### Configuration tunables
-The table records runtime fallbacks. The Quick-start template is an opinionated
+The table records runtime fallbacks. The Quick start template is an opinionated
configuration and may override them.
| Path | Runtime fallback | Type / Range | Description |
| _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.trade_price_target_method | `moving_average` | enum {`moving_average`,`quantile_interpolation`,`weighted_average`} | Trade NATR (Normalized Average True Range) computation method. |
| _Reversal confirmation_ | | | |
-| reversal_confirmation.lookback_period_candles | 0 | int >= 0 | Prior confirming candles; 0 = none. With confirmation enabled, unmeasurable history rejects entries, while a valid current exit may still reduce exposure; this does not imply profitability. |
+| reversal_confirmation.lookback_period_candles | 0 | int >= 0 | Prior confirming candles; 0 = none. With confirmation enabled, unmeasurable history rejects entries, while a valid current exit may still reduce exposure. |
| 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. |
| 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 and Quesenberry][knn-density]; [Silverman, §5.2][silverman-density]). 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 | `fixed` | enum {`fixed`,`knn`} | Per-pivot Gaussian bandwidth selector. `fixed` applies a constant `fill_sigma_candles` to every pivot. `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 and Quesenberry][knn-density]; [Silverman, §5.2][silverman-density]). 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 fall back only when label support collapses; shape or alignment errors remain fatal. |
+| 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; `fallback` logs a `WARNING` and uses sanitized base sample weights for that fit. Eval (test/val) weights bypass this policy and fall back only when label support collapses; shape or alignment errors remain fatal. |
| 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. |
| _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.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; (5) label weighting becomes causal: the `epsilon` baseline at each row uses only pivot weights available with that row's label. `false` is deprecated: 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 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.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, 4294967295] | 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`. |
+| 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` performs a 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; `false` evaluates every trial and the final fit with the same model seed. This does not change `freqai.optuna_hyperopt.seed`. |
The `label_weighting`, `label_smoothing`, `label_pipeline` and
`label_prediction` sections accept either the flat paths listed above or a
repositories / freqai-strategies.git / commitdiff
