Kalmanfilter

Library "Kalmanfilter"
A sophisticated Kalman Filter implementation for financial time series analysis
Author Rocky-Studio
version 1.0

initialize(initial_value, process_noise, measurement_noise)
  Initializes Kalman Filter parameters
  Parameters:
    initial_value (float): (float) The initial state estimate
    process_noise (float): (float) The process noise coefficient (Q)
    measurement_noise (float): (float) The measurement noise coefficient (R)
  Returns: [float, float] A tuple containing [initial_state, initial_covariance]

update(prev_state, prev_covariance, measurement, process_noise, measurement_noise)
  Update Kalman Filter state
  Parameters:
    prev_state (float)
    prev_covariance (float)
    measurement (float)
    process_noise (float)
    measurement_noise (float)

calculate_measurement_noise(price_series, length)
  Adaptive measurement noise calculation
  Parameters:
    price_series (array<float>)
    length (int)

calculate_measurement_noise_simple(price_series)
  Parameters:
    price_series (array<float>)

update_trading(prev_state, prev_velocity, prev_covariance, measurement, volatility_window)
  Enhanced trading update with velocity
  Parameters:
    prev_state (float)
    prev_velocity (float)
    prev_covariance (float)
    measurement (float)
    volatility_window (int)

model4_update(prev_mean, prev_speed, prev_covariance, price, process_noise, measurement_noise)
  Kalman Filter Model 4 implementation (Benhamou 2018)
  Parameters:
    prev_mean (float)
    prev_speed (float)
    prev_covariance (array<float>)
    price (float)
    process_noise (array<float>)
    measurement_noise (float)

model4_initialize(initial_price)
  Initialize Model 4 parameters
  Parameters:
    initial_price (float)

model4_default_process_noise()
  Create default process noise matrix for Model 4

model4_calculate_measurement_noise(price_series, length)
  Adaptive measurement noise calculation for Model 4
  Parameters:
    price_series (array<float>)
    length (int)