intro.py

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.filter(regex='^Adj\.')
df['HL_PCT'] = ((df['Adj. High'] - df['Adj. Low'])/df['Adj. Low']) * 100
df['PCT_CHANGE'] = ((df['Adj. Open'] - df['Adj. Close'])/df['Adj. Close']) * 100
df = df[['Adj. Close',  'HL_PCT',   'PCT_CHANGE',  'Adj. Volume', ]]
forecast_col = 'Adj. Close'
df.fillna(-9999, inplace = True)
forecast_out = int(math.ceil(0.01*len(df)))
df['label'] = df[forecast_col].shift(-forecast_out)
df.dropna(inplace=True)
x = np.array(df.drop(['label'], 1))
X = preprocessing.scale(x)
X = X[:-forecast_out+1]
x_lately = X[-forecast_out:]
df.dropna(inplace=True)

y = np.array(df['label'])
Y = y[:-forecast_out+1]

X_train, X_test, y_train, y_test = 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)
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]

print(df.tail())

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