In [1]:
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')
%matplotlib notebook
##Github Issues Resolved
import warnings
warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd")
warnings.filterwarnings(action="ignore", module="scipy", message="^This module")
df = quandl.get('WIKI/GOOGL')
#print(df.tail())
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' #Label variable
df.fillna(-99999,inplace=True)
forecast_out = int(math.ceil(0.01*len(df)))
print(df.tail())
df['label'] = df[forecast_col].shift(-forecast_out)
df.dropna(inplace=True)
print(df.tail())
##Make X,Y and Split Data
X = np.array(df.drop(['label'],1))
y = np.array(df['label'])
X = preprocessing.scale(X) #Is not used for high-performance applications
X_lately = X[-forecast_out:]
#X = X[:-forecast_out:] DON'T DO THIS
# X = X[:-forecast_out]
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X,y,test_size=0.2)
##Initialize a classifier and fit(train) data
clf = LinearRegression(n_jobs = -1)
#clf = svm.SVR(kernel="poly") Default is linear(citation needed)
clf.fit(X_train,y_train)
##Printing, Testing
accuracy = clf.score(X_test,y_test)
#print(accuracy)
forecast_set = clf.predict(X_lately)
#print(forecast_set)
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()
#print(df.tail())
Out[1]:
In [ ]: