PINE LIBRARY
Zaktualizowano kNNLib with turboQuant encoding
Library "kNNLib"
f_quantize_3bit(value, bounds)
Quantize a single feature to 3-bit index (0-7) using percentile boundaries
Parameters:
value (float): Feature value to quantize
bounds (array<float>): Array of 9 percentile boundaries [P0, P12.5, P25, P37.5, P50, P62.5, P75, P87.5, P100]
Returns: Integer index 0-7
f_compute_percentile_bounds(feature_array, history_len)
Compute percentile boundaries for a feature over a rolling window
Parameters:
feature_array (array<float>): Array of feature values (size = history_len)
history_len (int): Number of bars to use for percentile calculation
Returns: Array of 9 boundaries [P0, P12.5, P25, P37.5, P50, P62.5, P75, P87.5, P100]
f_turboquant_encode(f1, f2, f3, f4, f5, f6)
Encode 6 features into 18-bit TurboQuant state ID
Parameters:
f1 (int): Feature 1 (VectorOsc) quantized index 0-7
f2 (int): Feature 2 (BasketCorr) quantized index 0-7
f3 (int): Feature 3 (InnovZ) quantized index 0-7
f4 (int): Feature 4 (TE_Osc) quantized index 0-7
f5 (int): Feature 5 (OrthoZcvb) quantized index 0-7
f6 (int): Feature 6 (PhiDiv) quantized index 0-7
Returns: 18-bit state ID (0 to 262143)
f_normalize_basket_corr(rho, rho_history, norm_len)
Normalize BasketCorr using Fisher transform then z-score
Parameters:
rho (float): Raw correlation value [-1, +1]
rho_history (array<float>): Array of historical rho values
norm_len (int): Normalization window length
Returns: Normalized correlation
f_normalize_te_osc(te_osc, te_history, norm_len)
Normalize TE_Osc using rolling z-score
Parameters:
te_osc (float): Raw TE oscillator value
te_history (array<float>): Array of historical TE values
norm_len (int): Normalization window length
Returns: Normalized TE
f_normalize_phi_div(phi_div, phi_history, norm_len)
Normalize PhiDiv using rolling z-score
Parameters:
phi_div (float): Raw PhiDiv value
phi_history (array<float>): Array of historical PhiDiv values
norm_len (int): Normalization window length
Returns: Normalized PhiDiv
f_euclidean_distance(f1_current, f2_current, f3_current, f4_current, f5_current, f6_current, f1_hist, f2_hist, f3_hist, f4_hist, f5_hist, f6_hist)
Calculate Euclidean distance between two 6D feature vectors
Parameters:
f1_current (float): Current bar feature 1
f2_current (float): Current bar feature 2
f3_current (float): Current bar feature 3
f4_current (float): Current bar feature 4
f5_current (float): Current bar feature 5
f6_current (float): Current bar feature 6
f1_hist (float): Historical bar feature 1
f2_hist (float): Historical bar feature 2
f3_hist (float): Historical bar feature 3
f4_hist (float): Historical bar feature 4
f5_hist (float): Historical bar feature 5
f6_hist (float): Historical bar feature 6
Returns: Euclidean distance
f_find_k_nearest(f1_current, f2_current, f3_current, f4_current, f5_current, f6_current, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, k, max_history)
Find K nearest neighbors using linear scan
Parameters:
f1_current (float): Current bar feature 1
f2_current (float): Current bar feature 2
f3_current (float): Current bar feature 3
f4_current (float): Current bar feature 4
f5_current (float): Current bar feature 5
f6_current (float): Current bar feature 6
f1_history (array<float>): Array of historical feature 1 values
f2_history (array<float>): Array of historical feature 2 values
f3_history (array<float>): Array of historical feature 3 values
f4_history (array<float>): Array of historical feature 4 values
f5_history (array<float>): Array of historical feature 5 values
f6_history (array<float>): Array of historical feature 6 values
k (int): Number of neighbors to find
max_history (int): Maximum bars to search
Returns: Array of K nearest neighbor indices
f_calculate_confidence(f1_current, f2_current, f3_current, f4_current, f5_current, f6_current, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, k_nearest_indices)
Calculate epistemic confidence score from K-nearest distances
Parameters:
f1_current (float): Current bar feature 1
f2_current (float): Current bar feature 2
f3_current (float): Current bar feature 3
f4_current (float): Current bar feature 4
f5_current (float): Current bar feature 5
f6_current (float): Current bar feature 6
f1_history (array<float>): Array of historical feature 1 values
f2_history (array<float>): Array of historical feature 2 values
f3_history (array<float>): Array of historical feature 3 values
f4_history (array<float>): Array of historical feature 4 values
f5_history (array<float>): Array of historical feature 5 values
