def testInvalidDates(self):
instrument = "orcl"
# Don't skip errors.
with self.assertRaisesRegexp(Exception, "HTTP Error 404: Not Found"):
with common.TmpDir() as tmpPath:
bf = yahoofinance.build_feed([instrument], 2100, 2101, storage=tmpPath, frequency=bar.Frequency.DAY)
# Skip errors.
with common.TmpDir() as tmpPath:
bf = yahoofinance.build_feed(
[instrument], 2100, 2101, storage=tmpPath, frequency=bar.Frequency.DAY, skipErrors=True
)
bf.loadAll()
self.assertNotIn(instrument, bf)
def main(plot):
initialCash = 10000
instrumentsByClass = {
"Technology": ["MSFT", "ORCL", "IBM", "HPQ"],
"Services": ["WMT", "UPS", "TGT", "CCL"],
"Basic Materials": ["XOM", "CVX", "COP", "OXY"],
"Financial": ["BAC", "JPM", "WFC", "GS"],
"Consumer Goods": ["PG", "PEP", "CL", "KO"],
}
# Download the bars.
instruments = []
for assetClass in instrumentsByClass:
instruments.extend(instrumentsByClass[assetClass])
feed = yahoofinance.build_feed(instruments, 2005, 2013, "data", skipErrors=True)
strat = MarketTiming(feed, instrumentsByClass, initialCash)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, False, False, True)
plt.getOrCreateSubplot("cash").addCallback("Cash", lambda x: strat.getBroker().getCash())
strat.run()
print "Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05)
if plot:
plt.plot()
def main2(plot, instrument, entry_sma_period, exit_sma_period):
# Download the bars.
feed = yahoofinance.build_feed([instrument], 2014, 2014, ".")
# Evaluate the strategy with the feed's bars.
strat = SMACrossOver2(feed, instrument, entry_sma_period, exit_sma_period)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
strat.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(strat)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
plt.getInstrumentSubplot(instrument).addDataSeries("SMA", strat.getSMA())
plt.getInstrumentSubplot(instrument).addDataSeries("exit SMA", strat.get_exit_SMA())
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
# Run the strategy.
strat.run()
strat.info("Final portfolio value: $%.2f" % strat.getResult())
# Plot the strategy.
plt.plot()
def main(plot):
use_ex = True
instrument = "tcehy"
feed = yahoofinance.build_feed([instrument], 2015, 2016, ".")
if (use_ex):
strat = SMACrossOverEx(feed, instrument)
else:
strat = SMACrossOver(feed, instrument, 15)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
strat.attachAnalyzer(returnsAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, False, True)
if (use_ex):
plt.getInstrumentSubplot(instrument).addDataSeries("sma-15", strat.getSMA(15))
plt.getInstrumentSubplot(instrument).addDataSeries("sma-30", strat.getSMA(30))
else:
plt.getInstrumentSubplot(instrument).addDataSeries("sma", strat.getSMA())
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
strat.run()
strat.info("Final portfolio value: $%.2f" % strat.getResult())
print "Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05)
if plot:
plt.plot()
def main(plot):
al1 = Analyst('Ivy Kang')
al1.assign_weight('cmg', 0.673)
al1.assign_weight('aapl', 0.215)
al2 = Analyst('Charlie Brown')
al2.assign_weight('cmg', 0.420)
al2.assign_weight('orcl', 0.130)
al2.assign_weight('bk', 0.32)
al2.assign_weight('bk', 0.40)
al2.assign_weight('cmg', 0.30)
org = Organization()
org.add_analyst(al1)
org.add_analyst(al2)
# Download the bars.
feed = yahoofinance.build_feed(org.get_weights().keys(), 2014, 2015, ".instr_data")
strat = OrgStrat(feed, org)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, False, True)
strat.run()
if plot:
plt.plot()
def pickAsFunToLoop2(instrument):
#code = pickle.load(open(r'C:\Users\pmhgms\Desktop\machine_leaning\DataSet\stock_code.pickle','rb'))
#instrument = code[randint(0, len(code))]
# Load the yahoo feed from the CSV file
instruments = [instrument]
sy, ey = 2000, 2015
smaPeriod = 60
feed = yahoofinance.build_feed(instruments, sy, ey, "yhfeed", skipErrors=True)
# Evaluate the strategy with the feed's bars.
myStrategy = tut.SMACrossOver(feed, instrument, smaPeriod)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
plt.getInstrumentSubplot(instrument).addDataSeries("sma", myStrategy.getSMA())
# Plot the simple returns on each bar.
#plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
# Run the strategy.
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % (myStrategy.getResult()))
# Plot the strategy.
directory = 'smajpg'
picname = instrument+'_From_'+str(sy)+'_To_'+str(ey)+'_smaPeriod_'+str(smaPeriod)+'.jpg'
path = os.path.join(directory, picname)
plt.plot(savefige=True, path=path)
def pickAsFunToLoop(instrument,usevir=0):
#code = pickle.load(open(r'C:\Users\pmhgms\Desktop\machine_leaning\DataSet\stock_code.pickle','rb'))
#instrument = code[randint(0, len(code))]
# Load the yahoo feed from the CSV file
instruments = [instrument]
sy, ey = 2000, 2015
p1, p2 = 20, 10
usevir = usevir
feed = yahoofinance.build_feed(instruments, sy, ey, "yhfeed", skipErrors=True)
# Evaluate the strategy with the feed's bars.
myStrategy = tut.MyStrategy(feed, instrument, p1, p1, usevir)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
plt.getInstrumentSubplot(instrument).addDataSeries("high", myStrategy.getHigh())
plt.getInstrumentSubplot(instrument).addDataSeries("low", myStrategy.getLow())
# Plot the simple returns on each bar.
#plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
# Run the strategy.
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f, Stop loss %d time, Skip buy %d time" % (myStrategy.getResult(), myStrategy.stoplosstimes, myStrategy.skipbuy))
# Plot the strategy.
directory = 'temjpg'
picname = instrument+'_From_'+str(sy)+'_To_'+str(ey)+'_p1_'+str(p1)+'_p2_'+str(p2)+'_usevir_'+str(usevir)+'.jpg'
path = os.path.join(directory, picname)
plt.plot(savefige=True, path=path)
def algoparser(**kwargs):
"""
编译用户代码
:param args:
:param kwargs:
sid: 资产ID
start: 策略开始时间
end: 策略结束时间
code: 用户策略代码（字符串）
filename: 用户策略文件名(.py)
:return:
"""
sid = kwargs.pop('sid', None)
start = kwargs.pop('start', None)
end = kwargs.pop('end', None)
code = kwargs.pop('code',None)
Algo = compile(code, '<string>', 'exec')
exec Algo
instrument = sid
feed = yahoofinance.build_feed([instrument], start, end, ".")
