CyberCausalityLib

# CyberCausalityLib v17

CyberCausalityLib provides information-theoretic and econometric causality detection for identifying directional influence between price series, filtering spurious correlations, and detecting lead-lag relationships across assets.

## What it does

Delivers 15+ causality functions in three categories: Transfer Entropy (information-theoretic directional causality), Granger Causality (econometric lagged correlation), and PCMCI filtering (partial correlation mediation for spurious causality detection). Answers questions like "Does Bitcoin volume predict Ethereum price?" and "Is Asset A→B correlation direct or mediated by Asset C?"

Outputs causality scores, directional indicators, and optimal lag values for multi-basket aggregation, lead-lag pair trading, and filtering false correlations driven by common factors.

## How it works

Transfer Entropy measures directional information flow: `TE(X→Y) = H(Y_t | Y_{t-1}) - H(Y_t | Y_{t-1}, X_{t-lag})` using Shannon entropy. Discretizes price data into 4-8 bins, calculates joint/conditional entropies. Higher TE = X predicts future Y beyond Y's own history.

Granger Causality aggregates lagged correlations: `Granger = Σ(w_i × corr(Y_t, X_{t-i}))` with weights `w_i = |corr_i| / Σ|corr_j|`. Supports Pearson/Spearman/Kendall. Returns magnitude + directional coefficient.

PCMCI filtering detects spurious causality via partial correlation: `ρ(X,Y|Z)`. If partial << raw correlation, Z mediates X→Y (indirect causality). Reduces score proportionally.

## Why this is original

First TradingView library with information-theoretic causality. Pine has no native entropy, Granger tests, or partial correlation. Existing libraries offer only basic correlation without directionality or lag optimization.

Unique features:
- Transfer Entropy with Shannon entropy discretization
- Granger Causality with auto-lag search (1-10)
- PCMCI filtering (up to 4 mediators)
- Ensemble aggregation (5 baskets)
- Möbius transformation state tracking

No other Pine library combines information theory, econometrics, and graph-based causality with NA guards and graceful degradation.

## How to use it

```pine
//version=6
indicator("CyberCausalityLib Demo", overlay=false)
import cybermediaboy/CyberCausalityLib/17 as C
import cybermediaboy/NumLib/4 as N

// Transfer Entropy: Does volume predict price?
var price_buf = array.new<float>()
var vol_buf = array.new<float>()

if bar_index >= 99
array.clear(price_buf)
array.clear(vol_buf)
for i = 0 to 99
array.push(price_buf, close[99-i])
array.push(vol_buf, volume[99-i])

[te_score, te_dir] = C.f_calculate_te_score_v2(
price_buf, vol_buf, 100, 5, 2
)
// te_score > 0.15 → volume predicts price
plot(te_score, "TE Score", color.blue)

// Granger Causality: Lead-lag detection
var x_buf = array.new<float>()
var y_buf = array.new<float>()
// ...populate buffers...
[granger, beta, lag] = C.f_calculate_granger_score(
y_buf, x_buf, 100, 5, "Pearson"
)
// granger > 0.3 → X Granger-causes Y

// PCMCI Filtering: Remove spurious correlation
[raw, _, _] = C.f_calculate_granger_score(y_buf, x_buf, 100, 5, "Pearson")
filtered = C.f_pcmci_filter_score(
raw, y_buf, x_buf, mediator_buf,
array.new<float>(), array.new<float>(), array.new<float>(), 100
)
// filtered << raw → indirect causality
```

## Key functions

- `f_calculate_te_score_v2()` - Transfer Entropy (recommended)
- `f_calculate_granger_score()` - Granger Causality with lag search
- `f_pcmci_filter_score()` - Spurious correlation filtering
- `f_compute_te_ensemble()` - Multi-basket aggregation
- `f_shannon_entropy()` - Shannon entropy calculation
- `MobiusState` UDT - Non-linear state tracking

## Dependencies

Requires NumLib v4 for correlation functions (Pearson/Spearman/Kendall).

```pine
import cybermediaboy/CyberCausalityLib/17 as C
import cybermediaboy/NumLib/4 as N
```

## Version history

- **v17** (2026-04-17): Added HAR-RV forecast, Durbin-Watson autocorrelation test, PCA explained variance, improved entropy discretization (v2 algorithm)
- **v16** (2026-03-xx): Added PCMCI filtering, Möbius state tracking UDT
- **v15** (2026-02-xx): Added Granger causality, multi-lag search, correlation at lag
- **v14** (2026-01-xx): Initial release with Transfer Entropy, ensemble aggregation

## License

Mozilla Public License 2.0

## Author

© cybermediaboy

## Support

For questions, bug reports, or feature requests, comment on the library publication page or reference the source code documentation.

