buildBySector.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, plot_confusion_matrix\n",
    "from catboost import CatBoostClassifier, Pool\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from collections import Counter\n",
    "from statistics import mean\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "import os,sys\n",
    "\n",
    "modelNames = ['ZScorePredictor','FiveDayPredictor','TmmrwUpPredictor']\n",
    "verbose = False\n",
    "secretColumns = ['zScoreOfChangeTmmrw','percentChangeInFiveDays', 'sector', 'tmmrwChngAsPerc']\n",
    "sectorFolder = 'data/by_sector'\n",
    "catLabels = ['ticker','industry']\n",
    "trainDir = 'trainData/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scoreCalculator(predictor, attributes, labels):\n",
    "    predicted = predictor.predict(attributes)\n",
    "    errorScore = 0\n",
    "    for guessIndex in range(len(predicted)):\n",
    "        guess = predicted[guessIndex]\n",
    "        actual = labels[guessIndex]\n",
    "        if guess == 'hold' :\n",
    "            errorScore += 0\n",
    "        elif guess == 'long':\n",
    "            if actual == 'short':\n",
    "                errorScore += -15\n",
    "            elif actual == 'hold':\n",
    "                errorScore += -5\n",
    "            else :#actual == 'long'\n",
    "                errorScore += 20\n",
    "        else: #short\n",
    "            if actual == 'long':\n",
    "                errorScore += -20\n",
    "            elif actual == 'hold':\n",
    "                errorScore += 0\n",
    "            else: #actual == short\n",
    "                errorScore += 20\n",
    "    if verbose:\n",
    "        myMatrix = plot_confusion_matrix(predictor, attributes, labels,\n",
    "                                     cmap=plt.cm.Blues, normalize = 'all')    \n",
    "        plt.show()\n",
    "    return  errorScore/len(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def catBoostMaker(df,zScoreAnswer,fiveDayChangeAnswer, tmmrwUpAnswer, count, toSave, sector):\n",
    "    catIndicies = []\n",
    "    counter = -1\n",
    "    for col in df.columns: #finding indices for cat variables\n",
    "        counter += 1\n",
    "        if col in catLabels:\n",
    "            catIndicies.append(counter)\n",
    "    masterList = df.values\n",
    "    masterTrainList, masterTestList, zScoreTrainList, zScoreTestList, fiveDayChangeTrainList, fiveDayChangeTestList, tmmrwUpTrainList, tmmrwUpTestList = \\\n",
    "    train_test_split(masterList, zScoreAnswer, fiveDayChangeAnswer, tmmrwUpAnswer, test_size = .25)\n",
    "    \n",
    "    answerTrainList = [zScoreTrainList,fiveDayChangeTrainList, tmmrwUpTrainList]\n",
    "    answerTestList = [zScoreTestList,fiveDayChangeTestList, tmmrwUpTestList]\n",
    "    trainPools = [Pool(data = masterTrainList, label = x, cat_features = catIndicies) for x in answerTrainList]\n",
    "    testPools = [Pool(data = masterTestList, label = x, cat_features = catIndicies) for x in answerTestList]\n",
    "    \n",
    "    folderRoot = 'catPredictors/'\n",
    "    accuracyList = []\n",
    "    scoreList = []\n",
    "    featureDF = pd.DataFrame(data = \n",
    "                         {'featureName': [col for col in df.columns],\n",
    "                        'importance': [0] * len([col for col in df.columns])}).set_index('featureName')\n",
    "    for name, train, test in zip(modelNames, trainPools, testPools):\n",
    "        train.set_feature_names([x for x in df.columns])\n",
    "        model = CatBoostClassifier(bootstrap_type = 'Bernoulli', iterations = 150, \n",
    "                                   train_dir = '{}{}_{}'.format(trainDir, sector, count))\n",
    "        model.fit(train, eval_set = test, logging_level = 'Silent')\n",
    "        if toSave:\n",
    "            sectorFolder = '{}{}'.format(folderRoot, sector)\n",
    "            if not os.path.exists(sectorFolder):\n",
    "                os.makedirs(sectorFolder)\n",
    "            model.save_model('{}/{}-{}.cbm'.format(sectorFolder, name, count))\n",
    "        accuracy =  model.score(test)\n",
    "        error = scoreCalculator(model, test,test.get_label())\n",
    "        accuracyList.append(accuracy)\n",
    "        scoreList.append(error)\n",
    "        featureList = CatBoostClassifier.get_feature_importance(model, prettified = True)\n",
    "        featureDFPart = pd.DataFrame(data = \n",
    "                     {'featureName': featureList['Feature Id'],\n",
    "                    'importance': featureList['Importances']}).set_index('featureName')\n",
    "        featureDF = featureDF + featureDFPart\n",
    "        if verbose:\n",
    "            print(name)\n",
    "            print('Accuracy: ', accuracy)\n",
    "            print('Error Score:', error)\n",
    "        \n",
    "    averageAccuracy = sum([x for x in accuracyList])/len(accuracyList)\n",
    "    averageScore = sum([x for x in scoreList])/len(scoreList)\n",
    "    featureList = CatBoostClassifier.get_feature_importance(model, prettified = True)\n",
    "    return averageAccuracy, averageScore, featureDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#returns feature to remove, mean error score, # of significant (non answer or date) columns\n",
    "def buildForest(file, droppedColumns, toSave):\n",
    "    sector = file.split('.')[0]\n",
    "    sector = sector.replace(' ', '-')\n",
    "    file = sectorFolder + '/' + file\n",
    "    secretColumns = ['zScoreOfChangeTmmrw','percentChangeInFiveDays', 'sector', 'tmmrwChngAsPerc']\n",
    "    catAccuracy = []\n",
    "    catError = []\n",
    "    masterFeatureDF = None\n",
    "    colCount = 0\n",
    "    combinedDF = None\n",
    "    if os.stat(file).st_size > 2000000:\n",
    "        chunks = pd.read_csv(file, parse_dates = True, chunksize = 150000)\n",
    "        for idx, chunk in enumerate(chunks):\n",
    "            chunk.drop(columns = ['Date'], inplace = True)\n",
    "            zScoreAnswer = chunk['zScoreOfChangeTmmrw']\n",
    "            zScoreAnswer = zScoreAnswer.astype('float')\n",
    "            zScoreAnswer = ['long' if x > 0 else 'short' for x in zScoreAnswer]\n",
    "            fiveDayChangeAnswer = chunk['percentChangeInFiveDays']\n",
    "            fiveDayChangeAnswer = ['long' if x > 0 else 'short' for x in fiveDayChangeAnswer]\n",
    "            tmmrwUpAnswer = chunk['tmmrwChngAsPerc']\n",
    "            tmmrwUpAnswer = ['long' if x > 0 else 'short' for x in tmmrwUpAnswer]\n",
    "\n",
    "            chunk.drop(columns = secretColumns, inplace = True)\n",
    "            accuracy, error, featureDF = \\\n",
    "            catBoostMaker(chunk,zScoreAnswer,fiveDayChangeAnswer, tmmrwUpAnswer, idx, toSave, sector)\n",
    "            catAccuracy.append(accuracy)\n",
    "            catError.append(error)\n",
    "            if idx == 0:\n",
    "                masterFeatureDF = featureDF\n",
    "            else:\n",
    "                masterFeatureDF = masterFeatureDF + featureDF\n",
    "            break\n",
    "\n",
    "\n",
    "    else :\n",
    "        combinedDF = pd.read_csv(file, parse_dates = True)\n",
    "        combinedDF.drop(columns = ['Date'], inplace = True)\n",
    "        zScoreAnswer = combinedDF['zScoreOfChangeTmmrw']\n",
    "        zScoreAnswer = zScoreAnswer.astype('float')\n",
    "        zScoreAnswer = ['long' if x > 0 else 'short' for x in zScoreAnswer]\n",
    "        fiveDayChangeAnswer = combinedDF['percentChangeInFiveDays']\n",
    "        fiveDayChangeAnswer = ['long' if x > 0 else 'short' for x in fiveDayChangeAnswer]\n",
    "        tmmrwUpAnswer = combinedDF['tmmrwChngAsPerc']\n",
    "        tmmrwUpAnswer = ['long' if x > 0 else 'short' for x in tmmrwUpAnswer]\n",
    "\n",
    "        combinedDF.drop(columns = secretColumns, inplace = True)\n",
    "        accuracy, error, featureDF = \\\n",
    "        catBoostMaker(chunk,zScoreAnswer,fiveDayChangeAnswer, tmmrwUpAnswer, catLabels, idx, toSave, sector)\n",
    "        catAccuracy.append(accuracy)\n",
    "        catError.append(error)\n",
    "        masterFeatureDF = featureDF\n",
    "    \n",
    "    if verbose:\n",
    "        print('-------------------------------------------\\n\\n')\n",
    "        print('Final Catboost accuracy: ', mean(catAccuracy))\n",
    "        print('Final Catboost score: ' ,mean(catError))\n",
    "        print('Final Feature Importance:', masterFeatureDF.sort_values(by = ['importance']))\n",
    "    lowestImportance = masterFeatureDF['importance'].min()\n",
    "    minValues = masterFeatureDF[masterFeatureDF['importance'] == lowestImportance]\n",
    "    toRemove = minValues.index.values.tolist()\n",
    "    if verbose:\n",
    "        print(type(toRemove))\n",
    "        print('Feature to remove: ', toRemove)\n",
    "    return toRemove, mean(catError), colCount, masterFeatureDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic-Materials\n",
      "droppedColumns: []\n",
      "valuesToTry: []\n",
      "droppedColumns: ['DPRIMEValue']\n",
      "valuesToTry: ['DPRIMElastChangeP', 'PRS85006092bPercent']\n",
      "change in score by removing DPRIMEValue is 0.004311111111109156\n",
      "droppedColumns: ['DPRIMEValue', 'DPRIMElastChangeP']\n",
      "valuesToTry: ['PRS85006092bPercent']\n",
      "change in score by removing DPRIMElastChangeP is -0.030755555555554537\n",
      "droppedColumns: ['DPRIMEValue', 'PRS85006092bPercent']\n",
      "valuesToTry: []\n",
      "change in score by removing PRS85006092bPercent is -0.03271111111111136\n",
      "Final dropped columns: ['DPRIMEValue']\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZScorePredictor\n",
      "Accuracy:  0.6582133333333333\n",
      "Error Score: 7.174933333333334\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FiveDayPredictor\n",
      "Accuracy:  0.7216\n",
      "Error Score: 9.462666666666667\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TmmrwUpPredictor\n",
      "Accuracy:  0.6704533333333333\n",
      "Error Score: 7.5254666666666665\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Final Catboost accuracy:  0.6834222222222222\n",
      "Final Catboost score:  8.054355555555555\n",
      "Final Feature Importance:                        importance\n",
      "featureName                      \n",
      "WPU0911Value             0.000000\n",
      "DPRIMElastChangeP        0.000000\n",
