Regression_Forecasting_and_Prediction_p5.py

import pandas as pd
import quandl, math, datetime
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. Close'])/df['Adj. Close']*100
df['PCT_change']=(df['Adj. Close']-df['Adj. Open'])/df['Adj. Open']*100

#        price       x          x            x
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)))
#print(forecast_out)

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'])
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)
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.xlabel('Date')
plt.ylabel('Price')
plt.show()