Yunta50 feature model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lu\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "C:\\Users\\lu\\Anaconda3\\lib\\site-packages\\sklearn\\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Load in our libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "mpl.style.use('seaborn')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import time\n",
    "import datetime as dt\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.ensemble import (RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier,\\\n",
    "                              GradientBoostingClassifier)\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "import math\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data_processing import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"tsharep.csv\",encoding=' big5-hkscs ').rename(columns={'代碼':'code','日期':'date','中文簡稱':'name','開盤價(元)':'open','最高價(元)':'high','最低價(元)':'low','收盤價(元)':'close','成交張數(張)':'volume'})\n",
    "Taiwan50 = extract_target_index('ETF50.xlsx',data)\n",
    "TXF = load_txt_TXF('TXF1-300-分鐘.txt',Taiwan50)\n",
    "EXF = load_txt_2('EXF1-1-日.txt',Taiwan50)\n",
    "FXF = load_txt_2('FXF1-1-日.txt',Taiwan50)\n",
    "E_F = EXF/FXF\n",
    "Nikkei = load_csv('Nikkei225.csv',Taiwan50)\n",
    "VIX = load_csv('VIX.csv',Taiwan50).drop(['volume'],axis = 1)\n",
    "Russell = load_csv('Russell2000.csv',Taiwan50)\n",
    "SP = load_csv('S&P500.csv',Taiwan50)\n",
    "DJ = load_csv('Dow Jones.csv',Taiwan50)\n",
    "SOX = load_csv('SOX.csv',Taiwan50).drop(['volume'],axis = 1)\n",
    "pcr = load_txt('put_call_ratio-日-成交價.txt',Taiwan50).drop(['volume','open','low'],axis = 1)\n",
    "FOI = load_txt('外資未平倉-成交價.txt',Taiwan50).drop(['volume'],axis = 1)\n",
    "IOI = load_txt('投信未平倉-日-成交價.txt',Taiwan50).drop(['volume'],axis = 1)\n",
    "DOI = load_txt('自營未平倉-日-成交價.txt',Taiwan50).drop(['volume'],axis = 1)\n",
    "orderbook_ = pd.read_hdf('twse_orderbook_comp.h5').rename(columns = {'時間':'date',\n",
    "                                                                    '累積委託買進筆數':'ask_vol',\n",
    "                                                                    '累積委託買進數量':'ask_count',\n",
    "                                                                    '累積委託賣出筆數':'bid_vol',\n",
    "                                                                    '累積委託賣出數量':'bid_count',\n",
    "                                                                    '累積成交筆數':'vol',\n",
    "                                                                    '累積成交數量':'count',\n",
    "                                                                    '累積成交金額':'amount'})\n",
    "orderbook = orderbook_[orderbook_.index==0].iloc[-1*Taiwan50.shape[0]-1:-1]\n",
    "orderbook['date'] = Taiwan50.index.values\n",
    "orderbook = orderbook.groupby('date').sum().drop(['vol','count','amount'],axis = 1)\n",
    "ETFtable = ETF_data_processing('tetfp.csv')\n",
    "Yunta50 = extract_target_ETF(ETFtable,50)\n",
    "Yunta50_rate = get_change_rate(Yunta50)\n",
    "oil_price = pd.read_csv('oil_price.csv')\n",
    "oil_price['date'] = oil_price['date'].map(lambda x:dt.datetime.strptime(str(x),'%Y/%m/%d'))\n",
    "oil_price = oil_price.set_index('date')\n",
    "\n",
    "fiveyear_bond = pd.read_excel('five_year_bond.xls').rename(columns = {'observation_date':'date'}).set_index('date')\n",
    "tenyear_bond = pd.read_excel('ten_year_bond.xls').rename(columns = {'observation_date':'date'}).set_index('date')\n",
    "bond = pd.concat([fiveyear_bond,tenyear_bond],axis = 1).loc[Taiwan50.index[0]:Taiwan50.index[-1]].rename(columns = {'DGS5':'DGSfive'\n",
    "                                                                                                                    ,'DGS10':'DGSten'})\n",
    "mrf = pd.Series(get_mean_rf(Yunta50,'close',20,5),index=Yunta50.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list = [Yunta50,Taiwan50,orderbook,oil_price,bond,TXF,EXF,FXF,E_F,Nikkei,SP,Russell,DJ,SOX,VIX,pcr,FOI,IOI,DOI]\n",
    "df_option = [True,True,True,True,True,True,True,True,False,True,True,True,True,True,True,False,True,True,True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "models  =  {\n",
    "    'DecisionTreeClassifier' : DecisionTreeClassifier(random_state = 0),\n",
    "         'RandomForestClassifier': RandomForestClassifier(random_state = 0),\n",
    "#     'ExtraTreesClassifier': ExtraTreesClassifier(random_state = 0),\n",
    "#     'AdaBoostClassifier': AdaBoostClassifier(base_estimator = DecisionTreeClassifier(),\\\n",
    "#                                              n_estimators = 10,random_state = 0),\n",
    "#     'GradientBoostingClassifier': GradientBoostingClassifier(random_state = 0),\n",
    "#     'SVC': SVC(probability=True,random_state = 0),\n",
    "}\n",
    "    \n",
    "model_grid_params = {\n",
    "        'DecisionTreeClassifier':{'max_depth': [None,1,5,10],'min_samples_leaf': [1,2,5,10]},\n",
    "    'RandomForestClassifier': {'max_features':[None],'n_estimators':[5,10],'max_depth':[2,10],\\\n",
    "                               'min_samples_split':[2],'criterion':['entropy'],\\\n",
    "                               'min_samples_leaf':[3]},\n",
    "#     'ExtraTreesClassifier': {'max_features':[None],'n_estimators':[10],'max_depth':[10],\\\n",
    "#                              'min_samples_split':[2],'criterion':['entropy'],\\\n",
    "#                              'min_samples_leaf':[3]},\n",
    "#     'AdaBoostClassifier': {\"base_estimator__criterion\" : [\"entropy\"],\\\n",
    "#                            \"base_estimator__max_depth\": [None],\\\n",
    "#                            \"base_estimator__min_samples_leaf\" : [3],\\\n",
    "#                            \"base_estimator__min_samples_split\" : [2],\\\n",
    "#                            \"base_estimator__max_features\" : [None]},\n",
    "#     'GradientBoostingClassifier': {'max_features':[None],'n_estimators':[10],'max_depth':[10],\\\n",
    "#                                    'min_samples_split':[2],'min_samples_leaf':[3],\\\n",
    "#                                    'learning_rate':[0.1],'subsample':[1.0]},\n",
    "#     'SVC': [{'kernel':['rbf'],'gamma':[1e-1],'C':[1]},\\\n",
    "#             {'kernel':['linear'],'C':[1,10]}]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model_Selection:\n",
    "    \n",
    "    def __init__(self,models,model_grid_params,stock_5,latest_day,pred_day,day):\n",
    "        \n",
    "        self.models = models\n",
    "        self.model_grid = model_grid_params\n",
    "        self.stock_5 = stock_5\n",
    "        self.latest_day= latest_day\n",
    "        self.pred_day = pred_day\n",
    "        self.day = day\n",
    "        self.keys = models.keys()\n",
    "        self.best_score = {}\n",
    "        self.grid = {}\n",
    "        \n",
    "        self.predict_values = {}\n",
    "        self.cv_acc = {}\n",
    "        self.acc = {}\n",
    "        self.fscore = {}\n",
    "        self.true_values = {}\n",
    "        \n",
    "        self.predict_values_day = {}\n",
    "        self.cv_acc_day = {}\n",
    "        self.acc_day = {}\n",
    "        self.fscore_day = {}\n",
    "        self.true_values_day = {}\n",
    "        self.summary_day = []\n",
    "        \n",
    "    def Grid_fit(self,X_train,y_train,cv = 5,scoring = 'accuracy'):\n",
    "        \n",
    "        for key in self.keys:\n",
    "            print (\"Running GridSearchCV for %s\" %(key))\n",
    "            model = self.models[key]\n",
    "            model_grid = self.model_grid[key]\n",
    "            Grid = GridSearchCV(model, model_grid, cv = cv, scoring = scoring)\n",
    "            Grid.fit(X_train,y_train) \n",
    "            self.grid[key] = Grid\n",
    "            print (Grid.best_params_)\n",
    "            print ('CV Best Score = %s'%(Grid.best_score_))\n",
    "            self.cv_acc[key].append(Grid.best_score_)  \n",
    "    \n",
    "    def model_fit(self,X_train, y_train, X_test, y_test):\n",
    "        \n",
    "        for key in self.keys:\n",
    "            print (\"Running training & testing for %s.\" %(key))\n",
    "            model = self.models[key]\n",
    "            model.set_params(**self.grid[key].best_params_)\n",
    "            model.fit(X_train, y_train)\n",
    "            predictions = model.predict(X_test)\n",
    "            #print 'Prediction latest 15 second = %s'%(predictions)\n",
    "            self.predict_values[key].append(predictions.tolist())\n",
    "            self.true_values[key].append(y_test.tolist())\n",
    "            acc = metrics.accuracy_score(y_test,predictions)\n",
    "            f_score = metrics.f1_score(y_test,predictions)\n",
    "            print ('Accuracy = %s'%(acc))\n",
    "            self.acc[key].append(acc)\n",
    "            self.fscore[key].append(f_score)\n",
    "\n",
    "\n",
    "    def pipline(self):\n",
    "        \n",
    "        self.set_list_day() # store day values\n",
    "        for day in range(0,self.day,1):\n",
    "            self.set_list() # store values\n",
    "            print ('Day = %s'%(day+1))\n",
    "            for i in range(0,500,self.pred_day):#9000-self.latest_day-600,self.pred_day):\n",
    "                \n",
    "                print ('--------------------Rolling Window Time = %s--------------------'%(i/pred_day))\n",
    "                # Train data\n",
    "                data_train = self.stock_5[day][i:i+self.latest_day]\n",
    "                X_train = data_train[:,:-3]\n",
    "                train_rise = data_train[:,-3]\n",
    "                train_fall = data_train[:,-2]\n",
    "                train_noise = data_train[:,-1]\n",
    "                y_train = train_rise\n",
    "\n",
    "                # Test data\n",
    "                data_test = self.stock_5[day][i + self.latest_day:i + self.latest_day + self.pred_day]\n",
    "                X_test = data_test[:,:-3]\n",
    "                test_rise = data_test[:,-3]\n",
    "                test_fall = data_test[:,-2]\n",
    "                test_noise = data_test[:,-1]\n",
    "                y_test = test_rise\n",
    "                \n",
    "                #start = time.time()\n",
    "                self.Grid_fit(X_train, y_train, cv = 2, scoring = 'accuracy')\n",
    "                self.model_fit(X_train, y_train,X_test,y_test)\n",
    "                #end = time.time()\n",
    "                #print 'Total Time = %s'%(end - start)\n",
    "                \n",
    "            for key in self.keys:\n",
    "                \n",
    "                self.cv_acc_day[key].append(self.cv_acc[key])\n",
    "                self.acc_day[key].append(self.acc[key])\n",
    "                self.fscore_day[key].append(self.fscore[key])\n",
    "                self.true_values_day[key].append(self.true_values[key])\n",
    "                self.predict_values_day[key].append(self.predict_values[key])\n",
    "            \n",
    "            self.summary_day.append(self.score_summary(sort_by = 'Accuracy_mean'))\n",
    "    \n",
    "    def set_list(self):\n",
    "        \n",
    "        for key in self.keys:\n",
    "            self.predict_values[key] = []\n",
    "            self.cv_acc[key] = []\n",
    "            self.acc[key] = []\n",
    "            self.fscore[key] = []\n",
    "            self.true_values[key] = []\n",
    "            \n",
    "    def set_list_day(self):\n",
    "        \n",
    "        for key in self.keys:\n",
    "            self.predict_values_day[key] = []\n",
    "            self.cv_acc_day[key] = []\n",
    "            self.acc_day[key] = []\n",
    "            self.fscore_day[key] = []\n",
    "            self.true_values_day[key] = []\n",
    "            \n",
    "    def score_summary(self,sort_by):\n",
    "        \n",
    "        summary = pd.concat([pd.Series(list(pip.acc.keys())),pd.Series(map(lambda x: sum(pip.acc[x])/len(pip.acc[x]), pip.acc)),\\\n",
    "                             pd.Series(list(map(lambda x: statistics.stdev(pip.acc[x]), pip.acc))),\\\n",
    "                             pd.Series(list(map(lambda x: max(pip.acc[x]), pip.acc))),\\\n",
    "                             pd.Series(list(map(lambda x: min(pip.acc[x]), pip.acc))),\\\n",
    "                             pd.Series(list(map(lambda x: sum(pip.fscore[x])/len(pip.fscore[x]), pip.fscore)))],axis=1)\n",
    "        summary.columns = ['Estimator','Accuracy_mean','Accuracy_std','Accuracy_max','Accuracy_min','F_score']\n",
    "        summary.index.rename('Ranking', inplace=True)\n",
    "        return summary.sort_values(by = [sort_by], ascending=False)\n",
    "          \n",
    "    def print_(self):\n",
    "\n",
    "        print (self.predict_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataframe(df_list,df_r,target,interval,window,ca=4,day = 1):\n",
    "    df_list_rate = []\n",
    "    df_rf = get_rise_fall(target,interval).shift(-1*day+1)\n",
    "    \n",
    "    #concat the df in the df_list\n",
    "    for index,df in enumerate(df_list):\n",
    "        if(df_r[index]):\n",
    "            df_rate = get_change_rate(df,window).iloc[:,df.shape[1]:]\n",
    "        else:\n",
    "            df_rate = get_backday(df,window).iloc[:,df.shape[1]:]\n",
    "            \n",
    "        df_av  = get_av_ca(stock=df_rate,rf_df=df_rf,ca=ca,window = window).iloc[:,:-3]\n",
    "        df_colname = list(map(lambda x:  df_name[index]+'_'+x,df_av.columns))\n",
    "        df_dum = pd.get_dummies(df_av,prefix=df_colname,columns=df_av.columns)\n",
    "        df_list_rate.append(df_dum)\n",
    "        \n",
    "    df_all = pd.concat(df_list_rate,axis =1)\n",
    "    df_all['mean_rise_fall'] = mrf\n",
    "    \n",
    "    #create null columns for the null data\n",
    "    df_null = df_all.copy()\n",
    "    for columnname in df_all.columns:\n",
    "        df_null[columnname+'isnull'] = list(map(lambda x: int(x),df_all.isna()[columnname]))\n",
    "    df_null.drop(df_all.columns,axis = 1,inplace = True)\n",
    "\n",
    "    df_total = pd.concat([df_all,df_null],axis = 1)\n",
    "    df_total = pd.concat([df_total,df_rf.loc[df_total.index]],axis = 1).loc[TXF.index[25]:TXF.index[-1],:].fillna(0)\n",
    "    \n",
    "    #get target rise fall data\n",
    "    Y = Yunta50.copy()\n",
    "    Y['trf'] = np.sign((Yunta50-Yunta50.shift(1))['close'].values)\n",
    "    Y = Y.shift(-1*day+1)\n",
    "    trf = Y.loc[df_total.index]['trf']\n",
    "\n",
    "    return [df_total ,trf]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataframe_feature(df_list,df_r,target,interval,window,ca=4,day = 1):\n",
    "    df_list_rate = []\n",
    "    df_rf = get_rise_fall(target,interval).shift(-1*day+1)\n",
    "    \n",
    "    #concat the df in the df_list\n",
    "    for index,df in enumerate(df_list):\n",
    "        if(df_r[index]):\n",
    "            df_rate = get_change_rate(df,window).iloc[:,df.shape[1]:]\n",
    "        else:\n",
    "            df_rate = get_backday(df,window).iloc[:,df.shape[1]:]\n",
    "            \n",
    "        df_av  = get_av_ca(stock=df_rate,rf_df=df_rf,ca=ca,window = window).iloc[:,:-3]\n",
    "        df_colname = list(map(lambda x:  df_name[index]+'_'+x,df_av.columns))\n",
    "        df_dum = pd.get_dummies(df_av,prefix=df_colname,columns=df_av.columns)\n",
    "        df_list_rate.append(df_dum)\n",
    "        \n",
    "    df_all = pd.concat(df_list_rate,axis =1)\n",
    "    df_all['mean_rise_fall'] = mrf\n",
    "    \n",
    "    #create null columns for the null data\n",
    "    df_null = df_all.copy()\n",
    "    for columnname in df_all.columns:\n",
    "        df_null[columnname+'isnull'] = list(map(lambda x: int(x),df_all.isna()[columnname]))\n",
    "    df_null.drop(df_all.columns,axis = 1,inplace = True)\n",
    "\n",
    "    df_total = pd.concat([df_all,df_null],axis = 1)\n",
    "    df_total = pd.concat([df_total,df_rf.loc[df_total.index]],axis = 1).loc[TXF.index[25]:TXF.index[-1],:].fillna(0)\n",
    "    \n",
    "    #get target rise fall data\n",
    "    Y = Yunta50.copy()\n",
    "    Y['trf'] = np.sign((Yunta50-Yunta50.shift(1))['close'].values)\n",
    "    Y = Y.shift(-1*day+1)\n",
    "    trf = Y.loc[df_total.index]['trf']\n",
    "\n",
    "    data = df_total.as_matrix()\n",
    "    result = int(data.shape[0]*0.9)\n",
    "    train_X = data[:result,:-3]\n",
    "    train_rise = data[:result,-3]\n",
    "    train_fall = data[:result,-2]\n",
    "    train_noise = data[:result,-1]\n",
    "\n",
    "    test_X = data[result:,:-3]\n",
    "    test_rise = data[result:,-3]\n",
    "    test_fall = data[result:,-2]\n",
    "    test_noise = data[result:,-1]\n",
    "    trf_v = trf.values[result:]\n",
    "\n",
    "    return [df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf_v]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataframe_rf(df_list,df_r,target,interval,window,ca=4,day = 1):\n",
    "    df_list_rate = []\n",
    "    df_rf = get_rise_fall(target,interval).shift(-1*day+1)\n",
    "    \n",
    "    #concat the df in the df_list\n",
    "    for index,df in enumerate(df_list):\n",
    "        if(df_r[index]):\n",
    "            df_rate = get_change_rate(df,window).iloc[:,df.shape[1]:]\n",
    "        else:\n",
    "            df_rate = get_backday(df,window).iloc[:,df.shape[1]:]\n",
    "            \n",
    "        df_av  = get_av_ca(stock=df_rate,rf_df=df_rf,ca=ca,window = window).iloc[:,:-3]\n",
    "        df_colname = list(map(lambda x:  df_name[index]+'_'+x,df_av.columns))\n",
    "        df_dum = pd.get_dummies(df_av,prefix=df_colname,columns=df_av.columns)\n",
    "        df_list_rate.append(df_dum)\n",
    "        \n",
    "    df_all = pd.concat(df_list_rate,axis =1)\n",
    "    df_all['mean_rise_fall'] = mrf\n",
    "    \n",
    "    df_total = df_all.dropna(axis = 0,how = 'any')\n",
    "    \n",
    "    #split rise and fall\n",
    "    dicu = {}\n",
    "    dicd = {}\n",
    "    for index, col in enumerate(df_total.columns[:-1]):\n",
    "        dicu[col] = col+'_r'\n",
    "        dicd[col] = col+'_f'\n",
    "\n",
    "    up = df_total[df_total['mean_rise_fall']==1].rename(columns = dicu).iloc[:,:-1]\n",
    "    dn = df_total[df_total['mean_rise_fall']==-1].rename(columns = dicd).iloc[:,:-1]\n",
    "    df_total_ud = pd.concat([up,dn],axis = 1).sort_index().fillna(0)\n",
    "    \n",
