Ai Kavach by Pooja v16✅ Fakeout Kavach by Pooja — Smart Fake Breakout Protector
Fakeout Kavach is designed to help traders understand when a breakout is strong and when it is likely to be a trap.
It works as a confirmation and filtering system, giving you a clear view of market strength, momentum, volume pressure, and potential reversal signs — without providing buy/sell recommendations.
This is a support tool for traders who want cleaner entries, fewer trap trades, and better clarity in fast-moving markets.This tool adds an intelligent multi-layer confirmation system on your chart so you can quickly understand:
✔ When the breakout is real
✔ When the market has strength
✔ When momentum is fading
✔ And when you should simply avoid the move
It doesn’t give buy/sell calls.
Instead, it helps you decide “Should I trust this move or not?”
⭐ Core Features (Explained in Simple Language)
🔹 1. Fake Breakout Filter (RSI + MA Logic)
Fakeouts often happen when price shows strength but momentum does not.
This module checks:
RSI strength
RSI–MA crossover behaviour
Momentum direction
Push/rejection zones
📌 Benefit:
Quickly see if the breakout has real strength behind it or it’s just a trap candle.
🔹 2. Trend Strength Filter (ADX Protection)
Most traders lose money in sideways markets.
ADX Filter helps you understand whether the market actually has trend strength or not.
📌 Benefit:
Avoid taking trades when the market is weak, choppy, or directionless.
Only focus on moves backed by strength.
🔹 3. SB/SS Smart Confirmation
SB (Strong Break) and SS (Strong Slide) confirmations highlight alignment between:
Momentum
Trend
Strength
RSI structure
📌 Benefit:
Cleaner entries, fewer false triggers, and more confidence in the move you take.
🔹 4. Divergence Detection (RSI Based)
Catches early signs of:
Bullish reversal
Bearish reversal
Exhaustion at highs/lows
📌 Benefit:
Helps you avoid entering at the worst possible points and improves exit timing.
🔹 5. VAD Module (Volume + ATR + Delta Pressure)
Fake moves usually have weak volume or no volatility.
This module checks:
Volume strength
Volatility (ATR)
Buying/selling pressure (Delta)
📌 Benefit:
Helps you understand whether the breakout is backed by real buyer/seller pressure.
🔹 6. Session Protection
Opening candles can be noisy and unpredictable.
Session block lets you avoid signals during high volatility windows.
📌 Benefit:
No more taking wrong entries during the rush at market open.
🔹 7. Fully Modular – Use Only What YOU Need
Every feature has its own ON/OFF switch.
You can create your perfect setup by enabling only what you prefer.
📌 Benefit:
Suitable for scalpers, intraday traders, swing traders, and even beginners.
🎨 Customization Power — Fully Modular Design
✔ Every section of Fakeout Kavach has its own ON/OFF toggle:
✔ Turn RSI visuals on/off
✔ Enable or disable MA & fills
✔ Activate or hide divergences
✔ Use or ignore ADX trend filter
✔ Show or hide SB/SS signals
✔ Enable or disable session block
✔ Choose label style, shapes, colors, sizes
✔ Keep chart clean or run full analysis mode
✔ You decide what appears.
✔ You control the complexity.
✔ One indicator fits all types of traders.
🌍 Works Across All Markets
✔ Stocks
✔ Crypto
✔ Forex
✔ Commodities
✔ Indices
All timeframes from scalping to swing trading.
⭐ What This Indicator Helps You With
Avoiding trap candles
✔ Understanding when a move is strong or weak
✔ Filtering bad breakouts
✔ Confirming market structure with momentum
✔ Spotting reversal signs early
✔ Building confidence in your entries
✔ Staying out of sideways/no-volume zones
🛠 Support
For indicator-related questions, clarification, or feature suggestions, you can contact the creator through TradingView’s comment section or direct message.
⚠ Disclaimer (TradingView Policy Safe)
This indicator does not provide buy/sell signals, does not predict market movements, and does not guarantee results or profitability.
It is a technical analysis tool intended to assist traders in making their own trading decisions.
Always use proper risk management and follow your own trading plan.
M-oscillator
Tether Dynamics - Statistical Exhaustion EngineOverview
This strategy detects statistical exhaustion in price movement by modeling price as a particle tethered to a dynamic anchor. When price stretches too far from equilibrium and multiple independent statistical detectors confirm anomalous behavior, the strategy identifies high-probability mean-reversion opportunities.
Unlike simple oversold/overbought indicators, this system fuses concepts from classical mechanics , stochastic filtering , multivariate statistics , and statistical process control into a unified detection framework.
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THEORETICAL FOUNDATION
1. The Tethered Particle Model
The framework draws inspiration from Polyak's heavy ball method in optimization theory, where a particle with momentum navigates a loss landscape. Here, price is modeled as a particle connected to a moving anchor (adaptive EMA) by an elastic "chain" whose length scales with volatility (ATR). This creates a natural physics framework:
Displacement (x) : Distance from anchor, normalized by chain length
Velocity (v) : Rate of change of displacement
Acceleration (a) : Rate of change of velocity
This state vector defines the system's "phase space" — a complete description of price dynamics relative to equilibrium.
2. Adaptive Anchor (Kaufman Efficiency)
The anchor uses an adaptive smoothing approach inspired by Perry Kaufman's Adaptive Moving Average. The Efficiency Ratio measures trend strength:
ER = |Direction| / Volatility = |Price - Price | / Σ|ΔPrice|
High efficiency (trending) → faster adaptation
Low efficiency (choppy) → slower, more stable anchor
This prevents whipsaws in ranging markets while staying responsive in trends.
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DETECTION ARCHITECTURE
The strategy employs three independent statistical detectors , each grounded in distinct mathematical frameworks. A signal fires when price shows extended tension AND any detector confirms anomalous behavior AND momentum is decelerating (exhaustion).
Detector 1: Mahalanobis Distance (Multivariate Outlier Detection)
The Mahalanobis distance measures how "unusual" the current state vector is, accounting for correlations between displacement, velocity, and acceleration:
D² = (x - μ)ᵀ Σ⁻¹ (x - μ)
Where Σ is the full 3×3 covariance matrix. Under multivariate normality, D² follows a chi-squared distribution with 3 degrees of freedom:
χ²(3, 0.90) = 6.25 → 10% of observations exceed this
χ²(3, 0.95) = 7.81 → 5% of observations exceed this
This detector identifies states that are jointly extreme — even if no single variable looks unusual alone.
Why it matters: A price might have moderate displacement and moderate velocity, but the combination could be highly improbable. Mahalanobis captures this multivariate structure that univariate indicators miss.
Detector 2: CUSUM Change-Point Detection
Cumulative Sum (CUSUM) is a sequential analysis technique from statistical process control. It accumulates standardized deviations from the mean:
S⁺ₜ = max(0, S⁺ₜ₋₁ + zₜ - drift)
S⁻ₜ = min(0, S⁻ₜ₋₁ + zₜ + drift)
When either cumulative sum breaches a threshold, a "change point" is detected — the process has shifted from its baseline regime.
Why it matters: CUSUM detects subtle, persistent shifts that might not trigger on any single bar. It's sensitive to regime changes that precede reversals.
Detector 3: Kalman Innovation Filter (Ornstein-Uhlenbeck Model)
This detector models displacement as an Ornstein-Uhlenbeck process — the continuous-time analog of AR(1) mean-reversion:
dx = θ(μ - x)dt + σdW
A Kalman filter tracks the expected displacement and computes the innovation (prediction error):
νₜ = (yₜ - x̂ₜ|ₜ₋₁) / √Sₜ
Under correct model specification, normalized innovations should be ~N(0,1). Large innovations indicate the mean-reversion model is breaking down — price is behaving "unexpectedly" relative to equilibrium dynamics.
Adaptive Q Estimation: The filter continuously adjusts its process noise estimate based on innovation autocorrelation, maintaining calibration across different volatility regimes.
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SIGNAL LOGIC
Long Signal Requirements:
Z-Displacement < -σ threshold (price stretched below anchor)
ANY detector fires (Mahalanobis outlier OR CUSUM change OR Kalman innovation < -2σ)
Z-Acceleration > 0 (downward momentum decelerating)
Short Signal Requirements:
Z-Displacement > +σ threshold (price stretched above anchor)
ANY detector fires
Z-Acceleration < 0 (upward momentum decelerating)
The deceleration requirement ensures we're catching exhaustion rather than fighting momentum.
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RISK MANAGEMENT
Scale-Out Exit Strategy
Rather than all-or-nothing exits, the strategy takes profits at multiple R-levels:
Scale 1: 20% at 0.5R
Scale 2: 20% at 1.0R
Scale 3: 10% at 1.5R (optional)
Remainder: Trailing stop
This locks in gains while allowing winners to run.
Adaptive Trailing Stop
After reaching the activation threshold (default 1R), the stop trails from the highest high (longs) or lowest low (shorts) at a configurable ATR multiple.
Reversal Logic
When an opposite signal fires while in position, the strategy can close and flip direction rather than waiting for a stop-out.
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PARAMETER GUIDANCE
Anchor Period (24) : Base period for adaptive anchor
ATR Period (14) : Volatility measurement
Chain Length Mult (2.5) : Tether elasticity — higher = more stretch allowed
Long Tension σ (1.5) : Lower = more signals
Short Tension σ (2.0) : Higher threshold for shorts (trend asymmetry)
Mahalanobis Threshold (6.25) : χ²(3, 0.90) — adjust for signal frequency
CUSUM Threshold (3.0) : Lower = more sensitive to regime shifts
Lookback Window (100) : Statistical estimation window
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BACKTEST NOTES
Historical testing on NQ (2020-2025) suggests:
Long signals show stronger edge than shorts in equity indices
1H and 30-min timeframes balance signal quality vs. frequency
"Long Only" mode recommended for equity index futures
Important: Past performance does not guarantee future results. This strategy involves significant risk of loss.
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MATHEMATICAL REFERENCES
Polyak, B.T. (1964). "Some methods of speeding up the convergence of iteration methods" (Heavy ball method)
Bertsekas, D.P. (1999). "Nonlinear Programming" (Heavy ball method / momentum dynamics)
Mahalanobis, P.C. (1936). "On the generalized distance in statistics"
Page, E.S. (1954). "Continuous inspection schemes" (CUSUM)
Kalman, R.E. (1960). "A new approach to linear filtering and prediction problems"
Uhlenbeck, G.E. & Ornstein, L.S. (1930). "On the theory of Brownian motion"
Kaufman, P. (1995). "Smarter Trading" (Adaptive Moving Average)
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DISCLAIMER
This strategy is provided for educational and research purposes. Trading futures involves substantial risk of loss. The statistical methods employed do not guarantee profitable outcomes. Always use appropriate position sizing and risk management.
Whale Trading Network Technical Indicator
Whale Trading Network — Technical Indicator (WTN)
What it does — signal families
WTN produces three signal types across three user‑selected timeframes: (1) Green : bottom setup candidates, (2) Gold : continuation confirmations, and (3) Red : early top warnings. It blends momentum with trend/structure context and suppresses prints during sustained downtrends or late‑stage rallies. Defaults target 4h, 1d, and 5d workflows.
Preamble — originality and invite‑only context
WTN is a controller‑driven, regime‑aware indicator that coordinates classic elements (RSI, MACD, Stochastic RSI, MAs, BBs) into a governed signal layer rather than a simple overlay. A latched Down‑Channel regime, a Top‑Zone swing gate, cross‑asset/timeframe normalization, confluence‑based dot permissions, and multi‑timeframe orchestration (gold‑only on the highest frame) work together to actively manage when signals are allowed. The sections below explain why this is not a mashup and why the closed‑source / vendor value resides in WTN’s state‑machine logic, interlock rules, normalization framework, and cross‑frame roles—presented at the concept level so traders and moderators can understand how it operates without exposing proprietary thresholds.
Why it’s not a simple mashup (originality & usefulness)
WTN is not a bundle of classic tools; it is a controller‑driven indicator with regime awareness, gating, and normalization that coordinates otherwise independent signals into a single, coherent decision layer. Instead of overlaying RSI + MACD + BB + MAs, WTN governs when those tools matter, how long their states persist, and when prints must be blocked—using rules a basic mashup does not provide.
What the controller actually governs
Identifies and latches regimes (e.g., sustained down‑channel) so print permissions change with context—not just oscillator ticks.
Applies gates (e.g., Top‑Zone) when swing positioning suggests late‑stage risk.
Normalizes and weights evidence so MACD, RSI, Stoch RSI, histogram behavior, and price context contribute coherently.
Coordinates timeframes so dots form a workflow (tactical → swing → continuation) rather than three unrelated overlays.
Regime awareness & hysteresis (stability by design)
A core source of originality is hysteresis : once WTN recognizes a down‑channel, it latches that regime and suppresses prints until persistent breakout evidence plus momentum stabilization appear. This prevents flip‑flopping during chop, “first‑bounce” head fakes, and lower‑high rallies that a simple overlay will often misclassify. The regime state is visible (tinted panel), so users know why signals are paused.
Context gates that actively refuse bad timing
Two key context gates reduce “chase‑the‑top” and “bottom‑fish” problems:
Down‑Channel Latch: Blocks bottom candidates while momentum/structure remain impaired, then re‑enables only after sustained improvement.
Top‑Zone Gate: Detects upper‑swing positioning with momentum decay and blocks prints until positioning resets, avoiding confirmations into exhaustion.
Normalization that makes confluence real
Classic indicators have incompatible scales that vary across assets and timeframes. WTN normalizes them:
MACD line/signal/histogram, RSI, and Stoch RSI are mapped to consistent ranges so slope tests and region checks are comparable.
This lets confluence be meaningful : no single tool dominates due to scale; each contributes proportionally to permissions.
Multi‑timeframe orchestration (coordinated, not duplicated)
WTN assigns roles across the three selected timeframes:
Shorter timeframe: Tactical green setups (higher risk), ideally validated by gold .
Middle timeframe: Swing validation with more selective gold .
Highest timeframe: Gold‑only continuation, prioritizing higher‑confidence confirmation.
On lower frames, gold requires a prior green ; on the highest frame, green never prints . This structure turns dots into a sequence rather than three independent overlays.
Permission lattice & precedence (how conflicts are resolved)
Signals must pass a permission lattice where evidence sources interlock:
Momentum alignment: MACD slope and histogram behavior must agree; a single crossover is not enough.
Oscillator state: RSI/Stoch RSI must be supportive (e.g., stabilization from weak zones for a bottom candidate).
Structure & volatility context: MA stack, BB basis/width, and ATR‑aware checks help confirm or veto timing.
Regime/gate status: Down‑Channel or Top‑Zone states can override otherwise bullish micro‑signals.
Precedence rules mean a strong veto (e.g., active latch) can inhibit a print even if oscillators briefly improve.
Debounce, persistence & resumption (time matters)
WTN emphasizes persistence windows and debounce behavior:
Breakouts must persist (not one‑bar spikes) before the latch releases.
Oscillator stabilization must sustain before green candidates are permitted.
Continuations ( gold ) require maintained alignment , not transient ticks, so you avoid prints on single‑bar noise.
Failure modes addressed by the controller
RSI oversold during falling MACD: Basic mashups flag “bottom”; WTN keeps the latch until histogram and RSI recover together .
Momentum crossover inside the Top‑Zone: Overlays confirm continuation; WTN blocks until price resets out of the upper swing.
Event‑driven spikes (gap/volatility bursts): Transient improvements are debounced ; permissions wait for sustained evidence.
Indicator scale drift across assets/timeframes: Normalization ensures confluence rules remain consistent when you switch symbols.
Interpretability: see the “why,” not just the “what”
WTN’s pane is structured for auditability :
Tinted background exposes regime state (e.g., down‑channel latch).
Histogram anchored at 0 , RSI in the upper sub‑pane (0–100), Stoch RSI in the lower sub‑pane (−100–0) with clear overbought/oversold coloring.
Traders can visually trace the permission path: regime → positioning → momentum → oscillator → dot allowed/blocked.
Bottom line: WTN’s originality lives in the controller, regime latch, context gates, normalization, permission lattice, and timeframe orchestration that actively manage when a print is allowed. It is a coordinated decision system—not a simple overlay of classic indicators—and that governance is the reason it adds practical value for traders.
Why closed‑source / vendor value
WTN is powered by a proprietary engine written from the ground up in Pine v6; the source does not reuse any third‑party open‑source code. Its originality lies in the controller architecture and interlock logic that govern regime detection, context gates, normalization, and cross‑frame coordination. While it reads familiar elements (RSI, MACD, Stochastic RSI, MAs, BBs), the value comes from how those elements are orchestrated—state‑machine gating with hysteresis, context‑aware suppression and resumption, normalized confluence tests, and gold‑only continuation on the highest timeframe—yielding behavior that is not achievable by simply overlaying built‑ins.
What is original (and protected)
State‑machine gating: Rules define regimes, transitions, hysteresis, and re‑enable conditions across evidence sources (momentum slope, histogram decay/recovery, oscillator zones, MA/BB context).
Permission graph & interlocks: RSI, MACD (line/signal/histogram), Stoch RSI, price‑structure gates, and MA/BB context vote together through precedence rules—this coordination is proprietary.
Normalization framework: Mapping and using normalized ranges for momentum/oscillators to make confluence tests stable across assets/timeframes is a deliberate design central to WTN’s consistency.
