Athenarabetaathenara:~$ registry show agents/buy-and-hold-baseline
Buy-and-hold baseline
The reference passive baseline and a beginner's first trading agent — buys the index once, holds, and shows why every result needs a benchmark.
added 2026-08-15 · MIT · native
$ git clone https://github.com/athenara-ai/buy-and-hold-baseline $ cd buy-and-hold-baseline $ python agent.py --ticker SPY --start 2024-01-02 --end 2024-12-31
requires python >= 3.9
2024-01-02 → 2024-12-31 · backtest · total return +25.6% · sharpe 1.47 · max drawdown -8.4% · volatility +12.6% Author-reported
The registry’s reference point, and a deliberate teaching piece. It holds a broad index ETF (SPY in the published results) with dividends reinvested, trades once, and never looks at the market again.
It exists because a return number in isolation is meaningless: an agent that made +15% in a year the index made +25% lost against doing nothing. Results published to the leaderboard should cite a comparable passive baseline over the same period — this agent provides those numbers.
To be clear about what this is: not a strategy, not an edge — the degenerate case of the
observe–decide–act loop, whose decide() is a constant. That makes it the right first read for
someone meeting trading agents for the first time: the definition is a single heavily commented,
standard-library-only Python script covering the agent loop, the four metrics every honest result
carries, and the conventions (adjusted closes, Sharpe risk-free rates) that trip up comparisons.
Having zero parameters, it is also the one agent whose backtest nothing was fitted to.
The full strategy disclosure: buy at the first close of the period, hold, reinvest dividends. There is no edge to protect, which is the point.
trading [●····] none ai [●····] none programming [●····] none setup [●····] none
extended by gauss314 Financial Market Skills · VectorBT Backtesting Skills · Can LLM-based Financial Investing Strategies Outperform the Market in Long Run? · CryptoTrade: A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading · Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models (SEP) · StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets? · When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents (AMA) · FINSABER · StockBench
athenara:~$