f6_history (array<float>): Array of historical feature 6 values
k_nearest_indices (array<int>): Array of K nearest neighbor indices
Returns: Confidence score [0, 1]
f_quantize_3bit(value, bounds)
Quantize a single feature to 3-bit index (0-7) using percentile boundaries
Parameters:
value (float): Feature value to quantize
bounds (array<float>): Array of 9 percentile boundaries [P0, P12.5, P25, P37.5, P50, P62.5, P75, P87.5, P100]
Returns: Integer index 0-7
f_compute_percentile_bounds(feature_array, history_len)
Compute percentile boundaries for a feature over a rolling window
Parameters:
feature_array (array<float>): Array of feature values (size = history_len)
history_len (int): Number of bars to use for percentile calculation
Returns: Array of 9 boundaries [P0, P12.5, P25, P37.5, P50, P62.5, P75, P87.5, P100]
f_turboquant_encode(f1, f2, f3, f4, f5, f6)
Encode 6 features into 18-bit TurboQuant state ID
Parameters:
f1 (int): Feature 1 (VectorOsc) quantized index 0-7
f2 (int): Feature 2 (BasketCorr) quantized index 0-7
f3 (int): Feature 3 (InnovZ) quantized index 0-7
f4 (int): Feature 4 (TE_Osc) quantized index 0-7
f5 (int): Feature 5 (OrthoZcvb) quantized index 0-7
f6 (int): Feature 6 (PhiDiv) quantized index 0-7
Returns: 18-bit state ID (0 to 262143)
f_normalize_basket_corr(rho, rho_history, norm_len)
Normalize BasketCorr using Fisher transform then z-score
Parameters:
rho (float): Raw correlation value [-1, +1]
rho_history (array<float>): Array of historical rho values
norm_len (int): Normalization window length
Returns: Normalized correlation
f_normalize_te_osc(te_osc, te_history, norm_len)
Normalize TE_Osc using rolling z-score
Parameters:
te_osc (float): Raw TE oscillator value
te_history (array<float>): Array of historical TE values
norm_len (int): Normalization window length
Returns: Normalized TE
f_normalize_phi_div(phi_div, phi_history, norm_len)
Normalize PhiDiv using rolling z-score
Parameters:
phi_div (float): Raw PhiDiv value
phi_history (array<float>): Array of historical PhiDiv values
norm_len (int): Normalization window length
Returns: Normalized PhiDiv
f_euclidean_distance(f1_current, f2_current, f3_current, f4_current, f5_current, f6_current, f1_hist, f2_hist, f3_hist, f4_hist, f5_hist, f6_hist)
Calculate Euclidean distance between two 6D feature vectors
Parameters:
f1_current (float): Current bar feature 1
f2_current (float): Current bar feature 2
f3_current (float): Current bar feature 3
f4_current (float): Current bar feature 4
f5_current (float): Current bar feature 5
f6_current (float): Current bar feature 6
f1_hist (float): Historical bar feature 1
f2_hist (float): Historical bar feature 2
f3_hist (float): Historical bar feature 3
f4_hist (float): Historical bar feature 4
f5_hist (float): Historical bar feature 5
f6_hist (float): Historical bar feature 6
Returns: Euclidean distance
f_find_k_nearest(f1_current, f2_current, f3_current, f4_current, f5_current, f6_current, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, k, max_history)
Find K nearest neighbors using linear scan
Parameters:
f1_current (float): Current bar feature 1
f2_current (float): Current bar feature 2
f3_current (float): Current bar feature 3
f4_current (float): Current bar feature 4
f5_current (float): Current bar feature 5
f6_current (float): Current bar feature 6
f1_history (array<float>): Array of historical feature 1 values
f2_history (array<float>): Array of historical feature 2 values
f3_history (array<float>): Array of historical feature 3 values
f4_history (array<float>): Array of historical feature 4 values
f5_history (array<float>): Array of historical feature 5 values
f6_history (array<float>): Array of historical feature 6 values
k (int): Number of neighbors to find
max_history (int): Maximum bars to search
Returns: Array of K nearest neighbor indices
f_calculate_confidence(f1_current, f2_current, f3_current, f4_current, f5_current, f6_current, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, k_nearest_indices)
Calculate epistemic confidence score from K-nearest distances
Parameters:
f1_current (float): Current bar feature 1
f2_current (float): Current bar feature 2
f3_current (float): Current bar feature 3
f4_current (float): Current bar feature 4
f5_current (float): Current bar feature 5
f6_current (float): Current bar feature 6
f1_history (array<float>): Array of historical feature 1 values
f2_history (array<float>): Array of historical feature 2 values
f3_history (array<float>): Array of historical feature 3 values
