predict_stock.py

import os
import sys
import requests
import numpy as np
from keras.models import Sequential
from keras.layers import Dense

FILE_NAME = 'historical.csv'

def get_historical(quote):
    # Download our file from google finance
    url = 'http://www.google.com/finance/historical?q=NASDAQ%3A'+quote+'&output=csv'
    r = requests.get(url, stream=True)

    if r.status_code != 400:
        with open(FILE_NAME, 'wb') as f:
            for chunk in r:
                f.write(chunk)

        return True

def stock_prediction():

    # Collect data points from csv
    dataset = []

    with open(FILE_NAME) as f:
        for n, line in enumerate(f):
            if n != 0:
                str = line.split(',')[1]
                if str != "-":
                    dataset.append(float(line.split(',')[1]))

    dataset = np.array(dataset)

    # Create dataset matrix (X=t and Y=t+1)
    def create_dataset(dataset):
        dataX = [dataset[n+1] for n in range(len(dataset)-2)]
        return np.array(dataX), dataset[2:]
        
    trainX, trainY = create_dataset(dataset)

    # Create and fit Multilinear Perceptron model
    model = Sequential()
    model.add(Dense(8, input_dim=1, activation='relu'))
    model.add(Dense(1))
    model.compile(loss='mean_squared_error', optimizer='adam')
    model.fit(trainX, trainY, nb_epoch=200, batch_size=2, verbose=2)

    # Our prediction for tomorrow
    prediction = model.predict(np.array([dataset[0]]))
    result = 'The price will move from %s to %s' % (dataset[0], prediction[0][0])

    return result

# Ask user for a stock quote
stock_quote = input('Enter a stock quote from NASDAQ (e.j: AAPL, FB, GOOGL): ').upper()

# Check if we got the historical data
if not get_historical(stock_quote):
    print ('Google returned a 404, please re-run the script and')
    print ('enter a valid stock quote from NASDAQ')
    sys.exit()

# We have our file so we create the neural net and get the prediction
print (stock_prediction())

# We are done so we delete the csv file
os.remove(FILE_NAME)