{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import copy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from alphamind.api import *\n",
    "from PyFin.api import *\n",
    "from PyFin.Math.Accumulators.StatefulAccumulators import MovingAverage\n",
    "from PyFin.Math.Accumulators.StatefulAccumulators import MovingSharp\n",
    "from PyFin.Math.Accumulators.StatefulAccumulators import MovingMaxDrawdown\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "start_date = '2010-01-01'\n",
    "end_date = '2018-02-1'\n",
    "benchmark_code = 300\n",
    "universe_name = ['zz800']\n",
    "universe = Universe(universe_name, universe_name)\n",
    "frequency = '10b'\n",
    "batch = 16\n",
    "method = 'risk_neutral'\n",
    "neutralize_risk = ['SIZE'] + industry_styles\n",
    "\n",
    "horizon = map_freq(frequency)\n",
    "hedging_ratio = 0.\n",
    "\n",
    "executor = NaiveExecutor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "engine = SqlEngine()\n",
    "#factor_coverage = engine.fetch_factor_coverage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#res = factor_coverage[(factor_coverage.universe == 'ashare_ex') & (factor_coverage.source != 'risk_exposure')].groupby('factor').mean().sort_values('coverage', ascending=False)\n",
    "#model_features = sorted(res[res.coverage >= 0.99].index.unique().tolist())\n",
    "\n",
    "base1 = LAST('roe_q')\n",
    "base2 = CSRes('ep_q', base1)\n",
    "\n",
    "const_features_keys = {'f01': base1,\n",
    "                       'f02': base2,\n",
    "                       'f03': CSRes(CSRes('market_confidence_15d', base1), base2),\n",
    "                       'f04': CSRes(CSRes('RecievableTO', base1), base2),\n",
    "                       'f05': CSRes(CSRes('val_q', base1), base2),\n",
    "                       'f06': CSRes(CSRes('BP', base1), base2),\n",
    "                       'f07': CSRes(CSRes('con_pe_rolling_order', base1), base2),\n",
    "                       'f08': CSRes(CSRes('con_pb_rolling_order', base1), base2),\n",
    "                       'f09': CSRes(CSRes('DebtEquityRatio', base1), base2),\n",
    "                      }\n",
    "\n",
    "const_model_features_keys = sorted(const_features_keys.keys())\n",
    "\n",
    "const_weights = [1.0,\n",
    "                 1.0,\n",
    "                 0.25,\n",
    "                 0.25,\n",
    "                 0.25,\n",
    "                 0.25,\n",
    "                 -0.25,\n",
    "                 -0.25,\n",
    "                 -0.25\n",
    "                ]\n",
    "\n",
    "model_features = copy.deepcopy(const_features_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-02-05 12:13:42,906 - ALPHA_MIND - INFO - Starting data package fetching ...\n",
      "2018-02-05 12:13:50,160 - ALPHA_MIND - INFO - factor data loading finished\n",
      "2018-02-05 12:14:05,184 - ALPHA_MIND - INFO - return data loading finished\n",
      "2018-02-05 12:14:07,334 - ALPHA_MIND - INFO - industry data loading finished\n",
      "2018-02-05 12:14:07,593 - ALPHA_MIND - INFO - benchmark data loading finished\n",
      "2018-02-05 12:14:14,012 - ALPHA_MIND - INFO - risk data loading finished\n",
      "2018-02-05 12:14:15,562 - ALPHA_MIND - INFO - data merging finished\n",
      "2018-02-05 12:14:16,745 - ALPHA_MIND - INFO - Loading data is finished\n",
      "2018-02-05 12:14:22,134 - ALPHA_MIND - INFO - Data processing is finished\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Data phase\n",
    "\"\"\"\n",
    "\n",
    "model_factor_data = fetch_data_package(engine,\n",
    "                                       alpha_factors=model_features,\n",
    "                                       start_date=start_date,\n",
    "                                       end_date=end_date,\n",
    "                                       frequency=frequency,\n",
    "                                       universe=universe,\n",
    "                                       benchmark=benchmark_code,\n",
    "                                       batch=batch,\n",
    "                                       neutralized_risk=neutralize_risk,\n",
    "                                       pre_process=[winsorize_normal, standardize],\n",
    "                                       post_process=[winsorize_normal, standardize],\n",
    "                                       warm_start=batch)\n",
    "\n",
    "model_features_keys = model_factor_data['x_names']\n",
    "\n",
    "train_x = model_factor_data['train']['x']\n",
    "train_y = model_factor_data['train']['y']\n",
    "train_risk = model_factor_data['train']['risk']\n",
    "ref_dates = sorted(train_x.keys())\n",
    "\n",
    "predict_x = model_factor_data['predict']['x']\n",
    "predict_y = model_factor_data['predict']['y']\n",
    "predict_risk = model_factor_data['predict']['risk']\n",
    "predict_code = model_factor_data['predict']['code']\n",
    "settlement = model_factor_data['settlement']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f01', 'f02', 'f03', 'f04', 'f05', 'f06', 'f07', 'f08', 'f09']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_features_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for key, val in train_y.items():\n",
    "#     train_y[key] = np.where(val > 0., 1, 0)\n",
    "    \n",
    "# for key, val in predict_y.items():\n",
    "#     predict_y[key] = np.where(val > 0., 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cross_product(x, y):\n",
    "    n, m = x.shape\n",
    "    res = []\n",
    "    \n",
    "    for j in range(m):\n",
    "        res.append(x[:, [j]] * y)\n",
    "        \n",
    "    return np.concatenate(res, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 41.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\"\"\"\n",
    "Training phase\n",
    "\"\"\"\n",
    "\n",
    "from dask.distributed import Client\n",
    "client = Client('10.63.6.176:8786')\n",
    "\n",
    "params = [(train_x[ref_date], train_y[ref_date], ref_date) for ref_date in ref_dates]\n",
    "\n",
    "def train_model(param):\n",
    "    from alphamind.api import XGBTrainer\n",
    "    from alphamind.api import alpha_logger\n",
    "    x = param[0]\n",
    "    y = param[1]\n",
