Calibration

Candlestick Edge Auto-Preset MTF Self-Calibrating Candlestick Edge only fires a candlestick pattern when it is "rightly placed" — confirmed by a higher-timeframe trend AND by where price sits in the developing volume profile. Then it does what most pattern tools don't: it forward-tests every signal and reports the MEASURED edge per pattern, so you read realized performance instead of a marketing claim. WHY THIS IS ONE TOOL (not a bundle) The parts answer one question about one candle: "is this pattern in a place that has historically paid, and does it beat a coin-flip here?" PATTERN detection says WHAT printed (24 classic candlestick patterns). HTF ALIGNMENT says whether the bigger trend agrees. VOLUME-PROFILE POSITIONING says WHERE it printed — reversals only at value-area edges, naked POC, HVN support/resistance, or liquidity sweeps; continuations only through low-volume voids or on a value breakout. The CALIBRATION SPINE forward-resolves each signal with a triple barrier and reports Hit% vs a matched Base% (Edge) with a Wilson confidence interval, so a placed-and-confirmed pattern can be told apart from a small-sample fluke. One pattern substrate, one location read, one calibration spine. MEASUREMENT (the differentiator) Each signal opens at close with target = ±TP·ATR, stop = ∓SL·ATR, over a fixed horizon. The first barrier touched decides win/loss (same-bar tie counts as the stop — conservative). Base% is the unconditional same-barrier win-rate for that direction. Edge = Hit% − Base%; a "*" marks rows whose Wilson 95% lower bound clears the base rate. A leave-one-out row prices each filter's marginal contribution, and a footer lists only the patterns that are green AND have enough samples to trust in the current configuration. AUTO PRESET (default on) Candlestick edges are timeframe-specific. Auto Preset reads the chart's timeframe and switches on the pattern subset plus higher-timeframe distance that performed best for that timeframe in the author's study of NSE index futures, and forces the two filters on. Turn it OFF for full manual research mode: all 24 patterns selectable, filters and HTF distance (3x / 5x / 15x / custom) under your control. Nothing is ever removed — the preset only curates which patterns are active by default per timeframe. HOW TO USE Leave Auto Preset on and read the labelled signals (teal = bullish, red = bearish, each tagged with the pattern name). Open "Show scoreboard" to see measured Edge per pattern — trust the EDGE column and the "*", never a raw hit-rate. Best behaviour is on intraday timeframes (1H and below). ORIGINALITY Standard techniques are credited below. What is original is the combination: a location-gated pattern engine whose every signal is forward-calibrated, a timeframe-adaptive auto-preset, a leave-one-out filter attribution, and an auto-surfaced tradeable set — measured edge, not asserted. NON-REPAINT Signals open on confirmed bars; triple-barrier outcomes resolve on bars AFTER the trigger; all higher-timeframe / lower-timeframe / prior-day-POC requests use lookahead_off and confirmed intrabars. Pivots used by sweeps confirm first. DATA & MARKETS Runs on any symbol that reports volume; the developing profile needs volume to be meaningful. Defaults are tuned for intraday index futures. On the Enhanced data tier the delta read uses intrabar aggregation (richer on paid plans) and auto-falls-back to an OHLCV proxy when intrabars aren't served — safe to leave on for any plan. CONCEPT CREDITS (methods operationalized — original Pine re-derivations) Candlestick patterns — Nison; pattern-performance framing per Bulkowski Market / auction profile, POC / Value Area — Steidlmayer; Dalton Bulk Volume Classification — Easley, Lopez de Prado & O'Hara (2012) Triple-barrier labelling — Lopez de Prado Wilson score interval — Wilson (1927) HONESTY / LIMITS The profile is an ATR-binned developing session profile (not tick POC). Delta is an estimate (proxy or intrabar reconstruction), not true bid/ask. Reported edge is context measured on loaded history — not a prediction or a promise. The preset defaults were tuned on one instrument over a recent window, so treat them as a well-measured hypothesis, not proven alpha. Educational tool. Not financial advice — you alone are responsible for your trading decisions.

