arima_and_nlp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jrizz_000\\anaconda3\\anaconda3\\envs\\learn-env\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n",
      "In C:\\Users\\jrizz_000\\anaconda3\\anaconda3\\envs\\learn-env\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\jrizz_000\\anaconda3\\anaconda3\\envs\\learn-env\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\jrizz_000\\anaconda3\\anaconda3\\envs\\learn-env\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In C:\\Users\\jrizz_000\\anaconda3\\anaconda3\\envs\\learn-env\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\jrizz_000\\anaconda3\\anaconda3\\envs\\learn-env\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\jrizz_000\\anaconda3\\anaconda3\\envs\\learn-env\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\jrizz_000\\anaconda3\\anaconda3\\envs\\learn-env\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\jrizz_000\\anaconda3\\anaconda3\\envs\\learn-env\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "#**************** IMPORT PACKAGES ********************\n",
    "from flask import Flask, render_template, request, flash, redirect, url_for\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import datetime\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "import math, random\n",
    "from datetime import datetime\n",
    "import datetime as dt\n",
    "import json\n",
    "import yfinance as yf\n",
    "import tweepy\n",
    "import preprocessor as p\n",
    "import re\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import LSTM\n",
    "import xgboost as xgboost\n",
    "import simplejson\n",
    "from collections import deque\n",
    "import random\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from textblob import TextBlob\n",
    "import constants as ct\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_historical(quote):\n",
    "    end = datetime.now()\n",
    "    start = datetime(end.year-2,end.month,end.day)\n",
    "    data = yf.download(quote, start=start, end=end)\n",
    "    df = pd.DataFrame(data=data)\n",
    "    df.to_csv(''+quote+'.csv')\n",
    "    if(df.empty):\n",
    "        from alpha_vantage.timeseries import TimeSeries\n",
    "        ts = TimeSeries(key='N6A6QT6IBFJOPJ70',output_format='pandas')\n",
    "        data, meta_data = ts.get_daily_adjusted(symbol='NSE:'+quote, outputsize='full')\n",
    "        #Format df\n",
    "        #Last 2 yrs rows => 502, in ascending order => ::-1\n",
    "        data=data.head(503).iloc[::-1]\n",
    "        data=data.reset_index()\n",
    "        #Keep Required cols only\n",
    "        df=pd.DataFrame()\n",
    "        df['Date']=data['date']\n",
    "        df['Open']=data['1. open']\n",
    "        df['High']=data['2. high']\n",
    "        df['Low']=data['3. low']\n",
    "        df['Close']=data['4. close']\n",
    "        df['Adj Close']=data['5. adjusted close']\n",
    "        df['Volume']=data['6. volume']\n",
    "        df.to_csv(''+quote+'.csv',index=False)\n",
    "    return\n",
    "\n",
    "#******************** ARIMA SECTION ********************\n",
    "def ARIMA_ALGO(df):\n",
    "    from pmdarima.arima import auto_arima\n",
    "    from scipy.ndimage.interpolation import shift\n",
    "    from statsmodels.tsa.arima_model import ARIMA\n",
    "    from pmdarima.arima import auto_arima\n",
    "    import json\n",
    "    uniqueVals = df[\"symbol\"].unique()  \n",
    "    len(uniqueVals)\n",
    "    df=df.set_index(\"symbol\")\n",
    "    #for daily bas'is\n",
    "    def parser(x):\n",
    "        from datetime import datetime\n",
    "        return datetime.strptime(x, '%Y-%m-%d')\n",
    "    d['Date'] = pd.to_datetime(d['date']).map(lambda x: x.date())\n",
    "    date = d.Date\n",
    "    price = d.adjClose\n",
    "    for company in uniqueVals[:10]:\n",
    "        data=(df.loc[company,:]).reset_index() \n",
    "        data[['Code','Open','Low','High','Close','Adj_close','Date']] = data[['symbol','open','low','high','close', 'adjClose','date']]\n",
    "        Quantity_date = data[['Code','Open','Low','High','Close','Adj_close', 'Date']]\n",
    "\n",
    "        Quantity_date['Date'] = pd.to_datetime(Quantity_date['Date']).map(lambda x: x.date())\n",
    "        Quantity_date = Quantity_date.fillna(Quantity_date.bfill())\n",
    "\n",
    "        code = Quantity_date['Code'].to_list()\n",
    "        close=Quantity_date['Close'].to_list()\n",
    "        date=Quantity_date['Date'].to_list()\n",
    "        open=Quantity_date['Open'].to_list()\n",
    "        high=Quantity_date['High'].to_list()\n",
    "        low=Quantity_date['Low'].to_list()\n",
    "        Adj_close=Quantity_date['Adj_close'].to_list()\n",
    "\n",
    "        close = pd.DataFrame(data = close, columns=[\"Close\"])\n",
    "        code = pd.DataFrame(data = code, columns=['Code'])\n",
