KF_AIKF-AI™ is a deterministic Pine Script v6 library providing a constitutional foundation for AI-oriented services.
The library is organized into ten modules covering identity, constants, enumerations, runtime contracts, utility functions, manifest governance, AI standards, health monitoring, diagnostics, and operational summaries.
KF-AI™ currently provides deterministic contracts, validation, state management, utilities, health classification, diagnostics, and summary services. It does not perform machine-learning inference, prediction, scoring, automated trading, or trade execution.
The architecture is designed as a reusable foundation for future AI-oriented Pine Script services while maintaining explicit separation between identity, runtime contracts, governance, health, diagnostics, and summary layers.
Version: 1.0.0
Build: 0001
Modules: AI-0001 through AI-0010
Constitution: Constitution v1.0 Library
MACD Pro: Presets by Trading Style with Trend and Chop Filters-------DESCRIPTION
There is no single best MACD setup. The right one depends on your timeframe and on how you trade.
Gerald Appel built the indicator around 12, 26 and 9 for daily and weekly charts. Put those numbers on a fast intraday chart and the signal lags behind the price action. MACD Pro carries the tested setup for each trading style, and it will pick one for you based on the chart you are on.
----------THE PRESETS
Swing (4H, daily, weekly) uses 12, 26 and 9. This is the original setup. It finds macro trends and major reversals without catching every bit of market noise.
Day trading (5m, 15m, 1H) uses 8, 17 and 9. Balanced responsiveness. It cuts the lag on intraday charts while keeping enough smoothing to avoid constant whipsaws.
Intraday momentum (3m, 5m) uses 5, 13 and 1. Dropping the signal length to 1 turns the signal into a direct zero-line cross, which is the sweet spot for catching quick intraday momentum pushes.
Scalping (1m, 3m) uses 3, 10 and 1. Maximum sensitivity to short-term price bursts. High frequency and noisy, so it demands strict risk management.
"Auto by timeframe" reads your chart and loads the right setup for you. Move from a 5-minute chart to a daily chart and the settings follow you. Custom is there when you want your own numbers.
-----------WHY A SIGNAL LENGTH OF 1 IS HANDLED DIFFERENTLY
An exponential average of one period returns the input unchanged. That means at a signal length of 1, the signal line sits exactly on top of the MACD line and a crossover can never fire.
In that mode the script hides the signal line and switches the trigger over to the zero-line cross. The table on the pane tells you which mode is running.
--------------THE FILTERS
200 EMA trend filter. Longs only while price is above the 200 EMA, shorts only while price is below it. A crossover that passes prints a solid triangle. A crossover that fails prints a hollow circle, so you still see that it happened and can judge it yourself.
Chop filter. The MACD is a trend-following momentum tool. In a sideways range, crossovers fire over and over, and every one of them is a whipsaw. When ADX sits under your threshold, the pane shades gray and crossovers are marked as failed. Rely on crossovers only when price is expanding or breaking out.
----------------THE HISTOGRAM AS AN EARLY WARNING
The histogram measures the distance between the MACD line and the signal line. When those bars start shrinking toward zero, momentum is exhausting, even before any crossover happens.
Bars print bright while the histogram is expanding and fade to translucent while it is contracting. A bright green run turning pale is your cue to tighten stops or take something off the table. Yellow dots mark two shrinking bars in a row if you want it called out explicitly.
--------------PAIR IT WITH STRUCTURE
Never take a MACD entry on its own. Line the signals up with breaks of support and resistance, or with an opening range breakout backed by above-average volume. The more elements in agreement, the more confidence you can carry into the trade.
---------------ALERTS
- Filtered bullish signal
- Filtered bearish signal
- Upside momentum exhausting
- Downside momentum exhausting
-----------------NOTES
Written in Pine Script version 6. Every setting is editable, so nothing here is locked to my preferences.
Indicator
EWCoreLibEWCoreLib — Elliott Wave Pattern Evaluation Library
Overview
EWCoreLib is a Pine Script v6 library of exported functions that validate and score Elliott Wave price patterns. It has no chart output of its own and is not meant to be added to a chart; a calling script supplies the pivot data and decides what to do with the results.
It is one of three scripts published together: the EWCore indicator, this library, and EWCore Docs, an on-chart reference panel for EWCore.
If you are using EWCore, there is nothing to do here. EWCore pulls in this library itself, as part of its own code — you add EWCore to your chart and the library comes with it. This page exists for Pine programmers who want to call these functions from a script of their own, and as the open source behind EWCore's evaluation stage. The dependency runs one way only: EWCore needs this library, the library needs nothing from EWCore, and any script that can produce its own pivot series can use it.
Relation to the earlier publication
An earlier version of this library was published under the name EWCoreEvaluators. This publication continues the same codebase under a new name; it is not a variant or a competing implementation. The name changed because the earlier title stays bound to the earlier publication and cannot be reused. Any script that imports the old path should be pointed at Wick-Sniper/EWCoreLib, which is the one that receives further work.
Concepts
Pivots in, verdicts out. Every evaluator takes a pivot series — prices and bar positions in two parallel arrays — plus a starting index, and asks one question about the window beginning there: does this stretch of price form the pattern I check for? The answer comes back as a filled WaveCount object carrying the wave points, the hard-rule verdict, the component scores and an invalidation level, or as an empty one if the window does not qualify.
Configuration travels as an object, not as a parameter list. An exported function cannot read the global variables of the script importing it, and these evaluators need a great many settings — tolerances, score weights, Fibonacci levels, feature toggles. Passing them individually would mean unwieldy signatures that break on every added option. Instead the calling script builds one EngineConfig object per bar and hands it to whichever functions it calls.
Every exported function is pure. Parameters in, values out, no hidden state. That is what makes it safe to call the same evaluator from several contexts in one bar — a forward search, a historical chain walk, a sub-wave decomposition — without behaviour drifting between them.
Marginal violations are penalised, not rejected. A candidate that misses a rule by a hair is scored down rather than discarded, so a near-miss reading stays visible and comparable instead of vanishing silently.
Exported types
WaveCount — one complete candidate scenario: pattern type, direction, degree label, wave points and their bars, hard-rule and overlap flags, component scores, confidence tier, invalidation level, extension info.
EngineConfig — the settings bundle described above.
Exported functions, by purpose
Pattern evaluation — one per pattern in the canon this project supports: evaluateImpulseWindow, evaluateDiagonalWindow, evaluateZigzagFlatWindow, evaluateTriangleWindow, evaluateComboWXYWindow, evaluateComboWXYXZWindow, evaluateImpulseRecoveredWindow (rebuilds an impulse whose fourth wave the coarse pivot pass swallowed), evaluateLegWhole (judges a whole leg as a single structure rather than a chain of fragments).
Type arbitration — f_discriminateWindow, f_evalWindowTrio: when impulse, diagonal and triangle all pass on the same window, these decide which type the window really is, so the least strict pattern cannot win by default.
Re-checking and scoring — revalidateCandidate, f_comboBonus, f_dpEdgeModifier, f_legDominance, f_legWholeDominance, f_evalCandDegStep, f_evalFloorLeg, f_candDegPasses, f_floorLegPasses.
Chain and gap handling — f_realGapFill (dynamic-programming best-path search for a continuous chain of valid patterns across a pivot series), f_gapNoiseMetrics, f_depth2InnerLegs.
Degree handling — getDegreeLabel, shiftDegreeUp, shiftDegreeDown, f_degreeNameFor, f_notatedLabelTextArray: translate between Elliott degrees and the notation that belongs to each.
Explanatory text — f_patternExplanation, f_extGlyph, f_extTooltip, f_atomicDiagText, f_gapBridgeText, f_rankStage, f_rankWhy, impulseFunnelText, comboFunnelText: turn a verdict into readable reasoning for labels and tooltips.
Drawing helpers — drawImpulseFunnel, drawComboFunnel, f_drawWholeLegFallback, f_drawWholeLegFallbackFromPivots, f_drawWholeLegFallbackFromArrays, f_drawExplanationLabel, f_drawDateMarker, f_drawRoutedLeg.
Utilities — subLowerBound, f_barValToTime (resolves a stored bar value to a timestamp; historical drawings need xloc.bar_time), getLabelSize, f_lineStyle, f_frameCount.
Calling it from your own script
This section is for Pine programmers writing their own script. EWCore users can skip it — EWCore handles the import itself.
import Wick-Sniper/EWCoreLib/1 as ewcore
Type the alias by hand. The "copy to clipboard" button on the library page appends the library name a second time and produces an import line that does not resolve.
From there, build an EngineConfig once per bar, then call the evaluator for the pattern and window you are testing and read the returned WaveCount.
Notes
This library validates structure; it does not detect pivots. The quality of everything it returns depends on the pivot series you feed it — a threshold that admits noise produces confident verdicts about noise. In this project's own testing, wave counting becomes reliable at 15-minute charts and above.
A passing verdict means a reading exists that satisfies the rules checked, not that it is the correct count. Elliott Wave analysis admits more than one valid interpretation of the same chart, and these functions score candidates rather than settle them.
This library is a technical analysis building block. It is not financial advice and generates no trading signals. Library
Arbor_Gradient_Boosting_GainzAlgoGainzAlgo is excited to bring the ability to perform gradient boosting and feature importance selection to Pine Script. Currently, there are no native capabilities within Pine Script for gradient boosting or feature importance selection. Arbor fills this significant gap by introducing a from-scratch Gradient Boosting Machine (GBM) engineered with XGBoost-style mechanics.
Designed to support both classification and regression tasks, and building on our Random Forest approach to Pinescript, Arbor utilizes depth-1 stumps, meaning it performs one split per round without column subsampling.
Because PulseWire automatically lists the exported types and function parameters, the following outlines the core mechanics and capabilities you unlock by importing Arbor.
Core Mechanics
Arbor brings advanced machine-learning concepts directly into your Pine Script workflows:Advanced Training: Utilizes Newton leaf steps (second-order hessian weighting) and the exact XGBoost gain formula.
Regularization & Pruning: Integrates L2 regularization (lambda), minimum gain pruning (gamma), and minimum child weight checks to manage model complexity and prevent overfitting.
Stochasticity: Implements Fisher-Yates row subsampling to provide genuine round-to-round stochasticity matching XGBoost's subsample behavior.
Reproducibility: You can pass an optional seed to any fit function to ensure reproducible training runs across reloads.
Model Tiers
The library supports models scaled across three specific feature tiers:
GBM (1 Feature): Built for rapid classification or regression implementations.
GBM3 (3 Features): Purpose-built specifically for classification tasks.
GBM4 (4 Features): Supports both classification and regression, and uniquely offers XGBoost-style, gain-based feature importance evaluation.
Library "Arbor_Gradient_Boosting_GainzAlgo"
Arbor — gradient boosting for Pine Script. From-scratch GBM v2
with XGBoost-style mechanics: Fisher-Yates row subsampling, Newton leaf steps
(second-order hessian weighting), exact XGBoost gain formula with L2
regularization (lambda), minimum gain pruning (gamma), and minimum child
weight. Trees are depth-1 stumps (one split per round) and there is no
column (feature) subsampling — this is an XGBoost-style boosting scheme,
not a full XGBoost reimplementation. Supports classification and regression
across three feature tiers:
- GBM (1 feature) : gbm_fit / gbm_predict
classification or regression via is_classifier
- GBM3 (3 features) : gbm3_fit / gbm3_predict
classification only
- GBM4 (4 features) : gbm4_fit / gbm4_predict / gbm4_importance_pct
classification or regression with XGBoost-style
gain-based feature importance
All variants use Newton leaf steps, exact gain formula, L2 regularization,
Fisher-Yates shuffle subsampling, and gamma/min_child_weight pruning. Pass
an optional seed to any fit function for reproducible training runs.
gbm_fit(feat, target, n_rounds, lr, n_thresh, is_classifier, lambda, gamma, min_child_w, subsample, seed)
Fits a single-feature gradient-boosted stump ensemble using
XGBoost-style mechanics: Newton leaf steps (second-order hessian weighting),
exact gain formula with L2 regularization, gamma pruning, minimum child
weight, and Fisher-Yates row subsampling. Each round fits one depth-1 stump
(this is not a full multi-level tree, and there is no column subsampling).
Supports both binary classification (log-odds + sigmoid) and regression (MSE).
Parameters:
feat (array) : Array of feature values, one per training row
target (array) : Array of targets — 0.0/1.0 for classification, continuous for regression
n_rounds (int) : Number of boosting rounds / stumps to fit
lr (float) : Learning rate / shrinkage applied to each round's leaf contribution
n_thresh (int) : Candidate split thresholds to scan per round
is_classifier (bool) : True = binary classification, False = squared-error regression
lambda (float) : L2 leaf regularization — Ridge-style shrinkage toward zero (XGBoost default: 1.0)
gamma (float) : Minimum gain required to accept a split — prunes weak splits (XGBoost default: 0.0)
min_child_w (float) : Minimum hessian sum per child node — prevents tiny noisy splits (XGBoost default: 1.0)
subsample (float) : Fraction of rows randomly sampled per round via Fisher-Yates (default: 1.0 = all rows)
seed (int) : Optional seed for the row-subsampling shuffle — pass a fixed value for reproducible fits across reloads (default: na = unseeded/random each time)
Returns: Fitted GBM object ready for gbm_predict()
gbm_predict(model, x)
Scores a single feature value against a fitted GBM ensemble.
Parameters:
model (GBM) : A GBM object previously returned by gbm_fit()
x (float) : Feature value to score (same feature definition used in training)
Returns: Predicted probability if classifier, raw predicted value if regressor
gbm3_fit(feat1, feat2, feat3, target, n_rounds, lr, n_thresh, lambda, gamma, min_child_w, subsample, seed)
Fits a 3-feature gradient-boosted classifier using XGBoost-style
mechanics: Newton leaf steps, exact gain formula, L2 regularization, gamma
pruning, minimum child weight, and Fisher-Yates row subsampling. Selects the
best (feature, threshold) pair each round and boosts in log-odds space.
Each round fits a single depth-1 stump; there is no column subsampling.
Parameters:
feat1 (array) : Array of feature 1 values, one per training row
feat2 (array) : Array of feature 2 values, one per training row
feat3 (array) : Array of feature 3 values, one per training row
target (array) : Array of binary targets (0.0 or 1.0), one per training row
n_rounds (int) : Number of boosting rounds
lr (float) : Learning rate / shrinkage
n_thresh (int) : Candidate thresholds scanned per feature per round
lambda (float) : L2 leaf regularization (Ridge shrinkage, XGBoost default: 1.0)
gamma (float) : Minimum gain to accept a split (XGBoost default: 0.0)
min_child_w (float) : Minimum hessian sum per child node (XGBoost default: 1.0)
subsample (float) : Row sampling fraction per round via Fisher-Yates (default: 1.0)
seed (int) : Optional seed for the row-subsampling shuffle — pass a fixed value for reproducible fits across reloads (default: na = unseeded/random each time)
Returns: Fitted GBM3 object ready for gbm3_predict()
gbm3_predict(model, x1, x2, x3)
Scores 3 feature values against a fitted GBM3 classifier.
Parameters:
model (GBM3) : GBM3 object from gbm3_fit()
x1 (float) : Current value of feature 1
x2 (float) : Current value of feature 2
x3 (float) : Current value of feature 3
Returns: Predicted probability
gbm4_fit(feat1, feat2, feat3, feat4, target, n_rounds, lr, n_thresh, is_classifier, lambda, gamma, min_child_w, subsample, seed)
Fits a 4-feature gradient-boosted ensemble with Newton steps, exact gain
formula, L2 regularization, gamma pruning, minimum child weight, Fisher-Yates
row subsampling, and gain-based feature importance tracking.
Supports both binary classification and regression. Each round fits a single
depth-1 stump; there is no column subsampling.
Parameters:
feat1 (array) : Array of feature 1 values, one per training row
feat2 (array) : Array of feature 2 values, one per training row
feat3 (array) : Array of feature 3 values, one per training row
feat4 (array) : Array of feature 4 values, one per training row
target (array) : Array of targets — 0.0/1.0 for classification, continuous for regression
n_rounds (int) : Number of boosting rounds
lr (float) : Learning rate / shrinkage
n_thresh (int) : Candidate thresholds scanned per feature per round
is_classifier (bool) : True = binary classification, False = regression
lambda (float) : L2 leaf regularization (Ridge shrinkage, XGBoost default: 1.0)
gamma (float) : Minimum gain to accept a split (XGBoost default: 0.0)
min_child_w (float) : Minimum hessian sum per child node (XGBoost default: 1.0)
subsample (float) : Row sampling fraction per round via Fisher-Yates (default: 1.0)
seed (int) : Optional seed for the row-subsampling shuffle — pass a fixed value for reproducible fits across reloads (default: na = unseeded/random each time)
Returns: Fitted GBM4 object with importance scores, ready for gbm4_predict() / gbm4_importance_pct()
gbm4_predict(model, x1, x2, x3, x4)
Scores 4 feature values against a fitted GBM4 ensemble.
Parameters:
model (GBM4) : GBM4 object from gbm4_fit()
x1 (float) : Current value of feature 1
x2 (float) : Current value of feature 2
x3 (float) : Current value of feature 3
x4 (float) : Current value of feature 4
Returns: Predicted probability if classifier, raw predicted value if regressor
gbm4_importance_pct(model, feat_idx)
Returns normalized feature importance as % of total gain for one feature.
Importance = accumulated gain credited to this feature across all boosting rounds,
matching XGBoost's xgb.importance() Gain column definition.
Parameters:
model (GBM4) : GBM4 object from gbm4_fit()
feat_idx (int) : Feature index to query (0-3)
Returns: Percentage of total ensemble gain attributed to this feature (0.0–100.0)
GBM
Holds a fitted gradient-boosted stump ensemble (1 feature).
Fields:
thresh (array) : Split threshold for each round's stump
left_val (array) : Newton leaf value when feature < threshold
right_val (array) : Newton leaf value when feature >= threshold
base_score (series float) : Log-odds of training mean (classifier) or mean (regressor)
lr (series float) : Learning rate stored for inference
is_classifier (series bool) : True = sigmoid probability output, False = raw regression output
GBM3
Holds a fitted 3-feature gradient-boosted stump ensemble (classification only).
Fields:
stump_feat (array) : Which feature index (0-2) each round's stump split on
thresh (array) : Split threshold for each round's stump
left_val (array) : Newton leaf value when feature < threshold
right_val (array) : Newton leaf value when feature >= threshold
base_score (series float) : Log-odds of training mean
lr (series float) : Learning rate stored for inference
GBM4
Holds a fitted 4-feature gradient-boosted ensemble with gain-based importance.
Fields:
stump_feat (array) : Which feature index (0-3) each round's stump split on
thresh (array) : Split threshold for each round's stump
left_val (array) : Newton leaf value when feature < threshold
right_val (array) : Newton leaf value when feature >= threshold
importance (array) : Accumulated gain per feature (indices 0-3), raw — normalize via gbm4_importance_pct()
base_score (series float) : Log-odds (classifier) or mean (regressor)
lr (series float) : Learning rate stored for inference
is_classifier (series bool) : True = sigmoid probability output, False = raw regression output Library
DeeptestLibrary "Deeptest"
Comprehensive quantitative backtesting library with 50+ metrics:
Sharpe/Sortino ratios, R-Expectancy, SQN, drawdown analysis, Monte Carlo
simulation, Walk-Forward Analysis, VaR/CVaR, benchmark comparison, and
interactive table rendering for PulseWire strategies.
@version 15 (20.06.2026)
@license MIT — opensource.org
IMPORTS:
fikira/Text/1 as FN — Font styling for table cells (Sans Bold / Sans-Serif Bold)
PUBLIC API:
runDeeptest(...) — Complete backtest analysis orchestrator (only export)
type Stats — 50+ metric container returned by runDeeptest
type ThresholdConfig — Metric threshold + color configuration
type RollingStats — Rolling window analysis results
══════════════════════════════════════════════════════════════════════════════════════
runDeeptest(tableBg, headerBg, borderColor, bullColor, bearColor, textSize, showComplementaryRow, showStressTestTable, showDrawdownRecoveryCards, showTradeCards, showRExpectancy, enableLogging)
runDeeptest — Complete backtest analysis orchestrator (PUBLIC API)
Calls calculateFromStrategy() for 50+ metrics, then renders:
├ Main backtest table (23 columns × 3 rows + complementary row + footer)
├ Stress test matrix (IS | Monte Carlo | OOS — if showStressTestTable)
├ Drawdown/recovery cards (if showDrawdownRecoveryCards)
└ Top/worst trade cards (if showTradeCards)
Execution model: heavy computation runs once on last confirmed bar, table
rendering on last bar. Benchmark returns accumulate per-bar from SPY daily.
Parameters:
tableBg (color) : Table background color
headerBg (color) : Header background color
borderColor (color) : Border color
bullColor (color) : Color for positive metric values
bearColor (color) : Color for negative metric values
textSize (string) : Cell font size
showComplementaryRow (bool) : Toggle 2nd data row
showStressTestTable (bool) : Toggle MC/WFA stress test table
showDrawdownRecoveryCards (bool) : Toggle drawdown/recovery card tables
showTradeCards (bool) : Toggle top/worst trade card tables
showRExpectancy (bool) : R-multiple display mode for expectancy
enableLogging (bool) : Output all metrics to Data Window via log.info()
Returns: Stats object with all computed metrics
═══════════════════════════════════════════════════════════════════════════
Stats
Stats — Comprehensive backtest statistics container (50+ fields)
Fields:
totalTrades (series int)
winTrades (series int)
lossTrades (series int)
evenTrades (series int)
winRate (series float)
lossRate (series float)
avgWinPct (series float)
avgLossPct (series float)
avgTradePct (series float)
profitFactor (series float)
payoffRatio (series float)
expectancy (series float)
rExpectancy (series float)
grossProfit (series float)
grossLoss (series float)
netProfit (series float)
netProfitPct (series float)
compEffect (series float)
sharpe (series float)
sortino (series float)
calmar (series float)
martin (series float)
maxDrawdownPct (series float)
currentDrawdownPct (series float)
maxEquity (series float)
minEquity (series float)
cagr (series float)
monthlyReturn (series float)
maxConsecWins (series int)
maxConsecLosses (series int)
avgTradeDuration (series float)
avgWinDuration (series float)
avgLossDuration (series float)
timeInMarketPct (series float)
tradesPerMonth (series float)
tradesPerYear (series float)
skewness (series float)
kurtosis (series float)
var95 (series float)
cvar95 (series float)
ulcerIndex (series float)
riskOfRuin (series float)
pValue (series float)
alpha (series float)
beta (series float)
buyHoldReturn (series float)
equityRSquared (series float)
firstTradeTime (series int)
lastTradeTime (series int)
tradingPeriodDays (series float)
sqn (series float) Library
AssetCorrelationUtilsAssetCorrelationUtils
Auto-detection library for correlated asset pairings across futures, CFD, and crypto markets. Given any chart, returns the correct secondary and tertiary (and optionally quaternary) tickers for multi-asset divergence analysis, along with inversion flags and asset-category metadata.
Designed to eliminate the boilerplate of hardcoded ticker lists and manual "if EURUSD then GBPUSD" branching in every indicator that needs correlated data.
What it does
Consumer scripts call one function — resolveCurrentChart() — and receive a fully resolved AssetConfig object describing the current chart's correlated pair or triad. The library handles:
Symbol root extraction from full ticker IDs (with expiry suffixes, exchange prefixes, micro variants)
Asset category routing (futures / CFD / crypto branches)
Family-specific triad or dyad selection
Inversion detection (e.g. 6C inverse of USDCAD, DXY inverse of EUR/GBP)
Futures session and back-adjustment modifiers
Optional GXT mode for metals (currency-cross triads on Gold/Silver)
Optional Quad mode for metals (four-leg configurations)
Micro contracts always resolve to their higher-volume full-size correlated partners — MNQ correlates against ES/YM, not MES/MYM — matching the "trade the micros, read the majors" convention.
Supported asset classes
Futures
Indices: NQ, ES, YM, RTY + micros (MNQ, MES, MYM, M2K)
Metals: GC, SI, HG + micros (MGC, SIL, MHG)
Forex: 6E, 6B, 6A, 6N, 6C + micros (M6E, M6B, M6A, M6C)
Energy: CL, RB, HO + micros (MCL, MRB, MHO)
Treasury: ZB, ZF, ZN
Crypto: BTC, ETH + micros (MBT, MET)
CFD / Spot
Forex: EURUSD, GBPUSD, DXY, USDJPY, USDCHF, USDCAD
Metals: XAUUSD, XAGUSD, COPPER + cross-pairs (XAUEUR, XAUGBP, XAGEUR, XAGGBP)
Indices: NAS100, SP500, DJ30
EU Stocks: GER40, EU50 (dyad only)
Crypto (spot / perp)
Major: BTC, ETH, SOL, XRP
Alt: ZEC, DOGE, ADA, BNB, TAO
All routed via BINANCE perpetual (.P) pairs for consistent OHLC quality
Core functions
resolveCurrentChart(gxtMode = false, quadMode = false)
The one-liner entry point for most consumers. Wraps resolveAssets() with sensible defaults (uses syminfo.ticker, syminfo.tickerid, syminfo.type, syminfo.session, back-adjustment on).
resolveAssets(ticker, tickerId, assetType, session, useBackadjust, gxtMode, quadMode)
The full-control entry point. Same detection logic, but with explicit control over back-adjustment and session modification — useful for indicators with a strategy toggle (e.g. RTH vs ETH sessions).
Category detectors
detectIndicesFutures(ticker)
detectMetalsFutures(ticker) / detectMetalsFuturesGxt(ticker) / detectMetalsFuturesQuad(ticker)
detectForexFutures(ticker) / detectCADFutures(ticker)
detectEnergyFutures(ticker)
detectTreasuryFutures(ticker)
detectCryptoFutures(ticker)
detectForexCFD(ticker, tickerId)
detectCrypto(ticker, tickerId)
detectMetalsCFD(ticker, tickerId) / detectMetalsCFDGxt(ticker, tickerId) / detectMetalsCFDQuad(ticker, tickerId)
detectIndicesCFD(ticker, tickerId)
detectEUStocks(ticker, tickerId)
Each returns an AssetPairing — usable directly if you want to bypass the automatic category routing.
Resolution helpers
resolveTriad(chartTickerId, pairing) — returns primary + secondary + tertiary with inversion flags
resolveDyad(chartTickerId, pairing) — returns primary + secondary for two-asset configs
resolveQuad(chartTickerId, pairing) — returns four-asset config with inversion flags
Utility functions
applySessionModifierWithBackadjust(ticker, session) / applySessionModifierNoBackadjust(ticker, session) — apply ticker.modify with back-adjustment on or off
isTriadMode(pairing) — check whether a pairing has a valid tertiary
getAssetTicker(tickerId) — extract the clean ticker string from a full ticker ID
Fallback
getDefaultFallback(tickerId) — returns a pairing with the chart ticker as primary and empty secondaries. Used automatically when no category matches.
Return types
AssetConfig
detected (bool) — true if the chart asset was recognized
isTriadMode (bool) — true if 3 assets resolved, false for dyad
isQuadMode (bool) — true if 4 assets resolved
primary (string) — resolved primary ticker ID
secondary (string) — resolved secondary ticker ID
tertiary (string) — resolved tertiary ticker ID (empty for dyad)
quaternary (string) — resolved quaternary ticker ID (empty unless quad mode)
invertSecondary (bool)
invertTertiary (bool)
invertQuaternary (bool)
assetCategory (string) — category tag (e.g. "index_futures", "metal_cfd_gxt")
AssetPairing
Internal pairing structure used by detector functions. Consumers rarely construct this directly, but resolveTriad / resolveDyad / resolveQuad accept it if you're bypassing the auto-routing.
Quick start
import I_quacker_I/AssetCorrelationUtils/7 as AC
AC.AssetConfig config = AC.resolveCurrentChart()
string secondary = config.secondary
string tertiary = config.tertiary
bool inv2 = config.invertSecondary
bool inv3 = config.invertTertiary
bool detected = config.detected
For metals with currency-cross triads:
AC.AssetConfig config = AC.resolveCurrentChart(true)
// On Gold: secondary = "FOREXCOM:XAUEUR", tertiary = "FOREXCOM:XAUGBP"
// On Copper or non-metals: identical to resolveCurrentChart(false)
Full integration patterns (Off / Auto / Manual tri-state, explicit back-adjust control, and manual pairing) are documented inline in the library source.
Design notes
Robust ticker matching. All detectors use str.contains() on the root symbol, so any ticker format is recognized — bare (NQ), continuous (NQ1!), or dated with expiry (NQZ2025). Exchange prefixes are ignored during detection.
Consistent inversion semantics. DXY as the third leg of USD-base forex triads is marked inverted (rises when the pair falls). 6C as USDCAD's futures counterpart is fully inverted. Micros carry their parent's inversion flags unchanged.
Category tags. Every resolved AssetConfig carries an assetCategory string ("index_futures", "metal_cfd_gxt", "crypto", "fallback", etc.). Useful for consumer scripts that want to conditionally enable features per category (e.g. "only compute GXT confluence on metals").
Fallback safety. When no category matches, the library returns the chart ticker as primary with empty secondary / tertiary, detected = false, and assetCategory = "fallback". Consumer scripts should check detected before assuming correlated data is available.
Credits
Original library concept — @fstarcapital
Modifications and extensions — @I_quacker_I
Crypto remapped to BINANCE .P perpetuals
Micro contracts always correlate against higher-volume mini/full contracts
AUD/NZD forex futures family (6A, M6A, 6N)
GXT mode for metals (currency-cross triads)
Quad mode for four-leg metal configurations
Crypto tertiary swapped from TOTAL3 (market-cap index, no clean OHLC) to XRP (tradeable asset with proper sweep behavior)
License: Mozilla Public License 2.0 Library
CandlePressure_UtilitiesCandlePressure_Utilities is a lightweight Pine library for converting raw OHLC candle structure into a normalized candle-pressure score, buy/sell percentage estimates, oscillator output, and compact display helpers.
The library is designed for scripts that want a reusable candle-pressure layer without rebuilding the same CLV/body/wick math every time.
It centralizes the pieces that commonly repeat across pressure-based scripts:
• close-location value / CLV calculation
• candle body dominance
• upper-vs-lower wick imbalance
• deadzone-filtered wick pressure
• normalized pressure output from -1 to +1
• buy/sell percentage conversion
• pressure oscillator conversion from -100 to +100
• alternate body/wick buy-sell allocation
• compact volume and relative-volume formatting
• table/label size and table-position helpers
• small percent and black/white text helpers
On the example chart, the pressure candles, pressure oscillator, buy/sell split, CLV/body/wick breakdown, alternate body/wick comparison, and compact table values are all materially driven by this library.
This library is intentionally focused on pure candle structure. It does not confirm trend, detect pivots, calculate RSI/DMI/ATR context, decide trade direction, or choose final signal logic for the calling script. Those layers remain script-level decisions.
➖Quick Start➖
Import the library near the top of your script in global scope, alongside any other imports, before calling its helpers.
Typical placement:
//@version=6
indicator(...) or strategy(...)
import MYNAMEISBRANDON/CandlePressure_Utilities/1 as cp
Replace /1 with the latest published version if a newer version is available.
The main helper for most scripts is candlePressureMetrics(), which returns:
• pressure
• buyPct
• sellPct
Example:
= cp.candlePressureMetrics(
open,
high,
low,
close,
volume)
string splitText = cp.fmtBuySellSplit(
buyPct,
sellPct,
volume)
float pressureOsc = cp.pressureOsc(
pressure)
The library uses standard OHLCV argument order:
open, high, low, close, volume
➖What The Library Measures➖
The default candle-pressure model uses:
• Wick Deadzone = 0.02
• CLV Weight = 0.55
• Body Weight = 0.30
• Wick Weight = 0.15
CLV measures where the close finished inside the candle range. Body contribution measures open-to-close directional dominance. Wick contribution measures lower-wick vs upper-wick imbalance.
The final pressure score is a weighted blend of those components, normalized from -1 to +1.
That pressure score can then be converted into buy/sell percentage estimates, a -100 to +100 pressure oscillator, candle-overlay colors, table values, labels, or dashboard outputs.
➖Function Reference➖
These helpers are grouped by purpose.
Most scripts will only need:
• candlePressureMetrics()
• fmtBuySellSplit()
• pressureOsc()
More advanced scripts can use the full component helpers for tables, tooltips, debug output, or custom pressure models.
➖Model + Math Helpers➖
modelDefaults()
Returns the default candle-pressure model values used by this library.
Returns:
Wick deadzone, CLV weight, body weight, wick weight
clamp(v, lo, hi)
Restricts a value between a lower and upper bound.
Parameters:
v (float): Input value
lo (float): Lower bound
hi (float): Upper bound
Returns:
Clamped value
safeDiv(numerator, denominator, fallback)
Safely divides two values and returns the fallback when division is not valid.
Parameters:
numerator (float): Numerator value
denominator (float): Denominator value
fallback (float): Value returned when division is unsafe
Returns:
numerator / denominator, or fallback when unsafe
➖Display + UI Helpers➖
fmtCompact(val, sigFigs, naText)
Formats large values into compact display text such as 1.5k, 2.4m, or 1.2b.
Parameters:
val (float): Value to format
sigFigs (simple int): Significant figures to keep
naText (simple string): Text returned when val is na
Returns:
Compact formatted string
fmtBuySellSplit(buyPct, sellPct, volumeValue)
Formats buy/sell percentages into rounded split text such as 62/38.
Parameters:
buyPct (float): Buy percentage
sellPct (float): Sell percentage
volumeValue (float): Volume value used to handle missing or no-volume bars
Returns:
Formatted buy/sell split text
contrastText(bg)
Chooses black or white text based on background brightness.
Parameters:
bg (color): Background color
Returns:
Readable contrast text color
stripLeadingZero(txt)
Removes the leading zero from decimal text.
Parameters:
txt (string): Input text
Returns:
Adjusted text, such as 0.25 -> .25 or -0.25 -> -.25
fmtRelVol(val, naText)
Formats relative volume with two decimals and strips the leading zero.
Parameters:
val (float): Relative volume value
naText (string): Text returned when val is na
Returns:
Formatted relative-volume text
pctChange(currentValue, baseValue)
Returns the percent change from a base value.
Parameters:
currentValue (float): Current or projected value
baseValue (float): Comparison baseline
Returns:
Percent change
fmtPctWhole(val, naText)
Formats a percent value as rounded whole-percent text.
Parameters:
val (float): Percent value
naText (string): Text returned when val is na
Returns:
Rounded percent string
pctInt(pct)
Rounds and clamps a percentage into 0–100 integer form.
Parameters:
pct (float): Percent value
Returns:
Integer percent from 0 to 100
pctIntVol(pct, volumeValue)
Rounds and clamps a percentage into 0–100 integer form, returning 0 on no-volume bars.
Parameters:
pct (float): Percent value
volumeValue (float): Volume value
Returns:
Integer percent from 0 to 100
tableTextSize(sizeText)
Converts user-facing table-size text into Pine table text-size enums.
Parameters:
sizeText (string): Size text. Expected values: "Tiny", "Small", "Normal", or "Large"
Returns:
Pine table text-size enum
labelSize(sizeText)
Converts user-facing label-size text into Pine label-size enums.
Parameters:
sizeText (string): Size text. Expected values: "Tiny", "Small", "Normal", "Large", or "Huge"
Returns:
Pine label-size enum
tablePos(posText)
Converts user-facing table-position text into Pine table position enums.
Parameters:
posText (string): Table position text
Returns:
Pine table position enum
bw(useBlack)
Returns black text when the condition is true, otherwise white.
Parameters:
useBlack (bool): Whether black text should be used
Returns:
Black or white text color
➖Candle Pressure Helpers➖
candlePressurePartsFull(openValue, highValue, lowValue, closeValue, wickDeadzone, weightClv, weightBody, weightWick)
Converts OHLC candle structure into the full normalized pressure component set.
Parameters:
openValue (float): Candle open
highValue (float): Candle high
lowValue (float): Candle low
closeValue (float): Candle close
wickDeadzone (float): Wick imbalance threshold below which wick contribution is forced to 0
weightClv (float): Weight assigned to the CLV component
weightBody (float): Weight assigned to the body component
weightWick (float): Weight assigned to the wick component
Returns:
CLV, body % of range, signed body term, raw wick imbalance, deadzoned wick imbalance, final pressure
Note:
wickDeadzone, weightClv, weightBody, and weightWick are optional. If omitted, the library uses its default model:
Wick Deadzone 0.02 / CLV 0.55 / Body 0.30 / Wick 0.15
candlePressureParts(openValue, highValue, lowValue, closeValue, wickDeadzone, weightClv, weightBody, weightWick)
Converts OHLC candle structure into the compact pressure component set.
Parameters:
openValue (float): Candle open
highValue (float): Candle high
lowValue (float): Candle low
closeValue (float): Candle close
wickDeadzone (float): Wick imbalance threshold below which wick contribution is forced to 0
weightClv (float): Weight assigned to the CLV component
weightBody (float): Weight assigned to the body component
weightWick (float): Weight assigned to the wick component
Returns:
CLV, body % of range, raw wick imbalance, deadzoned wick imbalance, final pressure
Note:
wickDeadzone, weightClv, weightBody, and weightWick are optional. If omitted, the library uses its default model:
Wick Deadzone 0.02 / CLV 0.55 / Body 0.30 / Wick 0.15
pressureToBuySell(pressure, volumeValue)
Converts normalized pressure into buy/sell percentages.
Parameters:
pressure (float): Candle pressure in the -1..+1 range
volumeValue (float): Volume value used to handle missing or no-volume bars
Returns:
Buy %, Sell %
pressureOsc(pressure)
Converts normalized pressure into a -100..+100 oscillator value.
Parameters:
pressure (float): Candle pressure in the -1..+1 range
Returns:
Pressure oscillator value
candlePressureMetrics(openValue, highValue, lowValue, closeValue, volumeValue, wickDeadzone, weightClv, weightBody, weightWick)
One-call convenience wrapper for scripts that need final pressure, buy %, and sell %.
Parameters:
openValue (float): Candle open
highValue (float): Candle high
lowValue (float): Candle low
closeValue (float): Candle close
volumeValue (float): Volume value used to handle missing or no-volume bars
wickDeadzone (float): Wick imbalance threshold below which wick contribution is forced to 0
weightClv (float): Weight assigned to the CLV component
weightBody (float): Weight assigned to the body component
weightWick (float): Weight assigned to the wick component
Returns:
Pressure, Buy %, Sell %
Note:
wickDeadzone, weightClv, weightBody, and weightWick are optional. If omitted, the library uses its default model:
Wick Deadzone 0.02 / CLV 0.55 / Body 0.30 / Wick 0.15
bodyWickRateBuyPct(openValue, highValue, lowValue, closeValue)
Returns an alternate buy percentage using body/wick structure only.
Parameters:
openValue (float): Candle open
highValue (float): Candle high
lowValue (float): Candle low
closeValue (float): Candle close
Returns:
Buy percentage
bodyWickRateBuySell(openValue, highValue, lowValue, closeValue, volumeValue)
Returns alternate body/wick buy and sell percentages.
Parameters:
openValue (float): Candle open
highValue (float): Candle high
lowValue (float): Candle low
closeValue (float): Candle close
volumeValue (float): Volume value used to handle missing or no-volume bars
Returns:
Buy %, Sell %
➖Important Notes➖
Candle Pressure is not order flow.
The buy/sell split produced by this library is an estimate derived from candle structure. It is not true bid/ask volume, footprint data, or exchange-level order flow.
The pressure model is intentionally pure OHLC structure:
• CLV measures where the close finished inside the candle range.
• Body contribution measures open-to-close directional dominance.
• Wick contribution measures lower-wick vs upper-wick imbalance.
• Final pressure is a weighted blend of those components.
Momentum filters such as RSI, DMI, ATR, trend state, relative volume, or multi-timeframe context should be added by the calling script when needed.
This library provides the reusable candle-pressure foundation only.
➖Release Notes➖
v1
Initial release of CandlePressure_Utilities.
This release provides a focused candle-pressure utility layer for Pine scripts that need reusable OHLC pressure calculations, buy/sell percentage estimates, pressure oscillator output, compact display formatting, and small table/label helper functions.
Included in this release:
• default candle-pressure model values
• safe math helpers
• compact number formatting
• buy/sell split formatting
• relative-volume formatting
• table/label size and table-position helpers
• percent and bias display helpers
• full candle-pressure component output
• compact candle-pressure component output
• pressure-to-buy/sell conversion
• pressure oscillator conversion
• alternate body/wick buy-sell allocation
The library is designed to stay focused on reusable candle-pressure mechanics. It does not decide trend, trade direction, signal confirmation, pivot structure, RSI/DMI filters, ATR filters, or final color logic. Calling scripts remain responsible for their own signal model and visual interpretation.
Library
KernelLens🟦 KernelLens is a professional kernel regression library for Pine Script v6, providing eight mathematically rigorous Nadaraya–Watson estimators, a three-mode filter layer, a unified string dispatcher, and a suite of trading utilities — all built from the ground up on correct non-parametric statistics. Unlike existing Pine smoothing libraries — which inherit a decade-old loop-bound bug that silently reduces every kernel window to a handful of bars, regardless of the bandwidth parameter — KernelLens is built with auditable math, NA-safe iteration, input validation at every entry point, and academic references cited inline next to the formulas they describe.
The library integrates eight independent kernel families — Rational Quadratic, Gaussian, Periodic, Locally Periodic, Epanechnikov, Tricube, Triangular, and Cosine — behind a consistent API, with every raw estimator wrapped in a filter layer (None / Smooth / Zero Lag), a unified dispatcher for dropdown-driven kernel selection, and five utility exports covering slope detection, trend state, crossover signaling, residual confidence bands, and Silverman's rule-of-thumb bandwidth recommendation. Every public function validates its inputs, raises descriptive runtime errors on misuse, and returns `na` only when there is genuinely no data — never as a silent fallback.
🟦 MATHEMATICAL FOUNDATION
**The Nadaraya–Watson Estimator**
Given a source series `y_t` and a symmetric kernel `K` with scale parameter `ℓ` (the "bandwidth"), the Nadaraya–Watson estimator of the regression function `m(x) = E ` evaluated at the current bar is:
```
Σᵢ K(dᵢ / ℓ) · y_{t−i}
ŷ(t) = ───────────────────────
Σᵢ K(dᵢ / ℓ)
```
where `dᵢ` is the bar-distance from the kernel center and the sum runs over a finite window determined by the effective support of `K`.