Indicator

by Market_Logic_India

Verdict Calibrator Edge vs Base Rate Overview A meta-tool that answers one honest question about any signal: does it actually beat chance? Point it at another indicator's output (via the source input) or use a built-in reference signal, and it forward-tests every long/short call with a triple-barrier outcome, compares the hit rate to the unconditional base rate of the same move, and only calls an edge "proven" when a confidence-interval lower bound clears that base rate. It splits the result by side (long vs short) and by regime (trend vs range). It grades a signal; it does not make one. Why this exists Most "win rate" readouts are misleading. 60% right means nothing until you know how often the same move happened anyway — if price rose 60% of the time regardless, your signal has an edge of exactly zero. And a 60% on 12 samples is noise, not evidence. This tool is built so it can only ever say "no proven edge" when there isn't one. The base-rate comparison and the significance gate make false confidence structurally hard to produce. Why these parts are ONE tool Signal as a parameter. Connect any plot on your chart through the source input, or pick a built-in reference. The signal is an input, not baked in — so you can audit your indicators, not just this one. The ten built-ins span the families traders actually use: FamilyBuilt-insTrend-followMA cross · MACD cross · Supertrend flipMomentumRSI 50-cross · Stochastic crossBreakoutDonchian breakout · VWAP crossMean-reversionRSI 30/70 reversal · Bollinger fadeControlRandom (coin flip) The random control — the tool's own self-test. A deterministic coin flip that by construction has no edge. Grade it and the verdict should read "Not proven" with an Edge near zero. If a random signal ever comes back "PROVEN", the harness is broken — distrust the tool, not the market. No other calibrator on PulseWire ships with a falsification test built in. It is also the single fastest way for a sceptical user to satisfy themselves that this thing is honest. Triple-barrier outcome. From each signal: did price reach +target, −target, or neither within the horizon? A well-defined outcome, not a vague "did it go up eventually". Base-rate comparison. The honest yardstick — the unconditional rate of the same outcome, matched to the signal's own side and regime mix. Edge = Hit% − Base%, never raw Hit%. Significance gate. A score-interval lower bound must clear the base rate before an edge is called proven — which matters most at small samples, exactly where point estimates lie. Regime and side split. Edge is reported for long vs short and trend vs range separately, because a real edge usually lives in one and not the other. Remove any one and the tool can be fooled into reporting confidence it hasn't earned. How to use it Pick the signal (external source or a built-in), set the outcome (horizon + target in ATR), and read the verdict: PROVEN +X% ★ / Not proven / Gathering data. The two plotted lines are the running Hit % (of the signal) and Base % (unconditional) — the gap between them IS the edge, and you can watch it stabilise as samples accumulate. Switch the dashboard to Pro to see where the edge lives (long/short, trend/range). If it says "no proven edge", believe it. That's the tool working, not failing. Data & scope Works on any symbol and timeframe — it needs only OHLC, no volume. Give it enough history to reach the minimum sample count, or the verdict will honestly read "Gathering data". Because the base rate is measured on the same chart, the yardstick always matches the instrument you're on. Non-repainting & honest limits Confirmed-bar reads; samples log at the signal bar and resolve on closed bars. This is an in-sample, forward-from-signal study aid — NOT a walk-forward backtest. No costs or slippage. Overlapping forward windows correlate samples (the minimum-gap setting mitigates this; it does not eliminate it). Small-n edges are provisional even when starred. A proven in-sample edge is not a guarantee out-of-sample. Nothing here predicts price. Concept credits Built on standard, published techniques — triple-barrier forward labelling (M. López de Prado), base-rate / skill-vs-chance evaluation (a long tradition in forecast verification), the efficiency-ratio regime read (Perry Kaufman), and the Wilson score interval for a proportion (Edwin B. Wilson). The signal-agnostic intake, the coupling and the plain-language verdict are this script's own. No third-party Pine code is reused. Disclaimer Research and educational tool only. Not financial advice, no recommendation, no guarantee of results. Indicators describe past behaviour; they do not predict the future. Trading carries risk of loss. Test out-of-sample and make your own decisions. The author accepts no liability.

