CausalityLib - granger casuality and transfer entropy helpers

Library "CausalityLib"
Causality Analysis Library - Transfer Entropy, Granger Causality, and Causality Filtering

f_shannon_entropy(data, num_bins)
Calculate Shannon entropy of data distribution
Parameters:
data (array<float>): Array of continuous values
num_bins (int): Number of bins for discretization
Returns: Entropy value (higher = more randomness)

f_calculate_te_score(primary_arr, ticker_arr, window, bins, lag)
Calculate Transfer Entropy from source to target
Parameters:
primary_arr (array<float>): Target series (e.g., primary ticker returns)
ticker_arr (array<float>): Source series (e.g., basket ticker returns)
window (int): Window size for TE calculation
bins (int): Number of bins for discretization
lag (int): Lag for source series
Returns: [te_score, direction] - TE score and direction (-1 or 1)

f_correlation_at_lag(primary_arr, ticker_arr, lag, window, correlation_method)
Calculate Pearson correlation at specific lag
Parameters:
primary_arr (array<float>): Primary series
ticker_arr (array<float>): Ticker series
lag (int): Lag value (positive = ticker lags primary)
window (int): Window size for correlation
correlation_method (string): Correlation method to use ("Pearson", "Spearman", "Kendall")
Returns: Correlation coefficient [-1, 1]

f_calculate_granger_score(primary_arr, ticker_arr, window, max_lag, correlation_method)
Calculate Granger causality score with lag testing
Parameters:
primary_arr (array<float>): Primary series
ticker_arr (array<float>): Ticker series
window (int): Window size for correlation
max_lag (int): Maximum lag to test
correlation_method (string): Correlation method to use
Returns: [granger_score, beta_sum] - Granger score and directional beta

f_partial_correlation(x_arr, y_arr, z_arr, window)
Calculate partial correlation between X and Y controlling for Z
Parameters:
x_arr (array<float>): First series
y_arr (array<float>): Second series
z_arr (array<float>): Mediator series
window (int): Window size for correlation
Returns: Partial correlation coefficient [-1, 1]

f_pcmci_filter_score(raw_score, primary_arr, ticker_arr, mediator1, mediator2, mediator3, mediator4, window)
PCMCI Filter: Adjust Granger score by checking for mediating tickers
Parameters:
raw_score (float): Original Granger score
primary_arr (array<float>): Primary series
ticker_arr (array<float>): Ticker series
mediator1 (array<float>): First potential mediator series
mediator2 (array<float>): Second potential mediator series
mediator3 (array<float>): Third potential mediator series
mediator4 (array<float>): Fourth potential mediator series
window (int): Window size for correlation
Returns: Filtered score (reduced if causality is indirect/spurious)

Catatan Rilis

v2

Updated:
f_calculate_granger_score(primary_arr, ticker_arr, window, max_lag, correlation_method)
Calculate Granger causality score with lag testing
Parameters:
primary_arr (array<float>): Primary series
ticker_arr (array<float>): Ticker series
window (int): Window size for correlation
max_lag (int): Maximum lag to test
correlation_method (string): Correlation method to use
Returns: [granger_score, beta_sum, best_lag] - Granger score, directional beta, and best lag (highest |correlation|)

Catatan Rilis

v4

Added:
f_discretize(data, bins)
Discretize continuous data into bins (for entropy calculations)
Parameters:
data (array<float>): Array of continuous values
bins (int): Number of bins
Returns: Array of bin indices

Catatan Rilis

v5

Added:
f_shannon_entropy_v2(data, num_bins)
Calculate Shannon entropy of data distribution using explicit discretize->probability pipeline
Parameters:
data (array<float>): Array of continuous values
num_bins (int): Number of bins for discretization
Returns: Entropy value (higher = more randomness)

f_calculate_te_score_v2(primary_arr, ticker_arr, window, bins, lag)
Calculate Transfer Entropy from source to target using normalized TE formulation
Parameters:
primary_arr (array<float>): Target series (e.g., primary ticker returns)
ticker_arr (array<float>): Source series (e.g., basket ticker returns)
window (int): Window size for TE calculation
bins (int): Number of bins for discretization
lag (int): Lag for source series
Returns: [te_score, direction] - normalized TE score and direction (-1 or 1)

Catatan Rilis

v6

Added:
f_calculate_te_multilag(primary_arr, ticker_arr, window, bins, max_lag)
Parameters:
primary_arr (array<float>)
ticker_arr (array<float>)
window (int)
bins (int)
max_lag (int)

f_calculate_te_gated(primary_arr, ticker_arr, window, bins, max_lag, coupling_damping, coupling_floor)
Parameters:
primary_arr (array<float>)
ticker_arr (array<float>)
window (int)
bins (int)
max_lag (int)
coupling_damping (float)
coupling_floor (float)

f_calculate_net_te(primary_arr, ticker_arr, window, bins, lag)
Parameters:
primary_arr (array<float>)
ticker_arr (array<float>)
window (int)
bins (int)
lag (int)

f_kalman_hedge_ratio(measurement, observation, prev_beta, prev_P, Q, R)
Parameters:
measurement (float)
observation (float)
prev_beta (float)
prev_P (float)
Q (float)
R (float)

