def reporting(myStrtegy, plot):
print "Final portfolio value: $%.2f" % myStrategy.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())
returns = tradesAnalyzer.getAllReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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
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())
returns = tradesAnalyzer.getPositiveReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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
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())
returns = tradesAnalyzer.getNegativeReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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)
if plot:
plt.plot()
def detail(self):
""""""
print "-------------------------------------------------------------------------"
print "Final portfolio value: $%.2f" % self.strat.getResult()
print "Cumulative returns: %.2f %%" % (self.returnsAnalyzer.getCumulativeReturns()[-1] * 100)
print "Sharpe ratio: %.2f" % (self.sharpeRatioAnalyzer.getSharpeRatio(0.05))
print "Max. drawdown: %.2f %%" % (self.drawdownAnalyzer.getMaxDrawDown() * 100)
print "Longest drawdown duration: (%s)" % (self.drawdownAnalyzer.getLongestDrawDownDuration())
print
print "Total trades: %d" % (self.tradesAnalyzer.getCount())
if self.tradesAnalyzer.getCount() > 0:
profits = self.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())
returns = self.tradesAnalyzer.getAllReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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
print "Profitable trades: %d" % (self.tradesAnalyzer.getProfitableCount())
if self.tradesAnalyzer.getProfitableCount() > 0:
profits = self.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())
returns = self.tradesAnalyzer.getPositiveReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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
print "Unprofitable trades: %d" % (self.tradesAnalyzer.getUnprofitableCount())
if self.tradesAnalyzer.getUnprofitableCount() > 0:
losses = self.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())
returns = self.tradesAnalyzer.getNegativeReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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 "-------------------------------------------------------------------------"
def analyzer(testResult):
# 回测信息
print("年化收益率: %.2f %%" % (testResult[0].getCumulativeReturns()[-1] * 100))
print("夏普比率: %.2f" % (testResult[1].getSharpeRatio(0.05)))
print("最大回撤: %.2f %%" % (testResult[2].getMaxDrawDown() * 100))
print("最大回撤期间: %s" % (testResult[2].getLongestDrawDownDuration()))
# 交易信息
td = testResult[3]
print("-----------------------")
print("总交易次数:%d" % (td.getCount()))
if td.getCount() > 0:
profits = td.getAll()
print("平均收益:%.2f" % (profits.mean()))
print("收益标准差:%.2f" % (profits.std()))
print("最大收益:%.2f" % (profits.max()))
print("最小收益:%.2f" % (profits.min()))
returns = td.getAllReturns()
print("平均收益率:%.2f%%" % (returns.mean() * 100))
print("收益率标准差:%.2f%%" % (returns.std() * 100))
print("最大收益率:%.2f%%" % (returns.max() * 100))
print("最小收益率:%.2f%%" % (returns.min() * 100))
print("-----------------------")
print("盈利的交易次数: %d" % (td.getProfitableCount()))
if td.getProfitableCount() > 0:
profits = td.getProfits()
print("平均收益:%.2f" % (profits.mean()))
print("收益标准差:%.2f" % (profits.std()))
print("最大收益:%.2f" % (profits.max()))
print("最小收益:%.2f" % (profits.min()))
returns = td.getPositiveReturns()
print("平均收益率:%.2f%%" % (returns.mean() * 100))
print("收益率标准差:%.2f%%" % (returns.std() * 100))
print("最大收益率:%.2f%%" % (returns.max() * 100))
print("最小收益率:%.2f%%" % (returns.min() * 100))
print("-----------------------")
print("未盈利的交易次数: %d" % (td.getUnprofitableCount()))
if td.getUnprofitableCount() > 0:
losses = td.getLosses()
print("平均收益:%.2f" % (losses.mean()))
print("收益标准差:%.2f" % (losses.std()))
print("最大收益:%.2f" % (losses.max()))
print("最小收益:%.2f" % (losses.min()))
returns = td.getNegativeReturns()
print("平均收益率:%.2f%%" % (returns.mean() * 100))
print("收益率标准差:%.2f%%" % (returns.std() * 100))
print("最大收益率:%.2f%%" % (returns.max() * 100))
print("最小收益率:%.2f%%" % (returns.min() * 100))
def report_profit_return(get_count, get_profit, get_returns, ree):
tmp = get_count()
if 0 < tmp:
profits = get_profit()
# ree.append((profits.mean(),profits.std(),profits.max(),profits.min()))
ree["profits_mean"] = profits.mean()
ree["profits_std"] = profits.std()
ree["profits_max"] = profits.max()
ree["profits_min"] = profits.min()
returns = get_returns()
# ree.append((returns.mean() * 100, returns.std() * 100, returns.max() * 100, returns.min() * 100))
ree["returns_mean"] = returns.mean() * 100
ree["returns_std"] = returns.std() * 100
ree["returns_max"] = returns.max() * 100
ree["returns_min"] = returns.min() * 100
else:
ree["profits_mean"] = NULLVALUE
ree["profits_std"] = NULLVALUE
ree["profits_max"] = NULLVALUE
ree["profits_min"] = NULLVALUE
ree["returns_mean"] = NULLVALUE
ree["returns_std"] = NULLVALUE
ree["returns_max"] = NULLVALUE
ree["returns_min"] = NULLVALUE
def Function_form( bBandsPeriod,smaPeriod_short,smaPeriod_long, slopePeriod,plot=False):
from pyalgotrade.stratanalyzer import returns
from pyalgotrade.stratanalyzer import sharpe
