Pinescript-V6

💡 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: ...