def test_capture(self):
res_a = empyrical.capture(ret['a'], benchmark_rets['a'])
res_b = empyrical.capture(ret['b'], benchmark_rets['b'])
res_c = empyrical.capture(ret['c'], benchmark_rets['c'])
assert isclose(ret['a'].vbt.returns.capture(benchmark_rets['a']),
res_a)
pd.testing.assert_series_equal(
ret.vbt.returns.capture(benchmark_rets),
pd.Series([res_a, res_b, res_c], index=ret.columns))
def test_capture(self):
res_a = empyrical.capture(ret['a'], benchmark_rets['a'])
res_b = empyrical.capture(ret['b'], benchmark_rets['b'])
res_c = empyrical.capture(ret['c'], benchmark_rets['c'])
assert isclose(ret['a'].vbt.returns.capture(benchmark_rets['a']),
res_a)
pd.testing.assert_series_equal(
ret.vbt.returns.capture(benchmark_rets),
pd.Series([res_a, res_b, res_c],
index=ret.columns).rename('capture'))
pd.testing.assert_series_equal(
ret.vbt.returns.rolling_capture(ret.shape[0],
benchmark_rets,
minp=1).iloc[-1],
pd.Series([res_a, res_b, res_c],
index=ret.columns).rename(ret.index[-1]))
def runstrategy(ticker_list,bench_ticker):
args = parse_args()
print(args)
# Create a cerebro
cerebro = bt.Cerebro()
# Get the dates from the args
fromdate = datetime.datetime.strptime(args.fromdate, '%Y-%m-%d')
todate = datetime.datetime.strptime(args.todate, '%Y-%m-%d')
# bench = bt.feeds.YahooFinanceData(
# dataname=bench_ticker,
# fromdate=fromdate,
# todate=todate,
# buffered=True,plot = False
# )
bench = bt.feeds.GenericCSVData(
dataname='/Users/joan/PycharmProjects/CSV_DB/IB/' + bench_ticker + '.csv',
fromdate=fromdate,
todate=todate,
nullvalue=0.0,
dtformat=('%Y%m%d'),
datetime=1,
open=2,
high=3,
low=4,
close=5,
volume=6,
reverse=False,
plot=False)
cerebro.adddata(bench, name=bench_ticker)
for i in ticker_list:
print('Loading data: '+ i)
# data = bt.feeds.YahooFinanceData(
# dataname=i,
# fromdate=fromdate,
# todate=todate,
# adjclose=True,
# buffered=True, plot = False
# )
data = bt.feeds.GenericCSVData(
dataname='/Users/joan/PycharmProjects/CSV_DB/IB/'+i+'.csv',
fromdate=fromdate,
todate=todate,
nullvalue=0.0,
dtformat=('%Y%m%d'),
datetime=1,
open=2,
high=3,
low=4,
close=5,
volume=6,
reverse=False,
plot= False)
cerebro.adddata(data,name = i)
# Add the strategy
cerebro.addstrategy(PairTradingStrategy,
period=args.period,
stake=args.stake)
# Add the commission - only stocks like a for each operation
cerebro.broker.setcash(args.cash)
# Add the commission - only stocks like a for each operation
# cerebro.broker.setcommission(commission=args.commperc)
comminfo = FixedCommisionScheme()
cerebro.broker.addcommissioninfo(comminfo)
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe_ratio')
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="ta")
cerebro.addanalyzer(bt.analyzers.SQN, _name="sqn")
cerebro.addanalyzer(bt.analyzers.SharpeRatio_A, _name='myysharpe', riskfreerate=args.rf_rate)
cerebro.addanalyzer(bt.analyzers.PyFolio, _name='mypyf')
cerebro.addanalyzer(bt.analyzers.TimeReturn, timeframe=bt.TimeFrame.Days,
data=bench, _name='benchreturns')
cerebro.addobserver(bt.observers.Value)
cerebro.addobserver(bt.observers.Benchmark,plot = False)
cerebro.addobserver(bt.observers.DrawDown)
# And run it
strat = cerebro.run(runonce=not args.runnext,
preload=not args.nopreload,
oldsync=args.oldsync
)
# Plot if requested
if args.plot:
cerebro.plot(style='candlestick', barup='green', bardown='red',figsize=(100,100))
bench_returns = strat[0].analyzers.benchreturns.get_analysis()
bench_df = pd.DataFrame.from_dict(bench_returns, orient='index', columns=['return'])
return_df = pd.DataFrame.from_dict(strat[0].analyzers.mypyf.get_analysis()['returns'], orient='index',
columns=['return'])