Indicator

by Market_Logic_India

Structural Language Model Overview Structural Language Model treats price action as a language. Each bar is tokenised into one of five structural symbols, and a low-order Markov model learns the grammar — the probability of what comes next given the recent context. Instead of "match the nearest historical shape" (fragile, overfit-prone k-NN), it estimates P(next token | last k tokens): a nonparametric conditional-move model that proves or disproves itself, live, on your symbol. It is a research/forecast read, not a signal service. The five-symbol grammar Every bar becomes one token, built from robust intrabar primitives (gap-immune, no fragile sweep/FVG detection), with adaptive thresholds so the alphabet stays balanced across symbols and timeframes: X− down impulse · d ordinary down · c compression / indecision · u ordinary up · X+ up impulse The model then learns grammar like c → X+ (breakout), X+ → X− (reversal), runs of u/X+ (trend), X+ → c (exhaustion), using order-1 or order-2 transition counts with Laplace smoothing, updated online. Why these parts are one tool The tokeniser turns raw OHLC into a balanced, information-rich alphabet — without it the Markov counts are dominated by whatever token is most common. The Markov model reads out, each bar, a directional bias (P up-ish − P down-ish), a predictability score (how peaked the next-token distribution is, via normalized entropy), a structural-surprise spike (−log P of the token that just printed — a grammar break), and the full next-bar probability ladder. The harness is the part that makes it honest. It's prequential (predict-then-update: each transition is scored from counts that exclude its own outcome, so every score is out-of-sample), it runs a walk-forward in-sample vs out-of-sample split with Wilson 95% intervals, and it draws a reliability curve — binning OOS predictions by predicted P(up) and showing the realized up-rate per bin. A rising, significant curve = real calibrated information; a flat one = none. Remove any part and you can no longer answer "is this model actually calibrated on this market?" How to use it Read the directional bias line against its conviction bands as context, not a trigger, and check predictability for how peaked the forecast is. Then read the harness — the model is only worth trusting where the out-of-sample up-lean lift is above 1 and/or the reliability spread is positive and significant (✓sig). A flat or insignificant curve means there's no calibrated edge here; treat it as descriptive only, or try another symbol/timeframe. The dashboard has a Compact layout (default: forecast + the one calibration line that matters) and a Pro layout (the full ladder, in/out-of-sample lift, and the three-bin reliability curve). Bias-turn crosses are optionally mirrored on the price chart. It is never a standalone signal. Non-repainting Tokens and counts update only on confirmed bars, and the score for bar t uses counts as they stood before bar t's transition was added — nothing reads its own future. The live next-bar forecast naturally refines as the current bar forms (it's a forecast, not a settled statistic). All harness figures are out-of-sample by construction. Honest limits OHLCV only. A per-bar tokeniser maximises samples but is coarser than a swing/event grammar (a documented future extension). Any edge is typically modest and market/timeframe-dependent — directional forecasting on noisy price is hard, and no indicator has an inherent edge. That's exactly why the harness is built in: validate it before trusting it. Outputs for other scripts Generic EXP_* plots — bias, predictability, structural surprise, live P(next up-ish), and the OOS lift — are published to the Data Window for use from other scripts via input.source(). Concept credits Markov chains / n-gram language models — A. Markov (1913); C. Shannon (1948) Prequential (predict-then-update) evaluation — A. P. Dawid (1984) Additive (Laplace) smoothing — P.-S. Laplace Entropy — C. Shannon (1948) Wilson score interval — E. B. Wilson (1927) Synthesis and Pine implementation are the author's own; no third-party Pine code reused. Disclaimer Research and education only. Not financial advice, not a signal service, not a guarantee of future results. Validate with your own testing, apply realistic costs, and manage risk.

