def main():
# Load the orders file.
ordersFile = OrdersFile("orders.csv")
print "First date", ordersFile.getFirstDate()
print "Last date", ordersFile.getLastDate()
print "Symbols", ordersFile.getInstruments()
# Load the data from QSTK storage. QS environment variable has to be defined.
if os.getenv("QS") is None:
raise Exception("QS environment variable not defined")
feed = yahoofeed.Feed()
feed.setBarFilter(csvfeed.DateRangeFilter(ordersFile.getFirstDate(), ordersFile.getLastDate()))
feed.setDailyBarTime(datetime.time(0, 0, 0)) # This is to match the dates loaded with the ones in the orders file.
for symbol in ordersFile.getInstruments():
feed.addBarsFromCSV(symbol, os.path.join(os.getenv("QS"), "QSData", "Yahoo", symbol + ".csv"))
# Run the strategy.
cash = 1000000
useAdjustedClose = True
myStrategy = MyStrategy(feed, cash, ordersFile, useAdjustedClose)
# Attach returns and sharpe ratio analyzers.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
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))
def main():
# Load the orders file.
ordersFile = OrdersFile("orders.csv")
print "First date", ordersFile.getFirstDate()
print "Last date", ordersFile.getLastDate()
print "Symbols", ordersFile.getInstruments()
# Load the data from QSTK storage. QS environment variable has to be defined.
if os.getenv("QS") == None:
raise Exception("QS environment variable not defined")
feed = yahoofeed.Feed()
feed.setBarFilter(csvfeed.DateRangeFilter(ordersFile.getFirstDate(), ordersFile.getLastDate()))
feed.setDailyBarTime(datetime.time(0, 0, 0)) # This is to match the dates loaded with the ones in the orders file.
for symbol in ordersFile.getInstruments():
feed.addBarsFromCSV(symbol, os.path.join(os.getenv("QS"), "QSData", "Yahoo", symbol + ".csv"))
# Run the strategy.
cash = 1000000
useAdjustedClose = True
myStrategy = MyStrategy(feed, cash, ordersFile, useAdjustedClose)
# Attach returns and sharpe ratio analyzers.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
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, 252))
def main(tick, year, cash, smaPeriod, order):
print 'Welcome, ' + os.environ['USER'] + '!'
print 'Backtesting ' + tick + ' in ' + str(year)
# Download daily bars
filename = tick + '-' + str(year) + '.csv'
yahoofinance.download_daily_bars(tick, year, filename)
# Load CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV(tick, filename)
# Evaluate Strategy
strategySMA = StrategySMA(feed, tick, smaPeriod, order, cash)
# Attach a returns analyzers to the strategy.
retAnalyzer = returns.Returns()
strategySMA.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strategySMA.attachAnalyzer(sharpeRatioAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(strategySMA)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
plt.getInstrumentSubplot(tick).addDataSeries("SMA", strategySMA.getSMA())
# Set portfolio cash
strategySMA.getBroker().setCash(cash)
initial = strategySMA.getBroker().getEquity()
# Run the strategy
strategySMA.run()
# Print the results
print '*' * 60
print 'Initial portfolio value: $%.2f' % initial
final = strategySMA.getBroker().getEquity()
print 'Final portfolio value: $%.2f' % final
net = final - initial
if net > 0:
print 'Net gain: $%.2f' % net
else:
print 'Net loss: $%.2f' % net
percentage = (final - initial) / initial * 100
if percentage > 0:
print 'Percentage gain: +%.2f%%' % percentage
else:
print 'Percentage loss: %.2f%%' % percentage
#print 'Final portfolio value: $%.2f' % strategySMA.getResult()
print 'Annual 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))
# Plot the strategy
plt.plot()
def getSharpeRatio(self, riskFreeRate, tradingPeriods, annualized=True):
"""
Returns the Sharpe ratio for the strategy execution.
If the volatility is 0, 0 is returned.
:param riskFreeRate: The risk free rate per annum.
:type riskFreeRate: int/float.
:param tradingPeriods: The number of trading periods per annum.
:type tradingPeriods: int/float.
:param annualized: True if the sharpe ratio should be annualized.
:type annualized: boolean.
.. note::
* If using daily bars, tradingPeriods should be set to 252.
* If using hourly bars (with 6.5 trading hours a day) then tradingPeriods should be set to 252 * 6.5 = 1638.
"""
ret = 0.0
# From http://en.wikipedia.org/wiki/Sharpe_ratio: if Rf is a constant risk-free return throughout the period,
# then stddev(R - Rf) = stddev(R).
volatility = stats.stddev(self.__netReturns, 1)
if volatility != 0:
excessReturns = [dailyRet - (riskFreeRate / float(tradingPeriods)) for dailyRet in self.__netReturns]
avgExcessReturns = stats.mean(excessReturns)
ret = avgExcessReturns / volatility
if annualized:
ret = ret * math.sqrt(tradingPeriods)
return ret
def main():
import dateutil.parser
startPeriod = dateutil.parser.parse('2005-06-30T08:00:00.000Z')
endPeriod = dateutil.parser.parse('2014-12-31T08:00:00.000Z')
