### 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. |
-| exit_pricing.thresholds_calibration.decline_quantile | 0.5 | float (0,1) | PnL decline quantile threshold. |
-| _Reversal confirmation_ | | | |
-| reversal_confirmation.lookback_period_candles | 0 | int >= 0 | Prior confirming candles; 0 = none. |
-| reversal_confirmation.decay_fraction | 0.5 | float (0,1] | Geometric per-candle volatility adjusted reversal threshold relaxation factor. |
-| reversal_confirmation.min_natr_multiplier_fraction | 0.0095 | float [0,1] | Lower bound fraction for volatility adjusted reversal threshold. |
-| reversal_confirmation.max_natr_multiplier_fraction | 0.0125 | float [0,1] | Upper bound fraction (>= lower bound) for volatility adjusted reversal threshold. |
-| _Regressor model_ | | | |
-| freqai.regressor | `xgboost` | enum {`xgboost`,`lightgbm`,`histgradientboostingregressor`,`ngboost`,`catboost`} | Machine learning regressor algorithm. |
-| _Model training parameters_ | | | |
-| freqai.model_training_parameters.gpu_vram_gb | 80 | enum {8,10,12,16,24,32,40,48,64,80} | Available GPU VRAM (GB) for CatBoost, not total. Constrains `depth`, `border_count`, and `max_ctr_complexity` ranges. |
-| _Data split parameters_ | | | |
-| freqai.data_split_parameters.method | `train_test_split` | enum {`train_test_split`,`timeseries_split`} | Data splitting strategy. `train_test_split` for sequential split, `timeseries_split` for chronological split with configurable gap. |
-| freqai.data_split_parameters.test_size | 0.1 / None | float (0,1) \| int >= 1 \| None | Test set size. Float for fraction, int for count. Default: 0.1 for `train_test_split`, None for `timeseries_split` (sklearn dynamic sizing). |
-| freqai.data_split_parameters.n_splits | 5 | int >= 2 | Controls train/test proportions for `timeseries_split` (higher = larger train set). |
-| freqai.data_split_parameters.gap | 0 | int >= 0 | Samples to exclude between train/test for `timeseries_split`. When 0, auto-calculated from `label_period_candles` to prevent look-ahead bias. |
-| freqai.data_split_parameters.max_train_size | None | int >= 1 \| None | Maximum training set size for `timeseries_split`. When set, creates a sliding window instead of expanding train set. None = no limit. |
-| _Label smoothing_ | | | |
-| freqai.label_smoothing.method | `gaussian` | enum {`none`,`gaussian`,`kaiser`,`kaiser_bessel_derived`,`triang`,`smm`,`sma`,`savgol`,`gaussian_filter1d`} | Label smoothing method (`kaiser_bessel_derived` uses an even-length Kaiser-Bessel-derived zero-phase kernel; `smm`=median, `sma`=mean, `savgol`=Savitzky–Golay). |
-| freqai.label_smoothing.window_candles | 5 | int >= 3 | Smoothing window length (candles). |
-| freqai.label_smoothing.beta | 8.0 | float > 0 | Shape parameter for `kaiser` and `kaiser_bessel_derived` kernels. |
-| freqai.label_smoothing.polyorder | 3 | int >= 0 | Polynomial order for `savgol` smoothing. |
-| freqai.label_smoothing.mode | `mirror` | enum {`mirror`,`constant`,`nearest`,`wrap`,`interp`} | Boundary mode for `savgol` and `gaussian_filter1d`. |
-| freqai.label_smoothing.sigma | 1.0 | float > 0 | Gaussian `sigma` for `gaussian_filter1d` smoothing. |
-| _Label weighting_ | | | |
-| freqai.label_weighting.strategy | `none` | enum {`none`,`uniform`,`amplitude`,`amplitude_threshold_ratio`,`volume_rate`,`speed`,`efficiency_ratio`,`volume_weighted_efficiency_ratio`,`combined`} | Label weighting metric: none (`none`), uniform unit weight on every detected pivot (`uniform`), 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 to realign training emphasis. |
