### Configuration tunables
-| Path | Default | Type / Range | Description |
-| -------------------------------------------------------------- | ----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| _Protections_ | | | |
-| custom_protections.trade_duration_candles | 72 | int >= 1 | Estimated trade duration in candles. Scales protections stop duration candles and trade limit. |
-| custom_protections.lookback_period_fraction | 0.5 | float (0,1] | Fraction of `fit_live_predictions_candles` used to calculate `lookback_period_candles` for _MaxDrawdown_ and _StoplossGuard_ protections. |
-| custom_protections.cooldown.enabled | true | bool | Enable/disable _CooldownPeriod_ protection. |
-| custom_protections.cooldown.stop_duration_candles | 4 | int >= 1 | Number of candles to wait before allowing new trades after a trade is closed. |
-| custom_protections.drawdown.enabled | true | bool | Enable/disable _MaxDrawdown_ protection. |
-| custom_protections.drawdown.max_allowed_drawdown | 0.2 | float (0,1) | Maximum allowed drawdown. |
-| custom_protections.stoploss.enabled | true | bool | Enable/disable _StoplossGuard_ protection. |
-| _Leverage_ | | | |
-| leverage | `proposed_leverage` | float [1.0, max_leverage] | Leverage. Fallback to `proposed_leverage` for the pair. |
-| _Exit pricing_ | | | |
-| exit_pricing.trade_price_target_method | `moving_average` | enum {`moving_average`,`quantile_interpolation`,`weighted_average`} | Trade NATR computation method. (Deprecated alias: `exit_pricing.trade_price_target`) |
-| exit_pricing.thresholds_calibration.decline_quantile | 0.75 | float (0,1) | PnL decline quantile threshold. |
-| _Reversal confirmation_ | | | |
-| reversal_confirmation.lookback_period_candles | 0 | int >= 0 | Prior confirming candles; 0 = none. (Deprecated alias: `reversal_confirmation.lookback_period`) |
-| reversal_confirmation.decay_fraction | 0.5 | float (0,1] | Geometric per-candle volatility adjusted reversal threshold relaxation factor. (Deprecated alias: `reversal_confirmation.decay_ratio`) |
-| reversal_confirmation.min_natr_multiplier_fraction | 0.0095 | float [0,1] | Lower bound fraction for volatility adjusted reversal threshold. (Deprecated alias: `reversal_confirmation.min_natr_ratio_percent`) |
-| reversal_confirmation.max_natr_multiplier_fraction | 0.075 | float [0,1] | Upper bound fraction (>= lower bound) for volatility adjusted reversal threshold. (Deprecated alias: `reversal_confirmation.max_natr_ratio_percent`) |
-| _Regressor model_ | | | |
-| freqai.regressor | `xgboost` | enum {`xgboost`,`lightgbm`,`histgradientboostingregressor`} | Machine learning regressor algorithm. |
-| _Extrema smoothing_ | | | |
-| freqai.extrema_smoothing.method | `gaussian` | enum {`gaussian`,`kaiser`,`triang`,`smm`,`sma`,`savgol`,`gaussian_filter1d`} | Extrema smoothing method (`smm`=median, `sma`=mean, `savgol`=Savitzky–Golay). |
-| freqai.extrema_smoothing.window_candles | 5 | int >= 3 | Smoothing window length (candles). (Deprecated alias: `freqai.extrema_smoothing.window`) |
-| freqai.extrema_smoothing.beta | 8.0 | float > 0 | Shape parameter for `kaiser` kernel. |
-| freqai.extrema_smoothing.polyorder | 3 | int >= 1 | Polynomial order for `savgol` smoothing. |
-| freqai.extrema_smoothing.mode | `mirror` | enum {`mirror`,`constant`,`nearest`,`wrap`,`interp`} | Boundary mode for `savgol` and `gaussian_filter1d`. |
-| freqai.extrema_smoothing.sigma | 1.0 | float > 0 | Gaussian `sigma` for `gaussian_filter1d` smoothing. |
-| _Extrema weighting_ | | | |