Multi‑timeframe controller roles: Gold‑only behavior on the highest timeframe and the green‑precedence rule on lower frames are coordinated workflows specific to WTN.
Context‑aware suppression/resumption: Suppressing dots during down‑channels and top‑zones, then resuming only on verified persistence, reduces “false‑print drift” common to naive mashups.
Why protection is appropriate
Not reproducible through overlays: While anyone can overlay RSI, MACD, and BBs, WTN’s controller decisions (state transitions, permission checks, persistence windows, evidence requirements) are not trivially inferred from outputs and are central to its behavior.
Integrity of the workflow: Protection preserves a single, tested implementation so users do not encounter fragmented clones with altered rules that undermine the controller’s intent.
Ongoing calibration: Profiles for Crypto vs. Stocks (across three timeframes each) are curated to typical volatility traits. Maintaining these calibrations and the permission graph is part of the product’s vendor value.
What traders get (concept level, not black‑box hype)
Regime‑aware signals: Fewer prints into multi‑leg downtrends or late‑stage tops because the system explicitly refuses to signal in those contexts.
Consistent confluence: Normalization makes cross‑asset/timeframe confluence checks meaningful; users aren’t whipsawed by indicator scale differences.
Coherent workflow: Green → Gold on tactical frames, Gold‑only on the highest frame for continuation—an interpretable sequence that is easy to audit on the pane.
Transparent context: Tinted backgrounds and sub‑pane organization show why a dot was allowed or blocked (regime, swing position, oscillator state), letting traders understand how the script does what it claims—without exposing proprietary thresholds.
How it works — components & flow (concept level)
1) Normalized momentum & context
WTN reads RSI , MACD (line, signal, histogram), Stochastic RSI , ATR‑aware volatility , moving averages , Bollinger Bands , and price‑structure gates . Internals normalize oscillator values to a common pane so slopes, threshold checks, and histogram behavior are comparable across assets and timeframes. The histogram remains centered on 0, RSI uses 0–100 in the upper sub‑pane, and Stoch RSI maps to the lower sub‑pane.
Conceptual effect:
Normalization mitigates asset‑specific amplitude differences (e.g., MACD’s variable scale) so confluence tests don’t break when you switch symbols/timeframes.
Visual cues (line colors for overbought/oversold) make state changes obvious.
2) Regime detection — Down‑Channel Latch
Synchronized evidence (weak MA stack, negative momentum slope, fading histogram, RSI/Stoch RSI weak zones, price‑structure traits) latches the down‑channel regime. When latched, green prints are suppressed . The latch releases only after breakout persistence and improvements in RSI/histogram confirm trend resumption. The panel tints red while latched.
Design intent: Cut bottom‑fishing noise during multi‑leg downtrends, then resume prints only after sustained recovery.
3) Swing‑positioning — Top‑Zone Gate
A “top‑zone” derived from recent swing bounds with BB/Fibonacci context and momentum checks blocks new prints when price is in the upper swing and momentum decays, reducing confirmations into exhaustion.
4) Dot permissions (confluence gating)
WTN coordinates RSI, MACD, Stoch RSI, histogram behavior, SMA/BB context , and regime gates to determine whether a dot is allowed:
Green (bottom setup): Requires momentum deceleration with histogram improvement, RSI stabilizing upward, and price firming vs recent closes. Suppressed in Down‑Channel latch or Top‑Zone gate.
Gold (continuation): On lower two timeframes, prints only after a prior green and requires aligned momentum/oscillator states and supportive price context; on the highest timeframe, gold‑only prints emphasize higher‑confidence continuation cues.
Red (early top warning): Requires synchronized local peaks/roll‑downs across oscillators with slowing histogram; blocked in specific exhaustion conditions to avoid warnings into capitulation.
5) Multi‑timeframe controller
A controller aligns permissions across the three selected timeframes . Shorter frames provide tactical entries; the middle frame favors swing setups; the highest frame prints gold‑only for major continuation confirmation. Signals are coordinated, not independent overlays.
How to use it
Choose timeframes: Defaults target 4h / 1d / 5d . Use the shorter frame to spot tactical green ; wait for gold on the same or higher frame to confirm. Use the middle frame for swing validation. The highest frame is gold‑only , helping avoid early greens during broader trends.
Watch the tint: A red background band denotes the Down‑Channel latch ; expect suppressed greens until breakout persistence and momentum improvement.
Read the panel: The pane shows normalized momentum (MACD, histogram) with RSI up top and Stoch RSI below, including clear overbought/oversold coloring.
Confirm, then manage exposure: Treat green → gold as the preferred sequence. MA/BB context helps gauge trend strength (e.g., price vs 50/100/200 SMA and BB basis). Greens are higher‑risk; favor gold confirmations.
Crypto vs Stocks — calibrated profiles
Profiles are tuned for typical volatility patterns in each asset class. Each timeframe has its own calibration, yielding six independent tuning sections (3 per asset class).
Screenshots — captions
Screenshot 1 — Down‑Channel latch & release
The red‑tinted band shows the Down‑Channel latch regime on the indicator pane. While latched, green prints are suppressed . The latch only releases after breakout persistence and momentum improvement are visible (MACD/histogram strengthening with RSI and Stochastic RSI stabilizing). Once released, if the Top‑Zone gate is open and price context is supportive, the controller may permit a green dot on the lower timeframes, followed by a gold confirmation when conditions remain aligned.
Screenshot 2 — Pane layout & normalization
The indicator pane is organized for quick audit: the histogram is centered on 0 ; RSI plots in the upper sub‑pane on a 0–100 range; Stochastic RSI plots in the lower sub‑pane on a −100 to 0 normalized range. MACD line/signal/histogram and oscillators are normalized so slope checks, region tests, and confluence are comparable across symbols/timeframes. Line colors reflect overbought/oversold states to make regime/context changes easy to read.
Screenshot 3 — Adaptive dot permissions (sequence example)
This sequence shows adaptive dot permissions at work. After breakout persistence from a latched down‑channel, the controller permits a gold dot on the 5‑day view to confirm continuation (the highest timeframe uses gold‑only ). Soon after, the Top‑Zone gate engages, momentum slows (RSI/Stochastic RSI roll down, histogram decays), and a red dot warns of an early top. If deterioration persists, the Down‑Channel re‑latches and prints remain suppressed until the next verified recovery.
Limits & notes
100% original work: The WTN engine and controller logic are programmed from the ground up. No third‑party open‑source code, educational snippets, or auto‑generated code are reused.
No external libraries: Built in Pine v6 using standard language features only; no external libraries or ports of community scripts are used.
Chart type: Designed for standard time‑based candles only; non‑standard charts (Heikin Ashi, Renko, Kagi, P&F, Range) can produce unrealistic results.
Data handling: No lookahead and no future offsets.
Risk disclosure & legal notice
This tool is provided for educational purposes only and does not constitute financial or investment advice or recommendations.
Trading and investing involve risk, including possible loss of principal.
No guarantees or warranties of performance are expressed or implied. Past results do not predict future outcomes.
This publication does not include solicitation, pricing, or promotional offers; it provides information on the indicator’s design and use.
Use at your own risk. Test settings on paper and consult a qualified investment professional familiar with your risk tolerance before any live use.
AI Reversal Signals Custom [wjdtks255]📊 Indicator Overview: AI Reversal Signals Custom
This indicator is a comprehensive trend-following and reversal detection tool. It combines the long-term trend bias of a 200 EMA with highly sensitive RSI-based reversal signals and momentum visualization. It is designed to capture market bottoms and tops by identifying exhaustion points in price action.
Key Features
200 EMA (Trend Filter): A gold line representing the long-term institutional trend. It helps traders distinguish between "buying the dip" and "catching a falling knife."
Reversal Buy/Sell Labels: Real-time signals that appear when the market recovers from extreme overbought or oversold conditions.
Dynamic Background Clouds: Visual indicators of trend strength changes, highlighting potential entry zones.
Momentum Histogram: Internal calculations mimic the "Bottom Bars" seen in professional suites to track the velocity of price movement.
📈 Trading Strategy (How to Trade)
1. High-Probability Long Setup (Buy)
Trend Confirmation: Price should ideally be trading above the 200 EMA for the highest success rate.
Signal: Wait for the "BUY" label to appear below the candle.
Momentum: Confirm with the Light Green background or histogram shift indicating recovery.
Entry: Enter on the close of the signal candle.
2. High-Probability Short Setup (Sell)
Trend Confirmation: Price should ideally be trading below the 200 EMA.
Signal: Wait for the "SELL" label to appear above the candle.
Momentum: Confirm with the Red background or histogram fading from green to red.
Entry: Enter on the close of the signal candle.
3. Risk Management
Stop Loss: Place your Stop Loss slightly below the recent swing low for Buy orders, or above the recent swing high for Sell orders.
Take Profit: Exit when the price reaches a major support/resistance level or when an opposing signal appears.
💡 Professional Tip
For the best results, use this indicator on the 15-minute or 1-hour timeframes. The most powerful "Ultimate Reversal" signals occur when there is a Bullish Divergence (Price making lower lows while the RSI makes higher lows) followed by a confirmed "BUY" label.
CVD Oscillator - Alphaomega18ORDER FLOW DASHBOARD OSCILLATOR - TRADINGVIEW PUBLICATION (ENGLISH)
Created by Alphaomega18
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📌 PUBLICATION TITLE
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Order Flow Dashboard - CVD Oscillator & Pressures - Alphaomega18
📝 COMPLETE DESCRIPTION
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🎯 TRACK INSTITUTIONAL FLOW IN REAL-TIME
Order Flow Dashboard Oscillator is an advanced indicator that displays CVD (Cumulative Volume Delta) as a percentage oscillator, combined with real-time buy/sell pressures.
Unlike traditional CVD indicators where raw CVD reaches millions and crushes other data, this oscillator displays CVD deviation from its average in %, allowing clear reading on the SAME scale as pressures.
🔥 THE PROBLEM SOLVED
Classic CVD indicator problem:
✗ Raw CVD climbs to 1,000,000+ → Unreadable
✗ Pressures stay small (0-500) → Invisible
✗ Impossible to see both simultaneously
✗ Cluttered and confusing chart
Solution with CVD Oscillator:
✅ CVD displayed as % deviation (oscillates around 0)
✅ Pressures normalized on same scale
✅ EVERYTHING visible simultaneously
✅ Clear and intuitive reading
📊 INDICATOR COMPONENTS
🔷 CVD OSCILLATOR (Thick white line)
Traditional CVD accumulates infinitely:
→ Raw CVD = 50,000 ... 100,000 ... 500,000 ... 1,000,000+
→ Hard to interpret
CVD Oscillator shows DEVIATION:
→ CVD Oscillator = +5% ... +12% ... -3% ... -8%
→ Easy to interpret!
**How it works:**
• Calculates distance between CVD and its moving average (20 periods default)
• Converts to percentage
• Oscillates around 0 (gray center line)
**Interpretation:**
• **Above 0** → CVD > Average = BULLISH trend
• **Below 0** → CVD < Average = BEARISH trend
• **+10% zone** (green dotted line) → Buyer strength
• **-10% zone** (red dotted line) → Seller strength
🔷 BUY/SELL PRESSURES (Green/Red zones)
**Buy Pressure (Green zone)**
→ Calculated on bullish candles
→ Proportional to candle size
→ Normalized for optimal visibility
**Sell Pressure (Red zone)**
→ Calculated on bearish candles
→ Proportional to candle size
→ Normalized for optimal visibility
**Extreme Pressures** (Background)
→ 🟢 Light green background = EXTREME buy pressure (delta > 2x average)
→ 🔴 Light red background = EXTREME sell pressure (delta < -2x average)
🔷 REAL-TIME DASHBOARD (Top right corner)
Displays 6 key metrics:
1. **CVD Osc**: Oscillator value in %
2. **CVD Raw**: Raw CVD value (reference)
3. **Trend**: 🟢 Bullish or 🔴 Bearish
4. **Delta**: Current candle delta
5. **Volume**: HIGH (spike) or Normal
6. **Pressure**: 🚀 BUY / 💥 SELL / Neutral
🎯 HOW TO USE IT
📌 CASE 1: HOLD TRADES LONGER
**Classic problem:**
→ You're in a LONG
→ Price pulls back slightly, you panic
→ You exit... then price resumes up
→ Frustration: "I was right but exited too early!"
**Solution with CVD Oscillator:**
Example LONG trade:
1. You enter LONG on breakout
2. You watch CVD Oscillator
3. **As long as it stays ABOVE 0** → Keep the trade
4. Institutions continue accumulating
5. Trend remains intact
Exit:
→ CVD Oscillator **crosses below 0**
→ Signal: Institutions now selling
→ You exit or take profits
**Result:**
✅ You maximize your gains
✅ You exit at right time (when flow changes)
✅ You don't panic on small corrections
📌 CASE 2: CONFIRM TREND STRENGTH
**Setup:**
→ Price in uptrend
→ But is it real trend or just noise?
**Check CVD Oscillator:**
STRONG trend:
→ CVD Oscillator **stays positive** (+5%, +8%, +12%)
→ Dominant buy pressures (green zones)
→ Few or no red backgrounds
WEAK trend:
→ CVD Oscillator **oscillates around 0** (+2%, -1%, +3%)
→ Mixed pressures (green and red alternate)
→ Lack of conviction
**Action:**
✅ Strong trend → Trade with confidence
⚠️ Weak trend → Be cautious or avoid
📌 CASE 3: DETECT TREND CHANGE
**CVD Oscillator Divergence:**
Price makes higher highs BUT:
→ CVD Oscillator makes lower highs
→ +15% ... +12% ... +8% (progressive decline)
→ Sell pressures increasing
Signal: Distribution in progress
→ Institutions selling into rally
→ Reversal likely
→ Prepare SHORT or exit LONG
📌 CASE 4: OPTIMAL ENTRY TIMING
**Situation:**
→ Price consolidating
→ You wait for signal to enter
**LONG entry signal:**
→ CVD Oscillator **crosses above 0**
→ Green background (extreme buy pressure) appears
→ Dashboard: 🚀 BUY
Action: Enter LONG immediately
**SHORT entry signal:**
→ CVD Oscillator **crosses below 0**
→ Red background (extreme sell pressure) appears
→ Dashboard: 💥 SELL
Action: Enter SHORT immediately
⚙️ CUSTOMIZABLE PARAMETERS
🔧 **CVD Moving Average Length** (default: 20)
→ Moving average period for oscillator
→ Shorter (10-15) = More reactive, more signals
→ Longer (30-50) = Smoother, fewer false signals
👁️ **Show CVD Oscillator** (On/Off)
→ Show/hide CVD Oscillator line
👁️ **Show Buy/Sell Pressure** (On/Off)
→ Show/hide pressure zones
👁️ **Show Info Dashboard** (On/Off)
→ Show/hide information table
📊 RECOMMENDED CONFIGURATIONS
**For Day Trading (15min-1H):**
```
CVD MA Length: 20
Show CVD Oscillator: ✅ ON
Show Buy/Sell Pressure: ✅ ON
Show Info Dashboard: ✅ ON
```
**For Scalping (1-5min):**
```
CVD MA Length: 10 (more reactive)
Show CVD Oscillator: ✅ ON
Show Buy/Sell Pressure: ✅ ON
Show Info Dashboard: ✅ ON
```
**For Swing Trading (4H-Daily):**
```
CVD MA Length: 30 (smoother)
Show CVD Oscillator: ✅ ON
Show Buy/Sell Pressure: ✅ ON
Show Info Dashboard: ✅ ON
```
💡 MARKETS AND TIMEFRAMES
✅ **ALL markets compatible:**
• Futures (ES, NQ, YM, RTY, MNQ, MES, etc.)
• Forex (EUR/USD, GBP/USD, USD/JPY, etc.)
• Crypto (BTC, ETH, altcoins)
• Stocks (Tesla, Apple, Nvidia, etc.)
• Indices (S&P 500, Nasdaq, Dow Jones)
✅ **All timeframes:**
• Scalping: 1min, 5min
• Day Trading: 15min, 30min, 1H ⭐ (optimal!)
• Swing Trading: 4H, Daily
Note: More reliable with real volume data
→ TradingView Premium recommended
🏆 UNIQUE ADVANTAGES
✅ **CVD Oscillator**: % deviation instead of raw value
✅ **Same scale**: CVD and pressures visible together
✅ **Intuitive reading**: Above/below 0
✅ **Normalized pressures**: Always visible
✅ **Real-time dashboard**: 6 key metrics
✅ **Strength zones**: +10% and -10% marked
✅ **Background alerts**: Visual extreme pressures
✅ **Optimized code**: Light and fast
✅ **No repaint**: Reliable signals
🔗 PERFECT COMPLEMENT
Use with **Order Flow Signals** for complete system:
• **Order Flow Signals** (overlay=true) → Signals on chart
→ 💎 Absorptions, ▲ Divergences, 🚀 Pressures
• **Order Flow Dashboard** (overlay=false) → CVD and metrics
→ CVD Oscillator, Pressures, Live dashboard
**Complete system = 360° order flow vision!**
🎓 QUICK INTERPRETATION
**CVD Oscillator:**
• +5% to +10% = Moderate bullish
• +10% and above = STRONG bullish
• -5% to -10% = Moderate bearish
• -10% and below = STRONG bearish
• Near 0 = Neutral / Consolidation
**Pressures:**
• Large green zones = Dominant buying
• Large red zones = Dominant selling
• Balanced = Indecision
**Dashboard:**
• 🟢 Bullish + 🚀 BUY = Strong LONG signal
• 🔴 Bearish + 💥 SELL = Strong SHORT signal
• Massive positive delta = Bullish momentum
• Massive negative delta = Bearish momentum
⚠️ DISCLAIMER
Technical indicators are decision support tools. No indicator guarantees profits. Always use:
• Appropriate risk management
• Stop loss on every trade
• Proper position sizing
• Demo account testing first
Order Flow Dashboard improves your analysis but doesn't replace a complete strategy.
