Design a Reinforcement Learning Trading System — System Design Interview Practice
Design a reinforcement learning-based trading system that learns optimal trading strategies from market data, manages risk, and executes trades with low latency while adapting to market regime changes. Work through the requirements, architecture trade-offs, and an interactive design review.
Requirements and concepts to consider
- Train RL agents on historical market dataRequirement
- Define reward functions balancing returns and riskRequirement
- Handle continuous action spaces for position sizingRequirement
- Implement market environment simulationRequirement
- Support multiple asset classesRequirement
- Implement risk management constraintsRequirement
- Detect and adapt to market regime changesRequirement
- Provide backtesting and paper trading capabilitiesRequirement
- mlConcept to explore
- reinforcement learningConcept to explore
- algorithmic tradingConcept to explore
- financeConcept to explore
- risk managementConcept to explore
- deep learningConcept to explore