QuantEdge Momentum ML [PRO]🟦 QuantEdge Momentum ML PRO is a k-Nearest Neighbors driven momentum oscillator built on an adaptive machine-learning core. Unlike RSI, Stochastic, or MACD — which apply the same static formula to every asset — QE-ML PRO learns the dual-horizon RSI fingerprints that have historically led to bullish versus bearish outcomes on the exact instrument being traded, then scores the current bar against the N closest historical matches. The result is a non-parametric, self-calibrating oscillator whose decision boundary is shaped by the asset's own behaviour rather than a hard-coded curve.
The indicator integrates nine independent layers — feature engine, training sampler, k-NN predictor, WMA signal line, stdev-adjusted OB/OS bands, filtered signal dots, gradient channel, theme-adaptive dashboard, and a nine-theme palette — all rendered on a single, clean oscillator panel.
🟦 HOW THE CORE ENGINE WORKS
**Dual-Horizon RSI Feature Vector**
Each bar, the Feature Engine computes two RSI values at different lookback windows and smooths both through a shared trend-length WMA:
- `rsiFast = WMA(RSI(close, FastPeriod), TrendLength)` — reactive short-term momentum
- `rsiSlow = WMA(RSI(close, SlowPeriod), TrendLength)` — structural mid-term momentum
The pair `(rsiSlow, rsiFast)` is a 2-dimensional point in RSI feature space. Every training sample stores one such point along with a ±1 label that records whether price rose or fell since the previous sample. Over time the dataset accumulates a cloud of labelled points that maps which RSI states historically preceded up-moves versus down-moves on this exact asset.
**Training Sampler — Multi-Trigger Collector**
Three collection modes decide when to append a new labelled sample:
| Mode | Trigger | Use Case |
|---|---|---|
| **MA Crossover** | Fast WMA crosses Slow WMA | Clean, sparse samples — classic single-trigger behaviour |
| **Periodic** | Every N bars (user-set) | Fills dataset fast on new / low-history charts |
| **Hybrid** | MA crossover **OR** every N bars | Richest training set — recommended for fresh assets |
Sampling is gated by `barstate.isconfirmed` so the dataset never absorbs unconfirmed values from a flickering live bar.
**k-NN Predictor with Adaptive k**
On every bar, the predictor computes Euclidean distance in the 2D RSI feature space between the live `(rsiSlow, rsiFast)` point and every historical sample:
```
d = sqrt((rsiSlow_now - rsiSlow_hist)² + (rsiFast_now - rsiFast_hist)²)
```
The K closest historical points vote by summing their ±1 labels. The effective K is resolved adaptively using the classical statistical heuristic:
```
kEff = max(3, min(kMax, floor(sqrt(N))))
```
This means early bars — when only a handful of samples exist — use a small K, and the value stabilises as the dataset fills. On a fresh chart you never get a noisy prediction from an undersized neighborhood, and on a mature dataset K automatically scales up for smoother output.
**Bias Correction — Label-Mean Recentering**
Raw k-NN output is biased whenever the label distribution is skewed. On a trending asset, Periodic sampling fills the dataset with mostly +1 (or mostly −1) labels, pushing every prediction off zero. QE-ML PRO subtracts the expected value from the raw sum:
```
prediction = neighborLabelSum − (kEff × meanLabelAcrossDataset)
```
This keeps the mid-level visually centred at zero regardless of how trending the underlying asset has been. The correction is applied on every bar and is what makes the oscillator read cleanly on both sideways and strongly trending markets.
**Minimum Sample Gate**
Until the dataset has reached the user-defined Minimum Training Samples threshold, the predictor outputs exactly zero. This prevents unreliable readings during the warm-up phase on fresh charts.
**FIFO Rotation**
The dataset is hard-capped at Max Dataset Size. Once the cap is reached, the oldest sample is discarded on every new insertion — classical rolling window memory that keeps the k-NN scan bounded and the indicator fast on long histories.
🟦 PREDICTION LINE — FIVE VISUAL STYLES
All five styles are line-based. Only the visual effect differs — the underlying k-NN math is identical across styles.
| Style | Character |
|---|---|
| **Stratum** | Thick adaptive line with zone-based opacity: solid in extreme zones, semi-transparent in the mid zone. Layered intensity aesthetic — default |
| **Neon** | Bright core line with an outer glow halo. Cyberpunk luminous effect, best on dark backgrounds |
| **Resonance** | LRI-style gradient line that fades near the midline and brightens toward the rolling extremes |
| **Pulse** | Adaptive bull/bear color (above midline = bull, below = bear) plus the WMA signal line. The QE-ML PRO classic look |
| **Mono** | Single flat theme-bull line, no gradient, no adaptive coloring. Minimalist single-color silhouette |
🟦 SIGNAL LINE
A WMA of the raw prediction output, used as a crossover trigger line in the MACD convention. Crossovers between the prediction and signal line mark momentum regime changes.
**Two Visual Styles**
| Style | Rendering |
|---|---|
| **Neon** | Bright core line wrapped in a wider semi-transparent glow halo — cyberpunk aesthetic |
| **Flat** | Plain single-color line, no halo, no gradient — minimalist clean look |
🟦 SIGNAL DOTS — FILTERED CROSSOVER MARKERS
A two-layer neon cross-dot renderer fires on every Prediction × Signal crossover that survives the active filter mode. Four progressive filters decide which raw crosses reach the chart:
| Filter Mode | Behaviour | Signal Count |
|---|---|---|
| **All Crosses** | Every cross becomes a dot | Highest — noisy on choppy assets |
| **Zone Only** | Only crosses inside an OB or OS strip | Mean-reversion triggers — strongest reversal setups |
| **Mid Aligned** | Bull dots only above mid, bear dots only below | Trend-following — keeps you on regime side |
| **Strict** | Zone Only + Mid Aligned + extra strength multiplier on mid-zone crosses | Fewest signals, highest conviction — default |
Two additional gates filter out whipsaws:
- **Cooldown (bars)** — minimum spacing between consecutive dots, prevents cluster spam in ranges
- **Min Strength** — minimum `|prediction − signal|` separation at the moment of the cross, drops razor-thin crossovers that close back on themselves
Each dot is a two-layer plot: an outer glow halo with user-adjustable size and opacity, and a bright solid core on top — independently sized and opacity-controlled so users can dial in the exact visual weight they want.
The dot is placed at the actual cross point: bull dots at `min(prediction, signalLine)`, bear dots at `max(prediction, signalLine)`.
🟦 DYNAMIC BANDS — STDEV-ADJUSTED OB / OS ZONES
QE-ML PRO does not use fixed 80 / 20 overbought / oversold levels. Instead, the bands adapt to the actual historical range of the prediction output:
- **Channel Extremes** — rolling highest / lowest of the prediction over a user-configurable lookback
- **Stdev Band** — EMA of rolling standard deviation of the prediction, multiplied by the user's stdev length
- **OB Level** = `rangeHi − stdevBand` (inner boundary of the overbought strip)
- **OS Level** = `rangeLo + stdevBand` (inner boundary of the oversold strip)
The result is a pair of mean-reversion zones that tighten during quiet markets and widen during volatile ones — no manual recalibration needed across assets.
The strips are rendered as gradient fills anchored on the live prediction plot, so they only appear visually while the prediction is actually inside the zone.
🟦 CHANNEL GRADIENT
Two symmetric gradient fills bracket the mid line. The upper fill stretches from `midValue` to `rangeHi`, the lower fill from `midValue` to `rangeLo`. Opacity fades from full intensity at the extremes to fully transparent at the midline — a visual range meter showing how close the prediction is sitting to its historical boundaries.
Colors are pulled from the active Theme. A single opacity slider controls the gradient intensity.
🟦 DASHBOARD — LIVE DATA PANEL
A compact 2-column × 7-row monospace panel drawn on the last bar only (zero historical overhead). Every field updates in real time on the live bar.
| Row | Left | Right |
|---|---|---|
| Header | QE-ML PRO | Regime (▲ BULL / ▼ BEAR / ■ NEUTRAL) |
| Row 1 | Prediction | Raw value + trend arrow vs previous bar |
| Row 2 | Signal | WMA trigger line value |
| Row 3 | Strength | 10-block gauge of `|prediction − signal|` normalised against rolling channel |
| Row 4 | Zone | OB / MID / OS tag |
| Row 5 | Dataset | Sample count / effective k |
| Row 6 | Mode | Active Learning Mode (MA Cross / Periodic / Hybrid) |
**Theme-Aware Auto-Invert**
The panel background scaffolds auto-switch:
- **Tropic / Amber / Pastel / Cyber / Gold / Electric / Candy** → dark panel with bright theme accent text
- **Midnight / Graphite** → light panel with dark theme accent text
This guarantees legibility on every theme without breaking the theme's color identity — because Midnight and Graphite use deep dark bull tones that would drown against a black panel.
**Direction via Glyphs, Not Color**
Both columns share the same full-strength theme tone. Regime direction is conveyed by `▲ ▼ ■` glyphs rather than color shifts, which keeps the panel reading cleanly even on the most minimal themes.
🟦 NINE COLOR THEMES
One theme selector drives every colored component — Prediction line, Signal line, Channel fill, OB / OS strips, Mid-level line, Signal Dots, and Dashboard panel. No per-color manual inputs.
| Theme | Character | Bull | Bear |
|---|---|---|---|
| **Tropic** | Cyan steel + deep orange — electric contrast (default) | Cyan | Deep Orange |
| **Amber** | Warm amber + indigo blue — fire tones | Amber | Red |
| **Pastel** | Sky blue + soft lavender — cool arctic glow | Sky Blue | Lavender |
| **Cyber** | Neon lime + hot crimson — cyber terminal | Neon Green | Crimson |
| **Gold** | Bright gold + scarlet — solar warmth | Yellow Gold | Red |
| **Electric** | Electric aqua + magenta — high-voltage neon | Aqua | Magenta |
| **Candy** | Neon green + hot pink — dark energy pop | Mint Green | Hot Pink |
| **Midnight** | Deep navy + dark crimson — dark depth (auto light dashboard) | Navy Blue | Dark Red |
| **Graphite** | Near-black + silver grey — monochrome minimal (auto light dashboard) | Near Black | Grey |
🟦 ALERT SYSTEM — TEN CONDITIONS
Every alert is gated by its matching "Show X" visibility toggle — if a component is hidden from the chart, its alerts are automatically suppressed. This eliminates the mismatch between visual signals and alert signals that plagues many indicators.
| Alert | Condition | Gated By |
|---|---|---|
| Crossover OB | Prediction crosses above the overbought boundary | Show OB/OS Fill |
| Crossunder OB | Prediction crosses back down through OB | Show OB/OS Fill |
| Crossover OS | Prediction crosses up through oversold boundary | Show OB/OS Fill |
| Crossunder OS | Prediction crosses below the oversold boundary | Show OB/OS Fill |
| Crossover Mid | Prediction crosses above the mid line — bullish regime flip | Show Mid Level |
| Crossunder Mid | Prediction crosses below the mid line — bearish regime flip | Show Mid Level |
| Crossover Signal | Prediction crosses above its WMA signal line (MACD bullish) | Show Signal Line |
| Crossunder Signal | Prediction crosses below its WMA signal line (MACD bearish) | Show Signal Line |
| Bull Signal Dot | A filtered Bull Signal Dot is plotted (uses Filter Mode + Cooldown + Min Strength) | Show Signal Dots |
| Bear Signal Dot | A filtered Bear Signal Dot is plotted (uses Filter Mode + Cooldown + Min Strength) | Show Signal Dots |
🟦 SETTINGS REFERENCE
**Visual**
- Theme — nine cohesive palettes. Default: Tropic
**Machine Learning**
- Neighbors (k) — upper bound on neighbors used by the predictor. Default: 100
- Adaptive k — scales k with dataset size using the `floor(sqrt(N))` heuristic. Default: ON
- Learning Mode — MA Crossover / Periodic / Hybrid. Default: MA Crossover
- Sample Every (bars) — bar interval for the Periodic / Hybrid trigger. Default: 5
- Minimum Training Samples — warm-up gate, predictor outputs zero until reached. Default: 30
- Max Dataset Size — hard FIFO cap. Default: 500 (safe on all timeframes)
**Feature Engine**
- Trend Length — WMA smoothing applied to both RSI features. Default: 20
- RSI Fast Period — first feature dimension. Default: 5
- RSI Slow Period — second feature dimension. Default: 20
- MA Fast Period — fast WMA for the crossover training trigger. Default: 5
- MA Slow Period — slow WMA for the crossover training trigger. Default: 20
**Prediction Line**
- Show Prediction Line — master toggle. Default: ON
- Prediction Style — Stratum / Neon / Resonance / Pulse / Mono. Default: Stratum
- Prediction Width — 1 to 5. Default: 2
**Signal Line**
- Show Signal Line — toggle. Default: ON
- Signal Style — Neon / Flat. Default: Neon
- Signal Period — WMA length of the signal line. Default: 20
- Signal Width — 1 to 5. Default: 1
**Signal Dots**
- Show Signal Dots — toggle. Default: ON
- Filter Mode — All Crosses / Zone Only / Mid Aligned / Strict. Default: Strict
- Cooldown (bars) — minimum spacing between dots. Default: 5
- Min Strength — minimum `|prediction − signal|` at the cross. Default: 0.5
- Core Dot Size — 1 to 8. Default: 3
- Core Dot Opacity — 0 to 100. Default: 100
- Glow Dot Size — 1 to 12. Default: 8
- Glow Dot Opacity — 0 to 100. Default: 30
**Channel Fill**
- Show Channel Fill — toggle. Default: ON
- Channel Opacity — 0 to 100. Default: 25
- Channel Lookback — rolling highest / lowest window. Default: 500
**OB / OS Fill**
- Show OB/OS Fill — toggle. Default: ON
- Zone Stdev Length — stdev window that offsets the OB / OS boundaries inward. Default: 20
**Mid Level**
- Show Mid Level — toggle. Default: ON
- Mid Level Value — Y-value of the reference line. Default: 0
- Mid Level Style — Solid / Dashed / Dotted. Default: Dashed
**Dashboard**
- Show Dashboard — toggle. Default: ON
- Panel Position — six slots (Top/Middle/Bottom × Right/Left). Default: Middle Right
- Panel Text Size — Tiny / Small / Normal / Large. Default: Small
**Alerts**
- Ten opt-in toggles, one per alert condition. All default: ON
🟦 TRADER PRESETS — SETTINGS BY STYLE
QE-ML PRO is volatility-agnostic thanks to the adaptive bands and bias correction, but the reactivity of the predictor scales directly with the feature and sampler parameters. The four presets below are tested starting points you can drop straight into the settings panel — adjust by ±20% to taste.
---
** SCALPER — 1m / 3m / 5m**
High-frequency entries, tight stops, many signals per session. Priority is reaction speed — you want the predictor to flip states within a handful of bars of an actual move.
| Setting | Value |
|---|---|
| Trend Length | 10 |
| RSI Fast Period | 3 |
| RSI Slow Period | 14 |
| MA Fast Period | 3 |
| MA Slow Period | 10 |
| Signal Period | 8 |
| Neighbors (k) | 40 |
| Adaptive k | ON |
| Learning Mode | **Hybrid** |
| Sample Every | 2 |
| Minimum Training Samples | 20 |
| Max Dataset Size | **300** (keeps 1m charts fast) |
| Filter Mode | **All Crosses** or Zone Only |
| Cooldown | 2 |
| Min Strength | 0.3 |
| Channel Lookback | 200 |
| Zone Stdev Length | 10 |
| Prediction Style | Neon or Stratum |
**Why:** Low smoothing (Trend=10) + short RSI pair (3/14) keeps the features razor-sharp. Hybrid learning means you never wait for an MA crossover during quiet 1m sessions. Max Dataset capped at 300 protects you from the TradingView per-bar calculation limit on long 1m histories.
---
** DAY TRADER — 15m / 30m / 1H**
Balanced reactivity and conviction — the default profile. You want clean crosses without noise spam, and signals that survive the open / close volatility spikes.
| Setting | Value |
|---|---|
| Trend Length | 20 (default) |
| RSI Fast Period | 5 (default) |
| RSI Slow Period | 20 (default) |
| MA Fast Period | 5 (default) |
| MA Slow Period | 20 (default) |
| Signal Period | 20 (default) |
| Neighbors (k) | 100 (default) |
| Adaptive k | ON |
| Learning Mode | **MA Crossover** (default) |
| Minimum Training Samples | 30 (default) |
| Max Dataset Size | 500 (default) |
| Filter Mode | **Strict** (default) |
| Cooldown | 5 (default) |
| Min Strength | 0.5 (default) |
| Channel Lookback | 500 (default) |
| Zone Stdev Length | 20 (default) |
| Prediction Style | Stratum (default) |
**Why:** Every default value was tuned for this range. Strict filter + 5-bar cooldown keeps the dot count honest on a 30m chart. MA Crossover sampling gives you clean sparse data since 15m+ charts already have enough crossover events.
---
** SWING TRADER — 4H / 1D**
Lower signal frequency, higher conviction per signal. You're holding for days or weeks — every dot needs to mean something.
| Setting | Value |
|---|---|
| Trend Length | 30 |
| RSI Fast Period | 7 |
| RSI Slow Period | 30 |
| MA Fast Period | 7 |
| MA Slow Period | 30 |
| Signal Period | 30 |
| Neighbors (k) | 150 |
| Adaptive k | ON |
| Learning Mode | MA Crossover |
| Minimum Training Samples | 50 |
| Max Dataset Size | 800 |
| Filter Mode | **Strict** |
| Cooldown | 10 |
| Min Strength | 0.8 |
| Channel Lookback | 800 |
| Zone Stdev Length | 30 |
| Prediction Style | Stratum or Mono |
**Why:** Longer feature periods mean the predictor only moves on genuine structural shifts. Larger k (150) + bigger dataset (800) gives the k-NN vote a wider base so outliers don't flip the sign. Cooldown of 10 bars on a 4H chart = 40 hours minimum between dots — exactly what a swing trader wants.
---
** POSITION / LONG-TERM — 1D / 1W / 1M**
Macro regime detection. You're looking for the handful of generational setups per year — noise is the enemy.
| Setting | Value |
|---|---|
| Trend Length | 50 |
| RSI Fast Period | 10 |
| RSI Slow Period | 40 |
| MA Fast Period | 10 |
| MA Slow Period | 40 |
| Signal Period | 40 |
| Neighbors (k) | 200 |
| Adaptive k | ON |
| Learning Mode | **Hybrid** |
| Sample Every | 3 |
| Minimum Training Samples | 40 |
| Max Dataset Size | 1000 |
| Filter Mode | **Strict** |
| Cooldown | 15 |
| Min Strength | 1.0 |
| Channel Lookback | 1000 |
| Zone Stdev Length | 40 |
| Prediction Style | Mono or Pulse |
**Why:** Weekly and monthly charts have few crossover events per year — without Hybrid mode the dataset starves. Sample Every = 3 on a weekly chart means one sample every 3 weeks, which is plenty of structural density. Min Strength 1.0 filters out every shallow cross — you only see dots on generational momentum inflections.
---
**Tuning Tip**
If the predictor feels **too reactive** → increase Trend Length and Signal Period by 25%, raise Cooldown.
If the predictor feels **too sluggish** → switch Learning Mode to Hybrid, decrease Min Samples, lower Trend Length.
If the dashboard shows **Dataset N is stuck low** → switch Learning Mode from MA Crossover to Hybrid — crossover events are too rare on your current settings.
If you see **runtime / timeout errors** on long histories → drop Max Dataset Size to 300 and Channel Lookback to 300.
🟦 COMPATIBILITY
Works on all asset classes and all timeframes in TradingView Pine Script v6.
- **Crypto** — Spot, futures, perpetual contracts
- **Forex** — All pairs
- **Equities** — Stocks, ETFs, indices
- **Commodities** — Metals, energy, agriculture
- **Timeframes** — 1m through Monthly
The k-NN engine learns each asset's own RSI fingerprint distribution, and the stdev-adjusted bands auto-scale to the volatility of that distribution, so the indicator is truly self-calibrating across assets and timeframes — no manual recalibration required.
🟦 TECHNICAL NOTES
- Pine Script v6
- No repainting — training samples are gated by `barstate.isconfirmed` so the dataset never absorbs unconfirmed live-bar values
- Dataset is hard-capped via FIFO rotation; no unbounded memory growth
- Dashboard renders only on `barstate.islast` — zero historical overhead
- All drawing objects are stateless plots (no label / box / line object pools), so `max_*_count` limits cannot be exceeded
- k-NN distance pass is O(N), sort is O(N log N), both bounded by Max Dataset Size
- Default Max Dataset Size of 500 is tuned to stay within TradingView's per-bar calculation budget on histories up to ~50,000 bars
- Bias correction uses a single extra accumulator pass during the distance sweep — no performance penalty
🟦 DISCLAIMER
This indicator is provided for educational and informational purposes only. It does not constitute financial advice. Past performance does not guarantee future results. The k-NN engine learns from historical patterns, but markets do not guarantee that historical patterns will repeat. Always conduct your own analysis and apply proper risk management. Indicador
Liquidity Zone Harvester [JOAT]Liquidity Zone Harvester
Introduction
Institutional order flow leaves footprints in market structure. When a large buyer or seller places a significant order, the execution of that order creates an imbalance between supply and demand at a specific price level — and markets frequently return to these levels to test whether the original interest remains. These price areas are commonly referred to as order blocks or liquidity zones, and they form one of the core concepts in institutional and Smart Money trading methodology.
The Liquidity Zone Harvester is an automated order block detection and management system that identifies these zones using statistically validated momentum signals rather than arbitrary manual placement. Instead of drawing boxes wherever a trader's eye thinks supply or demand may exist, this indicator uses Z-score cumulative impulse detection to identify when directional momentum has reached statistically significant levels — and only then marks the most recent opposing-close candle as the source order block. Volume quality gates ensure that only high-participation impulses create zones, filtering out low-conviction moves that are less likely to represent genuine institutional activity.