    "    df_total_ud = pd.concat([df_total_ud,df_rf.loc[df_total.index]],axis = 1).dropna(axis = 0,how = 'any')\n",
    "    \n",
    "    #get target rise fall data\n",
    "    Y = Yunta50.copy()\n",
    "    Y['trf'] = np.sign((Yunta50-Yunta50.shift(1))['close'].values)\n",
    "    Y = Y.shift(-1*day+1)\n",
    "    trf = Y.loc[df_total.index]['trf']\n",
    "    \n",
    "\n",
    "    return [df_total_ud,trf_v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataframe_rf_feature(df_list,df_r,target,interval,window,ca=4,day = 1):\n",
    "    df_list_rate = []\n",
    "    df_rf = get_rise_fall(target,interval).shift(-1*day+1)\n",
    "    \n",
    "    #concat the df in the df_list\n",
    "    for index,df in enumerate(df_list):\n",
    "        if(df_r[index]):\n",
    "            df_rate = get_change_rate(df,window).iloc[:,df.shape[1]:]\n",
    "        else:\n",
    "            df_rate = get_backday(df,window).iloc[:,df.shape[1]:]\n",
    "            \n",
    "        df_av  = get_av_ca(stock=df_rate,rf_df=df_rf,ca=ca,window = window).iloc[:,:-3]\n",
    "        df_colname = list(map(lambda x:  df_name[index]+'_'+x,df_av.columns))\n",
    "        df_dum = pd.get_dummies(df_av,prefix=df_colname,columns=df_av.columns)\n",
    "        df_list_rate.append(df_dum)\n",
    "        \n",
    "    df_all = pd.concat(df_list_rate,axis =1)\n",
    "    df_all['mean_rise_fall'] = mrf\n",
    "    \n",
    "    df_total = df_all.dropna(axis = 0,how = 'any')\n",
    "    \n",
    "    #split rise and fall\n",
    "    dicu = {}\n",
    "    dicd = {}\n",
    "    for index, col in enumerate(df_total.columns[:-1]):\n",
    "        dicu[col] = col+'_r'\n",
    "        dicd[col] = col+'_f'\n",
    "\n",
    "    up = df_total[df_total['mean_rise_fall']==1].rename(columns = dicu).iloc[:,:-1]\n",
    "    dn = df_total[df_total['mean_rise_fall']==-1].rename(columns = dicd).iloc[:,:-1]\n",
    "    df_total_ud = pd.concat([up,dn],axis = 1).sort_index().fillna(0)\n",
    "    \n",
    "    df_total_ud = pd.concat([df_total_ud,df_rf.loc[df_total.index]],axis = 1).dropna(axis = 0,how = 'any')\n",
    "    \n",
    "    #get target rise fall data\n",
    "    Y = Yunta50.copy()\n",
    "    Y['trf'] = np.sign((Yunta50-Yunta50.shift(1))['close'].values)\n",
    "    Y = Y.shift(-1*day+1)\n",
    "    trf = Y.loc[df_total_ud.index]['trf']\n",
    "    \n",
    "    data = df_total_ud.as_matrix()\n",
    "    result = int(data.shape[0]*0.9)\n",
    "    train_X = data[:result,:-3]\n",
    "    train_rise = data[:result,-3]\n",
    "    train_fall = data[:result,-2]\n",
    "    train_noise = data[:result,-1]\n",
    "\n",
    "    test_X = data[result:,:-3]\n",
    "    test_rise = data[result:,-3]\n",
    "    test_fall = data[result:,-2]\n",
    "    test_noise = data[result:,-1]\n",
    "    trf_v = trf.values[result:]\n",
    "\n",
    "    return [df_total_ud,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf_v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_summary(summary):\n",
    "    dic = {}\n",
    "    df = pd.concat(pip.summary_day,axis = 0,ignore_index = True)\n",
    "    for index,i in enumerate(df.index):\n",
    "        dic[i] = intervals[index//2]\n",
    "    df_rn = df.rename(index = dic)\n",
    "    df_rn.index.name = 'interval'\n",
    "    return df_rn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_params_test = {\n",
    "#     'n_jobs': -1,\n",
    "    'n_estimators': [1],\n",
    "#      'warm_start': True, \n",
    "     #'max_features': 0.2,\n",
    "    'max_depth': [None,1,5],\n",
    "    'min_samples_leaf': [1,2,5],\n",
    "#     'max_features' : 'sqrt',\n",
    "#     'verbose': 0\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list = [Yunta50,Taiwan50,orderbook,oil_price,bond,TXF,EXF,FXF,E_F,Nikkei,SP,Russell,DJ,SOX,VIX,pcr,FOI,IOI,DOI]\n",
    "df_option = [True,True,True,True,True,True,True,True,False,True,True,True,True,True,True,False,True,True,True]\n",
    "df_name = ['Yunta50','Taiwan50','orderbook','oil_price','bond','TXF','EXF','FXF','E_F','Nikkei','SP','Russell','DJ','SOX','VIX','pcr','FOI','IOI','DOI']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation of the rf data \n",
    "### single r or f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval is 0\n",
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      "feature 445\n",
      "feature 446\n",
      "feature 447\n",
      "feature 448\n",
      "feature 449\n",
      "feature 450\n",
      "feature 451\n",
      "feature 452\n",
      "feature 453\n",
      "feature 454\n",
      "feature 455\n",
      "feature 456\n",
      "feature 457\n",
      "feature 458\n",
      "feature 459\n",
      "feature 460\n",
      "feature 461\n",
      "feature 462\n",
      "feature 463\n",
      "feature 464\n",
      "feature 465\n",
      "feature 466\n",
      "feature 467\n",
      "feature 468\n",
      "feature 469\n",
      "feature 470\n",
      "feature 471\n",
      "feature 472\n",
      "feature 473\n",
      "feature 474\n",
      "feature 475\n",
      "feature 476\n",
      "feature 477\n",
      "feature 478\n",
      "feature 479\n",
      "feature 480\n",
      "feature 481\n",
      "feature 482\n",
      "feature 483\n",
      "feature 484\n",
      "feature 485\n",
      "feature 486\n",
      "feature 487\n",
      "feature 488\n",
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      "feature 490\n",
      "feature 491\n",
      "feature 492\n",
      "feature 493\n",
      "feature 494\n",
      "feature 495\n",
      "feature 496\n",
      "feature 497\n",
      "feature 498\n",
      "feature 499\n",
      "feature 500\n",
      "feature 501\n",
      "feature 502\n",
      "feature 503\n",
      "feature 504\n",
      "feature 505\n",
      "feature 506\n",
      "feature 507\n",
      "feature 508\n",
      "feature 509\n",
      "feature 510\n",
      "feature 511\n",
      "feature 512\n",
      "feature 513\n",
      "feature 514\n",
      "feature 515\n",
      "feature 516\n",
      "feature 517\n",
      "feature 518\n",
      "feature 519\n",
      "feature 520\n",
      "feature 521\n",
      "feature 522\n",
      "feature 523\n",
      "feature 524\n",
      "feature 525\n",
      "feature 526\n",
      "feature 527\n",
      "feature 528\n",
      "feature 529\n",
      "feature 530\n",
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      "feature 532\n",
      "feature 533\n",
      "feature 534\n",
      "feature 535\n",
      "feature 536\n",
      "feature 537\n",
      "feature 538\n",
      "feature 539\n",
      "feature 540\n",
      "feature 541\n",
      "feature 542\n",
      "feature 543\n",
      "feature 544\n",
      "feature 545\n",
      "feature 546\n",
      "feature 547\n",
      "feature 548\n",
      "feature 549\n",
      "feature 550\n",
      "feature 551\n",
      "feature 552\n",
      "feature 553\n",
      "feature 554\n",
      "feature 555\n",
      "feature 556\n",
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      "feature 559\n",
      "feature 560\n",
      "feature 561\n",
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      "feature 563\n",
      "feature 564\n",
      "feature 565\n",
      "feature 566\n",
      "feature 567\n",
      "feature 568\n",
      "feature 569\n",
      "feature 570\n",
      "feature 571\n",
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      "feature 573\n",
      "feature 574\n",
      "feature 575\n",
      "feature 576\n",
      "feature 577\n",
      "feature 578\n",
      "feature 579\n",
      "feature 580\n",
      "feature 581\n",
      "feature 582\n",
      "feature 583\n",
      "feature 584\n",
      "feature 585\n",
      "feature 586\n",
      "feature 587\n",
      "feature 588\n",
      "feature 589\n",
      "feature 590\n",
      "feature 591\n",
      "feature 592\n",
      "feature 593\n",
      "feature 594\n",
      "feature 595\n",
      "feature 596\n",
      "feature 597\n",
      "feature 598\n",
      "feature 599\n",
      "feature 600\n",
      "feature 601\n",
      "feature 602\n",
      "feature 603\n",
      "feature 604\n",
      "feature 605\n",
      "feature 606\n",
      "feature 607\n",
      "feature 608\n",
      "feature 609\n",
      "feature 610\n",
      "feature 611\n",
      "feature 612\n",
      "feature 613\n",
      "feature 614\n",
      "feature 615\n",
      "feature 616\n",
      "feature 617\n",
      "feature 618\n",
      "feature 619\n",
      "feature 620\n",
      "feature 621\n",
      "feature 622\n",
      "feature 623\n",
      "feature 624\n",
      "feature 625\n",
      "feature 626\n",
      "feature 627\n",
      "feature 628\n",
      "feature 629\n",
      "feature 630\n",
      "feature 631\n"
     ]
    }
   ],
   "source": [
    "df_list = [Yunta50,Taiwan50,orderbook,oil_price,bond,TXF,EXF,FXF,E_F,Nikkei,SP,Russell,DJ,SOX,VIX,pcr,FOI,IOI,DOI]\n",
    "df_option = [True,True,True,True,True,True,True,True,False,True,True,True,True,True,True,False,True,True,True]\n",
    "df_name = ['Yunta50','Taiwan50','orderbook','oil_price','bond','TXF','EXF','FXF','E_F','Nikkei','SP','Russell','DJ','SOX','VIX','pcr','FOI','IOI','DOI']\n",
    "\n",
    "intervals = [0,0.1,0.25,0.5,0.75,1,1.5]\n",
    "window = 1\n",
    "\n",
    "best_score = []\n",
    "best_params = []\n",
    "for interval in intervals:\n",
    "    print('interval is',interval)\n",
    "    df_total ,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf_v = get_dataframe_rf_feature(df_list,df_option,Yunta50_rate,interval,window,ca = 4)\n",
    "    amount_of_features = len(df_total.columns)-3\n",
    "    score = []\n",
    "    params = []\n",
    "    for index in range(amount_of_features):\n",
    "        print('feature',index)\n",
    "        train_X_f = train_X[:,index].reshape((-1,1))\n",
    "        test_X_f = test_X[:,index].reshape((-1,1))\n",
    "        param_test1 = rf_params_test\n",
    "        gsearch1 = GridSearchCV(estimator = RandomForestClassifier(random_state=0), param_grid = param_test1, scoring='accuracy',cv=2)\n",
    "        gsearch1.fit(train_X_f,train_rise)\n",
    "        params.append(gsearch1.best_params_)\n",
    "        score.append(gsearch1.best_score_)\n",
    "\n",
    "#         model = RandomForestClassifier()\n",
    "#         model.set_params(**gsearch1.best_params_)\n",
    "\n",
    "    best_score.append(score)\n",
    "    best_params.append(params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yunta50_open_1_1.0_r</th>\n",
       "      <th>Yunta50_open_1_2.0_r</th>\n",
       "      <th>Yunta50_open_1_3.0_r</th>\n",
       "      <th>Yunta50_open_1_4.0_r</th>\n",
       "      <th>Yunta50_high_1_1.0_r</th>\n",
       "      <th>Yunta50_high_1_2.0_r</th>\n",
       "      <th>Yunta50_high_1_3.0_r</th>\n",
       "      <th>Yunta50_high_1_4.0_r</th>\n",
       "      <th>Yunta50_low_1_1.0_r</th>\n",
       "      <th>Yunta50_low_1_2.0_r</th>\n",
       "      <th>...</th>\n",
       "      <th>DOI_high_1_3.0_f</th>\n",
       "      <th>DOI_high_1_4.0_f</th>\n",
       "      <th>DOI_low_1_1.0_f</th>\n",
       "      <th>DOI_low_1_2.0_f</th>\n",
       "      <th>DOI_low_1_3.0_f</th>\n",
       "      <th>DOI_low_1_4.0_f</th>\n",
       "      <th>DOI_close_1_1.0_f</th>\n",
       "      <th>DOI_close_1_2.0_f</th>\n",
       "      <th>DOI_close_1_3.0_f</th>\n",
       "      <th>DOI_close_1_4.0_f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.505233</td>\n",
       "      <td>0.502379</td>\n",
       "      <td>0.500476</td>\n",
       "      <td>0.491912</td>\n",
       "      <td>0.511893</td>\n",
       "      <td>0.503330</td>\n",
       "      <td>0.505233</td>\n",
       "      <td>0.505233</td>\n",
       "      <td>0.500476</td>\n",
       "      <td>0.506185</td>\n",
       "      <td>...</td>\n",
       "      <td>0.503330</td>\n",
       "      <td>0.497621</td>\n",
       "      <td>0.498573</td>\n",
       "      <td>0.505233</td>\n",
       "      <td>0.503330</td>\n",
       "      <td>0.497621</td>\n",
       "      <td>0.498573</td>\n",
       "      <td>0.505233</td>\n",
       "      <td>0.503330</td>\n",
       "      <td>0.497621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.530923</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.532826</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.534729</td>\n",
       "      <td>0.529971</td>\n",
       "      <td>0.535680</td>\n",
       "      <td>0.541389</td>\n",
       "      <td>0.534729</td>\n",
       "      <td>0.529971</td>\n",
       "      <td>0.535680</td>\n",
       "      <td>0.541389</td>\n",
       "      <td>0.534729</td>\n",
       "      <td>0.529971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.589914</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.603235</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.589914</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.603235</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.589914</td>\n",
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       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>...</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.50</th>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>...</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 632 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Yunta50_open_1_1.0_r  Yunta50_open_1_2.0_r  Yunta50_open_1_3.0_r  \\\n",
       "0.00              0.505233              0.502379              0.500476   \n",
       "0.10              0.543292              0.530923              0.543292   \n",
       "0.25              0.607992              0.607992              0.607992   \n",
       "0.50              0.727878              0.727878              0.727878   \n",
       "0.75              0.797336              0.797336              0.797336   \n",
       "1.00              0.860133              0.860133              0.860133   \n",
       "1.50              0.944814              0.944814              0.944814   \n",
       "\n",
       "      Yunta50_open_1_4.0_r  Yunta50_high_1_1.0_r  Yunta50_high_1_2.0_r  \\\n",
       "0.00              0.491912              0.511893              0.503330   \n",
       "0.10              0.543292              0.543292              0.543292   \n",
       "0.25              0.607992              0.607992              0.607992   \n",
       "0.50              0.727878              0.727878              0.727878   \n",
       "0.75              0.797336              0.797336              0.797336   \n",
       "1.00              0.860133              0.860133              0.860133   \n",
       "1.50              0.944814              0.944814              0.944814   \n",
       "\n",
       "      Yunta50_high_1_3.0_r  Yunta50_high_1_4.0_r  Yunta50_low_1_1.0_r  \\\n",
       "0.00              0.505233              0.505233             0.500476   \n",
       "0.10              0.543292              0.543292             0.532826   \n",
       "0.25              0.607992              0.607992             0.607992   \n",
       "0.50              0.727878              0.727878             0.727878   \n",
       "0.75              0.797336              0.797336             0.797336   \n",
       "1.00              0.860133              0.860133             0.860133   \n",
       "1.50              0.944814              0.944814             0.944814   \n",
       "\n",
       "      Yunta50_low_1_2.0_r        ...          DOI_high_1_3.0_f  \\\n",
       "0.00             0.506185        ...                  0.503330   \n",
       "0.10             0.543292        ...                  0.534729   \n",
       "0.25             0.607992        ...                  0.607992   \n",
       "0.50             0.727878        ...                  0.727878   \n",
       "0.75             0.797336        ...                  0.797336   \n",
       "1.00             0.860133        ...                  0.860133   \n",
       "1.50             0.944814        ...                  0.944814   \n",
       "\n",
       "      DOI_high_1_4.0_f  DOI_low_1_1.0_f  DOI_low_1_2.0_f  DOI_low_1_3.0_f  \\\n",
       "0.00          0.497621         0.498573         0.505233         0.503330   \n",
       "0.10          0.529971         0.535680         0.541389         0.534729   \n",
       "0.25          0.589914         0.607992         0.603235         0.607992   \n",
       "0.50          0.727878         0.727878         0.727878         0.727878   \n",
       "0.75          0.797336         0.797336         0.797336         0.797336   \n",
       "1.00          0.860133         0.860133         0.860133         0.860133   \n",
       "1.50          0.944814         0.944814         0.944814         0.944814   \n",
       "\n",
       "      DOI_low_1_4.0_f  DOI_close_1_1.0_f  DOI_close_1_2.0_f  \\\n",
       "0.00         0.497621           0.498573           0.505233   \n",
       "0.10         0.529971           0.535680           0.541389   \n",
       "0.25         0.589914           0.607992           0.603235   \n",
       "0.50         0.727878           0.727878           0.727878   \n",
       "0.75         0.797336           0.797336           0.797336   \n",
       "1.00         0.860133           0.860133           0.860133   \n",
       "1.50         0.944814           0.944814           0.944814   \n",
       "\n",
       "      DOI_close_1_3.0_f  DOI_close_1_4.0_f  \n",
       "0.00           0.503330           0.497621  \n",
       "0.10           0.534729           0.529971  \n",
       "0.25           0.607992           0.589914  \n",
       "0.50           0.727878           0.727878  \n",
       "0.75           0.797336           0.797336  \n",
       "1.00           0.860133           0.860133  \n",
       "1.50           0.944814           0.944814  \n",
       "\n",
       "[7 rows x 632 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_srf = pd.DataFrame(best_score,columns=df_total.columns[:-3],index = intervals)\n",
    "df_srf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: 0.0, dtype: float64)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_srf.iloc[0,:][df_srf.iloc[0,:]>0.6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine r and f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "feature 315\n",
      "interval is 1.5\n",
      "feature 0\n",
      "feature 1\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature 211\n",
      "feature 212\n",
      "feature 213\n",
      "feature 214\n",
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      "feature 305\n",
