Quick answer: what is AI arbitrage?
AI arbitrage uses models, statistical filters or machine learning to improve how a system detects and evaluates price gaps. It does not remove fees, latency or risk. Its practical role is to help decide which signals deserve attention.
Useful terms
A model transforms data into a decision or score. A signal is a possible opportunity, not an order. A feature is a variable used by the model: spread, depth, volatility, latency or fill history. Backtesting checks a strategy on historical data. Overfitting happens when a model learns the past too perfectly and fails in live markets.
Where AI helps
AI can rank opportunities by expected net value, estimate slippage, detect stale data and warn when current market behavior looks unusual. It can also reject trades when liquidity is too thin or latency rises above a safe threshold.
Where AI fails
It cannot guarantee fills. It cannot force an exchange to process withdrawals. It cannot know future news. It cannot solve poor custody design. Most importantly, it cannot replace hard risk limits.
Recommended pipeline
Collect order books, trades, fees and balances. Normalize symbols and precision. Calculate net spread and liquidity. Score the opportunity. Execute only if limits are respected. Log every decision. Review rejected signals as carefully as completed trades.
Next reading
AI works best after the basics are clear. Read arbitrage trading, then explore statistical arbitrage.