Indicator

by Market_Logic_India

Conformal Reversion Bands Self-Calibrating Coverage Conformal Reversion Bands — Self-Calibrating Coverage What it is Ordinary bands lie about themselves. A Bollinger "2σ" band or an ATR band asserts a coverage it does not deliver — real price isn't Gaussian, so the band that's supposed to contain 95% of bars might actually contain 88% or 98%, and that fraction drifts as volatility changes. The label and the chart disagree. Conformal Reversion Bands fix this. You choose the coverage you want (e.g. 90%), and the band's half-width is a nonconformity quantile that is tracked online so the realised coverage actually converges to your target — and self-corrects when it drifts. The indicator then displays target vs realised coverage live, so you can see the guarantee holding instead of taking it on faith. A breach of a calibrated 95% band means something precise: price did what it does under about 5% of the time — a genuine rare excursion, and a mean-reversion (fade) candidate back toward fair value. How it works (and why this specific method) Centre — a fair-value line the bands revert to: session VWAP by default (auto-fallback to a robust rolling median on symbols without reliable volume), or EMA / median by choice. Score — the absolute deviation of price from the centre. Its running quantile is the band half-width. Online calibration — this uses the quantile tracker ("conformal P control") of Angelopoulos, Candès & Tibshirani (2023), with an optional error integrator ("PI control"). This is deliberately chosen over the older Adaptive Conformal Inference (ACI): ACI adapts the significance level and can occasionally produce infinite or null intervals; tracking the quantile on the scale of the scores cannot degenerate that way, so the bands stay finite and well-behaved on live charts. ACI is in fact a special case of the tracker. Volatility-normalized scores (locally adaptive) — scores are normalized by a local volatility estimate before calibration (the Papadopoulos–Gammerman–Vovk normalized-nonconformity idea), so the band width breathes with volatility bar-by-bar. This targets conditional coverage — not too wide in calm tape, not too narrow in fast tape — instead of only a global average. Decaying step size — the learning step shrinks as calibration matures (Angelopoulos–Barber–Bates) for tighter long-run coverage, floored so the bands never stop adapting to new regimes. Self-check — a trailing window measures realised coverage for both bands and reports how closely it tracks target. That readout is the whole point: it makes the band's core claim verifiable on your own chart. Note the honest theoretical ceiling: exact conditional coverage is impossible distribution-free; normalization gets most of the practical way there at negligible cost. Everything advances only on confirmed bars: the band shown on a bar is calibrated on scores up to the previous bar, then that bar is tested against it — no hindsight fitting. How to use it Add to any liquid symbol/timeframe; set the coverage you want for the inner and outer bands. Defaults suit index futures; change the price/volume sources in Data source for any other market. Read the dashboard headline first: it states CALIBRATED / ADAPTING / WARMING in plain language, with a colour anyone can read at a glance. When the inner and outer rows show target and realised % matching (✓), the bands are provably doing their job. Treat an outer-band breach as a statistically rare excursion — a fade-toward-centre candidate (optional close-back-inside confirmation). Watch COMPRESSION: a low band-width percentile means the bands are unusually tight (a volatility squeeze — expansion often follows); a high percentile means unusually wide. Divergences (price vs the band's own normalized deviation, or RSI — your choice) are drawn as lines on price for context. The dashboard and the identity label are separate toggles; the price/volume sources, coverage targets and every window are adjustable. Works as an honest, self-calibrating replacement for Bollinger/Keltner/ATR bands anywhere you use deviation bands. What makes it original Almost nothing on PulseWire ships real conformal prediction, and — as far as the author is aware — nothing ships the modern quantile-tracker / PI-control variant with a live coverage readout that proves the band's claim on-chart. The contribution is bringing a 2023-frontier uncertainty-quantification method to price bands in a form a trader can verify at a glance, rather than a σ-multiplier that only pretends to a coverage level. The band-width compression read and the band-native divergence are natural, honest by-products of the same construction — context, not a signal service. Concept credits Conformal prediction — V. Vovk, A. Gammerman, G. Shafer. Normalized nonconformity — H. Papadopoulos, A. Gammerman, V. Vovk (2008). Adaptive Conformal Inference — I. Gibbs & E. Candès (2021). Quantile tracker / Conformal PID control — A. Angelopoulos, E. Candès & R. Tibshirani (2023). Decaying step — A. Angelopoulos, R. Barber, S. Bates (2024). VWAP — classical. Implementation and charting design are the author's own. Important disclaimer Research and education only. Not financial advice, not a signal service, not a guarantee of future results. Coverage is a statistical property of the band width — it is not a claim that fading breaches is profitable. The readout is descriptive of the past on the current chart, not a forward guarantee. Validate independently, apply realistic costs and slippage, and manage your own risk.