strat = Strategy(feed, instrument)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
plt = plotter.StrategyPlotter(strat,True,False,False)
strat.run()
stat = plt.plotjson()
print stat
return stat
def main(plot):
initialCash = 1000000
commodity_tickers=["B","TA","BR"]
instrumentsByClass = {
"commodity": commodity_tickers
}
# Download the bars.
instruments = []
for assetClass in instrumentsByClass:
instruments.extend(instrumentsByClass[assetClass])
feed = yahoofinance.build_feed(instruments, 2007, 2013, "data", skipErrors=True)
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 = "000300.ss"
feed = yahoofinance.build_feed([instrument], 2013, 2015, ".")
period = 100
strat = HurstBasedStrategy(feed, instrument, period)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
strat.attachAnalyzer(returnsAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, False, True)
plt.getOrCreateSubplot("hurst").addDataSeries("Hurst", strat.getHurst())
plt.getOrCreateSubplot("hurst").addLine("random", 0.5)
# Plot the simple returns on each bar.
# plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
strat.run()
strat.info("Final portfolio value: $%.2f" % strat.getResult())
# print "Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05)
if plot:
plt.plot()
def main(plot):
initialCash = 10000
instrumentsByClass = {
"US Stocks": ["VTI"],
"Foreign Stocks": ["VEU"],
"US 10 Year Government Bonds": ["IEF"],
"Real Estate": ["VNQ"],
"Commodities": ["DBC"],
}
# Download the bars.
instruments = ["SPY"]
for assetClass in instrumentsByClass:
instruments.extend(instrumentsByClass[assetClass])
feed = yahoofinance.build_feed(instruments, 2007, 2013, "data", skipErrors=True)
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 testBuildDailyFeed(self):
with common.TmpDir() as tmpPath:
instrument = "orcl"
bf = yahoofinance.build_feed([instrument], 2010, 2010, storage=tmpPath)
bf.loadAll()
self.assertEqual(bf[instrument][-1].getOpen(), 31.22)
self.assertEqual(bf[instrument][-1].getClose(), 31.30)
def main(plot, instruments, smaPeriod, cash):
# Download the bars.
# In the instruments,I can provide more instruments.The instruments name can be got from yahoo finance.
#instruments = ["c07.si","C09.SI","C31.SI","E5H.SI"]
#instruments = ["h78.si"]
#feed is a Feed type defined in yahoofeed.py
feed = yahoofinance.build_feed(instruments, 2008, 2009, "./Historical_Price")
for instrument in instruments:
myStrategy = MyStrategy(feed, instrument, smaPeriod, cash)
if plot == True :
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
#plt.getInstrumentSubplot(instrument).addDataSeries("SMA", myStrategy.getSMA())
# Plot adjusted close values instead of regular close.
plt.getInstrumentSubplot(instrument).setUseAdjClose(True)
# Plot the strategy returns at each bar.
#plt.getOrCreateSubplot("returns").addDataSeries("Net return", returnsAnalyzer.getReturns())
#plt.getOrCreateSubplot("returns").addDataSeries("Cum. return", returnsAnalyzer.getCumulativeReturns())
myStrategy.run()
print "Result for %s : %.2f" % (instrument, myStrategy.getResult())
# Plot the strategy.
if plot == True :
plt.plot()
def main2(plot):
instrument = "ivv"
smaPeriod = 20
# Download the bars.
feed = yahoofinance.build_feed([instrument], 2013, 2014, ".")
# Evaluate the strategy with the feed's bars.
myStrategy = SMACrossOver(feed, instrument, smaPeriod)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
plt.getInstrumentSubplot(instrument).addDataSeries("SMA", myStrategy.getSMA())
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
# Run the strategy.
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
# Plot the strategy.
plt.plot()
def main(plot):
instrument = "yhoo"
bBandsPeriod = 20
# Download the bars.
feed = yahoofinance.build_feed([instrument], 2011, 2015, ".")
# feed = ts.get_hist_data('601198')
print type(feed)
# import sys;sys.exit(0)
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 main(plot):
entrySMA = 200
exitSMA = 5
rsiPeriod = 2
overBoughtThreshold = 90
overSoldThreshold = 10
# Download the bars.
instrument = "tcehy"
feed = yahoofinance.build_feed([instrument], 2014, 2016, ".")
strat = 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()
strat.info("Final portfolio value: $%.2f" % strat.getResult())
print "Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05)
if plot:
plt.plot()
def testBuildWeeklyFeed(self):
with common.TmpDir() as tmpPath:
instrument = "aapl"
bf = yahoofinance.build_feed([instrument], 2013, 2013, storage=tmpPath, frequency=bar.Frequency.WEEK)
bf.loadAll()
self.assertEqual(round(bf[instrument][-1].getOpen(), 2), 557.46)
self.assertEqual(round(bf[instrument][-1].getHigh(), 2), 561.28)
self.assertEqual(round(bf[instrument][-1].getLow(), 2), 540.43)
self.assertEqual(round(bf[instrument][-1].getClose(), 2), 540.98)
self.assertTrue(bf[instrument][-1].getVolume() in (9852500, 9855900, 68991600))
def run_strategy(): # Load the yahoo feed from the CSV file securities = ["app"] feed = yahoofinance.build_feed(securities, 2006, 2014, "stockdata") # Evaluate the strategy with the feed. masterTrader = MasterTrader(feed, securities[0]) masterTrader.run() print "Final portfolio value: $%.2f" % masterTrader.getBroker().getEquity() masterTrader.datamanager.close() print "Highest portfolio value: $%.2f" % masterTrader.highValue
def acquire_daily_data(instrument, year):
if year == 'all':
this_year = date.today().year # Get current year
final_year = this_year
in_business = True
while in_business:
try:
yahoofinance.build_feed(
[instrument], this_year, this_year,
'./data/historical/daily-atrader',
frequency=86400, skipErrors=False)
except:
in_business = False
log_business_years(instrument, [this_year + 1, final_year])
else:
this_year -= 1
else:
yahoofinance.build_feed([instrument], year[0], year[1],
'./data/historical/daily-atrader',
frequency=86400, skipErrors=False)
def __init__(self):
instruments = get_sp500_2()
#instruments = ['A', 'AA']
tmp_dir = "/home/work/workplace/tmp"
if not os.path.exists(tmp_dir):
os.mkdir(tmp_dir)
feed = yahoofinance.build_feed(instruments, 2010,2014,"/home/work/workplace/tmp/")
strategy.BacktestingStrategy.__init__(self, feed)
self.__instruments = instruments;
self.__positions = {}
for instrument in instruments:
self.__positions[instrument] = []
def main(plot):
instruments = ["AA", "AES", "AIG"]
feed = yahoofinance.build_feed(instruments, 2008, 2009, ".")
