Module-level ATR_SL_MULT was 1.5, now 2.0 to match config.py and CLI defaults.
This ensures generate_dataset_vectorized() produces correct labels even without
explicit parameters.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- MLFilter.from_model() classmethod eliminates brittle __new__() private-attribute
manipulation in backtester walk-forward model injection
- backtest_validator._check_invariants() now accepts cfg and uses cfg.initial_balance
instead of a hardcoded 1000.0 for the negative-balance invariant check
- backtester.py walk-forward injection block simplified to use the new factory method
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add normalize=False parameter to MLXFilter.fit() so external callers
can skip internal normalization. Remove the external normalization +
manual _mean/_std reset hack from walk_forward_auc() in train_mlx_model.py.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add atr_sl_mult and atr_tp_mult parameters to _calc_labels_vectorized
and generate_dataset_vectorized, defaulting to existing constants (1.5,
2.0) for full backward compatibility. Callers (train scripts, backtester)
can now pass symbol-specific multipliers without modifying module-level
constants.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