-| freqai.extrema_weighting.strategy | `none` | enum {`none`,`amplitude`,`amplitude_threshold_ratio`,`volume_rate`,`speed`,`efficiency_ratio`,`volume_weighted_efficiency_ratio`,`combined`} | Extrema weighting metric: none (`none`), swing amplitude (`amplitude`), swing amplitude / median volatility-threshold ratio (`amplitude_threshold_ratio`), swing volume per candle (`volume_rate`), swing speed (`speed`), swing efficiency ratio (`efficiency_ratio`), swing volume-weighted efficiency ratio (`volume_weighted_efficiency_ratio`), or combined metrics aggregation (`combined`). |
-| freqai.extrema_weighting.metric_coefficients | {} | dict[str, float] | Per-metric coefficients for `combined` strategy. Keys: `amplitude`, `amplitude_threshold_ratio`, `volume_rate`, `speed`, `efficiency_ratio`, `volume_weighted_efficiency_ratio`. |
-| freqai.extrema_weighting.aggregation | `weighted_average` | enum {`weighted_average`,`geometric_mean`} | Metric aggregation method for `combined` strategy. `weighted_average`=Σ(coef·metric)/Σ(coef), `geometric_mean`=∏(metric^coef)^(1/Σcoef). |
-| freqai.extrema_weighting.standardization | `none` | enum {`none`,`zscore`,`robust`,`mmad`,`power_yj`} | Standardization method applied to smoothed weighted extrema before normalization. `none`=w, `zscore`=(w-μ)/σ, `robust`=(w-median)/IQR, `mmad`=(w-median)/(MAD·k), `power_yj`=YJ(w). |
-| freqai.extrema_weighting.robust_quantiles | [0.25, 0.75] | list[float] where 0 <= Q1 < Q3 <= 1 | Quantile range for robust standardization, Q1 and Q3. |
-| freqai.extrema_weighting.mmad_scaling_factor | 1.4826 | float > 0 | Scaling factor for MMAD standardization. |
-| freqai.extrema_weighting.normalization | `maxabs` | enum {`maxabs`,`minmax`,`sigmoid`,`none`} | Normalization method applied to smoothed weighted extrema. `maxabs`=w/max(\|w\|), `minmax`=low+(w-min)/(max-min)·(high-low), `sigmoid`=2·σ(scale·w)-1, `none`=w. |
-| freqai.extrema_weighting.minmax_range | [-1.0, 1.0] | list[float] | Target range for `minmax` normalization, min and max. |
-| freqai.extrema_weighting.sigmoid_scale | 1.0 | float > 0 | Scale parameter for `sigmoid` normalization, controls steepness. |
-| freqai.extrema_weighting.gamma | 1.0 | float (0,10] | Contrast exponent applied to smoothed weighted extrema after normalization: >1 emphasizes extrema, values between 0 and 1 soften. |
-| _Feature parameters_ | | | |
-| freqai.feature_parameters.label_period_candles | min/max midpoint | int >= 1 | Zigzag labeling NATR horizon. |
-| freqai.feature_parameters.min_label_period_candles | 12 | int >= 1 | Minimum labeling NATR horizon used for reversals labeling HPO. |
-| freqai.feature_parameters.max_label_period_candles | 24 | int >= 1 | Maximum labeling NATR horizon used for reversals labeling HPO. |
-| freqai.feature_parameters.label_natr_multiplier | min/max midpoint | float > 0 | Zigzag labeling NATR multiplier. (Deprecated alias: `freqai.feature_parameters.label_natr_ratio`) |
-| freqai.feature_parameters.min_label_natr_multiplier | 9.0 | float > 0 | Minimum labeling NATR multiplier used for reversals labeling HPO. (Deprecated alias: `freqai.feature_parameters.min_label_natr_ratio`) |
-| freqai.feature_parameters.max_label_natr_multiplier | 12.0 | float > 0 | Maximum labeling NATR multiplier used for reversals labeling HPO. (Deprecated alias: `freqai.feature_parameters.max_label_natr_ratio`) |
-| freqai.feature_parameters.label_frequency_candles | `auto` | int >= 2 \| `auto` | Reversals labeling frequency. `auto` = max(2, 2 \* number of whitelisted pairs). |
