00 is finished: turn_over: 0.0, levegare: 0.8891761461463109\n",
      "2017-07-14 00:00:00 is finished: turn_over: 0.0, levegare: 0.8891761461463109\n",
      "2017-07-17 00:00:00 is finished: turn_over: 0.0, levegare: 0.8891761461463109\n",
      "2017-07-18 00:00:00 is finished: turn_over: 0.8477572802640347, levegare: 0.7183334684734588\n",
      "2017-07-19 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-07-20 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-07-21 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-07-24 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-07-25 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-07-26 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-07-27 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-07-28 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-07-31 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-08-01 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-08-02 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-08-03 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-08-04 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-08-07 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-08-08 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-08-09 00:00:00 is finished: turn_over: 0.0, levegare: 0.7183334684734588\n",
      "2017-08-10 00:00:00 is finished: turn_over: 0.7013979087114548, levegare: 0.6859081049497048\n",
      "2017-08-11 00:00:00 is finished: turn_over: 0.0, levegare: 0.6859081049497048\n",
      "2017-08-14 00:00:00 is finished: turn_over: 0.0, levegare: 0.6859081049497048\n",
      "2017-08-15 00:00:00 is finished: turn_over: 0.0, levegare: 0.6859081049497048\n",
      "2017-08-16 00:00:00 is finished: turn_over: 0.0, levegare: 0.6859081049497048\n",
      "2017-08-17 00:00:00 is finished: turn_over: 0.0, levegare: 0.6859081049497048\n",
      "2017-08-18 00:00:00 is finished: turn_over: 0.0, levegare: 0.6859081049497048\n",
      "2017-08-21 00:00:00 is finished: turn_over: 0.0, levegare: 0.6859081049497048\n",
      "2017-08-22 00:00:00 is finished: turn_over: 0.0, levegare: 0.6859081049497048\n",
      "2017-08-23 00:00:00 is finished: turn_over: 0.0, levegare: 0.6859081049497048\n",
      "2017-08-24 00:00:00 is finished: turn_over: 0.0, levegare: 0.6859081049497048\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-08-25 00:00:00 is finished: turn_over: 0.739224482703535, levegare: 0.7008647982750378\n",
      "2017-08-28 00:00:00 is finished: turn_over: 0.0, levegare: 0.7008647982750378\n",
      "2017-08-29 00:00:00 is finished: turn_over: 0.8868912487505068, levegare: 1.236445919621484\n",
      "2017-08-30 00:00:00 is finished: turn_over: 0.0, levegare: 1.236445919621484\n",
      "2017-08-31 00:00:00 is finished: turn_over: 0.0, levegare: 1.236445919621484\n",
      "2017-09-01 00:00:00 is finished: turn_over: 0.0, levegare: 1.236445919621484\n",
      "2017-09-04 00:00:00 is finished: turn_over: 0.0, levegare: 1.236445919621484\n",
      "2017-09-05 00:00:00 is finished: turn_over: 0.865973236148595, levegare: 1.1323135552272348\n",
      "2017-09-06 00:00:00 is finished: turn_over: 0.0, levegare: 1.1323135552272348\n",
      "2017-09-07 00:00:00 is finished: turn_over: 0.0, levegare: 1.1323135552272348\n",
      "2017-09-08 00:00:00 is finished: turn_over: 0.0, levegare: 1.1323135552272348\n",
      "2017-09-11 00:00:00 is finished: turn_over: 0.0, levegare: 1.1323135552272348\n",
      "2017-09-12 00:00:00 is finished: turn_over: 0.0, levegare: 1.1323135552272348\n",
      "2017-09-13 00:00:00 is finished: turn_over: 0.7209346511704442, levegare: 0.8424886734229002\n",
      "2017-09-14 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-09-15 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-09-18 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-09-19 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-09-20 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-09-21 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-09-22 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-09-25 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-09-26 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-09-27 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-09-28 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-09-29 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-10-09 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-10-10 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-10-11 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-10-12 00:00:00 is finished: turn_over: 0.0, levegare: 0.8424886734229002\n",
      "2017-10-13 00:00:00 is finished: turn_over: 0.7067715592849281, levegare: 0.7567059328690577\n",