predicate = BuyOnGap(feed)
eventProfiler = eventprofiler.Profiler(predicate, 5, 5)
eventProfiler.run(feed, True)
results = eventProfiler.getResults()
print "%d events found" % (results.getEventCount())
if plot:
eventprofiler.plot(results)
def main(plot):
instruments = ["AA", "AES", "AIG"]
feed = yahoofinance.build_feed(instruments, 2008, 2009, ".")
predicate = BuyOnGap(feed)
eventProfiler = eventprofiler.Profiler(predicate, 5, 5)
eventProfiler.run(feed, True)
results = eventProfiler.getResults()
print "%d events found" % (results.getEventCount())
if plot:
eventprofiler.plot(results)
def testInvalidDates(self):
instrument = "orcl"
# Don't skip errors.
with self.assertRaisesRegexp(Exception, "404 Client Error: Not Found"):
with common.TmpDir() as tmpPath:
bf = yahoofinance.build_feed([instrument],
2100,
2101,
storage=tmpPath,
frequency=bar.Frequency.DAY)
# Skip errors.
with common.TmpDir() as tmpPath:
bf = yahoofinance.build_feed([instrument],
2100,
2101,
storage=tmpPath,
frequency=bar.Frequency.DAY,
skipErrors=True)
bf.loadAll()
self.assertNotIn(instrument, bf)
def __init__(self):
instruments = get_sp500_2()
#instruments = ['A', 'AA']
tmp_dir = "/home/work/workplace/tmp"
if not os.path.exists(tmp_dir):
os.mkdir(tmp_dir)
feed = yahoofinance.build_feed(instruments, 2010, 2014,
"/home/work/workplace/tmp/")
strategy.BacktestingStrategy.__init__(self, feed)
self.__instruments = instruments
self.__positions = {}
for instrument in instruments:
self.__positions[instrument] = []
def run_strategy(smaPeriod):
instrument = "orcl"
feed = yahoofinance.build_feed([instrument], 2000, 2000, ".")
# Load the yahoo feed from the CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV("orcl", "orcl-2000-yahoofinance.csv")
# Evaluate the strategy with the feed.
myStrategy = MyStrategy(feed, "orcl", smaPeriod)
myStrategy.run()
print "Final portfolio value: $%.2f" % myStrategy.getBroker().getEquity()
print "Profit %s Win: %s Loss: %s Win rate: %s%% " % (myStrategy.profit, myStrategy.win, myStrategy.loss, round(decimal.Decimal(myStrategy.win)/(decimal.Decimal(myStrategy.win)+decimal.Decimal(myStrategy.loss))*100,2))
def main(plot):
smaPeriod = 10
cash = 20000
# Download the bars.
# In the instruments,I can provide more instruments.The instruments name can be got from yahoo finance.
#instruments = ["c07.si","C09.SI","C31.SI"]
instruments = ["c09.si"]
#feed is a Feed type defined in yahoofeed.py
feed = yahoofinance.build_feed(instruments, 2012, 2013, ".")
for instrument in instruments:
myStrategy = MyStrategy(feed, instrument, smaPeriod, cash)
myStrategy.run()
print "Result for %s : %.2f" % (instrument, myStrategy.getResult())
def testBuildWeeklyFeed(self):
with common.TmpDir() as tmpPath:
instrument = "aapl"
bf = yahoofinance.build_feed([instrument],
2013,
2013,
storage=tmpPath,
frequency=bar.Frequency.WEEK)
bf.loadAll()
self.assertEqual(bf[instrument][-1].getOpen(), 557.46)
self.assertEqual(bf[instrument][-1].getHigh(), 561.28)
self.assertEqual(bf[instrument][-1].getLow(), 540.43)
self.assertEqual(bf[instrument][-1].getClose(), 540.98)
self.assertTrue(bf[instrument][-1].getVolume() in (9852500,
9855900,
68991600))
def main(plot, instrument, entry_sma_period, exit_sma_period):
# Download the bars.
feed = yahoofinance.build_feed([instrument], 2014, 2014, ".")
strat = SMACrossOver2(feed, instrument, entry_sma_period, exit_sma_period)
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 run_strategy(smaPeriod, rsiPeriod, instrument, amount, plot):
# Load the yahoo feed from the CSV file
try:
feed = yahoofinance.build_feed([instrument], 2009, 2015, "/home/pwu/stock/database")
except:
print "can't download..."
pass
else:
# Evaluate the strategy with the feed's bars.
myStrategy = MyStrategy(feed, instrument, smaPeriod, rsiPeriod, amount)
if plot:
plt = plotter.StrategyPlotter(myStrategy, True, True, True)
plt.getInstrumentSubplot(instrument).addDataSeries("upper", myStrategy.getBollingerBands().getUpperBand())
plt.getInstrumentSubplot(instrument).addDataSeries("middle", myStrategy.getBollingerBands().getMiddleBand())
plt.getInstrumentSubplot(instrument).addDataSeries("lower", myStrategy.getBollingerBands().getLowerBand())
myStrategy.run()
if plot:
plt.plot()
def main(plot):
instrument = "aapl"
vwapWindowSize = 5
# Download the bars.
feed = yahoofinance.build_feed([instrument], 2011, 2012, ".")
