Apex Adaptive Trend Navigator [Pineify]Apex Adaptive Trend Navigator
The Apex Adaptive Trend Navigator is a comprehensive trend-following indicator that combines adaptive moving average technology, dynamic volatility bands, and market structure analysis into a single, cohesive trading tool. Designed for traders who want to identify trend direction with precision while filtering out market noise, this indicator adapts its sensitivity based on real-time market efficiency calculations.
Key Features
Adaptive Moving Average with efficiency-based smoothing factor
Dynamic ATR-based volatility bands that expand and contract with market conditions
Market Structure detection including BOS (Break of Structure) and CHoCH (Change of Character)
Real-time performance dashboard displaying trend status and efficiency metrics
Color-coded cloud visualization for intuitive trend identification
How It Works
The core of this indicator is built on an Adaptive Moving Average that uses a unique efficiency-based calculation method inspired by the Kaufman Adaptive Moving Average (KAMA) and TRAMA concepts. The efficiency ratio measures the directional movement of price relative to total price movement over the lookback period:
Efficiency = |Price Change over N periods| / Sum of |Individual Bar Changes|
This ratio ranges from 0 to 1, where values closer to 1 indicate a strong trending market with minimal noise, and values closer to 0 indicate choppy, sideways conditions. The smoothing factor is then squared to penalize noisy markets more aggressively, causing the adaptive line to flatten during consolidation and respond quickly during strong trends.
The Dynamic Volatility Bands are calculated using the Average True Range (ATR) multiplied by a user-defined factor. These bands create a channel around the adaptive moving average, helping traders visualize the current volatility regime and potential support/resistance zones.
Trading Ideas and Insights
When price stays above the adaptive line with the bullish cloud forming, consider this a confirmation of uptrend strength
The efficiency percentage in the dashboard indicates trend quality - higher values suggest more reliable trends
Watch for price interactions with the upper and lower bands as potential reversal or continuation zones
A flat adaptive line indicates consolidation - wait for a clear directional break before entering trades
How Multiple Indicators Work Together
This indicator integrates three complementary analytical approaches:
The Adaptive Moving Average serves as the trend backbone, providing a dynamic centerline that automatically adjusts to market conditions. Unlike fixed-period moving averages, it reduces lag during trends while minimizing whipsaws during ranging markets.
The ATR Volatility Bands work in conjunction with the adaptive MA to create a volatility envelope. When the adaptive line is trending and price remains within the cloud (between the MA and outer band), this confirms trend strength. Price breaking through the opposite band may signal exhaustion or reversal.
The Market Structure Analysis using swing point detection adds a Smart Money Concepts (SMC) layer. BOS signals indicate trend continuation when price breaks previous swing highs in uptrends or swing lows in downtrends. CHoCH signals warn of potential reversals when the structure shifts against the prevailing trend.
Unique Aspects
The squared efficiency factor creates a non-linear response that dramatically reduces noise sensitivity
Cloud fills only appear on the trend side, providing clear visual distinction between bullish and bearish regimes
The integrated dashboard eliminates the need to switch between multiple indicators for trend assessment
Pivot-based swing detection ensures accurate market structure identification
How to Use
Add the indicator to your chart and adjust the Lookback Period based on your trading timeframe (shorter for scalping, longer for swing trading)
Monitor the cloud color - green clouds indicate bullish conditions, red clouds indicate bearish conditions
Use the efficiency reading in the dashboard to gauge trend reliability before entering positions
Consider entries when price pulls back to the adaptive line during strong trends (high efficiency)
Use the volatility bands as dynamic take-profit or stop-loss reference levels
Customization
Lookback Period : Controls the sensitivity of trend detection and swing point identification (default: 20)
Volatility Multiplier : Adjusts the width of the ATR bands (default: 2.0)
Show Market Structure : Toggle visibility of BOS and CHoCH labels
Show Performance Dashboard : Toggle the trend status table
Color Settings : Customize bullish, bearish, and neutral colors to match your chart theme
Conclusion
The Apex Adaptive Trend Navigator offers traders a sophisticated yet intuitive approach to trend analysis. By combining adaptive smoothing technology with volatility measurement and market structure concepts, it provides multiple layers of confirmation for trading decisions. Whether you are a day trader seeking quick trend identification or a swing trader looking for reliable trend-following signals, this indicator adapts to your market conditions and trading style. The efficiency-based calculations ensure you always know not just the trend direction, but also the quality and reliability of that trend.
볼래틸리티
Standard Deviation Channel with SignalsStandard Deviation Channel with Signals
This Pine Script is a **Standard Deviation Channel (or Linear Regression Channel) indicator** designed for TradingView. It automatically draws a channel around price action based on statistical deviation from a central linear regression trendline.
Here is a breakdown of its key features:
* **Trend Identification:** It calculates a linear regression line (the "mean" price) over a user-defined length (default 128 bars) to show the current trend direction.
* **Volatility Bands:** It plots parallel upper and lower bands at specific standard deviations (e.g., ±1 and ±2 deviations) from the center line. These act as dynamic support and resistance levels.
* **Actionable Signals:** It generates **"BUY"** signals when the price crosses below the lower deviation band (suggesting the asset is oversold) and **"SELL"** signals when the price crosses above the upper deviation band (suggesting it is overbought). This logic is based on a Mean Reversion strategy.
* **Historical & Live Visualization:** Unlike standard versions that only show the channel for the *current* moment, this script plots the historical path of the bands so you can backtest visual signals, while also projecting the live channel forward for real-time analysis.
TradingIndicator Academy TIA - Pro Scalping System
Beschrijving:
Deze indicator is een geavanceerde scalping tool, specifiek ontwikkeld voor geautomatiseerde Bybit futures trading. De strategie is ontworpen om snelle prijs-reversals ("wicks") te vangen die buiten de standaard deviatie van de VWMA (Volume Weighted Moving Average) vallen.
Het script combineert visuele analyse met volledige webhook-automatisering, waardoor handmatige fouten worden geëlimineerd en trades direct via JSON-commando's worden uitgevoerd.
🚀 Belangrijkste Kenmerken:
⚡ Wick Entry Strategie: Signalen worden gegenereerd wanneer de prijs (High/Low) agressief door de VWMA bands breekt. Dit duidt vaak op liquidaties of overreacties van de markt, gevolgd door een snelle correctie.
🤖 Volledige Automatisering: Ingebouwde, kant-en-klare JSON-alerts voor trading bots. Het script genereert automatisch payloads met side, size, leverage, en unieke UUIDs voor foutloze executie.
👁️ Smart Visuals:
Dynamische TP/SL Boxen: Zodra een positie opent, toont de chart direct de Take Profit (groen) en Stop Loss (rood) zones. Deze boxen updaten real-time mee met de candle, zodat je altijd ziet waar je staat.
Live Dashboard: Een tabel rechtsboven toont de actuele status van de bot, entry prijs, en targets.
🛡️ Risk Management: Ingebouwde (instelbare) logic voor Stoploss (standaard 2.5%) en Takeprofit (0.75%), visueel weergegeven om direct je Risk/Reward te beoordelen.
🛠️ Hoe te gebruiken:
Voeg de indicator toe aan een 1m of 5m chart (bijv. crypto perpetuals).
Vul in de instellingen je Bot / Alert UUIDs in (voor correcte order tracking).
Maak een TradingView Alert aan:
Condition: TIA - Pro Scalping System
Trigger: Any alert() function call
Webhook: De URL van je trading bot of webhook provider.
Zet Min Liq Size op 0 voor alle signalen, of hoger om alleen trades met hoog volume te pakken.
⚠️ Disclaimer:
Trading met leverage brengt risico's met zich mee. Deze tool is bedoeld ter ondersteuning van een geautomatiseerde strategie. Test resultaten altijd eerst met minimale size of in een demo-omgeving.
Liquidation Bubbles [OmegaTools]🔴🟢 Liquidation Bubbles — Advanced Volume & Price Stress Detector
Liquidation Bubbles is a professional-grade analytical tool designed to identify forced positioning events, stop-runs, and liquidation clusters by combining price displacement and volume imbalance into a single, statistically normalized framework.
This indicator is not a repainting signal tool and not a simple volume spike detector. It is a contextual market stress mapper, built to highlight areas where one-sided positioning becomes unstable and the probability of forced order execution (liquidations, stops, margin calls) materially increases.
---
## 🔬 Core Concept
Market liquidations do not occur randomly.
They emerge when price deviates aggressively from its volume-weighted equilibrium while volume itself becomes abnormal.
Liquidation Bubbles detects exactly this condition by:
* Estimating a **dynamic equilibrium price** using an *inverted volume-weighted moving average*
* Measuring **directional price stress** relative to that equilibrium
* Measuring **volume stress** relative to its own adaptive baseline
* Normalizing both into **Z-score–like metrics**
* Highlighting only **statistically extreme, asymmetric events**
The result is a clear visual map of stress points where market participants are most vulnerable.
---
⚙️ Methodology (How It Works)
1️⃣ Advanced Inverted VWMA (Equilibrium Engine)
The script uses a custom Advanced VWMA, where:
* High volume bars receive less weight
* Low volume bars receive more weight
This produces a **robust equilibrium level**, resistant to manipulation and volume bursts.
This equilibrium is used for **both price and volume normalization**, creating a consistent statistical framework.
---
2️⃣ Price Stress (Directional)
Price stress is calculated as:
* The **maximum deviation** between high/low and equilibrium
* Directionally signed (upside vs downside)
* Normalized by its own historical volatility
This allows the script to distinguish:
* Aggressive upside exhaustion
* Aggressive downside capitulation
---
3️⃣ Volume Stress
Volume stress is measured as:
* Deviation from volume equilibrium
* Normalized by historical volume dispersion
This filters out:
* Normal high-volume sessions
* Illiquid noise
And isolates abnormal participation imbalance.
---
4️⃣ Liquidation Logic
A liquidation event is flagged when:
* Both price stress and volume stress exceed adaptive thresholds
* The imbalance is directional and statistically extreme
Optional Combined Score Mode allows aggregation of price & volume stress into a single composite metric for smoother signals.
---
🔵 Bubble System (Signal Hierarchy)
The indicator plots **two tiers of bubbles**:
🟢🔴 Small Bubbles
* Early warning stress points
* Localized stop-runs
* Micro-liquidations
* Often precede reactions or short-term reversals
🟢🔴 Big Bubbles
* Full liquidation clusters
* Forced unwinds
* High probability exhaustion zones
* Frequently align with:
* Intraday extremes
* Range boundaries
* Reversal pivots
* Volatility expansions
Bubble color:
* **Green** → Downside liquidation (sell-side exhaustion)
* **Red** → Upside liquidation (buy-side exhaustion)
Bubble placement is **ATR-adjusted**, ensuring visual clarity without overlapping price.
---
🔄 Cross-Market Volume Analysis
The script allows optional **external volume sourcing**, enabling:
* Futures volume applied to CFDs
* Index volume applied to ETFs
* Spot volume applied to derivatives
This is critical when:
* Your traded instrument has unreliable volume
* You want **institutional-grade confirmation**
---
🧠 How to Use Liquidation Bubbles
This indicator is **not meant to be traded alone**.
Best use cases:
* 🔹 Confluence with support & resistance
* 🔹 Contextual confirmation for reversals
* 🔹 Identifying fake breakouts
* 🔹 Liquidity sweep detection
* 🔹 Risk management (avoid entering into liquidation zones)
Ideal for:
* Futures
* Indices
* Crypto
* High-liquidity FX pairs
* Intraday & swing trading
---
🎯 Who This Tool Is For
Liquidation Bubbles is designed for:
* Advanced discretionary traders
* Order-flow & liquidity-based traders
* Macro & index traders
* Professionals seeking **context**, not signals
If you want **where the market is fragile**, not just where price moved — this tool was built for you.
---
📌 Key Characteristics
✔ Non-repainting
✔ Statistically normalized
✔ Adaptive to volatility
✔ Works on all timeframes
✔ Futures & crypto ready
✔ No lagging indicators
✔ No moving average crosses
---
Liquidation Bubbles does not predict the future.
It shows you where the market is most likely to break.
— OmegaTools
Zenith MACD Evolution [JOAT]
Zenith MACD Evolution - Volatility-Normalized Momentum Oscillator
Introduction and Purpose
Zenith MACD Evolution is an open-source oscillator indicator that takes the classic MACD and normalizes it by ATR (Average True Range) to create consistent overbought/oversold levels across different market conditions. The core problem this indicator solves is that traditional MACD values are incomparable across different volatility regimes. A MACD reading of 50 might be extreme in a quiet market but normal in a volatile one.
This indicator addresses that by dividing MACD by ATR and scaling to a consistent range, allowing traders to use fixed overbought/oversold levels that work across all market conditions.
Why ATR Normalization Works
Traditional MACD problems:
- Values vary wildly based on price and volatility
- No consistent overbought/oversold levels
- Hard to compare across different instruments
- Extreme readings in one period may be normal in another
ATR-normalized MACD (Zenith) solves these:
- Values scaled to consistent range
- Fixed overbought/oversold levels work across all conditions
- Comparable across different instruments
- Extreme readings are truly extreme regardless of volatility
How the Normalization Works
// Classic MACD
= ta.macd(close, fastLength, slowLength, signalLength)
// ATR for normalization
float atrValue = ta.atr(atrNormLength)
// Volatility-Normalized MACD
float zenithMACD = atrValue != 0 ? (histLine / atrValue) * 100 : 0
float zenithSignal = ta.ema(zenithMACD, signalLength)
The result is a MACD that typically ranges from -200 to +200, with consistent levels:
- Above +150 = Overbought
- Below -150 = Oversold
- Above +200 = Extreme overbought
- Below -200 = Extreme oversold
Signal Types
Zero Cross Up/Down - Zenith crosses zero line (trend change)
Overbought/Oversold Entry - Zenith enters extreme zones
Overbought/Oversold Exit - Zenith leaves extreme zones (potential reversal)
Momentum Shift - Histogram direction changes (early warning)
Divergence - Price makes new high/low but Zenith does not
Histogram Coloring
The histogram uses four colors to show momentum state:
- Strong Bull (Teal) - Positive and rising
- Weak Bull (Light Teal) - Positive but falling
- Strong Bear (Red) - Negative and falling
- Weak Bear (Light Red) - Negative but rising
This helps identify momentum shifts before crossovers occur.
