PINE LIBRARY
Cập nhật ICOptimizerLib
Library "ICOptimizerLib"
ICOptimizerLib v2 — IC-based parameter optimization with 4 Bayesian strategies.
Publish target: ICOptimizer/2 (hard break from v1 — see §A below).
Layer 1: primitive IC estimators (Pearson, Spearman, Kendall, Partial).
Layer 2: Optimizer UDT with 4 strategies: argmax | ucb | thompson | bayesian.
Layer 3: RegimeGate, ObjectiveWeights, composite scoring, serialize/restore.
Layer 4: diagnostics table and panel.
L2 library — depends only on NumLib.
─── §A v1 BACKWARD-COMPAT DECISION (follow-up 1) ────────────────────────
HARD BREAK. v1 (ICOptimizer/1) used bare strings ("argmax", "ucb", …).
v2 uses the OptimizerKind enum. Reason: Pine v6 enums are type-safe and
produce CE10 errors at compile time if a caller passes an invalid string,
whereas bare strings fail silently at runtime. The compat shim route
(string→enum dispatch wrapper) was considered and rejected: it would
re-introduce series-string branching inside a hot method, defeating the
purpose of the enum migration.
Migration for v1 callers:
OLD: f_find_optimal_param(params, ics, cur, 0.2) ← v1 API
NEW: opt = f_optimizer_new(OptimizerKind.ARGMAX, …) ← v2 API
idx = opt.propose()
opt.observe(idx, ic)
The free function f_find_optimal_param() is retained in §4 as a one-line
compat wrapper producing identical output to v1 findOptimalParam() for
callers that only used ARGMAX and do not need the UDT.
Publish target: ICOptimizer/2 (same publisher namespace as kNNLib/28,
LearningLib/1, etc. Parallel to v1, not a rename.)
─── §B UDT INDEPENDENCE AUDIT (follow-up 2) ─────────────────────────────
All 4 UDTs are independently constructable with no required coupling:
UDT Constructor Depends on
─────────────── ─────────────────────────── ────────────────────────────
Optimizer f_optimizer_new(…) nothing (grid is caller-owned)
RollingIC f_rolling_ic_new(capacity) nothing
RegimeGate f_regime_gate_new(…) nothing
ObjectiveWeights f_obj_weights_new(…) nothing
Valid combinations:
• Optimizer alone — minimal usage (ARGMAX strategy, no IC classification)
• Optimizer + RollingIC — IC classification per bar, classify() method
• Optimizer + ObjectiveWeights — composite scoring for multi-objective grids
• Optimizer + RegimeGate — gate-filtered observe() calls
• All 4 — full stack
Initialization order: any order; there are no cross-UDT init dependencies.
The caller is responsible for pushing IC values into RollingIC before
calling classify(); a fresh buffer returns 0.0 thresholds (safe default).
─── §C GP MATH VERIFICATION (follow-up 3) ───────────────────────────────
Jacobi solver convergence domain: guaranteed for diagonally dominant K.
K is diagonally dominant when kernel_noise > 0 (K[i,i] = kernel(xi,xi) +
noise ≥ 1 + noise > Σ_{j≠i} kernel(xi,xj) for RBF/Matern52 with ls > 0).
NaN propagation guard: f_optimizer_new() enforces noise ≥ 1e-6 at
construction (see implementation below). NaN in ic_sample is gated by
the na(ic_sample) check in observe() before any array writes.
Grid size constraints (enforced at f_optimizer_new):
grid_size == 1 → runtime.error (BAYESIAN is undefined for a single cell)
grid_size > 30 → runtime.error for BAYESIAN only (Jacobi O(n²×20) budget)
grid_size ≥ 2 → all strategies valid
ARGMAX/UCB/THOMPSON have no upper grid-size constraint.
