Trade Decision MatrixTrade Decision Matrix (TDM)
Trade Decision Matrix (TDM) is a professional-grade, multi-phase market intelligence indicator designed to assist traders in understanding market structure, regime behavior, capital confidence, and execution readiness using a systematic, probabilistic framework.
This indicator does not generate trade signals. Instead, it provides a structured decision matrix similar to institutional trading desks, combining regime analytics, entropy confidence, Bayesian reliability, capital allocation logic, and scenario interpretation.
🔹 Core Architecture
TDM is built using a nine-phase institutional decision pipeline:
Phase 1 — Market Context
Spot–future basis, volatility normalization, and structural slope detection.
Phase 2 — Regime Engine
Probabilistic classification of Trend, Breakout, Range, or Mean Reversion environments.
Phase 3 — Orthogonal Model Cores
Independent statistical, trend, breakout, and mean-reversion cores.
Phase 4 — Bayesian Reliability Engine
Adaptive reliability scoring for each core using Bayesian reinforcement.
Phase 5 — Capital Engine
Capital confidence and capital mode based on opportunity quality, regime clarity, entropy confidence, and risk filters.
Phase 6 — Decision Matrix
Bias, participation level, and trade quality grading.
Phase 7 — Scenario Engine
Contextual scenario interpretation such as Trend Expansion, Breakout Failure, Range Compression, etc.
Phase 8 — Execution Gate
Execution readiness filter based on capital and model alignment.
Phase 9 — Reversal Engine
Probabilistic reversal risk estimation using multi-factor logic.
🔹 Regime Entropy Confidence
TDM uses Shannon entropy to measure regime uncertainty and converts it into a confidence score.
Lower entropy = higher regime confidence.
Higher entropy = unstable or transitional market state.
This prevents over-confidence in noisy conditions.
🔹 Institutional Commentary Engine
A professional commentary layer interprets all internal engines and outputs institutional-style guidance such as:
• Institutional Alignment
• Capital Protection Mode
• Regime Uncertainty
• Momentum Continuation
• Structural Breakout
• Volatility Coiling
• Reversal Risk Elevated
This commentary is designed for situational awareness, not signal generation.
🔹 Dashboard
The dark-theme dashboard provides a compact institutional decision panel:
• Regime
• Entropy Confidence
• Scenario
• Bias
• Strength
• Capital Confidence
• Capital Mode
• Trade Quality
• Execution State
• Commentary
• Reversal Risk
All values are color-coded with heat shading for instant visual interpretation.
🔹 How To Use
TDM is best used as a decision support layer alongside your own trading strategy.
Typical workflow:
Identify regime and entropy confidence.
Observe capital confidence and capital mode.
Check scenario and bias alignment.
Confirm execution readiness.
Monitor reversal risk before entering or holding positions.
This tool is ideal for:
• Intraday traders
• Swing traders
• Options traders
• Index traders
• Systematic discretionary traders
🔹 Important Notes
• This indicator does NOT produce buy/sell signals.
• It is a decision intelligence framework.
• It should not be used as a standalone trading system.
• Always apply personal risk management.
🔹 Disclaimer
This indicator is provided for educational and informational purposes only.It does not constitute financial advice or investment recommendations.Trading involves risk. Users are responsible for their own trading decisions.
Probability
SA_ORB_ONR_CLOUD_vwapBandsSIGNAL ARCHITECT™ — ORB / ONR Cloud with VWAP Bands
Optimized for the 15-Minute Timeframe
Overview
The Signal Architect™ ORB / ONR Cloud is a session-structure and probability framework designed to help traders understand where price is statistically compressed, transitioning, or escaping value during the regular trading session.
On the 15-minute chart, this study excels at identifying:
High-probability consolidation zones
Early session directional intent
Fade vs continuation environments
Context for VWAP-based mean reversion or trend extension
Rather than predicting price, the indicator classifies market behavior using time-anchored ranges and volume-weighted statistics.
Core Components (15-Minute Context)
1️⃣ Overnight Range (ONR)
The Overnight Range captures price extremes formed before the regular session opens.
On the 15-minute timeframe, ONR acts as:
A higher-timeframe reference level
A source of institutional liquidity memory
A boundary where early session reactions often occur
2️⃣ Opening Range (ORB)
The Opening Range is defined as the first X minutes after the session open (default: 15 minutes).
On a 15-minute chart:
The ORB often forms entirely within a single candle
It represents initial institutional positioning
It helps differentiate initiative vs responsive behavior
3️⃣ ORB–ONR Cloud (Key Feature)
The Cloud is the overlapping area between the Overnight Range and the Opening Range.
This zone is critical on the 15-minute timeframe because it often represents:
Compressed auction
Balance / indecision
Liquidity absorption
Interpretation:
Price inside the cloud → Higher probability of consolidation, fade, or contraction
Price exiting the cloud → Transition toward expansion or trend resolution
The cloud is not a signal — it is a probability environment.
4️⃣ VWAP with Session-Weighted σ Bands
The study plots VWAP starting from the regular session open, along with true volume-weighted standard deviation bands (±1σ, ±2σ).
On the 15-minute timeframe:
VWAP defines fair value
σ bands help distinguish normal rotation vs statistical extension
Interaction with VWAP while inside the cloud often suggests mean-reverting conditions
Interaction with VWAP after leaving the cloud often confirms trend continuation
5️⃣ Breakout Classification (BRK)
A BRK event occurs when price closes outside BOTH:
The Overnight Range
The Opening Range
On the 15-minute chart:
BRK events often mark session regime changes
They are contextual markers, not entries
Arrows are color-matched to the candle (green candle → green arrow, red candle → red arrow)
To avoid clutter, breakouts can be limited to first-occurrence only.
Probability Layer (15-Minute Edge)
The indicator includes rolling probability calculations to quantify market behavior:
📊 Inside-Cloud Probability
Shows how often price remains inside the ORB–ONR cloud over the selected lookback.
Higher values → balance / compression dominant
Lower values → trend / expansion dominant
📉 Fade / Contraction Probability (Inside Cloud)
When price is inside the cloud, the study measures volatility contraction using ATR behavior.
Higher contraction % → Greater likelihood of rotation or fade
Lower contraction % → Cloud acting as launchpad rather than balance
📈 State Occupancy (5-State Model)
Tracks how price distributes its time across:
Above both ranges
Below both ranges
Inside ORB only
Inside ONR only
Inside the Cloud
This helps traders understand where the market statistically prefers to trade on the 15-minute structure.
Best Use Cases (15-Minute Chart)
✔ Contextual bias for intraday swing trades
✔ Identifying fade vs trend conditions
✔ VWAP-based execution alignment
✔ Avoiding low-probability entries inside compression
✔ Session structure awareness without lower-timeframe noise
What This Indicator Is NOT
❌ Not a buy/sell system
❌ Not predictive
❌ Not a guarantee of outcomes
It is a market structure and probability framework — designed to improve decision quality, not replace risk management.
Recommended Settings (15-Minute)
ORB Length: 15 minutes
VWAP Bands: ±1σ / ±2σ
Probability Lookback: 100–200 bars
Breakout Mode: First-occurrence only
Cloud Enabled: Yes
Risk & Compliance Notice
This tool is provided for educational and informational purposes only.
It does not constitute financial advice, investment recommendations, or trade instructions.
All trading involves risk, including the possible loss of capital.
Standalone Signal - trianchor.gumroad.com
chatgpt.com
chatgpt.com
chatgpt.com
SA_Multi-Timeframe Execution_ORB_RANGE_CLOUDCORE IDEA (Read This First)
This indicator does not give entries by itself.
It answers three critical questions:
Where is weekly balance vs expansion? (Cloud)
Has the week declared intent? (BRK)
Is price aligned across auction speeds? (30 → 15 → 5)
When multiple timeframes agree, price reacts strongly because you’re trading institutional reference points, not indicators.
SECTION I — TIMEFRAME ROLES (VERY IMPORTANT)
🟦 30-Minute Chart — STRUCTURE / BIAS
Role: Weekly map
Defines whether the week is:
Balanced (inside cloud)
Expanding (outside cloud)
You do not enter from 30m
You decide directional bias only
Think of 30m as the “Where am I allowed to trade?” chart.
🟨 15-Minute Chart — CONFIRMATION
Role: Decision layer
Confirms whether price is:
Holding weekly support/resistance
Accepting or rejecting the cloud
First place where BRK matters meaningfully
15m answers:
“Is the market agreeing with the weekly map?”
🟥 5-Minute Chart — EXECUTION
Role: Timing & precision
Used only after 30m + 15m alignment
Provides:
Entry timing
Stop placement
Early failure detection
5m answers:
“When do I engage?”
SECTION II — PAIRING SETUPS
🔷 SETUP A: 30-15-5 (Highest Conviction)
When to Use
You want swing-intraday hybrids
Best for NQ, ES
Best on trend days
Conditions
30m
Price outside weekly cloud
Or clean rejection from cloud boundary
15m
BRK confirms in same direction
OR price holds above/below cloud edge
5m
Pullback or continuation trigger
No counter-BRK against higher frames
Behavior Expectation
Clean directional follow-through
Strong support/resistance reactions
Fewer fakeouts
🔷 SETUP B: 30-15 (Context Only)
When to Use
You’re not trading actively
Or managing runners / swings
Conditions
Use 30m + 15m to:
Hold bias
Avoid counter-trend trades
Stay patient
This pairing prevents over-trading.
🔷 SETUP C: 15-5 (Intraday Precision)
When to Use
You want scalps or short intraday swings
Works extremely well after the weekly cloud is broken
Conditions
15m
Either:
First BRK of the week
Or price firmly outside cloud
5m
Entry after pullback
Or first continuation after compression
This is where you’ll see the “crazy support” you mentioned.
SECTION III — ENVIRONMENT RULES (DO NOT SKIP)
🟧 Price INSIDE the Weekly Cloud
Market State: Compression / Balance
What to expect:
Chop
Fades
Failed breakouts
Best actions:
Smaller targets
Faster exits
Mean-reversion mindset
Avoid:
Holding large trend positions
Forcing continuation trades
🟩 Price OUTSIDE the Weekly Cloud (with BRK)
Market State: Expansion / Trend
What to expect:
Momentum
Strong pullback support
One-directional pressure
Best actions:
Pullback entries
Let winners run
Scale instead of scalp
SECTION IV — PROFIT TARGETS BY PRODUCT
(REALISTIC, STRUCTURE-BASED)
🟦 NASDAQ FUTURES (/NQ)
High volatility, fast expansion
5-Minute
Inside cloud: 5–10 points
Outside cloud / BRK: 10–20 points
15-Minute
Continuation: 15–30 points
Strong weekly trend: 30–50 points
🟨 S&P 500 (/ES)
Cleaner structure, slower moves
5-Minute
Inside cloud: 2–4 points
Outside cloud: 4–8 points
15-Minute
Trend continuation: 6–12 points
Strong week: 12–20 points
🟥 Russell 2000 (/RTY)
Choppy but explosive when aligned
5-Minute
Inside cloud: 3–6 points
Outside cloud: 6–12 points
15-Minute
Trend days: 10–20 points
🟪 Dow Jones (/YM)
Slow but persistent
5-Minute
Inside cloud: 30–60 points
Outside cloud: 60–120 points
15-Minute
Trend continuation: 120–250 points
🟨 Gold Futures (/GC)
Mean-reverting, respect structure
5-Minute
Inside cloud: 3–6 points
Outside cloud: 6–10 points
15-Minute
Continuation: 10–20 points
Weekly trend: 20–35 points
SECTION V — GOLDEN RULES (PRINT THESE)
✅ Trade with the weekly map
✅ Let 30m define bias
✅ Let 15m confirm
✅ Let 5m execute
❌ Do NOT fight the cloud
❌ Do NOT over-size inside balance
Final Thought
When the 15m and 5m align with the weekly structure, you’re trading where liquidity must react — that’s why the support and continuation feel so strong.
Signal Inside the Indicator - trianchor.gumroad.com
chatgpt.com
chatgpt.com
chatgpt.com
QSS Confluence EngineQSS Confluence Engine - User Guide
1. System Overview
The QSS Confluence Engine is a sophisticated, multi-layered trading algorithm designed for TradingView (Pine Script v6). Unlike standard indicators that rely on a single data point (like RSI or MACD), QSS aggregates data from five distinct "Engines" to generate high-probability trade signals.
The core philosophy is Confluence: A trade signal is only valid when price structure, volume flow, statistical probability, and momentum cycles align.
2. The Five Core Engines
🔥 A. Signal Engine (The Backbone)
This is the primary trend detector. It determines the bias (Bullish/Bearish).
Modes:
OTT (Optimized Trend Tracker): Smoother, better for volatile assets (Crypto/Forex).
SuperTrend: Faster, better for trending stocks or scalping.
Function:
If this engine says "Bearish," the system ignores all Buy signals from other modules.
🏰 B. Liquidity Structure (Apex Logic)
Identifies "Structural" Support and Resistance based on market pivots.
Logic: Draws zones from significant Pivot Highs/Lows to the candle body (Wick-to-Body).
Usage: Acts as a filter. The system will block a Buy signal if price is currently smashing into a Supply (Red) Liquidity Zone.
