- Introduced `strategy_backtest.py` as a single source of truth for scoring and backtesting.
- Implemented functions to load live model bundles, build feature frames, and execute backtests.
- Ensured consistency between optimized models and their validation/trading counterparts.
- Added functionality for generating trading signals and retrieving the latest trading decision.
This commit introduces a new CSV file containing the results of a lot escalation what-if analysis. The report includes various regimes, timeframes, trade counts, net profits, final balances, final equities, returns, maximum drawdowns, and profit factors for different escalation scenarios.
- Introduced `pipeline_verification.py` containing multiple verifier classes for dataset integrity, feature leakage, evaluation integrity, prediction alignment, and more.
- Added `SessionFilter` class in `session_filter.py` for unified session/time-of-day filtering.
- Implemented `VerificationResult` data class for structured verification results.
- Created utility functions for hashing and manifest building.
- Established a test suite for the session filter to ensure correct functionality.
- Updated `__init__.py` to include the new shared module.
- Deleted `test_lifecycle_parity.py`, which contained tests for lifecycle event timelines in live and backtest scenarios.
- Deleted `test_lifecycle_units.py`, which included unit tests for lifecycle configurations and floating PnL calculations.
- Deleted `test_three_state_contract.py`, which verified the enforcement of three semantic states across various components.
- Updated parallel processing output times to reflect new execution durations.
- Modified strategy results table with new entries and adjusted values for fixed_tp and fixed_tp_plus_mr strategies.
- Increased maximum drawdown percentage from 30.0 to 35.0.
- Enhanced filtering logic to ensure proper retrieval of results from all_results or valid_results.
- Implemented `generate_bridge_csv.py` to generate reports for strategy winners from the Strategy Tester notebook.
- Added `smoke_test_explorer.py` to perform smoke tests on the Explorer notebook integration, validating core logic and data processing.
- Resolve 5 committed git merge conflict markers in GoldRegimeX_Explorer.ipynb
that rendered it invalid JSON and unexecutable in Jupyter
- Conflict resolution: keep wfo_mode_w dropdown, reg_lambda5_w / min_child_weight5_w
sliders, Run WFO Score Comparison button, and Section 6 CV Path Inspector from
copilot branch; keep ensemble API imports (load_xgb_ensemble, get_predictions_ensemble)
from HEAD
- Fix notebook Sections 1 and 5 to use load_xgb_ensemble / train_xgb_ensemble /
get_predictions_ensemble instead of old single-model API; ensures equity curves
and WFO scores match production vol-bucketed ensemble
- Update README changelog with merge conflict resolution and ensemble API fix entry
- Updated MIN_OOS_TRADES_HARD for M15 from 30 to 60.
- Introduced WFO_PARAMS_FAST for quicker intraday regime detection.
- Adjusted WFO_TRIALS and added WFO_TRIALS_FAST to accommodate wider search space.
- Revised scoring formula in compute_cpcv_score to improve accuracy and range.
- Enhanced _run_wfo to support different WFO modes and adjusted CV folds based on TF.
- Aligned feature preparation and state alignment for consistency in backtesting.
- Updated make_objective to include new parameters and improved tree depth and learning rate settings.
- Changed pruner strategy based on TF in run_optimization for better trial management.
Updated the out-of-sample (OOS) backtesting process to ensure that the states used are aligned with the OOS data frame index. This change addresses potential mismatches by applying a mask to the states based on the aligned index, improving the accuracy of the backtest results.
Luke Mosoti