Indicator

by Market_Logic_India

Trend Efficiency Exhaustion Regime-Gated & Calibrated Trend Efficiency Exhaustion — Regime-Gated & Calibrated What it is A single-pane oscillator that measures when a trend is losing efficiency and turns that into graded, forward-calibrated exhaustion and ignition signals. It is built around one question — "is this efficiency-exhaustion event actually worth acting on?" — and every component in the script exists to answer that one question rather than to add an independent signal. It plots, in one pane: an efficiency-gap histogram, a fast efficiency line, event markers, a regime "weather-strip" ribbon, and an information table that states the read in plain language. It is symbol- and timeframe-agnostic; defaults are tuned for NIFTY / BANKNIFTY but a Source input and a VIX-symbol input let you use it on any instrument in any market. The core idea — efficiency, not a magic multiplier The Efficiency Ratio is the net move divided by the total path travelled over a window: ER = |close − close | / Σ|close − close |, bounded 0–1. A value near 1 means price moved in a straight, efficient line (trend); near 0 means it wandered (chop). Reading efficiency at two horizons gives the central signal: Efficiency Gap = ER_fast − ER_slow. When the fast read rolls over while the slow read is still elevated, the trend is losing efficiency under an otherwise intact trend — the classic exhaustion tell. Displacement percentile ranks the current leg's travel against recent completed legs on this symbol and timeframe, so "stretched" is defined by the instrument's own recent behaviour rather than a fixed price > k·ATR multiplier. Exhaustion = a stretched leg with fast efficiency rolling over, under a genuine trend. Ignition = fast efficiency surging from a young leg (continuation). Why these components belong in ONE script (how the mashup works together) This is a mashup by design, but it is not a stack of indicators each drawing its own signal. Every layer is a gate or a grade on the same event, feeding one decision pipeline: Efficiency (dual-horizon Efficiency Ratio) — detects the candidate event (exhaustion / ignition). Regime engine (Efficiency + ADX + a self-exciting volatility-cluster intensity) — decides when the event is even allowed to fire. Exhaustion is only meaningful inside a real trend; it is suppressed in chaotic, news-driven volatility. The regime is rendered as a continuous 5-state read (strong-trend / trend / neutral / reversion / chaos). Variance ratio (Lo-MacKinlay) — a second, short-window-reliable lens that confirms a real trend existed to exhaust (VR > 1 = trending, < 1 = mean-reverting, ≈ 1 = random walk), with a significance z-statistic. Ornstein-Uhlenbeck half-life — quality gate: if the estimated mean-reversion half-life is longer than the evaluation horizon, the expected reversion is too slow to pay off in time, so the exhaustion call is rejected. Implied-volatility (VIX) state — quality gate: exhaustion is more reliable when implied volatility is elevated but stable (fear present, not spiking). The gate blocks exhaustion during a volatility spike. Divergence quality — grades each exhaustion on the price↔efficiency divergence at the extreme: the slope of the efficiency drop between successive same-side pivots, how developed the swing is, and whether volume waned into the extreme. Weak-divergence setups are filtered out. Forward calibration — the scorekeeper. Each fired event is logged as a hypothesis and resolved a fixed number of bars later against an ATR-scaled move, then summarised as a realised hit-rate versus an unconditional base rate. Take any single layer away and the remaining pipeline still describes the same one event — they are complementary measurements of a single hypothesis (a trend running out of efficiency), which is precisely why they belong together rather than as separate scripts. The regime, variance-ratio, OU and VIX layers never plot their own buy/sell calls; they only decide whether the efficiency-exhaustion event is trustworthy. The part most scripts skip — forward calibration Most indicators emit a score and never check whether that score was right. Here, every event is queued and resolved N bars later against moveATR × ATR, in R-multiples. The information table reports, per class (Exhaustion / Ignition): n — resolved sample size Hit% with a Wilson 95% interval (so you see how stable the rate is) Base% — the unconditional same-horizon move rate (the honest benchmark) Edge = Hit% − Base%, marked * when a z-test clears 95% MFE / MAE in R (how far it ran for you vs against you) a recency-weighted hit-rate and a regime-conditional hit-rate for the current regime If Edge is not positive, the signal is not adding information over chance on your chart — and the script tells you so instead of hiding it. How to use it Ribbon = context. Don't fade a strong trend; stand aside in chaos. Histogram rolling over + a marker = the trigger. Verdict line = the plain-language call (e.g. "TREND · watch for exhaustion", "EXHAUSTION ↓ · fade the up-move (edge +12%*)", "CHAOS · stand aside"), with the calibrated edge appended when the live class is calibrated. Chart View: Clean (default) shows only the decision elements; Full adds the slow-ER line, displacement %, all reference levels and the divergence glow for analysis. Information Table: Compact (default) is the key-info panel — verdict, efficiency/displacement/regime, variance-ratio/OU/VIX, best calibrated edge. Pro adds the full per-class calibration table with confidence intervals, recency and regime-conditional rows. Treat it as a context-and-confirmation overlay on your own process, not an autotrading signal. Paper-trade first and confirm the Edge column is positive on your symbol and timeframe before relying on a class. Originality The novelty is not any single formula — those are credited below — but the closed loop: a self-referential displacement percentile (no fixed multiplier), a regime engine and four independent quality gates that all condition one event, and a forward-calibration layer that scores that event against its own base rate with confidence intervals, recency weighting and regime conditioning. Everything is original Pine; no third-party script code is reused. Inputs, data & markets Source (group 1) sets the raw series the whole engine reads — change it to use any instrument in any market. Defaults are tuned for NIFTY / BANKNIFTY; the VIX Symbol defaults to NSE:INDIAVIX. For other markets, change the Source, the ER horizons and the VIX symbol (e.g. CBOE:VIX). A missing VIX symbol auto-disables that gate. Inputs are organised institutionally: Source & Efficiency · Regime & Variance-Ratio · Displacement · Events · Quality Gates · Calibration · Display · Theme · Exports. The table colour scheme adapts automatically to a light or dark chart background. Non-repaint Efficiency is read on confirmed closes, legs are taken from confirmed pivots, events fire on barstate.isconfirmed, and there are no dynamic-length ta.* calls. Forward statistics are in-sample, close-to-close, with no costs, slippage or stops — a study aid, not a backtest. Concept credits (original Pine re-derivations) Efficiency Ratio — Perry Kaufman Variance-ratio test — Andrew Lo & Craig MacKinlay (1988) ADX / Directional Movement — J. Welles Wilder Self-exciting (Hawkes) intensity — Alan G. Hawkes (1971) Mean-reversion half-life — Ornstein & Uhlenbeck process Score confidence interval — Edwin B. Wilson (1927) Dominant-cycle homodyne discriminator — John F. Ehlers Disclaimer For education and information only. Not financial advice and not a recommendation to buy or sell anything. Past performance does not guarantee future results. The forward statistics are in-sample and idealised (close-to-close, no costs/slippage/stops). Always do your own analysis and manage your own risk; paper-trade before risking real money.