    "    ref_date = param[2]\n",
    "    model = XGBTrainer(features=model_features_keys,\n",
    "                       objective='reg:linear',\n",
    "                       booster='gbtree',\n",
    "                       tree_method='hist',\n",
    "                       n_estimators=2000,\n",
    "                       learning_rate=0.01,\n",
    "                       early_stopping_rounds=30,\n",
    "                       subsample=0.25,\n",
    "                       colsample_bytree=1.,\n",
    "                       n_jobs=1,\n",
    "                       eval_sample=0.3)\n",
    "    model.fit(x, y)\n",
    "    alpha_logger.info('trade_date: {0} with {1} obs training finished'.format(ref_date, len(x)))\n",
    "    return ref_date, model\n",
    "\n",
    "parameters = [(train_x[ref_date], train_y[ref_date], ref_date) for ref_date in ref_dates]\n",
    "tasks = client.map(train_model, params)\n",
    "res = client.gather(tasks) \n",
    "\n",
    "index, data = list(zip(*res))\n",
    "xgb_models_series = pd.Series(data, index=index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "const_model = ConstLinearModel(features=const_features_keys,\n",
    "                               weights=const_weights)\n",
    "\n",
    "const_model_factor_data = engine.fetch_data_range(universe,\n",
    "                                                  const_features_keys,\n",
    "                                                  dates=ref_dates,\n",
    "                                                  benchmark=benchmark_code)['factor']\n",
    "\n",
    "industry_data = engine.fetch_industry_matrix_range(universe,\n",
    "                                                   dates=ref_dates,\n",
    "                                                   category='sw_adj',\n",
    "                                                   level=1)\n",
    "industries_list = industry_list(category='sw_adj', level=1)\n",
    "\n",
    "horizon = map_freq(frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-02-05 12:15:21,938 - ALPHA_MIND - INFO - 2010-08-30 00:00:00: 799\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start 0.005 weight gap simulation ...\n",
      "using backbone model and alpha model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\IntelPython3_2018\\lib\\site-packages\\ipykernel_launcher.py:92: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "2018-02-05 12:15:22,158 - ALPHA_MIND - INFO - 2010-08-30 00:00:00 is finished\n",
      "2018-02-05 12:15:22,163 - ALPHA_MIND - INFO - 2010-09-13 00:00:00: 799\n"
     ]
    },
    {
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     "output_type": "stream",
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      "using backbone model and alpha model\n"
     ]
    },
    {
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      "2018-02-05 12:15:22,439 - ALPHA_MIND - INFO - 2010-09-13 00:00:00 is finished\n",
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    },
    {
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      "using backbone model and alpha model\n"
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    },
    {
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      "2018-02-05 12:15:22,719 - ALPHA_MIND - INFO - 2010-09-30 00:00:00 is finished\n",
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      "using backbone model and alpha model\n"
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      "using backbone model and alpha model\n"
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      "using backbone model and alpha model\n"
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      "using backbone model and alpha model\n"
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8+eWXvPvuuzg4OODu7s7rr78OwMSJE4mJiaFz585VHsIcMmQI27dvp1WrVpe8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\nuIiIiIiIH/0/go0YN1YMHXAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b3e4614828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QEydOPG/dS5cupaKigmuvvZZJkyYxbtw4+vbte8H3sGDBAp5//nkWLVpE9+7diY6Otkp4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\nBXOwps7EigpsvWpaWzDrVmKeeyzQ5/vyeT1Sh4iIiEhP6m99wLURTz/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X86UvfYm4uLhg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\nWEoxrmbRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b380ad29b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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twsLCKodW3OjOnz/P7Nmz+eSTT6ptW1BQgLu7O0VFRYwbN45//OMfdO3atcZ9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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b3e5c90518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7oyTcl0pTtY1ZABISEli6dCm6rjN06FBGjhxpc/7f//43hw8fBqC8vLzO62mn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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b380c7dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicting and re-balance phase\n",
    "\n",
    "weight_gaps = [0.005, 0.010, 0.015, 0.020]\n",
    "\n",
    "model1_weight = 1.0\n",
    "model2_weight = 0.5\n",
    "\n",
    "factor_groups = const_model_factor_data.groupby('trade_date')\n",
    "industry_group = industry_data.groupby('trade_date')\n",
    "constraint_risk = ['SIZE', 'LEVERAGE'] + industries_list\n",
    "turn_over_target_base = 0.30\n",
    "size_risk_lower = 0\n",
    "size_risk_upper = 0\n",
    "benchmark_total_lower = 0.8\n",
    "benchmark_total_upper = 1.\n",
    "industry_lower = 1.\n",
    "industry_upper = 1.\n",
    "\n",
    "for s_i, weight_gap in enumerate(weight_gaps):\n",
    "    print(\"start {0} weight gap simulation ...\".format(weight_gap))\n",
    "\n",
    "    rets = []\n",
    "    turn_overs = []\n",
    "    leverags = []\n",
    "    previous_pos = pd.DataFrame()\n",
    "\n",
    "    index_dates = []\n",
    "\n",
    "    for i, value in enumerate(factor_groups):\n",
    "        date = value[0]\n",
    "        data = value[1]\n",
    "        ref_date = date.strftime('%Y-%m-%d')\n",
    "\n",
    "        total_data = data.fillna(data[const_model_features_keys].median())\n",
    "        alpha_logger.info('{0}: {1}'.format(date, len(total_data)))\n",