      "feature 306\n",
      "feature 307\n",
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      "feature 311\n",
      "feature 312\n",
      "feature 313\n",
      "feature 314\n",
      "feature 315\n"
     ]
    }
   ],
   "source": [
    "df_list = [Yunta50,Taiwan50,orderbook,oil_price,bond,TXF,EXF,FXF,E_F,Nikkei,SP,Russell,DJ,SOX,VIX,pcr,FOI,IOI,DOI]\n",
    "df_option = [True,True,True,True,True,True,True,True,False,True,True,True,True,True,True,False,True,True,True]\n",
    "df_name = ['Yunta50','Taiwan50','orderbook','oil_price','bond','TXF','EXF','FXF','E_F','Nikkei','SP','Russell','DJ','SOX','VIX','pcr','FOI','IOI','DOI']\n",
    "\n",
    "intervals = [0,0.1,0.25,0.5,0.75,1,1.5]\n",
    "window = 1\n",
    "\n",
    "best_score = []\n",
    "best_params = []\n",
    "for interval in intervals:\n",
    "    print('interval is',interval)\n",
    "    df_total ,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf_v = get_dataframe_rf_feature(df_list,df_option,Yunta50_rate,interval,window,ca = 4)\n",
    "    amount_of_features = len(df_total.columns)-3\n",
    "    half = int(amount_of_features/2)\n",
    "    score = []\n",
    "    params = []\n",
    "    for index in range(half):\n",
    "        print('feature',index)\n",
    "        train_X_f = train_X[:,[index,index+half]]\n",
    "#         test_X_f = test_X[:,index].reshape((-1,1))\n",
    "        param_test1 = rf_params_test\n",
    "        gsearch1 = GridSearchCV(estimator = RandomForestClassifier(random_state=0), param_grid = param_test1, scoring='accuracy',cv=2)\n",
    "        gsearch1.fit(train_X_f,train_rise)\n",
    "        params.append(gsearch1.best_params_)\n",
    "        score.append(gsearch1.best_score_)\n",
    "\n",
    "#         model = RandomForestClassifier()\n",
    "#         model.set_params(**gsearch1.best_params_)\n",
    "\n",
    "    best_score.append(score)\n",
    "    best_params.append(params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yunta50_open_1_1.0_r</th>\n",
       "      <th>Yunta50_open_1_2.0_r</th>\n",
       "      <th>Yunta50_open_1_3.0_r</th>\n",
       "      <th>Yunta50_open_1_4.0_r</th>\n",
       "      <th>Yunta50_high_1_1.0_r</th>\n",
       "      <th>Yunta50_high_1_2.0_r</th>\n",
       "      <th>Yunta50_high_1_3.0_r</th>\n",
       "      <th>Yunta50_high_1_4.0_r</th>\n",
       "      <th>Yunta50_low_1_1.0_r</th>\n",
       "      <th>Yunta50_low_1_2.0_r</th>\n",
       "      <th>...</th>\n",
       "      <th>DOI_high_1_3.0_r</th>\n",
       "      <th>DOI_high_1_4.0_r</th>\n",
       "      <th>DOI_low_1_1.0_r</th>\n",
       "      <th>DOI_low_1_2.0_r</th>\n",
       "      <th>DOI_low_1_3.0_r</th>\n",
       "      <th>DOI_low_1_4.0_r</th>\n",
       "      <th>DOI_close_1_1.0_r</th>\n",
       "      <th>DOI_close_1_2.0_r</th>\n",
       "      <th>DOI_close_1_3.0_r</th>\n",
       "      <th>DOI_close_1_4.0_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.514748</td>\n",
       "      <td>0.502379</td>\n",
       "      <td>0.500476</td>\n",
       "      <td>0.491912</td>\n",
       "      <td>0.515699</td>\n",
       "      <td>0.503330</td>\n",
       "      <td>0.505233</td>\n",
       "      <td>0.506185</td>\n",
       "      <td>0.500476</td>\n",
       "      <td>0.509039</td>\n",
       "      <td>...</td>\n",
       "      <td>0.506185</td>\n",
       "      <td>0.503330</td>\n",
       "      <td>0.507136</td>\n",
       "      <td>0.503330</td>\n",
       "      <td>0.506185</td>\n",
       "      <td>0.503330</td>\n",
       "      <td>0.507136</td>\n",
       "      <td>0.503330</td>\n",
       "      <td>0.506185</td>\n",
       "      <td>0.503330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.530923</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.532826</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.531874</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.531874</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.543292</td>\n",
       "      <td>0.531874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "      <td>0.607992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>...</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>0.727878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.797336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "      <td>0.860133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.50</th>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>...</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "      <td>0.944814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 316 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Yunta50_open_1_1.0_r  Yunta50_open_1_2.0_r  Yunta50_open_1_3.0_r  \\\n",
       "0.00              0.514748              0.502379              0.500476   \n",
       "0.10              0.543292              0.530923              0.543292   \n",
       "0.25              0.607992              0.607992              0.607992   \n",
       "0.50              0.727878              0.727878              0.727878   \n",
       "0.75              0.797336              0.797336              0.797336   \n",
       "1.00              0.860133              0.860133              0.860133   \n",
       "1.50              0.944814              0.944814              0.944814   \n",
       "\n",
       "      Yunta50_open_1_4.0_r  Yunta50_high_1_1.0_r  Yunta50_high_1_2.0_r  \\\n",
       "0.00              0.491912              0.515699              0.503330   \n",
       "0.10              0.543292              0.543292              0.543292   \n",
       "0.25              0.607992              0.607992              0.607992   \n",
       "0.50              0.727878              0.727878              0.727878   \n",
       "0.75              0.797336              0.797336              0.797336   \n",
       "1.00              0.860133              0.860133              0.860133   \n",
       "1.50              0.944814              0.944814              0.944814   \n",
       "\n",
       "      Yunta50_high_1_3.0_r  Yunta50_high_1_4.0_r  Yunta50_low_1_1.0_r  \\\n",
       "0.00              0.505233              0.506185             0.500476   \n",
       "0.10              0.543292              0.543292             0.532826   \n",
       "0.25              0.607992              0.607992             0.607992   \n",
       "0.50              0.727878              0.727878             0.727878   \n",
       "0.75              0.797336              0.797336             0.797336   \n",
       "1.00              0.860133              0.860133             0.860133   \n",
       "1.50              0.944814              0.944814             0.944814   \n",
       "\n",
       "      Yunta50_low_1_2.0_r        ...          DOI_high_1_3.0_r  \\\n",
       "0.00             0.509039        ...                  0.506185   \n",
       "0.10             0.543292        ...                  0.543292   \n",
       "0.25             0.607992        ...                  0.607992   \n",
       "0.50             0.727878        ...                  0.727878   \n",
       "0.75             0.797336        ...                  0.797336   \n",
       "1.00             0.860133        ...                  0.860133   \n",
       "1.50             0.944814        ...                  0.944814   \n",
       "\n",
       "      DOI_high_1_4.0_r  DOI_low_1_1.0_r  DOI_low_1_2.0_r  DOI_low_1_3.0_r  \\\n",
       "0.00          0.503330         0.507136         0.503330         0.506185   \n",
       "0.10          0.531874         0.543292         0.543292         0.543292   \n",
       "0.25          0.607992         0.607992         0.607992         0.607992   \n",
       "0.50          0.727878         0.727878         0.727878         0.727878   \n",
       "0.75          0.797336         0.797336         0.797336         0.797336   \n",
       "1.00          0.860133         0.860133         0.860133         0.860133   \n",
       "1.50          0.944814         0.944814         0.944814         0.944814   \n",
       "\n",
       "      DOI_low_1_4.0_r  DOI_close_1_1.0_r  DOI_close_1_2.0_r  \\\n",
       "0.00         0.503330           0.507136           0.503330   \n",
       "0.10         0.531874           0.543292           0.543292   \n",
       "0.25         0.607992           0.607992           0.607992   \n",
       "0.50         0.727878           0.727878           0.727878   \n",
       "0.75         0.797336           0.797336           0.797336   \n",
       "1.00         0.860133           0.860133           0.860133   \n",
       "1.50         0.944814           0.944814           0.944814   \n",
       "\n",
       "      DOI_close_1_3.0_r  DOI_close_1_4.0_r  \n",
       "0.00           0.506185           0.503330  \n",
       "0.10           0.543292           0.531874  \n",
       "0.25           0.607992           0.607992  \n",
       "0.50           0.727878           0.727878  \n",
       "0.75           0.797336           0.797336  \n",
       "1.00           0.860133           0.860133  \n",
       "1.50           0.944814           0.944814  \n",
       "\n",
       "[7 rows x 316 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_crf = pd.DataFrame(best_score,columns=df_total.columns[:half],index = intervals)\n",
    "df_crf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SP_close_1_4.0_r    0.615604\n",
       "DJ_close_1_4.0_r    0.603235\n",
       "Name: 0.0, dtype: float64"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_crf.iloc[0,:][df_crf.iloc[0,:]>0.6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SP_low_1_1.0_r', 'Russell_close_1_4.0_r', 'DJ_close_1_1.0_r',\n",
       "       'SP_close_1_1.0_r', 'VIX_close_1_1.0_r', 'VIX_close_1_4.0_r',\n",
       "       'SOX_close_1_4.0_r', 'SP_high_1_4.0_r', 'DJ_close_1_4.0_r',\n",
       "       'SP_close_1_4.0_r'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg_i1 = np.argsort(df_crf.iloc[0,:].values)[-10:]\n",
    "df_crf.columns[arg_i1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([176, 203, 220, 180, 256, 259, 243, 175, 223, 183], dtype=int64)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg_i1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### combine rf is better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigation of ca"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get all feature model  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval is 0\n",
      "feature 0\n",
      "feature 1\n",
      "feature 2\n",
      "feature 3\n",
      "feature 4\n",
      "feature 5\n",
      "feature 6\n",
      "feature 7\n",
      "feature 8\n",
      "feature 9\n",
      "feature 10\n",
      "feature 11\n",
      "feature 12\n",
      "feature 13\n",
      "feature 14\n",
      "feature 15\n",
      "feature 16\n",
      "feature 17\n",
      "feature 18\n",
      "feature 19\n",
      "feature 20\n",
      "feature 21\n",
      "feature 22\n",
      "feature 23\n",
      "feature 24\n",
      "feature 25\n",
      "feature 26\n",
      "feature 27\n",
      "feature 28\n",
      "feature 29\n",
      "feature 30\n",
      "feature 31\n",
      "feature 32\n",
      "feature 33\n",
      "feature 34\n",
      "feature 35\n",
      "feature 36\n",
      "feature 37\n",
      "feature 38\n",
      "feature 39\n",
      "feature 40\n",
      "feature 41\n",
      "feature 42\n",
      "feature 43\n",
      "feature 44\n",
      "feature 45\n",
      "feature 46\n",
      "feature 47\n",
      "feature 48\n",
      "feature 49\n",
      "feature 50\n",
      "feature 51\n",
      "feature 52\n",
      "feature 53\n",
      "feature 54\n",
      "feature 55\n",
      "feature 56\n",
      "feature 57\n",
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      "feature 60\n",
      "feature 61\n",
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      "feature 64\n",
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      "feature 86\n",
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      "feature 88\n",
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      "feature 95\n",
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      "feature 98\n",
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      "feature 100\n",
      "feature 101\n",
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      "feature 110\n",
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      "feature 119\n",
      "feature 120\n",
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      "feature 148\n",
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      "feature 225\n",
      "feature 226\n",
      "feature 227\n",
      "feature 228\n",
      "feature 229\n",
      "feature 230\n",
      "interval is 0.1\n",
      "feature 0\n",
      "feature 1\n",
      "feature 2\n",
      "feature 3\n",
      "feature 4\n",
      "feature 5\n",
      "feature 6\n",
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      "feature 228\n",
      "feature 229\n",
      "feature 230\n",
      "interval is 0.25\n",
      "feature 0\n",
      "feature 1\n",
      "feature 2\n",
      "feature 3\n",
      "feature 4\n",
      "feature 5\n",
      "feature 6\n",
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      "feature 86\n",
      "feature 87\n",
      "feature 88\n",
      "feature 89\n",
      "feature 90\n",
      "feature 91\n",
      "feature 92\n",
      "feature 93\n",
      "feature 94\n",
      "feature 95\n",
      "feature 96\n",
      "feature 97\n",
      "feature 98\n",
      "feature 99\n",
      "feature 100\n",
      "feature 101\n",
      "feature 102\n",
      "feature 103\n",
      "feature 104\n",
      "feature 105\n",
      "feature 106\n",
      "feature 107\n",
      "feature 108\n",
      "feature 109\n",
      "feature 110\n",
      "feature 111\n",
      "feature 112\n",
      "feature 113\n",
      "feature 114\n",
      "feature 115\n",
      "feature 116\n",
      "feature 117\n",
      "feature 118\n",
      "feature 119\n",
      "feature 120\n",
      "feature 121\n",
      "feature 122\n",
      "feature 123\n",
      "feature 124\n",
      "feature 125\n",
      "feature 126\n",
      "feature 127\n",
      "feature 128\n",
      "feature 129\n",
      "feature 130\n",
      "feature 131\n",
      "feature 132\n",
      "feature 133\n",
      "feature 134\n",
      "feature 135\n",
      "feature 136\n",
      "feature 137\n",
      "feature 138\n",
      "feature 139\n",
      "feature 140\n",
      "feature 141\n",
      "feature 142\n",
      "feature 143\n",
      "feature 144\n",
      "feature 145\n",
      "feature 146\n",
      "feature 147\n",
      "feature 148\n",
      "feature 149\n",
      "feature 150\n",
      "feature 151\n",
      "feature 152\n",
      "feature 153\n",
      "feature 154\n",
      "feature 155\n",
      "feature 156\n",
      "feature 157\n",
      "feature 158\n",
      "feature 159\n",
      "feature 160\n",
      "feature 161\n",
      "feature 162\n",
      "feature 163\n",
      "feature 164\n",
      "feature 165\n",
      "feature 166\n",
      "feature 167\n",
      "feature 168\n",
      "feature 169\n",
      "feature 170\n",
      "feature 171\n",
      "feature 172\n",
      "feature 173\n",
      "feature 174\n",
      "feature 175\n",
      "feature 176\n",
      "feature 177\n",
      "feature 178\n",
      "feature 179\n",
      "feature 180\n",
      "feature 181\n",
      "feature 182\n",
      "feature 183\n",
      "feature 184\n",
      "feature 185\n",
      "feature 186\n",
      "feature 187\n",
      "feature 188\n",
      "feature 189\n",
      "feature 190\n",
      "feature 191\n",
      "feature 192\n",
      "feature 193\n",
      "feature 194\n",
      "feature 195\n",
      "feature 196\n",
      "feature 197\n",
      "feature 198\n",
      "feature 199\n",
      "feature 200\n",
      "feature 201\n",
      "feature 202\n",
      "feature 203\n",
      "feature 204\n",
      "feature 205\n",
      "feature 206\n",
      "feature 207\n",
      "feature 208\n",
      "feature 209\n",
      "feature 210\n",
      "feature 211\n",
      "feature 212\n",
      "feature 213\n",
      "feature 214\n",
      "feature 215\n",
      "feature 216\n",
      "feature 217\n",
      "feature 218\n",
      "feature 219\n",
      "feature 220\n",
      "feature 221\n",
      "feature 222\n",
      "feature 223\n",
      "feature 224\n",
      "feature 225\n",
      "feature 226\n",
      "feature 227\n",
      "feature 228\n",
      "feature 229\n",
      "feature 230\n"
     ]
    }
   ],
   "source": [
    "df_list = [SP,Russell,DJ,SOX,VIX]\n",
    "df_option = [True,True,True,True,True]\n",
    "df_name = ['SP','Russell','DJ','SOX','VIX']\n",
    "# df_list = [Yunta50,Taiwan50,orderbook,oil_price,bond,TXF,EXF,FXF,E_F,Nikkei,SP,Russell,DJ,SOX,VIX,pcr,FOI,IOI,DOI]\n",
    "# df_option = [True,True,True,True,True,True,True,True,False,True,True,True,True,True,True,False,True,True,True]\n",
    "\n",
    "intervals = [0,0.1,0.25,0.5,0.75,1,1.5]\n",
    "ca = 10\n",
    "window = 1\n",
    "\n",
    "best_score = []\n",
    "best_params = []\n",
    "for interval in intervals:\n",
    "    print('interval is',interval)\n",
    "    df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = ca)\n",
    "    amount_of_features = len(df_total.columns)-3\n",
    "    half = int(amount_of_features/2)\n",
    "    score = []\n",
    "    params = []\n",
    "    for index in range(half):\n",
    "        print('feature',index)\n",
    "        train_X_f = train_X[:,index].reshape((-1,1))\n",
    "        param_test1 = rf_params_test\n",
    "        gsearch1 = GridSearchCV(estimator = RandomForestClassifier(random_state=0), param_grid = param_test1, scoring='accuracy',cv=2)\n",
    "        gsearch1.fit(train_X_f,train_rise)\n",
    "        params.append(gsearch1.best_params_)\n",
    "        score.append(gsearch1.best_score_)\n",
    "\n",
    "#         model = RandomForestClassifier()\n",
    "#         model.set_params(**gsearch1.best_params_)\n",
    "\n",
    "    best_score.append(score)\n",
    "    best_params.append(params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP_open_1_1.0</th>\n",
       "      <th>SP_open_1_2.0</th>\n",
       "      <th>SP_open_1_3.0</th>\n",
       "      <th>SP_open_1_4.0</th>\n",
       "      <th>SP_open_1_5.0</th>\n",
       "      <th>SP_open_1_6.0</th>\n",
       "      <th>SP_open_1_7.0</th>\n",
       "      <th>SP_open_1_8.0</th>\n",
       "      <th>SP_open_1_9.0</th>\n",
       "      <th>SP_open_1_10.0</th>\n",
