ULTIMATE ORDER FLOW SYSTEM🔥 ULTIMATE ORDER FLOW SYSTEM
Overview
This comprehensive order flow analysis tool combines **Volume Profile**, **Cumulative Delta**, and **Large Order Detection** to identify high-probability trading setups. The script analyzes institutional order flow patterns and volume distribution to pinpoint key levels where price is likely to react.
📊 Core Components & Methodology
🔥 ULTIMATE ORDER FLOW SYSTEM
Overview
This comprehensive order flow analysis tool combines Volume Profile, Cumulative Delta, and Large Order Detection to identify high-probability trading setups. The script analyzes institutional order flow patterns and volume distribution to pinpoint key levels where price is likely to react.
________________________________________
📊 Core Components & Methodology
1. Volume Profile Analysis
The script constructs a horizontal volume profile by:
• Dividing the price range into configurable rows (default: 20)
• Accumulating volume at each price level over a lookback period (default: 50 bars)
• Separating buy volume (green bars close > open) from sell volume (red bars)
• Identifying three critical levels:
o POC (Point of Control): Price level with highest traded volume - acts as a strong magnet
o VAH/VAL (Value Area High/Low): Contains 70% of total volume - defines fair value zone
o HVN (High Volume Nodes): Resistance zones where institutions accumulated positions
o LVN (Low Volume Nodes): Thin zones that price moves through quickly - ideal targets
Why This Matters: Institutional traders leave footprints through volume. HVN zones show where large players defended levels, making them reliable support/resistance.
________________________________________
2. Cumulative Delta (Order Flow)
Tracks the running total of buying vs selling pressure:
• Bar Delta: Difference between buy and sell volume per candle
• Cumulative Delta: Sum of all bar deltas - shows net directional pressure
• Delta Moving Average: Smoothed delta (20-period) to identify trend
• Delta Divergences:
o Bullish: Price makes lower low, but delta makes higher low (absorption at bottom)
o Bearish: Price makes higher high, but delta makes lower high (exhaustion at top)
How It Works: When cumulative delta trends up while price consolidates, it signals accumulation. Delta divergences reveal when smart money is positioned opposite to retail expectations.
________________________________________
3. Large Order Detection
Identifies institutional-sized orders in real-time:
• Compares current bar volume to 20-period moving average
• Flags orders exceeding 2.5x average volume (configurable multiplier)
• Distinguishes bullish (green circles below) vs bearish (red circles above) large orders
Rationale: Sudden volume spikes at key levels indicate institutional participation - the "fuel" needed for breakouts or reversals.
________________________________________
🎯 Trading Signal Logic
Combined Setup Criteria
The script generates SHORT and LONG signals when multiple conditions align:
SHORT Signal Requirements:
1. Price reaches an HVN resistance zone (within 0.2%)
2. Large sell order detected (volume spike + red candle)
3. Cumulative delta is bearish OR bearish divergence present
4. 10-bar cooldown between signals (prevents overtrading)
LONG Signal Requirements:
1. Price reaches an HVN support zone
2. Large buy order detected (volume spike + green candle)
3. Cumulative delta is bullish OR bullish divergence present
4. 10-bar cooldown enforced
________________________________________
🔧 Customization Options
Setting - Purpose - Recommendation
Volume Profile Rows - Granularity of level detection - 20 (balanced)
Lookback Period - Historical data analyzed - 50 bars (intraday), 200 (swing)
Large Order Multiplier - Sensitivity to volume spikes - 2.5x (standard), 3.5x (conservative)
HVN Threshold - Resistance zone detection - 1.3 (default)
LVN Threshold - Target zone identification - 0.6 (default)
Divergence Lookback - Pivot detection period - 5 bars (responsive)
________________________________________
📈 Dashboard Indicators
The real-time panel displays:
• POC: Current Point of Control price
• Location: Whether price is at HVN resistance
• Orders: Current large buy/sell activity
• Cumulative Δ: Net order flow value + trend direction
• Divergence: Active bullish/bearish divergences
• Bar Strength: % of candle volume that's directional (>65% = strong)
• SETUP: Current trade signal (LONG/SHORT/WAIT)
________________________________________
🎨 Visual System
• Yellow POC Line: Highest volume level - primary pivot
• Blue Value Area Box: Fair value zone (VAH to VAL)
• Red HVN Zones: Resistance/support from institutional accumulation
• Green LVN Zones: Low-liquidity targets for quick moves
• Volume Bars: Green (buy pressure) vs Red (sell pressure) distribution
• Triangles: LONG (green up) and SHORT (red down) entry signals
• Diamonds: Divergence warnings (cyan=bullish, fuchsia=bearish)
________________________________________
💡 How This Script Is Unique
Unlike standalone volume profile or delta indicators, this script:
1. Synthesizes three complementary methods - volume structure, order flow momentum, and liquidity detection
2. Requires multi-factor confirmation - signals only trigger when price, volume, and delta align at key zones
3. Adapts to market regime - delta filters ensure you're trading with the dominant order flow direction
4. Provides context, not just signals - the dashboard helps you understand why a setup is forming
________________________________________
⚙️ Best Practices
Timeframes:
• 5-15 min: Scalping (use 30-50 bar lookback)
• 1-4 hour: Swing trading (use 100-200 bar lookback)
Risk Management:
• Enter on signal candle close
• Stop loss: Beyond nearest HVN/LVN zone
• Target 1: Next LVN level
• Target 2: Opposite value area boundary
Filters:
• Avoid signals during major news events
• Require bar delta strength >65% for aggressive entries
• Wait for delta MA cross confirmation in ranging markets
________________________________________
🚨 Alerts Available
• Long Setup Trigger
• Short Setup Trigger
• Bullish/Bearish Divergence Detection
• Large Buy/Sell Order Execution
________________________________________
📚 Educational Context
This methodology is based on principles used by professional order flow traders:
• Market Profile Theory: Volume distribution reveals fair value
• Tape Reading: Large orders show institutional intent
• Auction Theory: Price seeks areas of liquidity imbalance (LVN zones)
The script automates pattern recognition that discretionary traders spend years learning to identify manually.
________________________________________
⚠️ Disclaimer
This indicator is a trading tool, not a trading system. It identifies high-probability setups based on order flow analysis but requires proper risk management, market context, and trader discretion. Past performance does not guarantee future results.
________________________________________
Version: 6 (Pine Script)
Type: Overlay + Separate Pane (Delta Panel)
Resource Usage: Moderate (500 bars history, 500 lines/boxes)
________________________________________
For questions or support, please comment below. If you find this script valuable, please boost and favorite! 🚀
1. Volume Profile Analysis
The script constructs a horizontal volume profile by:
- Dividing the price range into configurable rows (default: 20)
- Accumulating volume at each price level over a lookback period (default: 50 bars)
- Separating buy volume (green bars close > open) from sell volume (red bars)
- Identifying three critical levels:
- POC (Point of Control): Price level with highest traded volume - acts as a strong magnet
- VAH/VAL (Value Area High/Low): Contains 70% of total volume - defines fair value zone
- HVN (High Volume Nodes): Resistance zones where institutions accumulated positions
- LVN (Low Volume Nodes): Thin zones that price moves through quickly - ideal targets
Why This Matters: Institutional traders leave footprints through volume. HVN zones show where large players defended levels, making them reliable support/resistance.
---
2. Cumulative Delta (Order Flow)
Tracks the running total of buying vs selling pressure:
- **Bar Delta**: Difference between buy and sell volume per candle
- **Cumulative Delta**: Sum of all bar deltas - shows net directional pressure
- **Delta Moving Average**: Smoothed delta (20-period) to identify trend
- **Delta Divergences**:
- **Bullish**: Price makes lower low, but delta makes higher low (absorption at bottom)
- **Bearish**: Price makes higher high, but delta makes lower high (exhaustion at top)
**How It Works**: When cumulative delta trends up while price consolidates, it signals accumulation. Delta divergences reveal when smart money is positioned opposite to retail expectations.
---
### 3. **Large Order Detection**
Identifies **institutional-sized orders** in real-time:
- Compares current bar volume to 20-period moving average
- Flags orders exceeding 2.5x average volume (configurable multiplier)
- Distinguishes bullish (green circles below) vs bearish (red circles above) large orders
**Rationale**: Sudden volume spikes at key levels indicate institutional participation - the "fuel" needed for breakouts or reversals.
