Disclaimer: On System Calibration and Market-Specific Performance

The performance metrics and case studies presented for JINN, including those for Palantir (PLTR), are based on the script's default, out-of-the-box configuration. These default settings have been meticulously calibrated to reflect the unique market dynamics, volatility profile, and behavioral characteristics exhibited by PLTR during the analyzed period.

JINN is not a "one-size-fits-all" solution; it is a professional-grade, adaptive framework that requires intelligent calibration.

Users must understand that applying these default settings to other tickers, asset classes (e.g., forex, cryptocurrencies), or different market conditions may not yield similar results. The statistical edge demonstrated in our case studies is a direct result of the synergy between JINN's architecture and the specific market it is analyzing.

Key adaptive components are inherently market-specific:

The Auto-Tuner

This online learning system develops its strategy based on the reward signals generated by a specific asset's price action. Its optimal weight distribution is path-dependent and will differ for each unique market.

Context-Aware & Dynamic Thresholds

The baseline parameters for volatility, entropy, and regime detection are calibrated to a specific asset's profile. These must be recalibrated to ensure the system correctly interprets the behavior of a different market.

The JINN Pattern Veto (JPV)

The pattern-matching templates learned by the JPV are a signature of a specific asset's price "rhythm" and are not universally transferable.

We strongly advise all users to conduct their own rigorous testing and calibration for each specific market they intend to trade. JINN provides the complete suite of tools necessary for this professional-level task. The default settings should be considered a powerful and well-engineered starting point for your own analysis, not a final, universal configuration.

Trading involves substantial risk, and past performance is not indicative of future results.