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AI Trading Core Framework: From Signals to Guardrails

Jan 2026 12 min read

This is a diagram-ready summary of an AI trading core framework. The key idea is that AI is not just issuing signals—it acts as a Trader executing within strict rules and guardrails, while you, as the PM, own objectives, risk boundaries, approvals, and post-trade review. This is a bidirectional human–AI alignment workflow.

One-sentence vision

AI executes, PM sets goals and boundaries; structured evidence, guardrails, and review make decisions transparent, controllable, and auditable.

System modules: six layers

1) Inputs

  • Quant signals: classic factors, technical indicators, statistical models.
  • Text streams: news, research, filings, social content, extracted with LLMs.
  • Charts/technical structure: candles, patterns, key levels, volatility regimes.

2) Sensemaking / Structuring

  • Text extraction: event → entity → time → direction → confidence → evidence/provenance.
  • Chart outputs: annotated visuals (triggers, patterns, support/resistance, risk zones).

Making decision evidence explicit improves trust and collaboration with AI, especially in trading where uncertainty disclosure matters.

3) Policy + Planner

  • Fuse quant + text + technical inputs into intent (long/short/flat), size, pricing, and invalidation.
  • Design the AI as a policy-constrained agent that can act only inside approved risk policies.

4) Guardrails / Pre-trade Controls

  • Quantity/amount/frequency limits: max single order, daily totals, order rate.
  • Price sanity checks: bid/ask deviation thresholds, drift detection.
  • Market depth checks: avoid sweeping the book.
  • Self-trade prevention, instrument bans, account-level limits.

The critical point: AI decisions do not equal orders. Every trade must pass rule-based, auditable, and backtestable checks.

5) Execution + Monitoring

  • Execution: place, cancel, modify, slice orders; slippage control.
  • Monitoring: P&L, risk exposure, volatility spikes, data quality, model drift.
  • Emergency shutdown/degrade: explicit triggers (repeated anomalies, data failures, limit breaches).

6) Audit / Post-trade Review

  • Record: data versions, features, signals, prompts/outputs, rationale, guardrail results, orders.
  • Review: which rules held up, failure modes, and new guardrails to add.

Borrow from model risk management: effective challenge, ongoing validation, monitoring, outcome analysis, and documentation.

Human–AI collaboration: what the PM console should show

  • Strategy intent panel: targets (return/drawdown), risk budget, allowed instruments, leverage ranges.
  • Evidence & rationale panel: cited text evidence + structured extraction; annotated charts.
  • Guardrail status panel: which checks passed/failed and why.
  • Review & learning panel: timeline of signal → decision → order → outcome → explanation.

Minimum viable version (MVP)

  • Start with paper trading / sandbox—avoid real capital at first.
  • Focus on 1 market + 3 actions (open/close/stop-loss).
  • Start with 5 guardrails: single-order size, single-order value, price deviation, order rate, emergency stop.
  • UI first: “annotated chart” + “guardrail pass/fail reasons”.
  • Log reviews from day one—stability depends on it.
Build the system as an explainable execution machine, not a black-box signal generator— that is the path to scale and long-term reliability.