       "      <th>...</th>\n",
       "      <th>VIX_close_1_2.0</th>\n",
       "      <th>VIX_close_1_3.0</th>\n",
       "      <th>VIX_close_1_4.0</th>\n",
       "      <th>VIX_close_1_5.0</th>\n",
       "      <th>VIX_close_1_6.0</th>\n",
       "      <th>VIX_close_1_7.0</th>\n",
       "      <th>VIX_close_1_8.0</th>\n",
       "      <th>VIX_close_1_9.0</th>\n",
       "      <th>VIX_close_1_10.0</th>\n",
       "      <th>mean_rise_fall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.498657</td>\n",
       "      <td>0.502238</td>\n",
       "      <td>0.498657</td>\n",
       "      <td>0.496867</td>\n",
       "      <td>0.499552</td>\n",
       "      <td>0.506714</td>\n",
       "      <td>0.490600</td>\n",
       "      <td>0.521038</td>\n",
       "      <td>0.505819</td>\n",
       "      <td>0.499552</td>\n",
       "      <td>...</td>\n",
       "      <td>0.541629</td>\n",
       "      <td>0.524620</td>\n",
       "      <td>0.518353</td>\n",
       "      <td>0.478066</td>\n",
       "      <td>0.506714</td>\n",
       "      <td>0.514772</td>\n",
       "      <td>0.508505</td>\n",
       "      <td>0.529991</td>\n",
       "      <td>0.546106</td>\n",
       "      <td>0.481647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.533572</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.504924</td>\n",
       "      <td>0.522829</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.547001</td>\n",
       "      <td>0.544315</td>\n",
       "      <td>0.546106</td>\n",
       "      <td>...</td>\n",
       "      <td>0.565801</td>\n",
       "      <td>0.552372</td>\n",
       "      <td>0.545210</td>\n",
       "      <td>0.475380</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.524620</td>\n",
       "      <td>0.540734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.605192</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>...</td>\n",
       "      <td>0.627574</td>\n",
       "      <td>0.605192</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.722471</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.50</th>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>...</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 231 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SP_open_1_1.0  SP_open_1_2.0  SP_open_1_3.0  SP_open_1_4.0  \\\n",
       "0.00       0.498657       0.502238       0.498657       0.496867   \n",
       "0.10       0.533572       0.540734       0.504924       0.522829   \n",
       "0.25       0.609669       0.609669       0.609669       0.609669   \n",
       "0.50       0.726052       0.726052       0.726052       0.726052   \n",
       "0.75       0.800358       0.800358       0.800358       0.800358   \n",
       "1.00       0.862131       0.862131       0.862131       0.862131   \n",
       "1.50       0.946285       0.946285       0.946285       0.946285   \n",
       "\n",
       "      SP_open_1_5.0  SP_open_1_6.0  SP_open_1_7.0  SP_open_1_8.0  \\\n",
       "0.00       0.499552       0.506714       0.490600       0.521038   \n",
       "0.10       0.540734       0.540734       0.540734       0.547001   \n",
       "0.25       0.609669       0.609669       0.609669       0.605192   \n",
       "0.50       0.726052       0.726052       0.726052       0.726052   \n",
       "0.75       0.800358       0.800358       0.800358       0.800358   \n",
       "1.00       0.862131       0.862131       0.862131       0.862131   \n",
       "1.50       0.946285       0.946285       0.946285       0.946285   \n",
       "\n",
       "      SP_open_1_9.0  SP_open_1_10.0       ...        VIX_close_1_2.0  \\\n",
       "0.00       0.505819        0.499552       ...               0.541629   \n",
       "0.10       0.544315        0.546106       ...               0.565801   \n",
       "0.25       0.609669        0.609669       ...               0.627574   \n",
       "0.50       0.726052        0.726052       ...               0.722471   \n",
       "0.75       0.800358        0.800358       ...               0.800358   \n",
       "1.00       0.862131        0.862131       ...               0.862131   \n",
       "1.50       0.946285        0.946285       ...               0.946285   \n",
       "\n",
       "      VIX_close_1_3.0  VIX_close_1_4.0  VIX_close_1_5.0  VIX_close_1_6.0  \\\n",
       "0.00         0.524620         0.518353         0.478066         0.506714   \n",
       "0.10         0.552372         0.545210         0.475380         0.540734   \n",
       "0.25         0.605192         0.609669         0.609669         0.609669   \n",
       "0.50         0.726052         0.726052         0.726052         0.726052   \n",
       "0.75         0.800358         0.800358         0.800358         0.800358   \n",
       "1.00         0.862131         0.862131         0.862131         0.862131   \n",
       "1.50         0.946285         0.946285         0.946285         0.946285   \n",
       "\n",
       "      VIX_close_1_7.0  VIX_close_1_8.0  VIX_close_1_9.0  VIX_close_1_10.0  \\\n",
       "0.00         0.514772         0.508505         0.529991          0.546106   \n",
       "0.10         0.540734         0.540734         0.540734          0.524620   \n",
       "0.25         0.609669         0.609669         0.609669          0.609669   \n",
       "0.50         0.726052         0.726052         0.726052          0.726052   \n",
       "0.75         0.800358         0.800358         0.800358          0.800358   \n",
       "1.00         0.862131         0.862131         0.862131          0.862131   \n",
       "1.50         0.946285         0.946285         0.946285          0.946285   \n",
       "\n",
       "      mean_rise_fall  \n",
       "0.00        0.481647  \n",
       "0.10        0.540734  \n",
       "0.25        0.609669  \n",
       "0.50        0.726052  \n",
       "0.75        0.800358  \n",
       "1.00        0.862131  \n",
       "1.50        0.946285  \n",
       "\n",
       "[7 rows x 231 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca_am = pd.DataFrame(best_score,columns=df_total.columns[:half],index = intervals)\n",
    "df_ca_am"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_ca_am' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-da30f2b256c4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0marg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_ca_am\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mintervals\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf_ca_am\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mintervals\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df_ca_am' is not defined"
     ]
    }
   ],
   "source": [
    "arg = np.argsort(df_ca_am.loc[intervals[0]].values)[-10:]\n",
    "df_ca_am.loc[intervals[0]][arg].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### choose the good features and rebuild the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval is 0\n",
      "interval is 0.1\n",
      "interval is 0.25\n",
      "interval is 0.5\n",
      "interval is 0.75\n",
      "interval is 1\n",
      "interval is 1.5\n"
     ]
    }
   ],
   "source": [
    "best_score = []\n",
    "best_params = []\n",
    "numbers = [1,5,10,20,100,0]\n",
    "for interval in intervals:\n",
    "    print('interval is',interval)\n",
    "    df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = ca)\n",
    "    score = []\n",
    "    params = []\n",
    "    #select the best ten feature\n",
    "    for number in numbers:\n",
    "        arg = np.argsort(df_ca_am.loc[interval].values)[number*-1:]\n",
    "        # train\n",
    "        train_X_f = train_X[:,arg]\n",
    "        param_test1 = rf_params_test\n",
    "        gsearch1 = GridSearchCV(estimator = RandomForestClassifier(random_state=0), param_grid = param_test1, scoring='accuracy',cv=2)\n",
    "        gsearch1.fit(train_X_f,train_rise)\n",
    "        params.append(gsearch1.best_params_)\n",
    "        score.append(gsearch1.best_score_)\n",
    "\n",
    "#         model = RandomForestClassifier()\n",
    "#         model.set_params(**gsearch1.best_params_)\n",
    "    best_score.append(score)\n",
    "    best_params.append(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>20</th>\n",
       "      <th>100</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.554163</td>\n",
       "      <td>0.579230</td>\n",
       "      <td>0.587287</td>\n",
       "      <td>0.565801</td>\n",
       "      <td>0.571173</td>\n",
       "      <td>0.553268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.584602</td>\n",
       "      <td>0.612355</td>\n",
       "      <td>0.613250</td>\n",
       "      <td>0.615040</td>\n",
       "      <td>0.598030</td>\n",
       "      <td>0.554163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.649955</td>\n",
       "      <td>0.655327</td>\n",
       "      <td>0.660698</td>\n",
       "      <td>0.660698</td>\n",
       "      <td>0.648165</td>\n",
       "      <td>0.631155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.730528</td>\n",
       "      <td>0.732319</td>\n",
       "      <td>0.730528</td>\n",
       "      <td>0.729633</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.717995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.802149</td>\n",
       "      <td>0.800358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.50</th>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1         5         10        20        100       0  \n",
       "0.00  0.554163  0.579230  0.587287  0.565801  0.571173  0.553268\n",
       "0.10  0.584602  0.612355  0.613250  0.615040  0.598030  0.554163\n",
       "0.25  0.649955  0.655327  0.660698  0.660698  0.648165  0.631155\n",
       "0.50  0.730528  0.732319  0.730528  0.729633  0.726052  0.717995\n",
       "0.75  0.800358  0.800358  0.800358  0.800358  0.802149  0.800358\n",
       "1.00  0.862131  0.862131  0.862131  0.862131  0.862131  0.862131\n",
       "1.50  0.946285  0.946285  0.946285  0.946285  0.946285  0.946285"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca_am_com = pd.DataFrame(best_score,columns=numbers,index = intervals)\n",
    "df_ca_am_com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5684870188003581"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca_am_com.loc[intervals[0]].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### combining the good feature(10 is better) makes the model better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### choose the feature by outlier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval is 0\n",
      "feature 0\n",
      "feature 1\n",
      "feature 2\n",
      "feature 3\n",
      "feature 4\n",
      "feature 5\n",
      "feature 6\n",
      "feature 7\n",
      "feature 8\n",
      "feature 9\n",
      "feature 10\n",
      "feature 11\n",
      "feature 12\n",
      "feature 13\n",
      "feature 14\n",
      "feature 15\n",
      "feature 16\n",
      "feature 17\n",
      "feature 18\n",
      "feature 19\n",
      "feature 20\n",
      "feature 21\n",
      "feature 22\n",
      "feature 23\n",
      "feature 24\n",
      "feature 25\n",
      "feature 26\n",
      "feature 27\n",
      "feature 28\n",
      "feature 29\n",
      "feature 30\n",
      "feature 31\n",
      "feature 32\n",
      "feature 33\n",
      "feature 34\n",
      "feature 35\n",
      "feature 36\n",
      "feature 37\n",
      "feature 38\n",
      "feature 39\n",
      "feature 40\n",
      "feature 41\n",
      "feature 42\n",
      "feature 43\n",
      "feature 44\n",
      "feature 45\n",
      "feature 46\n",
      "feature 47\n",
      "feature 48\n",
      "feature 49\n",
      "feature 50\n",
      "feature 51\n",
      "feature 52\n",
      "feature 53\n",
      "feature 54\n",
      "feature 55\n",
      "feature 56\n",
      "feature 57\n",
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      "feature 60\n",
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      "feature 64\n",
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      "feature 66\n",
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      "feature 70\n",
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      "feature 230\n",
      "interval is 0.1\n",
      "feature 0\n",
      "feature 1\n",
      "feature 2\n",
      "feature 3\n",
      "feature 4\n",
      "feature 5\n",
      "feature 6\n",
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      "feature 230\n",
      "interval is 0.25\n",
      "feature 0\n",
      "feature 1\n",
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    {
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     "text": [
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      "feature 226\n",
      "feature 227\n",
      "feature 228\n",
      "feature 229\n",
      "feature 230\n",
      "interval is 1.5\n",
      "feature 0\n",
      "feature 1\n",
      "feature 2\n",
      "feature 3\n",
      "feature 4\n",
      "feature 5\n",
      "feature 6\n",
      "feature 7\n",
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      "feature 10\n",
      "feature 11\n",
      "feature 12\n",
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      "feature 20\n",
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      "feature 22\n",
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      "feature 29\n",
      "feature 30\n",
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      "feature 32\n",
      "feature 33\n",
      "feature 34\n",
      "feature 35\n",
      "feature 36\n",
      "feature 37\n",
      "feature 38\n",
      "feature 39\n",
      "feature 40\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature 41\n",
      "feature 42\n",
      "feature 43\n",
      "feature 44\n",
      "feature 45\n",
      "feature 46\n",
      "feature 47\n",
      "feature 48\n",
      "feature 49\n",
      "feature 50\n",
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      "feature 57\n",
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      "feature 59\n",
      "feature 60\n",
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      "feature 70\n",
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      "feature 85\n",
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      "feature 100\n",
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      "feature 128\n",
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      "feature 158\n",
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      "feature 160\n",
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      "feature 162\n",
      "feature 163\n",
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      "feature 165\n",
      "feature 166\n",
      "feature 167\n",
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      "feature 169\n",
      "feature 170\n",
      "feature 171\n",
      "feature 172\n",
      "feature 173\n",
      "feature 174\n",
      "feature 175\n",
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      "feature 180\n",
      "feature 181\n",
      "feature 182\n",
      "feature 183\n",
      "feature 184\n",
      "feature 185\n",
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      "feature 187\n",
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      "feature 189\n",
      "feature 190\n",
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      "feature 192\n",
      "feature 193\n",
      "feature 194\n",
      "feature 195\n",
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      "feature 197\n",
      "feature 198\n",
      "feature 199\n",
      "feature 200\n",
      "feature 201\n",
      "feature 202\n",
      "feature 203\n",
      "feature 204\n",
      "feature 205\n",
      "feature 206\n",
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      "feature 209\n",
      "feature 210\n",
      "feature 211\n",
      "feature 212\n",
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      "feature 214\n",
      "feature 215\n",
      "feature 216\n",
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      "feature 218\n",
      "feature 219\n",
      "feature 220\n",
      "feature 221\n",
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      "feature 223\n",
      "feature 224\n",
      "feature 225\n",
      "feature 226\n",
      "feature 227\n",
      "feature 228\n",
      "feature 229\n",
      "feature 230\n"
     ]
    }
   ],
   "source": [
    "df_list = [SP,Russell,DJ,SOX,VIX]\n",
    "df_option = [True,True,True,True,True]\n",
    "df_name = ['SP','Russell','DJ','SOX','VIX']\n",
    "intervals = [0,0.1,0.25,0.5,0.75,1,1.5]\n",
    "cas = [10]\n",
    "window = 1\n",
    "\n",
    "ca_scores_15 = []\n",
    "ca_params_15 = []\n",
    "for ca in cas:\n",
    "    best_score = []\n",
    "    best_params = []\n",
    "    for interval in intervals:\n",
    "        print('interval is',interval)\n",
    "        df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = ca)\n",
    "        amount_of_features = len(df_total.columns)-3\n",
    "        half = int(amount_of_features/2)\n",
    "        score = []\n",
    "        params = []\n",
    "        for index in range(half):\n",
    "            print('feature',index)\n",
    "            if (df_total.columns[index].find('1.0') != -1):\n",
    "                train_X_f = train_X[:,[index,index+half,index+1,index+1+half,index+2,index+2+half,index+3,index+3+half,\n",
    "                                     index+6,index+6+half,index+7,index+7+half,index+8,index+8+half,index+9,index+9+half]]\n",
    "#                 test_X_f = test_X[:,[index,index+half]]\n",
    "#             elif (df_total.columns[index].find('7.0') != -1):\n",
    "#                 train_X_f = train_X[:,[index,index+half,index+1,index+1+half,index+2,index+2+half,index+3,index+3+half]]\n",
    "# #                 test_X_f = test_X[:,[index,index+half]]\n",
    "            elif (df_total.columns[index].find('5.0') != -1):\n",
    "                train_X_f = train_X[:,[index,index+half,index+1,index+1+half]]\n",
    "#                 test_X_f = test_X[:,[index,index+half]]\n",
    "            else:\n",
    "                continue\n",
    "            param_test1 = rf_params_test\n",
    "            gsearch1 = GridSearchCV(estimator = RandomForestClassifier(random_state=0), param_grid = param_test1, scoring='accuracy',cv=2)\n",
    "            gsearch1.fit(train_X_f,train_rise)\n",
    "            params.append(gsearch1.best_params_)\n",
    "            score.append(gsearch1.best_score_)\n",
    "\n",