Sürüm Notları

v2

Added:
f_lcg_next(state)
Parameters:
state (int)

f_block_shuffle(x, block_len, seed)
Parameters:
x (array<float>)
block_len (int)
seed (int)

f_calculate_ete(primary_arr, ticker_arr, window, bins, lag, n_shuf, block_len, seed_base)
Parameters:
primary_arr (array<float>)
ticker_arr (array<float>)
window (int)
bins (int)
lag (int)
n_shuf (int)
block_len (int)
seed_base (int)

f_calculate_ete_multilag(primary_arr, ticker_arr, window, bins, max_lag, n_shuf, block_len, z_threshold, seed_base)
Parameters:
primary_arr (array<float>)
ticker_arr (array<float>)
window (int)
bins (int)
max_lag (int)
n_shuf (int)
block_len (int)
z_threshold (float)
seed_base (int)

f_calculate_ete_gated(primary_arr, ticker_arr, window, bins, max_lag, coupling_damping, coupling_floor, n_shuf, block_len, z_threshold, seed_base)
Parameters:
primary_arr (array<float>)
ticker_arr (array<float>)
window (int)
bins (int)
max_lag (int)
coupling_damping (float)
coupling_floor (float)
n_shuf (int)
block_len (int)
z_threshold (float)
seed_base (int)

f_calculate_net_ete(primary_arr, ticker_arr, window, bins, lag, n_shuf, block_len, seed_base)
Parameters:
primary_arr (array<float>)
ticker_arr (array<float>)
window (int)
bins (int)
lag (int)
n_shuf (int)
block_len (int)
seed_base (int)

f_ete_state_new()

f_ete_state_decay(s, decay_per_bar)
Parameters:
s (EteState)
decay_per_bar (float)

f_ete_state_update(ete_new, z_new, dir_new, lag_new)
Parameters:
ete_new (float)
z_new (float)
dir_new (float)
lag_new (int)

f_ete_regime(z, z_marginal, z_strong)
Parameters:
z (float)
z_marginal (float)
z_strong (float)

EteResult
Fields:
ete (series float): Effective TE = TE_obs - <TE_surrogate> (bias-corrected)
z (series float): Bootstrap z-score = (TE_obs - mean_surr) / std_surr
dir (series float): Sign from Pearson at best lag (-1 / +1)
lag (series int): Argmax-|ETE| lag selected
te_raw (series float): Pre-correction plug-in TE (for diagnostics)
surr_mu (series float): Surrogate mean (bias estimate)
surr_sd (series float): Surrogate std (significance scale)
signif (series bool): True if |z| >= z_threshold

EteState
Fields:
ete_held (series float)
z_held (series float)
dir_held (series float)
lag_held (series int)
bars_since_recomp (series int)

Sürüm Notları

v7

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

Sürüm Notları

v8

Added:
f_rate_return(price, price_prev, invert)
Convert SOFR/rate futures price to implied rate return
Parameters:
price (float): Current close price of rate futures (e.g., SR3M = 96.38)
price_prev (float): Previous bar close
invert (bool): If true, rate = 100 - price (SOFR convention). If false, pass through.
Returns: Rate return in bps (first difference of implied rate)

f_bar_is_live(bar_range, abs_ret, range_ma, ret_ma, range_pct, ret_pct)
Classify bar as live (active trading) or stale (flat/illiquid)
Parameters:
bar_range (float): Current bar high - low
abs_ret (float): Absolute return |close - close[1]|
range_ma (float): EMA of bar_range (caller provides; typically ema(range, 20))
ret_ma (float): EMA of abs_ret (caller provides; typically ema(abs_ret, 20))
range_pct (float): Minimum fraction of range_ma to qualify as live (default 0.05)
ret_pct (float): Minimum fraction of ret_ma to qualify as live (default 0.10)
Returns: true if bar is actively traded, false if stale

f_carry_forward(src, is_live)
Carry-forward filter: returns src on live bars, last valid value on stale bars
Parameters:
src (float): Input series value
is_live (bool): Activity flag from f_bar_is_live
Returns: src if live, last valid src otherwise (preserves time index for TE lag alignment)

f_adaptive_wilder(src, alpha_fast, alpha_slow, is_live)
Adaptive Wilder smoother: updates fast on live bars, slow on stale bars
Parameters:
src (float): Input value
alpha_fast (float): Alpha for live bars (e.g., 2/(fast_win+1))
alpha_slow (float): Alpha for stale bars (e.g., 2/(slow_win*4+1)); near-zero = freeze
is_live (bool): Activity flag from f_bar_is_live
Returns: Smoothed value that ignores dead-zone contamination

