mlfinlab.features.kca

Module implements Kinetic Component Analysis (KCA) as presented by Marcos Lopez de Prado and Riccardo Rebonato in the following paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2422183

Module Contents

Classes

KCA	Kinetic Component Analysis (KCA), a state-space application that

class KCA(time_series: pandas.DataFrame, include_std: bool | None = False, sigma: int | None = 0, seed: int | None = 0.01)

Bases: mlfinlab.features.base_noise_reduction.NoiseReductionMethod

Kinetic Component Analysis (KCA), a state-space application that extracts a signal from a series of noisy measurements by applying a Kalman Filter on a Taylor expansion of a stochastic process.

KCA decomposes the noisy signal in terms of three hidden components, which can be intuitively associated with position, velocity and acceleration.

property seed: float

Returns the current seed value.

Returns:

(float) Current seed value.

property forecast_df: pandas.DataFrame

Returns DataFrame with forecast.

Returns:

(pd.DataFrame) DataFrame with component forecasts.

property dataframe: pandas.DataFrame

Returns dataframe with original time series and relevant signals generated by the noise reduction method.

Returns:

(pd.DataFrame) Pandas DataFrame with relevant signals.

property signal: numpy.array

Returns main signal generated from noise reduction method.

Returns:

(np.array) Main signal generated from noise reduction method.

__slots__ = ()

update_seed(new_seed: float)

Update seed value.

Parameters:

new_seed – (float) New seed value to set.

get_position_signal() → numpy.array

Returns KCA position component signal.

Returns:

(np.array) Position signal.

get_velocity_signal() → numpy.array

Returns KCA velocity component signal.

Returns:

(np.array) Velocity component signal.

get_acceleration_signal() → numpy.array

Returns KCA acceleration component signal.

Returns:

(np.array) Acceleration component signal.

generate_signal() → numpy.array

Generates signal (position) and other components (velocity and acceleration) by running core KCA fit method.

Returns:

(np.array) Main generated signal (position).

generate_next_point_forecast() → pandas.Series

Generate forecast for next point in signal (position).

Returns:

(pd.Series) Next point forecast row with all components.

generate_signal_forecast(num_to_forecast: float) → numpy.array

Generate forecast of main signal (position) and its other components.

Parameters:

num_to_forecast – (int) Number of points to forecast

Returns:

(np.array) Main signal (position) forecast.

generate_signal_with_forecast(num_to_forecast: int) → numpy.array

Generate forecast of signal (plus components) and return main signal (position) with forecast included.

Parameters:

num_to_forecast – (int) Number of points to forecast

Returns:

(np.array) Main signal (position) with forecast included.