Layering Rules, Indicators, and AI in Quantitative Trading

Summary

This essay proposes assigning deterministic trading tasks to explicit rules and reserving AI for decisions that require interpretation of unstructured information. Moving-average signals, position limits, and stop-losses are presented as rule-based tasks, while news sentiment, unusual social activity, and translating a trader's hypothesis into testable factors are suggested uses for language models. The example architecture uses a moving-average crossover as the trade gate, lets an AI sentiment score affect position size or suppress entry, and keeps exits and a hard stop under deterministic control.

The article illustrates the design with a simplified crypto example and recommends comparing an indicator-only baseline with a version that adds AI, while logging AI outputs. It argues that predictable fallback behavior matters when model responses are invalid. However, it supplies no empirical comparison demonstrating that the AI layer improves returns or risk-adjusted performance. The sentiment input, thresholds, sizing, and stop are examples, and the code is explicitly incomplete for live trading; model errors, news quality, execution, and conventional risk controls remain material concerns.

Key ideas

• Use explicit rules for repeatable signals, position constraints, and protective exits.
• Use AI for interpreting text or other inputs whose meaning is difficult to specify with fixed rules.
• Treat AI sentiment as an input to a gated strategy rather than an independent trade trigger.
• Provide a defined fallback when model output is invalid and record decisions for review.
• Compare an AI-enhanced version with a rule-based baseline before drawing performance conclusions.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.