python/google.py at master · punkers01/python · GitHub

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import csv
import urllib
import numpy
import matplotlib.pyplot as plt

import datetime


def get_data(url):
    response = urllib.urlopen(url).readlines()
    cr = csv.DictReader(response)
    stock_train = []
    # with open('/Users/Fabio/Documents/aapl.csv') as csvfile:
    # cr = csv.DictReader(csvfile)

    for row in cr:
        stock_train.append(
            (float(row["Open"]), float(row["High"]), float(row["Low"]), float(row["Close"]), float(row["Volume"])))

    labels_train = [None] * len(stock_train)
    labels_train[0] = -1
    for idx, _ in enumerate(stock_train):
        if idx > 0:
            if stock_train[idx][3] >= stock_train[idx - 1][3]:
                labels_train[idx] = 1
            else:
                labels_train[idx] = -1

    return stock_train, labels_train


def main():
    start = datetime.date(int("2010"), int("01"), int("01"))
    end = datetime.date(int("2014"), int("12"), int("31"))
    url_string = "http://www.google.com/finance/historical?q={0}".format("MMM")
    url_string += "&startdate={0}&enddate={1}&output=csv".format(
        start.strftime('%b %d, %Y'), end.strftime('%b %d, %Y'))

    stock_train, labels_train = get_data(url_string)

    train_data = numpy.array(stock_train)
    train_label = numpy.array(labels_train)

    start = datetime.date(int("2015"), int("01"), int("01"))
    end = datetime.date(int("2015"), int("12"), int("31"))
    url_string = "http://www.google.com/finance/historical?q={0}".format("MMM")
    url_string += "&startdate={0}&enddate={1}&output=csv".format(
        start.strftime('%b %d, %Y'), end.strftime('%b %d, %Y'))

    stock_test, labels_test = get_data(url_string)
    from sklearn.ensemble import RandomForestClassifier

    clf = RandomForestClassifier()  # default 10
    clf.fit(train_data, train_label)
    test_data = numpy.array(stock_test)
    test_label = numpy.array(labels_test)

    accuracy = []
    count = 0
    while count < len(test_data):
        accuracy.append(clf.score(test_data[count:count + 10], test_label[count:count + 10]))
        count += 10

    plt.plot(numpy.arange(0, len(test_data), 10), accuracy)
    plt.ylabel('accuracy')
    plt.xlabel('days')
    plt.axis([0, len(test_data) - 1, 0, 1])
    plt.show()

    # 'http://www.google.com/finance/historical?q=AAPL&startdate=Jan 01, 2011&enddate=Dec 31, 2013&output=csv'


main()