myStrategy = MyStrategy(feed, instrument, vwapWindowSize)
if plot:
plt = plotter.StrategyPlotter(myStrategy, True, False, True)
plt.getInstrumentSubplot(instrument).addDataSeries("vwap", myStrategy.getVWAPDS())
myStrategy.run()
print "Result: %.2f" % myStrategy.getResult()
if plot:
plt.plot()
def main(plot):
instruments = ["gld", "gdx"]
windowSize = 50
# Download the bars.
feed = yahoofinance.build_feed(instruments, 2006, 2012, ".")
myStrategy = MyStrategy(feed, instruments[0], instruments[1], windowSize)
if plot:
plt = plotter.StrategyPlotter(myStrategy, False, False, True)
plt.getOrCreateSubplot("hedge").addDataSeries("Hedge Ratio", myStrategy.getHedgeRatioDS())
plt.getOrCreateSubplot("spread").addDataSeries("Spread", myStrategy.getSpreadDS())
myStrategy.run()
print "Result: %.2f" % myStrategy.getResult()
if plot:
plt.plot()
def main(plot):
initialCash = 10000
instrumentsByClass = {
"US Stocks": ["VTI"],
"Foreign Stocks": ["VEU"],
"US 10 Year Government Bonds": ["IEF"],
"Real Estate": ["VNQ"],
"Commodities": ["DBC"],
}
# Download the bars.
instruments = ["SPY"]
for assetClass in instrumentsByClass:
instruments.extend(instrumentsByClass[assetClass])
feed = yahoofinance.build_feed(instruments,
2007,
2013,
"data",
skipErrors=True)
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"
vwapWindowSize = 5
# Download the bars.
feed = yahoofinance.build_feed([instrument], 2011, 2012, ".")
myStrategy = MyStrategy(feed, instrument, vwapWindowSize)
if plot:
plt = plotter.StrategyPlotter(myStrategy, True, False, True)
plt.getInstrumentSubplot(instrument).addDataSeries(
"vwap", myStrategy.getVWAPDS())
myStrategy.run()
print "Result: %.2f" % myStrategy.getResult()
if plot:
plt.plot()
def main(plot):
instrument = "aapl"
smaPeriod = 20
feed = yahoofinance.build_feed([instrument],2014,2015,".")
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 = "yhoo"
bBandsPeriod = 40
# Download the bars.
feed = yahoofinance.build_feed([instrument], 2011, 2012, ".")
myStrategy = MyStrategy(feed, instrument, bBandsPeriod)
if plot:
plt = plotter.StrategyPlotter(myStrategy, True, True, True)
plt.getInstrumentSubplot(instrument).addDataSeries("upper", myStrategy.getBollingerBands().getUpperBand())
plt.getInstrumentSubplot(instrument).addDataSeries("middle", myStrategy.getBollingerBands().getMiddleBand())
plt.getInstrumentSubplot(instrument).addDataSeries("lower", myStrategy.getBollingerBands().getLowerBand())
myStrategy.run()
print "Result: %.2f" % myStrategy.getResult()
if plot:
plt.plot()
def main(plot):
instrument = "aapl"
smaPeriod = 20
feed = yahoofinance.build_feed([instrument], 2014, 2015, ".")
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 = "yhoo"
bBandsPeriod = 40
# Download the bars.
feed = yahoofinance.build_feed([instrument], 2011, 2012, ".")
myStrategy = MyStrategy(feed, instrument, bBandsPeriod)
if plot:
plt = plotter.StrategyPlotter(myStrategy, True, True, True)
plt.getInstrumentSubplot(instrument).addDataSeries("upper", myStrategy.getBollingerBands().getUpperBand())
plt.getInstrumentSubplot(instrument).addDataSeries("middle", myStrategy.getBollingerBands().getMiddleBand())
plt.getInstrumentSubplot(instrument).addDataSeries("lower", myStrategy.getBollingerBands().getLowerBand())
myStrategy.run()
print "Result: %.2f" % myStrategy.getResult()
if plot:
plt.plot()
def main(plot):
instruments = ["gld", "gdx"]
windowSize = 50
# Download the bars.
feed = yahoofinance.build_feed(instruments, 2006, 2012, ".")
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 main(plot):
instrument = "aapl"
vwapWindowSize = 5
threshold = 0.01
# Download the bars.
feed = yahoofinance.build_feed([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 = "yhoo"
bBandsPeriod = 40
# Download the bars.
feed = yahoofinance.build_feed([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 main(plot):
instrument = "aapl"
vwapWindowSize = 5
threshold = 0.01
# Download the bars.
feed = yahoofinance.build_feed([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):
symbol_file = open("TFEX", "r")
#Read each symbol and remove \n from each symbol
symbols = [x.strip('\n') for x in symbol_file.readlines()]
#symbols = ["aot.bk","bbl.bk","tpipl.bk","ichi.bk"]
bBandsPeriod = 21
f = open("bbands_result.txt", "w")
# Download the bars.
for instrument in symbols:
instrument = instrument + ".BK"
feed = yahoofinance.build_feed([instrument], 2015, 2016, ".")
