def main(plot):
instruments = ["gld", "gdx"]
windowSize = 50
# Load the bars. These files were manually downloaded from Yahoo Finance.
feed = yahoofeed.Feed()
for year in range(2006, 2012 + 1):
for instrument in instruments:
fileName = "%s-%d-yahoofinance.csv" % (instrument, year)
print("Loading bars from %s" % fileName)
feed.addBarsFromCSV(instrument, fileName)
strat = StatArb(feed, instruments[0], instruments[1], windowSize)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, False, False, True)
plt.getOrCreateSubplot("hedge").addDataSeries("Hedge Ratio",
strat.getHedgeRatioDS())
plt.getOrCreateSubplot("spread").addDataSeries("Spread",
strat.getSpreadDS())
strat.run()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
def __testIGE_BrokerImpl(self, quantity):
initialCash = 42.09 * quantity
# This testcase is based on an example from Ernie Chan's book:
# 'Quantitative Trading: How to Build Your Own Algorithmic Trading Business'
barFeed = yahoofeed.Feed()
barFeed.addBarsFromCSV(
"ige", common.get_data_file_path("sharpe-ratio-test-ige.csv"))
strat = strategy_test.BaseStrategy(barFeed, initialCash)
strat.setUseAdjustedValues(True)
strat.setBrokerOrdersGTC(True)
stratAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(stratAnalyzer)
# Disable volume checks to match book results.
strat.getBroker().getFillStrategy().setVolumeLimit(None)
# Manually place the order to get it filled on the first bar.
order = strat.getBroker().createMarketOrder(broker.Order.Action.BUY,
"ige", quantity,
True) # Adj. Close: 42.09
order.setGoodTillCanceled(True)
strat.getBroker().submitOrder(order)
strat.addOrder(datetime.datetime(2007, 11, 13),
strat.getBroker().createMarketOrder,
broker.Order.Action.SELL, "ige", quantity,
True) # Adj. Close: 127.64
strat.run()
self.assertEqual(round(strat.getBroker().getCash(), 2),
initialCash + (127.64 - 42.09) * quantity)
self.assertEqual(strat.orderUpdatedCalls, 6)
# The results are slightly different only because I'm taking into account the first bar as well.
self.assertEqual(round(stratAnalyzer.getSharpeRatio(0.04, True), 4),
0.7889)
self.assertEqual(round(stratAnalyzer.getSharpeRatio(0.04, False), 4),
0.0497)
def testIntraDay(self):
barFeed = ninjatraderfeed.Feed(ninjatraderfeed.Frequency.MINUTE,
marketsession.USEquities.getTimezone())
barFeed.setBarFilter(csvfeed.USEquitiesRTH())
barFeed.addBarsFromCSV(
"spy", common.get_data_file_path("nt-spy-minute-2011.csv"))
strat = strategy_test.BaseStrategy(barFeed, 1000)
stratAnalyzer = sharpe.SharpeRatio(False)
strat.attachAnalyzer(stratAnalyzer)
strat.marketOrder("spy", 1)
strat.run()
tradingPeriods = 252 * 6.5 * 60
manualAnnualized = sharpe.sharpe_ratio(stratAnalyzer.getReturns(),
0.04, tradingPeriods, True)
manualNotAnnualized = sharpe.sharpe_ratio(stratAnalyzer.getReturns(),
0.04, tradingPeriods, False)
analyzerAnnualized = stratAnalyzer.getSharpeRatio(0.04)
analyzerNotAnnualized = stratAnalyzer.getSharpeRatio(0.04, False)
self.assertEqual(round(analyzerAnnualized, 10), -1.1814830854)
self.assertEqual(round(analyzerNotAnnualized, 10), -0.0037659686)
# They should be similar, but not identical because the analyzer uses 365 days/year
# when useDailyReturns is set to False.
self.assertEqual(round(analyzerAnnualized, 1),
round(manualAnnualized, 1))
self.assertEqual(round(analyzerNotAnnualized, 3),
round(manualNotAnnualized, 3))
def main(plot):
instrument = "yhoo"
bBandsPeriod = 40
# Download the bars.
feed = quandl.build_feed("WIKI", [instrument], 2011, 2012, ".")
strat = BBands(feed, instrument, bBandsPeriod)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
plt.getInstrumentSubplot(instrument).addDataSeries(
"upper",
strat.getBollingerBands().getUpperBand())
plt.getInstrumentSubplot(instrument).addDataSeries(
"middle",
strat.getBollingerBands().getMiddleBand())
plt.getInstrumentSubplot(instrument).addDataSeries(
"lower",
strat.getBollingerBands().getLowerBand())
strat.run()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
def testNoTrades(self):
barFeed = yahoofeed.Feed()
barFeed.addBarsFromCSV(
"ige", common.get_data_file_path("sharpe-ratio-test-ige.csv"))
strat = strategy_test.BaseStrategy(barFeed, 1000)
stratAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(stratAnalyzer)
strat.run()
self.assertTrue(strat.getBroker().getCash() == 1000)
self.assertTrue(stratAnalyzer.getSharpeRatio(0.04, True) == 0)
self.assertTrue(stratAnalyzer.getSharpeRatio(0) == 0)
self.assertTrue(stratAnalyzer.getSharpeRatio(0, True) == 0)
def main(plot):