Dashboard Information
Zenith - Current normalized MACD value with signal line
Zone - Current zone (EXTREME OB/OVERBOUGHT/NORMAL/OVERSOLD/EXTREME OS)
Momentum - Direction (RISING/FALLING/FLAT)
Histogram - Current histogram value
ATR Norm - Current ATR value used for normalization
Classic - Traditional MACD value for reference
How to Use This Indicator
For Mean-Reversion:
1. Wait for Zenith to reach extreme zones (+200/-200)
2. Look for momentum shift (histogram color change)
3. Enter counter-trend when exiting extreme zone
For Trend Following:
1. Enter long on zero cross up
2. Enter short on zero cross down
3. Use histogram color to gauge momentum strength
For Divergence Trading:
1. Watch for DIV labels (price vs Zenith divergence)
2. Bullish divergence at support = potential long
3. Bearish divergence at resistance = potential short
Input Parameters
Fast/Slow/Signal Length (12/26/9) - Standard MACD parameters
ATR Normalization Period (26) - Period for ATR calculation
Overbought/Oversold Zone (150/-150) - Zone thresholds
Extreme Level (200) - Extreme threshold
Show Classic MACD Lines (false) - Toggle traditional lines
Show Divergence Detection (true) - Toggle divergence signals
Divergence Lookback (14) - Bars to scan for divergence
Timeframe Recommendations
All timeframes work due to normalization
Higher timeframes provide smoother signals
Normalization makes cross-timeframe comparison meaningful
Limitations
ATR normalization adds slight lag
Divergence detection is simplified
Extreme zones can persist in strong trends
Works best when combined with price action analysis
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes.
This indicator does not constitute financial advice. Momentum analysis does not guarantee profitable trades. Always use proper risk management.
- Made with passion by officialjackofalltrades
Trade Confidence BoosterNOT FINANCIAL ADVICE. TRADE AT YOUR OWN RISK.
The Smart Day Trader’s and Scalpers Secret Weapon
Stop guessing. Start trading with confidence .
The Trade Confidence Booster is a comprehensive trading system that transforms chaotic price action into crystal-clear entry and exit signals. Built for day traders and scalpers who demand a clean, rule-based indicator with structure, clarity, and consistency — without clutter. This indicator combines institutional-level analysis with simple, actionable signals.
What Makes This Different?
While others chase random breakouts and get stopped out repeatedly, Trade Confidence Booster waits for the market to show its hand through the coveted "Confidence Candle" pattern - a powerful consolidation signal that appears within clear trends and explosive moves. This isn't another repainted indicator making false promises. It's a complete trading framework that shows you:
WHEN to enter (Confidence Candles + Entry Signals)
Also, WHEN to trim and lock in gains (3 Customizable Trim Tiers)
WHERE to exit (Dynamic and Customizable Trend Break Triggers)
HOW MUCH confidence to put into the trade (Confluence Scoring System)
Key Features
📊 Smart Trend Detection - Multi-layered trend analysis combining price structure, momentum, and volume that also has the ability to AVOID CHOP
💪 Confidence Candle Technology - Identifies low-risk, high-probability entry zones
🎯 7-Point Confluence System - Never guess if a setup is worth taking
📈 Clear Entry Signals - CALL/PUT labels complete with quality scores
💰 Automated Profit Management - Built-in trim levels with default settings at 0.35%, 0.80%, and 1.25% that are completely customizable
🛡️ Adaptive Trailing Stops - Protects profits while letting winners run with adjustable buffers that compliment your trading and risk style
📍 Multi-Timeframe Support Levels - Hourly S/R zones visible on any timeframe
📐 Dynamic Fibonacci Levels - Auto-adjusting to current trend
🔵 Opening Range Breakout (ORB) - Visually see three (3) days of Original Ranges based on your desired timeframe (default set at 15 minutes) and easily identify a breakout in either direction
📊 Volume Profile with POC - See where smart money is positioned
This indicator isn’t built try and predict the market — it’s designed to help you stay aligned with structure, avoid chop, and manage trades with discipline.
Trader Otto - Christmas Tree V2.1 [Pearson Flux]Trader Otto - Pearson Flux is a high-performance statistical engine designed to visualize the true strength and direction of the market trend without the lag of traditional indicators.
Instead of cluttered lines, this system processes price action through a dual-core statistical algorithm (Pearson Correlation + Deviation Logic) to color-code candles directly based on their probability state.
**Core Intelligence:**
* **Statistical Trend Flow (Pearson R-Value):** The system continuously measures the correlation coefficient of price against time.
* **Cyan/Magenta Candles:** Indicate a statistically significant trend (High R-Value). This is the "Flux" state where momentum is strongest.
* **Grey Candles:** Indicate a low-correlation environment (Sideways/Chop). The system automatically filters out these noise periods.
* **Gold/Purple Candles:** Indicate a neutral but directional bias, useful for early trend anticipation.
* **Smart Reversion Signals:**
* **BUY/SELL Signals:** Generated only when price deviates statistically from its mean *AND* the correlation signature confirms a high-probability reversal point.
* **Zone Logic:** Automatically identifies "Overextended" price zones invisible to the naked eye.
* **Live Flux Dashboard:**
* A professional panel (position adjustable) displays the real-time R-Value, current Trend Status, and Signal State, giving you a complete statistical readout of the asset in seconds.
**Proprietary Calibration:**
The system uses a calibrated "Correlation Threshold" to filter signals.
* **Sensitivity Control:** Users can fine-tune the strictness of the trend filter via the settings menu (default 1.0). This protects the core statistical constants while allowing adaptation to different asset volatilities.
**Session Control:**
* Includes an optional "Time Filter" to restrict signals to specific trading hours (e.g., Active Market Session), perfect for filtering out low-volume noise in 24h markets.
*This script is protected to maintain the integrity of the underlying statistical engine.*
FVVO Oscillator 2.0 [RayAlgo]RayAlgo Flux Velocity & Volume Oscillator 2.0 (FVVO)
Premium volatility bands • Fisher-normalized momentum • Volume-weighted flow • Divergences • Sniper vertices • Pressure Burst diamonds • Momentum Ribbon
FVVO is a purpose-built oscillator designed to visualize momentum “flow” with a smooth, liquid core line while highlighting statistically meaningful extremes, turning-point vertices, and divergence-based momentum shifts. The 2.0 build focuses on signal clarity (infrequent event markers + full alert suite).
Core Concept (What this oscillator measures)
FVVO combines:
Normalized momentum (range-normalized + Fisher transform) to make momentum swings more readable.
Optional relative-volume weighting to slightly amplify momentum when volume supports the move.
Dynamic volatility bands around the oscillator mean (volatility-adaptive “overbought/oversold” context).
Vertex logic (“Sniper” dots) that marks local turning points only when the oscillator has stretched into band extremes.
What you see on the chart
1) Flux Oscillator (main line)
A smooth momentum stream that reacts to directional strength while avoiding noisy micro-flips as much as possible.
2) Volatility Bands
Three styles:
Minimal – subtle channel
Flag – inner/outer structure for cleaner “zone reading”
Glow – layered gradient feel toward extremes + optional edge glow
3) Sniper Dots (Turning-point vertices)
Bull sniper dot prints when a local bottom forms after oversold stretch.
Bear sniper dot prints when a local top forms after overbought stretch.
4) Divergence Engine (Regular divergence)
Bullish divergence: price makes a lower low while oscillator makes a higher low
Bearish divergence: price makes a higher high while oscillator makes a lower high
Divergences are drawn with dashed lines + “DIV” label.
5) Pressure Burst Diamonds
Detects COMPRESSION → EXPANSION transitions in oscillator velocity.
They are calculated by measuring the bar-to-bar velocity of the oscillator and comparing it to its own recent average. When oscillator velocity stays below a compression threshold, momentum is considered compressed. When velocity then expands above an expansion threshold, a burst event is triggered.
Prints a diamond symbol:
Bull diamond (cyan) when above the oscillator mean
Bear diamond (pink) when below the oscillator mean
Think of these as “energy released” moments—useful for breakout continuation or reversal readiness depending on context.
6) Momentum Ribbon (top/bottom strip)
A lightweight regime strip showing direction + strength:
Becomes more visible as the oscillator stretches away from its mean/bands.
Can be placed at the Top or Bottom of the pane.
How to Use (practical workflow)
A) Trend + Momentum Context
Treat the oscillator mean / zero area as a regime guide.
Stronger “impulses” often appear when the oscillator pushes toward band extremes.
B) Sniper Dots = “confirmed vertex at an extreme”
Best used as a timing trigger, not as a standalone strategy.
Higher confidence when aligned with:
Market structure / trend direction
Support/resistance
Higher timeframe bias
C) Divergence = “momentum disagreement”
Use divergences as an early warning, then look for confirmation (structure break, candle confirmation, etc.).
D) Pressure Burst Diamonds = “compression release”
Use as an event marker:
In trends: can signal continuation momentum returning.
Near key levels: can signal a meaningful “decision moment”.
Settings Guide:
Signal Smoothing: main control for reactivity (higher = smoother)
Weight by Relative Volume: boosts/dampens oscillator slightly based on rVOL
Band Style / Multipliers: controls your volatility envelope behavior
Divergence pivots: controls divergence strictness (higher = fewer, cleaner)
Burst thresholds: controls how selective burst events are
Ribbon contrast: visual strength indicator
Alerts (Full suite included)
Sniper Buy / Sniper Sell
Bullish Divergence / Bearish Divergence
Burst Release (Compression → Expansion)
Bull Burst / Bear Burst (directional burst variants)
Notes & Best Practices
No oscillator is perfect in chop—combine signals with structure, levels, and HTF bias.
Divergences can persist; treat them as probabilities, not guarantees.
Disclaimer
This indicator is an analytical and educational tool only. It does not provide financial advice or trade recommendations. All signals, levels, and visual elements are meant to assist in market analysis and must be used alongside proper risk management and independent decision-making. Trading involves risk, and past performance does not guarantee future results.
Advanced Scalping Suite v1.0# Advanced Scalping Suite - Quick Reference Guide
## Overview
A comprehensive all-in-one scalping indicator designed for 1-15 minute timeframes that combines multiple technical analysis tools, intelligent signal generation, and automatic market condition detection to help traders identify high-probability entry and exit points.
---
## Core Capabilities
### 1. **Multi-Indicator Analysis**
- **RSI (Relative Strength Index)** - Identifies overbought/oversold conditions with automatic divergence detection for early reversal warnings
- **EMA Ribbon (8/21/55)** - Visual trend identification with color-coded fills showing trend direction and strength
- **VWAP (Volume Weighted Average Price)** - Tracks institutional interest and intraday bias
- **ATR (Average True Range)** - Measures market volatility and filters out low-volatility periods
- **ADX (Average Directional Index)** - Quantifies trend strength (>25 = trending, <25 = range-bound)
- **Higher Timeframe Trend Confirmation** - Aligns trades with larger market direction
### 2. **Intelligent Signal Generation**
The indicator provides three tiers of buy/sell signals:
**STRONG Signals** (3+ confirmations)
- High-probability setups combining RSI extremes, divergences, VWAP position, EMA alignment, and chart patterns
- Best for conservative traders and high-confidence entries
- Includes HTF trend confirmation when enabled
**MEDIUM Signals** (2 confirmations)
- Balanced risk/reward setups for active scalpers
- Good for trend-following entries during established moves
**WEAK Signals** (1 confirmation)
- Early warning signals for aggressive traders
- Useful for quick scalp entries when market momentum is clear
### 3. **Automatic Market Condition Detection**
The indicator intelligently identifies market phases:
**Trending Markets** - Strong directional moves with stacked EMAs and high ADX
- Trades with the trend using momentum signals
- EMA ribbon shows clear green (uptrend) or red (downtrend) fill
**Choppy/Consolidation** - Range-bound price action with compressed EMAs and low ADX
- Yellow background warning alerts you to reduce position size or avoid trading
- Prevents false signals during sideways markets
**Low Volatility** - Insufficient ATR for profitable scalping
- Orange background warning indicates tight price ranges
- Automatically filters signals when volatility is too low
### 4. **Chart Pattern Recognition**
Automatically detects and draws common reversal patterns:
- **Double Tops** - Bearish reversal pattern marked with "DT" label
- **Double Bottoms** - Bullish reversal pattern marked with "DB" label
- Pattern lines drawn directly on chart for visual confirmation
- Contributes to signal strength calculations
### 5. **RSI Divergence Detection**
Identifies powerful leading indicators of trend reversals:
- **Bullish Divergence** - Price makes lower low while RSI makes higher low (marked with diamond)
- **Bearish Divergence** - Price makes higher high while RSI makes lower high (marked with diamond)
- Often precedes major price movements by several candles
### 6. **Real-Time Dashboard**
Top-right info panel displays:
- Current market condition (Trending/Choppy/Consolidation)
- Live RSI, ADX, and ATR% values with color-coded status
- Trend strength assessment (Weak/Moderate/Strong)
- Higher timeframe trend direction (UP ▲ / DOWN ▼ / NEUTRAL)
- Position relative to VWAP (Above ▲ / Below ▼)
- Reversal Probability percentage (0-100%)
- Current active signal status
### 7. **Customizable Alert System**
Built-in alerts for all signal types:
- Strong/Medium/Weak buy and sell signals
- Confirmed signals (candle close confirmation)
- Trend changes and EMA crossovers
- RSI divergences
- Chart pattern detection
- Choppy market warnings
---
## Practical Use Cases
### For Day Traders
- Use on 5-15 minute charts with higher timeframe set to 1H or 4H
- Filter for "Strong Only" or "Medium & Strong" signals
- Enable HTF confirmation to trade with daily trend
- Avoid trading during yellow (choppy) or orange (low volatility) backgrounds
### For Scalpers
- Use on 1-3 minute charts with higher timeframe set to 15-30 minutes
- Enable "All Signals" to catch quick momentum moves
- Watch for weak signals that align with EMA ribbon direction
- Focus on high ATR periods (morning session, news events)
### For Swing Traders
- Use indicator on lower timeframes to fine-tune entries for swing positions
- Wait for "Confirmed Only" signals that align with your swing direction
- Use reversal probability % to gauge timing of position exits
### For Risk Management
- Yellow background = Reduce position size by 50%
- Orange background = Consider sitting out
- Only take signals that have HTF confirmation enabled
- Reversal probability >50% = Prepare to exit or take profits
---
## Key Settings to Adjust
**Signal Filter Options:**
- "All Signals" - See everything (best for experienced traders)
- "Strong Only" - High-probability setups only (conservative)
- "Medium & Strong" - Balanced approach (recommended for most)
- "Confirmed Only" - Wait for candle close (reduces false signals)
**Higher Timeframe:** Set to 3-5x your chart timeframe (e.g., 15-min on 3-min chart, 1H on 15-min chart)
**RSI Period:** 7 for ultra-fast response, 9 for balanced (default), 14 for smoother
**Require HTF Confirmation:** Toggle on to only trade with larger trend (reduces trades, increases win rate)
---
## What Makes This Different
Unlike single-purpose indicators, this suite combines multiple confirmation factors to generate high-probability signals while automatically adjusting to market conditions. It won't spam you with signals during choppy markets, it respects volatility requirements, and it provides clear visual feedback about market state through the EMA ribbon, background colors, and comprehensive dashboard.
The multi-tier signal system allows traders of all styles to use the same tool - conservatives can wait for strong signals, while aggressive scalpers can act on weak signals during confirmed trends. The pattern detection and divergence identification provide additional edge by spotting setups before price action confirms them.
