regression.py

import math, datetime
import pandas as pd
import quandl
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[['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)
# Treats nans as outliers

forecast_out = int(math.ceil(0.1*len(df)))
# rounds up to an integer

df['label'] = df[forecast_col].shift(-forecast_out)
# shifts columns negatively

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)
# test 20% of our test data

#clf = LinearRegression(n_jobs=-1)
#clf.fit(X_train, y_train)
# train classifier to avoid training multiple times {use pickle}
#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()