      "2017-10-16 00:00:00 is finished: turn_over: 0.0, levegare: 0.7567059328690577\n",
      "2017-10-17 00:00:00 is finished: turn_over: 0.0, levegare: 0.7567059328690577\n",
      "2017-10-18 00:00:00 is finished: turn_over: 0.0, levegare: 0.7567059328690577\n",
      "2017-10-19 00:00:00 is finished: turn_over: 0.0, levegare: 0.7567059328690577\n",
      "2017-10-20 00:00:00 is finished: turn_over: 0.0, levegare: 0.7567059328690577\n",
      "2017-10-23 00:00:00 is finished: turn_over: 0.0, levegare: 0.7567059328690577\n",
      "2017-10-24 00:00:00 is finished: turn_over: 0.0, levegare: 0.7567059328690577\n",
      "2017-10-25 00:00:00 is finished: turn_over: 0.0, levegare: 0.7567059328690577\n",
      "2017-10-26 00:00:00 is finished: turn_over: 0.0, levegare: 0.7567059328690577\n",
      "2017-10-27 00:00:00 is finished: turn_over: 0.0, levegare: 0.7567059328690577\n",
      "2017-10-30 00:00:00 is finished: turn_over: 0.7725359160047902, levegare: 0.6849862633393917\n",
      "2017-10-31 00:00:00 is finished: turn_over: 0.0, levegare: 0.6849862633393917\n",
      "2017-11-01 00:00:00 is finished: turn_over: 0.0, levegare: 0.6849862633393917\n",
      "2017-11-02 00:00:00 is finished: turn_over: 0.0, levegare: 0.6849862633393917\n"
     ]
    }
   ],
   "source": [
    "rets = []\n",
    "turn_overs = []\n",
    "target_vol = 0.002\n",
    "turn_over_threshold = 0.70\n",
    "window = 30\n",
    "executor1 = TargetVolExecutor(window=window, target_vol=target_vol)\n",
    "executor2 = ThresholdExecutor(turn_over_threshold=turn_over_threshold, is_relative=False)\n",
    "execution_pipeline = ExecutionPipeline(executors=[executor1, executor2])\n",
    "leverags = []\n",
    "execution_dates = []\n",
    "horizon = _map_freq(freq)\n",
    "\n",
    "for i, value in enumerate(factor_groups):\n",
    "    date = value[0]\n",
    "    data = value[1]\n",
    "    \n",
    "    # get the latest model\n",
    "    models = model_df[model_df.index <= date]\n",
    "    \n",
    "    if models.empty:\n",
    "        continue\n",
    "    \n",
    "    execution_dates.append(date)\n",
    "    model = models[-1]\n",
    "    \n",
    "    codes = data.code.tolist()\n",
    "    ref_date = date.strftime('%Y-%m-%d')\n",
    "    total_data = data.dropna()\n",
    "    dx_return = None\n",
    "    risk_exp = total_data[neutralize_risk].values.astype(float)\n",
    "    industry = total_data.industry.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",
    "    risk_names = constraint_risk + ['total']\n",
    "    risk_target = risk_exp_expand.T @ benchmark_w\n",
    "    lbound = np.zeros(len(total_data))\n",
    "    ubound = 0.01 + benchmark_w\n",
    "\n",
    "    constraint = Constraints(risk_exp_expand, risk_names)\n",
    "    for i, name in enumerate(risk_names):\n",
    "        if name == 'total' or name == 'SIZE':\n",
    "            constraint.set_constraints(name, lower_bound=risk_target[i], upper_bound=risk_target[i])\n",
    "        else:\n",
    "            constraint.set_constraints(name, lower_bound=risk_target[i]*industry_lower, upper_bound=risk_target[i]*industry_upper)\n",
    "        \n",
    "    factors_values = factor_processing(total_data[features].values,\n",
    "                                       pre_process=[winsorize_normal],\n",
    "                                       post_process=[winsorize_normal])\n",
    "\n",
    "    er = model.predict(factors_values)\n",
    "    \n",
    "    target_pos, _ = er_portfolio_analysis(er,\n",
    "                                   industry,\n",
    "                                   dx_return,\n",
    "                                   constraint,\n",
    "                                   False,\n",
    "                                   benchmark_w)\n",
    "    target_pos['code'] = total_data['code'].values\n",
    "    \n",
    "    turn_over, executed_pos = execution_pipeline.execute(target_pos=target_pos)\n",
    "    \n",
    "    executed_codes = executed_pos.code.tolist()\n",
    "    dx_retuns = engine.fetch_dx_return(date, executed_codes, horizon=horizon)\n",
    "    \n",
    "    result = pd.merge(executed_pos, total_data, on=['code'], how='inner')\n",
    "    result = pd.merge(result, dx_retuns, on=['code'])\n",
    "    \n",
    "    leverage = result.weight_x.abs().sum()\n",
    "    \n",
    "    ret = (result.weight_x - result.weight_y * leverage / result.weight_y.sum()).values @ result.dx.values\n",
    "    rets.append(ret)\n",
    "    execution_pipeline.update({'return': ret})\n",
    "    turn_overs.append(turn_over)\n",
    "    leverags.append(executed_pos.weight.abs().sum())\n",