🌊 C. Imbalance Engine (BigBeluga Logic)
Identifies "Momentum" Supply/Demand based on volume and candle sequences.
Logic: Detects aggressive institutional buying/selling (e.g., 3 consecutive green candles with rising volume).
Usage: Shows where "Smart Money" has previously entered. These zones are often more reactive than standard pivots.
📊 D. Statistical Engine (Zeiierman Logic)
Calculates the raw mathematical probability of price movement.
Logic: It tracks every single bar in history. If the last candle was Green, it calculates: "Historically, after a Green candle, how often did price move up another 0.5 ATR?"
Filter: If enabled, it blocks trades where the historical probability of success is < 50%.
🧠 E. Neural KDE (Kernel Density Estimation)
Estimates the probability of a market reversal using statistical density.
Logic: Uses Gaussian math to find "Overextended" states in the RSI.
Usage: Prints Arrow labels when a reversal is statistically likely.
3. Configuration Guide (Settings Menu)
Signal Engine
Strategy Engine: Choose OTT for most assets. Switch to SuperTrend if you want faster, tighter signals.
OTT %: Lower (e.g., 1.0) for scalping, Higher (e.g., 2.0) for swing trading.
Liquidity & Imbalance
Filter: Respect Liquidity Zones: Keep this ON. It prevents buying into resistance.
Show S/D Imbalances: Default is OFF to keep the chart clean. Turn ON to see exactly where institutional volume entered.
Statistical Probability
Show Probability Lines: Default is ON.
Green Line: The price target for a "Bullish" continuation.
Red Line: The price target for a "Bearish" reversal.
Label: Shows the exact % chance (e.g., Prob Up: 65%).
Quant Filters
Koncorde / Institutional Activity: Checks for volume patterns associated with large players.
ADX Filter: Ensures the market is actually trending (ADX > 20) before signaling.
Cycle Filter (STC): A momentum cycle oscillator. Keeps you out of trades when the cycle is exhausted.
4. How to Trade (The Strategy)
The Buy Signal (Long)
A BUY label appears only when ALL of the following occur simultaneously:
Trend: The Core Engine (OTT/SuperTrend) turns Green.
Structure: Price is NOT inside a Supply/Resistance zone.
Volume: Volume is above average (if Volume Filter is on).
Probability: Statistical probability of an Up move is > 50%.
Momentum: STC Cycle is moving up.
The Sell Signal (Short)
A SELL label appears when:
Trend: The Core Engine turns Red.
Structure: Price is NOT inside a Demand/Support zone.
Volume: Volume is below average (if Volume Filter is on).
Probability: Statistical probability of a Down move is > 50%.
Momentum: STC Cycle is moving down.
Risk Management (R:R)
The script automatically draws entry, stop-loss, and take-profit lines when a signal fires.
SL (Stop Loss): Placed at 2.0 x ATR (Average True Range).
TP 1/2/3: Placed at 2x, 4x, and 6x ATR.
Strategy: Close half your position at TP1 and move SL to Breakeven.
5. The Dashboard (HUD)
Located at the bottom right, the HUD gives you a comprehensive real-time health check of the asset.
Current Engine: Displays the active strategy mode (OTT or SuperTrend).
Current Session: Identifies the active market session (London, New York, Tokyo).
Trend Bias: The overall direction (Bullish/Bearish).
Zone Status: Tells you if you are currently blocked by a zone (e.g., LIQ SUPPLY means "Don't Buy, we are at resistance").
KDE Prob: Indicates if a market Top or Bottom is statistically likely based on neural density.
Math Probability: The raw % chance of the current move continuing based on historical analysis.
Inst. Activity: Shows if institutions are Accumulating (Buying) or Distributing (Selling).
Trend Strength: Uses ADX to classify the market as Ranging, Trending, or Parabolic.
Trend Pressure: Shows if momentum is Rising or Falling.
System Status: A vital debugger. It tells you exactly why a trade isn't firing (e.g., "Wait: Cycle", "Wait: Ribbon") or if it's "Scanning...".
Active Trade Data: When a trade is live, this section replaces "System Status" to show Entry Price, Stop Loss, and Take Profit levels
6. Troubleshooting
"No Signals Appearing": You likely have too many filters enabled. Try disabling the Koncorde or Liquidity Filter temporarily to see if signals appear. The stricter the confluence, the fewer (but higher quality) the signals.
"Chart is too messy": Go to settings and uncheck Show Liquidity Zones, Show Probability Lines, or Show KDE Arrows. The logic will still work in the background even if visuals are hidden.
Directional Movement Probability (DMP Indicator) [whodatop]The Directional Movement Probability (DMP) indicator is an intraday-oriented analytical tool designed to identify probabilistic phases of directional price movement using a Z-score calculation based on the deviation of the closing price from its moving average.
The indicator is primarily intended for lower intraday timeframes , with 3-minute and 5-minute charts being the preferred operating range, where directional transitions and regime shifts are more clearly expressed.
Its primary objective is to detect the start and end of a positive Z-score zone, interpreted as a phase of dominant directional behavior.
It has demonstrated particularly consistent behavior on Forex instruments and currency futures , where mean-deviation dynamics and session-based liquidity patterns are well defined.
Core Calculation Logic
Z-score
The indicator uses a Z-score calculated from the closing price relative to its moving average.
The Lookback Length defines the calculation window for both the moving average and standard deviation.
If the standard deviation is zero, the Z-score defaults to 0.
Deadband (Hysteresis)
A symmetric deadband around zero is applied to reduce signal noise when Z fluctuates near the midpoint.
Setting Deadband = 0 disables this behavior.
Signal Filters
Filters do not alter the Z-score calculation and are applied only at the signal level.
Toxic Bar Filter
Suppresses signals on abnormally large candles by comparing bar height to recent volatility.
Session Filter
Optionally ignores signals during the Asian session (23:00–07:00 UTC) to reduce low-liquidity noise.
Limitations and Usage Notes
This is an intraday indicator, not a standalone trading system.
Best performance is typically observed on 3-minute and 5-minute charts.
Particularly well-suited for Forex markets and currency futures.
Can be applied to other asset classes and timeframes, but signal characteristics may vary.
Most effective when combined with:
- higher-timeframe directional bias,
- market structure or liquidity-based analysis,
- additional confirmation logic.
Not designed for prolonged range-bound conditions without supplementary filters.
Advanced Momentum TrackerThe Advanced Momentum Tracker (AMT) is a technical indicator designed to identify high-probability trend reversals and momentum shifts in real-time. Unlike traditional indicators that rely solely on mathematical formulas, AMT analyzes price action structure and historical patterns to detect when market momentum is shifting from bullish to bearish (and vice versa).
Core Methodology:
The indicator tracks consecutive price movements and maintains a comprehensive database of historical momentum patterns. It identifies trend changes by analyzing:
Sequential candle relationships (opens and closes)
Break of key trailing stop levels formed by recent price action
Historical success rates of similar momentum patterns
Key Features
1. Dynamic Levels:
Automatically plots real-time dynamic trailing stop levels based on current momentum
Color-coded lines: Green for bullish momentum, Red for bearish momentum
These levels act as trigger points for potential trend changes
2. Entry Signal Markers:
Clear BUY (↑) and SELL (↓) arrows when momentum shifts are detected
Arrows positioned above/below candles for maximum visibility ,Signals only appear on confirmed trend changes
3. Momentum Score Display:
Shows statistical probability based on historical pattern analysis
Displays strength percentage of current momentum continuation
Helps traders assess confidence level of the current trend
4. Exit Zone Indicator:
Plots recommended exit levels for active positions
Dynamic color coding: Red for long exits, Green for short exits
Warning system (orange) when price breaches exit zones
5. Position Management Filter:
Optional risk filter to avoid trades with excessive distance from trigger level
Customizable position threshold percentage
Helps maintain consistent risk-reward ratios
6. Comprehensive Alert System:
Customizable alert messages for both long and short signals
Configurable alert frequency (once per bar or once per bar close)
Real-time notifications for all signal types
Customization Options-
Visual Settings:
Toggle visibility of current price level, momentum score, and exit zones
Customizable colors for all elements (bullish/bearish themes)
Adjustable line thickness for dynamic levels
Entry Markers:
Custom colors for long and short entry signals
Adjustable arrow distance from candles
Core Parameters:
Historical Depth: Amount of past data to analyze (default: 20,000 bars)
Sensitivity Level: Controls how strong a move must be to trigger signals (default: 4)
Higher values = fewer but stronger signals
Lower values = more signals with earlier entries
Position Management:
Enable/disable position filter
Set maximum acceptable risk threshold as percentage
How It Works:-
Momentum Detection Engine: The script continuously monitors price action, tracking each bullish and bearish leg. It maintains arrays of opens, closes, and counts to build a comprehensive picture of market structure.
Pattern Recognition: When price breaks key levels (minimum/maximum of recent candles based on sensitivity), the indicator recognizes a potential momentum shift.
Statistical Validation: The script compares the current pattern against its historical database to calculate the probability of momentum continuation.
Signal Generation: When a valid trend change is detected (and passes the position filter if enabled), entry signals are displayed with corresponding exit zones.
Best Use Cases:
Swing trading on any timeframe (works on 1m to 1D charts)
Trend reversal identification
Momentum trading strategies
Works on all markets: Forex, Stocks, Crypto, Indices, Commodities etc
Recommended Settings:
Scalping/Day Trading: Sensitivity 2-3, Historical Depth 10,000-20,000
Swing Trading: Sensitivity 3-4, Historical Depth 20,000-30,000
Position Trading: Sensitivity 4-5, Historical Depth 30,000+
Important Notes:
Signals appear only on confirmed bars (not on real-time candles unless confirmed)
The momentum score becomes more accurate as more historical data is processed
Position filter should be adjusted based on the volatility of the instrument being traded
Best used in conjunction with proper risk management and position sizing
What Makes This Indicator Unique:
Unlike indicators that simply apply mathematical formulas to price data, AMT learns from historical price behavior. It doesn't just tell you what happened—it tells you what's likely to happen next based on thousands of similar situations in the past. The statistical momentum score provides an edge that pure technical indicators cannot offer.
Disclaimer: This indicator is a tool for technical analysis and should not be used as the sole basis for trading decisions. Always use proper risk management and combine with your own analysis. Happy Trading !!
Probability-Based Adaptive Detection🙏🏻 PBAD (Probability-Based Adaptive Detection) : adaptive control tool for outliers || novelty detection, made for worst case data & processes, for the highest time complexity O(n^2) compared with the alternatives (would be explained in a sec). Thresholds are completely data driven and axiomatic, no need in provided hyperparameters, are not learned or optimized. The method accepts multiple weights, e.g. both temporal and volatility weights.
Method briefly explained (I can go deeper if any1 asks explicitly):
Performs weighted KDE on initial input data, finds KDE global maximum (mode), creates new “residuals” dataset by centering initial data around this value;
Performs weighted KDE on residuals, uses sigmoid based probability mass targets with increasing probability coverage to construct a set of non-disjoint High Density Intervals (also called HDR, HPD in Bayesian terms);
Uses these intervals to calculate analogs of centralized & standardized moments;
Uses these ^^ moments to construct a set of control thresholds. The scheme used in PBAD is not only based on a central threshold, or on neighboring ones, it utilizes all previous thresholds, gaining more information.
...
The most important part is to understand whether you really need PBAD. Because even tho it seems to be the best one given highest algocomplexity, irl it would work worse in cases when it’s not required by your data.
Here’s the menu (aka taxonomy omg) of methods you can use that would let you make the right choice:
Moment-Based Adaptive Detection (MBAD) :
Norm: L2
Time complexity: original O(n), successfully reduced to O(1) in online version
Use case: default, general purpose
Based on: method of moments (powers of residuals from mean)
Thresholds architecture: centralized
Quantile-Based Adaptive Detection (QBAD):
Norm: L1
Time complexity: O(nlogn)
Use case: either bad data Or process instability
Based on: quantile moments (dyadic percentiles of residuals from median)
Thresholds architecture: chained/recursive/sequential
Probability-Based Adaptive Detection (PBAD):
Norm: L0
Time complexity: O(n^2)
Use case: both bad data And process instability
Based on: probability moments (target probability masses of residuals from KDE mode)
Thresholds architecture: decentralized (for lack of a better name xd, the idea is that these thresholds gain information from the all other threshold and are Not exclusively based on the central or neighboring thresholds)
...
Examples of true use cases:
^^ an appropriate financial instrument to use PBAD
^^ and another one
...
Additional details about how to use it:
Keep the student5 kernel, it’s the best you can do. I added others mostly for comparisons and if you want to use the tool Not for its primary purpose (on a fine data)
“Calculate for N bars” and “Starting at bar N” options allow to reduce calculation period only on the N number of last bars or next bars from a chosen one. It's vital, because calculations here are heavy
Keep plotting offset at 1 (allows to visually compare current bar with the previous threshold values). This is the way it should be done on price data.
HLC3 is the optimal source input, unless you want to use your own better one point estimate of each datapoint (in the best case done by using PBAD itself on OHLC+ values).
In essence it should be used just like MBAD or QBAD, fade/push extensions and limit, fade/push/skip deviations & basis, or other strategies of your. Again, the only reason for 3 methods to exist is to be chosen for according data characteristics.