    "        date = pd.DataFrame(data = date, columns=[\"Date\"]).astype(str)\n",
    "        open = pd.DataFrame(data = open, columns=[\"Open\"])\n",
    "        high = pd.DataFrame(data = high, columns=[\"High\"])\n",
    "        low = pd.DataFrame(data = low, columns=[\"Low\"])\n",
    "        Adj_close = pd.DataFrame(data = Adj_close, columns=[\"Adj_close\"])\n",
    "\n",
    "        result = pd.concat([code, date, open, low, high, close, Adj_close], axis=1, ignore_index=True)\n",
    "\n",
    "        result.columns = ['Code','Date','Open','Low','High','Close','Adj_close']\n",
    "\n",
    "        Quantity_date = Quantity_date.drop(['Code','Date','Open','Low','High','Close'],axis =1)\n",
    "\n",
    "        Quantity_date = Quantity_date.Adj_close\n",
    "        print()\n",
    "        fig = plt.figure(figsize=(7.2,4.8),dpi=65)\n",
    "        plt.plot(Quantity_date)\n",
    "        plt.savefig('Trends.png')\n",
    "        plt.close(fig)\n",
    "        plt.show()\n",
    "        import statsmodels.api as sm\n",
    "        from statsmodels.tsa.stattools import adfuller    \n",
    "        test_result=adfuller(Quantity_date)\n",
    "        #Ho: It is non stationary\n",
    "        #H1: It is stationary\n",
    "\n",
    "        def adfuller_test(sales):\n",
    "            result=adfuller(sales)\n",
    "            labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
    "            for value,label in zip(result,labels):\n",
    "                print(label+' : '+str(value) )\n",
    "            if result[1] <= 0.05:\n",
    "                print(\"strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\")\n",
    "            else:\n",
    "                print(\"weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \")\n",
    "        print(adfuller_test(Quantity_date))\n",
    "\n",
    "        from pandas.plotting import autocorrelation_plot\n",
    "        autocorrelation_plot(Quantity_date)\n",
    "        plt.show()\n",
    "        from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
    "        fig = plt.figure(figsize=(12,8))\n",
    "        ax1 = fig.add_subplot(211)\n",
    "        fig = sm.graphics.tsa.plot_acf(Quantity_date,ax=ax1)\n",
    "        print(fig)\n",
    "        ax2 = fig.add_subplot(212)\n",
    "        fig = sm.graphics.tsa.plot_pacf(Quantity_date,ax=ax2)\n",
    "        print(fig)\n",
    "   \n",
    "        quantity = Quantity_date.values\n",
    "        size = int(len(quantity) * 0.65)\n",
    "        train, test = quantity[0:size], quantity[size:len(quantity)]\n",
    "        #fit in model\n",
    "        \n",
    "        model_autoARIMA = auto_arima(train, start_p=0, start_q=0,\n",
    "                              test='adf',       # use adftest to find             optimal 'd'\n",
    "                              max_p=3, max_q=3, # maximum p and q\n",
    "                              m=1,              # frequency of series\n",
    "                              d=None,           # let model determine 'd'\n",
    "                              seasonal=False,   # No Seasonality\n",
    "                              start_P=0, \n",
    "                              D=0, \n",
    "                              trace=True,\n",
    "                              error_action='ignore',  \n",
    "                              suppress_warnings=True, \n",
    "                              stepwise=True)\n",
    "        print(model_autoARIMA.summary())\n",
    "        \n",
    "\n",
    "        model_autoARIMA.plot_diagnostics(figsize=(15,8))\n",
    "        plt.show()\n",
    "        \n",
    "        def arima_model(train, test):\n",
    "            history = [x for x in train]\n",
    "            predictions = [x for x in train]\n",
    "            onlypreds = []\n",
    "            for t in range(len(test)+7):\n",
    "                model = ARIMA(history, order=(0,1 ,0))\n",
    "                model = model.fit(disp=0)\n",
    "                output = model.forecast()\n",
    "                output = pd.DataFrame(output)\n",
    "                yhat = output[0]\n",
    "                predictions.append(yhat[0])\n",
    "                onlypreds.append(yhat[0])\n",
    "                if t < len(test):\n",
    "                    obs = test[t]\n",
    "                    history.append(obs)\n",
    "                else:\n",
    "                    obs = yhat[0]\n",
    "                    history.append(obs)\n",
    "            return predictions, onlypreds\n",
    "\n",
    "        preds, onlypreds = arima_model(train, test)\n",
    "\n",
    "        error_arima = math.sqrt(mean_squared_error(test, onlypreds[0:len(test)]))\n",
    "\n",
    "        result[\"Date\"] = result['Date'].astype(str).str.replace(\"-\",\"/\")\n",
    "        x = np.append(train, onlypreds)\n",
    "\n",