🚀 INSTALLATION
1. Copy the Pine Script code
2. Open Pine Editor in TradingView
3. Paste the code
4. Click "Add to Chart"
5. Indicator displays in separate pane (below)
6. Configure parameters to your preferences
7. Combine with Order Flow Signals for complete system!
💡 USAGE TIPS
**Golden Rule for Holding Trades:**
→ LONG: Keep as long as CVD Osc > 0
→ SHORT: Keep as long as CVD Osc < 0
**Strength Signals:**
→ CVD Osc > +10% = Very bullish
→ CVD Osc < -10% = Very bearish
**Trend Change:**
→ CVD Osc crosses 0 = Potential change
→ + Extreme background = Strong confirmation
📞 CONTACT AND SUPPORT
Created by Alphaomega18
For questions, bugs or suggestions:
Find my other indicators:
• Order Flow Signals (signals on chart)
• VWAP Multi-Timeframe Pro
• Fair Value Gap Detector
• Volume & Volatility Crisis Detector
Omni-Trend Analytics + Live PnL DashboardOverview
The Omni-Trend Analytics suite is an all-in-one technical command center. It integrates the battle-tested UT Bot signal logic with a sophisticated real-time dashboard, session tracking, and multi-timeframe trend analysis.
📊 The "Nexus" Dashboard
The heart of this script is the 6-row dynamic dashboard, designed to give you "at-a-glance" confluence:
RSI & RSI-MA: Tracks the standard RSI alongside a custom RSI-based Moving Average to spot momentum shifts before they hit the price.
Selectable Trend Status: Unlike static indicators, you can toggle the "Trend" source between EMA 9, 20, or 200 in the settings to match your trading style (Scalping vs. Swing).
Distance to EMA: Shows exactly how "overextended" the price is from your selected trend line.
ATR Volatility (Color-Coded): Turns Green when volatility is expanding (ideal for trend following) and Red when the market is contracting (ideal for range-trading or caution).
Live PnL Tracking: Automatically calculates the profit or loss of the most recent UT Bot signal in real-time.
🛠️ Key Features & Settings
Precision Signals: Combines UT Bot Buy/Sell labels with RSI "!" reversal warnings for high-probability entries.
Institutional Moving Averages: Includes 5 SMAs (including the 610 SMA) and 3 EMAs (9, 20, 200) all set to a professional Thickness 2 for clarity.
Session Highlighting: Automatically shades the background for London and New York sessions to help you trade when liquidity is highest.
VWAP Integration: Includes a purple VWAP line to ensure you are trading at a "fair value" relative to volume.
🔔 Strategic Alert Suite
The script comes pre-loaded with 6 specialized alert conditions:
UT Bot Signal: Standard entry alerts.
RSI Cross RSI-MA: Early warning for momentum reversals.
High-Prob UT + VWAP: Signals that only trigger when aligned with institutional volume.
EMA 9/20 Momentum Cross: Classic trend-shift notification.
ATR Volatility Spike: Alerts you to 50% increases in market volatility.
PnL Target / Break-Even: Pings you when your live trade reaches a user-defined profit threshold.
💡 Trading Pro-Tip
The Convergence Strategy: Look for a UT Bot Buy signal that occurs during the London/NY Overlap while the ATR is Green (expanding) and the RSI is crossing over its RSI-MA. This "triple confluence" is the primary design intent of the Omni-Trend suite.
Ichimoku Cloud Strategy - 1H HyperliquidStategy for Hyperliquid 1hr time frame using Ichimoku's Cloud.
Ultimate Confluence Oscillator PROUltimate Confluence Oscillator PRO
Multi-indicator momentum confluence with real-time bias, divergence, and expansion detection — all in one oscillator.
Ultimate Confluence Oscillator PRO is a professional-grade momentum indicator that combines RSI, Stochastic, MACD, divergence analysis, and higher-timeframe context into a single, clean oscillator designed for fast, confident decision-making.
Built for crypto, forex, futures, and equities, it helps traders identify when momentum conditions are aligned and when the market is transitioning from compression into expansion.
🔍 What This Indicator Does
Combines RSI, Stochastic, and MACD into a unified confluence framework
Highlights momentum agreement and disagreement across indicators
Detects momentum divergence using both RSI and MACD
Identifies compression → expansion conditions
Incorporates higher-timeframe trend context for directional awareness
📌 Real-Time Momentum HUD (Built-In)
The indicator includes a locked, on-chart information panel that updates in real time:
Current RSI value
Current Stochastic value
Automatic market state classification
Bullish
Bearish
Neutral
This allows traders to instantly evaluate momentum at any candle without switching indicators or performing manual checks.
Hover → Read → Decide.
📈 Signals & Alerts
Confluence-based BUY / SELL markers
Custom alert conditions for:
Strong momentum confluence
RSI divergence
MACD divergence
Alerts are informational and designed to support — not replace — a trading plan.
⚙️ Key Features
Non-repainting logic
Works on all timeframes
Clean visuals optimized for fast decision-making
Fully adjustable inputs
Suitable for scalping, intraday, and swing trading
🎯 Best Use Cases
Momentum confirmation before entries
Filtering low-quality setups in choppy markets
Identifying early expansion after consolidation
Aligning lower-timeframe trades with broader momentum context
⚠️ Disclaimer
This indicator is a technical analysis tool and does not provide financial advice. Always use proper risk management and your own trading plan.
MACD with Buy/Sell SignalsThe MACD (Moving Average Convergence Divergence) is a momentum indicator that shows the relationship between two exponential moving averages (EMAs) of a security's price. It consists of three key components:
configured to display o main chart
Sniper V53 - Forex Reactive + DashboardRSI + OBV calculation on 4 time frames for trend changes.
The indicator warns of possible trend changes; use additional confirmations for areas of interest.
Composite Index [Auto Signals]Composite Index
Description (描述正文):
Overview This is an enhanced version of the famous Composite Index (CI) developed by Connie Brown. While the traditional RSI is confined between 0 and 100, often masking true momentum in strong trends, the Composite Index is uncapped and incorporates a momentum component to reveal the market's true structural strength.
I have engineered this script to include Automated Signal Markers based on the crossover of the Composite Index and its Slow Moving Average. This helps traders instantly identify momentum shifts and "Timing" entries/exits without manual guesswork.
Key Features
Uncapped Momentum: Unlike RSI, the CI can go anywhere, preventing the "flattening" effect seen in strong trending markets (e.g., TSLA, NVDA).
Automated Signals:
▲ Green Triangle (Launch): Triggers when the Gray CI line crosses ABOVE the Red Slow MA. This indicates bearish momentum is exhausted and bulls are regaining control.
▼ Red Triangle (Warning): Triggers when the Gray CI line crosses BELOW the Red Slow MA. This indicates bullish momentum is failing, serving as an early warning for exits or tightening stops.
Classic Formula: Uses the standard Connie Brown parameters (14, 9, 3) + SMA smoothing for reliable divergence detection.
How to Use This Indicator This script is best used as a companion to trend indicators like TTM Squeeze or Moving Average Ribbons.
For Entries (The "Dip Buy"): In an uptrend, wait for a pullback. When the Green Triangle (▲) appears, it confirms that the pullback is over and momentum has turned back up.
For Exits (The "Top"): Look for Divergence. If Price makes a Higher High but the Composite Index makes a Lower High—followed by a Red Triangle (▼)—this is a high-probability sell signal.
The "Slow MA" Filter: The signals are generated only when the CI crosses the Slow MA (Red Line). This filters out the noise of minor fluctuations (crossing the Green line) and focuses on significant momentum changes.
Settings
RSI Period: 14 (Default)
Momentum Period: 9 (Default)
Signal Logic: Crossover/Crossunder of the Slow MA (33 Period).
Disclaimer This tool is for educational purposes only. Always combine momentum signals with price action and structure analysis.
Market Efficiency Ratio [Interakktive]The Market Efficiency Ratio decomposes price movement into two components: net progress vs wasted movement. This tool exposes the underlying math that most traders never see, helping you understand when price is moving efficiently versus chopping sideways.
Unlike simple trend indicators, this shows you WHY price movement matters — not just whether it's up or down, but how much of that movement was useful directional progress versus noisy oscillation.
█ WHAT IT DOES
• Calculates Efficiency Ratio (0–1 or 0–100) measuring directional progress
• Exposes Net Displacement (how far price actually moved)
• Exposes Path Length (total distance price traveled)
• Calculates Chop Cost (wasted movement)
• Visual zones for high/mid/low efficiency states
█ WHAT IT DOES NOT DO
• NO signals, NO entries/exits, NO buy/sell
• NO performance claims
• NO predictions — purely diagnostic
• This is a tool for understanding price behavior
█ HOW IT WORKS
The efficiency ratio answers one question: "Of all the movement price made, how much was useful progress?"
🔹 THE MATH
Over a lookback period of N bars:
Net Displacement = |Close - Close |
Path Length = Σ |Close - Close | for all bars
Efficiency Ratio = Net Displacement / Path Length
🔹 INTERPRETATION
• Efficiency = 1.0 (100%): Price moved in a straight line — every tick was progress
• Efficiency = 0.5 (50%): Half the movement was wasted in back-and-forth chop
• Efficiency = 0.0 (0%): Price ended exactly where it started — all movement was noise
🔹 CHOP COST
This is the "wasted movement" — how much price traveled without making progress:
Chop Cost = Path Length - Net Displacement
Chop % = Chop Cost / Path Length
High chop cost means lots of effort for little result — a warning sign for trend traders.
█ VISUAL GUIDE
Three efficiency zones:
• GREEN (≥70): High efficiency — strong directional movement
• YELLOW (30-70): Mixed efficiency — some progress, some chop
• RED (<30): Low efficiency — mostly noise, little progress
█ INPUTS
Lookback Length (default: 14)
Number of bars to calculate efficiency over. Higher values produce smoother readings but respond slower to changes.
Smoothing Length (default: 5)
EMA smoothing applied to the output. Reduces noise in the efficiency reading.
Apply Smoothing (default: true)
Toggle EMA smoothing on/off.
Scale Mode (default: 0–100)
Display as percentage (0-100) or decimal ratio (0-1).
Show Reference Bands (default: true)
Display the high/low efficiency threshold lines.
Low/High Efficiency Level (default: 30/70)
Thresholds for classifying efficiency zones.
Overlay Effect (default: None)
• None: No overlay
• Background Tint: Subtle chart background color in high/low zones
• Bar Highlight: Color bars during low efficiency periods
Show Data Window Values (default: true)
Export all raw values (Net Displacement, Path Length, Efficiency, Chop Cost, Chop %) to the data window for analysis.
█ USE CASES
This indicator helps traders understand:
• Why some trends are "clean" and others are "messy"
• When price is consolidating vs trending (without using volume)
• The relationship between movement and progress
• Why high-chop environments are difficult to trade
This is the foundational concept behind more advanced regime detection systems.
█ SUITABLE MARKETS
Works on: Stocks, Futures, Forex, Crypto
Timeframes: All timeframes
Note: This is a price-only indicator — no volume required
█ DISCLAIMER
This indicator is for informational and educational purposes only. It does not constitute financial advice. It does not generate trading signals. Past performance does not guarantee future results. Always conduct your own analysis.
Custom Reversal Oscillator [wjdtks255]📊 Indicator Overview: Custom Reversal Oscillator
This indicator is a momentum-based oscillator designed to identify potential trend reversals by analyzing price velocity and relative strength. It visualizes market exhaustion and recovery through a dynamic histogram and signal dots, similar to premium institutional tools.
Key Components
Dynamic Histogram (Bottom Bars): Changes color based on momentum strength. Bright Green/Red indicates accelerating momentum, while Darker shades suggest fading strength.
Signal Line: A white line tracing the core momentum, helping to visualize the "wave" of the market.
Buy/Sell Dots: Small circles at the bottom (Mint) or top (Red) that signal high-probability reversal points when the market is overextended.
📈 Trading Strategy (How to Trade)
1. Long Entry (Buy Signal)
Condition 1: The price should ideally be near or above the 200 EMA (for trend following) or showing a Bullish Divergence.
Condition 2: The Histogram bars transition from Dark Red to Bright Green.
Condition 3: A Mint Buy Dot appears at the bottom of the oscillator (near the -25 level).
Entry: Enter on the close of the candle where the Buy Dot is confirmed.
2. Short Entry (Sell Signal)
Condition 1: The price is struggling at resistance or showing a Bearish Divergence.
Condition 2: The Histogram bars transition from Dark Green to Bright Red.
Condition 3: A Red Sell Dot appears at the top of the oscillator (near the +25 level).
Entry: Enter on the close of the candle where the Sell Dot is confirmed.
3. Exit & Take Profit
Take Profit: Close the position when the Signal Line reaches the opposite extreme or when the histogram color starts to fade (loses its brightness).
Stop Loss: Place your stop loss slightly below the recent swing low (for Longs) or above the recent swing high (for Shorts).
💡 Pro Tips for Accuracy
Watch for Divergences: The most powerful signals occur when the price makes a lower low, but the Custom Reversal Oscillator makes a higher low. This indicates "Hidden Strength" and a massive reversal is often imminent.
Stochastic X-Score Signal📊 Stochastic X-Score Signal
This indicator is designed to analyze market momentum, direction, and strength in a single tool.
It combines Z-Score, Stochastic, Trend Filter, ADX/DI, and Volume to filter out high-quality trading signals.
🎯 Key Highlights
Measures price deviation using Z-Score
Converts data into Stochastic (0–100) to identify Overbought / Oversold
Uses HMA + ALMA to separate short-term momentum from long-term trend
Offers 4 signal sources, adjustable to different trading styles
Includes a Trend Filter to distinguish with-trend vs against-trend signals
Confirms real market strength with ADX/DI and Volume Gauge
⚙️ Signal System
🔺 BUY / 🔻 SELL from Reversal, Z-Score, ALMA, or MA Cross
With-trend signals = darker colors (stronger confirmation)
Against-trend signals = lighter colors (higher risk)
📊 Signal Quality Confirmation
ADX > 25 = strong trend
DI+ / DI- defines trend direction
Volume Candles clearly show buy vs sell pressure
🎨 Visualization
On-chart signals (Triangles + Bar Colors)
Indicator panel: Z-Score Histogram, Oscillator, ALMA, OB/OS zones
Gauge table for instant trend strength reading
🔔 Alerts Included
Bullish / Bearish (with-trend & against-trend)
MA Golden / Death Cross
Strong / Weak Trend alerts
High Buy / Sell Volume alerts
💡 Best For
Trend & Pullback traders
Traders who prefer one powerful indicator instead of many
Those who need signals with full market context
⚠️ This indicator is a market analysis tool and does not guarantee profits.
Always apply proper risk management when trading.
💬 Interested in our Indicator? Feel free to contact us via INBOX
📱 Facebook Page: Overdue Logic Indicator
www.facebook.com
ETHThe Indicator is using the combination of below indicators:
Relative Strength Index (RSI): A momentum oscillator used to identify overbought (above 70) or oversold (below 30) conditions, which can signal potential price reversals.
Moving Averages (MA & EMA): These smooth out price data to help identify the direction of the overall trend. Crossovers between different period MAs (e.g., a short-term MA crossing above a long-term MA) can generate buy or sell signals.
Moving Average Convergence Divergence (MACD): A trend-following momentum indicator that shows the relationship between two moving averages. A bullish crossover (MACD line above signal line) suggests upward momentum, while a bearish crossover (MACD line below signal line) indicates downward momentum.
Bollinger Bands: This volatility indicator consists of a middle band (moving average) and two outer bands based on standard deviation. Price touching the upper band may signal overbought conditions, while touching the lower band may signal oversold conditions or a potential bounce.
Volume Indicators (e.g., On-Balance Volume - OBV): Volume confirms the strength of a price movement. A price increase with high volume suggests strong buying pressure, validating the trend.
Ethereum Long/Short Ratio: This sentiment indicator compares the number of traders holding long positions versus short positions. A high ratio might indicate excessive bullish sentiment, potentially preceding a market correction.
Mystic Scales Dual Energy PRO [Destiny Quant]Mystic Scales Dual Energy PRO - Destiny Quant | 【天機衡】雙向能量
English Description
Balancing Momentum and Structure. Mystic Scales Dual Energy PRO utilizes a unique split-axis design to evaluate the balance between Market Momentum (WE2) and Market Health (WH1/WH2). It ensures you only execute trades when momentum is supported by a healthy market structure.
Custom Thresholds: Fully adjustable Entry/Exit score triggers with built-in hysteresis logic to prevent whipsaws.
Structural Health: Monitors DMI flows and Volume Ratios (VR) across Daily, Weekly, and Monthly timeframes.