Catatan Rilis

v7

Added:
f_compute_te_ensemble(pri_arr, b1, b2, b3, b4, b5, window, bins, smoothing, self1, self2, self3, self4, self5, use5)
Parameters:
pri_arr (array<float>)
b1 (array<float>)
b2 (array<float>)
b3 (array<float>)
b4 (array<float>)
b5 (array<float>)
window (int)
bins (int)
smoothing (simple int)
self1 (bool)
self2 (bool)
self3 (bool)
self4 (bool)
self5 (bool)
use5 (bool)

Catatan Rilis

v8

Updated:
f_compute_te_ensemble(pri_arr, b1, b2, b3, b4, b5, window, bins, smoothing, self1, self2, self3, self4, self5, use5)
Parameters:
pri_arr (array<float>)
b1 (array<float>)
b2 (array<float>)
b3 (array<float>)
b4 (array<float>)
b5 (array<float>)
window (int)
bins (int)
smoothing (int)
self1 (bool)
self2 (bool)
self3 (bool)
self4 (bool)
self5 (bool)
use5 (bool)

Catatan Rilis

v9

Added:
f_calculate_te_score_debug(primary_arr, ticker_arr, window, bins, lag)
Calculate Transfer Entropy with diagnostics for debugging data sufficiency and bin occupancy
Parameters:
primary_arr (array<float>): Target series (e.g., primary ticker returns)
ticker_arr (array<float>): Source series (e.g., basket ticker returns)
window (int): Window size for TE calculation
bins (int): Number of bins for discretization
lag (int): Lag for source series
Returns: [te_score, direction, win_size, valid_count, min_x, max_x, min_y, max_y, b0_share, b1_share, b2_share, b3_share, b4_share, b5_share, b6_share, b7_share]

Catatan Rilis

v10

Catatan Rilis

v11

Catatan Rilis

v12

Catatan Rilis

v13

Updated:
f_compute_te_ensemble(pri_arr, b1, b2, b3, b4, b5, window, bins, smoothing, self1, self2, self3, self4, self5, use5)
Parameters:
pri_arr (array<float>)
b1 (array<float>)
b2 (array<float>)
b3 (array<float>)
b4 (array<float>)
b5 (array<float>)
window (int)
bins (int)
smoothing (simple int)
self1 (bool)
self2 (bool)
self3 (bool)
self4 (bool)
self5 (bool)
use5 (bool)

Catatan Rilis

v14

Added:
f_compute_te_wrapper(te_primary, te_b1, te_b2, te_b3, te_b4, te_win, te_b, te_smooth, self1, self2, self3, self4, is_hard, bar_idx)
Wrapper for Transfer Entropy calculation with ensemble and fallback logic
Parameters:
te_primary (array<float>): Primary asset return array
te_b1 (array<float>): Basket asset 1 return array
te_b2 (array<float>): Basket asset 2 return array
te_b3 (array<float>): Basket asset 3 return array
te_b4 (array<float>): Basket asset 4 return array
te_win (int): TE window size
te_b (int): TE bins count
te_smooth (simple int): TE smoothing length
self1 (bool): Whether basket 1 is self-reference
self2 (bool): Whether basket 2 is self-reference
self3 (bool): Whether basket 3 is self-reference
self4 (bool): Whether basket 4 is self-reference
is_hard (bool): Whether in hard zone (recent bars)
bar_idx (int): Current bar index
Returns: [te_osc, te_strength, te_direction] - TE oscillator, strength, and direction

Catatan Rilis

v15

Catatan Rilis

v16

Added:
fmobiusnew()

fmobiusupdate(st, cur, prev, alpha)
Parameters:
st (MobiusState)
cur (float)
prev (float)
alpha (float)

fmobiuscompose(s1, s2)
Parameters:
s1 (MobiusState)
s2 (MobiusState)

fmobiusapply(st, x)
Parameters:
st (MobiusState)
x (float)

fcausalcovupdate(sigma_tri, state, mu, net_te, te_threshold, alpha)
Parameters:
sigma_tri (array<float>)
state (array<float>)
mu (array<float>)
net_te (float)
te_threshold (float)
alpha (float)

fcausalweights(te0, te1, te2, alpha)
Parameters:
te0 (float)
te1 (float)
te2 (float)
alpha (float)

MobiusState
Fields:
a (series float)
b (series float)
c (series float)

Catatan Rilis

v17

Added:
fharrvforecast(rv_buffer, beta_d, beta_w, beta_m)
Parameters:
rv_buffer (array<float>)
beta_d (float)
beta_w (float)
beta_m (float)

fdurbinwatson(window)
Parameters:
window (array<float>)

fpcaexplained(cov_tri, shrinkage)
Parameters:
cov_tri (array<float>)
shrinkage (float)

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