Btw:
This is the initial version, I don’t consider it perfected tbh, even tho it works as expected, however this method is very situational anyways.
In this script KDE function is modified to ensure the outcoming probabilities Do sum up to 1. I didn’t do this normalization in Weighted KDE Mode script , but there it’s not required since we just need a KDE global max.
see ya
∞
Session ATR Progression Tracker📊 Session ATR Progression Tracker - SIYL Regression Trading Tool
Track how much of your instrument's 7-day Average True Range (ATR) has been covered during the current trading session. This indicator is specifically designed for regression traders who follow the "Stay In Your Lane" (SIYL) methodology, helping you identify when the probability of mean reversion significantly increases. If you are interested in more on that check out Rod Casselli and tradersdevgroup.com.
🎯 Key Features:
• Real-time ATR Coverage Percentage - See at a glance what percentage of the 7-day ATR has been covered in the current session
• SIYL-Optimized Thresholds - See at a glance when the instrument has achieved 80% and 100% ATR coverage, the proven thresholds where mean reversion probability increases (customizable)
• Flexible Session Modes:
- Daily: Resets at calendar day change
- Session: Uses exchange-defined trading sessions
- Custom Session: Set your exact session start/end times (perfect for futures traders and international markets)
• Visual Alerts - Color-coded display (gray → orange → red) and optional background highlighting
• Repositionable Display - Choose from 9 screen positions to avoid chart clutter
• Session Markers - Green triangles mark the start of each new session
• Detailed Stats - View current range, ATR value, session high/low, and session status
💡 Why Use This Indicator?
This tool is built around a proven concept: regression trading becomes significantly more effective once a session has achieved at least 80% of its 7-day ATR. At this threshold, the probability of price reverting to mean increases substantially, creating higher-probability trade setups for SIYL practitioners.
Benefits for regression traders:
- Identify optimal entry points when mean reversion probability is highest (≥80% ATR coverage)
- Avoid premature regression entries before adequate range has been established
- Recognize when daily moves have "earned their range" and are ripe for reversal
- Time fade-the-move and counter-trend strategies with statistical backing
- Improve win rates by trading only after proven probability thresholds are met
⚙️ Setup Instructions:
1. Add the indicator to your chart
2. Select your preferred "Reset Mode" (recommend "Custom Session" for futures/international markets)
3. If using Custom Session, enter your session times in 24-hour format (e.g., 0930-1600 for US stocks, 1700-1600 for CME futures)
4. Adjust alert thresholds if desired (default: 80% and 100% - proven SIYL thresholds)
5. Position the display where it's most visible on your chart
📈 Works Across All Markets:
Stocks • Futures • Forex • Indices • Crypto • Commodities
Perfect for regression traders, mean reversion specialists, and SIYL practitioners who want to trade with probability on their side by entering only after the session has "earned its range."
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Tip: For futures contracts with overnight sessions that span calendar days (like MES, MNQ, MYM), use "Custom Session" mode with your exchange's official session times for accurate tracking.
SMC N-Gram Probability Matrix [PhenLabs]📊 SMC N-Gram Probability Matrix
Version: PineScript™ v6
📌 Description
The SMC N-Gram Probability Matrix applies computational linguistics methodology to Smart Money Concepts trading. By treating SMC patterns as a discrete “alphabet” and analyzing their sequential relationships through N-gram modeling, this indicator calculates the statistical probability of which pattern will appear next based on historical transitions.
Traditional SMC analysis is reactive—traders identify patterns after they form and then anticipate the next move. This indicator inverts that approach by building a transition probability matrix from up to 5,000 bars of pattern history, enabling traders to see which SMC formations most frequently follow their current market sequence.
The indicator detects and classifies 11 distinct SMC patterns including Fair Value Gaps, Order Blocks, Liquidity Sweeps, Break of Structure, and Change of Character in both bullish and bearish variants, then tracks how these patterns transition from one to another over time.
🚀 Points of Innovation
First indicator to apply N-gram sequence modeling from computational linguistics to SMC pattern analysis
Dynamic transition matrix rebuilds every 50 bars for adaptive probability calculations
Supports bigram (2), trigram (3), and quadgram (4) sequence lengths for varying analysis depth
Priority-based pattern classification ensures higher-significance patterns (CHoCH, BOS) take precedence
Configurable minimum occurrence threshold filters out statistically insignificant predictions
Real-time probability visualization with graphical confidence bars
🔧 Core Components
Pattern Alphabet System: 11 discrete SMC patterns encoded as integers for efficient matrix indexing and transition tracking
Swing Point Detection: Uses ta.pivothigh/pivotlow with configurable sensitivity for non-repainting structure identification
Transition Count Matrix: Flattened array storing occurrence counts for all possible pattern sequence transitions
Context Encoder: Converts N-gram pattern sequences into unique integer IDs for matrix lookup
Probability Calculator: Transforms raw transition counts into percentage probabilities for each possible next pattern
🔥 Key Features
Multi-Pattern SMC Detection: Simultaneously identifies FVGs, Order Blocks, Liquidity Sweeps, BOS, and CHoCH formations
Adjustable N-Gram Length: Choose between 2-4 pattern sequences to balance specificity against sample size
Flexible Lookback Range: Analyze anywhere from 100 to 5,000 historical bars for matrix construction
Pattern Toggle Controls: Enable or disable individual SMC pattern types to customize analysis focus
Probability Threshold Filtering: Set minimum occurrence requirements to ensure prediction reliability
Alert Integration: Built-in alert conditions trigger when high-probability predictions emerge
🎨 Visualization
Probability Table: Displays current pattern, recent sequence, sample count, and top N predicted patterns with percentage probabilities
Graphical Probability Bars: Visual bar representation (█░) showing relative probability strength at a glance
Chart Pattern Markers: Color-coded labels placed directly on price bars identifying detected SMC formations
Pattern Short Codes: Compact notation (F+, F-, O+, O-, L↑, L↓, B+, B-, C+, C-) for quick pattern identification
Customizable Table Position: Place probability display in any corner of your chart
📖 Usage Guidelines
N-Gram Configuration
N-Gram Length: Default 2, Range 2-4. Lower values provide more samples but less specificity. Higher values capture complex sequences but require more historical data.
Matrix Lookback Bars: Default 500, Range 100-5000. More bars increase statistical significance but may include outdated market behavior.
Min Occurrences for Prediction: Default 2, Range 1-10. Higher values filter noise but may reduce prediction availability.
SMC Detection Settings
Swing Detection Length: Default 5, Range 2-20. Controls pivot sensitivity for structure analysis.
FVG Minimum Size: Default 0.1%, Range 0.01-2.0%. Filters insignificant gaps.
Order Block Lookback: Default 10, Range 3-30. Bars to search for OB formations.
Liquidity Sweep Threshold: Default 0.3%, Range 0.05-1.0%. Minimum wick extension beyond swing points.
Display Settings
Show Probability Table: Toggle the probability matrix display on/off.
Show Top N Probabilities: Default 5, Range 3-10. Number of predicted patterns to display.
Show SMC Markers: Toggle on-chart pattern labels.
✅ Best Use Cases
Anticipating continuation or reversal patterns after liquidity sweeps
Identifying high-probability BOS/CHoCH sequences for trend trading
Filtering FVG and Order Block signals based on historical follow-through rates
Building confluence by comparing predicted patterns with other technical analysis
Studying how SMC patterns typically sequence on specific instruments or timeframes
⚠️ Limitations
Predictions are based solely on historical pattern frequency and do not account for fundamental factors
Low sample counts produce unreliable probabilities—always check the Samples display
Market regime changes can invalidate historical transition patterns
The indicator requires sufficient historical data to build meaningful probability matrices
Pattern detection uses standardized parameters that may not capture all institutional activity
💡 What Makes This Unique
Linguistic Modeling Applied to Markets: Treats SMC patterns like words in a language, analyzing how they “flow” together
Quantified Pattern Relationships: Transforms subjective SMC analysis into objective probability percentages
Adaptive Learning: Matrix rebuilds periodically to incorporate recent pattern behavior
Comprehensive SMC Coverage: Tracks all major Smart Money Concepts in a unified probability framework
🔬 How It Works
1. Pattern Detection Phase
Each bar is analyzed for SMC formations using configurable detection parameters
A priority hierarchy assigns the most significant pattern when multiple detections occur
2. Sequence Encoding Phase
Detected patterns are stored in a rolling history buffer of recent classifications
The current N-gram context is encoded into a unique integer identifier
3. Matrix Construction Phase
Historical pattern sequences are iterated to count transition occurrences
Each context-to-next-pattern transition increments the appropriate matrix cell
4. Probability Calculation Phase
Current context ID retrieves corresponding transition counts from the matrix
Raw counts are converted to percentages based on total context occurrences
5. Visualization Phase
Probabilities are sorted and the top N predictions are displayed in the table
Chart markers identify the current detected pattern for visual reference
💡 Note:
This indicator performs best when used as a confluence tool alongside traditional SMC analysis. The probability predictions highlight statistically common pattern sequences but should not be used as standalone trading signals. Always verify predictions against price action context, higher timeframe structure, and your overall trading plan. Monitor the sample count to ensure predictions are based on adequate historical data.
Bayesian Order Flow Predictor📌 Bayesian Order Flow Predictor — Advanced Probability Engine for Nasdaq and Futures
This indicator is a next-generation probabilistic forecasting system designed for Nasdaq traders who rely on Order Flow, Auction Market Theory, Value Area dynamics, market structure, DOM imbalance, and Bayesian probability models.
It combines 7 professional-grade factors (DOM, CVD, RSI, EMA trend, ATR volatility, Market Structure, Value Area positioning) into a unified Bayesian probability panel that outputs a clean bullish/bearish probability curve with high-confidence reversal and trend-continuation signals.
Engineered for scalpers, day traders, futures traders, and ICT-style order flow technicians, it delivers real-time directional probability, session-aware signals, and optional news-filter exclusion.
⭐ Features
Bayesian Probability Model (0–100%)
DOM imbalance scoring across dynamic depth levels
Cumulative Volume Delta (CVD) scoring
Market structure detection (HH/LL micro-trend shifts)
RSI momentum and overbought/oversold scoring
EMA directional bias + ATR-normalized deviation
Value Area positioning (VAH / VAL / POC) with optional previous-session mode
Session filtering (only signals during active hours)
Automated news filter (exclude signals around scheduled macro events)
Bull/Bear probability zones with background coloring
Anti-repetition system (no double signals in same direction)
Designed for future scalping, futures order flow, and high-precision timing
🧠 Bayesian Probability Engine — How It Works
The model evaluates 7 independent market factors simultaneously:
DOM imbalance
CVD pressure
Market structure
RSI deviation
EMA trend
Value Area position
ATR volatility shift
Each factor is transformed into a normalized score, multiplied by its weighting parameter, and aggregated into a global score.
This score is then passed through a Bayesian logistic function to convert uncertainty into a smooth probability curve, giving traders a clean, mathematically stable, and noise-resistant forecast.
📈 Buy & Sell Signal Logic
Signals trigger when:
Bullish Probability crosses above the user threshold
Bearish Probability crosses below the opposite threshold
Session is active
No protected news event is occurring
This avoids noise, prevents over-signaling, and focuses only on high-confidence inflection points.
🎯Fully compatible with the indicator: ➡️ AI Probabilistic Orderflow scalper
Both indicators synchronize perfectly when used together:
Bayesian panel → trend probability
Scalper v1 → timing + TP/SL engine
Together they create a complete probability-driven revenue management system for scalping Future.
📘 How to Use
Add the indicator to your chart
Set your trading session (e.g., 09:30–16:00 EST)
Adjust weights depending on your style (Order Flow / Momentum / Value Area)
Watch the probability curve:
Above threshold → bullish bias
Below threshold → bearish bias
Take signals when the curve crosses thresholds, not when flat
Combine with "AI Probabilistic Orderflow scalper" indicator for execution timing
Avoid high-impact news using the News Filter
💎 Advantages
Professional-grade Bayesian model
Works in all volatility regimes
Noise-resistant and smoother than traditional oscillators
Integrates Order Flow + Auction Theory + Momentum + Volatility
Perfect for NQ scalpers seeking an AI-style probability dashboard
Reduces emotional decision-making
Compatible with any execution strategy
Optimized for high winrate scalping and sniper entries
AI Probabilistic OrderFlow Scalper⭐ Description:
📌 AI Probabilistic OrderFlow Scalper
This script combines Order Flow, Auction Market Theory, Volume Imbalance, Market Structure (HH/LL), RSI bias filtering, and a probability-based direction model inspired by AI and statistics.
It produces high-precision scalping entries designed for fast markets such as Futures, while remaining compatible with all markets (indices, crypto, forex, metals).
This is not a typical indicator — it is a probabilistic predictive model engineered to provide sniper entries, a tick-based Take Profit, a volatility-adaptive ATR Stop Loss, and optional Value Area levels (VAH/VAL/POC).