f4_history (array<float>): Array of historical feature 4 values
f5_history (array<float>): Array of historical feature 5 values
f6_history (array<float>): Array of historical feature 6 values
k_nearest_indices (array<int>): Array of K nearest neighbor indices
Returns: Confidence score [0, 1]
Informacje o Wersji
v2Informacje o Wersji
v3Added:
f_turboquant(val)
Quantize a single feature to 3-bit index + 1-bit residual (4 bits total)
Parameters:
val (float): Feature value (should be clamped to [-3, +3] range)
Returns: [int, int] Tuple of (3-bit index, 1-bit residual)
f_pack_vector(i0, r0, i1, r1, i2, r2, i3, r3, i4, r4, i5, r5)
Pack 6 features into single 24-bit integer
Parameters:
i0 (int)
r0 (int)
i1 (int)
r1 (int)
i2 (int)
r2 (int)
i3 (int)
r3 (int)
i4 (int)
r4 (int)
i5 (int)
r5 (int)
Returns: int Packed 24-bit vector
f_unpack_centroid(packed, feat_idx)
Unpack and reconstruct centroid value with half-step refinement
Parameters:
packed (int): 24-bit packed vector
feat_idx (int): Feature index (0-5)
Returns: float Reconstructed feature value
f_hamming_screen(packed_cur, packed_hist)
Hamming screen: count features with >1 level difference
Parameters:
packed_cur (int): Current packed vector
packed_hist (int): Historical packed vector
Returns: int Number of features with significant difference
Updated:
f_normalize_te_osc(te_osc, te_history, norm_len)
Normalize TE_Osc using rolling z-score
Parameters:
te_osc (float): Raw TE oscillator value
te_history (array<float>): Array of historical TE values
norm_len (int): Normalization window length
Returns: Normalized TE oscillator
f_normalize_phi_div(phi_div, phi_history, norm_len)
Normalize PhiDiv using rolling z-score
Parameters:
phi_div (float): Raw PhiDiv value (PhiLatch - PhiTotal_orth)
phi_history (array<float>): Array of historical PhiDiv values
norm_len (int): Normalization window length
Returns: Normalized PhiDiv
f_euclidean_distance(f1_cur, f2_cur, f3_cur, f4_cur, f5_cur, f6_cur, f1_hist, f2_hist, f3_hist, f4_hist, f5_hist, f6_hist)
Calculate Euclidean distance between current state and historical state
Parameters:
f1_cur (float)
f2_cur (float)
f3_cur (float)
f4_cur (float)
f5_cur (float)
f6_cur (float)
f1_hist (float)
f2_hist (float)
f3_hist (float)
f4_hist (float)
f5_hist (float)
f6_hist (float)
Returns: float Euclidean distance
f_find_k_nearest(f1_cur, f2_cur, f3_cur, f4_cur, f5_cur, f6_cur, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, k, max_history)
Find K nearest neighbors using Hamming screening + Euclidean distance
Parameters:
f1_cur (float)
f2_cur (float)
f3_cur (float)
f4_cur (float)
f5_cur (float)
f6_cur (float)
f1_history (array<float>)
f2_history (array<float>)
f3_history (array<float>)
f4_history (array<float>)
f5_history (array<float>)
f6_history (array<float>)
k (int): Number of neighbors to retrieve
max_history (int): Maximum history length to search
Returns: array<int> Array of K nearest neighbor indices
f_calculate_confidence(f1_cur, f2_cur, f3_cur, f4_cur, f5_cur, f6_cur, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, k_nearest)
Calculate epistemic confidence from K-nearest distance distribution
Parameters:
f1_cur (float)
f2_cur (float)
f3_cur (float)
f4_cur (float)
f5_cur (float)
f6_cur (float)
f1_history (array<float>)
f2_history (array<float>)
f3_history (array<float>)
f4_history (array<float>)
f5_history (array<float>)
f6_history (array<float>)
k_nearest (array<int>): Array of K nearest neighbor indices
Returns: float Confidence score [0, 1]
Removed:
f_quantize_3bit(value, bounds)
Quantize a single feature to 3-bit index (0-7) using percentile boundaries
f_compute_percentile_bounds(feature_array, history_len)
Compute percentile boundaries for a feature over a rolling window
f_turboquant_encode(f1, f2, f3, f4, f5, f6)
Encode 6 features into 18-bit TurboQuant state ID
Informacje o Wersji
v4Added:
f_pack_vector_7(i0, r0, i1, r1, i2, r2, i3, r3, i4, r4, i5, r5, i6, r6)
Pack 7 features into single 28-bit integer
Parameters:
i0 (int)
r0 (int)
i1 (int)
r1 (int)
i2 (int)
r2 (int)
i3 (int)
r3 (int)
i4 (int)
r4 (int)
i5 (int)
r5 (int)
i6 (int)
r6 (int)
Returns: int Packed 28-bit vector
f_hamming_screen_7(packed_cur, packed_hist)
Hamming screen for 7-feature packed vectors
Parameters:
packed_cur (int): Current packed vector
packed_hist (int): Historical packed vector
Returns: int Number of features with significant difference
f_normalize_burst(burst_raw, burst_history, norm_len)
Normalize a non-negative burst score using log1p + robust IQR z-score
Parameters:
burst_raw (float): Raw burst score
