Momentum Master v1Momentum Master v1 - Advanced Multi-Filter Confluence Trading System
### Technical Methodology
Multi-timeframe EMA crossover system with institutional flow analysis, proprietary Fair Value Gap (FVG) retracement detection, and Point of Control (POC) proximity filtering.
The script combines six distinct confirmation filters: 3/21 EMA crossover signals, RSI momentum analysis (14-period), proprietary FVG retracement algorithm with 200-bar lookback, multi-timeframe POC proximity calculation (Volume/Session/Daily/Weekly), institutional order block detection with retest confirmation, and adaptive ATR-based risk management.
### Unique Features
1. Proprietary FVG Retracement Algorithm - Institutional Flow Analysis
2. Multi-Timeframe POC Proximity Filtering - Key Level Analysis
3. Adaptive Confidence Scoring System - Dynamic Risk Management
### How It Works
Long entries require: Fast EMA (3) crosses above Slow EMA (21) + RSI < 70 + volume > 1.1x average + FVG retracement confirmation + POC proximity within 2.0x ATR + order block direction alignment.
Uses ATR-based stop loss placement with 1.0x multiplier. Take profit levels at 2:1, 4:1, 6:1, 8:1, 10:1, and 12:1 risk/reward ratios.
### Value Proposition
This script combines 6 different institutional flow analysis techniques that would require multiple free scripts to replicate. The proprietary FVG retracement algorithm, multi-timeframe POC analysis, and adaptive confidence scoring system are not available in any single free script.
### Use Cases
Best timeframes: 5-minute for scalping, 15-minute for swing trades
Suitable markets: Forex major pairs, Crypto, major indices
Market conditions: Trending markets with high volume sessions
### Access Instructions
To request access to this invite-only script:
Contact: with your TradingView username
Requirements: Include your TradingView username and brief trading experience
Process: I will review requests within 24 hours and grant access to qualified traders
2 days ago
Release Notes
Momentum Master v1 - Multi-Filter EMA Crossover with Institutional Flow Analysis
### Technical Methodology
The script uses a 3/21 EMA crossover system combined with six confirmation filters: RSI momentum analysis (14-period), proprietary Fair Value Gap (FVG) retracement detection with 200-bar lookback, multi-timeframe Point of Control (POC) proximity calculation, institutional order block detection with retest confirmation, volume analysis (1.1x average threshold), and adaptive ATR-based risk management (14-period ATR with 1.0x multiplier).
### Unique Features
1. Proprietary FVG Retracement Algorithm - Tracks whether price retraces into recent Fair Value Gaps before generating signals, using 200-bar lookback with 20% ATR tolerance for retest confirmation
2. Multi-Timeframe POC Analysis - Combines Volume Profile POC (30-bar), Session POC (previous session HLC/3), Daily POC (previous day HLC/3), and Weekly POC (previous week HLC/3) with 2.0x ATR proximity filtering
3. Adaptive Confidence Scoring - Proprietary algorithm scores signal confidence 0-100% based on filter confluence, adjusting stop loss distance (0.9x to 1.2x ATR) based on signal quality
### How It Works
Long entries require: Fast EMA (3) crosses above Slow EMA (21) + RSI < 70 + volume > 1.1x average + FVG retracement confirmation within 15 bars + POC proximity within 2.0x ATR + order block direction alignment. Optional filters include ADX > 20 for trending markets and divergence confirmation.
Exit strategy uses ATR-based stop loss (1.0x multiplier) with take profit levels at 2:1, 4:1, 6:1, 8:1, 10:1, and 12:1 risk/reward ratios. Multiple concurrent trades allowed with 5-bar cooldown between entries.
### Value Proposition
This script combines 6 different institutional flow analysis techniques that would require multiple free scripts to replicate. The proprietary FVG retracement algorithm, multi-timeframe POC analysis, and adaptive confidence scoring system are not available in any single free script. Most free scripts only provide basic EMA crossover signals without institutional context.
### Use Cases
Best timeframes: 5-minute for scalping, 15-minute for swing trades, 1-hour for position entries
Suitable markets: Forex major pairs (EUR/USD, GBP/USD), Crypto (BTC/USD, ETH/USD), major indices (S&P 500, NASDAQ)
Market conditions: Trending markets with ADX > 20, high volume sessions (London/NY overlap)
### Access Instructions
To request access to this invite-only script:
Contact: with your TradingView username
Requirements: Include your TradingView username and brief trading experience
Process: I will review requests within 24 hours and grant access to qualified traders
Statistics
ATR %ATR % Oscillator
A simple and effective Average True Range (ATR) indicator displayed as a percentage of the current price in a separate panel.
FEATURES:
• ATR displayed as percentage of current price for easy cross-asset comparison
• EMA smoothing line using the same period as ATR
• Configurable ATR period (default: 20)
• Clean visualization with zero reference line
HOW IT WORKS:
The indicator calculates ATR and converts it to a percentage: (ATR / Close) × 100
This normalization allows you to:
- Compare volatility across different instruments regardless of price
- Identify high and low volatility periods
- Use the EMA line to spot volatility trends
PARAMETERS:
ATR Period - The lookback period for ATR calculation (default: 20)
Timeframe - Choose any timeframe for ATR calculation independently from the chart timeframe (default: chart timeframe)
IPDA Ranges – ProIPDA Ranges – Pro
This indicator plots Institutional Price Delivery Algorithm (IPDA) ranges based on lookback periods of 20, 40, and 60 days, as taught by ICT (Inner Circle Trader). It visualizes premium and discount zones, equilibrium levels, quadrants, and sub-quadrants to help traders identify key price areas and potential market biases.
Key Features:
- Displays IPDA ranges as boxes or lines, with customizable colors for discount, equilibrium, and premium zones.
- Optionally shades the 25%-75% mid-zone for each range.
- Supports quadrants (25% steps) and sub-quadrants with lines and labels for detailed price segmentation.
- Includes a table displaying either discount/premium status or percentage from equilibrium for each range.
- Configurable alerts for entry/exit into the mid-zone.
- Visual options include line styles, label sizes, price display on labels, and buffers for zone extension.
Settings Overview:
- IPDA Intervals: Enable/disable IPDA20, IPDA40, IPDA60; toggle quadrants, sub-quadrants, mid-zone shading, and drawing with lines vs. boxes.
- Colors and Styles: Customize colors for zones, lines, labels; select solid/dotted/dashed styles for borders and lines.
- Appearance: Adjust label and table sizes, table position, and background opacity.
- Labels: Show/hide per-range labels and include prices.
- Alerts: Enable mid-zone entry/exit alerts.
Usage:
Add the indicator to your chart and select the desired IPDA intervals. The ranges update dynamically based on daily highs and lows. Use the table for quick reference to current positioning (discount/premium or percentage). The mid-zone shading helps identify consolidation areas, while quadrants and sub-quadrants assist in pinpointing potential support/resistance levels.
© MadMonkTrading
Kelly Wave Position Matrix 20251024 V1 ZENYOUNGA simple table is designed for use when opening a position. It applies the Kelly formula to calculate a more scientific position size based on win rate and risk–reward ratio. At the same time, it displays 1.65× ATR stop-loss levels for both long and short positions to serve as a reference for comparing with existing stop-loss placements.
Additionally, the table back-calculates the corresponding position size based on a 2% total capital loss limit, using the actual loss ratio. It also shows the current wave trend status as a pre-filtering condition.
Overall, this table integrates the core elements of trading — trend (wave confirmation), win rate, risk–reward ratio, and position sizing — making it an effective checklist before entering a trade. Its purpose is to help achieve a probabilistic edge and ensure positive expected value in trading decisions.
CNN Fear and Greed Index📊 CNN Fear & Greed Index — by @victhoreb
Tap into the emotional heartbeat of the U.S. stock market with this powerful CNN-inspired Fear & Greed Index! 🧠📉📈 Designed to mirror the sentiment framework popularized by CNN Business, this indicator blends 7 key market signals into a single score from 0 (😱 Extreme Fear) to 100 (🚀 Extreme Greed), helping you navigate volatility with confidence.
🧩 What’s Inside?
Each component captures a unique behavioral or macroeconomic force:
- ⚡ Market Momentum: Tracks how far the S&P 500 is from its 125-day average — a pulse check on trend strength.
- 🏛️ Stock Price Strength: Measures the NYSE Highs vs. Lows — are more stocks breaking out or breaking down?
- 🌊 Stock Price Breadth: Uses the McClellan Volume Summation Index to assess market-wide participation.
- ☎️ Put/Call Ratio: A 5-day average of the equity options market — are traders hedging or chasing?
- 🌪️ Volatility (VIX): Compares the VIX to its 50-day average — rising fear or calming nerves?
- 🛡️ Safe Haven Demand: Contrasts stock returns with bond returns — are investors seeking shelter or risk?
- 💣 Junk Bond Demand: Inverted high-yield spread — tighter spreads = more risk-on appetite.