⭐ Main Features:
🔥 Directional probability model (AI-style weighted scoring)
📊 Order Flow imbalance (delta-like logic)
📈 HH/LL market structure detection
🎯 Smart RSI bias filter
🚀 One signal per trend shift (anti-spam)
🎯 Tick-based Take Profit (perfect for NQ / futures)
🛡️ ATR-based dynamic Stop Loss
📉 Value Area display: VAH, VAL, POC
🔊 Volume confirmation filter
📡 Directional probability plot
✔️ Works for Futures, Crypto, Forex, Indices
🧠 Probabilistic AI Approach
The model uses a 3-factor scoring system:
Order Flow imbalance
Market structure (HH/LL)
RSI trend bias
Each validated condition = 1 point.
The total score is converted into Buy/Sell probabilities, and the higher-probability direction is selected.
When probability exceeds the threshold (e.g. 80%), the system triggers a high-confidence sniper signal.
This mirrors Revenue Management logic:
→ Only take a decision when probability of success is maximized.
🎯 Buy/Sell Signals (Sniper Entries)
🔵 Green triangle under the candle = high-probability Buy
🔴 Red triangle above the candle = high-probability Sell
✔️ Only one signal per directional shift
✔️ Signals appear only when all strict filters are satisfied
📌 Automatic TP / SL
TP: fixed tick-based (e.g. 100 ticks for NQ scalping)
SL: ATR-based, adapts to volatility
TP/SL display can be enabled or disabled
Perfectly calibrated for high-speed scalping.
📘 How to Use
Use on every timeframe
Adjust probability threshold (75–90 recommended)
Enable strict mode for maximum precision
Let the model filter entries automatically
Choose a TP suitable for your market
Optionally display VAH/VAL/POC for Auction Theory context
Always test using backtesting before going live
🏆 Advantages
Extremely fast for scalping
High win-rate potential via probabilistic filtering
Clean signals (no noise or spam)
Combines the strongest trading frameworks:
Order Flow
Market Structure
Statistical modeling
Volume profiling
Automated risk management
Dynamic Breakout Odds [RayAlgo]█ OVERVIEW
Dynamic Breakout Odds is a probability-based breakout tool that uses ATR and pattern matching to estimate how likely price is to expand up or down from the current candle.
Instead of guessing, the indicator scans historical candles that look like the current one and measures how often price broke above or below by a volatility-based amount.
It then projects those probabilities forward as clean levels and a bias dashboard on your chart.
Use it to quickly answer:
• “Is the next move statistically more likely up or down?”
• “How far does price typically travel from here, in ATR terms?”
█ CONCEPTS
Candle Profile Matching
The script builds a “profile” of the current setup using two elements:
• The color of the previous candle (bullish close vs bearish close)
• The trend environment (above/below EMA, if the filter is enabled)
Only historical candles with the same profile are used for statistics. This keeps the probabilities specific to the current context instead of mixing all market conditions together.
ATR-Based Expansion
For every matching historical candle, the script checks how far price moved away from the open using ATR:
• Upward move thresholds
• Moderate expansion (≈ 0.5 ATR above the open)
• Stronger expansion (≈ 1.0 ATR above the open)
• Downward move thresholds
• Moderate expansion (≈ 0.5 ATR below the open)
• Stronger expansion (≈ 1.0 ATR below the open)
It counts how often each expansion happened, then converts those counts into probabilities.
Normalized Probability Scores
The indicator doesn’t just show raw percentages; it normalizes them so that all scenarios together form a consistent probability set.
Internally it tracks four outcomes for similar candles:
• Chance of a moderate move upward
• Chance of a strong move upward
• Chance of a moderate move downward
• Chance of a strong move downward
These are then normalized so the total is roughly 100%. From this, two main metrics are derived:
• Bullish Strength = combined normalized odds of upside moves
• Bearish Strength = combined normalized odds of downside moves
Whichever side has the higher score defines the current directional bias .
█ WHAT YOU SEE ON THE CHART
1. Breakout Projection Levels
Four horizontal levels are projected around the open of the current bar:
• Two upside levels
• Nearer upside expansion (~0.5 ATR above the open)
• Further upside expansion (~1.0 ATR above the open)
• Two downside levels
• Nearer downside expansion (~0.5 ATR below the open)
• Further downside expansion (~1.0 ATR below the open)
Each line extends a configurable number of bars into the future, so you visually see a breakout “corridor” above and below price.
2. Probability Labels
At the right edge of each line, you’ll see a label such as:
• “X% – near upside”
• “Y% – further downside”
These labels tell you how frequently similar candles in the chosen lookback reached that expansion. You immediately know which scenario has been more common historically.
3. Breakout Zones
Between the paired upside lines and the paired downside lines, shaded “probability zones” can be shown:
• The upper shaded band highlights the typical upside expansion range
• The lower shaded band highlights the typical downside expansion range
These zones visually group probable target areas instead of just single lines.
4. Background Tint
The background behind price is softly tinted towards:
• Bullish color when Bullish Strength > Bearish Strength
• Bearish color when Bearish Strength > Bullish Strength
The stronger the statistical imbalance between the two, the more pronounced the tint. This gives you an instant feel for whether conditions lean more Long, more Short, or are nearly Neutral.
5. Directional Bias Arrow
On the last bar the script can plot a clean arrow:
• Up-arrow below price when bullish odds dominate
• Down-arrow above price when bearish odds dominate
The arrow is positioned beyond all projection lines, making it easy to see even on cluttered charts and reminding you of the current statistical bias without text.
6. Origin Marker
A small horizontal mark is drawn at the open of the current candle.
This acts as the “starting point” from which all ATR-based expansions above and below are measured.
7. Dashboard Panel
A compact dashboard is drawn in a corner of the chart (location configurable). It displays:
• Bullish Strength – combined normalized probability for upside expansions
• Bearish Strength – combined normalized probability for downside expansions
• Bias – “Long Bias”, “Short Bias”, or “Neutral”
• Trend Filter – shows whether EMA-based filtering is ON or OFF and which length is used
This gives you a quick, text-based summary of the current statistical environment.
█ SETTINGS
Analysis Lookback Period
• Controls how many historical bars the script inspects when searching for similar candles.
• Larger values = more history, smoother statistics, slower adaptation.
• Smaller values = faster adaptation, but more noise and less stability.
ATR Length
• The period used to compute ATR volatility.
• Defines how “big” 0.5 ATR and 1.0 ATR moves are on your current symbol and timeframe.
Trend Filter (EMA)
• Filter by Trend?
• When ON, only historical candles in a similar trend regime are used.
• When OFF, all past candles with similar color are considered, regardless of trend.
• Trend EMA Length
• EMA period used to classify trend.
• Price above EMA → uptrend environment.
• Price below EMA → downtrend environment.
This filter helps you separate behavior in uptrends from downtrends, which can significantly change breakout dynamics.
Visual Settings
• Projection Width (bars)
• How far the lines and zones extend into the future.
• Show Probability Zones
• Toggle shaded bands between each pair of levels.
• Label Size
• Choose smaller or larger text for the probability labels on the right.
• Tint Background by Bias
• Turn the bias-based background on or off.
• Show Bias Marker on Last Candle
• Toggle the up/down arrow marker.
• Dashboard Location
• Select top/bottom left/right corner for the panel.
█ HOW TO USE IT
1. Start With the Dashboard
Look at Bullish Strength vs Bearish Strength:
• If bullish is clearly larger → environment statistically favors upside expansion.
• If bearish is clearly larger → environment statistically favors downside expansion.
• If they are close → treat the situation as Neutral; consider reducing position size or waiting for more clarity.
2. Use Levels as Dynamic Targets
The projected lines and zones can serve as:
• Profit targets based on typical expansion distance
• Logical regions for scaling out
• Areas where you expect price behavior to change (e.g., loss of momentum)
Short-term traders often focus on the nearer expansion levels, while swing traders may use the farther levels as extended targets.
3. Align With Trend (Optional)
With the trend filter ON:
• Prefer Long setups when price is above the EMA and bullish probabilities dominate.
• Prefer Short setups when price is below the EMA and bearish probabilities dominate.
With the filter OFF, you get pure color-plus-pattern statistics across the whole lookback, which can be useful if you deliberately trade counter-trend or range conditions.
4. Combine With Your Existing System
Dynamic Breakout Odds is best used as a confirmation and targeting layer :
• Combine it with structure (support/resistance, supply/demand, order blocks).
• Combine it with volume or orderflow tools if you use them.
• Use the probability zones to validate whether your planned target is realistic relative to recent volatility.
It is not designed to be a standalone “buy/sell” signal generator, but a statistical map around your entries.
█ PRACTICAL EXAMPLES
Example A – Bullish, Moderate Expansion Frequently Hit
• Bullish Strength significantly higher than Bearish Strength.
• The nearer upside level shows a strong historical hit rate.
Interpretation: similar setups often produce at least a moderate push upward before failing.
Use case: trade pullbacks in the direction of the bias, targeting the nearer upside projection as an initial take-profit.
Example B – Bearish, Deeper Downside Often Reached
• Bearish Strength clearly dominant.
• Both the nearer and farther downside levels show decent probabilities.
Interpretation: similar conditions historically saw follow-through to the downside.
Use case: use rallies against the direction of the bias to position into shorts, planning partial exits around the first downside projection and runners toward the second.
Example C – Neutral, Balanced Probabilities
• Bullish and Bearish Strength scores are close.
• Background tint is very light or absent.
Interpretation: the market is statistically indecisive; expansions up or down are similarly likely.
Use case: consider range trading tactics, mean-reversion ideas, or simply standing aside until a clearer skew develops.
█ BEST PRACTICES
• Use on liquid symbols and reasonable timeframes to avoid distorted ATR behavior.
• Don’t overfit lookback length to a single instrument; test across markets.
• Let the indicator provide context, not absolute certainty.
• Always combine with proper risk management (position sizing, max loss per trade, etc.).
• Be cautious with very small sample sizes (e.g., very short lookbacks on low-volume assets).
█ LIMITATIONS & NOTES
• All probabilities are based on historical behavior ; markets can change regime.
• ATR distances are relative to recent volatility and may shrink/expand over time.
• The script intentionally does not guarantee any direction or target; it only reports what has been most common in similar past situations.
█ DISCLAIMER
This tool is for educational and informational purposes only.
It does not constitute financial advice or a guarantee of performance.
Always do your own research, test on demo or historical data, and use appropriate risk management when trading live capital.
Per Bak Self-Organized CriticalityTL;DR: This indicator measures market fragility. It measures the system's vulnerability to cascade failures and phase transitions. I've added four independent stress vectors: tail risk, volatility regime, credit stress, and positioning extremes. This allows us to quantify how susceptible markets are to disproportionate moves from small shocks, similar to how a steep sandpile is primed for avalanches.
Avalanches, forest fires, earthquakes, pandemic outbreaks, and market crashes. What do they all have in common? They are not random.
These events follow power laws - stable systems that naturally evolve toward critical states where small triggers can unleash catastrophic cascades.
For example, if you are building a sandpile, there will be a point with a little bit additional sand will cause a landslide.
Markets build fragility grain by grain, like a sandpile approaching avalanche.
The Per Bak Self-Organized Criticality (SOC) indicator detects when the markets are a few grains away from collapse.
This indicator is highly inspired by the work of Per Bak related to the science of self-organized criticality .
As Bak said:
"The earthquake does not 'know how large it will become'. Thus, any precursor state of a large event is essentially identical to a precursor state of a small event."
For markets, this means:
We cannot predict individual crash size from initial conditions
We can predict statistical distribution of crashes
We can identify periods of increased systemic risk (proximity to critical state)
BTW, this is a forwarding looking indicator and doesn't reprint. :)
The Story of Per Bak
In 1987, Danish physicist Per Bak and his colleagues discovered an important pattern in nature: self-organized criticality.
Their sandpile experiment revealed something: drop grains of sand one by one onto a pile, and the system naturally evolves toward a critical state. Most grains cause nothing. Some trigger small slides. But occasionally a single grain triggers a massive avalanche.
The key insight is that we cannot predict which grain will trigger the avalanche, but you can measure when the pile has reached a critical state.
Why Markets Are the Ultimate SOC System?
Financial markets exhibit all the hallmarks of self-organized criticality:
Interconnected agents (traders, institutions, algorithms) with feedback loops
Non-linear interactions where small events can cascade through the system
Power-law distributions of returns (fat tails, not normal distributions)
Natural evolution toward fragility as leverage builds, correlations tighten, and positioning crowds
Phase transitions where calm markets suddenly shift to crisis regimes
Mathematical Foundation
Power Law Distributions
Traditional finance assumes returns follow a normal distribution. "Markets return 10% on average." But I disagree. Markets follow power laws:
P(x) ∝ x^(-α)
Where P(x) is the probability of an event of size x, and α is the power law exponent (typically 3-4 for financial markets).
What this means: Small moves happen constantly. Medium moves are less frequent. Catastrophic moves are rare but follow predictable probability distributions. The "fat tails" are features of critical systems.
Critical Slowing Down
As systems approach phase transitions, they exhibit critical slowing down—reduced ability to absorb shocks. Mathematically, this appears as:
τ ∝ |T - T_c|^(-ν)
Where τ is the relaxation time, T is the current state, T_c is the critical threshold, and ν is the critical exponent.
Translation: Near criticality, markets take longer to recover from perturbations. Fragility compounds.
