Scorecard — buy_and_hold

This project is a research and evaluation tool. It is not financial, investment, or trading advice. It does not execute trades or connect to brokerages. It exists to help researchers and practitioners rigorously measure how trading-agent frameworks actually perform — including, and especially, when they perform badly. Backtest results are not predictive of live performance. You are responsible for any decisions you make.

Run

Universe: bundled_synthetic (3 tickers)
Window: 2020-01-06 → 2024-12-31 (1259 trading days)
Cost model: constant_bps_5
Annualization: 252
Generated: 2026-05-15T09:25:14.229817+00:00

Net-of-cost performance

Metric	Value
Net total return	+55.6254%
Net annualized return	+9.2563%
Sharpe (annualized)	+0.829
Sharpe 95% CI (annualized) — HAC q=7, η=0.97	[-0.034, +1.693]
PSR (vs SR=0)	0.9678
Sortino (annualized, MAR=0)	+1.228

Drawdown

Metric	Value
Max drawdown	-16.6426%
Longest underwater stretch	242 trading days
Calmar ratio	+0.556

Charts

Per-ticker breakdown

Ticker	Decisions	Non-FLAT	Hit rate	Mean contribution	Sharpe (ann.)
SYN-A	1259	1259	0.507	+0.0049%	+0.185
SYN-B	1259	1259	0.514	+0.0213%	+0.771
SYN-C	1259	1259	0.498	+0.0116%	+0.441

Baselines (same window, same cost model)

Baseline	Net total return	Sharpe (ann.)
buy_and_hold	+55.6254%	+0.829
naive_momentum_20	+13.0526%	+0.333
random	-45.8816%	-1.197

Calibration

Not evaluable: the adapter did not emit a confidence. We do not fabricate a probability where the framework provided none.

Flags

No flags raised.

agent-backtest-lab is a research tool. Not financial advice. Not a trading system. Backtests don't predict the future.

Built by Betty Guo (Dongxin Guo / 郭东欣), PhD candidate, University of Hong Kong, advised by Prof. Siu-Ming Yiu. ORCID: 0009-0000-2388-1072. Apache-2.0.