🎯 Why Use It?
This index gives you a quantified view of Wall Street’s mood, helping you:
- Spot emotional extremes that often precede reversals
- Confirm or challenge your directional bias
- Stay grounded when the market gets irrational
🧭 Visual Sentiment Meter
A custom offset sentiment meter shows current positioning with intuitive labels:
- 😱 Extreme Fear
- 😨 Fear
- 😐 Neutral
- 😄 Greed
- 🚀 Extreme Greed
Color gradients and dynamic labels make it easy to interpret at a glance.
Ready to trade with the crowd—or against it? Add this indicator to your chart and let sentiment guide your strategy! 📈🧠
Crypto Fear and Greed Index📊 Crypto Fear & Greed Index — by @victhoreb
Decode the emotional pulse of the crypto market with this all-in-one Fear & Greed Index! 🧠💰 This custom-built indicator blends 7 powerful market signals into a single sentiment score ranging from 0 (😱 Extreme Fear) to 100 (🚀 Extreme Greed), helping you spot potential tops, bottoms, and trend shifts with clarity.
🔍 What’s under the hood?
Each component reflects a unique psychological or macroeconomic force:
- ⚡ Market Momentum: Measures how far BTC is from its 125-day average — are we overextended or undervalued?
- 📈 Crypto Price Strength: Tracks the dominance of altcoins (OTHERS.D) — rising dominance = growing risk appetite.
- 💵 Digital Dollar Dominance (USDT.D): A proxy for stablecoin demand — more USDT dominance = risk-off behavior.
- 🐦 Twitter Sentiment (LunarCrush): Captures real-time posts on TWITTER about Bitcoin — are the crowds euphoric or panicking?
- 🌪️ Volatility (VIX): Inverted VIX deviation — higher fear in traditional markets often spills into crypto.
- 🛡️ Safe Haven Demand: Compares BTC returns vs. US10Y bonds — are investors fleeing to safety or embracing risk?
- 🧨 Junk Bond Demand (BAMLH0A0HYM2): Inverted high-yield spread — tighter spreads = more greed in credit markets.
🎯 Why use it?
This index gives you a quantified view of market sentiment, helping you:
- Anticipate reversals during emotional extremes
- Confirm trend strength or weakness
- Stay objective when the market gets irrational
🧭 Visual Dashboard
A custom offset sentiment meter shows current positioning with intuitive labels:
- 😱 Extreme Fear
- 😨 Fear
- 😐 Neutral
- 😄 Greed
- 🚀 Extreme Greed
Color gradients and dynamic labels make it easy to interpret at a glance.
Ready to trade with the crowd—or against it? Add this indicator to your chart and let sentiment guide your strategy! 📈🧠
Statistical Price Deviation Index (MAD/VWMA)SPDI is a statistical oscillator designed to detect potential price reversal zones by measuring how far price deviates from its typical behavior within a defined rolling window.
Instead of using momentum or moving averages like traditional indicators, SPDI applies robust statistics - a rolling median and Mean Absolute Deviation (MAD) - to calculate a normalized measure of price displacement. This normalization keeps the output bounded (from −1 to +1 by default), producing a stable and consistent oscillator that adapts to changing volatility conditions.
The second line in SPDI uses a Volume-Weighted Moving Average (VWMA) instead of a simple price median. This creates a complementary oscillator showing statistically weighted deviations based on traded volume. When both oscillators align in their extremes, strong confluence reversal signals are generated.
How It Works
For each bar, SPDI calculates the median price of the last N bars (default 100).
It then measures how far the current bar’s midpoint deviates from that rolling median.
The Mean Absolute Deviation (MAD) of those distances defines a “normal” range of fluctuation.
The deviation is normalized and compressed via a tanh mapping, keeping the oscillator in fixed boundaries (−1 to +1).
The same logic is applied to the VWMA line to gauge volume-weighted deviations.
How to Use
The blue line (Price MAD) represents pure price deviation.
The green line (VWMA Disp) shows the volume-weighted deviation.
Overbought (red) zones indicate statistically extreme upward deviation -> potential short-term overextension.
Oversold (green) zones indicate statistically extreme downward deviation -> potential rebound area.
Confluence signals (both lines hitting the same extreme) often mark strong reversal points.
Settings Tips
Lookback length controls how much historical data defines “normal” behavior. Larger = smoother, smaller = more sensitive.
Smoothing (RMA length) can reduce noise without changing the overall statistical logic.
Output scale can be set to either −1..+1 or 0..100, depending on your visual preference.
Alerts and color fills are fully customizable in the Style tab.
Summary:
SPDI transforms raw price and volume data into a statistically bounded deviation index. When both Price MAD and VWMA Disp reach joint extremes, it highlights probable market turning points - offering traders a clean, data-driven way to spot potential reversals ahead of time.
EURUSD vs GBPUSD — Alexio Script que muestra que par es más fuerte entre GBP y EUR vs USD en un rango determinado.
OBTrendDelta Volume Delta & Order Block SuiteOB Trend Delta V1 - Order Block & Volume Delta Indicator
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📊 OVERVIEW
OB Trend Delta V1 is a technical indicator that combines Order Blocks analysis (institutional support/resistance zones) with Volume Delta (buying vs selling pressure) to provide insights on setup quality and market dynamics.
The indicator visually displays zones of interest, volume pressure, and a quality scoring system to assist in technical analysis of any market.
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🎯 CORE CONCEPT
▸ ORDER BLOCKS
Order Blocks are price zones where large institutions executed significant operations. These areas tend to act as support (Bull OB) or resistance (Bear OB) when price returns to them.
How to interpret:
🟢 Bull Order Block: Green zone where institutional buyers entered strongly → Potential support
🔴 Bear Order Block: Red zone where institutional sellers entered strongly → Potential resistance
▸ VOLUME DELTA
Volume Delta measures the difference between buying and selling volume in each candle, revealing which side of the market is dominating.
How to interpret:
✅ Positive Delta (green histogram): Buyers dominating → Bullish pressure
❌ Negative Delta (red histogram): Sellers dominating → Bearish pressure
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📈 WHAT THE INDICATOR SHOWS
1️⃣ TREND DETECTION
The indicator identifies the main market direction using moving averages and trend strength analysis (ADX), visually highlighting when the market is in:
Uptrend (Bullish Trend)
Downtrend (Bearish Trend)
Ranging (Sideways market/no clear trend)
2️⃣ SETUP QUALITY SYSTEM
Each trading opportunity is evaluated on 6 independent criteria:
✅ Price inside a valid Order Block
✅ Volume Delta confirming the direction
✅ Order Block is recent and "fresh"
✅ Few previous retests (OB still strong)
✅ Volume confirmation above average
✅ Favorable market regime
Setup Quality Score: 0 to 6 points
Score 6: Perfect setup (all criteria met)
Score 5: Excellent setup (5 of 6 criteria)
Score 4: Good setup (4 of 6 criteria)
Score 0-3: Weak setup or forming
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🔧 VISUAL COMPONENTS IN THE INDICATOR
▸ VOLUME DELTA HISTOGRAM
🟢 Green Bars: Buying volume > selling volume (bullish pressure)
🔴 Red Bars: Selling volume > buying volume (bearish pressure)
📊 Intensity: The larger the bar, the greater the pressure
▸ ORDER BLOCK ZONES
🟢 Green Boxes (Bull OB): Institutional support zones
🔴 Red Boxes (Bear OB): Institutional resistance zones
🔄 Projection: OBs are extended to the right until invalidated
▸ SETUP QUALITY SIGNALS
📊 Score Labels: Show setup quality (Q4, Q5, Q6)
• Q6: Perfect setup (all 6 criteria met)
• Q5: Excellent setup (5 of 6 criteria)
• Q4: Good setup (4 of 6 criteria)
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💡 HOW TO INTERPRET THE INFORMATION
Observe trend direction (EMAs and ADX)
Identify active Order Blocks:
• Bull OBs (green): Potential support zones
• Bear OBs (red): Potential resistance zones
Analyze Volume Delta:
• Green bars: Dominant buying pressure
• Red bars: Dominant selling pressure
Check Setup Quality Score:
• Q5-Q6: Setups with multiple confirmations
• Q4: Setup with moderate confirmations
• Q0-Q3: Few criteria met
⚠️ NOTE: The indicator provides technical information. Trading decisions are exclusively yours.