      "DPRIMEValue              0.000000\n",
      "CONSUMERValue            0.000000\n",
      "PRS85006092bPercent      0.000000\n",
      "...                           ...\n",
      "DTWEXAFEGSlastChangeP    5.483356\n",
      "DEXUSUKlastChangeP       6.153721\n",
      "industry                 6.409651\n",
      "typPrice                 8.282367\n",
      "aroon                   11.282433\n",
      "\n",
      "[158 rows x 1 columns]\n",
      "<class 'list'>\n",
      "Feature to remove:  ['CONSUMERValue', 'DPRIMEValue', 'DPRIMElastChangeP', 'PRS85006092bPercent', 'WPU0911Value']\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Communication-Services\n",
      "droppedColumns: []\n",
      "valuesToTry: []\n",
      "droppedColumns: ['CPIAUCSLfiveVsTenTickAverage']\n",
      "valuesToTry: ['DPRIMElastChangeP', 'South America']\n",
      "change in score by removing CPIAUCSLfiveVsTenTickAverage is -0.03413333333333313\n",
      "droppedColumns: ['DPRIMElastChangeP']\n",
      "valuesToTry: ['South America']\n",
      "change in score by removing DPRIMElastChangeP is -0.09177777777777862\n",
      "droppedColumns: ['South America']\n",
      "valuesToTry: []\n",
      "change in score by removing South America is 0.009333333333334082\n",
      "Final dropped columns: ['South America']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZScorePredictor\n",
      "Accuracy:  0.63712\n",
      "Error Score: 6.436266666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FiveDayPredictor\n",
      "Accuracy:  0.6912533333333334\n",
      "Error Score: 8.388666666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TmmrwUpPredictor\n",
      "Accuracy:  0.6353866666666667\n",
      "Error Score: 6.2804\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Final Catboost accuracy:  0.6545866666666668\n",
      "Final Catboost score:  7.035111111111111\n",
      "Final Feature Importance:                              importance\n",
      "featureName                            \n",
      "South America                  0.000000\n",
      "Oceania                        0.000000\n",
      "DPRIMElastChangeP              0.000000\n",
      "East & Central Europe          0.000000\n",
      "Misery Index                   0.028243\n",
      "...                                 ...\n",
      "highVsLowPerc                  4.381288\n",
      "thisDayAveragePercentChange    4.556486\n",
      "typPrice                       5.670109\n",
      "DEXCHUSlastChangeP             5.913813\n",
      "aroon                         14.605131\n",
      "\n",
      "[158 rows x 1 columns]\n",
      "<class 'list'>\n",
      "Feature to remove:  ['DPRIMElastChangeP', 'East & Central Europe', 'Oceania', 'South America']\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Consumer-Cyclical\n",
      "droppedColumns: []\n",
      "valuesToTry: []\n",
      "droppedColumns: ['BUSLOANSbPercent']\n",
      "valuesToTry: ['CASS-CTLI-Index', 'CONSUMERbPercent', 'DPRIMEValue', 'DPRIMElastChangeP', 'PCEC96Value', 'PCEC96bPercent', 'PRS85006092bPercent', 'PRS85006092fiveVsTenTickAverage', 'TCUValue', 'TCUfiveVsTenTickAverage']\n",
      "change in score by removing BUSLOANSbPercent is -0.06857777777777763\n",
      "droppedColumns: ['CASS-CTLI-Index']\n",
      "valuesToTry: ['CONSUMERbPercent', 'DPRIMEValue', 'DPRIMElastChangeP', 'PCEC96Value', 'PCEC96bPercent', 'PRS85006092bPercent', 'PRS85006092fiveVsTenTickAverage', 'TCUValue', 'TCUfiveVsTenTickAverage']\n",
      "change in score by removing CASS-CTLI-Index is -0.012311111111111828\n",
      "droppedColumns: ['CONSUMERbPercent']\n",
      "valuesToTry: ['DPRIMEValue', 'DPRIMElastChangeP', 'PCEC96Value', 'PCEC96bPercent', 'PRS85006092bPercent', 'PRS85006092fiveVsTenTickAverage', 'TCUValue', 'TCUfiveVsTenTickAverage']\n",
      "change in score by removing CONSUMERbPercent is 0.079600000000001\n",
      "droppedColumns: ['CONSUMERbPercent', 'DPRIMEValue']\n",
      "valuesToTry: ['DPRIMElastChangeP', 'PCEC96Value', 'PCEC96bPercent', 'PRS85006092bPercent', 'PRS85006092fiveVsTenTickAverage', 'TCUValue', 'TCUfiveVsTenTickAverage']\n",
      "change in score by removing DPRIMEValue is -0.038133333333333574\n",
      "droppedColumns: ['CONSUMERbPercent', 'DPRIMElastChangeP']\n",
      "valuesToTry: ['PCEC96Value', 'PCEC96bPercent', 'PRS85006092bPercent', 'PRS85006092fiveVsTenTickAverage', 'TCUValue', 'TCUfiveVsTenTickAverage']\n",
      "change in score by removing DPRIMElastChangeP is -0.08422222222222331\n",
      "droppedColumns: ['CONSUMERbPercent', 'PCEC96Value']\n",
      "valuesToTry: ['PCEC96bPercent', 'PRS85006092bPercent', 'PRS85006092fiveVsTenTickAverage', 'TCUValue', 'TCUfiveVsTenTickAverage']\n",
      "change in score by removing PCEC96Value is 0.12044444444444569\n",
      "droppedColumns: ['CONSUMERbPercent', 'PCEC96Value', 'PCEC96bPercent']\n",
      "valuesToTry: ['PRS85006092bPercent', 'PRS85006092fiveVsTenTickAverage', 'TCUValue', 'TCUfiveVsTenTickAverage']\n",
      "change in score by removing PCEC96bPercent is -0.025377777777777943\n",
      "droppedColumns: ['CONSUMERbPercent', 'PCEC96Value', 'PRS85006092bPercent']\n",
      "valuesToTry: ['PRS85006092fiveVsTenTickAverage', 'TCUValue', 'TCUfiveVsTenTickAverage']\n",
      "change in score by removing PRS85006092bPercent is -0.06657777777777785\n",
      "droppedColumns: ['CONSUMERbPercent', 'PCEC96Value', 'PRS85006092fiveVsTenTickAverage']\n",
      "valuesToTry: ['TCUValue', 'TCUfiveVsTenTickAverage']\n",
      "change in score by removing PRS85006092fiveVsTenTickAverage is -0.016444444444444706\n",
      "droppedColumns: ['CONSUMERbPercent', 'PCEC96Value', 'TCUValue']\n",
      "valuesToTry: ['TCUfiveVsTenTickAverage']\n",
      "change in score by removing TCUValue is -0.08351111111111198\n",
      "droppedColumns: ['CONSUMERbPercent', 'PCEC96Value', 'TCUfiveVsTenTickAverage']\n",
      "valuesToTry: []\n",
      "change in score by removing TCUfiveVsTenTickAverage is 0.10448888888888952\n",
      "Final dropped columns: ['CONSUMERbPercent', 'PCEC96Value', 'TCUfiveVsTenTickAverage']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZScorePredictor\n",
      "Accuracy:  0.6595466666666666\n",
      "Error Score: 7.3045333333333335\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FiveDayPredictor\n",
      "Accuracy:  0.70496\n",
      "Error Score: 8.956133333333334\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TmmrwUpPredictor\n",
      "Accuracy:  0.65952\n",
      "Error Score: 7.2572\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Final Catboost accuracy:  0.6746755555555556\n",
      "Final Catboost score:  7.8392888888888885\n",
      "Final Feature Importance:                                importance\n",
      "featureName                              \n",
      "DPRIMEValue                      0.000000\n",
      "BUSLOANSValue                    0.000000\n",
      "CONSUMERValue                    0.000000\n",
      "DPRIMElastChangeP                0.000000\n",
      "South America                    0.000000\n",
      "...                                   ...\n",
      "NASDAQ100fiveVsTenTickAverage    6.022834\n",
      "GOLDAMGBD228NLBMlastChangeP      6.077343\n",
      "typPrice                         6.107253\n",
      "thisDayPercentChangeStdev        7.125171\n",
      "aroon                           14.468026\n",
      "\n",
      "[158 rows x 1 columns]\n",
      "<class 'list'>\n",
      "Feature to remove:  ['BUSLOANSValue', 'CONSUMERValue', 'DPRIMEValue', 'DPRIMElastChangeP', 'South America']\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Consumer-Defensive\n",
      "droppedColumns: []\n",
      "valuesToTry: []\n",
      "droppedColumns: ['BUSLOANSValue']\n",
      "valuesToTry: ['DPRIMElastChangeP', 'UNRATEfiveVsTenTickAverage']\n",
      "change in score by removing BUSLOANSValue is -0.11315555555555523\n",
      "droppedColumns: ['DPRIMElastChangeP']\n",
      "valuesToTry: ['UNRATEfiveVsTenTickAverage']\n",
      "change in score by removing DPRIMElastChangeP is 0.137422222222221\n",
      "droppedColumns: ['DPRIMElastChangeP', 'UNRATEfiveVsTenTickAverage']\n",
      "valuesToTry: []\n",
      "change in score by removing UNRATEfiveVsTenTickAverage is -0.007600000000000051\n",
      "Final dropped columns: ['DPRIMElastChangeP']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZScorePredictor\n",
      "Accuracy:  0.6351733333333334\n",
      "Error Score: 6.340133333333333\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FiveDayPredictor\n",
      "Accuracy:  0.6864533333333334\n",
      "Error Score: 8.252933333333333\n"
     ]
    },
    {
     "data": {
      "image/png": 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3/WrgeqA/MAC4AbgmQz4zs1ZV8FGl2cDukoam4zMnAdOblLULMA04JSKWNByPiOdIxnf2SA8dScG90uZkGTBSRFxRsH+lpEkZ8pmZFZWMtlfmWhFRl8amO4BaYGpELJR0Rnp+MnAe0Bu4NA3IdQW3As4ErkoD7zLg88XKKza3vVf68e70ealrSe4fnAjcWub3MzN7myq7GHJEzABmNDk2ueDzRGBiC3nnA8XuqTZSrOU5lyRYNnyzLxWWA5yftRAzs5Z0xNlDWRSb2z60PStiZl1PJbvt7S3TDCNJ+wDDgC0bjkXEn9qqUmbWdXS6lmcDSd8HDiMJnjOAccD9gIOnmb1r1Rk6sz2q9AmSYfvnIuLzwAhgizatlZl1CRLU1ijT1tFk6bavj4hNkurS+Z8vkExlMjN71zpttx2YI2lH4L9JRuBfBx5uy0qZWddRpbEz09z2r6QfJ0u6Hdg+Iha0bbXMrCsQ6nzvbZfU4rxOSSMjYl7bVMnMuowSFjruaIq1PH9e5FwAR1S4LhU1Yo+B3Hv3/8u7GlaCnu/3rN9q8tbipypynU53zzMiDm/PiphZ1yOgtrMFTzOz9tABn0LKxMHTzHLl4GlmVqLkFRvVGT2zrCQvSSdLOi/d30XSgW1fNTPrCmqUbetoskzPvBT4APDpdP81ktd7mpm9a5V8b3t7ytJtHx0RIyU9ApC+77hHG9fLzLoAAd06YmTMIEvw3Ji+TD4AJPUFNrVprcysy6jS2JkpeP4GuBnoJ+knJKssfa9Na2VmXYLUCadnNoiIqyTNJVmWTsDHIuLxNq+ZmXUJVRo7My2GvAuwDril8FhEVGZulpl1aR1xJD2LLN32W3n7RXBbAkOBxcDebVgvM+sCBBVd6FjSWODXJK8eviwiftrk/ATgP9Ld14EvR8SjBedrgTnAMxFxXLGysnTb921S+Egav0nTzKw8FXyGMw18lwBHAyuB2ZKmR8SigmTLgUPTp4bGAVOA0QXnvwY8DmzfWnlZnvNsJF2K7v2l5jMza44y/pPBgcDSiFgWERuAa4HxhQkiYlZEvJTuPggM2lwPaRBwLHBZlsKy3PP894LdGmAksDrLxc3Miqnwq4cHAk8X7K+kcauyqdOA2wr2fwV8B9guS2FZ7nkWXqiO5B7oTVkubmbWmhKCZx9Jcwr2p0TElIL95q4UzV1I0uEkwXNMun8c8EJEzJV0WJbKFA2e6T2EbSPi21kuZmZWqhIWBlkTEaOKnF8JDC7YHwSsaqa84SRd83ERsTY9fDDwUUnHkAyMby/pyog4uaXCWrznKalbRNSTdNPNzCouefVwti2D2cDukoamU8hPAqY3Lk+7ANOAUyJiScPxiPhuRAyKiCFpvruKBU4o3vJ8mCRwzpc0HbgBeKOgsGmZvo6ZWRGVmmEUEXWSJgF3kDyqNDUiFko6Iz0/GTgP6A1cmrZ461ppzbYoyz3PXsBakncWNTzvGSTR28ysbBUeMCIiZgAzmhybXPB5IjCxlWvcA9zTWlnFgme/dKT9n7wdNDdfv7ULm5ll0RmnZ9YC21LCCJaZWWlETbZnODucYsHz2Yj4UbvVxMy6HNE5W55V+pXMrGoIulXpyiDFgueR7VYLM+uSOmXLMyJebM+KmFnX1GkXQzYza0tVGjsdPM0sP6KMpd06CAdPM8uP3G03MytZMsPIwdPMrGTVGTodPM0sZ1Xa8HTwNLM8qZT1PDsUB08zy41H283MyuQBIzOzUqmk13B0KA6eZpYbd9vNzMrklqeZWRmqM3Q6eJpZjgTUuuVpZla6Ko2dDp5mliehKu24V+tAl5l1ElK2Ldu1NFbSYklLJZ3dzPkJkhak2yxJI9LjgyXdLelxSQslfa21stzyNLPcJI8qVablKakWuAQ4GlgJzJY0PSIWFSRbDhwaES9JGgdMAUYDdcA3I2KepO2AuZLubJK3Ebc8zSw/GVudGVueBwJLI2JZRGwArgXGFyaIiFkR8VK6+yAwKD3+bETMSz+/BjwODCxWmFueZparEqZn9pE0p2B/SkRMKdgfCDxdsL+SpFXZktOA25oelDQE2B94qFhlHDzNLDfJYsiZk6+JiFGtXK6paDahdDhJ8BzT5Pi2wE3A1yPi1WKVcfA0s1xVcLR9JTC4YH8QsOod5UnDgcuAcRGxtuB4d5LAeVVETGutMN/zNLNcVfCe52xgd0lDJfUATgKmNy5LuwDTgFMiYknBcQF/AB6PiF9kKcwtzw7krn8s4nu/mkZ9/SYmfPQDnPXZoxud/9eK5/naT67iscVP890vHcdXJhy5+dzka+7m6lv+ARJ77dafX587gS236N7eX6FLOfIDe3HBNz9BbU0NV/xlFr+6/M5G58cdsi/nnnEcmyKoq9vEOb+4kQcfXcbAnXbkdz/4LP16b8+mCC6/+QF+f+09+XyJDqBSLc+IqJM0CbgDqAWmRsRCSWek5ycD5wG9gUvTOfV16a2Ag4FTgMckzU8veU5EzGipvHYNnpJWAKMiYk2Z+fcDBhT7QtWqvn4TZ//8Bq7/9VcZ0G9HPvyFi/nwh/Zhj6H9N6fZcfut+ck3Ps5tMx9rlPfZF17mshvu5b6rz2GrLXvwxXOn8uf/ncdJxxa7V27vRk2NuOg7n+L4Sb9l1fMvc9fl3+a2mY+xePlzm9PMnL1482+193sHMPWCLzD6kz+mrm4T3/vVNBYsXsm2W2/B3X/6D+556IlGebuKEu95tiqNDTOaHJtc8HkiMLGZfPdT4jT7qum2S+oG7Acck3NV2sS8RU8ydFBfhgzsQ4/u3fjYUSO5vUmQ7NtrO/Yf9h66d3vnz1Zfv4k339pIXV09697cyM59tm+vqndJB+w9hGVPr+HJZ9aysa6eaXfO45hDhzdK88b6DZs/b73VFkQ6dPH82ldZsHglAK+ve4slK56jf98d26vqHYtETcato2mzlqekbYDrSW7a1gLnp6fOlPQRoDvwyYh4QlIvYCqwK7AOOD0iFkj6ATAAGAKsIRkZ20rSGOCCiLiurerf3p5b/TID+u24eX9Avx2Zt/DJTHn799uRL3/mCEYe/3222qI7hx64J4eN3quNamoA/fvuwDPPv7R5f9XzL3HAPkPeke7Yw4Zz3lc/St+e23HiNya/4/zg/r0Yvscg5i5c0Ya17dg6XljMpi1bnmOBVRExIiL2AW5Pj6+JiJHA74Bvpcd+CDwSEcOBc4A/FVznAGB8RHyG5H7FdRGxX3OBU9LpkuZImrN29eo2+lptI5p7oCLj/21ffnUdt9/3GLNv+j6P3vJj1r25gRtvn13ZClojza1B2dxveOs9Cxj9yR9z8rencM4ZxzY6t81WPfjThRP57i9u4rU33myrqnZoDe9tr8aWZ1sGz8eAoyRdKOlDEfFKerzhEYC5JC1KSFqUVwBExF1Ab0k7pOemR8T6LAVGxJSIGBURo3r37VuRL9Fe+vfbkVUvvLx5f9ULL2fues+cvZhd+vemT8/t6N6tlmMPHcHsx5a3UU0Nkt9n4E49N+8P2Kknz615pcX0sx75P4YO7EOvHbYBoFttDZdf+EVuuH0Of7370Tavb0emjFtH02bBM30M4ACSIHqBpPPSU2+l/67n7dsGxR5ufaOt6tiR7L/XLix7ejVPrlrLho11/Pl/5/HhD+2bKe/AnXsyb+EK1r25gYjgvjlL2H3ITm1c465t3qIn2W2XvuwyoDfdu9VywtEjuW3mgkZphg7qs/nz8D0G0b17N158JfnP+b/+cwJLVjzHpVff1a717pCqNHq25T3PAcCLEXGlpNeBzxVJPhOYAJwv6TCSrv2rzXSNXgO2q3xt89etWy0XfPMTnPT1S6nftIlPH3cQe+7an8un3Q/AqSeM4YW1r/Jvn7+I1954k5qaGqZcdw/3XXMOB+w9hOMO34+jT/0Ztd1q2fd9Azll/Adz/kadW339Jr7zs+u56TdfpbZWXDX9QZ5Y9hyfPyGZsPLHaffz0SP248RjR1NXV8/6Nzdy2jlTAThoxK6cdOxoFv7rGWZelSz8c/4l07lzVotrUHRqHbFLnoWi2ZttFbiw9GHgImATsBH4MnAj6aNKkkYBF0fEYemA0R+BobxzwOj1iLg4vWYvkme4utPKgNH+B4yKex94uE2+m7WNnT5wVt5VsBK8tfh6Nq174V1Fvr323T/+9Jd7MqU9cLcd57YyPbNdtVnLMyLuIAl0hYYUnJ8DHJZ+fpEmq5+kx3/QZP9F4P2VramZ5ao6G56eYWRm+UluZ1Zn9HTwNLP8lLBKfEfj4GlmuarS2OngaWZ5UrMTDqqBg6eZ5apKY6eDp5nlp4M+/56Jg6eZ5atKo6eDp5nlyo8qmZmVwfc8zcxK5ec8zczK4267mVmJhFueZmZlqdLY6eBpZjmr0uhZNW/PNLPOqZLvMJI0VtJiSUslnd3M+QmSFqTbLEkjsuZ9R71L+pZmZhVWqbdwSKoFLgHGAcOAT0sa1iTZcuDQ9GWT5wNTSsjbiIOnmeWrcu8wOhBYGhHLImIDcC1NFlmPiFkR0fDO6AdJXo2eKW9TDp5mlpuGxZCz/AP0aXi1eLqd3uRyA4GnC/ZXpsdachpwW5l5PWBkZjkq7SH5Na28w6jYW3gbJ5QOJwmeY0rN28DB08xyVcHB9pXA4IL9QcCqd5QnDQcuA8ZFxNpS8hZyt93McpQshpxly2A2sLukoZJ6ACcB0xuVJu0CTANOiYglpeRtyi1PM8tVpWYYRUSdpEkkb+2tBaZGxEJJZ6TnJwPnAb2BS9OAXBcRo1rKW6w8B08zy02lF0OOiBnAjCbHJhd8nghMzJq3GAdPM8tXlc4wcvA0s1x5VSUzszJ4VSUzs1IJahw8zczKUZ3R08HTzHLjxZDNzMpUpbHTwdPM8uWWp5lZGTJOvexwHDzNLFfVGTodPM0sR/J7283MyuMZRmZm5ajO2OngaWb5qtLY6eBpZnnK/lrhjsbB08xyU80zjPwaDjOzMrjlaWa5qtaWp4OnmeXKjyqZmZXKD8mbmZWumgeMHDzNLFfV2m33aLuZ5aphfntrW7ZraaykxZKWSjq7mfN7SvqHpLckfavJuW9IWijpn5KukbRlsbIcPM0sV8q4tXodqRa4BBgHDAM+LWlYk2QvAmcBFzfJOzA9Pioi9gFqgZOKlefgaWb5qlT0hAOBpRGxLCI2ANcC4wsTRMQLETEb2NhM/m7AVpK6AVsDq4oV5uBpZrkRUCNl2oA+kuYUbKc3udxA4OmC/ZXpsVZFxDMkrdGngGeBVyLib8XydNoBo/nz5q7ZYavaJ/OuRxvoA6zJuxJWks76m73n3V5g3ry5d2zVXX0yJl8TEWOLnG+ufRpZLiypJ0krdSjwMnCDpJMj4sqW8nTa4BkRffOuQ1uQNCciRuVdD8vOv1nLWgmGpVoJDC7YH0QrXe8CRwHLI2I1gKRpwAeBFoOnu+1m1lnMBnaXNFRSD5IBn+kZ8z4FHCRpayUvVToSeLxYhk7b8jSzriUi6iRNAu4gGS2fGhELJZ2Rnp8saWdgDrA9sEnS14FhEfGQpBuBeUAd8AgwpVh5ish0S8A6CEmnR0TRH9U6Fv9mnZODp5lZGXzP08ysDA6eZmZlcPDsICS9nncdrHySVkiZn1dsLv9+ko6pZJ2sbTl4muUsnQ64H+DgWUUcPDsYJS5KV3Z5TNKJ6fHDJN0j6UZJT0i6Kn0eDUnHpMful/QbSX/N91t0bpK2kXSrpEfT3+nE9NSZkualv9ueadpekv4saYGkByUNT4//QNIUSX8D/gT8CDhR0vyC61kH5uc8O54TSFohI0im9c2WNDM9tz+wN8msiQeAgyXNAX4PHBIRyyVd0/5V7nLGAqsi4lgASTsAF5JMHxwp6SvAt4CJwA+BRyLiY5KOIAmU+6XXOQAYExHrJX2OZEWfSe37Vaxcbnl2PGOAayKiPiKeB+4F3p+eezgiVkbEJmA+MATYE1gWEcvTNA6ebe8x4ChJF0r6UES8kh6flv57LslvA8nveQVARNwF9E6DLcD0iFjfTnW2CnPLs+MptvjWWwWf60l+v+pchruKRcQSSQeQ3KO8IO16w9u/T8NvA8UXq3ij7Wppbc0tz45nJsm9r1pJfYFDgIeLpH8C2FXSkHTf98vamKQBwLp0xZ2LgZFFks8EJqT5DiPp2r/aTLrXgO0qW1NrSw6eHc/NwALgUeAu4DsR8VxLidNu31eA2yXdDzwPvNJSequIfYGHJc0HzgV+XCTtD4BRkhYAPwVObSHd3cAwDxhVD0/P7AQkbRsRr6ej75cA/4qIX+ZdL7POzC3PzuGLaStoIbADyei7mbUhtzzNzMrglqeZWRkcPM3MyuDgaWZWBgfPLkpSffpYzD8l3SBp63dxrf+R9In082WShhVJe5ikD5ZRRrOrFmVZzajUFavSeeffKrWO1rU4eHZd6yNiv4jYB9gAnFF4UlJtOReNiIkRsahIksNI3kpoVtUcPA3gPuC9aavwbklXA4+ls5wukjQ7XRXoS7B55affSlok6VagX8OF0pWfRqWfx6arDD0q6e/pLKgzgG+krd4PSeor6aa0jNmSDk7z9pb0N0mPSPo9GaahpqsXzZW0UNLpTc79PK3L39OZW0jaTdLtaZ77GlZCMsvCc9u7OCVrSY4Dbk8PHQjsk67QdDrwSkS8X9IWwAPpPO79gT1IZtrsBCwCpja5bl/gv3l7tadeEfGipMnA6xFxcZruauCXEXG/pF1I3ny4F/B94P6I+JGkY4FGwbAFX0jL2IpkNaqbImItsA0wLyK+Kem89NqTSN6OeEZE/EvSaOBS4Igy/ozWBTl4dl1bpQ/WQ9Ly/ANJd/rhghWa/g0Y3nA/k+QB/N1J5ttfExH1wCpJdzVz/YOAmQ3XiogXW6jHUSTTEhv2t5e0XVrGCWneWyW9lOE7nSXp+PTz4LSua4FNwHXp8SuBaZK2Tb/vDQVlb5GhDDPAwbMrWx8R+xUeSINI4Uo/As6MiDuapDuGt1cGaokypIHk1tEHmi7NltYl8wyOdNGNo9JrrZN0D7BlC8kjLfflpn8Ds6x8z9OKuQP4sqTuAJLeJ2kbkpWCTkrvifYHDm8m7z+AQyUNTfP2So83XT3obyRdaNJ0+6UfC1cjGgf0bKWuOwAvpYFzT5KWb4MaoKH1/BmS2wGvAsslfTItQ5JGtFKG2WYOnlbMZST3M+dJ+ifJnPluJCs//YtkUeDfkSzY3EhErCa5TzlN0qO83W2+BTi+YcAIOIt01SFJi3h71P+HwCGS5pHcPniqlbreDnRLVy86H3iw4NwbwN6S5pLc0/xRenwCcFpav4XA+Ax/EzPAc9vNzMrilqeZWRkcPM3MyuDgaWZWBgdPM7MyOHiamZXBwdPMrAwOnmZmZfj/PuWngnfM5n8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TmmrwUpPredictor\n",
      "Accuracy:  0.6366666666666667\n",
      "Error Score: 6.386\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Final Catboost accuracy:  0.6527644444444445\n",
      "Final Catboost score:  6.993022222222222\n",
      "Final Feature Importance:                                importance\n",
      "featureName                              \n",
      "UNRATEValue                      0.000000\n",
      "DPRIMEValue                      0.000000\n",
      "CONSUMERValue                    0.000000\n",
      "DPRIMElastChangeP                0.034589\n",
      "South America                    0.048317\n",
      "...                                   ...\n",
      "DTWEXAFEGSlastChangeP            5.087714\n",
      "GOLDAMGBD228NLBMlastChangeP      6.407764\n",
      "NASDAQ100fiveVsTenTickAverage    6.903080\n",
      "typPrice                         8.643420\n",
      "aroon                           15.362747\n",
      "\n",
      "[158 rows x 1 columns]\n",
      "<class 'list'>\n",
      "Feature to remove:  ['CONSUMERValue', 'DPRIMEValue', 'UNRATEValue']\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Energy\n",
      "droppedColumns: []\n",
      "valuesToTry: []\n",
      "droppedColumns: ['BUSLOANSValue']\n",
      "valuesToTry: ['BUSLOANSbPercent', 'CONSUMERValue', 'DPRIMEValue', 'DPRIMElastChangeP', 'South America', 'TCUValue']\n",
      "change in score by removing BUSLOANSValue is -0.0011555555555560204\n",
      "droppedColumns: ['BUSLOANSbPercent']\n",
      "valuesToTry: ['CONSUMERValue', 'DPRIMEValue', 'DPRIMElastChangeP', 'South America', 'TCUValue']\n",
      "change in score by removing BUSLOANSbPercent is 0.026177777777778743\n",
      "droppedColumns: ['BUSLOANSbPercent', 'CONSUMERValue']\n",
      "valuesToTry: ['DPRIMEValue', 'DPRIMElastChangeP', 'South America', 'TCUValue']\n",
      "change in score by removing CONSUMERValue is -0.048577777777776276\n",
      "droppedColumns: ['BUSLOANSbPercent', 'DPRIMEValue']\n",
      "valuesToTry: ['DPRIMElastChangeP', 'South America', 'TCUValue']\n",
      "change in score by removing DPRIMEValue is -0.008044444444445631\n",
      "droppedColumns: ['BUSLOANSbPercent', 'DPRIMElastChangeP']\n",
      "valuesToTry: ['South America', 'TCUValue']\n",
      "change in score by removing DPRIMElastChangeP is -0.025155555555555154\n",
      "droppedColumns: ['BUSLOANSbPercent', 'South America']\n",
      "valuesToTry: ['TCUValue']\n",
      "change in score by removing South America is 0.04039999999999999\n",
      "droppedColumns: ['BUSLOANSbPercent', 'South America', 'TCUValue']\n",
      "valuesToTry: []\n",
      "change in score by removing TCUValue is 0.0519999999999996\n",
      "Final dropped columns: ['BUSLOANSbPercent', 'South America', 'TCUValue']\n"
     ]
    },
    {
     "data": {
      "image/png": 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eeKxcEC0m1Yi4psmFe0XEm+UuZmbWWnmNpxIRtZImATOBKmBqRCySNDHdPxn4PtAf+EXaT1vbUPuVtA3JkwNfaHLpSyWNImm5L2tmfyObvfsv6SPAr4HewC7pc11fiIgvZvuqZmYtyH4TKpOImAHMaFI2uWT9bODsFs59iyThNi0/vTUxZLlR9T/AJ4G16Qc8AoxtzYeYmbVEyrZ0FpmeU42I5U3+Nalrm3DMrCsR+T3831FkSarLJX0UiPRxg/OAx9s2LDPrKiptkOoszf+JwJdIHqJ9nuRZrS+1YUxm1kVkbfp3psrsZmuqEVEDnNYOsZhZF1Rpzf/N1lQl7S7pz5JelPSCpFsl7d4ewZlZ5VPGpbPI0vz/A3AjMAjYieT1rabvxZqZvSc5vvvfIWRJqoqI30VEbbpcS5nXV83Mskru/md+979TKPfuf7909Z50sNfrSZLpycDt7RCbmVU6ZR+AurMod6PqYZIk2vCNS1/NCuCHbRWUmXUdnalpn0W5d/93a89AzKzraWj+V5JMb1RJ2gfYC3hfQ1lE/LatgjKzrqPL1FQbSPov4FCSpDqDZLzB+wEnVTPbYpWVUrPd/f93kjlbVkfEfwAjga3aNCoz6xIkqOqmTEtnkaX5vy4i6iXVStoWeAHww/9mlosu1/wH5knaDriK5ImAN4CH2jIoM+s6KiynZnr3v2Ew6smS7gS2jYiFbRuWmXUFQhX37n+5h/9Hl9sXEfPbJiQz6zI62QhUWZSrqZabhjWAw3OOJVd7DRvMH2dcUnQY1gp7fuVPRYdgrfDC8ldyuU6efaqSxgGXk8xRdXVEXNxk/2lsnJb6DeDcdDYTJC0DXicZhL907qp+wA3AUJI5qj4VES+3FEO5h/8Pey9fyswsKwFVOSVVSVXAlSST960A5kqaHhGLSw57BvhYRLwsaTwwBTiwZP9h6XCnpc4HZkXExekr++ezMTFvIssjVWZmbSbHAVUOAJZExNKIeJdkvJIJpQdExJySWuaDwOAM150ANMwufQ1wXNnvkylUM7M2kmNS3RlYXrK9Ii1ryVnAHSXbAfxF0sOSzikpHxgRqwDSP3coF0Sm11TNzNpCMlVK5uZ/taR5JdtTImJK6eWaOafZYUolHUaSVA8uKT4oIlZK2gG4S9ITETE7a3ANsrymKpLpVHaPiAsl7QLsGBF+VtXMtlgrXpaqabh51IIVwJCS7cHAyqYHSRoBXA2Mj4i1DeURsTL98wVJ00i6E2YDayQNiohVkgaRvADVoizN/18AHwFOTbdfJ+kMNjPbYjlO/DcXGCZpt3Tm51OA6Y0/S7sAtwCnR8S/Ssp7SerTsA4cCTyW7p4OnJGunwHcWi6ILM3/AyNitKR/AqR3zXpmOM/MrCwB3XO6+x8RtZImATNJHqmaGhGLJE1M908Gvg/0B36Rdjs0PDo1EJiWlnUH/hARd6aXvhi4UdJZwHPASeXiyJJU16ePKgSApAFAfWu+rJlZS/J8+D8iZpCMpldaNrlk/Wzg7GbOW0oyWFRz11xLMqhUJlmS6s+BacAOkv4vyahV38v6AWZmLZG60GuqDSLi95IeJsnUAo6LiMfbPDIz6xIqLKdmuvu/C/AW8OfSsoh4ri0DM7OuoRMNlZpJlub/7WycAPB9wG7Ak8DebRiXmXUBgk41AHUWWZr/+5Zup6NXfaGFw83Mssv+tlSn0eo3qiJivqT92yIYM+t6VGGzVGXpU/3Pks1uwGjgxTaLyMy6jK46RXWfkvVakj7Wm9smHDPrarpUUk0f+u8dEd9sp3jMrIvpMhP/SeqevvbV4rQqZmZbIpmiuugo8lWupvoQSf/pAknTgZuANxt2RsQtbRybmXUBXe6NKqAfsJZkTqqG51WDZKQXM7P3rKvdqNohvfP/