sddr = matrix_v_port.pct_change().std()
cr = matrix_v_port.iloc[-1] / matrix_v_port.iloc[0] - 1
# Get the statistics for the benchmark
daily_ret_bench = matrix_v_bench.pct_change()
adr_bench = matrix_v_bench.pct_change().mean()
sddr_bench = matrix_v_bench.pct_change().std()
cr_bench = matrix_v_bench.iloc[-1] / matrix_v_port.iloc[0] - 1
# Get the normalized data for the two portfolios
port_norm = matrix_v_port / matrix_v_port.iloc[0]
bench_norm = matrix_v_bench / matrix_v_bench.iloc[0]
print 'Cumulative return of the benchmark is ', cr_bench
print 'Cumulative return of the portfolio is ', cr
print 'Stdev of the daily return of the benchmark is ', sddr_bench
print 'Stdev of the daily return of the portfolio is ', sddr
print 'Mean of the daily return of the benchmark is ', adr_bench
print 'Mean of the daily return of the portfolio is ', adr
print 'The trading strategy is \n', df_trades
pl_data = pd.concat([port_norm, bench_norm], axis=1)
pl_data.columns = ['Manual Strategy', 'Benchmark']
ms.plot_data(pl_data, orders=orders)
if __name__ == '__main__':
ms.testStrategy(symbol='JPM', sd=dt.datetime(2008, 1, 1), ed=dt.datetime(2009, 12, 31), sv=100000)
testStrategy(symbol='JPM', sd=dt.datetime(2008, 1, 1), ed=dt.datetime(2009, 12, 31), sv=100000)
print('Author', author())
symbol = "JPM"
start_date = dt.datetime(2008, 1, 1)
end_date = dt.datetime(2009, 12, 31)
start_val = 100000
commission = 9.95
impact = 0.005
verbose = False
print('------ Indicators Chart ------')
getCombinedIndicators([symbol], start_date, end_date, lookback=20)
print('------ Manual Strategy ------')
ms.testStrategy(symbol,
start_date,
end_date,
start_val,
name='Portvals_MS_Insample.png',
verbose=verbose)
ms.testStrategy(symbol,
dt.datetime(2010, 1, 1),
dt.datetime(2011, 12, 31),
start_val,
name='Portvals_MS_Outsample.png',
verbose=verbose)
print()
print(
'------ Strategy Learner ------ \n------ BagLearner with Random Tree Learner ------'
)
learner = sl.StrategyLearner(verbose=verbose,
commission=commission,