What sets this indicator apart from standard order block tools is what happens after zone creation. Every active zone is tracked through a dual-mechanism aging system. The Bayesian exponential decay model progressively reduces zone visual intensity over time with a configurable half-life, providing a continuous probability signal about zone freshness. Simultaneously, a Kaplan-Meier survival analysis engine — borrowed from medical statistics — estimates the probability that a given zone will survive future price tests, based on the historical survival rates of all previously observed zones in the training window. Each zone displays both its current age and its estimated survival probability directly on the chart, turning static boxes into dynamically updated probability estimates.
Core Concepts
1. Z-Score Cumulative Impulse Detection
Zone creation is triggered only when directional momentum reaches a statistically defined threshold. The system accumulates a running streak of directional closes — when consecutive bars close higher than their open, the bull accumulator grows; when consecutive bars close lower, the bear accumulator grows. The streak resets when direction reverses. This cumulative streak is then normalized against its own rolling mean and standard deviation, producing a Z-score that measures how unusual the current momentum streak is relative to recent history.
cumBull := close > open ? nz(cumBull ) + (close - open) : 0
cumBear := close < open ? nz(cumBear ) + (open - close) : 0
zBull = (cumBull - ta.sma(cumBull, zLen)) / ta.stdev(cumBull, zLen)
zBear = (cumBear - ta.sma(cumBear, zLen)) / ta.stdev(cumBear, zLen)
bullEvent = ta.crossover(zBull, zThresh) and barstate.isconfirmed and volOK
bearEvent = ta.crossover(zBear, zThresh) and barstate.isconfirmed and volOK
When a bullEvent fires (bull Z-score crosses the threshold with volume confirmation), the system looks backward to find the most recent down-close candle — the last bar where sellers were dominant before the impulse began. This becomes the demand zone. Similarly, a bearEvent marks the most recent up-close candle as the supply zone.
2. Volume Quality Gate
Not all Z-score impulses are created equal. An impulse that occurs on abnormally low volume represents weak conviction — possibly a thin-market price drift rather than genuine institutional momentum. The volume gate applies RSI to the volume series to normalize it against its own history. Only when volume RSI exceeds the configurable threshold is the volOK condition true, enabling zone creation.
volRsi = ta.rsi(volume, 14)
volOK = volRsi > volThresh
This filter meaningfully reduces the number of zones created during low-participation conditions such as pre-market sessions, lunch hours, or holiday-period trading — precisely the times when order block levels are least likely to represent significant institutional interest.
3. Order Block Zone Construction
When a signal event is confirmed, the most recent opposing candle is identified using ta.valuewhen(). For a bullEvent, the system finds the most recent bar where close was less than open (a down candle) — its high and low define the demand zone boundaries. For a bearEvent, it finds the most recent up candle — its high and low define the supply zone boundaries. A box object is created spanning from that historical bar to the current bar, with height defined by the candle's actual high-low range.
lastDnHigh = ta.valuewhen(close < open, high, 0)
lastDnLow = ta.valuewhen(close < open, low, 0)
lastDnBar = ta.valuewhen(close < open, bar_index, 0)
if bullEvent
newBox = box.new(lastDnBar, lastDnHigh, bar_index, lastDnLow, ...)
bullBoxes.push(newBox)
4. Overlap Prevention (f_no_overlap)
To avoid cluttering the chart with redundant zones that occupy the same price territory, an overlap check function evaluates whether a proposed new zone overlaps with any existing zone of the same type. The function iterates over all existing bull or bear boxes and compares the new zone's top and bottom against each existing box's top and bottom. A guard condition (nBull > 0) prevents the iteration from running on an empty array, which would cause an index -1 crash.
f_no_overlap(newTop, newBot, boxes) =>
noOverlap = true
if boxes.size() > 0
for i = 0 to boxes.size() - 1
b = boxes.get(i)
if newTop >= box.get_bottom(b) and newBot <= box.get_top(b)
noOverlap := false
noOverlap
5. Bayesian Exponential Decay
Each zone's visual transparency is driven by an exponential decay function that represents the diminishing probability of zone relevance over time. The half-life parameter (default: 75 bars) defines how quickly a zone fades. At age 0, the zone is fully opaque. At age 75 bars, the zone is at 50% opacity. At age 150 bars, 25% opacity. This continuous decay — rather than a binary active/expired switch — provides an analog probability signal directly encoded in the zone's visual intensity.
decayFactor = math.exp(-0.693 * age / halfLife)
zoneAlpha = math.round(decayFactor * 200)
box.set_bgcolor(b, color.new(zoneColor, 255 - zoneAlpha))
6. Kaplan-Meier Survival Analysis
The Kaplan-Meier estimator is a nonparametric statistical method originally developed to measure survival probabilities in clinical trial data. In this indicator, "survival" is defined as a liquidity zone remaining unmitigated (not breached by a closing price on two separate occasions). Each time a zone is mitigated, it is recorded as a "death event" at its current age. Zones that expire by age limit without mitigation are recorded as "censored events" — incomplete observations. The KM formula multiplies survival probabilities across all observed events up to a given age.
// For each completed event (death at age t_i with n_i at-risk zones):
S_t := S_t * (1.0 - d_i / n_i)
// Product over all event times <= query age
For each active zone, the indicator queries the KM estimate at the zone's current age and displays the result as a percentage label. A zone at age 40 showing "Age 40 | 72%" means that historically, 72% of zones survived to at least 40 bars without being mitigated — giving traders a quantitative assessment of how likely the zone is to hold on the next test.
Features
Z-Score Cumulative Impulse: Statistical momentum threshold using normalized cumulative directional streaks to gate zone creation.
Volume Quality Gate: Volume RSI filter ensures only high-participation impulses create zones.
Precise Order Block Identification: Most recent opposing candle (last down-close for bull event, last up-close for bear event) defines zone boundaries.
Overlap Prevention: f_no_overlap function checks all existing zones before creating a new one, preventing chart clutter from redundant levels.
Bayesian Exponential Decay: Zone opacity decays over time with configurable half-life, encoding freshness as a visual probability signal.
Kaplan-Meier Survival Analysis: Medical-statistics survival estimator applied to zone longevity, displayed as a percentage probability label on each active zone.
Dynamic Zone Extension: Box right edge extends to the current bar on every update, keeping zones visually connected to the present.
Mitigation Tracking: Zones that are closed through twice are flagged as mitigated and removed, with the event recorded for KM analysis.
Seven-Row Dashboard: Active demand count, active supply count, bull Z, bear Z, volume RSI, KM training size, and signal status.
Two Alert Conditions: Zone created alert and zone rejection (price tests and bounces back) alert.
Input Parameters
Z-Score Settings:
Z Lookback: Rolling window for Z-score normalization (default: 50)
Z Threshold: Sigma level required to trigger an impulse event (default: 2.0)
Volume Gate Settings:
Volume RSI Period: RSI lookback for volume normalization (default: 14)
Volume RSI Threshold: Minimum volume RSI for zone creation eligibility (default: 55)
Zone Management Settings:
Max Zone Age: Maximum bars a zone remains active before forced removal (default: 300)
Mitigation Count: Number of closes through a zone required for mitigation (default: 2)
Max Active Zones Per Side: Maximum simultaneous demand or supply zones displayed (default: 5)
Decay Settings:
Decay Half-Life: Number of bars at which zone opacity reaches 50% of initial value (default: 75)
KM Settings:
KM Training Window: Bar lookback for Kaplan-Meier training data collection (default: 500)
Show Survival Labels: Toggle KM probability labels on active zones (default: true)
Display Settings:
Show Demand Zones: Toggle demand (bull) zone boxes (default: true)
Show Supply Zones: Toggle supply (bear) zone boxes (default: true)
Show Dashboard: Toggle the seven-row information table (default: true)
How to Use This Indicator
Step 1: Understand Zone Creation Conditions
Zones are not created on every bar — they are created only when a statistically significant directional impulse (Z-score above threshold) occurs on above-average volume. This selectivity is intentional. In any given trading session, you will likely see only a few zone creation events, each backed by a genuine momentum surge that suggests institutional participation. When you see a new zone appear, note the Z-score values in the dashboard and the volume RSI reading — higher values on both indicate a stronger impulse and more confident zone placement.
Step 2: Prioritize Fresh, High-Survival Zones
Not all zones on the chart are equally relevant. A fresh zone (low age, full opacity) at a KM survival rate of 80% is a far stronger candidate for price reaction than an old zone (high age, near-transparent) at 30% survival probability. Use both the visual opacity and the KM label together: as a zone ages and fades, reduce your expectation that it will provide meaningful support or resistance. When price approaches a zone that is both visually fresh and shows high KM survival probability, the statistical expectation of reaction is at its highest.
Step 3: Watch for Zone Rejection Alerts
The zone rejection alert fires when price tests a zone (enters the box boundary) and then closes back away from it without mitigating it. This is the core trade setup: price returning to the institutional order block level, briefly penetrating it, and then reversing. The rejection alert provides a timely notification for potential entries in the direction of the original impulse that created the zone, with the zone's near boundary serving as the natural stop-loss reference.
Step 4: Monitor KM Training Size for Statistical Validity
The dashboard displays the KM training sample size — the number of completed zone events (both mitigated and aged-out) available for the survival analysis. With fewer than 10 training events, the KM estimate has high variance and should be treated as rough guidance. With 30 or more training events, the estimate becomes statistically stable. On instruments or timeframes where the indicator has run for extended periods, the KM estimates become increasingly reliable as the training dataset grows.
Indicator Limitations
The Z-score cumulative impulse and volume gate require sufficient chart history for the rolling normalization periods to be seeded. In the first Z-lookback bars of a new chart, zone creation signals may be less reliable as the mean and standard deviation are not yet fully established.
Kaplan-Meier survival estimates are only as reliable as the training dataset. On instruments or timeframes that have not accumulated many completed zone events, the survival probabilities should be treated as rough estimates rather than statistically precise values.
The mitigation definition (two closes through the zone) is a configurable approximation. In real order block theory, mitigation can be defined in several ways; this indicator's specific definition may not match every trader's conceptual framework.
Zones are based on the most recent opposing candle at the time of the impulse event. In fast markets where multiple large candles cluster closely together, the marked candle may not represent the most significant institutional order location.
This indicator requires volume data. On instruments where volume is unavailable or unreliable (some synthetic indices, certain forex pairs), the volume gate will not function as intended and should be disabled or its threshold lowered significantly.
The exponential decay model assumes a constant half-life across all market conditions. In reality, zone relevance can be regime-dependent — a zone formed during a trending market may remain relevant longer than one formed during a range, or vice versa.
Maximum active zones per side is a hard limit. If the limit is reached, new valid zone creation events will be rejected until an existing zone is mitigated or aged out.
Originality Statement
The Liquidity Zone Harvester is a genuinely original indicator that applies statistical and mathematical frameworks from outside the trading domain to a problem common in technical analysis.
The Z-score cumulative impulse detection — using consecutive close-open accumulation normalized against rolling sma/stdev — as the primary trigger for order block marking is an original signal architecture. Most order block indicators use visual pattern matching (e.g., a large candle followed by a gap) rather than statistical significance thresholds.
Applying the Kaplan-Meier survival estimator — a nonparametric method from biostatistics — to estimate the probability that a liquidity zone will survive future price tests is a novel application of medical statistics to market analysis. This provides a mathematically grounded probability estimate that no standard order block indicator offers.
The Bayesian exponential decay applied to zone visual transparency — using a configurable half-life to continuously encode zone freshness as opacity — is an original visual design that treats zone relevance as a continuously diminishing probability rather than a binary active/inactive state.
The overlap prevention function that iterates over all existing zone arrays before creating a new zone — with the index-crash guard for empty arrays — is a specific engineering solution to a concrete problem in box-based indicator design.
The volume RSI quality gate, applied specifically to filter Z-score impulse events rather than as a standalone signal, is an original confluence filter design that specifically addresses the problem of thin-market false signals in order block detection.
Disclaimer
The Liquidity Zone Harvester is provided for educational and informational purposes only. It is a technical analysis tool and does not constitute financial advice. Liquidity zones and order blocks are analytical constructs; they do not guarantee price reactions. Past zone behavior as encoded in Kaplan-Meier estimates does not predict future zone performance. All trading involves risk of loss. Users are solely responsible for their own trading decisions. Please consider your individual risk tolerance and consult a licensed financial professional before engaging in any trading activity.
-Made with passion by officialjackofalltrades
Indicador
Self-Aware Trend System [WillyAlgoTrader]🧠 Self-Aware Trend System (SATS) is an adaptive SuperTrend-based trend-following system that continuously measures its own operating environment through a 4-factor Trend Quality Index (TQI) and modulates band width, asymmetry, and flip logic in real time. Unlike a fixed SuperTrend — which uses the same ATR multiplier forever — SATS knows when the market is trending vs. chopping, compresses bands in clean trends to lock profit tighter, widens them in noisy conditions to avoid whipsaws, and can detect regime collapse through a "character-flip" even when price hasn't broken the band yet. Each confirmed signal comes with a full trade plan (Entry, SL, TP1/TP2/TP3 at user-defined R multiples), and the system tracks its own realized R, win rate, drawdown, and per-regime edge — building an honest, instrument-specific performance log directly on your chart.
The name "Self-Aware" refers to one specific property: the indicator measures the quality of its own environment every bar and feeds that measurement back into its band width and flip conditions. It doesn't predict the future — it reacts to present conditions with mathematically defined adaptation rules.
🧩 WHY THESE COMPONENTS WORK TOGETHER
A classic SuperTrend has one problem: its ATR multiplier is fixed. In a clean trending market the bands are too wide, giving back profit on every pullback. In a choppy market the bands are too tight, generating whipsaw after whipsaw. Traders try to fix this by manually switching multipliers per timeframe or per instrument — but that's guesswork.
SATS chains a different approach:
Market state measurement (TQI) → Non-linear band modulation → Asymmetric band widths → Character-flip detection → R-multiple trade plan → Outcome tracking → Regime-aware statistics
The TQI engine measures market quality from four independent angles each bar (efficiency, volatility regime, structure, momentum persistence). The non-linear modulation translates that quality into band width — high quality compresses bands, low quality expands them, using a power curve that avoids both over-reacting to mild fluctuations and under-reacting to severe regime changes. Asymmetric bands tighten the active side (in the direction of the trend) while loosening the passive side — creating a "ratchet with leverage" that locks in profit faster than it invalidates the trend. Character-flip detection catches regime collapses (high quality → low quality) even when price hasn't broken the band — critical for exiting stale trends before they fully reverse. And performance tracking records every signal's realized R, building a real statistical picture of how the system performs on your specific instrument and timeframe.
Without TQI, the bands are blind. Without asymmetry, profit-taking lags. Without character-flip, exits happen too late. Without performance tracking, you have no idea if the system has a real edge on your instrument. All four work together — each layer addresses a specific weakness of classic SuperTrend.
🔍 WHAT MAKES IT ORIGINAL
1️⃣ Trend Quality Index (TQI) — 4-factor continuous quality measurement.
TQI is computed every bar as a weighted combination of four independent 0..1 factors:
— 🧭 Efficiency (default weight 0.35) : Kaufman Efficiency Ratio = |close − close | / sum(|close − close |). Measures directional movement vs. total path. 1.0 = perfect straight line, 0.0 = pure noise. Default window 20 bars.
— 📊 Volatility Regime (weight 0.20) : uses Volume Z-score when volume data is available (z = (volume − sma) / stdev, mapped from to ), or falls back to ATR ratio (current ATR vs. long-baseline ATR) on volume-less instruments.
— 🏗️ Structure (weight 0.25) : price position within its recent range. pricePos = (close − lowest) / (highest − lowest). Then tqiStruct = |pricePos − 0.5| × 2. Trends pin price to one edge (1.0), chop oscillates around the midpoint (0.0). No ATR dependency.
— ⏩ Momentum Persistence (weight 0.20) : of the last N bars, what fraction moved in the same direction as the overall window change? alignedBars / N. Default 10 bars.
Final TQI = (factor1 × w1 + factor2 × w2 + factor3 × w3 + factor4 × w4) / sum(weights), clamped to 0..1. Each weight is user-configurable.
2️⃣ Non-linear band modulation with power curve.
Instead of a linear "multiplier × (1 − tqi)", SATS uses a power curve:
qualityDeviation = (1 − tqi)^curvePower
tqiMult = 1 − qStrength + qStrength × (0.6 + 0.8 × qualityDeviation)
With curvePower = 1.5 (default), mild quality drops (from 0.9 to 0.7) cause small band expansion, but severe drops (0.5 to 0.2) cause rapid expansion. This matches how traders actually think: ignore small wobbles, react strongly to clear regime changes.
3️⃣ Asymmetric band widths — ratchet with leverage.
In a strong uptrend, the lower band (active, trailing price up) tightens while the upper band (passive, not used as stop) widens:
activeMult = symMult × (1 − asymStrength × tqi × 0.3)
passiveMult = symMult × (1 + asymStrength × tqi × 0.4)
Effect: as trend quality rises, the trailing stop moves closer to price (locking profit faster) while the opposite band moves away (so an accidental pullback doesn't trigger a flip). This is the "leverage" — asymmetric response to confirmed trend strength.
4️⃣ EMA-smoothed multipliers before ratchet application.
Raw TQI can spike bar-to-bar. If those spikes fed directly into the SuperTrend ratchet logic, bands would compress at a high-TQI bar and stay stuck there (SuperTrend math never loosens active bands against the trend). SATS EMA-smooths the multipliers (alpha 0.15) before ratchet application — preventing stickiness. This is the critical fix that makes adaptive SuperTrend actually work in practice.
5️⃣ Efficiency-weighted ATR.
Used for band construction and SL/TP sizing (not for TQI itself, to avoid circular feedback):
effATR = rawATR × (0.5 + 0.5 × ER)
Clean trending volatility counts full (ER = 1.0 → effATR = rawATR). Noisy chop volatility is halved (ER = 0.0 → effATR = 0.5 × rawATR). This makes SL/TP distances proportional to "useful" volatility, not total volatility.
6️⃣ Character-flip detection with age guard.
Classic SuperTrend only flips on price breaks. But a trend can die internally — quality collapses, momentum fades — before price actually breaches the band. Character-flip catches this:
charFlipDown = prevTQI > 0.55 (high) AND currentTQI < 0.25 (low) AND trendAge ≥ minAge AND close < source
The age guard (default 5 bars) prevents whipsaw on fresh trends — a newborn trend hasn't had time to establish quality, so early TQI noise can't kill it. After the age threshold, a quality collapse triggers an immediate flip even without price break.
7️⃣ Auto-fixed TP order.
If a user accidentally sets TP1 > TP2 (or TP3 < TP2), the indicator automatically sorts them. Math: fixedMin = min(all), fixedMax = max(all), middle = sum − min − max. The three TP lines always end up in correct order on the chart regardless of user input order.
8️⃣ R-multiple trade planning with pivot-anchored SL.
On each signal:
— Entry = close at bar of confirmed flip
— SL = min(pivot − slMult×ATR, entry − slMult×ATR) for longs (mirror for shorts)
— TP1/2/3 = entry ± risk × R-multiple
The SL uses whichever is further from entry — the recent pivot (if available) or a pure ATR distance. This ensures the stop always has a minimum ATR buffer regardless of how close the nearest pivot is.
9️⃣ Performance tracking with realized R accounting.
Every signal is tracked bar-by-bar for TP hits, SL hits, and timeout (default 100 bars). On close-out, realized R is calculated assuming 1/3 position per TP:
— TP3 hit: realized = (tp1R + tp2R + tp3R) / 3 (all three filled)
— SL hit after TP1: realized = (1/3) × tp1R + (2/3) × (−1R)
— SL hit after TP1+TP2: realized = (1/3) × tp1R + (1/3) × tp2R + (1/3) × (−1R)
— Pure SL: realized = −1R
— Timeout: realized = sum of already-hit TP portions (no penalty)
Results feed a rolling buffer (up to 100 signals), which drives:
— Rolling Win Rate
— Rolling Avg R
— Rolling drawdown (window DD)
— All-time drawdown
— Current and max win/loss streaks
🔟 9-cell regime edge tracking.
Every completed signal is bucketed by the market regime at entry time: Efficiency bin (low/mid/high) × Volatility bin (low/normal/high) = 3×3 = 9 cells. Each cell accumulates its own EWMA of realized R. The dashboard shows the current regime's historical edge — e.g., "Trending + High Vol: +0.85R (23 trades)". This lets you see which market conditions the system actually profits in.
1️⃣1️⃣ Experimental self-calibration (off by default).
When enabled, the system monitors its rolling avg R and drifts the Quality Influence parameter toward the user default if recent edge is poor (below threshold). This is explicitly marked experimental — no claim of improved results — and recommended off until validated on your instrument.
⚙️ HOW IT WORKS — CALCULATION FLOW
Step 1 — TQI computation : Compute four factors (Efficiency, Volatility Regime, Structure, Momentum Persistence). Weight and combine into a single 0..1 value.
Step 2 — ATR and effective ATR : rawATR = ta.atr(len). effATR = rawATR × (0.5 + 0.5 × ER).
Step 3 — Adaptive multiplier : Apply legacy ER adaptation (optional) and non-linear TQI curve. If asymmetric bands enabled, split into active/passive multipliers.
Step 4 — EMA smoothing : Smooth both multipliers with alpha 0.15 to prevent ratchet stickiness.
Step 5 — SuperTrend bands : upperBand = source + upperMult × effATR. lowerBand = source − lowerMult × effATR. Ratchet logic: lower only rises, upper only falls, until a flip.
Step 6 — Flip detection : Price flip (close crosses opposite band) OR character-flip (TQI collapse + age guard). On flip: reset trend age, start new segment.
Step 7 — Trade plan : On confirmed flip, compute Entry/SL/TP1/TP2/TP3. Draw lines and labels. Cache the market regime (ER bin × Vol bin) for later edge attribution.
Step 8 — Outcome tracking : Each bar, check active trade for TP1/TP2/TP3/SL hits and timeout. On close-out, calculate realized R, push to history buffer, update rolling stats, drawdown, streaks, and regime cell.