# Load the orders file.
#ordersFile = OrdersFile("MasterOrders_Both_Abhi-26.csv", filterAction='sell', rank=500)
#ordersFile = OrdersFile("MasterOrders_Both_SP-500.csv", filterAction='both', rank=500)
ordersFile = OrdersFile("orders.csv", filterAction='both', rank=20)
#ordersFile = OrdersFile("Problem-Orders.csv", filterAction='both', rank=20)
#startPeriod = yearFromTimeSinceEpoch(ordersFile.getFirstDate())
#endPeriod = yearFromTimeSinceEpoch(ordersFile.getLastDate())
print "First Year", startPeriod
print "Last Year", endPeriod
print "Instruments", ordersFile.getInstruments()
#instrument = ordersFile.getInstruments()[0]
k = 0
feed = None
for instrument in ordersFile.getInstruments():
if k == 0:
feed = xiquantPlatform.redis_build_feed_EOD_RAW(instrument, startPeriod, endPeriod)
else:
feed = xiquantPlatform.add_feeds_EODRAW_CSV(feed, instrument, startPeriod, endPeriod)
k += 1
# Add the SPY bars to support the simulation of whether we should have
# entered certain trades or not -- based on the SPY opening higher/lower
# than 20 SMA value for bullish/bearish trades.
feed = xiquantPlatform.add_feeds_EODRAW_CSV(feed, 'SPY', startPeriod, endPeriod)
barsDictForCurrAdj = {}
for instrument in ordersFile.getInstruments():
barsDictForCurrAdj[instrument] = feed.getBarSeries(instrument)
barsDictForCurrAdj['SPY'] = feed.getBarSeries('SPY')
feedAdjustedToEndDate = xiquantPlatform.adjustBars(barsDictForCurrAdj, startPeriod, endPeriod, keyFlag=False)
cash = 100000
useAdjustedClose = True
#myStrategy = MyStrategy(feedAdjustedToEndDate, endPeriod, cash, ordersFile, useAdjustedClose)
myStrategy = MyStrategy(feedAdjustedToEndDate, endPeriod, cash, ordersFile, useAdjustedClose)
# Attach returns and sharpe ratio analyzers.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
myStrategy.run()
filteredOrders = ordersFile.getOrders()
for key in sorted(filteredOrders.iterkeys()):
print key, filteredOrders[key]
# Print the results.
print "Final Portfolio Value: $%.2f" % myStrategy.getResult()
print "Total 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))
print "Strategy Results:\n"
print myStrategy.getStrategiesOutput()
print myStrategy.getResults()
def getLosingStdDev(self, ddof=1):
"""Returns the profit's standard deviation for the losing trades, or None if there are no losing trades.
:param ddof: Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
:type ddof: int.
"""
return stats.stddev(self.__losingTrades, ddof)
def getLosingStdDev(self, ddof=1): """Returns the profit's standard deviation for the losing trades, or None if there are no losing trades. :param ddof: Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. :type ddof: int. """ return stats.stddev(self.__losingTrades, ddof)
def main():
# Load the orders file.
ordersFile = OrdersFile("./OrderFiles/orders.csv")
print "First date", ordersFile.getFirstDate()
print "Last date", ordersFile.getLastDate()
print "Symbols", ordersFile.getInstruments()
qsPath = "/Users/LunaFire/Anaconda/lib/python2.7/site-packages/QSTK"
feed = yahoofeed.Feed()
feed.setBarFilter(csvfeed.DateRangeFilter(ordersFile.getFirstDate(), ordersFile.getLastDate()))
feed.setDailyBarTime(datetime.time(0, 0, 0)) # This is to match the dates loaded with the ones in the orders file.
for symbol in ordersFile.getInstruments():
feed.addBarsFromCSV(symbol, os.path.join(qsPath, "QSData", "Yahoo", symbol + ".csv"))
# Run the strategy.
cash = 1000000
useAdjustedClose = True
myStrategy = MyStrategy(feed, cash, ordersFile, useAdjustedClose)
# Attach returns and sharpe ratio analyzers.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
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))
def volatility(returns, tradingPeriods):
ret = 0.0
# From http://en.wikipedia.org/wiki/Sharpe_ratio: if Rf is a constant risk-free return throughout the period,
# then stddev(R - Rf) = stddev(R).
volatility = stats.stddev(returns, 1)
ret = volatility * math.sqrt(tradingPeriods)
return ret
def main(printinfo):
firstyear = 2007
lastyear = 2013
num_instr = 3
posinstruments = daq.find_instruments_by_year(firstyear)
instruments = sample(posinstruments, num_instr)
instruments = ['JCI', 'AAPL', 'YUM', 'ABC']
feed = daq.build_stock_feed(instruments, [firstyear, lastyear])
this_Strategy = sgs.SLTRIXCrossover(feed,
instruments,
100000,
12,
27,
.43,
.43,
printinfo=printinfo)
# this_Strategy = sgs.EMACrossover(feed, instruments, 100000, 28, .5, ('FixedPerTrade', 10))
retAnalyzer = returns.Returns()
this_Strategy.attachAnalyzer(retAnalyzer)
# this_Strategy.getBroker().setCommission(
# backtesting.TradePercentage(.01)) # 1.% commission per trade
this_Strategy.getBroker().setCommission(
backtesting.FixedPerTrade(10)) # $10 commission per trade
# Attach plotters
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(this_Strategy)
