### 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. |
-| _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 (< upper bound) 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. |
-| freqai.continual_learning | false | bool | Continue XGBoost or LightGBM training from the previously deployed model, so its booster grows at every retrain; delete trained models to reset. With `test_size` two-stage selection, HPO and the pre-refit selection model always cold-start (including when causal purging leaves no scorable holdout rows) and only the final refit continues, growing by the selection model's round count. Other regressors ignore any prior model. |
-| _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 | float [0,1) \| int >= 0 \| None | Outer holdout size; `0` disables the holdout (single-stage fit, `train_test_split` only). The same parameter reserves the chronological tail of the remaining training rows as inner validation for HPO and early stopping; a fractional value is relative to those remaining rows, not the original window. The holdout is predicted once and reported as weighted `holdout_rmse` in the original label scale; it measures the cold-started pre-refit selection model, not the refitted deployed model. `None` (sklearn dynamic sizing) applies only to `timeseries_split`; `train_test_split` requires a float or int; inner validation then falls back to `0.1`. |
-| 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` and `causal_mode=true` (default), auto-set from `label_horizon_candles`; when `0` and `causal_mode=false`, auto-set from `label_period_candles`. Under `causal_mode=true`, an explicit `gap<label_horizon_candles` is rejected. |
-| 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` | `savgol`: enum {`mirror`,`constant`,`nearest`,`wrap`,`interp`}; `gaussian_filter1d`: enum {`mirror`,`constant`,`nearest`,`wrap`}; ignored otherwise | 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)`; in causal mode, each row's baseline uses only pivot weights available with that row's label. `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 | 25.0 | 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"`. Under `causal_mode=true`, bumps are zero outside the finite support `ceil(4 * fill_sigma_candles)` tracked by exact availability; non-causal baselines retain the legacy unbounded Gaussian tails. Lower bound 0.5 prevents severe underflow inside the causal support. 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"`. |
-| freqai.label_weighting.support_policy | `fallback` | enum {`fallback`,`raise`} | Policy when active label weighting fails support checks (after causal split guards and feature-pipeline row filtering). `raise` aborts the fit with `ValueError`; `fallback` logs a `WARNING` and uses sanitized base sample weights for that fit. Eval (test/val) weights bypass this policy and always fall back on composition errors. |
-| freqai.label_weighting.min_pivot_equivalent_count | 3 | int >= 1 | Minimum number of surviving pivot-equivalent label weights required after filtering. Pivot-equivalent rows are weights at least 10% of the surviving maximum label weight. |
-| freqai.label_weighting.min_positive_label_weight_fraction | 0.01 | float [0,1] | Minimum fraction of filtered training rows with finite positive label weights. |
-| freqai.label_weighting.min_effective_sample_size | 3.0 | float >= 1 | Minimum Kish effective sample size of the final composed training weights. |
-| _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 period. |
-| freqai.feature_parameters.label_horizon_candles | `label_period_candles` | int >= 1 | Conservative fixed purge used by causal train/test guards and as the default `timeseries_split` gap. Zigzag labels additionally expose their exact row-wise confirmation time; centered smoothing composes the maximum availability time across each kernel support. When unset, falls back to `label_period_candles`. |
-| freqai.feature_parameters.causal_mode | true | bool | Causal split guard toggle. When `true` (default): rejects `data_split_parameters.shuffle=true`, `shuffle_after_split=true`, `reverse_train_test_order=true`; for `timeseries_split` auto-sets `gap=label_horizon_candles` when unset/`0` (rejects explicit `gap<label_horizon_candles`); for `train_test_split` applies the same fixed purge; both split methods additionally remove train rows whose exact Zigzag confirmation and active weight availability, including k-NN bandwidth confirmation, plus smoothing availability reaches the test boundary. `false` is deprecated; acausal baselines only. |
