def runstrat():
args = parse_args()
# Create a cerebro entity
cerebro = bt.Cerebro(stdstats=False)
# Add a strategy
cerebro.addstrategy(bt.Strategy)
# Load the Data
datapath = args.dataname or '../../datas/2006-day-001.txt'
data = btfeeds.BacktraderCSVData(dataname=datapath)
# Handy dictionary for the argument timeframe conversion
tframes = dict(daily=bt.TimeFrame.Days,
weekly=bt.TimeFrame.Weeks,
monthly=bt.TimeFrame.Months)
# Resample the data
if args.oldrs:
data = bt.DataResampler(dataname=data,
timeframe=tframes[args.timeframe],
compression=args.compression)
else:
data.resample(timeframe=tframes[args.timeframe],
compression=args.compression)
# Add the resample data instead of the original
cerebro.adddata(data)
# Run over everything
cerebro.run()
# Plot the result
cerebro.plot(style='bar')
def test_run(main=False):
# for i in [True, False]:
for rstype in [True, False]:
if rstype:
runonces = [True]
else:
runonces = [True, False]
for runonce in runonces:
data = testcommon.getdata(0)
if rstype:
data = bt.DataResampler(dataname=data,
timeframe=bt.TimeFrame.Weeks,
compression=1)
else:
data.resample(timeframe=bt.TimeFrame.Weeks, compression=1)
datas = [data]
testcommon.runtest(datas,
testcommon.TestStrategy,
main=main,
runonce=runonce,
plot=main,
chkind=chkind,
chkmin=chkmin,
chkvals=chkvals,
chkargs=chkargs)
def runstrat():
args = parse_args()
# Create a cerebro entity
cerebro = bt.Cerebro(stdstats=False)
# 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/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
if args.noresample:
datapath = args.dataname2 or '../datas/sample/2006-week-001.txt'
data2 = btfeeds.BacktraderCSVData(
dataname=datapath)
else:
data2 = bt.DataResampler(
dataname=data,
timeframe=tframes[args.timeframe],
compression=args.compression)
# First add the original data - smaller timeframe
cerebro.adddata(data)
# And then the large timeframe
cerebro.adddata(data2)
# Run over everything
cerebro.run()
# Plot the result
cerebro.plot(style='bar')
def test_run(main=False):
data = testcommon.getdata(0)
data = bt.DataResampler(dataname=data,
timeframe=bt.TimeFrame.Weeks,
compression=1)
datas = [data]
testcommon.runtest(datas,
testcommon.TestStrategy,
main=main,
plot=main,
chkind=chkind,
chkmin=chkmin,
chkvals=chkvals,
chkargs=chkargs)
def runstrat():
args = parse_args()
# Create a cerebro entity
cerebro = bt.Cerebro(stdstats=False)
# Add a strategy
cerebro.addstrategy(testStrategy)
# Load the Data
datapath = args.dataname or 'D:/backtradertutorial/datas/2006-min-005.txt'
data = btfeeds.BacktraderCSVData(
dataname=datapath, timeframe=bt.TimeFrame.Minutes,todate=datetime(2006, 1, 3))
# Handy dictionary for the argument timeframe conversion
tframes = dict(
daily=bt.TimeFrame.Days,
weekly=bt.TimeFrame.Weeks,
monthly=bt.TimeFrame.Months)
# Resample the data
if args.oldrs:
# Old resampler, fully deprecated
data = bt.DataResampler(
dataname=data,
timeframe=bt.TimeFrame.Minutes,
compression=1)
# Add the resample data instead of the original
cerebro.adddata(data)
else:
# New resampler
print('new resample',tframes[args.timeframe],args.compression,)
cerebro.resampledata(
data,
timeframe=bt.TimeFrame.Minutes,
compression=2)
# Run over everything
cerebro.run()