-| freqai.feature_parameters.label_weights | [1/7,1/7,1/7,1/7,1/7,1/7,1/7] | list[float] | Per-objective weights used in distance calculations to ideal point. Objectives: (1) number of detected reversals, (2) median swing amplitude, (3) median (swing amplitude / median volatility-threshold ratio), (4) median swing volume per candle, (5) median swing speed, (6) median swing efficiency ratio, (7) median swing volume-weighted efficiency ratio. |
-| freqai.feature_parameters.label_p_order | `None` | float \| None | p-order parameter for distance metrics. Used by minkowski (default 2.0) and power_mean (default 1.0). Ignored by other metrics. |
-| freqai.feature_parameters.label_method | `compromise_programming` | enum {`compromise_programming`,`topsis`,`kmeans`,`kmeans2`,`kmedoids`,`knn`,`medoid`} | HPO `label` Pareto front trial selection method. |
-| freqai.feature_parameters.label_distance_metric | `euclidean` | string | Distance metric for `compromise_programming` and `topsis` methods. |
-| freqai.feature_parameters.label_cluster_metric | `euclidean` | string | Distance metric for `kmeans`, `kmeans2`, and `kmedoids` methods. |
-| freqai.feature_parameters.label_cluster_selection_method | `topsis` | enum {`compromise_programming`,`topsis`} | Cluster selection method for clustering-based label methods. |
-| freqai.feature_parameters.label_cluster_trial_selection_method | `topsis` | enum {`compromise_programming`,`topsis`} | Best cluster trial selection method for clustering-based label methods. |
-| freqai.feature_parameters.label_density_metric | method-dependent | string | Distance metric for `knn` and `medoid` methods. |
-| freqai.feature_parameters.label_density_aggregation | `power_mean` | enum {`power_mean`,`quantile`,`min`,`max`} | Aggregation method for KNN neighbor distances. |
-| freqai.feature_parameters.label_density_n_neighbors | 5 | int >= 1 | Number of neighbors for KNN. |
-| freqai.feature_parameters.label_density_aggregation_param | aggregation-dependent | float \| None | Tunable for KNN neighbor distance aggregation: p-order (`power_mean`) or quantile value (`quantile`). |
-| _Predictions extrema_ | | | |
-| freqai.predictions_extrema.selection_method | `rank_extrema` | enum {`rank_extrema`,`rank_peaks`,`partition`} | Extrema selection method. `rank_extrema` ranks extrema values, `rank_peaks` ranks detected peak values, `partition` uses sign-based partitioning. |
-| freqai.predictions_extrema.threshold_smoothing_method | `mean` | enum {`mean`,`isodata`,`li`,`minimum`,`otsu`,`triangle`,`yen`,`median`,`soft_extremum`} | Thresholding method for prediction thresholds smoothing. (Deprecated alias: `freqai.predictions_extrema.thresholds_smoothing`) |
-| freqai.predictions_extrema.soft_extremum_alpha | 12.0 | float >= 0 | Alpha for `soft_extremum` thresholds smoothing. (Deprecated alias: `freqai.predictions_extrema.thresholds_alpha`) |
-| freqai.predictions_extrema.outlier_threshold_quantile | 0.999 | float (0,1) | Quantile threshold for predictions outlier filtering. (Deprecated alias: `freqai.predictions_extrema.threshold_outlier`) |
-| freqai.predictions_extrema.keep_extrema_fraction | 1.0 | float (0,1] | Fraction of extrema used for thresholds. `1.0` uses all, lower values keep only most significant. Applies to `rank_extrema` and `rank_peaks`; ignored for `partition`. (Deprecated alias: `freqai.predictions_extrema.extrema_fraction`) |
-| _Optuna / HPO_ | | | |
-| freqai.optuna_hyperopt.enabled | false | bool | Enables HPO. |
-| freqai.optuna_hyperopt.sampler | `tpe` | enum {`tpe`,`auto`} | HPO sampler algorithm for `hp` namespace. `tpe` uses [TPESampler](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.TPESampler.html) with multivariate and group, `auto` uses [AutoSampler](https://hub.optuna.org/samplers/auto_sampler). |