-| freqai.label_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.label_weighting.aggregation | `arithmetic_mean` | enum {`arithmetic_mean`,`geometric_mean`,`harmonic_mean`,`quadratic_mean`,`weighted_median`,`softmax`} | Metric aggregation method for `combined` strategy. `arithmetic_mean`=(Σ(w·m)/Σ(w)), `geometric_mean`=(∏(m^w))^(1/Σw), `harmonic_mean`=Σ(w)/(Σ(w/m)), `quadratic_mean`=(Σ(w·m²)/Σ(w))^(1/2), `weighted_median`=Q₀.₅(m,w), `softmax`=Σ(m·s_i) where s_i=w_i·exp(m_i/T)/Σ(w_j·exp(m_j/T)). |
-| freqai.label_weighting.softmax_temperature | 1.0 | float > 0 | Temperature T for `softmax` aggregation, controls distribution sharpness. |
-| freqai.label_weighting.fill_method | `zero` | enum {`zero`,`epsilon`,`gaussian`} | Off-pivot weighting scheme. `zero` hard-zeros off-pivot rows; `epsilon` applies a flat baseline `fill_epsilon * <fill_epsilon_baseline>(pivot_weights)`; `gaussian` applies heatmap-style decay around each pivot. Switching away from `zero` may require retuning tree-leaf regularization (`min_child_weight`, `lambda`) and resetting any prior Optuna study. Changing this parameter requires deleting trained models. |
-| freqai.label_weighting.fill_epsilon | 0.000001 | float [0,1] | Off-pivot fraction of the pivot baseline. Ignored when `fill_method != "epsilon"`. |
-| freqai.label_weighting.fill_epsilon_baseline | `mean` | enum {`mean`,`median`} | Pivot baseline statistic. `mean` tracks central tendency; `median` is robust against pivot-weight skew. Ignored when `fill_method != "epsilon"`. |
-| freqai.label_weighting.fill_sigma_candles | 10.0 | float >= 0.5 | Gaussian standard deviation in candles for `fill_method == "gaussian"`. Acts as the upper bound on per-pivot sigma when `fill_bandwidth == "knn"`. Lower bound 0.5 prevents severe underflow in the Gaussian tail. Ignored when `fill_method != "gaussian"`. |
-| freqai.label_weighting.fill_sigma_min_candles | 0.5 | float >= 0.5 | Lower bound on per-pivot sigma in candles when `fill_bandwidth == "knn"`. Clipped to `fill_sigma_candles` when larger. Ignored when `fill_method != "gaussian"` or `fill_bandwidth != "knn"`. |
-| freqai.label_weighting.fill_bandwidth | `fixed` | enum {`fixed`,`knn`} | Per-pivot Gaussian bandwidth selector. `fixed` applies a constant `fill_sigma_candles` to every pivot (legacy behavior). `knn` adapts each pivot's sigma to local pivot density via `sigma_p = clip(fill_bandwidth_alpha * d_k(p), fill_sigma_min_candles, fill_sigma_candles)` where `d_k(p)` is the index distance to the `k`-th nearest pivot neighbor (Loftsgaarden & Quesenberry 1965; Silverman 1986, §5.2). Mitigates the crushing of weaker pivots by stronger neighbors in dense clusters. Ignored when `fill_method != "gaussian"`. |
-| freqai.label_weighting.fill_bandwidth_neighbors | 1 | int >= 1 | `k` for the k-nearest-neighbor bandwidth selector. Ignored when `fill_method != "gaussian"` or `fill_bandwidth != "knn"`. |
-| freqai.label_weighting.fill_bandwidth_alpha | 0.5 | float > 0 | Multiplicative factor on the k-th neighbor distance. Smaller values produce sharper, more separated Gaussians; larger values approach the `fixed` behavior. Ignored when `fill_method != "gaussian"` or `fill_bandwidth != "knn"`. |
-| _Label pipeline_ | | | |
-| freqai.label_pipeline.standardization | `none` | enum {`none`,`zscore`,`robust`,`mmad`,`power_yj`} | Standardization method applied to labels before normalization. `none`=w, `zscore`=(w-μ)/σ, `robust`=(w-median)/(Q₃-Q₁), `mmad`=(w-median)/(MAD·k), `power_yj`=YJ(w). |
-| freqai.label_pipeline.robust_quantiles | [0.25, 0.75] | list[float] where 0 <= Q1 < Q3 <= 1 | Quantile range for robust standardization, Q1 and Q3. |
-| freqai.label_pipeline.mmad_scaling_factor | 1.4826 | float > 0 | Scaling factor for MMAD standardization. |