    "    print('{0} is finished: turn_over: {1}, levegare: {2}'.format(date, turn_over, leverags[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ret_df3 = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverags}, index=execution_dates)\n",
    "ret_df3.loc[advanceDateByCalendar('china.sse', dates[-1], freq)] = 0.\n",
    "ret_df3 = ret_df3.shift(1)\n",
    "ret_df3.iloc[0] = 0.\n",
    "ret_df3['tc_cost'] = ret_df3.turn_over * 0.002"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19309d4b1d0>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UzZs3ydfXl0xNTXkawiogEFVTZ0bGXmKbNm3CpEmTkJ6ertWnnzFW96nVahgb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\nERERET/6f9/jYu7vPhX5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19333d73f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_df3[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6),\n",
    "                                             title='Threshold tc + Target vol rebalanced: Monitored freq {0}, {1} tc, {2} vol target'.format(freq,\n",
    "                                                                                                                                             turn_over_threshold,\n",
    "                                                                                                                                             target_vol),\n",
    "                                             secondary_y='tc_cost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x193028b71d0>"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ov06pI5yxnVVdhE1T2HwgdyeGvtfgwQQ+UeOK3VZbYkM3oaE7mL2BZUGstCau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oyhvzfm3nQY3n9UFb+4eH703Ja7bHzplEZOby6j0xemssjG7dv/N5qord37MKJxuvrtg0loinU9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x193014ec198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_df3[['returns', 'leverage']].rolling(window=60).std().plot(figsize=(12, 6), title='rolling std', secondary_y='leverage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sharp:  1.53034639569\n"
     ]
    }
   ],
   "source": [
    "ret_atfer_tc = ret_df3.returns - ret_df3.tc_cost\n",
    "print(\"sharp: \", ret_atfer_tc.mean() / ret_atfer_tc.std() * np.sqrt(252))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>leverage</th>\n",
       "      <th>returns</th>\n",
       "      <th>turn_over</th>\n",
       "      <th>tc_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>0.756706</td>\n",
       "      <td>0.004698</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>0.684986</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.772536</td>\n",
       "      <td>0.001545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>0.684986</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02</th>\n",
       "      <td>0.684986</td>\n",
       "      <td>0.002921</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-03</th>\n",
       "      <td>0.684986</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            leverage   returns  turn_over   tc_cost\n",
       "2017-10-30  0.756706  0.004698   0.000000  0.000000\n",
       "2017-10-31  0.684986  0.001833   0.772536  0.001545\n",
       "2017-11-01  0.684986  0.001583   0.000000  0.000000\n",
       "2017-11-02  0.684986  0.002921   0.000000  0.000000\n",
       "2017-11-03  0.684986  0.000171   0.000000  0.000000"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_df3.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Target Turn Over + Strategy\n",
    "------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "2013-12-30 00:00:00 is finished: 0.040000000000000126\n",
      "2013-12-31 00:00:00 full rebalance\n",
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      "2014-12-08 00:00:00 is finished: 0.04000000000000009\n",
      "2014-12-09 00:00:00 is finished: 0.0400000000000001\n",
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      "2014-12-12 00:00:00 is finished: 0.04000000000000006\n",
      "2014-12-15 00:00:00 is finished: 0.040000000000000084\n",
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      "2015-01-05 00:00:00 is finished: 0.04000000000000012\n",
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      "2015-04-01 00:00:00 is finished: 0.040000000000000084\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "2015-04-02 00:00:00 is finished: 0.04000000000000009\n",
      "2015-04-03 00:00:00 is finished: 0.04000000000000012\n",
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      "2015-04-13 00:00:00 is finished: 0.04000000000000012\n",
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      "2015-04-15 00:00:00 is finished: 0.040000000000000105\n",
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      "2015-04-20 00:00:00 is finished: 0.040000000000000105\n",