burst_history (array<float>): History array of raw burst scores
norm_len (int): Normalization window length
Returns: Normalized burst score
Informacje o Wersji
v5Added:
f_find_k_nearest_7(f1_cur, f2_cur, f3_cur, f4_cur, f5_cur, f6_cur, f7_cur, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, f7_history, k, max_history)
Find K nearest neighbors using Hamming screening + Euclidean distance for 7 features
Parameters:
f1_cur (float)
f2_cur (float)
f3_cur (float)
f4_cur (float)
f5_cur (float)
f6_cur (float)
f7_cur (float)
f1_history (array<float>)
f2_history (array<float>)
f3_history (array<float>)
f4_history (array<float>)
f5_history (array<float>)
f6_history (array<float>)
f7_history (array<float>)
k (int): Number of neighbors to retrieve
max_history (int): Maximum history length to search
Returns: array<int> Array of K nearest neighbor indices
f_calculate_confidence_7(f1_cur, f2_cur, f3_cur, f4_cur, f5_cur, f6_cur, f7_cur, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, f7_history, k_nearest)
Calculate confidence from 7-feature K-nearest distance distribution
Parameters:
f1_cur (float)
f2_cur (float)
f3_cur (float)
f4_cur (float)
f5_cur (float)
f6_cur (float)
f7_cur (float)
f1_history (array<float>)
f2_history (array<float>)
f3_history (array<float>)
f4_history (array<float>)
f5_history (array<float>)
f6_history (array<float>)
f7_history (array<float>)
k_nearest (array<int>): Array of K nearest neighbor indices
Returns: float Confidence score [0, 1]
Informacje o Wersji
v6Added:
f_merge_tier(pool_dist, pool_id, target_ids, target_dist, target_weights, knn_k, decluster_gap)
Merge one tier into the final kNN set using sorted indices and decluster gating
Parameters:
pool_dist (array<float>): Candidate distances for the tier
pool_id (array<int>): Candidate history indices for the tier
target_ids (array<int>): Final output neighbor ids
target_dist (array<float>): Final output distances
target_weights (array<float>): Final output weights
knn_k (int): Target neighbor count
decluster_gap (int): Minimum spacing between accepted neighbor ids
f_hierarchical_tournament_live(s_packed, s_f1, s_f2, s_f3, s_f4, s_f5, s_f6, s_f7, s_family, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, f7_history, arr_packed_vec, arr_setup_id, arr_setup_family, current_setup_code, oracle_horizon, knn_k, hamming_thresh, decluster_gap, pool_max)
Hierarchical 4-tier tournament selection for live execution
Parameters:
s_packed (int)
s_f1 (float)
s_f2 (float)
s_f3 (float)
s_f4 (float)
s_f5 (float)
s_f6 (float)
s_f7 (float)
s_family (int)
f1_history (array<float>)
f2_history (array<float>)
f3_history (array<float>)
f4_history (array<float>)
f5_history (array<float>)
f6_history (array<float>)
f7_history (array<float>)
arr_packed_vec (array<int>)
arr_setup_id (array<int>)
arr_setup_family (array<int>)
current_setup_code (int)
oracle_horizon (int)
knn_k (int)
hamming_thresh (int)
decluster_gap (int)
pool_max (int)
Returns: [ids, distances, weights, dbg_iter, dbg_hskip, dbg_nskip, dbg_cands]
f_hierarchical_tournament_backtest(s_packed, s_f1, s_f2, s_f3, s_f4, s_f5, s_f6, s_f7, s_family, s_setup_code, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, f7_history, arr_packed_vec, arr_setup_id, arr_setup_family, knn_limit, knn_k, hamming_thresh, decluster_gap, pool_max)
Hierarchical 3-tier tournament selection for backtest execution
Parameters:
s_packed (int)
s_f1 (float)
s_f2 (float)
s_f3 (float)
s_f4 (float)
s_f5 (float)
s_f6 (float)
s_f7 (float)
s_family (int)
s_setup_code (int)
f1_history (array<float>)
f2_history (array<float>)
f3_history (array<float>)
f4_history (array<float>)
f5_history (array<float>)
f6_history (array<float>)
f7_history (array<float>)
arr_packed_vec (array<int>)
arr_setup_id (array<int>)
arr_setup_family (array<int>)
knn_limit (int)
knn_k (int)
hamming_thresh (int)
decluster_gap (int)
pool_max (int)
Informacje o Wersji
v7Updated:
f_normalize_phi_div(phi_div, history, len)
Normalize PhiDiv using rolling z-score
Parameters:
phi_div (float): Raw PhiDiv value (PhiLatch - PhiTotal_orth)
history (array<float>)
len (int)
Returns: Normalized PhiDiv
Informacje o Wersji
v8Updated:
f_hierarchical_tournament_live(s_packed, s_f1, s_f2, s_f3, s_f4, s_f5, s_f6, s_f7, s_family, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, f7_history, arr_packed_vec, arr_setup_id, arr_setup_family, current_setup_code, oracle_horizon, mc_horizon, knn_k, hamming_thresh, decluster_gap, pool_max)
Hierarchical 4-tier tournament selection for live execution
Parameters:
s_packed (int)
s_f1 (float)
s_f2 (float)
s_f3 (float)