# Include the SMA in the instrument's subplot to get it displayed along
# with the closing prices.
# for pltinstr in instruments:
# plt.getInstrumentSubplot(pltinstr).addDataSeries(
# "Short TRIX", this_Strategy.getshorttrix(pltinstr))
# plt.getInstrumentSubplot(pltinstr).addDataSeries(
# "Long TRIX", this_Strategy.getlongtrix(pltinstr))
# for pltinstr in instruments:
# plt.getInstrumentSubplot(pltinstr).addDataSeries(
# "EMA", this_Strategy.getEMA(pltinstr))
# # Plot the simple returns on each bar.
# plt.getOrCreateSubplot("returns").addDataSeries(
# "Simple returns", retAnalyzer.getReturns())
# Run the strategy
this_Strategy.run()
# Print the results.
print "Final portfolio value: $%.2f" % this_Strategy.getResult()
print "Annual return: %.2f %%" % (
(retAnalyzer.getCumulativeReturns()[-1] * 100) /
float(lastyear - firstyear))
print "Average daily return: %.2f %%" % (
stats.mean(retAnalyzer.getReturns()) * 100)
print "Std. dev. daily return: %.4f" % (stats.stddev(
retAnalyzer.getReturns()))
plt.plot()
return this_Strategy.getResult()
def getBeta(self, riskRate):
ret = 0.0
# tushare获取的涨跌幅数据已*100 故在此/100
volatility = stats.stddev(np.array(riskRate) / 100, 1)
if volatility != 0 and not np.isnan(volatility):
ret = cov(np.array(self.__returns),
np.array(riskRate) / 100) / pow(volatility, 2)
self.__beta = ret
return ret
def algo_test(target_name,
target_csv,
model_name,
model_csv,
model_prediction_std=0.0):
# Load the bar feed from the CSV file
feed = csvfeed.GenericBarFeed(pyalgotrade.bar.Frequency.DAY)
feed.addBarsFromCSV(target_name, target_csv)
feed.addBarsFromCSV(model_name, model_csv)
# Evaluate the strategy with the feed's bars.
myStrategy = MyStrategy(feed, target_name, model_prediction_std)
# myStrategy = MyBasicStrategy(feed, target_name)
# Attach analyzers.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
myStrategy.attachAnalyzer(drawDownAnalyzer)
sortinoRatioAnalyzer = sortino.SortinoRatio()
myStrategy.attachAnalyzer(sortinoRatioAnalyzer)
# Run the strategy
myStrategy.run()
# Print the results.
print("Final portfolio value: $%.2f" % myStrategy.getResult())
print("Total 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)))
print("Sortino ratio: %.2f" % (sortinoRatioAnalyzer.getSortinoRatio(0)))
returnsPerDay = stats.mean(retAnalyzer.getReturns())
# print("shares list : ")
# print(myStrategy.getShares())
output = {
'sharpe': sharpeRatioAnalyzer.getSharpeRatio(0),
'sortino': sortinoRatioAnalyzer.getSortinoRatio(0),
'returnAnalyzer': retAnalyzer,
'annualReturns': returnsPerDay * 252
}
return output
def sharpe_ratio(returns, riskFreeRate, tradingPeriods, annualized=True):
ret = 0.0
# From http://en.wikipedia.org/wiki/Sharpe_ratio: if Rf is a constant risk-free return throughout the period,
# then stddev(R - Rf) = stddev(R).
volatility = stats.stddev(returns, 1)
if volatility != 0:
excessReturns = [dailyRet - (riskFreeRate / float(tradingPeriods)) for dailyRet in returns]
avgExcessReturns = stats.mean(excessReturns)
ret = avgExcessReturns / volatility
if annualized:
ret = ret * math.sqrt(tradingPeriods)
return ret
def sharpe_ratio(returns, riskFreeRate, tradingPeriods, annualized=True):
ret = 0.0
# From http://en.wikipedia.org/wiki/Sharpe_ratio: if Rf is a constant risk-free return throughout the period,
# then stddev(R - Rf) = stddev(R).
volatility = stats.stddev(returns, 1)
if volatility != 0:
rfPerReturn = riskFreeRate / float(tradingPeriods)
avgExcessReturns = stats.mean(returns) - rfPerReturn
ret = avgExcessReturns / volatility
if annualized:
ret = ret * math.sqrt(tradingPeriods)
return ret
def __onReturns(self, dateTime, returnsAnalyzerBase):
netReturn = returnsAnalyzerBase.getNetReturn()
# Calculate daily returns.
if dateTime.date() == self.__currentDate:
self.__retBuffer.append(netReturn)
else:
if self.__retBuffer:
self.__retBuffer.append(netReturn)
netReturn = self.__retBuffer[0]
for aReturn in self.__retBuffer[1:]:
netReturn = (1 + netReturn) * (1 + aReturn) - 1
self.__retBuffer = []
self.__currentDate = dateTime.date()
self.__returns.append(netReturn)
if len(self.__returns) == self.__sessions:
self.__volaSeries.appendWithDateTime(dateTime.date(), stats.stddev(self.__returns, 1) * math.sqrt(252) * 100)
def main(printinfo):
firstyear = 2007
lastyear = 2013
num_instr = 3
posinstruments = daq.find_instruments_by_year(firstyear)
instruments = sample(posinstruments, num_instr)
instruments = ['JCI', 'AAPL', 'YUM', 'ABC']
feed = daq.build_stock_feed(instruments, [firstyear, lastyear])
this_Strategy = sgs.SLTRIXCrossover(
feed, instruments, 100000, 12, 27, .43, .43, printinfo=printinfo)
# this_Strategy = sgs.EMACrossover(feed, instruments, 100000, 28, .5, ('FixedPerTrade', 10))
retAnalyzer = returns.Returns()
this_Strategy.attachAnalyzer(retAnalyzer)
# this_Strategy.getBroker().setCommission(
# backtesting.TradePercentage(.01)) # 1.% commission per trade
this_Strategy.getBroker().setCommission(
backtesting.FixedPerTrade(10)) # $10 commission per trade
# Attach plotters
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(this_Strategy)