---
## 🎯 Trading Signal Logic
### Combined Setup Criteria
The script generates **SHORT** and **LONG** signals when multiple conditions align:
**SHORT Signal Requirements:**
1. Price reaches an HVN resistance zone (within 0.2%)
2. Large sell order detected (volume spike + red candle)
3. Cumulative delta is bearish OR bearish divergence present
4. 10-bar cooldown between signals (prevents overtrading)
**LONG Signal Requirements:**
1. Price reaches an HVN support zone
2. Large buy order detected (volume spike + green candle)
3. Cumulative delta is bullish OR bullish divergence present
4. 10-bar cooldown enforced
---
## 🔧 Customization Options
| Setting | Purpose | Recommendation |
|---------|---------|----------------|
| **Volume Profile Rows** | Granularity of level detection | 20 (balanced) |
| **Lookback Period** | Historical data analyzed | 50 bars (intraday), 200 (swing) |
| **Large Order Multiplier** | Sensitivity to volume spikes | 2.5x (standard), 3.5x (conservative) |
| **HVN Threshold** | Resistance zone detection | 1.3 (default) |
| **LVN Threshold** | Target zone identification | 0.6 (default) |
| **Divergence Lookback** | Pivot detection period | 5 bars (responsive) |
---
## 📈 Dashboard Indicators
The real-time panel displays:
- **POC**: Current Point of Control price
- **Location**: Whether price is at HVN resistance
- **Orders**: Current large buy/sell activity
- **Cumulative Δ**: Net order flow value + trend direction
- **Divergence**: Active bullish/bearish divergences
- **Bar Strength**: % of candle volume that's directional (>65% = strong)
- **SETUP**: Current trade signal (LONG/SHORT/WAIT)
---
## 🎨 Visual System
- **Yellow POC Line**: Highest volume level - primary pivot
- **Blue Value Area Box**: Fair value zone (VAH to VAL)
- **Red HVN Zones**: Resistance/support from institutional accumulation
- **Green LVN Zones**: Low-liquidity targets for quick moves
- **Volume Bars**: Green (buy pressure) vs Red (sell pressure) distribution
- **Triangles**: LONG (green up) and SHORT (red down) entry signals
- **Diamonds**: Divergence warnings (cyan=bullish, fuchsia=bearish)
---
## 💡 How This Script Is Unique
Unlike standalone volume profile or delta indicators, this script:
1. **Synthesizes three complementary methods** - volume structure, order flow momentum, and liquidity detection
2. **Requires multi-factor confirmation** - signals only trigger when price, volume, and delta align at key zones
3. **Adapts to market regime** - delta filters ensure you're trading with the dominant order flow direction
4. **Provides context, not just signals** - the dashboard helps you understand *why* a setup is forming
---
## ⚙️ Best Practices
**Timeframes:**
- 5-15 min: Scalping (use 30-50 bar lookback)
- 1-4 hour: Swing trading (use 100-200 bar lookback)
**Risk Management:**
- Enter on signal candle close
- Stop loss: Beyond nearest HVN/LVN zone
- Target 1: Next LVN level
- Target 2: Opposite value area boundary
**Filters:**
- Avoid signals during major news events
- Require bar delta strength >65% for aggressive entries
- Wait for delta MA cross confirmation in ranging markets
---
## 🚨 Alerts Available
- Long Setup Trigger
- Short Setup Trigger
- Bullish/Bearish Divergence Detection
- Large Buy/Sell Order Execution
---
## 📚 Educational Context
This methodology is based on principles used by professional order flow traders:
- **Market Profile Theory**: Volume distribution reveals fair value
- **Tape Reading**: Large orders show institutional intent
- **Auction Theory**: Price seeks areas of liquidity imbalance (LVN zones)
The script automates pattern recognition that discretionary traders spend years learning to identify manually.
---
## ⚠️ Disclaimer
This indicator is a **trading tool, not a trading system**. It identifies high-probability setups based on order flow analysis but requires proper risk management, market context, and trader discretion. Past performance does not guarantee future results.
---
**Version**: 6 (Pine Script)
**Type**: Overlay + Separate Pane (Delta Panel)
**Resource Usage**: Moderate (500 bars history, 500 lines/boxes)
---
*For questions or support, please comment below. If you find this script valuable, please boost and favorite!* 🚀
ابحث في النصوص البرمجية عن "imbalance"
Piku Pips📌 Piku Pips — Multi-Confluence Smart Signal System (EMA + Supertrend + Volume Profile + ATR Trailing + SR + RSI Climax Engine)
Piku Pips is a complete multi-confluence trading system designed for scalpers, intraday traders, and swing traders who rely on precision entries and institutional-grade confirmation layers.
This indicator combines trend, momentum, volatility, volume imbalance, structure breaks, smart money pivots, and exhaustion events—into a single unified charting system.
It does NOT repaint, supports alerts, and works across all assets (crypto, forex, indices, stocks).
🔥 What Makes This Indicator Special?
Piku Pips is built on stacked confluences instead of single-indicator signals.
Each signal is only printed when multiple conditions align, significantly increasing accuracy and reducing noise.
It includes:
✔ Trend Identification
Fast & Slow EMA cross
SuperTrend with custom ATR & factor
Parabolic SAR for micro-trend confirmation
ATR-based trailing stop engine (dual version for Buy & Sell)
✔ Momentum Confirmation
RSI Midline model
HH/LL structure detection
Bull/Bear volume imbalance model
✔ Smart Volume Analysis
Bullish vs Bearish VWMA volume
Flat-volume filters
RSI + Volume Spike + MFI exhaustion detection (Climax Module)
✔ Institutional Structure Mapping
Dynamic Support & Resistance
Automatic Zone Strength Ranking
Breakout detection with zone coloring
Pivot-based structure scanning
✔ Exhaustion + Divergence Engine (Climax Module)
RSI / Stochastic RSI hybrid
Macro trend smoothing (EMA/RMA/SMA/WMA selectable)
High-precision RSI divergence detection (HH/LH and LL/HL)
Volume spike detection
Buy Climax (potential top)
Sell Climax (potential bottom)
This module acts like a “smart momentum brain” that identifies major reversals.
🎯 Signal Logic (Simplified)
🔹 Buy Signal (Green Triangle)
Triggered when:
Fast EMA crosses above Slow EMA
Higher High structure forms
RSI > midline or crosses above it
Volume profile is bullish
SuperTrend is bullish (direction < 0)
🔹 Sell Signal (Red Triangle)
Triggered when:
Fast EMA crosses below Slow EMA
Lower Low structure forms
RSI < midline or crosses below it
Volume profile is bearish
SuperTrend is bearish (direction > 0)
🔸 Secondary ATR Signals (Orange & Maroon)
Uses Heikin-Ashi ATR trailing stop
Detects micro-shifts in trend momentum
Works excellent in scalping timeframes
🧠 Support & Resistance Engine
The script builds dynamic SR zones based on:
Pivot clustering
Channel width filtering
Strength scoring
Automated sorting and plotting
Zones:
Red tint = Resistance
Green tint = Support
Gray tint = Neutral / In-Play
Alerts trigger on clean SR breaks.
⚡ Climax Module (Exhaustion System)
This system overlays major exhaustion points:
🔻 Buy Climax
High-volume upward exhaustion → potential top.
🔺 Sell Climax
High-volume downward exhaustion → potential bottom.
🔼 RSI Divergences
Bullish divergence labeled "RSI⬆"
Bearish divergence labeled "RSI⬇"
Combined, these give early insight into possible reversals.
