import pandas as pd
import quandl,math,datetime
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
from sklearn import preprocessing, model_selection, svm
from sklearn.linear_model import LinearRegression
style.use('ggplot')

# this should be removed if you're not using a quandl account (can still be used anonymously)
with open("quandl.txt") as fp:  
   line = fp.readline()
   quandl.ApiConfig.api_key = line

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. Open'])/df['Adj. Open']
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open'])/df['Adj. Open']

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

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


forecast_out = int(math.ceil(0.1*len(df)))

df['label'] = df[forecast_cl].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)

#define features and labels


#scaling data for easier analysis
#preprocessing will mess up real time analysis, you'd have to preprocess real time



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

#get training and testing sets
X_train, X_test, y_train, y_test = model_selection.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_in

accuracy = clf.score(X_test,y_test)
#find accuracy of model
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

#populate dataframe with new dates and forecast values
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.xlabel('Date')
plt.ylabel('Price')
plt.show()
