Skip to main content
Volatility factors analyze price volatility patterns across multiple time horizons to generate trading signals based on volatility regime changes and trends.

​
Volatility Model Components

StageMain FunctionSubcomponents/Components
Dynamic Windowget_dynamic_window_nb()short_term, medium_term, long_term
Volatility Computationget_volatilities_nb()short_vol, medium_vol, long_vol
Score Componentsscores_nb()medium_term_impact, long_term_impact, trend_impact, direction
Final Scoringweighted_average_scores_nb()volatility_nb() (with tanh() amplification)

​
Volatility API Classes

ClassDescriptionKey Features
RollingVolatilityFactorRolling window volatility analysisDynamic window sizing, trend detection
VolatilityFactorCVCross-validated volatility analysisWalk-forward validation, parameter stability

​
RollingVolatilityFactor

RollingVolatilityFactor(
    window: int = 365,
    minp: int = None,
    vol_window: int = 30,
    min_periods: int = 10,
    medium_term_weight: float = 0.5,
    long_term_weight: float = 0.2,
    trend_weight: float = 0.3,
    signal_impact_multiplier: float = 5.0,
)
Rolling Volatility variation factor.
  • Mutliple Dynamic Window: Model is determines through three different time horizons — short, medium and long-term horizons.
  • Volatility Insigths: The standard deviations (volatilities) over the defined dynamic windows.
  • Scoring Mechanism: Relative scores between periods, trend and direction are encapsulated.
  • Signal Calculation: Aggregates signals and applies a hyperbolic tangent (tanh)
Workflow:
  1. Calculate Returns: compute log returns from closing prices.
  2. Compute Volatility on Different Time Horizons: Derive signals through weighted average standard deviation calculated on multiple periods
  3. Aggregate Signals: Combine the individual signals using their mean and compute the hyperbolic tangent element-wise.
  4. Handling Multiple Symbols: The process is repeated for each pair, offering a broader market view.
Method generated by attrs for class RollingVolatilityFactor.

​
Ancestors

  • systematica.models.base.BaseStatArb
  • abc.ABC

​
Instance variables

  • long_term_weight: float: Long term weight coeff. Defaults to 0.2.
  • medium_term_weight: float: Medium term weight coeff. Defaults to 0.5.
  • min_periods: int: Mininim period for calculating volatility. Defaults to 10.
  • minp: int: Mininim period. Defaults to None, which means no minimum period is applied.
  • signal_impact_multiplier: float: Signal impact multiplier. Defaults to 5.0.
  • trend_weight: float: Trend weights. Defaults to 0.3.
  • vol_window: int: Volatility window size. Defaults to 30.
  • window: int: The size of the rolling window. Defaults to 365.

​
VolatilityFactorCV

VolatilityFactorCV(
    training_window: int = 365,
    testing_window: int = 60,
    splitter: str = 'from_custom_rolling',
    custom_splitter: str | None = None,
    custom_splitter_kwargs: dict | None = None,
    vol_window: int = 30,
    min_periods: int = 10,
    medium_term_weight: float = 0.5,
    long_term_weight: float = 0.2,
    trend_weight: float = 0.3,
    signal_impact_multiplier: float = 5.0,
)
Volatility variation factor cross validation.
  • Mutliple Dynamic Window: Model is determines through three different time horizons — short, medium and long-term horizons.
  • Volatility Insigths: The standard deviations (volatilities) over the defined dynamic windows.
  • Scoring Mechanism: Relative scores between periods, trend and direction are encapsulated.
  • Signal Calculation: Aggregates signals and applies a hyperbolic tangent (tanh)
Workflow:
  1. Calculate Returns: compute log returns from closing prices.
  2. Compute Volatility on Different Time Horizons: Derive signals through weighted average standard deviation calculated on multiple periods
  3. Aggregate Signals: Combine the individual signals using their mean and compute the hyperbolic tangent element-wise.
  4. Handling Multiple Symbols: The process is repeated for each pair, offering a broader market view.
Method generated by attrs for class VolatilityFactorCV.

​
Ancestors

  • systematica.models.base.BaseStatArb
  • abc.ABC

​
Instance variables

  • custom_splitter: str | None: Custom splitter to use for data partitioning. Defaults to None, which means no custom splitter is used.
  • custom_splitter_kwargs: dict | None: Additional keyword arguments for the custom splitter. Defaults to None, which means no additional arguments are passed.
  • long_term_weight: float: Long term weight coefficient. Defaults to 0.2. This is the weight applied to the long-term volatility in the final score calculation.
  • medium_term_weight: float: Medium term weight coefficient. Defaults to 0.5. This is the weight applied to the medium-term volatility in the final score calculation.
  • min_periods: int: Mininim period for calculating volatility. Defaults to 10. This is the minimum number of periods required to calculate volatility.
  • signal_impact_multiplier: float: Signal impact multiplier. Defaults to 5.0. This factor amplifies the impact of the signals 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.
  • training_window: int: The size of the training window. Defaults to 365.
  • trend_weight: float: Trend weights. Defaults to 0.3. This is the weight applied to the trend in the final score calculation.
  • vol_window: int: Volatility window size. Defaults to 30. This is the window size used for calculating volatility.