🛠 Inputs Overview
📌 Trend Inputs
Fast EMA Length
Slow EMA Length
SuperTrend ATR + Factor
SAR multipliers
Buy/Sell ATR trailing stop parameters
📌 Momentum Inputs
RSI length / midline
Bull/Bear volume variance filter
HH/LL confirmation
📌 Structure Inputs
Pivot sensitivity
Max SR Zones
Loopback length
Zone strength minimum
📌 Climax Module Inputs
RSI / Stochastic lengths
Smoothing method (EMA, SMA, RMA, WMA)
Macro trend slope settings
Pivot sensitivity for divergence
Volume spike multiplier
MFI thresholds
Bull/Bear RSI levels
📈 How to Use Piku Pips
Best Use-Cases:
Scalping (1m–15m)
Intraday (15m–1H)
Swing trading (4H–1D)
Crypto / Forex / Indices / Stocks
Recommended Approach
Trade in direction of EMA + Supertrend + Macro RSI regime.
Enter when Piku Buy/Sell signal aligns with the trend.
Use SR zones as targets or invalidation levels.
Watch Climax signals for tops & bottoms.
Use divergence signals for early reversals.
🔔 Alerts Included
Buy Signal
Sell Signal
ATR Buy / Sell
Buy Climax
Sell Climax
RSI Divergence (bullish & bearish)
All-Signals alert
⚠️ Disclaimer
This indicator is created for educational purposes only and does not constitute financial advice.
Trading involves risk. Do your own research and backtesting before using any tool in live markets.
Timeframe Fast EMA Slow EMA ATR Period Factor RSI Length Overbought/Oversold
5 Min 9 21 10 2 8 80 / 20
15 Min 10 25 10 2.5 10 75/25
1 Hour 20 50 14 3 12 70/30
4 Hour 21 50 14 3 14 70/30
1 Day 20 100 14 3.5 14 70/30
Please use this settings for accurate results
Algorithm Predator - ProAlgorithm Predator - Pro: Advanced Multi-Agent Reinforcement Learning Trading System
Algorithm Predator - Pro combines four specialized market microstructure agents with a state-of-the-art reinforcement learning framework . Unlike traditional indicator mashups, this system implements genuine machine learning to automatically discover which detection strategies work best in current market conditions and adapts continuously without manual intervention.
Core Innovation: Rather than forcing traders to interpret conflicting signals, this system uses 15 different multi-armed bandit algorithms and a full reinforcement learning stack (Q-Learning, TD(λ) with eligibility traces, and Policy Gradient with REINFORCE) to learn optimal agent selection policies. The result is a self-improving system that gets smarter with every trade.
Target Users: Swing traders, day traders, and algorithmic traders seeking systematic signal generation with mathematical rigor. Suitable for stocks, forex, crypto, and futures on liquid instruments (>100k daily volume).
Why These Components Are Combined
The Fundamental Problem
No single indicator works consistently across all market regimes. What works in trending markets fails in ranging conditions. Traditional solutions force traders to manually switch indicators (slow, error-prone) or interpret all signals simultaneously (cognitive overload).
This system solves the problem through automated meta-learning: Deploy multiple specialized agents designed for specific market microstructure conditions, then use reinforcement learning to discover which agent (or combination) performs best in real-time.
Why These Specific Four Agents?
The four agents provide orthogonal failure mode coverage —each agent's weakness is another's strength:
Spoofing Detector - Optimal in consolidation/manipulation; fails in trending markets (hedged by Exhaustion Detector)
Exhaustion Detector - Optimal at trend climax; fails in range-bound markets (hedged by Liquidity Void)
Liquidity Void - Optimal pre-breakout compression; fails in established trends (hedged by Mean Reversion)
Mean Reversion - Optimal in low volatility; fails in strong trends (hedged by Spoofing Detector)
This creates complete market state coverage where at least one agent should perform well in any condition. The bandit system identifies which one without human intervention.
Why Reinforcement Learning vs. Simple Voting?
Traditional consensus systems have fatal flaws: equal weighting assumes all agents are equally reliable (false), static thresholds don't adapt, and no learning means past mistakes repeat indefinitely.
Reinforcement learning solves this through the exploration-exploitation tradeoff: Continuously test underused agents (exploration) while primarily relying on proven winners (exploitation). Over time, the system builds a probability distribution over agent quality reflecting actual market performance.
Mathematical Foundation: Multi-armed bandit problem from probability theory, where each agent is an "arm" with unknown reward distribution. The goal is to maximize cumulative reward while efficiently learning each arm's true quality.
The Four Trading Agents: Technical Explanation
Agent 1: 🎭 Spoofing Detector (Institutional Manipulation Detection)
Theoretical Basis: Market microstructure theory on order flow toxicity and information asymmetry. Based on research by Easley, López de Prado, and O'Hara on high-frequency trading manipulation.
What It Detects:
1. Iceberg Orders (Hidden Liquidity Absorption)
Method: Monitors volume spikes (>2.5× 20-period average) with minimal price movement (<0.3× ATR)
Formula: score += (close > open ? -2.5 : 2.5) when volume > vol_avg × 2.5 AND abs(close - open) / ATR < 0.3
Interpretation: Large volume without price movement indicates institutional absorption (buying) or distribution (selling) using hidden orders
Signal Logic: Contrarian—fade false breakouts caused by institutional manipulation
2. Spoofing Patterns (Fake Liquidity via Layering)
Method: Analyzes candlestick wick-to-body ratios during volume spikes
Formula: if upper_wick > body × 2 AND volume_spike: score += 2.0
Mechanism: Spoofing creates large wicks (orders pulled before execution) with volume evidence
Signal Logic: Wick direction indicates trapped participants; trade against the failed move
3. Post-Manipulation Reversals
Method: Tracks volume decay after manipulation events
Formula: if volume > vol_avg × 3 AND volume / volume < 0.3: score += (close > open ? -1.5 : 1.5)
Interpretation: Sharp volume drop after manipulation indicates exhaustion of manipulative orders
Why It Works: Institutional manipulation creates detectable microstructure anomalies. While retail traders see "mysterious reversals," this agent quantifies the order flow patterns causing them.
Parameter: i_spoof (sensitivity 0.5-2.0) - Controls detection threshold
Best Markets: Consolidations before breakouts, London/NY overlap windows, stocks with institutional ownership >70%
Agent 2: ⚡ Exhaustion Detector (Momentum Failure Analysis)
Theoretical Basis: Technical analysis divergence theory combined with VPIN reversals from market microstructure literature.
What It Detects:
1. Price-RSI Divergence (Momentum Deceleration)
Method: Compares 5-bar price ROC against RSI change
Formula: if price_roc > 5% AND rsi_current < rsi : score += 1.8
Mathematics: Second derivative detecting inflection points
Signal Logic: When price makes higher highs but momentum makes lower highs, expect mean reversion
2. Volume Exhaustion (Buying/Selling Climax)
Method: Identifies strong price moves (>5% ROC) with declining volume (<-20% volume ROC)
Formula: if price_roc > 5 AND vol_roc < -20: score += 2.5
Interpretation: Price extension without volume support indicates retail chasing while institutions exit
3. Momentum Deceleration (Acceleration Analysis)
Method: Compares recent 3-bar momentum to prior 3-bar momentum
Formula: deceleration = abs(mom1) < abs(mom2) × 0.5 where momentum significant (> ATR)
Signal Logic: When rate of price change decelerates significantly, anticipate directional shift
Why It Works: Momentum is lagging, but momentum divergence is leading. By comparing momentum's rate of change to price, this agent detects "weakening conviction" before reversals become obvious.
Parameter: i_momentum (sensitivity 0.5-2.0)
Best Markets: Strong trends reaching climax, parabolic moves, instruments with high retail participation
Agent 3: 💧 Liquidity Void Detector (Breakout Anticipation)
Theoretical Basis: Market liquidity theory and order book dynamics. Based on research into "liquidity holes" and volatility compression preceding expansion.
What It Detects:
1. Bollinger Band Squeeze (Volatility Compression)
Method: Monitors Bollinger Band width relative to 50-period average
Formula: bb_width = (upper_band - lower_band) / middle_band; triggers when < 0.6× average
Mathematical Foundation: Regression to the mean—low volatility precedes high volatility
Signal Logic: When volatility compresses AND cumulative delta shows directional bias, anticipate breakout
2. Volume Profile Gaps (Thin Liquidity Zones)
Method: Identifies sharp volume transitions indicating few limit orders
Formula: if volume < vol_avg × 0.5 AND volume < vol_avg × 0.5 AND volume > vol_avg × 1.5
Interpretation: Sudden volume drop after spike indicates price moved through order book to low-opposition area
Signal Logic: Price accelerates through low-liquidity zones
3. Stop Hunts (Liquidity Grabs Before Reversals)
Method: Detects new 20-bar highs/lows with immediate reversal and rejection wick
Formula: if new_high AND close < high - (high - low) × 0.6: score += 3.0
Mechanism: Market makers push price to trigger stop-loss clusters, then reverse
Signal Logic: Enter reversal after stop-hunt completes
Why It Works: Order book theory shows price moves fastest through zones with minimal liquidity. By identifying these zones before major moves, this agent provides early entry for high-reward breakouts.