Unit test specification (see test_icoptimizer_v2_unit.pine):
T1: grid=[1.0] + BAYESIAN → should hit error log (na guard)
T2: grid=[1.0,2.0] + BAYESIAN, 50 observe() calls → ic_var shrinks
T3: grid size=30 + BAYESIAN → no silent NaN on bar 500 / 1000
T4: rising IC synthetic trajectory → propose() returns idx 6 after warmup
T5: peak-in-middle IC → propose() converges to idx 3 (center)
─── §D v5→v6 DELTA (Phase E-2a) ─────────────────────────────────────────
1. //version=5 → //version=6
2. type ICOptimizer → decomposed to 4 independent UDTs (§B)
3. `series float` qualifiers explicit; `simple int` for all ta.* lengths (CE10297)
4. Enum OptimizerKind / KernelKind / ReturnMode replaces bare strings
5. classifyIC scalar bug → RollingIC ring buffer + sort-based percentile
6. detectAndAdjustDomination orphan → method check_domination on Optimizer
7. Monotonic counter → reset_decay(decay) method
8. S9: all multi-line ternaries collapsed to single lines
9. f_ma_for_idx() dispatch in demo for simple-int ta.sma constraint
10. Nested array.get() in f_build_gram / observe() split to locals (COMMA_STATEMENTS)
f_ic_pearson(signal, ret, n)
Pearson IC: correlation of signal with forward return
Parameters:
signal (float): Signal series (e.g. z-score oscillator)
ret (float): Forward return series (aligned: ret[0] = realized return for signal[horizon])
n (simple int): Rolling window (simple int — required by ta.correlation)
f_ic_spearman(signal, ret, n)
Spearman IC via rank correlation approximation
Parameters:
signal (float): Signal series
ret (float): Forward return series
n (simple int): Rolling window
Returns: Spearman rank-correlation approximation
f_ic_kendall(signal, ret, n)
Kendall IC approximation (via concordant/discordant sign correlation)
Parameters:
signal (float): Signal series
ret (float): Forward return series
n (simple int): Rolling window
Returns: Kendall tau approximation
f_ic_partial(signal, ret, control, n)
Partial IC: correlation of signal with ret after removing control variable
Parameters:
signal (float): Signal series
ret (float): Forward return series
control (float): Control variable to partial out
n (simple int): Rolling window
Returns: Partial Pearson IC
f_forward_return(src, horizon, mode, benchmark)
Compute forward return from source series
Parameters:
src (float): Source price series
horizon (simple int): Look-forward bars
mode (series ReturnMode): ReturnMode enum
benchmark (float): Optional benchmark (used only in EXCESS mode; pass na otherwise)
f_label_from_signal(sig, ret, eps)
Label from signal × return sign match
Parameters:
sig (float): Signal value
ret (float): Realized return
eps (float): Dead-zone threshold (returns within ±eps labelled 0)
Returns: 1 = correct direction, -1 = wrong direction, 0 = inside dead-zone
f_rolling_ic_new(capacity)
Create a new RollingIC buffer
Parameters:
capacity (simple int): Number of IC samples to retain
method push(self, ic_val)
Push a new IC observation into the ring buffer
Namespace types: RollingIC
Parameters:
self (RollingIC)
ic_val (float)
method classify(self, ic_val, good_pct, bad_pct)
Classify current IC against ring buffer distribution
Namespace types: RollingIC
Parameters:
self (RollingIC): RollingIC buffer (must have been pushed at least once)
ic_val (float): Current IC to classify
good_pct (float): Percentile above which IC is "good" (0–100)
bad_pct (float): Percentile below which IC is "bad" (0–100)
Returns: [is_good, is_bad, good_thresh, bad_thresh]
f_optimizer_new(kind, grid, lr, c_ucb, cooldown, kernel_kind, kernel_ls, kernel_noise)
Create a new Optimizer
Parameters:
kind (series OptimizerKind): Strategy
grid (array<float>): Parameter grid (array<float>, size ≤ 30 for BAYESIAN)
lr (float): EWM learning rate for ic_ema / ic_var updates (0–1)
c_ucb (float): UCB exploration constant (ignored for non-UCB)
cooldown (simple int): Minimum bars between switches
kernel_kind (series KernelKind): Kernel for BAYESIAN (ignored otherwise)
kernel_ls (float): Kernel lengthscale (ignored otherwise)
kernel_noise (float): Observation noise (ignored otherwise)
method propose(self)
Propose next parameter index to try