-| freqai.label_pipeline.normalization | `maxabs` | enum {`maxabs`,`minmax`,`sigmoid`,`none`} | Normalization method applied to labels. `maxabs`=w/max(\|w\|), `minmax`=low+(w-min)/(max-min)·(high-low), `sigmoid`=2·σ(scale·w)-1, `none`=w. |
-| freqai.label_pipeline.minmax_range | [-1.0, 1.0] | list[float] | Target range for `minmax` normalization, min and max. |
-| freqai.label_pipeline.sigmoid_scale | 1.0 | float > 0 | Scale parameter for `sigmoid` normalization, controls steepness. |
-| freqai.label_pipeline.gamma | 1.0 | float (0,10] | Contrast exponent applied to labels 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. |
-| freqai.feature_parameters.min_label_natr_multiplier | 9.0 | float > 0 | Minimum labeling NATR multiplier used for reversals labeling HPO. |
-| freqai.feature_parameters.max_label_natr_multiplier | 12.0 | float > 0 | Maximum labeling NATR multiplier used for reversals labeling HPO. |
-| 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 for trial selection methods. 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 | Lp exponent for parameterized metrics. Used by `minkowski` distance (default 2.0) and `power_mean` aggregation (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: Lp exponent (`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. |
-| _Label prediction_ | | | |
-| freqai.label_prediction.method | `thresholding` | enum {`none`,`thresholding`} | Prediction method. `none` disables threshold computation, `thresholding` enables adaptive threshold calculation. |
-| freqai.label_prediction.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.label_prediction.threshold_method | `mean` | enum {`mean`,`isodata`,`li`,`minimum`,`otsu`,`triangle`,`yen`,`median`,`soft_extremum`} | Thresholding method for prediction thresholds. |
-| freqai.label_prediction.soft_extremum_alpha | 12.0 | float >= 0 | Alpha for `soft_extremum` threshold method. |
-| freqai.label_prediction.outlier_quantile | 0.999 | float (0,1) | Quantile threshold for predictions outlier filtering. |
-| freqai.label_prediction.keep_fraction | 0.0075 | float (0,1] | Fraction of extrema used for thresholds. 1 uses all, lower values keep only most significant. Applies to `rank_extrema` and `rank_peaks`; ignored for `partition`. |
-| _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, group, and constant_liar (when multiple workers), `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. |
-| 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. |
+| exit_pricing.thresholds_calibration.decline_quantile | 0.5 | float (0,1) | PnL decline quantile threshold. |
+| _Reversal confirmation_ | | | |
+| reversal_confirmation.lookback_period_candles | 0 | int >= 0 | Prior confirming candles; 0 = none. |
+| reversal_confirmation.decay_fraction | 0.5 | float (0,1] | Geometric per-candle volatility adjusted reversal threshold relaxation factor. |
+| reversal_confirmation.min_natr_multiplier_fraction | 0.0095 | float [0,1] | Lower bound fraction for volatility adjusted reversal threshold. |
+| reversal_confirmation.max_natr_multiplier_fraction | 0.0125 | float [0,1] | Upper bound fraction (>= lower bound) for volatility adjusted reversal threshold. |
+| _Regressor model_ | | | |
+| freqai.regressor | `xgboost` | enum {`xgboost`,`lightgbm`,`histgradientboostingregressor`,`ngboost`,`catboost`} | Machine learning regressor algorithm. |
+| _Model training parameters_ | | | |
+| freqai.model_training_parameters.gpu_vram_gb | 80 | enum {8,10,12,16,24,32,40,48,64,80} | Available GPU VRAM (GB) for CatBoost, not total. Constrains `depth`, `border_count`, and `max_ctr_complexity` ranges. |