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      "2015-04-29 00:00:00 is finished: 0.04000000000000009\n",
      "2015-04-30 00:00:00 full rebalance\n",
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      "2015-06-23 00:00:00 is finished: 0.04000000000000017\n",
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      "2015-06-29 00:00:00 is finished: 0.04000000000000015\n",
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      "2015-06-30 00:00:00 is finished: 0.7881418877403082\n",
      "2015-07-01 00:00:00 is finished: 0.04000000000000011\n",
      "2015-07-02 00:00:00 is finished: 0.04000000000000011\n",
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      "2015-08-07 00:00:00 is finished: 0.040000050821371466\n",
      "2015-08-10 00:00:00 is finished: 0.040000050818160285\n",
      "2015-08-11 00:00:00 is finished: 0.04000000000603847\n",
      "2015-08-12 00:00:00 is finished: 0.04000000000000009\n",
      "2015-08-13 00:00:00 is finished: 0.04000000000216164\n",
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      "2015-08-17 00:00:00 is finished: 0.040000000000000084\n",
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      "2015-08-19 00:00:00 is finished: 0.04000000000000012\n",
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      "2015-08-31 00:00:00 is finished: 0.7308491488248379\n",
      "2015-09-01 00:00:00 is finished: 0.040000000000000126\n",
      "2015-09-02 00:00:00 is finished: 0.040000000000000105\n",
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      "2015-10-15 00:00:00 is finished: 0.050920000000000104\n",
      "2015-10-16 00:00:00 is finished: 0.04000000000000009\n",
      "2015-10-19 00:00:00 is finished: 0.04000000000000014\n",
      "2015-10-20 00:00:00 is finished: 0.04000000000000019\n",
      "2015-10-21 00:00:00 is finished: 0.04000000000000018\n",
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      "2016-06-30 00:00:00 is finished: 1.0033068720388965\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-07-01 00:00:00 is finished: 0.0400000000000001\n",
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      "2016-12-01 00:00:00 is finished: 0.04000000000000012\n",
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      "2016-12-30 00:00:00 full rebalance\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "2017-06-29 00:00:00 is finished: 0.04000000000000009\n",
      "2017-06-30 00:00:00 full rebalance\n",
      "2017-06-30 00:00:00 is finished: 0.9423686999739542\n",
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      "2017-07-31 00:00:00 full rebalance\n",
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      "2017-09-25 00:00:00 full rebalance\n",
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      "2017-09-29 00:00:00 is finished: 0.04000000000000002\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-10-09 00:00:00 is finished: 0.04000000000000013\n",
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      "2017-10-27 00:00:00 is finished: 0.04000000000000013\n",
      "2017-10-30 00:00:00 is finished: 0.040000000000000126\n",
      "2017-10-31 00:00:00 is finished: 0.04000000000000002\n",
      "2017-11-01 00:00:00 is finished: 0.040000000000000105\n",
      "2017-11-02 00:00:00 is finished: 0.04000000000000009\n"
     ]
    }
   ],
   "source": [
    "rets = []\n",
    "turn_overs = []\n",
    "turn_over_target_base = 0.04\n",
    "executor = NaiveExecutor()\n",
    "execution_pipeline = ExecutionPipeline(executors=[executor])\n",
    "leverags = []\n",
    "previous_pos = pd.DataFrame()\n",
    "execution_dates = []\n",
    "horizon = _map_freq(freq)\n",
    "\n",
    "for i, value in enumerate(factor_groups):\n",
    "    date = value[0]\n",
    "    data = value[1]\n",
    "    \n",
    "    # get the latest model\n",
    "    models = model_df[model_df.index <= date]\n",
    "    \n",
    "    if models.empty:\n",
    "        continue\n",
    "    \n",
    "    execution_dates.append(date)\n",
    "    model = models[-1]\n",
    "    \n",
    "    codes = data.code.tolist()\n",
    "    ref_date = date.strftime('%Y-%m-%d')\n",
    "    total_data = data.dropna()\n",
    "    dx_return = None\n",
    "    risk_exp = total_data[neutralize_risk].values.astype(float)\n",
    "    industry = total_data.industry.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",
    "    risk_names = constraint_risk + ['total']\n",
    "    risk_target = risk_exp_expand.T @ benchmark_w\n",
    "    lbound = np.zeros(len(total_data))\n",
    "    ubound = 0.01 + benchmark_w\n",
    "\n",
    "    constraint = Constraints(risk_exp_expand, risk_names)\n",
    "    for i, name in enumerate(risk_names):\n",
    "        if name == 'total' or name == 'SIZE':\n",
