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💡 요약 / TL;DR - Look-Ahead Bias & Stop-Loss Causality Executive Summary (BLUF) Insidious Causality Leaks: Look-ahead bias and structural causality violations inflate backtest metrics, presenting a statistical illusion of high performance that translates into immediate capital loss in live execution. AI Semantic Blindspot: LLM coding assistants only validate syntactic compilation rather than semantic causality, generating flawless-compiling trading code that silently snoops into the future. Algorithmic Defenses: Enforcing constant level stop locks, prioritizing pre-finalized historical indicators (t-1), and implementing automated Pandas forward-mask unit tests are vital to ensure statistical survival. Three Systemic Defects in AI-Generated Backtests When developers ask AI coding assistants (such as ChatGPT, Claude, or Copilot) to generate trading bot backtests, the resulting code almost always compiles without syntactic runtime errors. However, beneath the clean syntax, these models routinely embed three fatal causality leaks that guarantee bankruptcy in real-world markets. ...

💡 TL;DR - Order Book & Slippage Simulation Executive Summary (BLUF) Empirical Reality of Slippage: Standard backtesting engines that assume instant, guaranteed limit fills or rely on static, flat slippage parameters heavily distort the empirical reality of execution friction. In live cryptocurrency markets, ignoring non-linear Market Impact Costs can easily erode over $20$% of net algorithmic profits. Virtual Queue & Power-Law: Quantitative developers must incorporate dynamic power-law slippage modeling conditioned on localized pool depth and implement fill-probability filters ($P(\text{fill})$) that simulate queue position and time-to-fill priority under various volatility regimes. Execution Integrity: Incorporate defensive prompt constraints (Adaptive Sizing) to dynamically scale down entry sizes or perform split executions when trade volumes exceed 10% of the active order book depth. The Limit Order Mirage: The Flaw of 100% Fill Certainty When quantitative developers instruct AI coding engines (such as ChatGPT, Claude, or Copilot) to generate backtesting scripts using limit orders, the systems typically produce basic execution logic that looks like this: ...

💡 TL;DR — The Self-Audit Verdict (BLUF) The headline claim does not reproduce: across 6,467 trading days (2000–2026), the NDX–ZC volatility cross-correlation is flat at +0.07 to +0.09 at every lag from −10 to +10. The original post’s 5-day-lag negative correlation (printed as −0.6355) could not be reproduced with real data. The “GARCH” column was not GARCH: the original table’s conditional-volatility columns are pure exponential decay — log-linear R² = 0.99987 (NDX) and 0.99849 (ZC) — with zero shock response. A pipeline error, now documented and corrected. The window was a lottery: 12.3% of all 17-day windows in the full sample reach a correlation ≤ −0.6355 by chance alone. A 17-day correlation carries no evidential weight. Verdict: C1 refuted, C2 partially confirmed, C3 refuted. The original post is corrected; this audit is the public record. Seventeen days ago we published a hedging study claiming that Nasdaq-100 volatility shocks transmit to corn futures with a 5-business-day lag and a correlation of −0.6355. Seventeen is a fitting number, because seventeen rows of data is exactly what that claim stood on. A reader suggested we re-check the relationship with a proper DCC-GARCH model instead of a static correlation table — so we did, against 6,467 trading days of real NDX and ZC data spanning 2000–2026. ...

💡 Bottom Line A crypto perpetual-futures backtest fails live for five reasons, but only two are crypto-specific: notional-based funding drag and silent data gaps. Fix those here. The other three (limit-fill illusion, slippage and latency, look-ahead bias via joins) are universal backtest sins that apply to stocks, FX, and futures alike. We measured each one with real data in the Backtest Autopsy series, so this guide links out instead of repeating them. The funding numbers below are worked calculations from stated assumptions, not a measured study. The measured forensics live in the linked spokes. This is a methodology guide, not a forensic report. Where a claim needs measured proof, we point to the specific Backtest Autopsy spoke that measured it, rather than reproducing numbers here. ...