from pyalgotrade.stratanalyzer import drawdown
from pyalgotrade.stratanalyzer import trades
from pyalgotrade import strategy
from pyalgotrade.strategy import position
from pyalgotrade import plotter
from pyalgotrade.tools import yahoofinance
from pyalgotrade.barfeed import yahoofeed
from pyalgotrade.technical import bollinger
from pyalgotrade.stratanalyzer import sharpe
import BBands_mod
#import report
import matplotlib.pyplot as plt
# Load the yahoo feed from the CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV("orcl", "data_daily.csv")
#def main(plot):
instrument = "orcl"
# Evaluate the strategy with the feed's bars.
myStrategy = BBands_mod.BBands(feed, instrument, bBandsPeriod,smaPeriod_short,smaPeriod_long,slopePeriod)
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())
# Attach different analyzers to a strategy before executing it.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
myStrategy.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
myStrategy.attachAnalyzer(tradesAnalyzer)
# Run the strategy.
myStrategy.run()
print "Final portfolio value: $%.2f" % myStrategy.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())
returns = tradesAnalyzer.getAllReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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
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())
returns = tradesAnalyzer.getPositiveReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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
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())
returns = tradesAnalyzer.getNegativeReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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)
if plot:
plt.plot()
return (myStrategy.getResult(),retAnalyzer.getCumulativeReturns()[-1] * 100,
sharpeRatioAnalyzer.getSharpeRatio(0.05), drawDownAnalyzer.getMaxDrawDown() * 100)
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])
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()))
returns = tradesAnalyzer.getAllReturns()
print("Avg. return: %2.f %%" % (returns.mean() * 100))
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("")
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()))
returns = tradesAnalyzer.getPositiveReturns()
print("Avg. return: %2.f %%" % (returns.mean() * 100))
print("Returns std. dev.: %2.f %%" % (returns.std() * 100))
print("Max. return: %2.f %%" % (returns.max() * 100))
myStrategy.info("Cumulative returns: %.2f %%" % (returnsAnalyzer.getCumulativeReturns()[-1] * 100))
myStrategy.info("Sharpe ratio: %.2f" % (sharpeRatioAnalyzer.getSharpeRatio(0.05)))
myStrategy.info("Max. drawdown: %.2f %%" % (drawDownAnalyzer.getMaxDrawDown() * 100))
myStrategy.info("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())
returns = tradesAnalyzer.getAllReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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
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())
returns = tradesAnalyzer.getPositiveReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
print "Returns std. dev.: %2.f %%" % (returns.std() * 100)
print "Max. return: %2.f %%" % (returns.max() * 100)
def run_strategy(smaPeriod):
# Load the yahoo feed from the CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV("zggf", "E:/PythonData/CSV/000938.csv")
# Evaluate the strategy with the feed.
myStrategy = MyStrategy(feed, "zggf", smaPeriod)
# Attach different analyzers to a strategy before executing it.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
myStrategy.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
myStrategy.attachAnalyzer(tradesAnalyzer)
myStrategy.run()
print("Final portfolio value: $%.2f" % myStrategy.getResult())
print("Cumulative returns: %.2f %%" %
(returnsAnalyzer.getCumulativeReturns()[-1] * 100))
print(returnsAnalyzer.getCumulativeReturns())
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()))
returns = tradesAnalyzer.getAllReturns()
print("Avg. return: %2.f %%" % (returns.mean() * 100))
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()
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()))
returns = tradesAnalyzer.getPositiveReturns()
print("Avg. return: %2.f %%" % (returns.mean() * 100))
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()
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()))
returns = tradesAnalyzer.getNegativeReturns()
print("Avg. return: %2.f %%" % (returns.mean() * 100))
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))
def setup_strategy(files, name, show_plot=False):
#from pyalgotrade.feed import csvfeed, yahoofeed
# Load the bar feed from the CSV file
#feed = csvfeed.GenericBarFeed(frequency=Frequency.DAY)
feed = yahoofeed.Feed(Frequency.DAY)
#feed = csvfeed.Feed("Date", "%Y-%m-%d")
for sym, filename in files:
#print('loading', sym, filename)
if 'predictions' not in filename:
feed.addBarsFromCSV(sym, filename)
else:
feed.addBarsFromCSV('states', filename)
instrument_1 = files[0][0]
instrument_2 = files[1][0]
instrument_3 = files[2][0]
states_instrument = 'states'
#print('got these instruments', instrument_1, instrument_2, instrument_3, states_instrument)
#input()
#print(files)
