linear_regr.py

import quandl
import numpy as np
import math, datetime
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import preprocessing, 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. Close'])/df['Adj. Close']*100
df['PCT_change']=(df['Adj. Close']-df['Adj. Open'])/df['Adj. Open']*100

df=df[['Adj. Close','HL_PCT','PCT_change','Adj. Volume']]
forecast_col='Adj. Close'
df.fillna(-99999,inplace=True)
forecast_out=int(math.ceil(len(df)*0.01))

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 = train_test_split(x,y,test_size=0.2)

clf= LinearRegression()
clf.fit(x_train,y_train)

accuracy= clf.score(x_test,y_test)
forecast_set=clf.predict(x_lately)


print(accuracy*100,'%')
print(forecast_set)
print(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 = 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()