-| freqai.optuna_hyperopt.label_sampler | `auto` | enum {`auto`,`tpe`,`nsgaii`,`nsgaiii`} | HPO sampler algorithm for multi-objective `label` namespace. `nsgaii` uses [NSGAIISampler](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.NSGAIISampler.html), `nsgaiii` uses [NSGAIIISampler](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.NSGAIIISampler.html). |
-| freqai.optuna_hyperopt.storage | `file` | enum {`file`,`sqlite`} | HPO storage backend. |
-| freqai.optuna_hyperopt.continuous | true | bool | Continuous HPO. |
-| freqai.optuna_hyperopt.warm_start | true | 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.n_jobs | CPU threads / 4 | int >= 1 | Parallel HPO workers. |
-| 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 `label` search space. |
-| freqai.optuna_hyperopt.space_reduction | false | bool | Enable/disable `hp` search space reduction based on previous best parameters. |
-| freqai.optuna_hyperopt.space_fraction | 0.4 | float [0,1] | Fraction of the `hp` search space to use with `space_reduction`. Lower values create narrower search ranges around the best parameters. (Deprecated alias: `freqai.optuna_hyperopt.expansion_ratio`) |
-| freqai.optuna_hyperopt.min_resource | 3 | int >= 1 | Minimum resource per [HyperbandPruner](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.pruners.HyperbandPruner.html) rung. |
-| freqai.optuna_hyperopt.seed | 1 | int >= 0 | HPO RNG seed. |
+| Path | Default | Type / Range | Description |
+| -------------------------------------------------------------- | ----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| _Protections_ | | | |
+| custom_protections.trade_duration_candles | 72 | int >= 1 | Estimated trade duration in candles. Scales protections stop duration candles and trade limit. |
+| custom_protections.lookback_period_fraction | 0.5 | float (0,1] | Fraction of `fit_live_predictions_candles` used to calculate `lookback_period_candles` for _MaxDrawdown_ and _StoplossGuard_ protections. |
+| custom_protections.cooldown.enabled | true | bool | Enable/disable _CooldownPeriod_ protection. |
+| custom_protections.cooldown.stop_duration_candles | 4 | int >= 1 | Number of candles to wait before allowing new trades after a trade is closed. |
+| custom_protections.drawdown.enabled | true | bool | Enable/disable _MaxDrawdown_ protection. |
+| custom_protections.drawdown.max_allowed_drawdown | 0.2 | float (0,1) | Maximum allowed drawdown. |
+| custom_protections.stoploss.enabled | true | bool | Enable/disable _StoplossGuard_ protection. |
+| _Leverage_ | | | |
+| leverage | `proposed_leverage` | float [1.0, max_leverage] | Leverage. Fallback to `proposed_leverage` for the pair. |
+| _Exit pricing_ | | | |
+| exit_pricing.trade_price_target_method | `moving_average` | enum {`moving_average`,`quantile_interpolation`,`weighted_average`} | Trade NATR computation method. (Deprecated alias: `exit_pricing.trade_price_target`) |
+| exit_pricing.thresholds_calibration.decline_quantile | 0.75 | float (0,1) | PnL decline quantile threshold. |
+| _Reversal confirmation_ | | | |
+| reversal_confirmation.lookback_period_candles | 0 | int >= 0 | Prior confirming candles; 0 = none. (Deprecated alias: `reversal_confirmation.lookback_period`) |
+| reversal_confirmation.decay_fraction | 0.5 | float (0,1] | Geometric per-candle volatility adjusted reversal threshold relaxation factor. (Deprecated alias: `reversal_confirmation.decay_ratio`) |
+| reversal_confirmation.min_natr_multiplier_fraction | 0.0095 | float [0,1] | Lower bound fraction for volatility adjusted reversal threshold. (Deprecated alias: `reversal_confirmation.min_natr_ratio_percent`) |