    "    #         model = RandomForestClassifier()\n",
    "    #         model.set_params(**gsearch1.best_params_)\n",
    "\n",
    "        best_score.append(score)\n",
    "        best_params.append(params)\n",
    "    ca_scores_15.append(best_score)\n",
    "    ca_params_15.append(best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP_open_1_1.0</th>\n",
       "      <th>SP_open_1_2.0</th>\n",
       "      <th>SP_high_1_1.0</th>\n",
       "      <th>SP_high_1_2.0</th>\n",
       "      <th>SP_low_1_1.0</th>\n",
       "      <th>SP_low_1_2.0</th>\n",
       "      <th>SP_close_1_1.0</th>\n",
       "      <th>SP_close_1_2.0</th>\n",
       "      <th>SP_volume_1_1.0</th>\n",
       "      <th>SP_volume_1_2.0</th>\n",
       "      <th>...</th>\n",
       "      <th>SOX_close_1_1.0</th>\n",
       "      <th>SOX_close_1_2.0</th>\n",
       "      <th>VIX_open_1_1.0</th>\n",
       "      <th>VIX_open_1_2.0</th>\n",
       "      <th>VIX_high_1_1.0</th>\n",
       "      <th>VIX_high_1_2.0</th>\n",
       "      <th>VIX_low_1_1.0</th>\n",
       "      <th>VIX_low_1_2.0</th>\n",
       "      <th>VIX_close_1_1.0</th>\n",
       "      <th>VIX_close_1_2.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.512086</td>\n",
       "      <td>0.510295</td>\n",
       "      <td>0.561325</td>\n",
       "      <td>0.507610</td>\n",
       "      <td>0.592659</td>\n",
       "      <td>0.497762</td>\n",
       "      <td>0.632945</td>\n",
       "      <td>0.507610</td>\n",
       "      <td>0.495971</td>\n",
       "      <td>0.505819</td>\n",
       "      <td>...</td>\n",
       "      <td>0.588183</td>\n",
       "      <td>0.476276</td>\n",
       "      <td>0.556849</td>\n",
       "      <td>0.524620</td>\n",
       "      <td>0.566697</td>\n",
       "      <td>0.514772</td>\n",
       "      <td>0.567592</td>\n",
       "      <td>0.496867</td>\n",
       "      <td>0.617726</td>\n",
       "      <td>0.485228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.530886</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.590868</td>\n",
       "      <td>0.535363</td>\n",
       "      <td>0.589973</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.640107</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.529991</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>...</td>\n",
       "      <td>0.572963</td>\n",
       "      <td>0.523724</td>\n",
       "      <td>0.557744</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.584602</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.588183</td>\n",
       "      <td>0.527305</td>\n",
       "      <td>0.617726</td>\n",
       "      <td>0.485228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.648165</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.638317</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.682184</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.604297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.634736</td>\n",
       "      <td>0.587287</td>\n",
       "      <td>0.620412</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.630260</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.641898</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.648165</td>\n",
       "      <td>0.609669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.730528</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.50</th>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>...</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SP_open_1_1.0  SP_open_1_2.0  SP_high_1_1.0  SP_high_1_2.0  \\\n",
       "0.00       0.512086       0.510295       0.561325       0.507610   \n",
       "0.10       0.530886       0.540734       0.590868       0.535363   \n",
       "0.25       0.609669       0.609669       0.648165       0.609669   \n",
       "0.50       0.726052       0.726052       0.726052       0.726052   \n",
       "0.75       0.800358       0.800358       0.800358       0.800358   \n",
       "1.00       0.862131       0.862131       0.862131       0.862131   \n",
       "1.50       0.946285       0.946285       0.946285       0.946285   \n",
       "\n",
       "      SP_low_1_1.0  SP_low_1_2.0  SP_close_1_1.0  SP_close_1_2.0  \\\n",
       "0.00      0.592659      0.497762        0.632945        0.507610   \n",
       "0.10      0.589973      0.540734        0.640107        0.540734   \n",
       "0.25      0.638317      0.609669        0.682184        0.609669   \n",
       "0.50      0.726052      0.726052        0.730528        0.726052   \n",
       "0.75      0.800358      0.800358        0.800358        0.800358   \n",
       "1.00      0.862131      0.862131        0.862131        0.862131   \n",
       "1.50      0.946285      0.946285        0.946285        0.946285   \n",
       "\n",
       "      SP_volume_1_1.0  SP_volume_1_2.0       ...         SOX_close_1_1.0  \\\n",
       "0.00         0.495971         0.505819       ...                0.588183   \n",
       "0.10         0.529991         0.540734       ...                0.572963   \n",
       "0.25         0.609669         0.604297       ...                0.634736   \n",
       "0.50         0.726052         0.726052       ...                0.726052   \n",
       "0.75         0.800358         0.800358       ...                0.800358   \n",
       "1.00         0.862131         0.862131       ...                0.862131   \n",
       "1.50         0.946285         0.946285       ...                0.946285   \n",
       "\n",
       "      SOX_close_1_2.0  VIX_open_1_1.0  VIX_open_1_2.0  VIX_high_1_1.0  \\\n",
       "0.00         0.476276        0.556849        0.524620        0.566697   \n",
       "0.10         0.523724        0.557744        0.540734        0.584602   \n",
       "0.25         0.587287        0.620412        0.609669        0.630260   \n",
       "0.50         0.726052        0.726052        0.726052        0.726052   \n",
       "0.75         0.800358        0.800358        0.800358        0.800358   \n",
       "1.00         0.862131        0.862131        0.862131        0.862131   \n",
       "1.50         0.946285        0.946285        0.946285        0.946285   \n",
       "\n",
       "      VIX_high_1_2.0  VIX_low_1_1.0  VIX_low_1_2.0  VIX_close_1_1.0  \\\n",
       "0.00        0.514772       0.567592       0.496867         0.617726   \n",
       "0.10        0.540734       0.588183       0.527305         0.617726   \n",
       "0.25        0.609669       0.641898       0.609669         0.648165   \n",
       "0.50        0.726052       0.726052       0.726052         0.726052   \n",
       "0.75        0.800358       0.800358       0.800358         0.800358   \n",
       "1.00        0.862131       0.862131       0.862131         0.862131   \n",
       "1.50        0.946285       0.946285       0.946285         0.946285   \n",
       "\n",
       "      VIX_close_1_2.0  \n",
       "0.00         0.485228  \n",
       "0.10         0.485228  \n",
       "0.25         0.609669  \n",
       "0.50         0.726052  \n",
       "0.75         0.800358  \n",
       "1.00         0.862131  \n",
       "1.50         0.946285  \n",
       "\n",
       "[7 rows x 46 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = 2)\n",
    "half = int((len(df_total.columns)-3)/2)\n",
    "\n",
    "df_ca_15 = pd.DataFrame(ca_scores_15[0],columns=df_total.columns[:half-1],index = intervals)\n",
    "df_ca_15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5947179946284692"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg = np.argsort(df_ca_15.loc[intervals[0]].values)[-10:]\n",
    "df_ca_15.loc[intervals[0]][arg].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature 44\n",
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     ]
    }
   ],
   "source": [
    "df_list = [SP,Russell,DJ,SOX,VIX]\n",
    "df_option = [True,True,True,True,True]\n",
    "df_name = ['SP','Russell','DJ','SOX','VIX']\n",
    "intervals = [0,0.1,0.25,0.5,0.75,1,1.5]\n",
    "cas = [10]\n",
    "window = 1\n",
    "\n",
    "ca_scores_14 = []\n",
    "ca_params_14 = []\n",
    "for ca in cas:\n",
    "    best_score = []\n",
    "    best_params = []\n",
    "    for interval in intervals:\n",
    "        print('interval is',interval)\n",
    "        df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = ca)\n",
    "        amount_of_features = len(df_total.columns)-3\n",
    "        half = int(amount_of_features/2)\n",
    "        score = []\n",
    "        params = []\n",
    "        for index in range(half):\n",
    "            print('feature',index)\n",
    "            if (df_total.columns[index].find('1.0') != -1):\n",
    "                train_X_f = train_X[:,[index,index+half,index+1,index+1+half,index+2,index+2+half,\n",
    "                                     index+7,index+7+half,index+8,index+8+half,index+9,index+9+half]]\n",
    "#                 test_X_f = test_X[:,[index,index+half]]\n",
    "#             elif (df_total.columns[index].find('7.0') != -1):\n",
    "#                 train_X_f = train_X[:,[index,index+half,index+1,index+1+half,index+2,index+2+half,index+3,index+3+half]]\n",
    "# #                 test_X_f = test_X[:,[index,index+half]]\n",
    "            elif (df_total.columns[index].find('4.0') != -1):\n",
    "                train_X_f = train_X[:,[index,index+half,index+1,index+1+half,index+2,index+2+half,index+3,index+3+half]]\n",
    "#                 test_X_f = test_X[:,[index,index+half]]\n",
    "            else:\n",
    "                continue\n",
    "            param_test1 = rf_params_test\n",
    "            gsearch1 = GridSearchCV(estimator = RandomForestClassifier(random_state=0), param_grid = param_test1, scoring='accuracy',cv=2)\n",
    "            gsearch1.fit(train_X_f,train_rise)\n",
    "            params.append(gsearch1.best_params_)\n",
    "            score.append(gsearch1.best_score_)\n",
    "\n",
    "    #         model = RandomForestClassifier()\n",
    "    #         model.set_params(**gsearch1.best_params_)\n",
    "\n",
    "        best_score.append(score)\n",
    "        best_params.append(params)\n",
    "    ca_scores_14.append(best_score)\n",
    "    ca_params_14.append(best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP_open_1_1.0</th>\n",
       "      <th>SP_open_1_2.0</th>\n",
       "      <th>SP_high_1_1.0</th>\n",
       "      <th>SP_high_1_2.0</th>\n",
       "      <th>SP_low_1_1.0</th>\n",
       "      <th>SP_low_1_2.0</th>\n",
       "      <th>SP_close_1_1.0</th>\n",
       "      <th>SP_close_1_2.0</th>\n",
       "      <th>SP_volume_1_1.0</th>\n",
       "      <th>SP_volume_1_2.0</th>\n",
       "      <th>...</th>\n",
       "      <th>SOX_close_1_1.0</th>\n",
       "      <th>SOX_close_1_2.0</th>\n",
       "      <th>VIX_open_1_1.0</th>\n",
       "      <th>VIX_open_1_2.0</th>\n",
       "      <th>VIX_high_1_1.0</th>\n",
       "      <th>VIX_high_1_2.0</th>\n",
       "      <th>VIX_low_1_1.0</th>\n",
       "      <th>VIX_low_1_2.0</th>\n",
       "      <th>VIX_close_1_1.0</th>\n",
       "      <th>VIX_close_1_2.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.512086</td>\n",
       "      <td>0.510295</td>\n",
       "      <td>0.561325</td>\n",
       "      <td>0.507610</td>\n",
       "      <td>0.592659</td>\n",
       "      <td>0.497762</td>\n",
       "      <td>0.632945</td>\n",
       "      <td>0.507610</td>\n",
       "      <td>0.495971</td>\n",
       "      <td>0.505819</td>\n",
       "      <td>...</td>\n",
       "      <td>0.588183</td>\n",
       "      <td>0.476276</td>\n",
       "      <td>0.556849</td>\n",
       "      <td>0.524620</td>\n",
       "      <td>0.566697</td>\n",
       "      <td>0.514772</td>\n",
       "      <td>0.567592</td>\n",
       "      <td>0.496867</td>\n",
       "      <td>0.617726</td>\n",
       "      <td>0.485228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.530886</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.590868</td>\n",
       "      <td>0.535363</td>\n",
       "      <td>0.589973</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.640107</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.529991</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>...</td>\n",
       "      <td>0.572963</td>\n",
       "      <td>0.523724</td>\n",
       "      <td>0.557744</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.584602</td>\n",
       "      <td>0.540734</td>\n",
       "      <td>0.588183</td>\n",
       "      <td>0.527305</td>\n",
       "      <td>0.617726</td>\n",
       "      <td>0.485228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.648165</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.638317</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.682184</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.604297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.634736</td>\n",
       "      <td>0.587287</td>\n",
       "      <td>0.620412</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.630260</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.641898</td>\n",
       "      <td>0.609669</td>\n",
       "      <td>0.648165</td>\n",
       "      <td>0.609669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.730528</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "      <td>0.726052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "      <td>0.800358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "      <td>0.862131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.50</th>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>...</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "      <td>0.946285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SP_open_1_1.0  SP_open_1_2.0  SP_high_1_1.0  SP_high_1_2.0  \\\n",
       "0.00       0.512086       0.510295       0.561325       0.507610   \n",
       "0.10       0.530886       0.540734       0.590868       0.535363   \n",
       "0.25       0.609669       0.609669       0.648165       0.609669   \n",
       "0.50       0.726052       0.726052       0.726052       0.726052   \n",
       "0.75       0.800358       0.800358       0.800358       0.800358   \n",
       "1.00       0.862131       0.862131       0.862131       0.862131   \n",
       "1.50       0.946285       0.946285       0.946285       0.946285   \n",
       "\n",
       "      SP_low_1_1.0  SP_low_1_2.0  SP_close_1_1.0  SP_close_1_2.0  \\\n",
       "0.00      0.592659      0.497762        0.632945        0.507610   \n",
       "0.10      0.589973      0.540734        0.640107        0.540734   \n",
       "0.25      0.638317      0.609669        0.682184        0.609669   \n",
       "0.50      0.726052      0.726052        0.730528        0.726052   \n",
       "0.75      0.800358      0.800358        0.800358        0.800358   \n",
       "1.00      0.862131      0.862131        0.862131        0.862131   \n",
       "1.50      0.946285      0.946285        0.946285        0.946285   \n",
       "\n",
       "      SP_volume_1_1.0  SP_volume_1_2.0       ...         SOX_close_1_1.0  \\\n",
       "0.00         0.495971         0.505819       ...                0.588183   \n",
       "0.10         0.529991         0.540734       ...                0.572963   \n",
       "0.25         0.609669         0.604297       ...                0.634736   \n",
       "0.50         0.726052         0.726052       ...                0.726052   \n",
       "0.75         0.800358         0.800358       ...                0.800358   \n",
       "1.00         0.862131         0.862131       ...                0.862131   \n",
       "1.50         0.946285         0.946285       ...                0.946285   \n",
       "\n",
       "      SOX_close_1_2.0  VIX_open_1_1.0  VIX_open_1_2.0  VIX_high_1_1.0  \\\n",
       "0.00         0.476276        0.556849        0.524620        0.566697   \n",
       "0.10         0.523724        0.557744        0.540734        0.584602   \n",
       "0.25         0.587287        0.620412        0.609669        0.630260   \n",
       "0.50         0.726052        0.726052        0.726052        0.726052   \n",
       "0.75         0.800358        0.800358        0.800358        0.800358   \n",
       "1.00         0.862131        0.862131        0.862131        0.862131   \n",
       "1.50         0.946285        0.946285        0.946285        0.946285   \n",
       "\n",
       "      VIX_high_1_2.0  VIX_low_1_1.0  VIX_low_1_2.0  VIX_close_1_1.0  \\\n",
       "0.00        0.514772       0.567592       0.496867         0.617726   \n",
       "0.10        0.540734       0.588183       0.527305         0.617726   \n",
       "0.25        0.609669       0.641898       0.609669         0.648165   \n",
       "0.50        0.726052       0.726052       0.726052         0.726052   \n",
       "0.75        0.800358       0.800358       0.800358         0.800358   \n",
       "1.00        0.862131       0.862131       0.862131         0.862131   \n",
       "1.50        0.946285       0.946285       0.946285         0.946285   \n",
       "\n",
       "      VIX_close_1_2.0  \n",
       "0.00         0.485228  \n",
       "0.10         0.485228  \n",
       "0.25         0.609669  \n",
       "0.50         0.726052  \n",
       "0.75         0.800358  \n",
       "1.00         0.862131  \n",
       "1.50         0.946285  \n",
       "\n",
       "[7 rows x 46 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = 2)\n",
    "half = int((len(df_total.columns)-3)/2)\n",
    "\n",
    "df_ca_14 = pd.DataFrame(ca_scores_15[0],columns=df_total.columns[:half-1],index = intervals)\n",
    "df_ca_14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VIX_high_1_1.0         0.630260\n",
       "VIX_low_1_1.0          0.641898\n",
       "Russell_low_1_1.0      0.632945\n",
       "SOX_close_1_1.0        0.634736\n",
       "SP_low_1_1.0           0.638317\n",
       "Russell_close_1_1.0    0.645479\n",
       "DJ_low_1_1.0           0.617726\n",
       "DJ_close_1_1.0         0.669651\n",
       "VIX_close_1_1.0        0.648165\n",
       "SP_close_1_1.0         0.682184\n",
       "Name: 0.25, dtype: float64"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg = np.argsort(df_ca_14.loc[intervals[0]].values)[-10:]\n",
    "df_ca_14.loc[intervals[2]][arg]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval is 0\n",
      "interval is 0.1\n",
      "interval is 0.25\n",
      "interval is 0.5\n"
     ]
    }
   ],
   "source": [
    "best_score = []\n",
    "best_params = []\n",
    "numbers = [1,2,3,5,10,20,0]\n",