Step 9 — Dashboard render : On last bar, render live state (Trend, TQI, regime, performance stats, TQI breakdown, regime edge).
📖 HOW TO USE
🎯 Quick start:
1. Add indicator — preset is "Auto" (adapts to your current timeframe)
2. Green line = bullish trend, red = bearish trend
3. Line transparency reflects TQI: bright = high quality, faded = low quality
4. ▲ BUY / ▼ SELL labels appear on confirmed flips
5. Entry, SL, TP1, TP2, TP3 lines drawn automatically at the signal
6. Copy levels to your exchange, let the dashboard track outcomes
👁️ Reading the chart:
— 🟢 Bright green line = bullish trend with high TQI — aggressive participation
— 🟢 Faded green line = bullish trend with low TQI — cautious, possible regime shift
— 🔴 Bright red line = bearish trend with high TQI
— 🔴 Faded red line = bearish trend with low TQI
— Line flip + label = new trade signal
— Dashed TP lines turning solid + "✓" = TP was hit
— Score on label (e.g., "85/102") = multi-factor confluence strength
📊 Dashboard fields:
— Preset: Auto-resolved (Scalping / Default / Swing / Crypto)
— Trend: Bullish ▲ / Bearish ▼
— TQI: current quality index (0..1)
— Q.Strength: effective Quality Influence (may drift if auto-calibration enabled)
— Signal: current bar signal (BUY / SELL / —)
— Regime: Trending / Mixed / Choppy + Low/Norm/High Vol
— ER / RSI / Vol Z: raw filter values
— TQI Components breakdown: Efficiency / Volatility / Structure / Momentum (each 0..1)
— Performance section: Win Rate, Avg R, Window DD, All-Time DD, Streak W/L, Regime Edge
🔧 Tuning guide:
— Too many whipsaws : increase Quality Influence (0.5–0.7), increase Structure weight, increase Base Band Width
— Missing moves / signals too late : decrease Quality Influence (0.2–0.3), decrease Base Band Width, increase asymmetry
— Choppy instrument : use Swing preset, enable Character-Flip, raise minAge to 10+
— Strong trending instrument : use Scalping preset, enable Asymmetric Bands with strength 0.6+
— No volume data : automatically falls back to ATR ratio for volatility regime — no action needed
⚙️ KEY SETTINGS REFERENCE
⚙️ Main:
— Preset : Auto / Custom / Scalping / Default / Swing / Crypto 24/7 (auto-adapts ATR, band width, ER window, RSI, SL multiplier)
— ATR Length (13), Base Band Width (2.0 × ATR)
📐 Trend Quality Engine:
— Enable TQI (default On)
— Quality Influence (0.4): how strongly TQI compresses/expands bands
— Quality Curve Power (1.5): non-linearity
— Smooth Adaptive Multipliers (On): critical fix for ratchet stickiness
— Asymmetric Bands (On) + Asymmetry Strength (0.5)
— Efficiency-Weighted ATR (On)
— Character-Flip (On) + Min Age (5) + High/Low TQI thresholds (0.55 / 0.25)
— TQI factor weights : ER 0.35, Volatility 0.20, Structure 0.25, Momentum 0.20
🎯 Risk:
— SL Buffer (1.5 × ATR), TP1/2/3 R-multiples (1.0 / 2.0 / 3.0), Trade Timeout (100 bars)
🤖 Self-Learning (experimental):
— Auto-calibration (default Off), calibration window, bad/good R thresholds, quality step, cooldown, floor/ceiling
— Reset Learning Memory button
📊 Dashboard: position, TQI breakdown toggle, performance stats toggle, score breakdown toggle
🔔 Alerts
— 🟢 BUY — ticker, TF, price, TQI, score, SL, TP1, TP2, TP3
— 🔴 SELL — same payload
Plain text and JSON webhook formats supported. Bar-close confirmed.
⚠️ IMPORTANT NOTES
— 🚫 No repainting. All signals require barstate.isconfirmed. SuperTrend ratchet logic is monotonic — once the trailing band moves, it cannot move back against the trend until a flip. Character-flip uses only previous-bar TQI and current-bar close, both available at bar close.
— 📊 TQI is descriptive, not predictive. It measures current market quality from 4 factors — it does not forecast future price. A high TQI reading means "the market is currently behaving like a trend" — it can still fail on the next bar.
— 📏 Performance stats are walk-forward, not backtested. The rolling buffer records signals as they happen, bar by bar. Drawdown, win rate, and regime edge are honest forward-looking statistics on your specific instrument and timeframe — not curve-fitted optimization results.
— ⚖️ The realized R accounting assumes 1/3 position per TP . This mirrors a standard "scale out at each target" approach. Traders who hold full position to a single target should interpret the R values accordingly.
— 🔄 Auto-calibration is experimental. It's off by default and should stay off until you've validated it on your specific instrument. The drift is mean-reverting (toward your user default), not profit-maximizing — no claim of improvement is made.
— 🔒 The Reset Learning Memory button clears the rolling buffer, regime cells, drawdown, and streak stats. Use when changing instruments or after significant market regime shifts.
— 🛠️ SATS is a decision-support and trade-planning tool , not an automated bot. It identifies trend conditions, measures environmental quality, provides structured trade plans with R-based targets, and tracks outcomes — trade decisions and execution remain yours.
— 🌐 Works on all markets and timeframes. Volume-dependent features (Volume Z in TQI) auto-fall-back to ATR-based measurement when volume data is unavailable. Indicador
Golden Pocket Syndicate Mini (GPSM)This indicator is an overlay toolkit that combines multi-timeframe Golden Pocket-style zones (Fibonacci-derived ranges between user-defined high/low ratios), optional GP-anchored VWAPs that reset when price interacts with the matching zone, and a confluence framework with optional visuals (signals, divergences, order-block-style markers, sweeps, trails). It is intended to help traders see where higher-timeframe ranges and optional filters overlap on the chart—not to automate trading or promise outcomes.
What it does
Pulls prior completed higher-timeframe highs/lows via request.security() and derives upper/lower pocket levels from your fib inputs.
Plots pocket bands (and fills where used) for the timeframes you enable.
Optionally plots volume-weighted averages anchored to touches of the corresponding pocket.
Combines user-toggled filters into a confluence score and optional bull/bear markers; all signal logic can be turned off in settings.
How to use
Open settings, enable only the pocket timeframes and visuals you need. Adjust fib inputs, touch tolerance, and filter groups to match your process. If you use alerts, treat them as notifications only—confirm every trade in your own plan.
Important limitations
This is not financial, investment, or tax advice. Markets involve risk; past or hypothetical chart behavior does not guarantee future results.
Higher-timeframe data and request.security() behavior depend on symbol, session, and chart timeframe. Validate outputs on your instruments before relying on them.
Scripts cannot execute orders; you are responsible for compliance, sizing, and risk.
Companion
For separate 1H / 4H / 8H pocket bands (to reduce plot limits when combined with heavy scripts), use the author’s “Golden Pocket Syndicate mini” (GPSM) publication if offered.
Golden Pocket Syndicate mini (GPSM) — public description
Use this in the publication description field (English first).
GPSM is a lightweight companion overlay focused on 1-hour, 4-hour, and 8-hour Golden Pocket-style zones: two fib ratios applied to the prior completed bar’s range on each timeframe, with optional filled bands and optional GP-anchored VWAPs (off by default) that reset when price touches the matching pocket. The 1-hour band can optionally switch color using a simple prior closed 1H close vs EMA rule so you can see a regime-style split at a glance.
What it does
Uses request.security() on "60", "240", and "480" minute timeframes with the same prior-bar anchoring idea as the author’s main GPS Pro script.
Keeps the script small so it can run alongside heavier indicators without hitting Pine’s plot limits as quickly.
How to use
Add it to your chart, toggle 1H/4H/8H zones and fills, then optionally enable individual VWAPs. Match fib settings to your main workflow if you use GPS Pro on the same chart.
Important limitations
Not financial advice. No performance or profitability claims. Past chart behavior does not predict future prices.
HTF behavior varies by symbol and session (especially 8H). Confirm levels on your market.
You are solely responsible for trading decisions and risk.
Relationship to GPS Pro
GPSM does not duplicate the full confluence, SMC filters, or alerts stack from GPS Pro; it is meant as a focused HTF pocket + optional VWAP add-on. Indicador
Pulse Trend Radar [WillyAlgoTrader]⦿ Pulse Trend Radar is an overlay indicator built on a Kaufman Adaptive Moving Average (KAMA) core with median-ATR volatility bands — producing an adaptive trend system that speeds up in trending markets and slows down in noise. Every trend flip generates a signal scored by a 4-factor quality engine (0–100) with letter grades (A+ through C). The indicator also detects and visualizes liquidity zones from pivot highs/lows, marks order blocks from the last opposite candle before each trend flip, tracks real-time P&L with a live trade tracker, and monitors win/loss outcomes — creating a complete trend-following framework with Smart Money context.
🧩 WHY THESE COMPONENTS WORK TOGETHER
A trend indicator alone tells you direction — but not whether the entry is near a liquidity pool (where stops cluster), not whether there's institutional supply/demand nearby (order blocks), not how strong the signal is (all flips treated equally), and not how the system performs over time (no feedback).
This indicator layers four analysis dimensions onto the adaptive trend core:
KAMA adaptive trend + median ATR bands → Trend direction and flip detection
Liquidity zones from pivots → Where stop-hunts and liquidity grabs are likely
Order blocks from pre-flip candles → Where institutional supply/demand was established
4-factor signal scoring → Quality filtering — not all flips are equal
Win/loss tracker → Performance feedback on this instrument and timeframe
The KAMA core adapts its speed via the Efficiency Ratio — in a strong trend, the MA tracks price closely and the bands tighten, producing early signals. In choppy conditions, the MA barely moves and the bands widen, filtering out noise. The liquidity zones show where clusters of stops sit (above pivot highs, below pivot lows) — entries near these zones have higher follow-through because the liquidity grab fuels the move. The order blocks mark the institutional footprint before each trend change — these zones often act as support/resistance on retests. And the signal score combines trend strength, volume delta, efficiency acceleration, and liquidity proximity into a single quality metric — letting you prioritize A+ setups over C-grade ones.
🔍 WHAT MAKES IT ORIGINAL
1️⃣ Kaufman Adaptive Moving Average (KAMA) trend core.
The KAMA computes a smoothing constant from the Efficiency Ratio:
ER = |price − price | / sum(|price − price |, N)
fastSc = 2 / (fastLen + 1), slowSc = 2 / (slowLen + 1)
sc = (ER × (fastSc − slowSc) + slowSc)²
KAMA = KAMA + sc × (price − KAMA )
When ER → 1 (pure trend): sc approaches fastSc² → KAMA tracks price tightly. When ER → 0 (pure noise): sc approaches slowSc² → KAMA barely moves. This produces a line that accelerates into trends and goes flat in chop — without any manual period switching.
2️⃣ Median ATR volatility bands.
Instead of standard ATR (arithmetic mean of true ranges), the indicator uses a median of recent true ranges computed via a ring buffer over the volatility lookback (default 50 bars). The median is more robust to outlier spikes (gap bars, flash wicks) than the mean — producing smoother, more stable band widths.
Bands: upper = KAMA + medianATR × multiplier, lower = KAMA − medianATR × multiplier. Trend flips when the previous bar's source price crosses beyond a band: source > upper → bullish, source < lower → bearish. The active band (lower in uptrend, upper in downtrend) is plotted as the trend line.
3️⃣ Displacement-based gradient fill.
The fill between the trend line and price is not a fixed transparency — it scales with displacement: displacement = |price − KAMA| / (medianATR × multiplier). The further price stretches from KAMA, the more intense the fill becomes (transparency decreases from 95 to 60). This creates a visual "heat map" effect: faint fill near KAMA (low extension), bright fill far from KAMA (overbought/oversold). This gives immediate visual feedback on how extended the current move is without needing a separate oscillator.
4️⃣ Liquidity zone detection and sweep tracking.
Pivot highs and lows (configurable lookback, default 4 bars) are marked as liquidity zones:
— Above pivot highs → bearish liquidity (buy stops cluster above swing highs — potential sell-side liquidity)
— Below pivot lows → bullish liquidity (sell stops cluster below swing lows — potential buy-side liquidity)
Each zone extends rightward as a thin box (height = 0.15× medianATR). Zones are automatically removed when price sweeps through them (high crosses above bearish zone top, or low crosses below bullish zone bottom) — representing the liquidity grab event. Up to 15 zones per side (configurable).
The signal scoring engine measures the nearest liquidity zone distance on each trend flip — entries closer to a liquidity pool receive a higher quality score because the stop-hunt provides fuel for the ensuing move.
5️⃣ Order block detection on trend flips.
When the trend flips, the previous bar is marked as an order block:
— Bullish flip → demand order block (the last bearish candle before the reversal — where institutional buying absorbed selling pressure)
— Bearish flip → supply order block (the last bullish candle before the drop — where institutions distributed)
Each OB is drawn as a box from the previous candle's high to low, extending rightward. OBs are automatically invalidated (deleted) when price closes beyond the opposite edge after 3+ bars — indicating the zone has been broken. Up to 10 OBs per side (configurable).
6️⃣ 4-factor signal quality scoring (0–100).
Each trend flip is scored on four factors:
— 📐 Trend strength (25 pts) : combined from ER (directional efficiency) and displacement from KAMA — measures how strong the trend is at the moment of the flip
— 📊 Volume delta alignment (25 pts) : buy volume vs sell volume accumulated during the previous trend leg — bullish flip with positive volume delta scores higher (smart money was accumulating)
— ⚡ Efficiency acceleration (25 pts) : current ER minus previous ER — positive acceleration means the trend is gaining momentum, not losing it
— 💧 Liquidity proximity (25 pts) : distance to the nearest liquidity zone — closer = higher score (the flip is near a liquidity grab point)
Grades: A+ (≥ 80), A (≥ 60), B (≥ 40), C (< 40). Signal labels display "Long A+" / "Short B" etc.
7️⃣ OBV-based volume regime detection.
On Balance Volume (OBV) delta = OBV − SMA(OBV, 20). Classified as:
— Accumulation : OBV delta > 0 — more volume on up-moves than down-moves (institutional buying)
— Distribution : OBV delta < 0 — more volume on down-moves (institutional selling)
Displayed in the dashboard with directional coloring. Auto-displays "N/A" on instruments without volume data.
8️⃣ Live trade tracker with P&L.
On each signal: a dashed entry line extends horizontally, a vertical connector line tracks from entry to current price, and a P&L label updates in real-time showing percentage gain/loss. Green = profit, red = loss. Replaced on each new signal.
9️⃣ Win/loss markers + win rate tracking.
Each signal is tracked as a mini-trade: entry at signal close, SL at entry ± medianATR × SL multiplier, TP1 at entry ± risk × TP1 multiplier. If TP1 is reached before SL → green ● marker at the signal bar (win). If SL is reached first → red ● marker (loss). Running win rate displayed in the dashboard as "67% (4W/2L)".
🔟 ATR-based TP/SL with hit tracking.
Three take-profit levels as risk multiples (default 1.0/2.0/3.0 × risk) plus SL (default 3× medianATR from entry). Lines extend rightward with labels showing price + percentage. Labels update with ✓ on hit (green) or ✗ on SL hit (red). Active until the next signal replaces them.
⚙️ HOW IT WORKS — CALCULATION FLOW
Step 1 — KAMA: Efficiency Ratio from configurable lookback → adaptive smoothing constant → KAMA line that accelerates in trends, goes flat in chop.
Step 2 — Median ATR bands: True ranges stored in ring buffer → median computed → upper/lower bands = KAMA ± median × multiplier.
Step 3 — Trend detection: Previous bar's source > upper band → bullish flip. Source < lower band → bearish flip. Active band plotted as trend line. Gradient fill scales with displacement.
Step 4 — Liquidity zones: Pivot highs/lows → boxes above/below. Swept zones auto-deleted.
Step 5 — Order blocks: On flip → previous candle becomes OB. Invalidated when price closes beyond opposite edge.
Step 6 — Signal scoring: 4 factors (trend strength, volume delta, ER acceleration, liquidity proximity) → 0–100 → A+/A/B/C grade.
Step 7 — Trade tracking: SL/TP placed, lines extend, win/loss evaluated per trade.
📖 HOW TO USE
🎯 Quick start:
1. Add the indicator — adaptive trend line, liquidity zones, and order blocks appear
2. "Long A+" / "Short B" labels = trend flip signals with quality grade
3. Green/red liquidity zone boxes = where stops cluster (potential sweep targets)
4. Green/red order blocks = institutional supply/demand zones
5. SL/TP lines auto-appear with P&L tracker
👁️ Reading the chart:
— 🟢 Green trend line = bullish (lower band active)
— 🔴 Red trend line = bearish (upper band active)
— 🟢/🔴 Gradient fill = displacement from KAMA (brighter = more extended)
— 🟢 Small boxes below price = bullish liquidity zones (buy-side stops)
— 🔴 Small boxes above price = bearish liquidity zones (sell-side stops)
— 🟢 Larger boxes = demand order blocks (institutional buying zone)
— 🔴 Larger boxes = supply order blocks (institutional selling zone)
— 🟢 ● = win (TP1 reached), 🔴 ● = loss (SL hit)
— Dashed line + PnL label = live trade tracker
📊 Dashboard fields:
— Trend: ▲ Bullish / ▼ Bearish
— Last Signal: BUY/SELL with grade
— Score: 0–100 quality rating
— Strength: trend strength percentage
— P&L: current trade percentage
— Win Rate: wins/losses with percentages
— SL / TP1: current trade levels with ✓/✗ status
— Vol Regime: Accumulation / Distribution
— Vol Delta: buy vs sell volume percentage
— Efficiency: current ER percentage
🔧 Tuning guide:
— Too many signals: increase Band Multiplier (2.0–2.5) or ER Length (15–20)
— Too few signals: decrease Band Multiplier (1.2–1.5) or ER Length (8–10)
— Signals too late: decrease Slow Smoothing (15–20), decrease Volatility Length (20–30)
— Stops too tight: increase SL ATR Multiplier (2.5–4.0)
— Want only A+/A signals: monitor grades in dashboard, skip B/C entries
⚙️ KEY SETTINGS REFERENCE
⚙️ Main:
— Efficiency Ratio Length (default 13): KAMA lookback — higher = smoother
— Fast/Slow Smoothing (default 2/30): KAMA acceleration/deceleration
— Band Multiplier (default 1.8): band width in median ATR
— Volatility Length (default 50): median ATR ring buffer size
🎯 SL/TP:
— SL (× ATR) (default 3): stop distance in median ATR
— TP1/TP2/TP3 (× risk) (default 1.0/2.0/3.0): R:R multiples
💧 Liquidity:
— Pivot Lookback (default 4) / Max Zones (default 15)
🟧 Order Blocks:
— Max Order Blocks (default 10)
🎨 Visual:
— Gradient fill, trade tracker, win/loss markers (all toggleable)
— Configurable signal label size (Tiny–Large)
— Configurable dashboard font size (Tiny–Normal)
— Auto / Dark / Light theme
🔔 Alerts
— 🟢 BUY / 🔴 SELL — ticker, price, TF, SL, TP1, TP3
All support plain text and JSON webhook format. Bar-close confirmed.
⚠️ IMPORTANT NOTES
— 🚫 No repainting. All signals require barstate.isconfirmed. Trend flips use the previous bar's source vs the previous bar's band value — the signal fires on the bar after the crossing bar closes. KAMA and band values are deterministic once a bar is confirmed.
— 📐 The median ATR is more robust than standard ATR . A single flash wick or gap bar shifts the mean (standard ATR) significantly but barely affects the median. This produces more stable band widths and fewer false flips during anomalous bars.
— 📊 Volume delta is accumulated within each trend leg and resets on every trend flip. It represents the buy/sell balance during the specific move — not the overall volume profile. The pre-reset delta value is used for the signal score (capturing the exiting leg's character).
— 💧 Liquidity zones are automatically swept and removed when price touches them. This prevents stale zones from cluttering the chart. If a zone disappears, it means price swept through it — the liquidity has been taken.
— 🟧 Order blocks are invalidated after 3+ bars if price closes beyond the opposite edge. This prevents old OBs that have clearly failed from persisting.
— ⚖️ The 4-factor score uses the volume delta from before the trend reset (preResetVolDelta) — not the current leg's delta, which would be zero at the moment of the flip. This correctly captures whether the previous leg had accumulation or distribution behind it.
— 📏 Win/loss tracking evaluates TP1 vs SL only — if TP1 is reached before SL, it's a win. The trade closes on the first event and is not re-evaluated.
— 🛠️ This is a trend-following signal and analysis tool , not an automated trading bot. It provides adaptive trend detection, liquidity context, order block zones, and signal quality grading — trade decisions remain yours.
— 🌐 Works on all markets and timeframes. Volume features auto-adapt to instruments without volume data (OBV and volume delta show "N/A"). Indicador
Dynamic Support & Resistance V3Dynamic SRT V3 by Anonycryptous inspired by Ilja V.
Compared to the previous version, a completely new and accelerated concept.
Dynamic SRT V3 is a professional structural mapping suite that identifies high-density liquidity zones through a dual-engine calculation process. By merging a 6-Tiered Pivot Architecture with a Validated Diagonal Scoring Engine, it provides a surgical view of market boundaries, allowing traders to distinguish between minor price fluctuations and major institutional walls.
*How the Engine Operates
This indicator functions as a mathematical filter for price action, operating on two distinct layers:
-1. Tiered Institutional Anchors (Horizontal)
Instead of looking at a single fractal period, V3 tracks six different "memory depths" simultaneously (ranging from 5 to 200 bars).
The Concept: Markets move in cycles. Small cycles (Pivot 1-2) represent retail positioning, while large cycles (Pivot 5-6) represent institutional buy/sell walls.
State-Aware Logic: Each level uses an ATR-Volatility Buffer to determine its current state. If price is above the level, it acts as Support (Green); if below, it is Resistance (Red). If price is currently slicing through it, the level turns Grey (Neutral), signaling a "No-Trade Zone" or a consolidation phase.
