> ## Documentation Index > Fetch the complete documentation index at: https://systematica.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Config > systematica.config ## Variables * `DATE_FORMAT`: Default date format used in the systematica package. * `SIGNAL_PARAMS`: Signal parameters used in the systematica package. These parameters are used to define the trading signals and their behavior. * `DATA_SELECTION_PARAMS`: Data selection parameters used in the systematica package. These parameters are used to select the data for the trading signals. * `LOADER_PARAMS`: Loader parameters used in the systematica package. These parameters are used to load the data for the trading signals. * `VBT_PORT_KWARGS`: VectorBT portfolio keyword arguments used in the systematica package. These parameters are used to define the portfolio simulation and its behavior. * `NON_TUNABLE_VBT_PORT_KWARGS`: Non-tunable VectorBT portfolio keyword arguments used in the systematica package. These parameters are not tunable and are used to define the portfolio simulation and its behavior. * `TUNABLE_VBT_PORT_KWARGS`: Tunable VectorBT portfolio keyword arguments used in the systematica package. These parameters are tunable and are used to define the portfolio simulation and its behavior. * `ALL_PARAMS`: All parameters used in the systematica package. These parameters are used to define the trading signals, data selection, and portfolio simulation. * `REGISTRED_SIGNAL_MODELS`: Registered signal models used in the systematica package. These models are used to compute trading signals based on different strategies such as crossover, spread, and mixed strategies. * `DEFAULT_PORTFOLIO_PARAMS`: Default VectorBT portfolio parameters used in the systematica package. These parameters are used to define the portfolio simulation and its behavior. The default values are set to None or specific values as per the requirements of the systematica package. * `SPLITTER_KWARGS_MAP: Dict[str, Callable[..., Dict[str, Any]]]`: Mapping of splitters to their respective functions and required parameters. Each key corresponds to a splitter type, and the value is a function that generates the required keyword arguments for that splitter type. * `SPLITTER_REQUIRED_PARAMS: Dict[str, set]`: Required parameters for each splitter type. Each key corresponds to a splitter type, and the value is a set of required parameters for that splitter. * `DEFAULT_NEPTUNE_INIT_RUN`: Default Neptune predefined `init_run` fields and metadata. Please find more information about the `init_run` method in the Neptune [Documentation](https://docs.neptune.ai/api/experiment/init_run#neptune.init_run). * `DEFAULT_NEPTUNE_LOG_KWARGS`: Default Neptune predefined `log_kwargs` fields and metadata. * `DEFAULT_NEPTUNE_LOG_LIVE_STREAM_KWARGS`: Default Neptune predefined `log_kwargs` fields and metadata in `live_stream` model. ## `set_bfc_theme` ```python theme={null} set_bfc_theme() ``` Set the Blockforce Capital theme for Plotly figures. 1. Initialize the theme once for all figures. 2. Apply the theme to specific figures. 3. Use the theme with `vectorbtpro`. ## `default_splitter_kwargs` ```python theme={null} default_splitter_kwargs( splitter: str, **kwargs, ) ‑> Dict[str, Any] ``` Generate default keyword arguments for the specified splitter type. **Parameters**: | Name | Type | Default | Description | | ---------- | ----------- | ------- | ------------------------------------------------------------------------- | | `splitter` | `str` | `--` | The type of splitter. | | `kwargs` | `tp.Kwargs` | `--` | Arbitrary keyword arguments, will be filtered based on the splitter type. | **Raises**: | Type | Description | | ------------ | ------------------------------------ | | `ValueError` | If the splitter type is unsupported. | **Returns**: | Type | Description | | ----------- | ------------------------------------------------------------------------- | | `tp.Kwargs` | A dictionary containing the necessary keyword arguments for the