How to Build a Simple Arbitrage Bot Between Exchanges — A Practical Guide

Overview: Arbitrage opportunities arise from temporary price differences between exchanges. This guide outlines a practical architecture for a low-latency arbitrage bot, covers basic strategies, and highlights the operational and risk controls needed to run live.

"Arbitrage is often simple in concept but complex in execution — execution speed and reliability are everything."

1. Strategy selection

Common beginner-friendly arbitrage strategies include triangular arbitrage within one exchange and spatial arbitrage across exchanges. For simplicity, this guide focuses on the cross-exchange spatial arbitrage model: buy on exchange A where the asset is cheaper and sell on exchange B where the price is higher, profiting from the spread after fees.

2. Architectural components

Design a resilient pipeline that includes these components:

• Market data layer: Low-latency websocket feeds or REST polling with deduplication and normalization across exchanges.
• Decision engine: Evaluates price spreads, incorporates fees, and triggers trade signals based on configured thresholds.
• Execution layer: Manages order submission, cancellation, and status tracking with robust retry logic.
• Fund management: Monitors balances and performs rebalancing to maintain liquidity on both sides.
• Risk controls: Circuit breakers, max exposure limits, and slippage tolerances to avoid catastrophic losses.

3. Data fidelity and latency

Latency matters. Use native websocket streams and co-located infrastructure where feasible. Normalize timestamps and implement sequence checks to avoid reacting to stale data. For small spreads, even tens of milliseconds can be the difference between profit and loss.

4. Fees, funding and realistic returns

Factor in trading fees, withdrawal fees, and potential capital costs for moving assets between exchanges. Consider prefunding both exchanges to avoid withdrawal latency during execution and implement periodic rebalancing to limit on-chain transfer costs.

5. Execution challenges and mitigations

Common execution problems include partial fills, front-running, and unexpected order rejections. Mitigation tactics:

• Use limit orders at aggressive prices; fall back to market orders only as a last resort.
• Monitor orderbook depth to avoid taking liquidity that moves the price unfavorably.
• Implement position limits and time-based order expiry to reduce inventory risk.

6. Monitoring, logging and alerting

Implement observability to catch anomalies early. Log order lifecycle events, latency metrics, and PnL snapshots. Set alerts for unusual slippage, failed rebalances, or API degradation from an exchange.

7. Legal and compliance considerations

Understand the regulatory frameworks governing trading in your jurisdiction and the jurisdictions of the exchanges you use. Maintain records for auditability and consider AML/KYC obligations when opening exchange accounts.

8. Backtesting and simulation

Before deploying real capital, backtest strategy logic using historical orderbook snapshots and simulate execution with realistic slippage models. Use paper trading with small capital to validate the live environment before scaling up.

9. Final checklist before going live

• Pre-fund exchange accounts and verify balances.
• Verify all API keys and rate limits.
• Set automated rebalancing schedules and emergency stops.
• Confirm monitoring dashboards and alerting channels are active.

Conclusion

Building an arbitrage bot can be a rewarding exercise in engineering and market microstructure. Success hinges on reliable infrastructure, realistic accounting for costs, and disciplined risk management. Start small, instrument thoroughly, and iterate based on measured live performance.