-2. Slope-Intercept Validation Engine (Diagonal)
The dynamic trendlines are not just simple "peak-to-peak" connectors. They are calculated using a Linear Regression Scoring System.
*The Concept: A trendline's strength is defined by its "cleanliness."
-The Filter: Unlike standard tools, V3 uses a Price-Action Scan. It calculates the path of a potential trendline and automatically discards it if it cuts through the bodies of intermediate candles. This ensures that the wedges and channels you see are statistically valid structural boundaries.
-Strategic Application: LTF vs. HTF
*Performance & User Manual
-Optimized Execution: V3 utilizes Last-Bar Offloading. It scans 1000+ bars of history in milliseconds by executing the heavy diagonal math only on the most recent candle, ensuring zero chart lag.
-Price Tags: Dynamic labels are pinned professionally above the levels. Use these as your Take-Profit (TP) or Stop-Loss (SL) targets.
-Customization: Adjust the Touch Tolerance in the settings to make the trendlines more or less strict depending on the asset's "wickiness" (e.g., higher for BTC, lower for Forex).
*Lower Timeframes (1m – 15m): Scalping & Intraday
-LTF Focus: Prioritize Pivot Levels 1, 2, and 3. These are highly reactive and will map the micro-pullbacks of the current session.
-Early Signal: Look for price to reject a Dynamic Trendline while a micro-pivot (Level 1) is acting as support. This provides an aggressive "Early Entry" with a very tight risk-to-reward ratio.
-The Trap: Avoid trading when the LTF candles are consistently Grey, as this indicates the market is trapped inside a static pivot zone.
-Higher Timeframes (1H – Daily): Swing & Position Trading
*HTF Focus: Prioritize Pivot Levels 5 and 6. These represent the "Major Floors and Ceilings" of the weekly or monthly trend.
-The Macro Wall: If price hits a Level 6 Pivot (200-bar lookback), expect a significant reaction. Institutional orders are often clustered at these depths.
-Structural Confluence: The most powerful HTF setup is "The Confluence Cross." This occurs when a diagonal Resistance Trendline and a horizontal Level 5/6 Resistance meet at the same price point. This is the mathematical "End of Trend" zone where heavy reversals typically begin.
! Notice: This tool is for institutional-grade structural mapping and educational purposes only. It is not financial advice. Structural levels are areas of high probability, not guaranteed reversal points. Always trade with a stop-loss.
Indicador
Prismatic Trend Matrix [JOAT]Prismatic Trend Matrix
Introduction
The Prismatic Trend Matrix is an advanced open-source multi-dimensional trend analysis system that combines Hull Moving Average, SuperTrend, ADX strength filtering, and moving average confluence into a unified trend detection engine. This indicator analyzes trend across multiple dimensions simultaneously, creating a prismatic view of market direction with gradient visualization that reveals trend strength and conviction.
Unlike single-indicator trend systems, the Prismatic Trend Matrix provides multi-layered trend intelligence through Hull MA smoothing, SuperTrend band analysis, ADX strength measurement, and EMA/SMA alignment detection. The indicator is designed for traders who understand that strong trends require confirmation across multiple analytical dimensions.
Why This Indicator Exists
This indicator addresses the need for comprehensive trend analysis that goes beyond simple moving averages. By combining four distinct trend methodologies with gradient visualization, it reveals:
Hull Moving Average: Weighted moving average with reduced lag for responsive trend detection
SuperTrend Component: ATR-based bands that identify trend direction and support/resistance
ADX Strength Filter: Measures trend strength to separate strong trends from weak/choppy conditions
Moving Average Matrix: Three EMAs and two SMAs create alignment-based trend confirmation
Trend Classification: Five-state system (Strong Bull, Weak Bull, Sideways, Weak Bear, Strong Bear)
Counter-Trend Detection: Identifies potential reversals when price moves against established trend
Prismatic Gradient: Visual fill between Hull MA and SuperTrend shows trend intensity
Core Components Explained
1. Hull Moving Average (HMA)
The Hull MA uses weighted moving averages to create a smooth trend line with minimal lag:
The calculation involves three steps:
Step 1: Calculate WMA of half-length period
Step 2: Calculate WMA of full-length period
Step 3: Calculate WMA of the difference using square root of length
The result is a moving average that responds quickly to price changes while maintaining smoothness. The indicator applies additional EMA smoothing (default 3 periods) to reduce noise.
2. SuperTrend Calculation
SuperTrend uses ATR-based bands to identify trend direction:
Source: Average of high and low (HL2)
Upper Band: Source + (ATR × Factor)
Lower Band: Source - (ATR × Factor)
Direction: Bullish when close > upper band, bearish when close < lower band
Three modes control band adjustment:
Strict Mode: Bands adjust only when price crosses or previous band is breached
Quick Mode: Bands adjust when price crosses previous band
Quicker Mode: Bands adjust immediately with price
SuperTrend provides dynamic support/resistance levels that adapt to volatility.
3. ADX Strength Measurement
The Average Directional Index measures trend strength:
DI+ (Directional Indicator Plus): Measures upward directional movement
DI- (Directional Indicator Minus): Measures downward directional movement
ADX: Smoothed average of the difference between DI+ and DI-, normalized
Threshold: ADX above threshold (default 25) indicates strong trend
Rising ADX: Indicates strengthening trend momentum
ADX filters out weak trends and choppy conditions, ensuring signals occur only during strong directional movement.
4. Moving Average Matrix
Five moving averages create a trend alignment system:
Fast EMA (9): Short-term trend direction
Medium EMA (21): Intermediate trend direction
Slow EMA (50): Primary trend direction
Fast SMA (50): Smoothed primary trend
Slow SMA (200): Long-term institutional trend
Alignment is measured by comparing the order of these averages:
Bullish Alignment: EMA9 > EMA21 > EMA50 > SMA50 (all in ascending order)
Bearish Alignment: EMA9 < EMA21 < EMA50 < SMA50 (all in descending order)
Mixed Alignment: Averages not in order (choppy or transitional conditions)
Perfect alignment indicates strong institutional conviction in the trend direction.
5. Composite Trend Classification [/b>
The indicator combines all components into a five-state trend classification:
Strong Bull (State 2): Hull rising + SuperTrend bullish + MA alignment bullish + ADX strong
Weak Bull (State 1): Hull rising + (SuperTrend bullish OR MA alignment bullish)
Sideways (State 0): Mixed signals or weak trend conditions
Weak Bear (State -1): Hull falling + (SuperTrend bearish OR MA alignment bearish)
Strong Bear (State -2): Hull falling + SuperTrend bearish + MA alignment bearish + ADX strong
This classification provides clear trend assessment at a glance.
6. Counter-Trend Detection [/b>
The indicator identifies potential reversals when price moves against established trend:
Counter-Trend Bull: Trend state neutral/bearish + close > open + price rising + DI+ > DI- + close > Hull + ADX > 20
Counter-Trend Bear: Trend state neutral/bullish + close < open + price falling + DI- > DI+ + close < Hull + ADX > 20
Counter-trend signals include anti-overlap logic to prevent signal clustering and ensure clean placement.
7. Prismatic Gradient Visualization
The indicator creates a gradient fill between Hull MA and SuperTrend:
Gradient Layers: Multiple intermediate values calculated between Hull and SuperTrend (default 15 layers)
Color Intensity: Transparency increases from Hull (solid) to SuperTrend (transparent)
Dynamic Coloring: Gradient color matches trend state (green = bullish, red = bearish, cyan = sideways)
Visual Effect: Creates a glowing prismatic effect that emphasizes trend strength
The gradient provides intuitive visual feedback on trend intensity and direction.
8. Platform Levels
Platform levels are horizontal lines at the current Hull MA value:
Extension: Lines extend forward and backward from current bar (default 7 bars each direction)
Color Coding: Platform color matches current trend state
Purpose: Provides visual reference for potential support/resistance at Hull MA level
Platforms help identify key levels where price may find support or resistance.
Visual Elements
Hull Trend Line: Thick line (3px) with regime-based coloring showing primary trend
SuperTrend Line: Medium line (2px) with step-line style showing dynamic support/resistance
EMA Matrix: Three thin lines showing fast, medium, and slow EMAs with transparency
Prismatic Gradient: Multi-layer fill between Hull and SuperTrend creating glow effect
Platform Levels: Horizontal lines at Hull MA value extending forward/backward
Counter-Trend Signals: Triangles marking potential reversal points
Background Coloring: Subtle background tint for strong bull/bear states
Information Dashboard: Displays trend state, Hull direction, ADX strength, momentum, alignment, SuperTrend, DI balance, price vs Hull, gradient zone, counter-trend status, and signal
How to Use This Indicator
Step 1: Check Trend State
Monitor the dashboard for current trend state (Strong Bull, Weak Bull, Sideways, Weak Bear, Strong Bear). Trade in the direction of strong states.
Step 2: Verify ADX Strength
Ensure ADX is above threshold (default 25) for strong trends. Low ADX indicates choppy conditions - avoid trend-following strategies.
Step 3: Confirm MA Alignment
Check if moving averages are aligned (Bullish/Bearish/Mixed). Perfect alignment confirms institutional conviction.
Step 4: Monitor Hull Direction
Hull rising = bullish bias, Hull falling = bearish bias. Hull provides the primary trend direction signal.
Step 5: Use SuperTrend for Support/Resistance
SuperTrend line acts as dynamic support in uptrends and resistance in downtrends. Breaks of SuperTrend warn of trend changes.
Step 6: Watch for Counter-Trend Signals
Counter-trend signals at extreme levels may indicate reversals. Use these cautiously and confirm with other factors.
Step 7: Assess Gradient Zone
Price in upper gradient zone (near Hull) = strong trend, price in lower zone (near SuperTrend) = weak trend or potential reversal.
Best Practices
Trade only in Strong Bull or Strong Bear states for highest probability
Avoid trading in Sideways state - wait for clear trend establishment
Use ADX as a filter - only trade when ADX > 25 for strong trends
Confirm trend with MA alignment before entering positions
Use SuperTrend as trailing stop level in trending markets
Counter-trend signals work best at extreme levels with divergence
Monitor gradient zone - price near SuperTrend may indicate trend exhaustion
Combine with higher timeframe trend for additional confirmation
Input Parameters
Hull Trend Engine:
Hull Length: Period for Hull MA calculation (default: 20)
Hull Smoothing: Additional EMA smoothing (default: 3)
SuperTrend Layer:
ATR Period: Period for ATR calculation (default: 10)
ATR Factor: Multiplier for band width (default: 3.0)
Mode: Strict, Quick, or Quicker (default: Quick)
Trend Strength:
ADX Length: Period for ADX calculation (default: 14)
ADX Smoothing: Smoothing period for ADX (default: 14)
Strength Threshold: Minimum ADX for strong trend (default: 25)
MA Matrix:
Fast EMA: Short-term EMA (default: 9)
Medium EMA: Intermediate EMA (default: 21)
Slow EMA: Primary EMA (default: 50)
Fast SMA: Smoothed primary (default: 50)
Slow SMA: Long-term institutional (default: 200)
Visual Configuration:
Bullish/Bearish Trend Colors: Customizable colors for trend states
Sideways/Weak Trend Colors: Colors for neutral and weak states
Gradient Layers: Number of gradient fills (default: 15)
Show Platforms: Toggle platform level display (default: enabled)
Platform Extension: Bars to extend platforms (default: 7)
Originality Statement
This indicator is original in its multi-dimensional trend approach. While individual components (Hull MA, SuperTrend, ADX, EMAs) are established concepts, this indicator is justified because:
It combines four distinct trend methodologies into a unified classification system
The five-state trend classification provides clear trend assessment
Prismatic gradient visualization creates intuitive trend intensity display
Counter-trend detection with anti-overlap logic identifies potential reversals
MA alignment analysis measures institutional conviction
Integration of Hull MA smoothness with SuperTrend adaptability creates balanced trend detection
The comprehensive dashboard presents all trend dimensions simultaneously
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss. Trend analysis does not guarantee profitable trades. Past trends do not guarantee future trends. Always use proper risk management and never risk more than you can afford to lose.
-Made with passion by officialjackofalltrades Indicador
Smart Reversal EntrySmart Reversal Entry
Smart Reversal Entry is an open-source reversal-entry indicator built around one specific analytical idea:
after a short, directional three-candle expansion move, the first confirmed candle closing back in the opposite direction can create a structured reversal-entry opportunity when it appears in the correct EMA context.
This script is not designed to mark every bullish or bearish candle, and it is not intended to behave like a generic trend-following overlay, a standard candlestick-pattern indicator, or a broad “signal generator” that reacts to every small reversal. Its purpose is to measure short-term directional exhaustion in a standardized way, filter that move through an EMA context, require close-confirmed reversal behavior, and then project a fixed-risk trade structure directly on the chart for analysis and review.
The script also includes an internal background optimizer and review tables so users can compare how the same reversal framework behaves under different parameter combinations. These review tools are included to support study and comparison, not to imply future performance.
OPEN-SOURCE NOTE
This script is published open-source so users can inspect the logic directly, verify what the script is doing, and adapt parts of the workflow for their own research if they wish.
Even though the code is open, this description is intentionally detailed because many TradingView users do not read Pine Script. The goal is for a user to understand what the script does, how it works, why its parts belong together, and how it may be used in practice without having to study the code line by line.
OVERVIEW
At a high level, the script does six things:
1. It measures whether the last three candles produced a directional move large enough to matter in pip terms.
2. It checks whether price is positioned on the correct side of a selected EMA filter.
3. It requires the current candle to close in the opposite direction as confirmation of a possible reversal.
4. It maps a fixed stop-loss and a selectable take-profit multiple directly onto the chart.
5. It tracks projected trade outcomes and summarizes them in a review table and a daily PnL table.
6. It runs a hidden background optimizer over multiple EMA and move-threshold combinations so the user can compare the current settings to an internal parameter sweep.
The script is therefore meant to function as a complete reversal-entry and review framework rather than as a single-purpose candle-pattern marker.
CORE IDEA
Many reversal-style tools identify isolated candles or basic candlestick formations, but they do not standardize the market context around them.
This script is built around the idea that a reversal signal becomes more meaningful when three specific things happen together:
1. price has already made a clear short-term directional move,
2. that move is large enough to matter relative to the chosen pip structure,
3. and the next confirmed candle closes back in the opposite direction while price remains on the correct side of an EMA filter.
The model is intentionally narrow.
It does not try to identify every turning point in the market.
It does not try to classify broad market structure.
It does not use discretionary support and resistance interpretation.
It does not rely on vague candle descriptions such as “looks weak” or “looks exhausted”.
Instead, it defines reversal-entry conditions using a fixed sequence:
first measure a three-candle directional push,
then filter it using EMA context,
then require an opposite close-confirmed candle,
then project a standardized risk framework,
then review the resulting projected outcomes over time.
That narrower focus is the main reason this script exists in its current form.
WHY THIS SCRIPT IS NOT A SIMPLE MASHUP
This script combines multiple components, but they are not included simply to place more features into one publication.
Each component has a specific function inside the same analytical workflow:
- The EMA filter defines directional context.
- The three-candle move measurement defines whether a short-term push is large enough to qualify.
- The reversal candle confirmation defines the actual entry trigger.
- The pip-based stop-loss and RR framework standardize trade projection.
- The summary and daily review tables organize projected outcomes into a readable review structure.
- The internal optimizer compares the same reversal logic across multiple hidden EMA and move-threshold combinations.
These layers are interdependent.
Without the three-candle move measurement, the script would react to many small candles that do not represent meaningful short-term expansion.
Without the EMA filter, the script would lose its directional context and become a more generic reversal marker.
Without the close-confirmed reversal candle, the script would identify momentum but not the actual reversal-entry moment.
Without the risk projection layer, the user would still need to manually draw the entry, stop, and target after every signal.
Without the review tables, the user would have less organized feedback when reviewing results under the selected settings.
Without the internal optimizer, the user would see only the current configuration and not how the same logic behaves across a broader parameter range.
For that reason, the script is intended as a single reversal-entry framework, not as a random collection of unrelated features.
WHAT THE SCRIPT DOES
The script identifies reversal-entry setups using a strict, rule-based structure.
Long setup requirements:
- price must be above the selected EMA,
- the prior three candles must all be bearish,
- the combined bearish move across that sequence must reach the minimum pip threshold,
- the current candle must close bullish.
Short setup requirements:
- price must be below the selected EMA,
- the prior three candles must all be bullish,
- the combined bullish move across that sequence must reach the minimum pip threshold,
- the current candle must close bearish.
When a valid signal appears, the script can:
- place a BUY or SELL label,
- project a fixed stop loss in pips,
- project a take-profit level using the selected RR multiple,
- draw TP/SL boxes,
- draw an entry line,
- keep historical projected trades visible for later review,
- summarize projected outcomes in a summary table,
- summarize recent daily projected behavior in a daily PnL table.
The script also evaluates an internal optimizer in the background. That optimizer tests multiple EMA lengths and minimum-move combinations using the same reversal logic and displays the best-performing parameter combination inside the summary table over the shared analysis window.
HOW THE SCRIPT WORKS
1) EMA CONTEXT FILTER
The script uses a single EMA as a directional filter.
For long setups:
price must close above the selected EMA.
For short setups:
price must close below the selected EMA.
This does not turn the script into a pure trend-following system. Instead, it acts as a directional context filter so that reversal entries are only considered when price is positioned on the chosen side of the EMA.
In practical terms, the EMA filter is used to reduce context-free reversal signals. A bullish candle appearing after a bearish push is not enough by itself. The script still wants price to be trading above the selected EMA for longs, and below it for shorts.
2) THREE-CANDLE DIRECTIONAL MOVE MEASUREMENT
The script looks at the three candles immediately before the signal candle.
For a long setup:
those three candles must all be bearish.
For a short setup:
those three candles must all be bullish.
The script then measures the total directional move across that sequence in pip terms.
For long setups, it calculates the bearish move from the open of the first candle in the sequence to the close of the third bearish candle.
For short setups, it calculates the bullish move from the open of the first candle in the sequence to the close of the third bullish candle.
That move must be at least as large as the user-defined “Minimum 3-Candle Move (Pips)” setting.
This is one of the key parts of the script’s logic. It ensures that the setup is not based on three arbitrary candles, but on a directional push that is large enough to meet the minimum threshold selected by the user.
3) REVERSAL CANDLE CONFIRMATION
After the three-candle directional push is identified, the current candle must close in the opposite direction.
For long setups:
the current candle must close bullish.
For short setups:
the current candle must close bearish.
This requirement is intentionally strict. The script does not treat intrabar movement or unfinished candles as a valid signal. Signals are confirmed only when the bar closes.
This matters because a reversal that looks valid intrabar can disappear by the close. By waiting for close confirmation, the script reduces premature signal marking.
4) COOLDOWN FILTER
The script includes a cooldown period between signals.
Once a signal has fired, a new signal is not allowed until a defined number of bars has passed. In the current implementation, that cooldown is handled internally.
The purpose of this filter is to reduce signal clustering and prevent the chart from producing multiple nearby entries from the same short-term market behavior.
5) PIP-BASED RISK PROJECTION
When a valid signal appears, the script creates a projected trade framework using:
- entry at the signal close,
- a fixed stop-loss distance in pips,
- a take-profit level based on the selected risk/reward multiple.
This makes the projection logic standardized across signals.
For long setups:
- stop loss is placed below entry,
- take profit is placed above entry.
For short setups:
- stop loss is placed above entry,
- take profit is placed below entry.
The script can draw:
- entry line,
- TP box,
- SL box,
- BUY / SELL label,
- TP / SL hit labels.
This projection layer is not meant to claim that a setup will succeed. Its purpose is to reduce manual chart annotation and make the behavior of the signal model easier to inspect after the fact.
6) SAME-BAR TP/SL PRIORITY RULE
The script uses a strict and conservative rule when both target and stop would appear to be touched on the same bar after entry:
if TP and SL are both reached on the same bar, SL takes priority.
This is an important implementation detail because it directly affects projected statistics. It makes the review logic more conservative and avoids optimistic ambiguity when bar data alone cannot determine exact intrabar order.
7) SHARED ANALYSIS WINDOW
The script uses a shared analysis window internally.
Projected results and optimizer comparisons are evaluated over a rolling historical range rather than over the full unlimited chart history. This keeps the internal review process more controlled and makes the optimizer comparison consistent inside the same defined lookback window.
8) INTERNAL OPTIMIZER
One of the script’s more advanced components is the internal optimizer.
The optimizer runs in the background and is intentionally not exposed as a user-facing optimization panel. Instead of asking the user to manually test every variation, the script internally evaluates combinations of:
- 10 EMA values,
- 10 minimum-move thresholds.
That produces 100 total internal combinations.
Each combination uses the same reversal logic:
- EMA context,
- three-candle directional sequence,
- minimum move threshold,
- opposite close-confirmed candle,
- same stop-loss and RR structure.
The optimizer then tracks projected wins, losses, net R, gross profit, and gross loss for each combination, and the summary table displays the current best combination based on the script’s internal comparison rules.
This optimizer is not intended to present a “perfect setting”. It is a comparative review aid that helps the user understand how the same reversal framework behaves across multiple hidden parameter combinations.
WHAT MAKES THIS SCRIPT ORIGINAL
This script uses familiar technical-analysis building blocks such as:
- EMA filtering,
- candle-sequence logic,
- pip-based move measurement,
- fixed stop-loss projection,
- risk/reward mapping,
- performance review tables.
Those building blocks are not original by themselves.
The originality of this script is not in inventing a completely new primitive indicator. The originality lies in how these familiar elements are arranged into one tightly defined reversal-entry workflow:
EMA context
→ three-candle directional expansion
→ minimum pip-threshold validation
→ opposite candle close confirmation
→ fixed-risk trade projection
→ on-chart review
→ internal background parameter comparison
That full sequence is the main reason this script exists as its own publication.
It is not intended to be simply another EMA filter, another candlestick marker, another TP/SL visualizer, or another optimizer dashboard. It is specifically a short-term reversal-entry framework that combines directional context, expansion measurement, confirmation logic, risk mapping, and review in one workflow.
