tradeguard ai

The problem

Traders often fail not due to bad strategy, but due to psychological collapse, revenge trading, and poor risk discipline. It is difficult to remain objective when emotions or drawdowns cloud judgment during a live session.

What it does

• Calculates a weighted Risk Score (0-100) based on real-time trading activity and emotional state.
• Detects dangerous patterns like overtrading, revenge trading, and consecutive loss streaks.
• Enforces hard stop rules when daily loss limits or personal rule violations are detected.
• Mandates specific cooling-off periods or session termination based on data, not intuition.
• Evaluates fatigue and time-of-day risks to prevent trading during low-probability windows.

Why this beats prompting it yourself

General LLMs tend to be overly encouraging or easily swayed by a user's justification for "one last trade." This skill uses a strict weighted scoring matrix and non-negotiable protocol levels that refuse to validate rule-breaking, even if the user reports a profit.

Use cases

• Pre-trade checklists to ensure psychological readiness before entering a position.
• Mid-session audits after a series of losses to prevent account blowouts.
• End-of-day reviews to flag discipline violations and calculate behavioral risk scores.

Known limitations

Requires manual input of trade data and emotional state if not integrated via an external data provider. Cannot execute trades or lock broker accounts directly.