    "        pre = pd.DataFrame(x, columns=[\"ARIMA\"])\n",
    "        pre = pd.concat([pre, result['Adj_close']], axis=1)\n",
    "        pre = pd.concat([pre, result['Date']], axis=1)\n",
    "\n",
    "        idx = pd.date_range(np.array(result.Date)[-1], periods=8, freq='D')\n",
    "        pre.Date[-8:] = idx.map(lambda x: x.date()).astype(str).str.replace(\"-\",\"/\")\n",
    "\n",
    "\n",
    "        #plot graph\n",
    "        print()\n",
    "        #print(\"ARIMA model Accuracy: \")\n",
    "        fig = plt.figure(figsize=(7.2,4.8),dpi=65)\n",
    "        plt.plot(result['Adj_close'], label='History')\n",
    "        plt.plot(pre['Date'], pre[\"ARIMA\"], label='Predicted')\n",
    "        plt.legend(loc=4)\n",
    "        plt.savefig('ARIMA.png')\n",
    "        plt.close(fig)\n",
    "\n",
    "        arima_test=quantity\n",
    "        arima_predi=preds\n",
    "        tomorrow_ar = arima_predi[-7]\n",
    "\n",
    "\n",
    "        #plt.show()\n",
    "        print()\n",
    "        print(\"####arima_predi##########################################################################\")\n",
    "        print(\"Tomorrow's\",quote,\" Closing Price Prediction by ARIMA:\",tomorrow_ar )\n",
    "        print(\"ARIMA RMSE:\",error_arima)\n",
    "        print(\"##############################################################################\")\n",
    "        print()\n",
    "        prices = {\"Date\": date, \"History\": price, \"Forecast\": arima_predi}\n",
    "\n",
    "        return arima_predi, error_arima, tomorrow_ar, result, pre\n",
    "\n",
    "\n",
    "#******************** LSTM SECTION ********************\n",
    "def LSTM_ALGO(d):\n",
    "    n = 100\n",
    "    df1=d.reset_index()['close']\n",
    "    from sklearn.preprocessing import MinMaxScaler\n",
    "    scaler=MinMaxScaler(feature_range=(0,1))\n",
    "    df1=scaler.fit_transform(np.array(df1).reshape(-1,1))\n",
    "\n",
    "    ##splitting dataset into train and test split\n",
    "    training_size=int(len(df1)*0.65)\n",
    "    test_size=len(df1)-training_size\n",
    "    train_data,test_data=df1[0:training_size,:],df1[training_size:len(df1),:1]\n",
    "\n",
    "    import numpy\n",
    "    # convert an array of values into a dataset matrix\n",
    "    def create_dataset(dataset, time_step=1):\n",
    "        dataX, dataY = [], []\n",
    "        for i in range(len(dataset)-time_step-1):\n",
    "            a = dataset[i:(i+time_step), 0]   ###i=0, 0,1,2,3-----99   100 \n",
    "            dataX.append(a)\n",
    "            dataY.append(dataset[i + time_step, 0])\n",
    "        return numpy.array(dataX), numpy.array(dataY)\n",
    "\n",
    "    # reshape into X=t,t+1,t+2,t+3 and Y=t+4\n",
    "    time_step = n\n",
    "    X_train, y_train = create_dataset(train_data, time_step)\n",
    "    X_test, ytest = create_dataset(test_data, time_step)\n",
    "\n",
    "\n",
    "    # reshape input to be [samples, time steps, features] which is required for LSTM\n",
    "    X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
    "    X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)\n",
    "\n",
    "    model=Sequential()\n",
    "    model.add(LSTM(50,return_sequences=True,input_shape=(n,1)))\n",
    "    model.add(Dropout(p=0.1))\n",
    "\n",
    "    #Add 2nd LSTM layer\n",
    "    model.add(LSTM(units=50,return_sequences=True))\n",
    "    model.add(Dropout(p=0.1))\n",
    "\n",
    "    #Add 3rd LSTM layer\n",
    "    model.add(LSTM(units=50,return_sequences=True))\n",
    "    model.add(Dropout(p=0.1))\n",
    "\n",
    "    #Add 4th LSTM layer\n",
    "    model.add(LSTM(units=50))\n",
    "    model.add(Dropout(p=0.1))\n",
    "    model.add(Dense(1))\n",
    "    model.compile(loss='mean_squared_error',optimizer='adam')\n",
    "\n",
    "    model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=10,batch_size=100,verbose=1)\n",
    "\n",
    "    ### Lets Do the prediction and check performance metrics\n",
    "    train_predict=model.predict(X_train)\n",
    "    test_predict=model.predict(X_test)\n",
    "\n",
    "    train_predict=scaler.inverse_transform(train_predict)\n",
    "    test_predict=scaler.inverse_transform(test_predict)\n",
    "\n",
    "    import math\n",
    "    from sklearn.metrics import mean_squared_error\n",
    "\n",
    "    ### Test Data RMSE\n",
    "    error_lstm = math.sqrt(mean_squared_error(ytest,test_predict))\n",
    "\n",
    "    x_input=test_data[len(test_data)-n:].reshape(1,-1)\n",
    "\n",
    "    temp_input=list(x_input)\n",
    "    temp_input=temp_input[0].tolist()\n",
    "\n",
    "\n",
    "    # demonstrate prediction for next 10 days\n",
    "    from numpy import array\n",
    "\n",
    "    lst_output=[]\n",
    "    n_steps=n\n",
    "    i=0\n",
    "    while(i<7):\n",
    "\n",
    "        if(len(temp_input)>n):\n",