+| _Data split parameters_ | | | |
+| freqai.data_split_parameters.method | `train_test_split` | enum {`train_test_split`,`timeseries_split`} | Data splitting strategy. `train_test_split` for sequential split, `timeseries_split` for chronological split with configurable gap. |
+| freqai.data_split_parameters.test_size | 0.1 / None | float (0,1) \| int >= 1 \| None | Test set size. Float for fraction, int for count. Default: 0.1 for `train_test_split`, None for `timeseries_split` (sklearn dynamic sizing). |
+| freqai.data_split_parameters.n_splits | 5 | int >= 2 | Controls train/test proportions for `timeseries_split` (higher = larger train set). |
+| freqai.data_split_parameters.gap | 0 | int >= 0 | Samples to exclude between train/test for `timeseries_split`. When 0, auto-calculated from `label_period_candles` to prevent look-ahead bias. |
+| freqai.data_split_parameters.max_train_size | None | int >= 1 \| None | Maximum training set size for `timeseries_split`. When set, creates a sliding window instead of expanding train set. None = no limit. |
+| _Label smoothing_ | | | |
+| freqai.label_smoothing.method | `gaussian` | enum {`none`,`gaussian`,`kaiser`,`kaiser_bessel_derived`,`triang`,`smm`,`sma`,`savgol`,`gaussian_filter1d`} | Label smoothing method (`kaiser_bessel_derived` uses an even-length Kaiser-Bessel-derived zero-phase kernel; `smm`=median, `sma`=mean, `savgol`=Savitzky–Golay). |
+| freqai.label_smoothing.window_candles | 5 | int >= 3 | Smoothing window length (candles). |
+| freqai.label_smoothing.beta | 8.0 | float > 0 | Shape parameter for `kaiser` and `kaiser_bessel_derived` kernels. |
+| freqai.label_smoothing.polyorder | 3 | int >= 0 | Polynomial order for `savgol` smoothing. |
+| freqai.label_smoothing.mode | `mirror` | enum {`mirror`,`constant`,`nearest`,`wrap`,`interp`} | Boundary mode for `savgol` and `gaussian_filter1d`. |
+| freqai.label_smoothing.sigma | 1.0 | float > 0 | Gaussian `sigma` for `gaussian_filter1d` smoothing. |
+| _Label weighting_ | | | |
+| freqai.label_weighting.strategy | `none` | enum {`none`,`uniform`,`amplitude`,`amplitude_threshold_ratio`,`volume_rate`,`speed`,`efficiency_ratio`,`volume_weighted_efficiency_ratio`,`combined`} | Label weighting metric: none (`none`), uniform unit weight on every detected pivot (`uniform`), 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 to realign training emphasis. |
+| freqai.label_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.label_weighting.aggregation | `arithmetic_mean` | enum {`arithmetic_mean`,`geometric_mean`,`harmonic_mean`,`quadratic_mean`,`weighted_median`,`softmax`} | Metric aggregation method for `combined` strategy. `arithmetic_mean`=(Σ(w·m)/Σ(w)), `geometric_mean`=(∏(m^w))^(1/Σw), `harmonic_mean`=Σ(w)/(Σ(w/m)), `quadratic_mean`=(Σ(w·m²)/Σ(w))^(1/2), `weighted_median`=Q₀.₅(m,w), `softmax`=Σ(m·s_i) where s_i=w_i·exp(m_i/T)/Σ(w_j·exp(m_j/T)). |
+| freqai.label_weighting.softmax_temperature | 1.0 | float > 0 | Temperature T for `softmax` aggregation, controls distribution sharpness. |
+| freqai.label_weighting.fill_method | `zero` | enum {`zero`,`epsilon`,`gaussian`,`epsilon_gaussian`} | Off-pivot weighting scheme. `zero` hard-zeros off-pivot rows; `epsilon` applies the epsilon floor `fill_epsilon * <fill_epsilon_baseline>(pivot_weights)`; `gaussian` applies per-pivot Gaussian bumps; `epsilon_gaussian` sums the `epsilon` floor and the `gaussian` bumps. Pivot rows take the max of their raw weight and the off-pivot field at their index (no-op for `zero`). Switching away from `zero` may require retuning tree-leaf regularization (`min_child_weight`, `lambda`) and resetting any prior Optuna study. Changing this parameter requires deleting trained models. |
