def runstrat():
args = parse_args()
cerebro = bt.Cerebro()
# Data feed kwargs
# '15min', '30min', '60min',
dataframe = read_dataframe(args.data, args.years, ['d'])
for i in range(len(dataframe)):
cerebro.adddata(bt.feeds.PandasData(dataname=dataframe[i]))
cerebro.addstrategy(St)
cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')
strat = cerebro.run(stdstats=True, runonce=False)
pyfoliozer = strat[0].analyzers.getbyname('pyfolio')
returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()
transactions.to_csv("transtion.csv")
if args.plot:
cerebro.plot(style='line')
cerebro.plot(style='candle')
def runstrat():
args = parse_args()
cerebro = bt.Cerebro()
# Data feed kwargs
# '15min', '30min', '60min',
dataframe = read_dataframe(args.data, args.years, ['15min', '60min', 'd'])
for i in range(len(dataframe)):
temp_df = macd_extend_data(dataframe[i])
cerebro.adddata(
pandas_divergence(
dataname=temp_df,
divergence_top=temp_df.columns.to_list().index(
'divergence_top'),
divergence_bottom=temp_df.columns.to_list().index(
'divergence_bottom')))
cerebro.addstrategy(St)
cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')
strat = cerebro.run(stdstats=True, runonce=False)
pyfoliozer = strat[0].analyzers.getbyname('pyfolio')
returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()
transactions.to_csv("transtion.csv")
if args.plot:
cerebro.plot(style='line')
def runstrat():
args = parse_args()
cerebro = bt.Cerebro(cheat_on_open=True)
cerebro.broker.setcommission(commission=0.0012, stocklike=True)
# cerebro.broker.set_coo(True)
# Data feed kwargs
# '15min', '30min', '60min',
# dataframe = read_dataframe(args.data, args.years, ['15min', '60min', 'd'])
# dataframe = read_dataframe(args.data, args.years, ['d'])
# for i in range(len(dataframe)):
# temp_df = macd_extend_data(dataframe[i])
# cerebro.adddata(pandas_divergence(dataname=temp_df,
# divergence_top=temp_df.columns.to_list().index('divergence_top'),
# divergence_bottom=temp_df.columns.to_list().index('divergence_bottom')))
# dataframe = QAIndex2btData("512000", '2017-01-01', '2020-10-13')
# dataframe = QAStock2btData("000651", '2014-01-01', '2020-10-13')
dataframe = read_dataframe('000651.csv', '2014-2020', ['d'])[0]
cerebro.adddata(bt.feeds.PandasData(dataname=dataframe))
cerebro.addstrategy(St)
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='DrawDown')
cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='AnnualReturn')
cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')
strat = cerebro.run(stdstats=True, runonce=False)
drawDown = strat[0].analyzers.DrawDown.get_analysis()
print("max drawdown: %f", drawDown.max.drawdown)
annualReturn = strat[0].analyzers.AnnualReturn.get_analysis()
print("annualReturn: %s", annualReturn)
pyfoliozer = strat[0].analyzers.getbyname('pyfolio')
returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()
transactions.to_csv("transtion.csv")
if args.plot:
# cerebro.plot(style='line')
cerebro.plot(style='candle')
import pandas as pd
import talib
import ta
from BackTraderTest.BackTraderFunc.DataReadFromCsv import read_dataframe
from BackTraderTest.BackTraderFunc.DataResample import data_min_resample
df = pd.DataFrame()
df['close'] = [2, 2, 2, 2, 2, 3, 4, 2, 2, 2]
b = talib.LINEARREG_SLOPE(df['close'], 4)
df = read_dataframe("000651.csv", "2015-2016", "d")[0]
c = ta.add_volume_ta(df,
high="high",
low="low",
close="close",
volume="volume")
print(b)
df_large.loc[(df_large['slope'] > 0) & (df_large['macdTrade']),
'firstSignal'] = True
df_large.loc[(df_large['slope'] > 0) & (df_large['macdTrade'] == False),
'firstSignal'] = False
df_small = data_min_resample(df, timeScaleSmall)
df_small['fi'] = ta.volume.ForceIndexIndicator(close=df_small['close'],
volume=df_small["volume"],
n=2,
fillna=False).force_index()
df_small['fi_pre'] = df_small['fi'].shift()
df_small.loc[(df_small['fi'] > 0) & (df_small['fi_pre'] < 0),
'secondSignal'] = True
df_temp = df_large.resample("d", ).pad()['firstSignal']
res_df = pd.concat([df_small, df_temp], axis=1, join_axes=[df_small.index])
res_df['buyPoint'] = 0
res_df.loc[(res_df['secondSignal']) & (res_df['firstSignal']),
'buyPoint'] = True
return res_df
if __name__ == '__main__':
df = read_dataframe("../samples/mixing-timeframes/000651.csv", "2010-2020",
["60min"])
TripleScreen_extend_data(df[0], "d", 'w')