-| freqai.feature_parameters.min_label_period_candles | 12 | int >= 1 | Minimum labeling NATR period used for reversals labeling HPO. |
-| freqai.feature_parameters.max_label_period_candles | 24 | int >= 1 | Maximum labeling NATR period 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` | enum {`euclidean`,`minkowski`,`chebyshev`,`cityblock`,`sqeuclidean`,`seuclidean`,`mahalanobis`,`harmonic_mean`,`geometric_mean`,`arithmetic_mean`,`quadratic_mean`,`cubic_mean`,`power_mean`,`weighted_sum`} | Distance metric for `compromise_programming` and `topsis` methods. Invalid values warn and fall back to `euclidean`. |
-| freqai.feature_parameters.label_cluster_metric | `euclidean` | enum {`euclidean`,`minkowski`,`chebyshev`,`cityblock`,`sqeuclidean`,`seuclidean`,`mahalanobis`} | Distance metric for `kmeans`, `kmeans2`, and `kmedoids` methods. Invalid values warn and fall back to `euclidean`. |
-| 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 | enum {`euclidean`,`minkowski`,`chebyshev`,`cityblock`,`sqeuclidean`,`seuclidean`,`mahalanobis`} | Distance metric for `knn` and `medoid` methods. Invalid values warn and fall back to the method's natural default (`minkowski` for `knn`, `euclidean` for `medoid`). |
-| 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 regressor and dynamic label 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. Forced for both namespaces in backtest and hyperopt, resetting the study on each optimization. |
-| freqai.optuna_hyperopt.warm_start | true | bool | Warm start HPO with previous best value(s). Persisted values are loaded and saved only in live and dry-run modes; non-live runs reuse only values produced earlier in the same run. |
-| 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 period `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 used by the Optuna samplers and label-candle shuffling. |
-| freqai.optuna_hyperopt.reset_label_study_on_schema_mismatch | true | bool | Reset a persisted `label` study when its selection schema is missing, invalid, or incompatible. `true` keeps the historical destructive reset, deleting the study before recreating it; `false` preserves its trials and stored metadata, permits caller-managed reuse in memory, and does not persist selected params until the schema is reconciled. Both fail closed: an inspection error, or (under `true`) a deletion error, aborts study creation. Has no effect when `continuous=true` or outside live/dry-run modes, where studies are always reset. |
-| freqai.optuna_hyperopt.vary_model_seed_by_trial | true | bool | Add `trial.number` to each regressor's configured model seed (or its default seed of `1`) during HPO. `true` samples model randomness across trials and preserves the historical behavior; `false` evaluates every trial and the final fit with the same model seed. This does not change `freqai.optuna_hyperopt.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. |
+| _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 (< upper bound) 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. |
+| freqai.continual_learning | false | bool | Continue XGBoost or LightGBM training from the previously deployed model, so its booster grows at every retrain; delete trained models to reset. Under `test_size` two-stage selection, HPO and the pre-refit selection model cold-start and only the final refit continues, growing by the selection model's round count (see `test_size`). Other regressors ignore any prior model. |
+| _Model training parameters_ | | | |
+| freqai.model_training_parameters.gpu_vram_gb | 80 | int > 0 | Available GPU VRAM (GB) for CatBoost, not total. Any positive value is floored to the nearest supported tier `<= value` (tiers 8, 10, 12, 16, 24, 32, 40, 48, 64, 80; values below 8 use tier 8). 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 | float [0,1) \| int >= 0 \| None | Outer holdout size; `0` disables the holdout (single-stage fit, `train_test_split` only). The same parameter reserves the chronological tail of the remaining training rows as inner validation for HPO and early stopping; a fractional value is relative to those remaining rows, not the original window. The holdout is predicted once and reported as weighted `holdout_rmse` in the original label scale; it measures the cold-started pre-refit selection model, not the refitted deployed model. `None` (sklearn dynamic sizing) applies only to `timeseries_split`; `train_test_split` requires a float or int; inner validation then falls back to `0.1`. |
