#Predicting future closing stock prices on last 1% dates of available data 

import pandas as pd
import quandl, math, datetime
import numpy as np
from sklearn import preprocessing, svm
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from matplotlib import style
import pickle

style.use('ggplot')

df = quandl.get('WIKI/GOOGL')
df = df[['Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume']]
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low'])/df['Adj. Low'] * 100.0
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open'])/df['Adj. Open'] * 100.0

df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]

#print(df.head())
forecast_col = 'Adj. Close'
df.fillna(-99999, inplace=True)

forecast_out =  int(math.ceil(0.1*len(df)))
df['label'] = df[forecast_col].shift(-forecast_out)

X = np.array(df.drop(['label'],1))
X = preprocessing.scale(X)
X_lately = X[-forecast_out:]
X = X[:-forecast_out]

df.dropna(inplace=True)
y = np.array(df['label'])

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)

clf=LinearRegression(n_jobs=-1)	#use n_jobs to specify the number of thread you want to run your program parallely on, -1 means use as many threads as possible.
clf.fit(X_train,y_train)	#train
###########################################################
'''pickling: kinda saving the trained model for future use without training again and again'''
with open('linearregression.pickle','wb') as f:
	pickle.dump(clf,f)

pickle_in = open('linearregression.pickle','rb')
clf = pickle.load(pickle_in)
###########################################################
accuracy = clf.score(X_test,y_test)	#test
forecast_set = clf.predict(X_lately)

#print(forecast_set, accuracy, forecast_out)
df['Forecast'] = np.nan	#adding one more column for forecast..add forecast later only for forecast_set

last_date = df.iloc[-1].name	# date of last row till we had the non-predicted Adj. close price 
last_unix = last_date.timestamp()
one_day = 86400	#seconds in one day
next_unix = last_unix + one_day

for i in forecast_set:
	next_date = datetime.datetime.fromtimestamp(next_unix)
	next_unix+=one_day
	df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)] + [i]

print (df.tail())
df['Adj. Close'].plot()
df['Forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()