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 "%s" % instrument
print "Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.03)
f.write("%s\n" % instrument)
f.write("Sharpe ratio: %.2f\n" %
sharpeRatioAnalyzer.getSharpeRatio(0.03))
if plot:
plt.plot()
from pyalgotrade.optimizer import server
import sys, os
from pyalgotrade.barfeed import yahoofeed
from pyalgotrade.technical import ma
from pyalgotrade.tools import yahoofinance
def parameters_generator():
entrySMA = range(251, 281)
exitSMA = range(11, 21)
rsiPeriod = range(10, 14)
overBoughtThreshold = range(75, 90)
overSoldThreshold = range(5, 21)
return itertools.product(entrySMA, exitSMA, rsiPeriod, overBoughtThreshold,
overSoldThreshold)
# The if __name__ == '__main__' part is necessary if running on Windows.
if __name__ == '__main__':
# Load the feed from the CSV files.
#feed = yahoofeed.Feed()
instrument = sys.argv[1]
os.system("rm -rf %s-*" % (instrument))
feed = yahoofinance.build_feed([instrument], 2011, 2014, "./database")
#feed.addBarsFromCSV(instrument, "%s-2011-yahoofinance.csv"%(instrument))
#feed.addBarsFromCSV(instrument, "%s-2012-yahoofinance.csv"%(instrument))
#feed.addBarsFromCSV(instrument, "%s-2013-yahoofinance.csv"%(instrument))
#feed.addBarsFromCSV(instrument, "%s-2014-yahoofinance.csv"%(instrument))
# Run the server.
server.serve(feed, parameters_generator(), "localhost", 5000)
def setStats(self, instrument):
y_info = yahoofinance.build_feed([instrument], 2008, 2008, ".")
t = Test(y_info, instrument, self.Weight)
stats = Statistics(t)
self.stats = stats
def rsi2Trade(plot, storage, instrument, entrySMA, exitSMA, rsiPeriod,
overBoughtThreshold, overSoldThreshold):
# Download the bars.
feed = yahoofinance.build_feed([instrument], 2015, 2017, storage)
tm_hour = int(time.strftime('%H', time.localtime()))
weekdayNumber = datetime.datetime.today().weekday()
if (weekdayNumber >= 0 and weekdayNumber <= 4) and (tm_hour >= 9
and tm_hour <= 23):
feed.addBarsFromCSV(
instrument,
sinafinance.download_current_trade(instrument, storage))
strat = rsi2d.RSI2(feed, instrument, entrySMA, exitSMA, rsiPeriod,
overBoughtThreshold, overSoldThreshold)
strat.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
strat.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
strat.attachAnalyzer(tradesAnalyzer)
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 "Correct Ratio: %.2f" % (
(float(tradesAnalyzer.getProfitableCount()) /
tradesAnalyzer.getCount()) if tradesAnalyzer.getCount() else -1.0)
print "Evaluation Final portfolio value: %.2f" % strat.getResult()
print "Evaluation Cumulative returns: %.2f" % (
retAnalyzer.getCumulativeReturns()[-1] * 100)
print "Evaluation Sharpe ratio: %.2f" % (
sharpeRatioAnalyzer.getSharpeRatio(0.05))
print "Evaluation Max. drawdown: %.2f" % (
drawDownAnalyzer.getMaxDrawDown() * 100)
print "Evaluation Longest drawdown duration: %s" % (
drawDownAnalyzer.getLongestDrawDownDuration())
print
print "Final trades: %d" % (tradesAnalyzer.getCount())
if tradesAnalyzer.getCount() > 0:
profits = tradesAnalyzer.getAll()
print "Final Avg. profit: %.2f" % (profits.mean())
print "Final Profits std. dev.: %.2f" % (profits.std())
print "Final Max. profit: %.2f" % (profits.max())
print "Final Min. profit: %.2f" % (profits.min())
returns = tradesAnalyzer.getAllReturns()
print "Final Avg. return: %2.f" % (returns.mean() * 100)
print "Final Returns std. dev.: %2.f" % (returns.std() * 100)
print "Final Max. return: %2.f" % (returns.max() * 100)
print "Final Min. return: %2.f" % (returns.min() * 100)
print
print "Profitable trades: %d" % (tradesAnalyzer.getProfitableCount())
if tradesAnalyzer.getProfitableCount() > 0:
profits = tradesAnalyzer.getProfits()
print "Profitable Avg. profit: $%.2f" % (profits.mean())
print "Profitable Profits std. dev.: $%.2f" % (profits.std())
print "Profitable Max. profit: $%.2f" % (profits.max())
print "Profitable Min. profit: $%.2f" % (profits.min())
returns = tradesAnalyzer.getPositiveReturns()
print "Profitable Avg. return: %2.f %%" % (returns.mean() * 100)
print "Profitable Returns std. dev.: %2.f %%" % (returns.std() * 100)
print "Profitable Max. return: %2.f %%" % (returns.max() * 100)
print "Profitable Min. return: %2.f %%" % (returns.min() * 100)
print
print "Unprofitable trades: %d" % (tradesAnalyzer.getUnprofitableCount())
if tradesAnalyzer.getUnprofitableCount() > 0:
losses = tradesAnalyzer.getLosses()
print "Unprofitable Avg. loss: $%.2f" % (losses.mean())
print "Unprofitable Losses std. dev.: $%.2f" % (losses.std())
print "Unprofitable Max. loss: $%.2f" % (losses.min())
print "Unprofitable Min. loss: $%.2f" % (losses.max())
returns = tradesAnalyzer.getNegativeReturns()
print "Unprofitable Avg. return: %2.f %%" % (returns.mean() * 100)
print "Unprofitable Returns std. dev.: %2.f %%" % (returns.std() * 100)
print "Unprofitable Max. return: %2.f %%" % (returns.max() * 100)
print "Unprofitable Min. return: %2.f %%" % (returns.min() * 100)
if plot:
plt.plot()
return bar.getClose() < self.__exitSMA[-1]
def parameters_generator():
entrySMA = range(150, 251)
exitSMA = range(5, 16)
rsiPeriod = range(2, 11)
overBoughtThreshold = range(75, 96)
overSoldThreshold = range(5, 26)
return itertools.product(entrySMA, exitSMA, rsiPeriod, overBoughtThreshold,
overSoldThreshold)
# The if __name__ == '__main__' part is necessary if running on Windows.
instrument = sys.argv[1]
if __name__ == '__main__':
# Load the feed from the CSV files.
#feed = yahoofeed.Feed()
#feed.addBarsFromCSV("dia", "dia-2009.csv")
#feed.addBarsFromCSV("dia", "dia-2010.csv")
#feed.addBarsFromCSV("dia", "dia-2011.csv")
try:
feed = yahoofinance.build_feed([instrument], 2011, 2014, ".")
except:
print "can't download..."