    "intervals = [0,0.1,0.25,0.5]\n",
    "\n",
    "for interval in intervals:\n",
    "    print('interval is',interval)\n",
    "    df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = 10)\n",
    "    score = []\n",
    "    params = []\n",
    "    #select the best ten feature\n",
    "    for number in numbers:\n",
    "        \n",
    "        #find the good feature\n",
    "        arg = np.argsort(df_ca_14.loc[interval].values)[number*-1:]\n",
    "        col = list(df_ca_14.loc[intervals[0]][arg].index)\n",
    "        index_list = []\n",
    "        for c in col:\n",
    "            if (c.find('1.0') != -1):\n",
    "                for categroy in [1,2,3,8,9,10]:\n",
    "                    index_list.append(c.replace('1.0',f'{categroy}.0'))\n",
    "        indexs = [i for i,value in enumerate(df_total.columns.tolist()) if value in index_list]\n",
    "        \n",
    "        # train\n",
    "        train_X_f = train_X[:,indexs]\n",
    "        param_test1 = rf_params_test\n",
    "        gsearch1 = GridSearchCV(estimator = RandomForestClassifier(random_state=0), param_grid = param_test1, scoring='accuracy',cv=2)\n",
    "        gsearch1.fit(train_X_f,train_rise)\n",
    "        params.append(gsearch1.best_params_)\n",
    "        score.append(gsearch1.best_score_)\n",
    "\n",
    "#         model = RandomForestClassifier()\n",
    "#         model.set_params(**gsearch1.best_params_)\n",
    "    best_score.append(score)\n",
    "    best_params.append(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>10</th>\n",
       "      <th>20</th>\n",
       "      <th>0</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.624888</td>\n",
       "      <td>0.580125</td>\n",
       "      <td>0.583706</td>\n",
       "      <td>0.584602</td>\n",
       "      <td>0.574754</td>\n",
       "      <td>0.578335</td>\n",
       "      <td>0.567592</td>\n",
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       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.640107</td>\n",
       "      <td>0.581916</td>\n",
       "      <td>0.615040</td>\n",
       "      <td>0.578335</td>\n",
       "      <td>0.607878</td>\n",
       "      <td>0.584602</td>\n",
       "      <td>0.557744</td>\n",
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       "      <th>0.25</th>\n",
       "      <td>0.682184</td>\n",
       "      <td>0.662489</td>\n",
       "      <td>0.669651</td>\n",
       "      <td>0.666965</td>\n",
       "      <td>0.659803</td>\n",
       "      <td>0.623993</td>\n",
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       "      <td>0.729633</td>\n",
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       "      <td>0.732319</td>\n",
       "      <td>0.737690</td>\n",
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      "text/plain": [
       "            1         2         3         5         10        20        0 \n",
       "0.00  0.624888  0.580125  0.583706  0.584602  0.574754  0.578335  0.567592\n",
       "0.10  0.640107  0.581916  0.615040  0.578335  0.607878  0.584602  0.557744\n",
       "0.25  0.682184  0.662489  0.669651  0.666965  0.659803  0.623993  0.617726\n",
       "0.50  0.726052  0.729633  0.726052  0.726052  0.726052  0.732319  0.737690"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca_14_com = pd.DataFrame(best_score,columns=numbers,index = intervals)\n",
    "df_ca_14_com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### It's bad for this to combine the good features\n",
    "## Conclusion\n",
    "* choosing the feature by the outlier is better than choosing the feature by the performance in the American data\n",
    "* when choosing the feature by the outlier,use the best feature is better than use the combine feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All data in ca "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval is 0\n",
      "feature 0\n",
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      "feature 498\n",
      "feature 499\n",
      "feature 500\n",
      "feature 501\n",
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      "feature 503\n",
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      "feature 505\n",
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      "feature 539\n",
      "feature 540\n",
      "feature 541\n",
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      "feature 543\n",
      "feature 544\n",
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      "feature 548\n",
      "feature 549\n",
      "feature 550\n",
      "feature 551\n",
      "feature 552\n",
      "feature 553\n",
      "feature 554\n",
      "feature 555\n",
      "feature 556\n",
      "feature 557\n",
      "feature 558\n",
      "feature 559\n",
      "feature 560\n",
      "feature 561\n",
      "feature 562\n",
      "feature 563\n",
      "feature 564\n",
      "feature 565\n",
      "feature 566\n",
      "feature 567\n",
      "feature 568\n",
      "feature 569\n",
      "feature 570\n",
      "feature 571\n",
      "feature 572\n",
      "feature 573\n",
      "feature 574\n",
      "feature 575\n",
      "feature 576\n",
      "feature 577\n",
      "feature 578\n",
      "feature 579\n",
      "feature 580\n",
      "feature 581\n",
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      "feature 583\n",
      "feature 584\n",
      "feature 585\n",
      "feature 586\n",
      "feature 587\n",
      "feature 588\n",
      "feature 589\n",
      "feature 590\n",
      "feature 591\n",
      "feature 592\n",
      "feature 593\n",
      "feature 594\n",
      "feature 595\n",
      "feature 596\n",
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      "feature 599\n",
      "feature 600\n",
      "feature 601\n",
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      "feature 603\n",
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      "feature 606\n",
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      "feature 608\n",
      "feature 609\n",
      "feature 610\n",
      "feature 611\n",
      "feature 612\n",
      "feature 613\n",
      "feature 614\n",
      "feature 615\n",
      "feature 616\n",
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      "feature 619\n",
      "feature 620\n",
      "feature 621\n",
      "feature 622\n",
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      "feature 625\n",
      "feature 626\n",
      "feature 627\n",
      "feature 628\n",
      "feature 629\n",
      "feature 630\n",
      "feature 631\n",
      "feature 632\n",
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      "feature 634\n",
      "feature 635\n",
      "feature 636\n",
      "feature 637\n",
      "feature 638\n",
      "feature 639\n",
      "feature 640\n",
      "feature 641\n",
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      "feature 644\n",
      "feature 645\n",
      "feature 646\n",
      "feature 647\n",
      "feature 648\n",
      "feature 649\n",
      "feature 650\n",
      "feature 651\n",
      "feature 652\n",
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      "feature 654\n",
      "feature 655\n",
      "feature 656\n",
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      "feature 660\n",
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      "feature 667\n",
      "feature 668\n",
      "feature 669\n",
      "feature 670\n",
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      "feature 690\n",
      "feature 691\n",
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      "feature 694\n",
      "feature 695\n",
      "feature 696\n",
      "feature 697\n",
      "feature 698\n",
      "feature 699\n",
      "feature 700\n",
      "feature 701\n",
      "feature 702\n",
      "feature 703\n",
      "feature 704\n",
      "feature 705\n",
      "feature 706\n",
      "feature 707\n",
      "feature 708\n",
      "feature 709\n",
      "feature 710\n",
      "feature 711\n",
      "feature 712\n",
      "feature 713\n",
      "feature 714\n",
      "feature 715\n",
      "feature 716\n",
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      "feature 718\n",
      "feature 719\n",
      "feature 720\n",
      "feature 721\n",
      "feature 722\n",
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      "feature 727\n",
      "feature 728\n",
      "feature 729\n",
      "feature 730\n",
      "feature 731\n",
      "feature 732\n",
      "feature 733\n",
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      "feature 736\n",
      "feature 737\n",
      "feature 738\n",
      "feature 739\n",
      "feature 740\n",
      "feature 741\n",
      "feature 742\n",
      "feature 743\n",
      "feature 744\n",
      "feature 745\n",
      "feature 746\n",
      "feature 747\n",
      "feature 748\n",
      "feature 749\n",
      "feature 750\n",
      "feature 751\n",
      "feature 752\n",
      "feature 753\n",
      "feature 754\n",
      "feature 755\n",
      "feature 756\n",
      "feature 757\n",
      "feature 758\n",
      "feature 759\n",
      "feature 760\n",
      "feature 761\n",
      "feature 762\n",
      "feature 763\n",
      "feature 764\n",
      "feature 765\n",
      "feature 766\n",
      "feature 767\n",
      "feature 768\n",
      "feature 769\n",
      "feature 770\n",
      "feature 771\n",
      "feature 772\n",
      "feature 773\n",
      "feature 774\n",
      "feature 775\n",
      "feature 776\n",
      "feature 777\n",
      "feature 778\n",
      "feature 779\n",
      "feature 780\n",
      "feature 781\n",
      "feature 782\n",
      "feature 783\n",
      "feature 784\n",
      "feature 785\n",
      "feature 786\n",
      "feature 787\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature 788\n",
      "feature 789\n",
      "feature 790\n"
     ]
    }
   ],
   "source": [
    "df_list = [Yunta50,Taiwan50,orderbook,oil_price,bond,TXF,EXF,FXF,E_F,Nikkei,SP,Russell,DJ,SOX,VIX,pcr,FOI,IOI,DOI]\n",
    "df_option = [True,True,True,True,True,True,True,True,False,True,True,True,True,True,True,False,True,True,True]\n",
    "df_name = ['Yunta50','Taiwan50','orderbook','oil_price','bond','TXF','EXF','FXF','E_F','Nikkei','SP','Russell','DJ','SOX','VIX','pcr','FOI','IOI','DOI']\n",
    "\n",
    "\n",
    "intervals = [0,0.1,0.25,0.5,0.75,1,1.5]\n",
    "ca = 10\n",
    "window = 1\n",
    "\n",
    "best_score = []\n",
    "best_params = []\n",
    "for interval in intervals:\n",
    "    print('interval is',interval)\n",
    "    df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = ca)\n",
    "    amount_of_features = len(df_total.columns)-3\n",
    "    half = int(amount_of_features/2)\n",
    "    score = []\n",
    "    params = []\n",
    "    for index in range(half):\n",
    "        print('feature',index)\n",
    "        train_X_f = train_X[:,index].reshape((-1,1))\n",
    "        param_test1 = rf_params_test\n",
    "        gsearch1 = GridSearchCV(estimator = RandomForestClassifier(random_state=0), param_grid = param_test1, scoring='accuracy',cv=2)\n",
    "        gsearch1.fit(train_X_f,train_rise)\n",
    "        params.append(gsearch1.best_params_)\n",
    "        score.append(gsearch1.best_score_)\n",
    "\n",
    "#         model = RandomForestClassifier()\n",
    "#         model.set_params(**gsearch1.best_params_)\n",
    "\n",
    "    best_score.append(score)\n",
    "    best_params.append(params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yunta50_open_1_1.0</th>\n",
       "      <th>Yunta50_open_1_2.0</th>\n",
       "      <th>Yunta50_open_1_3.0</th>\n",
       "      <th>Yunta50_open_1_4.0</th>\n",
       "      <th>Yunta50_open_1_5.0</th>\n",
       "      <th>Yunta50_open_1_6.0</th>\n",
       "      <th>Yunta50_open_1_7.0</th>\n",
       "      <th>Yunta50_open_1_8.0</th>\n",
       "      <th>Yunta50_open_1_9.0</th>\n",
       "      <th>Yunta50_open_1_10.0</th>\n",
       "      <th>...</th>\n",
       "      <th>DOI_close_1_2.0</th>\n",
       "      <th>DOI_close_1_3.0</th>\n",
       "      <th>DOI_close_1_4.0</th>\n",
       "      <th>DOI_close_1_5.0</th>\n",
       "      <th>DOI_close_1_6.0</th>\n",
       "      <th>DOI_close_1_7.0</th>\n",
       "      <th>DOI_close_1_8.0</th>\n",
       "      <th>DOI_close_1_9.0</th>\n",
       "      <th>DOI_close_1_10.0</th>\n",
       "      <th>mean_rise_fall</th>\n",
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       "      <td>0.499139</td>\n",
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       "      <td>0.537005</td>\n",
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       "      <td>0.605852</td>\n",
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       "      <td>0.605852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
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       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
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       "    <tr>\n",
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       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
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       "      <td>0.796041</td>\n",
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       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
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       "      <th>1.00</th>\n",
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       "      <td>0.860585</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.860585</td>\n",
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       "      <td>0.860585</td>\n",
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       "      <td>0.860585</td>\n",
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       "    <tr>\n",
       "      <th>1.50</th>\n",
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       "      <td>0.946644</td>\n",
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       "      <td>0.946644</td>\n",
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       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>...</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 791 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Yunta50_open_1_1.0  Yunta50_open_1_2.0  Yunta50_open_1_3.0  \\\n",
       "0.00            0.498279            0.499139            0.499139   \n",
       "0.10            0.537005            0.537005            0.537005   \n",
       "0.25            0.605852            0.605852            0.605852   \n",
       "0.50            0.722031            0.722031            0.722031   \n",
       "0.75            0.796041            0.796041            0.796041   \n",
       "1.00            0.860585            0.860585            0.860585   \n",
       "1.50            0.946644            0.946644            0.946644   \n",
       "\n",
       "      Yunta50_open_1_4.0  Yunta50_open_1_5.0  Yunta50_open_1_6.0  \\\n",
       "0.00            0.499139            0.516351            0.499139   \n",
       "0.10            0.537005            0.537005            0.537005   \n",
       "0.25            0.605852            0.605852            0.605852   \n",
       "0.50            0.722031            0.722031            0.722031   \n",
       "0.75            0.796041            0.796041            0.796041   \n",
       "1.00            0.860585            0.860585            0.860585   \n",
       "1.50            0.946644            0.946644            0.946644   \n",
       "\n",
       "      Yunta50_open_1_7.0  Yunta50_open_1_8.0  Yunta50_open_1_9.0  \\\n",
       "0.00            0.499139            0.506024            0.493115   \n",
       "0.10            0.537005            0.536145            0.537005   \n",
       "0.25            0.605852            0.591222            0.605852   \n",
       "0.50            0.722031            0.722031            0.722031   \n",
       "0.75            0.796041            0.796041            0.796041   \n",
       "1.00            0.860585            0.860585            0.860585   \n",
       "1.50            0.946644            0.946644            0.946644   \n",
       "\n",
       "      Yunta50_open_1_10.0       ...        DOI_close_1_2.0  DOI_close_1_3.0  \\\n",
       "0.00             0.499139       ...               0.502582         0.499139   \n",
       "0.10             0.537005       ...               0.536145         0.537005   \n",
       "0.25             0.605852       ...               0.605852         0.605852   \n",
       "0.50             0.722031       ...               0.722031         0.722031   \n",
       "0.75             0.796041       ...               0.796041         0.796041   \n",
       "1.00             0.860585       ...               0.860585         0.860585   \n",
       "1.50             0.946644       ...               0.946644         0.946644   \n",
       "\n",
       "      DOI_close_1_4.0  DOI_close_1_5.0  DOI_close_1_6.0  DOI_close_1_7.0  \\\n",
       "0.00         0.499139         0.506024         0.499139         0.500000   \n",
       "0.10         0.537005         0.537005         0.537005         0.537005   \n",
       "0.25         0.605852         0.605852         0.605852         0.605852   \n",
       "0.50         0.722031         0.722031         0.722031         0.722031   \n",
       "0.75         0.796041         0.796041         0.796041         0.796041   \n",
       "1.00         0.860585         0.860585         0.860585         0.860585   \n",
       "1.50         0.946644         0.946644         0.946644         0.946644   \n",
       "\n",
       "      DOI_close_1_8.0  DOI_close_1_9.0  DOI_close_1_10.0  mean_rise_fall  \n",
       "0.00         0.499139         0.499139          0.499139        0.499139  \n",
       "0.10         0.530120         0.537005          0.537005        0.537005  \n",
       "0.25         0.605852         0.605852          0.605852        0.605852  \n",
       "0.50         0.722031         0.722031          0.722031        0.722031  \n",
       "0.75         0.796041         0.796041          0.796041        0.796041  \n",
       "1.00         0.860585         0.860585          0.860585        0.860585  \n",
       "1.50         0.946644         0.946644          0.946644        0.946644  \n",
       "\n",
       "[7 rows x 791 columns]"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca_am = pd.DataFrame(best_score,columns=df_total.columns[:half],index = intervals)\n",
    "df_ca_am"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5425989672977625"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg = np.argsort(df_ca_am.loc[intervals[0]].values)[-10:]\n",
