> ## 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. # Momentum > systematica.api.models.momentum Momentum factors capture price trends across multiple time horizons, incorporating volatility adjustments and weighted scoring mechanisms to generate normalized signals between -1 and 1. ## Momentum Calculation Components The momentum model computes scores based on the following key components: | Component | Description | Function | | --------------------- | -------------------------- | --------------------------------- | | Short-term Momentum | Recent price trends | `get_momentum_components_nb()` | | Medium-term Momentum | Intermediate price trends | `get_momentum_components_nb()` | | Long-term Momentum | Full dataset price trends | `get_momentum_components_nb()` | | Volatility Adjustment | Risk normalization | `get_volatility_adjustments_nb()` | | Weighted Scoring | Combined signal generation | `weighted_average_scores_nb()` | ## API Classes for Momentum The momentum API provides four main classes: | Class | Purpose | Core Function | | ------------------------------ | ----------------- | ------------------------------- | | `RollingMomentumFactor` | Per-asset rolling | `rolling_momentum_nb()` | | `RollingAverageMomentumFactor` | Averaged rolling | `rolling_momentum_average_nb()` | | `MomentumFactorCV` | Per-asset CV | `momentum_cv(combined=False)` | | `AverageMomentumFactorCV` | Averaged CV | `momentum_cv(combined=True)` | ## `RollingAverageMomentumFactor` ```python theme={null} RollingAverageMomentumFactor( window: int = 365, minp: int = None, short_term_mom: int = 30, medium_term_mom: int = 50, short_term_vol: int = 30, short_term_weight: float = 0.5, medium_term_weight: float = 0.3, long_term_weight: float = 0.2, score_scale: float = 2.0, ) ``` Momentum variation factor for calculating and scoring momentum between two symbols: * **Multiple Periods**: The Momentum indicator is calculated through different time horizons, giving more weight to recent data for faster response. * **Volatility Adjustments** : It incorporates both momentum and volatility adjustments to generate a normalized score. **Workflow**: 1. **Trend Components Calculation**: For each symbol, calculate the short, medium and long-term momentum using the average price returns over specified periods. 2. **Volatility Adjustment**: Calculate the short-term and long-term volatility using the standard deviation of returns over the respective periods. 3. **Standardization**: Normalize the momentum components by dividing them by the corresponding volatility values to account for risk. 4. **Scoring Mechanism**: Combine the normalized scores from the short, medium and long-term periods, applying different weights to each based on their importance (e.g., more weight for short-term momentum). 5. **Signal Calculation**: Amplify the combined score by a scaling factor and clip the final signal to a range between -1 and 1 to represent the direction and strength of the momentum. 6. **Handling Multiple Symbols**: The process is repeated for each pair, with the final signal being the equal-weighted average, offering a broader market view. Method generated by attrs for class RollingAverageMomentumFactor. ### Ancestors * `systematica.models.base.BaseStatArb` * `abc.ABC` ### Descendants * `systematica.api.models.momentum.RollingMomentumFactor` ### Instance variables * `long_term_weight: float`: Long term weight coefficient. Defaults to `0.2`. * `medium_term_mom: int`: Medium term momentum window. Defaults to `50`. * `medium_term_weight: float`: Medium term weight coefficient. Defaults to `0.3`. * `minp: int`: Mininim period. Defaults to `None`, which means no minimum period is applied. * `score_scale: float`: Scale final score. Defaults to `2.0`. This factor amplifies the final score, enhancing the signal strength. * `short_term_mom: int`: Short term momentum window. Defaults to `30`. * `short_term_vol: int`: Short term volatility window. Defaults to `30`. * `short_term_weight: float`: Short term weight coeff. Defaults to `0.5`. * `window: int`: The size of the rolling window. Defaults to `365`. ## `RollingMomentumFactor` ```python theme={null} RollingMomentumFactor( window: int = 365, minp: int = None, short_term_mom: int = 30, medium_term_mom: int = 50, short_term_vol: int = 30, short_term_weight: float = 0.5, medium_term_weight: float = 0.3, long_term_weight: float = 0.2, score_scale: float = 2.0, ) ``` Rolling momentum variation factor for calculating and scoring momentum on mulitple symbols. See `RollingAverageMomentumFactor`. Method generated by attrs for class RollingMomentumFactor. ### Ancestors * `systematica.api.models.momentum.RollingAverageMomentumFactor` * `systematica.models.base.BaseStatArb` * `abc.ABC` ## `MomentumFactorCV` ```python theme={null} MomentumFactorCV( training_window: int = 365, testing_window: int = 60, splitter: str = 'from_custom_rolling', custom_splitter: str | None = None, custom_splitter_kwargs: dict | None = None, short_term_mom: int = 30, medium_term_mom: int = 50, short_term_vol: int = 30, short_term_weight: float = 0.5, medium_term_weight: float = 0.3, long_term_weight: float = 0.2, score_scale: float = 2.0, ) ``` Momentum cross-validation variation factor for calculating and scoring momentum between two symbols. More information, see `RollingMomentumAverageFactor`. Method generated by attrs for class MomentumFactorCV. ### Ancestors * `systematica.api.models.momentum.AverageMomentumFactorCV` * `systematica.models.base.BaseStatArb` * `abc.ABC` ## `AverageMomentumFactorCV` ```python theme={null} AverageMomentumFactorCV( training_window: int = 365, testing_window: int = 60, splitter: str = 'from_custom_rolling', custom_splitter: str | None = None, custom_splitter_kwargs: dict | None = None, short_term_mom: int = 30, medium_term_mom: int = 50, short_term_vol: int = 30, short_term_weight: float = 0.5, medium_term_weight: float = 0.3, long_term_weight: float = 0.2, score_scale: float = 2.0, ) ``` Momentum cross-validation variation factor for calculating and scoring momentum between two symbols. More information, see `RollingMomentumAverageFactor`. Method generated by attrs for class AverageMomentumFactorCV. ### Ancestors * `systematica.models.base.BaseStatArb` * `abc.ABC` ### Descendants * `systematica.api.models.momentum.MomentumFactorCV` ### Instance variables * `custom_splitter: str | None`: Custom splitter to use for data partitioning. Defaults to `None`. If set, it should be a string that matches a custom splitter function. * `custom_splitter_kwargs: dict | None`: Additional keyword arguments for the custom splitter. Defaults to `None`. If `custom_splitter` is set, this should contain any necessary parameters for the custom splitter function. * `long_term_weight: float`: Long term weight coeff. Defaults to `0.2`. This is the weight applied to the long-term momentum in the final score calculation. * `medium_term_mom: int`: Medium term momentum window. Defaults to `50`. This is the window size used for medium-term momentum calculations. * `medium_term_weight: float`: Medium term weight coeff. Defaults to `0.3`. This is the weight applied to the medium-term momentum in the final score calculation. * `score_scale: float`: Scale final score. Defaults to `2.0`. This factor amplifies the final score, enhancing the signal strength. * `short_term_mom: int`: Short term momentum window. Defaults to `30`. This is the window size used for short-term momentum calculations. * `short_term_vol: int`: Short term volatility window. Defaults to `30`. This is the window size used for short-term volatility calculations. * `short_term_weight: float`: Short term weight coeff. Defaults to `0.5`. This is the weight applied to the short-term momentum in the final score calculation. * `splitter: str`: Default splitter to be used if custom\_splitter is not passed. Choices are `from_rolling`, `from_custom_rolling`, `from_expanding`, `from_custom_expanding`. Defaults to `from_custom_rolling`. * `testing_window: int`: The size of the testing window. Defaults to `60`. This is the period used for testing the model after training on the training window. * `training_window: int`: The size of the training window. Defaults to `365`.