Component Aggregation & Non-Linear Emergence
The Per Bak SOC our index aggregates four normalized components (each scaled 0-100) with tunable weights:
SOC = w₁·C_tail + w₂·C_vol + w₃·C_credit + w₄·C_position
Default weights (you can change this):
w₁ = 0.34 (Tail Risk via SKEW)
w₂ = 0.26 (Volatility Regime via VIX term structure)
w₃ = 0.18 (Credit Stress via HYG/LQD + TED spread)
w₄ = 0.22 (Positioning Extremes via Put/Call ratio)
Each component uses percentile ranking over a 252-day lookback combined with absolute thresholds to capture both relative regime shifts and extreme absolute levels.
The Four Pillars Explained
1. Tail Risk (SKEW Index)
Measures options market pricing of fat-tail events. High SKEW indicates elevated outlier probability.
C_tail = 0.7·percentrank(SKEW, 252) + 0.3·((SKEW - 115)/0.5)
2. Volatility Regime (VIX Term Structure)
Combines VIX level with term structure slope. Backwardation signals acute stress.
C_vol = 0.4·VIX_level + 0.35·VIX_slope + 0.25·VIX_ratio
3. Credit Stress (HYG/LQD + TED Spread)
Tracks high-yield deterioration versus investment-grade and interbank lending stress.
C_credit = 0.65·percentrank(LQD/HYG, 252) + 0.35·(TED/0.75)·100
4. Positioning Extremes (Put/Call Ratio)
Detects extreme hedging demand through percentile ranking and z-score analysis.
C_position = 0.6·percentrank(P/C, 252) + 0.4·zscore_normalized
What the Indicator Really Measures?
Not Volatility but Fragility
Markets Going Down ≠ Fragility Building (actually when markets go down, risk and fragility are released)
The 0-100 Scale & Regime Thresholds
The indicator outputs a 0-100 fragility score with four regimes:
🟢 Safe (0-39): System resilient, can absorb normal shocks
🟡 Building (40-54): Early fragility signs, watch for deterioration
🟠 Elevated (55-69): System vulnerable
🔴 Critical (70-100): Highly susceptible to cascade failures
Further Reading for Nerds
Bak, P., Tang, C., & Wiesenfeld, K. (1987). "Self-organized criticality: An explanation of 1/f noise." Physical Review Letters.
Bak, P. & Chen, K. (1991). "Self-organized criticality." Scientific American.
Bak, P. (1996). How Nature Works: The Science of Self-Organized Criticality. Copernicus.
Feedback is appreciated :)
Weighted KDE Mode🙏🏻 The ‘ultimate’ typical value estimator, for the highest computational cost @ time complexity O(n^2). I am not afraid to say: this is the last resort BFG9000 you can ‘ever’ get to make dem market demons kneel before y’all
Quickguide
pls read it, you won’t find it anywhere else in open access
When to use:
If current market activity is so crazy || things on your charts are really so bad (contaminated data && (data has very heavy tails || very pronounced peak)), the only option left is to use the peak (mode) of Kernel Density Estimate , instead of median not even mentioning mean. So when WMA won’t help, when WPNR won’t help, you need this thing.
Setting it up:
Interval: choose what u need, you can use usual moving windows, but I also added yearly and session anchors alike in old VWAP (always prefer 24h instead of Session if your plan allows). Other options like cumulative window are also there.
Parameters: this script ain't no joke, it needs time to make calculations, so I added a setting to calculate only for the last N bars (when “starting at bar N” is put on 0). If it’s not zero it acts as a starting point after which the calculations happen (useful for backtesting). Other parameters keep em as they are, keep student5 kernel , turn off appropriate weights if u apply it to other than chart data, on other studies etc.
But instead of listening to me just experiment with parameters and see what they change, would take 5 mins max
Been always saying that VWAP is ish, not time-aware etc, volume info is incorporated in a lil bit wrong way… So I decided not just to fix VWAP (you can do it yourself in 5 mins), but instead to drop there the Ultimate xD typical value estimator that is ever possible to do. Time aware, volume / inferred volume aware, resistant to all kinds of BS. This is your shieldwall.
How it works:
You can easily do a weighted kernel density estimation, in our case including temporal and intensity information while accumulating densities. Here are some details worth mentioning about the thing:
Kernels are raw (not unit variance), that’s easier to work with later.
h_constants for each kernel were calculated ^^ given that ^^ with python mpmath module with high decimal precision.
In bandwidth calculation instead of using empirical standard deviation as a scaler, I use... ta.range(src, len) / math.sqrt(12)
...that takes data range and converts it to standard deviation, assuming data is uniformly distributed. That’s exactly what we need: a scaler that is coherent with the KDE, that has nothing to do with stdevs, as the kernels except for gaussian ones (that we don’t even need to use). More importantly, if u take multiple windows and see over time which distro they approach on the long term, that would be the uniform one (not the normal one as many think). Sometimes windows are multimodal, sometimes Laplace like etc, so in general all together they are uniform ish.
The one and only kernel you really need is Student t with v = 5 , for the use case I highlighted in the first part of the post for TV users. It’s as far as u can get until ish becomes crazy like undefined variance etc. It has the highest kurtosis = 9 of all distros, perfect for the real use case I mentioned. Otherwise, you don’t even need KDE 4 real, but still I included other senseful kernels for comparison or in case I am trippin there.
Btw, don’t believe in all that hype about Epanechnikov kernel which in essence is made from beta distribution with alpha = beta = 2, idk why folk call it with that weird name, it’s beta2 kernel. Yes on papers it really minimises AMISE (that’s how I calculated h constants for all dem kernels in the script), but for really crazy data (proper use case for us), it ain't provides even ‘closely’ compared with student5 kernel. Not much else to add.
Shout out to @RicardoSantos for inspiration, I saw your KDE script a long time ago brotha, finna got my hands on it.
∞
Institutional Edge Pro v1.0 - 9.3/10 ConfidenceEducational 5-layer confirmation system combining institutional order flow concepts, trend analysis, and risk management principles. Features Order Block detection, adaptive stop losses (EMA 9x21), and probability scoring. For educational purposes only.
## ⚡ KEY FEATURES
### 🔍 5-Layer Confirmation System
- **Layer 0:** Market Regime Detection (30% weight) - ADX, Choppiness Index, Volatility, Volume
- **Layer 1:** Golden/Death Cross Trend Filter (20% weight) - EMA 50/200 with gradient confirmation
- **Layer 1.5:** Fast Death Cross Stop Loss - EMA 9/21 dynamic exits
- **Layer 2:** Smart Order Block Detection (20% weight) - Institutional footprint tracking
- **Layer 3:** Probabilistic Confirmations (20% weight) - RSI, MACD, Volume, Structure, Volatility
- **Layer 4:** Dynamic Risk Management (10% weight) - ATR-based adaptive stops
### 📊 Visual Dashboard
- **Regime Score:** 0-100 market health indicator
- **Trend Status:** Real-time BULL/BEAR/NONE classification
- **Trend Quality:** Freshness metric (degrades over time)
- **Order Block Status:** Active OB tracking with validation
- **Probability Scores:** Live Long/Short setup probabilities
NBarForwardOdds# N Bar Forward Odds
## Description
Calculates the probability of a closing price exceeding a closing price at a specified interval away from the
current bar. It does this by iterating through a series of intervals (1 to 20) and determining if the closing
price of the current bar is greater than the closing price of the bar at that interval.
## Usage:
Selectable base interval from the input configuration panel is calculated with a value step in a range `1:20` to get the final interval displayed.
Quantum Market Harmonics [QMH]# Quantum Market Harmonics - TradingView Script Description
## 📊 OVERVIEW
Quantum Market Harmonics (QMH) is a comprehensive multi-dimensional trading indicator that combines four independent analytical frameworks to generate high-probability trading signals with quantifiable confidence scores. Unlike simple indicator combinations that display multiple tools side-by-side, QMH synthesizes temporal analysis, inter-market correlations, behavioral psychology, and statistical probabilities into a unified confidence scoring system that requires agreement across all dimensions before generating a confirmed signal.
---
## 🎯 WHAT MAKES THIS SCRIPT ORIGINAL
### The Core Innovation: Weighted Confidence Scoring
Most indicators provide binary signals (buy/sell) or display multiple indicators separately, leaving traders to interpret conflicting information. QMH's originality lies in its weighted confidence scoring system that:
1. **Combines Four Independent Methods** - Each framework (described below) operates independently and contributes points to an overall confidence score
2. **Requires Multi-Dimensional Agreement** - Signals only fire when multiple frameworks align, dramatically reducing false positives
3. **Quantifies Signal Strength** - Every signal includes a numerical confidence rating (0-100%), allowing traders to filter by quality
4. **Adapts to Market Conditions** - Different market regimes activate different component combinations
### Why This Combination is Useful
Traditional approaches suffer from:
- **Single-dimension bias**: RSI shows oversold, but trend is still down
- **Conflicting signals**: MACD says buy, but volume is weak
- **No prioritization**: All signals treated equally regardless of strength
QMH solves these problems by requiring multiple independent confirmations and weighting each component's contribution to the final signal. This multi-dimensional approach mirrors how professional traders analyze markets - not relying on one indicator, but waiting for multiple pieces of evidence to align.
---
## 🔬 THE FOUR ANALYTICAL FRAMEWORKS
### 1. Temporal Fractal Resonance (TFR)
**What It Does:**
Analyzes trend alignment across four different timeframes simultaneously (15-minute, 1-hour, 4-hour, and daily) to identify periods of multi-timeframe synchronization.
**How It Works:**
- Uses `request.security()` with `lookahead=barmerge.lookahead_off` to retrieve confirmed price data from each timeframe
- Calculates "fractal strength" for each timeframe using this formula:
```
Fractal Strength = (Rate of Change / Standard Deviation) × 100
```
This creates a momentum-to-volatility ratio that measures trend strength relative to noise
- Computes a Resonance Index when all four timeframes show the same directional bias
- The index averages the absolute strength values when all timeframes align
**Why This Method:**
Fractal Market Hypothesis suggests that price patterns repeat across different time scales. When trends align from short-term (15m) to long-term (Daily), the probability of trend continuation increases substantially. The momentum/volatility ratio filters out low-conviction moves where volatility dominates direction.
**Contribution to Confidence Score:**
- TFR Bullish = +25 points
- TFR Bearish = +25 points (to bearish confidence)
- No alignment = 0 points
---
### 2. Cross-Asset Quantum Entanglement (CAQE)
**What It Does:**
Analyzes correlation patterns between the current asset and three reference markets (Bitcoin, US Dollar Index, and Volatility Index) to identify both normal correlation behavior and anomalous breakdowns that often precede significant moves.
**How It Works:**
- Retrieves price data from BTC (BINANCE:BTCUSDT), DXY (TVC:DXY), and VIX (TVC:VIX) using confirmed bars
- Calculates Pearson correlation coefficient between the main asset and each reference:
```
Correlation = Covariance(X,Y) / (StdDev(X) × StdDev(Y))
```
- Computes an Intermarket Pressure Index by weighting each reference asset's momentum by its correlation strength:
```
Pressure = (Corr₁ × ROC₁ + Corr₂ × ROC₂ + Corr₃ × ROC₃) / 3
```
- Detects "correlation breakdowns" when average correlation drops below 0.3
**Why This Method:**
Markets don't operate in isolation. Inter-market analysis (developed by John Murphy) recognizes that:
- Crypto assets often correlate with Bitcoin
- Risk assets inversely correlate with VIX (fear gauge)
- Dollar strength affects commodity and crypto prices
When these normal correlations break down, it signals potential regime changes. The term "quantum" reflects the interconnected nature of these relationships - like quantum entanglement where distant particles influence each other.
**Contribution to Confidence Score:**
- CAQE Bullish (positive pressure, stable correlations) = +25 points
- CAQE Bearish (negative pressure, stable correlations) = +25 points (to bearish)
- Correlation breakdown = Warning marker (potential reversal zone)
---
### 3. Adaptive Market Psychology Matrix (AMPM)
**What It Does:**
Classifies the current market emotional state into six distinct categories by analyzing the interaction between momentum (RSI), volume behavior, and volatility acceleration (ATR change).
**How It Works:**
The system evaluates three metrics:
1. **RSI (14-period)**: Measures overbought/oversold conditions
2. **Volume Analysis**: Compares current volume to 20-period average
3. **ATR Rate of Change**: Detects volatility acceleration
Based on these inputs, the market is classified into:
- **Euphoria**: RSI > 80, volume spike present, volatility rising (extreme bullish emotion)
- **Greed**: RSI > 70, normal volume (moderate bullish emotion)
- **Neutral**: RSI 40-60, declining volatility (balanced state)
- **Fear**: RSI 40-60, low volatility (uncertainty without panic)
- **Panic**: RSI < 30, volume spike present, volatility rising (extreme bearish emotion)
- **Despair**: RSI < 20, normal volume (capitulation phase)
**Why This Method:**
Behavioral finance principles (Kahneman, Tversky) show that markets follow predictable emotional cycles. Extreme psychological states often mark reversal points because:
- At Euphoria/Greed peaks, everyone bullish has already bought (no buyers left)
- At Panic/Despair bottoms, everyone bearish has already sold (no sellers left)
AMPM provides contrarian signals at these extremes while respecting trends during Fear and Greed intermediate states.
