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    AI Trading Bot Reality Checker ⭐

    1

    Adversarial auditor for trading bots that detects look-ahead bias, logic flaws, and missing risk controls.

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    AI Trading Bot Reality Checker ⭐

    AI Trading Bot Reality Checker ⭐

    Example session with this skill installed

    Audit this Python snippet for a Mean Reversion bot. Check for look-ahead bias in the RSI calculation and verify if the stop-loss logic handles gap-downs. Rank findings by severity.

    • Read your context and instructions
    • Compiled the ai trading bot

    I have audited the Mean Reversion bot logic. The review identified a critical look-ahead bias in the signal generation and insufficient handling of overnight price gaps.

    Adversarial Audit Report

    Executive Verdict

    Verdict: Needs Major Fixes. The strategy relies on future data for entry signals, making backtest results invalid.

    Findings Table

    IDSeverityEvidenceComponentProblemImpactRecommended Fix
    001CriticalConfirmedSignal LogicRSI uses df['close'] at index i for signal at i-1100% Look-ahead biasShift signal window by 1 period
    002HighConfirmedExit LogicStop-loss assumes fill at exact priceIgnores slippage/gapsUse limit orders or simulated slippage
    003MediumIndicationRiskNo max daily drawdownUncapped daily lossImplement a daily circuit breaker

    Logic Walkthrough

    The bot calculates RSI on a rolling window that includes the current closing price to decide an entry for the same period. In a live environment, this price is unknown at the time of the calculation. Additionally, the stop-loss function check_exit() uses a simple comparison that fails to account for market gaps where the opening price is significantly below the stop trigger.

    Failure Scenarios

    The "Perfect" Backtest: The bot shows 90% win rate in testing because it effectively "sees" the close before entering, but will fail immediately in live trading.
    2.

    Flash Crash/Gap: An overnight gap down of 5% would bypass the if price <= stop_loss check if the logic only triggers on specific price touches rather than boundary breaches.

    Next steps

    • Correct the RSI window indexing to ensure only historical data is used for the current signal.
    • Add a slippage model (e.g., 5-10 bps) to the backtest environment to test sensitivity.
    • Implement an explicit check for price gaps in the exit logic.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Trading bots generated by AI often hide catastrophic flaws like look-ahead bias, unrealistic slippage assumptions, or missing kill switches. This skill acts as an adversarial auditor to find these failure modes before you risk capital.

    What it does

    • Detect Logic Flaws by tracing signal generation, entry/exit triggers, and order management code for defects.
    • Identify Backtest Bias including look-ahead bias, data leakage, survivorship bias, and cherry-picked results.
    • Audit Risk Controls by inspecting stop-loss logic, position sizing, exposure limits, and drawdown safeguards.
    • Evaluate Execution Realism to ensure commissions, spreads, slippage, and market impact are correctly modeled.
    • Assess Deployment Readiness by reviewing logging, error handling, API disconnect recovery, and stale price detection.

    How it works

    1. Quick Reality Check provides a high-level scan of strategy claims and obvious validation gaps.
    2. Code Logic Audit traces data transformations and order handling to confirm specific implementation defects.
    3. Risk-Control Audit evaluates the effectiveness of safety mechanisms like kill switches and exposure limits.
    4. Fix-First Plan generates a prioritized list of corrections required before moving to paper or live trading.

    Frameworks & tools

    Works with any algorithmic trading stack including Python (Pandas, Backtrader, Zipline), Pine Script, MQL4/5, C++, and REST/WebSocket API integrations for major brokerages.

    Why this beats prompting it yourself

    Generic AI prompts often suffer from confirmation bias and fail to check for subtle quantitative errors like timestamp misalignment. This skill uses an adversarial framework designed to falsify claims and uncover hidden edge cases that standard code reviews miss.

    Use cases

    • Auditing AI-generated Python trading scripts for hidden look-ahead bias.
    • Reviewing Pine Script strategies for unrealistic backtest performance claims.
    • Verifying risk management logic in automated crypto trading bots.
    • Assessing deployment readiness for algorithmic systems before moving from backtest to paper trading.

    Known limitations

    Does not execute live trades or connect to brokerage APIs. Requires manual provision of code or strategy logic for analysis. Cannot guarantee future profitability or prevent all market losses.

    Cannot execute trades or access live brokerages. Requires user to provide source code or strategy logic. Findings are based on static analysis and logic tracing, not live market execution.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Security scan

    Security scan passed 15 days ago

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    Listed15 days ago

    What's inside

    Frequently Asked Questions

    The skill focuses on identifying high-risk failures like look-ahead bias, data leakage, and flawed risk controls that generic AI reviews often miss. It acts as an adversarial auditor, specifically designed to stress-test the logic and execution assumptions of trading algorithms.

    This skill is compatible with any AI agent or platform capable of processing code blocks, including popular Python libraries like Pandas and Backtrader, as well as Pine Script and MQL4/5. You can provide your strategy logic or raw code directly to the skill for analysis.

    No, the skill is designed for auditing and safety verification only; it does not have the capability to execute trades or connect to live brokerage APIs. Its primary purpose is to provide a 'Fix-First Plan' to secure your code before you deploy it to a live environment.

    Your purchase includes the full adversarial auditing framework, which provides a multi-stage review process covering code logic, risk-control effectiveness, and deployment readiness. You will also receive guidance on how to interpret the audit results to prioritize necessary fixes.

    The skill evaluates your strategy's assumptions regarding slippage, commissions, and market impact to ensure your backtest results aren't mathematically impossible. It identifies if your bot is 'cheating' by using future data points to make past decisions, which is a common cause of failed live deployments.

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