Strategy · 12 min read
Backtesting AI Trading Strategies for Monad
How to backtest autonomous trading strategies for Monad honestly: data, costs, overfitting, and moving from simulation to live trading.
Why backtests lie
Backtests are easy to make look good. Look-ahead bias, survivorship bias, ignoring gas and slippage, and tuning parameters until the chart rises all produce results that collapse live.
Getting the data right
Use block-level on-chain data: swaps, pool reserves, and liquidity changes, timestamped to the block. Model what your agent would actually have seen at decision time, including data delays.
Modeling costs
Include gas per transaction, protocol fees, price impact based on pool depth, and failed transactions. On a low-fee chain, slippage and adverse selection usually dominate gas.
Avoiding overfitting
Split data into training, validation, and untouched test periods. Use walk-forward testing. Prefer simple models with fewer parameters. If a tiny parameter change ruins the result, the edge probably is not real.
Paper trading and forking
Before risking funds, run the agent against live data without executing, or against a forked chain state. Compare paper results with the backtest; large gaps reveal modeling errors.
Going live
Start small, scale gradually, and keep comparing live results with expectations. Learn more about AI trading on Monad at monaf.ai.
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