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

style.use('ggplot')
df = quandl.get('WIKI/GOOGL')
# print(df.head())
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']]

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

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

df['label'] = df[forecast_col].shift(-forecast_out)
print (df.tail())

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 = cross_validation.train_test_split(X, y, test_size=0.2)

# clf = LinearRegression(n_jobs=-1)
# clf.fit(X_train, y_train)
# 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)

# print(accuracy)
forecast_set = clf.predict(X_lately)
print(forecast_set, accuracy, forecast_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 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]

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

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

style.use('ggplot')
df = quandl.get('WIKI/GOOGL')
# print(df.head())
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']]

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

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

df['label'] = df[forecast_col].shift(-forecast_out)
print (df.tail())

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 = cross_validation.train_test_split(X, y, test_size=0.2)

# clf = LinearRegression(n_jobs=-1)
# clf.fit(X_train, y_train)
# 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)

# print(accuracy)
forecast_set = clf.predict(X_lately)
print(forecast_set, accuracy, forecast_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 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]

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

# print(df.head()) # print(df.head()) # clf = LinearRegression(n_jobs=-1) # clf.fit(X_train, y_train) # with open ('linearregression.pickle', 'wb') as f: # 	pickle.dump(clf,f) # print(accuracy)