---
## Quick Start Checklist
1. Add indicator to your chart
2. Set Higher Timeframe to 3-5x your chart timeframe
3. Choose your Signal Filter preference (start with "Medium & Strong")
4. Enable "Require HTF Trend Confirmation" for safety
5. Set alerts for your preferred signal types
6. Watch the info panel for market condition changes
7. Avoid trading during yellow/orange backgrounds
8. Take signals that align with EMA ribbon direction
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**Remember:** No indicator is perfect. This tool is designed to stack probabilities in your favor by combining multiple technical factors, but always use proper risk management, position sizing, and stop losses on every trade.
Strength Relative to XXX [Hysteresis Smoothed]Strength Relative to XXX
█ OVERVIEW
This versatile indicator measures the relative strength of the current charted asset against any user-selected benchmark symbol (e.g., BTC, ETH, SP:SPX, TVC:GOLD, or any other asset). Green fill = Current asset outperforming the benchmark (bullish relative strength).
Red fill = Current asset underperforming the benchmark (bearish relative weakness). Perfect for rotation strategies across crypto, stocks, forex, and commodities — quickly identify assets gaining momentum edge over a chosen benchmark.
█ HOW IT WORKS
• Relative Ratio : Calculates current close / benchmark close for normalized comparison.
• Smoothing : Applies a Simple Moving Average (SMA) to the ratio (adjustable length).
• Oscillator : Plots deviation from the SMA, centered around zero.
• Hysteresis Enhancement : Adds a small relative threshold (~0.03% default) to prevent rapid color flips from minor noise. Color persists until a convincing cross — stable blocks without lag.
█ FEATURES & INPUTS
• Compare to : Symbol input for any benchmark (match exchange for accuracy).
• MA Length : Smoothing period (default 10).
• Relative Hysteresis Threshold : Noise filter strength (default 0.0003; tweak for responsiveness vs. stability).
█ USAGE TIPS
• Apply to ALT/BTC pairs for crypto rotations, stocks vs. SP:SPX for sector strength, or any custom comparison.
• Works on all timeframes — ideal for short-term scans on 4H/daily.
• Green zones = potential outperformance; red = caution.
• Combine with volume or momentum for confluence.
This refined relative strength oscillator delivers clean, reliable visuals in volatile markets.
Volume-Weighted RSI [VWRSI 2D Pro]A modular, volume-weighted RSI indicator built for clarity and control.
✅ Profile-based auto modes (Scalping → Macro)
✅ Toggleable Buy/Sell signals with strict mode
✅ RSI MA overlays for smoother entries
Buy Signal
RSI crosses above RSI MA
RSI > 50 (or > 55 in strict mode)
Sell Signal
RSI crosses below RSI MA
RSI < 50 (or < 45 in strict mode)
Strict mode filters out weak signals for higher conviction entries.
Volatility-Adaptive RSI Thresholds:
Traditional RSI uses static levels (70/30).
VWRSI Pro replaces these with dynamic bands:
🔹dynHigh = mean + mult × deviation
🔹 dynLow = mean − mult × deviation
Technical write-up can be found here: github.com
Adaptive Market Wave Theory - ProAdaptive Market Wave Theory
🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS
Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation.
The Wave Analysis Problem:
Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses:
1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition.
2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw.
3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management.
4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024.
5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve.
The AMWT Solution:
AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning:
• Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed.
• Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated.
• No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3."
• Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time.
• No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance.
The Core Insight:
"Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'"
🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS
Why Hidden Markov Models?
Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables.
This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations.
HMM Components:
1. Hidden States (S) : The unobservable market conditions
2. Observations (O) : What we can measure (price, volume, indicators)
3. Transition Matrix (A) : Probability of moving between states
4. Emission Matrix (B) : Probability of observations given each state
5. Initial Distribution (π) : Starting state probabilities
AMWT's Six Market States:
State 0: IMPULSE_UP
• Definition: Strong bullish momentum with high participation
• Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising
• Typical Duration: 5-20 bars depending on timeframe
• What It Means: Institutional buying pressure, trend acceleration phase
State 1: IMPULSE_DN
• Definition: Strong bearish momentum with high participation
• Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling
• Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster)
• What It Means: Institutional selling pressure, panic or distribution acceleration
State 2: CORRECTION
• Definition: Counter-trend consolidation with declining momentum
• Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing
• Typical Duration: 8-30 bars
• What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg
State 3: ACCUMULATION
• Definition: Base-building near lows where informed participants absorb supply
• Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range
• Typical Duration: 15-50 bars
• What It Means: Smart money buying from weak hands, preparing for markup phase
State 4: DISTRIBUTION
• Definition: Top-forming near highs where informed participants distribute holdings
• Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range
• Typical Duration: 15-50 bars
• What It Means: Smart money selling to late buyers, preparing for markdown phase
State 5: TRANSITION
• Definition: Regime change period with mixed signals and elevated uncertainty
• Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction
• Typical Duration: 5-15 bars
• What It Means: Market deciding next direction, dangerous for directional trades
The Transition Matrix:
The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online:
From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS
IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02
IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02
CORR 0.15 0.15 0.50 0.10 0.10 0.00
ACCUM 0.30 0.05 0.15 0.40 0.05 0.05
DIST 0.05 0.30 0.15 0.05 0.40 0.05
TRANS 0.20 0.20 0.20 0.15 0.15 0.10
Key Insights from Transition Probabilities:
• Impulse states are sticky (70% self-transition): Once trending, markets tend to continue
• Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain
• Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies
• Distribution strongly favors IMP_DN transition (30%): Topping leads to declines
The Viterbi Algorithm:
Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space.
Mathematical Formulation:
δ_t(j) = max_i × B_j(O_t)
Where:
δ_t(j) = probability of most likely path ending in state j at time t
A_ij = transition probability from state i to state j
B_j(O_t) = emission probability of observation O_t given state j
AMWT Implementation:
AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting:
• Current state estimate
• State confidence (probability of current state vs alternatives)
• State sequence for pattern detection
Online Learning (Baum-Welch Adaptation):
Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior:
f_onlineUpdateHMM(prev_state, curr_state, observation, decay) =>
// Update transition matrix
A *= decay
A += (1.0 - decay)
// Renormalize row
// Update emission matrix
B *= decay
B += (1.0 - decay)
// Renormalize row
The decay parameter (default 0.85) controls adaptation speed:
• Higher decay (0.95): Slower adaptation, more stable, better for consistent markets
• Lower decay (0.80): Faster adaptation, more reactive, better for regime changes
Why This Matters for Trading:
Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment :
"There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION."
This enables:
• Position sizing by confidence : 90% confidence = full size; 60% confidence = half size
• Risk management by transition probability : High correction probability = tighten stops
• Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in
🎰 THE 3-BANDIT CONSENSUS SYSTEM
The Multi-Agent Philosophy:
No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets.
AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions.
Agent 1: TREND AGENT
Philosophy : Markets trend. Follow the trend until it ends.
Analytical Components:
• EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish)
• MACD Histogram: Direction and rate of change
• Price Momentum: Close relative to ATR-normalized movement
• VWAP Position: Price above/below volume-weighted average price
Signal Generation:
Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP
→ Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4
Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1
→ Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3
Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP
→ Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4
Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1
→ Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3
When Trend Agent Excels:
• Trend days (IB extension >1.5x)
• Post-breakout continuation
• Institutional accumulation/distribution phases
When Trend Agent Fails:
• Range-bound markets (ADX <20)
• Chop zones after volatility spikes
• Reversal days at major levels
Agent 2: REVERSION AGENT
Philosophy: Markets revert to mean. Extreme readings reverse.
Analytical Components:
• Bollinger Band Position: Distance from bands, percent B
• RSI Extremes: Overbought (>70) and oversold (<30)
• Stochastic: %K/%D crossovers at extremes
• Band Squeeze: Bollinger Band width contraction
Signal Generation:
Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25
→ Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01
Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75
→ Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01
Squeeze Fire Bull: Band squeeze ending AND close > upper band
→ Signal: +1 (Long), Confidence: 0.65
Squeeze Fire Bear: Band squeeze ending AND close < lower band
→ Signal: -1 (Short), Confidence: 0.65
When Reversion Agent Excels:
• Rotation days (price stays within IB)
• Range-bound consolidation
• After extended moves without pullback
When Reversion Agent Fails:
• Strong trend days (RSI can stay overbought for days)
• Breakout moves
• News-driven directional moves
Agent 3: STRUCTURE AGENT
Philosophy: Market structure reveals institutional intent. Follow the smart money.
Analytical Components:
• Break of Structure (BOS): Price breaks prior swing high/low
• Change of Character (CHOCH): First break against prevailing trend
• Higher Highs/Higher Lows: Bullish structure
• Lower Highs/Lower Lows: Bearish structure
• Liquidity Sweeps: Stop runs that reverse
Signal Generation:
BOS Bull: Price breaks above prior swing high with momentum
→ Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bull: First higher low after downtrend, breaking structure
→ Signal: +1 (Long), Confidence: 0.75
BOS Bear: Price breaks below prior swing low with momentum
→ Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bear: First lower high after uptrend, breaking structure
→ Signal: -1 (Short), Confidence: 0.75
Liquidity Sweep Long: Price sweeps below swing low then reverses strongly
→ Signal: +1 (Long), Confidence: 0.80
Liquidity Sweep Short: Price sweeps above swing high then reverses strongly
→ Signal: -1 (Short), Confidence: 0.80
When Structure Agent Excels:
• After liquidity grabs (stop runs)
• At major swing points
• During institutional accumulation/distribution
When Structure Agent Fails:
• Choppy, structureless markets
• During news events (structure becomes noise)
• Very low timeframes (noise overwhelms structure)
Thompson Sampling: The Bandit Algorithm
With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives).
Thompson Sampling Solution:
Each agent maintains a Beta distribution representing its success/failure history:
Agent success rate modeled as Beta(α, β)
Where:
α = number of successful signals + 1
β = number of failed signals + 1
On Each Bar:
1. Sample from each agent's Beta distribution
2. Weight agent signals by sampled probabilities
3. Combine weighted signals into consensus
4. Update α/β based on trade outcomes
Mathematical Implementation:
// Beta sampling via Gamma ratio method
f_beta_sample(alpha, beta) =>
g1 = f_gamma_sample(alpha)
g2 = f_gamma_sample(beta)
g1 / (g1 + g2)
// Thompson Sampling selection
for each agent:
sampled_prob = f_beta_sample(agent.alpha, agent.beta)
weight = sampled_prob / sum(all_sampled_probs)
consensus += agent.signal × agent.confidence × weight
Why Thompson Sampling?
• Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution)
• Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff
• Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation
• Self-Correcting : Poor performers naturally get lower weights over time
Example Evolution:
Day 1 (Initial):
Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
After 50 Signals:
Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence)
Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming)
Structure Agent: Beta(32,19) → samples ~0.63 (outperforming)
Result: Structure Agent now receives highest weight in consensus
Consensus Requirements by Mode:
Aggressive Mode:
• Minimum 1/3 agents agreeing
• Consensus threshold: 45%
• Use case: More signals, higher risk tolerance
Balanced Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 55%
• Use case: Standard trading
Conservative Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 65%
• Use case: Higher quality, fewer signals
Institutional Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 75%
• Additional: Session quality >0.65, mode adjustment +0.10
• Use case: Highest quality signals only
🌀 INTELLIGENT CHOP DETECTION ENGINE
The Chop Problem:
Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike.
AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades.
Five-Factor Chop Analysis:
Factor 1: ADX Component (25% weight)
ADX (Average Directional Index) measures trend strength regardless of direction.
ADX < 15: Very weak trend (high chop score)
ADX 15-20: Weak trend (moderate chop score)
ADX 20-25: Developing trend (low chop score)
ADX > 25: Strong trend (minimal chop score)
adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100
Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop.
Factor 2: Choppiness Index (25% weight)
The Choppiness Index measures price efficiency using the ratio of ATR sum to price range:
CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n)
CI > 61.8: Choppy (range-bound, inefficient movement)
CI < 38.2: Trending (directional, efficient movement)
CI 38.2-61.8: Transitional
chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100
Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range).
Factor 3: Range Compression (20% weight)
Compares recent range to longer-term range, detecting volatility squeezes:
recent_range = Highest(20) - Lowest(20)
longer_range = Highest(50) - Lowest(50)
compression = 1 - (recent_range / longer_range)
compression > 0.5: Strong squeeze (potential breakout imminent)
compression < 0.2: No compression (normal volatility)
range_compression_score = compression × 100
Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet.
Factor 4: Channel Position (15% weight)
Tracks price position within the macro channel:
channel_position = (close - channel_low) / (channel_high - channel_low)
position 0.4-0.6: Center of channel (indecision zone)
position <0.2 or >0.8: Near extremes (potential reversal or breakout)
channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score
Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability.
Factor 5: Volume Quality (15% weight)
Assesses volume relative to average:
vol_ratio = volume / SMA(volume, 20)
vol_ratio < 0.7: Low volume (lack of conviction)
vol_ratio 0.7-1.3: Normal volume
vol_ratio > 1.3: High volume (conviction present)
volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0
Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall.
Combined Chop Intensity:
chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) +
(range_compression_score × 0.20) + (channel_chop × 0.15) +
(volume_chop × i_volumeChopWeight × 0.15)
Regime Classifications:
Based on chop intensity and component analysis:
• Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively
• Trending (20-35%): ADX >20, moderate directional bias, trade normally
• Transitioning (35-50%): Mixed signals, regime change possible, reduce size
• Mid-Range (50-60%): Price trapped in channel center, avoid new positions
• Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only
• Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout
• Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely
Signal Suppression:
When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification.
Chop Box Visualization:
When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions.
💧 LIQUIDITY ANCHORING SYSTEM
The Liquidity Concept:
Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing.
AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence.
Liquidity Event Types:
Type 1: Volume Spikes
Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x)
Interpretation: Sudden volume surge indicates institutional activity
• Near swing low + reversal: Likely accumulation
• Near swing high + reversal: Likely distribution
• With continuation: Institutional conviction in direction
Type 2: Stop Runs (Liquidity Sweeps)
Definition: Price briefly exceeds swing high/low then reverses within N bars
Detection:
• Price breaks above recent swing high (triggering buy stops)
• Then closes back below that high within 3 bars
• Signal: Bullish stop run complete, reversal likely
Or inverse for bearish:
• Price breaks below recent swing low (triggering sell stops)
• Then closes back above that low within 3 bars
• Signal: Bearish stop run complete, reversal likely
Type 3: Absorption Events
Definition: High volume with small candle body
Detection:
• Volume > 2x average
• Candle body < 30% of candle range
• Interpretation: Large orders being filled without moving price
• Implication: Accumulation (at lows) or distribution (at highs)
Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity)
BSL (Buy-Side Liquidity):
• Cluster of swing highs within ATR proximity
• Stop losses from shorts sit above these highs
• Breaking BSL triggers short covering (fuel for rally)
SSL (Sell-Side Liquidity):
• Cluster of swing lows within ATR proximity
• Stop losses from longs sit below these lows
• Breaking SSL triggers long liquidation (fuel for decline)
Liquidity Pool Mapping:
AMWT continuously scans for and maps liquidity pools:
// Detect swing highs/lows using pivot function
swing_high = ta.pivothigh(high, 5, 5)
swing_low = ta.pivotlow(low, 5, 5)
// Track recent swing points
if not na(swing_high)
bsl_levels.push(swing_high)
if not na(swing_low)
ssl_levels.push(swing_low)
// Display on chart with labels
Confluence Scoring Integration:
When signals fire near identified liquidity events, confluence scoring increases:
• Signal near volume spike: +10% confidence
• Signal after liquidity sweep: +15% confidence
• Signal at BSL/SSL pool: +10% confidence
• Signal aligned with absorption zone: +10% confidence
Why Liquidity Anchoring Matters:
Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst.