pass
else:
#strategy1 = MyStrategy(feed, instrument, parameters_generator())
local.run(MyStrategy, feed, parameters_generator())
#strategy1.run()
def main(plot, code):
#instrument = "yhoo"
#.SS .SZ
instrument = code + (".SS" if (code[0:3] == '600') else ".SZ")
bBandsPeriod = 13
# Download the bars.
feed = yahoofinance.build_feed([instrument], 2015, 2016, "./data")
strat = BBandsAll(feed, instrument, bBandsPeriod)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
retAnalyzer = returns.Returns()
strat.attachAnalyzer(retAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
strat.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
strat.attachAnalyzer(tradesAnalyzer)
tradesAnalyzer = trades.Trades()
strat.attachAnalyzer(tradesAnalyzer)
##print 'retAnalyzer.getReturns()[-1]:', retAnalyzer.getReturns()[-1]
##print 'size retAnalyzer.getReturns()[-1]:', len(retAnalyzer.getReturns()[-1])
#print 'last price:',strat.getLastPrice(instrument)
#print len(feed)
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())
plt.getInstrumentSubplot(instrument).addDataSeries(
"price", feed[instrument].getAdjCloseDataSeries())
strat.run()
#print "Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05)
#logger.info("%s value: $%.2f" % (code, strat.getResult()))
#logger.getLogger("custom").info("ble")
logging.info("%s value: $%.2f" % (code, strat.getResult()))
print "Final portfolio value: $%.2f" % strat.getResult()
print "Cumulative returns: %.2f %%" % (
retAnalyzer.getCumulativeReturns()[-1] * 100)
print "Sharpe ratio: %.2f" % (sharpeRatioAnalyzer.getSharpeRatio(0.05))
print "Max. drawdown: %.2f %%" % (drawDownAnalyzer.getMaxDrawDown() * 100)
print "Longest drawdown duration: %s" % (
drawDownAnalyzer.getLongestDrawDownDuration())
print
print "Total trades: %d" % (tradesAnalyzer.getCount())
if tradesAnalyzer.getCount() > 0:
profits = tradesAnalyzer.getAll()
print "Avg. profit: $%2.f" % (profits.mean())
print "Profits std. dev.: $%2.f" % (profits.std())
print "Max. profit: $%2.f" % (profits.max())
print "Min. profit: $%2.f" % (profits.min())
returns0 = tradesAnalyzer.getAllReturns()
print "Avg. return: %2.f %%" % (returns0.mean() * 100)
print "Returns std. dev.: %2.f %%" % (returns0.std() * 100)
print "Max. return: %2.f %%" % (returns0.max() * 100)
print "Min. return: %2.f %%" % (returns0.min() * 100)
print
print "Profitable trades: %d" % (tradesAnalyzer.getProfitableCount())
if tradesAnalyzer.getProfitableCount() > 0:
profits = tradesAnalyzer.getProfits()
print "Avg. profit: $%2.f" % (profits.mean())
print "Profits std. dev.: $%2.f" % (profits.std())
print "Max. profit: $%2.f" % (profits.max())
print "Min. profit: $%2.f" % (profits.min())
returns0 = tradesAnalyzer.getPositiveReturns()
print "Avg. return: %2.f %%" % (returns0.mean() * 100)
print "Returns std. dev.: %2.f %%" % (returns0.std() * 100)
print "Max. return: %2.f %%" % (returns0.max() * 100)
print "Min. return: %2.f %%" % (returns0.min() * 100)
print
print "Unprofitable trades: %d" % (tradesAnalyzer.getUnprofitableCount())
if tradesAnalyzer.getUnprofitableCount() > 0:
losses = tradesAnalyzer.getLosses()
print "Avg. loss: $%2.f" % (losses.mean())
print "Losses std. dev.: $%2.f" % (losses.std())
print "Max. loss: $%2.f" % (losses.min())
print "Min. loss: $%2.f" % (losses.max())
returns0 = tradesAnalyzer.getNegativeReturns()
print "Avg. return: %2.f %%" % (returns0.mean() * 100)
print "Returns std. dev.: %2.f %%" % (returns0.std() * 100)
print "Max. return: %2.f %%" % (returns0.max() * 100)
print "Min. return: %2.f %%" % (returns0.min() * 100)
if plot:
plt.plot()
from numpy import asarray
from numpy import diag
from numpy import zeros_like
from numpy import genfromtxt
from UtilityFunctions import GetReturns
from CoreFunctions import ObjectiveFunction
from CoreFunctions import ComputeF
from CoreFunctions import RewardFunction
from CoreFunctions import SharpeRatio
#### MAIN ####
# Download the testing data from yahoo Finance.
instruments = ["aapl"] # Set the instruments to download data.
feed = yahoofinance.build_feed(instruments, 2011, 2012, "./Data") # Download the specified data.
# Retrieve data as an NP array.
trainingData = genfromtxt('./Data/aapl-2011-yahoofinance.csv', delimiter=',') # Read the data from CSV file.
trainingData = trainingData[1:, 6] # Select the adjusted close
trainingData2012 = genfromtxt('./Data/aapl-2012-yahoofinance.csv', delimiter=',') # Read the data from CSV file.
trainingData2012 = trainingData2012[1:, 6] # Select the adjusted close
#print(trainingData.size)
trainingData = np.concatenate([trainingData,trainingData2012],axis=0)
#print (trainingData.size)
# Set the parameters
M = 10
T = 150
from pyalgotrade import strategy
from pyalgotrade.barfeed import yahoofeed
from pyalgotrade.tools import yahoofinance
from pyalgotrade.technical import ma
from pyalgotrade.technical import rsi
from pyalgotrade import dataseries
from pyalgotrade import technical
import technicalfilters as tf
feed = yahoofinance.build_feed(['GE', 'JCI', 'AAPL', 'ADI'],
2000,
2015,
'./data/historical/daily-atrader',
frequency=86400,
skipErrors=False)
class MyStrategy(strategy.BacktestingStrategy):
def __init__(self, feed, instrument, cash):
strategy.BacktestingStrategy.__init__(self, feed, cash)
self.__rsi = rsi.RSI(feed[instrument].getCloseDataSeries(), 14)
self.__sma = ma.SMA(self.__rsi, 15)
self.__instrument = instrument
self.__EMA = tf.EMA(feed[instrument].getCloseDataSeries(), 16, .3)
self.__DT = tf.Derivative(feed[instrument])
def onBars(self, bars):
bar = bars[self.__instrument]
self.info(
def main():
filedir = "testresults"
iteration = 0
resultsdict = {}
for instrument in instrumentListGetter():
if len(instrument.split()) > 1:
instrument, instrumentSource = instrument.split()
print "instrument: %s, source: %s" % (instrument, instrumentSource)
else:
instrumentSource = ""
#feed = yahoofeed.Feed()
#feed.addBarsFromCSV("hm", DATAPATH + "/hm_table.csv")