    "        risk_exp = total_data[neutralize_risk].values.astype(float)\n",
    "        benchmark_w = total_data.weight.values\n",
    "        \n",
    "        this_industry_data = industry_group.get_group(date)\n",
    "        \n",
    "        total_data = pd.merge(total_data, this_industry_data, on=['trade_date', 'code'])\n",
    "        constraint_exp = total_data[constraint_risk].values\n",
    "        industry = total_data.industry_name.values\n",
    "        risk_exp_expand = np.concatenate((constraint_exp, np.ones((len(risk_exp), 1))), axis=1).astype(float)\n",
    "\n",
    "        risk_names = constraint_risk + ['total']\n",
    "        risk_target = risk_exp_expand.T @ benchmark_w\n",
    "\n",
    "        lbound = np.maximum(0., benchmark_w - weight_gap)  # np.zeros(len(total_data))\n",
    "        ubound = weight_gap + benchmark_w\n",
    "\n",
    "        is_in_benchmark = (benchmark_w > 0.).astype(float)\n",
    "\n",
    "        risk_exp_expand = np.concatenate((risk_exp_expand, is_in_benchmark.reshape((-1, 1))), axis=1).astype(float)\n",
    "        risk_names.append('benchmark_total')\n",
    "\n",
    "        constraint = Constraints(risk_exp_expand, risk_names)\n",
    "\n",
    "        for j, name in enumerate(risk_names):\n",
    "            if name == 'total':\n",
    "                constraint.set_constraints(name,\n",
    "                                           lower_bound=risk_target[j],\n",
    "                                           upper_bound=risk_target[j])\n",
    "            elif name == 'SIZE':\n",
    "                base_target = abs(risk_target[j])\n",
    "                constraint.set_constraints(name,\n",
    "                                           lower_bound=risk_target[j] + base_target * size_risk_lower,\n",
    "                                           upper_bound=risk_target[j] + base_target * size_risk_upper)\n",
    "            elif name == 'benchmark_total':\n",
    "                base_target = benchmark_w.sum()\n",
    "                constraint.set_constraints(name,\n",
    "                                           lower_bound=benchmark_total_lower * base_target,\n",
    "                                           upper_bound=benchmark_total_upper * base_target)\n",
    "            else:\n",
    "                constraint.set_constraints(name,\n",
    "                                           lower_bound=risk_target[j] * industry_lower,\n",
    "                                           upper_bound=risk_target[j] * industry_upper)\n",
    "                \n",
    "        values = total_data[const_model_features_keys].dropna()\n",
    "        if values.empty:\n",
    "            continue\n",
    "        \n",
    "        factor_values = factor_processing(total_data[const_model_features_keys].values,\n",
    "                                          pre_process=[winsorize_normal, standardize],\n",
    "                                          risk_factors=risk_exp,\n",
    "                                          post_process=[winsorize_normal, standardize])\n",
    "\n",
    "        # const linear model\n",
    "        er1 = const_model.predict(factor_values)\n",
    "        \n",
    "        try:\n",
    "            model = xgb_models_series.ix[date]\n",
    "        except KeyError:\n",
    "            alpha_logger.info(\"model is not available. Omit this date\")\n",
    "            continue\n",
    "            \n",
    "        index_dates.append(date)\n",
    "\n",
    "        x = predict_x[date]\n",
    "        this_predict_code = predict_code[date]\n",
    "        er2 = model.predict(x)\n",
    "\n",
    "        # combine model\n",
    "        er1_table = pd.DataFrame({'er1': er1, 'code': total_data.code.values})\n",
    "        er2_table = pd.DataFrame({'er2': er2, 'code': this_predict_code})\n",
    "        er_table = pd.merge(er1_table, er2_table, on=['code'], how='left').fillna(0)\n",
    "\n",
    "        er1 = er_table.er1.values\n",
    "        er2 = er_table.er2.values\n",
    "        \n",
    "        if er2.std() > 0:\n",
    "            print('using backbone model and alpha model')\n",
    "            er = model1_weight * er1 / er1.std() +  model2_weight * er2 / er2.std()\n",
    "        else:\n",
    "            print('using only backbone modedl')\n",
    "            er = model1_weight * er1\n",
    "            \n",
    "        codes = total_data['code'].values\n",
    "\n",
    "        if previous_pos.empty:\n",
    "            current_position = None\n",
    "            turn_over_target = None\n",
    "        else:\n",
    "            previous_pos.set_index('code', inplace=True)\n",
    "            remained_pos = previous_pos.loc[codes]\n",
    "\n",
    "            remained_pos.fillna(0., inplace=True)\n",
    "            turn_over_target = turn_over_target_base\n",
    "            current_position = remained_pos.weight.values\n",
    "\n",
    "        try:\n",
    "            target_pos, _ = er_portfolio_analysis(er,\n",
    "                                                  industry,\n",
    "                                                  None,\n",
    "                                                  constraint,\n",
    "                                                  False,\n",
    "                                                  benchmark_w,\n",
    "                                                  method=method,\n",
    "                                                  turn_over_target=turn_over_target,\n",
    "                                                  current_position=current_position,\n",
    "                                                  lbound=lbound,\n",
    "                                                  ubound=ubound)\n",
    "        except ValueError:\n",
    "            alpha_logger.info('{0} full re-balance'.format(date))\n",
    "            target_pos, _ = er_portfolio_analysis(er,\n",
    "                                                  industry,\n",
    "                                                  None,\n",
    "                                                  constraint,\n",