The estimator is a locally weighted average: bars close to the kernel center contribute heavily, distant bars contribute proportionally less, and bars outside the support contribute nothing. It is asymptotically unbiased up to `O(ℓ²)` for twice-differentiable `m`, with variance of order `(n·ℓ)⁻¹` — the classical bias–variance trade-off that defines all non-parametric smoothers.
**Why Kernel Regression Beats Rolling Means**
A simple moving average gives every bar in the window the same weight. Kernel regression gives each bar a weight that decays smoothly with distance, producing:
- **Smoother output** — no step artifacts when bars enter / leave the window
- **Better bias control** — the peak of the kernel sits exactly on the point being estimated
- **Kernel-specific behavior** — compact-support kernels eliminate tail contamination entirely; Rational Quadratic's `α` parameter exposes multi-scale mixing; Periodic kernels resonate with known cycle lengths
The math has been the academic standard for non-parametric regression since Nadaraya (1964) and Watson (1964). KernelLens brings it to Pine Script v6 in its correct, bug-free form.
🟦 THE EIGHT KERNELS
All eight kernels implement the Nadaraya–Watson weighting scheme. They differ in support (compact versus infinite), smoothness (how many times differentiable), and how weight decays with distance.
| # | Kernel | Formula | Support | Smoothness | Character |
|---|---|---|---|---|---|
| 1 | **Rational Quadratic** | `(1 + d² / (2·α·ℓ²))^(−α)` | ℝ | C∞ | Multi-scale mixer — `α` controls stretch versus wiggle |
| 2 | **Gaussian (RBF)** | `exp(−d² / (2·ℓ²))` | ℝ | C∞ | The canonical smoother — smoothest possible with L² optimality |
| 3 | **Periodic** | `exp(−2·sin²(π·d/p) / ℓ²)` | ℝ | C∞ | Resonates with repetition distance `p` — ideal for cycles |
| 4 | **Locally Periodic** | Periodic · Gaussian | ℝ | C∞ | Seasonal patterns that slowly drift with trend |
| 5 | **Epanechnikov** | `(3/4)(1 − u²) · 𝟙{|u|≤1}` | | C⁰ | Asymptotically MSE-optimal (Watson 1964) — no tail contamination |
| 6 | **Tricube** | `(70/81)(1 − \|u\|³)³ · 𝟙{|u|≤1}` | | C² | The LOWESS standard — near-Gaussian with compact support |
| 7 | **Triangular** | `(1 − \|u\|) · 𝟙{|u|≤1}` | | C⁰ | Simplest non-uniform kernel — fastest to compute |
| 8 | **Cosine** | `(π/4)·cos(π·u/2) · 𝟙{|u|≤1}` | | C¹ | Raised-cosine taper — smoother boundary than Epanechnikov |
where `u = d/ℓ` and `𝟙` is the indicator function.
**Infinite-Support vs Compact-Support — Why Both Matter**
| | Infinite Support (RQ, Gauss, Periodic, LocPeriodic) | Compact Support (Epa, Tricube, Triangular, Cosine) |
|---|---|---|
| **Tail weight** | Never exactly zero | Exactly zero beyond ±ℓ |
| **Loop depth** | `3·ℓ` (3-σ cutoff, ≈99.7% mass) | Exactly `ℓ` |
| **Bar contamination** | Distant bars still pull the estimate a tiny amount | Distant bars cannot affect the estimate at all |
| **Best for** | Smooth trends, Gaussian-process intuition | Robust regression, outlier resistance |
KernelLens picks the correct loop depth automatically based on kernel family: `_depthInfinite` for Gaussian-family kernels, `_depthCompact` for bounded kernels, `_depthPeriodic` for Periodic (which must span enough cycles to reach stable weights).
**Why Eight, Not Four**
Most Pine kernel libraries ship only the four kernels from MacKay's Gaussian process tutorial. KernelLens adds the four compact-support classical kernels because:
- **Epanechnikov** minimises asymptotic mean squared error among all non-negative kernels of bounded support (Watson 1964) — it is the MSE-optimal baseline against which all other kernels are measured
- **Tricube** is the kernel used by LOWESS (Cleveland 1979), the de-facto standard for robust locally weighted scatterplot smoothing
- **Triangular** is the cheapest non-uniform compact kernel — useful when loop-budget matters on intraday charts with huge dataset size
- **Cosine** is C¹-continuous at the support boundary, unlike Epanechnikov's C⁰ discontinuity, producing visibly smoother transitions at kernel edges
Adding them makes the library an academically complete toolkit, not just a Pine port of one tutorial.
🟦 FILTER LAYER — NONE / SMOOTH / ZERO LAG
Every kernel export accepts a `_filter` parameter with three valid values. The filter layer is implemented identically across all eight kernels, so switching kernel families does not change filter behavior.
**"No Filter" — Single-Pass Raw Estimate**
```
ŷ = K(y)
```
One Nadaraya–Watson pass over the source. Cheapest mode, most reactive, fully represents the underlying kernel. Use this when you want the kernel's raw behavior with no additional smoothing or lag correction.
**"Smooth" — Double-Pass Estimate**
```
ŷ = K(K(y))
```
The kernel is applied once to the source, then applied again to its own output using the same bandwidth and the same parameters. The result is a more strongly smoothed curve at the cost of one extra loop pass per bar.
This is mathematically equivalent to convolving the kernel with itself — the effective kernel is wider and flatter, pulling longer-range context into each estimate without requiring the user to double the bandwidth.
**"Zero Lag" — Ehlers De-Lagged Estimate**
```
ŷ = 2·K(y) − K(K(y))
```
The ZLEMA identity from Ehlers (*Rocket Science for Traders*, 2000): subtract the smoothing lag from the raw estimate, effectively shifting the output back in time to match the source more closely.
The intuition: `K(y)` lags `y` by some amount; `K(K(y))` lags `K(y)` by the same amount; so `K(y) − K(K(y))` is an estimate of the lag itself, and adding it back to `K(y)` cancels out. The result tracks the source more tightly than either pass alone, at the cost of slightly noisier turning points.
**Lazy Evaluation — No Wasted Cycles**
In `"No Filter"` mode, the second pass is skipped entirely — it never runs. The filter branch uses an `if` block (not a ternary), so Pine's short-circuit semantics prevent the unused computation. A single kernel call costs one pass; `"Smooth"` or `"Zero Lag"` costs two. You only pay for what you use.
🟦 KERNEL CENTER OFFSET — THE `_phase` PARAMETER
Every KernelLens kernel takes a `_phase` parameter that shifts the kernel center into the past by `_phase` bars. It is the library's non-repainting knob.
**_phase = 0 — Live Estimate**
The kernel is centered on the current bar. The most recent price has maximum weight, and the estimate is as fresh as possible. Suitable for live signal generation, but the most recent bar can re-evaluate as it develops within its interval — standard Pine real-time behavior.
**_phase > 0 — Non-Repainting Historical Estimate**
The kernel center is moved `_phase` bars into the past. The estimate becomes the smoothed value *at that historical bar*, not the current bar. Once the bar at `bar_index − _phase` is fully confirmed (`barstate.isconfirmed`), its estimate cannot change again.
This is the standard trick for publishing kernel indicators that do not repaint: you get a stable, historically accurate curve at the cost of shifting the entire output `_phase` bars to the right on the chart. A `_phase = 25` call gives a curve that lags live price by 25 bars but is guaranteed stable for every past bar.
**Why It Belongs in the Library, Not the Caller**
Pushing `_phase` into the kernel's own loop is not the same as evaluating the kernel at a shifted source (`K(src )`). Shifting the source just uses a stale input with a current-bar-centered kernel, which still produces a fresh estimate of a stale series. KernelLens's `_phase` genuinely moves the kernel center, producing a historical-bar estimate that computes over the correct surrounding window.
🟦 NON-REPAINTING BEHAVIOR
Repainting is the single most-asked question about any Pine indicator, and the single most common source of silent failure when a retail trader moves from backtest to live. A strategy that looks flawless on historical bars and then bleeds money the moment it is deployed is almost always suffering from some form of repainting. KernelLens is engineered from first principles to eliminate every class of repainting by construction — not by patching symptoms, but by removing the dependencies that cause repainting in the first place.
**The Two Forms of Repainting**
| Form | Symptom | Typical Cause |
|---|---|---|
| **Historical repainting** | A bar that was closed days or weeks ago silently changes its plotted value when the chart is refreshed or scrolled | `request.security()` with `lookahead = barmerge.lookahead_on`, un-gated higher-timeframe data, or incorrect array rotation that reads into future bars |
| **Real-time repainting** | The plotted value on the live (current developing) bar flickers tick-by-tick as new price ticks arrive, then freezes at a final value when the bar closes | The indicator reads `close ` (or any current-bar value) inside a weighted sum — the current-bar weight changes every tick |
KernelLens avoids the first kind **entirely and unconditionally**: the library contains no `request.security` calls, no higher-timeframe lookups, no `lookahead_on` usage, and no array rotation that could leak future bars into the window. Every historical bar plotted by any KernelLens kernel is computed exclusively from bars that existed at the time that bar was closed. The plotted history is immutable.
Real-time repainting is controlled explicitly by the `_phase` parameter — it is the user's choice whether to accept tick-by-tick flicker on the live bar in exchange for zero lag (`_phase = 0`) or to eliminate the flicker entirely at the cost of a small fixed lag (`_phase ≥ 1`).
**Why Kernel Regression Normally Repaints (And How KernelLens Stops It)**
A traditional Nadaraya–Watson call centered on the current bar evaluates:
```
ŷ(t) = Σᵢ K(dᵢ/ℓ) · y_{t−i} for i = 0 … depth
```
On the live bar, the term `y_{t−0} = close ` is the current real-time price — which changes on every tick. Every tick moves the weighted sum, every tick moves the estimate, and the trader watching the chart sees the kernel plot flicker as the bar develops. The historical bars (where `close ` for that past bar is now fixed) are stable, but the live plot is unstable.
KernelLens's `_phase` parameter shifts the loop so the kernel runs over `i = _phase … _phase + depth`. With `_phase = 2`:
```
ŷ(t) = Σᵢ K((i−2)/ℓ) · y_{t−i} for i = 2 … 2 + depth
```
The sum no longer touches `close ` or `close ` — every bar it reads is already confirmed and cannot change. The live-bar kernel output is therefore identical from the first tick of the bar to the last tick of the bar, and identical again when the bar finally closes. There is no flicker and nothing to repaint.
**The Lag / Stability Trade-Off**
| `_phase` | Lag on Live Bar | Live-Bar Flicker | Historical Repainting | Best For |
|---|---|---|---|---|
| **0** | 0 bars | Yes (real-time only; history is stable) | None | Scalping, academic research, calibration |
| **1** | 1 bar | None | None | Fast day-trading; minimum acceptable lag for a live trading desk |
| **2** | 2 bars | None | None | Default for most users — the sweet spot between freshness and stability |
| **3** | 3 bars | None | None | Swing trading — extra margin against false flickers from erratic ticks |
| **5+** | 5+ bars | None | None | Position trading, long-term chart analysis, published signal marks |
Even at `_phase = 0`, **historical repainting never occurs** — only the live bar flickers during its own development. Once a bar closes, its plotted value is final; scrolling away and back, refreshing the chart, or re-opening PulseWire will never change that historical plot. The flicker is exclusively a live-bar tick-by-tick phenomenon.
**KernelLens as a Non-Repainting Primitive**
KernelLens exposes real-time flicker as an explicit, user-controlled trade-off rather than a hidden behavior. The caller picks any point on the spectrum from "fully live" (`_phase = 0`, maximum reactivity with tick-by-tick flicker) to "fully confirmed" (`_phase ≥ 1`, one or more bars of lag in exchange for a curve that never redraws) with a single integer parameter. Historical repainting — the dangerous form that silently rewrites past plots — is eliminated unconditionally regardless of `_phase`.
**How to Verify Non-Repainting Yourself**
Do not trust the word "non-repainting" from any library — always verify. KernelLens can be verified in about thirty seconds:
1. Load a chart with KernelLens on it using `_phase = 2` (or any value > 0).
2. Take a screenshot at any specific historical bar.
3. Scroll far to the left, refresh the chart, or reload the indicator.
4. Return to the same bar. The plotted value at that bar must be pixel-identical to the screenshot — because the computation on that bar used only the bars before it, which have not changed.
5. Repeat with `_phase = 0`. The historical bars must still be pixel-identical — only the live bar's plot can differ between observations, and only because the live bar's `close` is now a different number than it was when you took the screenshot.
For a stricter test, use PulseWire's **Bar Replay** mode. Enable Bar Replay, step forward one bar at a time, and watch the kernel plot on each newly-closed bar. With `_phase ≥ 1`, the value plotted on each newly-closed bar will exactly match what the indicator shows after you exit replay mode and view the same bar normally. This is the gold-standard test — Bar Replay reproduces live-bar tick arrival in a controlled way.
**Common Misconceptions**
> *"Any Pine indicator that uses `close` repaints."*
False. Using `close` on a confirmed bar does not repaint — the confirmed bar's close is locked. What can repaint is using `close` on the live bar, and only within that live bar's interval. KernelLens with `_phase > 0` never reads the live-bar close at all.
> *"`lookahead = barmerge.lookahead_on` is always wrong."*
Context-dependent. `lookahead_on` is used correctly in some multi-timeframe indicators to request a higher-TF value that is already settled on the lower TF. KernelLens does not use `request.security` at all, so this question does not apply — but for libraries that do, `lookahead_on` is only problematic when it leaks values from bars that were not yet closed at the lower-TF time of evaluation.
> *"Non-repainting means zero lag."*
False. Zero lag and non-repainting are orthogonal properties. KernelLens `_phase = 0` is zero lag with real-time flicker; `_phase = 2` is two-bar lag with no flicker. You can have any combination of the two, and the right choice depends on the trading style.
> *"The `FILTER_ZEROLAG` mode makes the indicator non-repainting."*
False. `FILTER_ZEROLAG` is an Ehlers-style de-lagging filter applied to the kernel output; it reduces the perceived lag of the estimate, but it does not affect whether the live bar flickers. Non-repainting is controlled exclusively by `_phase`. Choose `_phase` for repainting behavior, and `_filter` for smoothness / lag shape — they are independent knobs.
**When to Accept Real-Time Flicker (`_phase = 0`)**
Despite everything above, there are legitimate reasons to deliberately use `_phase = 0`:
- **Academic research and backtesting** — you want the kernel mathematics in its classical form, centered on the point being estimated, with no phase adjustment
- **Scalping on very short timeframes** — a 2-bar lag on a 1-minute chart is a 2-minute delay, which can matter when you are exiting within a 4-minute window
- **Visual calibration** — when you are choosing a bandwidth by eye, the live-bar flicker actually helps: you see how sensitive the curve is to each incoming tick, which is diagnostic information
- **Indicators that read the kernel output only on `barstate.isconfirmed`** — if your signal logic is gated by `if barstate.isconfirmed`, then live-bar flicker is invisible to your signal (it sees only the frozen close-of-bar value), and you can safely use `_phase = 0` with no practical consequence
For every other case — and especially for any live alert or automated trading system — use `_phase ≥ 1`. Two bars of lag on a clean, stable curve is almost always worth more than zero lag on a curve that redraws itself several times per bar.
🟦 UNIFIED DISPATCHER — `estimate()`
For indicators where the user picks a kernel from a dropdown, writing eight separate ternary branches is tedious and error-prone. KernelLens ships with a unified dispatcher that routes to the correct kernel based on a string argument:
```pine
import a_jabbaroff/KernelLens/1 as kl
line = kl.estimate(
kernelType = kl.KERNEL_GAUSS,
src = close,
bandwidth = 32,
shapeAlpha = 1.0,
period = 1,
phase = 2,
filter = kl.FILTER_SMOOTH)
```
The dispatcher forwards to the matching typed export, so there is no performance penalty versus calling the kernel directly — it is a compile-time routing pass. Unknown kernel names raise a descriptive `runtime.error` naming every valid alternative, so typos fail loudly instead of silently returning `na`.
**Public Constants**
KernelLens exposes its string constants so callers never type the magic values by hand:
| Constant | Value |
|---|---|
| `FILTER_NONE` | `"No Filter"` |
| `FILTER_SMOOTH` | `"Smooth"` |
| `FILTER_ZEROLAG` | `"Zero Lag"` |
| `KERNEL_RQ` | `"Rational Quadratic"` |
| `KERNEL_GAUSS` | `"Gaussian"` |
| `KERNEL_PERIODIC` | `"Periodic"` |
| `KERNEL_LOCPER` | `"Locally Periodic"` |
| `KERNEL_EPA` | `"Epanechnikov"` |
| `KERNEL_TRICUBE` | `"Tricube"` |
| `KERNEL_TRIANG` | `"Triangular"` |
| `KERNEL_COSINE` | `"Cosine"` |
Using the constants in your caller code means the Pine compiler — not a runtime string compare — catches typos at edit time.
🟦 UTILITY LAYER — FIVE PROFESSIONAL HELPERS
KernelLens ships with five utility exports that complement the core estimators. They are the functions you almost always write immediately after getting a smoothed line, factored out so you don't rewrite them in every indicator.
**`slope(estimate, step)` — Discrete First Derivative**
Returns `(y_t − y_{t−step}) / step`, the normalized rate of change over `step` bars. Use it to detect whether a kernel output is trending up, flat, or down — the foundation for any trend-following signal built on top of KernelLens.
```pine
rising = kl.slope(line, 3) > 0.0
```
**`trendState(estimate, step)` — Ternary Trend Indicator**
Returns `+1` if the estimate is rising, `−1` if falling, `0` if exactly flat over the window. A single-call replacement for hand-rolled `line > line ? 1 : line < line ? -1 : 0` ladders.
**`crossSignal(fast, slow)` — Bi-directional Crossover**
Returns `+1` on the bar where `fast` crosses above `slow` (bullish), `−1` on a bearish cross, and `0` otherwise. Built on `ta.crossover` / `ta.crossunder`, so the signal is non-repainting once the bar is confirmed.
**`confidenceBand(src, estimate, window)` — Residual Standard Deviation**
Computes the rolling standard deviation of `(src − estimate)` over a user-defined window. Use the return value as the half-width of a confidence band around the estimate:
```pine
est = kl.gaussian(close, 32, 2, kl.FILTER_SMOOTH)
sigma = kl.confidenceBand(close, est, 50)
upper = est + 1.96 * sigma
lower = est - 1.96 * sigma
```
This is a computationally cheap proxy for the full kernel-weighted local variance — ideal when you need visual bands without paying for a second weighted pass.
**`silvermanBandwidth(src, window)` — Optimal ℓ Suggestion**
Returns the Silverman rule-of-thumb bandwidth:
```
h ≈ 1.06 · σ · n^(−1/5)
```
where `σ` is the rolling standard deviation of the source and `n` is the window size. This is the classical starting point for Gaussian-family bandwidths in academic texts (Silverman 1986). Because Pine requires `simple int` for kernel bandwidth, the returned value is intended for diagnostic display — plot it, read it off the chart, then hard-code the rounded integer into the kernel call.
🟦 INPUT VALIDATION — FAIL LOUDLY, FAIL EARLY
Every public function in KernelLens validates its inputs through a set of internal `_assert*` helpers. Invalid arguments never produce silent `na` fallbacks or buried zero-divisions — they raise `runtime.error` with a descriptive message identifying the function, the parameter, and the expected range.
| Helper | Checks | Raises On |
|---|---|---|
| `_assertFilter` | Filter string is `FILTER_NONE`, `FILTER_SMOOTH`, or `FILTER_ZEROLAG` | Typos like `"No FIlter"` (capital I) — a bug that exists in at least one published kernel indicator |
| `_assertBandwidth` | Bandwidth is a strictly positive integer | Negative or zero bandwidth, which would cause division by zero or infinite loops |
| `_assertPeriod` | Period is a strictly positive integer | Zero period, which would cause `sin(π·d/0)` in Periodic kernels |
| `_assertAlpha` | Rational Quadratic shape parameter is strictly positive | Zero or negative `α`, which would invert the RQ formula |
Error messages are prefixed `KernelLens:` (or `KernelLens.:`) so they are easy to spot in the PulseWire runtime log. Every message names the parameter that failed, the value that was passed, and the set of valid alternatives — so a misconfigured chart tells you exactly what to fix.
🟦 LOOP DEPTH — THE BUG FIX THAT MOTIVATED KERNELLENS
The two most popular Pine kernel libraries on PulseWire share the same fatal bug: both compute their loop depth as
```pine
_size = array.size(array.from(_src))
```
where `array.from(_src)` creates a **one-element array containing the current value of `_src`**, so `_size` is always `1`. The loop then runs `for i = 0 to 1 + startAtBar`, effectively using only `startAtBar + 2` bars — completely ignoring the user's bandwidth. Every published kernel indicator built on those libraries inherits this silent miscalculation.
