Editing Algorithmic Trading Systems (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
ML in algorithmic trading operates at multiple levels: **signal generation** (finding predictive features), **strategy construction** (combining signals into positions), **execution** (minimizing market impact), and **risk management** (controlling portfolio risk).

**NLP alpha signals**: Earnings call transcripts, news articles, and analyst reports contain information that moves markets. NLP models (FinBERT, Llama fine-tuned on financial text) extract sentiment, detect management tone changes, and identify key forward-looking statements. Studies show earnings call sentiment predicts post-announcement price movements with statistical significance. Alternative data NLP (app review sentiment, job posting analysis, social media) provides additional edges.

**Deep learning for price prediction**: LSTMs, Temporal Fusion Transformers, and TCNs model price dynamics across multiple timeframes. However, financial markets are notoriously adversarial — any published strategy is quickly arbitraged away ("efficient market hypothesis"). ML signals typically have very low predictive power (information coefficient IC ~0.02–0.05) but generate alpha when applied at scale across thousands of instruments.

**Reinforcement learning for execution**: Optimal execution (VWAP, TWAP, implementation shortfall) minimizes market impact and slippage when entering/exiting large positions. Deep RL agents (Q-learning, PPO on market simulators) learn optimal order placement strategies that adapt to real-time order book conditions. Amazon, JPMorgan, and Jane Street have published RL-based execution work.

**Regime detection and adaptive strategies**: Market regimes change — volatility spikes, correlations shift, momentum strategies fail in mean-reverting markets. HMM (Hidden Markov Models) and ML classifiers detect regime shifts, switching the active strategy to match market conditions. Regime-adaptive models significantly improve Sharpe ratios vs. static strategies.
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