    "            #print(temp_input)\n",
    "            x_input=np.array(temp_input[1:])\n",
    "            print(\"{} day input {}\".format(i,x_input))\n",
    "            x_input=x_input.reshape(1,-1)\n",
    "            x_input = x_input.reshape((1, n_steps, 1))\n",
    "            #print(x_input)\n",
    "            yhat = model.predict(x_input, verbose=0)\n",
    "            print(\"{} day output {}\".format(i,yhat))\n",
    "            temp_input.extend(yhat[0].tolist())\n",
    "            temp_input=temp_input[1:]\n",
    "            #print(temp_input)\n",
    "            lst_output.extend(yhat.tolist())\n",
    "            i=i+1\n",
    "        else:\n",
    "            x_input = x_input.reshape((1, n_steps,1))\n",
    "            yhat = model.predict(x_input, verbose=0)\n",
    "            print(yhat[0])\n",
    "            temp_input.extend(yhat[0].tolist())\n",
    "            print(len(temp_input))\n",
    "            lst_output.extend(yhat.tolist())\n",
    "            i=i+1\n",
    "\n",
    "    df3=df1.tolist()\n",
    "    df3.extend(lst_output)\n",
    "    df3=scaler.inverse_transform(df3).tolist()\n",
    "    plt.plot(df3)\n",
    "    tomorrow_lstm=df3[-7]\n",
    "    return df3, error_lstm, tomorrow_lstm\n",
    "\n",
    "\n",
    "\n",
    "#**************** SENTIMENT ANALYSIS **************************\n",
    "def retrieving_tweets_polarity(symbol):\n",
    "    auth = tweepy.OAuthHandler(ct.consumer_key, ct.consumer_secret)\n",
    "    auth.set_access_token(ct.access_token, ct.access_token_secret)\n",
    "    user = tweepy.API(auth)\n",
    "\n",
    "    tweets = tweepy.Cursor(user.search, q=str(symbol), tweet_mode='extended', lang='en',exclude_replies=True).items(ct.num_of_tweets)\n",
    "\n",
    "    tweet_list = [] #List of tweets alongside polarity\n",
    "    global_polarity = 0 #Polarity of all tweets === Sum of polarities of individual tweets\n",
    "    tw_list=[] #List of tweets only => to be displayed on web page\n",
    "    #Count Positive, Negative to plot pie chart\n",
    "    pos=0 #Num of pos tweets\n",
    "    neg=1 #Num of negative tweets\n",
    "    for tweet in tweets:\n",
    "        count=20 #Num of tweets to be displayed on web page\n",
    "        #Convert to Textblob format for assigning polarity\n",
    "        tw2 = tweet.full_text\n",
    "        tw = tweet.full_text\n",
    "        #Clean\n",
    "        tw=p.clean(tw)\n",
    "        #print(\"-------------------------------CLEANED TWEET-----------------------------\")\n",
    "        #print(tw)\n",
    "        #Replace &amp; by &\n",
    "        tw=re.sub('&amp;','&',tw)\n",
    "        #Remove :\n",
    "        tw=re.sub(':','',tw)\n",
    "        #print(\"-------------------------------TWEET AFTER REGEX MATCHING-----------------------------\")\n",
    "        #print(tw)\n",
    "        #Remove Emojis and Hindi Characters\n",
    "        tw=tw.encode('ascii', 'ignore').decode('ascii')\n",
    "\n",
    "        #print(\"-------------------------------TWEET AFTER REMOVING NON ASCII CHARS-----------------------------\")\n",
    "        #print(tw)\n",
    "        blob = TextBlob(tw)\n",
    "        polarity = 0 #Polarity of single individual tweet\n",
    "        for sentence in blob.sentences:\n",
    "\n",
    "            polarity += sentence.sentiment.polarity\n",
    "            if polarity>0:\n",
    "                pos=pos+1\n",
    "            if polarity<0:\n",
    "                neg=neg+1\n",
    "\n",
    "            global_polarity += sentence.sentiment.polarity\n",
    "        if count > 0:\n",
    "            tw_list.append(tw2)\n",
    "\n",
    "        tweet_list.append(Tweet(tw, polarity))\n",
    "        count=count-1\n",
    "    global_polarity = global_polarity / len(tweet_list)\n",
    "    neutral=ct.num_of_tweets-pos-neg\n",
    "    if neutral<0:\n",
    "        neg=neg+neutral\n",
    "        neutral=20\n",
    "    print()\n",
    "    print(\"##############################################################################\")\n",
    "    print(\"Positive Tweets :\",pos,\"Negative Tweets :\",neg,\"Neutral Tweets :\",neutral)\n",
    "    print(\"##############################################################################\")\n",
    "    print()\n",
    "    labels=['Positive','Negative','Neutral']\n",
    "    sizes = [abs(pos),abs(neg),abs(neutral)]\n",
    "    explode = (0, 0, 0)\n",
    "\n",
    "    pie = pd.DataFrame(sizes, columns = [\"sizes\"])\n",
    "    pie['labels'] = labels\n",
    "    big_data = {\"sizes\": np.array(pie.sizes), \"labels\": np.array(pie.labels)}\n",
    "    df2=pd.DataFrame(big_data)\n",
    "    k = df2.to_dict('records')\n",
    "    out_file = open(\"static/assets/js/dashboard/pie.json\", \"w\", encoding='utf-8') \n",
    "    simplejson.dump(k, out_file, ensure_ascii=False, indent=4)\n",
    "\n",