Parameter: i_liquidity (sensitivity 0.5-2.0)
Best Markets: Range-bound pre-breakout setups, volatility compression zones, instruments prone to gap moves
Agent 4: 📊 Mean Reversion (Statistical Arbitrage Engine)
Theoretical Basis: Statistical arbitrage theory, Ornstein-Uhlenbeck mean-reverting processes, and pairs trading methodology applied to single instruments.
What It Detects:
1. Z-Score Extremes (Standard Deviation Analysis)
Method: Calculates price distance from 20-period and 50-period SMAs in standard deviation units
Formula: zscore_20 = (close - SMA20) / StdDev(50)
Statistical Interpretation: Z-score >2.0 means price is 2 standard deviations above mean (97.5th percentile)
Trigger Logic: if abs(zscore_20) > 2.0: score += zscore_20 > 0 ? -1.5 : 1.5 (fade extremes)
2. Ornstein-Uhlenbeck Process (Mean-Reverting Stochastic Model)
Method: Models price as mean-reverting stochastic process: dx = θ(μ - x)dt + σdW
Implementation: Calculates spread = close - SMA20, then z-score of spread vs. spread distribution
Formula: ou_signal = (spread - spread_mean) / spread_std
Interpretation: Measures "tension" pulling price back to equilibrium
3. Correlation Breakdown (Regime Change Detection)
Method: Compares 50-period price-volume correlation to 10-period correlation
Formula: corr_breakdown = abs(typical_corr - recent_corr) > 0.5
Enhancement: if corr_breakdown AND abs(zscore_20) > 1.0: score += zscore_20 > 0 ? -1.2 : 1.2
Why It Works: Mean reversion is the oldest quantitative strategy (1970s pairs trading at Morgan Stanley). While simple, it remains effective because markets exhibit periodic equilibrium-seeking behavior. This agent applies rigorous statistical testing to identify when mean reversion probability is highest.
Parameter: i_statarb (sensitivity 0.5-2.0)
Best Markets: Range-bound instruments, low-volatility periods (VIX <15), algo-dominated markets (forex majors, index futures)
Multi-Armed Bandit System: 15 Algorithms Explained
What Is a Multi-Armed Bandit Problem?
Origin: Named after slot machines ("one-armed bandits"). Imagine facing multiple slot machines, each with unknown payout rates. How do you maximize winnings?
Formal Definition: K arms (agents), each with unknown reward distribution with mean μᵢ. Goal: Maximize cumulative reward over T trials. Challenge: Balance exploration (trying uncertain arms to learn quality) vs. exploitation (using known-best arm for immediate reward).
Trading Application: Each agent is an "arm." After each trade, receive reward (P&L). Must decide which agent to trust for next signal.
Algorithm Categories
Bayesian Approaches (probabilistic, optimal for stationary environments):
Thompson Sampling
Bootstrapped Thompson Sampling
Discounted Thompson Sampling
Frequentist Approaches (confidence intervals, deterministic):
UCB1
UCB1-Tuned
KL-UCB
SW-UCB (Sliding Window)
D-UCB (Discounted)
Adversarial Approaches (robust to non-stationary environments):
EXP3-IX
Hedge
FPL-Gumbel
Reinforcement Learning Approaches (leverage learned state-action values):
Q-Values (from Q-Learning)
Policy Network (from Policy Gradient)
Simple Baseline:
Epsilon-Greedy
Softmax
Key Algorithm Details
Thompson Sampling (DEFAULT - RECOMMENDED)
Theoretical Foundation: Bayesian decision theory with conjugate priors. Published by Thompson (1933), rediscovered for bandits by Chapelle & Li (2011).
How It Works:
Model each agent's reward distribution as Beta(α, β) where α = wins, β = losses
Each step, sample from each agent's beta distribution: θᵢ ~ Beta(αᵢ, βᵢ)
Select agent with highest sample: argmaxᵢ θᵢ
Update winner's distribution after observing outcome
Mathematical Properties:
Optimality: Achieves logarithmic regret O(K log T) (proven optimal)
Bayesian: Maintains probability distribution over true arm means
Automatic Balance: High uncertainty → more exploration; high certainty → exploitation
⚠️ CRITICAL APPROXIMATION: This is a pseudo-random approximation of true Thompson Sampling. True implementation requires random number generation from beta distributions, which Pine Script doesn't provide. This version uses Box-Muller transform with market data (price/volume decimal digits) as entropy source. While not mathematically pure, it maintains core exploration-exploitation balance and learns agent preferences effectively.
When To Use: Best all-around choice. Handles non-stationary markets reasonably well, balances exploration naturally, highly sample-efficient.
UCB1 (Upper Confidence Bound)
Formula: UCB_i = reward_mean_i + sqrt(2 × ln(total_pulls) / pulls_i)
Interpretation: First term (exploitation) + second term (exploration bonus for less-tested arms)
Mathematical Properties:
Deterministic : Always selects same arm given same state
Regret Bound: O(K log T) — same optimality as Thompson Sampling
Interpretable: Can visualize confidence intervals
When To Use: Prefer deterministic behavior, want to visualize uncertainty, stable markets
EXP3-IX (Exponential Weights - Adversarial)
Theoretical Foundation: Adversarial bandit algorithm. Assumes environment may be actively hostile (worst-case analysis).
How It Works:
Maintain exponential weights: w_i = exp(η × cumulative_reward_i)
Select agent with probability proportional to weights: p_i = (1-γ)w_i/Σw_j + γ/K
After outcome, update with importance weighting: estimated_reward = observed_reward / p_i
Mathematical Properties:
Adversarial Regret: O(sqrt(TK log K)) even if environment is adversarial
No Assumptions: Doesn't assume stationary or stochastic reward distributions
Robust: Works even when optimal arm changes continuously
When To Use: Extreme non-stationarity, don't trust reward distribution assumptions, want robustness over efficiency
KL-UCB (Kullback-Leibler Upper Confidence Bound)
Theoretical Foundation: Uses KL-divergence instead of Hoeffding bounds. Tighter confidence intervals.
Formula (conceptual): Find largest q such that: n × KL(p||q) ≤ ln(t) + 3×ln(ln(t))
Mathematical Properties:
Tighter Bounds: KL-divergence adapts to reward distribution shape
Asymptotically Optimal: Better constant factors than UCB1
Computationally Intensive: Requires iterative binary search (15 iterations)
When To Use: Maximum sample efficiency needed, willing to pay computational cost, long-term trading (>500 bars)
Q-Values & Policy Network (RL-Based Selection)
Unique Feature: Instead of treating agents as black boxes with scalar rewards, these algorithms leverage the full RL state representation .
Q-Values Selection:
Uses learned Q-values: Q(state, agent_i) from Q-Learning
Selects agent via softmax over Q-values for current market state
Advantage: Selects based on state-conditional quality (which agent works best in THIS market state)
Policy Network Selection:
Uses neural network policy: π(agent | state, θ) from Policy Gradient
Direct policy over agents given market features
Advantage: Can learn non-linear relationships between market features and agent quality
When To Use: After 200+ RL updates (Q-Values) or 500+ updates (Policy Network) when models converged
Machine Learning & Reinforcement Learning Stack
Why Both Bandits AND Reinforcement Learning?
Critical Distinction:
Bandits treat agents as contextless black boxes: "Agent 2 has 60% win rate"
Reinforcement Learning adds state context: "Agent 2 has 60% win rate WHEN trend_score > 2 and RSI < 40"
Power of Combination: Bandits provide fast initial learning with minimal assumptions. RL provides state-dependent policies for superior long-term performance.