📊 SIGNAL GRADING SYSTEM
The Quality Problem:
Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence.
Confluence Components (100 points total):
1. Bandit Consensus Strength (25 points)
consensus_str = weighted average of agent confidences
score = consensus_str × 25
Example:
Trend Agent: +1 signal, 0.80 confidence, 0.35 weight
Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight
Structure Agent: +1 signal, 0.75 confidence, 0.40 weight
Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77
Score = 0.77 × 25 = 19.25 points
2. HMM State Confidence (15 points)
score = hmm_confidence × 15
Example:
HMM reports 82% probability of IMPULSE_UP
Score = 0.82 × 15 = 12.3 points
3. Session Quality (15 points)
Session quality varies by time:
• London/NY Overlap: 1.0 (15 points)
• New York Session: 0.95 (14.25 points)
• London Session: 0.70 (10.5 points)
• Asian Session: 0.40 (6 points)
• Off-Hours: 0.30 (4.5 points)
• Weekend: 0.10 (1.5 points)
4. Energy/Participation (10 points)
energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25
score = min(energy, 1.0) × 10
5. Volume Confirmation (10 points)
if volume > SMA(volume, 20) × 1.5:
score = 10
else if volume > SMA(volume, 20):
score = 5
else:
score = 0
6. Structure Alignment (10 points)
For long signals:
• Bullish structure (HH + HL): 10 points
• Higher low only: 6 points
• Neutral structure: 3 points
• Bearish structure: 0 points
Inverse for short signals
7. Trend Alignment (10 points)
For long signals:
• Price > EMA21 > EMA50: 10 points
• Price > EMA21: 6 points
• Neutral: 3 points
• Against trend: 0 points
8. Entry Trigger Quality (5 points)
• Strong trigger (multiple confirmations): 5 points
• Moderate trigger (single confirmation): 3 points
• Weak trigger (marginal): 1 point
Grade Scale:
Total Score → Grade
85-100 → A+ (Exceptional—all factors aligned)
70-84 → A (Strong—high probability)
55-69 → B (Acceptable—proceed with caution)
Below 55 → C (Marginal—filtered by default)
Grade-Based Signal Brightness:
Signal arrows on the chart have transparency based on grade:
• A+: Full brightness (alpha = 0)
• A: Slight fade (alpha = 15)
• B: Moderate fade (alpha = 35)
• C: Significant fade (alpha = 55)
This visual hierarchy helps traders instantly identify signal quality.
Minimum Grade Filter:
Configurable filter (default: C) sets the minimum grade for signal display:
• Set to "A" for only highest-quality signals
• Set to "B" for moderate selectivity
• Set to "C" for all signals (maximum quantity)
🕐 SESSION INTELLIGENCE
Why Sessions Matter:
Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality.
Session Definitions:
Asian Session (18:00-03:00 ET)
• Characteristics: Lower volatility, range-bound tendency, fewer institutional participants
• Quality Score: 0.40 (40% of peak quality)
• Strategy Implications: Fade extremes, expect ranges, smaller position sizes
• Best For: Mean-reversion setups, accumulation/distribution identification
London Session (03:00-12:00 ET)
• Characteristics: European institutional activity, volatility pickup, trend initiation
• Quality Score: 0.70 (70% of peak quality)
• Strategy Implications: Watch for trend development, breakouts more reliable
• Best For: Initial trend identification, structure breaks
New York Session (08:00-17:00 ET)
• Characteristics: Highest liquidity, US institutional activity, major moves
• Quality Score: 0.95 (95% of peak quality)
• Strategy Implications: Best environment for directional trades
• Best For: Trend continuation, momentum plays
London/NY Overlap (08:00-12:00 ET)
• Characteristics: Peak liquidity, both European and US participants active
• Quality Score: 1.0 (100%—maximum quality)
• Strategy Implications: Highest probability for successful breakouts and trends
• Best For: All signal types—this is prime time
Off-Hours
• Characteristics: Thin liquidity, erratic price action, gaps possible
• Quality Score: 0.30 (30% of peak quality)
• Strategy Implications: Avoid new positions, wider stops if holding
• Best For: Waiting
Smart Weekend Detection:
AMWT properly handles the Sunday evening futures open:
// Traditional (broken):
isWeekend = dayofweek == saturday OR dayofweek == sunday
// AMWT (correct):
anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime)
isWeekend = calendarWeekend AND NOT anySessionActive
This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend."
Session Transition Boosts:
Certain session transitions create trading opportunities:
• Asian → London transition: +15% confidence boost (volatility expansion likely)
• London → Overlap transition: +20% confidence boost (peak liquidity approaching)
• Overlap → NY-only transition: -10% confidence adjustment (liquidity declining)
• Any → Off-Hours transition: Signal suppression recommended
📈 TRADE MANAGEMENT SYSTEM
The Signal Spam Problem:
Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance.
Trade Lock Mechanism:
Once a signal fires, the system enters a "trade lock" state:
Trade Lock Duration: Configurable (default 30 bars)
Early Exit Conditions:
• TP3 hit (full target reached)
• Stop Loss hit (trade failed)
• Lock expiration (time-based exit)
During lock:
• No new signals of same type displayed
• Opposite signals can override (reversal)
• Trade status tracked in dashboard
Target Levels:
Each signal generates three profit targets based on ATR:
TP1 (Conservative Target)
• Default: 1.0 × ATR
• Purpose: Quick partial profit, reduce risk
• Action: Take 30-40% off position, move stop to breakeven
TP2 (Standard Target)
• Default: 2.5 × ATR
• Purpose: Main profit target
• Action: Take 40-50% off position, trail stop
TP3 (Extended Target)
• Default: 5.0 × ATR
• Purpose: Runner target for trend days
• Action: Close remaining position or continue trailing
Stop Loss:
• Default: 1.9 × ATR from entry
• Purpose: Define maximum risk
• Placement: Below recent swing low (longs) or above recent swing high (shorts)
Invalidation Level:
Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies:
invalidation = entry - (ATR × INVALIDATION_MULT × 1.5)
If price reaches invalidation, the current market interpretation is wrong—not just the trade.
Visual Trade Management:
During active trades, AMWT displays:
• Entry arrow with grade label (▲A+, ▼B, etc.)
• TP1, TP2, TP3 horizontal lines in green
• Stop Loss line in red
• Invalidation line in orange (dashed)
• Progress indicator in dashboard
Persistent Execution Markers:
When targets or stops are hit, permanent markers appear:
• TP hit: Green dot with "TP1"/"TP2"/"TP3" label
• SL hit: Red dot with "SL" label
These persist on the chart for review and statistics.
💰 PERFORMANCE TRACKING & STATISTICS
Tracked Metrics:
• Total Trades: Count of all signals that entered trade lock
• Winning Trades: Signals where at least TP1 was reached before SL
• Losing Trades: Signals where SL was hit before any TP
• Win Rate: Winning / Total × 100%
• Total R Profit: Sum of R-multiples from winning trades
• Total R Loss: Sum of R-multiples from losing trades
• Net R: Total R Profit - Total R Loss
Currency Conversion System:
AMWT can display P&L in multiple formats:
R-Multiple (Default)
• Shows risk-normalized returns
• "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades
• Best for comparing across different position sizes
Currency Conversion (USD/EUR/GBP/JPY/INR)
• Converts R-multiples to currency based on:
- Dollar Risk Per Trade (user input)
- Tick Value (user input)
- Selected currency
Example Configuration:
Dollar Risk Per Trade: $100
Display Currency: USD
If Net R = +4.2R
Display: Net P&L: +$420.00 | 78 trades
Ticks
• For futures traders who think in ticks
• Converts based on tick value input
Statistics Reset:
Two reset methods:
1. Toggle Reset
• Turn "Reset Statistics" toggle ON then OFF
• Clears all statistics immediately
2. Date-Based Reset
• Set "Reset After Date" (YYYY-MM-DD format)
• Only trades after this date are counted
• Useful for isolating recent performance
🎨 VISUAL FEATURES
Macro Channel:
Dynamic regression-based channel showing market boundaries:
• Upper/lower bounds calculated from swing pivot linear regression
• Adapts to current market structure
• Shows overall trend direction and potential reversal zones
Chop Boxes:
Semi-transparent overlay during high-chop periods:
• Purple/orange coloring indicates dangerous conditions
• Visual reminder to avoid new positions
Confluence Heat Zones:
Background shading indicating setup quality:
• Darker shading = higher confluence
• Lighter shading = lower confluence
• Helps identify optimal entry timing
EMA Ribbon:
Trend visualization via moving average fill:
• EMA 8/21/50 with gradient fill between
• Green fill when bullish aligned
• Red fill when bearish aligned
• Gray when neutral
Absorption Zone Boxes:
Marks potential accumulation/distribution areas:
• High volume + small body = absorption
• Boxes drawn at these levels
• Often act as support/resistance
Liquidity Pool Lines:
BSL/SSL levels with labels:
• Dashed lines at liquidity clusters
• "BSL" label above swing high clusters
• "SSL" label below swing low clusters
Six Professional Themes:
• Quantum: Deep purples and cyans (default)
• Cyberpunk: Neon pinks and blues
• Professional: Muted grays and greens
• Ocean: Blues and teals
• Matrix: Greens and blacks
• Ember: Oranges and reds
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Learning the System (Week 1)
Goal: Understand AMWT concepts and dashboard interpretation
Setup:
• Signal Mode: Balanced
• Display: All features enabled
• Grade Filter: C (see all signals)
Actions:
• Paper trade ONLY—no real money
• Observe HMM state transitions throughout the day
• Note when agents agree vs disagree
• Watch chop detection engage and disengage
• Track which grades produce winners vs losers
Key Learning Questions:
• How often do A+ signals win vs B signals? (Should see clear difference)
• Which agent tends to be right in current market? (Check dashboard)
• When does chop detection save you from bad trades?
• How do signals near liquidity events perform vs signals in vacuum?
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to your instrument and timeframe
Signal Mode Testing:
• Run 5 days on Aggressive mode (more signals)
• Run 5 days on Conservative mode (fewer signals)
• Compare: Which produces better risk-adjusted returns?
Grade Filter Testing:
• Track A+ only for 20 signals
• Track A and above for 20 signals
• Track B and above for 20 signals
• Compare win rates and expectancy
Chop Threshold Testing:
• Default (80%): Standard filtering
• Try 70%: More aggressive filtering
• Try 90%: Less filtering
• Which produces best results for your instrument?
Phase 3: Strategy Development (Weeks 3-4)
Goal: Develop personal trading rules based on system signals
Position Sizing by Grade:
• A+ grade: 100% position size
• A grade: 75% position size
• B grade: 50% position size
• C grade: 25% position size (or skip)
Session-Based Rules:
• London/NY Overlap: Take all A/A+ signals
• NY Session: Take all A+ signals, selective on A
• Asian Session: Only A+ signals with extra confirmation
• Off-Hours: No new positions
Chop Zone Rules:
• Chop >70%: Reduce position size 50%
• Chop >80%: No new positions
• Chop <50%: Full position size allowed
Phase 4: Live Micro-Sizing (Month 2)
Goal: Validate paper trading results with minimal risk
Setup:
• 10-20% of intended full position size
• Take ONLY A+ signals initially
• Follow trade management religiously
Tracking:
• Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus
• Calculate: Win rate by grade, by session, by chop level
• Compare to paper trading (should be within 15%)
Red Flags:
• Win rate diverges significantly from paper trading: Execution issues
• Consistent losses during certain sessions: Adjust session rules
• Losses cluster when specific agent dominates: Review that agent's logic
Phase 5: Scaling Up (Months 3-6)
Goal: Gradually increase to full position size
Progression:
• Month 3: 25-40% size (if micro-sizing profitable)
• Month 4: 40-60% size
• Month 5: 60-80% size
• Month 6: 80-100% size
Scale-Up Requirements:
• Minimum 30 trades at current size
• Win rate ≥50%
• Net R positive
• No revenge trading incidents
• Emotional control maintained
💡 DEVELOPMENT INSIGHTS
Why HMM Over Simple Indicators:
Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend.
The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP."
The Multi-Agent Evolution:
Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way.
The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection.
Chop Detection Revelation:
Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones.
Liquidity Anchoring Discovery:
Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals.
The Grade System Impact:
Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What AMWT Is NOT:
• NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty.
• NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment.
• NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis.
• NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate.
Core Assumptions:
1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure.
2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals.
3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns.
4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation.
Performs Best On:
• Liquid Futures : ES, NQ, MNQ, MES, CL, GC
• Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY
• Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap)
• Liquid Crypto : BTC, ETH on major exchanges
Performs Poorly On:
• Illiquid Instruments : Low volume stocks, exotic pairs
• Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal)
• Binary Event Days : Earnings, FDA approvals, court rulings
• Manipulated Markets : Penny stocks, low-cap altcoins
Known Weaknesses:
• Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable.
• Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance.
• Complexity : More parameters than simple indicators. Requires understanding to use effectively.
⚠️ RISK DISCLOSURE
Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses.
AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results.
Hidden Markov Model assumptions may not hold during:
• Major news events disrupting normal market behavior
• Flash crashes or circuit breaker events
• Low liquidity periods with erratic price action
• Algorithmic manipulation or spoofing
Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear.
Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing.
Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception.
By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes.
"Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable."
Initial Public Release
Core Engine:
• True Hidden Markov Model with online Baum-Welch learning
• Viterbi algorithm for optimal state sequence decoding
• 6-state market regime classification
Agent System:
• 3-Bandit consensus (Trend, Reversion, Structure)
• Thompson Sampling with true Beta distribution sampling
• Adaptive weight learning based on performance
Signal Generation:
• Quality-based confluence grading (A+/A/B/C)
• Four signal modes (Aggressive/Balanced/Conservative/Institutional)
• Grade-based visual brightness
Chop Detection:
• 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume)
• 7 regime classifications
• Configurable signal suppression threshold
Liquidity:
• Volume spike detection
• Stop run (liquidity sweep) identification
• BSL/SSL pool mapping
• Absorption zone detection
Trade Management:
• Trade lock with configurable duration
• TP1/TP2/TP3 targets
• ATR-based stop loss
• Persistent execution markers
Session Intelligence:
• Asian/London/NY/Overlap detection
• Smart weekend handling (Sunday futures open)
• Session quality scoring
Performance:
• Statistics tracking with reset functionality
• 7 currency display modes
• Win rate and Net R calculation
Visuals:
• Macro channel with linear regression
• Chop boxes
• EMA ribbon
• Liquidity pool lines
• 6 professional themes
Dashboards:
• Main Dashboard: Market State, Consensus, Trade Status, Statistics
• AMWT Advisor : Market Pulse, Agent Matrix, Structure, Watch For
Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
Adaptive Market Wave TheoryAdaptive Market Wave Theory
🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS
Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation.