+| 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`. `0` auto-derives the gap (source and lower-bound rule depend on `causal_mode`; see `causal_mode`). Not used by `train_test_split`. |
+| 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 >= 1 | Smoothing window length (candles). Values below 3 are raised to 3 at runtime. |
+| 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` | `savgol`: enum {`mirror`,`constant`,`nearest`,`wrap`,`interp`}; `gaussian_filter1d`: enum {`mirror`,`constant`,`nearest`,`wrap`}; ignored otherwise | 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`). Under `causal_mode=true` the epsilon baseline is computed causally (see `causal_mode`). 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 | 25.0 | 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. Under `causal_mode=true` the bumps use a finite support `ceil(4 * fill_sigma_candles)` (see `causal_mode`). 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"`. |
+| freqai.label_weighting.support_policy | `fallback` | enum {`fallback`,`raise`} | Policy when active label weighting fails support checks (evaluated on the training rows surviving upstream filtering). `raise` aborts the fit with `ValueError`; `fallback` logs a `WARNING` and uses sanitized base sample weights for that fit. Eval (test/val) weights bypass this policy and always fall back on composition errors. |
+| freqai.label_weighting.min_pivot_equivalent_count | 3 | int >= 1 | Minimum number of surviving pivot-equivalent label weights required after filtering. Pivot-equivalent rows are weights at least 10% of the surviving maximum label weight. |
+| freqai.label_weighting.min_positive_label_weight_fraction | 0.01 | float [0,1] | Minimum fraction of filtered training rows with finite positive label weights. |
+| freqai.label_weighting.min_effective_sample_size | 3.0 | float >= 1 | Minimum Kish effective sample size of the final composed training weights. |
+| _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], low < high | 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 period. |
+| freqai.feature_parameters.label_horizon_candles | `label_period_candles` | int >= 1 | Conservative fixed purge horizon in candles: the magnitude of the causal guards' purge and of the default `timeseries_split` gap (see `causal_mode` for how the guards consume it). When unset, falls back to `label_period_candles`. |
+| freqai.feature_parameters.causal_mode | true | bool | Causal split-guard master toggle. When `true` (default): (1) rejects `data_split_parameters.shuffle=true`, `feature_parameters.shuffle_after_split=true`, and `feature_parameters.reverse_train_test_order=true` (two of these rejections are independent of this toggle: `timeseries_split` rejects `shuffle_after_split` structurally, and an active holdout `test_size != 0` rejects all three at evaluation); (2) for `timeseries_split`, auto-sets `gap=label_horizon_candles` when `gap` is unset or `0` and rejects an explicit `gap<label_horizon_candles`; (3) for `train_test_split`, applies the same fixed `label_horizon_candles` purge around the train/test boundary; (4) both split methods additionally drop any train row whose label-aware availability reaches the test boundary, computed as the row-wise maximum over exact Zigzag confirmation time, centered-smoothing availability across each kernel support, and active label-weight availability (closing-pivot backfill, finite-support Gaussian bands `ceil(4 * fill_sigma_candles)`, k-NN adaptive-bandwidth sigma confirmation, and deferral of prefix-unstable non-finite weight imputations to the frame boundary); (5) label weighting becomes causal: the `epsilon` baseline at each row uses only pivot weights available with that row's label. `false` is deprecated (acausal baselines only): the causal split-guard rejections are lifted, but the toggle-independent ones remain (`timeseries_split` still rejects `shuffle_after_split`, and an active holdout still rejects all three at evaluation); `timeseries_split` `gap` auto-sets from `label_period_candles`, and Gaussian fills keep the legacy unbounded tails. |
