fee drag reality checker

    by nowrich

    1

    Quantify post-cost trading performance by calculating commissions, spreads, funding rates, and annualized fee drag.

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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 a crypto perpetual strategy: 150 trades/year, $50k average position, 0.04% taker fee, 0.02% spread, held 12 hours on average with a 0.01% 8h funding rate. Gross return is 35%.

    Your agent does

    Fee Drag Reality Check

    Strategy Snapshot

    • Holding period: 12 hours
    • Turnover: 150 round-trips/year
    • Principal: $50,000

    Cost Breakdown

    | Component | Per trade | Annualized | % of principal | |-----------|----------|------------|----------------| | Commissions | $40.00 | $6,000 | 12.00% | | Spread | $20.00 | $3,000 | 6.00% | | Funding | $7.50 | $1,125 | 2.25% | | Total fee drag | $67.50 | $10,125 | 20.25% |

    Verdict

    • Gross expected return: 35.00%
    • Net expected return: 14.75%
    • Viable? Yes, but highly cost-sensitive.
    • Primary fee killer: Commissions + Turnover.

    What you get

    Quantify how turnover and slippage erode gross strategy returns.Analyze perpetual funding rate impact on long-term position viability.Determine break-even alpha requirements for new automated trading bots.Stress-test strategies against high-spread and low-liquidity market conditions.

    About this skill

    The problem

    High-frequency or leveraged strategies often look profitable on paper but fail in production due to hidden overhead. Developers and traders frequently underestimate how turnover, funding rates, and slippage compound to erase their edge.

    What it does

    • Quantifies every cost component including commissions, bid-ask spreads, and exchange fees.
    • Calculates time-based costs like perpetual funding rates and margin borrow interest.
    • Computes annualized fee drag and determines the break-even alpha required for viability.
    • Stress-tests strategies against worst-case liquidity and high-cost execution scenarios.

    Why this beats prompting it yourself

    Generic LLM prompts often miss specialized costs like funding periods for perpetuals or the linear growth of turnover drag. This skill uses a structured mathematical framework to ensure no cost component is overlooked, providing a standardized reality check that separates viable alpha from fee-burning noise.

    Use cases

    • Audit a crypto perpetual strategy to see if funding rates kill the long-term yield.
    • Compare different broker fee schedules for a high-turnover equity scalping bot.
    • Calculate the impact of slippage on large order sizes relative to thin order books.
    • Determine if a short-selling strategy is viable after factoring in hard-to-borrow fees.

    How to install

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

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