s_f4 (float)
s_f5 (float)
s_f6 (float)
s_f7 (float)
s_family (int)
f1_history (array<float>)
f2_history (array<float>)
f3_history (array<float>)
f4_history (array<float>)
f5_history (array<float>)
f6_history (array<float>)
f7_history (array<float>)
arr_packed_vec (array<int>)
arr_setup_id (array<int>)
arr_setup_family (array<int>)
current_setup_code (int)
oracle_horizon (int)
mc_horizon (int)
knn_k (int)
hamming_thresh (int)
decluster_gap (int)
pool_max (int)
Returns: [ids, distances, weights, dbg_iter, dbg_hskip, dbg_nskip, dbg_cands]
Informacje o Wersji
v9Informacje o Wersji
v10Informacje o Wersji
v11Informacje o Wersji
v12Informacje o Wersji
v13Added:
f_knn_scan(cur_features, h0, h1, h2, h3, h4, h5, h6, arr_packed, packed_cur, nf, scan_from, scan_to, scan_step, hamming_thresh, max_candidates)
Parameters:
cur_features (array<float>)
h0 (array<float>)
h1 (array<float>)
h2 (array<float>)
h3 (array<float>)
h4 (array<float>)
h5 (array<float>)
h6 (array<float>)
arr_packed (array<int>)
packed_cur (int)
nf (int)
scan_from (int)
scan_to (int)
scan_step (int)
hamming_thresh (int)
max_candidates (int)
f_knn_select(t1_ids, t1_dist, t2_ids, t2_dist, t3_ids, t3_dist, t4_ids, t4_dist, k, gap)
Parameters:
t1_ids (array<int>)
t1_dist (array<float>)
t2_ids (array<int>)
t2_dist (array<float>)
t3_ids (array<int>)
t3_dist (array<float>)
t4_ids (array<int>)
t4_dist (array<float>)
k (int)
gap (int)
Informacje o Wersji
v14Added:
f_sign_aware_filter(s_f2, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, f7_history, arr_setup_id, arr_setup_family, arr_packed_vec, limit)
Sign-aware neighbor filtering to prevent canceling paths from mixed correlation phases
Parameters:
s_f2 (float): Current F2 value
f1_history (array<int>): Feature 1 history (quantized int)
f2_history (array<float>): Feature 2 history (float correlation)
f3_history (array<float>): Feature 3 history (float)
f4_history (array<float>): Feature 4 history (float)
f5_history (array<float>): Feature 5 history (float)
f6_history (array<float>): Feature 6 history (float)
f7_history (array<float>): Feature 7 history (float)
arr_setup_id (array<int>): Setup ID history
arr_setup_family (array<int>): Setup family history
arr_packed_vec (array<int>): Packed vector history
limit (int): Maximum number of history items to consider
Returns: Filtered arrays with same F2 sign as current setup
f_hierarchical_tournament_backtest_signaware(s_packed, s_f1, s_f2, s_f3, s_f4, s_f5, s_f6, s_f7, s_family, s_setup_code, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, f7_history, arr_setup_id, arr_setup_family, arr_packed_vec, knn_limit, knn_k, hamming_thresh, decluster_gap, pool_max)
Sign-aware hierarchical tournament for backtest with quantized F1
Parameters:
s_packed (int)
s_f1 (float)
s_f2 (float)
s_f3 (float)
s_f4 (float)
s_f5 (float)
s_f6 (float)
s_f7 (float)
s_family (int)
s_setup_code (int)
f1_history (array<int>)
f2_history (array<float>)
f3_history (array<float>)
f4_history (array<float>)
f5_history (array<float>)
f6_history (array<float>)
f7_history (array<float>)
arr_setup_id (array<int>)
arr_setup_family (array<int>)
arr_packed_vec (array<int>)
knn_limit (int)
knn_k (int)
hamming_thresh (int)
decluster_gap (int)
pool_max (int)
Returns: [ids, weights] for backtest MC validation
f_median_distance(k_nearest, f1_cur, f2_cur, f3_cur, f4_cur, f5_cur, f6_cur, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history)
Calculate median distance from k-nearest neighbors (robust confidence metric)
Parameters:
k_nearest (array<int>): Array of K nearest neighbor indices
f1_cur (float)
f2_cur (float)
f3_cur (float)
f4_cur (float)
f5_cur (float)
f6_cur (float)
f1_history (array<float>)
f2_history (array<float>)
f3_history (array<float>)
f4_history (array<float>)
f5_history (array<float>)
f6_history (array<float>)
Returns: float Median distance (more robust to outliers than mean)
Informacje o Wersji
v15Informacje o Wersji
v16Informacje o Wersji
v17Updated:
f_hierarchical_tournament_live(s_packed, s_f1, s_f2, s_f3, s_f4, s_f5, s_f6, s_f7, category_a, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, f7_history, arr_packed_vec, arr_category_b_history, arr_category_a_history, target_category_b, oracle_horizon, mc_horizon, knn_k, hamming_thresh, decluster_gap, pool_max)
Hierarchical 4-tier tournament selection for live execution (domain-agnostic)
Parameters:
s_packed (int)
s_f1 (float)
s_f2 (float)
s_f3 (float)
s_f4 (float)
s_f5 (float)
s_f6 (float)
s_f7 (float)
category_a (int): Target category A (e.g., family/regime). Use -1 to disable filtering.
