Zero-Lookahead Academic Validator Lite
Zero-Lookahead Academic Validator (Lite)
Subtitle: Statistical Sanity Check for Algorithmic Blueprints
Description
This agent provides a rigorous academic simulation environment for evaluating algorithmic strategies. It focuses on Data Integrity and Generalization Boundaries rather than performance projection. Utilizing the proprietary ZLAF (Zero-Lookahead Academic Framework) scoring system, this tool identifies common pitfalls such as look-ahead bias and over-parameterization. The agent parses natural language, code snippets, or CSV uploads to generate a structural audit report. Output language adapts automatically to the user's input (English, Traditional Chinese, Simplified Chinese, etc.).
Key Methodology: Built upon the generalization principles outlined in Advances in Financial Machine Learning (López de Prado, 2018) and information criteria (Akaike, 1974).
Target Audience: Students, retail algo-traders, and strategy developers seeking academic rigor.
Core Features (Lite Exclusive)
ZLAF Academic Score (ASS): A 0-100 score adjusting raw statistical ratios by a complexity penalty (C_df, C_rule) to reflect the "True Complexity" of a strategy.
Academic Grade (Text): Assigns a qualitative grade — Excellent, Good, Fair, Poor, or Unacceptable — based on an anonymous repository of 12,400+ simulated strategies, providing immediate context on where your logic stands academically. Research Quality Only; not investment advice.
Strategy Fingerprinting: Compares your logic against a library of 10 canonical academic blueprints (e.g., Dual Thrust, RSI variants) to quantify similarity and originality.
Three Laws Compliance: Enforces strict data partitioning rules to prevent future function leakage during simulation.
Interactive HTML Dashboard: Generates responsive dark-themed HTML reports with SVG gauges, progress bars, and color-coded KPI cards — viewable in any browser.
Pricing & Limits
• Subscription: $3.00 / day (Run Online)
• Execution: Optimized for Capafy sandbox (< 5 min per operation). Standard timeouts apply.
Technical Specs
• Dependencies: pandas>=2.0.0,<3.0.0, numpy>=1.23.0 (Lightweight, no heavy ML libs required).
• Input: Natural Language, Python Code, CSV Data.
• Support: [email protected]
Test Cases (Capafy Validation)
• Case 1: Input: "Audit RSI(14) 30/70 on TSLA, daily 2020-2024" → Output: ASS score, Academic Grade (text), Three Laws table, Sharpe/MaxDD/WinRate (historical simulation), similarity to RSI blueprint.
• Case 2: Input: (CSV upload) "Check this MA(12,26) strategy" → Output: ASS score, Grade, compliance status, backtest metrics with transaction cost deduction.
• Case 3: Input: def strategy(df): return df.close.shift(1) > df.close.rolling(12).mean().shift(1) → Output: ASS score, Grade, Law I PASS (shift detected), similarity to MA Crossover.
Academic Disclaimer
This tool is designed for Academic Research and Educational Simulation ONLY. It constitutes a theoretical stress-test of logical structures, not a prediction of future outcomes. All outputs (ASS, Academic Grades, Similarity indices) are derived from historical data hypothesis testing. This is not financial advice, investment advice, or an offer to buy/sell securities. Consult a licensed financial advisor before making any investment decisions. Users assume all liability for any application of these theoretical frameworks. Use at your own risk.
References
López de Prado, M. (2018). Advances in Financial Machine Learning. Wiley. Akaike, H. (1974). A new look at the statistical model identification.