**Contribution to Confidence Score:**
- Psychology Bullish (Panic/Despair + RSI < 35) = +15 points
- Psychology Bearish (Euphoria/Greed + RSI > 65) = +15 points
- Neutral states = 0 points
---
### 4. Time-Decay Probability Zones (TDPZ)
**What It Does:**
Creates dynamic support and resistance zones based on statistical probability distributions that adapt to changing market volatility, similar to Bollinger Bands but with enhancements for trend environments.
**How It Works:**
- Calculates a 20-period Simple Moving Average as the basis line
- Computes standard deviation of price over the same period
- Creates four probability zones:
- **Extreme Upper**: Basis + 2.5 standard deviations (≈99% probability boundary)
- **Upper Zone**: Basis + 1.5 standard deviations
- **Lower Zone**: Basis - 1.5 standard deviations
- **Extreme Lower**: Basis - 2.5 standard deviations (≈99% probability boundary)
- Dynamically adjusts zone width based on ATR (Average True Range):
```
Adjusted Upper = Upper Zone + (ATR × adjustment_factor)
Adjusted Lower = Lower Zone - (ATR × adjustment_factor)
```
- The adjustment factor increases during high volatility, widening the zones
**Why This Method:**
Traditional support/resistance levels are static and don't account for volatility regimes. TDPZ zones are probability-based and mean-reverting:
- Price has ≈99% probability of staying within extreme zones in normal conditions
- Touches to extreme zones represent statistical outliers (high-probability reversal opportunities)
- Zone expansion/contraction reflects volatility regime changes
- ATR adjustment prevents false signals during unusual volatility
The "time-decay" concept refers to mean reversion - the further price moves from the basis, the higher the probability of eventual return.
**Contribution to Confidence Score:**
- Price in Lower Extreme Zone = +15 points (bullish reversal probability)
- Price in Upper Extreme Zone = +15 points (bearish reversal probability)
- Price near basis = 0 points
---
## 🎯 HOW THE CONFIDENCE SCORING SYSTEM WORKS
### Signal Generation Formula
QMH calculates separate Bullish and Bearish confidence scores each bar:
**Bullish Confidence (0-100%):**
```
Base Score: 20 points
+ TFR Bullish: 25 points (if all 4 timeframes aligned bullish)
+ CAQE Bullish: 25 points (if intermarket pressure positive)
+ AMPM Bullish: 15 points (if Panic/Despair contrarian signal)
+ TDPZ Bullish: 15 points (if price in lower probability zones)
─────────
Maximum Possible: 100 points
```
**Bearish Confidence (0-100%):**
```
Base Score: 20 points
+ TFR Bearish: 25 points (if all 4 timeframes aligned bearish)
+ CAQE Bearish: 25 points (if intermarket pressure negative)
+ AMPM Bearish: 15 points (if Euphoria/Greed contrarian signal)
+ TDPZ Bearish: 15 points (if price in upper probability zones)
─────────
Maximum Possible: 100 points
```
### Confirmed Signal Requirements
A **QBUY** (Quantum Buy) signal generates when:
1. Bullish Confidence ≥ User-defined threshold (default 60%)
2. Bullish Confidence > Bearish Confidence
3. No active sell signal present
A **QSELL** (Quantum Sell) signal generates when:
1. Bearish Confidence ≥ User-defined threshold (default 60%)
2. Bearish Confidence > Bullish Confidence
3. No active buy signal present
### Why This Approach Is Different
**Example Comparison:**
Traditional RSI Strategy:
- RSI < 30 → Buy signal
- Result: May buy into falling knife if trend remains bearish
QMH Approach:
- RSI < 30 → Psychology shows Panic (+15 points)
- But requires additional confirmation:
- Are all timeframes also showing bullish reversal? (+25 points)
- Is intermarket pressure turning positive? (+25 points)
- Is price at a statistical extreme? (+15 points)
- Only when total ≥ 60 points does a QBUY signal fire
This multi-layer confirmation dramatically reduces false signals while maintaining sensitivity to genuine opportunities.
---
## 🚫 NO REPAINT GUARANTEE
**QMH is designed to be 100% repaint-free**, which is critical for honest backtesting and reliable live trading.
### Technical Implementation:
1. **All Multi-Timeframe Data Uses Confirmed Bars**
```pinescript
tf1_close = request.security(syminfo.tickerid, "15", close , lookahead=barmerge.lookahead_off)
```
Using `close ` instead of `close ` ensures we only reference the previous confirmed bar, not the current forming bar.
2. **Lookahead Prevention**
```pinescript
lookahead=barmerge.lookahead_off
```
This parameter prevents the function from accessing future data that wouldn't be available in real-time.
3. **Signal Timing**
Signals appear on the bar AFTER all conditions are met, not retroactively on the bar where conditions first appeared.
### What This Means for Users:
- **Backtest Accuracy**: Historical signals match exactly what you would have seen in real-time
- **No Disappearing Signals**: Once a signal appears, it stays (though price may move against it)
- **Honest Performance**: Results reflect true predictive power, not hindsight optimization
- **Live Trading Reliability**: Alerts fire at the same time signals appear on the chart
The dashboard displays "✓ NO REPAINT" to confirm this guarantee.
---
## 📖 HOW TO USE THIS INDICATOR
### Basic Trading Strategy
**For Trend Followers:**
1. **Wait for Signal Confirmation**
- QBUY label appears below a bar = Confirmed bullish entry opportunity
- QSELL label appears above a bar = Confirmed bearish entry opportunity
2. **Check Confidence Score**
- 60-70%: Moderate confidence (consider smaller position size)
- 70-85%: High confidence (standard position size)
- 85-100%: Very high confidence (consider larger position size)
3. **Enter Trade**
- Long entry: Market or limit order near signal bar
- Short entry: Market or limit order near signal bar
4. **Set Targets Using Probability Zones**
- Long trades: Target the adjusted upper zone (lime line)
- Short trades: Target the adjusted lower zone (red line)
- Alternatively, target the basis line (yellow) for conservative exits
5. **Set Stop Loss**
- Long trades: Below recent swing low minus 1 ATR
- Short trades: Above recent swing high plus 1 ATR
**For Mean Reversion Traders:**
1. **Wait for Extreme Zones**
- Price touches extreme lower zone (dotted red line below)
- Price touches extreme upper zone (dotted lime line above)
2. **Confirm with Psychology**
- At lower extreme: Look for Panic or Despair state
- At upper extreme: Look for Euphoria or Greed state
3. **Wait for Confidence Build**
- Monitor dashboard until confidence exceeds threshold
- Requires patience - extreme touches don't always reverse immediately
4. **Enter Reversal**
- Target: Return to basis line (yellow SMA 20)
- Stop: Beyond the extreme zone
**For Position Traders (Longer Timeframes):**
1. **Use Daily Timeframe**
- Set chart to daily for longer-term signals
- Signals will be less frequent but higher quality
2. **Require High Confidence**
- Filter setting: Min Confidence Score 80%+
- Only take the strongest multi-dimensional setups
3. **Confirm with Resonance Background**
- Green tinted background = All timeframes bullish aligned
- Red tinted background = All timeframes bearish aligned
- Only enter when background tint matches signal direction
4. **Hold for Major Targets**
- Long trades: Hold until extreme upper zone or opposite signal
- Short trades: Hold until extreme lower zone or opposite signal
---
## 📊 DASHBOARD INTERPRETATION
The QMH Dashboard (top-right corner) provides real-time market analysis across all four dimensions:
### Dashboard Elements:
1. **✓ NO REPAINT**
- Green confirmation that signals don't repaint
- Always visible to remind users of signal integrity
2. **SIGNAL: BULL/BEAR XX%**
- Shows dominant direction (whichever confidence is higher)
- Displays current confidence percentage
- Background color intensity reflects confidence level
3. **Psychology: **
- Current market emotional state
- Color coded:
- Orange = Euphoria (extreme bullish emotion)
- Yellow = Greed (moderate bullish emotion)
- Gray = Neutral (balanced state)
- Purple = Fear (uncertainty)
- Red = Panic (extreme bearish emotion)
- Dark red = Despair (capitulation)
4. **Resonance: **
- Multi-timeframe alignment strength
- Positive = All timeframes bullish aligned
- Negative = All timeframes bearish aligned
- Near zero = Timeframes not synchronized
- Emoji indicator: 🔥 (bullish resonance) ❄️ (bearish resonance)
5. **Intermarket: **
- Cross-asset pressure measurement
- Positive = BTC/DXY/VIX correlations supporting upside
- Negative = Correlations supporting downside
- Warning ⚠️ if correlation breakdown detected
6. **RSI: **
- Current RSI(14) reading
- Background colors: Red (>70 overbought), Green (<30 oversold)
- Status: OB (overbought), OS (oversold), or • (neutral)
7. **Status: READY BUY / READY SELL / WAIT**
- Quick trade readiness indicator
- READY BUY: Confidence ≥ threshold, bias bullish
- READY SELL: Confidence ≥ threshold, bias bearish
- WAIT: Confidence below threshold
### How to Use Dashboard:
**Before Entering a Trade:**
- Verify Status shows READY (not WAIT)
- Check that Resonance matches signal direction
- Confirm Psychology isn't contradicting (e.g., buying during Euphoria)
- Note Intermarket value - breakdowns (⚠️) suggest caution
**During a Trade:**
- Monitor Psychology shifts (e.g., from Fear to Greed in a long)
- Watch for Resonance changes that could signal exit
- Check for Intermarket breakdown warnings
---
## ⚙️ CUSTOMIZATION SETTINGS
### TFR Settings (Temporal Fractal Resonance)
- **Enable/Disable**: Turn TFR analysis on/off
- **Fractal Sensitivity** (5-50, default 14):
- Lower values = More responsive to short-term changes
- Higher values = More stable, slower to react
- Recommendation: 14 for balanced, 7 for scalping, 21 for position trading
### CAQE Settings (Cross-Asset Quantum Entanglement)
- **Enable/Disable**: Turn CAQE analysis on/off
- **Asset 1** (default BTC): Reference asset for correlation analysis
- **Asset 2** (default DXY): Second reference asset
- **Asset 3** (default VIX): Third reference asset
- **Correlation Length** (10-100, default 20):
- Lower values = More sensitive to recent correlation changes
- Higher values = More stable correlation measurements
- Recommendation: 20 for most assets, 50 for less volatile markets
### Psychology Settings (Adaptive Market Psychology Matrix)
- **Enable/Disable**: Turn AMPM analysis on/off
- **Volume Spike Threshold** (1.0-5.0x, default 2.0):
- Lower values = Detect smaller volume increases as spikes
- Higher values = Only flag major volume surges
- Recommendation: 2.0 for stocks, 1.5 for crypto
### Probability Settings (Time-Decay Probability Zones)
- **Enable/Disable**: Turn TDPZ visualization on/off
- **Probability Lookback** (20-200, default 50):
- Lower values = Zones adapt faster to recent price action
- Higher values = Zones based on longer statistical history
- Recommendation: 50 for most uses, 100 for position trading
### Filter Settings
- **Min Confidence Score** (40-95%, default 60%):
- Lower threshold = More signals, more false positives
- Higher threshold = Fewer signals, higher quality
- Recommendation: 60% for active trading, 75% for selective trading
### Visual Settings
- **Show Entry Signals**: Toggle QBUY/QSELL labels on chart
- **Show Probability Zones**: Toggle zone visualization
- **Show Psychology State**: Toggle dashboard display
---
## 🔔 ALERT CONFIGURATION
QMH includes four alert conditions that can be configured via TradingView's alert system:
### Available Alerts:
1. **Quantum Buy Signal**
- Fires when: Confirmed QBUY signal generates
- Message includes: Confidence percentage
- Use for: Entry notifications
2. **Quantum Sell Signal**
- Fires when: Confirmed QSELL signal generates
- Message includes: Confidence percentage
- Use for: Entry notifications or exit warnings
3. **Market Panic**
- Fires when: Psychology state reaches Panic
- Use for: Contrarian opportunity alerts
4. **Market Euphoria**
- Fires when: Psychology state reaches Euphoria
- Use for: Reversal warning alerts
### How to Set Alerts:
1. Right-click on chart → "Add Alert"
2. Condition: Select "Quantum Market Harmonics"
3. Choose alert type from dropdown
4. Configure expiration, frequency, and notification method
5. Create alert
**Recommendation**: Set alerts for Quantum Buy/Sell signals with "Once Per Bar Close" frequency to avoid intra-bar false triggers.
