cash_buffer_percentage=0.01,
data_handler=data_handler
)
strategy_backtest.run()
# Construct benchmark assets (buy & hold SPY)
benchmark_assets = ['EQ:SPY']
benchmark_universe = StaticUniverse(benchmark_assets)
# Construct a benchmark Alpha Model that provides
# 100% static allocation to the SPY ETF, with no rebalance
benchmark_alpha_model = FixedSignalsAlphaModel({'EQ:SPY': 1.0})
benchmark_backtest = BacktestTradingSession(
start_dt,
end_dt,
benchmark_universe,
benchmark_alpha_model,
rebalance='buy_and_hold',
cash_buffer_percentage=0.01,
data_handler=data_handler
)
benchmark_backtest.run()
# Performance Output
tearsheet = TearsheetStatistics(
strategy_equity=strategy_backtest.get_equity_curve(),
benchmark_equity=benchmark_backtest.get_equity_curve(),
title='60/40 US Equities/Bonds'
)
tearsheet.plot_results()
def cli(start_date, end_date, allocations, strat_title, strat_id, tearsheet):
csv_dir = os.environ.get('QSTRADER_CSV_DATA_DIR', '.')
start_dt = pd.Timestamp('%s 00:00:00' % start_date, tz=pytz.UTC)
if end_date is None:
# Use yesterday's date
yesterday = (datetime.now() - timedelta(1)).strftime('%Y-%m-%d')
end_dt = pd.Timestamp('%s 23:59:00' % yesterday, tz=pytz.UTC)
else:
end_dt = pd.Timestamp('%s 23:59:00' % end_date, tz=pytz.UTC)
alloc_dict = obtain_allocations(allocations)
# Assets and Data Handling
strategy_assets = list(alloc_dict.keys())
strategy_symbols = [
symbol.replace('EQ:', '') for symbol in strategy_assets
]
strategy_universe = StaticUniverse(strategy_assets)
strategy_data_source = CSVDailyBarDataSource(csv_dir,
Equity,
csv_symbols=strategy_symbols)
strategy_data_handler = BacktestDataHandler(
strategy_universe, data_sources=[strategy_data_source])
strategy_assets = alloc_dict.keys()
strategy_alpha_model = FixedSignalsAlphaModel(alloc_dict)
strategy_backtest = BacktestTradingSession(
start_dt,
end_dt,
strategy_universe,
strategy_alpha_model,
rebalance='end_of_month',
account_name=strat_title,
portfolio_id='STATIC001',
portfolio_name=strat_title,
long_only=True,
cash_buffer_percentage=0.01,
data_handler=strategy_data_handler)
strategy_backtest.run()
# Benchmark: 60/40 US Equities/Bonds
benchmark_symbols = ['SPY', 'AGG']
benchmark_assets = ['EQ:SPY', 'EQ:AGG']
benchmark_universe = StaticUniverse(benchmark_assets)
benchmark_data_source = CSVDailyBarDataSource(
csv_dir, Equity, csv_symbols=benchmark_symbols)
benchmark_data_handler = BacktestDataHandler(
benchmark_universe, data_sources=[benchmark_data_source])
benchmark_signal_weights = {'EQ:SPY': 0.6, 'EQ:AGG': 0.4}
benchmark_title = '60/40 US Equities/Bonds'
benchmark_alpha_model = FixedSignalsAlphaModel(benchmark_signal_weights)
benchmark_backtest = BacktestTradingSession(
start_dt,
end_dt,
benchmark_universe,
benchmark_alpha_model,
rebalance='end_of_month',
account_name='60/40 US Equities/Bonds',
portfolio_id='6040EQBD',
portfolio_name=benchmark_title,
long_only=True,
cash_buffer_percentage=0.01,
data_handler=benchmark_data_handler)
benchmark_backtest.run()
output_filename = ('%s_monthly.json' % strat_id).replace('-', '_')
stats = JSONStatistics(
equity_curve=strategy_backtest.get_equity_curve(),
target_allocations=strategy_backtest.get_target_allocations(),
strategy_id=strat_id,
strategy_name=strat_title,
benchmark_curve=benchmark_backtest.get_equity_curve(),
benchmark_id='6040-us-equitiesbonds',
benchmark_name=benchmark_title,
output_filename=output_filename)
stats.to_file()
if tearsheet:
tearsheet = TearsheetStatistics(
strategy_equity=strategy_backtest.get_equity_curve(),
benchmark_equity=benchmark_backtest.get_equity_curve(),
title=strat_title)
tearsheet.plot_results()