This is basically a continuous EV estimation problem under adversarial conditions.
Frame your answer around a systematic, data-driven decision process: define an objective (e.g., expected value, risk-adjusted return), set thresholds for action, and continuously update your model as new information arrives. Emphasize the trade-off between exploiting known edges and exploring to learn, and how you'd adapt in real-time.
Pro tip: Show you think in terms of expected value and risk management, not just profit. Mention that you'd start with a simple, robust rule and only add complexity when data justifies it—this demonstrates pragmatism and avoids overfitting.
Clarify what you're optimizing for (e.g., expected P&L, Sharpe ratio) and any constraints (e.g., risk limits, latency). This sets the foundation for all decisions.
Start with a simple heuristic, such as taking trades with positive expected value above a threshold, and passing otherwise. This provides a benchmark to improve upon.
Use Bayesian updating or online learning to adjust your estimates of value and risk as new bids/offers arrive. Recalculate thresholds and re-evaluate open decisions.
Apply position sizing, stop-losses, or diversification to limit exposure. When uncertainty is high, widen thresholds or pass entirely to avoid adverse selection.
Continuously track performance and market conditions. If the rule underperforms, diagnose whether it's due to model error or regime change, and update accordingly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.