The Wave Analysis Problem:
Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses:
1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition.
2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw.
3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management.
4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024.
5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve.
The AMWT Solution:
AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning:
• Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed.
• Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated.
• No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3."
• Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time.
• No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance.
The Core Insight:
"Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'"
🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS
Why Hidden Markov Models?
Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables.
This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations.
HMM Components:
1. Hidden States (S) : The unobservable market conditions
2. Observations (O) : What we can measure (price, volume, indicators)
3. Transition Matrix (A) : Probability of moving between states
4. Emission Matrix (B) : Probability of observations given each state
5. Initial Distribution (π) : Starting state probabilities
AMWT's Six Market States:
State 0: IMPULSE_UP
• Definition: Strong bullish momentum with high participation
• Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising
• Typical Duration: 5-20 bars depending on timeframe
• What It Means: Institutional buying pressure, trend acceleration phase
State 1: IMPULSE_DN
• Definition: Strong bearish momentum with high participation
• Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling
• Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster)
• What It Means: Institutional selling pressure, panic or distribution acceleration
State 2: CORRECTION
• Definition: Counter-trend consolidation with declining momentum
• Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing
• Typical Duration: 8-30 bars
• What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg
State 3: ACCUMULATION
• Definition: Base-building near lows where informed participants absorb supply
• Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range
• Typical Duration: 15-50 bars
• What It Means: Smart money buying from weak hands, preparing for markup phase
State 4: DISTRIBUTION
• Definition: Top-forming near highs where informed participants distribute holdings
• Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range
• Typical Duration: 15-50 bars
• What It Means: Smart money selling to late buyers, preparing for markdown phase
State 5: TRANSITION
• Definition: Regime change period with mixed signals and elevated uncertainty
• Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction
• Typical Duration: 5-15 bars
• What It Means: Market deciding next direction, dangerous for directional trades
The Transition Matrix:
The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online:
From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS
IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02
IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02
CORR 0.15 0.15 0.50 0.10 0.10 0.00
ACCUM 0.30 0.05 0.15 0.40 0.05 0.05
DIST 0.05 0.30 0.15 0.05 0.40 0.05
TRANS 0.20 0.20 0.20 0.15 0.15 0.10
Key Insights from Transition Probabilities:
• Impulse states are sticky (70% self-transition): Once trending, markets tend to continue
• Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain
• Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies
• Distribution strongly favors IMP_DN transition (30%): Topping leads to declines
The Viterbi Algorithm:
Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space.
Mathematical Formulation:
δ_t(j) = max_i × B_j(O_t)
Where:
δ_t(j) = probability of most likely path ending in state j at time t
A_ij = transition probability from state i to state j
B_j(O_t) = emission probability of observation O_t given state j
AMWT Implementation:
AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting:
• Current state estimate
• State confidence (probability of current state vs alternatives)
• State sequence for pattern detection
Online Learning (Baum-Welch Adaptation):
Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior:
f_onlineUpdateHMM(prev_state, curr_state, observation, decay) =>
// Update transition matrix
A *= decay
A += (1.0 - decay)
// Renormalize row
// Update emission matrix
B *= decay
B += (1.0 - decay)
// Renormalize row
The decay parameter (default 0.85) controls adaptation speed:
• Higher decay (0.95): Slower adaptation, more stable, better for consistent markets
• Lower decay (0.80): Faster adaptation, more reactive, better for regime changes
Why This Matters for Trading:
Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment :
"There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION."
This enables:
• Position sizing by confidence : 90% confidence = full size; 60% confidence = half size
• Risk management by transition probability : High correction probability = tighten stops
• Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in
🎰 THE 3-BANDIT CONSENSUS SYSTEM
The Multi-Agent Philosophy:
No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets.
AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions.
Agent 1: TREND AGENT
Philosophy : Markets trend. Follow the trend until it ends.
Analytical Components:
• EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish)
• MACD Histogram: Direction and rate of change
• Price Momentum: Close relative to ATR-normalized movement
• VWAP Position: Price above/below volume-weighted average price
Signal Generation:
Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP
→ Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4
Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1
→ Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3
Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP
→ Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4
Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1
→ Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3
When Trend Agent Excels:
• Trend days (IB extension >1.5x)
• Post-breakout continuation
• Institutional accumulation/distribution phases
When Trend Agent Fails:
• Range-bound markets (ADX <20)
• Chop zones after volatility spikes
• Reversal days at major levels
Agent 2: REVERSION AGENT
Philosophy: Markets revert to mean. Extreme readings reverse.
Analytical Components:
• Bollinger Band Position: Distance from bands, percent B
• RSI Extremes: Overbought (>70) and oversold (<30)
• Stochastic: %K/%D crossovers at extremes
• Band Squeeze: Bollinger Band width contraction
Signal Generation:
Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25
→ Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01
Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75
→ Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01
Squeeze Fire Bull: Band squeeze ending AND close > upper band
→ Signal: +1 (Long), Confidence: 0.65
Squeeze Fire Bear: Band squeeze ending AND close < lower band
→ Signal: -1 (Short), Confidence: 0.65
When Reversion Agent Excels:
• Rotation days (price stays within IB)
• Range-bound consolidation
• After extended moves without pullback
When Reversion Agent Fails:
• Strong trend days (RSI can stay overbought for days)
• Breakout moves
• News-driven directional moves
Agent 3: STRUCTURE AGENT
Philosophy: Market structure reveals institutional intent. Follow the smart money.
Analytical Components:
• Break of Structure (BOS): Price breaks prior swing high/low
• Change of Character (CHOCH): First break against prevailing trend
• Higher Highs/Higher Lows: Bullish structure
• Lower Highs/Lower Lows: Bearish structure
• Liquidity Sweeps: Stop runs that reverse
Signal Generation:
BOS Bull: Price breaks above prior swing high with momentum
→ Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bull: First higher low after downtrend, breaking structure
→ Signal: +1 (Long), Confidence: 0.75
BOS Bear: Price breaks below prior swing low with momentum
→ Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bear: First lower high after uptrend, breaking structure
→ Signal: -1 (Short), Confidence: 0.75
Liquidity Sweep Long: Price sweeps below swing low then reverses strongly
→ Signal: +1 (Long), Confidence: 0.80
Liquidity Sweep Short: Price sweeps above swing high then reverses strongly
→ Signal: -1 (Short), Confidence: 0.80
When Structure Agent Excels:
• After liquidity grabs (stop runs)
• At major swing points
• During institutional accumulation/distribution
When Structure Agent Fails:
• Choppy, structureless markets
• During news events (structure becomes noise)
• Very low timeframes (noise overwhelms structure)
Thompson Sampling: The Bandit Algorithm
With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives).
Thompson Sampling Solution:
Each agent maintains a Beta distribution representing its success/failure history:
Agent success rate modeled as Beta(α, β)
Where:
α = number of successful signals + 1
β = number of failed signals + 1
On Each Bar:
1. Sample from each agent's Beta distribution
2. Weight agent signals by sampled probabilities
3. Combine weighted signals into consensus
4. Update α/β based on trade outcomes
Mathematical Implementation:
// Beta sampling via Gamma ratio method
f_beta_sample(alpha, beta) =>
g1 = f_gamma_sample(alpha)
g2 = f_gamma_sample(beta)
g1 / (g1 + g2)
// Thompson Sampling selection
for each agent:
sampled_prob = f_beta_sample(agent.alpha, agent.beta)
weight = sampled_prob / sum(all_sampled_probs)
consensus += agent.signal × agent.confidence × weight
Why Thompson Sampling?
• Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution)
• Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff
• Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation
• Self-Correcting : Poor performers naturally get lower weights over time
Example Evolution:
Day 1 (Initial):
Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
After 50 Signals:
Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence)
Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming)
Structure Agent: Beta(32,19) → samples ~0.63 (outperforming)
Result: Structure Agent now receives highest weight in consensus
Consensus Requirements by Mode:
Aggressive Mode:
• Minimum 1/3 agents agreeing
• Consensus threshold: 45%
• Use case: More signals, higher risk tolerance
Balanced Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 55%
• Use case: Standard trading
Conservative Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 65%
• Use case: Higher quality, fewer signals
Institutional Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 75%
• Additional: Session quality >0.65, mode adjustment +0.10
• Use case: Highest quality signals only
🌀 INTELLIGENT CHOP DETECTION ENGINE
The Chop Problem:
Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike.
AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades.
Five-Factor Chop Analysis:
Factor 1: ADX Component (25% weight)
ADX (Average Directional Index) measures trend strength regardless of direction.
ADX < 15: Very weak trend (high chop score)
ADX 15-20: Weak trend (moderate chop score)
ADX 20-25: Developing trend (low chop score)
ADX > 25: Strong trend (minimal chop score)
adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100
Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop.
Factor 2: Choppiness Index (25% weight)
The Choppiness Index measures price efficiency using the ratio of ATR sum to price range:
CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n)
CI > 61.8: Choppy (range-bound, inefficient movement)
CI < 38.2: Trending (directional, efficient movement)
CI 38.2-61.8: Transitional
chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100
Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range).
Factor 3: Range Compression (20% weight)
Compares recent range to longer-term range, detecting volatility squeezes:
recent_range = Highest(20) - Lowest(20)
longer_range = Highest(50) - Lowest(50)
compression = 1 - (recent_range / longer_range)
compression > 0.5: Strong squeeze (potential breakout imminent)
compression < 0.2: No compression (normal volatility)
range_compression_score = compression × 100
Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet.
Factor 4: Channel Position (15% weight)
Tracks price position within the macro channel:
channel_position = (close - channel_low) / (channel_high - channel_low)
position 0.4-0.6: Center of channel (indecision zone)
position <0.2 or >0.8: Near extremes (potential reversal or breakout)
channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score
Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability.
Factor 5: Volume Quality (15% weight)
Assesses volume relative to average:
vol_ratio = volume / SMA(volume, 20)
vol_ratio < 0.7: Low volume (lack of conviction)
vol_ratio 0.7-1.3: Normal volume
vol_ratio > 1.3: High volume (conviction present)
volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0
Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall.
Combined Chop Intensity:
chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) +
(range_compression_score × 0.20) + (channel_chop × 0.15) +
(volume_chop × i_volumeChopWeight × 0.15)
Regime Classifications:
Based on chop intensity and component analysis:
• Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively
• Trending (20-35%): ADX >20, moderate directional bias, trade normally
• Transitioning (35-50%): Mixed signals, regime change possible, reduce size
• Mid-Range (50-60%): Price trapped in channel center, avoid new positions
• Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only
• Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout
• Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely
Signal Suppression:
When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification.
Chop Box Visualization:
When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions.
💧 LIQUIDITY ANCHORING SYSTEM
The Liquidity Concept:
Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing.
AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence.
Liquidity Event Types:
Type 1: Volume Spikes
Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x)
Interpretation: Sudden volume surge indicates institutional activity
• Near swing low + reversal: Likely accumulation
• Near swing high + reversal: Likely distribution
• With continuation: Institutional conviction in direction
Type 2: Stop Runs (Liquidity Sweeps)
Definition: Price briefly exceeds swing high/low then reverses within N bars
Detection:
• Price breaks above recent swing high (triggering buy stops)
• Then closes back below that high within 3 bars
• Signal: Bullish stop run complete, reversal likely
Or inverse for bearish:
• Price breaks below recent swing low (triggering sell stops)
• Then closes back above that low within 3 bars
• Signal: Bearish stop run complete, reversal likely
Type 3: Absorption Events
Definition: High volume with small candle body
Detection:
• Volume > 2x average
• Candle body < 30% of candle range
• Interpretation: Large orders being filled without moving price
• Implication: Accumulation (at lows) or distribution (at highs)
Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity)
BSL (Buy-Side Liquidity):
• Cluster of swing highs within ATR proximity
• Stop losses from shorts sit above these highs
• Breaking BSL triggers short covering (fuel for rally)
SSL (Sell-Side Liquidity):
• Cluster of swing lows within ATR proximity
• Stop losses from longs sit below these lows
• Breaking SSL triggers long liquidation (fuel for decline)
Liquidity Pool Mapping:
AMWT continuously scans for and maps liquidity pools:
// Detect swing highs/lows using pivot function
swing_high = ta.pivothigh(high, 5, 5)
swing_low = ta.pivotlow(low, 5, 5)
// Track recent swing points
if not na(swing_high)
bsl_levels.push(swing_high)
if not na(swing_low)
ssl_levels.push(swing_low)
// Display on chart with labels
Confluence Scoring Integration:
When signals fire near identified liquidity events, confluence scoring increases:
• Signal near volume spike: +10% confidence
• Signal after liquidity sweep: +15% confidence
• Signal at BSL/SSL pool: +10% confidence
• Signal aligned with absorption zone: +10% confidence
Why Liquidity Anchoring Matters:
Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst.
📊 SIGNAL GRADING SYSTEM
The Quality Problem:
Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence.
Confluence Components (100 points total):
1. Bandit Consensus Strength (25 points)
consensus_str = weighted average of agent confidences
score = consensus_str × 25
Example:
Trend Agent: +1 signal, 0.80 confidence, 0.35 weight
Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight
Structure Agent: +1 signal, 0.75 confidence, 0.40 weight
Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77
Score = 0.77 × 25 = 19.25 points
2. HMM State Confidence (15 points)
score = hmm_confidence × 15
Example:
HMM reports 82% probability of IMPULSE_UP
Score = 0.82 × 15 = 12.3 points
3. Session Quality (15 points)
Session quality varies by time:
• London/NY Overlap: 1.0 (15 points)
• New York Session: 0.95 (14.25 points)
• London Session: 0.70 (10.5 points)
• Asian Session: 0.40 (6 points)
• Off-Hours: 0.30 (4.5 points)
• Weekend: 0.10 (1.5 points)
4. Energy/Participation (10 points)
energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25
score = min(energy, 1.0) × 10
5. Volume Confirmation (10 points)
if volume > SMA(volume, 20) × 1.5:
score = 10
else if volume > SMA(volume, 20):
score = 5
else:
score = 0
6. Structure Alignment (10 points)
For long signals:
• Bullish structure (HH + HL): 10 points
• Higher low only: 6 points
• Neutral structure: 3 points
• Bearish structure: 0 points
Inverse for short signals
7. Trend Alignment (10 points)
For long signals:
• Price > EMA21 > EMA50: 10 points
• Price > EMA21: 6 points
• Neutral: 3 points
• Against trend: 0 points
8. Entry Trigger Quality (5 points)
• Strong trigger (multiple confirmations): 5 points
• Moderate trigger (single confirmation): 3 points
• Weak trigger (marginal): 1 point
Grade Scale:
Total Score → Grade
85-100 → A+ (Exceptional—all factors aligned)
70-84 → A (Strong—high probability)
55-69 → B (Acceptable—proceed with caution)
Below 55 → C (Marginal—filtered by default)
Grade-Based Signal Brightness:
Signal arrows on the chart have transparency based on grade:
• A+: Full brightness (alpha = 0)
• A: Slight fade (alpha = 15)
• B: Moderate fade (alpha = 35)
• C: Significant fade (alpha = 55)
This visual hierarchy helps traders instantly identify signal quality.