GBuTaYNmB341M2utCquolk2qVUBvWjGatplZ64huXeg51VURcWG7RWJmXY7oWjXVCvuqZtbhCLpXWKdquaSaeVBWM7P3okvVVCPipfYMxMy6pkp7pKrCHrs1s84mx4n/kDRO0pOSlkg6v5n9p0lamC5zJI1My4dIukfS45IWSfpKyTkXSHpe0oJ0OapcDK0epcrMLC8iv5pd+lr9lcAnSKarnitpekQsLjnsGeBj6QSm44EpwIEk45p8PR2Frw/wsKS7Ss69LCJ+kiUOJ1UzK45ybf4fACxJJ/FD0vXABGBDUo2IOSXHPwgMTstXAavS9dclPQ7sXHpuVm7+m1lhkjeqlGnJYGdgecn2irSsJWcBd2wSkzQU2A/4R0nxpLTLYKqk7csF4aRqZoVSxgWoljSvZDmnmUs11eyLSpIOI0mq325S3ptkaNOvRsRrafEvgT2AUSS12Z+W+z5u/ptZoVrR+q+JiDFl9q8AhpRsDwZWbvp5GgFcDYyPiLUl5T1IEurvSweMiog1JcdcBdxWLkjXVM2sQELKtmQwFxgmaTdJPYFTgOmNPi2ZHfoW4PSI+FdJuYBfA49HxM+anDOoZPN4kvFQWuSaqpkVJs+7/+n4z5OAmSRjl0yNiEWSJqb7JwPfB/oDv0gTdW1a+z0IOB14VNKC9JLfiYgZwKWSRpF0JSxjMxOfOqmaWaHyfPg/TYIzmpRNLlk/Gzi7mfPup4VX8yPi9NbE4KRqZsVRF5pOxcysreXZ/O8onFTNrFCuqZqZ5aiyUqqTqpkVSECVa6pmZvmpsJzqpGpmRRKqsA4AJ1UzK5RrqmZmOUkeqaqsrOqkambFacWo/p2Fk6qZFarS5qhyUjWzwiSDVBcdRb6cVM2sUL77b2aWowpr/TupdiQPzHuSH/9qOvX1wXGf3J8zP3VYo/3PLH+B/7rsJp5Y8jyTzvgknz3xYxv2XTvtb0yb+RCS2HPojvzgayexVc8e7f0VupSxw3fgeyfsS1U3ceODz/Krvz7VaP8R++zIV4/+IPX1UFdfz4+mPcrDS18C4KJT9+PwvXdk7RvvcNTFdxcRfodRaTXVdh0gRtIySdVbcP6ozc253VnV1dVz8S/+xBUXnsnNk/+TO+97hKefW9PomL59tuHbE4/lsyeObVT+Qs2rXDf9AX5/+Xn88Zf/SX1dPTPve6Q9w+9yugkuOGkkZ/3q74y7aBbHjB7MngP7NDpmzr9e5JhL7uHYH9/D+df9k/8+Zb8N+2556DnOnDyn6WW7nIY+1SxLZ9FpRt2S1J1k4q2KTKqP/Ws5Q3bqz+BB/enRozufHDuSe//eeHbcftv1Zu/3D6F7VdUm59fV1fPOu+upravj7XfWM6D/tu0Vepc0ctftefbFN1i+9i3W1wW3z1/BEfvu2OiYt96t27C+Tc8qomQKurlPr+WVt9a3V7gdV8aZVDvTEwJt1vyX1Au4kWTyrSrgh+muL0v6N6AHcFJEPCGpHzAV2B14CzgnIhZKugDYCRgK1AAHA1tLOhi4KCJuaKv429sLa19lYPV2G7YHVvflsSefy3TuDtV9+ewJYxl/xkVs1bMHHxk9jI+Mfn8bRWoAA/tuzapX1m3YXv3K24zcddOZiz8xYhDfOGYv+vfeis9P+Xt7hthpdJ50mU1b1lTHASsjYmRE7APcmZbXRMRokmlfv5GW/QD4Z0SMAL4D/LbkOh8CJkTEp0nml7khIkY1l1AlndMwfe3La2va6Gu1keYm0s34r/Nrr7/FvQ8u5rbffJu/XPtd1r39LrffPT/f+KyR5n41zf0K71q4ik/+9yzO/fU/+OpRH2zzuDqbpPmfX01V0jhJT0paIun8ZvafJmlhusyRNHJz50rqJ+kuSU+lf276r2eJtkyqjwJHSLpE0iER8Wpa3jD168MkNVBIaqC/A4iIu4H+kvqm+6ZHxMYqQRkRMSUixkTEmO37v+eu20LsUN2XNTWvbNheU/MqA/pla8L/Y8ESdtpxe/r17U2P7lUcftA+PPL4s20UqQGsfmUdg7bbesP2jtu9jxdebfl/07lPr2WX6l5s36tne4TXqSjjstnrSFXAlcB4YC/gVEl7NTnsGeBjaQXuh8CUDOeeD8yKiGHArHS7RW2WVNPpXz9EklwvkvT9dNc76Z91bOx+aO5n1vAP/5ttFWNHsvf7B/PcyrU8v/ol1q+vZebsRzj0w9lqNjsO2I5Hn3iOdW+/S0Tw0IIl7DZkhzaOuGtb+Nwr7DqgN4P7bUOPKnH06MHMemx1o2N2re61YX3vwX3pUdWNl998t71D7fjyyqpwALAkIpZGxLvA9cCE0gMiYk5EvJxuPkjSPbm5cycA16Tr1wDHlQuiLftUdwJeiohrJb0BfK7M4bOB04AfSjqUpIvgtWamWXgd6NO0sBJ0r6ri2+dO4Ivf+zX19fVMOHJ/9th1R266/UEATjr6w9S89DqnfeXnvPnWO6ib+P2f7ufmX32dfYfvwhEH78unz/s5VVXdGL77Tpw4/sCCv1Flq6sPfnDzQn5z7kep6iZuevBZnlr9OqceNBSA6x5YxidH7sTx+w9hfV3wzvo6vnLN3A3nX/bZMRy4ZzXb9+7J/T/4JJff8QQ3Pdg1Wxc53oTaGVhesr0CKPcX4SzgjgznDoyIVQARsUpS2RpLWz6nui/wY0n1wHrgXOCPLRx7AfAbSQtJblSd0cJx9wDnp/NyV9SNKoBD9h/OIfsPb1R20tEf3rBe3a8PM3/33WbPPfczR3LuZ45s0/issfsWr+G+xY0fe7vugWUb1qfMeoops56iOV/77by2DK1TaUVKrZZU+oObEhFTNnOp5rq6kXQYSVI9uLXnbk6bJdWImAnMbFI8tGT/PODQdP0lmlTT0/ILmmy/BOyfb6RmVqjsWbUmIsaU2b8CGFKyPRhYucnHSSOAq4HxEbE2w7lrJA1Ka6mDgBfKBdlpnlM1s8qTdJdm+y+DucAwSbtJ6gmcAkxv9HnSLiQ3y09P7/tkOXc6G1vPZwC3lgvCr6maWXFyHE81ImolTSJpIVcBUyNikaSJ6f7JJI9l9gd+kd6zqU2fGGr23PTSFwM3SjoLeA44qVwcTqpmVqg8H/6PiBnAjCZlk0vWzwbOznpuWr4W+HjWGJxUzaxAopmnfDo1J1UzK1SF5VQnVTMrTvbn+jsPJ1UzK1aFZVUnVTMrVKUNUu2kamaFcp+qmVlecnxOtaNwUjWzQrn5b2aWE+GaqplZriospzqpmlnBKiyrOqmaWaE600ypWTipmlmhKiulOqmaWdEqLKs6qZpZYRoGqa4kTqpmVhw//G9mlq8Ky6lOqmZWpMobpNoT/5lZoaRsS7ZraZykJyUtkXR+M/uHS/q7pHckfaOk/AOSFpQsr0n6arrvAknPl+w7qlwMrqmaWWHyHKRaUhVwJfAJkimn50qaHhGLSw57CTgPOK703Ih4EhhVcp3ngWklh1wWET/JEodrqmZWLGVcNu8AYElELI2Id4HrgQmlB0TECxExF1hf5jofB56OiGdb90USTqpmVihl/C+DnYHlJdsr0rLWOgW4rknZJEkLJU2VtH25k51UzaxQrehTrZY0r2Q5p+mlmrl8tC4W9QSOBW4qKf4lsAdJ98Aq4KflruE+VTMrjqBb9k7VmogYU2b/CmBIyfZgYGUrIxoPzI+INQ0FpeuSrgJuK3cB11TNrGC5darOBYZJ2i2tcZ4CTG9lMKfSpOkvaVDJ5vHAY+Uu4JqqmRUmz0GqI6JW0iRgJlAFTI2IRZImpvsnS9oRmAdsC9Snj03tFRGvSdqG5MmBLzS59KWSRpF0JSxrZn8jTqpmVqg8H/2PiBnAjCZlk0vWV5N0CzR37ltA/2bKT29NDE6qZlaoCnuhyknVzIpVaa+pOqmaWaEqK6U6qZpZgVrzXn9n4aRqZoXyINVmZnmqrJzqpGpmxaqwnOqkamZFkqeoNjPLS55vVHUUfvffzCxHrqmaWaEqrabqpGpmhfIjVWZmefHD/2Zm+anEG1VOqmZWKDf/zcxy5JqqmVmOKiynOqmaWcEqLKs6qZpZYQQV95qqIlo1LXanIelF4Nmi42gD1UBN0UFYq1Tq72zXiBiwJReQdCfJzyeLmogYtyWf1x4qNqlWKknzNjP3uXUw/p11LX7338wsR06qZmY5clLtfKYUHYC1mn9nXYj7VM3McuSaqplZjpxUzcxy5KTaQUh6o+gY7L2TtExS1uctmzt/lKSj8ozJiuGkalYwSd2BUYCTagVwUu1glPixpMckPSrp5LT8UEn3SvqjpCck/V5K3u+TdFRadr+kn0u6rdhvUdkk9ZJ0u6RH0t/TyemuL0uan/7ehqfH9pP0J0kLJT0oaURafoGkKZL+AvwWuBA4WdKCkutZJ+R3/zueE0hqLSNJXt+bK2l2um8/YG9gJfAAcJCkecCvgLER8Yyk69o/5C5nHLAyIo4GkNQXuITkNcrRkr4IfAM4G/gB8M+IOE7S4SQJdFR6nQ8BB0fEOkmfA8ZExKT2/SqWN9dUO56Dgesioi4i1gD3Afun+x6KiBURUQ8sAIYCw4GlEfFMeoyTatt7FDhC0iWSDomIV9PyW9I/Hyb53UDy+/wdQETcDfRPkzDA9IhY104xWztxTbXjKTdkzzsl63Ukv7/KGuKnE4iIf0n6EEkf6EVpEx42/n4afjfQ/O+n4eHwN9suSiuKa6odz2ySvrUqSQOAscBDZY5/Athd0tB02/1xbUzSTsBbEXEt8BNgdJnDZwOnpecdStJF8Fozx70O9Mk3UiuCk2rHMw1YCDwC3A18KyJWt3Rw2nz8InCnpPuBNcCrLR1vudgXeEjSAuC7wI/KHHsBMEbSQuBi4IwWjrsH2Ms3qjo/v6ZaAST1jog30qcBrgSeiojLio7LrCtyTbUyfD6tNS0C+pI8DWBmBXBN1cwsR66pmpnlyEnVzCxHTqpmZjlyUu2iJNWlj+88JukmSdtswbX+V9K/p+tXS9qrzLGHSvroe/iMZkeByjI6VGtHAEvfy/9Ga2M0AyfVrmxdRIyKiH2Ad4GJpTslVb2Xi0bE2RGxuMwhhwKtTqpmnYWTqgH8DdgzrUXeI+kPwKPpW10/ljQ3HWXpC7BhJK0rJC2WdDuwQ8OF0pG0xqTr49JRmx6RNCt962si8LW0lnyIpAGSbk4/Y66kg9Jz+0v6i6R/SvoVGV7HTUeDeljSIknnNNn30zSWWembakjaQ9Kd6Tl/axhZymxL+N3/Lk7JWJ7jgTvTogOAfdIRr84BXo2I/SVtBTyQvue+H/ABkjeLBgKLgalNrjsAuIqNo2f1i4iXJE0G3oiIn6TH/QG4LCLul7QLMBP4IPBfwP0RcaGko4FGSbIFZ6afsTXJ6F43R8RaoBcwPyK+Lun76bUnkUzINzEinpJ0IPAL4PD38GM028BJtevaOn1hAJKa6q9JmuUPlYx4dSQwoqG/lOTFgmEk4xFcFxF1wEpJdzdz/Q8DsxuuFREvtRDHESSvZzZsbyupT/oZJ6Tn3i7p5Qzf6TxJx6frQ9JY1wL1wA1p+bXALZJ6p9/3ppLP3irDZ5iV5aTada2LiFGlBWlyKR05ScCXI2Jmk+OOYuNISy1RhmMg6YL6SNMh8NJYMr+Zkg5WckR6rbck3Qu8r4XDI/3cV5r+DMy2lPtUrZyZwLmSegBIer+kXiQjL52S9rkOAg5r5ty/Ax+TtFt6br+0vOloTH8haYqTHjcqXS0d3Wk8sP1mYu0LvJwm1OEkNeUG3YCG2vanSboVXgOekXRS+hmSNHIzn2G2WU6qVs7VJP2l8yU9RjKmQHeSkbSeIhms+ZckA2k3EhEvkvSD3iLpETY2v/8MHN9wowo4j3QUJ0mL2fgUwg+AsZLmk3RDPLeZWO8EuqejQf0QeLBk35vA3pIeJukzvTAtPw04K41vETAhw8/ErCy/+29mliPXVM3McuSkamaWIydVM7McOamameXISdXMLEdOqmZmOXJSNTPL0f8HzxA5Xb03Dq8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZScorePredictor\n",
      "Accuracy:  0.66264\n",
      "Error Score: 7.429466666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FiveDayPredictor\n",
      "Accuracy:  0.71752\n",
      "Error Score: 9.357066666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TmmrwUpPredictor\n",
      "Accuracy:  0.6668533333333333\n",
      "Error Score: 7.460933333333333\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Final Catboost accuracy:  0.6823377777777777\n",
      "Final Catboost score:  8.082488888888888\n",
      "Final Feature Importance:                                importance\n",
      "featureName                              \n",
      "DPRIMEValue                      0.000000\n",
      "DPRIMElastChangeP                0.000000\n",
      "UNRATEValue                      0.000000\n",
      "CONSUMERValue                    0.000000\n",
      "CASS-CTLI-Index                  0.000000\n",
      "...                                   ...\n",
      "NASDAQ100fiveVsTenTickAverage    5.935730\n",
      "DEXJPUSlastChangeP               6.794000\n",
      "DTWEXAFEGSlastChangeP            7.160248\n",
      "DEXUSUKlastChangeP               7.269405\n",
      "aroon                           11.485511\n",
      "\n",
      "[158 rows x 1 columns]\n",
      "<class 'list'>\n",
      "Feature to remove:  ['CASS-CTLI-Index', 'CONSUMERValue', 'DPRIMEValue', 'DPRIMElastChangeP', 'UNRATEValue']\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Financial-Services\n",
      "droppedColumns: []\n",
      "valuesToTry: []\n",
      "droppedColumns: ['BUSLOANSValue']\n",
      "valuesToTry: ['CASS-CTLI-Index', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'China (Estimated)', 'DPRIMEValue', 'DPRIMElastChangeP', 'Misery Index', 'North America', 'Oceania', 'PCEC96lastChangeP', 'PRS85006092Value', 'PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing BUSLOANSValue is 0.03137777777777728\n",
      "droppedColumns: ['BUSLOANSValue', 'CASS-CTLI-Index']\n",
      "valuesToTry: ['CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'China (Estimated)', 'DPRIMEValue', 'DPRIMElastChangeP', 'Misery Index', 'North America', 'Oceania', 'PCEC96lastChangeP', 'PRS85006092Value', 'PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing CASS-CTLI-Index is -0.10084444444444607\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue']\n",
      "valuesToTry: ['CPIAUCSLfiveVsTenTickAverage', 'China (Estimated)', 'DPRIMEValue', 'DPRIMElastChangeP', 'Misery Index', 'North America', 'Oceania', 'PCEC96lastChangeP', 'PRS85006092Value', 'PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing CPIAUCSLValue is 0.05137777777778041\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage']\n",
      "valuesToTry: ['China (Estimated)', 'DPRIMEValue', 'DPRIMElastChangeP', 'Misery Index', 'North America', 'Oceania', 'PCEC96lastChangeP', 'PRS85006092Value', 'PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing CPIAUCSLfiveVsTenTickAverage is 0.09622222222222021\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'China (Estimated)']\n",
      "valuesToTry: ['DPRIMEValue', 'DPRIMElastChangeP', 'Misery Index', 'North America', 'Oceania', 'PCEC96lastChangeP', 'PRS85006092Value', 'PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing China (Estimated) is -0.08293333333333308\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue']\n",
      "valuesToTry: ['DPRIMElastChangeP', 'Misery Index', 'North America', 'Oceania', 'PCEC96lastChangeP', 'PRS85006092Value', 'PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing DPRIMEValue is 0.0986222222222235\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue', 'DPRIMElastChangeP']\n",
      "valuesToTry: ['Misery Index', 'North America', 'Oceania', 'PCEC96lastChangeP', 'PRS85006092Value', 'PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing DPRIMElastChangeP is -0.007777777777777217\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue', 'Misery Index']\n",
      "valuesToTry: ['North America', 'Oceania', 'PCEC96lastChangeP', 'PRS85006092Value', 'PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing Misery Index is -0.009377777777778817\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue', 'North America']\n",
      "valuesToTry: ['Oceania', 'PCEC96lastChangeP', 'PRS85006092Value', 'PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing North America is 0.06160000000000032\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue', 'North America', 'Oceania']\n",
