trading-expert_skill

Overview

This skill provides expert-level guidance for building, testing, and operating algorithmic trading systems, quantitative strategies, market data pipelines, and execution engines. It focuses on practical implementations for strategy design, backtesting, order management, risk controls, and real-time market processing. The content emphasizes reliable execution, measurable performance, and production readiness.

How this skill works

The skill inspects strategy design patterns (momentum, mean reversion, SMA crossover, arbitrage) and maps them to concrete implementations for backtesting and live execution. It reviews market data handling (tick processing, order book maintenance, VWAP/spread calculation), order lifecycle management (routing, placement, cancellation) and risk modules (position sizing, VaR, stop-loss enforcement). Outputs include recommended code structures, performance metrics, and operational checks for deployment.

When to use it

• Designing and backtesting quantitative trading strategies
• Building low-latency market data pipelines and order books
• Implementing execution logic and smart order routing
• Creating risk management and position-sizing modules
• Preparing strategies for paper trading and live deployment

Best practices

• Always backtest on realistic historical data including transaction costs and slippage
• Use conservative position sizing and enforce portfolio-level risk limits
• Instrument full audit logging for orders, fills, and state changes
• Prefer limit orders where appropriate and monitor execution quality metrics
• Run strategies in paper mode and stress-test failover and latency scenarios
• Review performance regularly and avoid overfitting with out-of-sample validation

Example use cases

• Prototype an SMA crossover with a backtester, track Sharpe, drawdown, and trade count
• Process real-time quote and trade ticks to maintain a top-of-book order book and compute VWAP
• Implement an OrderManager that performs smart order routing across venues and handles cancels/fills
• Build a RiskManager that computes position size, portfolio VaR, and enforces stop-loss thresholds
• Create a paper trading environment to validate execution algorithms (TWAP, VWAP) before going live