KernelLens replaces the broken helper with three explicit depth selectors:
| Helper | Depth | Used By |
|---|---|---|
| `_depthInfinite(bw)` | `max(bw · 3, 4)` | Gaussian, Rational Quadratic, Locally Periodic |
| `_depthCompact(bw)` | `max(bw, 4)` | Epanechnikov, Tricube, Triangular, Cosine |
| `_depthPeriodic(bw, p)` | `max(bw · 3, p · 10, 4)` | Periodic |
For Gaussian-family kernels, the `3·ℓ` cutoff captures approximately 99.7% of the kernel mass (the three-sigma rule). For compact-support kernels, the depth equals the bandwidth exactly — the loop terminates at the kernel's natural zero point. For Periodic kernels, the depth is the larger of the scale-based and cycle-based minima, so the loop always spans enough periods to produce a stable weighted average.
The loop counter `i` runs over bar offsets starting at `_phase`, every bar lookup is NA-checked before being incorporated into the sum, and the final `num / den` division is guarded against zero denominators. On a fresh chart, the kernel gracefully returns `na` for bars where the window extends past available history, rather than producing poisoned sums from implicit NA arithmetic.
🟦 API REFERENCE
**Core Kernel Estimators — Eight Exports**
| Export | Signature |
|---|---|
| `rationalQuadratic` | `(src, bandwidth, shapeAlpha, phase, filter) → float` |
| `gaussian` | `(src, bandwidth, phase, filter) → float` |
| `periodic` | `(src, bandwidth, period, phase, filter) → float` |
| `locallyPeriodic` | `(src, bandwidth, period, phase, filter) → float` |
| `epanechnikov` | `(src, bandwidth, phase, filter) → float` |
| `tricube` | `(src, bandwidth, phase, filter) → float` |
| `triangular` | `(src, bandwidth, phase, filter) → float` |
| `cosineKernel` | `(src, bandwidth, phase, filter) → float` |
**Unified Dispatcher**
| Export | Signature |
|---|---|
| `estimate` | `(kernelType, src, bandwidth, shapeAlpha, period, phase, filter) → float` |
**Utility Layer — Five Exports**
| Export | Signature |
|---|---|
| `slope` | `(estimate, step) → float` |
| `trendState` | `(estimate, step) → int` |
| `crossSignal` | `(fast, slow) → int` |
| `confidenceBand` | `(src, estimate, window) → float` |
| `silvermanBandwidth` | `(src, window) → float` |
**Parameter Types**
| Name | Pine Type | Description |
|---|---|---|
| `src` | `series float` | Source series (close, hl2, ohlc4, or any other price-derived series) |
| `bandwidth` | `simple int` | Kernel scale `ℓ`, must be `> 0` |
| `shapeAlpha` | `simple float` | Rational Quadratic shape parameter, must be `> 0` |
| `period` | `simple int` | Periodic repetition distance, must be `> 0` |
| `phase` | `simple int` | Kernel center offset in bars, must be `≥ 0` |
| `filter` | `simple string` | One of `FILTER_NONE`, `FILTER_SMOOTH`, `FILTER_ZEROLAG` |
| `kernelType` | `simple string` | One of the eight `KERNEL_*` constants |
| `step` | `simple int` | Finite-difference step for `slope` / `trendState`, must be `≥ 1` |
| `window` | `simple int` | Rolling window for `confidenceBand` / `silvermanBandwidth`, must be `≥ 2` |
🟦 USAGE EXAMPLES
**Minimal — One Gaussian Curve**
```pine
//@version=6
indicator("KernelLens — Gaussian Demo", overlay = true)
import a_jabbaroff/KernelLens/1 as kl
line = kl.gaussian(close, 32, 2, kl.FILTER_SMOOTH)
plot(line, "Gaussian", color = color.orange, linewidth = 2)
```
**Fast / Slow Crossover System**
```pine
//@version=6
indicator("KernelLens — RQ Crossover", overlay = true)
import a_jabbaroff/KernelLens/1 as kl
fast = kl.rationalQuadratic(close, 8, 1.0, 2, kl.FILTER_NONE)
slow = kl.rationalQuadratic(close, 32, 1.0, 2, kl.FILTER_SMOOTH)
cross = kl.crossSignal(fast, slow)
plot(fast, "Fast", color = color.aqua, linewidth = 2)
plot(slow, "Slow", color = color.orange, linewidth = 2)
plotshape(cross == 1, "Bull", location = location.belowbar,
color = color.lime, style = shape.triangleup, size = size.tiny)
plotshape(cross == -1, "Bear", location = location.abovebar,
color = color.red, style = shape.triangledown, size = size.tiny)
```
**Confidence Band Envelope**
```pine
//@version=6
indicator("KernelLens — Confidence Band", overlay = true)
import a_jabbaroff/KernelLens/1 as kl
est = kl.tricube(close, 48, 2, kl.FILTER_SMOOTH)
sigma = kl.confidenceBand(close, est, 50)
k = 1.96
upper = est + k * sigma
lower = est - k * sigma
plot(est, "Estimate", color = color.orange, linewidth = 2)
p1 = plot(upper, "+1.96σ", color = color.new(color.aqua, 70))
p2 = plot(lower, "−1.96σ", color = color.new(color.aqua, 70))
fill(p1, p2, color = color.new(color.aqua, 92))
```
**Dropdown-Driven Kernel Selection**
```pine
//@version=6
indicator("KernelLens — Dropdown", overlay = true)
import a_jabbaroff/KernelLens/1 as kl
kernelType = input.string(kl.KERNEL_GAUSS, "Kernel",
options = )
bandwidth = input.int(32, "Bandwidth", minval = 2)
alphaRQ = input.float(1.0,"RQ Alpha", minval = 0.01, step = 0.25)
period = input.int(20, "Period", minval = 1)
phase = input.int(2, "Phase", minval = 0)
filter = input.string(kl.FILTER_SMOOTH, "Filter",
options = )
line = kl.estimate(kernelType, close, bandwidth, alphaRQ, period, phase, filter)
plot(line, "KernelLens", color = color.orange, linewidth = 2)
```
🟦 TIMEFRAME PRESETS — BANDWIDTH BY STYLE
Kernel bandwidth is the single most important parameter. It controls the trade-off between reactivity (small `ℓ`, tight fit, noisier) and stability (large `ℓ`, smooth curve, slower to react). The presets below are tested starting points — adjust by ±25 % to taste.
---
**SCALPER — 1m / 3m / 5m**
| Parameter | Value |
|---|---|
| Bandwidth (ℓ) | 8 |
| Phase | 1 |
| Filter | `FILTER_NONE` |
| Best Kernel | Rational Quadratic or Gaussian |
| RQ shapeAlpha | 1.0 |
**Why:** Short bandwidth means the kernel reacts within a handful of bars. `FILTER_NONE` removes the double-pass lag, so the estimate tracks price as tightly as possible. Phase 1 keeps the estimate nearly live while still avoiding the current-bar tick noise.
---
**DAY TRADER — 15m / 30m / 1H**
| Parameter | Value |
|---|---|
| Bandwidth (ℓ) | 16 |
| Phase | 2 |
| Filter | `FILTER_SMOOTH` |
| Best Kernel | Gaussian or Tricube |
| RQ shapeAlpha | 1.0 |
**Why:** Balanced reactivity — the 16-bar Gaussian is the default Silverman range for intraday price data, and `FILTER_SMOOTH` removes most of the bar-to-bar chop without significantly increasing lag. Tricube provides near-identical behaviour with strict compact support and is preferred on noisy assets where outlier bars should not influence the curve.
---
**SWING TRADER — 4H / 1D**
| Parameter | Value |
|---|---|
| Bandwidth (ℓ) | 32 |
| Phase | 3 |
| Filter | `FILTER_SMOOTH` |
| Best Kernel | Rational Quadratic |
| RQ shapeAlpha | 2.0 |
**Why:** Swing trades need structural signals, not intraday noise. Rational Quadratic with `α = 2.0` mixes medium and long length scales, producing a curve that ignores transient spikes but catches genuine regime shifts. Phase 3 shifts the estimate three bars back so each swing decision is made against a fully confirmed kernel output.
---
**POSITION / LONG-TERM — 1D / 1W / 1M**
| Parameter | Value |
|---|---|
| Bandwidth (ℓ) | 64 |
| Phase | 5 |
| Filter | `FILTER_SMOOTH` or `FILTER_ZEROLAG` |
| Best Kernel | Gaussian or Locally Periodic |
| Period (if LP) | 52 (weekly cycle) |
**Why:** Position traders care about the macro trajectory. A Gaussian with ℓ = 64 produces a curve that only turns on genuine multi-month inflections. Locally Periodic with `period = 52` is the ideal choice when a clear seasonal cycle is present — it uses both the long-range Gaussian envelope and the 52-bar periodicity to highlight cycle turns that align with trend.
---
**RESEARCH — Academic / Backtest**
| Parameter | Value |
|---|---|
| Bandwidth (ℓ) | Compute via `silvermanBandwidth(src, 200)` |
| Phase | 0 |
| Filter | `FILTER_NONE` |
| Best Kernel | Epanechnikov |
**Why:** Epanechnikov is the MSE-optimal kernel; `FILTER_NONE` keeps the estimator in its classical single-pass form; `phase = 0` centers the kernel on the bar being evaluated. This is the configuration that matches the statistical literature exactly — use it when publishing research, running Monte-Carlo studies, or calibrating against reference implementations.
🟦 BANDWIDTH SELECTION
Bandwidth `ℓ` is the single most consequential choice in kernel regression. Too small and the estimate overfits local noise; too large and it flattens real structure. KernelLens exposes two helpers to support both manual and semi-automated bandwidth selection.
**Manual — Start with ℓ ≈ √n**
A practical starting point for financial time series: set `ℓ ≈ √window_of_interest`. If you care about 100-bar structure, try `ℓ = 10`. If you care about 400-bar structure, try `ℓ = 20`. Adjust by ±25 % based on how noisy the result looks.
**Silverman's Rule of Thumb**
The closed-form optimal bandwidth for Gaussian-family kernels under Gaussian source assumptions:
```
h ≈ 1.06 · σ · n^(−1/5)
```
Call `silvermanBandwidth(src, window)` to compute this value live. Because Pine requires `simple int` bandwidth at compile time, the returned value is for diagnostic use — plot it, read the stable value off the chart, then hard-code the rounded integer into your kernel calls.
**Leave-One-Out Cross-Validation (Manual)**
For academic rigor, compute the leave-one-out mean squared error for a range of bandwidths and pick the minimum. KernelLens does not automate this (it would require `series int` bandwidth, which Pine does not support inside kernel loops), but the formula is straightforward:
```
LOOCV(ℓ) = (1/n) · Σᵢ (yᵢ − ŷᵢ⁻ⁱ(ℓ))²
```
where `ŷᵢ⁻ⁱ` is the kernel estimate at bar `i` computed without including bar `i` in the sum. Evaluate offline, pick the minimum, hard-code the result.
🟦 FILTER SELECTION — WHEN TO USE EACH
| Filter | Best For | Avoid When |
|---|---|---|
| `FILTER_NONE` | Live signal generation, research / calibration, compact-support kernels on noisy data | Choppy markets where you need extra smoothing |
| `FILTER_SMOOTH` | Swing and position trades, confidence band midlines, most day-trading setups | Scalping — the double pass adds measurable lag |
| `FILTER_ZEROLAG` | Regime detection, crossover systems that need the curve to track price tightly | Low-volume assets — Zero Lag amplifies high-frequency noise |
The three filters use the same underlying kernel with the same bandwidth, so switching between them does not require re-tuning. Default to `FILTER_SMOOTH` when in doubt — it is the best-behaved option across the widest range of assets and timeframes.
🟦 COMPATIBILITY
KernelLens targets Pine Script v6 and runs on every PulseWire chart — no exchange, asset class, or timeframe restriction.
- **Crypto** — Spot, futures, perpetual contracts
- **Forex** — All majors, minors, and exotics
- **Equities** — Stocks, ETFs, indices
- **Commodities** — Metals, energy, agriculture
- **Timeframes** — 1 minute through Monthly
The library is deterministic — given the same source and parameters, every bar of every symbol produces the same estimate. No calibration is needed across assets; the bandwidth parameter alone controls smoothness, and the kernel formulas are scale-free in the source dimension. Silverman's bandwidth helper automatically adapts to each asset's volatility.
🟦 TECHNICAL NOTES
- **Pine Script v6** — uses the modern type system, strict type checking, and the `switch` expression in the unified dispatcher
- **Non-repainting** — kernel outputs for any confirmed bar depend only on that bar's history; there is no look-ahead, no `request.security` with lookahead, and no dependency on the unconfirmed current bar unless `_phase = 0` is deliberately chosen
- **NA-safe iteration** — every bar lookup inside a kernel loop is guarded by `if not na(y)`, so chart history gaps and warm-up bars cannot poison the weighted sum
- **Division-by-zero protection** — every kernel's final division checks `den > 0.0` and returns `na` if the denominator collapses (which can only happen on truly empty windows)
- **Input validation** — every public function asserts its preconditions up front via `_assertFilter`, `_assertBandwidth`, `_assertPeriod`, `_assertAlpha`, and raises `runtime.error` with a descriptive message on misuse — no silent `na` fallbacks
- **Lazy filter evaluation** — the `"No Filter"` path never executes the second kernel pass; the `if`-branch check short-circuits, so single-pass mode is as cheap as a raw kernel call
- **Correct loop bounds** — `_depthInfinite`, `_depthCompact`, and `_depthPeriodic` compute the correct window size per kernel family, fixing the silent `_size = 1` bug that plagues every other published Pine kernel library
- **No persistent state** — the library is purely functional: no `var`, no arrays, no history buffers that grow over time; every export is a pure expression of `(inputs) → output`, so Pine's `max_*_count` limits cannot be exceeded and the library cannot leak memory
- **O(bandwidth) per bar per kernel call** — the loop depth is bounded by the constants in Section 0; there is no hidden quadratic behavior and the cost scales linearly with the user-chosen bandwidth
- **Unicode-safe comments** — the source uses academic notation (`σ`, `ℓ`, `α`, `ŷ`, `ℝ`) where it improves readability; all strings are plain ASCII for runtime compatibility
🟦 ACADEMIC REFERENCES
Every kernel and every formula in KernelLens is cited inline in the source. The combined bibliography:
- **Nadaraya, E. A. (1964).** On estimating regression. *Theory of Probability & Its Applications*, 9(1), 141–142.
- **Watson, G. S. (1964).** Smooth regression analysis. *Sankhyā: The Indian Journal of Statistics, Series A*, 26(4), 359–372.
- **Cleveland, W. S. (1979).** Robust locally weighted regression and smoothing scatterplots. *Journal of the American Statistical Association*, 74(368), 829–836. *(Tricube kernel, LOWESS.)*
- **Silverman, B. W. (1986).** *Density Estimation for Statistics and Data Analysis*. Chapman & Hall, London. *(Bandwidth rule of thumb.)*
- **Wand, M. P. & Jones, M. C. (1995).** *Kernel Smoothing*. Chapman & Hall. *(Unified treatment of all eight kernels.)*
- **MacKay, D. J. C. (1998).** Introduction to Gaussian Processes. *NIPS Tutorial*. *(Periodic and Rational Quadratic kernels.)*
- **Ehlers, J. F. (2000).** *Rocket Science for Traders*. John Wiley & Sons. *(Zero-lag smoothing trick.)*
- **Rasmussen, C. E. & Williams, C. K. I. (2006).** *Gaussian Processes for Machine Learning*. MIT Press. *(Locally Periodic and Rational Quadratic kernels.)*
🟦 VERSIONING & LICENSE
- **Version** — 1.0.0
- **Pine Script** — v6
- **License** — Mozilla Public License 2.0
- **Status** — Production-ready
KernelLens follows semantic versioning. Minor versions add new exports without breaking existing ones; patch versions fix bugs; major versions may change function signatures and will be announced in the changelog.
🟦 DISCLAIMER
KernelLens is a mathematical library for non-parametric regression on financial time series using the Nadaraya–Watson method. The library is provided solely for educational and research purposes and does not constitute financial, investment, or trading advice.
Kernel regression is a local smoothing technique. It estimates the mean of a source series in the neighborhood of the current bar based on historical data, but it does not predict future prices, does not generate trading signals on its own, and does not guarantee the profitability of any strategy built on top of its output.
Past performance of any model does not guarantee future results. Markets contain systemic risks that cannot be eliminated by any amount of mathematical rigor in the kernel itself. Responsibility for any trading decisions made using this library rests entirely with the user. Always apply sound capital management, conduct your own independent analysis, and never risk capital you are not prepared to lose.
The author assumes no liability for direct or indirect losses incurred through the use of KernelLens or any indicator built on top of it. Library
Indicator
VisualStructureToolsLibrary "VisualStructureTools"
MTF-safe drawing library (Unix-Time). Designed for high visual discrimination and efficient debugging of complex logic without cluttering the main script.
Optimized for Pine Script® v6 to prevent runtime errors in multi-timeframe environments.
setLine(price, startTime, labelText, labelPos, is_extend, l_width, l_col, l_style)
Draws a horizontal level or a segment with an optional label.
Parameters:
price (float) : Price level for the line.
startTime (int) : UNIX timestamp (ms) for the starting point.
labelText (string) : Text to display on the label. Use "none" to hide.
labelPos (string) : Position of the label relative to the price ('above' or 'below', 'none').
is_extend (bool) : If true, the line extends infinitely (extend.both).
l_width (int) : Width of the line in pixels.
l_col (color) : Color for the line and label text.
l_style (string) : Style of the line ('solid', 'dashed', 'dotted').
setBox(top, bottom, startTime, endTime, boxText, b_col, b_width, b_style, b_transp)
Draws a filled box with an optional synchronized text label.
Parameters:
top (float) : Price of the upper boundary.
bottom (float) : Price of the lower boundary.
startTime (int) : UNIX timestamp (ms) for the left side of the box.
endTime (int) : UNIX timestamp (ms) for the right side (defaults to current 'time').
boxText (string) : Optional text label for the box. Use "" to hide.
b_col (color) : Border and fill color.
b_width (int) : Border width.
b_style (string) : Border style ('solid', 'dashed', 'dotted').
b_transp (int) : Transparency for the background fill (0-100). Library
Library
Library
BarCoreLibrary "BarCore"
BarCore is a foundational library for technical analysis, providing essential functions for evaluating the structural properties of candlesticks and inter-bar relationships.
It prioritizes ratio-based metrics (0.0 to 1.0) over absolute prices, making it asset-agnostic and ideal for robust pattern recognition, momentum analysis, and volume-weighted pressure evaluation.
Key modules:
- Structure & Range: High-precision bar and body metrics with relative positioning.
- Wick Dynamics: Absolute and relative wick analysis for identifying price rejection.
- Inter-bar Logic: Containment, coverage, and quantitative price overlap (Ratio-based).
- Gap Intelligence: Real body and price gaps with customizable significance thresholds.
- Flow & Pressure: Volume-weighted buying/selling pressure and Money Flow metrics.
isBuyingBar()
Checks if the bar is a bullish (up) bar, where close is greater than open.
Returns: bool True if the bar closed higher than it opened.
isSellingBar()
Checks if the bar is a bearish (down) bar, where close is less than open.
Returns: bool True if the bar closed lower than it opened.
barMidpoint()
Calculates the absolute midpoint of the bar's total range (High + Low) / 2.
Returns: float The midpoint price of the bar.
barRange()
Calculates the absolute size of the bar's total range (High to Low).
Returns: float The absolute difference between high and low.
barRangeMidpoint()
Calculates half of the bar's total range size.
Returns: float Half the bar's range size.
realBodyHigh()
Returns the higher price between the open and close.
Returns: float The top of the real body.
realBodyLow()
Returns the lower price between the open and close.
Returns: float The bottom of the real body.
realBodyMidpoint()
Calculates the absolute midpoint of the bar's real body.
Returns: float The midpoint price of the real body.
realBodyRange()
Calculates the absolute size of the bar's real body.
Returns: float The absolute difference between open and close.
realBodyRangeMidpoint()
Calculates half of the bar's real body size.
Returns: float Half the real body size.
upperWickRange()
Calculates the absolute size of the upper wick.
Returns: float The range from high to the real body high.
lowerWickRange()
Calculates the absolute size of the lower wick.
Returns: float The range from the real body low to low.
openRatio()
Returns the location of the open price relative to the bar's total range (0.0 at low to 1.0 at high).
Returns: float The ratio of the distance from low to open, divided by the total range.
closeRatio()
Returns the location of the close price relative to the bar's total range (0.0 at low to 1.0 at high).
Returns: float The ratio of the distance from low to close, divided by the total range.
realBodyRatio()
Calculates the ratio of the real body size to the total bar range.
Returns: float The real body size divided by the bar range. Returns 0 if barRange is 0.
upperWickRatio()
Calculates the ratio of the upper wick size to the total bar range.
Returns: float The upper wick size divided by the bar range. Returns 0 if barRange is 0.
lowerWickRatio()
Calculates the ratio of the lower wick size to the total bar range.
Returns: float The lower wick size divided by the bar range. Returns 0 if barRange is 0.
upperWickToBodyRatio()
Calculates the ratio of the upper wick size to the real body size.
Returns: float The upper wick size divided by the real body size. Returns 0 if realBodyRange is 0.
lowerWickToBodyRatio()
Calculates the ratio of the lower wick size to the real body size.
Returns: float The lower wick size divided by the real body size. Returns 0 if realBodyRange is 0.
totalWickRatio()
Calculates the ratio of the total wick range (Upper Wick + Lower Wick) to the total bar range.
Returns: float The total wick range expressed as a ratio of the bar's total range. Returns 0 if barRange is 0.
isBodyExpansion()
Checks if the current bar's real body range is larger than the previous bar's real body range (body expansion).
