overtrading trigger detector

    by nowrich

    1

    Detects and quantifies overtrading patterns by analyzing behavioral signals and execution quality trends.

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    Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLI+17 more

    See it in action

    You say

    Analyze these logs: 12 trades today (avg is 5). 4 stops hit, each re-entered within 2 minutes. Journal notes: 'Market is wrong, I need to get back to green now.' Entries were before confirmation.

    Your agent does

    OPS: 8.5 (High). Urgency: 9/10, Frustration: 8/10, Repeated-entry: 9/10. Frequency is rising while execution quality dropped from 95% to 30%. Diagnosis: Active overtrading and revenge behavior. Action: Immediate trading halt. Reset max daily trade limits.

    What you get

    Audit trade logs for emotional urgency and plan drift.Calculate a weighted Overtrading Pressure Score from session data.Identify the divergence between trading frequency and execution quality.Generate behavioral intervention steps for revenge trading cycles.

    About this skill

    The problem

    Traders often fail to recognize when emotional volatility leads to excessive trading frequency. This disconnect results in revenge trading, abandoned plans, and rapid capital erosion that feels like a mystery until the account is blown.

    What it does

    • Analyzes trade logs and journal entries to score four psychological signals: Urgency, Frustration, Fear, and Repeated-entry behavior.
    • Calculates an Overtrading Pressure Score (OPS) using a weighted behavioral formula.
    • Correlates trading frequency against execution quality to confirm if a trader is spiraling.
    • Identifies specific behavioral triggers like immediate re-entries or unconfirmed entries.
    • Provides actionable intervention steps to restore discipline based on the calculated risk level.

    Why this beats prompting it yourself

    A standard LLM prompt often gives vague advice about staying calm. This skill applies a specific weighted mathematical model (OPS) to raw data, forcing an objective diagnosis based on frequency vs. quality trends rather than subjective feelings.

    Use cases

    • Post-session debriefs to identify why a trading day turned negative.
    • Behavioral coaching audits for professional or retail traders.
    • Risk management checkpoints to detect early signs of revenge trading.
    • Journal analysis to quantify the cost of emotional execution errors.

    Known limitations

    Relies on accurate inputs such as timestamps and journal notes. It is a diagnostic tool for behavioral risk, not a financial advisor or a trade signal generator.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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