      "valuesToTry: ['PCEC96lastChangeP', 'PRS85006092Value', 'PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing Oceania is -0.15071111111111257\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue', 'North America', 'PCEC96lastChangeP']\n",
      "valuesToTry: ['PRS85006092Value', 'PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing PCEC96lastChangeP is -0.02071111111111179\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue', 'North America', 'PRS85006092Value']\n",
      "valuesToTry: ['PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing PRS85006092Value is 0.12435555555555666\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue', 'North America', 'PRS85006092Value', 'PRS85006092bPercent']\n",
      "valuesToTry: ['SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing PRS85006092bPercent is -0.16644444444444417\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue', 'North America', 'PRS85006092Value', 'SFTPINDM114SFRBSFValue']\n",
      "valuesToTry: ['SFTPINDM114SFRBSFbPercent', 'Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing SFTPINDM114SFRBSFValue is 0.14822222222222337\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue', 'North America', 'PRS85006092Value', 'SFTPINDM114SFRBSFValue', 'SFTPINDM114SFRBSFbPercent']\n",
      "valuesToTry: ['Shipments Index', 'UNRATEbPercent']\n",
      "change in score by removing SFTPINDM114SFRBSFbPercent is -0.0011111111111112848\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue', 'North America', 'PRS85006092Value', 'SFTPINDM114SFRBSFValue', 'Shipments Index']\n",
      "valuesToTry: ['UNRATEbPercent']\n",
      "change in score by removing Shipments Index is -0.015733333333333377\n",
      "droppedColumns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue', 'North America', 'PRS85006092Value', 'SFTPINDM114SFRBSFValue', 'UNRATEbPercent']\n",
      "valuesToTry: []\n",
      "change in score by removing UNRATEbPercent is -0.025111111111112194\n",
      "Final dropped columns: ['BUSLOANSValue', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'DPRIMEValue', 'North America', 'PRS85006092Value', 'SFTPINDM114SFRBSFValue']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZScorePredictor\n",
      "Accuracy:  0.6674666666666667\n",
      "Error Score: 7.5452\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FiveDayPredictor\n",
      "Accuracy:  0.7297333333333333\n",
      "Error Score: 9.867866666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TmmrwUpPredictor\n",
      "Accuracy:  0.6670133333333333\n",
      "Error Score: 7.469333333333333\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Final Catboost accuracy:  0.6880711111111112\n",
      "Final Catboost score:  8.294133333333333\n",
      "Final Feature Importance:                             importance\n",
      "featureName                           \n",
      "China (Estimated)             0.000000\n",
      "Expenditures Index            0.000000\n",
      "UNRATEfiveVsTenTickAverage    0.000000\n",
      "CPIAUCSLValue                 0.000000\n",
      "WPU0911bPercent               0.000000\n",
      "...                                ...\n",
      "CCLACBW027SBOGValue           6.624016\n",
      "ticker                       12.421522\n",
      "aroon                        13.179424\n",
      "industry                     13.323141\n",
      "slowVSFastKAMA               13.654862\n",
      "\n",
      "[158 rows x 1 columns]\n",
      "<class 'list'>\n",
      "Feature to remove:  ['BOPGSTBbPercent', 'BUSLOANSfiveVsTenTickAverage', 'CONSUMERlastChangeP', 'CPIAUCSLValue', 'CPIAUCSLfiveVsTenTickAverage', 'China (Estimated)', 'DPRIMEValue', 'DPRIMElastChangeP', 'Expenditures Index', 'PRS85006092Value', 'PRS85006092fiveVsTenTickAverage', 'Shipments Index', 'South America', 'UNRATEfiveVsTenTickAverage', 'WPU0911bPercent']\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Financial\n",
      "droppedColumns: []\n",
      "valuesToTry: []\n",
      "droppedColumns: ['CPIAUCSLfiveVsTenTickAverage']\n",
      "valuesToTry: ['ticker']\n",
      "change in score by removing CPIAUCSLfiveVsTenTickAverage is -0.21314553990610374\n",
      "droppedColumns: ['ticker']\n",
      "valuesToTry: []\n",
      "change in score by removing ticker is -0.04694835680751197\n",
      "Final dropped columns: []\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZScorePredictor\n",
      "Accuracy:  0.5504225352112676\n",
      "Error Score: 3.0591549295774647\n"
     ]
    },
    {
     "data": {
      "image/png": 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RcW9r18sSPK8E7gIGSrqYZJalb5fy4czMmmrLh+QjYgbJ1JmF+6YUvD8TOLOZfAtJZozLLMvY9hslzSWZlk7AhyPiH8VcxMysOeqgywpnkaW3fRjwJnB34b6IeKmcBTOzrqFCY2em2/Z72LQQ3LbAcOBZYGQZy2VmXUSFTueZ6bZ9/8LtdLalL7SQ3MwsM9G5J0NuJCIek+RnPM1s62UfPdThZGnz/HrBZhUwBlhZthKZWZeiDrlC0ZZlqXn2KnhfR9IGekd5imNmXUmnXXo4fTi+Z0Sc107lMbMuptMFT0k16RP7LS7HYWa2tTrjAnCPkrRvPi5pOnAb8EbDwYi4s8xlM7NOLll6OO9SlCZLm2dfYBXJrEoNz3sGyeB6M7Ot0hlHGA1Me9qfZlPQbNDsHHlmZsXorB1G1UBPiphg1MysWBVa8Ww1eL4SERe2W0nMrAsSVZ3wOc/K/ERmVjFE56x5vrfdSmFmXZOgpkIbPVsMnhHxansWxMy6ns5a8zQzK7vO+KiSmVnZVWjsdPA0s/yIbEv4dkQOnmaWH/m23cysaMkIIwdPM7OiVWbodPA0s5xVaMXTwdPM8qROOZ+nmVlZubfdzKxE7jAyMyuWKncZjkqtMZtZJ9Bw257llel80omSnpW0QNL5zRw/XdKT6esvkg7ImrcpB08zy5WkTK8M56kGrgbGAyOAj0sa0STZC8B7ImIUcBEwtYi8jTh4mlmulPGVwSHAgohYGBHvANOACYUJIuIvEbE63ZwFDMmatykHTzPLjYBqKdMrg8HA4oLtJem+lnwO+H2Jed1hZGb5KqK/qL+kOQXbUyNiauGpmsnT7Hprko4hCZ5HFpu3gYOnmeVIKPsAzdqIGNvK8SXA0ILtIcDSza4ojQKuA8ZHxKpi8hbybbuZ5UrK9spgNrCXpOGSugOnAdMbX0vDgDuBT0XEP4vJ25RrnmaWm+RRpbZ5zjMi6iRNBu4jWTr9+oiYJ2lSenwK8F2gH3BN2oNfFxFjW8rb2vUcPM0sP9lrlZlExAxgRpN9UwrenwmcmTVvaxw8zSxXHp5pZlakZDLkvEtRGgdPM8tVEb3tHYqDZwfy17nP8qNr72bDhmDC8Qfz6Y+Oa3R80eIVXPjT23n2+Zf54qfexydPOXrjsdfXruPin93B8y8uR4Jvf+UjjNp313b+BF3LkXv35z8mjKBa4vZHF3PdwwsbHT92xEDOft/eREDdhuCS6fN5bNFqutdU8etJh9G9poqaKnH/U8u46oHncvoU+avQu/b2DZ6SFgFjI6K2xPyjgV3Sht1Opb5+A5dN+S1XXfQ5Bvbrw6e/fhVHHbofuw8btDFN717bc+5ZH+ThWfM3y/+ja+/msDF7c8l/fJL16+t46+317Vn8LqdK8O2TR3LmtY+yfM1b3HL2ETw0fwXPr1i7Mc2sBat4cP4jAOy9Uy/+85MH8oErZvJO3QbOmPo33nynnpoqccOXDmfmsyt58qV/5fRp8lWpNc+Kec5TUg0wGjgp56KUxbznFjNk534M3qkf3brVcMLRBzDzb42DZN8dejJi76HU1DT+2ta++RZ/f/oFJpxwMADdutXQq+d27Vb2rmj/oTvwUu2bLHl1Hevrg98/8QrHjhzUKM2b79RvfL9d92oiNj9WUy1qqkWjg11IQ5tnlldHU7aap6QewK0kT+pXk8xgAnC2pA8C3YCPRsQzkvoC1wO7A28CZ0XEk5IuAHYBdgNqSYZSbSfpSOCHEXFLucrf3laueo1B/fts3B7Yrw/z/rm4lRybLF32Kjv26cGFP7mN5xa9wr57DOYbZ32I7bbtXq7idnmD+mzLsjVvbdxetmYdo4busFm6944cxNfG70O/nt2ZdP2mkYVVgtu/ciTD+m3PTX95kScXr2mPYnc8UsX2tpez5nkisDQiDoiIdwH3pvtrI2IM8F/Auem+7wN/T6eJ+n/ArwvOcxAwISI+QfKA6y0RMbozBU6AaK7mkfFvqq5+A88+v5RTTzqMG376Fbbbtju/uv3hNi2fNZb1n/sf5i3nA1fMZPKv5nLO+/beuH9DwCk/eYRjLn6Q/YftwJ6DepanoBWgDWdValflDJ5PAcdJulTSURHR8L/WO9Ofc0lqlJDUKH8DEBEPAv0kNVTDpkfEuiwXlHSWpDmS5qx+taRm1dwM7N+H5bWbah8rVq1hQN/emfMO7N+bd+0zDIBjj9ifZ59/uSzltMSyNW+xU59tN27v1Gc7Vrz2dovp576wmqH9tmeH7bs12v/6W3XMfn4VR+0zoGxl7cga1m3P8upoyhY803GjB5EE0R9K+m56qOEvrJ5NzQatzWjyRhHXnJoOtRq7Y9/+JZQ6PyP2GsLipat4edmrrF9fx/0zn+CoQ1qdi3Wj/jv2YmD/HXhxyUoAZj+xgOFDB20hl22Np5esYdf+PRi843Z0qxbjD9iZh+Yvb5RmWL/tN77fb3BvulVX8a8317Njj+702jb509+mporD9+rPwpWZ/8w7nUqteZazzXMX4NWIuEHSWuAzrSSfCZwOXCRpHMmt/WvNzB79OtCr7Uubv5rqas6b9CHO+d71bNiwgQ8eN5Y9dh3EHb+fBcCp4w+jdvXrfOZrP+ONN99GVWLa9EeYds3X6bn9tpz3hQ/xnR9No66unl0G9eW7X/1Izp+oc6vfEFz823lce+YhVFXBXbOXsGD5WiYeltT+b5n1EsfvvxMTxgymbkPw1vp6vnHj3wEY0GsbfjhxFFVVSY3q3idf4Y//WJHnx8lXR4yMGajZtra2OLH0PuByYAOwHvgicDvpo0qSxgJXRMS4tMPoF8BwNu8wWhsRV6Tn7EsycL8bW+gwGjlqTEybMbMsn83KY8J/+vuqJC/f+BXeXv7cVoW+/fY/MH7124czpT10jx3mbmFKunZVtppnRNxHEugK7VZwfA4wLn3/Ks1MeR8RFzTZfhU4uG1LamZ5qtCKp0cYmVnOKjR6OniaWW6SzqDKjJ4OnmaWnzaez7M9OXiaWa4qNHY6eJpZnkQzjyRWBAdPM8tVhcZOB08zy09HHT2UhYOnmeWrQqOng6eZ5cqPKpmZlcBtnmZmxfJznmZmpfFtu5lZkYRrnmZmJanQ2OngaWY5q9DoWTFLD5tZ59SWaxhJOlHSs5IWSDq/meP7SvqrpLclndvk2CJJT0l6XNKcpnmbcs3TzHLVVhVPSdXA1cDxwBJgtqTpETG/INmrwDnAh1s4zTERkWn1SNc8zSxfbbcC3CHAgohYGBHvANNoskJFRKyIiNkkSwNtFQdPM8tNw2TIWf7LYDCwuGB7SbovqwDulzRX0llbSuzbdjPLT3EPyfdv0hY5NSKmNj7bZopZ4fKIiFgqaSDwgKRnIqLFVQkdPM0sV0W0edZuYfXMJcDQgu0hwNKsJ4+IpenPFZLuImkGaDF4+rbdzHKUTIac5ZXBbGAvScMldQdOA6ZnKoXUQ1KvhvfACcDTreVxzdPMctVWI4wiok7SZJIlz6uB6yNinqRJ6fEpknYC5gC9gQ2SvgqMAPoDd6VBuga4KSLube16Dp5mlpu2ngw5ImYAM5rsm1LwfhnJ7XxTrwEHFHMtB08zy1eFjjBy8DSzXHlWJTOzEnhWJTOzYgmqHDzNzEpRmdHTwdPMcuPJkM3MSlShsdPB08zy5ZqnmVkJMg697HAcPM0sV5UZOh08zSxH8rrtZmal8QgjM7NSVGbsdPA0s3xVaOx08DSzPGVfVrijcfA0s9xU8ggjL8NhZlYC1zzNLFeVWvN08DSzXPlRJTOzYvkheTOz4lVyh5GDp5nlyrftZmYlcM3TzKwEFRo7HTzNLGcVGj0dPM0sN4KKHZ6piMi7DGUhaSXwYt7lKIP+QG3ehbCidNbvbNeIGLA1J5B0L8nvJ4vaiDhxa67Xljpt8OysJM2JiLF5l8Oy83fWOXlsu5lZCRw8zcxK4OBZeabmXQArmr+zTshtnmZmJXDN08ysBA6eHYSktXmXwUonaZGkrI/cNJd/tKST2rJMVl4OnmY5k1QDjAYcPCuIg2cHo8Tlkp6W9JSkien+cZIelnS7pGck3SglQzMknZTue0TSlZJ+l++n6Nwk9ZB0j6Qn0u9pYnrobEmPpd/bvmnavpL+R9KTkmZJGpXuv0DSVEn3A78GLgQmSnq84HzWgXl4ZsdzCkkt5ACSkRezJc1Mjx0IjASWAn8GjpA0B/g5cHREvCDp5vYvcpdzIrA0It4PIKkPcCnJCJgxkr4EnAucCXwf+HtEfFjSsSSBcnR6noOAIyNinaTPAGMjYnL7fhQrlWueHc+RwM0RUR8Ry4E/Agenxx6NiCURsQF4HNgN2BdYGBEvpGkcPMvvKeA4SZdKOioi1qT770x/ziX5biD5Pn8DEBEPAv3SYAswPSLWtVOZrY255tnxtDZLwtsF7+tJvr/KnFWhgkXEPyUdRNJG+cP01hs2fT8N3w00//00PB/4RvlKaeXmmmfHM5Ok7ata0gDgaODRVtI/A+wuabd02+1lZSZpF+DNiLgBuAIY00rymcDpab5xJLf2rzWT7nWgV9uW1MrJwbPjuQt4EngCeBD4ZkQsaylxetv3JeBeSY8Ay4E1LaW3NrE/8Kikx4FvAT9oJe0FwFhJTwKXAJ9uId1DwAh3GFUOjzDqBCT1jIi1ae/71cBzEfHjvMtl1pm55tk5fD6tBc0D+pD0vptZGbnmaWZWAtc8zcxK4OBpZlYCB08zsxI4eHZRkurTx2KelnSbpO234ly/lPSR9P11kka0knacpHeXcI1mZy3KMptRsTNWpePOzy22jNa1OHh2XesiYnREvAt4B5hUeFBSdSknjYgzI2J+K0nGAUUHT7OOxsHTAP4E7JnWCh+SdBPwVDrK6XJJs9NZgb4AG2d+ukrSfEn3AAMbTpTO/DQ2fX9iOsvQE5L+kI6CmgR8La31HiVpgKQ70mvMlnREmrefpPsl/V3Sz8kwDDWdvWiupHmSzmpy7EdpWf6QjtxC0h6S7k3z/KlhJiSzLDy2vYtTMpfkeODedNchwLvSGZrOAtZExMGStgH+nI7jPhDYh2SkzSBgPnB9k/MOAK5l02xPfSPiVUlTgLURcUWa7ibgxxHxiKRhwH3AfsD3gEci4kJJ7wcaBcMWnJFeYzuS2ajuiIhVQA/gsYj4hqTvpueeTLK20KSIeE7SocA1wLEl/BqtC3Lw7Lq2Sx+sh6Tm+d8kt9OPFszQdAIwqqE9k+QB/L1IxtvfHBH1wFJJDzZz/sOAmQ3niohXWyjHcSTDEhu2e0vqlV7jlDTvPZJWZ/hM50g6OX0/NC3rKmADcEu6/wbgTkk90897W8G1t8lwDTPAwbMrWxcRowt3pEGkcKYfAWdHxH1N0p3EppmBWqIMaSBpOjq86dRsaVkyj+BIJ904Lj3Xm5IeBrZtIXmk1/1X09+BWVZu87TW3Ad8UVI3AEl7S+pBMlPQaWmb6M7AMc3k/SvwHknD07x90/1NZw+6n+QWmjTd6PRt4WxE44Edt1DWPsDqNHDuS1LzbVAFNNSeP0HSHPAa8IKkj6bXkKQDtnANs40cPK0115G0Zz4m6WmSMfM1JDM/PUcyKfB/kUzY3EhErCRpp7xT0hNsum2+Gzi5ocMIOId01iFJ89nU6/994GhJj5E0H7y0hbLeC9SksxddBMwqOPYGMFLSXJI2zQvT/acDn0vLNw+YkOF3YgZ4bLuZWUlc8zQzK4GDp5lZCRw8zcxK4OBpZlYCB08zsxI4eJqZlcDB08ysBA6eZmYl+D917NTzTuwPDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FiveDayPredictor\n",