WHAT APPEARS ON THE CHART
Depending on settings, the chart may display:
- EMA line,
- BUY labels,
- SELL labels,
- signal-bar background highlights,
- entry line,
- TP box,
- SL box,
- TP hit labels,
- SL hit labels,
- summary table,
- daily PnL table.
Users who want a cleaner chart can disable some visual layers and keep only the ones most relevant to their workflow.
HOW TO USE THE SCRIPT
A practical workflow is:
1. Add the script to a standard candlestick chart.
2. Select the EMA length you want to use as directional context.
3. Set the minimum three-candle move threshold in pips.
4. Set the pip preset correctly for the instrument, or use manual pip size if needed.
5. Choose the stop-loss distance in pips.
6. Select the RR mode used for take-profit projection.
7. Wait for a valid long or short setup to appear.
8. Use the projected entry, stop, and target structure as a chart-analysis framework rather than as a blind instruction.
9. Review projected trade behavior in the summary table and daily table.
10. Compare your selected settings with the optimizer’s best internal combination, but do not treat the optimizer output as a guaranteed best future configuration.
This script is best understood as a structured decision-support and reversal-review tool, not as a self-sufficient trading system.
SETTINGS REFERENCE
Signal Settings
- EMA Length: sets the EMA used as the directional filter.
- Minimum 3-Candle Move (Pips): defines how large the directional three-candle move must be before a reversal candle can qualify.
Pip Settings
- Pip Preset: selects a predefined pip-size interpretation for common instrument types.
- Manual Pip Size: allows direct control when the selected symbol needs a custom pip conversion.
Risk Management
- Stop Loss (Pips): sets the fixed stop-loss distance in pip units.
- Take Profit RR: sets the projected target multiple relative to the stop-loss distance.
Visual Settings
- Show Buy/Sell Labels: shows or hides the signal labels.
- Highlight Signal Bars: adds background color to signal bars.
- Show Entry Line: shows or hides the projected entry line.
- Show TP/SL Hit Labels: controls whether projected outcomes are labeled.
- Show TP Hit Labels: controls TP hit labels specifically.
- Show SL Hit Labels: controls SL hit labels specifically.
Summary Table
- Show Summary Table: enables or disables the main review table.
- Table Position: sets the table location.
- Table Text Size: controls summary-table text size.
Daily PnL Table
- Show Daily PnL Table: enables or disables the daily review table.
- Daily Table Position: sets the daily table location.
- Daily Table Text Size: controls daily-table text size.
INTERNAL LOGIC NOTES
The current code also includes internal settings that are not exposed as user-facing optimization controls. These include:
- signal cooldown,
- shared analysis window,
- maximum stored closed-trade visuals,
- hidden optimizer activation,
- internal optimizer parameter combinations.
These internal elements exist to keep the public interface simpler while still allowing the script to maintain consistent review behavior in the background.
IMPORTANT PRACTICAL NOTE ON PIP SIZE
The script uses pip-based calculations for:
- the minimum three-candle move,
- stop-loss distance,
- take-profit distance,
- optimizer comparison logic.
Because of that, correct pip interpretation is extremely important.
If signals appear too frequent, too rare, too compressed, or visually inconsistent for the instrument being analyzed, the first setting to verify is Pip Preset or Manual Pip Size.
This matters especially for:
- gold symbols,
- 5-digit forex symbols,
- JPY forex pairs,
- indices and CFD-style instruments,
- custom broker symbols with unusual decimal formatting.
LIMITATIONS AND SHORTCOMINGS
This script has important limitations:
- It is a short-term reversal model, not a full market-structure engine.
- It only evaluates one specific reversal pattern based on a three-candle directional push and an opposite close-confirmed candle.
- It does not use support/resistance structure, volume profile, or discretionary context.
- It relies on pip conversion, so poor pip settings can distort signal behavior.
- The internal optimizer compares parameter combinations only inside the defined shared analysis window.
- The optimizer output is a comparative review tool, not a guarantee that the best historical combination will remain best in future market conditions.
- Projected results depend on the script’s own simplified outcome logic.
- If TP and SL are both touched on the same bar, SL is prioritized by design, which makes the logic more conservative but also affects outcome statistics.
- Historical projected trades and review metrics are chart-based review aids, not proof of tradable real-world execution.
- No reversal-entry model can remove all false signals or all regime-dependent behavior.
For those reasons, the script should be used as a structured analysis and review framework, not as a promise of future profitability.
WHO THIS SCRIPT MAY BE USEFUL FOR
This script may be useful for traders who:
- want a rules-based short-term reversal-entry model,
- want EMA-based directional context,
- want a minimum expansion threshold before a reversal is allowed,
- want fixed-risk trade projection on the chart,
- want review tables for projected outcomes,
- want background comparison of multiple EMA and move-threshold combinations.
It may be less suitable for traders who:
- want a broad trend-following system,
- want a discretionary support/resistance engine,
- want a multi-pattern candlestick library,
- want a fully automated strategy with no outside confirmation,
- want outcome metrics interpreted as live performance promises.
DISCLAIMER
This script is provided for educational and informational purposes only.
It does not constitute financial, investment, or trading advice.
Market conditions change, historical behavior does not guarantee future results, and users should perform their own analysis, validation, and risk management before using the script in live decision-making. Indicador
Alpha Signal Engine [MarkitTick]💡 The Alpha Signal Engine is an advanced, multi-dimensional trend-following system designed to provide traders with highly filtered, high-probability market signals. At its core, it dynamically calculates a volatility-adjusted trailing band to determine the primary market direction. However, unlike traditional trend indicators that rely on a single data point, this engine passes every potential trend reversal through a rigorous, six-layer filtering mechanism. By requiring confluence across higher timeframe trends, momentum, volume, volatility regimes, and price action strength, it drastically reduces the noise and false signals inherent in choppy markets. It also features a built-in heads-up dashboard and fully formatted JSON webhook capabilities for automated trading integration.
✨ Originality and Utility
● A Dynamic, Adaptive Baseline
Standard trailing stop or trend indicators, such as the classic Supertrend, typically use a static multiplier against the Average True Range (ATR). The Alpha Signal Engine innovates by introducing a "Dynamic Factor." This factor continuously adapts the band's distance from price by factoring in the current baseline multiplier, the relative volatility (ATR normalized by price), and the immediate price change momentum. This allows the bands to tighten during periods of strong, directional momentum and widen during erratic volatility, providing a more responsive and intelligent trailing mechanism.
● The Six-Pillar Filtering Gateway
The true utility of this indicator lies in its modular filtering engine. Traders often have to clutter their charts with half a dozen indicators to confirm a setup. This script centralizes that logic. Users can selectively enable or disable filters based on their specific asset and trading style, turning the indicator into a customizable algorithmic engine. Whether you need volume confirmation, ADX trend strength, or simple RSI momentum, the script handles the complex boolean logic internally and only outputs a signal when your precise market conditions are met.
🔬 Methodology and Concepts
● Dynamic Factor Calculation
The indicator establishes its baseline trend using an upper and lower band. The distance of these bands from the median price is dictated by a dynamically calculated factor. This factor is the sum of a base value, a volatility component (ATR divided by Close, scaled by a user weight), and a price movement component (percentage change of the close, scaled by a user weight). This raw factor is then smoothed using a Simple Moving Average (SMA) to prevent erratic band shifts.
● Trend Determination
The trend direction flips when the closing price crosses the active dynamic band. If the price closes above the upper band, the trend shifts bullish, and the lower band becomes the active support. Conversely, closing below the lower band shifts the trend bearish, making the upper band the active resistance.
● The Filter Matrix
A signal is only generated when a trend flip aligns with all activated filters:
HTF Alignment: Uses the request context to pull the trend direction from a higher timeframe, ensuring you are not trading against the macro trend.
ADX Trending: Measures the Average Directional Index to ensure the market is in an active trending phase (above a defined threshold) rather than a sideways chop.
Volume Surge: Compares current volume against a Volume SMA. The current bar must exhibit a volume spike greater than the defined multiplier to confirm institutional participation.
RSI Momentum: A simple but effective gatekeeper requiring the Relative Strength Index to be above 50 for longs and below 50 for shorts.
ATR Volatility Regime: Compares the current ATR against a 50-period SMA of the ATR. It ensures the market is operating within a "normal" volatility ratio, preventing entries during extreme, unpredictable volatility spikes or dead, illiquid periods.
Candle Body Strength: Calculates the absolute size of the candle body (Open to Close) and mandates it must be larger than a specific fraction of the ATR, ensuring the signal candle has true directional conviction.
🎨 Visual Guide
● Chart Elements
Up Trend Line: Displayed as a solid, teal-colored line trailing below the price action during a bullish phase. It acts as dynamic support.
Down Trend Line: Displayed as a solid, bright pink/red line trailing above the price action during a bearish phase. It acts as dynamic resistance.
Trend Cloud (Fill): A colored gradient fill exists between the median price and the active trend line. A teal cloud visually represents bullish dominance, while a pink/red cloud represents bearish dominance.
Buy Signals: Indicated by small, teal "B" labels positioned below the signal candle.
Sell Signals: Indicated by small, pink/red "S" labels positioned above the signal candle.
● Filter Dashboard
Located in the top right corner of the chart, this HUD (Heads-Up Display) provides a real-time status check of your system.
The left column lists the available filters (HTF Align, ADX Trend, Vol Surge, RSI Gate, ATR Regime, Body Str).
The right column displays the current status of each filter.
A gray "OFF" indicator means the user has disabled the filter in the settings.
A green "ON" or "Aligned" text indicates the condition is currently met.
A red "Opposed" or unlit indicator means the condition is active but currently failing to meet the required criteria.
The bottom rows clearly state the current overarching trend direction and whether a signal is pending or waiting.
📖 How to Use
• Interpreting the System
To effectively use the Alpha Signal Engine, begin by observing the main trend lines and the color of the cloud. This provides your baseline bias. Do not take trades purely on the band flipping. Instead, rely on the explicit "B" and "S" labels.
• Signal Execution
When a "B" (Buy) or "S" (Sell) label appears, it means the price has successfully flipped the trend AND all user-activated filters in the dashboard are glowing green. This is your entry trigger. The active trend line (the teal line for longs, the pink line for shorts) serves as an ideal, dynamic stop-loss placement.
• Customizing the Engine
The system is designed to be tuned. If you are trading a highly liquid asset like major forex pairs, you may want to enable the ADX and HTF filters to catch long, sustained moves. If you are trading volatile crypto assets, enabling the Volume Surge and Candle Body filters can help you avoid fake-outs and trap wicks. Monitor the on-chart dashboard to see which filters are keeping you out of bad trades and adjust your settings accordingly.
⚙️ Inputs and Settings
• Supertrend Settings
ATR Length: The lookback period for calculating the Average True Range.
Base Factor: The starting multiplier for the dynamic bands.
Volatility & Price Change Weights: Determines how aggressively the bands react to sudden spikes in relative volatility and price momentum.
Factor Smoothing: Applies an SMA to the final dynamic multiplier to keep the bands stable.
• Filter Settings
Enable HTF Alignment: Toggle and define the higher timeframe (e.g., Daily) to align with.
ADX Settings: Toggle the filter, define the lookback length, and set the minimum trend strength threshold (default is 20).
Volume Settings: Toggle the filter, define the Volume MA length, and set the multiplier required to classify as a "surge."
RSI Settings: Toggle the filter and set the RSI lookback length.
ATR Regime Settings: Define the minimum and maximum acceptable ratios of current ATR versus historical ATR.
Candle Body Settings: Define the minimum required size of the candle body as a fraction of the current ATR.
• Webhook Action Names
These text inputs allow you to define specific payload strings (e.g., "long", "closeshort") that the indicator will output via JSON alerts, perfectly formatting the data for third-party automation services like 3Commas or PineConnector.
🔍 Deconstruction of the Underlying Scientific and Academic Framework
The Alpha Signal Engine is grounded in several well-documented tenets of quantitative financial analysis and statistical market theory.
● Volatility-Adjusted Trailing Stops
The foundation of the indicator relies on the Average True Range (ATR), introduced by J. Welles Wilder Jr. The ATR is a measure of the degree of price volatility. By tying the trailing stop (the dynamic band) to the ATR, the system acknowledges the statistical reality of market variance. The innovation here is the dynamic multiplier. By adjusting the distance based on the normalized rate of change (momentum), the script attempts to solve the lagging nature of fixed-multiplier trailing stops, utilizing principles found in adaptive moving averages (like Kaufman's AMA), where sensitivity increases alongside directional conviction.
● Multi-Dimensional Confluence Theory
The filtering engine operates on the academic principle of conditional probability and confluence. In market microstructure, no single indicator holds a permanent statistical edge.
The HTF filter is rooted in Dow Theory, prioritizing the primary trend over secondary reactions.
The ADX filter utilizes Wilder's Directional Movement Index to mathematically separate trending environments from mean-reverting environments, applying a statistical threshold to directional strength.
The Volume Surge filter relies on the Volume Price Trend concepts, positing that significant price movements must be sponsored by outsized volume to validate institutional participation and avoid anomalous low-liquidity spikes.
The ATR Regime filter applies mean-reverting principles to volatility itself (volatility clustering), ensuring that entries are only taken when the variance of the asset is within historically "normal" parameters, avoiding the fat tails of extreme market shocks.
By chaining these disparate mathematical models (trend, momentum, volume, volatility) via Boolean logic, the system mathematically reduces the frequency of trades while theoretically increasing the probability of the remaining sample size.
⚠️ Disclaimer
All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion. Indicador
ORB Pro Suite v6ORB Pro Suite v6 — Multi-Session + HTF ORB Build
ORB Pro Suite v6 is an advanced Opening Range Breakout (ORB) tool designed for traders who want clarity, structure, and adaptability across NY, London, and Asia sessions — without changing the core ORB logic that works.
This update expands the original ORB Pro Suite to support overnight markets and multi-timeframe workflows, while keeping the strategy behavior consistent and familiar.
✅ Multi-Session Presets
Choose from built-in session presets:
NY AM (RTH) — original behavior (unchanged)
London
Asia
Custom
Each preset aligns the ORB window with the selected session and pairs seamlessly with session-appropriate filters.
✅ ORB Build Mode
You now have two ways to build your ORB:
1️⃣ Time Window (Classic ORB)
Uses session start/end times
Identical to previous versions
2️⃣ HTF Candle Count (Advanced)
Build the ORB from 5m / 15m / 30m / 60m candles
Works on any chart timeframe
Ideal for traders who want ORB consistency across TFs
Example:
Build a 15-minute ORB from 1× 15m candle, even while trading on a 5m chart.
✅ Session Profile Defaults
ORB Pro Suite introduces Session Profiles that automatically tune filters for different market conditions — without changing the strategy logic.
Profiles include:
NY (Default)
London (Breakout)
Asia (Slow Session)
Custom
You can toggle Profile Defaults ON or OFF at any time.
🧠 Core ORB Logic (Unchanged)
Original ORB framework:
Opening range high/low
Breakout confirmation
Optional retest logic
Golden Pocket (0.5–0.618) validation
Local + higher-timeframe trend filters
Cooldown protection
Visual risk/reward mapping
If you traded NY with earlier versions, nothing has changed.
⚙️ Recommended Starting Settings
For most users:
ORB Build Mode: Time Window
Session Profile: Auto
Strictness: Balanced
Advanced users:
Enable HTF Candle Count
Select desired ORB TF (5m–60m)
Adjust candle count to match your style
All inputs remain fully customizable.
📊 Designed For
Futures (ES, NQ, YM, RTY)
Forex pairs
Gold & major indices
Intraday price-action traders
Session-based trading workflows
⚠️ Disclaimer
This indicator is for educational and informational purposes only.
It does not constitute financial advice or trade recommendations.
Trading involves risk. Always manage risk appropriately and trade responsibly. Indicador
Sessions + Prev + PDH/PDL + Killzones SuiteDescription
This indicator is designed to provide time-based and price-based market context by combining session ranges with commonly referenced prior levels into a single, unified framework.
The purpose of the script is contextual analysis, not signal generation.
What the script does
The script tracks and plots the following elements directly on the price chart:
• High and Low ranges for multiple trading sessions (Asia, London, New York morning, and New York afternoon)
• High and Low levels from the previous occurrence of each session
• Prior Day High (PDH) and Prior Day Low (PDL)
• Optional session “killzone” boxes that visually mark active session time windows
All calculations are performed using time-based session boundaries and price extrema (high/low) within those windows.
Why these components are combined
Sessions, previous session levels, and prior day levels are frequently analyzed together by discretionary traders because they represent:
• Where liquidity formed earlier in the day or previous day
• Where price previously paused, expanded, or reversed
• Natural reference points for intraday structure and range analysis
Instead of plotting these elements using multiple separate scripts, this indicator integrates them into one consistent framework so that all levels are calculated using the same timezone, session logic, and display rules.
This avoids mismatched session times, duplicate levels, or conflicting calculations that can occur when multiple scripts are used simultaneously.
How the script works (high-level)
• Each session is defined using user-selectable session times and timezone
• During a session, the script tracks the highest and lowest traded price
• When a session ends, its final high and low are stored as the “previous session” levels
• PDH and PDL are calculated using the completed trading day
• Lines and labels are anchored to the bars where levels are formed, rather than extending indefinitely
• Optional display filters allow users to show only the current trading day to reduce chart clutter
No forward-looking logic, prediction, alerts, or trade execution logic is included.
How to use it
This script is intended to be used as a visual reference tool to help traders:
• Identify session boundaries and intraday ranges
• Observe how price reacts near prior session highs and lows
• Assess where price is trading relative to PDH and PDL
• Maintain consistent session timing across different timezones
The script does not provide trade entries, exits, alerts, or performance claims.
Important notes
• This indicator does not generate buy or sell signals
• It does not predict future price movement
• It is not a trading strategy
• All decisions remain the responsibility of the user
Disclaimer
This script is provided for educational and informational purposes only.
It does not constitute financial advice. Trading involves risk, and users should apply appropriate risk management and personal judgment when using any technical tool. Indicador
Kalman Volume Trend [BigBeluga]🔵 OVERVIEW
Kalman Volume Trend is an advanced trend-following system that combines the predictive power of a Kalman Filter with real-time volume delta analysis. Unlike standard moving averages that suffer from significant lag, the Kalman Filter uses a recursive mathematical algorithm to estimate the "true" trend by filtering out market noise.
The indicator not only identifies directional regimes but also visualizes the intensity of buying and selling pressure directly on the trend line, providing a multi-dimensional view of market conviction.
🔵 CONCEPT
Kalman Filter Logic — A state-space model that predicts price movement and then corrects itself based on new data, resulting in a smoother yet more responsive trend line than traditional EMAs.
Adaptive ATR Bands — The trend direction is determined by price breaking through volatility-adjusted bands, reducing whipsaws in sideways markets.
Volume-Weighted Trend Lines — The indicator plots "Volume Bars" extending from the trend line, where the length and color represent the relative strength of the volume delta.
Cumulative Trend Statistics — It tracks the total buy volume, sell volume, and net delta from the exact moment a new trend begins.
🔵 HOW IT WORKS (IN-DEPTH)
1️⃣ The Kalman Filtering Process
The script utilizes two primary parameters: Process Noise (Q) and Measurement Noise (R) .
It calculates a "State Estimate" (the trend) by balancing its previous prediction against the current price.
If the price is "jittery" (high R), the filter smooths the line; if the trend is moving decisively (low Q), it tracks the price more aggressively.
2️⃣ Trend Direction & Volatility Bands
Two bands are projected around the Kalman line based on a multiplier of the Average True Range (ATR) .
A Bullish trend is triggered when price closes above the upper band.
A Bearish trend is triggered when price closes below the lower band.
Once a trend is established, the opposite band acts as the trailing "Trend Line" to provide a clear buffer for price fluctuations.
3️⃣ Volume Delta Visualization
Small vertical candles ("Volume Bars") are plotted along the trend line.
These bars represent the Normalized Volume Delta (Close vs. Open and Volume intensity).
Large bars indicate high-conviction participation, while small bars suggest waning interest or consolidation.
4️⃣ Extreme Volume & Cumulative Dashboard
When volume exceeds 1.5x its recent average, an "X" label appears on the chart to mark an Exhaustion or Ignition point.
A bottom-right dashboard displays a vertical histogram showing the balance of power (BUY vs. SELL vs. DELTA) for the current trend only .
🔵 KEY FEATURES
Recursive Kalman Algorithm: High-accuracy trend tracking with minimal lag.
Integrated Volume Profiling: See volume delta without needing a separate sub-window.
Dynamic Trend Dashboard: Automatically resets at every trend flip to show fresh volume stats.
Volatility-Aware: Uses 200-period ATR to ensure bands adapt to changing market conditions.
Volume Extreme Alerts: Identifies high-volume spikes that often precede trend reversals.
🔵 DASHBOARD METRICS
BUY — Total volume accumulated on bullish candles since the trend started.
SELL — Total volume accumulated on bearish candles since the trend started.
DELTA — The net difference between buying and selling pressure.
TOTAL VOLUME — The total "fuel" spent during the current directional regime.
🔵 HOW TO USE
Riding the Trend: Stay in the trade as long as the Kalman line color remains consistent.
Spotting Weakness: If the Kalman line is Bullish (Blue) but the Volume Bars are consistently negative or shrinking, the trend may be losing steam.
High-Volume Breakouts: Look for the "X" labels at the start of a trend shift; this confirms institutional participation in the new direction.
Dashboard Confirmation: Use the vertical histogram to confirm if the buyers or sellers are truly in control during a pullback to the trend line.
🔵 CONCLUSION
Kalman Volume Trend offers a sophisticated approach to trend analysis by merging high-level signal processing with raw volume data. By focusing on "clean" price data and weighting it with volume delta, it helps traders filter out market noise and focus on high-conviction movements. Indicador
StealthTrail SuperTrend ML Pro [WillyAlgoTrader]🤖 StealthTrail SuperTrend ML Pro is an overlay indicator that builds on the adaptive SuperTrend core from StealthTrail and adds three layers of intelligence: an instrument profiling engine that classifies the market into Trending, Ranging, or Volatile regimes and auto-tunes all SuperTrend parameters accordingly; a 13-feature machine learning scoring system that evaluates every candidate signal on momentum, trend, volatility, structure, volume, HTF alignment, divergence, session quality, and regime context — producing a 0–100 confidence score; and a self-learning mechanism that tracks signal outcomes over time and dynamically adjusts the confidence gate to optimize signal quality. The result is a SuperTrend that configures itself, scores its own signals, and learns from its results.