# Include the SMA in the instrument's subplot to get it displayed along
# with the closing prices.
# for pltinstr in instruments:
# plt.getInstrumentSubplot(pltinstr).addDataSeries(
# "Short TRIX", this_Strategy.getshorttrix(pltinstr))
# plt.getInstrumentSubplot(pltinstr).addDataSeries(
# "Long TRIX", this_Strategy.getlongtrix(pltinstr))
# for pltinstr in instruments:
# plt.getInstrumentSubplot(pltinstr).addDataSeries(
# "EMA", this_Strategy.getEMA(pltinstr))
# # Plot the simple returns on each bar.
# plt.getOrCreateSubplot("returns").addDataSeries(
# "Simple returns", retAnalyzer.getReturns())
# Run the strategy
this_Strategy.run()
# Print the results.
print "Final portfolio value: $%.2f" % this_Strategy.getResult()
print "Annual return: %.2f %%" % ((retAnalyzer.getCumulativeReturns()[-1] * 100)/float(lastyear - firstyear))
print "Average daily return: %.2f %%" % (stats.mean(retAnalyzer.getReturns()) * 100)
print "Std. dev. daily return: %.4f" % (stats.stddev(retAnalyzer.getReturns()))
plt.plot()
return this_Strategy.getResult()
def sharpe_ratio_2(returns, riskFreeRate, firstDateTime, lastDateTime, annualized=True):
ret = 0.0
# From http://en.wikipedia.org/wiki/Sharpe_ratio:
# if Rf is a constant risk-free return throughout the period, then stddev(R - Rf) = stddev(R).
volatility = stats.stddev(returns, 1)
if volatility != 0:
# We use 365 instead of 252 becuase we wan't the diff from 1/1/xxxx to 12/31/xxxx to be 1 year.
yearsTraded = days_traded(firstDateTime, lastDateTime) / 365.0
riskFreeRateForPeriod = riskFreeRate * yearsTraded
rfPerReturn = riskFreeRateForPeriod / float(len(returns))
avgExcessReturns = stats.mean(returns) - rfPerReturn
ret = avgExcessReturns / volatility
if annualized:
ret = ret * math.sqrt(len(returns) / yearsTraded)
return ret
def sharpe_ratio_2(returns, riskFreeRate, firstDateTime, lastDateTime, annualized=True):
ret = 0.0
# From http://en.wikipedia.org/wiki/Sharpe_ratio:
# if Rf is a constant risk-free return throughout the period, then stddev(R - Rf) = stddev(R).
volatility = stats.stddev(returns, 1)
if volatility != 0:
# We use 365 instead of 252 becuase we wan't the diff from 1/1/xxxx to 12/31/xxxx to be 1 year.
yearsTraded = days_traded(firstDateTime, lastDateTime) / 365.0
riskFreeRateForPeriod = riskFreeRate * yearsTraded
rfPerReturn = riskFreeRateForPeriod / float(len(returns))
avgExcessReturns = stats.mean(returns) - rfPerReturn
ret = avgExcessReturns / volatility
if annualized:
ret = ret * math.sqrt(len(returns) / yearsTraded)
return ret
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
myStrategy.attachAnalyzer(drawDownAnalyzer)
# Run the strategy
myStrategy.run()
# Print the results.
print("")
print("最终资产价值 Final portfolio value: $%.2f" % myStrategy.getResult())
print("年收益 Anual return: %.2f %%" %
(retAnalyzer.getCumulativeReturns()[-1] * 100))
print("平均日收益率 Average daily return: %.2f %%" %
(stats.mean(retAnalyzer.getReturns()) * 100))
print("日收益率方差 Std. dev. daily return: %.4f" %
(stats.stddev(retAnalyzer.getReturns())))
print("夏普指数 Sharpe ratio: %.2f" % (sharpeRatioAnalyzer.getSharpeRatio(0)))
print("最大回撤率 Max. drawdown: %.2f %%" %
(drawDownAnalyzer.getMaxDrawDown() * 100))
print("最长回撤时间 Longest drawdown duration: %s" %
(drawDownAnalyzer.getLongestDrawDownDuration()))
print('')
fss = 'tmp\dret010.csv'
xs1 = ret2csv(fss)
mpl.use('seaborn-whitegrid')
xs1.plot()
def onBars(self, bars):
pass
priceCurrency = "USD"
# Load the bar feed from the CSV file
feed = quandlfeed.Feed()
feed.addBarsFromCSV("ibm/USD", "WIKI-IBM-2011-quandl.csv")
feed.addBarsFromCSV("aes/USD", "WIKI-AES-2011-quandl.csv")
feed.addBarsFromCSV("aig/USD", "WIKI-AIG-2011-quandl.csv")
feed.addBarsFromCSV("orcl/USD", "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(priceCurrency)
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio(priceCurrency)
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)))
capStart = myStrategy.getBroker().getEquity()
myStrategy.info("CAPITAL INICIAL: $%.4f" % capStart)
# Run the strategy
myStrategy.run()
# Show basic information
allRet = returnsAnalyzer.getReturns()
capEnd = myStrategy.getBroker().getEquity()
myStrategy.info("CAPITAL FINAL: $%.4f" % capEnd)
myStrategy.info(" ")
myStrategy.info("Rentabilidad: %.4f%%" % (100 *
(capEnd - capStart) / capStart))
myStrategy.info("Rentabilidad Anualizada: %.4f%%" % (100 * (math.pow(
(capEnd / capStart),
(365.0 /
((myStrategy.endDateTime - myStrategy.startDateTime).days))) - 1)))
myStrategy.info("Volatilidad Anualizada: %.4f%%" %
(100 * stats.stddev(allRet, 1) * math.sqrt(252)))
myStrategy.info("Ratio de Sharpe Anualizado: %.4f" %
(sharpeAnalyzer.getSharpeRatio(0.0036, True)))
myStrategy.info("DrawDown Maximo: %.4f%%" %
(100 * drawDownAnalyzer.getMaxDrawDown()))
myStrategy.info("DrawDown Mas Largo: %s dias" %
(drawDownAnalyzer.getLongestDrawDownDuration().days))