    "                                                  False,\n",
    "                                                  benchmark_w,\n",
    "                                                  method=method,\n",
    "                                                  lbound=lbound,\n",
    "                                                  ubound=ubound)\n",
    "\n",
    "        target_pos['code'] = total_data['code'].values\n",
    "\n",
    "        turn_over, executed_pos = executor.execute(target_pos=target_pos)\n",
    "\n",
    "        executed_codes = executed_pos.code.tolist()\n",
    "        dx_returns = engine.fetch_dx_return(date, executed_codes, horizon=horizon, offset=1)\n",
    "\n",
    "        result = pd.merge(executed_pos, total_data[['code', 'weight']], on=['code'], how='inner')\n",
    "        result = pd.merge(result, dx_returns, on=['code'])\n",
    "\n",
    "        leverage = result.weight_x.abs().sum()\n",
    "\n",
    "        ret = result.weight_x.values @ (np.exp(result.dx.values) - 1.)\n",
    "        rets.append(np.log(1. + ret))\n",
    "        executor.set_current(executed_pos)\n",
    "        turn_overs.append(turn_over)\n",
    "        leverags.append(leverage)\n",
    "\n",
    "        previous_pos = executed_pos\n",
    "        alpha_logger.info('{0} is finished'.format(date))\n",
    "\n",
    "    ret_df = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverags}, index=index_dates)\n",
    "\n",
    "    # index return\n",
    "    index_return = engine.fetch_dx_return_index_range(benchmark_code, dates=ref_dates, horizon=horizon,\n",
    "                                                      offset=1).set_index('trade_date')\n",
    "    ret_df['index'] = index_return['dx']\n",
    "\n",
    "    ret_df.loc[advanceDateByCalendar('china.sse', ref_dates[-1], frequency)] = 0.\n",
    "    ret_df = ret_df.shift(1)\n",
    "    ret_df.iloc[0] = 0.\n",
    "    ret_df['tc_cost'] = ret_df.turn_over * 0.002\n",
    "    ret_df['returns'] = ret_df['leverage'] * (ret_df['returns'] - ret_df['index'])\n",
    "\n",
    "    ret_df[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6),\n",
    "                                                 title='Fixed frequency rebalanced: {0}'.format(frequency),\n",
    "                                                 secondary_y='tc_cost')\n",
    "\n",
    "    ret_df['ret_after_tc'] = ret_df['returns'] - ret_df['tc_cost']\n",
    "    \n",
    "\n",
    "    sharp_calc = MovingSharp(49)\n",
    "    drawdown_calc = MovingMaxDrawdown(49)\n",
    "    max_drawdown_calc = MovingMaxDrawdown(len(ret_df))\n",
    "\n",
    "    res_df = pd.DataFrame(columns=['daily_return', 'cum_ret', 'sharp', 'drawdown', 'max_drawn', 'leverage'])\n",
    "\n",
    "    total_returns = 0.\n",
    "\n",
    "    for i, ret in enumerate(ret_df['ret_after_tc']):\n",
    "        date = ret_df.index[i]\n",
    "        total_returns += ret\n",
    "        sharp_calc.push({'ret': ret, 'riskFree': 0.})\n",
    "        drawdown_calc.push({'ret': ret})\n",
    "        max_drawdown_calc.push({'ret': ret})\n",
    "\n",
    "        res_df.loc[date, 'daily_return'] = ret\n",
    "        res_df.loc[date, 'cum_ret'] = total_returns\n",
    "        res_df.loc[date, 'drawdown'] = drawdown_calc.result()[0]\n",
    "        res_df.loc[date, 'max_drawn'] = max_drawdown_calc.result()[0]\n",
    "        res_df.loc[date, 'leverage'] = ret_df.loc[date, 'leverage']\n",
    "\n",
    "        if i < 10:\n",
    "            res_df.loc[date, 'sharp'] = 0.\n",
    "        else:\n",
    "            res_df.loc[date, 'sharp'] = sharp_calc.result() * np.sqrt(49)\n",
    "\n",
    "    res_df.to_csv('hs300_rf2_{0}.csv'.format(int(s_i)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regression\n",
    "----------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-02-05 14:12:28,211 - ALPHA_MIND - INFO - trade_date: 2010-08-30 00:00:00 with 12774 obs training finished\n",
      "2018-02-05 14:12:28,219 - ALPHA_MIND - INFO - trade_date: 2010-09-13 00:00:00 with 12775 obs training finished\n",
      "2018-02-05 14:12:28,226 - ALPHA_MIND - INFO - trade_date: 2010-09-30 00:00:00 with 12776 obs training finished\n",
      "2018-02-05 14:12:28,233 - ALPHA_MIND - INFO - trade_date: 2010-10-21 00:00:00 with 12776 obs training finished\n",
      "2018-02-05 14:12:28,238 - ALPHA_MIND - INFO - trade_date: 2010-11-04 00:00:00 with 12777 obs training finished\n",
      "2018-02-05 14:12:28,245 - ALPHA_MIND - INFO - trade_date: 2010-11-18 00:00:00 with 12778 obs training finished\n",
      "2018-02-05 14:12:28,251 - ALPHA_MIND - INFO - trade_date: 2010-12-02 00:00:00 with 12778 obs training finished\n",
      "2018-02-05 14:12:28,256 - ALPHA_MIND - INFO - trade_date: 2010-12-16 00:00:00 with 12778 obs training finished\n",
      "2018-02-05 14:12:28,262 - ALPHA_MIND - INFO - trade_date: 2010-12-30 00:00:00 with 12778 obs training finished\n",
      "2018-02-05 14:12:28,268 - ALPHA_MIND - INFO - trade_date: 2011-01-14 00:00:00 with 12778 obs training finished\n",
      "2018-02-05 14:12:28,274 - ALPHA_MIND - INFO - trade_date: 2011-01-28 00:00:00 with 12778 obs training finished\n",
      "2018-02-05 14:12:28,281 - ALPHA_MIND - INFO - trade_date: 2011-02-18 00:00:00 with 12744 obs training finished\n",
      "2018-02-05 14:12:28,288 - ALPHA_MIND - INFO - trade_date: 2011-03-04 00:00:00 with 12742 obs training finished\n",
      "2018-02-05 14:12:28,294 - ALPHA_MIND - INFO - trade_date: 2011-03-18 00:00:00 with 12741 obs training finished\n",
      "2018-02-05 14:12:28,300 - ALPHA_MIND - INFO - trade_date: 2011-04-01 00:00:00 with 12740 obs training finished\n",
      "2018-02-05 14:12:28,307 - ALPHA_MIND - INFO - trade_date: 2011-04-19 00:00:00 with 12740 obs training finished\n",
      "2018-02-05 14:12:28,313 - ALPHA_MIND - INFO - trade_date: 2011-05-04 00:00:00 with 12740 obs training finished\n",
      "2018-02-05 14:12:28,319 - ALPHA_MIND - INFO - trade_date: 2011-05-18 00:00:00 with 12740 obs training finished\n",
      "2018-02-05 14:12:28,325 - ALPHA_MIND - INFO - trade_date: 2011-06-01 00:00:00 with 12740 obs training finished\n",
      "2018-02-05 14:12:28,332 - ALPHA_MIND - INFO - trade_date: 2011-06-16 00:00:00 with 12740 obs training finished\n",