f_variance_ok(src_var, var_floor)
Check if a series has enough variance for meaningful TE/Granger computation
Parameters:
src_var (float): Rolling variance of the input series (caller provides: ta.variance(src, win))
var_floor (float): Minimum variance to allow TE update (default 1e-8)
Returns: true if variance is sufficient for TE/Granger computation

f_gated_correlation(x, y, win, src_var, var_floor)
Variance-gated correlation: computes ta.correlation only when source has enough variance.
On insufficient variance, returns last valid correlation (carry-forward).
Parameters:
x (float): Series X (e.g., F1[lag_vix])
y (float): Series Y (e.g., btc_ret)
win (int): Correlation window length
src_var (float): Rolling variance of x (caller provides: ta.variance(x, win))
var_floor (float): Minimum variance threshold (default 1e-8)
Returns: Correlation coefficient with carry-forward on low-variance bars

f_live_push(buf, val, is_live, max_size)
Push value to TE array only on live bars (stale bars skipped entirely).
Preserves distribution quality for TE joint histogram — stale bars don't
contribute zero-change entries that pull distribution toward diagonal.
Parameters:
buf (array<float>): Target array for TE computation
val (float): Value to push (typically z-score or rate return)
is_live (bool): Activity flag from f_bar_is_live
max_size (int): Maximum buffer size (trim oldest when exceeded)
Returns: Current buffer size after operation

f_calculate_te_score_robust(primary_arr, ticker_arr, live_mask, window, bins, lag, min_live_pct)
TE score with dead-bar exclusion. Filters ticker_arr to include
only bars marked live (parallel boolean mask). Reduces effective n but
prevents zero-variance contamination of the 3D contingency table.
Parameters:
primary_arr (array<float>): Target series (e.g., BTC returns)
ticker_arr (array<float>): Source series (e.g., factor z-scores)
live_mask (array<float>): Parallel array: 1.0 if live, 0.0 if stale (same length as ticker_arr)
window (int): TE window size
bins (int): Number of discretization bins
lag (int): Source series lag
min_live_pct (float): Minimum fraction of live bars in window required (0.3 = 30%)
Returns: [te_score, direction] — 0.0 if insufficient live bars

f_calculate_te_gated_robust(primary_arr, ticker_arr, live_mask, window, bins, max_lag, coupling_damping, coupling_floor, min_live_pct)
Robust gated TE multilag with dead-bar filtering.
Drop-in replacement for f_calculate_te_gated that adds live_mask filtering.
Parameters:
primary_arr (array<float>): Target series (BTC returns)
ticker_arr (array<float>): Source series (factor z-scores)
live_mask (array<float>): Parallel activity mask (1.0=live, 0.0=stale)
window (int): TE window size
bins (int): Discretization bins
max_lag (int): Maximum lag to scan
coupling_damping (float): Coupling strength (volatility proxy)
coupling_floor (float): Minimum coupling to allow TE through
min_live_pct (float): Minimum live fraction required (default 0.3)
Returns: [gated_te, direction, best_lag]

f_symbolize(ret, threshold)
Symbolize a return value into 3 states: -1 (down), 0 (dead), +1 (up)
Parameters:
ret (float): Return or rate change value
threshold (float): Minimum magnitude to register as non-zero (dead-zone width)
Returns: -1, 0, or +1

f_effective_n(sym_arr)
Count effective (non-zero) symbols in a symbolized array.
Used to determine if TE has enough live data after dead-zone exclusion.
Parameters:
sym_arr (array<int>): Array of symbolized values (-1, 0, +1)
Returns: Count of non-zero entries (effective sample size for TE)

Sürüm Notları

v10

Pine kitaplığı

Gerçek TradingView ruhuyla, yazar bu Pine kodunu açık kaynaklı bir kütüphane olarak yayınladı, böylece topluluğumuzdaki diğer Pine programcıları onu yeniden kullanabilir. Yazarı tebrik ederiz! Bu kütüphaneyi özel olarak veya diğer açık kaynaklı yayınlarda kullanabilirsiniz, ancak bu kodun yayınlarda yeniden kullanılması Topluluk Kuralları tarafından yönetilir.

cybermediaboy

Feragatname

Bilgiler ve yayınlar, TradingView tarafından sağlanan veya onaylanan finansal, yatırım, alım satım veya diğer türden tavsiye veya öneriler anlamına gelmez ve teşkil etmez. Kullanım Koşulları bölümünde daha fazlasını okuyun.

Pine kitaplığı

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