# Plot the strategy.
plt.plot()
def protfolio(self, myStrategy):
retAnalyzer = returns.Returns()
sharpeRatioAnalyzer = sharpe.SharpeRatio()
drawDownAnalyzer = drawdown.DrawDown()
tradesAnalyzer = trades.Trades()
myStrategy.attachAnalyzer(retAnalyzer)
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
myStrategy.attachAnalyzer(drawDownAnalyzer)
myStrategy.attachAnalyzer(tradesAnalyzer)
myStrategy.run()
dfjson = myStrategy.EchoDF()
if self._Debug:
print dfjson
print "--------------"
finalProt = myStrategy.getResult()
brjObject = brj()
#交易过程
brjObject.db(dfjson.to_json(orient="split"))
## ...
brjObject.formats("markPoint")
brjObject.name("df")
brjObject.buildData()
brjObject.db(dfjson.to_json(orient="split"))
## ...
brjObject.formats("table")
brjObject.name("交易细则")
brjObject.buildData()
base = {}
#总资产
base["Final portfolio value: "] = "$%.2f" % finalProt
#累计收益率
base["Anual return: "] = "%.2f %%" % (
retAnalyzer.getCumulativeReturns()[-1] * 100)
# 平均收益率
base["Average daily return:"] = " %.2f %%" % (
stats.mean(retAnalyzer.getReturns()) * 100)
#方差收益率
base["Std. dev. daily return:"] = " %.4f" % (stats.stddev(
retAnalyzer.getReturns()))
#夏普比率
base["Sharpe ratio: "] = "%.2f" % (
sharpeRatioAnalyzer.getSharpeRatio(0))
#最大回撤
base["DrawDown :"] = " %.2f" % (drawDownAnalyzer.getMaxDrawDown())
dfbase = pd.DataFrame(base, index=["val"])
if self._Debug:
print dfbase.T
print "--------------"
baseJson = dfbase.T.reset_index().to_json(orient="split")
brjObject.db(baseJson)
brjObject.formats("table")
brjObject.name("base")
brjObject.buildData()
AllTrades = {}
#总交易笔数
AllTrades["Total trades:"] = " %d" % (tradesAnalyzer.getCount())
if tradesAnalyzer.getCount() > 0:
profits = tradesAnalyzer.getAll()
AllTrades["Avg. profit:"] = " $%2.f" % (profits.mean())
AllTrades["Profits std. dev.:"] = " $%2.f" % (profits.std())
AllTrades["Max. profit: "] = "$%2.f" % (profits.max())
AllTrades["Min. profit:"] = " $%2.f" % (profits.min())
returns_trade = tradesAnalyzer.getAllReturns()
AllTrades["Avg. return:"] = " %2.f %%" % (returns_trade.mean() *
100)
AllTrades["Returns std. dev.: "] = "%2.f %%" % (
returns_trade.std() * 100)
AllTrades["Max. return: "] = "%2.f %%" % (returns_trade.max() *
100)
AllTrades["Min. return: "] = "%2.f %%" % (returns_trade.min() *
100)
df = pd.DataFrame(AllTrades, index=["val"])
if self._Debug:
print df.T
print "--------------"
baseJson = df.T.reset_index().to_json(orient="split")
brjObject.db(baseJson)
brjObject.formats("table")
brjObject.name("AllTrades")
brjObject.buildData()
#盈利笔数
proTrades = {}
proTrades["Profitable trades: "] = "%d" % (
tradesAnalyzer.getProfitableCount())
if tradesAnalyzer.getProfitableCount() > 0:
profits = tradesAnalyzer.getProfits()
proTrades["Avg. profit: "] = "$%2.f" % (profits.mean())
proTrades["Profits std. dev.: "] = "$%2.f" % (profits.std())
proTrades["Max. profit:"] = " $%2.f" % (profits.max())
proTrades["Min. profit:"] = " $%2.f" % (profits.min())
returns_trade = tradesAnalyzer.getPositiveReturns()
proTrades["Avg. return: "] = "%2.f %%" % (returns_trade.mean() *
100)
proTrades["Returns std. dev.:"] = " %2.f %%" % (
returns_trade.std() * 100)
proTrades["Max. return: "] = "%2.f %%" % (returns_trade.max() *
100)
proTrades["Min. return: "] = "%2.f %%" % (returns_trade.min() *
100)
df = pd.DataFrame(proTrades, index=["val"])
if self._Debug:
print df.T
print "--------------"
baseJson = df.T.reset_index().to_json(orient="split")
brjObject.db(baseJson)
brjObject.formats("table")
brjObject.name("proTrades")
brjObject.buildData()
#亏损笔数
unproTrades = {}
unproTrades["Unprofitable trades:"] = " %d" % (
tradesAnalyzer.getUnprofitableCount())
if tradesAnalyzer.getUnprofitableCount() > 0:
losses = tradesAnalyzer.getLosses()
unproTrades["Avg. loss:"] = " $%2.f" % (losses.mean())
unproTrades["Losses std. dev.:"] = " $%2.f" % (losses.std())
unproTrades["Max. loss: "] = "$%2.f" % (losses.min())
unproTrades["Min. loss: "] = "$%2.f" % (losses.max())
returns_trade = tradesAnalyzer.getNegativeReturns()
unproTrades["Avg. return: "] = "%2.f %%" % (returns_trade.mean() *
100)
unproTrades["Returns std. dev.: "] = "%2.f %%" % (
returns_trade.std() * 100)
unproTrades["Max. return: "] = "%2.f %%" % (returns_trade.max() *
100)