Strategic Confluence: The perfect companion for the Celestial Mirror to confirm high-conviction entries.
中文說明
權衡動能與結構的平衡之衡 【天機衡】雙向能量 PRO 採用獨特的雙軸分離設計，同時權衡 「市場動能 (WE2)」 與 「市場健康度 (WH1/WH2)」。它確保您只在市場結構健康的前提下發動動能交易。
自訂門檻觸發：具備可調式進場/出場分數門檻，並內建遲滯邏輯 (Hysteresis) 有效過濾頻繁洗盤。
結構健康偵測：即時監控日、週、月線級別的 DMI 流向與成交量比率 (VR)。
策略共振：作為【天機鏡】的最佳拍檔，用來確認高勝率的共振進場時機。
🚀 Get Access / 獲取授權 This is an Invite-only script. To unlock the Celestial Mirror, please:
Visit the link in my profile.
Send a direct message for subscription details.
本指標為 僅限邀請 (Invite-only)。欲獲取授權，請：
點擊我個人主頁的連結（官網/商店）。
透過 TradingView 私訊聯繫我了解訂閱詳情。
Celestial Mirror AI Score PRO - Destiny QuantCelestial Mirror AI Score PRO - Destiny Quant | 【天機鏡】AI 評分系統
English Description
The Strategic Brain of Quantitative Trading. The Celestial Mirror AI Score PRO is a multi-factor weighting engine designed by Destiny Quant Lab. It acts as a digital "Mirror," revealing the hidden truth of market quality. By integrating over 10+ quantitative factors, including the proprietary Zanger Explosion Algorithm, it provides a real-time AI Score (0-99).
Institutional Detection: Uses advanced VSA logic to track "Smart Money" footprints.
Dual Engine: Switch between "Factor Analysis" (Swing) and "Explosion" (Momentum) modes.
Quant Dashboard: Real-time monitoring of momentum, volume structure, and pivot hierarchy.
中文說明
量化交易的策略大腦 【天機鏡】AI 評分系統 PRO 是由 天機量化實驗室 開發的多因子加權引擎。它如同數位之鏡，照見市場體質的虛實。本指標結合了 10 多項量化因子與獨家 Zanger 爆發演算法，將複雜盤面轉化為 0-99 的即時評分。
機構追蹤：透過進階量價分析 (VSA) 偵測大戶資金流向。
雙模式引擎：提供適合波段的「因子分析」與捕捉飆股噴發的「爆發預測」模式。
天機數據面板：即時監測動能、量能與樞軸位置，讓數據一目了然。
🚀 Get Access / 獲取授權 This is an Invite-only script. To unlock the Celestial Mirror, please:
Visit the link in my profile.
Send a direct message for subscription details.
本指標為 僅限邀請 (Invite-only)。欲獲取授權，請：
點擊我個人主頁的連結（官網/商店）。
透過 TradingView 私訊聯繫我了解訂閱詳情。
MTF rsi/stoch imdI just built this indicator.
It displays a multi-timeframe (MTF) table directly on the chart, showing Stoch RSI K and RSI values per timeframe.
Cell background colors are driven by predefined value ranges, while text color turns green or red depending on whether the value is rising or falling compared to the previous candle on the same timeframe.
The RSI color conditions are based on the levels 36, 46, 56, and 65.
The Timeframe Pack selector works as follows:
Pack 1 (BNC): 3m, 9m, 27m, 1h, 81m, 3h, 9h, 12h, 1D, 3D, 1W, 9D
Pack 2: 1h through 24h
Pack 3: 1D through 24D
Pack 4 (Custom): fully user-defined timeframes via the 24 slots
Only when Pack 4 (Custom) is selected do the custom timeframe slots apply; in Packs 1–3 they are ignored.
All visual behavior (box colors, text colors, transparency, or a single-color override) is configurable under Style, and the entire table can be toggled on or off.
BK AK-Zenith💥 Introducing BK AK-ZENITH — Adaptive Rhythm RSI for Peak/Valley Warfare 💥
This is not another generic RSI. This is ZENITH: it measures where momentum is on the scale, then tells you when it’s hitting extremes, when it’s turning, and when price is lying through its teeth with divergence.
At its core, ZENITH does one thing ruthlessly well:
it matches the oscillator’s period to the market’s current rhythm—adaptive when the market is fast, adaptive when the market is slow—so your signals stop being “late because the settings were wrong.”
🎖 Full Credit — Respect the Origin (AlgoAlpha)
The core RSI architecture in this form belongs to AlgoAlpha—one of the best introducers and coders on TradingView. They originated this adaptive/Rhythm-RSI framework and the way it’s presented and engineered.
BK AK-ZENITH is my enhancement layer on top of AlgoAlpha’s foundation.
I kept the spine intact, and I added tactical systems: clearer Peak/Valley warfare logic, pivot governance (anti-spam), divergence strike markers, momentum flip confirmation, and a war-room readout—so it trades like a weapon, not a toy.
Respect where it started: AlgoAlpha built the engine. I tuned it for battlefield use.
🧠 What Exactly is BK AK-ZENITH?
BK AK-ZENITH is an Adaptive Period RSI (or fixed if you choose), designed to read momentum like a range of intent rather than a single overbought/oversold gimmick.
Core Systems Inside ZENITH
✅ Adaptive Period RSI (Rhythm Engine)
Automatically adjusts its internal RSI length to match current market cadence.
(Optional fixed length mode if you want static.)
✅ Optional HMA Smoothing
Cleaner shape without turning it into a laggy moving average.
✅ Peak / Valley Zones (default 80/20)
Hard boundaries that define “true extremes” so you stop treating every wiggle like a signal.
✅ Pivot-Based BUY/SELL Triangles + Cooldown
Signals are governed by pivots and a cooldown so it doesn’t machine-gun trash.
✅ Momentum Flip Diamonds (◇)
Shows when the oscillator’s slope flips—clean confirmation for “engine change.”
✅ Divergence Lightning (⚡)
Exposes when price is performing confidence while momentum is quietly breaking.
✅ War-Room Table / Meter
Bias, zone, reading, and adaptive period printed so you don’t “interpret”—you execute.
✅ Alerts Suite
Pivots, divergences, zone entries—so the chart calls you, not your emotions.
🎯 How to use it (execution rules)
1) Zones = permission
Valley (≤ Valley level): demand territory. Stalk reversal structure; stop chasing breakdown candles.
Peak (≥ Peak level): supply territory. Harvest, tighten, stop adding risk at the top.
2) Pivot triangles = the shot clock
Your ▲/▼ signals are pivot-confirmed with a cooldown. That’s intentional.
This is designed to force patience and prevent overtrading.
3) Divergence = truth serum
When price makes the “confident” high/high or low/low but ZENITH disagrees, you’re seeing internal change before the crowd does.
Treat divergence as warning + timing context, not a gambling button.
4) Meter/Table = discipline
If you can’t summarize the state in one glance, you’ll overtrade. ZENITH prints the state so your brain stops inventing stories.
🔧 Settings that actually matter
Adaptive Period ON (default): the whole point of ZENITH
Peak/Valley levels: how strict extremes must be
Pivot strength + Cooldown: your anti-spam governor
Divergence pivot length: controls how “major” divergence must be
The “AK” in the name is an acknowledgment of my mentor A.K. His standards—patience, precision, clarity, emotional control—are why this tool is built with governors instead of hype.
And above all: all praise to Gd—the true source of wisdom, restraint, and right timing.
👑 King Solomon Lens — ZENITH Discernment
Solomon asked Gd for something most people never ask for: not wealth, not victory—discernment. The ability to separate what looks true from what is true.
That is exactly what momentum work is supposed to do.
1) Honest weights, honest measures.
In Solomon’s world, crooked scales were an abomination because they disguised reality. In trading, the crooked scale is your own excitement: you see one green candle and call it strength. ZENITH forces an honest measure—0 to 100—so you deal in degree, not drama. A Peak is not “bullish.” A Peak is “momentum priced in.” A Valley is not “bearish.” A Valley is “selling pressure reaching exhaustion.”
2) Wisdom adapts to seasons.
Solomon’s order wasn’t chaos—there was a time to build, a time to harvest, a time to wait. Markets have seasons too: trend seasons, chop seasons, compression seasons, expansion seasons. Fixed-length RSI pretends every season is the same. ZENITH does not. It listens for rhythm and adjusts its internal timing so your read stays relevant to today’s market tempo—not last month’s.
3) The sword test: revealing what’s hidden.
Solomon’s most famous judgment wasn’t about theatrics—it was about revealing the truth beneath appearances. Divergence is that same test in markets: price can perform strength while the engine quietly weakens, or perform weakness while momentum secretly repairs. The ⚡ is not a prophecy. It’s a revelation: “what you see on price is not the full story.”
That’s ZENITH discipline: measure → discern → execute.
And may Gd bless your judgment to act only when the measure is clean.
⚔️ Final
BK AK-ZENITH is a momentum fire-control system: adaptive rhythm + extreme zones + pivot timing + divergence truth.
Use it to stop feeling trades and start weighing them. Praise to Gd always. 🙏
Kinetic Elasticity Reversion System - Adaptive Genesis Engine🧬 KERS-AGE - EVOLVED KINETIC ELASTICITY REVERSION SYSTEM
EDUCATIONAL GUIDE & THEORETICAL FOUNDATION
⚠️ IMPORTANT DISCLAIMER
This indicator and guide are provided for educational and informational purposes only. This is NOT financial advice, investment advice, or a recommendation to buy or sell any security.
Trading involves substantial risk of loss. Past performance does not guarantee future results. The performance metrics, win rates, and examples shown are from historical backtesting and do not represent actual trading results. Always conduct your own research, paper trade extensively, and never risk capital you cannot afford to lose.
The developers assume no responsibility for any trading losses incurred through use of this indicator.
INTRODUCTION
KERS-AGE (Kinetic Elasticity Reversion System - Adaptive Genetic Evolution) represents an educational exploration of adaptive trading systems. Unlike traditional indicators with fixed parameters, KERS-AGE demonstrates a dynamic, evolving approach that adjusts to market conditions through genetic algorithms and machine learning techniques.
This guide explains the theoretical concepts, technical implementation, and educational examples of how the system operates.
CONCEPTUAL FRAMEWORK
Traditional Indicators vs. Adaptive Systems:
Traditional Indicators:
Fixed parameters
Single strategy approach
Static behavior
Designed for specific conditions
Require manual optimization
Adaptive System Approach (KERS-AGE):
Dynamic parameters (adjust based on conditions)
Multiple strategies tested simultaneously
Pattern recognition (cluster analysis)
Regime-aware (speciation)
Automated optimization (genetic algorithms)
Transparent operation (detailed dashboard)
CORE CONCEPTS EXPLAINED
1. THE ELASTICITY ANALOGY 🎯
The indicator models price behavior as if connected to a moving average by an elastic band:
Price extends away → Elastic tension builds → Potential reversion point identified
Key Measurements:
STRETCH: Distance from price to equilibrium (MA)
TENSION: Normalized force calculation
THRESHOLD: Point where multiple factors align
Theoretical Foundation:
Markets have historically shown mean-reverting tendencies around fair value. This concept quantifies the deviation and identifies potential reversal zones based on multiple confluence factors.
Mathematical Approach:
text
Tension Score = (Price Distance from MA) / (Band Width) × Volatility Scaling
Signal Threshold = Multiple of ATR × Dynamic Volatility Ratio
Confluence = Tension Score + Additional Factors
2. THE 6 SIGNAL TYPES 📊
The system recognizes 6 distinct pattern categories:
A. ELASTIC SIGNALS
Pattern: Price reaches statistical band extremes
Theory: Maximum deviation from mean suggests potential reversion
Detection: Price touches outer zones (typically 2-3× ATR from MA)
Component: Mathematical band extension measurement
Historical Context: Often observed in markets with clear swing patterns
B. WICK SIGNALS
Pattern: Extended rejection wicks on candles
Theory: Failed breakout attempts may indicate directional exhaustion
Detection: Upper/lower wick exceeding 2× body size
Component: Real-time price rejection measurement
Historical Context: Common in volatile conditions with rapid reversals
C. EXHAUSTION SIGNALS
Pattern: Decelerating momentum despite price extension
Theory: Velocity and acceleration divergence may precede reversals
Detection: Decreasing velocity with negative acceleration
Component: Momentum derivative analysis
Historical Context: Often seen at trend maturity points
D. CLIMAX SIGNALS
Pattern: Volume spike at price extreme
Theory: Unusual volume at extremes historically correlates with turning points
Detection: Volume 1.5-2.5× average at band extreme
Component: Volume-price relationship analysis
Historical Context: Associated with institutional activity or capitulation
E. STRUCTURE SIGNALS
Pattern: Fractal pivot formations (swing highs/lows)
Theory: Market structure points have historically acted as support/resistance
Detection: 2-4 bar pivot patterns
Component: Classical technical analysis
Historical Context: Universal across timeframes and markets
F. DIVERGENCE SIGNALS
Pattern: RSI divergence versus price
Theory: Momentum divergence has historically preceded price reversals
Detection: Price makes new extreme but RSI does not
Component: Oscillator divergence detection
Historical Context: Considered a leading indicator in technical analysis
Pattern Confluence:
Historical testing suggests stronger signals when multiple types align:
Elastic + Wick + Volume = Higher confluence score
Elastic + Exhaustion + Divergence = Multiple confirmation factors
Any 3+ types = Increased pattern strength
Note: Past pattern performance does not guarantee future occurrence.
3. REGIME DETECTION 🌍
The system attempts to classify market conditions into three behavioral regimes:
📈 TREND REGIME
Detection Methodology:
text
Efficiency Ratio = Net Movement / Total Movement
Classification: Efficiency > 0.5 AND Volatility < 1.3 → TREND
Characteristics Observed:
Directional price movement
Relatively lower volatility
Defined higher highs/lower lows
Persistent directional momentum
System Response:
Reduces signal frequency
Prioritizes trend-specialist strategies
Applies additional filtering to counter-trend signals
Increases confluence requirements
Educational Note:
In trending conditions, counter-trend mean reversion signals historically have shown reduced reliability. Users may consider additional confirmation when trend regime is detected.
↔️ RANGE REGIME
Detection Methodology:
text
Classification: Efficiency < 0.5 AND Volatility 0.9-1.4 → RANGE
Characteristics Observed:
Oscillating price action
Defined support/resistance zones
Mean-reverting behavior patterns
Relatively balanced directional flow
System Response:
Increases signal frequency
Activates range-specialist strategies
Adjusts bands relative to volatility
Reduces confluence threshold
Educational Note:
Historical backtesting suggests mean reversion systems have performed better in ranging conditions. This does not guarantee future performance.
🌊 VOLATILE REGIME
Detection Methodology:
text
Classification: DVS (Dynamic Volatility Scaling) > 1.5 → VOLATILE
Characteristics Observed:
Erratic price swings
Expanded ranges
Elevated ATR readings
Often news or event-driven
System Response:
Activates volatility-specialist strategies
Widens bands automatically
Prioritizes wick rejection signals
Emphasizes volume confirmation
Educational Note:
Volatile conditions historically present both opportunity and increased risk. Wider stops may be appropriate for risk management.
4. GENETIC EVOLUTION EXPLAINED 🧬
The system employs genetic algorithms to optimize parameters - an approach used in computational finance research.
The Evolution Process:
STEP 1: INITIALIZATION
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Initial State: System creates 4 starter strategies
- Strategy 0: Range-optimized parameters
- Strategy 1: Trend-optimized parameters
- Strategy 2: Volatility-optimized parameters
- Strategy 3: Balanced parameters
Each contains 14 adjustable parameters (genes):
- Band sensitivity
- Extension multiplier
- Wick threshold
- Momentum threshold
- Volume multiplier
- Component weights (elastic, wick, momentum, volume, fractal)
- Target percentage
STEP 2: COMPETITION (Shadow Trading)
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Early Bars: All strategies generate signals in parallel
- Each tracks hypothetical performance independently
- Simulated P&L, win rate, Sharpe ratio calculated
- No actual trades executed (educational simulation)
- Performance metrics recorded for analysis
STEP 3: FITNESS EVALUATION
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Fitness Calculation =
0.25 × Win Rate +
0.25 × PnL Score +
0.15 × Drawdown Score +
0.30 × Sharpe Ratio Score +
0.05 × Trade Count Score
With Walk-Forward enabled:
Fitness = 0.60 × Test Score + 0.40 × Train Score
With Speciation enabled:
Fitness adjusted by Diversity Penalty
STEP 4: SELECTION (Tournament)
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Periodically (default every 50 bars):
- Randomly select 4 active strategies
- Compare fitness scores
- Top 2 selected as "parents"
STEP 5: CROSSOVER (Breeding)
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Parent 1 Fitness: 0.65
Parent 2 Fitness: 0.55
Weight calculation: 0.65/(0.65+0.55) = 54%
For each parameter:
Child Parameter = (0.54 × Parent1) + (0.46 × Parent2)
Example:
Band Sensitivity: (0.54 × 1.5) + (0.46 × 2.0) = 1.73
STEP 6: MUTATION
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For each parameter:
if random(0-1) < Mutation Rate (default 0.15):
Add random variation: -12% to +12%
Purpose: Prevents premature convergence
Enables: Discovery of novel parameter combinations
ADAPTIVE MUTATION:
If population fitness converges → Mutation rate × 1.5
(Encourages exploration when diversity decreases)
STEP 7: INSERTION
text
New strategy added to population:
- Assigned unique ID number
- Generation counter incremented
- Begins shadow trading
- Competes with existing strategies
STEP 8: CULLING (Selection Pressure)
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Periodically (default every 100 bars):
- Identify lowest fitness strategy
- Verify not elite (protected top performers)
- Verify not last of species
- Remove from population
Result: Maintains selection pressure
Effect: Prevents weak strategies from diluting signals
STEP 9: SIGNAL GENERATION LOGIC
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When determining signals to display:
If Ensemble enabled:
- All strategies cast weighted votes
- Weights based on fitness scores
- Specialists receive boost in matching regime
- Signal generated if consensus threshold reached
If Ensemble disabled:
- Single highest-fitness strategy used
STEP 10: ADAPTATION OBSERVATION
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Over time: Population characteristics may shift
- Lower-performing strategies removed
- Higher-performing strategies replicated
- Parameters adjust toward observed optima
- Fitness scores generally trend upward
Long-term: Population reaches maturity
- Strategies become specialized
- Parameters optimized for recent conditions
- Performance stabilizes
Educational Context:
Genetic algorithms are a recognized computational method for optimization problems. This implementation applies those concepts to trading parameter optimization. Past optimization results do not guarantee future performance.