      "2018-02-05 14:12:28,339 - ALPHA_MIND - INFO - trade_date: 2011-06-30 00:00:00 with 12740 obs training finished\n",
      "2018-02-05 14:12:28,345 - ALPHA_MIND - INFO - trade_date: 2011-07-14 00:00:00 with 12739 obs training finished\n",
      "2018-02-05 14:12:28,351 - ALPHA_MIND - INFO - trade_date: 2011-07-28 00:00:00 with 12738 obs training finished\n",
      "2018-02-05 14:12:28,358 - ALPHA_MIND - INFO - trade_date: 2011-08-11 00:00:00 with 12712 obs training finished\n",
      "2018-02-05 14:12:28,363 - ALPHA_MIND - INFO - trade_date: 2011-08-25 00:00:00 with 12710 obs training finished\n",
      "2018-02-05 14:12:28,369 - ALPHA_MIND - INFO - trade_date: 2011-09-08 00:00:00 with 12707 obs training finished\n",
      "2018-02-05 14:12:28,374 - ALPHA_MIND - INFO - trade_date: 2011-09-23 00:00:00 with 12705 obs training finished\n",
      "2018-02-05 14:12:28,379 - ALPHA_MIND - INFO - trade_date: 2011-10-14 00:00:00 with 12737 obs training finished\n",
      "2018-02-05 14:12:28,386 - ALPHA_MIND - INFO - trade_date: 2011-10-28 00:00:00 with 12738 obs training finished\n",
      "2018-02-05 14:12:28,394 - ALPHA_MIND - INFO - trade_date: 2011-11-11 00:00:00 with 12737 obs training finished\n",
      "2018-02-05 14:12:28,401 - ALPHA_MIND - INFO - trade_date: 2011-11-25 00:00:00 with 12737 obs training finished\n",
      "2018-02-05 14:12:28,408 - ALPHA_MIND - INFO - trade_date: 2011-12-09 00:00:00 with 12737 obs training finished\n",
      "2018-02-05 14:12:28,414 - ALPHA_MIND - INFO - trade_date: 2011-12-23 00:00:00 with 12736 obs training finished\n",
      "2018-02-05 14:12:28,421 - ALPHA_MIND - INFO - trade_date: 2012-01-10 00:00:00 with 12735 obs training finished\n",
      "2018-02-05 14:12:28,427 - ALPHA_MIND - INFO - trade_date: 2012-01-31 00:00:00 with 12734 obs training finished\n",
      "2018-02-05 14:12:28,434 - ALPHA_MIND - INFO - trade_date: 2012-02-14 00:00:00 with 12733 obs training finished\n",
      "2018-02-05 14:12:28,440 - ALPHA_MIND - INFO - trade_date: 2012-02-28 00:00:00 with 12732 obs training finished\n",
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      "2018-02-05 14:12:28,460 - ALPHA_MIND - INFO - trade_date: 2012-04-13 00:00:00 with 12751 obs training finished\n",
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      "2018-02-05 14:12:28,473 - ALPHA_MIND - INFO - trade_date: 2012-05-15 00:00:00 with 12751 obs training finished\n",
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      "2018-02-05 14:12:28,486 - ALPHA_MIND - INFO - trade_date: 2012-06-12 00:00:00 with 12753 obs training finished\n",
      "2018-02-05 14:12:28,491 - ALPHA_MIND - INFO - trade_date: 2012-06-27 00:00:00 with 12753 obs training finished\n",
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      "2018-02-05 14:12:28,503 - ALPHA_MIND - INFO - trade_date: 2012-07-25 00:00:00 with 12754 obs training finished\n",
      "2018-02-05 14:12:28,509 - ALPHA_MIND - INFO - trade_date: 2012-08-08 00:00:00 with 12753 obs training finished\n",
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      "2018-02-05 14:12:28,522 - ALPHA_MIND - INFO - trade_date: 2012-09-05 00:00:00 with 12757 obs training finished\n",
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      "2018-02-05 14:12:28,550 - ALPHA_MIND - INFO - trade_date: 2012-11-07 00:00:00 with 12766 obs training finished\n",
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      "2018-02-05 14:12:28,565 - ALPHA_MIND - INFO - trade_date: 2012-12-05 00:00:00 with 12772 obs training finished\n",
      "2018-02-05 14:12:28,572 - ALPHA_MIND - INFO - trade_date: 2012-12-19 00:00:00 with 12774 obs training finished\n",
      "2018-02-05 14:12:28,578 - ALPHA_MIND - INFO - trade_date: 2013-01-07 00:00:00 with 12777 obs training finished\n",
      "2018-02-05 14:12:28,585 - ALPHA_MIND - INFO - trade_date: 2013-01-21 00:00:00 with 12778 obs training finished\n",
      "2018-02-05 14:12:28,592 - ALPHA_MIND - INFO - trade_date: 2013-02-04 00:00:00 with 12779 obs training finished\n",
      "2018-02-05 14:12:28,600 - ALPHA_MIND - INFO - trade_date: 2013-02-25 00:00:00 with 12781 obs training finished\n",
      "2018-02-05 14:12:28,607 - ALPHA_MIND - INFO - trade_date: 2013-03-11 00:00:00 with 12783 obs training finished\n",
      "2018-02-05 14:12:28,615 - ALPHA_MIND - INFO - trade_date: 2013-03-25 00:00:00 with 12785 obs training finished\n",
      "2018-02-05 14:12:28,623 - ALPHA_MIND - INFO - trade_date: 2013-04-10 00:00:00 with 12788 obs training finished\n",
      "2018-02-05 14:12:28,631 - ALPHA_MIND - INFO - trade_date: 2013-04-24 00:00:00 with 12789 obs training finished\n",
      "2018-02-05 14:12:28,638 - ALPHA_MIND - INFO - trade_date: 2013-05-13 00:00:00 with 12790 obs training finished\n",
      "2018-02-05 14:12:28,646 - ALPHA_MIND - INFO - trade_date: 2013-05-27 00:00:00 with 12791 obs training finished\n",
      "2018-02-05 14:12:28,653 - ALPHA_MIND - INFO - trade_date: 2013-06-13 00:00:00 with 12792 obs training finished\n",
      "2018-02-05 14:12:28,660 - ALPHA_MIND - INFO - trade_date: 2013-06-27 00:00:00 with 12793 obs training finished\n",
      "2018-02-05 14:12:28,667 - ALPHA_MIND - INFO - trade_date: 2013-07-11 00:00:00 with 12794 obs training finished\n",
      "2018-02-05 14:12:28,674 - ALPHA_MIND - INFO - trade_date: 2013-07-25 00:00:00 with 12795 obs training finished\n",
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      "2018-02-05 14:12:28,686 - ALPHA_MIND - INFO - trade_date: 2013-08-22 00:00:00 with 12797 obs training finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-02-05 14:12:28,693 - ALPHA_MIND - INFO - trade_date: 2013-09-05 00:00:00 with 12798 obs training finished\n",