🧩 WHY THESE COMPONENTS WORK TOGETHER
A standard SuperTrend has fixed parameters that work well in one market regime and fail in another. Manually tuning ATR length, multiplier, and filters for each instrument and timeframe is time-consuming and becomes outdated when conditions change.
This indicator solves the problem through a three-layer intelligence stack:
Layer 1 — Regime classification → Auto-tuning: The instrument profiler continuously measures efficiency ratio (trend strength), autocorrelation (serial dependence), volatility clustering, and normalized volatility — classifying the market into TRENDING, RANGING, or VOLATILE. Each regime produces different optimal SuperTrend parameters. The auto-tuner interpolates ATR length, multiplier, cushion, cooldown, and RSI threshold using regime-weighted blending — so the SuperTrend self-configures for current conditions.
Layer 2 — ML signal scoring: Even with auto-tuned parameters, not every SuperTrend flip is a good trade. The 13-feature ML engine evaluates each flip against momentum, volume, trend efficiency, volatility shock, band distance, MACD, price structure, regime confidence, MTF alignment, ADX strength, RSI divergence, volume profile zone, and session quality. Each feature is normalized to 0–100, weighted, passed through a sigmoid function, and combined into a single confidence score. Signals below the confidence gate are rejected — they pass the classic SuperTrend logic but fail the multi-dimensional quality check.
Layer 3 — Self-learning gate: The ML confidence gate itself adapts over time. The system tracks each signal's outcome (win/loss evaluated after N bars using the ATR at entry for fair comparison). When the win rate exceeds 70%, the gate lowers (allowing more signals). When it drops below 50%, the gate raises (becoming stricter). A decay factor prevents old signals from dominating. This creates a feedback loop: the indicator learns which confidence level produces profitable signals on this specific instrument and timeframe.
Without auto-tuning, the SuperTrend uses static parameters. Without ML scoring, good and bad flips are treated equally. Without self-learning, the confidence gate is a fixed guess. Each layer eliminates a specific category of bad signals that the previous layer can't catch.
🔍 WHAT MAKES IT ORIGINAL
1️⃣ Instrument profiling and regime classification.
The profiler computes four metrics over a configurable lookback (default 100 bars):
— 📐 Efficiency Ratio : ER = |close − close | / sum(|close − close |, N). Measures net directional movement vs total path. ER → 1.0 = pure trend, ER → 0.0 = pure chop.
— 🔄 Autocorrelation : correlation between return and return over a sliding window. High positive autocorrelation = trending persistence. Near-zero = random. Negative = mean-reverting.
— 📏 Volatility Clustering : ratio of short-term ATR (20 bars) to long-term ATR (lookback). Values > 1.0 indicate a volatility spike (breakout or crash). Values < 1.0 indicate compression.
— 📈 Normalized Volatility : ATR / close × 100. Measures absolute volatility as percentage of price — allows cross-instrument comparison.
These are smoothed with EMA and combined into three regime scores:
— trendScore = ER × regimeSensitivity
— rangeScore = (1 − ER) × (1 − |autocorrelation|) × regimeSensitivity
— volatScore = clamp(volClustering − 1, 0, 2) × regimeSensitivity
The highest score determines the regime: TRENDING, RANGING, or VOLATILE. Regime confidence = highest_score / total_scores × 100%.
2️⃣ Regime-weighted auto-tuning (6 parameters).
Each SuperTrend parameter is computed as a weighted blend of regime-optimal values:
effectiveParam = wT × trendOptimal + wR × rangeOptimal + wV × volatileOptimal
Where wT, wR, wV are the normalized regime weights. The parameters and their regime-optimal ranges:
— ATR Length: Trending=10, Ranging=16, Volatile=21 (further scaled by normalized volatility)
— Base Multiplier: Trending=2.0, Ranging=3.2, Volatile=3.8 (scaled by normalized vol)
— Flip Cushion: Trending=0.05, Ranging=0.25, Volatile=0.15
— Signal Cooldown: Trending=2, Ranging=5, Volatile=3
— RSI Threshold: Trending=40, Ranging=52, Volatile=45
— RSI Length: derived as ATR Length × 0.9
— Adaptive Smoothing: ATR Length × 4.0
This means in a trending regime, the SuperTrend uses shorter ATR, lower multiplier (tighter bands), minimal cushion, and permissive RSI — catching trends early. In a ranging regime: longer ATR, wider multiplier, large cushion, strict RSI — avoiding chop. In volatile conditions: intermediate settings with wider bands to accommodate spikes.
3️⃣ 13-feature ML scoring engine.
Each feature is normalized to 0–100, multiplied by its weight, summed, normalized by total weight, and passed through a sigmoid function to produce the final 0–100 confidence score.
Features and their weights:
— 💪 F1: Momentum (RSI alignment with trend) — w=0.15
— 📈 F2: Volume Surge (volume / SMA ratio) — w=0.08
— 📐 F3: Trend Efficiency (ER × 100) — w=0.15
— ⚡ F4: Volatility Shock (inverted vol clustering) — w=−0.08 (negative = penalizes vol spikes)
— 📏 F5: Band Distance (sigmoid of close-to-band ATR distance) — w=0.10
— 📊 F6: MACD (normalized histogram vs ATR) — w=0.08
— 🏗️ F7: Price Structure (HH/HL for bull, LL/LH for bear over 10 bars) — w=0.08
— 🧠 F8: Regime Confidence (% confidence in current regime) — w=0.04
— 🌐 F9: MTF Confluence (aligned with HTF = 100, not = 0) — w=0.12
— 💪 F10: ADX Strength (ADX × 2.5, capped at 100) — w=0.10
— 🔀 F11: RSI Divergence (aligned div = 100, counter div = 10) — w=0.08
— 📊 F12: Volume Profile Zone (proximity to highest-volume bar) — w=0.06
— 🕐 F13: Session Quality (hour-based scoring for optimal trading sessions) — w=0.04
The raw weighted sum is normalized, then passed through: mlScore = 100 / (1 + exp(−0.08 × (normalizedScore − 50))). This sigmoid compresses extreme values and centers the output around 50.
All 13 weights plus the bias term are user-configurable — you can adjust how much each factor contributes. Negative weights penalize a factor (e.g., W4 = −0.08 means high volatility shock reduces the score).
4️⃣ Self-learning confidence gate.
The system maintains 5 pending signal slots. Each stores the entry price, direction, bar index, and ATR at entry. After the evaluation horizon (default 15 bars), the outcome is assessed:
win = (close − entryPrice) × direction > 0.5 × entryATR (profitable by at least half an ATR, using the ATR at entry time — not current ATR — for fair comparison)
A decay factor (0.98) is applied to historical totals before adding new results, preventing stale data from dominating. When total tracked signals ≥ 8:
— Win rate > 70% → gate decreases by 1.5 (more permissive)
— Win rate < 50% → gate increases by 1.5 (more restrictive)
— Gate is clamped to
The dashboard shows the current gate value, whether it's auto-adjusted, and the tracked win rate with sample size.
5️⃣ Multi-timeframe confluence with auto-HTF selection.
The indicator automatically selects the appropriate higher timeframe based on your current TF: 1M→15M, 5M→30M, 15M→1H, 1H→4H, 4H→D, D→W. Or you can manually specify the HTF.
Three strictness levels:
— Loose: MTF misalignment adds negative ML score but doesn't block
— Moderate: MTF misalignment reduces signal quality
— Strict: MTF misalignment hard-blocks the signal entirely
HTF trend is determined by EMA(20) vs EMA(50) on the higher timeframe.
6️⃣ Adaptive SuperTrend core (from StealthTrail).
The band calculation uses pre-computed interpolated ATR values (ATR bank at 9 fixed periods with lerp interpolation) for smooth adaptation to any auto-tuned ATR length. Same mechanics as StealthTrail: adaptive multiplier (ATR / SMA ratio), band ratcheting, flip cushion, and cooldown — but now all parameters are regime-driven.
7️⃣ Three-mode trailing TP/SL system.
Three SL modes: ATR (entry ± mult × ATR), Band (SuperTrend band as SL), Fixed % (percentage from entry).
Three trailing modes:
— ATR : trail at fixed ATR distance from price — simple, consistent
— Band : use the SuperTrend band itself as trailing stop — structurally anchored
— ATR → Band (hybrid) : start with tight ATR trail, switch to band when it catches up — best of both
The trail only ratchets in the profit direction (never moves away from price). TP1/TP2/TP3 hit markers (✓) appear on the chart. On TP3 or SL hit, the trade closes and lines are removed.
8️⃣ RSI divergence detection.
Scans for bullish divergence (price lower low, RSI higher low, RSI < 40) and bearish divergence (price higher high, RSI lower high, RSI > 60). Divergences aligned with the signal direction boost the ML score (F11 = 100). Counter-divergences penalize it (F11 = 10). Optional visualization as dots on the chart.
9️⃣ Volume profile zone proximity.
Tracks the highest-volume bar in the last 50 bars. Proximity to this bar's price level is scored: within 1.5× ATR = 100 (near institutional activity), further = proportionally lower. Signals near high-volume zones tend to have more follow-through.
🔟 Session quality scoring.
For intraday timeframes, each hour receives a quality score based on typical institutional activity: London/NY overlap (13–17 UTC) = 100, European session (8–12) = 80, US afternoon (18–20) = 70, Asian session (0–7) = 30. An optional kill zone filter suppresses all signals during specified hours.
⚙️ HOW IT WORKS — CALCULATION FLOW
Step 1 — Instrument profiling: ER, autocorrelation, vol clustering, normalized vol → EMA-smoothed → regime classification (TRENDING / RANGING / VOLATILE) with confidence %.
Step 2 — Auto-tuning: Regime weights blend optimal parameters for ATR length, multiplier, cushion, cooldown, RSI threshold/length.
Step 3 — SuperTrend calculation: Interpolated ATR from pre-computed bank → adaptive multiplier (ATR/SMA ratio) → upper/lower bands → ratcheting → flip detection with cushion + cooldown.
Step 4 — Classic filters: Momentum (RSI), volume, session, MTF hard-block (if strict).
Step 5 — ML feature extraction: 13 features normalized to 0–100 from current market state.
Step 6 — ML scoring: Weighted sum → weight normalization → sigmoid → 0–100 confidence score.
Step 7 — Signal decision: Classic filters pass AND (ML disabled OR mlScore ≥ gate) → confirmed signal.
Step 8 — Self-learning: Signal stored in pending slot → evaluated after horizon bars → win rate updated → gate adjusted.
Step 9 — TP/SL placement: SL from band/ATR/fixed% → TPs as ATR multiples → trailing stop ratchets per mode.
📖 HOW TO USE
🎯 Quick start:
1. Add the indicator — Auto-Tune is ON by default, parameters self-configure
2. The SuperTrend band and Long/Short labels appear
3. Dashboard shows: trend, strength, regime, ML score, gate, win rate, TP/SL status
4. Enable ML Filter for quality scoring (off by default — enable after reviewing signal quality)
5. Self-Learning auto-adjusts the gate over time
👁️ Reading the chart:
— 🟢 Green band + "Long" label = confirmed bullish signal
— 🔴 Red band + "Short" label = confirmed bearish signal
— ⚫ Gray dot = classic filter blocked the flip
— 🟡 Yellow triangle = ML rejected the signal (score below gate)
— 🟢 Green dot below bar = bullish RSI divergence
— 🔴 Red dot above bar = bearish RSI divergence
— 📈 Regime badge = current market classification (📈 TRENDING / 📊 RANGING / ⚡ VOLATILE)
— 🟢 Green dashed = TP1/TP2/TP3, 🔴 Red dashed = SL, 🔵 Blue dotted = entry
— "TP1 ✓" / "SL ✗" labels = outcome markers
📊 Dashboard sections:
— Main: trend, signal, strength, ADX, HTF alignment
— 🤖 ML Engine: ML score, confidence gate (fixed or auto), win rate with sample size
— 🎯 Position: status (LONG/SHORT/FLAT), entry, SL (with trail mode icon), TP levels (✓ for hit), R:R ratio
— 🧠 Regime: classification + confidence %
🔧 Tuning guide:
— Start simple: Auto-Tune ON, ML OFF — let the regime engine handle parameters
— Add ML: Enable ML Filter after 50+ signals to see which score level produces winners
— Enable Self-Learning: after 100+ bars of ML being active — let the gate auto-calibrate
— Adjust weights: if you know your market (e.g., volume is unreliable on forex → set W2 to 0)
— Strict MTF: for higher-timeframe alignment — reduces signals, increases quality
— Trail Mode: Band for trend-following, ATR for scalping, ATR→Band for hybrid
⚙️ KEY SETTINGS REFERENCE
⚙️ Main:
— Auto-Tune (default On): regime-driven parameter self-configuration
— ATR Length / Base Multiplier : manual overrides when auto-tune is off
🧠 Adaptive Engine:
— Profiling Lookback (default 100): bars for regime classification
— Regime Sensitivity (default 1.0): scaling factor for regime scores
📐 Multi-Timeframe:
— MTF Confluence (default On): Auto/Manual HTF selection
— Strictness (default Moderate): Loose / Moderate / Strict
🤖 ML Signal Filter:
— Enable ML (default Off): activate 13-feature scoring
— Confidence Gate (default 21): minimum ML score to pass
— Self-Learning (default On): auto-adjust gate from tracked outcomes
— Evaluation Horizon (default 15 bars): outcome assessment window
— 13 individual weights + bias : fully configurable feature importance
🔍 Filters:
— Flip Cushion, Cooldown, RSI Momentum, Volume, Session Kill Zone
🎯 TP/SL:
— SL Mode (default Band): ATR / Band / Fixed %
— Trail Mode (default Band): ATR / Band / ATR → Band
— TP Levels (1–3): ATR multipliers (default 1.5 / 2.5 / 4.0)
🔔 Alerts
— 🟢 LONG / 🔴 SHORT — ticker, price, TF, band, ML score, ADX, HTF, SL, TP1, regime
— 🎯 TP1 / TP2 / TP3 HIT — trade progress
— 🛑 SL HIT — stopped out
All support plain text and JSON webhook format. Bar-close confirmed.
⚠️ IMPORTANT NOTES
— 🚫 No repainting. All signals require barstate.isconfirmed. The confirmed trend direction is stored separately from the real-time calculation — signals only fire on closed bars. HTF data uses lookahead_off. A warmup period (max of profiling lookback and 55 bars) prevents signals during insufficient data.
— 🤖 The ML scoring is not machine learning in the neural network sense . It's a weighted linear model with sigmoid activation — a logistic regression analog. The 13 features are hand-crafted from market microstructure, and the weights are configurable by the user. There is no gradient descent, backpropagation, or training phase. The "self-learning" adjusts only the confidence gate threshold, not the feature weights.
— 📐 Auto-tuning produces different parameters on every bar as the regime shifts. The ATR length and multiplier change continuously — this is by design. If you prefer fixed parameters, disable Auto-Tune.
— ⚖️ The self-learning gate requires at least 8 tracked signals before it begins adjusting. With the decay factor (0.98), the effective sample is weighted toward recent signals. The gate moves slowly (±1.5 per adjustment) and is clamped to .
— 📊 Win rate evaluation uses ATR at entry time , not current ATR. A signal is a "win" if price moves > 0.5× entry ATR in the signal direction within the evaluation horizon. This prevents volatile periods from inflating win counts.
— 🔄 The pre-computed ATR bank (9 fixed periods with lerp interpolation) is a performance optimization that allows smooth ATR adaptation to any auto-tuned length without calling ta.atr() dynamically — which Pine Script doesn't support with variable-length arguments.
— 📏 Volume Profile Zone tracks the single highest-volume bar in the last 50 bars, not a full volume profile. It decays after 50 bars with no new volume peak.
— 🕐 Session scoring uses UTC-based hours. On daily or higher timeframes, session quality defaults to 50 (neutral) as intraday session distinctions don't apply.
— 🛠️ This is a signal scoring and analysis tool , not an automated trading bot. It classifies regimes, scores signals, tracks outcomes, and manages TP/SL — trade decisions remain yours.
— 🌐 Works on all markets and timeframes. Volume features auto-adapt to instruments without volume data. Indicador
Trend Resonance Oscillator [JOAT]Trend Resonance Oscillator
Introduction
The Trend Resonance Oscillator is an open-source non-overlay indicator that measures multi-timeframe trend alignment and produces a composite resonance score. It fetches trend data from up to five configurable timeframes, calculates whether they agree on direction, and outputs an oscillator that reflects the degree of alignment. When most or all timeframes point the same way, the oscillator reaches extreme values and the indicator declares a state of "resonance" — a condition where directional conviction is high across the time spectrum. It also includes quantum-inspired coherence scoring, harmonic pattern detection, and momentum alignment visualization.
Built with Pine Script v6, the indicator uses custom types for trend state, resonance state, timeframe data, quantum state, and harmonic patterns.
Why This Indicator Exists
A trade taken in the direction of the 5-minute trend may fail if the 1-hour and daily trends disagree. Multi-timeframe alignment is one of the most reliable filters for trade quality, but checking multiple timeframes manually is tedious and subjective. This indicator automates that process by:
Simultaneous MTF analysis: Fetches close, EMA, and rate-of-change data from five configurable timeframes in a single indicator
Alignment scoring: Quantifies how many timeframes agree on direction and how strong each trend is, producing a single composite score
Resonance detection: Identifies periods when alignment exceeds a configurable threshold, signaling high-conviction directional conditions
Confluence signals: Generates labeled signals when a minimum number of timeframes align, providing clear entry confirmation
Coherence and entanglement metrics: Measures the consistency and correlation between timeframe trends, adding depth beyond simple directional agreement
Core Components Explained
1. Multi-Timeframe Trend Detection
For each of the five timeframes (default: 5m, 15m, 1H, 4H, Daily), the indicator fetches close price, EMA, and rate-of-change using `request.security()` with proper lookahead settings to avoid repainting:
float _tf1Close = request.security(syminfo.tickerid, tf1, close, barmerge.gaps_off, barmerge.lookahead_off)
float _tf1EMA = request.security(syminfo.tickerid, tf1, _globalEMA, barmerge.gaps_off, barmerge.lookahead_off)
Each timeframe's trend is classified as bullish, bearish, or flat based on the percentage difference between close and EMA relative to a configurable threshold (default 0.5%). The trend strength is calculated as the magnitude of that percentage difference, capped at 100.
2. Alignment Score Calculation
The alignment score counts how many timeframes are bullish versus bearish, then produces a normalized score from -100 (all bearish) to +100 (all bullish):
+100: All active timeframes are bullish — maximum bullish alignment
+60: Majority bullish with some neutral — strong bullish bias
0: Equal bullish and bearish — no directional consensus
-60: Majority bearish — strong bearish bias
-100: All bearish — maximum bearish alignment
The alignment score is weighted by the average trend strength across all active timeframes, so a +80 alignment with strong individual trends produces a higher oscillator value than +80 alignment with weak trends.
3. Resonance Detection
Resonance occurs when the ratio of aligned timeframes to total active timeframes exceeds the resonance threshold (default 0.7) and the aligned count meets the minimum confluence requirement (default 4 timeframes). During resonance, the background is tinted to indicate the directional bias, and a duration counter tracks how long the resonance state has persisted.
Sustained resonance (high duration) suggests a strong, established trend. New resonance (low duration) may signal the beginning of a directional move. The dashboard displays the resonance score, aligned count, and duration for quick assessment.
The Trend Resonance Oscillator panel showing the main oscillator line with gradient coloring, MTF trend bars at the bottom showing individual timeframe directions, resonance background shading during a strong bullish alignment, and confluence/resonance signal labels
4. Quantum Coherence and Entanglement
The indicator calculates two additional metrics inspired by quantum physics concepts (used as analytical metaphors, not literal physics):
Coherence: The ratio of aligned timeframes to total timeframes. A coherence of 1.0 means perfect agreement. When coherence exceeds the threshold (default 0.8), the indicator enters a "coherent" state, which is visualized as a subtle wave pattern on the oscillator.
Entanglement: Measures the pairwise correlation between all timeframe trends. For each pair of timeframes, if they agree on direction, the entanglement score increases; if they disagree, it decreases. High entanglement means timeframes are moving in lockstep.
for i = 0 to 3
for j = i + 1 to 4
if trend_i != 0 and trend_j != 0
correlation = trend_i == trend_j ? 1.0 : -1.0
entanglement += correlation
pairs += 1
When the quantum superposition score (combination of coherence and entanglement) exceeds a threshold, a "quantum collapse" signal fires, indicating that all timeframes have converged to a single directional state.
5. Harmonic Pattern Detection
The harmonic module detects cyclical patterns in the resonance data. When resonance is sustained for more than 10 bars, the pattern is classified as a sine wave (smooth, established trend). When resonance is new or intermittent, it is classified as a square wave (choppy, emerging trend). The harmonic wave is plotted as a subtle overlay on the oscillator.
6. Confluence and Signal System
The indicator generates three tiers of signals, with higher tiers taking priority:
CONF (Confluence): Minimum timeframes aligned with alignment score >= 70
RES (Resonance): Strong resonance with score >= 80
QTM (Quantum): Quantum collapse — all metrics converge to a single state
Each signal fires only on its first bar (not continuously), preventing chart clutter. Signals are color-coded with gradient intensity based on the underlying strength.