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📊 TECHNICAL CHARACTERISTICS
▸ RECOMMENDED TIMEFRAMES
5 minutes: Scalping / Fast day trading
15 minutes: Day trading
1 hour: Swing trading
4 hours: Medium-term positions
Daily: Long-term analysis
▸ COMPATIBLE MARKETS
✅ Forex (all pairs)
✅ Cryptocurrencies (BTC, ETH, altcoins)
✅ Indices (S&P500, Nasdaq, etc)
✅ Commodities (Gold, Oil, etc)
✅ Stocks and CFDs
⚠️ Requirement: Volume data is necessary for Volume Delta calculation
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⚠️ IMPORTANT WARNINGS
▸ EDUCATIONAL USE
📊 This indicator is an educational technical analysis tool
⚠️ The indicator does NOT provide buy or sell signals
⚠️ The indicator does NOT guarantee results
⚠️ All trading decisions are your responsibility
▸ RISK MANAGEMENT
⚠️ Always use proper risk management
⚠️ Never trade with money you cannot afford to lose
⚠️ Test the indicator on a demo account before using real money
⚠️ Combine with your own analysis and strategy
▸ LIMITATIONS
❌ No indicator is 100% accurate
❌ Markets can behave unpredictably
❌ Requires confirmation with other analyses
❌ Volume Delta requires reliable volume data
▸ DISCLAIMER
📢 This indicator is educational and does not constitute investment advice.
The indicator shows technical information, not trading signals
Past results do not guarantee future results
Trading involves risk of total capital loss
You are 100% responsible for your trading decisions
Consult a financial professional before investing
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📚 ADVANCED CONCEPTS
▸ WHAT ARE ORDER BLOCKS?
Order Blocks represent zones where "smart money" (institutions, whales) accumulated or distributed positions. When price returns to these zones, there is high probability of reaction due to:
Pending limit orders
Psychological levels
Institutional value zones
▸ VOLUME DELTA VS NORMAL VOLUME
Normal volume shows only QUANTITY of trades.
Volume Delta shows DIRECTION (who is winning the battle):
High volume + Positive delta = Strong accumulation 🚀
High volume + Negative delta = Strong distribution 📉
▸ MARKET REGIME (ADX)
ADX measures TREND STRENGTH:
ADX > 25: Strong trend (best time to trade)
ADX < 20: Sideways/ranging market (avoid trades)
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✅ BEFORE USING THIS INDICATOR
Make sure you:
☑ Understand the Order Blocks concept
☑ Know how to interpret Volume Delta
☑ Understand trend analysis
☑ Have your own trading strategy
☑ Know risk management
☑ Understand the indicator does NOT provide buy/sell signals
☑ Are aware of trading risks
☑ Test on demo account before using real money
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📊 USE AS AN ANALYSIS TOOL, NOT AS AN AUTOMATIC DECISION SYSTEM!
The indicator provides information. You make the decisions.
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Version: 1.0 | Type: Order Block + Volume Delta + Trend Analysis | Update: October 2024
WorldCup Dashboard + Institutional Sessions© 2025 NewMeta™ — Educational use only.
# Full, Premium Description
## WorldCup Dashboard + Institutional Sessions
**A trade-ready, intraday framework that combines market structure, real flow, and institutional timing.**
This toolkit fuses **Institutional Sessions** with a **price–volume decision engine** so you can see *who is active*, *where value sits*, and *whether the drive is real*. You get: **CVD/Delta**, volume-weighted **Momentum**, **Aggression** spikes, **FVG (MTF)** with nearest side, **Daily Volume Profile (VAH/POC/VAL)**, **ATR regime**, a **24h position gauge**, classic **candle patterns**, IBH/IBL + **first-hour “true close”** lines, and a **10-vote confluence scoreboard**—all in one view.
---
## What’s inside (and how to trade it)
### 🌍 Institutional Sessions (Sydney • Tokyo • London • New York)
* Session boxes + a highlighted **first hour**.
* Plots the **true close** (first-hour close) as a running line with a label.
**Use:** Many desks anchor risk to this print. Above = bullish bias; below = bearish. **IBH/IBL** breaks during London/NY carry the most signal.
### 📊 CVD / Delta (Flow)
* Net buyer vs seller pressure with smooth trend state.
**Use:** **Rising CVD + acceptance above mid/POC** confirms continuation. Bearish price + rising CVD = caution (possible absorption).
### ⚡ Volume-Weighted Momentum
* Momentum adjusted by participation quality (volume).
**Use:** Momentum>MA and >0 → trend drive is “real”; <0 and falling → distribution risk.
### 🔥 Aggression Detector
* ROC × normalized volume × wick factor to flag **forceful** candles.
**Use:** On spikes, avoid fading blindly—wait for pullbacks into **aligned FVG** or for aggression to cool.
### 🟦🟪 Fair Value Gaps (with MTF)
* Detects up to 3 recent FVGs and marks the **nearest** side to price.
**Use:** Trend pullbacks into **bullish FVG** for longs; bounces into **bearish FVG** for shorts. Optional threshold to filter weak gaps.
### 🧭 24h Gauge (positioning)
* Shows current price across the 24h low⇢high with a mid reference.
**Use:** Above mid and pushing upper third = momentum continuation setups; below mid = sell the rips bias.
### 🧱 Daily Volume Profile (manual per day)
* **VAH / POC / VAL** derived from discretized rows.
**Use:** **POC below** supports longs; **POC above** caps rallies. Fade VAH/VAL in ranges; treat them as break/hold levels in trends.
### 📈 ATR Regime
* **ATR vs ATR-avg** with direction and regime flag (**HIGH / NORMAL / LOW**).
**Use:** HIGH ⇒ give trades room & favor trend following. LOW ⇒ fade edges, scale targets.
### 🕯️ Candle Patterns (contextual, not standalone)
* Engulfings, Morning/Evening Star, 3 Soldiers/Crows, Harami, Hammer/Shooting Star, Double Top/Bottom.
**Use:** Only with session + flow + momentum alignment.
### 🤝 Price–Volume Classification
* Labels each bar as **continuation**, **exhaustion**, **distribution**, or **healthy pullback**.
**Use:** Align continuation reads with trend; treat “Price↑ + Vol↓” as a caution flag.
### 🧪 Confluence Scoreboard & B/S Meter
* Ten elements vote: 🔵 bull, ⚪ neutral, 🟣 bear.
**Use:** Execution filter—take setups when the board’s skew matches your trade direction.
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## Playbooks (actionable)
**Trend Pullback (Long)**
1. London/NY active, Momentum↑, CVD↑, price above 24h mid & POC.
2. Pullback into **nearest bullish FVG**.
3. Invalidate under FVG low or **true-close** line.
4. Targets: IBH → VAH → 24h high.
**Range Fade (Short)**
1. Asia/quiet regime, **Price↑ + Vol↓** into **VAH**, ATR low.
2. Nearest FVG bearish or scoreboard skew bearish.
3. Invalidate above VAH/IBH.
4. Targets: POC → VAL.
**News/Impulse**
Aggression spike? Don’t chase. Let it pull back into the aligned FVG; require CVD/Momentum agreement before entry.
---
## Alerts (included)
* **Bull/Bear Confluence ≥ 7/10**
* **Intraday Target Achieved** / **Daily Target Achieved**
* **Session True-Close Retests** (Sydney/Tokyo/London/NY)
*(Keep alerts “Once per bar” unless you specifically want intrabar triggers.)*
---
## Setup Tips
* **UTC**: Choose the reference that matches how you track sessions (default UTC+2).
* **Volume threshold**: 2.0× is a strong baseline; raise for noisy alts, lower for majors.
* **CVD smoothing**: 14–24 for scalps; 24–34 for slower markets.
* **ATR lengths**: Keep defaults unless your asset has a persistent regime shift.
---
## Why this framework?
Because **timing (sessions)**, **truth (flow)**, and **location (value/FVG)** together beat any single signal. You get *who is trading*, *how strong the push is*, and *where risk lives*—on one screen—so execution is faster and cleaner.
---
**Disclaimer**: Educational use only. Not financial advice. Markets are risky—backtest and size responsibly.
IIR One-Pole Price Filter [BackQuant]IIR One-Pole Price Filter
A lightweight, mathematically grounded smoothing filter derived from signal processing theory, designed to denoise price data while maintaining minimal lag. It provides a refined alternative to the classic Exponential Moving Average (EMA) by directly controlling the filter’s responsiveness through three interchangeable alpha modes: EMA-Length , Half-Life , and Cutoff-Period .
Concept overview
An IIR (Infinite Impulse Response) filter is a type of recursive filter that blends current and past input values to produce a smooth, continuous output. The "one-pole" version is its simplest form, consisting of a single recursive feedback loop that exponentially decays older price information. This makes it both memory-efficient and responsive , ideal for traders seeking a precise balance between noise reduction and reaction speed.
Unlike standard moving averages, the IIR filter can be tuned in physically meaningful terms (such as half-life or cutoff frequency) rather than just arbitrary periods. This allows the trader to think about responsiveness in the same way an engineer or physicist would interpret signal smoothing.
Why use it
Filters out market noise without introducing heavy lag like higher-order smoothers.
Adapts to various trading speeds and time horizons by changing how alpha (responsiveness) is parameterized.
Provides consistent and mathematically interpretable control of smoothing, suitable for both discretionary and algorithmic systems.
Can serve as the core component in adaptive strategies, volatility normalization, or trend extraction pipelines.