5. SPECIATION (Niche Specialization) 🐟🦎🦅
Inspired by biological speciation theory applied to algorithmic trading.
The Three Species:
RANGE SPECIALISTS 📊
text
Optimized for: Sideways market conditions
Parameter tendencies:
- Tighter bands (1.0-1.5× ATR)
- Higher sensitivity to elastic stretch
- Emphasis on fractal structure
- More frequent signal generation
Typically emerge when:
- Range regime detected
- Clear support/resistance present
- Mean reversion showing historical success
Historical backtesting observations:
- Win rates often in 55-65% range
- Smaller reward/risk ratios (0.5-1.5R)
- Higher trade frequency
TREND SPECIALISTS 📈
text
Optimized for: Directional market conditions
Parameter tendencies:
- Wider bands (2.0-2.5× ATR)
- Focus on momentum exhaustion
- Emphasis on divergence patterns
- More selective signal generation
Typically emerge when:
- Trend regime detected
- Strong directional movement observed
- Counter-trend exhaustion signals sought
Historical backtesting observations:
- Win rates often in 40-55% range
- Larger reward/risk ratios (1.5-3.0R)
- Lower trade frequency
VOLATILITY SPECIALISTS 🌊
text
Optimized for: High-volatility conditions
Parameter tendencies:
- Expanded bands (1.5-2.0× ATR)
- Priority on wick rejection patterns
- Strong volume confirmation requirement
- Very selective signals
Typically emerge when:
- Volatile regime detected
- High DVS ratio (>1.5)
- News-driven or event-driven conditions
Historical backtesting observations:
- Win rates often in 50-60% range
- Variable reward/risk ratios (1.0-2.5R)
- Opportunistic trade timing
Species Protection Mechanism:
text
Minimum Per Species: Configurable (default 2)
If Range specialists = 1:
→ Preferential spawning of Range type
→ Protection from culling process
Purpose: Ensures coverage across regime types
Theory: Markets cycle between behavioral states
Goal: Prevent extinction of specialized approaches
Fitness Sharing:
text
If Species has 4 members:
Individual Fitness × 1 / (4 ^ 0.3)
Individual Fitness × 0.72
Purpose: Creates pressure toward species diversity
Effect: Prevents single approach from dominating population
Educational Note: Speciation is a theoretical framework for maintaining strategy diversity. Past specialization performance does not guarantee future regime classification accuracy or signal quality.
6. WALK-FORWARD VALIDATION 📈
An out-of-sample testing methodology used in quantitative research to reduce overfitting risk.
The Overfitting Problem:
text
Hypothetical Example:
In-Sample Backtest: 85% win rate
Out-of-Sample Results: 35% win rate
Explanation: Strategy may have optimized to historical noise
rather than repeatable patterns
Walk-Forward Methodology:
Timeline Structure:
text
┌──────────────────────────────────────────────────────┐
│ Train Window │ Test Window │ Train │ Test │
│ (200 bars) │ (50 bars) │ (200) │ (50) │
└──────────────────────────────────────────────────────┘
In-Sample Out-of-Sample IS OOS
(Optimize) (Validate) Cycle 2...
TRAIN PHASE (In-Sample):
text
Example Bars 1-200: Strategies optimize parameters
- Performance tracked
- Not yet used for primary fitness
- Learning period
TEST PHASE (Out-of-Sample):
text
Example Bars 201-250: Strategies use optimized parameters
- Performance tracked separately
- Validation period
- Out-of-sample evaluation
FITNESS CALCULATION EXAMPLE:
text
Train Win Rate: 65%
Test Win Rate: 58%
Composite Fitness:
= (0.40 × 0.65) + (0.60 × 0.58)
= 0.26 + 0.35
= 0.61
Note: Test results weighted 60%, Train 40%
Theory: Out-of-sample may better indicate forward performance
OVERFIT DETECTION MECHANISM:
text
Gap = Train WR - Test WR = 65% - 58% = 7%
If Gap > Overfit Threshold (default 25%):
Fitness Penalty = Gap × 2
Example with 30% gap:
Strategy shows: Train 70%, Test 40%
Gap: 30% → Potential overfit flagged
Penalty: 30% × 2 = 60% fitness reduction
Result: Strategy likely to be culled
WINDOW ROLLING:
text
Example Bar 250: Test window complete
→ Reset both windows
→ Start new cycle
→ Previous results retained for analysis
Cycle Count increments
Historical performance tracked across multiple cycles
Educational Context:
Walk-forward analysis is a recognized approach in quantitative finance research for evaluating strategy robustness. However, past out-of-sample performance does not guarantee future results. Market conditions can change in ways not represented in historical data.
7. CLUSTER ANALYSIS 🔬
An unsupervised machine learning approach for pattern recognition.
The Concept:
text
Scenario: System identifies a price pivot that wasn't signaled
→ Extract pattern characteristics
→ Store features for analysis
→ Adjust detection for similar future patterns
Implementation:
STEP 1: FEATURE EXTRACTION
text
When significant move occurs without signal:
Extract 5-dimensional feature vector:
Feature Vector =
Example:
Observed Pattern:
STEP 2: CLUSTER ASSIGNMENT
text
Compare to existing cluster centroids using distance metric:
Cluster 0:
Cluster 1: ← Minimum distance
Cluster 2:
...
Assign to nearest cluster
STEP 3: CENTROID UPDATE
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Old Centroid 1:
New Pattern:
Decay Rate: 0.95
Updated Centroid:
= 0.95 × Old + 0.05 × New
= Exponential moving average update
=
STEP 4: PROFIT TRACKING
text
Cluster Average Profit (hypothetical):
Old Average: 2.5R
New Observation: 3.2R
Updated: 0.95 × 2.5 + 0.05 × 3.2 = 2.535R
STEP 5: LEARNING ADJUSTMENT
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If Cluster Average Profit > Threshold (e.g., 2.0R):
Cluster Learning Boost += increment (e.g., 0.1)
(Maximum cap: 2.0)
Effect: Future signals resembling this cluster receive adjustment
STEP 6: SCORE MODIFICATION
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For signals matching cluster characteristics:
Base Score × Cluster Learning Boost
Example:
Base Score: 5.2
Cluster Boost: 1.3
Adjusted Score: 5.2 × 1.3 = 6.76
Result: Pattern more likely to generate signal
Cluster Interpretation Example:
text
CLUSTER 0: "High elastic, low volume"
Centroid:
Avg Profit: 3.5R (historical backtest)
Interpretation: Pure elastic signals in ranges historically favorable
CLUSTER 1: "Wick rejection, volatile"
Centroid:
Avg Profit: 2.8R (historical backtest)
Interpretation: Wick signals in volatility showed positive results
CLUSTER 2: "Exhaustion divergence"
Centroid:
Avg Profit: 4.2R (historical backtest)
Interpretation: Momentum exhaustion in trends performed well
Learning Progress Metrics:
text
Missed Total: 47
Clusters Updated: 142
Patterns Learned: 28
Interpretation:
- System identified 47 significant moves without signals
- Clusters updated 142 times (incremental refinement)
- Made 28 parameter adjustments
- Theoretically improving pattern recognition
Educational Note: Cluster analysis is a recognized machine learning technique. This implementation applies it to trading pattern recognition. Past cluster performance does not guarantee future pattern profitability or accurate classification.
8. ENSEMBLE VOTING 🗳️
A collective decision-making approach common in machine learning.
The Wisdom of Crowds Concept:
text
Single Model:
- May have blind spots
- Subject to individual bias
- Limited perspective
Ensemble of Models:
- Blind spots may offset
- Biases may average out
- Multiple perspectives considered
Implementation:
STEP 1: INDIVIDUAL VOTES
text
Example Bar 247:
Strategy 0 (Range): LONG (fitness: 0.65)
Strategy 1 (Trend): FLAT (fitness: 0.58)
Strategy 2 (Volatile): LONG (fitness: 0.52)
Strategy 3 (Balanced): SHORT (fitness: 0.48)
Strategy 4 (Range): LONG (fitness: 0.71)
Strategy 5 (Trend): FLAT (fitness: 0.55)
STEP 2: WEIGHT CALCULATION
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Base Weight = Fitness Score
If strategy's species matches current regime:
Weight × Specialist Boost (configurable, default 1.5)
If strategy has recent positive performance:
Weight × Recent Performance Factor
Example for Strategy 0:
Base: 0.65
Range specialist in Range regime: 0.65 × 1.5 = 0.975
Recent performance adjustment: 0.975 × 1.13 = 1.10
STEP 3: WEIGHTED TALLYING
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LONG votes:
S0: 1.10 + S2: 0.52 + S4: 0.71 = 2.33
SHORT votes:
S3: 0.48 = 0.48
FLAT votes:
S1: 0.58 + S5: 0.55 = 1.13
Total Weight: 2.33 + 0.48 + 1.13 = 3.94
STEP 4: CONSENSUS CALCULATION
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LONG %: 2.33 / 3.94 = 59.1%
SHORT %: 0.48 / 3.94 = 12.2%
FLAT %: 1.13 / 3.94 = 28.7%
Minimum Consensus Setting: 60%
Result: NO SIGNAL (59.1% < 60%)
STEP 5: SIGNAL DETERMINATION
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If LONG % >= Min Consensus:
→ Display LONG signal
→ Show consensus percentage in dashboard
If SHORT % >= Min Consensus:
→ Display SHORT signal
If neither threshold reached:
→ No signal displayed
Practical Examples:
text
Strong Consensus (85%):
5 strategies LONG, 0 SHORT, 1 FLAT
→ High agreement among models
Moderate Consensus (62%):
3 LONG, 2 SHORT, 1 FLAT
→ Borderline agreement
No Consensus (48%):
3 LONG, 2 SHORT, 1 FLAT
→ Insufficient agreement, no signal shown
Educational Note: Ensemble methods are widely used in machine learning to improve model robustness. This implementation applies ensemble concepts to trading signals. Past ensemble performance does not guarantee future signal quality or profitability.
9. THOMPSON SAMPLING 🎲
A Bayesian reinforcement learning technique for balancing exploration and exploitation.
The Exploration-Exploitation Dilemma:
text
EXPLOITATION: Use what appears to work
Benefit: Leverages observed success patterns
Risk: May miss better alternatives
EXPLORATION: Try less-tested approaches
Benefit: May discover superior methods
Risk: May waste resources on inferior options
Thompson Sampling Solution:
STEP 1: BETA DISTRIBUTIONS
text
For each signal type, maintain:
Alpha = Successes + 1
Beta = Failures + 1
Example for Elastic signals:
15 wins, 10 losses
Alpha = 16, Beta = 11
STEP 2: PROBABILITY SAMPLING
text
Rather than using simple Win Rate = 15/25 = 60%
Sample from Beta(16, 11) distribution:
Possible samples: 0.55, 0.62, 0.58, 0.64, 0.59...
Rationale: Incorporates uncertainty
- Type with 5 trades: High uncertainty, wide sample variation
- Type with 50 trades: Lower uncertainty, narrow sample range
STEP 3: TYPE PRIORITIZATION
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Example Bar 248:
Elastic sampled: 0.62
Wick sampled: 0.58
Exhaustion sampled: 0.71 ← Highest this sample
Climax sampled: 0.52
Structure sampled: 0.63
Divergence sampled: 0.45
Exhaustion type receives temporary boost
STEP 4: SIGNAL ADJUSTMENT
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If current signal is Exhaustion type:
Score × (0.7 + 0.71 × 0.6)
Score × 1.126
If current signal is other type with lower sample:
Score × (0.7 + sample × 0.6)
(smaller adjustment)
STEP 5: OUTCOME FEEDBACK
text
When trade completes:
If WIN:
Alpha += 1
(Beta unchanged)
If LOSS:
Beta += 1
(Alpha unchanged)
Effect: Shifts probability distribution for future samples
Educational Context:
Thompson Sampling is a recognized Bayesian approach to the multi-armed bandit problem. This implementation applies it to signal type selection. The mathematical optimality assumes stationary distributions, which may not hold in financial markets. Past sampling performance does not guarantee future type selection accuracy.
10. DYNAMIC VOLATILITY SCALING (DVS) 📉
An adaptive approach where parameters adjust based on current vs. baseline volatility.
The Adaptation Problem:
text
Fixed bands (e.g., always 1.5 ATR):
In low volatility environment (vol = 0.5):
Bands may be too wide → fewer signals
In high volatility environment (vol = 2.0):
Bands may be too tight → excessive signals
The DVS Approach:
STEP 1: BASELINE ESTABLISHMENT
text
Calculate volatility over baseline period (default 100 bars):
Method options: ATR / Close, Parkinson, or Garman-Klass
Example average volatility = 1.2%
This represents "normal" for recent conditions
STEP 2: CURRENT VOLATILITY
text
Current bar volatility = 1.8%
STEP 3: DVS RATIO
text
DVS Ratio = Current / Baseline
= 1.8 / 1.2
= 1.5
Interpretation: Volatility currently 50% above baseline
STEP 4: BAND ADJUSTMENT
text
Base Band Width: 1.5 ATR
Adjusted Band Width:
Upper: 1.5 × DVS = 1.5 × 1.5 = 2.25 ATR
Lower: Same
Result: Bands expand 50% to accommodate higher volatility
STEP 5: THRESHOLD ADJUSTMENT
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Base Thresholds:
Wick: 0.15
Momentum: 0.6
Adjusted:
Wick: 0.15 / DVS = 0.10 (easier to trigger in high vol)
Momentum: 0.6 × DVS = 0.90 (harder to trigger in high vol)
DVS Calculation Methods:
text
ATR RATIO (Simplest):
DVS = (ATR / Close) / SMA(ATR / Close, 100)
PARKINSON (Range-based):
σ = √(∑(ln(H/L))² / (4×n×ln(2)))
DVS = Current σ / Baseline σ
GARMAN-KLASS (Comprehensive):
σ = √(0.5×(ln(H/L))² - (2×ln(2)-1)×(ln(C/O))²)
DVS = Current σ / Baseline σ
ENSEMBLE (Robust):
DVS = Median(ATR_Ratio, Parkinson, Garman_Klass)
Educational Note: Dynamic volatility scaling is an approach to normalize indicators across varying market conditions. The effectiveness depends on the assumption that recent volatility patterns continue, which is not guaranteed. Past volatility adjustment performance does not guarantee future normalization accuracy.
11. PRESSURE KERNEL 💪
A composite measurement attempting to quantify directional force beyond simple price movement.
Components:
1. CLOSE LOCATION VALUE (CLV)
text
CLV = ((Close - Low) - (High - Close)) / Range
Examples:
Close at top of range: CLV = +1.0 (bullish position)
Close at midpoint: CLV = 0.0 (neutral)
Close at bottom: CLV = -1.0 (bearish position)
2. WICK ASYMMETRY
text
Wick Pressure = (Lower Wick - Upper Wick) / Range
Additional factors:
If Lower Wick > Body × 2: +0.3 (rejection boost)
If Upper Wick > Body × 2: -0.3 (rejection penalty)
3. BODY MOMENTUM
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Body Ratio = Body Size / Range
Body Momentum = Close > Open ? +Body Ratio : -Body Ratio
Strong bullish candle: +0.9
Weak bullish candle: +0.2
Doji: 0.0
4. PATH ESTIMATE
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Close Position = (Close - Low) / Range
Open Position = (Open - Low) / Range
Path = Close Position - Open Position
Additional adjustments:
If closed high with lower wick: +0.2
If closed low with upper wick: -0.2
5. MOMENTUM CONFIRMATION
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Price Change / ATR
Examples:
+1.5 ATR move: +1.0 (capped)
+0.5 ATR move: +0.5
-0.8 ATR move: -0.8
COMPOSITE CALCULATION:
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Pressure =
CLV × 0.25 +
Wick Pressure × 0.25 +
Body Momentum × 0.20 +
Path Estimate × 0.15 +
Momentum Confirm × 0.15
Volume context applied:
If Volume > 1.5× avg: × 1.3
If Volume < 0.5× avg: × 0.7
Final smoothing: 3-period EMA
Pressure Interpretation:
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Pressure > 0.3: Suggests buying pressure
→ May support LONG signals
→ May reduce SHORT signal strength
Pressure < -0.3: Suggests selling pressure
→ May support SHORT signals
→ May reduce LONG signal strength
-0.3 to +0.3: Neutral range
→ Minimal directional bias
Educational Note: The Pressure Kernel is a custom composite indicator combining multiple price action metrics. These weightings are theoretical constructs. Past pressure readings do not guarantee future directional movement or signal quality.