      "2018-02-05 14:12:28,700 - ALPHA_MIND - INFO - trade_date: 2013-09-23 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,707 - ALPHA_MIND - INFO - trade_date: 2013-10-14 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,714 - ALPHA_MIND - INFO - trade_date: 2013-10-28 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,722 - ALPHA_MIND - INFO - trade_date: 2013-11-11 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,729 - ALPHA_MIND - INFO - trade_date: 2013-11-25 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,735 - ALPHA_MIND - INFO - trade_date: 2013-12-09 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,741 - ALPHA_MIND - INFO - trade_date: 2013-12-23 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,747 - ALPHA_MIND - INFO - trade_date: 2014-01-07 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,754 - ALPHA_MIND - INFO - trade_date: 2014-01-21 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,762 - ALPHA_MIND - INFO - trade_date: 2014-02-11 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,768 - ALPHA_MIND - INFO - trade_date: 2014-02-25 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,775 - ALPHA_MIND - INFO - trade_date: 2014-03-11 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,780 - ALPHA_MIND - INFO - trade_date: 2014-03-25 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,785 - ALPHA_MIND - INFO - trade_date: 2014-04-09 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,790 - ALPHA_MIND - INFO - trade_date: 2014-04-23 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,796 - ALPHA_MIND - INFO - trade_date: 2014-05-09 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,802 - ALPHA_MIND - INFO - trade_date: 2014-05-23 00:00:00 with 12799 obs training finished\n",
      "2018-02-05 14:12:28,810 - ALPHA_MIND - INFO - trade_date: 2014-06-09 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,816 - ALPHA_MIND - INFO - trade_date: 2014-06-23 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,824 - ALPHA_MIND - INFO - trade_date: 2014-07-07 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,830 - ALPHA_MIND - INFO - trade_date: 2014-07-21 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,836 - ALPHA_MIND - INFO - trade_date: 2014-08-04 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,842 - ALPHA_MIND - INFO - trade_date: 2014-08-18 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,848 - ALPHA_MIND - INFO - trade_date: 2014-09-01 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,854 - ALPHA_MIND - INFO - trade_date: 2014-09-16 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,860 - ALPHA_MIND - INFO - trade_date: 2014-09-30 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,866 - ALPHA_MIND - INFO - trade_date: 2014-10-21 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,873 - ALPHA_MIND - INFO - trade_date: 2014-11-04 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,881 - ALPHA_MIND - INFO - trade_date: 2014-11-18 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,889 - ALPHA_MIND - INFO - trade_date: 2014-12-02 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,896 - ALPHA_MIND - INFO - trade_date: 2014-12-16 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,903 - ALPHA_MIND - INFO - trade_date: 2014-12-30 00:00:00 with 12800 obs training finished\n",
      "2018-02-05 14:12:28,909 - ALPHA_MIND - INFO - trade_date: 2015-01-15 00:00:00 with 12764 obs training finished\n",
      "2018-02-05 14:12:28,915 - ALPHA_MIND - INFO - trade_date: 2015-01-29 00:00:00 with 12764 obs training finished\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-02-05 14:12:29,188 - ALPHA_MIND - INFO - trade_date: 2016-09-19 00:00:00 with 12781 obs training finished\n",
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      "2018-02-05 14:12:29,408 - ALPHA_MIND - INFO - trade_date: 2017-12-26 00:00:00 with 12794 obs training finished\n",
      "2018-02-05 14:12:29,417 - ALPHA_MIND - INFO - trade_date: 2018-01-10 00:00:00 with 12794 obs training finished\n",
      "2018-02-05 14:12:29,424 - ALPHA_MIND - INFO - trade_date: 2018-01-24 00:00:00 with 12794 obs training finished\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Training phase\n",
    "\"\"\"\n",
    "\n",
    "lasso_models_series = pd.Series()\n",
    "previous_model = None\n",
    "\n",
    "for ref_date in ref_dates:\n",
    "    x = train_x[ref_date]\n",
    "    y = train_y[ref_date]\n",
    "    if len(x) > 5 * len(model_features_keys):\n",
    "        model = LassoRegression(alpha=0.005, features=model_features_keys, fit_intercept=True) \n",
    "        model.fit(x, y)\n",
    "        lasso_models_series.loc[ref_date] = model\n",
    "        previous_model = copy.deepcopy(model)\n",
    "    else:\n",
    "        models_series.loc[ref_date] = previous_model\n",
    "    \n",
    "    alpha_logger.info('trade_date: {0} with {1} obs training finished'.format(ref_date, len(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-02-05 14:12:30,629 - ALPHA_MIND - INFO - 2010-08-30 00:00:00: 799\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start 0.005 weight gap simulation ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\IntelPython3_2018\\lib\\site-packages\\ipykernel_launcher.py:81: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "2018-02-05 14:12:30,881 - ALPHA_MIND - INFO - 2010-08-30 00:00:00 is finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using backbone model and alpha model\n"
     ]
    },
    {
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     "output_type": "stream",
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      "2018-02-05 14:12:30,889 - ALPHA_MIND - INFO - 2010-09-13 00:00:00: 799\n"
     ]
    },
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      "using backbone model and alpha model\n"
     ]
    },
    {
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      "2018-02-05 14:12:31,173 - ALPHA_MIND - INFO - 2010-09-13 00:00:00 is finished\n",
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    {
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      "using backbone model and alpha model\n"
     ]
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      "2018-02-05 14:12:31,446 - ALPHA_MIND - INFO - 2010-09-30 00:00:00 is finished\n",
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      "using backbone model and alpha model\n"
     ]
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      "2018-02-05 14:12:31,738 - ALPHA_MIND - INFO - 2010-10-21 00:00:00 is finished\n",
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      "using backbone model and alpha model\n"
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      "2018-02-05 14:12:32,028 - ALPHA_MIND - INFO - 2010-11-04 00:00:00 is finished\n",
