I am trying to test a hypothesis on outperformance of a trading strategy over the buy and hold. I have original data's returns containing 1261 observations as a sample to be used for bootstrap.
I want to know if I have applied it correctly.
def back_test_series(x):
df= pd.DataFrame(x, columns= ['Close'])
return df.Close
from arch.bootstrap import CircularBlockBootstrap
bs = CircularBlockBootstrap(40, sample_return)
results = bs.apply(back_test_series, 2500)
Above, sample_return is the sample containing 2761 returns on actual data. I created 2500 bootstrapped samples containing 2761 observations each.
and then created a cummulative return to get price time series.
time_series = []
for simu in results:
df = pd.DataFrame(simu, columns=["Close"])
df['Close'] = (1+df).cumprod()
time_series.append(df)
and finally ran my backtesting in the price series obatained from bootstrap.
final_results = []
for simulation in enumerate(time_series):
x = Backtesting.scrip_backtest(simulation)
final_results.append(x)
Backtesting.scrip_backtest is my trading strategy which will return stats like buy and hold cagr, strategy cagr, std dev of strategy returns.
My question is can I use bootstrap in this way? Should I use MovingBlockBootstrap or CircularBlockBootstrap?
It it correct to run trading strategy on bootstrapped time series as mentioned above?
