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




style.use('ggplot')


df = quandl.get('WIKI/GOOGL') #Gets stock data for google

df = df[ ['Adj. Open','Adj. High','Adj. Low','Adj. Close','Adj. Volume',]]

df['HL_PCT'] = (df['Adj. High']-df['Adj. Close'])/ df['Adj. Close']*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']]

forcast_col = 'Adj. Close'
df.fillna(-99999, inplace=True)

forcast_out = int(math.ceil(0.01*len(df)))
print(forcast_out)

df['label'] = df[forcast_col].shift(-forcast_out)


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


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


#training and testing sets
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size = 0.2)

clf = LinearRegression(n_jobs=-1)
clf.fit(X_train, y_train)
accuracy = clf.score(X_test,y_test)



#print('Accuracy is',accuracy)

#pridicting based on the X data 
forcast_set = clf.predict(X_lately)
print(forcast_set, accuracy, forcast_out)

df['Forecast'] = np.nan
last_date = df.iloc[-1].name
last_unix = last_date.timestamp()
one_day=86400
next_unix = last_unix + one_day

for i in forcast_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]

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