# Load the feed from the CSV files.
#for params in parameters_generator():
faileddataforinstrument = False
for params in teststrategy.parameters_generator():
# Do not move to outer loop or severe performance problems arise # take the data we have and use
yiter = 0
if "quandl" in instrumentSource: # https://www.quandl.com/data/
'''/home/carl/Projects/algotrader/algotrader/lib64/python2.7/site-packages/pyalgotrade/barfeed/csvfeed.py:171 needs try hack '''
try:
feed = GenericBarFeed(frequency=bar.Frequency.DAY)
feed.setDateTimeFormat("%Y-%m-%d")
feed.setColumnName("datetime", "Date")
feed.setColumnName("adj_close", "Adjusted Close")
#feed.addBarsFromCSV(instrument, DATAPATH)
feed.addBarsFromCSV(instrument,
DATAPATH + "/" + instrument + ".csv")
except:
print sys.exc_info()
faileddataforinstrument = True
else:
for year in range(YEARSOFDATA, 0, -1):
startyear = localtime().tm_year - (YEARSOFDATA - yiter)
try:
feed = build_feed([instrument],
startyear,
2016,
DATAPATH,
frequency=86400,
timezone=None,
skipErrors=False)
except:
print "\n\nFailed downloading %s for year %d yiter %d \n\n" % (
instrument, startyear, yiter)
yiter += 1
if year == (YEARSOFDATA - 1):
faileddataforinstrument = True
if faileddataforinstrument:
break
strat = teststrategy.TestStrat(feed, instrument, *params)
iteration += 1
paramnames = teststrategy.parameters_generator(paramnames=True)
paramsarray = zip(paramnames, [str(p) for p in params])
paramsstring = str([
":".join(t) for t in zip(paramnames, [str(p) for p in params])
])
print "\n\nIteration %d / %d; Instrument %s ; Params %s" % (
iteration, iterations, instrument, paramsstring)
#retAnalyzer = returns.Returns()
#strat.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
#drawDownAnalyzer = drawdown.DrawDown()
#strat.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
strat.attachAnalyzer(tradesAnalyzer)
# with instrument
#csvdict["Params"] = [":".join(t) for t in zip(("instrument", "entrySMA", "exitDays", "rsiPeriod", "overBoughtThreshold", "overSoldThreshold"), [str(p) for p in params] )]
# without instrument
print paramsstring
strat.run()
tradetotal = 0
tradetotal = sum(
[t * 100.00 for t in tradesAnalyzer.getAllReturns()])
nr_of_trades = tradesAnalyzer.getCount()
try:
profitable_tradeprcnt = "%.2f" % (
(float(tradesAnalyzer.getProfitableCount()) /
float(tradesAnalyzer.getCount())) * 100)
trade_avg_result_prcnt = "%.2f" % (
tradetotal / float(tradesAnalyzer.getCount()))
except ZeroDivisionError:
profitable_tradeprcnt = "0.0"
trade_avg_result_prcnt = "0.0"
sharpe_ratio = "%.2f" % (sharpeRatioAnalyzer.getSharpeRatio(0.05))
#print "Cumulative returns: %.2f %%" % (retAnalyzer.getCumulativeReturns()[-1] * 100)
print "Trade stats"
print "Nr of trades:\t\t\t%d" % (nr_of_trades)
print "Trade avg:\t\t\t%s%%" % (trade_avg_result_prcnt)
print "Profitable trades:\t\t%s%%" % (profitable_tradeprcnt)
print "Sharpe ratio:\t\t\t", sharpe_ratio
print
returns = tradesAnalyzer.getAllReturns()
if tradesAnalyzer.getProfitableCount() > 0:
trade_max = "%.2f" % (returns.max() * 100)
trade_min = "%.2f" % (returns.min() * 100)
trade_stddev = "%.2f" % (returns.std() * 100)
else:
trade_max = "%.2f" % 0
trade_min = "%.2f" % 0
trade_stddev = "%.2f" % 0
print "Returns std. dev.: %s %%\t\t\t" % trade_stddev
print "Max. return: %s %%\t\t\t" % trade_max
print "Min. return: %s %%\t\t\t" % trade_min
#print "Trade total: %.2f" % ( tradetotal )
#print "Max. drawdown: %.2f %%" % (drawDownAnalyzer.getMaxDrawDown() * 100)
#if tradesAnalyzer.getProfitableCount() > 0:
#profits = tradesAnalyzer.getProfits()
#losses = tradesAnalyzer.getLosses()
#print "Avg. profit: $%2.f" % (profits.mean())
#print "Avg. loss: $%2.f" % (losses.mean())
#returns = tradesAnalyzer.getPositiveReturns()
#returns = tradesAnalyzer.getAllReturns()
#print "Avg. return: %2.f %%" % (returns.mean() * 100) # too much rounding
#print "Returns std. dev.: %2.f %%" % (returns.std() * 100)
#print "Max. return: %2.f %%" % (returns.max() * 100)
#print "Min. return: %2.f %%" % (returns.min() * 100)
#print "Trades std. dev.: $%2.f" % (profits.std())
#print "Max. profit: $%2.f" % (profits.max())
#print "Min. profit: $%2.f" % (profits.min())
if paramsstring in resultsdict.keys():
try:
resultsdict[paramsstring]['nr_of_trades'] = int(
resultsdict[paramsstring]['nr_of_trades']) + int(
nr_of_trades)
resultsdict[paramsstring][
'profitable_tradeprcnt'] = "%.2f" % (
(float(resultsdict[paramsstring]
['profitable_tradeprcnt']) +
float(profitable_tradeprcnt)) / 2.00)
resultsdict[paramsstring][
'trade_avg_result_prcnt'] = "%.2f" % (
(float(resultsdict[paramsstring]
['trade_avg_result_prcnt']) +
float(trade_avg_result_prcnt)) / 2.00)
resultsdict[paramsstring]['sharpe_ratio'] = "%.2f" % (
(float(resultsdict[paramsstring]['sharpe_ratio']) +
