Jérôme Benoit [Fri, 2 Jan 2026 14:21:16 +0000 (15:21 +0100)]
refactor(quickadapter): unify validation helpers with ValidationMode support
- Add ValidationMode type for "warn", "raise", "none" behavior
- Rename _UNSUPPORTED_CLUSTER_METRICS to _UNSUPPORTED_WEIGHTS_METRICS
- Refactor _validate_minkowski_p, _validate_quantile_q with mode support
- Add _validate_power_mean_p, _validate_metric_weights_support, _validate_label_weights
- Add _validate_enum_value for generic enum validation
- Add _prepare_knn_kwargs for sklearn-specific weight handling
- Remove deprecated _normalize_weights function
- Update all call sites to use new unified API
Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>
* fix: use unbounded cache for constant-returning helper methods
Replace @lru_cache(maxsize=1) with @lru_cache(maxsize=None) for all
static methods that return constant sets. Using maxsize=None is more
idiomatic and efficient for parameterless functions that always return
the same value.
* refactor: add _prepare_distance_kwargs to centralize distance kwargs preparation
Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>
* refactor: cleanup extrema weighting API
Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>
* refactor: cleanup extrema smoothing API
- Remove ClusterSelectionMethod type and related constants
- Unify selection methods to use DistanceMethod for both cluster and trial selection
- Add separate trial_selection_method parameter for within-cluster selection
- Change power_mean default from 2.0 to 1.0 for internal consistency
- Add validation for selection_method and trial_selection_method parameters
* fix: add missing validations for label_distance_metric and label_density_aggregation_param
- Add validation for label_distance_metric parameter at configuration time
- Add early validation for label_density_aggregation_param (quantile and power_mean)
- Ensures invalid configuration values fail fast with clear error messages
- Harmonizes error messages with existing validation patterns in the codebase
* fix: add validation for label_cluster_metric and custom metrics support in topsis
- Add validation that label_cluster_metric is in _distance_metrics_set()
- Implement custom metrics support in _topsis_scores (hellinger, shellinger,
harmonic/geometric/arithmetic/quadratic/cubic/power_mean, weighted_sum)
matching _compromise_programming_scores implementation
* docs: update README.md with refactored label selection methods
Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>
* docs: fix config parameter and bump to v3.9.0
- Fix config-template.json: label_metric -> label_method
- Bump version from 3.8.5 to 3.9.0 in model and strategy
Parameter names now match QuickAdapterRegressorV3.py implementation.
Jérôme Benoit [Sun, 28 Dec 2025 18:51:56 +0000 (19:51 +0100)]
refactor(quickadapter)!: normalize tunables namespace for semantic consistency (#26)
* refactor(quickadapter): normalize tunables namespace for semantic consistency
Rename config keys and internal variables to follow consistent naming conventions:
- `_candles` suffix for time periods in candle units
- `_fraction` suffix for values in [0,1] range
- `_multiplier` suffix for scaling factors
- `_method` suffix for algorithm selectors
Internal variable renames for code consistency:
- threshold_outlier → outlier_threshold_fraction
- thresholds_alpha → soft_extremum_alpha
- extrema_fraction → keep_extrema_fraction (local vars and function params)
- _reversal_lookback_period → _reversal_lookback_period_candles
- natr_ratio → natr_multiplier (zigzag function param)
All deprecated aliases emit warnings and remain functional for backward compatibility.
* chore(quickadapter): remove temporary audit file from codebase
* refactor(quickadapter): align constant names with normalized tunables
Rename class constants to match the normalized config key names:
- PREDICTIONS_EXTREMA_THRESHOLD_OUTLIER_DEFAULT → PREDICTIONS_EXTREMA_OUTLIER_THRESHOLD_FRACTION_DEFAULT
- PREDICTIONS_EXTREMA_THRESHOLDS_ALPHA_DEFAULT → PREDICTIONS_EXTREMA_SOFT_EXTREMUM_ALPHA_DEFAULT
- PREDICTIONS_EXTREMA_EXTREMA_FRACTION_DEFAULT → PREDICTIONS_EXTREMA_KEEP_EXTREMA_FRACTION_DEFAULT
* fix(quickadapter): rename outlier_threshold_fraction to outlier_threshold_quantile
The value (e.g., 0.999) represents the 99.9th percentile, which is
mathematically a quantile, not a fraction. This aligns the naming with
the semantic meaning of the parameter.