Returns: bool True if realBodyRange() > realBodyRange() .
isBodyContraction()
Checks if the current bar's real body range is smaller than the previous bar's real body range (body contraction).
Returns: bool True if realBodyRange() < realBodyRange() .
isWithinPrevBar(inclusive)
Checks if the current bar's range is entirely within the previous bar's range.
Parameters:
inclusive (bool) : If true, allows equality (<=, >=). Default is false.
Returns: bool True if High < High AND Low > Low .
isCoveringPrevBar(inclusive)
Checks if the current bar's range fully covers the entire previous bar's range.
Parameters:
inclusive (bool) : If true, allows equality (<=, >=). Default is false.
Returns: bool True if High > High AND Low < Low .
isWithinPrevBody(inclusive)
Checks if the current bar's real body is entirely inside the previous bar's real body.
Parameters:
inclusive (bool) : If true, allows equality (<=, >=). Default is false.
Returns: bool True if the current body is contained inside the previous body.
isCoveringPrevBody(inclusive)
Checks if the current bar's real body fully covers the previous bar's real body.
Parameters:
inclusive (bool) : If true, allows equality (<=, >=). Default is false.
Returns: bool True if the current body fully covers the previous body.
isOpenWithinPrevBody(inclusive)
Checks if the current bar's open price falls within the real body range of the previous bar.
Parameters:
inclusive (bool) : If true, includes the boundary prices. Default is false.
Returns: bool True if the open price is between the previous bar's real body high and real body low.
isCloseWithinPrevBody(inclusive)
Checks if the current bar's close price falls within the real body range of the previous bar.
Parameters:
inclusive (bool) : If true, includes the boundary prices. Default is false.
Returns: bool True if the close price is between the previous bar's real body high and real body low.
isPrevOpenWithinBody(inclusive)
Checks if the previous bar's open price falls within the current bar's real body range.
Parameters:
inclusive (bool) : If true, includes the boundary prices. Default is false.
Returns: bool True if open is between the current bar's real body high and real body low.
isPrevCloseWithinBody(inclusive)
Checks if the previous bar's closing price falls within the current bar's real body range.
Parameters:
inclusive (bool) : If true, includes the boundary prices. Default is false.
Returns: bool True if close is between the current bar's real body high and real body low.
isOverlappingPrevBar()
Checks if there is any price overlap between the current bar's range and the previous bar's range.
Returns: bool True if the current bar's range has any intersection with the previous bar's range.
bodyOverlapRatio()
Calculates the percentage of the current real body that overlaps with the previous real body.
Returns: float The overlap ratio (0.0 to 1.0). 1.0 means the current body is entirely within the previous body's price range.
isCompletePriceGapUp()
Checks for a complete price gap up where the current bar's low is strictly above the previous bar's high, meaning there is zero price overlap between the two bars.
Returns: bool True if the current low is greater than the previous high.
isCompletePriceGapDown()
Checks for a complete price gap down where the current bar's high is strictly below the previous bar's low, meaning there is zero price overlap between the two bars.
Returns: bool True if the current high is less than the previous low.
isRealBodyGapUp()
Checks for a gap between the current and previous real bodies.
Returns: bool True if the current body is completely above the previous body.
isRealBodyGapDown()
Checks for a gap between the current and previous real bodies.
Returns: bool True if the current body is completely below the previous body.
gapRatio()
Calculates the percentage difference between the current open and the previous close, expressed as a decimal ratio.
Returns: float The gap ratio (positive for gap up, negative for gap down). Returns 0 if the previous close is 0.
gapPercentage()
Calculates the percentage difference between the current open and the previous close.
Returns: float The gap percentage (positive for gap up, negative for gap down). Returns 0 if previous close is 0.
isGapUp()
Checks for a basic gap up, where the current bar's open is strictly higher than the previous bar's close. This is the minimum condition for a gap up.
Returns: bool True if the current open is greater than the previous close (i.e., gapRatio is positive).
isGapDown()
Checks for a basic gap down, where the current bar's open is strictly lower than the previous bar's close. This is the minimum condition for a gap down.
Returns: bool True if the current open is less than the previous close (i.e., gapRatio is negative).
isSignificantGapUp(minRatio)
Checks if the current bar opened significantly higher than the previous close, as defined by a minimum percentage ratio.
Parameters:
minRatio (float) : The minimum required gap percentage ratio. Default is 0.03 (3%).
Returns: bool True if the gap ratio (open vs. previous close) is greater than or equal to the minimum ratio.
isSignificantGapDown(minRatio)
Checks if the current bar opened significantly lower than the previous close, as defined by a minimum percentage ratio.
Parameters:
minRatio (float) : The minimum required gap percentage ratio. Default is 0.03 (3%).
Returns: bool True if the absolute value of the gap ratio (open vs. previous close) is greater than or equal to the minimum ratio.
trueRangeComponentHigh()
Calculates the absolute distance from the current bar's High to the previous bar's Close, representing one of the components of the True Range.
Returns: float The absolute difference: |High - Close |.
trueRangeComponentLow()
Calculates the absolute distance from the current bar's Low to the previous bar's Close, representing one of the components of the True Range.
Returns: float The absolute difference: |Low - Close |.
isUpperWickDominant(minRatio)
Checks if the upper wick is significantly long relative to the total range.
Parameters:
minRatio (float) : Minimum ratio of the wick to the total bar range. Default is 0.7 (70%).
Returns: bool True if the upper wick dominates the bar's range.
isUpperWickNegligible(maxRatio)
Checks if the upper wick is very small relative to the total range.
Parameters:
maxRatio (float) : Maximum ratio of the wick to the total bar range. Default is 0.05 (5%).
Returns: bool True if the upper wick is negligible.
isLowerWickDominant(minRatio)
Checks if the lower wick is significantly long relative to the total range.
Parameters:
minRatio (float) : Minimum ratio of the wick to the total bar range. Default is 0.7 (70%).
Returns: bool True if the lower wick dominates the bar's range.
isLowerWickNegligible(maxRatio)
Checks if the lower wick is very small relative to the total range.
Parameters:
maxRatio (float) : Maximum ratio of the wick to the total bar range. Default is 0.05 (5%).
Returns: bool True if the lower wick is negligible.
isSymmetric(maxTolerance)
Checks if the upper and lower wicks are roughly equal in length.
Parameters:
maxTolerance (float) : Maximum allowable percentage difference between the two wicks. Default is 0.15 (15%).
Returns: bool True if wicks are symmetric within the tolerance level.
isMarubozuBody(minRatio)
Candle with a very large body relative to the total range (minimal wicks).
Parameters:
minRatio (float) : Minimum body size ratio. Default is 0.9 (90%).
Returns: bool True if the bar has minimal wicks (Marubozu body).
isLargeBody(minRatio)
Candle with a large body relative to the total range.
Parameters:
minRatio (float) : Minimum body size ratio. Default is 0.6 (60%).
Returns: bool True if the bar has a large body.
isSmallBody(maxRatio)
Candle with a small body relative to the total range.
Parameters:
maxRatio (float) : Maximum body size ratio. Default is 0.4 (40%).
Returns: bool True if the bar has small body.
isDojiBody(maxRatio)
Candle with a very small body relative to the total range (indecision).
Parameters:
maxRatio (float) : Maximum body size ratio. Default is 0.1 (10%).
Returns: bool True if the bar has a very small body.
isLowerWickExtended(minRatio)
Checks if the lower wick is significantly extended relative to the real body size.
Parameters:
minRatio (float) : Minimum required ratio of the lower wick length to the real body size. Default is 2.0 (Lower wick must be at least twice the body's size).
Returns: bool True if the lower wick's length is at least `minRatio` times the size of the real body.
isUpperWickExtended(minRatio)
Checks if the upper wick is significantly extended relative to the real body size.
Parameters:
minRatio (float) : Minimum required ratio of the upper wick length to the real body size. Default is 2.0 (Upper wick must be at least twice the body's size).
Returns: bool True if the upper wick's length is at least `minRatio` times the size of the real body.
isStrongBuyingBar(minCloseRatio, maxOpenRatio)
Checks for a bar with strong bullish momentum (open near low, close near high), indicating high conviction.
Parameters:
minCloseRatio (float) : Minimum required ratio for the close location (relative to range, e.g., 0.7 means close must be in the top 30%). Default is 0.7 (70%).
maxOpenRatio (float) : Maximum allowed ratio for the open location (relative to range, e.g., 0.3 means open must be in the bottom 30%). Default is 0.3 (30%).
Returns: bool True if the bar is bullish, opened in the low extreme, and closed in the high extreme.
isStrongSellingBar(maxCloseRatio, minOpenRatio)
Checks for a bar with strong bearish momentum (open near high, close near low), indicating high conviction.
Parameters:
maxCloseRatio (float) : Maximum allowed ratio for the close location (relative to range, e.g., 0.3 means close must be in the bottom 30%). Default is 0.3 (30%).
minOpenRatio (float) : Minimum required ratio for the open location (relative to range, e.g., 0.7 means open must be in the top 30%). Default is 0.7 (70%).
Returns: bool True if the bar is bearish, opened in the high extreme, and closed in the low extreme.
isWeakBuyingBar(maxCloseRatio, maxBodyRatio)
Identifies a bar that is technically bullish but shows significant weakness, characterized by a failure to close near the high and a small body size.
Parameters:
maxCloseRatio (float) : Maximum allowed ratio for the close location relative to the range (e.g., 0.6 means the close must be in the bottom 60% of the bar's range). Default is 0.6 (60%).
maxBodyRatio (float) : Maximum allowed ratio for the real body size relative to the bar's range (e.g., 0.4 means the body is small). Default is 0.4 (40%).
Returns: bool True if the bar is bullish, but its close is weak and its body is small.
isWeakSellingBar(minCloseRatio, maxBodyRatio)
Identifies a bar that is technically bearish but shows significant weakness, characterized by a failure to close near the low and a small body size.
Parameters:
minCloseRatio (float) : Minimum required ratio for the close location relative to the range (e.g., 0.4 means the close must be in the top 60% of the bar's range). Default is 0.4 (40%).
maxBodyRatio (float) : Maximum allowed ratio for the real body size relative to the bar's range (e.g., 0.4 means the body is small). Default is 0.4 (40%).
Returns: bool True if the bar is bearish, but its close is weak and its body is small.
balanceOfPower()
Measures the net pressure of buyers vs. sellers within the bar, normalized to the bar's range.
Returns: float A value between -1.0 (strong selling) and +1.0 (strong buying), representing the strength and direction of the close relative to the open.
buyingPressure()
Measures the net buying volume pressure based on the close location and volume.
Returns: float A numerical value representing the volume weighted buying pressure.
sellingPressure()
Measures the net selling volume pressure based on the close location and volume.
Returns: float A numerical value representing the volume weighted selling pressure.
moneyFlowMultiplier()
Calculates the Money Flow Multiplier (MFM), which is the price component of Money Flow and CMF.
Returns: float A normalized value from -1.0 (strong selling) to +1.0 (strong buying), representing the net directional pressure.
moneyFlowVolume()
Calculates the Money Flow Volume (MFV), which is the Money Flow Multiplier weighted by the bar's volume.
Returns: float A numerical value representing the volume-weighted money flow. Positive = buying dominance; negative = selling dominance.
isAccumulationBar()
Checks for basic accumulation on the current bar, requiring both positive Money Flow Volume and a buying bar (closing higher than opening).
Returns: bool True if the bar exhibits buying dominance through its internal range location and is a buying bar.
isDistributionBar()
Checks for basic distribution on the current bar, requiring both negative Money Flow Volume and a selling bar (closing lower than opening).
Returns: bool True if the bar exhibits selling dominance through its internal range location and is a selling bar. Library
arraysLibrary "arrays"
Supplementary array methods.
method delete(arr, index)
remove int object from array of integers at specific index
Namespace types: array
Parameters:
arr (array) : int array
index (int) : index at which int object need to be removed
Returns: void
method delete(arr, index)
remove float object from array of float at specific index
Namespace types: array
Parameters:
arr (array) : float array
index (int) : index at which float object need to be removed
Returns: float
method delete(arr, index)
remove bool object from array of bool at specific index
Namespace types: array
Parameters:
arr (array) : bool array
index (int) : index at which bool object need to be removed
Returns: bool
method delete(arr, index)
remove string object from array of string at specific index
Namespace types: array
Parameters:
arr (array) : string array
index (int) : index at which string object need to be removed
Returns: string
method delete(arr, index)
remove color object from array of color at specific index
Namespace types: array
Parameters:
arr (array) : color array
index (int) : index at which color object need to be removed
Returns: color
method delete(arr, index)
remove chart.point object from array of chart.point at specific index
Namespace types: array
Parameters:
arr (array) : chart.point array
index (int) : index at which chart.point object need to be removed
Returns: void
method delete(arr, index)
remove line object from array of lines at specific index and deletes the line
Namespace types: array
Parameters:
arr (array) : line array
index (int) : index at which line object need to be removed and deleted
Returns: void
method delete(arr, index)
remove label object from array of labels at specific index and deletes the label
Namespace types: array
Parameters:
arr (array) : label array
index (int) : index at which label object need to be removed and deleted
Returns: void
method delete(arr, index)
remove box object from array of boxes at specific index and deletes the box
Namespace types: array
Parameters:
arr (array) : box array
index (int) : index at which box object need to be removed and deleted
Returns: void
method delete(arr, index)
remove table object from array of tables at specific index and deletes the table
Namespace types: array
Parameters:
arr (array) : table array
index (int) : index at which table object need to be removed and deleted
Returns: void
method delete(arr, index)
remove linefill object from array of linefills at specific index and deletes the linefill
Namespace types: array
Parameters:
arr (array) : linefill array
index (int) : index at which linefill object need to be removed and deleted
Returns: void
method delete(arr, index)
remove polyline object from array of polylines at specific index and deletes the polyline
Namespace types: array
Parameters:
arr (array) : polyline array
index (int) : index at which polyline object need to be removed and deleted
Returns: void
method popr(arr)
remove last int object from array
Namespace types: array
Parameters:
arr (array) : int array
Returns: int
method popr(arr)
remove last float object from array
Namespace types: array
Parameters:
arr (array) : float array
Returns: float
method popr(arr)
remove last bool object from array
Namespace types: array
Parameters:
arr (array) : bool array
Returns: bool
method popr(arr)
remove last string object from array
Namespace types: array
Parameters:
arr (array) : string array
Returns: string
method popr(arr)
remove last color object from array
Namespace types: array
Parameters:
arr (array) : color array
Returns: color
method popr(arr)
remove last chart.point object from array
Namespace types: array
Parameters:
arr (array) : chart.point array
Returns: void
method popr(arr)
remove and delete last line object from array
Namespace types: array
Parameters:
arr (array) : line array
Returns: void
method popr(arr)
remove and delete last label object from array
Namespace types: array
Parameters:
arr (array) : label array
Returns: void
method popr(arr)
remove and delete last box object from array
Namespace types: array
Parameters:
arr (array) : box array
Returns: void
method popr(arr)
remove and delete last table object from array
Namespace types: array
Parameters:
arr (array) : table array
Returns: void
method popr(arr)
remove and delete last linefill object from array
Namespace types: array
Parameters:
arr (array) : linefill array
Returns: void
method popr(arr)
remove and delete last polyline object from array
Namespace types: array
Parameters:
arr (array) : polyline array
Returns: void
method shiftr(arr)
remove first int object from array
Namespace types: array
Parameters:
arr (array) : int array
Returns: int
method shiftr(arr)
remove first float object from array
Namespace types: array
Parameters:
arr (array) : float array
Returns: float
method shiftr(arr)
remove first bool object from array
Namespace types: array
Parameters:
arr (array) : bool array
Returns: bool
method shiftr(arr)
remove first string object from array
Namespace types: array
Parameters:
arr (array) : string array
Returns: string
method shiftr(arr)
remove first color object from array
Namespace types: array
Parameters:
arr (array) : color array
Returns: color
method shiftr(arr)
remove first chart.point object from array
Namespace types: array
Parameters:
arr (array) : chart.point array
Returns: void
method shiftr(arr)
remove and delete first line object from array
Namespace types: array
Parameters:
arr (array) : line array
Returns: void
method shiftr(arr)
remove and delete first label object from array
Namespace types: array
Parameters:
arr (array) : label array
Returns: void
method shiftr(arr)
remove and delete first box object from array
Namespace types: array
Parameters:
arr (array) : box array
Returns: void
method shiftr(arr)
remove and delete first table object from array
Namespace types: array
Parameters:
arr (array) : table array
Returns: void
method shiftr(arr)
remove and delete first linefill object from array
Namespace types: array
Parameters:
arr (array) : linefill array
Returns: void
method shiftr(arr)
remove and delete first polyline object from array
Namespace types: array
Parameters:
arr (array) : polyline array
Returns: void
method push(arr, val, maxItems)
add int to the end of an array with max items cap. Objects are removed from start to maintain max items cap
Namespace types: array
Parameters:
arr (array) : int array
val (int) : int object to be pushed
maxItems (int) : max number of items array can hold
Returns: int
method push(arr, val, maxItems)
add float to the end of an array with max items cap. Objects are removed from start to maintain max items cap
Namespace types: array
Parameters:
arr (array) : float array
val (float) : float object to be pushed
maxItems (int) : max number of items array can hold
Returns: float
method push(arr, val, maxItems)
add bool to the end of an array with max items cap. Objects are removed from start to maintain max items cap
Namespace types: array
Parameters:
arr (array) : bool array
val (bool) : bool object to be pushed
maxItems (int) : max number of items array can hold
Returns: bool
method push(arr, val, maxItems)
add string to the end of an array with max items cap. Objects are removed from start to maintain max items cap
Namespace types: array
Parameters:
arr (array) : string array
val (string) : string object to be pushed
maxItems (int) : max number of items array can hold
Returns: string
method push(arr, val, maxItems)
add color to the end of an array with max items cap. Objects are removed from start to maintain max items cap
Namespace types: array
Parameters:
arr (array) : color array
val (color) : color object to be pushed
maxItems (int) : max number of items array can hold
Returns: color
method push(arr, val, maxItems)
add chart.point to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap
Namespace types: array
Parameters:
arr (array) : chart.point array
val (chart.point) : chart.point object to be pushed
maxItems (int) : max number of items array can hold
Returns: chart.point
method push(arr, val, maxItems)
add line to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap
Namespace types: array
Parameters:
arr (array) : line array
val (line) : line object to be pushed
maxItems (int) : max number of items array can hold
Returns: line
method push(arr, val, maxItems)
add label to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap
Namespace types: array
Parameters:
arr (array) : label array
val (label) : label object to be pushed
maxItems (int) : max number of items array can hold
Returns: label
method push(arr, val, maxItems)
add box to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap
Namespace types: array
Parameters:
arr (array) : box array
val (box) : box object to be pushed
maxItems (int) : max number of items array can hold
Returns: box
method push(arr, val, maxItems)
add table to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap
Namespace types: array
Parameters:
arr (array) : table array
val (table) : table object to be pushed
maxItems (int) : max number of items array can hold
Returns: table
method push(arr, val, maxItems)
add linefill to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap
Namespace types: array
Parameters:
arr (array) : linefill array
val (linefill) : linefill object to be pushed
maxItems (int) : max number of items array can hold
Returns: linefill
method push(arr, val, maxItems)
add polyline to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap
Namespace types: array
Parameters:
arr (array) : polyline array
val (polyline) : polyline object to be pushed
maxItems (int) : max number of items array can hold
Returns: polyline
method unshift(arr, val, maxItems)
add int to the beginning of an array with max items cap. Objects are removed from end to maintain max items cap
Namespace types: array
Parameters:
arr (array) : int array
val (int) : int object to be unshift
maxItems (int) : max number of items array can hold
Returns: int
method unshift(arr, val, maxItems)
add float to the beginning of an array with max items cap. Objects are removed from end to maintain max items cap
Namespace types: array
Parameters:
arr (array) : float array
val (float) : float object to be unshift
maxItems (int) : max number of items array can hold
Returns: float
method unshift(arr, val, maxItems)
add bool to the beginning of an array with max items cap. Objects are removed from end to maintain max items cap
Namespace types: array
Parameters:
arr (array) : bool array
val (bool) : bool object to be unshift
maxItems (int) : max number of items array can hold
Returns: bool
method unshift(arr, val, maxItems)
add string to the beginning of an array with max items cap. Objects are removed from end to maintain max items cap
Namespace types: array
Parameters:
arr (array) : string array
val (string) : string object to be unshift
maxItems (int) : max number of items array can hold
Returns: string
method unshift(arr, val, maxItems)
add color to the beginning of an array with max items cap. Objects are removed from end to maintain max items cap
Namespace types: array
Parameters:
arr (array) : color array
val (color) : color object to be unshift
maxItems (int) : max number of items array can hold
Returns: color
method unshift(arr, val, maxItems)
add chart.point to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap
Namespace types: array
Parameters:
arr (array) : chart.point array
val (chart.point) : chart.point object to be unshift
maxItems (int) : max number of items array can hold
Returns: chart.point
method unshift(arr, val, maxItems)
add line to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap
Namespace types: array
Parameters:
arr (array) : line array
val (line) : line object to be unshift
maxItems (int) : max number of items array can hold
Returns: line
method unshift(arr, val, maxItems)
add label to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap
Namespace types: array
Parameters:
arr (array) : label array
val (label) : label object to be unshift
maxItems (int) : max number of items array can hold
Returns: label
method unshift(arr, val, maxItems)
add box to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap
Namespace types: array
Parameters:
arr (array) : box array
val (box) : box object to be unshift
maxItems (int) : max number of items array can hold
Returns: box
method unshift(arr, val, maxItems)
add table to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap
Namespace types: array
Parameters:
arr (array) : table array
val (table) : table object to be unshift
maxItems (int) : max number of items array can hold
Returns: table
method unshift(arr, val, maxItems)
add linefill to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap
Namespace types: array
Parameters:
arr (array) : linefill array
val (linefill) : linefill object to be unshift
maxItems (int) : max number of items array can hold
Returns: linefill
method unshift(arr, val, maxItems)
add polyline to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap
Namespace types: array
Parameters:
arr (array) : polyline array
val (polyline) : polyline object to be unshift
maxItems (int) : max number of items array can hold
Returns: polyline
method isEmpty(arr)
checks if an int array is either null or empty
Namespace types: array
Parameters:
arr (array) : int array
Returns: bool
method isEmpty(arr)
checks if a float array is either null or empty
Namespace types: array
Parameters:
arr (array) : float array
Returns: bool
method isEmpty(arr)
checks if a string array is either null or empty
Namespace types: array
Parameters:
arr (array) : string array
Returns: bool
method isEmpty(arr)
checks if a bool array is either null or empty
Namespace types: array
Parameters:
arr (array) : bool array
Returns: bool
method isEmpty(arr)
checks if a color array is either null or empty
Namespace types: array
Parameters:
arr (array) : color array
Returns: bool
method isEmpty(arr)
checks if a chart.point array is either null or empty
Namespace types: array
Parameters:
arr (array) : chart.point array
Returns: bool
method isEmpty(arr)
checks if a line array is either null or empty
Namespace types: array
Parameters:
arr (array) : line array
Returns: bool
method isEmpty(arr)
checks if a label array is either null or empty
Namespace types: array
Parameters:
arr (array) : label array
Returns: bool
method isEmpty(arr)
checks if a box array is either null or empty
Namespace types: array
Parameters:
arr (array) : box array
Returns: bool
method isEmpty(arr)
checks if a linefill array is either null or empty
Namespace types: array
Parameters:
arr (array) : linefill array
Returns: bool
method isEmpty(arr)
checks if a polyline array is either null or empty
Namespace types: array
Parameters:
arr (array) : polyline array
Returns: bool
method isEmpty(arr)
checks if a table array is either null or empty
Namespace types: array
Parameters:
arr (array) : table array
Returns: bool
method isNotEmpty(arr)
checks if an int array is not null and has at least one item
Namespace types: array
Parameters:
arr (array) : int array
Returns: bool
method isNotEmpty(arr)
checks if a float array is not null and has at least one item
Namespace types: array
Parameters:
arr (array) : float array
Returns: bool
method isNotEmpty(arr)
checks if a string array is not null and has at least one item
Namespace types: array
Parameters:
arr (array) : string array
Returns: bool
method isNotEmpty(arr)
checks if a bool array is not null and has at least one item
Namespace types: array
Parameters:
arr (array) : bool array
Returns: bool
method isNotEmpty(arr)
checks if a color array is not null and has at least one item
Namespace types: array
Parameters:
arr (array) : color array
Returns: bool
method isNotEmpty(arr)
checks if a chart.point array is not null and has at least one item
Namespace types: array
Parameters:
arr (array) : chart.point array
Returns: bool
method isNotEmpty(arr)
checks if a line array is not null and has at least one item
Namespace types: array
Parameters:
arr (array) : line array
Returns: bool
method isNotEmpty(arr)
checks if a label array is not null and has at least one item
Namespace types: array
Parameters:
arr (array) : label array
Returns: bool
method isNotEmpty(arr)
checks if a box array is not null and has at least one item
Namespace types: array
Parameters:
arr (array) : box array
Returns: bool
method isNotEmpty(arr)
checks if a linefill array is not null and has at least one item
Namespace types: array
Parameters:
arr (array) : linefill array
Returns: bool
method isNotEmpty(arr)
checks if a polyline array is not null and has at least one item
Namespace types: array
Parameters:
arr (array) : polyline array
Returns: bool
method isNotEmpty(arr)
checks if a table array is not null and has at least one item
Namespace types: array
Parameters:
arr (array) : table array
Returns: bool
method flush(arr)
remove all int objects in an array
Namespace types: array
Parameters:
arr (array) : int array
Returns: int
method flush(arr)
remove all float objects in an array
Namespace types: array
Parameters:
arr (array) : float array
Returns: float
method flush(arr)
remove all bool objects in an array
Namespace types: array
Parameters:
arr (array) : bool array
Returns: bool
method flush(arr)
remove all string objects in an array
Namespace types: array
Parameters:
arr (array) : string array
Returns: string
method flush(arr)
remove all color objects in an array
Namespace types: array
Parameters:
arr (array) : color array
Returns: color
method flush(arr)
remove all chart.point objects in an array
Namespace types: array
Parameters:
arr (array) : chart.point array
Returns: chart.point
method flush(arr)
remove and delete all line objects in an array
Namespace types: array
Parameters:
arr (array) : line array
Returns: line
method flush(arr)
remove and delete all label objects in an array
Namespace types: array
Parameters:
arr (array) : label array
Returns: label
method flush(arr)
remove and delete all box objects in an array
Namespace types: array
Parameters:
arr (array) : box array
Returns: box
method flush(arr)
remove and delete all table objects in an array
Namespace types: array
Parameters:
arr (array) : table array
Returns: table
method flush(arr)
remove and delete all linefill objects in an array
Namespace types: array
Parameters:
arr (array) : linefill array
Returns: linefill
method flush(arr)
remove and delete all polyline objects in an array
Namespace types: array
Parameters:
arr (array) : polyline array
Returns: polyline Library
lower_tfLibrary "lower_tf"
█ OVERVIEW
This library is an enhanced (opinionated) version of the library originally developed by PineCoders contained in lower_tf .