Namespace types: Optimizer
Parameters:
self (Optimizer)
Returns: Selected grid index
method observe(self, idx, ic_sample)
Record observed IC for a grid cell and update posterior
Namespace types: Optimizer
Parameters:
self (Optimizer)
idx (int): Grid index that was evaluated
ic_sample (float): Observed IC value
method reset_decay(self, decay)
Apply exponential decay to ic_ema and ic_var (prevents monotonic drift)
Namespace types: Optimizer
Parameters:
self (Optimizer)
decay (float): Decay factor 0..1 (e.g. 0.95 = retain 95% of past)
method check_domination(self, long_n, short_n, ratio_threshold)
Detect directional signal domination and bump current grid index
Namespace types: Optimizer
Parameters:
self (Optimizer)
long_n (int): Count of long signals in evaluation window
short_n (int): Count of short signals in evaluation window
ratio_threshold (float): Domination ratio (e.g. 4 = 4:1 imbalance)
Returns: [was_dominated, direction_str] direction_str = "long" | "short" | "none"
method current_param(self)
Get current parameter value from grid
Namespace types: Optimizer
Parameters:
self (Optimizer)
f_find_optimal_param(testParams, icValues, currentParam, smoothing)
Find optimal parameter from arrays (v1-compatible, wraps Optimizer.propose)
Parameters:
testParams (array<float>): Grid array
icValues (array<float>): IC values for each grid cell (same size)
currentParam (float): Current param (for EWM smoothing)
smoothing (simple float): EWM lr (0–1)
Returns: [newParam, bestIC, bestIdx]
f_regime_gate_new(mode, threshold, confirm_bars)
Create RegimeGate
Parameters:
mode (string)
threshold (float)
confirm_bars (simple int)
method is_open(self, ic_val, bars_above)
Check if gate is open given current IC and a rolling counter
Namespace types: RegimeGate
Parameters:
self (RegimeGate): RegimeGate
ic_val (float): Current IC
bars_above (int): Rolling bars-above-threshold counter (caller maintains)
Returns: bool gate_open
f_obj_weights_new(w_ic, w_hitrate, w_freq_penalty, w_drawdown_penalty)
Create ObjectiveWeights
Parameters:
w_ic (float)
w_hitrate (float)
w_freq_penalty (float)
w_drawdown_penalty (float)
f_composite_score(w, ic, hitrate, freq, drawdown)
Compute composite score for a grid cell
Parameters:
w (ObjectiveWeights): ObjectiveWeights
ic (float): IC value for cell
hitrate (float): Hit rate 0..1 for cell
freq (float): Signal frequency 0..1 (higher = more signals = penalized)
drawdown (float): Max drawdown magnitude (positive float)
Returns: Composite score (higher = better)
f_optimizer_serialize(self)
Serialize Optimizer state to a compact CSV string
Parameters:
self (Optimizer): Optimizer to serialize
Returns: string blob (pass to f_optimizer_restore to reconstruct ic_ema/ic_var)
f_optimizer_restore(self, blob)
Restore ic_ema/ic_var/visits from serialized blob into an existing Optimizer
Parameters:
self (Optimizer): Optimizer (grid must already be initialized with correct size)
blob (string): String from f_optimizer_serialize
Returns: self (mutated in place)
f_diag_table(self, gate, weights, pos, max_rows)
Render diagnostics table for Optimizer state
Parameters:
self (Optimizer): Optimizer
gate (RegimeGate): RegimeGate (pass na if unused)
weights (ObjectiveWeights): ObjectiveWeights (pass na if unused)
pos (string): Table position (e.g. position.bottom_right)
max_rows (simple int): Maximum grid rows to display (capped at array.size(grid))
Returns: table reference
f_diag_panel(self, height)
Render sparkline-style panel (one plot bar per grid cell, height = ic_ema)
Parameters:
self (Optimizer): Optimizer
height (float): Panel height in price units (caller scales)
Returns: label(na) (renders labels directly)
f_kind_str(k)
Parameters:
k (series OptimizerKind)
f_kernel_str(k)
Parameters:
k (series KernelKind)
f_return_mode_str(m)
Parameters:
m (series ReturnMode)
RollingIC
Rolling IC ring buffer for proper percentile computation
Fields:
samples (array<float>): Circular buffer of IC observations
head (series int): Write head (mod capacity)
capacity (series int): Max samples to retain
Optimizer
Optimizer — unified UDT for all 4 strategies
Fields:
kind (series OptimizerKind): Strategy: ARGMAX | UCB | THOMPSON | BAYESIAN