unproTrades["Min. return: "] = "%2.f %%" % (returns_trade.min() *
100)
df = pd.DataFrame(unproTrades, index=["val"])
if self._Debug:
print df.T
print "--------------"
baseJson = df.T.reset_index().to_json(orient="split")
brjObject.db(baseJson)
brjObject.formats("table")
brjObject.name("unproTrades")
brjObject.buildData()
brjJson = brjObject.getResult()
if self._Debug == False:
print brjJson
def onEnterOk(self, position):
execInfo = position.getEntryOrder().getExecutionInfo()
print("BUY at $%.2f 数量:%d 手续费:%.2f" % (execInfo.getPrice(), execInfo.getQuantity(), execInfo.getCommission()))
def onBars(self, bars):
pass
if __name__ == "__main__":
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")
myStrategy = MyStrategy(feed)
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeAnalyzer)
myStrategy.run()
print("最终收益:%.2f" % myStrategy.getResult())
print("年化收益率:%.2f%%" % (retAnalyzer.getCumulativeReturns()[-1]*100))
print("平均每日收益率:%.2f %%" % (stats.mean(retAnalyzer.getReturns())*100))
print("每日收益标准差:%.4f" % (stats.stddev(retAnalyzer.getReturns())))
print("夏普比率:%.2f" % sharpeAnalyzer.getSharpeRatio(0))
def run_strategy(isBenchmark, instruments, posMax, entrySma, exitSma,
rsiPeriod, overSoldThreshold):
# Load the yahoo feed from the CSV file
feed = yahoofeed.Feed()
feed.sanitizeBars(True)
for instrument, startYear in instruments.items():
for year in range(startYear, assets.endYear):
if os.path.isfile(assets.folder + instrument + "-" + str(year) +
".csv"):
feed.addBarsFromCSV(
instrument,
assets.folder + instrument + "-" + str(year) + ".csv")
if isBenchmark:
myStrategy = MyBenchmark(feed, instruments, posMax, exitSma)
else:
myStrategy = MyStrategy(feed, instruments, posMax, entrySma, exitSma,
rsiPeriod, overSoldThreshold)
# Attach analyzers to the strategy.
# Returns first in case others use it (DataSeries)
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
returnsAnalyzer.getReturns().setMaxLen(1000000)
sharpeAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
myStrategy.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
myStrategy.attachAnalyzer(tradesAnalyzer)
volaAnalyzer = volatility.VolaAnalyzer(120)
myStrategy.attachAnalyzer(volaAnalyzer)
# Attach a plotter to the strategy
plt = plotter.StrategyPlotter(myStrategy, False)
volaSeries = volaAnalyzer.getVolaSeries()
plt.getOrCreateSubplot("Volatility").addDataSeries("Volatility",
volaSeries)
capStart = myStrategy.getBroker().getEquity()
myStrategy.info("CAPITAL INICIAL: $%.4f" % capStart)
# Run the strategy
myStrategy.run()
# Show basic information
allRet = returnsAnalyzer.getReturns()
capEnd = myStrategy.getBroker().getEquity()
myStrategy.info("CAPITAL FINAL: $%.4f" % capEnd)
myStrategy.info(" ")
myStrategy.info("Rentabilidad: %.4f%%" % (100 *
(capEnd - capStart) / capStart))
myStrategy.info("Rentabilidad Anualizada: %.4f%%" % (100 * (math.pow(
(capEnd / capStart),
(365.0 /
((myStrategy.endDateTime - myStrategy.startDateTime).days))) - 1)))
myStrategy.info("Volatilidad Anualizada: %.4f%%" %
(100 * stats.stddev(allRet, 1) * math.sqrt(252)))
myStrategy.info("Ratio de Sharpe Anualizado: %.4f" %
(100 * sharpeAnalyzer.getSharpeRatio(0.0036, True)))
myStrategy.info("DrawDown Maximo: %.4f%%" %
(100 * drawDownAnalyzer.getMaxDrawDown()))
myStrategy.info("DrawDown Mas Largo: %s dias" %
(drawDownAnalyzer.getLongestDrawDownDuration().days))
myStrategy.info(" ")
myStrategy.info("Rentabilidad Media: %.4f%%" % (100 * stats.mean(allRet)))
posRet = []
negRet = []
allRet = returnsAnalyzer.getReturns()
for ret in allRet:
if ret > 0:
posRet.append(ret)
elif ret < 0:
negRet.append(ret)
myStrategy.info("Ganancia Media: %.4f%%" % (100 * stats.mean(posRet)))
myStrategy.info("Perdida Media: %.4f%%" % (100 * stats.mean(negRet)))
myStrategy.info(" ")
myStrategy.info("Ganancia Media por Op: $%s" %
(stats.mean(tradesAnalyzer.getProfits())))
myStrategy.info("Perdida Media por Op: $%s" %
(stats.mean(tradesAnalyzer.getLosses())))
myStrategy.info("Comisiones Totales: $%s" %
(sum(tradesAnalyzer.getCommissionsForAllTrades())))
myStrategy.info("Num Ops Igual: %s" % (tradesAnalyzer.getEvenCount()))
myStrategy.info("Num Ops Gano: %s" % (tradesAnalyzer.getProfitableCount()))
myStrategy.info("Num Ops Pierdo: %s" %
(tradesAnalyzer.getUnprofitableCount()))