Visual Elements
Main Oscillator: Smoothed alignment score plotted as a line with gradient coloring from bearish to bullish
Reference Levels: Lines at 0 (neutral), +/-50 (moderate), +/-80 (strong)
MTF Trend Bars: Five colored column bars at the bottom of the panel, each representing one timeframe's trend direction and strength
Resonance Background: Tinted background during resonance states
Quantum Superposition Line: Step-line showing the quantum composite score
Coherence Wave: Subtle area plot showing coherence oscillation
Harmonic Pattern: Sine/square wave overlay during active resonance
Momentum Alignment: Area histogram showing aggregate momentum across timeframes
Convergence/Divergence: Histogram showing agreement between momentum and oscillator
Signal Labels: CONF, RES, and QTM labels at signal points
Entanglement Lines: Visual connections when timeframe entanglement is high
Dashboard: Comprehensive table showing each timeframe's trend, strength, and the aggregate resonance metrics
Input Parameters
Multi-Timeframe Settings:
Toggle and configure each of 5 timeframes (default: 5m, 15m, 1H, 4H, Daily)
Trend Detection:
Trend EMA Length (default 20), Momentum Length (default 14), Trend Threshold (default 0.5%)
Resonance Settings:
Resonance Lookback (default 20), Resonance Threshold (default 0.7)
Show Resonance Zones toggle
Alignment Scoring:
Min TFs for Confluence (default 4)
Show Alignment Score and Confluence Signals
Advanced Resonance:
Quantum Resonance, Coherence Waves, Entanglement Lines, Harmonic Patterns toggles
Coherence Threshold (default 0.8), Harmonic Period (default 8)
Visual Settings:
Show Oscillator, MTF Bars, Dashboard, Glow Effects, Waveform
Color Scheme: Quantum, Classic, Professional, Neon
How to Use This Indicator
Step 1: Check the MTF trend bars at the bottom of the panel. If all five bars are the same color (all bullish or all bearish), you have strong multi-timeframe alignment.
Step 2: Read the oscillator value. Values above +50 indicate moderate bullish alignment; above +80 indicates strong alignment. The inverse applies for bearish readings.
Step 3: Watch for resonance background shading. When the background turns bullish or bearish, the indicator has detected sustained multi-timeframe agreement — this is the highest-conviction environment for directional trades.
Step 4: Use CONF, RES, and QTM signals as entry confirmations. A CONF signal in the direction of the oscillator provides moderate confirmation. A RES or QTM signal provides strong confirmation.
Step 5: Monitor the momentum alignment area. When momentum and the oscillator agree, the move has both directional alignment and momentum behind it. When they diverge, the move may be losing steam.
Dashboard showing all five timeframes with their individual trend states, the aggregate resonance score, coherence level, entanglement reading, and harmonic pattern status
Indicator Limitations
Multi-timeframe data requires sufficient history on all selected timeframes. On newly listed instruments, higher timeframe data may be limited.
The indicator uses `request.security()` with `barmerge.lookahead_off` to prevent repainting, but the inherent delay of higher timeframe data means signals reflect confirmed (not real-time) higher timeframe states.
Alignment does not guarantee profitable trades. All timeframes can align in one direction and then reverse simultaneously.
The quantum and harmonic features are analytical metaphors that provide useful metrics, not literal physics simulations.
On very low timeframes (1m or less), higher timeframe data updates infrequently, which can make the oscillator appear static for extended periods.
The indicator makes multiple `request.security()` calls, which counts against TradingView's security call limit.
Originality Statement
This indicator is original in its comprehensive multi-timeframe resonance framework. While MTF trend indicators exist, this indicator is justified because:
It produces a quantified resonance score that measures not just direction but the degree and duration of multi-timeframe agreement
The coherence and entanglement metrics add pairwise correlation analysis between timeframes, going beyond simple directional counting
The three-tier signal system (CONF/RES/QTM) provides graduated confidence levels based on the strength of alignment
Harmonic pattern detection on the resonance data identifies whether alignment is sustained (sine) or emerging (square)
The momentum alignment overlay shows whether aggregate momentum across timeframes supports the directional reading
The weighted oscillator combines alignment direction with individual trend strength for a more nuanced composite score
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss. Multi-timeframe alignment is a powerful filter but does not guarantee profitable trades. Always use proper risk management. The author is not responsible for any losses incurred from using this indicator.
-Made with passion by officialjackofalltrades
Indicador
Gold Priceaction V2.001. Introduction
Welcome to Gold Priceaction V2.00, an incredibly powerful, all-in-one custom indicator built on Pine Script v5 specifically for TradingView. Unlike traditional lagging indicators that rely on past moving averages, this tool reads the raw footprint of the market. It is engineered to automatically map out institutional price action, dynamic support and resistance, exact trendlines, and high-probability entry zones in real-time.
Whether you are a scalper, day trader, or swing trader, Gold Priceaction V2.00 gives you an objective, crystal-clear view of market direction—removing emotion and guesswork from your trading routine.
2. Why it Works Exceptionally Well for Gold (XAUUSD)
The Gold (XAUUSD) market is notoriously volatile and highly manipulated by large institutional players (banks and hedge funds). Gold charts frequently experience "liquidity grabs"—sudden spikes that hunt retail traders' stop losses before reversing into the true trend direction.
Gold Priceaction V2.00 is tailored exactly for this environment. Instead of blindly following a breakout, this indicator mathematically calculates true swing points to identify areas where the market is most likely to reverse (Support/Resistance) or continue (Target Breakouts). By highlighting premium and discount zones alongside equal highs/lows (liquidity pools), it allows you to trade with the "Smart Money" rather than becoming their exit liquidity.
3. Core Features & Functions (What’s Inside & How It Works)
📈 Dynamic Trendlines: Forget drawing subjective lines manually. The indicator uses a complex algorithmic loop to find the sharpest, most accurate Ascending (Bullish) and Descending (Bearish) trendlines in real-time. It projects these lines forward, giving you dynamic, diagonal support and resistance levels.
🛑 Real-Time Support & Resistance: The script constantly analyzes market data to find minor and major pivot points.
• Strong Highs/Lows: Act as massive, rigid Support and Resistance boundaries.
• Weak Highs/Lows: Act as magnets. The indicator anticipates that these weak points will be broken, turning them into your primary Take Profit (Target) levels.
🎯 Clean Future Target Projection: Most indicators clutter your screen with dozens of old, useless lines. Gold Priceaction V2.00 features an advanced auto-cleanup system. It automatically deletes past targets that have already been hit and only projects a single, bold horizontal line deep into the future. This shows you exactly where the price is magnetically drawn to next.
📦 Institutional Zones (Order Blocks & FVGs): The indicator automatically highlights the hidden footprint of big banks:
• Order Blocks (Supply/Demand Zones): Identifies the last bearish candle before a strong bullish move (and vice versa). These colored boxes act as high-probability entry zones for reversals or continuations.
• Fair Value Gaps (FVG): Spots sudden imbalances in price where the market moved too fast, leaving a "gap." Price almost always returns to rebalance these zones.
🕰️ Multi-Timeframe Context (PDH/PDL): Context is everything. You can enable higher timeframe levels directly on your lower timeframe chart (e.g., 5-minute chart).
• PDH / PDL: Previous Daily High and Low.
• PWH / PWL: Previous Weekly High and Low.
• PMH / PML: Previous Monthly High and Low.
These act as massive macro support/resistance areas where major reversals frequently happen.
4. How to Use It in Live Trading (The 4-Step Playbook)
Step 1: Determine the Trend
Look at the real-time Dashboard on your screen. If the "Market Trend" says BULLISH, you only look for BUY setups. If it says BEARISH, you only look for SELL setups.
Step 2: Wait for Price to Reach an Entry Zone
Do not buy at the top! Let the price retrace (pullback) down into a Discount Zone, a Bullish Order Block, or touch the Ascending Trendline.
Step 3: Look for Confirmation
Wait for a minor trend shift on a lower timeframe. If you are in a Bullish zone, wait for the indicator to print a "Support Hold" or a lower-timeframe "Resistance Break" to prove buyers are stepping in.
Step 4: Execute & Manage Risk
• Entry: Enter the market when the setup is confirmed.
• Stop Loss (SL): Look at the Dashboard. It dynamically calculates the safest, tightest Stop Loss based on the "Immediate Swing Level" to minimize your risk.
• Take Profit (TP): Ride the trade directly to the "🎯 Future Target" line projected on your chart.
5. Recommended Timeframes
• Scalping (Quick Trades): 1-Minute (1m) or 3-Minute (3m) charts.
• Intraday (Day Trading): 5-Minute (5m) and 15-Minute (15m) charts. (Highly Recommended for Gold).
• Swing Trading (Holding for days): 1-Hour (1H) or 4-Hour (4H) charts.
6. The Interactive Dashboard & Customization
The indicator includes a completely clean, user-friendly settings menu. We have hidden all the messy background code values so your chart title remains pristine.
Dashboard Customization:
• Positioning: Using the settings menu (gear icon), you can move the dashboard anywhere on your screen (Top Right, Bottom Center, Right Center, Left Center, etc.) so it never blocks your price action.
• Target Probability Score: A built-in logic metric reading from 0% to 100%. It calculates the trend bias, verifies if the price is safely holding above support, and checks RSI confluence (Overbought/Oversold levels) to give you a live win-rate probability color-coded in Red, Yellow, or Green.
• Styling: You can fully customize the colors of your candles, Order Blocks, and zones to perfectly match TradingView's Light or Dark modes.
❓ Frequently Asked Questions (FAQ)
Q1: Does this indicator repaint?
A: No. The historical Support/Resistance lines and Order Blocks are drawn based on confirmed closed candles. Once a swing point is confirmed mathematically, it does not repaint or shift backward.
Q2: Is this only for Gold?
A: While it is highly optimized for the volatility and structure of XAUUSD, the pure price action mathematics behind it work excellently on Forex pairs (EURUSD, GBPUSD), Crypto (BTC, ETH), and Indices (US30, NAS100).
Q3: Why are my old Target lines disappearing?
A: That is by design! To keep your chart clean and easy to read, the indicator deletes old, irrelevant "past" targets and only shows you the "Future Target" that matters right now.
Q4: How do I get rid of all the text on my indicator title bar?
A: You don't have to! In Gold Priceaction V2.00, all input variables have been explicitly hidden (display.none). The indicator name on your chart will stay clean and professional without long strings of text.
Q5: What does "Premium" and "Discount" mean?
A: Think of it like shopping. The indicator draws a grid between the highest and lowest points of the current trend.
• Premium Level (Red): The price is too high/expensive. (Best place to SELL).
• Discount Level (Green): The price is cheap/on sale. (Best place to BUY).
Indicador
Indicador
Super AlligatorSuper Alligator 🐊 — A Bill Williams Tribute
Built on the shoulders of a legend.
The Origin
Bill Williams introduced the Alligator indicator in his 1995 book Trading Chaos as a way to identify trending markets and filter out the noise of consolidation. Three smoothed moving averages — offset forward in time — behave like the jaw, teeth, and lips of an alligator.
When the lines are intertwined, the alligator sleeps. The market is ranging, there is no edge, and most traders are losing money fighting the noise. When the lines fan apart, the alligator is awake and feeding — a trend is in motion and momentum is real. Williams argued that up to 70% of price action is consolidation. The Alligator's job is to keep you out of it.
In memory of Bill Williams (1932–2019) — trader, psychologist, and one of the most original thinkers in technical analysis.
What Super Alligator Adds
The classic Alligator tells you when a trend exists. Super Alligator adds two layers on top:
1. Gap-based momentum confirmation
The distance between the closing candle and the green (Lips) line acts as a confirmation gate. The alligator opening its mouth is the setup. Price pulling away from the green line is the confirmation. You control exactly how much distance is required — meaning you control the sensitivity.
2. Intraday trend filters
VWAP and a configurable SMA act as directional filters. Signals only fire when price is on the correct side of these levels, reducing counter-trend noise significantly.
Signal Logic
A BUY signal fires when all of the following are true:
Lips (green) is above Jaw (blue) — bullish fan
Close is above the green line by at least your gap threshold
Close is above VWAP (if enabled)
Close is above SMA (if enabled)
A SELL signal fires when the inverse is true:
Jaw (blue) is above Lips (green) — bearish fan
Close is below the green line by at least your gap threshold
Close is below VWAP (if enabled)
Close is below SMA (if enabled)
Signals fire once — on the first bar all conditions align. They do not repaint.
The Gap Setting — Your Sensitivity Control
The Min Gap (%) is the most important input. It scales automatically to whatever instrument you're trading.
Futures:
NQ (~25,000) — 0.10% = ~25 pts / 0.20% = ~50 pts / 0.30% = ~75 pts
ES (~5,500) — 0.10% = ~5.5 pts / 0.20% = ~11 pts / 0.30% = ~16.5 pts
Crypto:
Bitcoin (~85,000) — 0.10% = ~$85 / 0.20% = ~$170 / 0.30% = ~$255
Forex:
EUR/USD (~1.08) — 0.10% = ~10 pips / 0.20% = ~21 pips
Equities/ETFs:
SPY (~550) — 0.10% = ~$0.55 / 0.20% = ~$1.10
Start at 0.10% and adjust. Too many signals → increase. No signals → decrease.
NOTE: The above are just examples. Not limited to just those examples.
Filter Guide
VWAP Filter
Recommended ON for all intraday timeframes (1m through 1H). VWAP resets daily and represents the market's intraday fair value. Turn OFF on daily/weekly charts.
SMA Filter
OFF by default. Most useful on higher timeframes (4H, Daily) as a macro trend filter. On lower intraday timeframes a long-period SMA sits too far from price to be a useful signal filter — particularly for sells in instruments in long-term uptrends. If using intraday, reduce to 50–100.
Recommended Starting Settings
1m – 5m charts: VWAP on / SMA off / Gap 0.05–0.10%
15m – 1H charts: VWAP on / SMA optional (50–100) / Gap 0.10–0.20%
4H – Daily charts: VWAP off / SMA on (200) / Gap 0.15–0.30%
Notes
This is a signal tool, not a trading system. Use it alongside your own levels, risk management, and market context.
Signals do not repaint — they fire once and do not move.
Built-in alerts for both BUY and SELL. Set them up via TradingView's alert system after adding to your chart.
Works on all instruments and timeframes.
Indicador
Signal Qualification Engine [JOAT]Signal Qualification Engine
Introduction
The Signal Qualification Engine is a sophisticated multi-layer signal filtering system designed to identify high-probability trading opportunities through comprehensive confluence analysis. This indicator solves the universal trading problem of signal quality - not all signals are created equal, and distinguishing between mediocre setups and high-probability opportunities is what separates successful traders from the crowd. By evaluating signals across trend, momentum, volume, and structure layers, this engine provides institutional-grade signal qualification that helps traders focus only on the best opportunities.
This tool is built for traders who understand that edge in trading comes from the confluence of multiple factors rather than any single indicator. Whether you're a discretionary trader looking for confirmation, a systematic trader needing signal filtering, or an algorithm developer requiring quality scoring, this engine provides the comprehensive analysis needed to elevate your trading from random signals to systematic, high-quality setups.
Why This Indicator Exists
Most traders struggle with signal overload - too many signals, varying quality, and no systematic way to evaluate them. This indicator addresses that critical problem by:
Multi-Layer Analysis: Evaluates signals across four independent analytical layers
Quality Scoring: Provides objective, numerical quality scores for every signal
Confluence Detection: Identifies when multiple factors align for high-probability setups
Risk/Reward Validation: Ensures signals offer adequate profit potential relative to risk
Premium Signals: Flags exceptional setups with maximum confluence
Visual Zones: Shows entry zones, stop levels, and targets for clear risk management
The engine transforms subjective signal evaluation into an objective, systematic process that can be consistently applied across all market conditions and instruments.
Core Components Explained
1. Trend Analysis Layer
The trend layer evaluates the directional bias using multiple trend indicators:
// Trend scoring
int trend_bull_score = 0
int trend_bear_score = 0
// Moving average analysis
if price_above_fast_ma
trend_bull_score += 1
if price_above_slow_ma
trend_bull_score += 1
if ma_bullish_cross
trend_bull_score += 1
// ADX analysis
if adx > i_adx_thresh
trend_bull_score += plus_di > minus_di ? 2 : 0
trend_bear_score += minus_di > plus_di ? 2 : 0
Trend components:
Price vs MAs: Position relative to fast and slow moving averages
MA Crossovers: Recent trend changes and confirmation
ADX Strength: Trend strength above threshold (default 25)
Directional Movement: +DI vs -DI for trend direction
Trend Score: Cumulative trend strength (0-5 points)
The trend layer ensures we only trade in the direction of the established trend or during trend changes with confirmation.
2. Momentum Analysis Layer
Momentum is evaluated through multiple oscillators to ensure optimal timing:
// Momentum scoring
int momentum_bull_score = 0
int momentum_bear_score = 0
// RSI analysis
if rsi > 50 and rsi < 70 and rsi > rsi
momentum_bull_score += 1
if rsi < 50 and rsi > 30 and rsi < rsi
momentum_bear_score += 1
// Stochastic analysis
if stoch_k > stoch_d and stoch_k < 80
momentum_bull_score += 1
if stoch_k < stoch_d and stoch_k > 20
momentum_bear_score += 1
// MACD analysis
if macd_hist > 0 and macd_hist > macd_hist
momentum_bull_score += 1
if macd_hist < 0 and macd_hist < macd_hist
momentum_bear_score += 1
Momentum components:
RSI Direction: Momentum direction with overbought/oversold filters
Stochastic Crossovers: Entry timing with extreme level avoidance
MACD Histogram: Trend acceleration and deceleration
Momentum Score: Cumulative momentum strength (0-3 points)
Divergence Detection: Price/momentum divergences for early signals
The momentum layer ensures we enter when momentum supports our directional bias.
3. Volume Analysis Layer
Volume confirms the strength and conviction behind price movements:
// Volume analysis
float vol_sma = ta.sma(volume, 20)
float vol_ratio = vol_sma > 0 ? volume / vol_sma : 1.0
bool above_avg_vol = volume > vol_sma * 1.2
bool high_vol_session = session_vol_ratio > 1.5
// Volume scoring
int volume_score = 0
if above_avg_vol
volume_score += 1
if high_vol_session
volume_score += 1
if vol_ratio > 1.5
volume_score += 1
Volume components:
Volume Ratio: Current volume relative to 20-period average
Above Average Volume: Confirms signal strength (20% above average)
Session Volume Analysis: Compares current volume to historical session averages
Volume Score: Cumulative volume confirmation (0-3 points)
Volume Spike Detection: Exceptional volume that may signal institutional activity
The volume layer ensures signals have sufficient participation to be reliable.
4. Structure Analysis Layer
Structure identifies key levels where professional traders place orders:
// Structure analysis
float swing_high = ta.pivothigh(high, i_swing_left, i_swing_right)
float swing_low = ta.pivotlow(low, i_swing_left, i_swing_right)
bool near_resistance = math.abs(close - nearest_resistance) / close * 100 < i_level_proximity
bool near_support = math.abs(close - nearest_support) / close * 100 < i_level_proximity
bool sweep_high = high > nearest_resistance and close < nearest_resistance
bool sweep_low = low < nearest_support and close > nearest_support
Structure components:
Swing Points: Key highs and lows defining market structure
Level Proximity: Distance to nearest support/resistance
Liquidity Sweeps: Price moves beyond levels that quickly reverse
Break of Structure: Confirms trend changes
Structure Score: Cumulative structural confirmation (0-3 points)
The structure layer ensures entries occur at technically significant levels.
5. Signal Qualification System
All layers combine to produce a comprehensive qualification score:
// Total scores (max 14)
int bull_total = (
trend_bull_score + momentum_bull_score + volume_score + structure_score +
(near_support ? 1 : 0) + (sweep_low ? 1 : 0) + (rr_ratio >= i_min_rr ? 1 : 0)
)
int bear_total = (
trend_bear_score + momentum_bear_score + volume_score + structure_score +
(near_resistance ? 1 : 0) + (sweep_high ? 1 : 0) + (rr_ratio >= i_min_rr ? 1 : 0)
)
Qualification criteria:
Trend Score (0-5 points): Directional bias strength
Momentum Score (0-3 points): Timing confirmation
Volume Score (0-3 points): Participation confirmation
Structure Score (0-3 points): Level confirmation
Level Proximity (1 point): Entry at key level
Liquidity Sweep (1 point): Institutional activity
Risk/Reward (1 point): Adequate profit potential
Maximum Score: 14 points for perfect confluence
6. Quality Grading System
Signals are graded based on their qualification score:
// Quality grades
string bull_grade = bull_total >= 12 ? "A+" :
bull_total >= 10 ? "A" :
bull_total >= 8 ? "B" :
bull_total >= 6 ? "C" : "D"
string bear_grade = bear_total >= 12 ? "A+" :
bear_total >= 10 ? "A" :
bear_total >= 8 ? "B" :
bear_total >= 6 ? "C" : "D"
Grade meanings:
A+ (12-14 points): Exceptional setup with maximum confluence
A (10-11 points): High-quality setup with strong confluence
B (8-9 points): Good setup with moderate confluence
C (6-7 points): Acceptable setup with basic confluence
D (0-5 points): Weak setup, avoid trading
Only B-grade and above signals are typically considered for trading.
