ML.py

import pandas as pd
from Quandl import Quandl
import math, datetime, pickle
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')

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']]

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

forecast_out = int(math.ceil(0.01*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 = cross_validation.train_test_split(X, y, test_size=0.2)

# clf = LinearRegression()
# 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)

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()