# Plot the strategy.
plt.plot()
def __testStdDevImpl(self, values, ddof): self.__assertEqFloat(stats.stddev(values, ddof), stats.py_stddev(values, ddof))
def main(plot):
instrument = "FB"
#smaPeriod = 163
smaPeriod = 30
# Download the bars.
feed = yahoofinance.build_feed([instrument], 2015, 2016, "../../data/")
strat = SMACrossOver(feed, instrument, smaPeriod)
retAnalyzer = returns.Returns()
strat.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
tradesAnalyzer = trades.Trades()
strat.attachAnalyzer(tradesAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
strat.attachAnalyzer(drawDownAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
plt.getInstrumentSubplot(instrument).addDataSeries("sma", strat.getSMA())
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", retAnalyzer.getReturns())
#plt.getOrCreateSubplot("Slope").addDataSeries("slope", strat.getSlope())
plt.getOrCreateSubplot("BuySlope").addDataSeries("BuySlope", strat.getBuySlope())
strat.run()
print "Final portfolio value: $%.2f" % strat.getResult()
print "Cumulative returns: %.2f %%" % (retAnalyzer.getCumulativeReturns()[-1] * 100)
print "Average daily return: %.2f %%" % (pystats.mean(retAnalyzer.getReturns()) * 100)
print "Std. dev. daily return: %.4f" % (pystats.stddev(retAnalyzer.getReturns()))
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())
rets = tradesAnalyzer.getAllReturns()
print "Avg. return: %2.f %%" % (rets.mean() * 100)
print "Returns std. dev.: %2.f %%" % (rets.std() * 100)
print "Max. return: %2.f %%" % (rets.max() * 100)
print "Min. return: %2.f %%" % (rets.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())
rets = tradesAnalyzer.getPositiveReturns()
print "Avg. return: %2.f %%" % (rets.mean() * 100)
print "Returns std. dev.: %2.f %%" % (rets.std() * 100)
print "Max. return: %2.f %%" % (rets.max() * 100)
print "Min. return: %2.f %%" % (rets.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())
rets = tradesAnalyzer.getNegativeReturns()
print "Avg. return: %2.f %%" % (rets.mean() * 100)
print "Returns std. dev.: %2.f %%" % (rets.std() * 100)
print "Max. return: %2.f %%" % (rets.max() * 100)
print "Min. return: %2.f %%" % (rets.min() * 100)
if plot:
plt.plot()
def main(tick, year, cash, smaPeriod, order):
print 'Welcome, ' + os.environ['USER'] + '!'
print 'Backtesting ' + tick + ' in ' + str(year)
# Download daily bars
filename = tick + '-' + str(year) + '.csv'
yahoofinance.download_daily_bars(tick, year, filename)
# Load CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV(tick, filename)
# Evaluate Strategy
strategySMA = StrategySMA(feed, tick, smaPeriod, order, cash)
# Attach a returns analyzers to the strategy.
retAnalyzer = returns.Returns()
strategySMA.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strategySMA.attachAnalyzer(sharpeRatioAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(strategySMA)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
plt.getInstrumentSubplot(tick).addDataSeries("SMA", strategySMA.getSMA())
# Set portfolio cash
strategySMA.getBroker().setCash(cash)
initial = strategySMA.getBroker().getEquity()
# Run the strategy
strategySMA.run()
# Print the results
print '*' * 60
print 'Initial portfolio value: $%.2f' % initial
final = strategySMA.getBroker().getEquity()
print 'Final portfolio value: $%.2f' % final
net = final - initial
if net > 0:
print 'Net gain: $%.2f' % net
else:
print 'Net loss: $%.2f' % net
percentage = (final - initial) / initial * 100
if percentage > 0:
print 'Percentage gain: +%.2f%%' % percentage
else:
print 'Percentage loss: %.2f%%' % percentage
#print 'Final portfolio value: $%.2f' % strategySMA.getResult()
print 'Annual 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))
# Plot the strategy
plt.plot()
if hasattr(myStrategy, '_smaShort'):
subPlot = plt.getOrCreateSubplot("SMA")
subPlot.addDataSeries("SMALong", myStrategy._smaLong[instrument])
subPlot.addDataSeries("SMAShort", myStrategy._smaShort[instrument])
subPlot = plt.getOrCreateSubplot("MACD")
subPlot.addDataSeries("MACDPrice", myStrategy._macdPrice[instrument])
subPlot.addDataSeries("MACDVolume", myStrategy._macdVol[instrument])
capStart = myStrategy.getBroker().getEquity()
myStrategy.info("CAPITAL INICIAL: $%.4f" % capStart)
# Run the strategy
myStrategy.run()
# Show basic information
allRet = returnsAnalyzer.getReturns()
capEnd = myStrategy.getBroker().getEquity()
myStrategy.info("CAPITAL FINAL: $%.4f" % capEnd)
myStrategy.info(" ")
myStrategy.info("Rentabilidad: %.4f%%" % (100 * (capEnd - capStart) / capStart))
myStrategy.info("Rentabilidad Anualizada: %.4f%%" % (100 * (math.pow((capEnd / capStart), (365.0 / ((myStrategy.endDateTime - myStrategy.startDateTime).days))) - 1)))
myStrategy.info("Volatilidad Anualizada: %.4f%%" % (100 * stats.stddev(allRet, 1) * math.sqrt(252)))
myStrategy.info("Ratio de Sharpe Anualizado: %.4f" % (100 * sharpeAnalyzer.getSharpeRatio(0.0036, True)))
myStrategy.info("DrawDown Maximo: %.4f%%" % (100 * drawDownAnalyzer.getMaxDrawDown()))
myStrategy.info("DrawDown Mas Largo: %s dias" % (drawDownAnalyzer.getLongestDrawDownDuration().days))
