From b2462d332dca1beaa3ec283a41c9d4efe00c5701 Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Tue, 21 Oct 2025 21:12:26 +0200 Subject: [PATCH] feat(qav3): add tunable to enable HPO search space reduction MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- README.md | 102 +++++++++--------- .../freqaimodels/QuickAdapterRegressorV3.py | 9 +- quickadapter/user_data/strategies/Utils.py | 3 +- 3 files changed, 61 insertions(+), 53 deletions(-) diff --git a/README.md b/README.md index 3bc7314..b91a0e1 100644 --- a/README.md +++ b/README.md @@ -35,56 +35,57 @@ docker compose up -d --build ### Configuration tunables -| Path | Default | Type / Range | Description | -|------|---------|-------------|-------------| -| _Protections_ | | | | -| estimated_trade_duration_candles | 48 | int >= 1 | Heuristic for StoplossGuard tuning. | -| _Exit pricing_ | | | | -| exit_pricing.trade_price_target | `moving_average` | enum {`moving_average`,`interpolation`,`weighted_interpolation`} | Trade NATR computation method. | -| exit_pricing.thresholds_calibration.decline_quantile | 0.90 | float (0,1) | PNL decline quantile threshold. | -| _Reversal confirmation_ | | | | -| reversal_confirmation.lookback_period | 0 | int >= 0 | Prior confirming candles; 0 = none. | -| reversal_confirmation.decay_ratio | 0.5 | float (0,1] | Geometric per-step relaxation factor. | -| _Regressor model_ | | | | -| freqai.regressor | `xgboost` | enum {`xgboost`,`lightgbm`} | Machine learning regressor algorithm. | -| _Extrema smoothing_ | | | | -| freqai.extrema_smoothing | `gaussian` | enum {`gaussian`,`kaiser`,`triang`,`smm`,`sma`} | Extrema smoothing kernel (smm=simple moving median, sma=simple moving average). | -| freqai.extrema_smoothing_window | 5 | int >= 1 | Window size for extrema smoothing. | -| freqai.extrema_smoothing_beta | 8.0 | float > 0 | Kaiser kernel shape parameter. | -| _Feature parameters_ | | | | -| freqai.feature_parameters.label_period_candles | 24 | int >= 1 | Zigzag NATR horizon. | -| freqai.feature_parameters.label_natr_ratio | 9.0 | float > 0 | Zigzag NATR ratio. | -| freqai.feature_parameters.min_label_natr_ratio | 9.0 | float > 0 | Minimum NATR ratio bound used by label HPO. | -| freqai.feature_parameters.max_label_natr_ratio | 12.0 | float > 0 | Maximum NATR ratio bound used by label HPO. | -| freqai.feature_parameters.label_frequency_candles | 12 | int >= 2 | Reversals labeling frequency. | -| freqai.feature_parameters.label_metric | `euclidean` | string (supported: `euclidean`,`minkowski`,`cityblock`,`chebyshev`,`mahalanobis`,`seuclidean`,`jensenshannon`,`sqeuclidean`,...) | Metric used in distance calculations to ideal point. | -| freqai.feature_parameters.label_weights | [0.5,0.5] | list[float] | Per-objective weights used in distance calculations to ideal point. | -| freqai.feature_parameters.label_p_order | `None` | float | p-order used by Minkowski / power-mean calculations (optional). | -| freqai.feature_parameters.label_medoid_metric | `euclidean` | string | Metric used with `medoid`. | -| freqai.feature_parameters.label_kmeans_metric | `euclidean` | string | Metric used for k-means clustering. | -| freqai.feature_parameters.label_kmeans_selection | `min` | enum {`min`,`medoid`} | Strategy to select trial in the best kmeans cluster. | -| freqai.feature_parameters.label_kmedoids_metric | `euclidean` | string | Metric used for k-medoids clustering. | -| freqai.feature_parameters.label_kmedoids_selection | `min` | enum {`min`,`medoid`} | Strategy to select trial in the best k-medoids cluster. | -| freqai.feature_parameters.label_knn_metric | `minkowski` | string | Distance metric for KNN. | -| freqai.feature_parameters.label_knn_p_order | `None` | float | p-order for KNN Minkowski metric distance. (optional) | -| freqai.feature_parameters.label_knn_n_neighbors | 5 | int >= 1 | Number of neighbors for KNN. | -| _Prediction thresholds_ | | | | -| freqai.prediction_thresholds_smoothing | `mean` | enum {`mean`,`isodata`,`li`,`minimum`,`otsu`,`triangle`,`yen`,`soft_extremum`} | Thresholding method for prediction thresholds smoothing. | -| freqai.prediction_thresholds_alpha | 12.0 | float > 0 | Alpha for `soft_extremum`. | -| freqai.outlier_threshold | 0.999 | float (0,1) | Quantile threshold for predictions outlier filtering. | -| _Optuna / HPO_ | | | | -| freqai.optuna_hyperopt.enabled | true | bool | Enables HPO. | -| freqai.optuna_hyperopt.n_jobs | CPU threads / 4 | int >= 1 | Parallel HPO workers. | -| freqai.optuna_hyperopt.storage | `file` | enum {`file`,`sqlite`} | HPO storage backend. | -| freqai.optuna_hyperopt.continuous | false | bool | Continuous HPO. | -| freqai.optuna_hyperopt.warm_start | false | bool | Warm start HPO with previous best value(s). | -| freqai.optuna_hyperopt.n_startup_trials | 15 | int >= 0 | HPO startup trials. | -| freqai.optuna_hyperopt.n_trials | 50 | int >= 1 | Maximum HPO trials. | -| freqai.optuna_hyperopt.timeout | 7200 | int >= 0 | HPO wall-clock timeout in seconds. | -| freqai.optuna_hyperopt.label_candles_step | 1 | int >= 1 | Step for Zigzag NATR horizon search space. | -| freqai.optuna_hyperopt.train_candles_step | 10 | int >= 1 | Step for training sets size search space. | -| freqai.optuna_hyperopt.expansion_ratio | 0.4 | float [0,1] | HPO search space expansion ratio. | -| freqai.optuna_hyperopt.seed | 1 | int >= 0 | HPO RNG seed. | +| Path | Default | Type / Range | Description | +|------|------------------|-------------|---------------------------------------------------------------------------------| +| _Protections_ | | | | +| estimated_trade_duration_candles | 48 | int >= 1 | Heuristic for StoplossGuard tuning. | +| _Exit pricing_ | | | | +| exit_pricing.trade_price_target | `moving_average` | enum {`moving_average`,`interpolation`,`weighted_interpolation`} | Trade NATR computation method. | +| exit_pricing.thresholds_calibration.decline_quantile | 0.90 | float (0,1) | PNL decline quantile threshold. | +| _Reversal confirmation_ | | | | +| reversal_confirmation.lookback_period | 0 | int >= 0 | Prior confirming candles; 0 = none. | +| reversal_confirmation.decay_ratio | 0.5 | float (0,1] | Geometric per-step relaxation factor. | +| _Regressor model_ | | | | +| freqai.regressor | `xgboost` | enum {`xgboost`,`lightgbm`} | Machine learning regressor algorithm. | +| _Extrema smoothing_ | | | | +| freqai.extrema_smoothing | `gaussian` | enum {`gaussian`,`kaiser`,`triang`,`smm`,`sma`} | Extrema smoothing kernel (smm=simple moving median, sma=simple moving average). | +| freqai.extrema_smoothing_window | 5 | int >= 1 | Window size for extrema smoothing. | +| freqai.extrema_smoothing_beta | 8.0 | float > 0 | Kaiser kernel shape parameter. | +| _Feature parameters_ | | | | +| freqai.feature_parameters.label_period_candles | 24 | int >= 1 | Zigzag NATR horizon. | +| freqai.feature_parameters.label_natr_ratio | 9.0 | float > 0 | Zigzag NATR ratio. | +| freqai.feature_parameters.min_label_natr_ratio | 9.0 | float > 0 | Minimum NATR ratio bound used by label HPO. | +| freqai.feature_parameters.max_label_natr_ratio | 12.0 | float > 0 | Maximum NATR ratio bound used by label HPO. | +| freqai.feature_parameters.label_frequency_candles | 12 | int >= 2 | Reversals labeling frequency. | +| freqai.feature_parameters.label_metric | `euclidean` | string (supported: `euclidean`,`minkowski`,`cityblock`,`chebyshev`,`mahalanobis`,`seuclidean`,`jensenshannon`,`sqeuclidean`,...) | Metric used in distance calculations to ideal point. | +| freqai.feature_parameters.label_weights | [0.5,0.5] | list[float] | Per-objective weights used in distance calculations to ideal point. | +| freqai.feature_parameters.label_p_order | `None` | float | p-order used by Minkowski / power-mean calculations (optional). | +| freqai.feature_parameters.label_medoid_metric | `euclidean` | string | Metric used with `medoid`. | +| freqai.feature_parameters.label_kmeans_metric | `euclidean` | string | Metric used for k-means clustering. | +| freqai.feature_parameters.label_kmeans_selection | `min` | enum {`min`,`medoid`} | Strategy to select trial in the best kmeans cluster. | +| freqai.feature_parameters.label_kmedoids_metric | `euclidean` | string | Metric used for k-medoids clustering. | +| freqai.feature_parameters.label_kmedoids_selection | `min` | enum {`min`,`medoid`} | Strategy to select trial in the best k-medoids