grid (array<float>): Discrete parameter grid (size ≤ 30 for BAYESIAN)
ic_ema (array<float>): Posterior mean per cell (EWM updated)
ic_var (array<float>): Posterior variance per cell (UCB/Thompson/Bayes)
visits (array<int>): Visit count per cell
lr (series float): EWM learning rate for ic_ema / ic_var updates
c_ucb (series float): Exploration coefficient (UCB only)
cooldown (series int): Minimum bars between parameter changes
last_change_bar (series int): Bar index of last change
current_idx (series int): Currently selected grid index
kernel_matrix (array<float>): Flattened len(grid)² Gram matrix (BAYESIAN only)
kernel_kind (series KernelKind): RBF | MATERN52 (BAYESIAN only)
kernel_ls (series float): Kernel lengthscale (BAYESIAN only)
kernel_noise (series float): Observation noise σ² (BAYESIAN only)
total_visits (series int): Cumulative visit count (for UCB log normalizer)
decay_factor (series float): EWM decay applied by reset_decay (0..1; 1=no decay)
RegimeGate
RegimeGate — IC regime filter
Fields:
mode (series string): "positive" | "any" | "top_pct"
threshold (series float): IC threshold for "positive" or percentile for "top_pct"
confirm_bars (series int): Bars IC must stay above threshold before gate opens
ObjectiveWeights
ObjectiveWeights — composite scoring weights
Fields:
w_ic (series float): Weight on IC component
w_hitrate (series float): Weight on hit-rate component
w_freq_penalty (series float): Penalty for excessive signal frequency
w_drawdown_penalty (series float): Penalty for drawdown
ICOptimizerLib v2 — IC-based parameter optimization with 4 Bayesian strategies.
Publish target: ICOptimizer/2 (hard break from v1 — see §A below).
Layer 1: primitive IC estimators (Pearson, Spearman, Kendall, Partial).
Layer 2: Optimizer UDT with 4 strategies: argmax | ucb | thompson | bayesian.
Layer 3: RegimeGate, ObjectiveWeights, composite scoring, serialize/restore.
Layer 4: diagnostics table and panel.
L2 library — depends only on NumLib.
─── §A v1 BACKWARD-COMPAT DECISION (follow-up 1) ────────────────────────
HARD BREAK. v1 (ICOptimizer/1) used bare strings ("argmax", "ucb", …).
v2 uses the OptimizerKind enum. Reason: Pine v6 enums are type-safe and
produce CE10 errors at compile time if a caller passes an invalid string,
whereas bare strings fail silently at runtime. The compat shim route
(string→enum dispatch wrapper) was considered and rejected: it would
re-introduce series-string branching inside a hot method, defeating the
purpose of the enum migration.
Migration for v1 callers:
OLD: f_find_optimal_param(params, ics, cur, 0.2) ← v1 API
NEW: opt = f_optimizer_new(OptimizerKind.ARGMAX, …) ← v2 API
idx = opt.propose()
opt.observe(idx, ic)
The free function f_find_optimal_param() is retained in §4 as a one-line
compat wrapper producing identical output to v1 findOptimalParam() for
callers that only used ARGMAX and do not need the UDT.
Publish target: ICOptimizer/2 (same publisher namespace as kNNLib/28,
LearningLib/1, etc. Parallel to v1, not a rename.)
─── §B UDT INDEPENDENCE AUDIT (follow-up 2) ─────────────────────────────
All 4 UDTs are independently constructable with no required coupling:
UDT Constructor Depends on
─────────────── ─────────────────────────── ────────────────────────────
Optimizer f_optimizer_new(…) nothing (grid is caller-owned)
RollingIC f_rolling_ic_new(capacity) nothing
RegimeGate f_regime_gate_new(…) nothing
ObjectiveWeights f_obj_weights_new(…) nothing
Valid combinations:
• Optimizer alone — minimal usage (ARGMAX strategy, no IC classification)
• Optimizer + RollingIC — IC classification per bar, classify() method
• Optimizer + ObjectiveWeights — composite scoring for multi-objective grids
• Optimizer + RegimeGate — gate-filtered observe() calls
• All 4 — full stack
Initialization order: any order; there are no cross-UDT init dependencies.
The caller is responsible for pushing IC values into RollingIC before
calling classify(); a fresh buffer returns 0.0 thresholds (safe default).
─── §C GP MATH VERIFICATION (follow-up 3) ───────────────────────────────
Jacobi solver convergence domain: guaranteed for diagonally dominant K.