float(sharpe_ratio)) / 2.00)
resultsdict[paramsstring]['trade_max'] = "%.2f" % (
float(resultsdict[paramsstring]['trade_max']) +
float(trade_max) / 2.00)
resultsdict[paramsstring]['trade_min'] = "%.2f" % (
float(resultsdict[paramsstring]['trade_min']) +
float(trade_min) / 2.00)
resultsdict[paramsstring]['trade_stddev'] = "%.2f" % (
float(resultsdict[paramsstring]['trade_stddev']) +
float(trade_stddev) / 2.00)
except ZeroDivisionError:
print "\nError (ZeroDivisionError) trying averaging with: %s\n" % paramsstring
else: # First time with params
resultsdict[paramsstring] = dict(paramsarray)
resultsdict[paramsstring]['params'] = paramsstring
resultsdict[paramsstring]['nr_of_trades'] = nr_of_trades
resultsdict[paramsstring][
'profitable_tradeprcnt'] = profitable_tradeprcnt
resultsdict[paramsstring][
'trade_avg_result_prcnt'] = trade_avg_result_prcnt
resultsdict[paramsstring]['sharpe_ratio'] = sharpe_ratio
resultsdict[paramsstring]['trade_max'] = trade_max
resultsdict[paramsstring]['trade_min'] = trade_min
resultsdict[paramsstring]['trade_stddev'] = trade_stddev
feed.reset() # feed must be reset
del sharpeRatioAnalyzer
del tradesAnalyzer
with open(filedir + "/" + strat.STRATNAME + '.csv', 'wb') as csvfile:
fieldnames = ['params']
fieldnames.extend(paramnames)
fieldnames.extend(
('nr_of_trades', 'profitable_tradeprcnt', 'trade_avg_result_prcnt',
'sharpe_ratio', 'trade_max', 'trade_min', 'trade_stddev'))
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
#csvheader = {'params':'Params: ' + strat.STRATNAME, paramnames, 'nr_of_trades':'Nr of trades',\
# 'profitable_tradeprcnt':'Profitable trades%', 'trade_avg_result_prcnt':'Trade avg %', 'sharpe_ratio': 'Sharpe Ratio'}
csvheader = {'params': 'Params: ' + strat.STRATNAME}
for n in paramnames:
csvheader[n] = n
csvheader['nr_of_trades'] = 'Nr of trades'
csvheader['profitable_tradeprcnt'] = 'Profitable trades%'
csvheader['trade_avg_result_prcnt'] = 'Trade avg %'
csvheader['sharpe_ratio'] = 'Sharpe Ratio'
csvheader['trade_max'] = 'Trade Max'
csvheader['trade_min'] = 'Trade Min'
csvheader['trade_stddev'] = 'Trade Stddev'
writer.writerow(csvheader)
for result in resultsdict.keys():
writer.writerow(resultsdict[result])
def fetchMyPortfolio():
if not os.path.exists(dataDir):
os.makedirs(dataDir)
yahoofinance.build_feed(myPortfolio, 2017, 2017, dataDir,skipErrors=False)
def fitnessProgressTest(instrument, start, end):
start_year = int(start.split('-')[0])
data = get_stock(instrument, start, end, save=True)
tech_indicators = get_indicators(data)
result = parse_data_to_levels(data, tech_indicators)
data = result["data"]
maxDr = 0.0
ret = 0.0
unprofit = 0
profit = 0
sharpe = 0.0
streak = 0.0
train_maxDr = 0.0
train_ret = 0.0
train_profit = 0
train_unprofit = 0
train_sharpe = 0.0
train_streak = 0
lens = 1
for i in range(lens):
import time
start1 = time.time()
print "Start GENETIC"
calc_str = genetic_algoritm(100, 1, 0.25, 0.3, data, tech_indicators,
instrument, start)
end = time.time()
elapsed = end - start1
print "Total time: %f" % elapsed
train_maxDr += calc_str.stats.drawDownAnalyzer.getMaxDrawDown() * 100
train_ret += calc_str.stats.retAnalyzer.getCumulativeReturns(
)[-1] * 100
train_profit += calc_str.stats.tradesAnalyzer.getProfitableCount()
train_unprofit += calc_str.stats.tradesAnalyzer.getUnprofitableCount()
train_sharpe += calc_str.stats.sharpeRatioAnalyzer.getSharpeRatio(
0.025)
train_streak += calc_str.stats.streak
start2 = '%d-01-03' % (start_year + 1)
end2 = '%d-12-30' % (start_year + 1)
data2 = get_stock(instrument, start2, end2)
data2 = parse_data_to_levels(data2, tech_indicators)
getFitness(data2["data"], calc_str, setResults=True)
weights = data2["data"]["Weight"]
t = Test(
yahoofinance.build_feed([instrument], start_year + 1,
start_year + 1, "."), instrument, weights)
stats = Statistics(t)
maxDr += stats.drawDownAnalyzer.getMaxDrawDown() * 100
ret += stats.retAnalyzer.getCumulativeReturns()[-1] * 100
profit += stats.tradesAnalyzer.getProfitableCount()
unprofit += stats.tradesAnalyzer.getUnprofitableCount()
sharpe += stats.sharpeRatioAnalyzer.getSharpeRatio(0.025)
streak += stats.streak
result3 = []
result3.append(train_sharpe)
result3.append(train_ret)
result3.append(train_maxDr)
result3.append(train_profit)
result3.append(train_unprofit)
result3.append(train_streak)
result3.append(sharpe)
result3.append(ret)
result3.append(maxDr)
result3.append(profit)
result3.append(unprofit)
result3.append(streak)
return result3
def __getFeedFromYahoo(self,instrument):
from pyalgotrade.tools import yahoofinance
import sys,os
dataPath = os.path.abspath(os.path.join(os.path.dirname(__file__),'data','yahoodb','csv'))
feed = yahoofinance.build_feed([instrument], 2015, 2015, dataPath)
return feed