    "            constraint.set_constraints(name, lower_bound=risk_target[i], upper_bound=risk_target[i])\n",
    "        else:\n",
    "            constraint.set_constraints(name, lower_bound=risk_target[i]*industry_lower, upper_bound=risk_target[i]*industry_upper)\n",
    "        \n",
    "    factors_values = factor_processing(total_data[features].values,\n",
    "                                       pre_process=[winsorize_normal],\n",
    "                                       post_process=[winsorize_normal])\n",
    "\n",
    "    er = model.predict(factors_values)\n",
    "    \n",
    "    codes = total_data['code'].values\n",
    "    \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",
    "        \n",
    "        current_position = remained_pos.weight.values\n",
    "    \n",
    "    try:\n",
    "        target_pos, _ = er_portfolio_analysis(er,\n",
    "                                              industry,\n",
    "                                              dx_return,\n",
    "                                              constraint,\n",
    "                                              False,\n",
    "                                              benchmark_w,\n",
    "                                              current_position=current_position,\n",
    "                                              turn_over_target=turn_over_target)\n",
    "    except ValueError:\n",
    "        print('{0} full rebalance'.format(date))\n",
    "        target_pos, _ = er_portfolio_analysis(er,\n",
    "                                              industry,\n",
    "                                              dx_return,\n",
    "                                              constraint,\n",
    "                                              False,\n",
    "                                              benchmark_w)\n",
    "    \n",
    "    target_pos['code'] = codes\n",
    "    \n",
    "    turn_over, executed_pos = execution_pipeline.execute(target_pos=target_pos)\n",
    "    \n",
    "    executed_codes = executed_pos.code.tolist()\n",
    "    dx_retuns = engine.fetch_dx_return(date, executed_codes, horizon=horizon)\n",
    "    \n",
    "    result = pd.merge(executed_pos, total_data, on=['code'], how='inner')\n",
    "    result = pd.merge(result, dx_retuns, on=['code'])\n",
    "    leverage = result.weight_x.abs().sum()\n",
    "    \n",
    "    ret = (result.weight_x - result.weight_y * leverage / result.weight_y.sum()).values @ result.dx.values\n",
    "    rets.append(ret)\n",
    "    leverags.append(executed_pos.weight.abs().sum())\n",
    "    turn_overs.append(turn_over)\n",
    "    previous_pos = executed_pos\n",
    "    \n",
    "    print('{0} is finished: {1}'.format(date, turn_over))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret_df4 = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverags}, index=execution_dates)\n",
    "ret_df4.loc[advanceDateByCalendar('china.sse', dates[-1], freq)] = 0.\n",
    "ret_df4 = ret_df4.shift(1)\n",
    "ret_df4.iloc[0] = 0.\n",
    "ret_df4['tc_cost'] = ret_df4.turn_over * 0.002"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19315c40588>"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Uqh2EqU/HdFtbW1y9ehVAyYTkxcXFMDc3r3FlRFHk4LuBMzAwgCiK9V0Nxhhj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\nTAAgcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19301c3d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_df4[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6),\n",
    "                                             title='Target turn over rebalanced: Rebalance freq {0}, {1} turnover_target'.format(freq,\n",
    "                                                                                                                                 turn_over_target_base),\n",
    "                                             secondary_y='tc_cost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sharp:  2.7172844153\n"
     ]
    }
   ],
   "source": [
    "ret_atfer_tc = ret_df4.returns - ret_df4.tc_cost\n",
    "print(\"sharp: \", ret_atfer_tc.mean() / ret_atfer_tc.std() * np.sqrt(252))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19315c40a20>"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1a9aQlZVFdHQ0qamppKWlkZ+fT//+/U/4D6ilqemWiEjbYGwb9hVB4U6skWOw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\nIYQQQqQxSdiFEEIIIYRIY/8Phi3q0+mpWhIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1930a14b898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_df4[['returns', 'leverage']].rolling(window=60).std().plot(figsize=(12, 6), title='rolling std', secondary_y='leverage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