Alpha Modes Explained
EMA-Length : Classic exponential decay with alpha = 2 / (L + 1). Equivalent to a standard EMA but exposed directly for fine control.
Half-Life : Defines the number of bars it takes for the influence of a price input to decay by half. More intuitive for time-domain analysis.
Cutoff-Period : Inspired by analog filter theory, defines the cutoff frequency (in bars) beyond which price oscillations are heavily attenuated. Lower periods = faster response.
Formula in plain terms
Each bar updates as:
yₜ = yₜ₋₁ + alpha × (priceₜ − yₜ₋₁)
Where alpha is the smoothing coefficient derived from your chosen mode.
Smaller alpha → smoother but slower response.
Larger alpha → faster but noisier response.
Practical application
Trend detection : When the filter line rises, momentum is positive; when it falls, momentum is negative.
Signal timing : Use the crossover of the filter vs its previous value (or price) as an entry/exit condition.
Noise suppression : Apply on volatile assets or lower timeframes to remove flicker from raw price data.
Foundation for advanced filters : The one-pole IIR serves as a building block for multi-pole cascades, adaptive smoothers, and spectral filters.
Customization options
Alpha Scale : Multiplies the final alpha to fine-tune aggressiveness without changing the mode’s core math.
Color Painting : Candles can be painted green/red by trend direction for visual clarity.
Line Width & Transparency : Adjust the visual intensity to integrate cleanly with your charting style.
Interpretation tips
A smooth yet reactive line implies optimal tuning — minimal delay with reduced false flips.
A sluggish line suggests alpha is too small (increase responsiveness).
A noisy, twitchy line means alpha is too large (increase smoothing).
Half-life tuning often feels more natural for aligning filter speed with price cycles or bar duration.
Summary
The IIR One-Pole Price Filter is a signal smoother that merges simplicity with mathematical rigor. Whether you’re filtering for entry signals, generating trend overlays, or constructing larger multi-stage systems, this filter delivers stability, clarity, and precision control over noise versus lag, an essential tool for any quantitative or systematic trading approach.
Liquidity Stress Index SOFR - IORBLiquidity Stress Index (SOFR - IORB)
This indicator tracks the spread between the Secured Overnight Financing Rate (SOFR) and the Interest on Reserve Balances (IORB) set by the Federal Reserve.
A persistently positive spread may indicate funding stress or liquidity shortages in the repo market, as it suggests overnight lending rates exceed the risk-free rate banks earn at the Fed.
Useful for monitoring monetary policy transmission or market/liquidity stress.
Advanced HMM - 3 States CompleteHidden Markov Model
Aconsistent challenge for quantitative traders is the frequent behaviour modification of financial
markets, often abruptly, due to changing periods of government policy, regulatory environment
and other macroeconomic effects. Such periods are known as market regimes. Detecting such
changes is a common, albeit difficult, process undertaken by quantitative market participants.
These various regimes lead to adjustments of asset returns via shifts in their means, variances,
autocorrelation and covariances. This impacts the effectiveness of time series methods that rely
on stationarity. In particular it can lead to dynamically-varying correlation, excess kurtosis ("fat
tails"), heteroskedasticity (volatility clustering) and skewed returns.
There is a clear need to effectively detect these regimes. This aids optimal deployment of
quantitative trading strategies and tuning the parameters within them. The modeling task then
becomes an attempt to identify when a new regime has occurred adjusting strategy deployment,
risk management and position sizing criteria accordingly.
A principal method for carrying out regime detection is to use a statistical time series tech
nique known as a Hidden Markov Model . These models are well-suited to the task since they
involve inference on "hidden" generative processes via "noisy" indirect observations correlated
to these processes. In this instance the hidden, or latent, process is the underlying regime state,
while the asset returns are the indirect noisy observations that are influenced by these states.
MAIN FEATURES OF THE INDICATOR
The "Advanced HMM - 3 States Complete" indicator is an advanced technical analysis tool that uses Hidden Markov Model (HMM) to identify three main market regimes: BULL, BEAR, and SIDEWAYS.
🎯 KEY FEATURES:
1. HMM-based Trend Detection
3 market states: Bull (0), Bear (1), Sideways (2)
Dynamic probabilities: Calculates probability for each state based on price data
Transition matrix: Models state transitions between regimes
2. Analytical Features
Price volatility: Log returns and standard deviation
Momentum: Rate of Change (ROC)
Volume: Volume ratio vs moving average
Data normalization: Standardizes features to common scale
3. Visual Trading Signals
text
📍 BUY Signals:
- Green upward triangle below bars
- "LONG" label in green
📍 SELL Signals:
- Red downward triangle above bars
- "SHORT" label in red
📍 EXIT Signals:
- Orange X marks when transitioning to sideways
4. Information Display
Probability table (top-right): Shows percentage for each state
State label: Current regime with probability percentages
Chart background color: Reflects dominant market state
5. Automated Alerts
Alerts when new Bull/Bear market detected
Alerts when market transitions to sideways
Configurable TradingView notifications
6. Customizable Parameters
pinescript
length: 100 // Lookback period
smoothing_period: 20 // Probability smoothing
volatility_threshold: 0.5 // Volatility threshold
💡 PRACTICAL APPLICATIONS:
Identify primary trends with quantified probabilities
Entry/exit signals based on state transitions
Risk management during sideways markets
Trend confirmation when combined with other indicators
This indicator is particularly useful for market regime analysis and identifying trend transition points using advanced statistical probability methods.
🔧 TECHNICAL IMPLEMENTATION:
Composite observation: Weighted combination of returns (40%), momentum (30%), and volatility (30%)
Gaussian emission probabilities: Different distributions for each state
Manual HMM updates: Avoids matrix computation limitations in Pine Script
Real-time smoothing: EMA applied to state probabilities
The indicator provides institutional-grade regime detection in a visually intuitive package suitable for both discretionary and systematic traders.
ATR Gauge - Audiophile StyleThe ATR Gauge - Audiophile Style indicator is a custom visualization tool. It's designed to give you a quick, retro-inspired snapshot of market volatility using the Average True Range (ATR) metric. Think of it as a dashboard widget styled like the VU meters on old-school audiophile equipment (e.g., vintage stereo amps from brands like McIntosh or Marantz)—simple, elegant, and functional. It sits in one of the corners of your chart and helps you gauge how "hot" or "cool" the current price action is compared to recent levels.
Why This Gauge?: Standard ATR plots as a line on your chart, but this turns it into a visual "meter" focused on the last 24 hours. It's like a speedometer for volatility—quick to read at a glance. Useful for day traders, scalpers, or anyone monitoring intraday risk without cluttering the main chart.
CCT Gold Synthetic Market Cap🌎 Gold Synthetic Market Cap
Overview
The Gold Synthetic Market Cap indicator transforms the Gold Spot price (XAU/USD) into a synthetic market capitalization chart, allowing traders and analysts to visualize gold’s total estimated valuation as a global asset — similar to how cryptocurrencies are evaluated by total market cap.
This tool uses the current XAU/USD price multiplied by the total amount of gold ever mined (~210,000 metric tons), automatically converting the result into trillions of US dollars (USD T).
The outcome is a precise and dynamic representation of gold’s real-time market value — displayed as full OHLC candles in a separate chart panel.
🧠 Core Concept
Gold’s price per ounce doesn’t tell the full story of its global valuation.
By converting it to market capitalization, we can compare it to other asset classes such as:
Bitcoin’s total market cap (CRYPTOCAP:BTC)
Global equities and ETFs
Precious metals or commodities benchmarks
This indicator bridges the gap between price analysis and macro asset valuation, offering a quantitative visualization of gold’s total monetary footprint.
⚙️ Technical Mechanics
Base Symbol: OANDA:XAUUSD (or any gold pair available on your chart)
Conversion Constant:
210,000 tons × 32,150.7 oz/ton = 6.76 × 10⁹ ounces
Calculation:
MarketCap = (XAUUSD × total_ounces) / 1e12
Displayed Units: Trillions of USD (USD T)
Chart Type: Full OHLC candles (plotcandle)
Each candle represents the daily/weekly/monthly change in gold’s total market value.
🎛️ User Controls (Inputs)
Toggle Function
Show Average Line? Displays a 21-period SMA (in trillions) for trend-following analysis.
Show Info Table? Adds a small info table at the bottom-right corner showing the current market cap value.
Show Market Cap Label? Displays a live label above the last candle showing the latest market cap value.
Normalize Scale? Adjusts scaling for better visual fit. Leave enabled to avoid flat or off-screen candles.
📈 How to Use
1 - Add the indicator to your Gold Spot chart (XAUUSD).
2 - When added, TradingView automatically creates a separate panel below the main price chart.
3 - You can hide the original XAUUSD chart to focus solely on the synthetic market cap.
4 - Maximize the indicator panel (double-click or use the arrow icon) to view the synthetic market cap in full-screen mode.