# print('Sharpe Ratio(bt):', firstStrat.analyzers.myysharpe.get_analysis()['sharperatio'])
# print('Sharpe Ratio:', empyrical.sharpe_ratio(return_df, risk_free=args.rf_rate / 252, period='daily')[0])
# print('Sharpe Ratio Benchmark:', empyrical.sharpe_ratio(bench_df, risk_free=args.rf_rate / 252, period='daily')[0])
# print('')
#
# print('Sortino Ratio:', empyrical.sortino_ratio(return_df, period='daily')[0])
# print('Sortino Ratio Benchmark:', empyrical.sortino_ratio(bench_df, period='daily')[0])
# print('')
# print('VaR:', empyrical.value_at_risk(return_df) * 100, '%')
# print('VaR Benchmark:', empyrical.value_at_risk(bench_df) * 100, '%')
#
# print('')
#
# print('Capture:', round(empyrical.capture(return_df, bench_df, period='daily')[0] * 100), '%')
# print('')
#
# print('Max drawdown: ', round(empyrical.max_drawdown(return_df)[0] * 100), '%')
# print('Max drawdown Benchmark: ', round(empyrical.max_drawdown(bench_df)[0] * 100), '%')
#
# print('')
alpha, beta = empyrical.alpha_beta(return_df, bench_df, risk_free=args.rf_rate)
# print('Beta: ', beta)
# print('')
# print('Annual return:', round(empyrical.annual_return(return_df)[0] * 100), '%')
# print('Annual Vol:', round(empyrical.annual_volatility(return_df)[0] * 100), '%')
# print('')
# print('Annual return Benchmark:', round(empyrical.annual_return(bench_df)[0] * 100), '%')
# print('Annual Vol Benchmark:', round(empyrical.annual_volatility(bench_df)[0] * 100), '%')
# print('')
dic = {'SQN': printSQN(strat[0].analyzers.sqn.get_analysis()),
'sharpe': empyrical.sharpe_ratio(return_df, risk_free=args.rf_rate / 252, period='daily')[0],
'sharpe_bm': empyrical.sharpe_ratio(bench_df, risk_free=args.rf_rate / 252, period='daily')[0],
'sortino': empyrical.sortino_ratio(bench_df, period='daily')[0],
'sortino_bm': empyrical.sortino_ratio(bench_df, period='daily')[0],
'VaR': empyrical.value_at_risk(return_df) * 100,
'VaR_bm': empyrical.value_at_risk(bench_df) * 100,
'capture': round(empyrical.capture(return_df, bench_df, period='daily')[0] * 100),
'max_dd': round(empyrical.max_drawdown(return_df)[0] * 100),
'max_dd_bm':round(empyrical.max_drawdown(bench_df)[0] * 100),
'beta': beta,
'return_annual':round(empyrical.annual_return(return_df)[0] * 100,2),
'return_annual_bm':round(empyrical.annual_volatility(return_df)[0] * 100,2),
'vol_annual':round(empyrical.annual_return(bench_df)[0] * 100,2),
'vol_annual_bm':round(empyrical.annual_volatility(bench_df)[0] * 100,2)}
df = pd.DataFrame(dic,index = [0])
print(df)
def calc_stats(df):
df['perc_ret'] = (1 + df['return']).cumprod() - 1
# print(df.tail())
return df
s = return_df.rolling(30).std()
b = bench_df.rolling(30).std()
# Get final portfolio Value
portvalue = cerebro.broker.getvalue()
# Print out the final result
print('Final Portfolio Value: ${}'.format(round(portvalue)), 'PnL: ${}'.format(round(portvalue - args.cash)),
'PnL: {}%'.format(((portvalue / args.cash) - 1) * 100))
# Finally plot the end results
if args.plot:
fig, axs = plt.subplots(2, sharex=True)
fig.autofmt_xdate()
axs[1].plot(s)
axs[1].plot(b)
axs[1].set_title('Drawdown')
axs[1].legend(['Fund', 'Benchmark'])
axs[0].set_title('Returns')
axs[0].plot(calc_stats(return_df)['perc_ret'])
axs[0].plot(calc_stats(bench_df)['perc_ret'])
axs[0].legend(['Fund', 'Benchmark'])
plt.show()
empyrical.sharpe_ratio(return_df, risk_free=rf / 252, period='daily')[0])
print('Sharpe Ratio Benchmark:',
empyrical.sharpe_ratio(bench_df, risk_free=rf / 252, period='daily')[0])
print('')
print('Sortino Ratio:', empyrical.sortino_ratio(return_df, period='daily')[0])
print('Sortino Ratio Benchmark:',
empyrical.sortino_ratio(bench_df, period='daily')[0])
print('')
print('VaR:', empyrical.value_at_risk(return_df) * 100, '%')
print('VaR Benchmark:', empyrical.value_at_risk(bench_df) * 100, '%')
print('')
print('Capture:',
round(empyrical.capture(return_df, bench_df, period='daily')[0] * 100),
'%')
print('')
print('Max drawdown: ', round(empyrical.max_drawdown(return_df)[0] * 100), '%')
print('Max drawdown Benchmark: ',
round(empyrical.max_drawdown(bench_df)[0] * 100), '%')
print('')
alpha, beta = empyrical.alpha_beta(return_df, bench_df, risk_free=rf)
print('Beta: ', beta)
print('')
print('Annual return:', round(empyrical.annual_return(return_df)[0] * 100),
'%')
print('Annual Vol:', round(empyrical.annual_volatility(return_df)[0] * 100),
'%')