initialCash = 10000
instrumentsByClass = {
"US Stocks": ["VTI"],
"Foreign Stocks": ["VEU"],
"US 10 Year Government Bonds": ["IEF"],
"Real Estate": ["VNQ"],
"Commodities": ["DBC"],
}
# Load the bars. These files were manually downloaded from Yahoo Finance.
feed = yahoofeed.Feed()
instruments = ["SPY"]
for assetClass in instrumentsByClass:
instruments.extend(instrumentsByClass[assetClass])
for year in range(2007, 2013+1):
for instrument in instruments:
fileName = "%s-%d-yahoofinance.csv" % (instrument, year)
print("Loading bars from %s" % fileName)
feed.addBarsFromCSV(instrument, fileName)
# Build the strategy and attach some metrics.
strat = MarketTiming(feed, instrumentsByClass, initialCash)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
returnsAnalyzer = returns.Returns()
strat.attachAnalyzer(returnsAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, False, False, True)
plt.getOrCreateSubplot("cash").addCallback("Cash", lambda x: strat.getBroker().getCash())
# Plot strategy vs. SPY cumulative returns.
plt.getOrCreateSubplot("returns").addDataSeries("SPY", cumret.CumulativeReturn(feed["SPY"].getPriceDataSeries()))
plt.getOrCreateSubplot("returns").addDataSeries("Strategy", returnsAnalyzer.getCumulativeReturns())
strat.run()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
print("Returns: %.2f %%" % (returnsAnalyzer.getCumulativeReturns()[-1] * 100))
if plot:
plt.plot()
def main(plot):
instrument = "AAPL"
smaPeriod = 163
# Download the bars.
feed = quandl.build_feed("WIKI", [instrument], 2011, 2012, ".")
strat = sma_crossover.SMACrossOver(feed, instrument, smaPeriod)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, False, True)
plt.getInstrumentSubplot(instrument).addDataSeries(
"sma", strat.getSMA())
strat.run()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
def main(plot):
instrument = "AAPL"
vwapWindowSize = 5
threshold = 0.01
# Download the bars.
feed = quandl.build_feed("WIKI", [instrument], 2011, 2012, ".")
strat = VWAPMomentum(feed, instrument, vwapWindowSize, threshold)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, False, True)
plt.getInstrumentSubplot(instrument).addDataSeries("vwap", strat.getVWAP())
strat.run()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
def main(plot):
instrument = "DIA"
entrySMA = 200
exitSMA = 5
rsiPeriod = 2
overBoughtThreshold = 90
overSoldThreshold = 10
# Load the bars. These files were manually downloaded from Yahoo Finance.
feed = yahoofeed.Feed()
for year in range(2009, 2013):
fileName = "%s-%d-yahoofinance.csv" % (instrument, year)
print("Loading bars from %s" % fileName)
feed.addBarsFromCSV(instrument, fileName)
strat = rsi2.RSI2(feed, instrument, entrySMA, exitSMA, rsiPeriod,
overBoughtThreshold, overSoldThreshold)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, False, True)
plt.getInstrumentSubplot(instrument).addDataSeries(
"Entry SMA", strat.getEntrySMA())
plt.getInstrumentSubplot(instrument).addDataSeries(
"Exit SMA", strat.getExitSMA())
plt.getOrCreateSubplot("rsi").addDataSeries("RSI", strat.getRSI())
plt.getOrCreateSubplot("rsi").addLine("Overbought",
overBoughtThreshold)
plt.getOrCreateSubplot("rsi").addLine("Oversold", overSoldThreshold)
strat.run()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
pass
# Load the bar feed from the CSV file
feed = quandlfeed.Feed()
feed.addBarsFromCSV("ibm", "WIKI-IBM-2011-quandl.csv")
feed.addBarsFromCSV("aes", "WIKI-AES-2011-quandl.csv")
feed.addBarsFromCSV("aig", "WIKI-AIG-2011-quandl.csv")
feed.addBarsFromCSV("orcl", "WIKI-ORCL-2011-quandl.csv")
# Evaluate the strategy with the feed's bars.
myStrategy = MyStrategy(feed)
# Attach returns and sharpe ratio analyzers.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
# Run the strategy
myStrategy.run()
# Print the results.
print("Final portfolio value: $%.2f" % myStrategy.getResult())
print("Anual return: %.2f %%" % (retAnalyzer.getCumulativeReturns()[-1] * 100))
print("Average daily return: %.2f %%" %
(stats.mean(retAnalyzer.getReturns()) * 100))
print("Std. dev. daily return: %.4f" %
(stats.stddev(retAnalyzer.getReturns())))
print("Sharpe ratio: %.2f" % (sharpeRatioAnalyzer.getSharpeRatio(0)))