Apply any drawing tools, trendlines, or visual overlays directly on this panel (they won’t affect the base chart).
Optionally, compare it side by side with Bitcoin Market Cap (CRYPTOCAP:BTC) for macro-level correlation studies.
🪙 Practical Applications
Compare Gold’s global valuation to Bitcoin, equities, or global M2 supply.
Analyze macro rotation trends between risk-off and risk-on assets.
Estimate how much capital is stored in physical gold versus digital assets.
Integrate into broader multi-asset dashboards for portfolio allocation analysis.
💡 Suggested Workflow
Keep the normalize toggle enabled (default).
Maximize the lower panel for a full synthetic chart view.
Combine this tool with the F!72 SuperTrade or MarketMonitor indicators for contextual macro insight.
Use a weekly or monthly timeframe for clearer long-term structure visualization.
📊 Notes
This indicator uses public XAU/USD pricing and does not require any external API.
Works seamlessly with any TradingView theme (light or dark).
Best viewed with logarithmic scale off, as values are already represented in trillions.
Compatible with all resolutions and broker feeds that support XAUUSD.
🔬 Example Interpretation
If Gold trades around $4,000/oz,
the total market cap is approximately:
4,000 × 32,150.7 × 210,000 ≈ 27 Trillion USD
If Gold rises to $5,000/oz,
the global valuation crosses 33.9 Trillion USD —
a move equivalent to adding the entire market cap of all major tech stocks combined.
🧭 Final Recommendation
This script is designed as an analytical overlay, not a trading signal tool.
It complements technical analysis by providing macro context — showing where gold stands as a global store of value in relation to other capital markets.
For best experience:
Use higher timeframes (1W or 1M)
Maximize the indicator panel
Keep Normalize Scale = ON
⚠️ Disclaimer
This indicator is a visualization and educational tool.
It does not provide financial advice or investment recommendations.
Always perform your own research before making financial decisions.
Author: Central Crypto Traders
Version: 1.0 (October 2025)
Type: Informational Overlay
License: Open for personal and educational use
Match on Selectable Percentage Change + RangeIndicator Overview:
Match on Selectable Percentage Change + Range is a powerful analytical tool designed for traders and analysts who want to identify historical price bars that match a specific percentage variation, and then evaluate how price evolved in the following days. It combines precision filtering with visual tabular feedback, making it ideal for pattern recognition, backtesting, and scenario analysis.
What It Does
This indicator scans historical bars to find instances where the percentage change between two consecutive closes matches a user-defined target (± a customizable tolerance). Once matches are found, it displays:
The date of each match (most recent first)
The actual variation searched
The percentage change after 2, 10, 20, and 30 bars
The min-max range (in %) over those same periods
All results are shown in a dynamic table directly on the chart.
Inputs & Controls
Input Description
Which variation do you want to analyze? (%)
Set the target percentage change to look for (e.g. 2.5%)
% deviation from the variation to be considered (%) Define the tolerance range around the target (e.g. ±0.5%)
Bars to analyze (max 9999) Set how many past bars to scan
Show match table Toggle to enable/disable the entire table
Show percentage variations (2d, 10d, 20d, 30d) Toggle to show/hide post-match percentage changes
Show min-max ranges (2d, 10d, 20d, 30d) Toggle to show/hide post-match high/low ranges
Table Structure
Each row in the table represents a historical match. Columns include:
Date: When the match occurred
Variation in: The actual % change that triggered the match
2d / 10d / 20d / 30d: % change after those days
Min-Max 2d / 10d / 20d / 30d: Range of price movement after those days
Color coding helps quickly identify bullish (green) vs bearish (red) outcomes.
Use Cases
Backtesting: See how similar past moves evolved over time
Scenario modeling: Estimate potential outcomes after a known variation
Pattern recognition: Spot recurring setups or volatility clusters
Risk analysis: Understand post-variation drawdowns and upside potential
Tips for Use
Use tighter deviation (e.g. 0.3%) for precision, or wider (e.g. 1%) for broader pattern capture.
Combine with other indicators to validate setups (e.g. volume, RSI, trend filters).
Toggle off variation or range columns to focus only on the metrics you need.
Gold–Bitcoin Correlation (Offset Model) by KManus88This indicator analyzes the correlation between Gold (XAU/USD) and Bitcoin (BTC/USD) using a time-offset model adjustable by the user.
The goal is to detect cyclical leads or lags between both assets, highlighting how capital flows into Gold may precede or follow movements in the crypto market.
Key Features:
Dynamic correlation calculation between Gold and Bitcoin.
Adjustable offset in days (default: 107) to fine-tune the temporal shift.
Automatic labels and on-chart visualization.
Compatible with multiple timeframes and logarithmic scales.
Interpretation:
Positive correlation suggests synchronized trends between both assets.
Negative correlation signals divergence or rotation of liquidity.
The time-offset parameter helps estimate when a shift in Gold could later reflect in Bitcoin.
Recommended use:
For macro-financial and global liquidity cycle analysis.
As a complementary tool in cross-asset momentum strategies.
© 2025 – Developed by KManus88 | Inspired by monetary correlation studies and global liquidity cycles.
This script is for educational purposes only and does not constitute financial advice.
Smooth Theil-SenI wanted to build a Theil-Sen estimator that could run on more than one bar and produce smoother output than the standard implementation. Theil-Sen regression is a non-parametric method that calculates the median slope between all pairs of points in your dataset, which makes it extremely robust to outliers. The problem is that median operations produce discrete jumps, especially when you're working with limited sample sizes. Every time the median shifts from one value to another, you get a step change in your regression line, which creates visual choppiness that can be distracting even though the underlying calculations are sound.
The solution I ended up going with was convolving a Gaussian kernel around the center of the sorted lists to get a more continuous median estimate. Instead of just picking the middle value or averaging the two middle values when you have an even sample size, the Gaussian kernel weights the values near the center more heavily and smoothly tapers off as you move away from the median position. This creates a weighted average that behaves like a median in terms of robustness but produces much smoother transitions as new data points arrive and the sorted list shifts.
There are variance tradeoffs with this approach since you're no longer using the pure median, but they're minimal in practice. The kernel weighting stays concentrated enough around the center that you retain most of the outlier resistance that makes Theil-Sen useful in the first place. What you gain is a regression line that updates smoothly instead of jumping discretely, which makes it easier to spot genuine trend changes versus just the statistical noise of median recalculation. The smoothness is particularly noticeable when you're running the estimator over longer lookback periods where the sorted list is large enough that small kernel adjustments have less impact on the overall center of mass.
The Gaussian kernel itself is a bell curve centered on the median position, with a standard deviation you can tune to control how much smoothing you want. Tighter kernels stay closer to the pure median behavior and give you more discrete steps. Wider kernels spread the weighting further from the center and produce smoother output at the cost of slightly reduced outlier resistance. The default settings strike a balance that keeps the estimator robust while removing most of the visual jitter.
Running Theil-Sen on multiple bars means calculating slopes between all pairs of points across your lookback window, sorting those slopes, and then applying the Gaussian kernel to find the weighted center of that sorted distribution. This is computationally more expensive than simple moving averages or even standard linear regression, but Pine Script handles it well enough for reasonable lookback lengths. The benefit is that you get a trend estimate that doesn't get thrown off by individual spikes or anomalies in your price data, which is valuable when working with noisy instruments or during volatile periods where traditional regression lines can swing wildly.
The implementation maintains sorted arrays for both the slope calculations and the final kernel weighting, which keeps everything organized and makes the Gaussian convolution straightforward. The kernel weights are precalculated based on the distance from the center position, then applied as multipliers to the sorted slope values before summing to get the final smoothed median slope. That slope gets combined with an intercept calculation to produce the regression line values you see plotted on the chart.
What this really demonstrates is that you can take classical statistical methods like Theil-Sen and adapt them with signal processing techniques like kernel convolution to get behavior that's more suited to real-time visualization. The pure mathematical definition of a median is discrete by nature, but financial charts benefit from smooth, continuous lines that make it easier to track changes over time. By introducing the Gaussian kernel weighting, you preserve the core robustness of the median-based approach while gaining the visual smoothness of methods that use weighted averages. Whether that smoothness is worth the minor variance tradeoff depends on your use case, but for most charting applications, the improved readability makes it a good compromise.
Mean Reversion Oscillator [Alpha Extract]An advanced composite oscillator system specifically designed to identify extreme market conditions and high-probability mean reversion opportunities, combining five proven oscillators into a single, powerful analytical framework.
By integrating multiple momentum and volume-based indicators with sophisticated extreme level detection, this oscillator provides precise entry signals for contrarian trading strategies while filtering out false reversals through momentum confirmation.
🔶 Multi-Oscillator Composite Framework
Utilizes a comprehensive approach that combines Bollinger %B, RSI, Stochastic, Money Flow Index, and Williams %R into a unified composite score. This multi-dimensional analysis ensures robust signal generation by capturing different aspects of market extremes and momentum shifts.