Minimum Grade Filter:
Configurable filter (default: C) sets the minimum grade for signal display:
• Set to "A" for only highest-quality signals
• Set to "B" for moderate selectivity
• Set to "C" for all signals (maximum quantity)
🕐 SESSION INTELLIGENCE
Why Sessions Matter:
Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality.
Session Definitions:
Asian Session (18:00-03:00 ET)
• Characteristics: Lower volatility, range-bound tendency, fewer institutional participants
• Quality Score: 0.40 (40% of peak quality)
• Strategy Implications: Fade extremes, expect ranges, smaller position sizes
• Best For: Mean-reversion setups, accumulation/distribution identification
London Session (03:00-12:00 ET)
• Characteristics: European institutional activity, volatility pickup, trend initiation
• Quality Score: 0.70 (70% of peak quality)
• Strategy Implications: Watch for trend development, breakouts more reliable
• Best For: Initial trend identification, structure breaks
New York Session (08:00-17:00 ET)
• Characteristics: Highest liquidity, US institutional activity, major moves
• Quality Score: 0.95 (95% of peak quality)
• Strategy Implications: Best environment for directional trades
• Best For: Trend continuation, momentum plays
London/NY Overlap (08:00-12:00 ET)
• Characteristics: Peak liquidity, both European and US participants active
• Quality Score: 1.0 (100%—maximum quality)
• Strategy Implications: Highest probability for successful breakouts and trends
• Best For: All signal types—this is prime time
Off-Hours
• Characteristics: Thin liquidity, erratic price action, gaps possible
• Quality Score: 0.30 (30% of peak quality)
• Strategy Implications: Avoid new positions, wider stops if holding
• Best For: Waiting
Smart Weekend Detection:
AMWT properly handles the Sunday evening futures open:
// Traditional (broken):
isWeekend = dayofweek == saturday OR dayofweek == sunday
// AMWT (correct):
anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime)
isWeekend = calendarWeekend AND NOT anySessionActive
This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend."
Session Transition Boosts:
Certain session transitions create trading opportunities:
• Asian → London transition: +15% confidence boost (volatility expansion likely)
• London → Overlap transition: +20% confidence boost (peak liquidity approaching)
• Overlap → NY-only transition: -10% confidence adjustment (liquidity declining)
• Any → Off-Hours transition: Signal suppression recommended
📈 TRADE MANAGEMENT SYSTEM
The Signal Spam Problem:
Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance.
Trade Lock Mechanism:
Once a signal fires, the system enters a "trade lock" state:
Trade Lock Duration: Configurable (default 30 bars)
Early Exit Conditions:
• TP3 hit (full target reached)
• Stop Loss hit (trade failed)
• Lock expiration (time-based exit)
During lock:
• No new signals of same type displayed
• Opposite signals can override (reversal)
• Trade status tracked in dashboard
Target Levels:
Each signal generates three profit targets based on ATR:
TP1 (Conservative Target)
• Default: 1.0 × ATR
• Purpose: Quick partial profit, reduce risk
• Action: Take 30-40% off position, move stop to breakeven
TP2 (Standard Target)
• Default: 2.5 × ATR
• Purpose: Main profit target
• Action: Take 40-50% off position, trail stop
TP3 (Extended Target)
• Default: 5.0 × ATR
• Purpose: Runner target for trend days
• Action: Close remaining position or continue trailing
Stop Loss:
• Default: 1.9 × ATR from entry
• Purpose: Define maximum risk
• Placement: Below recent swing low (longs) or above recent swing high (shorts)
Invalidation Level:
Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies:
invalidation = entry - (ATR × INVALIDATION_MULT × 1.5)
If price reaches invalidation, the current market interpretation is wrong—not just the trade.
Visual Trade Management:
During active trades, AMWT displays:
• Entry arrow with grade label (▲A+, ▼B, etc.)
• TP1, TP2, TP3 horizontal lines in green
• Stop Loss line in red
• Invalidation line in orange (dashed)
• Progress indicator in dashboard
Persistent Execution Markers:
When targets or stops are hit, permanent markers appear:
• TP hit: Green dot with "TP1"/"TP2"/"TP3" label
• SL hit: Red dot with "SL" label
These persist on the chart for review and statistics.
💰 PERFORMANCE TRACKING & STATISTICS
Tracked Metrics:
• Total Trades: Count of all signals that entered trade lock
• Winning Trades: Signals where at least TP1 was reached before SL
• Losing Trades: Signals where SL was hit before any TP
• Win Rate: Winning / Total × 100%
• Total R Profit: Sum of R-multiples from winning trades
• Total R Loss: Sum of R-multiples from losing trades
• Net R: Total R Profit - Total R Loss
Currency Conversion System:
AMWT can display P&L in multiple formats:
R-Multiple (Default)
• Shows risk-normalized returns
• "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades
• Best for comparing across different position sizes
Currency Conversion (USD/EUR/GBP/JPY/INR)
• Converts R-multiples to currency based on:
- Dollar Risk Per Trade (user input)
- Tick Value (user input)
- Selected currency
Example Configuration:
Dollar Risk Per Trade: $100
Display Currency: USD
If Net R = +4.2R
Display: Net P&L: +$420.00 | 78 trades
Ticks
• For futures traders who think in ticks
• Converts based on tick value input
Statistics Reset:
Two reset methods:
1. Toggle Reset
• Turn "Reset Statistics" toggle ON then OFF
• Clears all statistics immediately
2. Date-Based Reset
• Set "Reset After Date" (YYYY-MM-DD format)
• Only trades after this date are counted
• Useful for isolating recent performance
🎨 VISUAL FEATURES
Macro Channel:
Dynamic regression-based channel showing market boundaries:
• Upper/lower bounds calculated from swing pivot linear regression
• Adapts to current market structure
• Shows overall trend direction and potential reversal zones
Chop Boxes:
Semi-transparent overlay during high-chop periods:
• Purple/orange coloring indicates dangerous conditions
• Visual reminder to avoid new positions
Confluence Heat Zones:
Background shading indicating setup quality:
• Darker shading = higher confluence
• Lighter shading = lower confluence
• Helps identify optimal entry timing
EMA Ribbon:
Trend visualization via moving average fill:
• EMA 8/21/50 with gradient fill between
• Green fill when bullish aligned
• Red fill when bearish aligned
• Gray when neutral
Absorption Zone Boxes:
Marks potential accumulation/distribution areas:
• High volume + small body = absorption
• Boxes drawn at these levels
• Often act as support/resistance
Liquidity Pool Lines:
BSL/SSL levels with labels:
• Dashed lines at liquidity clusters
• "BSL" label above swing high clusters
• "SSL" label below swing low clusters
Six Professional Themes:
• Quantum: Deep purples and cyans (default)
• Cyberpunk: Neon pinks and blues
• Professional: Muted grays and greens
• Ocean: Blues and teals
• Matrix: Greens and blacks
• Ember: Oranges and reds
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Learning the System (Week 1)
Goal: Understand AMWT concepts and dashboard interpretation
Setup:
• Signal Mode: Balanced
• Display: All features enabled
• Grade Filter: C (see all signals)
Actions:
• Paper trade ONLY—no real money
• Observe HMM state transitions throughout the day
• Note when agents agree vs disagree
• Watch chop detection engage and disengage
• Track which grades produce winners vs losers
Key Learning Questions:
• How often do A+ signals win vs B signals? (Should see clear difference)
• Which agent tends to be right in current market? (Check dashboard)
• When does chop detection save you from bad trades?
• How do signals near liquidity events perform vs signals in vacuum?
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to your instrument and timeframe
Signal Mode Testing:
• Run 5 days on Aggressive mode (more signals)
• Run 5 days on Conservative mode (fewer signals)
• Compare: Which produces better risk-adjusted returns?
Grade Filter Testing:
• Track A+ only for 20 signals
• Track A and above for 20 signals
• Track B and above for 20 signals
• Compare win rates and expectancy
Chop Threshold Testing:
• Default (80%): Standard filtering
• Try 70%: More aggressive filtering
• Try 90%: Less filtering
• Which produces best results for your instrument?
Phase 3: Strategy Development (Weeks 3-4)
Goal: Develop personal trading rules based on system signals
Position Sizing by Grade:
• A+ grade: 100% position size
• A grade: 75% position size
• B grade: 50% position size
• C grade: 25% position size (or skip)
Session-Based Rules:
• London/NY Overlap: Take all A/A+ signals
• NY Session: Take all A+ signals, selective on A
• Asian Session: Only A+ signals with extra confirmation
• Off-Hours: No new positions
Chop Zone Rules:
• Chop >70%: Reduce position size 50%
• Chop >80%: No new positions
• Chop <50%: Full position size allowed
Phase 4: Live Micro-Sizing (Month 2)
Goal: Validate paper trading results with minimal risk
Setup:
• 10-20% of intended full position size
• Take ONLY A+ signals initially
• Follow trade management religiously
Tracking:
• Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus
• Calculate: Win rate by grade, by session, by chop level
• Compare to paper trading (should be within 15%)
Red Flags:
• Win rate diverges significantly from paper trading: Execution issues
• Consistent losses during certain sessions: Adjust session rules
• Losses cluster when specific agent dominates: Review that agent's logic
Phase 5: Scaling Up (Months 3-6)
Goal: Gradually increase to full position size
Progression:
• Month 3: 25-40% size (if micro-sizing profitable)
• Month 4: 40-60% size
• Month 5: 60-80% size
• Month 6: 80-100% size
Scale-Up Requirements:
• Minimum 30 trades at current size
• Win rate ≥50%
• Net R positive
• No revenge trading incidents
• Emotional control maintained
💡 DEVELOPMENT INSIGHTS
Why HMM Over Simple Indicators:
Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend.
The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP."
The Multi-Agent Evolution:
Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way.
The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection.
Chop Detection Revelation:
Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones.
Liquidity Anchoring Discovery:
Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals.
The Grade System Impact:
Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What AMWT Is NOT:
• NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty.
• NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment.
• NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis.
• NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate.
Core Assumptions:
1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure.
2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals.
3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns.
4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation.
Performs Best On:
• Liquid Futures : ES, NQ, MNQ, MES, CL, GC
• Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY
• Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap)
• Liquid Crypto : BTC, ETH on major exchanges
Performs Poorly On:
• Illiquid Instruments : Low volume stocks, exotic pairs
• Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal)
• Binary Event Days : Earnings, FDA approvals, court rulings
• Manipulated Markets : Penny stocks, low-cap altcoins
Known Weaknesses:
• Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable.
• Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance.
• Complexity : More parameters than simple indicators. Requires understanding to use effectively.
⚠️ RISK DISCLOSURE
Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses.
AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results.
Hidden Markov Model assumptions may not hold during:
• Major news events disrupting normal market behavior
• Flash crashes or circuit breaker events
• Low liquidity periods with erratic price action
• Algorithmic manipulation or spoofing
Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear.
Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing.
Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception.
By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes.
"Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable."
Initial Public Release
Core Engine:
• True Hidden Markov Model with online Baum-Welch learning
• Viterbi algorithm for optimal state sequence decoding
• 6-state market regime classification
Agent System:
• 3-Bandit consensus (Trend, Reversion, Structure)
• Thompson Sampling with true Beta distribution sampling
• Adaptive weight learning based on performance
Signal Generation:
• Quality-based confluence grading (A+/A/B/C)
• Four signal modes (Aggressive/Balanced/Conservative/Institutional)
• Grade-based visual brightness
Chop Detection:
• 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume)
• 7 regime classifications
• Configurable signal suppression threshold
Liquidity:
• Volume spike detection
• Stop run (liquidity sweep) identification
• BSL/SSL pool mapping
• Absorption zone detection
Trade Management:
• Trade lock with configurable duration
• TP1/TP2/TP3 targets
• ATR-based stop loss
• Persistent execution markers
Session Intelligence:
• Asian/London/NY/Overlap detection
• Smart weekend handling (Sunday futures open)
• Session quality scoring
Performance:
• Statistics tracking with reset functionality
• 7 currency display modes
• Win rate and Net R calculation
Visuals:
• Macro channel with linear regression
• Chop boxes
• EMA ribbon
• Liquidity pool lines
• 6 professional themes
Dashboards:
• Main Dashboard: Market State, Consensus, Trade Status, Statistics
📋 AMWT vs AMWT-PRO:
This version includes all core AMWT functionality:
✓ Full Hidden Markov Model state estimation
✓ 3-Bandit Thompson Sampling consensus system
✓ Complete 5-factor chop detection engine
✓ All four signal modes
✓ Full trade management with TP/SL tracking
✓ Main dashboard with complete statistics
✓ All visual features (channels, zones, pools)
✓ Identical signal generation to PRO
✓ Six professional themes
✓ Full alert system
The PRO version adds the AMWT Advisor panel—a secondary dashboard providing:
• Real-time Market Pulse situation assessment
• Agent Matrix visualization (individual agent votes)
• Structure analysis breakdown
• "Watch For" upcoming setups
• Action Command coaching
Both versions generate identical signals . The Advisor provides additional guidance for interpreting those signals.
Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
TrintityTrendIntroducing TrinityTrend
A multi-signal indicator combining:
Candle TrendStrength
SuperTrend logic
TTM Squeeze detection
Built for clarity, momentum, and volatility awareness—across any timeframe.
TrendStrength Mode
Candle coloring reflects directional conviction.
Strong uptrend
Strong downtrend
Neutral or indecisive
Helps traders stay with momentum and avoid chop.
SuperTrend Overlay
SuperTrend Logic Dynamic trailing stop based on volatility.
🟩 Price above = bullish bias
🟥 Price below = bearish bias
Great for swing entries and exits.
TTM Squeeze Detection
TTM Squeeze Mode Detects compression zones before breakout.
Squeeze on = buildup (You can change the color of this)
Pairs well with TrendStrength for timing entries.
Multi-Timeframe Versatility
Multi-Timeframe Ready:
Intraday scalping
Daily swing setups
Weekly macro bias
Toggle modes to match your strategy
ATR Price ZoneThe ATR Price Zone is an indicator which takes the Daily Average True Range of a stock and shows how high and low the price of the stock could possibly go from the opening price.
Key features:
The ATR Price Zone is an indicator which takes the Daily Average True Range of a stock and shows how high and low the priced the stock could possibly go from the opening price.