    "df_ca_am.loc[intervals[0]][arg].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### choose the good features and rebuild the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval is 0\n",
      "interval is 0.1\n",
      "interval is 0.25\n",
      "interval is 0.5\n",
      "interval is 0.75\n",
      "interval is 1\n",
      "interval is 1.5\n"
     ]
    }
   ],
   "source": [
    "best_score = []\n",
    "best_params = []\n",
    "numbers = [1,5,10,20,100,0]\n",
    "for interval in intervals:\n",
    "    print('interval is',interval)\n",
    "    df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = ca)\n",
    "    score = []\n",
    "    params = []\n",
    "    #select the best ten feature\n",
    "    for number in numbers:\n",
    "        arg = np.argsort(df_ca_am.loc[interval].values)[number*-1:]\n",
    "        # train\n",
    "        train_X_f = train_X[:,arg]\n",
    "        param_test1 = rf_params_test\n",
    "        gsearch1 = GridSearchCV(estimator = RandomForestClassifier(random_state=0), param_grid = param_test1, scoring='accuracy',cv=2)\n",
    "        gsearch1.fit(train_X_f,train_rise)\n",
    "        params.append(gsearch1.best_params_)\n",
    "        score.append(gsearch1.best_score_)\n",
    "\n",
    "#         model = RandomForestClassifier()\n",
    "#         model.set_params(**gsearch1.best_params_)\n",
    "    best_score.append(score)\n",
    "    best_params.append(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>10</th>\n",
       "      <th>20</th>\n",
       "      <th>100</th>\n",
       "      <th>0</th>\n",
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       "      <th>0.00</th>\n",
       "      <td>0.547332</td>\n",
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       "      <td>0.561102</td>\n",
       "      <td>0.545611</td>\n",
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       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.579174</td>\n",
       "      <td>0.608434</td>\n",
       "      <td>0.593804</td>\n",
       "      <td>0.602410</td>\n",
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       "      <th>0.25</th>\n",
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       "      <td>0.645439</td>\n",
       "      <td>0.604131</td>\n",
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       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.734940</td>\n",
       "      <td>0.734079</td>\n",
       "      <td>0.736661</td>\n",
       "      <td>0.726334</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.694492</td>\n",
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       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.797762</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.783133</td>\n",
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       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
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       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
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       "    <tr>\n",
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       "      <td>0.946644</td>\n",
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      "text/plain": [
       "           1         5         10        20        100       0  \n",
       "0.00  0.547332  0.574010  0.574010  0.585198  0.561102  0.545611\n",
       "0.10  0.579174  0.608434  0.593804  0.602410  0.562823  0.563683\n",
       "0.25  0.644578  0.644578  0.639415  0.642857  0.645439  0.604131\n",
       "0.50  0.734940  0.734079  0.736661  0.726334  0.722031  0.694492\n",
       "0.75  0.796041  0.796041  0.797762  0.796041  0.796041  0.783133\n",
       "1.00  0.860585  0.860585  0.860585  0.860585  0.860585  0.860585\n",
       "1.50  0.946644  0.946644  0.946644  0.946644  0.946644  0.946644"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca_am_com = pd.DataFrame(best_score,columns=numbers,index = intervals)\n",
    "df_ca_am_com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5645438898450946"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca_am_com.loc[intervals[0]].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### combining the good feature(10 is better) makes the model better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### choose the feature by outlier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval is 0\n",
      "feature 0\n",
      "feature 1\n",
      "feature 2\n",
      "feature 3\n",
      "feature 4\n",
      "feature 5\n",
      "feature 6\n",
      "feature 7\n",
      "feature 8\n",
      "feature 9\n",
      "feature 10\n",
      "feature 11\n",
      "feature 12\n",
      "feature 13\n",
      "feature 14\n",
      "feature 15\n",
      "feature 16\n",
      "feature 17\n",
      "feature 18\n",
      "feature 19\n",
      "feature 20\n",
      "feature 21\n",
      "feature 22\n",
      "feature 23\n",
      "feature 24\n",
      "feature 25\n",
      "feature 26\n",
      "feature 27\n",
      "feature 28\n",
      "feature 29\n",
      "feature 30\n",
      "feature 31\n",
      "feature 32\n",
      "feature 33\n",
      "feature 34\n",
      "feature 35\n",
      "feature 36\n",
      "feature 37\n",
      "feature 38\n",
      "feature 39\n",
      "feature 40\n",
      "feature 41\n",
      "feature 42\n",
      "feature 43\n",
      "feature 44\n",
      "feature 45\n",
      "feature 46\n",
      "feature 47\n",
      "feature 48\n",
      "feature 49\n",
      "feature 50\n",
      "feature 51\n",
      "feature 52\n",
      "feature 53\n",
      "feature 54\n",
      "feature 55\n",
      "feature 56\n",
      "feature 57\n",
      "feature 58\n",
      "feature 59\n",
      "feature 60\n",
      "feature 61\n",
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      "feature 63\n",
      "feature 64\n",
      "feature 65\n",
      "feature 66\n",
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      "feature 68\n",
      "feature 69\n",
      "feature 70\n",
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     ]
    }
   ],
   "source": [
    "df_list = [Yunta50,Taiwan50,orderbook,oil_price,bond,TXF,EXF,FXF,E_F,Nikkei,SP,Russell,DJ,SOX,VIX,pcr,FOI,IOI,DOI]\n",
    "df_option = [True,True,True,True,True,True,True,True,False,True,True,True,True,True,True,False,True,True,True]\n",
    "df_name = ['Yunta50','Taiwan50','orderbook','oil_price','bond','TXF','EXF','FXF','E_F','Nikkei','SP','Russell','DJ','SOX','VIX','pcr','FOI','IOI','DOI']\n",
    "intervals = [0,0.1,0.25,0.5,0.75,1,1.5]\n",
    "cas = [10]\n",
    "window = 1\n",
    "\n",
    "ca_scores_15 = []\n",
    "ca_params_15 = []\n",
    "for ca in cas:\n",
    "    best_score = []\n",
    "    best_params = []\n",
    "    for interval in intervals:\n",
    "        print('interval is',interval)\n",
    "        df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = ca)\n",
    "        amount_of_features = len(df_total.columns)-3\n",
    "        half = int(amount_of_features/2)\n",
    "        score = []\n",
    "        params = []\n",
    "        for index in range(half):\n",
    "            print('feature',index)\n",
    "            if (df_total.columns[index].find('1.0') != -1):\n",
    "                train_X_f = train_X[:,[index,index+half,index+1,index+1+half,index+2,index+2+half,index+3,index+3+half,\n",
    "                                     index+6,index+6+half,index+7,index+7+half,index+8,index+8+half,index+9,index+9+half]]\n",
    "#                 test_X_f = test_X[:,[index,index+half]]\n",
    "#             elif (df_total.columns[index].find('7.0') != -1):\n",
    "#                 train_X_f = train_X[:,[index,index+half,index+1,index+1+half,index+2,index+2+half,index+3,index+3+half]]\n",
    "# #                 test_X_f = test_X[:,[index,index+half]]\n",
    "            elif (df_total.columns[index].find('5.0') != -1):\n",
    "                train_X_f = train_X[:,[index,index+half,index+1,index+1+half]]\n",
    "#                 test_X_f = test_X[:,[index,index+half]]\n",
    "            else:\n",
    "                continue\n",
    "            param_test1 = rf_params_test\n",
    "            gsearch1 = GridSearchCV(estimator = RandomForestClassifier(random_state=0), param_grid = param_test1, scoring='accuracy',cv=2)\n",
    "            gsearch1.fit(train_X_f,train_rise)\n",
    "            params.append(gsearch1.best_params_)\n",
    "            score.append(gsearch1.best_score_)\n",
    "\n",
    "    #         model = RandomForestClassifier()\n",
    "    #         model.set_params(**gsearch1.best_params_)\n",
    "\n",
    "        best_score.append(score)\n",
    "        best_params.append(params)\n",
    "    ca_scores_15.append(best_score)\n",
    "    ca_params_15.append(best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yunta50_open_1_1.0</th>\n",
       "      <th>Yunta50_open_1_2.0</th>\n",
       "      <th>Yunta50_high_1_1.0</th>\n",
       "      <th>Yunta50_high_1_2.0</th>\n",
       "      <th>Yunta50_low_1_1.0</th>\n",
       "      <th>Yunta50_low_1_2.0</th>\n",
       "      <th>Yunta50_close_1_1.0</th>\n",
       "      <th>Yunta50_close_1_2.0</th>\n",
       "      <th>Yunta50_volume_1_1.0</th>\n",
       "      <th>Yunta50_volume_1_2.0</th>\n",
       "      <th>...</th>\n",
       "      <th>IOI_close_1_1.0</th>\n",
       "      <th>IOI_close_1_2.0</th>\n",
       "      <th>DOI_open_1_1.0</th>\n",
       "      <th>DOI_open_1_2.0</th>\n",
       "      <th>DOI_high_1_1.0</th>\n",
       "      <th>DOI_high_1_2.0</th>\n",
       "      <th>DOI_low_1_1.0</th>\n",
       "      <th>DOI_low_1_2.0</th>\n",
       "      <th>DOI_close_1_1.0</th>\n",
       "      <th>DOI_close_1_2.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.505164</td>\n",
       "      <td>0.516351</td>\n",
       "      <td>0.498279</td>\n",
       "      <td>0.493115</td>\n",
       "      <td>0.490534</td>\n",
       "      <td>0.499139</td>\n",
       "      <td>0.517212</td>\n",
       "      <td>0.500861</td>\n",
       "      <td>0.506024</td>\n",
       "      <td>0.499139</td>\n",
       "      <td>...</td>\n",
       "      <td>0.499139</td>\n",
       "      <td>0.499139</td>\n",
       "      <td>0.510327</td>\n",
       "      <td>0.506024</td>\n",
       "      <td>0.510327</td>\n",
       "      <td>0.506024</td>\n",
       "      <td>0.510327</td>\n",
       "      <td>0.506024</td>\n",
       "      <td>0.510327</td>\n",
       "      <td>0.506024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>...</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.50</th>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>...</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 158 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Yunta50_open_1_1.0  Yunta50_open_1_2.0  Yunta50_high_1_1.0  \\\n",
       "0.00            0.505164            0.516351            0.498279   \n",
       "0.10            0.537005            0.537005            0.537005   \n",
       "0.25            0.605852            0.605852            0.605852   \n",
       "0.50            0.722031            0.722031            0.722031   \n",
       "0.75            0.796041            0.796041            0.796041   \n",
       "1.00            0.860585            0.860585            0.860585   \n",
       "1.50            0.946644            0.946644            0.946644   \n",
       "\n",
       "      Yunta50_high_1_2.0  Yunta50_low_1_1.0  Yunta50_low_1_2.0  \\\n",
       "0.00            0.493115           0.490534           0.499139   \n",
       "0.10            0.537005           0.537005           0.537005   \n",
       "0.25            0.605852           0.605852           0.605852   \n",
       "0.50            0.722031           0.722031           0.722031   \n",
       "0.75            0.796041           0.796041           0.796041   \n",
       "1.00            0.860585           0.860585           0.860585   \n",
       "1.50            0.946644           0.946644           0.946644   \n",
       "\n",
       "      Yunta50_close_1_1.0  Yunta50_close_1_2.0  Yunta50_volume_1_1.0  \\\n",
       "0.00             0.517212             0.500861              0.506024   \n",
       "0.10             0.537005             0.537005              0.537005   \n",
       "0.25             0.605852             0.605852              0.605852   \n",
       "0.50             0.722031             0.722031              0.722031   \n",
       "0.75             0.796041             0.796041              0.796041   \n",
       "1.00             0.860585             0.860585              0.860585   \n",
       "1.50             0.946644             0.946644              0.946644   \n",
       "\n",
       "      Yunta50_volume_1_2.0       ...         IOI_close_1_1.0  IOI_close_1_2.0  \\\n",
       "0.00              0.499139       ...                0.499139         0.499139   \n",
       "0.10              0.537005       ...                0.537005         0.537005   \n",
       "0.25              0.605852       ...                0.605852         0.605852   \n",
       "0.50              0.722031       ...                0.722031         0.722031   \n",
       "0.75              0.796041       ...                0.796041         0.796041   \n",
       "1.00              0.860585       ...                0.860585         0.860585   \n",
       "1.50              0.946644       ...                0.946644         0.946644   \n",
       "\n",
       "      DOI_open_1_1.0  DOI_open_1_2.0  DOI_high_1_1.0  DOI_high_1_2.0  \\\n",
       "0.00        0.510327        0.506024        0.510327        0.506024   \n",
       "0.10        0.537005        0.537005        0.537005        0.537005   \n",
       "0.25        0.605852        0.605852        0.605852        0.605852   \n",
       "0.50        0.722031        0.722031        0.722031        0.722031   \n",
       "0.75        0.796041        0.796041        0.796041        0.796041   \n",
       "1.00        0.860585        0.860585        0.860585        0.860585   \n",
       "1.50        0.946644        0.946644        0.946644        0.946644   \n",
       "\n",
       "      DOI_low_1_1.0  DOI_low_1_2.0  DOI_close_1_1.0  DOI_close_1_2.0  \n",
       "0.00       0.510327       0.506024         0.510327         0.506024  \n",
       "0.10       0.537005       0.537005         0.537005         0.537005  \n",
       "0.25       0.605852       0.605852         0.605852         0.605852  \n",
       "0.50       0.722031       0.722031         0.722031         0.722031  \n",
       "0.75       0.796041       0.796041         0.796041         0.796041  \n",
       "1.00       0.860585       0.860585         0.860585         0.860585  \n",
       "1.50       0.946644       0.946644         0.946644         0.946644  \n",
       "\n",
       "[7 rows x 158 columns]"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = 2)\n",
    "half = int((len(df_total.columns)-3)/2)\n",
    "\n",
    "df_ca_15 = pd.DataFrame(ca_scores_15[0],columns=df_total.columns[:half-1],index = intervals)\n",
    "df_ca_15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5896729776247848"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg = np.argsort(df_ca_15.loc[intervals[0]].values)[-10:]\n",
    "df_ca_15.loc[intervals[0]][arg].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval is 0\n",
      "feature 0\n",
      "feature 1\n",
      "feature 2\n",
      "feature 3\n",
      "feature 4\n",
      "feature 5\n",
      "feature 6\n",
      "feature 7\n",
      "feature 8\n",
      "feature 9\n",
      "feature 10\n",
      "feature 11\n",
      "feature 12\n",
      "feature 13\n",
      "feature 14\n",
      "feature 15\n",
      "feature 16\n",
      "feature 17\n",
      "feature 18\n",
      "feature 19\n",
      "feature 20\n",
      "feature 21\n",
      "feature 22\n",
      "feature 23\n",
      "feature 24\n",
      "feature 25\n",
      "feature 26\n",
      "feature 27\n",
      "feature 28\n",
      "feature 29\n",
      "feature 30\n",
      "feature 31\n",
      "feature 32\n",
      "feature 33\n",
      "feature 34\n",
      "feature 35\n",
      "feature 36\n",
      "feature 37\n",
      "feature 38\n",
      "feature 39\n",
      "feature 40\n",
      "feature 41\n",
      "feature 42\n",
      "feature 43\n",
      "feature 44\n",
      "feature 45\n",
      "feature 46\n",
      "feature 47\n",
      "feature 48\n",
      "feature 49\n",
      "feature 50\n",
      "feature 51\n",
      "feature 52\n",
      "feature 53\n",
      "feature 54\n",
      "feature 55\n",
      "feature 56\n",
      "feature 57\n",
      "feature 58\n",
      "feature 59\n",
      "feature 60\n",
      "feature 61\n",
      "feature 62\n",
      "feature 63\n",
      "feature 64\n",
      "feature 65\n",
      "feature 66\n",
      "feature 67\n",
      "feature 68\n",
      "feature 69\n",
      "feature 70\n",
      "feature 71\n",
      "feature 72\n",
      "feature 73\n",
      "feature 74\n",
      "feature 75\n",
      "feature 76\n",
      "feature 77\n",
      "feature 78\n",
      "feature 79\n",
      "feature 80\n",
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      "feature 82\n",
      "feature 83\n",
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      "feature 86\n",
      "feature 87\n",
      "feature 88\n",
      "feature 89\n",
      "feature 90\n",
      "feature 91\n",
      "feature 92\n",
      "feature 93\n",
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      "feature 95\n",
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      "feature 98\n",
      "feature 99\n",
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      "feature 101\n",
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      "feature 104\n",
      "feature 105\n",
      "feature 106\n",
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      "feature 108\n",
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      "feature 110\n",
      "feature 111\n",
      "feature 112\n",
      "feature 113\n",
      "feature 114\n",
      "feature 115\n",
      "feature 116\n",
      "feature 117\n",
      "feature 118\n",
      "feature 119\n",
      "feature 120\n",
      "feature 121\n",
      "feature 122\n",
      "feature 123\n",
      "feature 124\n",
      "feature 125\n",
      "feature 126\n",
      "feature 127\n",
      "feature 128\n",
      "feature 129\n",
      "feature 130\n",
      "feature 131\n",