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      "using backbone model and alpha model\n"
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      "using backbone model and alpha model\n"
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    },
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Wq5XHHnuMJUuWsGPHDrZv38706dMpLi5mzpw5zJ8/3+6+vUup8Qp4dnY2ZrPZ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\nzY0xmMf+jFn7CgzKhMOlEBWNNWuu/+LNDa/BsFH+ixndnc/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ZE7wDEhEREQkCBfCPUABv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\n4HlAfSi6VZYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b381b988d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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THh5e6SbMvn37smPHDkJCQq57DO3bt6dfv37ExMQQHBxMRETEVactvFzXrl35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\nf9aW7+7fKTVvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b3ec16d208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0iKCgIEaNGnXBvhcsWEB5eTk33XQTo0ePZvjw4XTr1u2Sr2H27Nm8+OKLzJ07l44dOxIT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\nAwBAomfbJyPcxUOKIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b3eb3d57b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Tp85l77cqPychhBBC3AQ5WZB5CYJqg8Pll3pXSrEnJZtv4s6wLTkTe6NGVGM/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\nAAwxgAMAAACGGMABAAAAQ/8A7CgBnFQAmUkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b387f3e668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicting and re-balance phase\n",
    "\n",
    "weight_gaps = [0.005, 0.010, 0.015, 0.020]\n",
    "\n",
    "model1_weight = 1.0\n",
    "model2_weight = 0.5\n",
    "\n",
    "factor_groups = const_model_factor_data.groupby('trade_date')\n",
    "\n",
    "for s_i, weight_gap in enumerate(weight_gaps):\n",
    "    print(\"start {0} weight gap simulation ...\".format(weight_gap))\n",
    "\n",
    "    rets = []\n",
    "    turn_overs = []\n",
    "    leverags = []\n",
    "    previous_pos = pd.DataFrame()\n",
    "\n",
    "    index_dates = []\n",
    "\n",
    "    for i, value in enumerate(factor_groups):\n",
    "        date = value[0]\n",
    "        data = value[1]\n",
    "        ref_date = date.strftime('%Y-%m-%d')\n",
    "\n",
    "        total_data = data.fillna(data[const_model_features_keys].median())\n",
    "        alpha_logger.info('{0}: {1}'.format(date, len(total_data)))\n",
    "        risk_exp = total_data[neutralize_risk].values.astype(float)\n",
    "        industry = total_data.industry_code.values\n",
    "        benchmark_w = total_data.weight.values\n",
    "        \n",
    "        this_industry_data = industry_group.get_group(date)\n",
    "        \n",
    "        total_data = pd.merge(total_data, this_industry_data, on=['trade_date', 'code'])\n",
    "        constraint_exp = total_data[constraint_risk].values\n",
    "        industry = total_data.industry_name.values\n",
    "\n",
    "        risk_exp_expand = np.concatenate((constraint_exp, np.ones((len(risk_exp), 1))), axis=1).astype(float)\n",
    "\n",
    "        risk_names = constraint_risk + ['total']\n",
    "        risk_target = risk_exp_expand.T @ benchmark_w\n",
    "\n",
    "        lbound = np.maximum(0., benchmark_w - weight_gap)  # np.zeros(len(total_data))\n",
    "        ubound = weight_gap + benchmark_w\n",
    "\n",
    "        is_in_benchmark = (benchmark_w > 0.).astype(float)\n",
    "\n",
    "        risk_exp_expand = np.concatenate((risk_exp_expand, is_in_benchmark.reshape((-1, 1))), axis=1).astype(float)\n",
    "        risk_names.append('benchmark_total')\n",
    "\n",
    "        constraint = Constraints(risk_exp_expand, risk_names)\n",
    "\n",
    "        for j, name in enumerate(risk_names):\n",
    "            if name == 'total':\n",
    "                constraint.set_constraints(name,\n",
    "                                           lower_bound=risk_target[j],\n",
    "                                           upper_bound=risk_target[j])\n",
    "            elif name == 'SIZE':\n",
    "                base_target = abs(risk_target[j])\n",
    "                constraint.set_constraints(name,\n",
    "                                           lower_bound=risk_target[j] + base_target * size_risk_lower,\n",
    "                                           upper_bound=risk_target[j] + base_target * size_risk_upper)\n",
    "            elif name == 'benchmark_total':\n",
    "                base_target = benchmark_w.sum()\n",
    "                constraint.set_constraints(name,\n",
    "                                           lower_bound=benchmark_total_lower * base_target,\n",
    "                                           upper_bound=benchmark_total_upper * base_target)\n",
    "            else:\n",
    "                constraint.set_constraints(name,\n",
    "                                           lower_bound=risk_target[j] * industry_lower,\n",
    "                                           upper_bound=risk_target[j] * industry_upper)\n",
    "        \n",
    "        factor_values = factor_processing(total_data[const_model_features_keys].values,\n",
    "                                          pre_process=[winsorize_normal, standardize],\n",
    "                                          risk_factors=risk_exp,\n",
    "                                          post_process=[winsorize_normal, standardize])\n",
    "\n",
    "        # const linear model\n",
    "        er1 = const_model.predict(factor_values)\n",
    "        \n",
    "        try:\n",
    "            model = lasso_models_series.ix[date]\n",
    "        except KeyError:\n",
    "            alpha_logger.info(\"model is not available. Omit this date\")\n",
    "            continue\n",
    "            \n",
    "        index_dates.append(date)\n",
    "\n",
    "        x = predict_x[date]\n",
    "        this_predict_code = predict_code[date]\n",
    "        er2 = model.predict(x)\n",
    "\n",
    "        # combine model\n",
    "        er1_table = pd.DataFrame({'er1': er1, 'code': total_data.code.values})\n",