+| freqai.feature_parameters.min_label_period_candles | 12 | int >= 1 | Minimum labeling NATR period used for reversals labeling HPO. |
+| freqai.feature_parameters.max_label_period_candles | 24 | int >= 1 | Maximum labeling NATR period 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, 10000] \| `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` | enum {`euclidean`,`minkowski`,`chebyshev`,`cityblock`,`sqeuclidean`,`seuclidean`,`mahalanobis`,`harmonic_mean`,`geometric_mean`,`arithmetic_mean`,`quadratic_mean`,`cubic_mean`,`power_mean`,`weighted_sum`} | Distance metric for `compromise_programming` and `topsis` methods. Invalid values warn and fall back to `euclidean`. |
+| freqai.feature_parameters.label_cluster_metric | `euclidean` | enum {`euclidean`,`minkowski`,`chebyshev`,`cityblock`,`sqeuclidean`,`seuclidean`,`mahalanobis`} | Distance metric for `kmeans`, `kmeans2`, and `kmedoids` methods. Invalid values warn and fall back to `euclidean`. |
+| 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 | enum {`euclidean`,`minkowski`,`chebyshev`,`cityblock`,`sqeuclidean`,`seuclidean`,`mahalanobis`} | Distance metric for `knn` and `medoid` methods. Invalid values warn and fall back to the method's natural default (`minkowski` for `knn`, `euclidean` for `medoid`). |
+| 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], low < high | 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 regressor and dynamic label 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. Forced for both namespaces in backtest and hyperopt, resetting the study on each optimization. |
+| freqai.optuna_hyperopt.warm_start | true | bool | Warm start HPO with previous best value(s). Persisted values are loaded and saved only in live and dry-run modes; non-live runs reuse only values produced earlier in the same run. |
+| 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 | 1 | int >= 1 | Parallel HPO workers. The effective value is capped at `max(1, CPU threads // 4)`; that cap is not the default. |
+| 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 period `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 used by the Optuna samplers and label-candle shuffling. |
+| freqai.optuna_hyperopt.reset_label_study_on_schema_mismatch | true | bool | Reset a persisted `label` study when its selection schema is missing, invalid, or incompatible. `true` keeps the historical destructive reset, deleting the study before recreating it; `false` preserves its trials and stored metadata, permits caller-managed reuse in memory, and does not persist selected params until the schema is reconciled. Both fail closed: an inspection error, or (under `true`) a deletion error, aborts study creation. Has no effect when `continuous=true` or outside live/dry-run modes, where studies are always reset. |
+| freqai.optuna_hyperopt.vary_model_seed_by_trial | true | bool | Add `trial.number` to each regressor's configured model seed (or its default seed of `1`) during HPO. `true` samples model randomness across trials and preserves the historical behavior; `false` evaluates every trial and the final fit with the same model seed. This does not change `freqai.optuna_hyperopt.seed`. |
## ReforceXY
)
-def _compute_combined_label_weights(
+def _invalid_weight_strategy_message(
+ strategy: str, metrics: dict[str, list[float]]
+) -> str:
+ return (
+ f"Invalid weighting strategy value {strategy!r}: "
+ f"supported values are {', '.join(WEIGHT_STRATEGIES)} or metric names {', '.join(metrics.keys())}"
+ )
+
+
+def _select_combined_metrics(
metrics: dict[str, list[float]],
metric_coefficients: dict[str, Any],
- aggregation: CombinedAggregation,
- softmax_temperature: float,
- *,
- impute: Callable[[NDArray[np.floating]], NDArray[np.floating]] = _impute_weights,
-) -> NDArray[np.floating]:
- if len(metrics) == 0:
- return np.asarray([], dtype=float)
+) -> list[tuple[str, NDArray[np.floating], float]]:
+ """Select the components feeding ``combined`` aggregation.
+ Shared selection logic (coefficient parsing, the all-unit default, the skip
+ rules for unselected or empty metrics), returning raw pre-imputation value
+ arrays paired with metric name and coefficient in ``metrics`` iteration
+ order. Imputation is left to the caller.