f1_history (array<float>)
f2_history (array<float>)
f3_history (array<float>)
f4_history (array<float>)
f5_history (array<float>)
f6_history (array<float>)
f7_history (array<float>)
arr_packed_vec (array<int>)
arr_category_b_history (array<int>)
arr_category_a_history (array<int>)
target_category_b (int)
oracle_horizon (int)
mc_horizon (int)
knn_k (int)
hamming_thresh (int)
decluster_gap (int)
pool_max (int)
Returns: [ids, distances, weights, dbg_iter, dbg_hskip, dbg_nskip, dbg_cands]
f_hierarchical_tournament_backtest(s_packed, s_f1, s_f2, s_f3, s_f4, s_f5, s_f6, s_f7, category_a, category_b, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, f7_history, arr_packed_vec, arr_category_b_history, arr_category_a_history, knn_limit, knn_k, hamming_thresh, decluster_gap, pool_max)
Hierarchical 3-tier tournament selection for backtest execution (domain-agnostic)
Parameters:
s_packed (int)
s_f1 (float)
s_f2 (float)
s_f3 (float)
s_f4 (float)
s_f5 (float)
s_f6 (float)
s_f7 (float)
category_a (int): Target category A. Use -1 to disable filtering.
category_b (int): Target category B. Use 0 to disable filtering.
f1_history (array<float>)
f2_history (array<float>)
f3_history (array<float>)
f4_history (array<float>)
f5_history (array<float>)
f6_history (array<float>)
f7_history (array<float>)
arr_packed_vec (array<int>)
arr_category_b_history (array<int>)
arr_category_a_history (array<int>)
knn_limit (int)
knn_k (int)
hamming_thresh (int)
decluster_gap (int)
pool_max (int)
f_hierarchical_tournament_backtest_signaware(s_packed, s_f1, s_f2, s_f3, s_f4, s_f5, s_f6, s_f7, category_a, category_b, f1_history, f2_history, f3_history, f4_history, f5_history, f6_history, f7_history, arr_category_b_history, arr_category_a_history, arr_packed_vec, knn_limit, knn_k, hamming_thresh, decluster_gap, pool_max)
Sign-aware hierarchical tournament for backtest with quantized F1 (domain-agnostic)
Parameters:
s_packed (int)
s_f1 (float)
s_f2 (float)
s_f3 (float)
s_f4 (float)
s_f5 (float)
s_f6 (float)
s_f7 (float)
category_a (int): Target category A. Use -1 to disable filtering.
category_b (int): Target category B. Use 0 to disable filtering.
f1_history (array<int>)
f2_history (array<float>)
f3_history (array<float>)
f4_history (array<float>)
f5_history (array<float>)
f6_history (array<float>)
f7_history (array<float>)
arr_category_b_history (array<int>)
arr_category_a_history (array<int>)
arr_packed_vec (array<int>)
knn_limit (int)
knn_k (int)
hamming_thresh (int)
decluster_gap (int)
pool_max (int)
Returns: [ids, weights] for backtest MC validation
Informacje o Wersji
v18Informacje o Wersji
v19Informacje o Wersji
v20Informacje o Wersji
v21Added:
fweightedmedian(values, weights)
Parameters:
values (array<float>)
weights (array<float>)
fweightedpercentile(values, weights, p)
Parameters:
values (array<float>)
weights (array<float>)
p (float)
fhorizonconfidence(horizons, weights)
Parameters:
horizons (array<int>)
weights (array<float>)
famplitudeconfidence(amplitudes, weights)
Parameters:
amplitudes (array<float>)
weights (array<float>)
fprojectionconfidence(horizons, amplitudes, weights, net_te, te_threshold, alpha_k, alpha_a, alpha_te)
Parameters:
horizons (array<int>)
amplitudes (array<float>)
weights (array<float>)
net_te (float)
te_threshold (float)
alpha_k (float)
alpha_a (float)
alpha_te (float)
fcausaleuclidean7(c1, c2, c3, c4, c5, c6, c7, h1, h2, h3, h4, h5, h6, h7, te_weights)
Parameters:
c1 (float)
c2 (float)
c3 (float)
c4 (float)
c5 (float)
c6 (float)
c7 (float)
h1 (float)
h2 (float)
h3 (float)
h4 (float)
h5 (float)
h6 (float)
h7 (float)
te_weights (array<float>)
Informacje o Wersji
v22Added:
fprojectionconfidence2(horizons, amplitudes, weights, nette, te_threshold, alpha_k, alpha_a, alpha_te, conf_mode)
Parameters:
horizons (array<int>)
amplitudes (array<float>)
weights (array<float>)
nette (float)
te_threshold (float)
alpha_k (float)
alpha_a (float)
alpha_te (float)
conf_mode (int)
Informacje o Wersji
v23Informacje o Wersji
v24Informacje o Wersji
v25Informacje o Wersji
v26Informacje o Wersji
v27Informacje o Wersji
v28Added:
f_config_default(k)
Parameters:
k (int)
f_query_new(feats, meta_a, meta_b, dir)
Parameters:
feats (array<float>)
meta_a (int)
meta_b (int)
dir (series TradeDirection enum from cybermediaboy/LearningLib/1)
f_mahalanobis_packed(a, b, sigma_inv_tri)
Mahalanobis distance using lower-triangular packed inverse cov.