Component 1: Q-Learning (Value-Based RL)
Algorithm: Temporal Difference Learning with Bellman equation.
State Space: 54 discrete states formed from:
trend_state = {0: bearish, 1: neutral, 2: bullish} (3 values)
volatility_state = {0: low, 1: normal, 2: high} (3 values)
RSI_state = {0: oversold, 1: neutral, 2: overbought} (3 values)
volume_state = {0: low, 1: high} (2 values)
Total states: 3 × 3 × 3 × 2 = 54 states
Action Space: 5 actions (No trade, Agent 1, Agent 2, Agent 3, Agent 4)
Total state-action pairs: 54 × 5 = 270 Q-values
Bellman Equation:
Q(s,a) ← Q(s,a) + α ×
Parameters:
α (learning rate): 0.01-0.50, default 0.10 - Controls step size for updates
γ (discount factor): 0.80-0.99, default 0.95 - Values future rewards
ε (exploration): 0.01-0.30, default 0.10 - Probability of random action
Update Mechanism:
Position opens with state s, action a (selected agent)
Every bar position is open: Calculate floating P&L → scale to reward
Perform online TD update
When position closes: Perform terminal update with final reward
Gradient Clipping: TD errors clipped to ; Q-values clipped to for stability.
Why It Works: Q-Learning learns "quality" of each agent in each market state through trial and error. Over time, builds complete state-action value function enabling optimal state-dependent agent selection.
Component 2: TD(λ) Learning (Temporal Difference with Eligibility Traces)
Enhancement Over Basic Q-Learning: Credit assignment across multiple time steps.
The Problem TD(λ) Solves:
Position opens at t=0
Market moves favorably at t=3
Position closes at t=8
Question: Which earlier decisions contributed to success?
Basic Q-Learning: Only updates Q(s₈, a₈) ← reward
TD(λ): Updates ALL visited state-action pairs with decayed credit
Eligibility Trace Formula:
e(s,a) ← γ × λ × e(s,a) for all s,a (decay all traces)
e(s_current, a_current) ← 1 (reset current trace)
Q(s,a) ← Q(s,a) + α × TD_error × e(s,a) (update all with trace weight)
Lambda Parameter (λ): 0.5-0.99, default 0.90
λ=0: Pure 1-step TD (only immediate next state)
λ=1: Full Monte Carlo (entire episode)
λ=0.9: Balance (recommended)
Why Superior: Dramatically faster learning for multi-step tasks. Q-Learning requires many episodes to propagate rewards backwards; TD(λ) does it in one.
Component 3: Policy Gradient (REINFORCE with Baseline)
Paradigm Shift: Instead of learning value function Q(s,a), directly learn policy π(a|s).
Policy Network Architecture:
Input: 12 market features
Hidden: None (linear policy)
Output: 5 actions (softmax distribution)
Total parameters: 12 features × 5 actions + 5 biases = 65 parameters
Feature Set (12 Features):
Price Z-score (close - SMA20) / ATR
Volume ratio (volume / vol_avg - 1)
RSI deviation (RSI - 50) / 50
Bollinger width ratio
Trend score / 4 (normalized)
VWAP deviation
5-bar price ROC
5-bar volume ROC
Range/ATR ratio - 1
Price-volume correlation (20-period)
Volatility ratio (ATR / ATR_avg - 1)
EMA50 deviation
REINFORCE Update Rule:
θ ← θ + α × ∇log π(a|s) × advantage
where advantage = reward - baseline (variance reduction)
Why Baseline? Raw rewards have high variance. Subtracting baseline (running average) centers rewards around zero, reducing gradient variance by 50-70%.
Learning Rate: 0.001-0.100, default 0.010 (much lower than Q-Learning because policy gradients have high variance)
Why Policy Gradient?
Handles 12 continuous features directly (Q-Learning requires discretization)
Naturally maintains exploration through probability distribution
Can converge to stochastic optimal policy
Component 4: Ensemble Meta-Learner (Stacking)
Architecture: Level-1 meta-learner combines Level-0 base learners (Q-Learning, TD(λ), Policy Gradient).
Three Meta-Learning Algorithms:
1. Simple Average (Baseline)
Final_prediction = (Q_prediction + TD_prediction + Policy_prediction) / 3
2. Weighted Vote (Reward-Based)
weight_i ← 0.95 × weight_i + 0.05 × (reward_i + 1)
3. Adaptive Weighting (Gradient-Based) — RECOMMENDED
Loss Function: L = (y_true - ŷ_ensemble)²
Gradient: ∂L/∂weight_i = -2 × (y_true - ŷ_ensemble) × agent_contribution_i
Updates weights via gradient descent with clipping and normalization
Why It Works: Unlike simple averaging, meta-learner discovers which base learner is most reliable in current regime. If Policy Gradient excels in trending markets while Q-Learning excels in ranging, meta-learner learns these patterns and weights accordingly.
Feature Importance Tracking
Purpose: Identify which of 12 features contribute most to successful predictions.
Update Rule: importance_i ← 0.95 × importance_i + 0.05 × |feature_i × reward|
Use Cases:
Feature selection: Drop low-importance features
Market regime detection: Importance shifts reveal regime changes
Agent tuning: If VWAP deviation has high importance, consider boosting agents using VWAP
RL Position Tracking System
Critical Innovation: Proper reinforcement learning requires tracking which decisions led to outcomes.
State Tracking (When Signal Validates):
active_rl_state ← current_market_state (0-53)
active_rl_action ← selected_agent (1-4)
active_rl_entry ← entry_price
active_rl_direction ← 1 (long) or -1 (short)
active_rl_bar ← current_bar_index
Online Updates (Every Bar Position Open):
floating_pnl = (close - entry) / entry × direction
reward = floating_pnl × 10 (scale to meaningful range)
reward = clip(reward, -5.0, 5.0)
Update Q-Learning, TD(λ), and Policy Gradient
Terminal Update (Position Close):
Final Q-Learning update (no next Q-value, terminal state)
Update meta-learner with final result
Update agent memory
Clear position tracking
Exit Conditions:
Time-based: ≥3 bars held (minimum hold period)
Stop-loss: 1.5% adverse move
Take-profit: 2.0% favorable move
Market Microstructure Filters
Why Microstructure Matters
Traditional technical analysis assumes fair, efficient markets. Reality: Markets have friction, manipulation, and information asymmetry. Microstructure filters detect when market structure indicates adverse conditions.
Filter 1: VPIN (Volume-Synchronized Probability of Informed Trading)
Theoretical Foundation: Easley, López de Prado, & O'Hara (2012). "Flow Toxicity and Liquidity in a High-Frequency World."
What It Measures: Probability that current order flow is "toxic" (informed traders with private information).
Calculation:
Classify volume as buy or sell (close > close = buy volume)
Calculate imbalance over 20 bars: VPIN = |Σ buy_volume - Σ sell_volume| / Σ total_volume
Compare to moving average: toxic = VPIN > VPIN_MA(20) × sensitivity
Interpretation:
VPIN < 0.3: Normal flow (uninformed retail)
VPIN 0.3-0.4: Elevated (smart money active)
VPIN > 0.4: Toxic flow (informed institutions dominant)
Filter Logic:
Block LONG when: VPIN toxic AND price rising (don't buy into institutional distribution)
Block SHORT when: VPIN toxic AND price falling (don't sell into institutional accumulation)
Adaptive Threshold: If VPIN toxic frequently, relax threshold; if rarely toxic, tighten threshold. Bounded .
Filter 2: Toxicity (Kyle's Lambda Approximation)
Theoretical Foundation: Kyle (1985). "Continuous Auctions and Insider Trading."
What It Measures: Price impact per unit volume — market depth and informed trading.
Calculation:
price_impact = (close - close ) / sqrt(Σ volume over 10 bars)
impact_zscore = (price_impact - impact_mean) / impact_std
toxicity = abs(impact_zscore)
Interpretation:
Low toxicity (<1.0): Deep liquid market, large orders absorbed easily
High toxicity (>2.0): Thin market or informed trading
Filter Logic: Block ALL SIGNALS when toxicity > threshold. Most dangerous when price breaks from VWAP with high toxicity.
