Principle:HKUDS AI Trader Benchmark Index Analysis
| Knowledge Sources | |
|---|---|
| Domains | Financial_Analysis, Benchmarking, Performance_Metrics |
| Last Updated | 2026-02-09 14:00 GMT |
Overview
Principle of computing standardized performance metrics for a market benchmark index to enable fair comparison against LLM trading agent portfolios.
Description
Benchmark index analysis establishes the baseline performance that trading agents must exceed to demonstrate value. By computing the same set of metrics (cumulative return, annualized return, Sharpe ratio, maximum drawdown, volatility, win rate) on the benchmark using the identical calculation functions as the agent evaluation pipeline, direct apple-to-apple comparisons become possible. Temporal alignment is critical: the benchmark analysis must cover exactly the same date range as the agent simulation to avoid measurement bias. For the crypto market, the CD5 (Crypto Diversified 5) index serves as the benchmark, weighted by market capitalization across BTC, ETH, XRP, SOL, and ADA.
Usage
Apply this principle after completing agent backtesting runs. The benchmark metrics are compared against agent metrics in the performance visualization and comparison reports.
Theoretical Basis
The benchmark analysis computes standard portfolio metrics:
# Abstract algorithm description
portfolio_values = {date: close_price for date in aligned_dates}
# Use same functions as agent evaluation for consistency
daily_returns = calculate_daily_returns(portfolio_values)
cumulative_return = (final_value / initial_value) - 1
annualized_return = (1 + cumulative_return) ** (365 / days) - 1
sharpe_ratio = mean(daily_returns) / std(daily_returns) * sqrt(trading_days)
max_drawdown = max(peak - trough) / peak
Key requirements:
- Temporal alignment: Use same start/end dates as agent simulation
- Function consistency: Use identical metric functions as agent evaluation
- Trading days: 365 for crypto (24/7 market), 252 for stocks