Key features:
ATR Price Zone uses zones looking forward to help strategize possible movements in price.
This indicator is customizable with zones, horizontal lines, a quick reference chart and colors.
The indicator continues to move forward with the chart.
It references the Daily True Average Range regardless of which Time Frame you are using.
It also references the opening candle with a blue arrow when using less than daily time frames.
Create by BothwellTrader
NICHI (NuwenPham's Ichimoku)NICHI (NuwenPham’s Ichimoku)
NICHI is a dual-engine Ichimoku indicator designed for modern, high-volatility markets.
It preserves a faithful traditional Ichimoku while introducing an advanced, filter-driven Ichimoku framework for research, visualization, and discretionary trading.
The goal of NICHI is not to replace Ichimoku — but to extend it.
Overview
NICHI includes two independent Ichimoku systems that can be enabled separately or together.
1. Standard Ichimoku
A clean, traditional Hosoda Ichimoku using Donchian midpoints:
Tenkan-sen (short period)
Kijun-sen (medium period)
Senkou Span A & B (forward displaced)
Chikou Span (lagging)
Design choice:
The Standard Ichimoku is intentionally plotted in a separate pane to avoid cluttering the price chart.
It serves as a reference / regime baseline, not a visual overlay.
2. Advanced Ichimoku
The Advanced system keeps the Ichimoku structure intact but replaces the Donchian calculations with selectable smoothing filters.
Each Ichimoku component (Tenkan, Kijun, Senkou B, Chikou) can be calculated using modern filters designed to handle volatility, noise, and regime shifts.
Supported filters include:
McGinley Dynamic (MD)
VWMA (exchange or tick-derived volume)
EMA / DEMA / SMA / SMMA / WMA
ALMA / LSMA / Hull MA
COVWMA / FRAMA / KAMA
50th Percentile
Moving Median
This allows Ichimoku to behave as:
A smoother trend system
A volatility-adaptive framework
A momentum-responsive overlay
Enhanced Cloud (Kumo) Modeling
Advanced Kumo logic includes:
Independent forward offsets for Span A and Span B
Bull / bear regime classification aligned with how the cloud is actually drawn
Adaptive cloud coloring
Neutral cloud state when spans disagree
This avoids misleading regime signals when different offsets are used.
Directional Persistence Tracking
NICHI tracks directional streaks for key components:
Tenkan direction
Kijun direction
Span A direction
Span B direction
These persistence counters stabilize coloring, reduce flicker, and improve visual clarity during transitions.
Bar Coloring Modes (Advanced)
Three bar-coloring frameworks are included.
Kumo-Based
Above cloud → bullish
Below cloud → bearish
Inside cloud → neutral
Tenkan / Kijun-Based
Above both → bullish
Below both → bearish
Chikou-Based
Chikou above past price → bullish
Chikou below past price → bearish
Each mode is intentionally distinct and serves a different trading style.
Moving Average Overlays
NICHI includes four optional moving average overlays (MA1–MA4):
Configurable type, length, width, and source
Intended for bias, confluence, or higher-timeframe context
Controlled as code-level constants by design
What Changed Since BETA
This release promotes NICHI from beta to stable with the following key improvements:
Chikou regime logic fixed:
Chikou comparisons now reference historical price only, eliminating any future lookahead behavior.
Kumo bull/bear alignment clarified:
Cloud regime classification now matches how the cloud is visually drawn when Span A and Span B use different forward offsets.
Kijun direction tracking corrected:
Kijun coloring now reflects Kijun movement, not Tenkan movement.
Bar coloring gated:
Bar coloring is applied only when Advanced Ichimoku is enabled, preventing unintended behavior when using Standard mode alone.
General stability and cleanup:
Minor bug fixes, consistency improvements, and documentation clarity.
Notes
Advanced Ichimoku is intended for research and visualization, not as a turnkey strategy.
Standard Ichimoku remains a faithful baseline.
If reporting issues, please include symbol, timeframe, and a screenshot.
Pinnacle ICT BasicOverview
Pinnacle ICT Basic (PICT Basic) is a contextual market regime overlay inspired by Inner Circle Trader (ICT) principles. It analyzes price behavior relative to recent structure and momentum to classify current conditions as orderly (directional progression), transitional (consolidation/stall), or unstable (chop/stand-down).
Important: This script provides no trade entries, exits, targets, alerts for execution, or performance predictions. It serves purely as a visual aid for discretionary decision-making, highlighting market context to help traders avoid low-quality conditions.
Originality and Value of This Integration
This script stands out by combining classic elements (EMA baseline for trend bias, pivot-based liquidity sweeps, displacement via candle body analysis, volume spikes, ATR-based separation, ADX/range for chop detection, and HTF EMA alignment) in a unique hierarchical filtering system. The proprietary tuning creates cleaner, more reliable contextual reads than simple individual indicators or basic mashups.
Key differentiators include:
Adaptive stall detection using a rolling baseline cross-count scaled as a percentage of lookback period (combined with ADX and range/ATR ratios) to identify hidden consolidation early, reducing false directional reads in ranging markets.
Deterministic market-mode adjustments (offsets for stocks vs. futures) for consistent behavior across asset classes without over-optimization.
Binary quality gating on setups (configurable OR/AND logic for volume + displacement) before confirmation, with limits like one-setup-per-leg, one-confirm-per-swing, cooldown bars, and micro-trend alignment.
Strict CONT (continuation first-touch) arming that requires pre-separation from baseline (ATR-scaled) and optional close-side requirements, preventing premature or noisy signals.
These interactions form a multi-layer filter: structure → quality → confirmation → regime shading. This reduces noise significantly compared to freely available scripts that plot sweeps or displacements independently, offering refined contextual awareness that justifies protected source code and selective access.
How It Works (Conceptual)
The script evaluates price movement progression, not just position.
At a high level:
A baseline EMA defines primary bias (bullish/bearish), with optional micro EMA for short-term alignment.
Market state detection combines traditional chop filters with proprietary stall logic to flag "stand down" periods of indecision.
Liquidity sweeps identify breaches of recent swing highs/lows (configurable key-swing strength or lookback).
Displacement requires strong candle bodies exceeding averages (with optional ATR filter).
Volume confirmation demands spikes above SMA.
HTF filter checks true bias alignment (not just LTF close vs. HTF EMA).
Setups trigger on recent sweeps or armed first-touch continuations at baseline.
Confirms require confluence of displacement, volume, micro alignment, and HTF OK—gated to avoid over-signaling.
The HUD displays regime (bullish/bearish/stand-down), bias, HTF status, alignment (OK or mismatch), and active filters (vol/disp). Background shading and optional labels/shapes provide visual cues for orderly vs. compressed/unstable action.
Visual Output
The script overlays:
Baseline (and optional micro) EMA.
Background regime shading.
Setup/confirm labels or shapes (configurable sizes/modes: Minimal, Standard, Debug).
On-chart HUD with real-time state summary.
No predictive elements, offsets into future, or non-standard chart assumptions are used.
What This Script Is Not
Does not generate buy/sell signals or alerts for direct execution.
Does not rely on fixed oscillator thresholds or simple MA crossovers alone.
Does not forecast direction or replace risk management.
Does not constitute a standalone system—all decisions remain discretionary.
Intended Use
Use as a contextual filter alongside your existing approach:
Avoid participation in "stand down" or mismatched conditions.
Monitor transitions from compression to expansion.
Assess structural continuity or disruption.
Apply across timeframes and assets (with auto-mode detection for stocks/futures).
Disclaimer
This script is for educational and informational purposes only. It does not constitute financial, investment, or trading advice. Trading involves risk; apply proper risk management. Past observations do not guarantee future behavior.
To request access, send a private message on TradingView with your username and brief intended use.
The RayAlgo Pro 3.0 (SMC+Filters)The RayAlgo Pro 3.0 (SMC+Filters) is a multi-module market-structure and trend framework designed to organize price action into a clear, repeatable workflow.
The core idea is layered confluence: each component answers a different “trading question” (direction, location, timing, and conditions). The goal is to build a coherent view where signals are interpreted in context.
Signal Filters introduce an additional confirmation layer, allowing classic SMC logic to be refined through strength, volatility, timeframe alignment, and session context.
The modules are designed to agree with each other, not compete.
Market Structure defines direction and key swing references (HH/HL/LH/LL, BOS/CHoCH).
Trend Engine transforms that structure into an actionable state (bull / bear) using volatility-adaptive bands.
Execution Context is added through continuation touches, reversal events, order blocks, and optional S/R zones.
Decision Support is handled via a dashboard that summarizes trend, strength, volatility, session, and higher-timeframe alignment.
1) Market Structure
Swing points derived from pivot highs/lows.
BOS / CHoCH lines drawn when price breaks the most recent active swing high/low.
Trend Change labels (“BUY” / “SELL”) triggered on a confirmed directional change in the structure state (ChoCH)
2) Trend Engine & Visual Layer (bands + candles)
Wireframe bands built from:
a WMA basis (trend anchor)
an ATR-smoothed volatility envelope
Trend Continuation markers (⬆ / ⬇) when price interacts with the active band in the direction of the trend (throttled to reduce clutter).
A bull / bear main line:
bullish state plots the lower band as the “active” trend line
bearish state plots the upper band as the “active” trend line
3) Clarity Candles (Trend-Based Coloring)
Candles are colored based on the active market structure trend state.
Bullish color during bullish structure, bearish color during bearish structure.
Helps visually separate trend phases, pullbacks, and transitions, reducing noise and improving directional focus.
4) Trend Reversal Reference
A dedicated reversal marker printed simultaneously with the main BUY / SELL signal.
Marks the exact previous swing high or low from which the trend changed.
Acts as a key structural reference and a natural trade invalidation level if price is reclaimed.
5) Smart Order Blocks
Bullish / bearish Order Block boxes created at structure break events.
Each block prints:
direction (BULL / BEAR)
a relative “strength” marker (+, ++, +++) based on box height vs ATR
the volume at the originating bar (formatted K/M/B)
Blocks are automatically managed:
a maximum number can remain active
blocks are removed when “mitigated” (price closes beyond the block boundary)
6) Optional Support & Resistance Zones
Pivot-based zones with:
minimum distance filtering (avoid stacking zones too close)
zone thickness scaled by ATR
extension into the future
7) Optional Sessions
Tight session boxes drawn around the price. Visualize high-activity trading hours and their price ranges.
London (08:00–17:00 UTC)
New York (13:00–22:00 UTC)
Tokyo (00:00–09:00 UTC)
Sydney (22:00–07:00 UTC)
8) Optional TP/SL levels
When a trend change triggers, the script can plot:
SL at the most recent opposite swing reference
TP1 / TP2 / TP3 derived from risk (entry → SL) and your selected R:R multipliers
9) Info Panel / Dashboard (table)
A compact dashboard (position + size configurable) showing:
current timeframe trend status (BULLISH / BEARISH / NEUTRAL)
strength using ADX classification (Weak / Range / Strong)
trend age (bars since last trend-change)
a volatility score (0–10) based on ATR vs its longer-term average
current session label
MTF alignment for 3 user-selected timeframes (Bull / Bear / Flat / N/A)
10) Optional Signal Filters
An optional confirmation layer applied only to Trend Change (BUY / SELL) signals.
Filters can require:
multi-timeframe directional alignment
a defined volatility regime
minimum trend strength (ADX)
a specific trading sessions
Signals failing the active filters are suppressed, and candles visually indicate filtered conditions until a valid signal appears.
HOW DOES IT WORK?
A) Market Structure State
1. The script detects swing highs/lows using pivot logic.
2. A swing is considered “active” until price breaks it.
3. When price breaks:
breaking a swing high flips/sets state to bullish
breaking a swing low flips/sets state to bearish
4. The break is drawn as a horizontal line and labeled:
BOS (break in the direction of the existing trend)
CHoCH (a change-of-character event — break against the prior direction)
B) Trend Engine (volatility-adaptive bands)
A WMA basis defines the baseline direction.
An ATR-based volatility measure is smoothed and multiplied
When market structure is bullish, the script emphasizes the lower band as the active trend line; in bearish states, it emphasizes the upper band.
This creates a trend framework that adapts to changing volatility, rather than using fixed-distance bands.
C) Timing Tools (continuation + reversal event markers)
Trend Change (BUY/SELL) appears only when the structure state flips (change-of-trend condition).
Continuation markers (⬆/⬇) appear when price “tags” the active band while the trend state remains intact.
Continuation signals are throttled (a minimum spacing between prints) to reduce signal clustering.
D) Order Blocks (structure-driven zones)
Order blocks are created at structure break moments, then managed dynamically:
Bullish OB prints on bullish structure resolution
bearish OB prints on bearish resolution.
Each OB box includes:
direction
strength grade based on box size relative to ATR
originating volume (formatted)
SETTINGS GUIDE
Structure Settings
Swing Size: controls pivot sensitivity.
lower = faster swing detection (more structure events, more noise)
higher = fewer, more “major” swings (cleaner structure, slower reactions)
BOS/CHoCH Line Style: visual preference (solid/dashed/dotted).
Show BOS / Show CHoCH: toggle structure event labeling.
Show Swing Labels: prints HH/HL/LH/LL at confirmed pivots.
Trend Reversal Signal
Show Trend Reversal Signals: adds a reversal label/line when the trend state flips.
Signal Line Length (bars): how far the reversal reference line extends.
Line/Text Colors: visuals.
Smart Order Blocks
Show Order Blocks: enables OB zones at structure events.
Bull/Bear OB Colors: visuals.
Max Active OBs: performance and chart cleanliness control.
How OB “strength” works:
Strength is graded by block height relative to ATR, giving a quick “size vs volatility” context.
Support & Resistance
Pivot Strength: how many bars left/right define a pivot (higher = fewer zones).
Minimum Zone Distance (%): prevents clustering.
Zone Thickness (ATR Mult): thickness adapts to volatility.
Max Zones / Extension (bars): visual management + horizon.
Main Trend Settings
Basis Length: WMA length (higher = smoother, slower).
ATR: volatility sampling window.
Smoothing: smooths volatility to prevent jitter.
Vol Multiplier: expands/contracts the envelope (higher = wider bands).
Visuals
Show Wireframe Bands + Wireframe Spread: adds multiple “distance lines” from the active band scaled by volatility.
Show Volatility Shadow: adds a separate volatility visualization layer (useful for regime awareness).
Sessions
Toggle session boxes (UTC-based).
TP/SL Settings
Show TP/SL Levels: plots SL + 3 targets on trend-change events.
TP risk/reward ratios: set R:R multipliers for TP1/2/3.
Info Panel
Show Info Panel / Position / Size
ADX Length: strength classification sensitivity.
Volume MA Length: average volume reference (currently used for averaging; the dashboard focuses on volatility/ADX/trend age).
MTF 1/2/3: three timeframes for trend alignment readout.
Appearance
Bull/Bear Colors
Trend Background: optional shading keyed off the trend state.