+| reversal_confirmation.max_natr_multiplier_fraction | 0.075 | float [0,1] | Upper bound fraction (>= lower bound) for volatility adjusted reversal threshold. (Deprecated alias: `reversal_confirmation.max_natr_ratio_percent`) |
+| _Regressor model_ | | | |
+| freqai.regressor | `xgboost` | enum {`xgboost`,`lightgbm`,`histgradientboostingregressor`} | Machine learning regressor algorithm. |
+| _Extrema smoothing_ | | | |
+| freqai.extrema_smoothing.method | `gaussian` | enum {`gaussian`,`kaiser`,`triang`,`smm`,`sma`,`savgol`,`gaussian_filter1d`} | Extrema smoothing method (`smm`=median, `sma`=mean, `savgol`=Savitzky–Golay). |
+| freqai.extrema_smoothing.window_candles | 5 | int >= 3 | Smoothing window length (candles). (Deprecated alias: `freqai.extrema_smoothing.window`) |
+| freqai.extrema_smoothing.beta | 8.0 | float > 0 | Shape parameter for `kaiser` kernel. |
+| freqai.extrema_smoothing.polyorder | 3 | int >= 1 | Polynomial order for `savgol` smoothing. |
+| freqai.extrema_smoothing.mode | `mirror` | enum {`mirror`,`constant`,`nearest`,`wrap`,`interp`} | Boundary mode for `savgol` and `gaussian_filter1d`. |
+| freqai.extrema_smoothing.sigma | 1.0 | float > 0 | Gaussian `sigma` for `gaussian_filter1d` smoothing. |
+| _Extrema weighting_ | | | |
+| freqai.extrema_weighting.strategy | `none` | enum {`none`,`amplitude`,`amplitude_threshold_ratio`,`volume_rate`,`speed`,`efficiency_ratio`,`volume_weighted_efficiency_ratio`,`combined`} | Extrema weighting metric: none (`none`), swing amplitude (`amplitude`), swing amplitude / median volatility-threshold ratio (`amplitude_threshold_ratio`), swing volume per candle (`volume_rate`), swing speed (`speed`), swing efficiency ratio (`efficiency_ratio`), swing volume-weighted efficiency ratio (`volume_weighted_efficiency_ratio`), or combined metrics aggregation (`combined`). Switching between `none` and any other strategy requires deleting trained models. |
+| freqai.extrema_weighting.metric_coefficients | {} | dict[str, float] | Per-metric coefficients for `combined` strategy. Keys: `amplitude`, `amplitude_threshold_ratio`, `volume_rate`, `speed`, `efficiency_ratio`, `volume_weighted_efficiency_ratio`. |
+| freqai.extrema_weighting.aggregation | `weighted_average` | enum {`weighted_average`,`geometric_mean`} | Metric aggregation method for `combined` strategy. `weighted_average`=Σ(coef·metric)/Σ(coef), `geometric_mean`=∏(metric^coef)^(1/Σcoef). |
+| freqai.extrema_weighting.standardization | `none` | enum {`none`,`zscore`,`robust`,`mmad`,`power_yj`} | Standardization method applied to smoothed weighted extrema before normalization. `none`=w, `zscore`=(w-μ)/σ, `robust`=(w-median)/IQR, `mmad`=(w-median)/(MAD·k), `power_yj`=YJ(w). |
+| freqai.extrema_weighting.robust_quantiles | [0.25, 0.75] | list[float] where 0 <= Q1 < Q3 <= 1 | Quantile range for robust standardization, Q1 and Q3. |
+| freqai.extrema_weighting.mmad_scaling_factor | 1.4826 | float > 0 | Scaling factor for MMAD standardization. |
+| freqai.extrema_weighting.normalization | `maxabs` | enum {`maxabs`,`minmax`,`sigmoid`,`none`} | Normalization method applied to smoothed weighted extrema. `maxabs`=w/max(\|w\|), `minmax`=low+(w-min)/(max-min)·(high-low), `sigmoid`=2·σ(scale·w)-1, `none`=w. |
+| freqai.extrema_weighting.minmax_range | [-1.0, 1.0] | list[float] | Target range for `minmax` normalization, min and max. |
+| freqai.extrema_weighting.sigmoid_scale | 1.0 | float > 0 | Scale parameter for `sigmoid` normalization, controls steepness. |