+ """
coefficients = _parse_metric_coefficients(metric_coefficients)
if len(coefficients) == 0:
coefficients = {k: 1.0 for k in metrics.keys()}
- imputed_metrics: list[NDArray[np.floating]] = []
- coefficients_list: list[float] = []
-
+ selected: list[tuple[str, NDArray[np.floating], float]] = []
for metric_name, metric_values in metrics.items():
if metric_name not in coefficients:
continue
- coefficient = coefficients[metric_name]
values_array = np.asarray(metric_values, dtype=float)
if values_array.size == 0:
continue
- imputed_metrics.append(impute(values_array))
- coefficients_list.append(float(coefficient))
+ selected.append((metric_name, values_array, float(coefficients[metric_name])))
+ return selected
- if len(imputed_metrics) == 0:
+
+def _aggregate_imputed_metrics(
+ imputed_metrics: list[NDArray[np.floating]],
+ coefficients: list[float],
+ aggregation: CombinedAggregation,
+ softmax_temperature: float,
+) -> NDArray[np.floating]:
+ return _aggregate_metrics(
+ np.vstack(imputed_metrics),
+ np.asarray(coefficients, dtype=float),
+ aggregation,
+ softmax_temperature,
+ )
+
+
+def _compute_combined_label_weights(
+ metrics: dict[str, list[float]],
+ metric_coefficients: dict[str, Any],
+ aggregation: CombinedAggregation,
+ softmax_temperature: float,
+ *,
+ impute: Callable[[NDArray[np.floating]], NDArray[np.floating]] = _impute_weights,
+) -> NDArray[np.floating]:
+ selected = _select_combined_metrics(metrics, metric_coefficients)
+ if len(selected) == 0:
return np.asarray([], dtype=float)
- stacked_metrics = np.vstack(imputed_metrics)
- coefficients_array = np.asarray(coefficients_list, dtype=float)
+ return _aggregate_imputed_metrics(
+ [impute(values) for _, values, _ in selected],
+ [coefficient for _, _, coefficient in selected],
+ aggregation,
+ softmax_temperature,
+ )
- return _aggregate_metrics(
- stacked_metrics, coefficients_array, aggregation, softmax_temperature
+
+def _nonfinite_imputation_dependency_mask(
+ values: NDArray[np.floating],
+) -> NDArray[np.bool_]:
+ """Mark values whose legacy full-frame imputation is not prefix-stable.
+
+ A non-finite value's imputation is prefix-unstable (default or zero at
+ boundaries, interior median otherwise) as later pivots arrive, so it stays
+ unavailable until the frame boundary.
+ """
+ return ~np.isfinite(values)
+
+
+def compute_label_weight_imputation_dependency_mask(
+ n_indices: int,
+ metrics: dict[str, list[float]],
+ weighting_config: dict[str, Any],
+) -> tuple[NDArray[np.bool_], NDArray[np.bool_]]:
+ """Identify pivot weights whose non-finite imputation can change by prefix.
+
+ Returns ``(dependency_mask, leading_stable_mask)``. A ``dependency_mask``
+ pivot remains causally unavailable until the frame boundary; for
+ ``combined``, dependency propagates from every selected component and from
+ the aggregate before its final imputation. ``leading_stable_mask`` (a subset
+ of ``dependency_mask``) marks the leading non-finite run of a single-metric
+ strategy: those pivots impute to ``0.0`` and stabilize once the first finite
+ pivot's weight is known, so they need not defer to the frame boundary. It is
+ empty for ``uniform``, ``combined``, and all-non-finite metrics.