7. Risk/Reward Validation
Each signal is validated for adequate profit potential:
// Risk/Reward calculation
float atr_val = ta.atr(14)
float stop_distance = atr_val * i_stop_mult
float target_distance = atr_val * i_target_mult
float rr_ratio = target_distance / stop_distance
// RR validation
bool valid_rr = rr_ratio >= i_min_rr
RR features:
ATR-Based Stops: Dynamic stop placement based on volatility
Multiple Targets: Primary and secondary profit targets
Minimum RR Ratio: Configurable minimum (default 1.5:1)
RR Validation: Signals without adequate RR are disqualified
Visual Targets: Clear stop and target levels on chart
Visual Elements
Signal Markers: Clear entry signals with quality grades
Entry Zones: Shaded areas showing optimal entry regions
Risk Levels: Visual stop loss and target levels
Quality Meter: Real-time confluence score display
Background Colors: Signal strength background shading
Dashboard: Comprehensive metrics panel
Premium Signals: Special markers for A+ grade setups
The dashboard displays:
1. Current signal qualification scores
2. Quality grades and confluence percentages
3. Individual layer scores (trend, momentum, volume, structure)
4. Risk/Reward ratio and validation status
5. Nearest support/resistance levels
6. Volume analysis and session context
7. Signal cooldown status
8. Premium signal indicators
Input Parameters
Trend Settings:
Fast MA Period: Short-term trend (default: 21)
Slow MA Period: Medium-term trend (default: 55)
ADX Period: Trend strength (default: 14)
ADX Threshold: Minimum trend strength (default: 25)
Momentum Settings:
RSI Period: Momentum oscillator (default: 14)
Stochastic K/D: Entry timing (default: 14/3)
MACD Fast/Slow/Signal: Trend acceleration (default: 12/26/9)
Structure Settings:
Swing Left/Right: Pivot point detection (default: 10/5)
Level Proximity %: Distance to key levels (default: 0.5%)
Max Levels: Maximum swing levels to track (default: 20)
Qualification Settings:
Minimum Score: Required qualification score (default: 6)
Signal Cooldown: Bars between signals (default: 5)
Minimum R:R: Required risk/reward ratio (default: 1.5)
Require Confirmation: Wait for bar close (default: true)
How to Use This Indicator
Step 1: Monitor Signal Quality
Watch for B-grade or higher signals. A-grade signals offer the highest probability but occur less frequently. Focus on quality over quantity - one A-grade signal is worth ten C-grade signals.
Step 2: Verify Layer Alignment
Check the dashboard to see which layers are contributing to the signal. The best signals have confirmation from all four layers (trend, momentum, volume, structure).
Step 3: Assess Risk/Reward
Ensure the signal offers adequate profit potential. The indicator automatically validates RR ratios, but you should manually verify that targets make sense in the current market context.
Step 4: Time Entry with Structure
Use the entry zones and structure levels to time your entry precisely. The best entries occur when price is near key support/resistance levels or after liquidity sweeps.
Step 5: Manage Risk Dynamically
Use the visual stop and target levels as guidelines, but adjust based on your personal risk tolerance and account size. Never risk more than you're comfortable losing.
Step 6: Track Premium Signals
Pay special attention to A+ grade premium signals. These rare setups with maximum confluence often lead to the largest moves and deserve larger position sizes.
Best Practices
Be patient for A-grade signals rather than forcing mediocre trades
Use the qualification score as your primary filter - ignore signals below your minimum threshold
Combine with your own analysis for additional confirmation
Adjust the minimum score based on market conditions - higher in choppy markets, lower in strong trends
Keep a trade journal to track which grade performs best in each market condition
Use the cooldown period to avoid overtrading - quality signals require patience
Pay attention to volume confirmation - signals without volume support often fail
Structure is key - signals at major levels have higher success rates
Liquidity sweeps provide high-probatility reversal opportunities
Always respect the risk/reward validation - poor RR setups destroy accounts
Strategy Integration
This indicator is designed to enhance any trading system:
Use as a signal filter for existing strategies
Import quality scores to weight trade decisions
Combine with trend-following systems for entry timing
Use structure levels for stop placement in other systems
Integrate volume analysis for signal confirmation
Apply risk/reward validation to all trades
Use premium signals as standalone trade opportunities
Export layer scores for custom signal development
The indicator includes 12 export functions for integration:
Bull/Bear Score Export: Total qualification scores
Quality Grade Export: Letter grade as numeric value
Trend Score Export: Trend layer score
Momentum Score Export: Momentum layer score
Volume Score Export: Volume layer score
Structure Score Export: Structure layer score
RR Ratio Export: Current risk/reward ratio
Signal Export: Binary signal output
Premium Signal Export: A+ grade signal flag
Technical Implementation
Built with Pine Script v6 featuring:
Multi-layer signal analysis across four independent systems
Dynamic qualification scoring with configurable weights
Advanced market structure detection with pivot points
Volume analysis with session context
Risk/reward validation with ATR-based calculations
Comprehensive visualization with entry zones and risk levels
Real-time dashboard with 12 key metrics
Alert conditions for all signal types and grades
Export functions for strategy integration
Premium signal detection for exceptional setups
The code uses confirmed bars for all calculations to prevent repainting and ensure reliable signals.
Originality Statement
This indicator is original in its comprehensive approach to signal qualification and multi-layer confluence analysis. While individual components (RSI, MACD, ADX, etc.) are established tools, this indicator is justified because:
It synthesizes four distinct analytical layers into a unified qualification system
The scoring system provides objective, numerical signal evaluation
Quality grading transforms subjective analysis into systematic decision-making
Risk/reward validation ensures only profitable setups are considered
Structure analysis integration provides context for market microstructure
Volume layer adds confirmation often missing from signal systems
Premium signal detection identifies exceptional opportunities
Comprehensive visualization makes complex analysis accessible
Export functions enable integration with any trading system
Each layer contributes unique insights: trend provides direction, momentum provides timing, volume provides confirmation, and structure provides context
The indicator's value lies in transforming signal evaluation from art to science - providing traders with a systematic, objective way to identify and focus only on the highest probability trading opportunities.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Signal qualification is a tool for improving trade selection, not a guarantee of success.
Even high-quality signals can fail due to unexpected market events, news, or changes in market conditions. Past performance of high-grade signals does not guarantee future results. The indicator's signals are mathematical calculations based on historical patterns and should be used in conjunction with proper risk management.
Always use stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose on any single trade, regardless of signal quality.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this system.
-Made with passion by officialjackofalltrades
Indicador
Breakout Trend Bar AlertsEvery trend has a starting point. It's rarely a gradual drift — it's one massive, decisive candle that breaks the market out of consolidation and kicks off a sustained move. Breakout Bar Alerts is built to catch that exact moment.
The indicator monitors price action in real time and identifies when a bar forms that dwarfs everything around it — the largest high-to-low range of any candle in the last 250 bars. These are the bars where conviction enters the market, weak hands get flushed, and a new trend begins. When one appears, you get an instant alert so you're never late to the move.
Why these bars matter:
Big range bars represent a sudden surge of momentum and volume-backed commitment from one side of the market. Bulls or bears have taken control decisively. What follows is often the beginning of a trend leg — not a random spike.
Built to filter out the noise:
The opening bar of every session is excluded entirely. That first chaotic candle never skews your data or triggers a false signal.
Only bars within your active session window are counted. Off-hours price action is completely ignored, so your benchmark is always built from real, tradeable market conditions.
Three alert conditions — Bull Breakout Bar, Bear Breakout Bar, or Both — so you only get notified for the setups you actually trade.
Inputs:
Lookback Period — how many bars back to measure the largest range (default: 250)
Enable Time Filter — restricts detection and calculations to your active trading session
Active Session — define your session window in exchange time
Bull / Bear colors — fully customizable
Best used on intraday timeframes (1m – 15m) on futures, forex, or high-volume equities. When this fires, pay attention — the trend may already be starting. Indicador
Indicador
Adaptive Momentum Fusion [WillyAlgoTrader]📡 Adaptive Momentum Fusion is a separate-pane oscillator that replaces the fixed EMAs inside a standard MACD with selectable adaptive moving averages — six different adaptation engines that each respond to a different market dimension (efficiency, volatility, fractal structure, momentum, volume, or a weighted composite of all five). The oscillator line and signal line are then processed through Jurik-style smoothing to reduce jitter without adding lag. The result is a MACD-like oscillator where the core moving averages automatically adjust their speed to current market conditions, producing cleaner crossovers, more meaningful histogram readings, and built-in divergence detection.
A standard MACD uses fixed-length EMAs (typically 12/26/9). This means the same smoothing is applied whether the market is trending strongly, ranging sideways, experiencing a volatility spike, or consolidating. The result: late crossovers in trends, whipsaws in ranges, and false divergences during choppy periods.
This indicator solves the problem at the source — the moving averages themselves. Instead of fixed EMAs, each adaptation engine dynamically adjusts the smoothing constant (alpha) on every bar based on a real-time market measurement. In a strong trend, the Efficiency engine detects high directional efficiency and increases alpha → faster MA → earlier crossover. In a range, efficiency drops and alpha decreases → slower MA → fewer whipsaws. Each engine uses a different measurement to achieve this adaptation, and the user selects which dimension matters most for their market.
🧩 WHY THESE COMPONENTS WORK TOGETHER
A MACD with adaptive MAs alone would still produce a noisy signal line (the EMA of the oscillator). Adding adaptation to the base MAs but keeping a fixed EMA for the signal line creates a mismatch: the oscillator adapts but the signal doesn't, causing crossover lag to reappear at the signal level.
This indicator addresses the full signal chain:
Adaptive fast MA → Adaptive slow MA → Oscillator (MACD or PPO) → Jurik-smoothed signal line → 4-state histogram → Divergence scanner
The adaptive MAs eliminate the fixed-speed problem at the source. The MACD/PPO dual mode lets you choose between absolute (price-scaled) and percentage (normalized) output. The Jurik smoother replaces the standard signal EMA with a 3-stage filter that reduces jitter while preserving crossover timing — critical because adaptive oscillators produce more variable readings than fixed ones. The 4-state histogram (accelerating/decelerating × bullish/bearish) quantifies momentum acceleration, not just direction. And the divergence scanner works on the adaptive oscillator itself, which produces more reliable pivots than a fixed MACD because the adaptive MAs have already filtered out noise-driven fluctuations.
Removing the adaptive engines returns you to a standard MACD. Removing the Jurik smoother reintroduces signal line jitter that the adaptive engines amplify. Removing the histogram states loses momentum acceleration information. The full pipeline produces cleaner signals than any subset.
🔍 WHAT MAKES IT ORIGINAL
1️⃣ Six selectable adaptation engines.
Each engine computes a dynamic smoothing constant (alpha) that controls how fast the moving average reacts. All engines feed into the same adaptive EMA core: result = alpha × source + (1 − alpha) × result . The difference is how alpha is calculated:
Efficiency (Kaufman AMA):
Computes the Efficiency Ratio: ER = |price − price | / sum(|price − price |, N). ER ranges from 0 (pure chop — price moves a lot bar-to-bar but goes nowhere net) to 1 (pure trend — every bar moves in the same direction). The smoothing constant: sc = (ER × (fastSc − slowSc) + slowSc)², where fastSc = 2/3, slowSc = 2/31. This is the classic Kaufman Adaptive Moving Average approach. Best all-around engine.
Volatility (ATR-based):
Computes volRatio = ATR(len) / SMA(ATR, len×2). When current volatility exceeds its average (ratio > 1), alpha increases → MA speeds up to track the expanding price action. When volatility contracts (ratio < 1), alpha decreases → MA slows down to avoid noise. Alpha = baseAlpha × volRatio, clamped to . Best for instruments with distinct volatility regimes (crypto, commodities).
Fractal (Hurst-inspired):
Estimates the fractal dimension of the price series using rescaled range analysis: the full-range is compared to the sum of two half-ranges. fracDim = 1 + log(sumHalf) / log(2 × rangeFull). A fractal dimension near 1.0 indicates trending behavior (smooth), near 2.0 indicates mean-reverting (rough). Alpha = exp(−4.6 × (fracDim − 1)), which maps: fracDim ≈ 1.0 → alpha ≈ 1.0 (fast, trending), fracDim ≈ 2.0 → alpha ≈ 0.01 (slow, ranging). Best for detecting regime changes between trending and mean-reverting markets.
Momentum (ROC-based):
Computes the Rate of Change normalized against its recent maximum: norm = |ROC| / highest(|ROC|, len×2). When momentum is strong relative to recent history, alpha increases. Alpha = baseAlpha + norm × (1 − baseAlpha) × 0.5. This makes the MA react faster during breakouts and momentum surges while staying smooth during low-momentum drift. Best for breakout-oriented strategies.
Volume:
Computes volRatio = volume / SMA(volume, len). When volume exceeds its average (institutional participation), the MA speeds up. Alpha = baseAlpha × √(volRatio). The square root prevents extreme volume spikes from making the MA too reactive. Auto-disabled on instruments without volume data (forex) — falls back to baseAlpha. Best for stocks and crypto where volume confirms moves.
Composite (weighted blend):
Runs all five engines simultaneously and blends the results: with volume = 30% Efficiency + 20% Volatility + 20% Fractal + 15% Momentum + 15% Volume. Without volume = 35% Efficiency + 25% Volatility + 25% Fractal + 15% Momentum. This produces the most robust adaptation but is slightly slower because it averages five different smoothing perspectives. Best for users who don't want to choose a single engine.
2️⃣ Jurik-style signal line smoothing.
The standard MACD signal line is a simple EMA of the oscillator. This indicator replaces it with a 3-stage Jurik-inspired filter:
beta = 0.45 × (len − 1) / (0.45 × (len − 1) + 2)
alphaJ = beta³
e0 = (1 − alphaJ) × source + alphaJ × e0
e1 = (source − e0) × (1 − beta) + beta × e1
e2 = (e0 + phase × e1 − e2 ) × (1 − alphaJ)² + alphaJ² × e2
The three stages progressively remove jitter while maintaining phase alignment. The phase parameter (Jitter Reduction, default 0.7) controls how much additional smoothing is applied: 0.0 = minimal smoothing (fast but noisy), 1.0 = maximum smoothing (smooth but slightly more lag). This is particularly important for adaptive oscillators because the variable-speed MAs produce a more erratic oscillator line than fixed EMAs — the Jurik filter absorbs this variability at the signal level.
3️⃣ MACD/PPO dual output mode.
— MACD mode : oscillator = fastMA − slowMA (absolute difference, price-scaled). Readings scale with instrument price — useful for single-instrument analysis.
— PPO mode : oscillator = (fastMA − slowMA) / slowMA × 100 (percentage difference, normalized). Readings are comparable across instruments and time — useful for scanning, multi-market analysis, or consistent threshold settings.
4️⃣ 4-state momentum histogram.
The histogram (oscillator − signal) is classified into four states based on value and direction of change:
— 🟢 Accelerating bullish : histogram > 0 AND rising (momentum strengthening)
— 🟢 Decelerating bullish : histogram > 0 AND falling (momentum fading, potential reversal ahead)
— 🔴 Accelerating bearish : histogram < 0 AND falling (selling pressure increasing)
— 🔴 Decelerating bearish : histogram < 0 AND rising (selling pressure easing, potential bounce)
Each state has a distinct color intensity: accelerating = fully saturated, decelerating = faded. This provides immediate visual recognition of whether momentum is building or exhausting — the most actionable information from a histogram.
5️⃣ Divergence detection with adaptive oscillator.
The indicator scans for regular divergences between price and the adaptive oscillator:
— Regular Bullish Divergence : price makes a lower low, but the oscillator makes a higher low → selling pressure is weakening despite lower prices → potential reversal up
— Regular Bearish Divergence : price makes a higher high, but the oscillator makes a lower high → buying pressure is weakening despite higher prices → potential reversal down
Pivots are detected using a custom function with configurable lookback (default 30 bars) and a fixed right-bar confirmation of 5 bars. Divergences are labeled on the oscillator pane and optionally drawn as connecting lines. The divergence state persists in the dashboard for 20 bars after detection, then decays.
Divergences detected on the adaptive oscillator are more reliable than on a standard MACD because the adaptive MAs have already filtered regime-inappropriate noise — the pivots in the oscillator correspond to genuine momentum shifts, not noise-driven wiggles.
6️⃣ Zero-line cross signals.
When enabled, the indicator marks when the oscillator crosses the zero line — which corresponds to the fast adaptive MA crossing the slow adaptive MA. In MACD terms, this is the equivalent of the "MACD crossing zero." Because the MAs are adaptive, these crossovers occur at more structurally meaningful points than with fixed EMAs.
7️⃣ Signal strength scoring (0–100).
Each bar's histogram absolute value is normalized against its recent maximum over 50 bars: strength = |histogram| / highest(|histogram|, 50) × 100. This provides a relative measure: 100 = histogram is at its strongest in the last 50 bars, 0 = no momentum. Classified as Strong (≥ 70), Medium (≥ 40), Weak (< 40). Displayed in the dashboard.
⚙️ HOW IT WORKS — CALCULATION FLOW
Step 1 — Engine selection: The selected adaptation engine computes a dynamic alpha value on each bar based on its specific market measurement (efficiency ratio, ATR ratio, fractal dimension, ROC normalization, volume ratio, or composite blend).
Step 2 — Adaptive MA calculation: The fast MA and slow MA are each computed using the adaptive EMA formula: result = alpha × source + (1 − alpha) × result . The same engine is used for both, with different length parameters (fast = default 8, slow = default 21).
Step 3 — Oscillator: MACD mode: osc = fastMA − slowMA. PPO mode: osc = (fastMA − slowMA) / slowMA × 100.
Step 4 — Signal line: The oscillator is passed through the Jurik 3-stage filter with the configured signal length (default 7) and jitter reduction factor (default 0.7).
Step 5 — Histogram: hist = oscillator − signal. Classified into 4 states (accelerating/decelerating × bullish/bearish) based on sign and direction of change.
Step 6 — Divergence scan: Custom pivot detection identifies local highs and lows in both the oscillator and price. When a new pivot is found, it's compared to the previous pivot to check for divergence conditions (price lower low + osc higher low, or price higher high + osc lower high).
Step 7 — Signals: Crossovers (oscillator crossing signal line) and zero-line crosses are detected. All signals require barstate.isconfirmed + warmup check.
📖 HOW TO USE
🎯 Quick start:
1. Add the indicator — the adaptive oscillator appears in a separate pane
2. Select an adaptation engine matching your market (Efficiency for general use)
3. Green line above red = bullish momentum, below = bearish
4. Histogram bars show momentum acceleration (bright) vs deceleration (faded)
5. Divergence labels mark potential reversals
👁️ Reading the pane:
— 🟢 Green oscillator line above signal line = bullish momentum
— 🔴 Red oscillator line below signal line = bearish momentum
— 🟢 Bright green histogram = accelerating bullish momentum
— 🟢 Faded green histogram = decelerating bullish (momentum fading)
— 🔴 Bright red histogram = accelerating bearish momentum
— 🔴 Faded red histogram = decelerating bearish (selling easing)
— ▲ label = buy crossover signal (oscillator crosses above signal)
— ▼ label = sell crossover signal
— ● dot on zero line = zero-line cross
— "Bull Div" / "Bear Div" labels = detected divergences
🔧 Engine selection guide:
— Efficiency : best default for most markets — adapts to trending vs ranging
— Volatility : best for instruments with clear vol regimes (crypto, commodities)
— Fractal : best for detecting regime shifts (trending ↔ mean-reverting)
— Momentum : best for breakout strategies — speeds up on strong moves
— Volume : best for stocks/crypto where volume confirms participation
— Composite : most robust — blends all dimensions, good when unsure
🔧 Tuning guide:
— Too many whipsaws: increase Slow Length, increase Signal Length, increase Jitter Reduction
— Signals too late: decrease Fast/Slow Length, decrease Signal Length, decrease Jitter Reduction
— Histogram too noisy: increase Jitter Reduction toward 0.8–1.0
— Divergences too frequent: increase Divergence Lookback (40–60)
— Cross-market comparison: switch to PPO mode for normalized output
⚙️ KEY SETTINGS REFERENCE
⚙️ Main:
— Adaptation Engine (default Efficiency): Efficiency / Volatility / Fractal / Momentum / Volume / Composite
— Fast Length (default 8): fast adaptive MA period
— Slow Length (default 21): slow adaptive MA period
— Signal Length (default 7): Jurik signal smoothing period
— Output Mode (default MACD): MACD (absolute) or PPO (percentage)
🔍 Filters:
— Detect Divergences (default On): scan for regular divergences
— Divergence Lookback (default 30): pivot search depth
— Zero Line Cross Signals (default On): mark zero crossings
🔧 Advanced:
— Jitter Reduction (default 0.7): Jurik phase parameter (0.0–1.0)
— Auto / Dark / Light theme
📊 Dashboard
— Trend: oscillator vs signal line direction (Bullish / Bearish / Neutral)
— Signal: last crossover (BUY / SELL / Wait)
— Strength: histogram normalized to 50-bar max (Strong / Medium / Weak with %)
— Momentum: 4-state acceleration (Accel ▲ / Decel ▽ / Accel ▼ / Decel △)
— Divergence: current divergence state (Bull Div / Bear Div / None)
— Engine / TF: selected engine and timeframe
— Version
🔔 Alerts
— 🟢 BUY / 🔴 SELL — oscillator-signal crossover (ticker, price, timeframe, engine)
— 🟡 ZERO BULL CROSS / ZERO BEAR CROSS — zero-line crossing
— 🔵 BULL DIVERGENCE / BEAR DIVERGENCE — detected divergence
All support plain text and JSON webhook format. Bar-close confirmed.
⚠️ IMPORTANT NOTES
— 🚫 No repainting. All signals and divergences require barstate.isconfirmed. A warmup period (2× slow length, minimum 50 bars) prevents signals during insufficient data. The adaptive MAs are deterministic — once a bar closes, the MA value for that bar never changes.
— 📐 This is a separate-pane oscillator, not an overlay. It displays in its own pane below the chart. The oscillator values are momentum readings, not price levels. Use it alongside your chart for confluence, not as a standalone trading system.
— ⚖️ The six engines produce different signals on the same data . Efficiency and Composite tend to produce the most similar results. Fractal can diverge significantly during regime transitions. Volume engine auto-falls back to base alpha on instruments without volume data.
— 📊 Signal strength is relative to recent 50 bars , not absolute. A "Strong" reading during a low-volatility week may be weaker in absolute terms than a "Weak" reading during a high-volatility week.
— 🔄 Divergences use a 5-bar right confirmation — they appear 5 bars after the actual pivot. This delay is inherent to pivot detection and cannot be eliminated without introducing false positives.
— 📏 PPO mode normalizes the output as a percentage, making threshold settings (e.g., "divergence when oscillator > 2") comparable across instruments. MACD mode values scale with price — a reading of 5.0 on a $100 stock is different from 5.0 on a $50,000 BTC chart.
— 🛠️ This is a momentum analysis tool , not an automated trading bot. It measures and visualizes adaptive momentum — trade decisions remain yours.
— 🌐 Works on all markets and timeframes. Volume engine auto-adapts to available data. Indicador
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