Filter 3: Regime Filter (Counter-Trend Protection)
Purpose: Prevent counter-trend trades during strong trends.
Trend Scoring:
trend_score = 0
trend_score += close > EMA8 ? +1 : -1
trend_score += EMA8 > EMA21 ? +1 : -1
trend_score += EMA21 > EMA50 ? +1 : -1
trend_score += close > EMA200 ? +1 : -1
Range:
Regime Classification:
Strong Bull: trend_score ≥ +3 → Block all SHORT signals
Strong Bear: trend_score ≤ -3 → Block all LONG signals
Neutral: -2 ≤ trend_score ≤ +2 → Allow both directions
Filter 4: Liquidity Boost (Signal Enhancer)
Unique: Unlike other filters (which block), this amplifies signals during low liquidity.
Logic: if volume < vol_avg × 0.7: agent_scores × 1.2
Why It Works: Low liquidity often precedes explosive moves (breakouts). By increasing agent sensitivity during compression, system catches pre-breakout signals earlier.
Technical Implementation & Approximations
⚠️ Critical Approximations Required by Pine Script
1. Thompson Sampling: Pseudo-Random Beta Distribution
Academic Standard: True random sampling from beta distributions using cryptographic RNG
This Implementation: Box-Muller transform for normal distribution using market data (price/volume decimal digits) as entropy source, then scale to beta distribution mean/variance
Impact: Not cryptographically random, may have subtle biases in specific price ranges, but maintains correct mean and approximate variance. Sufficient for bandit agent selection.
2. VPIN: Simplified Volume Classification
Academic Standard: Lee-Ready algorithm or exchange-provided aggressor flags with tick-by-tick data
This Implementation: Bar-based classification: if close > close : buy_volume += volume
Impact: 10-15% precision loss. Works well in directional markets, misclassifies in choppy conditions. Still captures order flow imbalance signal.
3. Policy Gradient: Simplified Per-Action Updates
Academic Standard: Full softmax gradient updating all actions (selected action UP, others DOWN proportionally)
This Implementation: Only updates selected action's weights
Impact: Valid approximation for small action spaces (5 actions). Slower convergence than full softmax but still learns optimal policy.
4. Kyle's Lambda: Simplified Price Impact
Academic Standard: Regression over multiple time scales with signed order flow
This Implementation: price_impact = Δprice_10 / sqrt(Σvolume_10); z_score calculation
Impact: 15-20% precision loss. No proper signed order flow. Still detects informed trading signals at extremes (>2σ).
5. Other Simplifications:
Hawkes Process: Fixed exponential decay (0.9) not MLE-optimized
Entropy: Ratio approximation not true Shannon entropy H(X) = -Σ p(x)·log₂(p(x))
Feature Engineering: 12 features vs. potential 100+ with polynomial interactions
RL Hybrid Updates: Both online and terminal (non-standard but empirically effective)
Overall Precision Loss Estimate: 10-15% compared to academic implementations with institutional data feeds.
Practical Trade-off: For retail trading with OHLCV data, these approximations provide 90%+ of the edge while maintaining full transparency, zero latency, no external dependencies, and runs on any TradingView plan.
How to Use: Practical Guide
Initial Setup (5 Minutes)
Select Trading Mode: Start with "Balanced" for most users
Enable ML/RL System: Toggle to TRUE, select "Full Stack" ML Mode
Bandit Configuration: Algorithm: "Thompson Sampling", Mode: "Switch" or "Blend"
Microstructure Filters: Enable all four filters, enable "Adaptive Microstructure Thresholds"
Visual Settings: Enable dashboard (Top Right), enable all chart visuals
Learning Phase (First 50-100 Signals)
What To Monitor:
Agent Performance Table: Watch win rates develop (target >55%)
Bandit Weights: Should diverge from uniform (0.25 each) after 20-30 signals
RL Core Metrics: "RL Updates" should increase when position open
Filter Status: "Blocked" count indicates filter activity
Optimization Tips:
Too few signals: Lower min_confidence to 0.25, increase agent sensitivities to 1.1-1.2
Too many signals: Raise min_confidence to 0.35-0.40, decrease agent sensitivities to 0.8-0.9
One agent dominates (>70%): Consider "Lock Agent" feature
Signal Interpretation
Dashboard Signal Status:
⚪ WAITING FOR SIGNAL: No agent signaling
⏳ ANALYZING...: Agent signaling but not confirmed
🟡 CONFIRMING 2/3: Building confirmation (2 of 3 bars)
🟢 LONG ACTIVE : Validated long entry
🔴 SHORT ACTIVE : Validated short entry
Kill Zone Boxes: Entry price (triangle marker), Take Profit (Entry + 2.5× ATR), Stop Loss (Entry - 1.5× ATR). Risk:Reward = 1:1.67
Risk Management
Position Sizing:
Risk per trade = 1-2% of capital
Position size = (Capital × Risk%) / (Entry - StopLoss)
Stop-Loss Placement:
Initial: Entry ± 1.5× ATR (shown in kill zone)
Trailing: After 1:1 R:R achieved, move stop to breakeven
Take-Profit Strategy:
TP1 (2.5× ATR): Take 50% off
TP2 (Runner): Trail stop at 1× ATR or use opposite signal as exit
Memory Persistence
Why Save Memory: Every chart reload resets the system. Saving learned parameters preserves weeks of learning.
When To Save: After 200+ signals when agent weights stabilize
What To Save: From Memory Export panel, copy all alpha/beta/weight values and adaptive thresholds
How To Restore: Enable "Restore From Saved State", input all values into corresponding fields
What Makes This Original
Innovation 1: Genuine Multi-Armed Bandit Framework
This implements 15 mathematically rigorous bandit algorithms from academic literature (Thompson Sampling from Chapelle & Li 2011, UCB family from Auer et al. 2002, EXP3 from Auer et al. 2002, KL-UCB from Garivier & Cappé 2011). Each algorithm maintains proper state, updates according to proven theory, and converges to optimal behavior. This is real learning, not superficial parameter changes.
Innovation 2: Full Reinforcement Learning Stack
Beyond bandits learning which agent works best globally, RL learns which agent works best in each market state. After 500+ positions, system builds 54-state × 5-action value function (270 learned parameters) capturing context-dependent agent quality.
Innovation 3: Market Microstructure Integration
Combines retail technical analysis with institutional-grade microstructure metrics: VPIN from Easley, López de Prado, O'Hara (2012), Kyle's Lambda from Kyle (1985), Hawkes Processes from Hawkes (1971). These detect informed trading, manipulation, and liquidity dynamics invisible to technical analysis.
Innovation 4: Adaptive Threshold System
Dynamic quantile-based thresholds: Maintains histogram of each agent's score distribution (24 bins, exponentially decayed), calculates 80th percentile threshold from histogram. Agent triggers only when score exceeds its own learned quantile. Proper non-parametric density estimation automatically adapts to instrument volatility, agent behavior shifts, and market regime changes.
Innovation 5: Episodic Memory with Transfer Learning
Dual-layer architecture: Short-term memory (last 20 trades, fast adaptation) + Long-term memory (condensed episodes, historical patterns). Transfer mechanism consolidates knowledge when STM reaches threshold. Mimics hippocampus → neocortex consolidation in human memory.
Limitations & Disclaimers
General Limitations
No Predictive Guarantee: Pattern recognition ≠ prediction. Past performance ≠ future results.
Learning Period Required: Minimum 50-100 bars for reliable statistics. Initial performance may be suboptimal.
Overfitting Risk: System learns patterns in historical data. May not generalize to unprecedented conditions.
Approximation Limitations: See technical implementation section (10-15% precision loss vs. academic standards)
Single-Instrument Limitation: No multi-asset correlation, sector context, or VIX integration.
Forward-Looking Bias Disclaimer
CRITICAL TRANSPARENCY: The RL system uses an 8-bar forward-looking window for reward calculation.
What This Means: System learns from rewards incorporating future price information (bars 101-108 relative to entry at bar 100).
Why Acceptable:
✅ Signals do NOT look ahead: Entry decisions use only data ≤ entry bar
✅ Learning only: Forward data used for optimization, not signal generation
✅ Real-time mirrors backtest: In live trading, system learns identically
⚠️ Implication: Dashboard "Agent Win%" reflects this 8-bar evaluation. Real-time performance may differ slightly if positions held longer, slippage/fees not captured, or market microstructure changes.