+| freqai.extrema_weighting.gamma | 1.0 | float (0,10] | Contrast exponent applied to smoothed weighted extrema after normalization: >1 emphasizes extrema, values between 0 and 1 soften. |
+| _Feature parameters_ | | | |
+| freqai.feature_parameters.label_period_candles | min/max midpoint | int >= 1 | Zigzag labeling NATR horizon. |
+| freqai.feature_parameters.min_label_period_candles | 12 | int >= 1 | Minimum labeling NATR horizon used for reversals labeling HPO. |
+| freqai.feature_parameters.max_label_period_candles | 24 | int >= 1 | Maximum labeling NATR horizon used for reversals labeling HPO. |
+| freqai.feature_parameters.label_natr_multiplier | min/max midpoint | float > 0 | Zigzag labeling NATR multiplier. (Deprecated alias: `freqai.feature_parameters.label_natr_ratio`) |
+| freqai.feature_parameters.min_label_natr_multiplier | 9.0 | float > 0 | Minimum labeling NATR multiplier used for reversals labeling HPO. (Deprecated alias: `freqai.feature_parameters.min_label_natr_ratio`) |
+| freqai.feature_parameters.max_label_natr_multiplier | 12.0 | float > 0 | Maximum labeling NATR multiplier used for reversals labeling HPO. (Deprecated alias: `freqai.feature_parameters.max_label_natr_ratio`) |
+| freqai.feature_parameters.label_frequency_candles | `auto` | int >= 2 \| `auto` | Reversals labeling frequency. `auto` = max(2, 2 \* number of whitelisted pairs). |
+| freqai.feature_parameters.label_weights | [1/7,1/7,1/7,1/7,1/7,1/7,1/7] | list[float] | Per-objective weights used in distance calculations to ideal point. Objectives: (1) number of detected reversals, (2) median swing amplitude, (3) median (swing amplitude / median volatility-threshold ratio), (4) median swing volume per candle, (5) median swing speed, (6) median swing efficiency ratio, (7) median swing volume-weighted efficiency ratio. |
+| freqai.feature_parameters.label_p_order | `None` | float \| None | p-order parameter for distance metrics. Used by minkowski (default 2.0) and power_mean (default 1.0). Ignored by other metrics. |
+| freqai.feature_parameters.label_method | `compromise_programming` | enum {`compromise_programming`,`topsis`,`kmeans`,`kmeans2`,`kmedoids`,`knn`,`medoid`} | HPO `label` Pareto front trial selection method. |
+| freqai.feature_parameters.label_distance_metric | `euclidean` | string | Distance metric for `compromise_programming` and `topsis` methods. |
+| freqai.feature_parameters.label_cluster_metric | `euclidean` | string | Distance metric for `kmeans`, `kmeans2`, and `kmedoids` methods. |
+| freqai.feature_parameters.label_cluster_selection_method | `topsis` | enum {`compromise_programming`,`topsis`} | Cluster selection method for clustering-based label methods. |
+| freqai.feature_parameters.label_cluster_trial_selection_method | `topsis` | enum {`compromise_programming`,`topsis`} | Best cluster trial selection method for clustering-based label methods. |
+| freqai.feature_parameters.label_density_metric | method-dependent | string | Distance metric for `knn` and `medoid` methods. |
+| freqai.feature_parameters.label_density_aggregation | `power_mean` | enum {`power_mean`,`quantile`,`min`,`max`} | Aggregation method for KNN neighbor distances. |
+| freqai.feature_parameters.label_density_n_neighbors | 5 | int >= 1 | Number of neighbors for KNN. |
+| freqai.feature_parameters.label_density_aggregation_param | aggregation-dependent | float \| None | Tunable for KNN neighbor distance aggregation: p-order (`power_mean`) or quantile value (`quantile`). |
+| freqai.feature_parameters.scaler | `minmax` | enum {`minmax`,`maxabs`,`standard`,`robust`} | Feature scaling method. `minmax`=MinMaxScaler, `maxabs`=MaxAbsScaler, `standard`=StandardScaler, `robust`=RobustScaler. Changing this parameter requires deleting trained models. |