    "\n",
    "    fig = plt.figure(figsize=(7.2,4.8),dpi=65)\n",
    "    fig1, ax1 = plt.subplots(figsize=(7.2,4.8),dpi=65)\n",
    "    ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "    # Equal aspect ratio ensures that pie is drawn as a circle\n",
    "    ax1.axis('equal')  \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('static/SA.png')\n",
    "    plt.close(fig)\n",
    "    #plt.show()\n",
    "    if global_polarity>0:\n",
    "        print()\n",
    "        print(\"##############################################################################\")\n",
    "        print(\"Tweets Polarity: Overall Positive\")\n",
    "        print(\"##############################################################################\")\n",
    "        print()\n",
    "        tw_pol=\"Overall Positive\"\n",
    "    else:\n",
    "        print()\n",
    "        print(\"##############################################################################\")\n",
    "        print(\"Tweets Polarity: Overall Negative\")\n",
    "        print(\"##############################################################################\")\n",
    "        print()\n",
    "        tw_pol=\"Overall Negative\"\n",
    "    return global_polarity,tw_list,tw_pol,pos,neg,neutral\n",
    "\n",
    "\n",
    "\n",
    "def recommending(df, global_polarity,today_stock,mean):\n",
    "    if today_stock.iloc[-1]['adjClose'] < mean:\n",
    "        if global_polarity > 0:\n",
    "            print()\n",
    "\n",
    "            idea=\"RISE\"\n",
    "            decision=\"BUY\"\n",
    "            print()\n",
    "            print(\"##############################################################################\")\n",
    "            print(\"According to the DL Predictions and Sentiment Analysis of Tweets, a\",idea,\"in\",quote,\"stock is expected: \",decision)\n",
    "        elif global_polarity < 0:\n",
    "            print()\n",
    "            idea=\"FALL\"\n",
    "            decision=\"SELL\"\n",
    "            print()\n",
    "            print(\"##############################################################################\")\n",
    "            print(\"According to the DL Predictions and Sentiment Analysis of Tweets, a\",idea,\"in\",quote,\"stock is expected: \",decision)\n",
    "    else:\n",
    "        print()\n",
    "        idea=\"FALL\"\n",
    "        decision=\"SELL\"\n",
    "        print()\n",
    "        print(\"##############################################################################\")\n",
    "        print(\"According to the DL Predictions and Sentiment Analysis of Tweets, a\",idea,\"in\",quote,\"stock is expected: \",decision)\n",
    "    return idea, decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############################################################################\n",
      "Today's AAPL Stock Data: \n",
      "     symbol                       date   close    high     low    open  \\\n",
      "1257   AAPL  2020-12-31 00:00:00+00:00  132.69  134.74  131.72  134.08   \n",
      "\n",
      "        volume  adjClose  adjHigh  adjLow  adjOpen  adjVolume  divCash  \\\n",
      "1257  99116586    132.69   134.74  131.72   134.08   99116586      0.0   \n",
      "\n",
      "      splitFactor  \n",
      "1257          1.0  \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic : 1.1247874109732012\n",
      "p-value : 0.9954227186632431\n",
      "#Lags Used : 9\n",
      "Number of Observations Used : 748\n",
      "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n",
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(864x576)\n",
      "Figure(864x576)\n",
      "Performing stepwise search to minimize aic\n",
      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=1191.168, Time=0.17 sec\n",
      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=1193.164, Time=0.08 sec\n",
      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=1193.163, Time=0.06 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=1191.252, Time=0.01 sec\n",
      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=1194.627, Time=0.19 sec\n",
      "\n",
      "Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n",
      "Total fit time: 0.526 seconds\n",
      "                           Statespace Model Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                  492\n",
      "Model:               SARIMAX(0, 1, 0)   Log Likelihood                -593.584\n",
      "Date:                Sun, 03 Jan 2021   AIC                           1191.168\n",
      "Time:                        04:39:47   BIC                           1199.561\n",
      "Sample:                             0   HQIC                          1194.464\n",
      "                                - 492                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "intercept      0.0529      0.038      1.402      0.161      -0.021       0.127\n",