      "Accuracy:  0.7284507042253521\n",
      "Error Score: 9.952112676056338\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TmmrwUpPredictor\n",
      "Accuracy:  0.5374647887323943\n",
      "Error Score: 2.636619718309859\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Final Catboost accuracy:  0.6054460093896714\n",
      "Final Catboost score:  5.215962441314554\n",
      "Final Feature Importance:                         importance\n",
      "featureName                       \n",
      "UNRATEValue               0.000000\n",
      "DPRIMEValue               0.000000\n",
      "ticker                    0.000000\n",
      "industry                  0.000000\n",
      "SFTPINDM114SFRBSFValue    0.000000\n",
      "...                            ...\n",
      "WilliamsR                 5.691048\n",
      "typPrice                  5.865870\n",
      "DGS2bPercent              6.080875\n",
      "STLFSI2lastChangeP        6.339774\n",
      "aroon                     8.385810\n",
      "\n",
      "[158 rows x 1 columns]\n",
      "<class 'list'>\n",
      "Feature to remove:  ['DPRIMEValue', 'SFTPINDM114SFRBSFValue', 'TCUfiveVsTenTickAverage', 'UNRATEValue', 'industry', 'ticker']\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Healthcare\n",
      "droppedColumns: []\n",
      "valuesToTry: []\n",
      "droppedColumns: ['CONSUMERfiveVsTenTickAverage']\n",
      "valuesToTry: ['DPRIMEValue', 'DPRIMElastChangeP', 'East & Central Europe', 'North America', 'PRS85006092Value', 'South America', 'UNRATEValue']\n",
      "change in score by removing CONSUMERfiveVsTenTickAverage is -0.049866666666666504\n",
      "droppedColumns: ['DPRIMEValue']\n",
      "valuesToTry: ['DPRIMElastChangeP', 'East & Central Europe', 'North America', 'PRS85006092Value', 'South America', 'UNRATEValue']\n",
      "change in score by removing DPRIMEValue is 0.03137777777777728\n",
      "droppedColumns: ['DPRIMEValue', 'DPRIMElastChangeP']\n",
      "valuesToTry: ['East & Central Europe', 'North America', 'PRS85006092Value', 'South America', 'UNRATEValue']\n",
      "change in score by removing DPRIMElastChangeP is -0.0775555555555556\n",
      "droppedColumns: ['DPRIMEValue', 'East & Central Europe']\n",
      "valuesToTry: ['North America', 'PRS85006092Value', 'South America', 'UNRATEValue']\n",
      "change in score by removing East & Central Europe is -0.040711111111111364\n",
      "droppedColumns: ['DPRIMEValue', 'North America']\n",
      "valuesToTry: ['PRS85006092Value', 'South America', 'UNRATEValue']\n",
      "change in score by removing North America is 0.15808888888888895\n",
      "droppedColumns: ['DPRIMEValue', 'North America', 'PRS85006092Value']\n",
      "valuesToTry: ['South America', 'UNRATEValue']\n",
      "change in score by removing PRS85006092Value is -0.1967555555555549\n",
      "droppedColumns: ['DPRIMEValue', 'North America', 'South America']\n",
      "valuesToTry: ['UNRATEValue']\n",
      "change in score by removing South America is 0.10017777777777681\n",
      "droppedColumns: ['DPRIMEValue', 'North America', 'South America', 'UNRATEValue']\n",
      "valuesToTry: []\n",
      "change in score by removing UNRATEValue is -0.047866666666665836\n",
      "Final dropped columns: ['DPRIMEValue', 'North America', 'South America']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZScorePredictor\n",
      "Accuracy:  0.63088\n",
      "Error Score: 6.1796\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FiveDayPredictor\n",
      "Accuracy:  0.68768\n",
      "Error Score: 8.2832\n"
     ]
    },
    {
     "data": {
      "image/png": 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sae91HGZmGyN59XDZTrc38GxELErOretJWslrk2dEPFRQ/mFgeKkXa6/m+QjJ/c25kqYDNwGrCoK4tdSLmpk1KWKE0cAWzekrIuKKgvWtgJcL1hfTvFbZ0heBPxWsB3C3pAB+2eLc68nS294feJPknUVNz3sG4ORpZhulyA6jZRExfgOnaylaLSgdSJI89y/YvF9ELJE0GLhH0sKImNHWxdpLnoPTnvbHWZc02w3IzKxYZRyeuRjYumB9OLBk/etpNHAlMDEi3mzaHhFL0j/fkDSN5DZAm8mzvbsN9UDPdOlV8LlpMTPbSKIu45LBTGBHSSPTaTNPAqY3u1oy3PxW4JSIeLpgew9JvZo+A58kqTi2qb2a56sR8f0sEZuZlUKUr+aZdnBPBu4iqfxNjYgFkial+6cA5wMDgMvTXv416a2AIcC0dFs34NqIuLO967WXPGv00VUzqxmCbmV8Sj4i7gDuaLFtSsHn04HTWzluETCm5fb2tJc8Dy7mRGZmxSpnzbOjtZk8I2J5RwZiZl1Tp50M2cyskmo0dzp5mll+RLYJNqqRk6eZ5UdutpuZFS0ZYeTkaWZWtNpMnU6eZpazGq14OnmaWZ5Utvk8O5qTp5nlxr3tZmYlcoeRmVmxVL7XcHQ0J08zy42b7WZmJXLN08ysBLWZOp08zSxHAupd8zQzK16N5k4nTzPLk1CNNtydPM0sV655mpkVKXlUqTazp5OnmeVHrnmamZXEwzPNzIqUTIacdxSlcfI0s1y5t93MrAQ12mp38qwm//vQE/yfn9xMQ2Mjpxy9L1877ZPN9j/9wmtM/v7VPLZwMf/+5SP5yimHrN03+qjz6blFd+rr6ujWrY4/X/VvHR1+l3Pwx3bmh9/4DPV1dfzutof42W/vabZ/4gG7851JR9IYwZo1jXz7v2/m4ccWAXDmSRM49Zh9QeKq3z/IlOvuz+EbVAfXPDOQ9AIwPiKWlXj8WGBYRNxRzriqQUNDI+dddCPTLp3MsCF9OejUi5l4wO6M2m7o2jL9evfgR9/4LLf/5bFWz/GHKV9lQN+eHRVyl1ZXJy7+5gkcO/lSlrz+Fvf99jz+NGM+Tz3/2toyM2Y+xZ9mzAdg1x2GMfWHX2Cfz/6AnbcfyqnH7MvBp17MB2sauPmSs7j7gQUsenlpXl8nN7V8z7NmZoOS1A0YCxyecygVMXvBC2y39UBGDB/Ippt047hDx3HHX+Y1KzOofy/G7botm3SrzylKa7LnriNY9PIyXnzlTT5c08Ct98zh8E+MblZm1eoP1n7eYvPuRCSfdxqxJTPnv8Dq9z+koaGRB+c8y5ETxnRk+NVDoi7jUm0qVvOU1AO4ERgO1AMXpLu+IunTwCbAZyNioaT+wFRgO+Bd4IyImCfpu8AwYASwDNgf2FzS/sAPI+KGSsXf0V5duoKthvRbuz5sSD9mP/5C5uMlcdzkS5HEacfux2nH7V+BKK3J0EF9eOX1f6xdX/L6P9hztxHrlTtiwmjOP/soBvXrxYlfmwLAk88t4d+//Gn69enBe+99wKH77srcJ1/qqNCrTvWlxWwq2Ww/DFgSEUcASOoDXAgsi4hxks4CzgVOB74HPBoRx0g6CLiKpJYJsCewf0SslnQaSbN/cmsXlHQGcAbA1ttsU7EvVgnRVC0pUMx/tnde+TWGDurL0uXvcOzkS9lxxJbsN26HMkZohVqbg7KVn5Db75/H7ffPY989tufbk47g2LMv5ekXXuf/XXUP0y6dzKp332fBM6+wpqGhA6KuPrX83vZKNtvnA4dIulDSxyNiRbr91vTP2SQ1SkhqlL8DiIj7gAFpsgWYHhGrs1wwIq6IiPERMX7QwEFl+RIdZdjgvuvVZLYc2KedI5obOqgvkDTtj5wwmjkLXihzhFZoyRtvrddSeG3ZijbLP/Toc4zcaiD9+/QA4Orpf2PCKRdyxJk/4x9vr+qS9zubKONSbSqWPCPiaZJa43zgh5LOT3e9n/7ZwLqab2t/N03/j6+qVIzVZNwu2/LcS0t58ZVlfPDhGm69Zw4TDxi94QOBVavf551V7639fN/DC9l5+2GVDLfLm/PEi2y/zSC2GTaATbrVc9yh4/jTjOb3qEcOH7j28+iPDGeTTbqxfEXyz3lgv6Rjb/iQfhx54BhuvmtWxwVfbWo0e1bynucwYHlEXC1pJXBaO8VnACcDF0iaQNK0f7uVptE7QK/yR5u/bt3queibJ3D8OZfR0BCcfNRH2Xn7oUy95a8AfOH4j/P6src56NSLeGfVe0hiyvX387cbvsPyt1bxuW/+CoCGNQ0cf9h4Dtl3lzy/TqfX0NDINy+6kVsuOZv6enHN9IdZuOg1Pp/ea/6fWx/gqIPGcuIR+7BmTQOr3/uQL3576trjr7rwdPr16cGaNQ2cd9GNrHgnU+OqU6rVZrtau9dWlhNLnwIuBhqBD4EvAzeTPqokaTzw44iYkHYY/Q8wkvU7jFZGxI/Tc/YH7iLpbGq3w2jPPcfHg3/vwv+b16B+e7V6K9uq1PtP3Ujju29sVObbefc94qrb7s9Udu/t+86OiPEbc71yqljNMyLuIkl0hUYU7J8FTEg/LweObuUc322xvhzYq7yRmlmuarPi6RFGZpaf5HZmbWZPJ08zy08Nz+dZMyOMzKxzKmdnu6TDJD0l6VlJ32pl/8mS5qXLQ5LGZD22JSdPM8uRkLItGzyTVA9cBkwEdgH+SVLLx06eBz4REaNJRj1eUcSxzTh5mlmupGxLBnsDz0bEooj4ALieFh3REfFQRDSNRnmYZPh4pmNbcvI0s9xkbbKnuXOgpFkFyxktTrcV8HLB+uJ0W1u+CPypxGPdYWRmOcveYbRsA895tjdSsXlB6UCS5Nk0g07mY5s4eZpZrsr4qNJiYOuC9eHAkvWuJ40GrgQmRsSbxRxbyM12M8tVGe95zgR2lDRS0qbAScD05tfSNiSTE52Szr+R+diWXPM0s/yU8TnPiFgjaTLJyMZ6YGpELJA0Kd0/BTgfGABcnvbgr0lnYmv12Pau5+RpZrkq5wij9BU9d7TYNqXg8+kkcwhnOrY9Tp5mlhtRuyOMnDzNLFc1mjudPM0sZzWaPZ08zSxXtToZspOnmeWqNlOnk6eZ5a1Gs6eTp5nlxpMhm5mVooYnQ3byNLNc1WjudPI0szxlm+i4Gjl5mlmuajR3OnmaWX6KeT9RtXHyNLN81Wj2dPI0s1z5USUzsxL4nqeZWbEEdU6eZmalqM3s6eRpZrnxZMhmZiWq0dzp5Glm+XLN08ysBB6eaWZWgtpMnU6eZpYjeUo6M7PSeISRmVkpajN3OnmaWb5qNHc6eZpZnuRXD5uZFauWRxjV5R2AmVktcs3TzHJVqzVPJ08zy5UfVTIzK5YfkjczK14tdxg5eZpZrtxsNzMrgWueZmYlqNHc6eRpZjmr0ezp5GlmuRHU7PBMRUTeMVSEpKXAi3nHUQEDgWV5B2FF6ay/2bYRMWhjTiDpTpK/nyyWRcRhG3O9cuq0ybOzkjQrIsbnHYdl59+sc/LYdjOzEjh5mpmVwMmz9lyRdwBWNP9mnZDveZqZlcA1TzOzEjh5mpmVwMmzSkhamXcMVjpJL0jK+rxia8ePlXR4OWOyynLyNMuZpG7AWMDJs4Y4eVYZJS6W9Lik+ZJOTLdPkHS/pJslLZR0jZSMa5N0eLrtAUmXSPpjvt+ic5PUQ9Ltkh5Lf6cT011fkTQn/d1GpWX7S/q9pHmSHpY0Ot3+XUlXSLobuAr4PnCipLkF57Mq5rHt1ec4klrIGJJhazMlzUj37QHsCiwBHgT2kzQL+CVwQEQ8L+m6jg+5yzkMWBIRRwBI6gNcSDJ8cJyks4BzgdOB7wGPRsQxkg4iSZRj0/PsCewfEaslnQaMj4jJHftVrFSueVaf/YHrIqIhIl4H/gLsle57JCIWR0QjMBcYAYwCFkXE82kZJ8/Kmw8cIulCSR+PiBXp9lvTP2eT/DaQ/J6/A4iI+4ABabIFmB4RqzsoZisz1zyrT3tTzLxf8LmB5PerzSlpalhEPC1pT5J7lD9Mm96w7vdp+m2g9d+n6eHqVZWL0irNNc/qM4Pk3le9pEHAAcAj7ZRfCGwnaUS67vtlFSZpGPBuRFwN/BgY107xGcDJ6XETSJr2b7dS7h2gV3kjtUpy8qw+04B5wGPAfcA3I+K1tgqnzb6zgDslPQC8Dqxoq7yVxe7AI5LmAt8BftBO2e8C4yXNA34EnNpGuT8Du7jDqHZ4eGYnIKlnRKxMe98vA56JiJ/mHZdZZ+aaZ+fwpbQWtADoQ9L7bmYV5JqnmVkJXPM0MyuBk6eZWQmcPM3MSuDk2UVJakgfi3lc0k2SttiIc/1G0mfSz1dK2qWdshMk7VvCNVqdtSjLbEbFzliVjjs/t9gYrWtx8uy6VkfE2IjYDfgAmFS4U1J9KSeNiNMj4ol2ikwAik6eZtXGydMA/grskNYK/yzpWmB+OsrpYkkz01mBzoS1Mz9dKukJSbcDg5tOlM78ND79fFg6y9Bjku5NR0FNAr6W1no/LmmQpFvSa8yUtF967ABJd0t6VNIvyTAMNZ29aLakBZLOaLHvJ2ks96Yjt5C0vaQ702P+2jQTklkWHtvexSmZS3IicGe6aW9gt3SGpjOAFRGxl6TuwIPpOO49gI+QjLQZAjwBTG1x3kHAr1g321P/iFguaQqwMiJ+nJa7FvhpRDwgaRvgLmBn4D+AByLi+5KOAJolwzZ8Ib3G5iSzUd0SEW8CPYA5EfENSeen555M8mK2SRHxjKR9gMuBg0r4a7QuyMmz69o8fbAekprnr0ma048UzND0SWB00/1MkgfwdyQZb39dRDQASyTd18r5PwrMaDpXRCxvI45DSIYlNq33ltQrvcZx6bG3S/pHhu90jqRj089bp7G+CTQCN6TbrwZuldQz/b43FVy7e4ZrmAFOnl3Z6ogYW7ghTSKFM/0I+EpE3NWi3OGsmxmoLcpQBpJbRx9rOTVbGkvmERzppBuHpOd6V9L9wGZtFI/0um+1/Dswy8r3PK09dwFflrQJgKSdJPUgmSnopPSe6FDgwFaO/RvwCUkj02P7p9tbzh50N0kTmrTc2PRj4WxEE4F+G4i1D/CPNHGOIqn5NqkDmmrP/0xyO+Bt4HlJn02vIUljNnANs7WcPK09V5Lcz5wj6XGSMfPdSGZ+eoZkUuBfkEzY3ExELCW5T3mrpMdY12z+A3BsU4cRcA7prEOSnmBdr//3gAMkzSG5ffDSBmK9E+iWzl50AfBwwb5VwK6SZpPc0/x+uv1k4ItpfAuAozP8nZgBHttuZlYS1zzNzErg5GlmVgInTzOzEjh5mpmVwMnTzKwETp5mZiVw8jQzK8H/B1MCLOgZYQgVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TmmrwUpPredictor\n",
      "Accuracy:  0.6395466666666667\n",
      "Error Score: 6.345066666666667\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Final Catboost accuracy:  0.6527022222222222\n",
      "Final Catboost score:  6.935955555555556\n",
      "Final Feature Importance:                             importance\n",
      "featureName                           \n",
      "Expenditures Index            0.000000\n",
      "DPRIMEValue                   0.000000\n",
      "DPRIMElastChangeP             0.000000\n",
      "PCEC96Value                   0.000000\n",
      "PCEC96fiveVsTenTickAverage    0.000000\n",
      "...                                ...\n",
      "highVsLowPerc                 6.017256\n",
      "DGS30lastChangeP              6.315795\n",
      "typPrice                      6.507030\n",
      "ticker                       11.877725\n",
      "aroon                        20.093991\n",
      "\n",
      "[158 rows x 1 columns]\n",
      "<class 'list'>\n",
      "Feature to remove:  ['DPRIMEValue', 'DPRIMElastChangeP', 'Expenditures Index', 'PCEC96Value', 'PCEC96fiveVsTenTickAverage']\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Industrials\n",
      "droppedColumns: []\n",
      "valuesToTry: []\n",
      "droppedColumns: ['CPIAUCSLbPercent']\n",
      "valuesToTry: ['DPRIMEValue', 'DPRIMElastChangeP', 'East & Central Europe', 'PCEC96Value', 'PRS85006092fiveVsTenTickAverage', 'South America', 'WPU0911bPercent']\n",
