stock.py

import numpy as np
from datetime import datetime
import smtplib
import time
import os
from selenium import webdriver
#For Prediction
from sklearn.linear_model import LinearRegression
from sklearn import preprocessing,svm
from sklearn.model_selection import train_test_split
#For Stock Data
from iexfinance.stocks import Stock
from iexfinance.stocks import get_historical_data
import pandas_datareader
import pandas as pd
from pandas_datareader import data


def getStocks(n):
    #Navigating to the Yahoo stock screener    
    # chrome_driver_path = './chromedriver'
    # driver = webdriver.Chrome(chrome_driver_path)
    # url = 'https://finance.yahoo.com/screener/predefined/aggressive_small_caps?offset=0&count=202'
    # driver.get(url)

    # stock_list = []
    # n += 1
    # for i in range(1, n):
    #     ticker = driver.find_element_by_xpath('//*[@id="scr-res-table"]/div[1]/table/tbody/tr[  {}  ]/td[1]/a'.format(i))
    #     stock_list.append(ticker.text)
    # driver.quit()
    #Using the stock list to predict the future price of the stock a specificed amount of days
    # for i in stock_list:        
    #     predictData(i, 2)
     
    predictData('AAPL',2)
  


def predictData(stock,days):
    start = datetime(2016, 1, 1)
    end = datetime.now()
    #Outputting the Historical data into a .csv for later use
    #df = get_historical_data(stock, start,output_format='pandas')
    df = data.get_data_yahoo(stock, start, end)
    print(stock)
    print("before",df.head(1))    
    # csv_name = ('Exports/' + stock + '_Export.csv')    
    # df.to_csv(csv_name)

    df['prediction'] = df['Close'].shift(-1)
    print("after",df.head(1))
    df.dropna(inplace=True)
    forecast_time = int(days)
    X = np.array(df.drop(['prediction'], 1))
    Y = np.array(df['prediction'])
    X = preprocessing.scale(X)
    X_prediction = X[-forecast_time:]
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.5)
    print(X_train)
    #Performing the Regression on the training data
    clf = LinearRegression()
    clf.fit(X_train, Y_train)
    prediction = (clf.predict(X_prediction))
    print("Prediction",prediction)
    print("hejfhiodhviodjivd")
    print(X_prediction)


getStocks(5)