Risk Warnings
No Guarantee of Profit: All trading involves risk of loss
System Failures: Bugs possible despite extensive testing
Market Conditions: Optimized for liquid markets (>100k daily volume). Performance degrades in illiquid instruments, major news events, flash crashes
Broker-Specific Issues: Execution slippage, commission/fees, overnight financing costs
Appropriate Use
This Indicator Is:
✅ Entry trigger system
✅ Risk management framework (stop/target)
✅ Adaptive agent selection engine
✅ Learning system that improves over time
This Indicator Is NOT:
❌ Complete trading strategy (requires position sizing, portfolio management)
❌ Replacement for fundamental analysis
❌ Guaranteed profit generator
❌ Suitable for complete beginners without training
Recommended Complementary Analysis: Market context (support/resistance), volume profile, fundamental catalysts, correlation with related instruments, broader market regime
Recommended Settings by Instrument
Stocks (Large Cap, >$1B):
Mode: Balanced | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling, Switch
Agent Sensitivity: Spoofing 1.0-1.2, Exhaustion 0.9-1.1, Liquidity 0.8-1.0, StatArb 1.1-1.3
Microstructure: All enabled, VPIN 1.2, Toxicity 1.5 | Timeframe: 15min-1H
Forex Majors (EURUSD, GBPUSD):
Mode: Balanced to Conservative | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling, Blend
Agent Sensitivity: Spoofing 0.8-1.0, Exhaustion 0.9-1.1, Liquidity 0.7-0.9, StatArb 1.2-1.5
Microstructure: All enabled, VPIN 1.0-1.1, Toxicity 1.3-1.5 | Timeframe: 5min-30min
Crypto (BTC, ETH):
Mode: Aggressive to Balanced | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling OR EXP3-IX
Agent Sensitivity: Spoofing 1.2-1.5, Exhaustion 1.1-1.3, Liquidity 1.2-1.5, StatArb 0.7-0.9
Microstructure: All enabled, VPIN 1.4-1.6, Toxicity 1.8-2.2 | Timeframe: 15min-4H
Futures (ES, NQ, CL):
Mode: Balanced | ML/RL: Enabled, Full Stack | Bandit: UCB1 or Thompson Sampling
Agent Sensitivity: All 1.0-1.2 (balanced)
Microstructure: All enabled, VPIN 1.1-1.3, Toxicity 1.4-1.6 | Timeframe: 5min-30min
Conclusion
Algorithm Predator - Pro synthesizes academic research from market microstructure theory, reinforcement learning, and multi-armed bandit algorithms. Unlike typical indicator mashups, this system implements 15 mathematically rigorous bandit algorithms, deploys a complete RL stack (Q-Learning, TD(λ), Policy Gradient), integrates institutional microstructure metrics (VPIN, Kyle's Lambda), adapts continuously through dual-layer memory and meta-learning, and provides full transparency on approximations and limitations.
The system is designed for serious algorithmic traders who understand that no indicator is perfect, but through proper machine learning, we can build systems that improve over time and adapt to changing markets without manual intervention.
Use responsibly. Risk disclosure applies. Past performance ≠ future results.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
GoldilocksTrader – Institutional Zones + Smart Money Market ModeThe GoldilocksTrader – Smart Money Trading System is a powerful institutional-grade tool designed for traders who want to follow real liquidity, identify institutional zones, and accurately read Smart Money market structure.
This indicator automatically detects Supply & Demand Zones, plots Institutional Pivot Levels, builds dynamic fade-strength heatmaps, and labels the current Market Mode (ACCUMULATE, DISTRIBUTE, WAIT)—all powered by a clean, real-time algorithm that updates with every candle.
This system helps you understand where banks, hedge funds, and institutions are likely to defend price, accumulate positions, or engineer liquidity sweeps. It makes complex Smart Money concepts simple, visual, and trader-friendly.
🧠 Core Features
✔ Institutional Supply & Demand Zones (auto-detected from swing pivots)
✔ Smart Money fade-strength heatmap using multi-layered boxes
✔ Market Mode Detection:
• ACCUMULATE – Smart Money loading long positions
• DISTRIBUTE – Smart Money unloading into premium levels
• WAIT – Neutral / imbalance zones
✔ EMA 9/21 Trend Filters
✔ VWAP Institutional Bias Filter
✔ Nearest Above/Below Liquidity Zones with clean readability
✔ Adjustable Transparency & Zone Thickness
✔ Compact On-Chart Legend (optional)
✔ Extremely lightweight, low-lag, optimized for all markets/timeframes
✔ Works for Forex, Crypto, Stocks, Indices, Futures, Commodities
📈 Trading Concepts Covered
This indicator is built around world-class concepts used by top proprietary desks and Smart Money traders, including:
ICT (Inner Circle Trader) Supply/Demand
Liquidity Zones & Institutional Order Blocks
Wyckoff Accumulation / Distribution
Imbalance & Fair Value Behavior (FVG-style fades)
Market Maker Models (MMXM + Premium/Discount Zones)
Pivot-based liquidity mapping
VWAP Institutional Bias
Trend Continuation vs. Reversal Zones
If you trade SMC, ICT, Wyckoff, Smart Money, Algo-based models, or institutional liquidity, this indicator is a perfect companion.
🚀 How It Helps You Trade
🔹 Identify hidden institutional levels where real accumulation or distribution occurs
🔹 Avoid bad trades by staying out of “WAIT” zones where most of the retail market enters.
🔹 Time entries during premium vs. discount pricing
🔹 Understand where price is expected to react, reverse, or continue
🔹 Visualize institutional pressure with fade-strength heatmaps
🔹 Combine with your own strategy to increase precision and confidence
🎨 Clean, Professional Visualization
Zones auto-extend to the left for historical context
Fade opacity increases or decreases depending on zone strength
Market Mode label plotted dynamically near relevant price zones
Optional compact legend for fast reading
All elements can be toggled and customized to your style.
⭐ Created by GoldilocksTrader™
For more institutional-level tools—including the new and soon to be popular "GoldilocksTrader Buy-Sell Signals with Built-In Optimizer"—search:
👉 “GoldilocksTrader” on TradingView
👉 Visit GoldilocksTrader.com for premium systems & education
Follow the institutions.
Trade Smart.
Trade Goldilocks™..."it's just right"
ICT Trading SuiteThe ICT Trading Suite is a complete price-action toolkit designed for traders who follow ICT concepts such as Fair Value Gaps (FVGs), Order Blocks (OBs), Supply & Demand Zones, Market Structure pivots, Liquidity Zones, and Moving Averages.
This indicator combines multiple institutional concepts into a single clean, optimized, high-performance script — allowing you to see the market the same way smart money does.
Each module can be toggled on/off to match your personal strategy.
🔥 FEATURE SET
1️⃣ Moving Averages (Fully Customisable)
5 MA slots
Multiple MA types: EMA, SMA, RMA, WMA, HMA, VWMA
Custom colours & visibility toggles
Supports all timeframes
Ideal for bias recognition and trend filtering.
2️⃣ Fair Value Gaps (FVG) – ICT 3-Candle Model
The script detects bullish and bearish FVGs using the classic ICT logic:
Bullish FVG → high < low
Bearish FVG → low > high
Features:
Automatic gap detection
Custom colours for up/down FVGs
CE (consequent encroachment) line
Optional deletion when filled
Extend FVGs dynamically
Lookback days filter
FVG blocks automatically update until price fills the imbalance.
3️⃣ Supply & Demand Zones (Swing-Based)
Built from confirmed swing highs/lows using ta.pivothigh and ta.pivotlow.
Features:
ATR-based zone thickness
Zone overlap filtering
Auto-cleaning oldest zones
POI (Point of Interest) marker
3 types of arrays:
Supply zone boxes
Demand zone boxes
POI midline boxes
Zones extend 100 bars by default and update dynamically.
Zones are deleted instantly when price breaks them (converted into BOS behavior).