      "change in score by removing CPIAUCSLbPercent is -0.028844444444445116\n",
      "droppedColumns: ['DPRIMEValue']\n",
      "valuesToTry: ['DPRIMElastChangeP', 'East & Central Europe', 'PCEC96Value', 'PRS85006092fiveVsTenTickAverage', 'South America', 'WPU0911bPercent']\n",
      "change in score by removing DPRIMEValue is -0.06591111111111125\n",
      "droppedColumns: ['DPRIMElastChangeP']\n",
      "valuesToTry: ['East & Central Europe', 'PCEC96Value', 'PRS85006092fiveVsTenTickAverage', 'South America', 'WPU0911bPercent']\n",
      "change in score by removing DPRIMElastChangeP is 0.0861333333333345\n",
      "droppedColumns: ['DPRIMElastChangeP', 'East & Central Europe']\n",
      "valuesToTry: ['PCEC96Value', 'PRS85006092fiveVsTenTickAverage', 'South America', 'WPU0911bPercent']\n",
      "change in score by removing East & Central Europe is -0.04582222222222221\n",
      "droppedColumns: ['DPRIMElastChangeP', 'PCEC96Value']\n",
      "valuesToTry: ['PRS85006092fiveVsTenTickAverage', 'South America', 'WPU0911bPercent']\n",
      "change in score by removing PCEC96Value is -0.04897777777777712\n",
      "droppedColumns: ['DPRIMElastChangeP', 'PRS85006092fiveVsTenTickAverage']\n",
      "valuesToTry: ['South America', 'WPU0911bPercent']\n",
      "change in score by removing PRS85006092fiveVsTenTickAverage is 0.1035555555555554\n",
      "droppedColumns: ['DPRIMElastChangeP', 'PRS85006092fiveVsTenTickAverage', 'South America']\n",
      "valuesToTry: ['WPU0911bPercent']\n",
      "change in score by removing South America is -0.06484444444444648\n",
      "droppedColumns: ['DPRIMElastChangeP', 'PRS85006092fiveVsTenTickAverage', 'WPU0911bPercent']\n",
      "valuesToTry: []\n",
      "change in score by removing WPU0911bPercent is -0.014977777777776424\n",
      "Final dropped columns: ['DPRIMElastChangeP', 'PRS85006092fiveVsTenTickAverage']\n"
     ]
    },
    {
     "data": {
      "image/png": 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qoExLW5Hl9n91RNRJWiNpG+BNwJ3/zSwX7e72H5ghaTvgRpIeAe8DT7ZkUGbWflRYTs307v/X0tVxkh4AtomI2S0blpm1B0IV9+5/uc7/Q8rti4iZLROSmbUbOY9AJWkY8N8kE//dFBFXNNh/BnBRuvk+cEE68D6SFgLvkYwXXTohYHfgTmAXkon/To2IlU3FUK6mek2ZfQEcVWZ/4fYc2JffTb6y6DCsGfqff1fRIVgzrHy1ybzSLHm1qUqqAq4jmRF1ETBd0qSIeK7ksAXA4RGxUtJwYDxwYMn+I9OR+UpdDDwUEVekb5dezCeJeT3lOv8f2axvZGbWTAKq8quqHgDMj4hXACTdAYwA1ibViHi85PgngL4ZrjuCpFspwM3Ao5RJqlm6VJmZtZhmDKjSU9KMkuW8BpfaCXi9ZHtRWtaUc4D7S7YD+LOkpxpce4eIWAqQ/ty+3PfJ9EaVmVlLaUYX1Jr6ds4mNHalRkfUk3QkSVI9pKT44IhYIml74EFJ8yJiauboUq6pmllhkqlSchtPdRHQr2S7L7Bk/c/UIOAmYERELK8vj4gl6c83gXtJmhMAlknqnZ7bm6SvfpOyjPwvSf8i6ZJ0u7+kAzZ0nplZFjmOpzodGChpQDpJ6enApNIDJPUHJgJnRsSLJeVbS+pavw58EZiT7p4EjErXRwF/KBdEltv/64E6kqf9l5F0ObgH2D/DuWZmZeX1nCoi1kgaA0wh6VI1ISLmShqd7h8HXAL0AK5Pa7/1Xad2AO5Ny6qB30bEA+mlrwDuknQO8BpwSrk4siTVAyNiiKSn08BWpn8FzMw2iYDqHDuqRsRkkoGfSsvGlayfC5zbyHmvkIxr0tg1l5OMf5JJlqT6cdr/KwAk9SKpuZqZbbIKe6EqU1L9BUmj7faS/otk1KoftmhUZtYuSO3oNdV6EXGbpKdIqr8CToyI51s8MjNrFyosp2YapLo/8AHwx9KyiHitJQMzs/ahDQ2VmkmW2//7+GQCwC2BAcALwF4tGJeZtQOCNjUAdRZZbv/3Kd1OR686v4nDzcyyy94Htc1o9muqETFTkvuomlkuVGGzVGVpU/2Pks0OwBDgrRaLyMzajfY6RXXXkvU1JG2s97RMOGbW3rSrpJp2+u8SEd9tpXjMrJ1pNxP/SapO36VtcloVM7NNkUxRXXQU+SpXU32SpP10lqRJwN3AqvqdETGxhWMzs3ag3b1RBXQHlpOMUlXfXzVIhs8yM9to7e1B1fbpk/85fJJM6zU6mraZWXNVWEW1bFKtArrQjCkKzMyaR3RoR/1Ul0bEZa0WiZm1O6J91VQr7Kua2WZHUF1hjarlkmrmka7NzDZGu6qpRsSK1gzEzNqnSutSVWHdbs2srUmmqd7wku1aGibpBUnzJV3cyP4zJM1Ol8cl7ZuW95P0iKTnJc2V9K2Scy6VtFjSrHQ5rlwMzR6lyswsLyK/ml36Wv11wDHAImC6pEkR8VzJYQuAw9MJTIcD44EDScY1+U46Cl9X4ClJD5ace21E/DRLHE6qZlYc5Xr7fwAwP50ZFUl3ACOAtUk1Ih4vOf4JoG9avhRYmq6/J+l5YKfSc7Py7b+ZFSZ5o0qZFqCnpBkly3kNLrcT8HrJ9qK0rCnnAPevF5O0C7Af8PeS4jFpk8EESd3KfSfXVM2sUM2op9ZExNBmXqrRF5UkHUmSVA9pUN6FZGjTb0fEu2nxDcDl6bUuB64Bzm4qCCdVMytUjg//FwH9Srb7AkvW/zwNAm4ChkfE8pLyjiQJ9bbSAaMiYlnJMTcCfyoXhG//zaxAQsq2ZDAdGChpgKROwOnApHU+LZkdeiJwZkS8WFIu4NfA8xHxswbn9C7ZHEkyHkqTXFM1s8Lk+fQ/Hf95DDCFZOySCRExV9LodP844BKgB3B9mqjXpE0KBwNnAs9KmpVe8vsRMRm4StJgktv/hWxg4lMnVTMrVJ6d/9MkOLlB2biS9XOBcxs5bxpNNO9GxJnNicFJ1cyKo3Y0nYqZWUvL8/Z/c+GkamaFck3VzCxHlZVSnVTNrEACqlxTNTPLT4XlVCdVMyuSUIU1ADipmlmhXFM1M8tJ0qWqsrKqk6qZFacZo/q3FU6qZlaoSpujyknVzAqTDFJddBT5clI1s0L56b+ZWY4q7O7fSXVz8tiMF7j6V5OoqwtOPHZ/zj71yHX2L3j9Tf7z2ruZN38xY0Ydy1knH752329/P42JU54kIjhp2AGcceKhrR1+u3P4Xjty6amDqeog7pi2gOunzFtn/zH79uHCE/amLoLauuBHd85i+ss1AFx91v4cvU9vlr/3IcdcNqWI8DcblVZTbdUBYiQtlNRzE84fvKE5t9uq2to6rrj+94y97GzuGfcfPPCXZ3j5tWXrHLNt185cNPoEzjr5sHXK5y98g4lTnuSWa8dw53XfZuqT83h1cU1rht/udJD48VeGMOqXf+XoS6dwwv79Gdh7m3WOeWzemxx7+Z8Z/uMHufDm6Vx51ifTK939twWc9YuprR32Zqe+TTXL0la0mVG3JFUDg4GKTKpzXnydfn160Ld3Dzp2rObYw/bl0b+tOztu9+26sNen+1FdVbVO+YLX32Sf3fuz1ZadqK6q4rN7D+CRx8vO+GCbaPCA7ix8831eq1nFx7V1/HHGa3xx3z7rHPPBh2vWrnfeopoomYLuyZdqePuDj1or3M1XxplU21IPgRa7/Ze0NXAXyeRbVSSzEAJ8Q9KXgY7AKRExT1J3YAKwK/ABcF5EzJZ0KdAH2AWoIZn5cCtJhwA/iYg7Wyr+1vbm8nfYoed2a7d36Lktc154LdO5u+28A2NvnsLb765ii04dmTbjBfYc2LeFIjWAHbfbiiUrP1i7vXTlagYP6L7ecccO3omLRu5Dz65b8NWx01ozxDaj7aTLbFqyTXUYsCQijgeQtC1wJck0s0MkfQ24kGRqgx8BT0fEiZKOAn5DUisF+CxwSESslvRVYGhEjGnsA9N5wM8D6LNTv8YO2Xw1NpFuxr/Ou/bfga+ecjgX/OAmttpyCz49oDfVVW3mJqRNyjoX8pRZi5kyazEHDOzJhSfszT///C8tHVqbktz+V1Zabcl/ec8CX5B0paRDI+KdtLx+6tenSGqgkNRAbwGIiIeBHmkSBpgUEauzfGBEjI+IoRExtFuPjW66LcT2PbdlWc3ba7eX1bxDr+7bNH1CAyOPPYDbf/ktJlw9mm27dqZ/n7b1/duapW+vpk+3zmu3e3fbijffbvr/pk++VEP/XlvTbetOrRFem6KMS6ZrScMkvSBpvqSLG9l/hqTZ6fK4pH03dK6k7pIelPRS+rNbuRhaLKmm079+liS5/kTSJemuD9OftXxSUy73h39VS8W4Odnr0315bclyFr+xgo8/XsOUqc9wxEGfyXz+irffB2Dpmyt5+PE5DDt83w2cYZvimYUrGLB9F/r12JqOVR348tD+PPjMulPM79yry9r1vfttR6eqDqxc5XbU9eSUVSVVAdcBw4E9ga9I2rPBYQuAwyNiEEmT5PgM514MPBQRA4GH0u0mtWSbah9gRUTcKul94KtlDp8KnAFcLukIkiaCdxuZZuE9oGv+0RavuqqKiy4Ywdd++Gvq6uoY8cX92W3nHbn7vicAOOX4g6hZ8R5nfOsXrPrgQ9RB3Pb7adzzq+/QpfOWXPhft/D2ux9QXV3FxV87kW26dt7AJ9qmqK0L/u8dM7nlW4dR1UHc+dgCXlz6Lv9y2G4A3Dr1ZY4b0peTD9qZj2vr+MfHtXz9xifWnv/Lcw7ic7v3oluXLfj7FV/iZ3+cy52PLSjq6xQqx9v/A4D5EfEKgKQ7gBHA2ie+EfF4yfFPkDzz2dC5I4Aj0uNuBh4FLmoqiJZsU90HuFpSHfAxcAHwuyaOvRT4H0mzSR5UjWriuEeAi9N5uSvqQRXAofvvwaH777FO2SnHH7R2vWf3rky55QeNnjvh6gtaNDZb3yNz3uCROfevU3br1JfXrt8wZR43NOi7Wu8bv36i0fL2qBkptaekGSXb4yNifMn2TsDrJduLgAPLXO8coP4XWO7cHSJiKUBELJW0fbkgWyypRsQUoGGv5l1K9s8gzf4RsYLkr0HDa1zaYHsFsH++kZpZobJn1ZqIGFpmf9bnh0g6kiSpHtLcczfEj4jNrDBJc2m2/2WwCCjt9tMXWNLwIEmDgJuAERGxPMO5yyT1Ts/tDbxZLggnVTMrTjqeapYlg+nAQEkDJHUCTgcmrfNxUn+SHkhnpg/Ts5w7iU+aJEcBfygXhN/9N7NC5fWYKiLWSBpD0uxYBUyIiLmSRqf7xwGXAD2A69MH4WvSbpiNnpte+grgLknnAK8Bp5SLw0nVzAokGunls9EiYjIwuUHZuJL1c0leOMp0blq+HDg6awxOqmZWqAp7ocpJ1cyK05y3pdoKJ1UzK1aFZVUnVTMrVKUNUu2kamaFcpuqmVlesvdBbTOcVM2sUL79NzPLiXBN1cwsVxWWU51UzaxgFZZVnVTNrFCVNkeVk6qZFaqyUqqTqpkVrcKyqpOqmRWmfpDqSuKkambFced/M7N8VVhOdVI1syLlO0j15sBJ1cwKVWE51UnVzIpTiYNUezZVMyuWMi5ZLiUNk/SCpPmSLm5k/x6S/ibpQ0kXlpTvLmlWyfKupG+n+y6VtLhk33HlYnBN1cwKlVeXKklVwHXAMcAiYLqkSRHxXMlhK4BvAieWnhsRLwCDS66zGLi35JBrI+KnWeJwTdXMCiVlWzI4AJgfEa9ExEfAHcCI0gMi4s2ImA58XOY6RwMvR8SrG/N9nFTNrDiCDhkXoKekGSXLeQ2uthPwesn2orSsuU4Hbm9QNkbSbEkTJHUrd7KTqpkVLHOjak1EDC1ZxjdyoYaiWZFInYATgLtLim8AdiNpHlgKXFPuGm5TNbPC5DxI9SKgX8l2X2BJM68xHJgZEcvqC0rXJd0I/KncBVxTNbNC5fjwfzowUNKAtMZ5OjCpmeF8hQa3/pJ6l2yOBOaUu4BrqmZWqLxqqhGxRtIYYApQBUyIiLmSRqf7x0naEZgBbAPUpd2m9oyIdyV1Juk5cH6DS18laTBJU8LCRvavw0nVzAqV52uqETEZmNygbFzJ+hskzQKNnfsB0KOR8jObE4OTqpkVqtLeqHJSNbPCNKMPapvhpGpmhfIg1WZmeaqsnOqkambFqrCc6qRqZkWSp6g2M8tLzm9UbRb8RpWZWY5cUzWzQlVaTdVJ1cwK5S5VZmZ5ced/M7P8VOKDKidVMyuUb//NzHLkmqqZWY4qLKc6qZpZwSosqzqpmllhBBX3mqoimjXZYJsh6S1go+bt3sz1BGqKDsKapVJ/ZztHRK9NuYCkB0j++2RRExHDNuXzWkPFJtVKJWlGRAwtOg7Lzr+z9sXv/puZ5chJ1cwsR06qbc/4ogOwZvPvrB1xm6qZWY5cUzUzy5GTqplZjpxUNxOS3i86Btt4khZKytrfsrHzB0s6Ls+YrBhOqmYFk1QNDAacVCuAk+pmRomrJc2R9Kyk09LyIyQ9Kul3kuZJuk1K3u+TdFxaNk3SLyT9qdhvUdkkbS3pPknPpL+n09Jd35A0M/297ZEe213S7yXNlvSEpEFp+aWSxkv6M/Ab4DLgNEmzSq5nbZDf/d/8nERSa9mX5PW96ZKmpvv2A/YClgCPAQdLmgH8CjgsIhZIur31Q253hgFLIuJ4AEnbAleSvEY5RNLXgAuBc4EfAU9HxImSjiJJoIPT63wWOCQiVkv6KjA0Isa07lexvLmmuvk5BLg9ImojYhnwF2D/dN+TEbEoIuqAWcAuwB7AKxGxID3GSbXlPQt8QdKVkg6NiHfS8onpz6dIfjeQ/D5vAYiIh4EeaRIGmBQRq1spZmslrqlufsoN2fNhyXotye+vsob4aQMi4kVJnyVpA/1JegsPn/x+6n830Pjvp75z+KqWi9KK4prq5mcqSdtalaRewGHAk2WOnwfsKmmXdNvtcS1MUh/gg4i4FfgpMKTM4VOBM9LzjiBpIni3kePeA7rmG6kVwUl183MvMBt4BngY+F5EvNHUwent49eAByRNA5YB7zR1vOViH+BJSbOAHwA/LnPspcBQSbOBK4BRTRz3CLCnH1S1fX5NtQJI6hIR76e9Aa4DXoqIa4uOy6w9ck21MvxbWmuaC2xL0hvAzArgmqqZWY5cUzUzy5GTqplZjpxUzcxy5KTaTkmqTbvvzJF0t6TOm3Ct/5X0T+n6TZL2LHPsEZI+vxGf0egoUFlGh2ruCGDpe/kXNjdGM3BSbc9WR8TgiNgb+AgYXbpTUtXGXDQizo2I58occgTQ7KRq1lY4qRrAX4FPpbXIRyT9Fng2favraknT01GWzoe1I2mNlfScpPuA7esvlI6kNTRdH5aO2vSMpIfSt75GA/+e1pIPldRL0j3pZ0yXdHB6bg9Jf5b0tKRfkeF13HQ0qKckzZV0XoN916SxPJS+qYak3SQ9kJ7z1/qRpcw2hd/9b+eUjOU5HHggLToA2Dsd8eo84J2I2F/SFsBj6Xvu+wG7k7xZtAPwHDChwXV7ATfyyehZ3SNihaRxwPsR8dP0uN8C10bENEn9gSnAZ4D/BKZFxGWSjgfWSZJNODv9jK1IRve6JyKWA1sDMyPiO5IuSa89hmRCvtER8ZKkA4HrgaM24j+j2VpOqu3XVukLA5DUVH9Nclv+ZMmIV18EBtW3l5K8WDCQZDyC2yOiFlgi6eFGrn8QMLX+WhGxook4vkDyemb99jaSuqafcVJ67n2SVmb4Tt+UNDJd75fGuhyoA+5My28FJkrqkn7fu0s+e4sMn2FWlpNq+7U6IgaXFqTJpXTkJAHfiIgpDY47jk9GWmqKMhwDSRPU5xoOgZfGkvnNlHSwki+k1/pA0qPAlk0cHunnvt3wv4HZpnKbqpUzBbhAUkcASZ+WtDXJyEunp22uvYEjGzn3b8Dhkgak53ZPyxuOxvRnkltx0uMGp6ulozsNB7ptINZtgZVpQt2DpKZcrwNQX9v+Z5JmhXeBBZJOST9DkvbdwGeYbZCTqpVzE0l76UxJc0jGFKgmGUnrJZLBmm8gGUh7HRHxFkk76ERJz/DJ7fcfgZH1D6qAb5KO4iTpOT7phfAj4DBJM0maIV7bQKwPANXpaFCXA0+U7FsF7CXpKZI208vS8jOAc9L45gIjMvw3MSvL7/6bmeXINVUzsxw5qZqZ5chJ1cwsR06qZmY5clI1M8uRk6qZWY6cVM3McvT/AXbLZdGRcinwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZScorePredictor\n",
      "Accuracy:  0.6601866666666667\n",
      "Error Score: 7.333466666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FiveDayPredictor\n",
      "Accuracy:  0.7178933333333334\n",
      "Error Score: 9.4692\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TmmrwUpPredictor\n",
      "Accuracy:  0.66152\n",
      "Error Score: 7.337866666666667\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Final Catboost accuracy:  0.6798666666666667\n",
      "Final Catboost score:  8.046844444444444\n",
      "Final Feature Importance:                              importance\n",
      "featureName                            \n",
      "Misery Index                   0.000000\n",
      "CONSUMERValue                  0.000000\n",
      "West Europe                    0.000000\n",
      "WPU0911fiveVsTenTickAverage    0.000000\n",
      "CASS-CTLI-Index                0.000000\n",
      "...                                 ...\n",
      "typPrice                       8.507238\n",
      "WALCLlastChangeP               9.221953\n",
      "thisDayPercentChangeStdev     10.470541\n",