Indicator

by Market_Logic_India

Self Calibrating Probability Channel SELF-CALIBRATING PROBABILITY CHANNEL A forecast channel whose width is set by conformal prediction, tuned by a parameter-free online calibrator, and proven on your own chart. You pick a coverage level - say 90% - and the indicator shows you, live, the percentage it has actually achieved over recent bars, on every timeframe. Most bands assert a width; this one measures whether the width was right and corrects itself until it is, with nothing to tune. WHAT IT IS Bollinger Bands, Keltner Channels, Donchian Channels and standard-deviation regression channels all draw a width from a formula and ask you to trust it. None of them tell you what fraction of price actually landed inside. A "2 standard deviation" band is only a true 95% band if returns are normally distributed and stationary - which markets are not - so the real hit-rate drifts, usually without the user ever knowing. This indicator inverts that. It forecasts where price should be next bar, measures how wrong that forecast has actually been, and builds the band directly from the empirical distribution of those errors. Then it watches its own hit-rate bar by bar and self-corrects. The result is a channel that earns its stated confidence level instead of assuming it - and reports, honestly, where it is and isn't holding. THE METHOD (plain language) 1. Forecast path. Each bar, a one-step-ahead forecast of price is formed. You can pick a Kalman level-and-velocity tracker, a linear-regression slope, an EMA projection, or an anchored VWAP - or leave it on Auto, which runs all of them and blends them online by recent accuracy, so the centre line self-calibrates too. The forecast for the current bar uses only prior bars, so it is genuinely out-of-sample. 2. Error window. The gap between forecast and outcome is the forecast error. A rolling window of recent errors is kept, stored in volatility (ATR) units so the band breathes with the market. Each error is recorded only after its band has already been scored, so the band never includes the bar it is being tested on. 3. Conformal bands. For a chosen confidence level, the band edges sit at the matching quantiles of the recent error distribution (split-conformal prediction). Because it uses the actual error quantiles - including their skew - the bands are asymmetric when the errors are, rather than forcing a symmetric width. Four levels are drawn at once (50 / 70 / 90 / 95%) as nested zones, so the channel doubles as a probability heatmap: the dark core is where price spends most of its time, the faint outer edge marks rare excursions. 4. Parameter-free self-calibration (DtACI). After each bar the indicator checks whether price fell inside each level and nudges the width to hold the target. Rather than asking you to pick a calibration speed, it runs several speeds as competing "experts" and continuously blends them by how well each has tracked coverage recently (Dynamically-tuned Adaptive Conformal Inference). There is no rate to tune - the calibration tunes itself. 5. Live coverage proof, including by regime. The dashboard shows, for every level, the target versus the actually-achieved coverage over a rolling window, each tagged calibrated / under / over. It also reports the realised 90% coverage broken down by market regime - so you can see, for instance, that the band holds 92% in a quiet range but 87% in a volatile breakout. You are not asked to trust the band; you are shown its track record on the symbol, timeframe and regime in front of you. 6. Forward cone. A widening cone projects the likely range several bars ahead. Its width is built from actual multi-step forecast errors (not a square-root-of-time assumption), and its centre curves as projected momentum decays rather than extrapolating in a straight line. An optional bootstrap cloud resamples the real errors into sample forward paths - a direct picture of the distribution the bands come from. 7. Context and early warning. A two-axis regime read (trend strength x volatility) labels conditions; a turbulence detector watches for clustering of outer-band breaches and flags, in advance, when coverage is likely to degrade; a coiled-spring marker notes when a compressed range begins to expand; and an optional higher-timeframe row shows whether the larger trend agrees. WHY THESE PARTS BELONG TOGETHER (one engine, not a bundle) This is a single forecasting loop, not a collection of separate indicators sharing a chart. Each part is a required step, and removing any one breaks the whole: - The forecast path produces an expected price and a drift. Without it there is no quantity whose error can be measured. - The conformal band converts that path's own recent errors into prediction intervals. Without the forecast there is no error to bound; without the band the forecast is an unqualified guess. - The online self-calibration adjusts the band to hold the target hit-rate as conditions change. Without it the intervals slowly drift out of calibration and the stated confidence becomes false. - The live coverage readout verifies the loop is actually working, overall