* fix(quickadapter): add missing deprecated alias support
* refactor(quickadapter): rename safe configuration value retrieval helper
Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>
* refactor(quickadapter): rename natr_ratio_fraction to natr_multiplier_fraction
- Align naming with label_natr_multiplier for consistency
- Rename get_config_value_with_deprecated_alias to get_config_value
* refactor(quickadapter): centralize label_natr_multiplier migration in get_label_defaults
- Move label_natr_ratio -> label_natr_multiplier migration to get_label_defaults()
- Update get_config_value to migrate in-place (pop old key, store new key)
- Remove redundant get_config_value calls in Strategy and Model __init__
- Simplify cached properties to use .get() since migration is done at init
- Rename _CUSTOM_STOPLOSS_NATR_RATIO_FRACTION to _CUSTOM_STOPLOSS_NATR_MULTIPLIER_FRACTION
* fix(quickadapter): check that df columns exist before using them
Jérôme Benoit [Sun, 28 Dec 2025 01:16:02 +0000 (02:16 +0100)]
feat(quickadapter): add HistGradientBoostingRegressor support (#25)
* feat(quickadapter): add HistGradientBoostingRegressor support
Add sklearn's HistGradientBoostingRegressor as a third regressor option.
- Add 'histgradientboostingregressor' to Regressor type and REGRESSORS
- Implement fit_regressor() with X_val/y_val support and early stopping
- Add native sklearn hyperparameters to get_optuna_study_model_parameters()
- Return empty callbacks list (no Optuna pruning callback support)
- Log warning when init_model is provided (not supported)
* fix(quickadapter): address PR review comments for HistGradientBoostingRegressor
Jérôme Benoit [Tue, 23 Dec 2025 19:13:18 +0000 (20:13 +0100)]
refactor(ReforceXY): PBRS refactoring, bug fix, and documentation harmonization
This commit includes three major improvements to the PBRS implementation:
1. Bug Fix: idle_factor calculation
- Fixed incorrect variable reference in reward_space_analysis.py:625
- Changed 'factor' to 'base_factor' in idle_factor formula
- Formula: idle_factor = base_factor * (profit_aim / risk_reward_ratio) / 4.0
- Also fixed in test_reward_components.py and ReforceXY.py
2. Refactoring: Separation of concerns in PBRS calculation
- Renamed apply_potential_shaping() → compute_pbrs_components()
- Removed base_reward parameter from PBRS functions
- PBRS functions now return only shaping components
- Caller responsible for: total = base_reward + shaping + entry + exit
- Kept deprecated wrapper for backward compatibility
- Updated ReforceXY.py with parallel changes
- Adapted tests to new function signatures
3. Documentation: Complete mathematical notation harmonization
- Achieved 100% consistent notation across both implementations
- Standardized on Greek symbols: Φ(s), γ, Δ(s,a,s')
- Eliminated mixing of word forms (Phi/gamma/Delta) with symbols
- Harmonized docstrings to 156-169 lines with identical theory sections
- Added cross-references between implementations
- Fixed all instances of Δ(s,s') → Δ(s,a,s') to include action parameter
Files modified:
- reward_space_analysis/reward_space_analysis.py: Core refactoring + docs
- user_data/freqaimodels/ReforceXY.py: Parallel refactoring + docs
- tests/components/test_additives.py: Adapted to new signature
- tests/components/test_reward_components.py: Bug fix
- tests/api/test_api_helpers.py: Adapted to new signature
All 50 tests pass. Behavior preserved except for intentional bug fix.
repositories / freqai-strategies.git / log