K is diagonally dominant when kernel_noise > 0 (K[i,i] = kernel(xi,xi) +
noise ≥ 1 + noise > Σ_{j≠i} kernel(xi,xj) for RBF/Matern52 with ls > 0).
NaN propagation guard: f_optimizer_new() enforces noise ≥ 1e-6 at
construction (see implementation below). NaN in ic_sample is gated by
the na(ic_sample) check in observe() before any array writes.
Grid size constraints (enforced at f_optimizer_new):
grid_size == 1 → runtime.error (BAYESIAN is undefined for a single cell)
grid_size > 30 → runtime.error for BAYESIAN only (Jacobi O(n²×20) budget)
grid_size ≥ 2 → all strategies valid
ARGMAX/UCB/THOMPSON have no upper grid-size constraint.
Unit test specification (see test_icoptimizer_v2_unit.pine):
T1: grid=[1.0] + BAYESIAN → should hit error log (na guard)
T2: grid=[1.0,2.0] + BAYESIAN, 50 observe() calls → ic_var shrinks
T3: grid size=30 + BAYESIAN → no silent NaN on bar 500 / 1000
T4: rising IC synthetic trajectory → propose() returns idx 6 after warmup
T5: peak-in-middle IC → propose() converges to idx 3 (center)
─── §D v5→v6 DELTA (Phase E-2a) ─────────────────────────────────────────
1. //version=5 → //version=6
2. type ICOptimizer → decomposed to 4 independent UDTs (§B)
3. `series float` qualifiers explicit; `simple int` for all ta.* lengths (CE10297)
4. Enum OptimizerKind / KernelKind / ReturnMode replaces bare strings
5. classifyIC scalar bug → RollingIC ring buffer + sort-based percentile
6. detectAndAdjustDomination orphan → method check_domination on Optimizer
7. Monotonic counter → reset_decay(decay) method
8. S9: all multi-line ternaries collapsed to single lines
9. f_ma_for_idx() dispatch in demo for simple-int ta.sma constraint
10. Nested array.get() in f_build_gram / observe() split to locals (COMMA_STATEMENTS)
f_ic_pearson(signal, ret, n)
Pearson IC: correlation of signal with forward return
Parameters:
signal (float): Signal series (e.g. z-score oscillator)
ret (float): Forward return series (aligned: ret[0] = realized return for signal[horizon])
n (simple int): Rolling window (simple int — required by ta.correlation)
f_ic_spearman(signal, ret, n)
Spearman IC via rank correlation approximation
Parameters:
signal (float): Signal series
ret (float): Forward return series
n (simple int): Rolling window
Returns: Spearman rank-correlation approximation
f_ic_kendall(signal, ret, n)
Kendall IC approximation (via concordant/discordant sign correlation)
Parameters:
signal (float): Signal series
ret (float): Forward return series
n (simple int): Rolling window
Returns: Kendall tau approximation
f_ic_partial(signal, ret, control, n)
Partial IC: correlation of signal with ret after removing control variable
Parameters:
signal (float): Signal series
ret (float): Forward return series
control (float): Control variable to partial out
n (simple int): Rolling window
Returns: Partial Pearson IC
f_forward_return(src, horizon, mode, benchmark)
Compute forward return from source series
Parameters:
src (float): Source price series
horizon (simple int): Look-forward bars
mode (series ReturnMode): ReturnMode enum
benchmark (float): Optional benchmark (used only in EXCESS mode; pass na otherwise)
f_label_from_signal(sig, ret, eps)
Label from signal × return sign match
Parameters:
sig (float): Signal value
ret (float): Realized return
eps (float): Dead-zone threshold (returns within ±eps labelled 0)
Returns: 1 = correct direction, -1 = wrong direction, 0 = inside dead-zone
f_rolling_ic_new(capacity)
Create a new RollingIC buffer
Parameters:
capacity (simple int): Number of IC samples to retain
method push(self, ic_val)
Push a new IC observation into the ring buffer
Namespace types: RollingIC
Parameters:
self (RollingIC)
ic_val (float)
method classify(self, ic_val, good_pct, bad_pct)
Classify current IC against ring buffer distribution
Namespace types: RollingIC
Parameters:
self (RollingIC): RollingIC buffer (must have been pushed at least once)