# Evaluate the strategy with the feed.
myStrategy = AccuracyStrat(feed, instrument_1, instrument_2, instrument_3,
states_instrument)
from pyalgotrade.stratanalyzer import returns
# Attach different analyzers to a strategy before executing it.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
myStrategy.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
myStrategy.attachAnalyzer(tradesAnalyzer)
# 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("orcl").addDataSeries("SMA", myStrategy.getSMA())
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns",
retAnalyzer.getReturns())
#
# Run the strategy.
myStrategy.run()
if show_plot:
plt.plot()
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(10, 5)
cum_returns = retAnalyzer.getCumulativeReturns()[-1] * 100
sharpe_ratio = sharpeRatioAnalyzer.getSharpeRatio(0.05)
results = {}
results['final_value'] = myStrategy.getResult()
results['cum_returns'] = cum_returns
results['sharpe_ratio'] = sharpe_ratio
if not show_plot and sharpe_ratio > 1 and cum_returns > 200:
plt.savePlot('./backtest_plots/%s_%s.png' %
(str(int(results['cum_returns'])), name))
del plt
results['max_drawdown_%'] = drawDownAnalyzer.getMaxDrawDown() * 100
results['longest_drawdown'] = str(
drawDownAnalyzer.getLongestDrawDownDuration())
results['total_trades'] = tradesAnalyzer.getCount()
results['profitable_trades'] = tradesAnalyzer.getProfitableCount()
try:
results['win_rate'] = tradesAnalyzer.getProfitableCount(
) / tradesAnalyzer.getCount()
profits = tradesAnalyzer.getAll()
results['avg_profit_$'] = profits.mean()
results['std_profit_$'] = profits.std()
results['max_profit_$'] = profits.max()
results['min_profit_$'] = profits.min()
returns = tradesAnalyzer.getAllReturns()
results['avg_profit_%'] = returns.mean() * 100
results['std_profit_%'] = returns.std() * 100
results['max_profit_%'] = returns.max() * 100
results['min_profit_%'] = returns.min() * 100
for sym, filename in files:
os.remove(filename)
except Exception as e:
#print('backtest exception', e)
pass
results = pd.DataFrame.from_dict(results, orient='index')
#print(results)
return results
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()
def Function_form(bBandsPeriod,
smaPeriod_short,
smaPeriod_long,
slopePeriod,
plot=False):
from pyalgotrade.stratanalyzer import returns
from pyalgotrade.stratanalyzer import sharpe
from pyalgotrade.stratanalyzer import drawdown
from pyalgotrade.stratanalyzer import trades
from pyalgotrade import strategy
from pyalgotrade.strategy import position
from pyalgotrade import plotter
from pyalgotrade.tools import yahoofinance
from pyalgotrade.barfeed import yahoofeed
from pyalgotrade.technical import bollinger
from pyalgotrade.stratanalyzer import sharpe
import BBands_mod
#import report
import matplotlib.pyplot as plt
# Load the yahoo feed from the CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV("orcl", "data_daily.csv")
#def main(plot):
instrument = "orcl"
# Evaluate the strategy with the feed's bars.
myStrategy = BBands_mod.BBands(feed, instrument, bBandsPeriod,
smaPeriod_short, smaPeriod_long,
slopePeriod)
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())
# Attach different analyzers to a strategy before executing it.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
myStrategy.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
myStrategy.attachAnalyzer(tradesAnalyzer)
# Run the strategy.
myStrategy.run()
print "Final portfolio value: $%.2f" % myStrategy.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())
returns = tradesAnalyzer.getAllReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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
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())
returns = tradesAnalyzer.getPositiveReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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
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())
returns = tradesAnalyzer.getNegativeReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
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)
if plot:
plt.plot()
return (myStrategy.getResult(),
retAnalyzer.getCumulativeReturns()[-1] * 100,
sharpeRatioAnalyzer.getSharpeRatio(0.05),
drawDownAnalyzer.getMaxDrawDown() * 100)
