quandl_sample.py

from pyalgotrade import strategy
from pyalgotrade import plotter
from pyalgotrade.tools import quandl
from pyalgotrade.feed import csvfeed
import datetime


class MyStrategy(strategy.BacktestingStrategy):
    def __init__(self, feed, quandlFeed, instrument):
        super(MyStrategy, self).__init__(feed)
        self.setUseAdjustedValues(True)
        self.__instrument = instrument

        # It is VERY important to add the the extra feed to the event dispatch loop before
        # running the strategy.
        self.getDispatcher().addSubject(quandlFeed)

        # Subscribe to events from the Quandl feed.
        quandlFeed.getNewValuesEvent().subscribe(self.onQuandlData)

    def onQuandlData(self, dateTime, values):
        self.info(values)

    def onBars(self, bars):
        self.info(bars[self.__instrument].getAdjClose())


def main(plot):
    instruments = ["GORO"]

    # Download GORO bars using WIKI source code.
    feed = quandl.build_feed("WIKI", instruments, 2006, 2012, ".")

    # Load Quandl CSV downloaded from http://www.quandl.com/OFDP-Open-Financial-Data-Project/GOLD_2-LBMA-Gold-Price-London-Fixings-P-M
    quandlFeed = csvfeed.Feed("Date", "%Y-%m-%d")
    quandlFeed.setDateRange(datetime.datetime(2006, 1, 1), datetime.datetime(2012, 12, 31))
    quandlFeed.addValuesFromCSV("quandl_gold_2.csv")

    myStrategy = MyStrategy(feed, quandlFeed, instruments[0])

    if plot:
        plt = plotter.StrategyPlotter(myStrategy, True, False, False)
        plt.getOrCreateSubplot("quandl").addDataSeries("USD", quandlFeed["USD"])
        plt.getOrCreateSubplot("quandl").addDataSeries("EUR", quandlFeed["EUR"])
        plt.getOrCreateSubplot("quandl").addDataSeries("GBP", quandlFeed["GBP"])

    myStrategy.run()

    if plot:
        plt.plot()


if __name__ == "__main__":
    main(True)