USAGE GUIDE - EDUCATIONAL EXAMPLES
Getting Started:
STEP 1: Add Indicator
Open TradingView
Add KERS-AGE to chart
Allow minimum 100 bars for initialization
Verify dashboard displays Gen: 1+
STEP 2: Initial Observation Period
text
First 200 bars:
- System is in learning phase
- Signal frequency typically low
- Population evolution occurring
- Fitness scores generally increasing
Recommendation: Observe without trading during initialization
STEP 3: Signal Evaluation Criteria
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Consider evaluating signals based on:
- Confidence percentage
- Grade assignment (A+, A, B+, B, C)
- Position within bands
- Historical win rate shown in dashboard
- Train vs. Test performance gap
Example Signal Evaluation Checklist:
Educational Criteria to Consider:
Signal appeared (⚡ arrow displayed)
Confidence level meets personal threshold
Grade meets personal quality standard
Ensemble consensus (if enabled) meets threshold
Historical win rate acceptable
Test performance reasonable vs. Train
Price location at band extreme
Regime classification appropriate for strategy
If trending: Signal direction aligns with personal analysis
Stop loss distance acceptable for risk tolerance
Position size appropriate (example: 1-2% account risk)
Note: This is an educational checklist, not trading advice. Users should develop their own criteria based on personal risk tolerance and strategy.
Risk Management Educational Examples:
POSITION SIZING EXAMPLE:
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Hypothetical scenario:
Account: $10,000
Risk tolerance: 1.5% per trade = $150
Indicated stop distance: 1.5 ATR = $300 per contract
Calculation: $150 / $300 = 0.5 contracts
This is an educational example only, not a recommendation.
STOP LOSS EXAMPLES:
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System provides stop level (red line)
Typically calculated as 1.5 ATR from entry
Alternative approaches users might consider:
LONG: Below recent swing low
SHORT: Above recent swing high
Users should determine stops based on personal risk management.
TAKE PROFIT EXAMPLES:
text
System provides target level (green line)
Typically calculated as price stretch × 60%
Alternative approaches users might consider:
Scale out: Partial exit at 1R, remainder at 2R
Trailing stop: Adjust stop after profit threshold
Users should determine targets based on personal strategy.
Educational Note: These are theoretical examples for educational purposes. Actual position sizing and risk management should be determined by each user based on their individual risk tolerance, account size, and trading plan.
OPTIMIZATION BY MARKET TYPE - EDUCATIONAL SUGGESTIONS
RANGE-BOUND MARKETS
Suggested Settings for Testing:
Population Size: 6-8
Min Confluence: 5.0-6.0
Min Consensus: 70%
Enable Speciation: Consider enabling
Min Per Species: 2
Theoretical Rationale:
More strategies may provide better coverage
Moderate confluence may generate more signals
Higher consensus may filter quality
Speciation may encourage range specialist emergence
Historical Backtest Observations:
Win rates in testing: Varied, often 50-65% range
Reward/risk ratios observed: 0.5-1.5R
Signal frequency: Relatively frequent
Disclaimer: Past backtesting results do not guarantee future performance.
TRENDING MARKETS
Suggested Settings for Testing:
Population Size: 4-5
Min Confluence: 6.0-7.0
Consider enabling MTF filter
MTF Timeframe: 3-5× current timeframe
Specialist Boost: 1.8-2.0
Theoretical Rationale:
Fewer strategies may adapt faster
Higher confluence may filter counter-trend noise
MTF may reduce counter-trend signals
Specialist boost may prioritize trend specialists
Historical Backtest Observations:
Win rates in testing: Varied, often 40-55% range
Reward/risk ratios observed: 1.5-3.0R
Signal frequency: Less frequent
Disclaimer: Past backtesting results do not guarantee future performance.
VOLATILE MARKETS (e.g., Cryptocurrency)
Suggested Settings for Testing:
Base Length: 25-30
Band Multiplier: 1.8-2.0
DVS: Consider enabling (Ensemble method)
Consider enabling Volume Filter
Volume Multiplier: 1.5-2.0
Theoretical Rationale:
Longer base may smooth noise
Wider bands may accommodate larger swings
DVS may be critical for adaptation
Volume filter may confirm genuine moves
Historical Backtest Observations:
Win rates in testing: Varied, often 45-60% range
Reward/risk ratios observed: 1.0-2.5R
Signal frequency: Moderate
Disclaimer: Cryptocurrency markets are highly volatile and risky. Past backtesting results do not guarantee future performance.
SCALPING (1-5min timeframes)
Suggested Settings for Testing:
Base Length: 15-20
Train Window: 150
Test Window: 30
Spawn Interval: 30
Min Confluence: 5.5-6.5
Consider enabling Ensemble
Min Consensus: 75%
Theoretical Rationale:
Shorter base may increase responsiveness
Shorter windows may speed evolution cycles
Quick spawning may enable rapid adaptation
Higher confluence may filter noise
Ensemble may reduce false signals
Historical Backtest Observations:
Win rates in testing: Varied, often 50-65% range
Reward/risk ratios observed: 0.5-1.0R
Signal frequency: Frequent but filtered
Disclaimer: Scalping involves high frequency trading with increased transaction costs and slippage risk. Past backtesting results do not guarantee future performance.
SWING TRADING (4H-Daily timeframes)
Suggested Settings for Testing:
Base Length: 25-35
Train Window: 300
Test Window: 100
Population Size: 7-8
Consider enabling Walk-Forward
Cooldown: 8-10 bars
Theoretical Rationale:
Longer timeframe may benefit from longer lookbacks
Larger windows may improve robustness testing
More population may increase stability
Walk-forward may be valuable for multi-day holds
Longer cooldown may reduce overtrading
Historical Backtest Observations:
Win rates in testing: Varied, often 45-60% range
Reward/risk ratios observed: 2.0-4.0R
Signal frequency: Infrequent but potentially higher quality
Disclaimer: Swing trading involves overnight and weekend risk. Past backtesting results do not guarantee future performance.
DASHBOARD GUIDE - INTERPRETATION EXAMPLES
Reading Each Section:
HEADER:
text
🧬 KERS-AGE EVOLVED 📈 TREND
Regime indication:
Color coding suggests current classification
(Green = Range, Orange = Trend, Purple = Volatile)
POPULATION:
text
Pop: 6/6
Gen: 42
Interpretation:
- Population at target size
- System at generation 42
- May indicate mature evolution
SPECIES (if enabled):
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R:2 T:3 V:1
Interpretation:
- 2 Range specialists
- 3 Trend specialists
- 1 Volatility specialist
In TREND regime this distribution may be expected
WALK-FORWARD (if enabled):
text
Phase: 🧪 TEST
Cycles: 5
Train: 65%
Test: 58%
Considerations:
- Currently in test phase
- Completed 5 full cycles
- 7% performance gap between train and test
- Gap under default 25% overfit threshold
ENSEMBLE (if enabled):
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Vote: 🟢 LONG
Consensus: 72%
Interpretation:
- Weighted majority voting LONG
- 72% agreement level
- Exceeds default 60% consensus threshold
SELECTED STRATEGY:
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ID:23
Trades: 47
Win%: 58%
P&L: +8.3R
Fitness: 0.62
Information displayed:
- Strategy ID 23, Trend specialist
- 47 historical simulated trades
- 58% historical win rate
- +8.3R historical cumulative reward/risk
- 0.62 fitness score
Note: These are historical simulation metrics
SIGNAL QUALITY:
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Conf: 78%
Grade: B+
Elastic: ████████░░
Wick: ██████░░░░
Momentum: ███████░░░
Pressure: ███████░░░
Information displayed:
- 78% confluence score
- B+ grade assignment
- Elastic component strongest
- Visual representation of component strengths
LEARNING (if enabled):
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Missed: 47
Learned: 28
Interpretation:
- System identified 47 moves without signals
- 28 pattern adjustments made
- Suggests ongoing learning process
POSITION:
text
POS: 🟢 LONG
Score: 7.2
Current state:
- Simulated long position active
- 7.2 confluence score
- Monitor for potential exit signal
Educational Note: Dashboard displays are for informational and educational purposes. All performance metrics are historical simulations and do not represent actual trading results or future expectations.
FREQUENTLY ASKED QUESTIONS - EDUCATIONAL RESPONSES
Q: Why aren't signals showing?
A: Several factors may affect signal generation:
System may still be initializing (check Gen: counter)
Confluence score may be below threshold
Ensemble consensus (if enabled) may be below requirement
Current regime may naturally produce fewer signals
Filters may be active (volume, noise reduction)
Consider adjusting settings or allowing more time for evolution.
Q: The win rate seems low compared to backtesting?
A: Consider these factors:
First 200 bars typically represent learning period
Focus on TEST % rather than TRAIN % for realistic expectations
Trend regime historically shows 40-55% win rates in backtesting
Different market conditions may affect performance
System emphasizes reward/risk ratio alongside win rate
Past performance does not guarantee future results
Q: Should I take all signals?
A: This is a personal decision. Some users may consider:
Taking higher grades (A+, A) in any regime
Being more selective in trend regimes
Requiring higher ensemble consensus
Only trading during specific regimes
Paper trading extensively before live trading
Each user should develop their own signal selection criteria.
Q: Signals appear then disappear?
A: This may be expected behavior:
Default requires 2-bar persistence
Designed to filter brief spikes
Confirmation delay intended to reduce false signals
Wait for persistence requirement to be met
This is an intentional feature, not a malfunction.
Q: Test % much lower than Train %?
A: This may indicate:
Overfit detection system functioning
Gap exceeding threshold triggers penalty
Strategy may be optimizing to in-sample noise
System designed to cull such strategies
Walk-forward protection working as intended
This is a safety feature to reduce overfitting risk.
Q: The population keeps culling strategies?
A: This is part of normal evolution:
Lower-performing strategies removed periodically
Higher-performing strategies replicate
Population quality theoretically improves over time
Total culled count shows selection pressure
This is expected evolutionary behavior.
Q: Which timeframe works best?
A: Backtesting suggests 15min to 4H may be suitable ranges:
Lower timeframes may be noisier, may need more filtering
Higher timeframes may produce fewer signals
Extensive historical testing recommended for chosen asset
Each asset may behave differently
Consider paper trading across multiple timeframes
Personal testing is recommended for your specific use case.
Q: Does it work on all asset types?
A: Historical testing suggests:
Cryptocurrency: Consider longer Base Length (25-30) due to volatility
Forex: Standard settings may be appropriate starting point
Stocks: Standard settings, possibly smaller population (4-5)
Indices: Trend-focused settings may be worth testing
Each asset class has unique characteristics. Extensive testing recommended.
Q: Can settings be changed after initialization?
A: Yes, but considerations:
Population will reset
Strategies restart evolution
Learning progress resets
Consider testing new settings on separate chart first
May want to compare performance before committing
Settings changes restart the evolutionary process.
Q: Walk-Forward enabled or disabled?
A: Educational perspective:
Walk-Forward adds out-of-sample validation
May reduce overfitting risk
Results may be more conservative
Considered best practice in quantitative research
Requires more bars for meaningful data
Recommended for those concerned about robustness
Individual users should assess based on their needs.
Q: Ensemble mode or single strategy?
A: Trade-offs to consider:
Ensemble approach:
Requires consensus threshold
May have higher consistency
Typically fewer signals
Multiple perspectives considered
Single strategy approach:
More signals (varying quality)
Faster response to conditions
Higher variability
More active signal generation
Personal preference and risk tolerance should guide this choice.
ADVANCED CONSIDERATIONS
Evolution Time: Consider allowing 200+ bars for population maturity
Regime Awareness: Historical performance varies by regime classification
Confluence Range: Testing suggests 70-85% may be informative range
Ensemble Levels: 80%+ consensus historically associated with stronger agreement
Out-of-Sample Focus: Test performance may be more indicative than train performance
Learning Metrics: "Learned" count shows pattern adjustment over time
Pressure Levels: >0.4 pressure historically added confirmation
DVS Monitoring: >1.5 DVS typically widens bands and affects frequency
Species Balance: Healthy distribution might be 2-2-2 or 3-2-1, avoid 6-0-0
Timeframe Testing: Match to personal trading style, test thoroughly
Volume Importance: May be more critical for stocks/crypto than forex
MTF Utility: Historically more impactful in trending conditions
Grade Significance: A+ in trend regime historically rare and potentially significant
Risk Parameters: Standard risk management suggests 1-2% per trade maximum
Stop Levels: System stops are pre-calculated, widening may affect reward/risk
THEORETICAL FOUNDATIONS
Genetic Algorithms in Finance:
Traditional Optimization Approaches:
Grid search: Exhaustive but computationally expensive
Gradient descent: Efficient but prone to local optima
Random search: Simple but inefficient
Genetic Algorithm Characteristics:
Explores parameter space through evolutionary process
Balances exploration (mutation) and exploitation (selection)
Mitigates local optima through population diversity
Parallel evaluation via population approach
Inspired by biological evolution principles
Academic Context: Genetic algorithms are studied in computational finance literature for parameter optimization. Effectiveness varies based on problem characteristics and implementation.
Ensemble Methods in Machine Learning:
Single Model Limitations:
May overfit to specific patterns
Can have blind spots in certain conditions
May be brittle to distribution shifts
Ensemble Theoretical Benefits:
Variance reduction through averaging
Robustness through diversity
Improved generalization potential
Widely used (Random Forests, Gradient Boosting, etc.)
Academic Context: Ensemble methods are well-studied in machine learning literature. Performance benefits depend on base model diversity and correlation structure.
Walk-Forward Analysis:
Alternative Approaches:
Simple backtest: Risk of overfitting to full dataset
Single train/test split: Limited validation
Cross-validation: May violate time-series properties
Walk-Forward Characteristics:
Continuous out-of-sample validation
Respects temporal ordering
Attempts to detect strategy degradation
Used in quantitative trading research
Academic Context: Walk-forward analysis is discussed in quantitative finance literature as a robustness check. However, it assumes future regimes will resemble recent test periods, which is not guaranteed.
FINAL EDUCATIONAL SUMMARY
KERS-AGE demonstrates an adaptive systems approach to technical analysis. Rather than fixed rules, it implements:
✓ Evolutionary Optimization: Parameter adaptation through genetic algorithms
✓ Regime Classification: Attempted market condition categorization
✓ Out-of-Sample Testing: Walk-forward validation methodology
✓ Pattern Recognition: Cluster analysis and learning systems
✓ Ensemble Methodology: Collective decision-making framework
✓ Full Transparency: Comprehensive dashboard and metrics
This indicator is an educational tool demonstrating advanced algorithmic concepts.
Critical Reminders:
The system:
✓ Attempts to identify potential reversal patterns
✓ Adapts parameters to changing conditions
✓ Provides multiple filtering mechanisms
✓ Offers detailed performance metrics
Users must understand:
✓ No system guarantees profitable results
✓ Past performance does not predict future results
✓ Extensive testing and validation recommended
✓ Risk management is user's responsibility
✓ Market conditions can change unpredictably
✓ This is educational software, not financial advice
Success in trading requires: Proper education, risk management, discipline, realistic expectations, and personal responsibility for all trading decisions.
For Educational Use
🧬 KERS-AGE Development Team
⚠️ FINAL DISCLAIMER
This indicator and documentation are provided strictly for educational and informational purposes.
NOT FINANCIAL ADVICE: Nothing in this guide constitutes financial advice, investment advice, trading advice, or any recommendation to buy, sell, or hold any security or to engage in any trading strategy.
NO GUARANTEES: No representation is made that any account will or is likely to achieve profits or losses similar to those shown in backtests, examples, or historical data. Past performance is not indicative of future results.
SUBSTANTIAL RISK: Trading stocks, forex, futures, options, and cryptocurrencies involves substantial risk of loss and is not suitable for every investor. The high degree of leverage can work against you as well as for you.
YOUR RESPONSIBILITY: You are solely responsible for your own investment and trading decisions. You should conduct your own research, perform your own analysis, and consult with qualified financial advisors before making any trading decisions.
NO LIABILITY: The developers, contributors, and distributors of this indicator disclaim all liability for any losses or damages, direct or indirect, that may result from use of this indicator or reliance on any information provided.
PAPER TRADE FIRST: Users are strongly encouraged to thoroughly test this indicator in a paper trading environment before risking any real capital.
By using this indicator, you acknowledge that you have read this disclaimer, understand the risks involved in trading, and agree that you are solely responsible for your own trading decisions and their outcomes.
Educational Software Only | Trade at Your Own Risk | Not Financial Advice
Taking you to school. — Dskyz , Trade with insight. Trade with anticipation.
RunRox - Pairs Screener📊 Pairs Screener is part of our premium suite for pair trading.
This indicator is designed to scan and rank the most profitable and optimal pairs for the Pairs Strategy. The screener can backtest multiple metrics on deep historical data and display results for many pairs against one base asset at the same time.