# Plot the strategy.
plt.plot()
def onBars(self, bars):
pass
# Load the yahoo feed from CSV files.
feed = yahoofeed.Feed()
feed.addBarsFromCSV("aeti", "aeti-2011-yahoofinance.csv")
feed.addBarsFromCSV("egan", "egan-2011-yahoofinance.csv")
feed.addBarsFromCSV("glng", "glng-2011-yahoofinance.csv")
feed.addBarsFromCSV("simo", "simo-2011-yahoofinance.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, 252))
def main(plot):
# Download the bars.
feed = build_feed(instFeed, start, endYear)
# Define Strategy
myStrategy = MyStrategy(feed, starting_cash)
# Attach returns and sharpe ratio analyzers.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
tradesAnalyzer = trades.Trades()
myStrategy.attachAnalyzer(tradesAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
myStrategy.attachAnalyzer(drawDownAnalyzer)
if plot:
symbol = "C"
etf = sym_dictionary[symbol]
spreadDS = dataseries.SequenceDataSeries(pltSpread[symbol])
returnDS = dataseries.SequenceDataSeries(marketValue[symbol])
MACD_ROC = dataseries.SequenceDataSeries(MFI_MACD[symbol][3])
middleBandDS = dataseries.SequenceDataSeries(bollingerBands[symbol][1])
upperBandDS = dataseries.SequenceDataSeries(bollingerBands[symbol][2])
lowerBandDS = dataseries.SequenceDataSeries(bollingerBands[symbol][0])
umiddleBandDS = dataseries.SequenceDataSeries(bollingerBands[symbol][3])
lmiddleBandDS = dataseries.SequenceDataSeries(bollingerBands[symbol][4])
MFI_MACD_ds = dataseries.SequenceDataSeries(MFI_MACD[symbol][0])
MFI_MACDtrigger_ds = dataseries.SequenceDataSeries(MFI_MACD[symbol][1])
MFI_MACDoscillator_ds = dataseries.SequenceDataSeries(MFI_MACD[symbol][2])
plt = plotter.StrategyPlotter(myStrategy, False, False, False)
plt.getOrCreateSubplot("spread").addDataSeries(symbol + ":" + etf, spreadDS)
plt.getOrCreateSubplot("spread").addDataSeries("Middle", middleBandDS)
plt.getOrCreateSubplot("spread").addDataSeries("Upper", upperBandDS)
plt.getOrCreateSubplot("spread").addDataSeries("Lower", lowerBandDS)
plt.getOrCreateSubplot("spread").addDataSeries("Mid-L", lmiddleBandDS)
plt.getOrCreateSubplot("spread").addDataSeries("Mid-U", umiddleBandDS)
plt.getOrCreateSubplot("returns").addDataSeries(symbol + "-Return", returnDS)
plt.getOrCreateSubplot("returns").addDataSeries("Cum. return", returnsAnalyzer.getCumulativeReturns())
#plt.getOrCreateSubplot("MFI").addDataSeries("MFI-MACD", MFI_MACD_ds)
#plt.getOrCreateSubplot("MFI").addDataSeries("Trigger", MFI_MACDtrigger_ds)
plt.getOrCreateSubplot("MACD").addDataSeries("Oscillator", MFI_MACDoscillator_ds)
#plt.getOrCreateSubplot("MACD").addDataSeries("ROC", MACD_ROC)
plt.getOrCreateSubplot("MACD").addDataSeries("0", [0])
# Run the strategy
print "Running Strategy..."
myStrategy.clearOrders(orders_file)
myStrategy.run()
print "Final portfolio value: $%.2f" % myStrategy.getResult()
print "Anual return: %.2f %%" % (returnsAnalyzer.getCumulativeReturns()[-1] * 100)
print "Average daily return: %.2f %%" % (stats.mean(returnsAnalyzer.getReturns()) * 100)
print "Std. dev. daily return: %.4f" % (stats.stddev(returnsAnalyzer.getReturns()))
print "Sharpe ratio: %.2f" % (sharpeRatioAnalyzer.getSharpeRatio(0, 252))
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())
returnz = tradesAnalyzer.getAllReturns()
print "Avg. return: %2.f %%" % (returnz.mean() * 100)
print "Returns std. dev.: %2.f %%" % (returnz.std() * 100)
print "Max. return: %2.f %%" % (returnz.max() * 100)
print "Min. return: %2.f %%" % (returnz.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())
returnz = tradesAnalyzer.getPositiveReturns()
print "Avg. return: %2.f %%" % (returnz.mean() * 100)
print "Returns std. dev.: %2.f %%" % (returnz.std() * 100)
print "Max. return: %2.f %%" % (returnz.max() * 100)
print "Min. return: %2.f %%" % (returnz.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())
returnz = tradesAnalyzer.getNegativeReturns()
print "Avg. return: %2.f %%" % (returnz.mean() * 100)
print "Returns std. dev.: %2.f %%" % (returnz.std() * 100)
print "Max. return: %2.f %%" % (returnz.max() * 100)
print "Min. return: %2.f %%" % (returnz.min() * 100)
print
for symbol in instruments:
print str(symbol)+ ": " + str(round(marketValue[symbol][-1], 4) * 100) + "%"
print bbandPeriod
if plot:
plt.plot(datetime.strptime('01/01/' + str(startYear), '%m/%d/%Y'))