      "sigma2         0.6570      0.028     23.211      0.000       0.602       0.713\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                       52.36   Jarque-Bera (JB):               167.34\n",
      "Prob(Q):                              0.09   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               1.71   Skew:                            -0.52\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         5.66\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "####arima_predi##########################################################################\n",
      "Tomorrow's AAPL  Closing Price Prediction by ARIMA: 132.8109164897872\n",
      "ARIMA RMSE: 2.532068849239711\n",
      "##############################################################################\n",
      "\n",
      "\n",
      "\n",
      "##############################################################################\n",
      "Positive Tweets : 39 Negative Tweets : 16 Neutral Tweets : 245\n",
      "##############################################################################\n",
      "\n",
      "\n",
      "##############################################################################\n",
      "Tweets Polarity: Overall Positive\n",
      "##############################################################################\n",
      "\n",
      "ARIMA Model Forecasted Prices for Next 7 days:\n",
      "[[132.81]\n",
      " [132.93]\n",
      " [133.05]\n",
      " [133.17]\n",
      " [133.29]\n",
      " [133.42]\n",
      " [133.54]]\n",
      "LSTM Forecasted Prices for Next 7 days:\n",
      "\n",
      "\n",
      "\n",
      "##############################################################################\n",
      "According to the DL Predictions and Sentiment Analysis of Tweets, a RISE in AAPL stock is expected:  BUY\n"
     ]
    },
    {
     "data": {
      "image/png": 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RvHNVLBqfaDuEpIcuOclRtZPrewBzSeEkBh3meRtvnPu/74xf9kZNpjd9y9sLmf3pOhqbPQZ3LuHonhU8uWgFIfzjmz8Yvi99S4t54qPlVBcXMb6qCws3bOaWtz6k0fP4XFVXrh+0z2fru+3thXxtaF/KCsL88aMV/HrhMvYpKSI2ciCpmN9fl5xkvX8DIyKuo5mycoxKM0fVTq5/FLjQdo5deF7j5Quenn3CohljbUcJMpVmTrgu4jr32w4hqaXdszmodnL9eIJYmADGFD086JRRj/c/ZprtKCJp9u1YNJ6Ry7wkc1SaOaZ2cr0Bgnid6HbGhB8f8PkjHh508lTbUUTSqBv+3Y8kh6g0c89ZpGFu2ZQzxjzXd9yEu4eeq+KUXHZlLBo/eO8vk2yh0swhtZPri/Dv/5k1plUfOuHbB1+m4pRcFca/haDkCJVmbrkG/zqxrPJG98ETbhl57cvNGF2vIbloYiwaP9N2CEkNlWaOqJ1c3xW403aO9lrQZd8jb6i9+ZUmE9pqO4tIGtwVi8Z3natRso5KM3fcToYmZE+XRWU9D7969K1zGkMFmlZHck1/4Cu2Q0jHqTRzQGJ+2Zy4Ee7ykm6jrhxzx9yN4aJ1trOIpNhNsWjc2pSWkhoqzdzwHSBn7uW3pqj80MvH3rlwbUHpKttZRFKoAv+8A8liKs0sVzu5/mCCOpFBB2wsKBl2xdhJy1YWdf7EdhaRFHJ1bDO7qTSz39fJ0Z9jY7jowKvG3L5+aUn3RbaziKRIT+By2yGk/XLywzZf1E6uHwScZjtHOm0NFQz48uivmv92qv7AdhaRFPlqLBovtB1C2kelmd1uJg9+hs0m3Nutdcvf69xvvu0sIinQjxw8pJIvcv4DN1fVTq7vCVxsO0emeCZUdevIa3u+WTHo37aziKTAbbFoXJ+/WUg/tOx1LTl0xmxSjKn41iFXDHil8qDXbUcR6aADgC/aDiFtp9LMQrFovOSScPUZ40NdpxRjVtnOk1HGlP942IXOP3rVzLIdRaSD7rAdQNpOpZmdzgsb4xwYKp14Qbhn4Ynh7lO7El5oO1TGGFP8s8FfHPl03/EzbEcR6YCDY9H4sbZDSNuoNLPTddseGGPKepmiCWeGK/udHa58pa8pettmsIwxpuDX+9eN+e1+x71sO4pIB1xmO4C0jfE8z3YGaYNYNH4k8NKeXtPoNb8zu3ndmri3YZTn35oopx29ZNaUL8/740TbOVKpsbB81YsTHqiwnUPSbjPQO+I6K20HkeRopJl9rtvbC4pMaNjYcJexl4SrlxwR6jK1ELMmE8Fs+cc+oyb+cNhFuienZKNi4ALbISR5Ks0sEovGuwGnJPv6kDF9h4Y6Tbgo3JPjQ92mdiacszPrzKo6aMLXRkSmeqBdJ5JtLrUdQJKn3bNZJBaNXwE81N7lPc/bupqmWdOaVld8zBYnhdGSMvuO4ynbdygAPUYeQ9WoEz773so36lk242kwhnBxJwaeN4lwSRkLn7yH9Yvm03PsyVTWHEfzls18+IfJ7HfeJIwxu2xjwLrF0yb/696xYbys3i2t3bN5pybiOrNth5C9K7AdQNrkvI4sbIwpqKDg8BMLerDZa37rtea1G9/1NtaSoT0ORV0rGXLV3bv9XsVBR9J9xFEALHrhV6yY83e6jziKxtXLGXLNfcx78EYqa47j4ymP0+tz5+y2MAE+LO897rrRX51576yfHFboNRWl7c2IpNalgEozC2j3bJaIReN9gPGpWl+xCR08Ltx19CXh6obRoc4vFWDWp2rdrdmydiXvPuiy4NFvsHnlxzt8L1SwfSrO5sZNlFQPwBQU0rxlM17TFkIFRWxeuYSmzRvo1HvQHrfzcWnl2KvG3P7W5lDhxrS8EZHUOy8WjefXZCVZSqWZPc4mDT+vsDH9h4fKxn8p3HPLsaGKKWWEPt77Uu0z/PbfM+TqKFWjT+TDP961y/c/mfUc79x9OWs/fJvS6gGEi0rpfshEFj7xE/ocdxlL6n9P1eg6/vuXB/jouYdo3rK51W19Wtyl5oqxk+avDxfn9ElQkjMqgNNth5C9U2lmjw7tmt0bY0zFvqGSieeEq3qcHu4xvYrCeaneRmFZVwC6Dq6l8dOlu3y/atQJDLvpYboNH8/HUx/3nxtdx8Dz76Rp8wbK+zssn/03uo/4HJ16D2LFnBf3uL11hZ0OuWLsnYtWF5atSPV7EUkDnRCUBVSaWSAWjR8IHJaJbRljCrubwiNOKegx+Pxw1euDTMlrpOCM1KbNG/GamwDYsOQ/FCQKdJvmLY2fPS4oKSdUuH1PVXPTVla+UU+PmuNo3rwRb+sWmpu20rR573tfNxUUD71yzB2rlhd3TdsIWiRFjopF471th5A904lA2SGto8zWlJrwoRPDFYzzvP/821u/+PXmdbVN7ZwkfuPShSx8Mkq4uBQw9D/dZcPiBayZP5teE8/m46mPs2aBPw97QafODPjiVz9bdtn0p+h5xGkYY6gcdTwf/vEnmFAB+1/wtaS2vSVcuP/Vo29ruPe1nzT03ri8X3vyi2SAAU4CYraDSOt0yUkWiEXj84ADbefwPG/5h97md2Y0r3E20lxlO09bhbzmj388+771A9ct3t92lr3RJSd56/mI6xxvO4S0TrtnAy4WjR9GAAoTwBhTuV+oZMJ54aoup4Z7vNyDgv/YztQWzSbU66uH3VAxt8uAubaziLTiqFg03tl2CGmdSjP4TrIdYGfGmOJKU3jkqeEeA88NV/1rP1Myx3amZHnG9Jh06NV9Zncf8qbtLCK7UQQcZzuEtE6lGXxH2w7QGmOMKTPhmqPDFSMvDlfPH2HKpoegce9LWmZMl+8Nv+TAl3uO+JftKCK7EbhflGU7HdMMsFg0Xg6sBAr39tqgaPa8pe97m96d2bzm4M