funded account drawdown shield

    by nowrich

    1

    Generates structured risk management rules and drawdown buffers for funded trading accounts.

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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

    Create a drawdown shield for a $50,000 account. Daily loss limit is 4%, max drawdown is 8%, and I want to risk 0.5% per trade with a 20-minute cooldown after any loss.

    Your agent does

    { "account_balance": 50000, "daily_loss_limit": 2000, "drawdown_buffer": 4000, "risk_amount_per_trade": 250, "cooldown_rules": { "after_loss_minutes": 20, "after_two_losses_minutes": 40 }, "emergency_stop_rules": { "daily_usage_pct_threshold": 80 } }

    What you get

    Define hard daily and weekly loss limits for funded account evaluations.Calculate position sizes based on equity and stop-loss distance.Set up emergency stop triggers to protect high-watermark drawdown.Implement structured cooldown timers to prevent revenge trading.

    About this skill

    The problem

    Traders often blow funded accounts by exceeding tight drawdown limits or revenge trading after a loss. Manually calculating risk per trade and tracking trailing high-watermarks while markets move leads to fatal execution errors.

    What it does

    • Calculates hard daily and weekly loss limits based on account balance and equity.
    • Determines real-time drawdown buffers to prevent breaching trailing high-watermark constraints.
    • Computes exact risk-per-trade amounts and required position sizing for specific stop-loss distances.
    • Generates structured cooldown periods and emergency stop triggers to halt trading during high-loss streaks.
    • Outputs a standardized JSON risk shield configuration for integration into automated execution systems.

    Why this beats prompting it yourself

    Standard LLMs often struggle with the precise mathematical logic required for trailing drawdown buffers and compound cooldown rules. This skill applies consistent, prudent defaults and structured logic to ensure risk calculations remain accurate and executable by code.

    Use cases

    • Generating a daily risk mandate for a new $100k funded evaluation account.
    • Calculating emergency stop levels for an automated trading bot to prevent account liquidation.
    • Designing a psychological cooldown protocol to mitigate emotional revenge trading after losses.

    How to install

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

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