---
## 💡 BEST PRACTICES
### For All Users:
1. **Backtest First**
- Test on your specific market and timeframe before live trading
- Different assets may perform better with different confidence thresholds
- Verify that the No Repaint guarantee works as described
2. **Paper Trade**
- Practice with signals on a demo account first
- Understand typical signal frequency for your timeframe
- Get comfortable with the dashboard interpretation
3. **Risk Management**
- Never risk more than 1-2% of capital per trade
- Use proper stop losses (not just mental stops)
- Position size based on confidence score (larger size at higher confidence)
4. **Consider Context**
- QMH signals work best in clear trends or at extremes
- During tight consolidation, false signals increase
- Major news events can invalidate technical signals
### Optimal Use Cases:
**QMH Works Best When:**
- ✅ Markets are trending (up or down)
- ✅ Volatility is normal to elevated
- ✅ Price reaches probability zone extremes
- ✅ Multiple timeframes align
- ✅ Clear inter-market relationships exist
**QMH Is Less Effective When:**
- ❌ Extremely low volatility (zones contract too much)
- ❌ Sideways choppy markets (conflicting timeframes)
- ❌ Flash crashes or news events (correlations break down)
- ❌ Very illiquid assets (irregular price action)
### Session Considerations:
- **24/7 Markets (Crypto)**: Works on all sessions, but signals may be more reliable during high-volume periods (US/European trading hours)
- **Forex**: Best during London/New York overlap when volume is highest
- **Stocks**: Most reliable during regular trading hours (not pre-market/after-hours)
---
## ⚠️ LIMITATIONS AND RISKS
### This Indicator Cannot:
- **Predict Black Swan Events**: Sudden unexpected events invalidate technical analysis
- **Guarantee Profits**: No indicator is 100% accurate; losses will occur
- **Replace Risk Management**: Always use stop losses and proper position sizing
- **Account for Fundamental Changes**: Company news, economic data, etc. can override technical signals
- **Work in All Market Conditions**: Less effective during extreme low volatility or major news events
### Known Limitations:
1. **Multi-Timeframe Lag**: Uses confirmed bars (`close `), so signals appear one bar after conditions met
2. **Correlation Dependency**: CAQE requires sufficient history; may be less reliable on newly listed assets
3. **Computational Load**: Multiple `request.security()` calls may cause slower performance on older devices
4. **Repaint of Dashboard**: Dashboard updates every bar (by design), but signals themselves don't repaint
### Risk Warnings:
- Past performance doesn't guarantee future results
- Backtesting results may not reflect actual trading results due to slippage, commissions, and execution delays
- Different markets and timeframes may produce different results
- The indicator should be used as a tool, not as a standalone trading system
- Always combine with your own analysis, risk management, and trading plan
---
## 🎓 EDUCATIONAL CONCEPTS
This indicator synthesizes several established financial theories and technical analysis concepts:
### Academic Foundations:
1. **Fractal Market Hypothesis** (Edgar Peters)
- Markets exhibit self-similar patterns across time scales
- Implemented via multi-timeframe resonance analysis
2. **Behavioral Finance** (Kahneman & Tversky)
- Investor psychology drives market inefficiencies
- Implemented via market psychology state classification
3. **Intermarket Analysis** (John Murphy)
- Asset classes correlate and influence each other predictably
- Implemented via cross-asset correlation monitoring
4. **Mean Reversion** (Statistical Arbitrage)
- Prices tend to revert to statistical norms
- Implemented via probability zones and standard deviation bands
5. **Multi-Timeframe Analysis** (Technical Analysis Standard)
- Higher timeframe trends dominate lower timeframe noise
- Implemented via fractal resonance scoring
### Learning Resources:
To better understand the concepts behind QMH:
- Read "Intermarket Analysis" by John Murphy (for CAQE concepts)
- Study "Thinking, Fast and Slow" by Daniel Kahneman (for psychology concepts)
- Review "Fractal Market Analysis" by Edgar Peters (for TFR concepts)
- Learn about Bollinger Bands (for TDPZ foundation)
---
## 🔄 VERSION HISTORY AND UPDATES
**Current Version: 1.0**
This is the initial public release. Future updates will be published using TradingView's Update feature (not as separate publications). Planned improvements may include:
- Additional reference assets for CAQE
- Optional machine learning-based weight optimization
- Customizable psychology state definitions
- Alternative probability zone calculations
- Performance metrics tracking
Check the "Updates" tab on the script page for version history.
---
## 📞 SUPPORT AND FEEDBACK
### How to Get Help:
1. **Read This Description First**: Most questions are answered in the detailed sections above
2. **Check Comments**: Other users may have asked similar questions
3. **Post Comments**: For general questions visible to the community
4. **Use TradingView Messaging**: For private inquiries (if available)
### Providing Useful Feedback:
When reporting issues or suggesting improvements:
- Specify your asset, timeframe, and settings
- Include a screenshot if relevant
- Describe expected vs. actual behavior
- Check if issue persists with default settings
### Continuous Improvement:
This indicator will evolve based on user feedback and market testing. Constructive suggestions for improvements are always welcome.
---
## ⚖️ DISCLAIMER
This indicator is provided for **educational and informational purposes only**. It does **not constitute financial advice, investment advice, trading advice, or any other type of advice**.
**Important Disclaimers:**
- You should **not** rely solely on this indicator to make trading decisions
- Always conduct your own research and due diligence
- Past performance is not indicative of future results
- Trading and investing involve substantial risk of loss
- Only trade with capital you can afford to lose
- Consider consulting with a licensed financial advisor before trading
- The author is not responsible for any trading losses incurred using this indicator
**By using this indicator, you acknowledge:**
- You understand the risks of trading
- You take full responsibility for your trading decisions
- You will use proper risk management techniques
- You will not hold the author liable for any losses
---
## 🙏 ACKNOWLEDGMENTS
This indicator builds upon the collective knowledge of the technical analysis and trading community. While the specific implementation and combination are original, the underlying concepts draw from:
- The Pine Script community on TradingView
- Academic research in behavioral finance and market microstructure
- Classical technical analysis methods developed over decades
- Open-source indicators that demonstrate best practices in Pine Script coding
Special thanks to TradingView for providing the platform and Pine Script language that make indicators like this possible.
---
## 📚 ADDITIONAL RESOURCES
**Pine Script Documentation:**
- Official Pine Script Manual: www.tradingview.com
**Related Concepts to Study:**
- Multi-timeframe analysis techniques
- Correlation analysis in financial markets
- Behavioral finance principles
- Mean reversion strategies
- Bollinger Bands methodology
**Recommended TradingView Tools:**
- Strategy Tester: To backtest signal performance
- Bar Replay: To see how signals develop in real-time
- Alert System: To receive notifications of new signals
---
**Thank you for using Quantum Market Harmonics. Trade safely and responsibly.**
Markov Chain Regime & Next‑Bar Probability Forecast✨ What it is
A regime-aware, math-driven panel that forecasts the odds for the very next candle. It shows:
• P(next r > 0)
• P(next r > +θ)
• P(next r < −θ)
• A 4-bucket split of next-bar outcomes (>+θ | 0..+θ | −θ..0 | <−θ)
• Next-regime probabilities: Calm | Neutral | Volatile
🧠 Why the math is strong
• Markov regimes: Markets cluster in volatility “moods.” We learn a 3-state regime S∈{Calm, Neutral, Volatile} with a transition matrix A, where A = P(Sₜ₊₁=j | Sₜ=i).
• Condition on the future state: We estimate event odds given the next regime j—
q_pos(j)=P(rₜ₊₁>0 | Sₜ₊₁=j), q_gt(j)=P(rₜ₊₁>+θ | Sₜ₊₁=j), q_lt(j)=P(rₜ₊₁<−θ | Sₜ₊₁=j)—
and mix them with transitions from the current (or frozen) state sNow:
P(event) = Σⱼ A · q(event | j).
This mixture-of-regimes view (HMM-style one-step prediction) ties next-bar outcomes to where volatility is likely headed.
• Statistical hygiene: Laplace/Beta smoothing, minimum-sample gating, and unconditional fallbacks keep estimates stable. Heavy computations run on confirmed bars; “Freeze at close” avoids intrabar flicker.
📊 What each value means
• Regime label & background: 🟩 Calm, 🟧 Neutral, 🟥 Volatile — quick read of market context.
• P(next r > 0): Directional tilt for the very next bar.
• P(next r > +θ): Odds of an outsized positive move beyond θ.
• P(next r < −θ): Odds of an outsized negative move beyond −θ.
• Partition row: Distributes next-bar probability across four intuitive buckets; they ≈ sum to 100%.
• Next Regime Probs: Likelihood of switching to Calm/Neutral/Volatile on the next bar (row of A for the current/frozen state).
• Samples row: How many next-bar samples support each next-state estimate (a confidence cue).
• Smoothing α: The Laplace prior used to stabilize binary event rates.
⚙️ Inputs you control
• Returns: Log (default) or %
• Include Volume (z-score) + lookback
• Include Range (HL/PrevClose)
• Rolling window N (transitions & estimates)
• θ as percent (e.g., 0.5%)
• Freeze forecast at last close (recommended)
• Display toggles (plots, partition, samples)
🎯 How to use it
• Volatility awareness & sizing: Rising P(next regime = Volatile) → consider smaller size, wider stops, or skipping marginal entries.
• Breakout preparation: Elevated P(next r > +θ) highlights environments where range expansion is more likely; pair with your setup/trigger.
• Defense for mean-reversion: If P(next r < −θ) lifts while you’re late long (or P(next r > +θ) lifts while late short), tighten risk or wait for better context.
• Calibration tip: Start θ near your market’s typical bar size; adjust until “>+θ” flags truly meaningful moves for your timeframe.
📝 Method notes & limits
Activity features (|r|, volume z, range) are standardized; only positive z’s feed the composite activity score. Estimates adapt to instrument/timeframe; rare regimes or small windows increase variance (hence smoothing, sample gating, fallbacks). This is a context/forecast tool, not a standalone signal—combine with your entry/exit rules and risk management.
🧩 Strategies too
We also develop full strategy versions that use these probabilities for entries, filters, and position sizing. Like this publication if you’d like us to release the strategy edition next.
⚠️ Disclaimer
Educational use only. Not financial advice. Markets involve risk. Past performance does not guarantee future results.
First Passage Time - Distribution AnalysisThe First Passage Time (FPT) Distribution Analysis indicator is a sophisticated probabilistic tool that answers one of the most critical questions in trading: "How long will it take for price to reach my target, and what are the odds of getting there first?"
Unlike traditional technical indicators that focus on what might happen, this indicator tells you when it's likely to happen.
Mathematical Foundation: First Passage Time Theory
What is First Passage Time?
First Passage Time (FPT) is a concept in stochastic processes that measures the time it takes for a random process to reach a specific threshold for the first time. Originally developed in physics and mathematics, FPT has applications in:
Quantitative Finance: Option pricing, risk management, and algorithmic trading
Neuroscience: Modeling neural firing patterns
Biology: Population dynamics and disease spread
Engineering: Reliability analysis and failure prediction
The Mathematics Behind It
This indicator uses Geometric Brownian Motion (GBM), the same stochastic model used in the Black-Scholes option pricing formula:
dS = μS dt + σS dW
Where:
S = Asset price
μ = Drift (trend component)
σ = Volatility (uncertainty component)
dW = Wiener process (random walk)
Through Monte Carlo simulation, the indicator runs 1,000+ price path simulations to statistically determine:
When each threshold (+X% or -X%) is likely to be hit
Which threshold is hit first (directional bias)
How often each scenario occurs (probability distribution)
🎯 How This Indicator Works
Core Algorithm Workflow:
Calculate Historical Statistics
Measures recent price volatility (standard deviation of log returns)
Calculates drift (average directional movement)
Annualizes these metrics for meaningful comparison
Run Monte Carlo Simulations
Generates 1,000+ random price paths based on historical behavior
Tracks when each path hits the upside (+X%) or downside (-X%) threshold
Records which threshold was hit first in each simulation
Aggregate Statistical Results
Calculates percentile distributions (10th, 25th, 50th, 75th, 90th)
Computes "first hit" probabilities (upside vs downside)
Determines average and median time-to-target
Visual Representation
Displays thresholds as horizontal lines
Shows gradient risk zones (purple-to-blue)
Provides comprehensive statistics table
📈 Use Cases
1. Options Trading
Selling Options: Determine if your strike price is likely to be hit before expiration
Buying Options: Estimate probability of reaching profit targets within your time window
Time Decay Management: Compare expected time-to-target vs theta decay
Example: You're considering selling a 30-day call option 5% out of the money. The indicator shows there's a 72% chance price hits +5% within 12 days. This tells you the trade has high assignment risk.
2. Swing Trading
Entry Timing: Wait for higher probability setups when directional bias is strong
Target Setting: Use median time-to-target to set realistic profit expectations
Stop Loss Placement: Understand probability of hitting your stop before target
Example: The indicator shows 85% upside probability with median time of 3.2 days. You can confidently enter long positions with appropriate position sizing.
3. Risk Management
Position Sizing: Larger positions when probability heavily favors one direction
Portfolio Allocation: Reduce exposure when probabilities are near 50/50 (high uncertainty)
Hedge Timing: Know when to add protective positions based on downside probability
Example: Indicator shows 55% upside vs 45% downside—nearly neutral. This signals high uncertainty, suggesting reduced position size or wait for better setup.
4. Market Regime Detection
Trending Markets: High directional bias (70%+ one direction)
Range-bound Markets: Balanced probabilities (45-55% both directions)
Volatility Regimes: Compare actual vs theoretical minimum time
Example: Consistent 90%+ bullish bias across multiple timeframes confirms strong uptrend—stay long and avoid counter-trend trades.
First Hit Rate (Most Important!)
Shows which threshold is likely to be hit FIRST:
Upside %: Probability of hitting upside target before downside
Downside %: Probability of hitting downside target before upside
These always sum to 100%
⚠️ Warning: If you see "Low Hit Rate" warning, increase this parameter!