This allows you to quickly detect market inefficiencies and select the most promising pairs for live trading.
HOW DOES THIS STRATEGY WORK⁉️
The core idea of the strategy is described in detail in our main indicator Pairs Strategy from the same product line.
There you can find a full explanation of the concept, the math behind pair trading, and the internal logic of the engine.
The Pairs Screener is built on top of the same core technology as the main indicator and uses the same internal logic and calculations.
It is designed as a key companion tool to the main strategy: it helps you find tradeable pairs, evaluate current deviations, sort and filter lists of candidates, and much more. All of these features will be described in this post.
✅ KEY FEATURES
More than 400+ assets available for scanning
Forex assets
Crypto assets
Lower Timeframe Backtester Strategy support
Invert signals mode
Hedge Coefficient (position size balancing between both legs)
6 hedge modes
Stop Loss support
Take Profit support
Whitelist with your own custom asset list
Blacklist to exclude unwanted assets
Custom filters
12 tracking metrics for pair evaluation
Customizable alerts
And many other tools for fine-tuning your search
The screener runs backtests simultaneously across a large number of assets and calculates metrics automatically.
This helps you very quickly find pairs with strong structural relationships or current inefficiencies that can be used as the basis for your pair trading strategies.
⚙️ MAIN SETTINGS
The first section controls the core parameters of the screener: Score, correlation, asset groups for scanning, and other base settings. All major crypto and forex symbols are embedded directly into the screener.
Since there are more than 400 assets, it is technically impossible to analyze everything at once, so we grouped them into batches of 40 assets per group.
The workflow is simple:
Open the chart of the asset you want to use as the base ticker.
In the screener settings choose the market (Crypto or Forex).
Select a Group (for example, Group 1) and the indicator will scan all assets inside that group against your base ticker.
Then you switch to Group 2, Group 3, etc., and repeat the scan.
Embedded universe:
400+ assets total
350+ Crypto – split into 10 groups
70+ Forex – split into 3 groups
Below is a description of each setting.
🔸 Exclude Dates
Allows you to specify a period that should be excluded from analysis.
Useful for removing abnormal spikes, news events, or any non-typical segments that distort the statistics for your pairs.
🔸 Market
Defines which universe will be used to build pairs with the current main asset:
Crypto – 350+ crypto symbols
Forex – 70+ FX symbols
Whitelist – your own custom list of assets
🔸 Group
Selects the asset group to scan.
As mentioned above, assets are split into groups of about 40 instruments:
350+ Crypto → 10 groups
70+ Forex → 3 groups
The screener will calculate all metrics only for the group you select.
🔸 Lower Timeframe
This option enables deep history analysis.
Each TradingView plan has a limit on the number of visible bars (for example, 5,000 bars on the basic plan). In standard mode you would only get statistics for the last 5,000 bars of your current timeframe.
If you want a deeper backtest on a lower timeframe, you can do the following:
Suppose your target timeframe for analysis is 5 minutes.
Switch your chart to a 30-minute timeframe.
Enable Lower Timeframe in the indicator.
Select 5 minutes as the lower timeframe inside the screener.
In this mode the screener can reconstruct and analyze up to 99,000 bars of data for your assets. This allows you to evaluate pairs on a much deeper history and see whether the results are stable over a larger sample.
🔸 Method
Here you choose the deviation model:
preferred Z-Score or S-Score for your analysis,
plus you can enable Invert to search for negatively correlated pairs and calculate their profit correctly.
🔸 Period
This is the lookback period for Z/S Score.
It defines how many bars are used to calculate the deviation metric for each pair.
🔸 Correlation Period
This is the number of bars used to calculate correlation between the base asset and each candidate in the group.
The resulting correlation value is also displayed in the results table.
🔀 HEDGE COEFFICIENT
The next block of settings is related to the hedge coefficient.
This defines how much margin is allocated to each leg of the pair.
The classic approach in pair trading is to split the position equally between both assets.
For example, if you allocate 100 USD to a trade , the standard model would open 50 USD long on one asset and 50 USD short on the other.
This works well for pairs with similar volatility , such as BTCUSDT / ETHUSDT
However, if you use a pair like BTCUSDT / DOGEUSDT , the volatility of these assets is very different.
They can still be correlated, but their amplitude is not the same. While Bitcoin might move 2% , Dogecoin can move 10% over the same period.
Because of that, for pairs with strongly different volatility, we can use a hedge coefficient and, for example, enter with 30 USD on one leg and 70 USD on the other, taking the volatility difference into account.
This is the main idea behind the Hedge Coefficient section and its primary use.
The indicator includes 6 methods of calculating the coefficient:
Cumulative RMA
Beta OLS
Beta TLS
Beta EMA
RMA Range
RMA Delta
Each method uses a different formula to compute the hedge coefficient and to size the position based on different metrics of the assets.
We leave it to the trader to decide which algorithm works best for their specific pair and style.
Below are the settings inside this section:
🔹 Method
When Auto Hedge is enabled, you can select which method to use from the list above.
The chosen method will automatically calculate the hedge coefficient between the two legs.
🔹 Hedge Coefficient
This is the manual hedge ratio per trade when Auto Hedge is disabled.
By default it is set to 1, which means the position is opened 50/50 between the two assets.
🔹 Min Allowed Hedge Coef.
This is the minimum allowed hedge coefficient.
By default it is 0.2, which means the model will not go below a 20% / 80% split between the legs.
🔹 MA Length
For methods that use moving averages (for example Beta EMA), this parameter sets the period used to calculate the hedge coefficient.
💰 STRATEGY SETTINGS
This section defines the base backtesting settings for all assets in the screener.
Here you configure entries, exits, Stop Loss, and other parameters used to find the most optimal pairs for your strategy. 🔸 Commission %
In this field you set your broker’s fee percentage per trade.
The indicator automatically calculates the correct commission for each leg of every trade. You only need to input the real commission rate that your broker charges for volume. No additional manual calculations are required.
🔸 Qty $
The margin amount used for backtesting across all assets in the screener.
This margin is split between both legs of the pair either equally or according to the selected hedge coefficient.
🔸 Entry
The Z/S Score deviation level at which the backtest opens a trade for each pair.
🔸 Exit
The Z/S Score level at which the backtest closes trades for the tested assets.
🔸 Stop Loss
PnL threshold at which a trade is force-closed during the historical test.
🔸 Cooldown
Number of bars the strategy will wait after a Stop Loss before opening the next trade.
This block gives you flexible control over how your strategy is tested on 400+ assets, helping you standardize the rules and compare pairs under the exact same conditions.
🗒️ WHITELIST
In this section you can define your own custom list of assets for monitoring and backtesting.
This is useful if you want to work with symbols that are not included in the built-in lists, such as exotic crypto from smaller exchanges, specific stocks, or any custom universe 🔹 Exchange Prefix
Enter the exchange prefix used for your tickers.
Example: BINANCE, OANDA, etc.
🔹 Ticker Postfix
Enable this option if the tickers require a postfix.
Example 1: .P for Binance Futures perpetual contracts.
Example 2: USDT if you only provide the base asset in the ticker list.
🔹 Ticker List
Enter a comma-separated list of tickers to analyze.
Example 1: BTCUSDT, ETHUSDT, BNBUSDT (when the exchange prefix is set).
Example 2: BTC, ETH, BNB (when using postfix USDT).
Example 3: BINANCE:BTCUSDT.P, OANDA:EURUSD (when different exchanges are used and the prefix option is disabled).
This gives you full flexibility to build a screener universe that matches exactly the assets you trade.
⛔ BLACKLIST
In this section you can enable a blacklist of unwanted assets that should be skipped during analysis. Enter a comma-separated list of tickers to exclude from the screener:
Example 1: BTCUSDT, ETHUSDT
Example 2: BTC, ETH (all tickers that contain these symbols will be excluded)
This helps you quickly remove illiquid, noisy, or unwanted instruments from the results without changing your main groups or whitelist.
📈 DASHBOARD
This section controls the results dashboard: table position, style, and sorting logic.
Here is what you can configure:
Result Table – position of the results table on the chart.
Background / Text – colors and opacity for the table background and text.
Table Size – overall size of the results table (from 0 to 30).
Show Results – how many rows (pairs) to display in the table.
Sort by (stat) – which metric to use for sorting the results.
Available options: Profit Factor, Profit, Winrate, Correlation, Score.
This lets you quickly focus on the most interesting pairs according to the exact metric that matters most for your strategy.
📎 FILTER SETTINGS
This section lets you filter the results table by metric values.
For example, you can show only pairs with a minimum correlation of 0.8 to focus on more stable relationships. 🔸 Min Correlation
Minimum allowed correlation between the two assets over the selected lookback period.
🔸 Min Score
Minimum absolute Score (Z-Score or S-Score) required to include a pair in the results.
For example, 2.0 means only pairs with Score >= 2.0 or <= -2.0 will be displayed.
🔸 Min Winrate
Minimum win rate percentage for a pair to be included in the table.
🔸 Min Profit Factor
Minimum profit factor required for a pair to stay in the results. These filters help you quickly narrow the list down to pairs that meet your quality criteria and match your risk profile.
📌 COLUMN SELECTION
This section lets you fully customize which metrics are displayed in the results table.
You can enable or hide any column to focus only on the data you need to identify the best pairs for trading. The screener allows you to show up to 12 metrics at the same time, which gives a detailed view of pair quality. Available columns:
🔹 Exchange Prefix
Show the exchange prefix in the ticker.
🔹 Correlation
Correlation between the two assets’ prices over the lookback period.
🔹 Score
Current Score value (Z-Score or S-Score).
On lower timeframe research, Score is not displayed.
🔹 Spread
Shows spread as % change since entry.
Positive value = profit on the main position.
🔹 Unrealized PnL
Shows unrealized PnL as a $ value based on current prices.
🔹 Profit
Total profit from all trades: Gross Profit − Gross Loss.
🔹 Winrate
Percentage of profitable trades out of all executed trades.
🔹 Profit Factor
Gross Profit / Gross Loss.
🔹 Trades
Total number of trades.
🔹 Max Drawdown
Maximum observed loss from peak to trough before a new peak is made.
🔹 Max Loss
Largest loss recorded on a single trade.
🔹 Long/Short Profit
Separate profit/loss for long trades and short trades.
🔹 Avg. Trade Time
Average duration of trades.
All these metrics are designed to help you quickly identify the strongest pairs for your strategy.
You can change colors, opacity, and hide any columns that are not relevant to your workflow.
🔔 ALERT
The alert system in this screener works in a specific way.
Alerts are tied directly to the filters you set in the Filter Settings section:
Minimum Correlation
Minimum Score
Minimum Winrate
Minimum Profit Factor
You can configure alerts to trigger when a new pair appears that matches all your filter conditions. 💡 Example
You set:
Minimum Score = 3
Then you create an alert based on the screener.
When any pair reaches a Score greater than +3 or less than −3, you will receive a notification.
This is how alerts work in this screener.
The idea is to deliver the most relevant information about the current market situation without forcing you to watch the screener all the time.
Supported placeholders for alert messages: {{ticker_1}} – main ticker (the one on the chart).
{{ticker_2}} – the paired ticker listed in the table.
{{corr}} – correlation value.
{{score}} – Score value (Z-Score or S-Score).
{{time}} – bar open time (UTC).
{{timenow}} – alert trigger time (UTC). You can use these placeholders to build alert text or JSON payloads in any format required by your tools.
The screener is designed to significantly enhance your pair trading workflow: it helps you quickly identify working pairs and current market inefficiencies, and with the alert system you can react to opportunities without constantly sitting in front of the screen.
Always remember that past performance does not guarantee future results.
Use the screener data within a risk-controlled trading system and adjust position sizing according to your own risk management rules.
RunRox - Pairs Strategy🧬 Pairs Strategy is a new indicator by RunRox included in our premium subscription.
It is a specialized tool for trading pairs, built around working with two correlated instruments at the same time.
The indicator is designed specifically for pair trading logic: it helps track the relationship between two assets, identify statistical deviations, and generate signals for opening and managing long/short combinations on both legs of the pair.
Below in this description I will go through the core functions of the indicator and the main concepts behind the strategy so you can clearly understand how to apply it in your trading.
📌 CONCEPT
The core idea of pair trading is to find and trade correlated instruments that usually move in a similar way.
When these two assets temporarily diverge from each other, a trading opportunity appears.
In such moments, the relatively overvalued asset is sold (short leg), and the relatively undervalued asset is bought (long leg).
When the spread between them narrows and both instruments revert back toward their typical relationship (mean), the position is closed and the trader captures the profit from this convergence.
In practice, one leg of the pair can end up in a loss while the other generates a larger profit.
Due to the difference in performance between the two assets, the combined result of the pair trade can still be positive.
✅ KEY FEATURES:
2 deviation types (Z-Score and S-Score)
Invert signals mode
Hedge Coefficient (position size balancing between both legs)
6 hedge modes
Entries based on Score or RSI
Extra entries based on Score or Spread
Stop Loss
Take Profit
RSI Filter
RSI Pivot Mode
Built-in Backtester Strategy
Lower Timeframe Backtester Strategy
Live trade panel for current position
Equity curve chart
21 performance metrics in the backtester
2 alert types
*And many more fine-tuning options for pair trading
🔗 SCORE
Score is the core deviation metric between the two assets in the pair.
For example, if you are trading ETHUSDT/BTCUSDT, the indicator analyzes the relationship ETH/BTC, and when one leg temporarily diverges from the other, this difference is reflected in the Score value.
In other words, Score shows how much the current spread between the two instruments deviates from its typical state and is used as the main signal source for pair entries and exits.
In the screenshot above you can see how Score looks in our indicator.
Depending on how large the difference is between the two assets, the Score value can move in a range from −N to +N
When Score is in the −N zone, this is a 🟢 long zone for the first asset and a short zone for the second.
Using the ETH/BTC example: when Score is deeply negative, you open a long on ETH and a short on BTC at the same time, then close both legs when Score returns back to the 0 zone (balance between the two assets).
When Score is in the +N zone, this is a 🔴 short zone for the first asset and a long zone for the second.
In the same ETH/BTC example: when Score is strongly positive, you short ETH and long BTC, and again close both positions when Score comes back to the neutral 0 zone.
☯️ Z/S SCORE
Inside the indicator we added two different formulas for calculating the spread between the two legs of the pair: Z-Score and S-Score.
These approaches measure deviation in different ways and can produce slightly different signals depending on the chosen pair and its behavior.
This allows you to switch between Z-Score and S-Score and choose the method that gives more stable and cleaner signals for your specific instruments.
As you can see in the screenshot above, we used the same pair but applied different Score types to measure the spread and deviation from the norm.
🟣 Z-Score – generated 9 entry signals .
It reacts to price fluctuations more smoothly and usually stays within a range of approximately −8 to +8 .
🟠 S-Score – generated 5 entry signals .
It reacts to price changes more aggressively and produces wider deviations, often reaching −15 to +15 .
This gives traders the choice between a more sensitive but smoother model (Z-Score) and a more selective, stronger-deviation model (S-Score)
⁉️ HOW DOES THE STRATEGY WORK
Here is a basic example of how you can trade this pair trading strategy using our indicator and its signals.
In the classic approach the trade consists of one initial entry and several scale-ins (averaging) if the spread continues to move against the position.
The first entry is opened when Score reaches a standard deviation of −2 or +2.
If price does not revert to the mean and moves further against the position so that Score expands to −3 or +3, the strategy performs the first scale-in.
If Score extends to −4 or +4, a second scale-in is added.
If the spread grows even more and Score reaches −5 or +5, a third scale-in is executed.
In our indicator the number of averaging steps can be up to 4 scale-ins .
After that the position waits until Score returns back to the 0 level , where the whole pair position is closed.
This is the standard model of classical pair trading.
However there are many variations:
using Stop Loss and Take Profit,
exiting earlier or later than the 0 zone,
scaling in not by Score but by Spread, since Score is not linear while Spread is linear,
entering when RSI on both tickers shows opposite extremes, for example RSI 20 on one asset and RSI 80 on the other, and so on.
The number of possible trading styles for this strategy is very large.
We designed the indicator to cover as many of these variations as possible and added flexible tools so you can build your own pair trading logic on top of it.
Below is an example of a classic pair trade with two entries: one main entry and one extra entry (scale-in) .
The pair SUIUSDT / PENGUUSDT shows a high correlation, and on one of the trades the sequence looked like this:
A −2 Score deviation occurred into the long zone and triggered the Main Entry .
🔹 Main Entry
Long SUIUSDT – Margin: 5,000 USD, Entry price: 1.5708
Short PENGUUSDT – Margin: 5,000 USD, Entry price: 0.011793
Price then moved further against the position, Score went deeper into deviation, and the strategy added one extra entry.
🔸 Extra Entry
Long SUIUSDT – Margin: 5,000 USD, Entry price: 1.5938
Short PENGUUSDT – Margin: 5,000 USD, Entry price: 0.012173
The trade was closed when Score reverted back toward the 0 zone (mean reversion of the spread):
❎ Exit
SUIUSDT P&L: −403.34 USD, Exit price: 1.5184
PENGUUSDT P&L: +743.73 USD, Exit price: 0.011089
✅ Total P&L: +340.39 USD
With a total margin of 10,000 USD used per side (20,000 USD combined), this trade yielded around +1.7% on the deployed margin.