+| freqai.label_weighting.fill_epsilon | 0.000001 | float [0,1] | Off-pivot fraction of the pivot baseline. Ignored when `fill_method` not in {`epsilon`,`epsilon_gaussian`}. |
+| freqai.label_weighting.fill_epsilon_baseline | `mean` | enum {`mean`,`median`} | Pivot baseline statistic. `mean` tracks central tendency; `median` is robust against pivot-weight skew. Ignored when `fill_method` not in {`epsilon`,`epsilon_gaussian`}. |
+| freqai.label_weighting.fill_sigma_candles | 10.0 | float >= 0.5 | Gaussian standard deviation in candles for the per-pivot bumps. Acts as the upper bound on per-pivot sigma when `fill_bandwidth == "knn"`. Lower bound 0.5 prevents severe underflow in the Gaussian tail. Ignored when `fill_method` not in {`gaussian`,`epsilon_gaussian`}. |
+| freqai.label_weighting.fill_sigma_min_candles | 0.5 | float >= 0.5 | Lower bound on per-pivot sigma in candles when `fill_bandwidth == "knn"`. Clipped to `fill_sigma_candles` when larger. Ignored when `fill_method` not in {`gaussian`,`epsilon_gaussian`} or `fill_bandwidth != "knn"`. |
+| freqai.label_weighting.fill_bandwidth | `fixed` | enum {`fixed`,`knn`} | Per-pivot Gaussian bandwidth selector. `fixed` applies a constant `fill_sigma_candles` to every pivot (legacy behavior). `knn` adapts each pivot's sigma to local pivot density via `sigma_p = clip(fill_bandwidth_alpha * d_k(p), fill_sigma_min_candles, fill_sigma_candles)` where `d_k(p)` is the index distance to the `k`-th nearest pivot neighbor (Loftsgaarden & Quesenberry 1965; Silverman 1986, §5.2). Mitigates the crushing of weaker pivots by stronger neighbors in dense clusters. Ignored when `fill_method` not in {`gaussian`,`epsilon_gaussian`}. |
+| freqai.label_weighting.fill_bandwidth_neighbors | 1 | int >= 1 | `k` for the k-nearest-neighbor bandwidth selector. Ignored when `fill_method` not in {`gaussian`,`epsilon_gaussian`} or `fill_bandwidth != "knn"`. |
+| freqai.label_weighting.fill_bandwidth_alpha | 0.5 | float > 0 | Multiplicative factor on the k-th neighbor distance. Smaller values produce sharper, more separated Gaussians; larger values approach the `fixed` behavior. Ignored when `fill_method` not in {`gaussian`,`epsilon_gaussian`} or `fill_bandwidth != "knn"`. |
+| _Label pipeline_ | | | |
+| freqai.label_pipeline.standardization | `none` | enum {`none`,`zscore`,`robust`,`mmad`,`power_yj`} | Standardization method applied to labels before normalization. `none`=w, `zscore`=(w-μ)/σ, `robust`=(w-median)/(Q₃-Q₁), `mmad`=(w-median)/(MAD·k), `power_yj`=YJ(w). |
+| freqai.label_pipeline.robust_quantiles | [0.25, 0.75] | list[float] where 0 <= Q1 < Q3 <= 1 | Quantile range for robust standardization, Q1 and Q3. |
+| freqai.label_pipeline.mmad_scaling_factor | 1.4826 | float > 0 | Scaling factor for MMAD standardization. |
+| freqai.label_pipeline.normalization | `maxabs` | enum {`maxabs`,`minmax`,`sigmoid`,`none`} | Normalization method applied to labels. `maxabs`=w/max(\|w\|), `minmax`=low+(w-min)/(max-min)·(high-low), `sigmoid`=2·σ(scale·w)-1, `none`=w. |
+| freqai.label_pipeline.minmax_range | [-1.0, 1.0] | list[float] | Target range for `minmax` normalization, min and max. |
+| freqai.label_pipeline.sigmoid_scale | 1.0 | float > 0 | Scale parameter for `sigmoid` normalization, controls steepness. |
+| freqai.label_pipeline.gamma | 1.0 | float (0,10] | Contrast exponent applied to labels 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. |
+| freqai.feature_parameters.min_label_natr_multiplier | 9.0 | float > 0 | Minimum labeling NATR multiplier used for reversals labeling HPO. |