It is a Pine Script® programming tool for advanced lower-timeframe selection and intra-bar analysis.
█ CONCEPTS
Lower Timeframe Analysis
Lower timeframe analysis refers to the analysis of price action and market microstructure using data from timeframes shorter than the current chart period. This technique allows traders and analysts to gain deeper insights into market dynamics, volume distribution, and the price movements occurring within each bar on the chart. In Pine Script®, the request.security_lower_tf() function allows this analysis by accessing intrabar data.
The library provides a comprehensive set of functions for accurate mapping of lower timeframes, dynamic precision control, and optimized historical coverage using request.security_lower_tf().
█ IMPROVEMENTS
The original library implemented ten precision levels. This enhanced version extends that to twelve levels, adding two ultra-high-precision options:
Coverage-Based Precision (Original 5 levels):
1. "Covering most chart bars (least precise)"
2. "Covering some chart bars (less precise)"
3. "Covering fewer chart bars (more precise)"
4. "Covering few chart bars (very precise)"
5. "Covering the least chart bars (most precise)"
Intrabar-Count-Based Precision (Expanded from 5 to 7 levels):
6. "~12 intrabars per chart bar"
7. "~24 intrabars per chart bar"
8. "~50 intrabars per chart bar"
9. "~100 intrabars per chart bar"
10. "~250 intrabars per chart bar"
11. "~500 intrabars per chart bar" ← NEW
12. "~1000 intrabars per chart bar" ← NEW
The key enhancements in this version include:
1. Extended Precision Range: Adds two ultra-high-precision levels (~500 and ~1000 intrabars) for advanced microstructure analysis requiring maximum granularity.
2. Market-Agnostic Implementation: Eliminates the distinction between crypto/forex and traditional markets, removing the mktFactor variable in favor of a unified, predictable approach across all asset classes.
3. Explicit Precision Mapping: Completely refactors the timeframe selection logic using native Pine Script® timeframe properties ( timeframe.isseconds , timeframe.isminutes , timeframe.isdaily , timeframe.isweekly , timeframe.ismonthly ) and explicit multiplier-based lookup tables. The original library used minute-based calculations with market-dependent conditionals that produced inconsistent results. This version provides deterministic, predictable mappings for every chart timeframe, ensuring consistent precision behavior regardless of asset type or market hours.
An example of the differences can be seen side-by-side in the chart below, where the original library is on the left and the enhanced version is on the right:
█ USAGE EXAMPLE
// This Pine Script® code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © andre_007
//@version=6
indicator("lower_tf Example")
import andre_007/lower_tf/1 as LTF
import PineCoders/Time/5 as PCtime
//#region ———————————————————— Example code
// ————— Constants
color WHITE = color.white
color GRAY = color.gray
string LTF1 = "Covering most chart bars (least precise)"
string LTF2 = "Covering some chart bars (less precise)"
string LTF3 = "Covering less chart bars (more precise)"
string LTF4 = "Covering few chart bars (very precise)"
string LTF5 = "Covering the least chart bars (most precise)"
string LTF6 = "~12 intrabars per chart bar"
string LTF7 = "~24 intrabars per chart bar"
string LTF8 = "~50 intrabars per chart bar"
string LTF9 = "~100 intrabars per chart bar"
string LTF10 = "~250 intrabars per chart bar"
string LTF11 = "~500 intrabars per chart bar"
string LTF12 = "~1000 intrabars per chart bar"
string TT_LTF = "This selection determines the approximate number of intrabars analyzed per chart bar. Higher numbers of
intrabars produce more granular data at the cost of less historical bar coverage, because the maximum number of
available intrabars is 200K.
The first five options set the lower timeframe based on a specified relative level of chart bar coverage.
The last five options set the lower timeframe based on an approximate number of intrabars per chart bar."
string TAB_TXT = "Uses intrabars at the {0} timeframe. Avg intrabars per chart bar:
{1,number,#.#} Chart bars covered: {2} of {3} ({4,number,#.##}%)"
string ERR_TXT = "No intrabar information exists at the {1}{0}{1} timeframe."
// ————— Inputs
string ltfModeInput = input.string(LTF3, "Intrabar precision", options = , tooltip = TT_LTF)
bool showInfoBoxInput = input.bool(true, "Show information box ")
string infoBoxSizeInput = input.string("normal", "Size ", inline = "01", options = )
string infoBoxYPosInput = input.string("bottom", "↕", inline = "01", options = )
string infoBoxXPosInput = input.string("right", "↔", inline = "01", options = )
color infoBoxColorInput = input.color(GRAY, "", inline = "01")
color infoBoxTxtColorInput = input.color(WHITE, "T", inline = "01")
// ————— Calculations
// @variable A "string" representing the lower timeframe for the data request.
// NOTE:
// This line is a good example where using `var` in the declaration can improve a script's performance.
// By using `var` here, the script calls `ltf()` only once, on the dataset's first bar, instead of redundantly
// evaluating unchanging strings on every bar. We only need one evaluation of this function because the selected
// timeframe does not change across bars in this script.
var string ltfString = LTF.ltf(ltfModeInput, LTF1, LTF2, LTF3, LTF4, LTF5, LTF6, LTF7, LTF8, LTF9, LTF10, LTF11, LTF12)
// @variable An array containing all intrabar `close` prices from the `ltfString` timeframe for the current chart bar.
array intrabarCloses = request.security_lower_tf(syminfo.tickerid, ltfString, close)
// Calculate the intrabar stats.
= LTF.ltfStats(intrabarCloses)
int chartBars = bar_index + 1
// ————— Visuals
// Plot the `avgIntrabars` and `intrabars` series in all display locations.
plot(avgIntrabars, "Average intrabars", color.silver, 6)
plot(intrabars, "Intrabars", color.blue, 2)
// Plot the `chartBarsCovered` and `chartBars` values in the Data Window and the script's status line.
plot(chartBarsCovered, "Chart bars covered", display = display.data_window + display.status_line)
plot(chartBars, "Chart bars total", display = display.data_window + display.status_line)
// Information box logic.
if showInfoBoxInput
// @variable A single-cell table that displays intrabar information.
var table infoBox = table.new(infoBoxYPosInput + "_" + infoBoxXPosInput, 1, 1)
// @variable The span of the `ltfString` timeframe formatted as a number of automatically selected time units.
string formattedLtf = PCtime.formattedNoOfPeriods(timeframe.in_seconds(ltfString) * 1000)
// @variable A "string" containing the formatted text to display in the `infoBox`.
string txt = str.format(
TAB_TXT, formattedLtf, avgIntrabars, chartBarsCovered, chartBars, chartBarsCovered / chartBars * 100, "'"
)
// Initialize the `infoBox` cell on the first bar.
if barstate.isfirst
table.cell(
infoBox, 0, 0, txt, text_color = infoBoxTxtColorInput, text_size = infoBoxSizeInput,
bgcolor = infoBoxColorInput
)
// Update the cell's text on the latest bar.
else if barstate.islast
table.cell_set_text(infoBox, 0, 0, txt)
// Raise a runtime error if no intrabar data is available.
if ta.cum(intrabars) == 0 and barstate.islast
runtime.error(str.format(ERR_TXT, ltfString, "'"))
//#endregion
█ EXPORTED FUNCTIONS
ltf(userSelection, choice1, choice2, ...)
Returns the optimal lower timeframe string based on user selection and current chart timeframe. Dynamically calculates precision to balance granularity with historical coverage within the 200K intrabar limit.
ltfStats(intrabarValues)
Analyzes an intrabar array returned by request.security_lower_tf() and returns statistics: number of intrabars in current bar, total chart bars covered, and average intrabars per bar.
█ CREDITS AND LICENSING
Original Concept : PineCoders Team
Original Lower TF Library :
License : Mozilla Public License 2.0 Library
TimeSeriesBenchmarkMeasuresLibrary "TimeSeriesBenchmarkMeasures"
Time Series Benchmark Metrics. \
Provides a comprehensive set of functions for benchmarking time series data, allowing you to evaluate the accuracy, stability, and risk characteristics of various models or strategies. The functions cover a wide range of statistical measures, including accuracy metrics (MAE, MSE, RMSE, NRMSE, MAPE, SMAPE), autocorrelation analysis (ACF, ADF), and risk measures (Theils Inequality, Sharpness, Resolution, Coverage, and Pinball).
___
Reference:
- github.com .
- medium.com .
- www.salesforce.com .
- towardsdatascience.com .
- github.com .
mae(actual, forecasts)
In statistics, mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement.
Parameters:
actual (array) : List of actual values.
forecasts (array) : List of forecasts values.
Returns: - Mean Absolute Error (MAE).
___
Reference:
- en.wikipedia.org .
- The Orange Book of Machine Learning - Carl McBride Ellis .
mse(actual, forecasts)
The Mean Squared Error (MSE) is a measure of the quality of an estimator. As it is derived from the square of Euclidean distance, it is always a positive value that decreases as the error approaches zero.
Parameters:
actual (array) : List of actual values.
forecasts (array) : List of forecasts values.
Returns: - Mean Squared Error (MSE).
___
Reference:
- en.wikipedia.org .
rmse(targets, forecasts, order, offset)
Calculates the Root Mean Squared Error (RMSE) between target observations and forecasts. RMSE is a standard measure of the differences between values predicted by a model and the values actually observed.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
order (int) : Model order parameter that determines the starting position in the targets array, `default=0`.
offset (int) : Forecast offset related to target, `default=0`.
Returns: - RMSE value.
nmrse(targets, forecasts, order, offset)
Normalised Root Mean Squared Error.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
order (int) : Model order parameter that determines the starting position in the targets array, `default=0`.
offset (int) : Forecast offset related to target, `default=0`.
Returns: - NRMSE value.
rmse_interval(targets, forecasts)
Root Mean Squared Error for a set of interval windows. Computes RMSE by converting interval forecasts (with min/max bounds) into point forecasts using the mean of the interval bounds, then compares against actual target values.
Parameters:
targets (array) : List of target observations.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - RMSE value for the combined interval list.
mape(targets, forecasts)
Mean Average Percentual Error.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
Returns: - MAPE value.
smape(targets, forecasts, mode)
Symmetric Mean Average Percentual Error. Calculates the Mean Absolute Percentage Error (MAPE) between actual targets and forecasts. MAPE is a common metric for evaluating forecast accuracy, expressed as a percentage, lower values indicate a better forecast accuracy.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
mode (int) : Type of method: default=0:`sum(abs(Fi-Ti)) / sum(Fi+Ti)` , 1:`mean(abs(Fi-Ti) / ((Fi + Ti) / 2))` , 2:`mean(abs(Fi-Ti) / (abs(Fi) + abs(Ti))) * 100`
Returns: - SMAPE value.
mape_interval(targets, forecasts)
Mean Average Percentual Error for a set of interval windows.
Parameters:
targets (array) : List of target observations.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - MAPE value for the combined interval list.
acf(data, k)
Autocorrelation Function (ACF) for a time series at a specified lag.
Parameters:
data (array) : Sample data of the observations.
k (int) : The lag period for which to calculate the autocorrelation. Must be a non-negative integer.
Returns: - The autocorrelation value at the specified lag, ranging from -1 to 1.
___
The autocorrelation function measures the linear dependence between observations in a time series
at different time lags. It quantifies how well the series correlates with itself at different
time intervals, which is useful for identifying patterns, seasonality, and the appropriate
lag structure for time series models.
ACF values close to 1 indicate strong positive correlation, values close to -1 indicate
strong negative correlation, and values near 0 indicate no linear correlation.
___
Reference:
- statisticsbyjim.com
acf_multiple(data, k)
Autocorrelation function (ACF) for a time series at a set of specified lags.
Parameters:
data (array) : Sample data of the observations.
k (array) : List of lag periods for which to calculate the autocorrelation. Must be a non-negative integer.
Returns: - List of ACF values for provided lags.
___
The autocorrelation function measures the linear dependence between observations in a time series
at different time lags. It quantifies how well the series correlates with itself at different
time intervals, which is useful for identifying patterns, seasonality, and the appropriate
lag structure for time series models.
ACF values close to 1 indicate strong positive correlation, values close to -1 indicate
strong negative correlation, and values near 0 indicate no linear correlation.
___
Reference:
- statisticsbyjim.com
adfuller(data, n_lag, conf)
: Augmented Dickey-Fuller test for stationarity.
Parameters:
data (array) : Data series.
n_lag (int) : Maximum lag.
conf (string) : Confidence Probability level used to test for critical value, (`90%`, `95%`, `99%`).
Returns: - `adf` The test statistic.
- `crit` Critical value for the test statistic at the 10 % levels.
- `nobs` Number of observations used for the ADF regression and calculation of the critical values.
___
The Augmented Dickey-Fuller test is used to determine whether a time series is stationary
or contains a unit root (non-stationary). The null hypothesis is that the series has a unit root
(is non-stationary), while the alternative hypothesis is that the series is stationary.
A stationary time series has statistical properties that do not change over time, making it
suitable for many time series forecasting models. If the test statistic is less than the
critical value, we reject the null hypothesis and conclude the series is stationary.
___
Reference:
- www.jstor.org
- en.wikipedia.org
theils_inequality(targets, forecasts)
Calculates Theil's Inequality Coefficient, a measure of forecast accuracy that quantifies the relative difference between actual and predicted values.
Parameters:
targets (array) : List of target observations.
forecasts (array) : Matrix with list of forecasts, ordered column wise.
Returns: - Theil's Inequality Coefficient value, value closer to 0 is better.
___
Theil's Inequality Coefficient is calculated as: `sqrt(Sum((y_i - f_i)^2)) / (sqrt(Sum(y_i^2)) + sqrt(Sum(f_i^2)))`
where `y_i` represents actual values and `f_i` represents forecast values.
This metric ranges from 0 to infinity, with 0 indicating perfect forecast accuracy.
___
Reference:
- en.wikipedia.org
sharpness(forecasts)
The average width of the forecast intervals across all observations, representing the sharpness or precision of the predictive intervals.
Parameters:
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - Sharpness The sharpness level, which is the average width of all prediction intervals across the forecast horizon.
___
Sharpness is an important metric for evaluating forecast quality. It measures how narrow or wide the
prediction intervals are. Higher sharpness (narrower intervals) indicates greater precision in the
forecast intervals, while lower sharpness (wider intervals) suggests less precision.
The sharpness metric is calculated as the mean of the interval widths across all observations, where
each interval width is the difference between the upper and lower bounds of the prediction interval.
Note: This function assumes that the forecasts matrix has at least 2 columns, with the first column
representing the lower bounds and the second column representing the upper bounds of prediction intervals.
___
Reference:
- Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. otexts.com
resolution(forecasts)
Calculates the resolution of forecast intervals, measuring the average absolute difference between individual forecast interval widths and the overall sharpness measure.
Parameters:
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - The average absolute difference between individual forecast interval widths and the overall sharpness measure, representing the resolution of the forecasts.
___
Resolution is a key metric for evaluating forecast quality that measures the consistency of prediction
interval widths. It quantifies how much the individual forecast intervals vary from the average interval
width (sharpness). High resolution indicates that the forecast intervals are relatively consistent
across observations, while low resolution suggests significant variation in interval widths.
The resolution is calculated as the mean absolute deviation of individual interval widths from the
overall sharpness value. This provides insight into the uniformity of the forecast uncertainty
estimates across the forecast horizon.