+ """
+ label_weighting = {**DEFAULTS_LABEL_WEIGHTING, **weighting_config}
+ strategy = label_weighting["strategy"]
+ if strategy == WEIGHT_STRATEGIES[0]: # "none"
+ raise ValueError(
+ "compute_label_weight_imputation_dependency_mask must not be called "
+ f"with strategy={strategy!r}; callers must skip invocation when "
+ "weighting is disabled"
+ )
+ if strategy == WEIGHT_STRATEGIES[1]: # "uniform"
+ return np.zeros(n_indices, dtype=bool), np.zeros(n_indices, dtype=bool)
+ if strategy in metrics:
+ values = np.asarray(metrics[strategy], dtype=float)
+ if values.size == 0:
+ return np.zeros(n_indices, dtype=bool), np.zeros(n_indices, dtype=bool)
+ if values.shape != (n_indices,):
+ raise ValueError(
+ f"Invalid metric {strategy!r} shape {values.shape}: "
+ f"must be ({n_indices},)"
+ )
+ dependency = _nonfinite_imputation_dependency_mask(values)
+ leading_stable = np.zeros(n_indices, dtype=bool)
+ finite = ~dependency
+ if finite.any():
+ leading_stable[: int(np.argmax(finite))] = True
+ return dependency, leading_stable
+ if strategy != WEIGHT_STRATEGIES[8]: # "combined"
+ raise ValueError(_invalid_weight_strategy_message(strategy, metrics))
+
+ dependency_mask = np.zeros(n_indices, dtype=bool)
+ imputed_metrics: list[NDArray[np.floating]] = []
+ coefficients_list: list[float] = []
+ for metric_name, values_array, coefficient in _select_combined_metrics(
+ metrics, label_weighting["metric_coefficients"]
+ ):
+ if values_array.shape != (n_indices,):
+ raise ValueError(
+ f"Invalid metric {metric_name!r} shape {values_array.shape}: "
+ f"must be ({n_indices},)"
+ )
+ dependency_mask |= _nonfinite_imputation_dependency_mask(values_array)
+ imputed_metrics.append(_impute_weights(values_array))
+ coefficients_list.append(coefficient)
+
+ if len(imputed_metrics) == 0:
+ return dependency_mask, np.zeros(n_indices, dtype=bool)
+
+ combined_weights = _aggregate_imputed_metrics(
+ imputed_metrics,
+ coefficients_list,
+ label_weighting["aggregation"],
+ label_weighting["softmax_temperature"],
)
+ if combined_weights.shape != (n_indices,):
+ raise ValueError(
+ f"Invalid combined weights shape {combined_weights.shape}: "
+ f"must be ({n_indices},)"
+ )
+ dependency_mask |= _nonfinite_imputation_dependency_mask(combined_weights)
+ return dependency_mask, np.zeros(n_indices, dtype=bool)
def _compute_epsilon_floor(
impute=impute,
)
else:
- raise ValueError(
- f"Invalid weighting strategy value {strategy!r}: "
- f"supported values are {', '.join(WEIGHT_STRATEGIES)} or metric names {', '.join(metrics.keys())}"
- )
+ raise ValueError(_invalid_weight_strategy_message(strategy, metrics))
return impute(weights)
indices: Sequence[int] | NDArray[np.integer],
fill_radius: int = 0,
*,
+ imputation_dependency_mask: Sequence[bool] | NDArray[np.bool_] | None = None,
+ imputation_leading_stable_mask: Sequence[bool] | NDArray[np.bool_] | None = None,
weighting_config: dict[str, Any] | None = None,
) -> pd.Series:
"""Per-row causal availability (in candles) of the label WEIGHT column.
off-center bands still wait for the pivot confirmation and sigma. Other
strategies and additive fills keep their existing competing-band
dependencies.
+
+ ``imputation_dependency_mask`` marks pivot weights whose non-finite
+ imputation can change as the available prefix grows. Those pivots and their
+ Gaussian bands are unavailable until the frame boundary. An unresolved
+ trailing pivot is excluded only when it has no such dependency.
+ ``imputation_leading_stable_mask`` (a subset) marks a leading non-finite run
+ that imputes to 0.0 and stabilizes at the first finite pivot's confirmation;
+ those pivots are released there instead of at the frame boundary and their
+ zero-weight bands are skipped.