Signal Filters
Enable Signal Filters: applies additional rules to BUY/SELL signals only.
Filtered Candle Color: candles turn gray when a signal is filtered out.
MTF Alignment
Require MTF 1/2/3: only allow signals aligned with selected higher timeframes.
ADX Strength
Require ADX ≥ Min: filters out weak or ranging conditions.
Volatility
Vol Score Min/Max: limits signals to a chosen volatility regime (0–10).
Sessions
Restrict to Selected Sessions: signals only fire during chosen sessions.
ALERTS
This script includes alert conditions for:
Trend state events
Bullish Trend Change (trend changed to bullish)
Bearish Trend Change (trend changed to bearish)
Structure events
Bullish BOS
Bearish BOS
Continuation events
Bullish Trend Continuation (price touched bullish band)
Bearish Trend Continuation (price touched bearish band)
Order block events
Bullish Order Block Detected
Bearish Order Block Detected
Disclaimer
This indicator is an analytical and educational tool only. It does not provide financial advice or trade recommendations. All signals, levels, and visual elements are meant to assist in market analysis and must be used alongside proper risk management and independent decision-making. Trading involves risk, and past performance does not guarantee future results.
P2P Indicator [MOT]P2P Indicator - Time & Structural Liquidity System
The P2P Indicator is a comprehensive market structure tool designed to assist traders in identifying structural reversal scenarios where Time, Price, and Momentum converge.
ORIGINALITY & SYSTEMIC SYNERGY
While indicators for Session times, Fibonacci levels, and Fair Value Gaps (FVG) exist individually, this script is not a simple collection of these tools. It is an algorithmic system where five distinct components must align to generate a valid signal:
Session High/Low & Daily Refs: These define the Interest Zone (Context).
Fair Value Gap: This serves as the Structural Trigger (Entry Mechanism).
Pivot Points: These confirm the Market Structure Shift (MSS) (Trend Validation).
ATR Volatility: This acts as the Momentum Filter (Quality Control).
Fibonacci Projections: These provide the Objective Targets (Trade Management). The script's originality lies in this conditional dependency : A signal is ONLY generated when a specific chronological sequence (Zone Test → Inversion → MSS → Volatility) occurs, and the trade is managed specifically by the coupled Fibonacci projections and opposing Fair Value Gaps. This automates a complex institutional workflow that would otherwise require manual interpretation of multiple disparate tools.
The P2P System in action: Visualizing the automated confluence of Time (Sessions), Structure (FVGs), and Momentum (Signals).
METHODOLOGY: THE ALGORITHMIC SEQUENCE
The script generates signals based on a strict logic flow. This ensures that trades are only identified when market structure actually shifts, rather than blindly fading support or resistance.
1. Context: Time-Based Reference Zones
First, the script defines the playing field. It tracks the High and Low of the Asia (20:00-00:00 NY) and London (02:00-05:00 NY) sessions, as well as Daily Reference Points (PDH/PDL).
The Concept: These are treated as "Interest Zones"—statistical areas where price is likely to react.
2. Setup: Boundary Interaction State
The algorithm constantly monitors price action relative to these defined pools.
The Logic: When price engages with these key levels, the system enters a "Primed State." It registers that the asset has reached a statistical extreme, but strictly refrains from signaling until a reversal is structurally confirmed. This prevents premature entries during strong trends by waiting for specific interaction criteria to be met.
3. Trigger: Internal Efficiency Check
Once the system is primed, the script monitors internal price action for specific inefficiencies created during the move.
The Concept: It validates that the interaction was a reversal by identifying aggressive counter-momentum that forcefully reclaims internal zones. This ensures the move has sufficient energy to invalidate the previous structure.
4. Confirmation: Trend Geometry Validation
To prevent false reversals, the script employs a secondary trend filter.
The Concept: It cross-references the reversal against recent swing points to confirm that the short-term market geometry has aligned with the new direction (Market Structure Shift). This step filters out "wicks" that don't result in a true trend change.
5. Filter: Volatility Validation (ATR)
Finally, the move is measured against the Average True Range (ATR) .
The Logic: The structural shift must meet a minimum volatility threshold (customizable via settings). If the price action is too compressed relative to recent volatility, the signal is suppressed to avoid consolidation chop.
Algorithmic Entry: Buy and Sell signals forming after key liquidity pools are taken out and reversals are confirmed.
FEATURES & SETTINGS
Session & Daily Management
Session Visualization: Toggle specific ranges for Asia and London to define the intraday liquidity pools.
Daily Reference Points: Automatically plots PDH, PDL, Daily Midpoint (PD50), and True Day Open as higher-timeframe targets. Just like the session levels, these function as major liquidity pools for the system's sweep detection logic.
Gap Analysis & Filtering
State-Aware FVG Architecture: Unlike standard gap indicators, this engine dynamically tracks the lifecycle of every inefficiency. It distinguishes between "Active" gaps (potential triggers) and "Mitigated" zones, specifically filtering for the structural arrays required by the P2P algorithm.
Noise Filtering: Custom "Min/Max Gap Size" inputs allow users to filter out insignificant noise, ensuring the "Inversion" logic only reacts to key structural arrays.
Fair Value Gap visualization with noise filtering enabled.
Signal Logic & Customization
MSS Confirmation: Option to enable/disable the "Market Structure Shift" requirement for looser or stricter entries.
ATR Sensitivity: Tuning the "ATR Percentage" adjusts the volatility requirement for the entry model. Higher values demand a more explosive structural reclaim.
Dynamic Fib Projections
Target Generation: Once a signal is active, the script uses a proprietary Fibonacci engine based on the session range (Global/Pre-Market) to project dynamic extension targets (e.g., -0.236, -0.786). Full customization control over sessions, colors, and sensitivity.
HOW TO USE & BEST PRACTICES
This tool is optimized for trading intraday reversals during key volume windows.
Identify: Wait for price to interact with a plotted Session or Daily Level (e.g., Asia Low).
Execute: Wait for the P2P Signal. This confirms the boundary interaction has transitioned into a valid structure shift.
Target: Use the opposing Session Liquidity, FVGs, or the plotted Fib Projections as take-profit levels.
Time of Day: Signals generated during key volume injection times (e.g., London Open, NY Open) tend to be more significant due to increased market participation.
ALERTS
The script simplifies automation by aggregating the complex logic flow into a single alert condition.
Any Signal: Triggers on valid Buy/Sell setups, allowing for automated notifications of these specific structural events.
⚠️ DISCLAIMER
This script is for educational and analytical purposes only. It does not constitute financial advice. Trading involves significant risk. Past performance of the logic described is not indicative of future results.
3 Green Candles StrategyThe script will do the following:
Detect three consecutive green candles: The script checks if the current candle and the two preceding candles are bullish (green).
Draw white lines: Once this condition is met, two white horizontal lines will be drawn:
One line at the low of the first green candle in the sequence of three.
One line at the high of the third green candle in the sequence of three. These lines extend across the width of the three candles.
Draw a box (SL & TP): Starting from the closing price of the third green candle (the 'entry' or signal candle), a box is drawn that extends a configurable number of bars to the right:
Stop Loss (SL - red): The bottom of the red box is at the low of the first green candle (as you described). The top of the red box is the entry price.
Take Profit (TP - green): The top of the green box is calculated based on the entry price plus a configurable Risk-Reward Ratio relative to the SL. The bottom of the green box is the entry price. You can adjust the "Take Profit Ratio" and the number of "Box Extension Bars" in the indicator's settings.
Trader Otto - QQA Matrix - Quant/Quali Analysis SystemTrader Otto - QQA Matrix - Quant/Quali Analysis is a sophisticated multi-engine signal system that combines quantitative momentum analysis with qualitative market structure validation (Smart Money Concepts) and trend filtering.
**Core System Architecture:**
The QQA Matrix operates through a four-layer decision framework:
1. **Signal Engine (Dual Motor):** Choose between Adaptive RSI (volatility-adjusted momentum with WWMA smoothing) or Inverse Fisher Transform (statistical oscillator with HMA normalization). Both engines detect high-probability momentum shifts with minimal lag.
2. **Context Layer (SMC Filter):** Validates signals only when price interacts with institutional zones - Fair Value Gaps (FVG) and Order Blocks (OB) - using pivot-based detection and ATR filtering to identify genuine liquidity areas.
3. **Trend Filter (HalfTrend V18):** Optional safety mechanism using amplitude-based trend detection with dual-deviation channels. Filters counter-trend signals to align with dominant market direction.
4. **Confluence Engine:** Employs adjustable time-window tolerance (default 3 bars) to allow slight timing mismatches between trigger and context, capturing confluences that rigid systems miss.
**Key Features:**
- **Dual Engine Selection:** Switch between Adaptive RSI (range-bound markets) and Inverse Fisher (trending markets) without changing charts
- **Smart Money Validation:** Signals fire only inside institutional zones (FVG/OB), avoiding random entries
- **Trend Safety Toggle:** Enable/disable trend filter based on your trading style (scalping vs swing)
- **Transparent Parameters:** All engine settings exposed in Advanced Configuration - no hidden values
- **Low Repaint Risk:** Uses confirmed bar logic and lookback buffers for stable signals
**Best Practices:**
- **Scalping (1-5min):** Use Inverse Fisher + Trend Filter OFF for faster entries
- **Intraday (15-60min):** Use Adaptive RSI + Trend Filter ON for higher win rate
- **Swing (4H-Daily):** Use Adaptive RSI + Trend Filter ON + wider Tolerance (5 bars)
**Technical Notes:**
- FVG Detection: 3-candle pattern (current low > high for bullish)
- Order Blocks: Pivot-based with ATR size filter (default 3.0x) to eliminate noise
- HalfTrend: Amplitude-based algorithm with SMA confirmation, not standard channel deviation
- Signal Cooldown: Built-in array cleanup prevents signal spam from expired zones
**Recommended Pairs:**
Works best on liquid markets with clear institutional footprint: ES, NQ, BTC, EUR/USD, GBP/USD, and index futures (WIN, WDO on B3).
This is an educational tool. Always backtest parameters for your specific market and timeframe before live trading.
ICT Smart Money Concepts SMC Malibu🔷 Overview
The Smart Money Concepts (SMC) indicator is a comprehensive toolkit designed for institutional-style trading analysis. It automatically identifies and visualizes key SMC structures including Order Blocks, Breaker Blocks, Fair Value Gaps, Liquidity Levels, and Market Structure shifts — all in real-time.
Built with precision and clarity in mind, this indicator eliminates chart clutter through intelligent zone clustering, ensuring only the most relevant and actionable levels are displayed.
🔷 Key Features
Order Blocks (OB) — Automatically detects bullish and bearish order blocks with mitigation tracking
Breaker Blocks (BB) — Identifies failed order blocks that convert into breaker zones
Fair Value Gaps (FVG/IFVG) — Spots imbalances and inverse FVGs with visual fill tracking
Liquidity Levels (BSL/SSL) — Maps buy-side and sell-side liquidity with smart clustering
Market Structure (BOS/ChoCH) — Tracks Break of Structure and Change of Character in real-time
Kill Zones — Highlights key trading sessions (Asia, London, NY AM, NY Lunch, NY PM)
HTF Dashboard — Displays higher timeframe OB, FVG, and BB zones for confluence
Unicorn Model — Detects the rare ICT Unicorn setup automatically
🔷 What Makes It Unique?
✅ Smart Overlap Prevention — When multiple zones form at the same price level, older zones are automatically removed, keeping only the most recent and relevant structure.
✅ Mitigation Tracking — Zones that have been mitigated fade automatically, allowing you to distinguish between fresh and used levels.
✅ Multi-Timeframe Confluence — The built-in HTF dashboard shows higher timeframe structures directly on your chart without switching timeframes.
✅ Clean & Professional Design — Every element is carefully styled for maximum clarity and minimal distraction.
🔷 How To Use
Enable the structures you want to see (OB, BB, FVG, Liquidity, etc.)
Use Kill Zones to focus on high-probability trading windows
Look for confluence between current timeframe structures and HTF dashboard levels
Trade reactions at fresh (non-mitigated) zones with proper risk management
🔷 Settings
All features are fully customizable:
Toggle each structure on/off independently
Adjust colors and transparency
Control maximum active zones
Show/hide historical (mitigated) levels
Customize Kill Zone sessions and times
🔷 Notes
Works on all markets (Forex, Crypto, Stocks, Indices)
Optimized for 1M to 4H timeframes
Best used in conjunction with your own analysis and risk management
📱 7-DAY FREE TRIAL: Website: harmonikprzmalibu.netlify.app/
[longshorti] FVG - Fair Value GapThis script is an educational tool designed to help traders and students of technical analysis visualize the concept of Fair Value Gaps (FVG) and price imbalances. It provides a mathematical framework to observe how these zones are formed and subsequently "mitigated" (filled) by price action over time.
By quantifying price gaps into data points like volume and percentage, this tool allows for a deeper study of market mechanics and liquidity concepts as described in various trading theories like Smart Money Concepts (SMC).
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📊 Educational Metrics Breakdown
The indicator provides a data label for each detected zone to help analyze the "life cycle" of an imbalance: Example: 17.86M / 13.66M USDT (75.4%)
Fill Progress (17.86M): * Weighted Mode: Shows a calculated value representing the physical fill of the gap relative to its initial volume.
Total Activity Mode: Tracks the total cumulative volume traded within the zone's coordinates since its inception.
Initial Impulse Volume (13.66M): The total volume of the candle that created the imbalance.
Remaining Open Gap ((75.4%)): A mathematical representation of the portion of the FVG that has not yet been touched by subsequent price action.
Relative Price Weight ( ): The height of the FVG expressed as a percentage of the asset's price at the time of creation.
Key Educational Features
Adaptive Step Visualization: Dynamically divides imbalances into "steps" to help students observe exactly where price finds support or resistance within a gap.
Price % Filtering: Teaches the user to distinguish between significant market imbalances and minor price noise based on a percentage threshold.
Historical Context: Past imbalances are kept on the chart in a subtle #363a45 color to allow for the study of "S/R Flip" phenomena (where a filled FVG later acts as support/resistance).
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⚠️ DISCLAIMER (Educational Purposes Only)
This script is provided for educational and research purposes only. It is not a financial advisor, and it does not provide financial advice or trade recommendations.
No Guarantees: Past performance as visualized by this tool does not guarantee future results.
Not a Signal Tool: This indicator should be used as a supplementary visualization aid and never as a standalone basis for making financial decisions.
Risk Warning: Trading in financial markets involves significant risk of loss. Always perform your own due diligence and consult with a certified financial professional before making any investment.
The author of this script is not responsible for any financial losses incurred through the use of this tool. By using this script, you acknowledge that you understand its educational nature and use it at your own risk.
How to Study with this Tool
Observe Mitigation: Watch how price reacts when it enters the "Remaining %" zone.
Volume Analysis: Compare the "Initial Volume" with the "Total Activity" to see how levels of high interest are formed.
Filtered Perspectives: Use the Price % filter to see how market structure changes when only major imbalances are considered.