    "        er2_table = pd.DataFrame({'er2': er2, 'code': this_predict_code})\n",
    "        er_table = pd.merge(er1_table, er2_table, on=['code'], how='left').fillna(0)\n",
    "\n",
    "        er1 = er_table.er1.values\n",
    "        er2 = er_table.er2.values\n",
    "        \n",
    "        if er2.std() > 0:\n",
    "            print('using backbone model and alpha model')\n",
    "            er = model1_weight * er1 / er1.std() +  model2_weight * er2 / er2.std()\n",
    "        else:\n",
    "            print('using only backbone modedl')\n",
    "            er = model1_weight * er1\n",
    "            \n",
    "        codes = total_data['code'].values\n",
    "\n",
    "        if previous_pos.empty:\n",
    "            current_position = None\n",
    "            turn_over_target = None\n",
    "        else:\n",
    "            previous_pos.set_index('code', inplace=True)\n",
    "            remained_pos = previous_pos.loc[codes]\n",
    "\n",
    "            remained_pos.fillna(0., inplace=True)\n",
    "            turn_over_target = turn_over_target_base\n",
    "            current_position = remained_pos.weight.values\n",
    "\n",
    "        try:\n",
    "            target_pos, _ = er_portfolio_analysis(er,\n",
    "                                                  industry,\n",
    "                                                  None,\n",
    "                                                  constraint,\n",
    "                                                  False,\n",
    "                                                  benchmark_w,\n",
    "                                                  method=method,\n",
    "                                                  turn_over_target=turn_over_target,\n",
    "                                                  current_position=current_position,\n",
    "                                                  lbound=lbound,\n",
    "                                                  ubound=ubound)\n",
    "        except ValueError:\n",
    "            alpha_logger.info('{0} full re-balance'.format(date))\n",
    "            target_pos, _ = er_portfolio_analysis(er,\n",
    "                                                  industry,\n",
    "                                                  None,\n",
    "                                                  constraint,\n",
    "                                                  False,\n",
    "                                                  benchmark_w,\n",
    "                                                  method=method,\n",
    "                                                  lbound=lbound,\n",
    "                                                  ubound=ubound)\n",
    "\n",
    "        target_pos['code'] = total_data['code'].values\n",
    "\n",
    "        turn_over, executed_pos = executor.execute(target_pos=target_pos)\n",
    "\n",
    "        executed_codes = executed_pos.code.tolist()\n",
    "        dx_returns = engine.fetch_dx_return(date, executed_codes, horizon=horizon, offset=1)\n",
    "\n",
    "        result = pd.merge(executed_pos, total_data[['code', 'weight']], on=['code'], how='inner')\n",
    "        result = pd.merge(result, dx_returns, on=['code'])\n",
    "\n",
    "        leverage = result.weight_x.abs().sum()\n",
    "\n",
    "        ret = result.weight_x.values @ (np.exp(result.dx.values) - 1.)\n",
    "        rets.append(np.log(1. + ret))\n",
    "        executor.set_current(executed_pos)\n",
    "        turn_overs.append(turn_over)\n",
    "        leverags.append(leverage)\n",
    "\n",
    "        previous_pos = executed_pos\n",
    "        alpha_logger.info('{0} is finished'.format(date))\n",
    "\n",
    "    ret_df = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverags}, index=index_dates)\n",
    "\n",
    "    # index return\n",
    "    index_return = engine.fetch_dx_return_index_range(benchmark_code, dates=ref_dates, horizon=horizon,\n",
    "                                                      offset=1).set_index('trade_date')\n",
    "    ret_df['index'] = index_return['dx']\n",
    "\n",
    "    ret_df.loc[advanceDateByCalendar('china.sse', ref_dates[-1], frequency)] = 0.\n",
    "    ret_df = ret_df.shift(1)\n",
    "    ret_df.iloc[0] = 0.\n",
    "    ret_df['tc_cost'] = ret_df.turn_over * 0.002\n",
    "    ret_df['returns'] = ret_df['leverage'] * (ret_df['returns'] - ret_df['index'])\n",
    "\n",
    "    ret_df[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6),\n",
    "                                                 title='Fixed frequency rebalanced: {0}'.format(frequency),\n",
    "                                                 secondary_y='tc_cost')\n",
    "\n",
    "    ret_df['ret_after_tc'] = ret_df['returns'] - ret_df['tc_cost']\n",
    "    \n",
    "\n",
    "    sharp_calc = MovingSharp(49)\n",
    "    drawdown_calc = MovingMaxDrawdown(49)\n",
    "    max_drawdown_calc = MovingMaxDrawdown(len(ret_df))\n",
    "\n",
    "    res_df = pd.DataFrame(columns=['daily_return', 'cum_ret', 'sharp', 'drawdown', 'max_drawn', 'leverage'])\n",
    "\n",
    "    total_returns = 0.\n",
    "\n",
    "    for i, ret in enumerate(ret_df['ret_after_tc']):\n",
    "        date = ret_df.index[i]\n",
    "        total_returns += ret\n",
    "        sharp_calc.push({'ret': ret, 'riskFree': 0.})\n",
    "        drawdown_calc.push({'ret': ret})\n",
    "        max_drawdown_calc.push({'ret': ret})\n",
    "\n",
    "        res_df.loc[date, 'daily_return'] = ret\n",
    "        res_df.loc[date, 'cum_ret'] = total_returns\n",
    "        res_df.loc[date, 'drawdown'] = drawdown_calc.result()[0]\n",
    "        res_df.loc[date, 'max_drawn'] = max_drawdown_calc.result()[0]\n",
    "        res_df.loc[date, 'leverage'] = ret_df.loc[date, 'leverage']\n",
    "\n",
    "        if i < 10:\n",
    "            res_df.loc[date, 'sharp'] = 0.\n",
    "        else:\n",
    "            res_df.loc[date, 'sharp'] = sharp_calc.result() * np.sqrt(49)\n",
    "\n",
    "    res_df.to_csv('hs300_rf_{0}.csv'.format(int(s_i)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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