"""
n = len(known_at_lookahead)
positions, known_at_lookahead_values = _sanitize_known_at_lookahead(
if n == 0:
return pd.Series(positions, index=known_at_lookahead.index, dtype=np.int64)
known_at_positions = positions + known_at_lookahead_values
- idx = np.asarray(indices, dtype=int)
- idx = np.sort(idx[(idx >= 0) & (idx < n)])
+ raw_idx = np.asarray(indices, dtype=int)
+
+ def _validate_pivot_mask(
+ mask: Sequence[bool] | NDArray[np.bool_] | None, name: str
+ ) -> NDArray[np.bool_]:
+ if mask is None:
+ return np.zeros(raw_idx.size, dtype=bool)
+ arr = np.asarray(mask)
+ if arr.shape != raw_idx.shape:
+ raise ValueError(
+ f"Invalid {name} shape {arr.shape}: must be {raw_idx.shape}"
+ )
+ if arr.dtype != np.bool_:
+ raise ValueError(f"Invalid {name} dtype {arr.dtype}: must be bool")
+ return arr
+
+ raw_dependency_mask = _validate_pivot_mask(
+ imputation_dependency_mask, "imputation_dependency_mask"
+ )
+ raw_leading_stable_mask = _validate_pivot_mask(
+ imputation_leading_stable_mask, "imputation_leading_stable_mask"
+ )
+ valid_mask = (raw_idx >= 0) & (raw_idx < n)
+ idx = raw_idx[valid_mask]
+ order = np.argsort(idx, kind="stable")
+ idx = idx[order]
+ dependency_mask = raw_dependency_mask[valid_mask][order]
+ leading_stable_mask = raw_leading_stable_mask[valid_mask][order]
base = known_at_positions.copy()
if idx.size:
weight_availability = np.empty(idx.size, dtype=np.int64)
n,
)
np.maximum(avail_pivot, sigma_availability, out=avail_pivot)
+ avail_pivot[dependency_mask] = n
+ if leading_stable_mask.any() and np.array_equal(order, np.arange(idx.size)):
+ # Leading non-finite run imputes to 0.0, stable once the first finite
+ # pivot's weight is known (backfilled confirmation weight_availability
+ # [first_finite]), not at the frame boundary. Guarded to the sorted
+ # (identity-order) case where the run is a contiguous prefix.
+ first_finite = int(leading_stable_mask.sum())
+ if first_finite < weight_availability.size:
+ avail_pivot[leading_stable_mask] = int(
+ weight_availability[first_finite]
+ )
base[idx] = np.maximum(base[idx], avail_pivot)
if fill_radius > 0:
- for pivot_pos, pivot_avail, weight_avail in zip(
+ for (
+ pivot_pos,
+ pivot_avail,
+ weight_avail,
+ pivot_dependency,
+ pivot_leading,
+ ) in zip(
idx.tolist(),
avail_pivot.tolist(),
band_weight_availability.tolist(),
+ dependency_mask.tolist(),
+ leading_stable_mask.tolist(),
):
- # A metric-based trailing pivot never resolves in-frame and its
- # Gaussian bump is 0. Pure-Gaussian uniform pivots use their own
- # confirmation, so only that path retains the trailing band.
- if weight_avail >= n:
+ # A leading-run pivot imputes to 0.0 (zero bump): its own row is
+ # released above; it spreads no band.
+ if pivot_leading:
+ continue
+ # Skip pivots whose band weight never resolves in-frame
+ # (weight_avail == n): their Gaussian bump is zero. Exception: an
+ # imputation-dependent pivot keeps the non-zero legacy default for
+ # an all-non-finite metric, so its band must defer to n.
+ if weight_avail >= n and not pivot_dependency:
continue
lo = max(0, pivot_pos - fill_radius)
hi = min(n, pivot_pos + fill_radius + 1)
repositories / freqai-strategies.git / commitdiff