Note: This function requires the forecasts matrix to have at least 2 columns (min, max) representing
the lower and upper bounds of prediction intervals.
___
Reference:
- (sites.stat.washington.edu)
- (www.jstor.org)
coverage(targets, forecasts)
Calculates the coverage probability, which is the percentage of target values that fall within the corresponding forecasted prediction intervals.
Parameters:
targets (array) : List of target values.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - Percent of target values that fall within their corresponding forecast intervals, expressed as a decimal value between 0 and 1 (or 0% and 100%).
___
Coverage probability is a crucial metric for evaluating the reliability of prediction intervals.
It measures how well the forecast intervals capture the actual observed values. An ideal forecast
should have a coverage probability close to the nominal confidence level (e.g., 90%, 95%, or 99%).
For example, if a 95% prediction interval is used, we expect approximately 95% of the actual
target values to fall within those intervals. If the coverage is significantly lower than the
nominal level, the intervals may be too narrow; if it's significantly higher, the intervals may
be too wide.
Note: This function requires the targets array and forecasts matrix to have the same number of
observations, and the forecasts matrix must have at least 2 columns (min, max) representing
the lower and upper bounds of prediction intervals.
___
Reference:
- (www.jstor.org)
pinball(tau, target, forecast)
Pinball loss function, measures the asymmetric loss for quantile forecasts.
Parameters:
tau (float) : The quantile level (between 0 and 1), where 0.5 represents the median.
target (float) : The actual observed value to compare against.
forecast (float) : The forecasted value.
Returns: - The Pinball loss value, which quantifies the distance between the forecast and target relative to the specified quantile level.
___
The Pinball loss function is specifically designed for evaluating quantile forecasts. It is
asymmetric, meaning it penalizes underestimates and overestimates differently depending on the
quantile level being evaluated.
For a given quantile τ, the loss function is defined as:
- If target >= forecast: (target - forecast) * τ
- If target < forecast: (forecast - target) * (1 - τ)
This loss function is commonly used in quantile regression and probabilistic forecasting
to evaluate how well forecasts capture specific quantiles of the target distribution.
___
Reference:
- (www.otexts.com)
pinball_mean(tau, targets, forecasts)
Calculates the mean pinball loss for quantile regression.
Parameters:
tau (float) : The quantile level (between 0 and 1), where 0.5 represents the median.
targets (array) : The actual observed values to compare against.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - The mean pinball loss value across all observations.
___
The pinball_mean() function computes the average Pinball loss across multiple observations,
making it suitable for evaluating overall forecast performance in quantile regression tasks.
This function leverages the asymmetric Pinball loss function to evaluate how well forecasts
capture specific quantiles of the target distribution. The choice of which column from the
forecasts matrix to use depends on the quantile level:
- For τ ≤ 0.5: Uses the first column (min) of forecasts
- For τ > 0.5: Uses the second column (max) of forecasts
This loss function is commonly used in quantile regression and probabilistic forecasting
to evaluate how well forecasts capture specific quantiles of the target distribution.
___
Reference:
- (www.otexts.com) Library
Correlation HeatMap Matrix Data [TradingFinder]🔵 Introduction
Correlation is a statistical measure that shows the degree and direction of a linear relationship between two assets.
Its value ranges from -1 to +1 : +1 means perfect positive correlation, 0 means no linear relationship, and -1 means perfect negative correlation.
In financial markets, correlation is used for portfolio diversification, risk management, pairs trading, intermarket analysis, and identifying divergences.
Correlation HeatMap Matrix Data TradingFinder is a Pine Script v6 library that calculates and returns raw correlation matrix data between up to 20 symbols. It only provides the data – it does not draw or render the heatmap – making it ideal for use in other scripts that handle visualization or further analysis. The library uses ta.correlation for fast and accurate calculations.
It also includes two helper functions for visual styling :
CorrelationColor(corr) : takes the correlation value as input and generates a smooth gradient color, ranging from strong negative to strong positive correlation.
CorrelationTextColor(corr) : takes the correlation value as input and returns a text color that ensures optimal contrast over the background color.
Library
"Correlation_HeatMap_Matrix_Data_TradingFinder"
CorrelationColor(corr)
Parameters:
corr (float)
CorrelationTextColor(corr)
Parameters:
corr (float)
Data_Matrix(Corr_Period, Sym_1, Sym_2, Sym_3, Sym_4, Sym_5, Sym_6, Sym_7, Sym_8, Sym_9, Sym_10, Sym_11, Sym_12, Sym_13, Sym_14, Sym_15, Sym_16, Sym_17, Sym_18, Sym_19, Sym_20)
Parameters:
Corr_Period (int)
Sym_1 (string)
Sym_2 (string)
Sym_3 (string)
Sym_4 (string)
Sym_5 (string)
Sym_6 (string)
Sym_7 (string)
Sym_8 (string)
Sym_9 (string)
Sym_10 (string)
Sym_11 (string)
Sym_12 (string)
Sym_13 (string)
Sym_14 (string)
Sym_15 (string)
Sym_16 (string)
Sym_17 (string)
Sym_18 (string)
Sym_19 (string)
Sym_20 (string)
🔵 How to use
Import the library into your Pine Script using the import keyword and its full namespace.
Decide how many symbols you want to include in your correlation matrix (up to 20). Each symbol must be provided as a string, for example FX:EURUSD .
Choose the correlation period (Corr\_Period) in bars. This is the lookback window used for the calculation, such as 20, 50, or 100 bars.
Call Data_Matrix(Corr_Period, Sym_1, ..., Sym_20) with your selected parameters. The function will return an array containing the correlation values for every symbol pair (upper triangle of the matrix plus diagonal).
For example :
var string Sym_1 = '' , var string Sym_2 = '' , var string Sym_3 = '' , var string Sym_4 = '' , var string Sym_5 = '' , var string Sym_6 = '' , var string Sym_7 = '' , var string Sym_8 = '' , var string Sym_9 = '' , var string Sym_10 = ''
var string Sym_11 = '', var string Sym_12 = '', var string Sym_13 = '', var string Sym_14 = '', var string Sym_15 = '', var string Sym_16 = '', var string Sym_17 = '', var string Sym_18 = '', var string Sym_19 = '', var string Sym_20 = ''
switch Market
'Forex' => Sym_1 := 'EURUSD' , Sym_2 := 'GBPUSD' , Sym_3 := 'USDJPY' , Sym_4 := 'USDCHF' , Sym_5 := 'USDCAD' , Sym_6 := 'AUDUSD' , Sym_7 := 'NZDUSD' , Sym_8 := 'EURJPY' , Sym_9 := 'EURGBP' , Sym_10 := 'GBPJPY'
,Sym_11 := 'AUDJPY', Sym_12 := 'EURCHF', Sym_13 := 'EURCAD', Sym_14 := 'GBPCAD', Sym_15 := 'CADJPY', Sym_16 := 'CHFJPY', Sym_17 := 'NZDJPY', Sym_18 := 'AUDNZD', Sym_19 := 'USDSEK' , Sym_20 := 'USDNOK'
'Stock' => Sym_1 := 'NVDA' , Sym_2 := 'AAPL' , Sym_3 := 'GOOGL' , Sym_4 := 'GOOG' , Sym_5 := 'META' , Sym_6 := 'MSFT' , Sym_7 := 'AMZN' , Sym_8 := 'AVGO' , Sym_9 := 'TSLA' , Sym_10 := 'BRK.B'
,Sym_11 := 'UNH' , Sym_12 := 'V' , Sym_13 := 'JPM' , Sym_14 := 'WMT' , Sym_15 := 'LLY' , Sym_16 := 'ORCL', Sym_17 := 'HD' , Sym_18 := 'JNJ' , Sym_19 := 'MA' , Sym_20 := 'COST'
'Crypto' => Sym_1 := 'BTCUSD' , Sym_2 := 'ETHUSD' , Sym_3 := 'BNBUSD' , Sym_4 := 'XRPUSD' , Sym_5 := 'SOLUSD' , Sym_6 := 'ADAUSD' , Sym_7 := 'DOGEUSD' , Sym_8 := 'AVAXUSD' , Sym_9 := 'DOTUSD' , Sym_10 := 'TRXUSD'
,Sym_11 := 'LTCUSD' , Sym_12 := 'LINKUSD', Sym_13 := 'UNIUSD', Sym_14 := 'ATOMUSD', Sym_15 := 'ICPUSD', Sym_16 := 'ARBUSD', Sym_17 := 'APTUSD', Sym_18 := 'FILUSD', Sym_19 := 'OPUSD' , Sym_20 := 'USDT.D'
'Custom' => Sym_1 := Sym_1_C , Sym_2 := Sym_2_C , Sym_3 := Sym_3_C , Sym_4 := Sym_4_C , Sym_5 := Sym_5_C , Sym_6 := Sym_6_C , Sym_7 := Sym_7_C , Sym_8 := Sym_8_C , Sym_9 := Sym_9_C , Sym_10 := Sym_10_C
,Sym_11 := Sym_11_C, Sym_12 := Sym_12_C, Sym_13 := Sym_13_C, Sym_14 := Sym_14_C, Sym_15 := Sym_15_C, Sym_16 := Sym_16_C, Sym_17 := Sym_17_C, Sym_18 := Sym_18_C, Sym_19 := Sym_19_C , Sym_20 := Sym_20_C
= Corr.Data_Matrix(Corr_period, Sym_1 ,Sym_2 ,Sym_3 ,Sym_4 ,Sym_5 ,Sym_6 ,Sym_7 ,Sym_8 ,Sym_9 ,Sym_10,Sym_11,Sym_12,Sym_13,Sym_14,Sym_15,Sym_16,Sym_17,Sym_18,Sym_19,Sym_20)
Loop through or index into this array to retrieve each correlation value for your custom layout or logic.
Pass each correlation value to CorrelationColor() to get the corresponding gradient background color, which reflects the correlation’s strength and direction (negative to positive).
For example :
Corr.CorrelationColor(SYM_3_10)
Pass the same correlation value to CorrelationTextColor() to get the correct text color for readability against that background.
For example :
Corr.CorrelationTextColor(SYM_1_1)
Use these colors in a table or label to render your own heatmap or any other visualization you need.
Library
TFPS_EngineLibrary "TFPS_Engine"
f_calculate_lead_lag(series1, series2, length, max_lag)
Parameters:
series1 (float)
series2 (float)
length (int)
max_lag (int)
f_calculate_pressure_score(spx_ticker, vix_ticker, dxy_ticker, us10y_ticker, benchmark_source, trend_lookback, score_smoothing, use_dynamic_weights, corr_lookback, w_spx, w_vix, w_dxy, w_us10y, zscore_lookback, max_lag)
Parameters:
spx_ticker (string)
vix_ticker (string)
dxy_ticker (string)
us10y_ticker (string)
benchmark_source (float)
trend_lookback (int)
score_smoothing (simple int)
use_dynamic_weights (bool)
corr_lookback (int)
w_spx (float)
w_vix (float)
w_dxy (float)
w_us10y (float)
zscore_lookback (int)
max_lag (int)
LeadLagOutput
Fields:
best_lag (series int)
max_corr (series float)
TFPS_Output
Fields:
historical_score (series float)
smoothed_score (series float)
z_score (series float)
regime_signal (series int)
lead_lag_bars (series int)
lead_lag_corr (series float)
weight_spx (series float)
weight_vix (series float)
weight_dxy (series float)
weight_us10y (series float) Library
UTSStrategyHelperLibrary "UTSStrategyHelper"
TODO: add library description here
stopLossPrice(sig, atr, factor, isLong)
Calculates the stop loss price using a distance determined by ATR multiplied by a factor. Example for Long trade SL: PRICE - (ATR * factor).
Parameters:
sig (float)
atr (float) : (float): The value of the atr.
factor (float)
isLong (bool) : (bool): The current trade direction.
Returns: (bool): A boolean value.
takeProfitPrice(sig, atr, factor, isLong)
Calculates the take profit price using a distance determined by ATR multiplied by a factor. Example for Long trade TP: PRICE + (ATR * factor). When take profit price is reached usually 50 % of the position is closed and the other 50 % get a trailing stop assigned.
Parameters:
sig (float)
atr (float) : (float): The value of the atr.
factor (float)
isLong (bool) : (bool): The current trade direction.
Returns: (bool): A boolean value.
trailingStopPrice(initialStopPrice, atr, factor, priceSource, isLong)
Calculates a trailing stop price using a distance determined by ATR multiplied by a factor. It takes an initial price and follows the price closely if it changes in a favourable way.
Parameters:
initialStopPrice (float) : (float): The initial stop price which, for consistency also should be ATR * factor behind price: e.g. Long trade: PRICE - (ATR * factor)
atr (float) : (float): The value of the atr. Ideally the ATR value at trade open is taken and used for subsequent calculations.
factor (float)
priceSource (float) : (float): The current price.
isLong (bool) : (bool): The current trade direction.
Returns: (bool): A boolean value.
hasGreaterPositionSize(positionSize)
Determines if the strategy's position size has grown since the last bar.
Parameters:
positionSize (float) : (float): The size of the position.
Returns: (bool): A boolean value.
hasSmallerPositionSize(positionSize)
Determines if the strategy's position size has decreased since the last bar.
Parameters:
positionSize (float) : (float): The size of the position.
Returns: (bool): A boolean value.
hasUnchangedPositionSize(positionSize)
Determines if the strategy's position size has changed since the last bar.
Parameters:
positionSize (float) : (float): The size of the position.
Returns: (bool): A boolean value.
exporthasLongPosition(positionSize)
Determines if the strategy has an open long position.
Parameters:
positionSize (float) : (float): The size of the position.
Returns: (bool): A boolean value.
hasShortPosition(positionSize)
Determines if the strategy has an open short position.
Parameters:
positionSize (float) : (float): The size of the position.
Returns: (bool): A boolean value.
hasAnyPosition(positionSize)
Determines if the strategy has any open position, regardless of short or long.
Parameters:
positionSize (float) : (float): The size of the position.
Returns: (bool): A boolean value.
hasSignal(value)
Determines if the given argument contains a valid value (means not 'na').
Parameters:
value (float) : (float): The actual value.
Returns: (bool): A boolean value. Library
UTSConvenienceToolsLibrary "UTSConvenienceTools"
Convenience tool library containing helper functions for drawing and charting.
isDarkColor(color)
Determines on base of the luminance of the given color if the color can be considered a 'dark' color. Usefull for determining the readable font color for arbitrary colored backgrounds. Credits out to:
Parameters:
color (color) : (color): The actual color value.
Returns: (bool): A boolean value.
smallLabelLowerRight(txt, yPos, bgColor)
Displays the specified `txt` in a small label at the `yPos` of the current bar. The label points to the lower right.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned above the candle pass 'high'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
smallLabelUpperRight(txt, yPos, bgColor)
Displays the specified `txt` in a small label at the `yPos` of the current bar. The label points to the upper right.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned below the candle pass 'low'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
smallLabelCenter(txt, yPos, bgColor)
Displays the specified `txt` in a small label at the `yPos` of the current bar. The label points to the center.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned above the candle pass 'high'. To have it positioned below the candle pass 'low'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
smallLabelDown(txt, yPos, bgColor)
Displays the specified `txt` in a small label at the `yPos` of the current bar. The label points down.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned above the candle pass 'high'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
smallLabelUp(txt, yPos, bgColor)
Displays the specified `txt` in a small label at the `yPos` of the current bar. The label points down.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned below the candle pass 'low'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
normalLabelLowerRight(txt, yPos, bgColor)
Displays the specified `txt` in a normal label at the `yPos` of the current bar. The label points to the lower right.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned above the candle pass 'high'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
normalLabelUpperRight(txt, yPos, bgColor)
Displays the specified `txt` in a normal label at the `yPos` of the current bar. The label points to the upper right.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned below the candle pass 'low'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
normalLabelCenter(txt, yPos, bgColor)
Displays the specified `txt` in a normal label at the `yPos` of the current bar. The label points to the center.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned above the candle pass 'high'. To have it positioned below the candle pass 'low'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
normalLabelDown(txt, yPos, bgColor)
Displays the specified `txt` in a normal label at the `yPos` of the current bar. The label points down.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned above the candle pass 'high'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
normalLabelUp(txt, yPos, bgColor)
Displays the specified `txt` in a normal label at the `yPos` of the current bar. The label points down.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned below the candle pass 'low'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
largeLabelLowerRight(txt, yPos, bgColor)
Displays the specified `txt` in a large label at the `yPos` of the current bar. The label points to the lower right.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned above the candle pass 'high'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
largeLabelUpperRight(txt, yPos, bgColor)
Displays the specified `txt` in a large label at the `yPos` of the current bar. The label points to the upper right.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned below the candle pass 'low'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
largeLabelCenter(txt, yPos, bgColor)
Displays the specified `txt` in a large label at the `yPos` of the current bar. The label points to the center.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned above the candle pass 'high'. To have it positioned below the candle pass 'low'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
largeLabelDown(txt, yPos, bgColor)
Displays the specified `txt` in a large label at the `yPos` of the current bar. The label points down.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned above the candle pass 'high'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
largeLabelUp(txt, yPos, bgColor)
Displays the specified `txt` in a large label at the `yPos` of the current bar. The label points down.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned below the candle pass 'low'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
autoLabelLowerRight(txt, yPos, bgColor)
Displays the specified `txt` in a auto label at the `yPos` of the current bar. The label points to the lower right.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned above the candle pass 'high'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
autoLabelUpperRight(txt, yPos, bgColor)
Displays the specified `txt` in a auto label at the `yPos` of the current bar. The label points to the upper right.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned below the candle pass 'low'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
autoLabelCenter(txt, yPos, bgColor)
Displays the specified `txt` in a auto label at the `yPos` of the current bar. The label points to the center.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned above the candle pass 'high'. To have it positioned below the candle pass 'low'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
autoLabelDown(txt, yPos, bgColor)
Displays the specified `txt` in a auto label at the `yPos` of the current bar. The label points down.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned above the candle pass 'high'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value.
autoLabelUp(txt, yPos, bgColor)
Displays the specified `txt` in a auto label at the `yPos` of the current bar. The label points down.
Parameters:
txt (string)
yPos (float) : (float): The y-position value. To have it positioned below the candle pass 'low'.
bgColor (color) : (color): The background color value.
Returns: (bool): A boolean value. Library
Zero Lag MTF Moving Average by CoffeeshopCryptoBased on Moving Average Types supplied by @PulseWire www.pulsewire.com
Ideas and code enhanced to show higher timeframe by @CoffeeShopCrypto
It’s time to take the guesswork out of moving averages and multiple timeframes when day trading. Moving averages are a cornerstone of many trading strategies, often viewed as dynamic support and resistance levels. Traders rely on these levels to anticipate price reactions, whether it’s a bounce in a trending market or a reversal in a ranging one. Additionally, the direction and alignment of multi timeframe moving averages—whether they’re moving in the same direction or diverging—provide critical clues about market momentum and potential reversals. However, the traditional higher timeframe moving average indicators force traders to wait for higher timeframe candles to close, creating lag and missed opportunities.
The Old Way
For example: If you are on a 5 minute chart and you want to observe the location and direction of a 30 minute chart Moving Average, you'll need to wait for a total of 6 candles to close, and again every 6 candles after that. This only creates more lag.
The New Way
Now there is no waiting for high timeframe session candles to close. No matter what timeframe Moving Average you want to know about, this indicator will show you its location on your current chart at any time in real time.
For those who prefer Bollinger Bands, this indicator adds a whole new dimension to your strategy. Traders often wait for price action to break outside the lower time frame Bollinger bands before considering a trade, while still seeking key support or resistance levels beyond them. But if you don't know the position of your higher time frame Bollinger, you could be trading into a trap. With Zero Lag Multi Timeframe Moving Average, you can view both your current and higher timeframe Bollinger Bands simultaneously with zero waiting. This lets you instantly see when price action is traveling between the bands of either timeframe or breaking through both—indicating a strong trend in that direction. Additionally, when both sets of Bollinger Bands overlap at the same price levels, it highlights areas of strong consolidation and ranging conditions, giving you a clear picture of market dynamics. This is a key element in price action that tells you there is currently no direction to the market and both the current and higher time frames are flat.
Enter Zero Lag Multi Timeframe Moving Average—the ultimate tool for real-time higher timeframe moving averages and Bollinger Bands. This innovative indicator eliminates the delay, delivering instant, precise values for higher timeframe averages and bands, even on open candles. Seamlessly combining current and higher timeframe data, it allows traders to identify key moments where moving averages or Bollinger Bands align or diverge, signaling market conditions. Whether you’re gauging the strength of a trend, pinpointing potential reversals, or identifying consolidation zones, Zero Lag Multi Timeframe Moving Average gives you the clarity needed to make better trading decisions according to market conditions.
Why is this "Mashup" of moving averages different and important?
Honestly its really about the calculation thats imported through the "import library" function.
Heres what it does:
The ZLMTF-MA is designed to help traders easily see where higher timeframe moving averages and Bollinger Bands are—without needing to switch chart timeframes or wait for those larger candles to close. It works by adjusting common moving average types like SMA, EMA, and VWMA to show what they would look like if they were based on a higher timeframe, right on your current chart. This helps users stay focused on their main timeframe while still having a clear view of the bigger picture, making it easier to spot trend direction, key support and resistance levels, and overall market structure. The goal is to keep things simple, fast, and more visually informative for everyday traders.
