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AnimateDread e372ce60a9 fix(vote): "0 fired" on USDJPY meant the threshold is above the highest vote the ensemble can cast 
USDJPY has taken no trades in 66 eras and its highest vote ever seen is 13
against a 25% threshold. Not a bug and not undertrained models - arithmetic.

Direction() divides the summed contributions by the CAPABLE weight, so a
unanimous vote returns the capability-weighted mean of the tier weights, which
is roughly the pooled holdout win rate. USDJPY's members pool at 15.6-19.4%
(its label base rate is 14.0% against SP500's 25.4%, because its derived
geometry resolves far fewer bars directionally: Buy 10.3% Sell 11.2% Neutral
78.6%). So the ensemble's CEILING is ~19 and the threshold is 25. Coverage can
never leave 0, and no amount of training moves it, because the ceiling IS the
win rate.

The report now computes that ceiling - every member voting at its best tier -
and says so when the threshold sits above it, instead of printing "0 fired at
vote>=25%" which reads as "the models are unsure".

Same class as the excursion head's disjoint gate (ee4d459) and the reason
ReportDetectability exists: a configuration that cannot reach its own bar has
to say that, not report a number that looks like evidence.

Also: VerboseMode and Run_Alglib_Baselines back to false. The per-era cadence
was for reading the horizon break-even and the excursion sigmas; both are
settled, and TrainLogDue still prints them every 25 eras. The baselines cost a
45 s single-threaded freeze at every attach and their forest row turned out to
be one deterministic observation that does not survive overlap deflation.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 08:16:11 -04:00
..
ConfidenceBridge.mqh refactor(kiss): drop the AI sub-vote early-exit route; certified == traded 2026-08-19 20:27:28 -04:00
IndicatorResources.mqh refactor(comments): box headers to stdlib length 2026-08-22 00:30:14 -04:00
IndicatorTuneRanges.mqh feat: extend ADWyckoffEventStream with new range-lifecycle parameters and update related features 2026-08-02 17:08:48 -04:00
Inputs.mqh fix(vote): "0 fired" on USDJPY meant the threshold is above the highest vote the ensemble can cast 2026-08-22 08:16:11 -04:00
README.md feat: Enhance README and documentation for Warrior_EA project 2026-04-20 19:28:34 -04:00
RiskBudget.mqh refactor(comments): box headers to stdlib length 2026-08-22 00:30:14 -04:00
TunedPeriods.mqh refactor(comments): box headers to stdlib length 2026-08-22 00:30:14 -04:00
Variables.mqh refactor(comments): box headers to stdlib length 2026-08-22 00:30:14 -04:00

README.md

Variables Subsystem (Variables/)

Overview

The Variables/ directory contains global input parameters and runtime variables for the Warrior EA. These files centralize configuration, feature toggles, and runtime state, supporting both user customization and internal logic.

Key Components

Inputs.mqh

  • Purpose: Defines all user-configurable input parameters for the EA.
  • Contents:
    • General EA settings (magic number, training mode, logging, etc.)
    • Money management strategy selection and parameters
    • Entry strategy and thresholds
    • Trailing stop strategy selection
    • Neural network/AI configuration (algorithm, layers, training years, etc.)
    • Indicator and feature toggles (enable/disable specific indicators and features)
    • Time/session filter settings
  • Integration: Used for both manual and programmatic configuration of the EA. Enables dynamic feature selection and AI/ML pipeline configuration.

Variables.mqh

  • Purpose: Stores global runtime variables and constants.
  • Contents:
    • EA name and database schema
    • Backtesting and feature enablement flags
    • AI/ML signal toggles (EnablePAI, EnableCONV, EnableLSTM)
  • Integration: Used throughout the EA for runtime logic, feature gating, and database operations.

Integration Notes

  • Centralized configuration and variable management improves maintainability and supports advanced, AI/ML-driven workflows.
  • Feature toggles allow for rapid experimentation and safe deployment of new logic.

Documented April 2026. For further details, see the main project documentation.