// Weighted composite (equal weights)
normalized_bb = bb_percent
normalized_rsi = rsi
normalized_stoch = stoch_d_val
normalized_mfi = mfi
normalized_williams = williams_r
composite_raw = (normalized_bb + normalized_rsi + normalized_stoch + normalized_mfi + normalized_williams) / 5
composite = ta.sma(composite_raw, composite_smooth)
🔶 Advanced Extreme Level Detection
Features a sophisticated dual-threshold system that distinguishes between moderate and extreme market conditions. This hierarchical approach allows traders to identify varying degrees of mean reversion potential, from moderate oversold/overbought conditions to extreme levels that demand immediate attention.
🔶 Momentum Confirmation System
Incorporates a specialized momentum histogram that confirms mean reversion signals by analyzing the rate of change in the composite oscillator. This prevents premature entries during strong trending conditions while highlighting genuine reversal opportunities.
// Oscillator momentum (rate of change)
osc_momentum = ta.mom(composite, 5)
histogram = osc_momentum
// Momentum confirmation
momentum_bullish = histogram > histogram
momentum_bearish = histogram < histogram
// Confirmed signals
confirmed_bullish = bullish_entry and momentum_bullish
confirmed_bearish = bearish_entry and momentum_bearish
🔶 Dynamic Visual Intelligence
The oscillator line adapts its color intensity based on proximity to extreme levels, providing instant visual feedback about market conditions. Background shading creates clear zones that highlight when markets enter moderate or extreme territories.
🔶 Intelligent Signal Generation
Generates precise entry signals only when the composite oscillator crosses extreme thresholds with momentum confirmation. This dual-confirmation approach significantly reduces false signals while maintaining sensitivity to genuine mean reversion opportunities.
How It Works
🔶 Composite Score Calculation
The indicator simultaneously tracks five different oscillators, each normalized to a 0-100 scale, then combines them into a smoothed composite score. This approach eliminates the noise inherent in single-oscillator analysis while capturing the consensus view of multiple momentum indicators.
// Mean reversion entry signals
bullish_entry = ta.crossover(composite, 100 - extreme_level) and composite < (100 - extreme_level)
bearish_entry = ta.crossunder(composite, extreme_level) and composite > extreme_level
// Bollinger %B calculation
bb_basis = ta.sma(src, bb_length)
bb_dev = bb_mult * ta.stdev(src, bb_length)
bb_percent = (src - bb_lower) / (bb_upper - bb_lower) * 100
🔶 Extreme Zone Identification
The system automatically identifies when markets reach statistically significant extreme levels, both moderate (65/35) and extreme (80/20). These zones represent areas where mean reversion has the highest probability of success based on historical market behavior.
🔶 Momentum Histogram Analysis
A specialized momentum histogram tracks the velocity of oscillator changes, helping traders distinguish between healthy corrections and potential trend reversals. The histogram's color-coded display makes momentum shifts immediately apparent.
🔶 Divergence Detection Framework
Built-in divergence analysis identifies situations where price and oscillator movements diverge, often signaling impending reversals. Diamond-shaped markers highlight these critical divergence patterns for enhanced pattern recognition.
🔶 Real-Time Information Dashboard
An integrated information table provides instant access to current oscillator readings, market status, and individual component values. This dashboard eliminates the need to manually check multiple indicators while trading.
🔶 Individual Component Display
Optional display of individual oscillator components allows traders to understand which specific indicators are driving the composite signal. This transparency enables more informed decision-making and deeper market analysis.
🔶 Adaptive Background Coloring
Intelligent background shading automatically adjusts based on market conditions, creating visual zones that correspond to different levels of mean reversion potential. The subtle color gradations make pattern recognition effortless.
1D
3D
🔶 Comprehensive Alert System
Multi-tier alert system covers confirmed entry signals, divergence patterns, and extreme level breaches. Each alert type provides specific context about the detected condition, enabling traders to respond appropriately to different signal strengths.
🔶 Customizable Threshold Management
Fully adjustable extreme and moderate levels allow traders to fine-tune the indicator's sensitivity to match different market volatilities and trading timeframes. This flexibility ensures optimal performance across various market conditions.
🔶 Why Choose AE - Mean Reversion Oscillator?
This indicator provides the most comprehensive approach to mean reversion trading by combining multiple proven oscillators with advanced confirmation mechanisms. By offering clear visual hierarchies for different extreme levels and requiring momentum confirmation for signals, it empowers traders to identify high-probability contrarian opportunities while avoiding false reversals. The sophisticated composite methodology ensures that signals are both statistically significant and practically actionable, making it an essential tool for traders focused on mean reversion strategies across all market conditions.
Historical Matrix Analyzer [PhenLabs]📊Historical Matrix Analyzer
Version: PineScriptv6
📌Description
The Historical Matrix Analyzer is an advanced probabilistic trading tool that transforms technical analysis into a data-driven decision support system. By creating a comprehensive 56-cell matrix that tracks every combination of RSI states and multi-indicator conditions, this indicator reveals which market patterns have historically led to profitable outcomes and which have not.
At its core, the indicator continuously monitors seven distinct RSI states (ranging from Extreme Oversold to Extreme Overbought) and eight unique indicator combinations (MACD direction, volume levels, and price momentum). For each of these 56 possible market states, the system calculates average forward returns, win rates, and occurrence counts based on your configurable lookback period. The result is a color-coded probability matrix that shows you exactly where you stand in the historical performance landscape.
The standout feature is the Current State Panel, which provides instant clarity on your active market conditions. This panel displays signal strength classifications (from Strong Bullish to Strong Bearish), the average return percentage for similar past occurrences, an estimated win rate using Bayesian smoothing to prevent small-sample distortions, and a confidence level indicator that warns you when insufficient data exists for reliable conclusions.
🚀Points of Innovation
Multi-dimensional state classification combining 7 RSI levels with 8 indicator combinations for 56 unique trackable market conditions
Bayesian win rate estimation with adjustable smoothing strength to provide stable probability estimates even with limited historical samples
Real-time active cell highlighting with “NOW” marker that visually connects current market conditions to their historical performance data
Configurable color intensity sensitivity allowing traders to adjust heat-map responsiveness from conservative to aggressive visual feedback
Dual-panel display system separating the comprehensive statistics matrix from an easy-to-read current state summary panel
Intelligent confidence scoring that automatically warns traders when occurrence counts fall below reliable thresholds
🔧Core Components
RSI State Classification: Segments RSI readings into 7 distinct zones (Extreme Oversold <20, Oversold 20-30, Weak 30-40, Neutral 40-60, Strong 60-70, Overbought 70-80, Extreme Overbought >80) to capture momentum extremes and transitions
Multi-Indicator Condition Tracking: Simultaneously monitors MACD crossover status (bullish/bearish), volume relative to moving average (high/low), and price direction (rising/falling) creating 8 binary-encoded combinations
Historical Data Storage Arrays: Maintains rolling lookback windows storing RSI states, indicator states, prices, and bar indices for precise forward-return calculations
Forward Performance Calculator: Measures price changes over configurable forward bar periods (1-20 bars) from each historical state, accumulating total returns and win counts per matrix cell
Bayesian Smoothing Engine: Applies statistical prior assumptions (default 50% win rate) weighted by user-defined strength parameter to stabilize estimated win rates when sample sizes are small
Dynamic Color Mapping System: Converts average returns into color-coded heat map with intensity adjusted by sensitivity parameter and transparency modified by confidence levels
🔥Key Features
56-Cell Probability Matrix: Comprehensive grid displaying every possible combination of RSI state and indicator condition, with each cell showing average return percentage, estimated win rate, and occurrence count for complete statistical visibility
Current State Info Panel: Dedicated display showing your exact position in the matrix with signal strength emoji indicators, numerical statistics, and color-coded confidence warnings for immediate situational awareness
Customizable Lookback Period: Adjustable historical window from 50 to 500 bars allowing traders to focus on recent market behavior or capture longer-term pattern stability across different market cycles
Configurable Forward Performance Window: Select target holding periods from 1 to 20 bars ahead to align probability calculations with your trading timeframe, whether day trading or swing trading
Visual Heat Mapping: Color-coded cells transition from red (bearish historical performance) through gray (neutral) to green (bullish performance) with intensity reflecting statistical significance and occurrence frequency
Intelligent Data Filtering: Minimum occurrence threshold (1-10) removes unreliable patterns with insufficient historical samples, displaying gray warning colors for low-confidence cells
Flexible Layout Options: Independent positioning of statistics matrix and info panel to any screen corner, accommodating different chart layouts and personal preferences
Tooltip Details: Hover over any matrix cell to see full RSI label, complete indicator status description, precise average return, estimated win rate, and total occurrence count
🎨Visualization
Statistics Matrix Table: A 9-column by 8-row grid with RSI states labeling vertical axis and indicator combinations on horizontal axis, using compact abbreviations (XOverS, OverB, MACD↑, Vol↓, P↑) for space efficiency
Active Cell Indicator: The current market state cell displays “⦿ NOW ⦿” in yellow text with enhanced color saturation to immediately draw attention to relevant historical performance
Signal Strength Visualization: Info panel uses emoji indicators (🔥 Strong Bullish, ✅ Bullish, ↗️ Weak Bullish, ➖ Neutral, ↘️ Weak Bearish, ⛔ Bearish, ❄️ Strong Bearish, ⚠️ Insufficient Data) for rapid interpretation
Histogram Plot: Below the price chart, a green/red histogram displays the current cell’s average return percentage, providing a time-series view of how historical performance changes as market conditions evolve
Color Intensity Scaling: Cell background transparency and saturation dynamically adjust based on both the magnitude of average returns and the occurrence count, ensuring visual emphasis on reliable patterns
Confidence Level Display: Info panel bottom row shows “High Confidence” (green), “Medium Confidence” (orange), or “Low Confidence” (red) based on occurrence counts relative to minimum threshold multipliers
📖Usage Guidelines
RSI Period
Default: 14
Range: 1 to unlimited
Description: Controls the lookback period for RSI momentum calculation. Standard 14-period provides widely-recognized overbought/oversold levels. Decrease for faster, more sensitive RSI reactions suitable for scalping. Increase (21, 28) for smoother, longer-term momentum assessment in swing trading. Changes affect how quickly the indicator moves between the 7 RSI state classifications.