and per regime. It is the proof step a formula-based band cannot offer. - The context layers (regime, turbulence early-warning, graded breaches, compression-release, higher-timeframe agreement) all read the same forecast errors and exist only to tell you WHEN the interval is most trustworthy and when it is about to fail. So the components are not combined for convenience; they form a closed measure-and-correct cycle - forecast, bound the error, recalibrate, verify - which is precisely why they are published as one script rather than several overlays. WHAT MAKES IT DIFFERENT Conformal prediction is a distribution-free framework - its coverage guarantee holds for any underlying distribution given exchangeable errors, with no assumption that returns are Gaussian. It is standard in machine-learning uncertainty quantification but essentially absent from charting tools, which lean almost entirely on standard-deviation or ATR multiples. Pairing it with a parameter-free online recalibrator, a self-weighting forecast centre, and an on-chart coverage readout - including a per-regime breakdown - is the original contribution here. No moving-average envelope, regression channel or volatility band can state "I targeted 90% and have actually delivered 90% over the last 250 bars, and here is exactly where I don't" - this one can, and shows it. WHAT YOU SEE ON THE CHART - A multi-zone channel around a forecast centre line, shaded from the high-probability core out to the rare-excursion edge, coloured by forecast direction, and adaptive to dark or light chart backgrounds. - A widening forward cone, optionally filled with a faint cloud of resampled paths. - Right-side labels marking the forecast and the 90 / 95% edges as price levels. - Small triangles when price breaks beyond the outer band; a ring when that breach is also high-quality (graded on displacement, close position, volume, range expansion and structure); an amber diamond when a quiet range starts to wake up. - A dashboard with the live forecast, the 90% band range and where price sits within it, the full calibration table, the per-regime coverage, a reliability score, the forecast bias, the sample count, the calibration mode, and an optional higher-timeframe row. - A plain-language "how to read" key, so the chart is approachable without any statistics background. HOW TO READ AND USE IT Mean reversion: when price reaches the outer (90 / 95%) zone in a ranging regime, it is statistically stretched and tends to revert toward the centre line. The "band position" readout and the calibration table tell you how stretched, and how trustworthy that edge currently is. Trend continuation: a sustained walk along one side of the channel, especially with the cone tilted that way and the higher-timeframe row aligned, indicates a directional regime rather than noise. Anomaly / breakout: a plain triangle is a volatility event; a ringed one is the same event confirmed as high-quality. A turbulence flag warns that the bands may be about to lose calibration. Reliability and regime: treat the bands as most actionable when reliability is high, the calibration rows read "calibrated", and turbulence is quiet. The per-regime coverage tells you which conditions the channel is currently most trustworthy in. SETTINGS OVERVIEW - Forecast path (Auto / Kalman / Linear Regression / EMA / Anchored VWAP) and smoothing lengths. - Calibration: residual window, recency window, volatility normalisation, parameter-free DtACI on/off (with a manual ACI rate as fallback), coverage-evaluation window. - Forward projection length, cone momentum decay, optional bootstrap cloud. - Anomaly sensitivity, swing pivot length, coiled-spring thresholds, turbulence sensitivity. - Higher-timeframe context, price source, and full theme controls. The price source is selectable and volume is borrowed where a symbol reports none, so it works across futures, equities, forex and crypto on any timeframe. Defaults read well intraday; longer windows suit higher timeframes. HONESTY AND LIMITATIONS - Non-repainting: each bar's forecast uses only prior bars, each error is recorded only after its band is scored, anomalies confirm on bar close, and the higher-timeframe row uses the last confirmed higher-timeframe value. Historical bands do not change after the fact. - Conformal coverage is a statistical expectation over a window, not a per-bar guarantee. In a sharp regime break the realised hit-rate will dip until the window and calibrator re-adapt - and the dashboard, including its per-regime breakdown, shows that dip honestly rather than hiding it. - The bands describe the distribution of short-horizon forecast error. They are a probabilistic context for price, not a prediction of direction and not a trading system. - Calibration needs enough samples; on a fresh chart the channel needs its warm-up window before the figures are meaningful, and the cone needs a few extra bars beyond that. This script is for research and education. It is not financial advice and not a solicitation to trade. Markets carry risk; test any tool on your own data and timeframe, and make your own decisions.