splitter. | ## `default_create_study_kwargs` ```python theme={null} default_create_study_kwargs( seed: None | int, ) ‑> Dict[str, Any] ``` Generate default keyword arguments for `optuna.create_study`. **Parameters**: | Name | Type | Default | Description | | ------ | ----- | ------- | ---------------------------------------- | | `seed` | `int` | `--` | or None Random seed for reproducibility. | **Returns**: | Type | Description | | ----------- | ------------------------------------------------------- | | `tp.Kwargs` | Default keyword arguments for creating an Optuna study. | ## `default_optimize_kwargs` ```python theme={null} default_optimize_kwargs( n_trials: int, n_jobs: int, n_completed_trials: int, gc_after_trial: bool = False, ) ‑> Dict[str, Any] ``` Generate default keyword arguments for `study.optimize`. **Parameters**: | Name | Type | Default | Description | | -------------------- | ------ | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `n_trials` | `int` | `--` | Number of trials to run. | | `n_jobs` | `int` | `--` | Number of parallel jobs (-1 to use all CPUs). | | `n_completed_trials` | `int` | `--` | Number of completed trials before stopping. | | `gc_after_trial` | `bool` | `False` | Flag to determine whether to automatically run garbage collection after each trial. Set to `True` to run the garbage collection, `False` otherwise. It runs a full collection by internally calling `gc.collect`. If you see an increase in memory consumption over several trials, try setting this flag to `True`. | **Returns**: | Type | Description | | ----------- | ------------------------------------------------------------------------------------ | | `tp.Kwargs` | A dictionary containing the default keyword arguments for Optuna study optimization. | ## `NeptuneSelector` ```python theme={null} NeptuneSelector( run_id: str, trial_selector: str | int | ForwardRef('BaseTrialSelector') = None, column_stack: bool = False, group_by: bool = True, ) ``` Method generated by attrs for class NeptuneSelector. ### Instance variables * `model: str`: The model name, which is the run ID of the Neptune run. * `column_stack: bool`: Compute `vbt.PF.column_stack` to combine portfolio when several trial numbers are passed. Default to `False`. * `group_by: bool`: Group strategy when `column_stack` is `True`. If `False`, compute individual backtests. Combined all strategy into a single run otherwise. Defaults to `True`. * `run_id: str`: The ID of the Neptune run to fetch. * `trial_selector: str | int | BaseTrialSelector`: Custom parameter selection. If `None`, retrieve `best/params` from neptune. if `int`, retrieve trial number. If `BaseTrialSelector`, retrieve params based on algorithm. Defaults to `None`. ## `FeatureConfig` ```python theme={null} FeatureConfig( model: Any, signal_model: str, long_entries: float, long_exits: float, short_entries: float, short_exits: float, select_symbols: str | int | List[str | int] = None, loader_or_data: Callable = , timeframe: str = None, start: str = '2018-01-01 UTC', end: str = None, s1: str = 'BTCUSDT', s2: str = 'ETHUSDT', suffix: str = 'h5', use_close: bool = True, freq: str = None, size: float | numpy.ndarray = 50, size_type: str | numpy.ndarray = 'valuepercent100', allow_partial: float | numpy.ndarray = None, fees: float | numpy.ndarray = None, fixed_fees: float | numpy.ndarray = None, slippage: float | numpy.ndarray = None, min_size: float | numpy.ndarray = None, max_size: float | numpy.ndarray = None, size_granularity: float | numpy.ndarray = None, sl_stop: float | numpy.ndarray = None, tsl_stop: float | numpy.ndarray = None, tp_stop: float | numpy.ndarray = None, accumulate: bool | str | numpy.ndarray = None, init_cash: float | numpy.ndarray = None, cash_sharing: bool = True, group_by: bool = True, call_seq: str | bool = 'auto', fill_pos_info: bool = False, log: bool | numpy.ndarray = None, portfolio_kwargs: Dict[str, Any] = _Nothing.NOTHING, model_params: Dict[str, Any] = _Nothing.NOTHING, id: str = None, study_name: str = None, ) ``` Configuration for a feature calculation. This class encapsulates the necessary configuration for computing a feature. It includes the feature's name, a callable to compute the feature, a list of required data keys, and optional parameters for the computation. Method generated by attrs for class FeatureConfig. ### Instance variables * `model_name: str`: Model name. * `frequency: str`: Frequency. * `symbols: Dict[str, Any]`: Function parameters. * `loader_name: str`: Loader name. * `data_selection_params: Dict[str, Any]`: Function parameters. * `signal_params: Dict[str, Any]`: Signal parameters. * `tunable_params: Dict[str, Any]`: Tunable parameters. * `accumulate: bool | str | numpy.ndarray`: Whether to accumulate orders. Defaults to `None`. * `allow_partial: float | numpy.ndarray`: Whether to allow partial fills. Defaults to `None`. * `call_seq: str | bool`: Call sequence. Defaults to `auto`. * `cash_sharing: bool`: Whether to share cash between assets. Defaults to `True`. * `constant_params: Dict[str, Any]`: * `data: vectorbtpro.data.base.Data`: * `distributed_params: Dict[str, Any]`: * `end: str`: The end date for the data in string format (e.g., `YYYY-MM UTC`). If None, use `datetie.utcnow()` to fetch the latest data possible. Default to `None`. * `fees: float | numpy.ndarray`: Fees per order. Defaults to `None`. * `fill_pos_info: bool`: Whether to fill position info. Defaults to `False`. * `fixed_fees: float | numpy.ndarray`: Fixed fees per order. Defaults to `None`. * `freq: str`: Frequency of the data. Used when `cross_validate` is True. Defaults to `None`. * `group_by: bool`: Whether to group by symbol. Defaults to `True`. * `id: str`: Custom searchable id. * `init_cash: float | numpy.ndarray`: Initial cash. Defaults to `None`. * `loader_or_data: Callable`: Default loader or data. If loader, it must accept timeframe as retriever. More information in `data/loader`. Defaults to `load_clean_data`. * `loader_params: Dict[str, Any]`: * `log: bool | numpy.ndarray`: Whether to log orders. Defaults to `None`. * `long_entries: float`: Long entry signals. * `long_exits: float`: Long exit signals. * `max_size: float | numpy.ndarray`: Maximum size of each order. Defaults to `None`. * `metadata: Dict[str, Any]`: * `min_size: float | numpy.ndarray`: Minimum size of each order. Defaults to `None`. * `model: Any`: Compute model, which defines the method to compute the feature. * `model_params: Dict[str, Any]`: A dictionary of optional keyword arguments needed to compute the model. * `non_tunable_portfolio_params: Dict[str, Any]`: * `portfolio_kwargs: Dict[str, Any]`: Additional keyword arguments for the portfolio simulation. * `s1: str`: First symbol. * `s2: str`: Second symbol. * `select_symbols: str | int | List[str | int]`: Symbol name (e.g., `BTCUSDT` or `ETHUSDT`) or column index (e.g., `0` or `1`) to keep. If None, no filtering. * `short_entries: float`: Short entry signals. * `short_exits: float`: Short exit signals. * `signal_model: str`: Signal model used for generating signals. * `size: float | numpy.ndarray`: Size of each order. Defaults to `50`. * `size_granularity: float | numpy.ndarray`: Granularity of size. Defaults to `None`. * `size_type: str | numpy.ndarray`: Type of size (default is "valuepercent100"). * `sl_stop: float | numpy.ndarray`: Stop-loss level. Defaults to `None`. * `slippage: float | numpy.ndarray`: Slippage per order. Defaults to `None`. * `start: str`: The start date for the data in string format (e.g., `YYYY-MM UTC`). * `study_name: str`: The name of the (optuna and tracker) study. * `suffix: str`: Extension (suffix) of the file to load. Defaults to `h5`. * `timeframe: str`: Timeframe of the file to be loaded. * `tp_stop: float | numpy.ndarray`: Take-profit level. Defaults to `None`. * `tsl_stop: float | numpy.ndarray`: Trailing stop-loss level. Defaults to `None`. * `tunable_portfolio_params: Dict[str, Any]`: * `use_close: bool`: Use close price if set to True. Otherwise, use open price.