Advanced Parameters
Drift Mode
Allows you to explore different scenarios:
Historical: Uses actual recent trend (default—most realistic)
Zero (Neutral): Assumes no trend, only volatility (symmetric probabilities)
50% Reduced: Dampens trend effect (conservative scenario)
Use Case: Switch to "Zero (Neutral)" to see what happens in a pure volatility environment, useful for range-bound markets.
Distribution Type
Percentile: Shows 10%, 25%, 50%, 75%, 90% levels (recommended for most users)
Sigma: Shows standard deviation levels (1σ, 2σ)—useful for statistical analysis
⚠️ Important Limitations & Best Practices
Limitations
Assumes GBM: Real markets have fat tails, jumps, and regime changes not captured by GBM
Historical Parameters: Uses recent volatility/drift—may not predict regime shifts
No Fundamental Events: Cannot predict earnings, news, or macro shocks
Computational: Runs only on last bar—doesn't give historical signals
Remember: Probabilities are not certainties. Use this indicator as part of a comprehensive trading plan with proper risk management.
Created by: Henrique Centieiro. feedback is more than welcome!
Hummingbird Probability Mapping IndicatorHummingbird Probability Mapping Indicator - A nature inspired indicator that utilizes combinations of the following trend patterns and projects a probability mapping with greater than 70% accuracy based on real-time analysis.
EMA Trend
MACD
RSI
VWAP Spread
Burst
Squeeze
Volatility (ATRp)
Qi Dass
Institutional Levels (CNN) - [PhenLabs]📊Institutional Levels (Convolutional Neural Network-inspired)
Version : PineScript™v6
📌Description
The CNN-IL Institutional Levels indicator represents a breakthrough in automated zone detection technology, combining convolutional neural network principles with advanced statistical modeling. This sophisticated tool identifies high-probability institutional trading zones by analyzing pivot patterns, volume dynamics, and price behavior using machine learning algorithms.
The indicator employs a proprietary 9-factor logistic regression model that calculates real-time reaction probabilities for each detected zone. By incorporating CNN-inspired filtering techniques and dynamic zone management, it provides traders with unprecedented accuracy in identifying where institutional money is likely to react to price action.
🚀Points of Innovation
● CNN-Inspired Pivot Analysis - Advanced binning system using convolutional neural network principles for superior pattern recognition
● Real-Time Probability Engine - Live reaction probability calculations using 9-factor logistic regression model
● Dynamic Zone Intelligence - Automatic zone merging using Intersection over Union (IoU) algorithms
● Volume-Weighted Scoring - Time-of-day volume Z-score analysis for enhanced zone strength assessment
● Adaptive Decay System - Intelligent zone lifecycle management based on touch frequency and recency
● Multi-Filter Architecture - Optional gradient, smoothing, and Difference of Gaussians (DoG) convolution filters
🔧Core Components
● Pivot Detection Engine - Advanced pivot identification with configurable left/right bars and ATR-normalized strength calculations
● Neural Network Binning - Price level clustering using CNN-inspired algorithms with ATR-based bin sizing
● Logistic Regression Model - 9-factor probability calculation including distance, width, volume, VWAP deviation, and trend analysis
● Zone Management System - Intelligent creation, merging, and decay algorithms for optimal zone lifecycle control
● Visualization Layer - Dynamic line drawing with opacity-based scoring and optional zone fills
🔥Key Features
● High-Probability Zone Detection - Automatically identifies institutional levels with reaction probabilities above configurable thresholds
● Real-Time Probability Scoring - Live calculation of zone reaction likelihood using advanced statistical modeling
● Session-Aware Analysis - Optional filtering to specific trading sessions for enhanced accuracy during active market hours
● Customizable Parameters - Full control over lookback periods, zone sensitivity, merge thresholds, and probability models
● Performance Optimized - Efficient processing with controlled update frequencies and pivot processing limits
● Non-Repainting Mode - Strict mode available for backtesting accuracy and live trading reliability
🎨Visualization
● Dynamic Zone Lines - Color-coded support and resistance levels with opacity reflecting zone strength and confidence scores
● Probability Labels - Real-time display of reaction probabilities, touch counts, and historical hit rates for active zones
● Zone Fills - Optional semi-transparent zone highlighting for enhanced visual clarity and immediate pattern recognition
● Adaptive Styling - Automatic color and opacity adjustments based on zone scoring and statistical significance
📖Usage Guidelines
● Lookback Bars - Default 500, Range 100-1000, Controls the historical data window for pivot analysis and zone calculation
● Pivot Left/Right - Default 3, Range 1-10, Defines the pivot detection sensitivity and confirmation requirements
● Bin Size ATR units - Default 0.25, Range 0.1-2.0, Controls price level clustering granularity for zone creation
● Base Zone Half-Width ATR units - Default 0.25, Range 0.1-1.0, Sets the minimum zone width in ATR units for institutional level boundaries
● Zone Merge IoU Threshold - Default 0.5, Range 0.1-0.9, Intersection over Union threshold for automatic zone merging algorithms
● Max Active Zones - Default 5, Range 3-20, Maximum number of zones displayed simultaneously to prevent chart clutter
● Probability Threshold for Labels - Default 0.6, Range 0.3-0.9, Minimum reaction probability required for zone label display and alerts
● Distance Weight w1 - Controls influence of price distance from zone center on reaction probability
● Width Weight w2 - Adjusts impact of zone width on probability calculations
● Volume Weight w3 - Modifies volume Z-score influence on zone strength assessment
● VWAP Weight w4 - Controls VWAP deviation impact on institutional level significance
● Touch Count Weight w5 - Adjusts influence of historical zone interactions on probability scoring
● Hit Rate Weight w6 - Controls prior success rate impact on future reaction likelihood predictions
● Wick Penetration Weight w7 - Modifies wick penetration analysis influence on probability calculations
● Trend Weight w8 - Adjusts trend context impact using ADX analysis for directional bias assessment
✅Best Use Cases
● Swing Trading Entries - Enter positions at high-probability institutional zones with 60%+ reaction scores
● Scalping Opportunities - Quick entries and exits around frequently tested institutional levels
● Risk Management - Use zones as dynamic stop-loss and take-profit levels based on institutional behavior
● Market Structure Analysis - Identify key institutional levels that define current market structure and sentiment
● Confluence Trading - Combine with other technical indicators for high-probability trade setups
● Session-Based Strategies - Focus analysis during high-volume sessions for maximum effectiveness
⚠️Limitations
● Historical Pattern Dependency - Algorithm effectiveness relies on historical patterns that may not repeat in changing market conditions
● Computational Intensity - Complex calculations may impact chart performance on lower-end devices or with multiple indicators
● Probability Estimates - Reaction probabilities are statistical estimates and do not guarantee actual market outcomes
● Session Sensitivity - Performance may vary significantly between different market sessions and volatility regimes
● Parameter Sensitivity - Results can be highly dependent on input parameters requiring optimization for different instruments
💡What Makes This Unique
● CNN Architecture - First indicator to apply convolutional neural network principles to institutional-level detection
● Real-Time ML Scoring - Live machine learning probability calculations for each zone interaction
● Advanced Zone Management - Sophisticated algorithms for zone lifecycle management and automatic optimization
● Statistical Rigor - Comprehensive 9-factor logistic regression model with extensive backtesting validation
● Performance Optimization - Efficient processing algorithms designed for real-time trading applications
🔬How It Works
● Multi-timeframe pivot identification - Uses configurable sensitivity parameters for advanced pivot detection
● ATR-normalized strength calculations - Standardizes pivot significance across different volatility regimes
● Volume Z-score integration - Enhanced pivot weighting based on time-of-day volume patterns
● Price level clustering - Neural network binning algorithms with ATR-based sizing for zone creation
● Recency decay applications - Weights recent pivots more heavily than historical data for relevance
● Statistical filtering - Eliminates low-significance price levels and reduces market noise
● Dynamic zone generation - Creates zones from statistically significant pivot clusters with minimum support thresholds
● IoU-based merging algorithms - Combines overlapping zones while maintaining accuracy using Intersection over Union
● Adaptive decay systems - Automatic removal of outdated or low-performing zones for optimal performance
● 9-factor logistic regression - Incorporates distance, width, volume, VWAP, touch history, and trend analysis
● Real-time scoring updates - Zone interaction calculations with configurable threshold filtering
● Optional CNN filters - Gradient detection, smoothing, and Difference of Gaussians processing for enhanced accuracy
💡Note
This indicator represents advanced quantitative analysis and should be used by traders familiar with statistical modeling concepts. The probability scores are mathematical estimates based on historical patterns and should be combined with proper risk management and additional technical analysis for optimal trading decisions.
Mean Reversion Probability Zones [BigBeluga]🔵 OVERVIEW
The Mean Reversion Probability Zones indicator measures the likelihood of price reverting back toward its mean . By analyzing oscillator dynamics (RSI, MFI, or Stochastic), it calculates probability zones both above and below the oscillator. These zones are visualized as histograms, colored regions on the main chart, and a compact dashboard, helping traders spot when the market is statistically stretched and more likely to revert.
🔵 CONCEPTS
Mean Reversion : The tendency of price to return to its average after significant extensions.
Oscillator-Based Analysis : Uses RSI, MFI, or Stochastic as the base signal for detecting overextension.
Probability Model : The probability of reversion is computed using three factors:
Whether the oscillator is rising or declining.
Whether the oscillator is above or below user-defined thresholds.
The oscillator’s actual value (distance from equilibrium).
Dual-Zone Output :
Upper histogram = probability of downward mean reversion.
Lower histogram = probability of upward mean reversion.
Historical Extremes : The dashboard highlights the recent maximum probability values for both upward and downward scenarios.
🔵 FEATURES
Oscillator Choice : Switch between RSI, MFI, and Stochastic.
Customizable Zones : User-defined upper/lower thresholds with independent colors.
Probability Histograms :
Above oscillator → down reversion probability.
Below oscillator → up reversion probability.
Colored Gradient Zones on Chart : Visual overlays showing where mean reversion probabilities are strongest.
Probability Labels : Percentages displayed next to histogram values for clarity.
Dashboard : Compact table in the corner showing the recent maximum probabilities for both upward and downward mean reversion.
Overlay Compatibility : Works in both chart pane and sub-pane with oscillators.
🔵 HOW TO USE
Set Oscillator : Choose RSI, MFI, or Stochastic depending on your strategy style.
Adjust Zones : Define upper/lower bounds for when oscillator values indicate strong overbought/oversold conditions.
Interpret Histograms :
Orange (upper) histogram → higher chance of a pullback/downward mean reversion.
Green (lower) histogram → higher chance of upward reversion/bounce.
Watch Gradient Zones : On the main chart, shaded areas highlight where probability of mean reversion is elevated.
Consult Dashboard : Use the “Recent MAX” values to understand how strong recent reversion probabilities have been in either direction.
Confluence Strategy : Combine with support/resistance, order flow, or trend filters to avoid counter-trend trades.
🔵 CONCLUSION
The Mean Reversion Probability Zones provides traders with an advanced way to quantify and visualize mean reversion opportunities. By blending oscillator momentum, threshold logic, and probability calculations, it highlights when markets are statistically stretched and primed for reversal. Whether you are a contrarian trader or simply looking for exhaustion signals to fade, this tool helps bring structure and clarity to mean reversion setups.
BUY & SELL Probability (M5..D1) - MTFMTF Probability Indicator (M5 to D1)
Indicator — Dual Histogram with Buy/Sell Labels
This indicator is designed to provide a probabilistic bias for bullish or bearish conditions by combining three different analytical components across multiple timeframes. The goal is to reduce noise from single-indicator signals and instead highlight confluence where trend, momentum, and strength agree.
Why this combination is useful
- EMA(200) Trend Filter: Identifies whether price is trading above or below a widely used long-term moving average.
- MACD Momentum: Detects short-term directional momentum through line crossovers.
- ADX Strength: Measures how strong the trend is, preventing signals in weak or flat markets.
By combining these, the indicator avoids situations where one tool signals a trade but others do not, helping to filter out low-probability setups.
How it works
- Each timeframe (M5, M15, H1, H4, D1) generates its own trend, momentum, and strength score.
- Scores are weighted according to user-defined importance and then aggregated into a single probability.
- Proximity to recent support and resistance levels can adjust the final score, accounting for nearby barriers.
- The final probability is displayed as:
- Histogram (subwindow): Green bars for bullish probability >50%, red bars for bearish <50%.
- On-chart labels: Showing exact buy/sell percentages on the last bar for quick reference.
Inputs
- EMA length (default 200), MACD settings, ADX period.
- Weights for each timeframe and component (trend, momentum, strength).
- Optional boost for the chart’s current timeframe.
- Smoothing length for probability values.
- Lookback period for support/resistance adjustment.
How to use it
- A green histogram above zero indicates bullish probability >50%.
- A red histogram below zero indicates bearish probability >50%.
- Neutral readings near 50% show low confluence and may be best avoided.
- Users can adjust weights to emphasize higher or lower timeframes, depending on their trading style.
Notes
- This script does not guarantee profitable trades.
- Best used together with price action, volume, or additional confirmation tools.
- Signals are calculated only on closed bars to avoid repainting.
- For testing and learning purposes — not financial advice.