ic_val (float): Current IC to classify
good_pct (float): Percentile above which IC is "good" (0–100)
bad_pct (float): Percentile below which IC is "bad" (0–100)
Returns: [is_good, is_bad, good_thresh, bad_thresh]
f_optimizer_new(kind, grid, lr, c_ucb, cooldown, kernel_kind, kernel_ls, kernel_noise)
Create a new Optimizer
Parameters:
kind (series OptimizerKind): Strategy
grid (array<float>): Parameter grid (array<float>, size ≤ 30 for BAYESIAN)
lr (float): EWM learning rate for ic_ema / ic_var updates (0–1)
c_ucb (float): UCB exploration constant (ignored for non-UCB)
cooldown (simple int): Minimum bars between switches
kernel_kind (series KernelKind): Kernel for BAYESIAN (ignored otherwise)
kernel_ls (float): Kernel lengthscale (ignored otherwise)
kernel_noise (float): Observation noise (ignored otherwise)
method propose(self)
Propose next parameter index to try
Namespace types: Optimizer
Parameters:
self (Optimizer)
Returns: Selected grid index
method observe(self, idx, ic_sample)
Record observed IC for a grid cell and update posterior
Namespace types: Optimizer
Parameters:
self (Optimizer)
idx (int): Grid index that was evaluated
ic_sample (float): Observed IC value
method reset_decay(self, decay)
Apply exponential decay to ic_ema and ic_var (prevents monotonic drift)
Namespace types: Optimizer
Parameters:
self (Optimizer)
decay (float): Decay factor 0..1 (e.g. 0.95 = retain 95% of past)
method check_domination(self, long_n, short_n, ratio_threshold)
Detect directional signal domination and bump current grid index
Namespace types: Optimizer
Parameters:
self (Optimizer)
long_n (int): Count of long signals in evaluation window
short_n (int): Count of short signals in evaluation window
ratio_threshold (float): Domination ratio (e.g. 4 = 4:1 imbalance)
Returns: [was_dominated, direction_str] direction_str = "long" | "short" | "none"
method current_param(self)
Get current parameter value from grid
Namespace types: Optimizer
Parameters:
self (Optimizer)
f_find_optimal_param(testParams, icValues, currentParam, smoothing)
Find optimal parameter from arrays (v1-compatible, wraps Optimizer.propose)
Parameters:
testParams (array<float>): Grid array
icValues (array<float>): IC values for each grid cell (same size)
currentParam (float): Current param (for EWM smoothing)
smoothing (simple float): EWM lr (0–1)
Returns: [newParam, bestIC, bestIdx]
f_regime_gate_new(mode, threshold, confirm_bars)
Create RegimeGate
Parameters:
mode (string)
threshold (float)
confirm_bars (simple int)
method is_open(self, ic_val, bars_above)
Check if gate is open given current IC and a rolling counter
Namespace types: RegimeGate
Parameters:
self (RegimeGate): RegimeGate
ic_val (float): Current IC
bars_above (int): Rolling bars-above-threshold counter (caller maintains)
Returns: bool gate_open
f_obj_weights_new(w_ic, w_hitrate, w_freq_penalty, w_drawdown_penalty)
Create ObjectiveWeights
Parameters:
w_ic (float)
w_hitrate (float)
w_freq_penalty (float)
w_drawdown_penalty (float)
f_composite_score(w, ic, hitrate, freq, drawdown)
Compute composite score for a grid cell
Parameters:
w (ObjectiveWeights): ObjectiveWeights
ic (float): IC value for cell
hitrate (float): Hit rate 0..1 for cell
freq (float): Signal frequency 0..1 (higher = more signals = penalized)
drawdown (float): Max drawdown magnitude (positive float)
Returns: Composite score (higher = better)
f_optimizer_serialize(self)
Serialize Optimizer state to a compact CSV string
Parameters:
self (Optimizer): Optimizer to serialize
Returns: string blob (pass to f_optimizer_restore to reconstruct ic_ema/ic_var)
f_optimizer_restore(self, blob)
Restore ic_ema/ic_var/visits from serialized blob into an existing Optimizer
Parameters:
self (Optimizer): Optimizer (grid must already be initialized with correct size)
blob (string): String from f_optimizer_serialize
Returns: self (mutated in place)
f_diag_table(self, gate, weights, pos, max_rows)
