profit lock safe scaling planner

    by nowrich

    1

    A disciplined risk management framework for protecting profits and scaling position size based on trade history.

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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

    I have 35 closed trades with a 58% win rate and 1.6 profit factor. My last 20 trades had 95% rule compliance. I'm in a 2R profit on my current long. Should I scale or lock gains?

    Your agent does

    Compliance Check

    • Rule compliance: 95% (Met)

    Trade History Check

    • Closed trades: 35
    • Win rate: 58%
    • Profit factor: 1.6 (Met)

    Decision

    • Action: SCALE RISK & LOCK PROFITS
    • Reason: Thresholds met.
    • Risk adjustment: Move stop to lock 50% profit; increase next size by 20%.

    What you get

    Calculate if trade history justifies increasing position size.Determine when to move stop losses to breakeven or lock partial profits.Audit rule compliance to prevent over-leveraging during losing streaks.Enforce drawdown-based exposure reduction to protect portfolio capital.

    About this skill

    The problem

    Traders often scale their position sizes based on optimism or recent winning streaks rather than objective data. This emotional bias leads to catastrophic drawdowns when a strategy reverts to the mean or rule compliance slips.

    What it does

    • Calculates rule compliance scores over the last 20 trades to prevent scaling during periods of poor discipline.
    • Enforces strict trade history minimums, requiring 30+ closed trades and specific profit factors before permitting risk increases.
    • Triggers automatic exposure reduction protocols when drawdown exceeds 10% or compliance drops below 80%.
    • Provides precise profit-locking instructions, such as moving stops to breakeven at 2R or trimming size based on volatility.

    Why this beats prompting it yourself

    This skill removes the "gut feeling" from risk management by applying a rigorous mathematical workflow to every decision. It prevents the common mistake of scaling during a lucky streak by forcing a check against historical win rates and disciplined execution metrics.

    Use cases

    • Deciding whether to increase size after a series of profitable trades.
    • Auditing a trading journal to determine if exposure needs to be trimmed.
    • Establishing automated stop-loss adjustments for open positions at specific R-multiples.
    • Setting clear thresholds for pausing a strategy during a period of low rule compliance.

    Known limitations

    Requires accurate manual entry of trade history, win rates, and compliance scores. It does not integrate directly with broker APIs to pull live data.

    How to install

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

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