143Wzn2SPPa7zivT/POX7xzDG2o+xMxzTz2jKgV8R19OEcQBppBtt4sqgwAULGVA8KlU64INyz+KRw95cqCH9oO1OrjCn6xQGn1v6h/9HTbEcRaaEncKjtELJ7Ks1gC+yu2b0xxnSqNkXjzwhX7ntOuGrWvqY4mMcQjQk/NuDYI3456GTdWkyCRMc1A0qlGWzH2A7QUcaYULkJjzo23O2QL4V7zh1uOs0wsNV2rh0YY57tO25CdOi5Kk4JCpVmQOmYZkDFovEqYCn+Bc85pdnzFs/3Nr43q3ntiEa8rntfInNGrJw35etv/XKi7Rw6ppn3tgIVEddJ+40UpG000gyuo8nBwgQIGdN7SKjThAvDPUMnhLq91IXwR7YzbfNG98ETbzv0yy81Y5ptZ5G8VgCMtB1CdqXSDK6sPZ6ZLGNM596h4vFfDFfu88Vw5Su9TdG/bWcCmN+1//gba2+a2WRCwdqNLPlmlO0AsiuVZnBl/fHMZBljwl1NwZgTwt0PujDc823HdHrFQJPNTB+VVR9x9ehb52wx4dbvPyaSXirNAFJpBlDihtMDbOewodiEhh8e7jLmknD14rH+zbHX2cqyvKTbqCvHTnpnU6hIx5XEBpVmAKk0g+kQ2wFsCxnTb5h/c+ymL4S6TS0ntMRGjtVF5SMvP3zSB2sLSlfZ2L7ktQGxaLzSdgjZkUozmA62HSAojDFd+4WKJ5wdrqo8I1w5o5rCjE+2vqGg9KArx05a+mlh+SeZ3rbkPY02A0alGUwqzZ0YYwq7mYLDTyroMfSCcM83DzSls0jBzbGTtTlcNDgy9o51y0q6Lc7UNkVQaQaOSjOYhtsOEGQlJnTI+HDXUZeEqz8cFSp/OQwbM7HdraGC/a4ZdYvX0Knnh5nYnggqzcBRaQZMLBovAgbbzpENwsbsd3Co/MiLw9UbjwlVTO1EaFm6t9kcCve5sfamsgWd+76X7m2JALW2A8iOVJrBM5Qsm6TdNmNM9wGhkgnnhqu6nhbuMa2SgrQWmmdCVbeMvK7qrYr930nndkSAylg0PsB2CNlOpRk8Op7ZTsaY4h6mcNypBZUHnBeumj3QlKTvfpnGVHzzkCv7v1o57PW0bUPEN8R2ANlOpRk8Ks0U6GTChx0Vrqi5OFy9YGSofFoIUj9JgTHlPxp20dD6XofNSvm6RbYbYDuAbKfSDB6VZgoVGDNoZKh83CXh6jWfC3WdWkJoRUo3YEzJ/YPPGvl03/EzUrpeke32sx1AtlNpBs9BtgPkImNM1f6h0gnnh6s6nRzu/nI3Cj5I4coLfr1/3Zjf7feFl1O2TpHtVJoBotIMkFg0XgzsYztHLjPGlPY0RUeeHu4x4Jxw1Wv9TfEbKVpx6E/9jz7ygQPP0D05JdUG2A4g26k0g6UvOXo7sKAxxphyE679fLjbiC+Fe757sCmbbmBLR9f7Yu/RE3407CIVp6SSRpoBotIMlr62A+SjQhMaMirc+YhLwtXLx4e6TC3GrOrI+l6tOmjC10ZEpnoZnLFIclplLBovtx1CfCrNYOlnO0A+Cxmzz4GhThMuCPcsPDHc/aWuhBe2d13vVOw/4SuH3TC9CWP1FmeSMzTaDAiVZrBopBkAxpiyXqZo/Jnhyn5nhStf7WuK3mrPej7o3Gfc9aO+OmurCXV4t6/kvQG2A4hPpRksOgkoQIwxoS6mYPRx4e4HXxTu+c4w02lmW2+OvaRT5dirxtz+5uZQYUbmx5WcpZFmQKg0g6XKdgDZvSITGjY23GXsJeHqJYeHukwtxKxNdtmVxV1rrhh7x/z14eI16cwoOU17oQJCpRksPW0HkD0LGdPXCXWacFG4p3d8qNvUzoQXJbPcusKyQ64Ye+dHawrLVqY7o+SkLrYDiE+lGSwqzSxhjOnSJ1Q84axwZfWZ4cqZvSiM722ZTQXFzhVj7li5oqjL0kxklJyis2cDQqUZLNo9m2WMMQUVpmDsiQU9nAvDPd8abEpfBZpbe/2WcOGgq8bcvnlxaWVDBmNK9lNpBoRKM1gqbAeQ9is2oYOPDHcdfUm4umF0qPNLBZj1u3tdUyi87/WjvlLwQdk+/8l0RslaKs2AUGkGS5HtANJxYWP6Dw+Vjf9SuOeWz4cqpnQitMvu2GYT2ucrNTdWzO0yYK6NjJJ1VJoBodIMiFg0btDPI6cYYyr6h0omnhuu6nZ6uMf0Kgrntfy+Z0yPSYde3XtO98Htug5U8opKMyD0IR0chbYDSHoYY4q6m8IjTinoMfj8cNXrg0zJa2ybYs+Yrt8dfumgaVWHzLabUgKus+0A4lNpBodKMw+UmvChE8MVtReHq98/LFT+chg2YUynu53zhj/fe8wrtvNJYGmkGRAqzeAosB1AMqfAmP0PDZUfeXG4et3RoYqppSa8+qEDTqv9475HTbOdTQJJpRkQKs3g0EgzDxljKvcLlUw4L1zV+ZSCypkv7FfX65H9T9KtxWRnRbFoXJ8RAaDRTXDoZ5HHjDElVRSOO7Wg0tsw4PRVU0r3mQGcYDuXBIoGOQGgD+rg0G+RgjHGlBGu2dprnO0oEjy6W04A6DeX4NAvMCLSmuaI67Q605RkjkozOFSaItIajTIDQqUZHJtsBxCRwFJpBoRKMzhW2Q4gIoG12XYA8ak0g2Md0GQ7hIgE0m4n/5fMU2kGRMR1PGCN7RwiEkjrbAcQn0ozWFbZDiAigaSRZkCoNINlle0AIhJIGmkGhEozWFbZDiAigbTWdgDxqTSDZZXtACISSIttBxCfSjNYVtsOICKB1GA7gPhUmsGyynYAEQkklWZAqDSDZbntACISSCrNgFBpBsv7tgOISCCpNANCpRksC2wHEJHA8YCPbIcQn0ozWN6zHUBEAueTiOto7tmAUGkGSMR1VgErbOcQkUDRrtkAUWkGj3bRikhLKs0AUWkGj3bRikhLKs0AUWkGj0aaItKSPhMCRKUZPBppikhLc2wHkO1UmsGj3ypFZJtm4A3bIWQ7lWbwaKQpItssiLiObgsWICrNgIm4zqfAIts5RCQQtGs2YFSawTTLdgARCQSVZsCoNINJpSkioNIMHJVmML1qO4CIBIJKM2BUmsH0L/yz5kQkfy1MnOMgAaLSDKCI66wF3rGdQ0Ss0igzgFSawfWy7QAiYtW/bAeQXak0g0ulKZLfXrQdQHal0gwulaZI/lqBRpqBpNIMqIjrLAI+sJ1DRKz4e8R1dDJgAKk0g+2ftgOIiBUv2A4gu6fSDLa/2A4gIlaoNANKpRlsfwM22A4hIhn1VsR1ltgOIbun0gywiOtsxC9OEckfz9sOIK1TaQbf07YDiEhGaddsgKk0g+8ZoMl2CBHJiPXANNshpHUqzYCLuM4K9D+RSL6oj7hOo+0Q0jqVZnb4s+0AIpIRf7IdQPZMpZkddFxTJPdtAp60HUL2TKWZBSKu8wHwlu0cIpJWzyTucCQBptLMHtptI5Lbfm87gOydSjN7/A+6MbVIrloFPGc7hOydSjNLRFznv8BfbecQkbR4TGfNZgeVZnZ5yHYAEUmLR2wHkOSoNLPLs8BHtkOISEq9HXGd12yHkOSoNLNIxHWa0G+kIrnmV7YDSPJUmtnnYTStnkiuaAR+azuEJE+lmWUirtOA7oIgkit+H3GdT2yHkOSpNLNTzHYAEekwD5hsO4S0jUozOz2HTggSyXbPRlwnbjuEtI1KMwslTgh60HYOEemQH9sOIG2n0sxe9wOf2g4hIu0yM+I6L9sOIW2n0sxSEddZA9xjO4eItIuOZWYplWZ2uxd/zkoRyR7z0O3+spZKM4tFXGc1Gm2KZJufRFxHN1/IUirN7HcvsNp2CBFJysfAo7ZDSPupNLNcxHVW4ReniATf3RHX2Ww7hLSfSjM33INGmyJB9yFwn+0Q0jEqzRwQcZ1P0f+MIkF3u0aZ2a/AdgBJmShwA9DFdhDJXp7n8diT3+O/H8Vpbt7KMRO+xIB9h/OLR29m6bIPuO7KGIMGHrbLcr/6/e2s/HQJAIsWz+Pi877P4EGjeeCRa9m0aT0XnvUt+vYZwkeL5zHnzRc4+fjrM/3WbHsl4jqP2Q4hHaeRZo5IjDa/ZzuHZLfFHy9gyccLuPWG3+Ne/Sue/utP6dqlihsiv+DQQ45tdblLzvsBN3/5f7g+8hClpZ0ZOvgI4vNmcNDQ8Xzx1FuZPuspAP5W/whfOOryTL2dIHFtB5DUUGnmlnuA92yHkOxV0aWKgoJCmpq2sGnzeso6daGoqJSysoqklv93fCpDDhhDYUERxcWlbNy4lsbGTRQXd+K1Oc9yyPCjKC7ulN43ETyPR1znFdshJDVUmjkk4jqN6Dda6YBOnbpSVdmfr/+gju/95AyO//xVbVr+1dn/x6iRdQAMOWAsjVs2MWvO/3F47anE582gR7fePP7UD3hx6q/TET+INgG32g4hqaPSzDER13kW+KvtHJKd5s6fwarVy/jOHX/lm7c+w9PP3sOWrY1JLbth4xoWLZnPgYNGARAKhTjz5K9y8bnf59XZz/CFoy7jmed/xhkn3cyyTxay7JOF6XwrQXFvxHXy4o3mC5VmbroR/47wIm3ieR5lnboQCoUpKSlja9MWvOampJad/cbzHDr8GIwxOzy/9JOFeJ5Hr+qBbNiwGg/YurWRTZs3pOEdBMonwPdth5DUUmnmoIjrzAd+ZDuHZJ+hB46l2Wtm8k8v4Mf3nc/nxp1PU3MT9zx4GXPnzeCJp3/EM8/fD8CMWU8Rnzfjs2Vfnf1/jD7spF3W+eI/f8WxR10KwIQjzuGun17Iho1r6ddnSGbelD1fS9xYQXKI8TzPdgZJg1g0XgK8DQyynUUkD00FPhdxHX3A5hiNNHNUxHU2AdfYziGSh9YDl6gwc5NKM4dFXOfvgC6oFsmsWyKu84HtEJIeKs3cdx2wxHYIkTzxD