cluster. | +| freqai.feature_parameters.label_knn_metric | `minkowski` | string | Distance metric for KNN. | +| freqai.feature_parameters.label_knn_p_order | `None` | float | p-order for KNN Minkowski metric distance. (optional) | +| freqai.feature_parameters.label_knn_n_neighbors | 5 | int >= 1 | Number of neighbors for KNN. | +| _Prediction thresholds_ | | | | +| freqai.prediction_thresholds_smoothing | `mean` | enum {`mean`,`isodata`,`li`,`minimum`,`otsu`,`triangle`,`yen`,`soft_extremum`} | Thresholding method for prediction thresholds smoothing. | +| freqai.prediction_thresholds_alpha | 12.0 | float > 0 | Alpha for `soft_extremum`. | +| freqai.outlier_threshold | 0.999 | float (0,1) | Quantile threshold for predictions outlier filtering. | +| _Optuna / HPO_ | | | | +| freqai.optuna_hyperopt.enabled | true | bool | Enables HPO. | +| freqai.optuna_hyperopt.n_jobs | CPU threads / 4 | int >= 1 | Parallel HPO workers. | +| freqai.optuna_hyperopt.storage | `file` | enum {`file`,`sqlite`} | HPO storage backend. | +| freqai.optuna_hyperopt.continuous | false | bool | Continuous HPO. | +| freqai.optuna_hyperopt.warm_start | false | bool | Warm start HPO with previous best value(s). | +| freqai.optuna_hyperopt.n_startup_trials | 15 | int >= 0 | HPO startup trials. | +| freqai.optuna_hyperopt.n_trials | 50 | int >= 1 | Maximum HPO trials. | +| freqai.optuna_hyperopt.timeout | 7200 | int >= 0 | HPO wall-clock timeout in seconds. | +| freqai.optuna_hyperopt.label_candles_step | 1 | int >= 1 | Step for Zigzag NATR horizon search space. | +| freqai.optuna_hyperopt.train_candles_step | 10 | int >= 1 | Step for training sets size search space. | +| freqai.optuna_hyperopt.space_reduction | false | bool | Enable/disable HPO search space reduction based on previous best parameters. | +| freqai.optuna_hyperopt.expansion_ratio | 0.4 | float [0,1] | HPO search space expansion ratio. | +| freqai.optuna_hyperopt.seed | 1 | int >= 0 | HPO RNG seed. | ## ReforceXY @@ -153,4 +154,3 @@ docker compose down ## Note > Do not expect any support of any kind on the Internet. Nevertheless, PRs implementing documentation, bug fixes, cleanups or sensible features will be discussed and might get merged. - diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py index c906355..cd33696 100644 --- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py +++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py @@ -81,6 +81,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel): "timeout": 7200, "label_candles_step": 1, "train_candles_step": 10, + "space_reduction": False, "expansion_ratio": 0.4, "seed": 1, } @@ -317,6 +318,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel): test_weights, self.get_optuna_params(dk.pair, "hp"), model_training_parameters, + self._optuna_config.get("space_reduction"), self._optuna_config.get("expansion_ratio"), ), direction=optuna.study.StudyDirection.MINIMIZE, @@ -1735,10 +1737,15 @@ def hp_objective( test_weights: NDArray[np.floating], model_training_best_parameters: dict[str, Any], model_training_parameters: dict[str, Any], + space_reduction: bool, expansion_ratio: float, ) -> float: study_model_parameters = get_optuna_study_model_parameters( - trial, regressor, model_training_best_parameters, expansion_ratio + trial, + regressor, + model_training_best_parameters, + space_reduction, + expansion_ratio, ) model_training_parameters = {**model_training_parameters, **study_model_parameters} diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py index 74dc2e9..c40118b 100644 --- a/quickadapter/user_data/strategies/Utils.py +++ b/quickadapter/user_data/strategies/Utils.py @@ -827,6 +827,7 @@ def get_optuna_study_model_parameters( trial: optuna.trial.Trial, regressor: str, model_training_best_parameters: dict[str, Any], + space_reduction: bool, expansion_ratio: float, ) -> dict[str, Any]: if regressor not in regressors: @@ -864,7 +865,7 @@ def get_optuna_study_model_parameters( } ranges = copy.deepcopy(default_ranges) - if model_training_best_parameters: + if space_reduction and model_training_best_parameters: for param, (default_min, default_max) in default_ranges.items(): center_value = model_training_best_parameters.get(param) -- 2.43.0