+| freqai.feature_parameters.range | [-1.0, 1.0] | list[float] | Target range for `minmax` scaler, min and max. Changing this parameter requires deleting trained models. |
+| _Predictions extrema_ | | | |
+| freqai.predictions_extrema.selection_method | `rank_extrema` | enum {`rank_extrema`,`rank_peaks`,`partition`} | Extrema selection method. `rank_extrema` ranks extrema values, `rank_peaks` ranks detected peak values, `partition` uses sign-based partitioning. |
+| freqai.predictions_extrema.threshold_smoothing_method | `mean` | enum {`mean`,`isodata`,`li`,`minimum`,`otsu`,`triangle`,`yen`,`median`,`soft_extremum`} | Thresholding method for prediction thresholds smoothing. (Deprecated alias: `freqai.predictions_extrema.thresholds_smoothing`) |
+| freqai.predictions_extrema.soft_extremum_alpha | 12.0 | float >= 0 | Alpha for `soft_extremum` thresholds smoothing. (Deprecated alias: `freqai.predictions_extrema.thresholds_alpha`) |
+| freqai.predictions_extrema.outlier_threshold_quantile | 0.999 | float (0,1) | Quantile threshold for predictions outlier filtering. (Deprecated alias: `freqai.predictions_extrema.threshold_outlier`) |
+| freqai.predictions_extrema.keep_extrema_fraction | 1.0 | float (0,1] | Fraction of extrema used for thresholds. `1.0` uses all, lower values keep only most significant. Applies to `rank_extrema` and `rank_peaks`; ignored for `partition`. (Deprecated alias: `freqai.predictions_extrema.extrema_fraction`) |
+| _Optuna / HPO_ | | | |
+| freqai.optuna_hyperopt.enabled | false | bool | Enables HPO. |
+| freqai.optuna_hyperopt.sampler | `tpe` | enum {`tpe`,`auto`} | HPO sampler algorithm for `hp` namespace. `tpe` uses [TPESampler](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.TPESampler.html) with multivariate and group, `auto` uses [AutoSampler](https://hub.optuna.org/samplers/auto_sampler). |
+| freqai.optuna_hyperopt.label_sampler | `auto` | enum {`auto`,`tpe`,`nsgaii`,`nsgaiii`} | HPO sampler algorithm for multi-objective `label` namespace. `nsgaii` uses [NSGAIISampler](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.NSGAIISampler.html), `nsgaiii` uses [NSGAIIISampler](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.NSGAIIISampler.html). |
+| freqai.optuna_hyperopt.storage | `file` | enum {`file`,`sqlite`} | HPO storage backend. |
+| freqai.optuna_hyperopt.continuous | true | bool | Continuous HPO. |
+| freqai.optuna_hyperopt.warm_start | true | 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.n_jobs | CPU threads / 4 | int >= 1 | Parallel HPO workers. |
+| 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 `label` search space. |
+| freqai.optuna_hyperopt.space_reduction | false | bool | Enable/disable `hp` search space reduction based on previous best parameters. |
+| freqai.optuna_hyperopt.space_fraction | 0.4 | float [0,1] | Fraction of the `hp` search space to use with `space_reduction`. Lower values create narrower search ranges around the best parameters. (Deprecated alias: `freqai.optuna_hyperopt.expansion_ratio`) |
+| freqai.optuna_hyperopt.min_resource | 3 | int >= 1 | Minimum resource per [HyperbandPruner](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.pruners.HyperbandPruner.html) rung. |
+| freqai.optuna_hyperopt.seed | 1 | int >= 0 | HPO RNG seed. |
## ReforceXY
import skimage
import sklearn
from datasieve.pipeline import Pipeline
+from datasieve.transforms import SKLearnWrapper
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from numpy.typing import NDArray
from optuna.study.study import ObjectiveFuncType