+BB2yEkfVSaOS7iOsuBLwHaVSSSXmuBy7RbNrepNPNAYjdt1HYOkRz3FV2TmftUmvnjduAN2yFEctQLEdd5yHYIST+VZp5ITLF3LpDzV5SLZNhqIC9noc9HKs08EnGdd4GbbOcQyTHXR1znI9shJDM0uUEeikXjTwGn2s4hkgMejrjOFbZDSOZopJmfLgcW2Q4hkuVmA9faDiGZpdLMQxHXWQFcCCQ3E7eI7OxT4MyI62y2HUQyS6WZpyKu8090702R9mgGLoi4zoe2g0jmqTTzWMR1fgr8zHYOkSzztYjrPGc7hNih0pQbgBdshxDJEo9FXEf3yMxjKs08F3GdJuAs4B3bWUQCbg5wqe0QYpcuOREAYtH4fsCrQJXtLCIBtBSojbhOg+0gYpdGmgJA4lZGpwI6G1BkR6uB41SYAipNaSHiOjOAy2znEAmQDUBdxHXesB1EgkGlKTuIuM7vgG/bziESAI3A6RHXmW47iASHSlN2EXGdbwB3284hYlETcH7EdXRmuexApSm7FXGdm4F7bOcQscADroy4zh9tB5HgUWlKqyKu4wL32c4hkmFfibjOI7ZDSDCpNGWPIq5zA3C/7RwiGfLdiOvo0IS0StdpSlJi0fjPgGts5xBJo3sSe1dEWqWRpiTrWuBB2yFE0uTrKkxJhkaakrRYNG7wizNiO4tIijQB10Rc5yHbQSQ7qDSlTRLFGcWf6F0km20Gzo24zlO2g0j2UGlKu8Si8ZuAuwBjO4tIO6wBTom4zhTbQSS7qDSl3WLR+FnAo0Cx7SwibfAxcLymxpP2UGlKh8Si8SOBPwPdLUcRScZ/gGMjrvO+7SCSnXT2rHRIxHVeBsYA82xnEdmL2cARKkzpCJWmdFjEdd7DL86/284i0opHgHER11lqO4hkN+2elZSJReMF+PPVftlyFJFtNgFf1rR4kioqTUm5WDR+CfBToMx2Fslr7wNnRlznddtBJHeoNCUtYtH4YOAxYITlKJKf/g+4MOI6q2wHkdyiY5qSFhHXmYd/nPNe21kkrzQBk4CTVZiSDhppStrFovE64FdAle0sktM+wZ/h5x+2g0ju0khT0i7iOs8ChwD6MJN0eR44VIUp6aaRpmRMLBoPAbcA3wEKLMeR3LAGuDniOg/bDiL5QaUpGReLxkcBvwQOsp1FstqLwGUR1/mv7SCSP1SaYkXims4bgW8A5XbTSJb5FLhFo0uxQaUpVsWi8b74Z9iebjuLZIXHgRs0s4/YotKUQIhF48cD9wMDbWeRQFqIf7Po52wHkfyms2clECKu81dgGP5JQpstx5HgWIN/3aWjwpQg0EhTAicWjR+IP+r8vO0sYk0j8CDw3YjrLLcdRmQblaYEViwaPxb4Fv7MQpIfPPzpFydFXOcD22FEdqbSlMBLHO/8FlBrO4uk1T/wz4qdYzuISGtUmpI1EtPxfQs4zHYWSak3gVsjrvNCKldqjBkAfACc5nnenxPPLfA8b1Ab13O953n3tXG7D3ued0xbtiPZQScCSdaIuM6zEdepAU4B3rAcRzpuKv7PcmSqC7OFd4HbjDGmA+u4fndPGmPCHVinZClNZSZZJ+I6f4lF488ApwJ3AiPtJpI22IJ/reXdGbrP5SJgPn45/xnAGNMV+AXQAzDAlZ7nLWg5CjXGPAz8Fv/fVh9jzBTgN/h3UakDCoGXEl18ItAZeM7zvG9k4D2JRdo9K1kvFo0fAVyHP0FCoeU4snsrgZ8DP4u4zuJMbHDbblLgYuCPwFjgvcTjNzzPe8wYcwjwNc/zztxdaXqeN2Wn5y8GzgZO8DzPM8aUeZ63PjGSnQ6cg78HT7tnc5RGmpL1Iq4zHZgei8b3ASLAlcA+dlNJwjzgHuDRiOtssBHA87yPjDGz8fdMAAwHJhhjrkr8fetuFtvT7txXvO2jjTOMMZfjn/U7EOiHP7qVHKXSlJwRcZ0lwDdj0fh38XehXQ4cD+jYU2ZtAJ4BHgX+GnGdIOzO+gHwp8Tjd4CZnuc9BWCMKUo8v9oY0wv/vpwj8HfHAjTvtK6mFo+/AwzBn5BjOnsuW8kBKk3JORHX2Qo8DTwdi8b7AJcAFwIHWg2W27YAfwP+F3g64jrrLOfZQWK0+RpwHPA94OfGmOvwS+5Z4C7gx8Df8Ut1WYvFZxpjnsI/FruzJ/HL8l0gUO9Z0kPHNCVvxKJxBzgt8UeXrXScB7yMX5RPRFxnheU8Immn0pS8FIvG++Mf4zoNGId24bbFHPxZex6LuE6D7TAimaTSlLwXi8argJPxC/RooMRuosD5BH+35QvA3yKu87HlPCLWqDRFWohF48X4u26PaPGn0mqozFuNv9t1KlAPvB6Qk3lErFNpiuxF4q4rLUt0iN1EKbUF/7KQt4HX8IvyjYjr7HzGqIig0hRps1g03gP/zitD8c/IPSDxtbfNXEn4CL8c30p8fRt4N+I6jVZTiWQRlaZIisSi8XL8At1WotsKtR/+Lt7iNG6+EVgKLAE+bvF1Mf7lEG9HXOfTNG5fJC+oNEUyJBaNd8Yvz0r8eU87A11afC3Hv4xja+JPU4vHOz+3nh3LcaWOO4qkn0pTREQkSbo1mIiISJJUmiIiIklSaYqIiCRJpSkiIpIklaaIiEiSVJoiIiJJUmmKiIgkSaUpIiKSJJWmiIhIklSaIiIiSVJpioiIJEmlKSIikiSVpoiISJJUmiIiIklSaYqIiCRJpSkiIpIklaaIiEiSVJoiIiJJUmmKiIgk6f8BZvXZcCMxcnMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 468x312 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Tweet(object):\n",
    "\n",
    "    def __init__(self, content, polarity):\n",
    "        self.content = content\n",
    "        self.polarity = polarity\n",
    "\n",
    "\n",
    "        \n",
    "import pandas_datareader as pdr\n",
    "quote = 'AAPL'\n",
    "key=\"60104c377746149d341afb340c95833238fe5e73\"\n",
    "d = pdr.get_data_tiingo(quote, api_key=key)\n",
    "d.dropna().to_csv(quote+'.csv')\n",
    "d=pd.read_csv(quote+'.csv')\n",
    "d = d[500:]\n",
    "today_stock=d.iloc[-1:]\n",
    "print(\"##############################################################################\")\n",
    "print(\"Today's\",quote,\"Stock Data: \")\n",
    "print(today_stock)\n",
    "\n",
    "plt.plot(d['close'])\n",
    "\n",
    "#predictions\n",
    "arima_predi, error_arima, tomorrow_ar, i, pre = ARIMA_ALGO(d)\n",
    "# df3, error_lstm, tomorrow_lstm = LSTM_ALGO(d)\n",
    "\n",
    "# df3 = pd.DataFrame(df3, columns = [\"LSTM\"])\n",
    "df3 = pd.concat([pre.Date, pre.Adj_close], axis=1)\n",
    "all_pred = pd.concat([df3, pre[\"ARIMA\"]], axis=1)\n",
    "\n",
    "print()\n",
    "#print(\"Recent %s related Tweets & News: \" % quote)\n",
    "polarity,tw_list,tw_pol,pos,neg,neutral = retrieving_tweets_polarity(quote)\n",
    "dates = np.array(all_pred[\"Date\"].tail(7)).reshape(-1,1)\n",
    "print(\"ARIMA Model Forecasted Prices for Next 7 days:\")\n",
    "forecast_set_ar = np.round(np.array(all_pred[\"ARIMA\"].tail(7)),2).reshape(-1,1)\n",
    "mean=d[\"adjClose\"].tail(7).mean()\n",
    "print(forecast_set_ar)\n",
    "# print(\"LSTM Forecasted Prices for Next 7 days:\")\n",
    "# forecast_set_ls = np.round(np.array(all_pred[\"LSTM\"].tail(7)), 2).reshape(-1,1)\n",
    "# print(forecast_set_ls)\n",
    "print()\n",
    "#print(\"Generating recommendation based on prediction & polarity...\")\n",
    "idea, decision=recommending(i, polarity,today_stock,mean)\n",
    "today_stock=today_stock.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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