On different assets the size and speed of the spread movement will vary, but the principle remains the same.
This is just one example to illustrate how the strategy works in practice using simplified theoretical balances.
⚙️ MAIN SETTINGS
After explaining how the strategy works, we can move to the indicator settings and their logic.
The first block is Main Settings, which controls how the pair is built, how the spread is calculated, and how the backtest is performed.
The core idea of the indicator is to backtest historical data, generate entry signals, show open-position parameters, and provide all necessary metrics for both discretionary and algorithmic trading.
This is a complete framework for analyzing a pair of assets and building a trading system around them. Below I will go through the main parameters one by one.
🔹 Exclude Dates
Allows you to exclude abnormal periods in the pair’s history to remove outlier trades from the backtest.
This is useful when the market experienced extreme news events, listing spikes, or other non-typical situations that distort statistics.
🔹 Pair
Here you select the second asset for your pair.
For example, if your main chart is BTCUSDT, in this field you choose a correlated asset such as ETHUSDT, and the working pair becomes BTCUSDT / ETHUSDT.
The indicator then calculates spread, Score, and all related metrics based on this asset combination.
🔹 Lower Timeframe
This is a special mode for backtesting on a lower timeframe while using a higher timeframe chart to extend the history limit.
For example, if your TradingView plan provides only 5,000 bars of history on the current timeframe, you can switch your chart to a higher timeframe and select a lower timeframe in this setting.
The indicator will then reconstruct the pair logic using up to 99,000 bars of lower timeframe data for backtesting.
This allows you to test the pair on a much longer historical period and find more stable combinations of assets.
🔹 Method
Here you choose which deviation model you want to use: Z-Score or S-Score.
Both methods calculate spread deviation but use different formulas, which can give different signal behavior depending on the pair.
Examples of these two methods are shown earlier in this description.
🔹 Period
This parameter defines how many bars are used to calculate the average deviation for the pair.
If you set Period = 300, the indicator looks back 300 bars and calculates the typical spread deviation over that window.
For example, if the average deviation over 300 bars is around 1%, then a move to 2% or more will push Z/S Score closer to its boundary levels, since such a deviation is considered abnormal for that lookback period.
A larger Period means that only bigger deviations will be treated as anomalies.
A smaller Period makes the model more sensitive and treats smaller deviations as anomalies.
This allows you to tune how aggressive or conservative your pair trading signals should be.
🔹 Invert
This setting is used for negatively correlated pairs.
Some instruments have a positive correlation in the range from +0.8 to +1.0 (strong positive correlation), while others show a negative correlation from −0.8 to −1.0, meaning they usually move in opposite directions.
A classic example is the pair EURUSD and DXY.
As shown in the screenshot above, these instruments often have strong negative correlation due to macro factors and typically move in opposite directions: when EURUSD is rising, DXY is falling, and vice versa.
Such pairs can also be traded with our indicator.
To do this, we use the Invert option, which effectively flips one of the assets (as shown in the screenshot below). After inversion, both instruments are brought to a “same-direction” behavior from the model’s point of view.
From there, you trade the pair in the same way as a positively correlated one:
you open both legs in the same direction (both long or both short) depending on the spread and Score, and then wait for the spread between the inverted pair to converge back toward its mean.
🔀 HEDGE COEFFICIENT
The next block of settings is related to the hedge coefficient.
This defines how much margin is allocated to each leg of the pair.
The classic approach in pair trading is to split the position equally between both assets.
For example, if you allocate 100 USD to a trade , the standard model would open 50 USD long on one asset and 50 USD short on the other.
This works well for pairs with similar volatility , such as BTCUSDT / ETHUSDT
However, if you use a pair like BTCUSDT / DOGEUSDT , the volatility of these assets is very different.
They can still be correlated, but their amplitude is not the same. While Bitcoin might move 2% , Dogecoin can move 10% over the same period.
Because of that, for pairs with strongly different volatility, we can use a hedge coefficient and, for example, enter with 30 USD on one leg and 70 USD on the other, taking the volatility difference into account.
This is the main idea behind the Hedge Coefficient section and its primary use.
The indicator includes 6 methods of calculating the coefficient:
Cumulative RMA
Beta OLS
Beta TLS
Beta EMA
RMA Range
RMA Delta
Each method uses a different formula to compute the hedge coefficient and to size the position based on different metrics of the assets.
We leave it to the trader to decide which algorithm works best for their specific pair and style.
Below are the settings inside this section:
🔹 Method
When Auto Hedge is enabled, you can select which method to use from the list above.
The chosen method will automatically calculate the hedge coefficient between the two legs.
🔹 Hedge Coefficient
This is the manual hedge ratio per trade when Auto Hedge is disabled.
By default it is set to 1, which means the position is opened 50/50 between the two assets.
🔹 Min Allowed Hedge Coef.
This is the minimum allowed hedge coefficient.
By default it is 0.2, which means the model will not go below a 20% / 80% split between the legs.
🔹 MA Length
For methods that use moving averages (for example Beta EMA), this parameter sets the period used to calculate the hedge coefficient.
🛠️ STRATEGY SETTINGS
The next important block is Strategy Settings .
Here you define the core parameters used for backtesting: trading commission, position size, entry / exit logic, Stop Loss, Take Profit, and other rules that describe how you want the strategy to operate.
Below are all parameters with a detailed explanation.
🔸 Commission %
In this field you set your broker’s fee percentage per trade .
The indicator automatically calculates the correct commission for each leg of every trade. You only need to input the real commission rate that your broker charges for volume. No additional manual calculations are required.
🔸 Main Entry Mode
There are two options for the main entry:
Score - This is the primary entry method based on Z/S Score.
When Score reaches the deviation level defined in the settings below, the strategy opens the first position.
For example, if you set “Entry at 2 deviations”, the trade will be opened when Score hits ±2.
RSI Only - Alternative entry method based on RSI divergence between the two assets.
The exact RSI levels are defined in the RSI settings section below.
For example, if you set the entry threshold at 30, then when one asset has RSI below 30 and the second one has RSI above 70, the first entry will be triggered.
🔸 Extra Entries Mode
This defines how scale-ins (averaging) are executed. There are two modes:
Score - Works the same way as the main entry, but for additional entries.
For example, the main entry can be at 2 deviations, the first scale-in at 3, the second at 4, etc.
Spread - This mode uses the Spread (difference between the two assets) starting from the main entry moment.
As the spread continues to widen, the strategy can add extra entries based on spread growth rather than Score.
Since Score is a non-linear metric and Spread is linear, in some configurations averaging by Spread can produce better results than averaging by Score. This is pair- and strategy-dependent. 🔸 Entry parameters
Deviation / Spread threshold
Entry size
Main Entry – first field (deviation / spread), second field (position size)
Entry 2 – first field (deviation / spread), second field (position size)
Entry 3 – first field (deviation / spread), second field (position size)
Entry 4 – first field (deviation / spread), second field (position size)
This allows you to define up to four scaling steps with different triggers and different sizing.
🔸 Exit Level
This parameter defines at what Score level you want to exit the trade.
By default it is 0, which means the backtester closes the position when Score returns to the neutral (0) zone.
You can also use positive or negative values. Example:
Assume your main entry is configured at a 3 deviation.
You can exit at the 0 level, or you can set Exit Level = 2.
If your initial entry was at −3, the position will be closed when Score reaches +2.
If your initial entry was at +3, the position will be closed when Score reaches −2.
This approach can increase the profit per trade due to a larger captured spread, but it may also increase the holding time of the position.
🔸 Stop Loss
Here you define the maximum loss per trade in PnL units.
If a trade reaches the negative PnL value specified in this field and the Stop Loss option is enabled, the indicator will close the trade at a loss.
The Cooldown parameter sets a pause after a losing trade:
the strategy will wait a specified number of bars before opening the next trade.
🔸 Take Profit
Works similar to Stop Loss but for profit targets.
You set the desired PnL value you want to reach.
The trade will be closed when either the Take Profit target is hit or when Score reaches the exit level defined in the settings, whichever occurs first (depending on your configuration).
🔸 Show Qty in currency
When enabled, trade size is displayed in currency (USD) instead of token quantity.
This is useful for quickly understanding position size in monetary terms.
You will see this in the Current Trade panel, which is described later.
🔸 Size Rounding
Controls how many decimal places are used when rounding position size (from 0 to 10 digits after the decimal).
This is also used for the Current Trade panel so you can adjust how detailed or compact the size display should be.
📊 RSI FILTERS
This section is used for additional trade filtering.
RSI can be used in two ways:
as a primary entry signal,
or as an extra filter for entries based on Z/S Score.
If in the Strategy Settings the Main Entry Mode is set to RSI, then RSI becomes the main trigger for opening a position.
In this case a trade is opened when the RSI of the two assets reaches opposite zones.
Example:
If the threshold is set to 30, then:
when one asset has RSI below 30, and
the second asset has RSI above 70 (100 − 30),
the strategy opens the first entry.
All extra entries after that will be executed either by Spread or by Z/S Score, depending on your Extra Entries Mode.
Below are the parameters in this block:
RSI Length – standard RSI period setting.
RSI Pivot Mode – when enabled, RSI is used as an additional filter together with Z/S Score. The indicator looks for a reversal pattern on RSI (pivot behavior). If RSI forms a reversal structure, the trade is allowed to open. If not, the signal is skipped until a proper RSI pivot is formed.
Entry RSI Filter – here you define the RSI thresholds used for RSI-based entries. These are the same boundary levels described in the example above.
Overall, this section helps filter out lower-quality trades using additional RSI conditions or lets you build RSI-only entry logic based on extreme levels.
🎨 MAIN CHART STYLING
This section controls the visual appearance of trades on the main chart.
You can customize how the second asset line is drawn, as well as the icons for entries, scale-ins, and exits, including their size and style.
▫️ Price Line
This is the line that shows the price of the second asset and the relative difference between the two instruments.
You can adjust the line thickness and color to make it more readable on your chart.
▫️ Adjust Price Line by Hedge Coefficient
When this option is enabled, the second asset’s line is normalized by the hedge coefficient.
If you turn it off, the hedge coefficient will not be applied to the second asset’s line, and it will be displayed in raw form.
▫️ Entry Label
Here you can customize how the entry markers look:
choose the color, icon style, and size of the label that marks each trade entry and scale-in on the chart.
▫️ Exit Label
Similarly, you can define the color, icon style, and size of the label used for exits.
This helps visually separate entries and exits and makes it easier to read the trade history directly from the chart.
🎯 INDICATOR PANEL
This section controls the settings of the indicator panel, which works like an oscillator and allows you to visualize multiple metrics in one place.
You can flexibly enable, style, and scale each parameter.
🔹 Score
Displays the main deviation metric between the two assets.
You can customize the color and line thickness of the Score plot.
🔹 Spread
Shows the spread between the two assets.
It starts calculating from the moment the trade is opened.
You can adjust its color and thickness for better visibility.
🔹 Total Profit
Displays the cumulative profit for this pair and strategy as a line that grows (or falls) over time.
Color, opacity, and line thickness can be customized.
🔹 Unrealized PNL
Once a trade is opened, this line shows the current PnL of the active position.
It also lets you see historical drawdowns on the pair.
Color and thickness can be adjusted.
🔹 Released PNL
Shows the realized PnL of each closed trade as bars.
Useful for quickly evaluating the result of every individual trade in the backtest.
🔹 Correlation
Plots the correlation coefficient between the two assets as a graph, so you can visually track how stable or unstable the relationship between them is over time.
🔹 Hedge Coefficient
Shows the hedge coefficient as a line, which helps understand how the model is rebalancing exposure between the two legs depending on their behavior.
For each metric there is also a 📎 Stretch option.
Stretch allows you to compress or expand the scale of a specific line to visually align metrics with different ranges on the same panel and make the chart easier to read.
📈 PROFIT CHART
Since TradingView does not natively support proper backtesting for pair trading, this indicator includes its own profit curve for the pair.
You can visually see how the strategy performed over historical data: whether there were deep drawdowns, abnormal profit spikes, or stable equity growth over time. This makes it much easier to evaluate the quality of the pair and the strategy on history.
In the settings of this section you can flexibly customize how the profit chart is displayed:
labels, position of the panel, padding, and other visual details.
Everything depends on your personal preferences, so we give full control over styling:
you can adjust the look of the profit chart to match your layout or completely hide it from the chart if you do not need it.
📌 CURRENT TRADE
This section controls the current trade table.
When there is an active trade on the chart, the panel displays all key information for the open position:
direction for each ticker (long or short),
required position size for each leg,
entry price for both assets,
and real-time PnL for each leg separately,
so you always have a clear view of the current situation.
The main thing you can do with this table is customize its appearance:
you can change the size, position on the chart, background and text colors, as well as separate coloring for positive / negative PnL and different colors for long and short positions.
📅 BACKTEST RESULTS
The next key block is Backtest Results.
This results table with detailed metrics gives you an extended view of how the pair and strategy perform: win rate, profit factor, long/short breakdown, and more than 20 additional stats that help you evaluate the potential of your setup.
⚠️ First of all, it is important to note ⚠️
past performance does not guarantee future results.
Every trader must keep this in mind and factor these risks into their strategy.
The table shows metrics in three cuts:
All Entries
Main Entries
Extra Entries (scale-ins)
Core metrics:
Profit – total profit for each entry type.
Winrate – win rate for this pair.
Profit Factor – ratio of gross profit to gross loss for the strategy.
Trades – number of trades in the backtest.
Wins – number of winning trades.
Losses – number of losing trades.
Long Profit – profit generated by long positions.
Short Profit – profit generated by short positions.
Longs – total number of long trades.
Shorts – total number of short trades.
Avg. Time – average time spent in a trade.
Additional metrics for a deeper evaluation of the pair:
Correlation – current correlation between the two assets in the pair.
Bars Processed – number of bars used in the analysis.
Max Drawdown – maximum historical drawdown of the strategy.
Biggest Loss – the largest single losing trade in the backtest.
Recommended Hedge – recommended hedge coefficient based on historical behavior.
Max Spread – maximum positive spread observed in history.
Min Spread – maximum negative spread observed in history.
Avg. Max Spread – average of positive extreme spread values (above 0).
Avg. Min Spread – average of negative extreme spread values (below 0).
Avg Positive Spread – average positive spread across all trades (only values above 0).
Avg Negative Spread – average negative spread across all trades (only values below 0).
Current Spread – current spread between the assets when a trade is open.
These metrics together allow you to quickly assess how stable the pair is, how the risk/return profile looks, and whether the strategy parameters are suitable for live trading. You can fully customize this results table to fit your workflow:
hide metrics you don’t need, change colors, opacity, and other visual styles, and reorder the focus of the stats according to your trading style.
This way the backtest block can show only the metrics that matter to you most and remain clean and readable during analysis.
📣 ALERTS
The next section is dedicated to alerts.
Here you can configure all signals you need, both for manual trading and for full automation of this pair trading strategy. This block is designed to cover most practical use cases. The indicator supports two alert modes:
Single Alert – one universal custom alert for all events.
Two Alerts – separate alerts for each ticker so you can receive different messages per asset.
Available alert events:
Main Entry – when the main entry is triggered.
Entry 2 – when the first scale-in is executed.
Entry 3 – when the second scale-in is executed.
Entry 4 – when the third scale-in is executed.
Exit Alert – when the position is closed.
StopLoss Alert – when Stop Loss is hit.
TakeProfit Alert – when Take Profit is hit.
All alerts are fully customizable and support a set of placeholders for building structured messages or JSON payloads.
🔹1 Alert Type
List of supported placeholders: {{event}} – trigger name ('Entry 1', 'Exit').
{{dir_1}} – 'Long' or 'Short' for the main ticker.
{{dir_2}} – 'Long' or 'Short' for the other ticker.
{{action_1}} – 'Buy', 'Sell' or 'Close' for the main ticker.
{{action_2}} – 'Buy', 'Sell' or 'Close' for the other ticker.
{{price_1}} – price for the main ticker.
{{price_2}} – price for the other ticker.
{{qty_1}} – order size for the main ticker.
{{qty_2}} – order size for the other ticker.
{{ticker_1}} – main ticker (e.g. 'BTCUSD').
{{ticker_2}} – other ticker (e.g. 'ETHUSD').
{{time}} – candle open time in UTC.
{{timenow}} – signal time in UTC.
🔹2 Alert Type
List of supported placeholders: {{event}} – trigger name ('Entry 1', 'Exit', 'SL', 'TP').
{{action}} – 'Buy', 'Sell' or 'Close'.
{{price}} – order price.
{{qty}} – order size.
{{ticker}} – ticker (e.g. 'BTCUSD').
{{time}} – candle open time in UTC.
{{timenow}} – signal time in UTC. You can use these placeholders to build any JSON structure or custom alert text required by your trading bot, exchange API, or automation service.
In this post I’ve explained how the indicator works, the core concept behind this pair trading strategy, and shown practical examples of trades together with a detailed breakdown of each unique feature inside the tool.
We have invested a lot of work into building this indicator and we truly hope it will help you trade pair strategies more efficiently and more profitably by giving you structured, strategy-specific information that is difficult to obtain in any other way.
⚠️ Please also remember that past performance does not guarantee future results.
Always evaluate the risks, the robustness of your setup, and your own risk tolerance before entering any position, and make independent, well-considered decisions when using this or any other strategy.