+from sklearn.preprocessing import (
+ MaxAbsScaler,
+ MinMaxScaler,
+ RobustScaler,
+ StandardScaler,
+)
from sklearn_extra.cluster import KMedoids
-from ExtremaWeightingTransformer import (
- ExtremaWeightingTransformer,
-)
+from ExtremaWeightingTransformer import ExtremaWeightingTransformer
from Utils import (
DEFAULT_FIT_LIVE_PREDICTIONS_CANDLES,
EXTREMA_COLUMN,
)
ExtremaSelectionMethod = Literal["rank_extrema", "rank_peaks", "partition"]
-OptunaNamespace = Literal["hp", "label"]
OptunaSampler = Literal["tpe", "auto", "nsgaii", "nsgaiii"]
+OptunaNamespace = Literal["hp", "label"]
+ScalerType = Literal["minmax", "maxabs", "standard", "robust"]
CustomThresholdMethod = Literal["median", "soft_extremum"]
SkimageThresholdMethod = Literal[
"mean", "isodata", "li", "minimum", "otsu", "triangle", "yen"
https://github.com/sponsors/robcaulk
"""
- version = "3.10.2"
+ version = "3.10.3"
_TEST_SIZE: Final[float] = 0.1
)
_OPTUNA_NAMESPACES: Final[tuple[OptunaNamespace, ...]] = ("hp", "label")
+ _SCALER_TYPES: Final[tuple[ScalerType, ...]] = (
+ "minmax",
+ "maxabs",
+ "standard",
+ "robust",
+ )
+
+ SCALER_DEFAULT: Final[ScalerType] = _SCALER_TYPES[0] # "minmax"
+ RANGE_DEFAULT: Final[tuple[float, float]] = (-1.0, 1.0)
+
_DISTANCE_METHODS: Final[tuple[DistanceMethod, ...]] = (
"compromise_programming",
"topsis",
def _optuna_namespaces_set() -> set[OptunaNamespace]:
return set(QuickAdapterRegressorV3._OPTUNA_NAMESPACES)
+ @staticmethod
+ @lru_cache(maxsize=None)
+ def _scaler_types_set() -> set[ScalerType]:
+ return set(QuickAdapterRegressorV3._SCALER_TYPES)
+
@staticmethod
@lru_cache(maxsize=None)
def _scipy_metrics_set() -> set[str]:
)
self._optuna_label_shuffle_rng.shuffle(self._optuna_label_candle_pool)
+ def define_data_pipeline(self, threads: int = -1) -> Pipeline:
+ scaler = self.ft_params.get("scaler", QuickAdapterRegressorV3.SCALER_DEFAULT)
+
+ QuickAdapterRegressorV3._validate_enum_value(
+ scaler,
+ QuickAdapterRegressorV3._scaler_types_set(),
+ QuickAdapterRegressorV3._SCALER_TYPES,
+ ctx="scaler",
+ )
+
+ feature_range = self.ft_params.get(
+ "range", QuickAdapterRegressorV3.RANGE_DEFAULT
+ )
+
+ if not isinstance(feature_range, (list, tuple)) or len(feature_range) != 2:
+ raise ValueError(
+ f"Invalid range {type(feature_range).__name__!r}: "
+ f"must be a list or tuple of 2 numbers"
+ )
+ min_val, max_val = float(feature_range[0]), float(feature_range[1])
+ if min_val >= max_val:
+ raise ValueError(f"Invalid range [{min_val}, {max_val}]: min must be < max")
+ feature_range = (min_val, max_val)
+
+ if (
+ scaler == QuickAdapterRegressorV3.SCALER_DEFAULT
+ and feature_range == QuickAdapterRegressorV3.RANGE_DEFAULT
+ ):
+ return super().define_data_pipeline(threads)
+
+ pipeline = super().define_data_pipeline(threads)
+
+ if scaler == QuickAdapterRegressorV3._SCALER_TYPES[1]: # "maxabs"
+ scaler_obj = SKLearnWrapper(MaxAbsScaler())
+ elif scaler == QuickAdapterRegressorV3._SCALER_TYPES[2]: # "standard"
+ scaler_obj = SKLearnWrapper(StandardScaler())
+ elif scaler == QuickAdapterRegressorV3._SCALER_TYPES[3]: # "robust"
+ scaler_obj = SKLearnWrapper(RobustScaler())
+ else:
+ scaler_obj = SKLearnWrapper(MinMaxScaler(feature_range=feature_range))
+
+ steps = [
+ (name, scaler_obj)
+ if name in ("scaler", "post-pca-scaler")
+ else (name, transformer)
+ for name, transformer in pipeline.steps
+ ]
+
+ return Pipeline(steps)
+
def define_label_pipeline(self, threads: int = -1) -> Pipeline:
extrema_weighting = self.freqai_info.get("extrema_weighting", {})
if not isinstance(extrema_weighting, dict):
repositories / freqai-strategies.git / commitdiff