Packing convention: sigma_inv_tri[idx] for (i,j) with i>=j is at
idx = i*(i+1)/2 + j. Diagonal at i*(i+1)/2 + i.
Caller-managed packing; sigma_inv_tri size = n*(n+1)/2.
Parameters:
a (array<float>)
b (array<float>)
sigma_inv_tri (array<float>)
f_dist_dispatch(kind, a, b, sigma_inv_tri)
Distance dispatch, including Mahalanobis when query carries
sigma_inv_tri.
Parameters:
kind (series DistanceKind enum from cybermediaboy/LearningLib/1)
a (array<float>)
b (array<float>)
sigma_inv_tri (array<float>)
f_knn_search(tb, q, cfg)
Hierarchical kNN search with tournament filtering and fallback.
Algorithm:
1. Try requested filter mode.
2. If filtered population < cfg.min_samples, fall back:
META_AB → META_A_ONLY → NONE
META_A_THEN_B / META_A_ONLY → NONE
META_B_ONLY → NONE
3. Compute distances, retain top-k via bounded insertion.
4. Populate weights = 1 / max(d, min_distance).
5. Aggregate per cfg.aggregate.
6. Compute confidence.
Returns KnnResult with effective filter and population counts.
Parameters:
tb (TrainingBuffer type from cybermediaboy/LearningLib/1)
q (KnnQuery)
cfg (KnnConfig)
f_aggregate_str(m)
Parameters:
m (series AggregateMode)
f_filter_str(f)
Parameters:
f (series FilterMode)
f_dirfilter_str(d)
Parameters:
d (series DirectionFilter)
KnnConfig
KnnConfig — search hyperparameters
Fields:
k (series int): number of neighbors
metric (series DistanceKind enum from cybermediaboy/LearningLib/1): distance metric
aggregate (series AggregateMode): how to combine top-k labels
filter (series FilterMode): tournament filter mode
dir_filter (series DirectionFilter): direction filter
min_samples (series int): minimum filtered population to use filter (else fallback)
min_distance (series float): floor for 1/d weighting (avoid singular)
require_distinct_t (series bool): require sample.timestamp != query.timestamp (anti-leak)
KnnQuery
KnnQuery — query specification
Fields:
features (array<float>): query feature vector
meta_a (series int): category-A label (e.g. setup family)
meta_b (series int): category-B label (e.g. setup code)
direction (series TradeDirection enum from cybermediaboy/LearningLib/1): query direction context (used by SAME_AS_QUERY filter)
timestamp (series int): query bar (used by require_distinct_t)
sigma_inv_tri (array<float>): inverse covariance (lower-triangular packed) for Mahalanobis;
KnnNeighbor
KnnNeighbor — single neighbor with distance and sample backref
Fields:
sample (TBSample type from cybermediaboy/LearningLib/1)
distance (series float)
weight (series float)
KnnResult
KnnResult — aggregated output
Fields:
neighbors (array<KnnNeighbor>): top-k sorted ascending by distance
prediction (series float): aggregated label (na if no neighbors)
confidence (series float): in [0,1] — fraction of neighbors agreeing on dominant class,
used_filter (series FilterMode): which filter was effectively applied (after fallback)
n_candidates (series int): number of samples scanned
n_filtered (series int): number passing filter
Removed:
f_turboquant(val)
Quantize a single feature to 3-bit index + 1-bit residual (4 bits total)
f_pack_vector(i0, r0, i1, r1, i2, r2, i3, r3, i4, r4, i5, r5)
Pack 6 features into single 24-bit integer
f_pack_vector_7(i0, r0, i1, r1, i2, r2, i3, r3, i4, r4, i5, r5, i6, r6)
Pack 7 features into single 28-bit integer
f_unpack_centroid(packed, feat_idx)
Unpack and reconstruct centroid value with half-step refinement
Informacje o Wersji
v29Updated:
f_query_new(feats, meta_a, meta_b, dir)
Parameters:
feats (array<float>)
meta_a (int)
meta_b (int)
dir (series TradeDirection enum from cybermediaboy/LearningLib/3)
f_dist_dispatch(kind, a, b, sigma_inv_tri)
Distance dispatch, including Mahalanobis when query carries
sigma_inv_tri.