MACD Fast Length
Default: 12
Range: 1 to unlimited
Description: Sets the faster exponential moving average for MACD calculation. Standard 12-period setting works well for daily charts and captures short-term momentum shifts. Decreasing creates more responsive MACD crossovers but increases false signals. Increasing smooths out noise but delays signal generation, affecting the bullish/bearish indicator state classification.
MACD Slow Length
Default: 26
Range: 1 to unlimited
Description: Defines the slower exponential moving average for MACD calculation. Traditional 26-period setting balances trend identification with responsiveness. Must be greater than Fast Length. Wider spread between fast and slow increases MACD sensitivity to trend changes, impacting the frequency of indicator state transitions in the matrix.
MACD Signal Length
Default: 9
Range: 1 to unlimited
Description: Smoothing period for the MACD signal line that triggers bullish/bearish state changes. Standard 9-period provides reliable crossover signals. Shorter values create more frequent state changes and earlier signals but with more whipsaws. Longer values produce more confirmed, stable signals but with increased lag in detecting momentum shifts.
Volume MA Period
Default: 20
Range: 1 to unlimited
Description: Lookback period for volume moving average used to classify volume as “high” or “low” in indicator state combinations. 20-period default captures typical monthly trading patterns. Shorter periods (10-15) make volume classification more reactive to recent spikes. Longer periods (30-50) require more sustained volume changes to trigger state classification shifts.
Statistics Lookback Period
Default: 200
Range: 50 to 500
Description: Number of historical bars used to calculate matrix statistics. 200 bars provides substantial data for reliable patterns while remaining responsive to regime changes. Lower values (50-100) emphasize recent market behavior and adapt quickly but may produce volatile statistics. Higher values (300-500) capture long-term patterns with stable statistics but slower adaptation to changing market dynamics.
Forward Performance Bars
Default: 5
Range: 1 to 20
Description: Number of bars ahead used to calculate forward returns from each historical state occurrence. 5-bar default suits intraday to short-term swing trading (5 hours on hourly charts, 1 week on daily charts). Lower values (1-3) target short-term momentum trades. Higher values (10-20) align with position trading and longer-term pattern exploitation.
Color Intensity Sensitivity
Default: 2.0
Range: 0.5 to 5.0, step 0.5
Description: Amplifies or dampens the color intensity response to average return magnitudes in the matrix heat map. 2.0 default provides balanced visual emphasis. Lower values (0.5-1.0) create subtle coloring requiring larger returns for full saturation, useful for volatile instruments. Higher values (3.0-5.0) produce vivid colors from smaller returns, highlighting subtle edges in range-bound markets.
Minimum Occurrences for Coloring
Default: 3
Range: 1 to 10
Description: Required minimum sample size before applying color-coded performance to matrix cells. Cells with fewer occurrences display gray “insufficient data” warning. 3-occurrence default filters out rare patterns. Lower threshold (1-2) shows more data but includes unreliable single-event statistics. Higher thresholds (5-10) ensure only well-established patterns receive visual emphasis.
Table Position
Default: top_right
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the 56-cell statistics matrix table. Position to avoid overlapping critical price action or other indicators on your chart. Consider chart orientation and candlestick density when selecting optimal placement.
Show Current State Panel
Default: true
Options: true, false
Description: Toggle visibility of the dedicated current state information panel. When enabled, displays signal strength, RSI value, indicator status, average return, estimated win rate, and confidence level for active market conditions. Disable to declutter charts when only the matrix table is needed.
Info Panel Position
Default: bottom_left
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the current state information panel (when enabled). Position independently from statistics matrix to optimize chart real estate. Typically placed opposite the matrix table for balanced visual layout.
Win Rate Smoothing Strength
Default: 5
Range: 1 to 20
Description: Controls Bayesian prior weighting for estimated win rate calculations. Acts as virtual sample size assuming 50% win rate baseline. Default 5 provides moderate smoothing preventing extreme win rate estimates from small samples. Lower values (1-3) reduce smoothing effect, allowing win rates to reflect raw data more directly. Higher values (10-20) increase conservatism, pulling win rate estimates toward 50% until substantial evidence accumulates.
✅Best Use Cases
Pattern-based discretionary trading where you want historical confirmation before entering setups that “look good” based on current technical alignment
Swing trading with holding periods matching your forward performance bar setting, using high-confidence bullish cells as entry filters
Risk assessment and position sizing, allocating larger size to trades originating from cells with strong positive average returns and high estimated win rates
Market regime identification by observing which RSI states and indicator combinations are currently producing the most reliable historical patterns
Backtesting validation by comparing your manual strategy signals against the historical performance of the corresponding matrix cells
Educational tool for developing intuition about which technical condition combinations have actually worked versus those that feel right but lack historical evidence
⚠️Limitations
Historical patterns do not guarantee future performance, especially during unprecedented market events or regime changes not represented in the lookback period
Small sample sizes (low occurrence counts) produce unreliable statistics despite Bayesian smoothing, requiring caution when acting on low-confidence cells
Matrix statistics lag behind rapidly changing market conditions, as the lookback period must accumulate new state occurrences before updating performance data
Forward return calculations use fixed bar periods that may not align with actual trade exit timing, support/resistance levels, or volatility-adjusted profit targets
💡What Makes This Unique
Multi-Dimensional State Space: Unlike single-indicator tools, simultaneously tracks 56 distinct market condition combinations providing granular pattern resolution unavailable in traditional technical analysis
Bayesian Statistical Rigor: Implements proper probabilistic smoothing to prevent overconfidence from limited data, a critical feature missing from most pattern recognition tools
Real-Time Contextual Feedback: The “NOW” marker and dedicated info panel instantly connect current market conditions to their historical performance profile, eliminating guesswork
Transparent Occurrence Counts: Displays sample sizes directly in each cell, allowing traders to judge statistical reliability themselves rather than hiding data quality issues
Fully Customizable Analysis Window: Complete control over lookback depth and forward return horizons lets traders align the tool precisely with their trading timeframe and strategy requirements
🔬How It Works
1. State Classification and Encoding
Each bar’s RSI value is evaluated and assigned to one of 7 discrete states based on threshold levels (0: <20, 1: 20-30, 2: 30-40, 3: 40-60, 4: 60-70, 5: 70-80, 6: >80)
Simultaneously, three binary conditions are evaluated: MACD line position relative to signal line, current volume relative to its moving average, and current close relative to previous close
These three binary conditions are combined into a single indicator state integer (0-7) using binary encoding, creating 8 possible indicator combinations
The RSI state and indicator state are stored together, defining one of 56 possible market condition cells in the matrix
2. Historical Data Accumulation
As each bar completes, the current state classification, closing price, and bar index are stored in rolling arrays maintained at the size specified by the lookback period
When the arrays reach capacity, the oldest data point is removed and the newest added, creating a sliding historical window
This continuous process builds a comprehensive database of past market conditions and their subsequent price movements
3. Forward Return Calculation and Statistics Update
On each bar, the indicator looks back through the stored historical data to find bars where sufficient forward bars exist to measure outcomes
For each historical occurrence, the price change from that bar to the bar N periods ahead (where N is the forward performance bars setting) is calculated as a percentage return
This percentage return is added to the cumulative return total for the specific matrix cell corresponding to that historical bar’s state classification
Occurrence counts are incremented, and wins are tallied for positive returns, building comprehensive statistics for each of the 56 cells
The Bayesian smoothing formula combines these raw statistics with prior assumptions (neutral 50% win rate) weighted by the smoothing strength parameter to produce estimated win rates that remain stable even with small samples
💡Note:
The Historical Matrix Analyzer is designed as a decision support tool, not a standalone trading system. Best results come from using it to validate discretionary trade ideas or filter systematic strategy signals. Always combine matrix insights with proper risk management, position sizing rules, and awareness of broader market context. The estimated win rate feature uses Bayesian statistics specifically to prevent false confidence from limited data, but no amount of smoothing can create reliable predictions from fundamentally insufficient sample sizes. Focus on high-confidence cells (green-colored confidence indicators) with occurrence counts well above your minimum threshold for the most actionable insights.