+| freqai.feature_parameters.max_label_natr_multiplier | 12.0 | float > 0 | Maximum labeling NATR multiplier used for reversals labeling HPO. |
+| 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 for trial selection methods. 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 | Lp exponent for parameterized metrics. Used by `minkowski` distance (default 2.0) and `power_mean` aggregation (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: Lp exponent (`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. |
+| _Label prediction_ | | | |
+| freqai.label_prediction.method | `thresholding` | enum {`none`,`thresholding`} | Prediction method. `none` disables threshold computation, `thresholding` enables adaptive threshold calculation. |
+| freqai.label_prediction.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.label_prediction.threshold_method | `mean` | enum {`mean`,`isodata`,`li`,`minimum`,`otsu`,`triangle`,`yen`,`median`,`soft_extremum`} | Thresholding method for prediction thresholds. |
+| freqai.label_prediction.soft_extremum_alpha | 12.0 | float >= 0 | Alpha for `soft_extremum` threshold method. |
+| freqai.label_prediction.outlier_quantile | 0.999 | float (0,1) | Quantile threshold for predictions outlier filtering. |
+| freqai.label_prediction.keep_fraction | 0.0075 | float (0,1] | Fraction of extrema used for thresholds. 1 uses all, lower values keep only most significant. Applies to `rank_extrema` and `rank_peaks`; ignored for `partition`. |
+| _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, group, and constant_liar (when multiple workers), `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. |
+| 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
WEIGHT_STRATEGIES,
CombinedAggregation,
CombinedMetric,
+ FillEpsilonBaseline,
SmoothingMethod,
SmoothingMode,
)
sigma_min_candles: float = 0.5,
logger: Logger | None = None,
) -> NDArray[np.floating]:
- """Per-row max of Gaussian-decayed pivot weights.
+ """Per-row max of per-pivot Gaussian bumps.
Out[i] = max over p of ``w_p * exp(-(i - p)**2 / (2 * sigma_p**2))``.
) -> NDArray[np.floating]:
"""Scatter per-pivot weights into a full-length array.
- Pivot rows (validated via ``valid_mask``) receive ``weights``; off-pivot
- rows receive the corresponding entry of ``fill_weights`` (shape
- ``(n_values,)``).
+ Pivot rows (validated via ``valid_mask``) take
+ ``max(weights, fill_weights)`` so a pivot row is never written below
+ the off-pivot field at its index. Off-pivot rows receive the
+ corresponding entry of ``fill_weights`` (shape ``(n_values,)``). The
+ ``max`` fixes the sub-floor / sub-bump pivot-row dip that arises when
+ the off-pivot field exceeds the pivot's raw weight: via the floor for
+ ``epsilon`` / ``epsilon_gaussian``, via a stronger neighbor's bump for
+ ``gaussian`` / ``epsilon_gaussian``. ``zero`` is bit-identical to a
+ plain assignment because its fill is 0.
"""
if fill_weights.shape != (n_values,):
raise ValueError(
f"got {indices_array.size} indices but {weights.size} weights"
)
weights_array = fill_weights.astype(float, copy=True)
- weights_array[indices_array[valid_mask]] = weights[valid_mask]
+ pivot_idx = indices_array[valid_mask]
+ weights_array[pivot_idx] = np.maximum(weights[valid_mask], weights_array[pivot_idx])
return weights_array
)
+def _compute_epsilon_floor(
+ weights: NDArray[np.floating],
+ valid_mask: NDArray[np.bool_],
+ eps: float,
+ baseline: FillEpsilonBaseline,
+) -> float:
+ """Flat off-pivot weight value ``phi = eps * B(W)``.
+
+ ``B(W)`` is the mean or median of valid pivot weights, selected by
+ ``baseline`` (``FILL_EPSILON_BASELINES``). Returns ``0.0`` on degenerate
+ inputs (no valid pivots, non-finite baseline).