Bollinger Bands
When working with Bollinger Bands, a common strategy is to take the trades once price action has escaped through the top or bottom of your current Bollinger Band.
A false breakout occurs when both Bollinger Bands are not moving in the same direction as eachother or when they are overlapping.
Moving Averages as Support and Resistance:
Traders who use Moving Averages as support or resistance, looking for rejections or failures of these areas can now see multiple timeframe price action instantly and simultaneously.
Trading Setup Examples:
Price Action Scenario 1:
Higher Timeframe Ranging-
When price action breaks through a current moving average headed toward a higher timeframe moving average, trades are taken with caution if the moving averages are converging.
Price Action Scenario 2:
Strong Trending Market -
If the moving averages are in the same direction, and your price action is now leading the low timeframe moving average, you have re-entered a strong trend.
Price Action Scenario 3:
High Timeframe Rejections -
If you have a rejection of a higher timeframe moving average, and your both averages are still diverging, this is the end of a pullback as you re-enter a strong trend in the original direction
Price Action Scenario 4:
Trend Reversals -
If you close beyond both the low and high timeframe moving averages, you can consider that price action is strong enough to change direction here and you should prepare for trade setups in the opposite direction of the previous.
HTF MA Label Information:
Even if your high timeframe moving average is turned off, you can still see this label.
It gives you a quick reminder of what high timeframe settings you have used to see MA values.
Indicator
lib_smcLibrary "lib_smc"
This is an adaptation of LuxAlgo's Smart Money Concepts indicator with numerous changes. Main changes include integration of object based plotting, plenty of performance improvements, live tracking of Order Blocks, integration of volume profiles to refine Order Blocks, and many more.
This is a library for developers, if you want this converted into a working strategy, let me know.
buffer(item, len, force_rotate)
Parameters:
item (float)
len (int)
force_rotate (bool)
buffer(item, len, force_rotate)
Parameters:
item (int)
len (int)
force_rotate (bool)
buffer(item, len, force_rotate)
Parameters:
item (Profile type from robbatt/lib_profile/32)
len (int)
force_rotate (bool)
swings(len)
INTERNAL: detect swing points (HH and LL) in given range
Parameters:
len (simple int) : range to check for new swing points
Returns: values are the price level where and if a new HH or LL was detected, else na
method init(this)
Namespace types: OrderBlockConfig
Parameters:
this (OrderBlockConfig)
method delete(this)
Namespace types: OrderBlock
Parameters:
this (OrderBlock)
method clear_broken(this, broken_buffer)
INTERNAL: delete internal order blocks box coordinates if top/bottom is broken
Namespace types: map
Parameters:
this (map)
broken_buffer (map)
Returns: any_bull_ob_broken, any_bear_ob_broken, broken signals are true if an according order block was broken/mitigated, broken contains the broken block(s)
create_ob(id, mode, start_t, start_i, top, end_t, end_i, bottom, break_price, early_confirmation_price, config, init_plot, force_overlay)
INTERNAL: set internal order block coordinates
Parameters:
id (int)
mode (int) : 1: bullish, -1 bearish block
start_t (int)
start_i (int)
top (float)
end_t (int)
end_i (int)
bottom (float)
break_price (float)
early_confirmation_price (float)
config (OrderBlockConfig)
init_plot (bool)
force_overlay (bool)
Returns: signals are true if an according order block was broken/mitigated
method align_to_profile(block, align_edge, align_break_price)
Namespace types: OrderBlock
Parameters:
block (OrderBlock)
align_edge (bool)
align_break_price (bool)
method create_profile(block, opens, tops, bottoms, closes, values, resolution, vah_pc, val_pc, args, init_calculated, init_plot, force_overlay)
Namespace types: OrderBlock
Parameters:
block (OrderBlock)
opens (array)
tops (array)
bottoms (array)
closes (array)
values (array)
resolution (int)
vah_pc (float)
val_pc (float)
args (ProfileArgs type from robbatt/lib_profile/32)
init_calculated (bool)
init_plot (bool)
force_overlay (bool)
method create_profile(block, resolution, vah_pc, val_pc, args, init_calculated, init_plot, force_overlay)
Namespace types: OrderBlock
Parameters:
block (OrderBlock)
resolution (int)
vah_pc (float)
val_pc (float)
args (ProfileArgs type from robbatt/lib_profile/32)
init_calculated (bool)
init_plot (bool)
force_overlay (bool)
track_obs(swing_len, hh, ll, top, btm, bull_bos_alert, bull_choch_alert, bear_bos_alert, bear_choch_alert, min_block_size, max_block_size, config_bull, config_bear, init_plot, force_overlay, enabled, extend_blocks, clear_broken_buffer_before, align_edge_to_value_area, align_break_price_to_poc, profile_args_bull, profile_args_bear, use_soft_confirm, soft_confirm_offset, use_retracements_with_FVG_out)
Parameters:
swing_len (int)
hh (float)
ll (float)
top (float)
btm (float)
bull_bos_alert (bool)
bull_choch_alert (bool)
bear_bos_alert (bool)
bear_choch_alert (bool)
min_block_size (float)
max_block_size (float)
config_bull (OrderBlockConfig)
config_bear (OrderBlockConfig)
init_plot (bool)
force_overlay (bool)
enabled (bool)
extend_blocks (simple bool)
clear_broken_buffer_before (simple bool)
align_edge_to_value_area (simple bool)
align_break_price_to_poc (simple bool)
profile_args_bull (ProfileArgs type from robbatt/lib_profile/32)
profile_args_bear (ProfileArgs type from robbatt/lib_profile/32)
use_soft_confirm (simple bool)
soft_confirm_offset (float)
use_retracements_with_FVG_out (simple bool)
method draw(this, config, extend_only)
Namespace types: OrderBlock
Parameters:
this (OrderBlock)
config (OrderBlockConfig)
extend_only (bool)
method draw(blocks, config)
INTERNAL: plot order blocks
Namespace types: array
Parameters:
blocks (array)
config (OrderBlockConfig)
method draw(blocks, config)
INTERNAL: plot order blocks
Namespace types: map
Parameters:
blocks (map)
config (OrderBlockConfig)
method cleanup(this, ob_bull, ob_bear)
removes all Profiles that are older than the latest OrderBlock from this profile buffer
Namespace types: array
Parameters:
this (array type from robbatt/lib_profile/32)
ob_bull (OrderBlock)
ob_bear (OrderBlock)
_plot_swing_points(mode, x, y, show_swing_points, linecolor_swings, keep_history, show_latest_swings_levels, trail_x, trail_y, trend)
INTERNAL: plot swing points
Parameters:
mode (int) : 1: bullish, -1 bearish block
x (int) : x-coordingate of swing point to plot (bar_index)
y (float) : y-coordingate of swing point to plot (price)
show_swing_points (bool) : switch to enable/disable plotting of swing point labels
linecolor_swings (color) : color for swing point labels and lates level lines
keep_history (bool) : weater to remove older swing point labels and only keep the most recent
show_latest_swings_levels (bool)
trail_x (int) : x-coordinate for latest swing point (bar_index)
trail_y (float) : y-coordinate for latest swing point (price)
trend (int) : the current trend 1: bullish, -1: bearish, to determine Strong/Weak Low/Highs
_pivot_lvl(mode, trend, hhll_x, hhll, super_hhll, filter_insignificant_internal_breaks)
INTERNAL: detect whether a structural level has been broken and if it was in trend direction (BoS) or against trend direction (ChoCh), also track the latest high and low swing points
Parameters:
mode (simple int) : detect 1: bullish, -1 bearish pivot points
trend (int) : current trend direction
hhll_x (int) : x-coordinate of newly detected hh/ll (bar_index)
hhll (float) : y-coordinate of newly detected hh/ll (price)
super_hhll (float) : level/y-coordinate of superior hhll (if this is an internal structure pivot level)
filter_insignificant_internal_breaks (bool) : if true pivot points / internal structure will be ignored where the wick in trend direction is longer than the opposite (likely to push further in direction of main trend)
Returns: coordinates of internal structure that has been broken (x,y): start of structure, (trail_x, trail_y): tracking hh/ll after structure break, (bos_alert, choch_alert): signal whether a structural level has been broken
_plot_structure(x, y, is_bos, is_choch, line_color, line_style, label_style, label_size, keep_history)
INTERNAL: plot structural breaks (BoS/ChoCh)
Parameters:
x (int) : x-coordinate of newly broken structure (bar_index)
y (float) : y-coordinate of newly broken structure (price)
is_bos (bool) : whether this structural break was in trend direction
is_choch (bool) : whether this structural break was against trend direction
line_color (color) : color for the line connecting the structural level and the breaking candle
line_style (string) : style (line.style_dashed/solid) for the line connecting the structural level and the breaking candle
label_style (string) : style (label.style_label_down/up) for the label above/below the line connecting the structural level and the breaking candle
label_size (string) : size (size.small/tiny) for the label above/below the line connecting the structural level and the breaking candle
keep_history (bool) : weater to remove older swing point labels and only keep the most recent
structure_values(length, super_hh, super_ll, filter_insignificant_internal_breaks)
detect (and plot) structural breaks and the resulting new trend
Parameters:
length (simple int) : lookback period for swing point detection
super_hh (float) : level/y-coordinate of superior hh (for internal structure detection)
super_ll (float) : level/y-coordinate of superior ll (for internal structure detection)
filter_insignificant_internal_breaks (bool) : if true pivot points / internal structure will be ignored where the wick in trend direction is longer than the opposite (likely to push further in direction of main trend)
Returns: trend: direction 1:bullish -1:bearish, (bull_bos_alert, bull_choch_alert, top_x, top_y, trail_up_x, trail_up): whether and which level broke in a bullish direction, trailing high, (bbear_bos_alert, bear_choch_alert, tm_x, btm_y, trail_dn_x, trail_dn): same in bearish direction
structure_plot(trend, bull_bos_alert, bull_choch_alert, top_x, top_y, trail_up_x, trail_up, hh, bear_bos_alert, bear_choch_alert, btm_x, btm_y, trail_dn_x, trail_dn, ll, color_bull, color_bear, show_swing_points, show_latest_swings_levels, show_bos, show_choch, line_style, label_size, keep_history)
detect (and plot) structural breaks and the resulting new trend
Parameters:
trend (int) : crrent trend 1: bullish, -1: bearish
bull_bos_alert (bool) : if there was a bullish bos alert -> plot it
bull_choch_alert (bool) : if there was a bullish choch alert -> plot it
top_x (int) : latest shwing high x
top_y (float) : latest swing high y
trail_up_x (int) : trailing high x
trail_up (float) : trailing high y
hh (float) : if there was a higher high
bear_bos_alert (bool) : if there was a bearish bos alert -> plot it
bear_choch_alert (bool) : if there was a bearish chock alert -> plot it
btm_x (int) : latest swing low x
btm_y (float) : latest swing low y
trail_dn_x (int) : trailing low x
trail_dn (float) : trailing low y
ll (float) : if there was a lower low
color_bull (color) : color for bullish BoS/ChoCh levels
color_bear (color) : color for bearish BoS/ChoCh levels
show_swing_points (bool) : whether to plot swing point labels
show_latest_swings_levels (bool) : whether to track and plot latest swing point levels with lines
show_bos (bool) : whether to plot BoS levels
show_choch (bool) : whether to plot ChoCh levels
line_style (string) : whether to plot BoS levels
label_size (string) : label size of plotted BoS/ChoCh levels
keep_history (bool) : weater to remove older swing point labels and only keep the most recent
structure(length, color_bull, color_bear, super_hh, super_ll, filter_insignificant_internal_breaks, show_swing_points, show_latest_swings_levels, show_bos, show_choch, line_style, label_size, keep_history, enabled)
detect (and plot) structural breaks and the resulting new trend
Parameters:
length (simple int) : lookback period for swing point detection
color_bull (color) : color for bullish BoS/ChoCh levels
color_bear (color) : color for bearish BoS/ChoCh levels
super_hh (float) : level/y-coordinate of superior hh (for internal structure detection)
super_ll (float) : level/y-coordinate of superior ll (for internal structure detection)
filter_insignificant_internal_breaks (bool) : if true pivot points / internal structure will be ignored where the wick in trend direction is longer than the opposite (likely to push further in direction of main trend)
show_swing_points (bool) : whether to plot swing point labels
show_latest_swings_levels (bool) : whether to track and plot latest swing point levels with lines
show_bos (bool) : whether to plot BoS levels
show_choch (bool) : whether to plot ChoCh levels
line_style (string) : whether to plot BoS levels
label_size (string) : label size of plotted BoS/ChoCh levels
keep_history (bool) : weater to remove older swing point labels and only keep the most recent
enabled (bool)
_check_equal_level(mode, len, eq_threshold, enabled)
INTERNAL: detect equal levels (double top/bottom)
Parameters:
mode (int) : detect 1: bullish/high, -1 bearish/low pivot points
len (int) : lookback period for equal level (swing point) detection
eq_threshold (float) : maximum price offset for a level to be considered equal
enabled (bool)
Returns: eq_alert whether an equal level was detected and coordinates of the first and the second level/swing point
_plot_equal_level(show_eq, x1, y1, x2, y2, label_txt, label_style, label_size, line_color, line_style, keep_history)
INTERNAL: plot equal levels (double top/bottom)
Parameters:
show_eq (bool) : whether to plot the level or not
x1 (int) : x-coordinate of the first level / swing point
y1 (float) : y-coordinate of the first level / swing point
x2 (int) : x-coordinate of the second level / swing point
y2 (float) : y-coordinate of the second level / swing point
label_txt (string) : text for the label above/below the line connecting the equal levels
label_style (string) : style (label.style_label_down/up) for the label above/below the line connecting the equal levels
label_size (string) : size (size.tiny) for the label above/below the line connecting the equal levels
line_color (color) : color for the line connecting the equal levels (and it's label)
line_style (string) : style (line.style_dotted) for the line connecting the equal levels
keep_history (bool) : weater to remove older swing point labels and only keep the most recent
equal_levels_values(len, threshold, enabled)
detect (and plot) equal levels (double top/bottom), returns coordinates
Parameters:
len (int) : lookback period for equal level (swing point) detection
threshold (float) : maximum price offset for a level to be considered equal
enabled (bool) : whether detection is enabled
Returns: (eqh_alert, eqh_x1, eqh_y1, eqh_x2, eqh_y2) whether an equal high was detected and coordinates of the first and the second level/swing point, (eql_alert, eql_x1, eql_y1, eql_x2, eql_y2) same for equal lows
equal_levels_plot(eqh_x1, eqh_y1, eqh_x2, eqh_y2, eql_x1, eql_y1, eql_x2, eql_y2, color_eqh, color_eql, show, keep_history)
detect (and plot) equal levels (double top/bottom), returns coordinates
Parameters:
eqh_x1 (int) : coordinates of first point of equal high
eqh_y1 (float) : coordinates of first point of equal high
eqh_x2 (int) : coordinates of second point of equal high
eqh_y2 (float) : coordinates of second point of equal high
eql_x1 (int) : coordinates of first point of equal low
eql_y1 (float) : coordinates of first point of equal low
eql_x2 (int) : coordinates of second point of equal low
eql_y2 (float) : coordinates of second point of equal low
color_eqh (color) : color for the line connecting the equal highs (and it's label)
color_eql (color) : color for the line connecting the equal lows (and it's label)
show (bool) : whether plotting is enabled
keep_history (bool) : weater to remove older swing point labels and only keep the most recent
Returns: (eqh_alert, eqh_x1, eqh_y1, eqh_x2, eqh_y2) whether an equal high was detected and coordinates of the first and the second level/swing point, (eql_alert, eql_x1, eql_y1, eql_x2, eql_y2) same for equal lows
equal_levels(len, threshold, color_eqh, color_eql, enabled, show, keep_history)
detect (and plot) equal levels (double top/bottom)
Parameters:
len (int) : lookback period for equal level (swing point) detection
threshold (float) : maximum price offset for a level to be considered equal
color_eqh (color) : color for the line connecting the equal highs (and it's label)
color_eql (color) : color for the line connecting the equal lows (and it's label)
enabled (bool) : whether detection is enabled
show (bool) : whether plotting is enabled
keep_history (bool) : weater to remove older swing point labels and only keep the most recent
Returns: (eqh_alert) whether an equal high was detected, (eql_alert) same for equal lows
_detect_fvg(mode, enabled, o, h, l, c, filter_insignificant_fvgs, change_tf)
INTERNAL: detect FVG (fair value gap)
Parameters:
mode (int) : detect 1: bullish, -1 bearish gaps
enabled (bool) : whether detection is enabled
o (float) : reference source open
h (float) : reference source high
l (float) : reference source low
c (float) : reference source close
filter_insignificant_fvgs (bool) : whether to calculate and filter small/insignificant gaps
change_tf (bool) : signal when the previous reference timeframe closed, triggers new calculation
Returns: whether a new FVG was detected and its top/mid/bottom levels
_clear_broken_fvg(mode, upper_boxes, lower_boxes)
INTERNAL: clear mitigated FVGs (fair value gaps)
Parameters:
mode (int) : detect 1: bullish, -1 bearish gaps
upper_boxes (array) : array that stores the upper parts of the FVG boxes
lower_boxes (array) : array that stores the lower parts of the FVG boxes
_plot_fvg(mode, show, top, mid, btm, border_color, extend_box)
INTERNAL: plot (and clear broken) FVG (fair value gap)
Parameters:
mode (int) : plot 1: bullish, -1 bearish gap
show (bool) : whether plotting is enabled
top (float) : top level of fvg
mid (float) : center level of fvg
btm (float) : bottom level of fvg
border_color (color) : color for the FVG box
extend_box (int) : how many bars into the future the FVG box should be extended after detection
fvgs_values(o, h, l, c, filter_insignificant_fvgs, change_tf, enabled)
detect (and plot / clear broken) FVGs (fair value gaps), and return alerts and level values
Parameters:
o (float) : reference source open
h (float) : reference source high
l (float) : reference source low
c (float) : reference source close
filter_insignificant_fvgs (bool) : whether to calculate and filter small/insignificant gaps
change_tf (bool) : signal when the previous reference timeframe closed, triggers new calculation
enabled (bool) : whether detection is enabled
Returns: (bullish_fvg_alert, bull_top, bull_mid, bull_btm): whether a new bullish FVG was detected and its top/mid/bottom levels, (bearish_fvg_alert, bear_top, bear_mid, bear_btm): same for bearish FVGs
fvgs_plot(bullish_fvg_alert, bull_top, bull_mid, bull_btm, bearish_fvg_alert, bear_top, bear_mid, bear_btm, color_bull, color_bear, extend_box, show)
Parameters:
bullish_fvg_alert (bool)
bull_top (float)
bull_mid (float)
bull_btm (float)
bearish_fvg_alert (bool)
bear_top (float)
bear_mid (float)
bear_btm (float)
color_bull (color) : color for bullish FVG boxes
color_bear (color) : color for bearish FVG boxes
extend_box (int) : how many bars into the future the FVG box should be extended after detection
show (bool) : whether plotting is enabled
Returns: (bullish_fvg_alert, bull_top, bull_mid, bull_btm): whether a new bullish FVG was detected and its top/mid/bottom levels, (bearish_fvg_alert, bear_top, bear_mid, bear_btm): same for bearish FVGs
fvgs(o, h, l, c, filter_insignificant_fvgs, change_tf, color_bull, color_bear, extend_box, enabled, show)
detect (and plot / clear broken) FVGs (fair value gaps)
Parameters:
o (float) : reference source open
h (float) : reference source high
l (float) : reference source low
c (float) : reference source close
filter_insignificant_fvgs (bool) : whether to calculate and filter small/insignificant gaps
change_tf (bool) : signal when the previous reference timeframe closed, triggers new calculation
color_bull (color) : color for bullish FVG boxes
color_bear (color) : color for bearish FVG boxes
extend_box (int) : how many bars into the future the FVG box should be extended after detection
enabled (bool) : whether detection is enabled
show (bool) : whether plotting is enabled
Returns: (bullish_fvg_alert): whether a new bullish FVG was detected, (bearish_fvg_alert): same for bearish FVGs
OrderBlock
Fields:
id (series int)
dir (series int)
left_top (chart.point)
right_bottom (chart.point)
break_price (series float)
early_confirmation_price (series float)
ltf_high (array)
ltf_low (array)
ltf_volume (array)
plot (Box type from robbatt/lib_plot_objects/49)
profile (Profile type from robbatt/lib_profile/32)
trailing (series bool)
extending (series bool)
awaiting_confirmation (series bool)
touched_break_price_before_confirmation (series bool)
soft_confirmed (series bool)
has_fvg_out (series bool)
hidden (series bool)
broken (series bool)
OrderBlockConfig
Fields:
show (series bool)
show_last (series int)
show_id (series bool)
show_profile (series bool)
args (BoxArgs type from robbatt/lib_plot_objects/49)
txt (series string)
txt_args (BoxTextArgs type from robbatt/lib_plot_objects/49)
delete_when_broken (series bool)
broken_args (BoxArgs type from robbatt/lib_plot_objects/49)
broken_txt (series string)
broken_txt_args (BoxTextArgs type from robbatt/lib_plot_objects/49)
broken_profile_args (ProfileArgs type from robbatt/lib_profile/32)
use_profile (series bool)
profile_args (ProfileArgs type from robbatt/lib_profile/32) Library
Library