Fair Value Lead-Lag Model [BackQuant]Fair Value Lead-Lag Model
A cross-asset model that estimates where price "should" be relative to a chosen reference series, then tracks the deviation as a normalized oscillator. It helps you answer two questions: 1) is the asset rich or cheap vs its driver, and 2) is the driver leading or lagging price over the next N bars.
Concept in one paragraph
Many assets co-move with a macro or sector driver. Think BTC vs DXY, gold vs real yields, a stock vs its sector ETF. This tool builds a rolling fair value of the charted asset from a reference series and shows how far price is above or below that fair value in standard deviation units. You can shift the reference forward or backward to test who leads whom, then use the deviation and its bands to structure mean-reversion or trend-following ideas.
What the model does
Reference mapping : Pulls a reference symbol at a chosen timeframe, with an optional lead or lag in bars to test causality.
Fair value engine : Converts the reference into a synthetic fair value of the chart using one of four methods:
Ratio : price/ref with a rolling average ratio. Good when the relationship is proportional.
Spread : price minus ref with a rolling average spread. Good when the relationship is additive.
Z-Score : normalizes both series, aligns on standardized units, then re-projects to price space. Good when scale drifts.
Beta-Adjusted : rolling regression style. Uses covariance and variance to compute beta, then builds a fair value = mean(price) + beta * (ref − mean(ref)).
Deviation and bands : Computes a z-scored deviation of price vs fair value and plots sigma bands (±1, ±2, ±3) around the fair value line on the chart.
Correlation context : Shows rolling correlation so you can judge if deviations are meaningful or just noise when co-movement is weak.
Visuals :
Fair value line on price chart with sigma envelopes.
Deviation as a column oscillator and optional line.
Threshold shading beyond user-set upper and lower levels.
Summary table with reference, deviation, status, correlation, and method.
Why this is useful
Mean reversion framework : When correlation is healthy and deviation stretches beyond your sigma threshold, probability favors reversion toward fair value. This is classic pairs logic adapted to a driver and a target.
Trend confirmation : If price rides the fair value line and deviation stays modest while correlation is positive, it supports trend persistence. Pullbacks to negative deviation in an uptrend can be buyable.
Lead-lag discovery : Shift the reference forward by +N bars. If correlation improves, the reference tends to lead. Shift backward for the reverse. Use the best setting for planning early entries or hedges.
Regime detection : Large persistent deviations with falling correlation hint at regime change. The relationship you relied on may be breaking down, so reduce confidence or switch methods.
How to use it step by step
Pick a sensible reference : Choose a macro, index, currency, or sector driver that logically explains the asset’s moves. Example: gold with DXY, a semiconductor stock with SOXX.
Test lead-lag : Nudge Lead/Lag Periods to small positive values like +1 to +5 to see if the reference leads. If correlation improves, keep that offset. If correlation worsens, try a small negative value or zero.
Select a method :
Start with Beta-Adjusted when the relationship is approximately linear with drift.
Use Ratio if the assets usually move in proportional terms.
Use Spread when they trade around a level difference.
Use Z-Score when scales wander or volatility regimes shift.
Tune windows :
Rolling Window controls how quickly fair value adapts. Shorter equals faster but noisier.
Normalization Period controls how deviations are standardized. Longer equals stabler sigma sizing.
Correlation Length controls how co-movement is measured. Keep it near the fair value window.
Trade the edges :
Mean reversion idea : Wait for deviation beyond your Upper or Lower Threshold with positive correlation. Fade back toward fair value. Exit at the fair value line or the next inner sigma band.
Trend idea : In an uptrend, buy pullbacks when deviation dips negative but correlation remains healthy. In a downtrend, sell bounces when deviation spikes positive.
Read the table : Deviation shows how many sigmas you are from fair value. Status tells you overvalued or undervalued. Correlation color hints confidence. Method tells you the projection style used.
Reading the display
Fair value line on price chart: the model’s estimate of where price should trade given the reference, updated each bar.
Sigma bands around fair value: a quick sense of residual volatility. Reversions often target inner bands first.
Deviation oscillator : above zero means rich vs fair value, below zero means cheap. Color bins intensify with distance.
Correlation line (optional): scale is folded to match thresholds. Higher values increase trust in deviations.
Parameter tips
Start with Rolling Window 20 to 30, Normalization Period 100, Correlation Length 50.
Upper and Lower Threshold at ±2.0 are classic. Tighten to ±1.5 for more signals or widen to ±2.5 to focus on outliers.
When correlation drifts below about 0.3, treat deviations with caution. Consider switching method or reference.
If the fair value line whipsaws, increase Rolling Window or move to Beta-Adjusted which tends to be smoother.
Playbook examples
Pairs-style reversion : Asset is +2.3 sigma rich vs reference, correlation 0.65, trend flat. Short the deviation back toward fair value. Cover near the fair value line or +1 sigma.
Pro-trend pullback : Uptrend with correlation 0.7. Deviation dips to −1.2 sigma while price sits near the −1 sigma band. Buy the dip, target the fair value line, trail if the line is rising.
Lead-lag timing : Reference leads by +3 bars with improved correlation. Use reference swings as early cues to anticipate deviation turns on the target.
Caveats
The model assumes a stable relationship over the chosen windows. Structural breaks, policy shocks, and index rebalances can invalidate recent history.
Correlation is descriptive, not causal. A strong correlation does not guarantee future convergence.
Do not force trades when the reference has low liquidity or mismatched hours. Use a reference timeframe that captures real overlap.
Bottom line
This tool turns a loose cross-asset intuition into a quantified, visual fair value map. It gives you a consistent way to find rich or cheap conditions, time mean-reversion toward a statistically grounded target, and confirm or fade trends when the driver agrees.
Relative Performance Tracker [QuantAlgo]🟢 Overview
The Relative Performance Tracker is a multi-asset comparison tool designed to monitor and rank up to 30 different tickers simultaneously based on their relative price performance. This indicator enables traders and investors to quickly identify market leaders and laggards across their watchlist, facilitating rotation strategies, strength-based trading decisions, and cross-asset momentum analysis.
🟢 Key Features
1. Multi-Asset Monitoring
Track up to 30 tickers across any market (stocks, crypto, forex, commodities, indices)
Individual enable/disable toggles for each ticker to customize your watchlist
Universal compatibility with any TradingView symbol format (EXCHANGE:TICKER)
2. Ranking Tables (Up to 3 Tables)
Each ticker's percentage change over your chosen lookback period, calculated as:
(Current Price - Past Price) / Past Price × 100
Automatic sorting from strongest to weakest performers
Rank: Position from 1-30 (1 = strongest performer)
Ticker: Symbol name with color-coded background (green for gains, red for losses)
% Change: Exact percentage with color intensity matching magnitude
For example, Rank #1 has the highest gain among all enabled tickers, Rank #30 has the lowest (or most negative) return.
3. Histogram Visualization
Adjustable bar count: Display anywhere from 1 to 30 top-ranked tickers (user customizable)
Bar height = magnitude of percentage change.
Bars extend upward for gains, downward for losses. Taller bars = larger moves.
Green bars for positive returns, red for negative returns.
4. Customizable Color Schemes
Classic: Traditional green/red for intuitive interpretation
Aqua: Blue/orange combination for reduced eye strain
Cosmic: Vibrant aqua/purple optimized for dark mode
Custom: Full personalization of positive and negative colors
5. Built-In Ranking Alerts
Six alert conditions detect when rankings change:
Top 1 Changed: New #1 leader emerges
Top 3/5/10/15/20 Changed: Shifts within those tiers
🟢 Practical Applications
→ Momentum Trading: Focus on top-ranked assets (Rank 1-10) that show strongest relative strength for trend-following strategies
→ Market Breadth Analysis: Monitor how many tickers are above vs. below zero on the histogram to gauge overall market health
→ Divergence Spotting: Identify when previously leading assets lose momentum (drop out of top ranks) as potential trend reversal signals
→ Multi-Timeframe Analysis: Use different lookback periods on different charts to align short-term and long-term relative strength
→ Customized Focus: Adjust histogram bars to show only top 5-10 strongest movers for concentrated analysis, or expand to 20-30 for comprehensive overview