      "feature 132\n",
      "feature 133\n",
      "feature 134\n",
      "feature 135\n",
      "feature 136\n",
      "feature 137\n",
      "feature 138\n",
      "feature 139\n",
      "feature 140\n",
      "feature 141\n",
      "feature 142\n",
      "feature 143\n",
      "feature 144\n",
      "feature 145\n",
      "feature 146\n",
      "feature 147\n",
      "feature 148\n",
      "feature 149\n",
      "feature 150\n",
      "feature 151\n",
      "feature 152\n",
      "feature 153\n",
      "feature 154\n",
      "feature 155\n",
      "feature 156\n",
      "feature 157\n",
      "feature 158\n",
      "feature 159\n",
      "feature 160\n",
      "feature 161\n",
      "feature 162\n",
      "feature 163\n",
      "feature 164\n",
      "feature 165\n",
      "feature 166\n",
      "feature 167\n",
      "feature 168\n",
      "feature 169\n",
      "feature 170\n",
      "feature 171\n",
      "feature 172\n",
      "feature 173\n",
      "feature 174\n",
      "feature 175\n",
      "feature 176\n",
      "feature 177\n",
      "feature 178\n",
      "feature 179\n",
      "feature 180\n",
      "feature 181\n",
      "feature 182\n",
      "feature 183\n",
      "feature 184\n",
      "feature 185\n",
      "feature 186\n",
      "feature 187\n",
      "feature 188\n",
      "feature 189\n",
      "feature 190\n",
      "feature 191\n",
      "feature 192\n",
      "feature 193\n",
      "feature 194\n",
      "feature 195\n",
      "feature 196\n",
      "feature 197\n",
      "feature 198\n",
      "feature 199\n",
      "feature 200\n",
      "feature 201\n",
      "feature 202\n",
      "feature 203\n",
      "feature 204\n",
      "feature 205\n",
      "feature 206\n",
      "feature 207\n",
      "feature 208\n",
      "feature 209\n",
      "feature 210\n",
      "feature 211\n",
      "feature 212\n",
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      "feature 214\n",
      "feature 215\n",
      "feature 216\n",
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      "feature 218\n",
      "feature 219\n",
      "feature 220\n",
      "feature 221\n",
      "feature 222\n",
      "feature 223\n",
      "feature 224\n",
      "feature 225\n",
      "feature 226\n",
      "feature 227\n",
      "feature 228\n",
      "feature 229\n",
      "feature 230\n",
      "feature 231\n",
      "feature 232\n",
      "feature 233\n",
      "feature 234\n",
      "feature 235\n",
      "feature 236\n",
      "feature 237\n",
      "feature 238\n",
      "feature 239\n",
      "feature 240\n",
      "feature 241\n",
      "feature 242\n",
      "feature 243\n",
      "feature 244\n",
      "feature 245\n",
      "feature 246\n",
      "feature 247\n",
      "feature 248\n",
      "feature 249\n",
      "feature 250\n",
      "feature 251\n",
      "feature 252\n",
      "feature 253\n",
      "feature 254\n",
      "feature 255\n",
      "feature 256\n",
      "feature 257\n",
      "feature 258\n",
      "feature 259\n",
      "feature 260\n",
      "feature 261\n",
      "feature 262\n",
      "feature 263\n",
      "feature 264\n",
      "feature 265\n",
      "feature 266\n",
      "feature 267\n",
      "feature 268\n",
      "feature 269\n",
      "feature 270\n",
      "feature 271\n",
      "feature 272\n",
      "feature 273\n",
      "feature 274\n",
      "feature 275\n",
      "feature 276\n",
      "feature 277\n",
      "feature 278\n",
      "feature 279\n",
      "feature 280\n",
      "feature 281\n",
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      "feature 283\n",
      "feature 284\n",
      "feature 285\n",
      "feature 286\n",
      "feature 287\n",
      "feature 288\n",
      "feature 289\n",
      "feature 290\n",
      "feature 291\n",
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      "feature 293\n",
      "feature 294\n",
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      "feature 297\n",
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      "feature 299\n",
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      "feature 352\n",
      "feature 353\n",
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     ]
    }
   ],
   "source": [
    "df_list = [Yunta50,Taiwan50,orderbook,oil_price,bond,TXF,EXF,FXF,E_F,Nikkei,SP,Russell,DJ,SOX,VIX,pcr,FOI,IOI,DOI]\n",
    "df_option = [True,True,True,True,True,True,True,True,False,True,True,True,True,True,True,False,True,True,True]\n",
    "df_name = ['Yunta50','Taiwan50','orderbook','oil_price','bond','TXF','EXF','FXF','E_F','Nikkei','SP','Russell','DJ','SOX','VIX','pcr','FOI','IOI','DOI']\n",
    "intervals = [0,0.1,0.25,0.5,0.75,1,1.5]\n",
    "cas = [10]\n",
    "window = 1\n",
    "\n",
    "ca_scores_14 = []\n",
    "ca_params_14 = []\n",
    "for ca in cas:\n",
    "    best_score = []\n",
    "    best_params = []\n",
    "    for interval in intervals:\n",
    "        print('interval is',interval)\n",
    "        df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = ca)\n",
    "        amount_of_features = len(df_total.columns)-3\n",
    "        half = int(amount_of_features/2)\n",
    "        score = []\n",
    "        params = []\n",
    "        for index in range(half):\n",
    "            print('feature',index)\n",
    "            if (df_total.columns[index].find('1.0') != -1):\n",
    "                train_X_f = train_X[:,[index,index+half,index+1,index+1+half,index+2,index+2+half,\n",
    "                                     index+7,index+7+half,index+8,index+8+half,index+9,index+9+half]]\n",
    "#                 test_X_f = test_X[:,[index,index+half]]\n",
    "#             elif (df_total.columns[index].find('7.0') != -1):\n",
    "#                 train_X_f = train_X[:,[index,index+half,index+1,index+1+half,index+2,index+2+half,index+3,index+3+half]]\n",
    "# #                 test_X_f = test_X[:,[index,index+half]]\n",
    "            elif (df_total.columns[index].find('4.0') != -1):\n",
    "                train_X_f = train_X[:,[index,index+half,index+1,index+1+half,index+2,index+2+half,index+3,index+3+half]]\n",
    "#                 test_X_f = test_X[:,[index,index+half]]\n",
    "            else:\n",
    "                continue\n",
    "            param_test1 = rf_params_test\n",
    "            gsearch1 = GridSearchCV(estimator = RandomForestClassifier(random_state=0), param_grid = param_test1, scoring='accuracy',cv=2)\n",
    "            gsearch1.fit(train_X_f,train_rise)\n",
    "            params.append(gsearch1.best_params_)\n",
    "            score.append(gsearch1.best_score_)\n",
    "\n",
    "    #         model = RandomForestClassifier()\n",
    "    #         model.set_params(**gsearch1.best_params_)\n",
    "\n",
    "        best_score.append(score)\n",
    "        best_params.append(params)\n",
    "    ca_scores_14.append(best_score)\n",
    "    ca_params_14.append(best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yunta50_open_1_1.0</th>\n",
       "      <th>Yunta50_open_1_2.0</th>\n",
       "      <th>Yunta50_high_1_1.0</th>\n",
       "      <th>Yunta50_high_1_2.0</th>\n",
       "      <th>Yunta50_low_1_1.0</th>\n",
       "      <th>Yunta50_low_1_2.0</th>\n",
       "      <th>Yunta50_close_1_1.0</th>\n",
       "      <th>Yunta50_close_1_2.0</th>\n",
       "      <th>Yunta50_volume_1_1.0</th>\n",
       "      <th>Yunta50_volume_1_2.0</th>\n",
       "      <th>...</th>\n",
       "      <th>IOI_close_1_1.0</th>\n",
       "      <th>IOI_close_1_2.0</th>\n",
       "      <th>DOI_open_1_1.0</th>\n",
       "      <th>DOI_open_1_2.0</th>\n",
       "      <th>DOI_high_1_1.0</th>\n",
       "      <th>DOI_high_1_2.0</th>\n",
       "      <th>DOI_low_1_1.0</th>\n",
       "      <th>DOI_low_1_2.0</th>\n",
       "      <th>DOI_close_1_1.0</th>\n",
       "      <th>DOI_close_1_2.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.505164</td>\n",
       "      <td>0.516351</td>\n",
       "      <td>0.498279</td>\n",
       "      <td>0.493115</td>\n",
       "      <td>0.490534</td>\n",
       "      <td>0.499139</td>\n",
       "      <td>0.517212</td>\n",
       "      <td>0.500861</td>\n",
       "      <td>0.506024</td>\n",
       "      <td>0.499139</td>\n",
       "      <td>...</td>\n",
       "      <td>0.499139</td>\n",
       "      <td>0.499139</td>\n",
       "      <td>0.510327</td>\n",
       "      <td>0.506024</td>\n",
       "      <td>0.510327</td>\n",
       "      <td>0.506024</td>\n",
       "      <td>0.510327</td>\n",
       "      <td>0.506024</td>\n",
       "      <td>0.510327</td>\n",
       "      <td>0.506024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "      <td>0.537005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "      <td>0.605852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>...</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "      <td>0.722031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "      <td>0.796041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "      <td>0.860585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.50</th>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>...</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "      <td>0.946644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 158 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Yunta50_open_1_1.0  Yunta50_open_1_2.0  Yunta50_high_1_1.0  \\\n",
       "0.00            0.505164            0.516351            0.498279   \n",
       "0.10            0.537005            0.537005            0.537005   \n",
       "0.25            0.605852            0.605852            0.605852   \n",
       "0.50            0.722031            0.722031            0.722031   \n",
       "0.75            0.796041            0.796041            0.796041   \n",
       "1.00            0.860585            0.860585            0.860585   \n",
       "1.50            0.946644            0.946644            0.946644   \n",
       "\n",
       "      Yunta50_high_1_2.0  Yunta50_low_1_1.0  Yunta50_low_1_2.0  \\\n",
       "0.00            0.493115           0.490534           0.499139   \n",
       "0.10            0.537005           0.537005           0.537005   \n",
       "0.25            0.605852           0.605852           0.605852   \n",
       "0.50            0.722031           0.722031           0.722031   \n",
       "0.75            0.796041           0.796041           0.796041   \n",
       "1.00            0.860585           0.860585           0.860585   \n",
       "1.50            0.946644           0.946644           0.946644   \n",
       "\n",
       "      Yunta50_close_1_1.0  Yunta50_close_1_2.0  Yunta50_volume_1_1.0  \\\n",
       "0.00             0.517212             0.500861              0.506024   \n",
       "0.10             0.537005             0.537005              0.537005   \n",
       "0.25             0.605852             0.605852              0.605852   \n",
       "0.50             0.722031             0.722031              0.722031   \n",
       "0.75             0.796041             0.796041              0.796041   \n",
       "1.00             0.860585             0.860585              0.860585   \n",
       "1.50             0.946644             0.946644              0.946644   \n",
       "\n",
       "      Yunta50_volume_1_2.0       ...         IOI_close_1_1.0  IOI_close_1_2.0  \\\n",
       "0.00              0.499139       ...                0.499139         0.499139   \n",
       "0.10              0.537005       ...                0.537005         0.537005   \n",
       "0.25              0.605852       ...                0.605852         0.605852   \n",
       "0.50              0.722031       ...                0.722031         0.722031   \n",
       "0.75              0.796041       ...                0.796041         0.796041   \n",
       "1.00              0.860585       ...                0.860585         0.860585   \n",
       "1.50              0.946644       ...                0.946644         0.946644   \n",
       "\n",
       "      DOI_open_1_1.0  DOI_open_1_2.0  DOI_high_1_1.0  DOI_high_1_2.0  \\\n",
       "0.00        0.510327        0.506024        0.510327        0.506024   \n",
       "0.10        0.537005        0.537005        0.537005        0.537005   \n",
       "0.25        0.605852        0.605852        0.605852        0.605852   \n",
       "0.50        0.722031        0.722031        0.722031        0.722031   \n",
       "0.75        0.796041        0.796041        0.796041        0.796041   \n",
       "1.00        0.860585        0.860585        0.860585        0.860585   \n",
       "1.50        0.946644        0.946644        0.946644        0.946644   \n",
       "\n",
       "      DOI_low_1_1.0  DOI_low_1_2.0  DOI_close_1_1.0  DOI_close_1_2.0  \n",
       "0.00       0.510327       0.506024         0.510327         0.506024  \n",
       "0.10       0.537005       0.537005         0.537005         0.537005  \n",
       "0.25       0.605852       0.605852         0.605852         0.605852  \n",
       "0.50       0.722031       0.722031         0.722031         0.722031  \n",
       "0.75       0.796041       0.796041         0.796041         0.796041  \n",
       "1.00       0.860585       0.860585         0.860585         0.860585  \n",
       "1.50       0.946644       0.946644         0.946644         0.946644  \n",
       "\n",
       "[7 rows x 158 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = 2)\n",
    "half = int((len(df_total.columns)-3)/2)\n",
    "\n",
    "df_ca_14 = pd.DataFrame(ca_scores_15[0],columns=df_total.columns[:half-1],index = intervals)\n",
    "df_ca_14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SOX_close_1_1.0        0.629088\n",
       "Russell_low_1_1.0      0.636833\n",
       "SOX_low_1_1.0          0.635112\n",
       "DJ_high_1_1.0          0.650602\n",
       "SP_low_1_1.0           0.634251\n",
       "VIX_close_1_1.0        0.624785\n",
       "Russell_close_1_1.0    0.636833\n",
       "DJ_low_1_1.0           0.605852\n",
       "DJ_close_1_1.0         0.660929\n",
       "SP_close_1_1.0         0.656627\n",
       "Name: 0.25, dtype: float64"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg = np.argsort(df_ca_14.loc[intervals[0]].values)[-10:]\n",
    "df_ca_14.loc[intervals[2]][arg]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval is 0\n",
      "interval is 0.1\n",
      "interval is 0.25\n",
      "interval is 0.5\n"
     ]
    }
   ],
   "source": [
    "best_score = []\n",
    "best_params = []\n",
    "numbers = [1,2,3,5,10,20,0]\n",
    "intervals = [0,0.1,0.25,0.5]\n",
    "\n",
    "for interval in intervals:\n",
    "    print('interval is',interval)\n",
    "    df_total,train_X ,train_rise,train_fall,test_X,test_rise,test_fall,trf = get_dataframe_feature(df_list,df_option,Yunta50_rate,interval,window,ca = 10)\n",
    "    score = []\n",
    "    params = []\n",
    "    #select the best ten feature\n",
    "    for number in numbers:\n",
    "        \n",
    "        #find the good feature\n",
    "        arg = np.argsort(df_ca_14.loc[interval].values)[number*-1:]\n",
    "        col = list(df_ca_14.loc[intervals[0]][arg].index)\n",
    "        index_list = []\n",
    "        for c in col:\n",
    "            if (c.find('1.0') != -1):\n",
    "                for categroy in [1,2,3,8,9,10]:\n",
    "                    index_list.append(c.replace('1.0',f'{categroy}.0'))\n",
    "        indexs = [i for i,value in enumerate(df_total.columns.tolist()) if value in index_list]\n",
    "        \n",
    "        # train\n",
    "        train_X_f = train_X[:,indexs]\n",
    "        param_test1 = rf_params_test\n",
    "        gsearch1 = GridSearchCV(estimator = RandomForestClassifier(random_state=0), param_grid = param_test1, scoring='accuracy',cv=2)\n",
    "        gsearch1.fit(train_X_f,train_rise)\n",
    "        params.append(gsearch1.best_params_)\n",
    "        score.append(gsearch1.best_score_)\n",
    "\n",
    "#         model = RandomForestClassifier()\n",
    "#         model.set_params(**gsearch1.best_params_)\n",
    "    best_score.append(score)\n",
    "    best_params.append(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>20</th>\n",
       "      <th>0</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.615318</td>\n",
       "      <td>0.583477</td>\n",
       "      <td>0.578313</td>\n",
       "      <td>0.572289</td>\n",
       "      <td>0.582616</td>\n",
       "      <td>0.563683</td>\n",
       "      <td>0.547332</td>\n",
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       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.623924</td>\n",
       "      <td>0.604991</td>\n",
       "      <td>0.602410</td>\n",
       "      <td>0.592943</td>\n",
       "      <td>0.593804</td>\n",
       "      <td>0.574010</td>\n",
       "      <td>0.545611</td>\n",
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       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.659208</td>\n",
       "      <td>0.645439</td>\n",
       "      <td>0.651463</td>\n",
       "      <td>0.651463</td>\n",
       "      <td>0.630809</td>\n",
       "      <td>0.623924</td>\n",
       "      <td>0.612737</td>\n",
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       "      <td>0.734940</td>\n",
       "      <td>0.734940</td>\n",
       "      <td>0.738382</td>\n",
       "      <td>0.733219</td>\n",
       "      <td>0.729776</td>\n",
       "      <td>0.732358</td>\n",
       "      <td>0.722031</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "            1         2         3         5         10        20        0 \n",
       "0.00  0.615318  0.583477  0.578313  0.572289  0.582616  0.563683  0.547332\n",
       "0.10  0.623924  0.604991  0.602410  0.592943  0.593804  0.574010  0.545611\n",
       "0.25  0.659208  0.645439  0.651463  0.651463  0.630809  0.623924  0.612737\n",
       "0.50  0.734940  0.734940  0.738382  0.733219  0.729776  0.732358  0.722031"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca_14_com = pd.DataFrame(best_score,columns=numbers,index = intervals)\n",
    "df_ca_14_com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### It's bad for this to combine the good features\n",
    "## Conclusion\n",
    "* choosing the feature by the outlier is better than choosing the feature by the performance in the American data\n",
    "* when choosing the feature by the outlier,use the best feature is better than use the combine feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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