def runstrat():
args = parse_args()
# Create a cerebro entity
cerebro = bt.Cerebro(stdstats=False)
cerebro.addstrategy(
SMAStrategy,
# args for the strategy
period=args.period,
)
# Load the Data
datapath = args.dataname or '../datas/sample/2006-day-001.txt'
data = btfeeds.BacktraderCSVData(dataname=datapath)
tframes = dict(daily=bt.TimeFrame.Days,
weekly=bt.TimeFrame.Weeks,
monthly=bt.TimeFrame.Months)
# Handy dictionary for the argument timeframe conversion
# Resample the data
data_replayed = bt.DataReplayer(dataname=data,
timeframe=tframes[args.timeframe],
compression=args.compression)
# First add the original data - smaller timeframe
cerebro.adddata(data_replayed)
# Run over everything
cerebro.run(preload=False)
# Plot the result
cerebro.plot(style='bar')
def test_run(main=False):
for i in [True, False]:
data = testcommon.getdata(0)
if i:
data = bt.DataReplayer(
dataname=data,
timeframe=bt.TimeFrame.Weeks,
compression=1)
else:
data.replay(
timeframe=bt.TimeFrame.Weeks,
compression=1)
datas = [data]
testcommon.runtest(datas,
testcommon.TestStrategy,
main=main,
plot=main,
chkind=chkind,
chkmin=chkmin,
chkvals=chkvals,
chknext=chknext,
chkargs=chkargs,
runonce=False, preload=False)
def runstrat():
args = parse_args()
# Create a cerebro entity
cerebro = bt.Cerebro()
# Add a strategy
if not args.indicators:
cerebro.addstrategy(bt.Strategy)
else:
cerebro.addstrategy(
SMAStrategy,
# args for the strategy
period=args.period,
onlydaily=args.onlydaily,
)
# Load the Data
datapath = args.dataname or '../../datas/2006-day-001.txt'
data = btfeeds.BacktraderCSVData(dataname=datapath)
tframes = dict(daily=bt.TimeFrame.Days,
weekly=bt.TimeFrame.Weeks,
monthly=bt.TimeFrame.Months)
# Handy dictionary for the argument timeframe conversion
# Resample the data
if args.noresample:
datapath = args.dataname2 or '../../datas/2006-week-001.txt'
data2 = btfeeds.BacktraderCSVData(dataname=datapath)
else:
if args.oldrs:
if args.replay:
data2 = bt.DataReplayer(dataname=data,
timeframe=tframes[args.timeframe],
compression=args.compression)
else:
data2 = bt.DataResampler(dataname=data,
timeframe=tframes[args.timeframe],
compression=args.compression)
else:
data2 = bt.DataClone(dataname=data)
if args.replay:
if args.timeframe == 'daily':
data2.addfilter(ReplayerDaily)
elif args.timeframe == 'weekly':
data2.addfilter(ReplayerWeekly)
elif args.timeframe == 'monthly':
data2.addfilter(ReplayerMonthly)
else:
if args.timeframe == 'daily':
data2.addfilter(ResamplerDaily)
elif args.timeframe == 'weekly':
data2.addfilter(ResamplerWeekly)
elif args.timeframe == 'monthly':
data2.addfilter(ResamplerMonthly)
# First add the original data - smaller timeframe
cerebro.adddata(data)
# And then the large timeframe
cerebro.adddata(data2)
# Run over everything
cerebro.run(runonce=not args.runnext,
preload=not args.nopreload,
stdstats=False)
# Plot the result
cerebro.plot(style='bar')