def main(self):
print("========== reset ==========")
self.reset()
print("========== simulate ==========")
self.rolling_online_manager.simulate(end_time=self.end_time)
print("========== collect results ==========")
print(self.rolling_online_manager.get_collector()())
print("========== signals ==========")
signals = self.rolling_online_manager.get_signals()
print(signals)
# Backtesting
# - the code is based on this example https://qlib.readthedocs.io/en/latest/component/strategy.html
CSI300_BENCH = "SH000903"
STRATEGY_CONFIG = {
"topk": 30,
"n_drop": 3,
"signal": signals.to_frame("score"),
}
strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
report_normal, positions_normal = backtest_daily(
start_time=signals.index.get_level_values("datetime").min(),
end_time=signals.index.get_level_values("datetime").max(),
strategy=strategy_obj,
)
analysis = dict()
analysis["excess_return_without_cost"] = risk_analysis(
report_normal["return"] - report_normal["bench"])
analysis["excess_return_with_cost"] = risk_analysis(
report_normal["return"] - report_normal["bench"] -
report_normal["cost"])
analysis_df = pd.concat(analysis) # type: pd.DataFrame
pprint(analysis_df)
def analyze(report_normal):
_analysis = dict()
_analysis["sub_bench"] = risk_analysis(report_normal["return"] -
report_normal["bench"])
_analysis["sub_cost"] = risk_analysis(report_normal["return"] -
report_normal["bench"] -
report_normal["cost"])
analysis_df = pd.concat(_analysis) # type: pd.DataFrame
print(analysis_df)
return analysis_df
###################################
STRATEGY_CONFIG = {
"topk": 50,
"n_drop": 5,
}
BACKTEST_CONFIG = {
"verbose": False,
"limit_threshold": 0.095,
"account": 100000000,
"benchmark": BENCHMARK,
"deal_price": "close",
"open_cost": 0.0005,
"close_cost": 0.0015,
"min_cost": 5,
}
# use default strategy
# custom Strategy, refer to: TODO: Strategy API url
strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)
###################################
# analyze
# If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb
###################################
analysis = dict()
analysis["excess_return_without_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"])
analysis["excess_return_with_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"] - report_normal["cost"])
analysis_df = pd.concat(analysis) # type: pd.DataFrame
print(analysis_df)
###################################
STRATEGY_CONFIG = {
"topk": 50,
"n_drop": 5,
}
BACKTEST_CONFIG = {
"verbose": False,
"limit_threshold": 0.095,
"account": 100000000,
"benchmark": BENCHMARK,
"deal_price": "close",
"open_cost": 0.0005,
"close_cost": 0.0015,
"min_cost": 5,
}
# use default strategy
# custom Strategy, refer to: TODO: Strategy API url
strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)
###################################
# analyze
# If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb
###################################
analysis = dict()
analysis["sub_bench"] = risk_analysis(report_normal["return"] - report_normal["bench"])
analysis["sub_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"] - report_normal["cost"])
analysis_df = pd.concat(analysis) # type: pd.DataFrame
print(analysis_df)
