An AI Trading Framework with Trade Reviews and Bounded Playbook Updates
Summary
This single-instrument framework combines technical indicators, relevant news, current positions, and stored trading rules to produce structured AI decisions. Its workflow records market snapshots, applies position and confidence limits, manages entries and exits, reviews closed trades, and uses accumulated reviews to update a Playbook of rules and selected parameters. Suggested changes to confidence thresholds, base position size, stop loss, and trailing take profit are clipped to human-set bounds; position caps, leverage, and add-on limits remain code-enforced.
The document describes implementation features and intended research uses, but provides no performance results or empirical evidence that the AI decisions or evolving rules improve returns. It warns that language models can misread indicators or news, produce unstable output, and overfit sparse trade histories. The default notification mode avoids live orders; the author recommends simulation and careful risk checks before enabling execution. The framework is presented as an auditable research system, not a profit guarantee or an automatically invented trading algorithm.
Key ideas
- The system combines indicator snapshots, news, position state, and stored experience for structured AI decisions.
- Closed trades are reviewed and saved so later Playbook updates can draw on accumulated trade history.
- AI parameter suggestions are constrained to human-defined ranges, while position and leverage limits are enforced separately.
- Hard stops, trailing exits, confidence thresholds, and add-on limits provide execution controls.
- The document gives no evidence of profitability and cautions against overfitting limited trade samples.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.