      "DEXUSUKfiveVsTenTickAverage   12.263821\n",
      "aroon                         12.404599\n",
      "\n",
      "[158 rows x 1 columns]\n",
      "<class 'list'>\n",
      "Feature to remove:  ['BUSLOANSValue', 'CASS-CTLI-Index', 'CONSUMERValue', 'CPIAUCSLbPercent', 'DPRIMEValue', 'DPRIMElastChangeP', 'Misery Index', 'PRS85006092bPercent', 'SFTPINDM114SFRBSFValue', 'UNRATEfiveVsTenTickAverage', 'WPU0911fiveVsTenTickAverage', 'West Europe']\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Real-Estate\n",
      "droppedColumns: []\n",
      "valuesToTry: []\n",
      "droppedColumns: ['BUSLOANSValue']\n",
      "valuesToTry: ['CASS-CTLI-Index', 'CONSUMERValue', 'DPRIMEValue', 'PCEC96Value', 'UNRATEValue']\n",
      "change in score by removing BUSLOANSValue is 0.05306666666666615\n",
      "droppedColumns: ['BUSLOANSValue', 'CASS-CTLI-Index']\n",
      "valuesToTry: ['CONSUMERValue', 'DPRIMEValue', 'PCEC96Value', 'UNRATEValue']\n",
      "change in score by removing CASS-CTLI-Index is 0.016488888888888553\n",
      "droppedColumns: ['BUSLOANSValue', 'CASS-CTLI-Index', 'CONSUMERValue']\n",
      "valuesToTry: ['DPRIMEValue', 'PCEC96Value', 'UNRATEValue']\n",
      "change in score by removing CONSUMERValue is -0.056977777777778016\n",
      "droppedColumns: ['BUSLOANSValue', 'CASS-CTLI-Index', 'DPRIMEValue']\n",
      "valuesToTry: ['PCEC96Value', 'UNRATEValue']\n",
      "change in score by removing DPRIMEValue is -0.01804444444444364\n",
      "droppedColumns: ['BUSLOANSValue', 'CASS-CTLI-Index', 'PCEC96Value']\n",
      "valuesToTry: ['UNRATEValue']\n",
      "change in score by removing PCEC96Value is 0.0752000000000006\n",
      "droppedColumns: ['BUSLOANSValue', 'CASS-CTLI-Index', 'PCEC96Value', 'UNRATEValue']\n",
      "valuesToTry: []\n",
      "change in score by removing UNRATEValue is -0.1197777777777791\n",
      "Final dropped columns: ['BUSLOANSValue', 'CASS-CTLI-Index', 'PCEC96Value']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZScorePredictor\n",
      "Accuracy:  0.68992\n",
      "Error Score: 8.4264\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FiveDayPredictor\n",
      "Accuracy:  0.7391466666666666\n",
      "Error Score: 10.233866666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TmmrwUpPredictor\n",
      "Accuracy:  0.6877066666666667\n",
      "Error Score: 8.3032\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Final Catboost accuracy:  0.7055911111111111\n",
      "Final Catboost score:  8.987822222222222\n",
      "Final Feature Importance:                                importance\n",
      "featureName                              \n",
      "DPRIMElastChangeP                0.000000\n",
      "South America                    0.000000\n",
      "PCEC96Value                      0.000000\n",
      "BUSLOANSValue                    0.000000\n",
      "UNRATEValue                      0.000000\n",
      "...                                   ...\n",
      "DEXUSUKlastChangeP               5.470655\n",
      "DEXCHUSlastChangeP               5.877547\n",
      "NASDAQ100fiveVsTenTickAverage    6.372815\n",
      "DGS2lastChangeP                  7.122209\n",
      "aroon                           10.052616\n",
      "\n",
      "[158 rows x 1 columns]\n",
      "<class 'list'>\n",
      "Feature to remove:  ['BUSLOANSValue', 'DPRIMElastChangeP', 'PCEC96Value', 'South America', 'UNRATEValue']\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Technology\n",
      "droppedColumns: []\n",
      "valuesToTry: []\n",
      "droppedColumns: ['CASS-CTLI-Index']\n",
      "valuesToTry: ['CONSUMERValue', 'DPRIMEValue', 'DPRIMElastChangeP', 'PCEC96Value', 'WPU0911Value']\n",
      "change in score by removing CASS-CTLI-Index is -0.00031111111111048473\n",
      "droppedColumns: ['CONSUMERValue']\n",
      "valuesToTry: ['DPRIMEValue', 'DPRIMElastChangeP', 'PCEC96Value', 'WPU0911Value']\n",
      "change in score by removing CONSUMERValue is -0.046088888888888846\n",
      "droppedColumns: ['DPRIMEValue']\n",
      "valuesToTry: ['DPRIMElastChangeP', 'PCEC96Value', 'WPU0911Value']\n",
      "change in score by removing DPRIMEValue is 0.09199999999999964\n",
      "droppedColumns: ['DPRIMEValue', 'DPRIMElastChangeP']\n",
      "valuesToTry: ['PCEC96Value', 'WPU0911Value']\n",
      "change in score by removing DPRIMElastChangeP is -0.05204444444444434\n",
      "droppedColumns: ['DPRIMEValue', 'PCEC96Value']\n",
      "valuesToTry: ['WPU0911Value']\n",
      "change in score by removing PCEC96Value is -0.0342222222222226\n",
      "droppedColumns: ['DPRIMEValue', 'WPU0911Value']\n",
      "valuesToTry: []\n",
      "change in score by removing WPU0911Value is 0.14235555555555646\n",
      "Final dropped columns: ['DPRIMEValue', 'WPU0911Value']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZScorePredictor\n",
      "Accuracy:  0.6492\n",
      "Error Score: 6.8656\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FiveDayPredictor\n",
      "Accuracy:  0.70096\n",
      "Error Score: 8.7928\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TmmrwUpPredictor\n",
      "Accuracy:  0.65136\n",
      "Error Score: 6.922266666666666\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Final Catboost accuracy:  0.6671733333333334\n",
      "Final Catboost score:  7.526888888888888\n",
      "Final Feature Importance:                              importance\n",
      "featureName                            \n",
      "PCEC96Value                    0.000000\n",
      "BOPGSTBbPercent                0.000000\n",
      "DPRIMElastChangeP              0.000000\n",
      "DPRIMEValue                    0.000000\n",
      "BUSLOANSbPercent               0.000000\n",
      "...                                 ...\n",
      "WALCLlastChangeP               7.510113\n",
      "DEXUSUKfiveVsTenTickAverage    9.076021\n",
      "typPrice                       9.596430\n",
      "GOLDAMGBD228NLBMlastChangeP   10.412169\n",
      "aroon                         16.007353\n",
      "\n",
      "[158 rows x 1 columns]\n",
      "<class 'list'>\n",
      "Feature to remove:  ['BOPGSTBbPercent', 'BUSLOANSbPercent', 'CPIAUCSLValue', 'DPRIMEValue', 'DPRIMElastChangeP', 'PCEC96Value', 'South America', 'UNRATEValue']\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Utilities\n",
      "droppedColumns: []\n",
      "valuesToTry: []\n",
      "droppedColumns: ['DPRIMEValue']\n",
      "valuesToTry: ['PRS85006092lastChangeP']\n",
      "change in score by removing DPRIMEValue is 0.011200000000000543\n",
      "droppedColumns: ['DPRIMEValue', 'PRS85006092lastChangeP']\n",
      "valuesToTry: []\n",
      "change in score by removing PRS85006092lastChangeP is -0.023288888888886916\n",
      "Final dropped columns: ['DPRIMEValue']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZScorePredictor\n",
      "Accuracy:  0.69904\n",
      "Error Score: 8.722266666666666\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FiveDayPredictor\n",
      "Accuracy:  0.7325333333333334\n",
      "Error Score: 10.003333333333334\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TmmrwUpPredictor\n",
      "Accuracy:  0.69872\n",
      "Error Score: 8.7068\n",
      "-------------------------------------------\n",
      "\n",
      "\n",
      "Final Catboost accuracy:  0.7100977777777778\n",
      "Final Catboost score:  9.144133333333334\n",
      "Final Feature Importance:                                       importance\n",
      "featureName                                     \n",
      "DPRIMEValue                             0.000000\n",
      "CONSUMERValue                           0.000000\n",
      "UNRATEValue                             0.000000\n",
      "BUSLOANSValue                           0.000000\n",
      "NASDAQ100Value                          0.059532\n",
      "...                                          ...\n",
      "articleSentChangeSinceYesterday         4.649567\n",
      "GOLDAMGBD228NLBMfiveVsTenTickAverage    4.651299\n",
      "GOLDAMGBD228NLBMlastChangeP             5.554398\n",
      "aroon                                  10.109761\n",
      "typPrice                               12.064815\n",
      "\n",
      "[158 rows x 1 columns]\n",
      "<class 'list'>\n",
      "Feature to remove:  ['BUSLOANSValue', 'CONSUMERValue', 'DPRIMEValue', 'UNRATEValue']\n",
      "-------------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for file in os.listdir(sectorFolder):\n",
    "    sector = file.split('.')[0]\n",
    "    sector = sector.replace(' ', '-')\n",
    "    print(sector)\n",
    "    droppedColumns = []\n",
    "    valuesToTry = []\n",
    "    seenCol = []\n",
    "    lastScore = 0\n",
    "    lastAccuracy = 0\n",
    "    toContinue = True\n",
    "    newToRemove = None\n",
    "    first = True\n",
    "    oldScore = None\n",
    "    while (toContinue):\n",
    "        print('droppedColumns: {}'.format(droppedColumns))\n",
    "        print('valuesToTry: {}'.format(valuesToTry))\n",
    "        toRemove, newScore, colCount, _ = buildForest(file, droppedColumns, False)\n",
    "        for x in toRemove:\n",
    "            seenCol.append(x)\n",
    "        if not first:\n",
    "            print('change in score by removing {} is {}'.format(droppedColumns[-1], newScore - oldScore))\n",
    "            if newScore <= oldScore :\n",
    "                droppedColumns.pop()\n",
    "                if len(valuesToTry) == 0:\n",
    "                    toContinue  = False\n",
    "                else:\n",
    "                    droppedColumns.append(valuesToTry.pop(0))\n",
    "            else:\n",
    "                for x in toRemove:\n",
    "                    if x not in seenCol:\n",
    "                        valuesToTry.append(x)\n",
    "                if len(valuesToTry) != 0:\n",
    "                    droppedColumns.append(valuesToTry.pop(0))\n",
    "                else:\n",
    "                    toContinue = False\n",
    "        else : #if first\n",
    "            first = False\n",
    "            valuesToTry = toRemove\n",
    "            droppedColumns.append(valuesToTry.pop(0))\n",
    "        oldScore = newScore\n",
    "    print('Final dropped columns:', droppedColumns)\n",
    "    verbose = True\n",
    "    buildForest(file, droppedColumns, True)     \n",
    "    verbose = False\n",
    "    print('-------------------------------------------\\n\\n')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}