Render diagnostics table for Optimizer state
Parameters:
self (Optimizer): Optimizer
gate (RegimeGate): RegimeGate (pass na if unused)
weights (ObjectiveWeights): ObjectiveWeights (pass na if unused)
pos (string): Table position (e.g. position.bottom_right)
max_rows (simple int): Maximum grid rows to display (capped at array.size(grid))
Returns: table reference
f_diag_panel(self, height)
Render sparkline-style panel (one plot bar per grid cell, height = ic_ema)
Parameters:
self (Optimizer): Optimizer
height (float): Panel height in price units (caller scales)
Returns: label(na) (renders labels directly)
f_kind_str(k)
Parameters:
k (series OptimizerKind)
f_kernel_str(k)
Parameters:
k (series KernelKind)
f_return_mode_str(m)
Parameters:
m (series ReturnMode)
RollingIC
Rolling IC ring buffer for proper percentile computation
Fields:
samples (array<float>): Circular buffer of IC observations
head (series int): Write head (mod capacity)
capacity (series int): Max samples to retain
Optimizer
Optimizer — unified UDT for all 4 strategies
Fields:
kind (series OptimizerKind): Strategy: ARGMAX | UCB | THOMPSON | BAYESIAN
grid (array<float>): Discrete parameter grid (size ≤ 30 for BAYESIAN)
ic_ema (array<float>): Posterior mean per cell (EWM updated)
ic_var (array<float>): Posterior variance per cell (UCB/Thompson/Bayes)
visits (array<int>): Visit count per cell
lr (series float): EWM learning rate for ic_ema / ic_var updates
c_ucb (series float): Exploration coefficient (UCB only)
cooldown (series int): Minimum bars between parameter changes
last_change_bar (series int): Bar index of last change
current_idx (series int): Currently selected grid index
kernel_matrix (array<float>): Flattened len(grid)² Gram matrix (BAYESIAN only)
kernel_kind (series KernelKind): RBF | MATERN52 (BAYESIAN only)
kernel_ls (series float): Kernel lengthscale (BAYESIAN only)
kernel_noise (series float): Observation noise σ² (BAYESIAN only)
total_visits (series int): Cumulative visit count (for UCB log normalizer)
decay_factor (series float): EWM decay applied by reset_decay (0..1; 1=no decay)
RegimeGate
RegimeGate — IC regime filter
Fields:
mode (series string): "positive" | "any" | "top_pct"
threshold (series float): IC threshold for "positive" or percentile for "top_pct"
confirm_bars (series int): Bars IC must stay above threshold before gate opens
ObjectiveWeights
ObjectiveWeights — composite scoring weights
Fields:
w_ic (series float): Weight on IC component
w_hitrate (series float): Weight on hit-rate component
w_freq_penalty (series float): Penalty for excessive signal frequency
w_drawdown_penalty (series float): Penalty for drawdown
Phát hành các Ghi chú
v2Thư viện Pine
Theo đúng tinh thần TradingView, tác giả đã công bố mã Pine này như một thư viện mã nguồn mở để các lập trình viên Pine khác trong cộng đồng có thể tái sử dụng. Chúc mừng tác giả! Bạn có thể sử dụng thư viện này cho mục đích cá nhân hoặc trong các ấn phẩm mã nguồn mở khác, nhưng việc tái sử dụng mã này trong các ấn phẩm phải tuân theo Nội Quy.
Thông báo miễn trừ trách nhiệm
Thông tin và các ấn phẩm này không nhằm mục đích, và không cấu thành, lời khuyên hoặc khuyến nghị về tài chính, đầu tư, giao dịch hay các loại khác do TradingView cung cấp hoặc xác nhận. Đọc thêm tại Điều khoản Sử dụng.
Thư viện Pine
Theo đúng tinh thần TradingView, tác giả đã công bố mã Pine này như một thư viện mã nguồn mở để các lập trình viên Pine khác trong cộng đồng có thể tái sử dụng. Chúc mừng tác giả! Bạn có thể sử dụng thư viện này cho mục đích cá nhân hoặc trong các ấn phẩm mã nguồn mở khác, nhưng việc tái sử dụng mã này trong các ấn phẩm phải tuân theo Nội Quy.
Thông báo miễn trừ trách nhiệm
Thông tin và các ấn phẩm này không nhằm mục đích, và không cấu thành, lời khuyên hoặc khuyến nghị về tài chính, đầu tư, giao dịch hay các loại khác do TradingView cung cấp hoặc xác nhận. Đọc thêm tại Điều khoản Sử dụng.