# The first language is the default language and the respective language server will be used as a fallback.
# Note that when using the JetBrains backend, language servers are not used and this list is correspondingly ignored.
languages:
- - python
+- python
# time budget (seconds) per tool call for the retrieval of additional symbol information
# such as docstrings or parameter information.
# Possible values: unset (use global setting), "lf", "crlf", or "native" (platform default)
# This does not affect Serena's own files (e.g. memories and configuration files), which always use native line endings.
line_ending:
+
+# list of regex patterns for memories to completely ignore.
+# Matching memories will not appear in list_memories or activate_project output
+# and cannot be accessed via read_memory or write_memory.
+# To access ignored memory files, use the read_file tool on the raw file path.
+# Extends the list from the global configuration, merging the two lists.
+# Example: ["_archive/.*", "_episodes/.*"]
+ignored_memory_patterns: []
+
+# advanced configuration option allowing to configure language server-specific options.
+# Maps the language key to the options.
+# Have a look at the docstring of the constructors of the LS implementations within solidlsp (e.g., for C# or PHP) to see which options are available.
+# No documentation on options means no options are available.
+ls_specific_settings: {}
| freqai.label_smoothing.method | `gaussian` | enum {`none`,`gaussian`,`kaiser`,`triang`,`smm`,`sma`,`savgol`,`gaussian_filter1d`} | Label smoothing method (`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` kernel. |
-| freqai.label_smoothing.polyorder | 3 | int >= 1 | Polynomial order for `savgol` smoothing. |
+| freqai.label_smoothing.polyorder | 3 | int >= 0 | Polynomial order for `savgol` smoothing. |
| freqai.label_smoothing.mode | `mirror` | enum {`mirror`,`constant`,`nearest`,`wrap`,`interp`} | Boundary mode for `savgol` and `gaussian_filter1d`. |
| freqai.label_smoothing.sigma | 1.0 | float > 0 | Gaussian `sigma` for `gaussian_filter1d` smoothing. |
| _Label weighting_ | | | |
| freqai.label_weighting.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. |
| _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)/IQR, `mmad`=(w-median)/(MAD·k), `power_yj`=YJ(w). |
+| 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.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 used in distance calculations to ideal point. Objectives: (1) number of detected reversals, (2) median swing amplitude, (3) median (swing amplitude / median volatility-threshold ratio), (4) median swing volume per candle, (5) median swing speed, (6) median swing efficiency ratio, (7) median swing volume-weighted efficiency ratio. |
-| freqai.feature_parameters.label_p_order | None | float \| None | p-order parameter for distance metrics. Used by `minkowski` (default 2.0) and `power_mean` (default 1.0). Ignored by other metrics. |
+| freqai.feature_parameters.label_weights | [1/7,1/7,1/7,1/7,1/7,1/7,1/7] | list[float] | Per-objective weights for trial selection methods. Objectives: (1) number of detected reversals, (2) median swing amplitude, (3) median (swing amplitude / median volatility-threshold ratio), (4) median swing volume per candle, (5) median swing speed, (6) median swing efficiency ratio, (7) median swing volume-weighted efficiency ratio. |
+| freqai.feature_parameters.label_p_order | None | float \| None | Lp exponent for parameterized metrics. Used by `minkowski` distance (default 2.0) and `power_mean` aggregation (default 1.0). Ignored by other metrics. |
| freqai.feature_parameters.label_method | `compromise_programming` | enum {`compromise_programming`,`topsis`,`kmeans`,`kmeans2`,`kmedoids`,`knn`,`medoid`} | HPO `label` Pareto front trial selection method. |
| freqai.feature_parameters.label_distance_metric | `euclidean` | string | Distance metric for `compromise_programming` and `topsis` methods. |
| freqai.feature_parameters.label_cluster_metric | `euclidean` | string | Distance metric for `kmeans`, `kmeans2`, and `kmedoids` methods. |
| freqai.feature_parameters.label_density_metric | method-dependent | string | Distance metric for `knn` and `medoid` methods. |
| freqai.feature_parameters.label_density_aggregation | `power_mean` | enum {`power_mean`,`quantile`,`min`,`max`} | Aggregation method for KNN neighbor distances. |
| freqai.feature_parameters.label_density_n_neighbors | 5 | int >= 1 | Number of neighbors for KNN. |
-| freqai.feature_parameters.label_density_aggregation_param | aggregation-dependent | float \| None | Tunable for KNN neighbor distance aggregation: p-order (`power_mean`) or quantile value (`quantile`). |
+| freqai.feature_parameters.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_ | | | |
repositories / freqai-strategies.git / commitdiff