Parameters:
kind (series DistanceKind enum from cybermediaboy/LearningLib/3)
a (array<float>)
b (array<float>)
sigma_inv_tri (array<float>)
f_knn_search(tb, q, cfg)
Hierarchical kNN search with tournament filtering and fallback.
Algorithm:
1. Try requested filter mode.
2. If filtered population < cfg.min_samples, fall back:
META_AB → META_A_ONLY → NONE
META_A_THEN_B / META_A_ONLY → NONE
META_B_ONLY → NONE
3. Compute distances, retain top-k via bounded insertion.
4. Populate weights = 1 / max(d, min_distance).
5. Aggregate per cfg.aggregate.
6. Compute confidence.
Returns KnnResult with effective filter and population counts.
Parameters:
tb (TrainingBuffer type from cybermediaboy/LearningLib/3)
q (KnnQuery)
cfg (KnnConfig)
KnnConfig
KnnConfig — search hyperparameters
Fields:
k (series int): number of neighbors
metric (series DistanceKind enum from cybermediaboy/LearningLib/3): distance metric
aggregate (series AggregateMode): how to combine top-k labels
filter (series FilterMode): tournament filter mode
dir_filter (series DirectionFilter): direction filter
min_samples (series int): minimum filtered population to use filter (else fallback)
min_distance (series float): floor for 1/d weighting (avoid singular)
require_distinct_t (series bool): require sample.timestamp != query.timestamp (anti-leak)
KnnQuery
KnnQuery — query specification
Fields:
features (array<float>): query feature vector
meta_a (series int): category-A label (e.g. setup family)
meta_b (series int): category-B label (e.g. setup code)
direction (series TradeDirection enum from cybermediaboy/LearningLib/3): query direction context (used by SAME_AS_QUERY filter)
timestamp (series int): query bar (used by require_distinct_t)
sigma_inv_tri (array<float>): inverse covariance (lower-triangular packed) for Mahalanobis;
KnnNeighbor
KnnNeighbor — single neighbor with distance and sample backref
Fields:
sample (TBSample type from cybermediaboy/LearningLib/3)
distance (series float)
weight (series float)
Informacje o Wersji
v30Informacje o Wersji
v31Added:
f_mahalanobis_shrinkage(x, X_buffer, alpha, weiszfeld_iters)
Shrinkage Mahalanobis distance with L1-median anchor (Cabana et al. 2019)
Parameters:
x (array<float>): Current observation vector (length d, typically 3)
X_buffer (matrix<float>): Matrix of past observations (N rows × d columns)
alpha (simple float): Shrinkage weight toward identity [0,1], default 0.1
weiszfeld_iters (simple int): Weiszfeld iterations for L1-median [1,10], default 5
Returns: Robust Mahalanobis distance, or na if invalid inputs
Biblioteka Pine
W zgodzie z duchem TradingView autor opublikował ten kod Pine jako bibliotekę open-source, aby inni programiści Pine z naszej społeczności mogli go ponownie wykorzystać. Ukłony dla autora. Można korzystać z tej biblioteki prywatnie lub w innych publikacjach open-source, jednak ponowne wykorzystanie tego kodu w publikacjach podlega Zasadom serwisu.
Wyłączenie odpowiedzialności
Informacje i publikacje nie stanowią i nie powinny być traktowane jako porady finansowe, inwestycyjne, tradingowe ani jakiekolwiek inne rekomendacje dostarczane lub zatwierdzone przez TradingView. Więcej informacji znajduje się w Warunkach użytkowania.
Biblioteka Pine
W zgodzie z duchem TradingView autor opublikował ten kod Pine jako bibliotekę open-source, aby inni programiści Pine z naszej społeczności mogli go ponownie wykorzystać. Ukłony dla autora. Można korzystać z tej biblioteki prywatnie lub w innych publikacjach open-source, jednak ponowne wykorzystanie tego kodu w publikacjach podlega Zasadom serwisu.
Wyłączenie odpowiedzialności
Informacje i publikacje nie stanowią i nie powinny być traktowane jako porady finansowe, inwestycyjne, tradingowe ani jakiekolwiek inne rekomendacje dostarczane lub zatwierdzone przez TradingView. Więcej informacji znajduje się w Warunkach użytkowania.