Indicator

by Market_Logic_India

Cross-Platform Price Offset Dashboard ## What it does A utility dashboard for traders who hold accounts across multiple brokers or trading platforms where the same underlying instrument quotes at slightly different prices. The indicator displays, in real time, the equivalent price of the chart's symbol on up to two external platforms — based on a fixed offset that you calibrate once, manually. ## Why this exists Traders who route the same idea to several brokers often see small but persistent price gaps between platforms: different contract specifications, broker-specific spreads, rollover timing differences between data feeds, or CFD-versus-futures pricing on the same underlying. When you mark a level on PulseWire (entry, stop, target), you still need to translate that level into the price scale of the platform where the trade is actually executed. This indicator removes that mental arithmetic. ## How it works At a single moment of your choosing, you take a simultaneous snapshot of three values: 1. The current price of the symbol on the PulseWire chart. 2. The price of the same instrument on Platform 1 (for example, your live MT4 or MT5 account). 3. The price of the same instrument on Platform 2 (another broker or account). You enter all three values into the indicator's inputs. From that moment on, the indicator computes a constant offset for each platform: offset_1 = platform_1_price − reference_price offset_2 = platform_2_price − reference_price It then displays the live equivalent on each platform as: platform_1_live = close + offset_1 platform_2_live = close + offset_2 If the PulseWire chart moves +20 points, the displayed prices for both platforms also move +20 points relative to their calibrated baseline. ## How to use 1. Add the indicator to any chart. It is symbol-agnostic and works on futures, indices, metals, stocks, and crypto. 2. Open the indicator settings, "Calibration" tab. 3. Fill in the three calibration prices, all taken at the same moment from your platforms. 4. Optionally, rename "Platform 1" and "Platform 2" to your broker names. 5. The dashboard now updates on every tick. ## Inputs - Reference price at calibration — the chart's price at the moment you took the snapshot. - Platform 1 name / Platform 2 name — labels shown in the table. - Platform 1 price / Platform 2 price at calibration — the corresponding prices on your external platforms at that same moment. - Display options — table position, text size (tiny, small, normal, large, huge), text and background colors, and an optional row that shows the raw offsets for sanity-checking. ## Assumptions and limitations - The offset is treated as constant until you recalibrate. If the broker changes the spread, rolls a contract, or shifts a data-feed timing, the displayed values will drift away from reality and you should recalibrate. - This is a display utility. It does not place orders, generate signals, or connect to any external broker. All three calibration prices are entered manually by the user. - Recalibration is manual. Whenever you observe a meaningful drift on your real platform, take a fresh snapshot and update the three input values. ## Notes - The indicator does not connect to brokers, does not fetch external data, and does not access any network resource. It operates purely on the chart's close series and on the three calibration inputs you provide. - Displayed prices use the chart symbol's mintick precision, so values appear at the same precision as your chart. - The source is fully open. Read the code for the exact formula and the table-rendering logic. This script is provided for informational and convenience purposes only and does not constitute trading advice.

Indicator

by razgamliel