agentic rag in stock trading
Below are 10 ways that Agentic Retrieval-Augmented Generation might be used in stock scanning and systematic trading applications.
- Dynamic Factor-Based Screen Construction The agent decomposes user investment criteria (e.g., "undervalued tech stocks with strong ROIC and low debt") into queryable factors. It retrieves financial data, constructs multi-factor screens, and refines the screen iteratively using real-time data and logical validation.
- Automated Thesis Validation Against Filings Given a ticker and a value thesis, the agent retrieves 10-K and 10-Q filings, parses sections such as risk factors and management discussion, then evaluates whether qualitative data confirms or contradicts the quantitative thesis, reducing thesis fragility.
- Earnings Signal Interpretation with Sentiment Overlay The agent retrieves earnings call transcripts, compares analyst expectations to actual results, synthesizes signals like “beat with weak guidance,” and integrates sentiment analysis to classify the tone of management discussions.
- Multi-Temporal Pattern Recognition Across Sectors The agent scans sector-level price action, retrieves analyst commentary and macroeconomic indicators, and uses memory to hypothesize sector rotations or reversals over multiple time frames (e.g., 3-week and 12-month perspectives).
- Quant Model Debugging and Rationalization When a quantitative trading model underperforms, the agent retrieves trade logs, cross-references market regimes and macro events, generates hypotheses about alpha decay, factor overfitting, or signal drift, and suggests improvements based on historical precedents.
- Risk Flagging via Footnote Mining and Contextual Analysis The agent parses financial statement footnotes (e.g., lease obligations, off-balance sheet items), correlates them with known hidden risk patterns, and flags potential risk areas for further review or exclusion.
- Multi-Leg Trade Strategy Generation Based on News Events Upon major news events (e.g., Federal Reserve rate hikes), the agent retrieves impacted industries, matches them with portfolios or watchlists, and synthesizes multi-leg options or equity strategies grounded in historical analogs.
- Value Trap Detection Using Agentic Reasoning The agent identifies seemingly cheap stocks and iteratively checks for deteriorating fundamentals, poor governance, or strategic misalignment by retrieving filings, conference calls, and insider activity, reasoning about whether the stock is a value trap.
- Autonomous Backtest Explanation & Benchmarking The agent analyzes raw backtest results, retrieves relevant benchmarks, market phases, and comparable strategies to contextualize performance, and generates attribution-style reports explaining sources of alpha or underperformance.
- Live Strategy Adaptation to Regime Change