Adapt the configuration to your needs: edit `user_data/config.json` to set your
exchange API keys and tune the `freqai` section.
+The API server is disabled by default. Before enabling it, replace its username,
+password, JWT secret and WebSocket token; keep the Compose port bound to
+localhost unless access is protected by a VPN or SSH tunnel.
+
Then build and start the container:
```shell
docker compose up -d --build
```
+The build intentionally follows Freqtrade's current `stable_freqai` image and
+resolves some dependencies at build time. Record the resolved image digest and
+dependency versions for reproducible evaluations, as required by the protocol
+below.
+
### Configuration tunables
-| Path | Default | Type / Range | Description |
+The table records runtime fallbacks. The Quick-start template is an opinionated
+configuration and may override them.
+
+| Path | Runtime fallback | 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.lookback_period_fraction | 0.5 | float (0,1] | Fraction of Freqtrade's [`fit_live_predictions_candles`][freqai-parameters] 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. |
| _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.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.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, LightGBM, or CPU CatBoost 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. |
+| freqai.continual_learning | false | bool | Continue XGBoost, LightGBM, or CPU CatBoost 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`). GPU CatBoost and other regressors cold-start instead. |
| _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.test_size | 0.1 | float [0,1) \| int >= 0 \| null | 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. `null` (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. |
+| freqai.data_split_parameters.max_train_size | null | int >= 1 \| null | Maximum training set size for `timeseries_split`. When set, creates a sliding window instead of expanding train set. `null` = 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 (`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.window_candles | 5 | int >= 1 | Requested smoothing window in candles. Runtime raises values below 3; Gaussian, Kaiser, triangular, SMM and SMA use the next odd length, `kaiser_bessel_derived` uses the next even length, and `savgol` uses an odd length greater than `polyorder`. `none` does not smooth. For `gaussian_filter1d`, this value only gates series shorter than the requested window; `sigma` defines the kernel. |
| 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`} | Boundary mode for `savgol` and `gaussian_filter1d`; ignored otherwise. |
| 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.metric_coefficients | {} | dict[str, finite float > 0] | Per-metric coefficients for `combined` strategy. Keys: `amplitude`, `amplitude_threshold_ratio`, `volume_rate`, `speed`, `efficiency_ratio`, `volume_weighted_efficiency_ratio`. Invalid entries are ignored; when none remain, all metrics are selected with coefficient `1.0`. |
| 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_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 | `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_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.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.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. |
| 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_weights | uniform | list of 7 finite floats >= 0; sum > 0 | Per-objective weights for trial selection methods, normalized internally. 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 | null | `minkowski`: finite float > 0; `power_mean`: finite float; null otherwise | Lp exponent for parameterized distance metrics. Used by `minkowski` distance (default 2.0) and `power_mean` distance (default 1.0). The KNN `power_mean` aggregation exponent is configured by `label_density_aggregation_param`. Ignored by other metrics. |
+| freqai.feature_parameters.label_method | `compromise_programming` | enum {`compromise_programming`,`topsis`,`kmeans`,`kmeans2`,`knn`,`medoid`} | HPO `label` Pareto front trial selection method. `kmedoids` is unavailable in the current Python 3.14 image. |
| 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_metric | `euclidean` | enum {`euclidean`,`minkowski`,`chebyshev`,`cityblock`,`sqeuclidean`,`seuclidean`,`mahalanobis`} | Distance metric for `kmeans` and `kmeans2`. 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.label_density_aggregation_param | aggregation-dependent | `power_mean`: finite float; `quantile`: float [0,1]; null otherwise | 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.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`. |
+The `label_weighting`, `label_smoothing`, `label_pipeline` and
+`label_prediction` sections accept either the flat paths listed above or a
+per-label form using `default` and `columns.<glob>`. Do not mix both forms in
+one section: once `default` or `columns` is present, sibling flat keys are
+ignored with a warning. Matching column patterns are applied from least to most
+specific; equally specific patterns follow declaration order, so the later one
+wins.
+
### Backtest evaluation protocol
Use this protocol before adopting a change to a QuickAdapter default. It is an
to the next window, or with a forward dry-run. Keep native-backtest and
full-loop results in separate report sections. This repository does not provide
that chronological runner. See the [FreqAI running guide][freqai-running] and
-the [FreqAI callback order][freqai-source].
+the [FreqAI train/predict/callback order][freqai-source] and
+[backtesting replay loop][freqai-replay].
#### Procedure
assumptions are not credible under a fixed or moving-window scheme. The
stationary bootstrap preserves local dependence under its assumptions; it
does not create information absent from a short backtest [Politis and
- Romano][stationary-bootstrap]. See [Politis and White][block-length] for
- data-driven block-length selection.
+ Romano][stationary-bootstrap]. Use the corrected automatic selector from
+ [Politis and White][block-length] together with the published
+ [Patton, Politis and White correction][block-length-correction].
9. **Control selection and decide once.** Orient effects so positive values
favor the candidate. A promotion requires the one-sided lower confidence
bound for the primary net-log-return effect to exceed its practical margin,
[afml]: https://www.wiley.com/en-us/Advances+in+Financial+Machine+Learning-p-9781119482086
[block-length]: https://doi.org/10.1081/ETC-120028836
+[block-length-correction]: https://doi.org/10.1080/07474930802459016
[dsr]: https://doi.org/10.3905/jpm.2014.40.5.094
+[freqai-parameters]: https://www.freqtrade.io/en/stable/freqai-parameter-table/#general-configuration-parameters
[freqai-running]: https://www.freqtrade.io/en/stable/freqai-running/
-[freqai-source]: https://github.com/freqtrade/freqtrade/blob/2026.6/freqtrade/freqai/freqai_interface.py#L396-L402
+[freqai-replay]: https://github.com/freqtrade/freqtrade/blob/2026.7/freqtrade/freqai/freqai_interface.py#L900-L927
+[freqai-source]: https://github.com/freqtrade/freqtrade/blob/2026.7/freqtrade/freqai/freqai_interface.py#L348-L408
[freqtrade-backtesting]: https://www.freqtrade.io/en/stable/backtesting/
[hansen-spa]: https://doi.org/10.1198/073500105000000063
[holm]: https://www.jstor.org/stable/4615733
+[knn-density]: https://doi.org/10.1214/aoms/1177700079
[lookahead-analysis]: https://www.freqtrade.io/en/stable/lookahead-analysis/
[pbo]: https://doi.org/10.21314/JCF.2016.322
[recursive-analysis]: https://www.freqtrade.io/en/stable/recursive-analysis/
+[silverman-density]: https://doi.org/10.1201/9781315140919
[stationary-bootstrap]: https://doi.org/10.1080/01621459.1994.10476870
[white-reality-check]: https://doi.org/10.1111/1468-0262.00152
repositories / freqai-strategies.git / commitdiff
