You are tasked with developing a high-frequency (HF) trading strategy for a highly liquid equity asset using machine learning. You have access to Level 3 Limit Order Book (LOB) data, historical trade data, and relevant market news data. Your goal is to predict short-term price movements (e.g., mid-price changes in the next 100 milliseconds) and design an optimal execution strategy.
Describe your end-to-end approach in detail, covering the following key aspects:
1. **Data Preprocessing and Feature Engineering:** How would you process the raw LOB and news data? What features would you extract and why? Specifically, address how you would handle challenges such as non-stationarity and microstructure noise.
2. **Model Selection and Architecture:** Which machine learning models or architectures would you consider, and how would you make your selection? Discuss specific considerations for high-frequency data (e.g., sequence modeling, latency, interpretability, robustness to changes in market microstructure).
3. **Backtesting and Evaluation:** How would you rigorously backtest your strategy to avoid common pitfalls (e.g., overfitting, look-ahead bias, data snooping) and ensure its robustness? What metrics are crucial for high-frequency trading?
4. **Risk Management and Execution:** How would you integrate risk management into your machine learning model and execution logic? Discuss how trading costs, market impact, and latency would influence your strategy design and performance.