+ """
+ if not valid_mask.any():
+ return 0.0
+ pivot_values = weights[valid_mask]
+ if baseline == FILL_EPSILON_BASELINES[0]: # "mean"
+ b = float(np.nanmean(pivot_values))
+ elif baseline == FILL_EPSILON_BASELINES[1]: # "median"
+ b = float(np.nanmedian(pivot_values))
+ else:
+ raise ValueError(
+ f"Invalid fill_epsilon_baseline value {baseline!r}: "
+ f"supported values are {', '.join(FILL_EPSILON_BASELINES)}"
+ )
+ if not np.isfinite(b):
+ b = 0.0
+ return float(eps) * b
+
+
+def _compute_gaussian_bumps(
+ n_values: int,
+ indices_array: NDArray[np.integer],
+ valid_mask: NDArray[np.bool_],
+ weights: NDArray[np.floating],
+ label_weighting: dict[str, Any],
+ *,
+ logger: Logger | None,
+) -> NDArray[np.floating]:
+ """Per-row max of per-pivot Gaussian bumps.
+
+ Adapter over ``_gaussian_fill_weights`` that pulls tunables from
+ ``label_weighting`` and applies the ``valid_mask``.
+ """
+ return _gaussian_fill_weights(
+ n_values=n_values,
+ pivot_indices=indices_array[valid_mask],
+ pivot_weights=weights[valid_mask],
+ sigma_candles=label_weighting["fill_sigma_candles"],
+ bandwidth=label_weighting["fill_bandwidth"],
+ bandwidth_neighbors=label_weighting["fill_bandwidth_neighbors"],
+ bandwidth_alpha=label_weighting["fill_bandwidth_alpha"],
+ sigma_min_candles=label_weighting["fill_sigma_min_candles"],
+ logger=logger,
+ )
+
+
def compute_label_weights(
n_values: int,
indices: Sequence[int] | NDArray[np.integer],
if fill_method == FILL_METHODS[0]: # "zero"
fill_weights = np.zeros(n_values, dtype=float)
elif fill_method == FILL_METHODS[1]: # "epsilon"
- eps = label_weighting["fill_epsilon"]
- baseline = label_weighting["fill_epsilon_baseline"]
- if valid_mask.any():
- pivot_values = weights[valid_mask]
- if baseline == FILL_EPSILON_BASELINES[0]: # "mean"
- pivot_baseline = float(np.nanmean(pivot_values))
- elif baseline == FILL_EPSILON_BASELINES[1]: # "median"
- pivot_baseline = float(np.nanmedian(pivot_values))
- else:
- raise ValueError(f"Invalid fill_epsilon_baseline value {baseline!r}")
- if not np.isfinite(pivot_baseline):
- pivot_baseline = 0.0
- else:
- pivot_baseline = 0.0
- fill_weights = np.full(n_values, eps * pivot_baseline, dtype=float)
+ fill_weights = np.full(
+ n_values,
+ _compute_epsilon_floor(
+ weights,
+ valid_mask,
+ label_weighting["fill_epsilon"],
+ label_weighting["fill_epsilon_baseline"],
+ ),
+ dtype=float,
+ )
elif fill_method == FILL_METHODS[2]: # "gaussian"
- fill_weights = _gaussian_fill_weights(
- n_values=n_values,
- pivot_indices=indices_array[valid_mask],
- pivot_weights=weights[valid_mask],
- sigma_candles=label_weighting["fill_sigma_candles"],
- bandwidth=label_weighting["fill_bandwidth"],
- bandwidth_neighbors=label_weighting["fill_bandwidth_neighbors"],
- bandwidth_alpha=label_weighting["fill_bandwidth_alpha"],
- sigma_min_candles=label_weighting["fill_sigma_min_candles"],
- logger=logger,
+ fill_weights = _compute_gaussian_bumps(
+ n_values, indices_array, valid_mask, weights, label_weighting, logger=logger
+ )
+ elif fill_method == FILL_METHODS[3]: # "epsilon_gaussian"
+ fill_weights = _compute_gaussian_bumps(
+ n_values, indices_array, valid_mask, weights, label_weighting, logger=logger
+ )
+ np.add(
+ fill_weights,
+ _compute_epsilon_floor(
+ weights,
+ valid_mask,
+ label_weighting["fill_epsilon"],
+ label_weighting["fill_epsilon_baseline"],
+ ),
+ out=fill_weights,
)
else:
- raise ValueError(f"Invalid fill_method value {fill_method!r}")
+ raise ValueError(
+ f"Invalid fill_method value {fill_method!r}: "
+ f"supported values are {', '.join(FILL_METHODS)}"
+ )
return _scatter_weights(
n_values=n_values,
repositories / freqai-strategies.git / commitdiff