4️⃣ Smart Money Order Blocks (Simple Engulfing Pattern)
OBs are detected using the classic engulfing model:
Bullish OB
Bearish candle → Engulfed by bullish candle where
close > high
Bearish OB
Bullish candle → Engulfed by bearish candle where
close < low
Each OB stores:
Original top/bottom
Current top/bottom
POI line (optional)
Engulfing candle structure
Mitigation state
Features:
Dynamic boundaries (OB shrinks as price mitigates)
POI line update
Automatic deletion (or recolour) when completely mitigated
Limit how many OBs stay on chart
Support for adding HTF OBs later
This creates very clean and very accurate ICT order blocks.
5️⃣ Liquidity / Vector Zones (Volume-Spread Analysis)
A built-in PVSRA-style logic marks areas of institutional activity.
Vector candles detected using:
Volume ≥ 200% of average
Or candle spread × volume ≥ highest in last 10 bars
Medium-volume vectors (150%) also included
Colour-coded zones extend to the right
Auto-cleanup once price clears the zone
Useful for detecting areas where algorithms (MMXs) aggressively buy/sell.
6️⃣ Pivot Levels
Multiple pivot methods supported:
Traditional
Fibonacci
Woodie
Classic
DM
Camarilla
Features:
Auto / Daily / Weekly / Monthly / Quarterly / Yearly pivots
Dynamic line extension
Labels with prices
Custom colours
Only draws selected pivot levels
Efficient matrix-based pivot system
💎 TECHNICAL EXCELLENCE
✔ Pine Script v6
✔ Efficient arrays & memory handling
✔ Clean dynamic updates
✔ Max-performance structure
✔ Modular design (each component can be toggled)
✔ Integrates all ICT concepts in one tool
🎯 Who Is This Indicator For?
Perfect for:
ICT Traders
Smart Money / Institutional Traders
Day Traders & Scalpers
Swing Traders using OB/FVG
Liquidity hunters
Market structure based traders
Volume-spread or PVSRA focused traders
This combines multiple institutional concepts without cluttering the chart.
🏆 Final Notes
This is a true all-in-one institutional suite, replacing up to 8 separate indicators.
Designed for precision, clarity, and professional price-action workflow.
FXGringo1.2FXGringo - Decision Points
This indicator identifies support and resistance zones based on reference points provided in the levels field, interpreting them as potential areas of price reaction. From these points, the script plots strength levels, allowing the trader to visualize regions where the price may encounter natural barriers to equilibrium between supply and demand.
Although the internal calculations do not directly reveal the complete methodology, its logic can be compared to concepts similar to gamma levels (GEX), insofar as it seeks to map zones where price movement tends to be more sensitive due to the concentration of positions or relevant market flows.
How the Indicator Works:
Input of External Points:
The user manually provides price points that represent potential support or resistance levels.
Strength Classification:
The indicator processes these points and plots each level based on criteria such as distance from the current price, frequency of occurrence in the history, and pre-calculated volatility variation. This generates a visual and quantitative hierarchy among the provided levels.
Context Analysis:
Based on the interaction between price and these levels, the script identifies and plots zones of greater relevance—where the price tends to react, consolidate, or reverse.
Confluence Analysis:
Observe how the external levels align with peaks, troughs, and volume zones. The overlap of strong levels often indicates areas of great institutional interest.
Risk Management:
Use the identified levels to plan entry and exit points and stop-loss or take-profit placement, based on the relative strength of the levels.
Modern Conceptual Basis: The methodology, although proprietary, can be compared to how gamma levels reflect zones of greater price sensitivity relative to the market's aggregate exposure.
Conclusion:
This indicator acts as an advanced tool for interpreting support and resistance levels, using external data to build a dynamic map of market interest zones. Its operation can be seen as an analogy to gamma levels (GEX), identifying regions where the price tends to react more significantly due to liquidity concentration or position imbalance. This approach provides the trader with a refined view of the areas of influence of large players, assisting in making decisions with greater precision and confidence.
Force DashboardScalping Dashboard - Complete User Guide
Overview
This scalping system consists of two complementary TradingView indicators designed for intraday trading with no overnight holds:
Force Dashboard - Single-row table showing real-time market bias and entry signals
Large Order Detection - Visual diamonds showing institutional order flow
Together, they provide a complete at-a-glance view of market conditions optimized for quick entries and exits.
Recommended Timeframes
Primary Scalping Timeframes
1-minute chart: Ultra-fast scalps (30 seconds - 3 minutes hold time)
2-minute chart: Quick scalps (2-5 minutes hold time)
5-minute chart: Standard scalps (5-15 minutes hold time)
Best Practices
Use 1-2 minute for highly liquid instruments (ES, NQ, major forex pairs)
Use 5-minute for less liquid markets or if you prefer fewer signals
Never hold past the last hour of trading to avoid overnight risk
Set hard stop times (e.g., exit all positions by 3:45 PM EST)
Dashboard Components Explained
Core Indicators (Circles ●)
MACD (5/13/5)
Green ● = Bullish momentum (MACD histogram positive)
Red ● = Bearish momentum (MACD histogram negative)
Gray ● = No clear momentum
Use: Confirms trend direction and momentum shifts
EMA (9/20/50)
Green ● = Price > EMA9 > EMA20 (uptrend)
Red ● = Price < EMA9 < EMA20 (downtrend)
Gray ● = Choppy/sideways
Use: Identifies the immediate micro-trend
Stoch (5-period Stochastic)
Green ● = Oversold (<20) - potential reversal up
Red ● = Overbought (>80) - potential reversal down
Gray ● = Neutral zone (20-80)
Use: Spots reversal opportunities at extremes
RSI (7-period)
Green ● = Oversold (<30)
Red ● = Overbought (>70)
Gray ● = Neutral
Use: Confirms overbought/oversold conditions
CVD (Cumulative Volume Delta)
Green ● = CVD above its moving average (buying pressure)
Red ● = CVD below its moving average (selling pressure)
Gray ● = Neutral
Use: Shows overall buying vs selling pressure
ΔCVD (Delta CVD - Rate of Change)
Green ● = CVD accelerating upward (buying acceleration)
Red ● = CVD accelerating downward (selling acceleration)
Gray ● = No acceleration
Use: Detects momentum shifts in order flow
Imbal (Order Flow Imbalance)
Green ● = Buy pressure >2x sell pressure
Red ● = Sell pressure >2x buy pressure
Gray ● = Balanced
Use: Identifies extreme one-sided order flow
Vol (Volume Strength)
Green ● = Volume >1.5x average (strong interest)
Red ● = Volume <0.7x average (low interest)
Gray ● = Normal volume
Yellow background = Volume surge (>2x average) - BIG MOVE ALERT
Use: Confirms conviction behind price moves
Tape (Tape Speed)
Green ● = Fast order flow (>1.3x normal)
Red ● = Slow order flow (<0.7x normal)
Gray ● = Normal speed
Yellow background = Very fast tape (>1.5x) - RAPID EXECUTION ALERT
Use: Measures urgency and speed of orders
Key Levels
Support (Supp)
Shows the nearest high-volume support level below current price
Bright Green background = Price is AT support (within 0.3%) - BOUNCE ZONE
Green background = Price above support (healthy)
Red background = Price below support (broken support, now resistance)
Resistance (Res)
Shows the nearest high-volume resistance level above current price
Bright Orange background = Price is AT resistance (within 0.3%) - REJECTION ZONE
Red background = Price below resistance (facing overhead supply)
Green background = Price above resistance (breakout)
These levels update automatically every 3 bars based on volume profile
Entry Signal Components
Score
Displays format: "6L" (6 long indicators) or "4S" (4 short indicators)
Bright Green = 6-7 indicators aligned for long
Light Green = 5 indicators aligned for long
Yellow = 4 indicators aligned (weaker setup)
Gray = No alignment
Red/Orange colors = Same scale for short setups
Score of 5+ indicates high-probability setup
SCALP (Main Entry Signal)
BRIGHT GREEN "LONG" = High-quality long scalp (Score 5+)
Green "LONG" = Decent long scalp (Score 4)
BRIGHT ORANGE "SHORT" = High-quality short scalp (Score 5+)
Red "SHORT" = Decent short scalp (Score 4)
Gray "WAIT" = No clear setup - STAY OUT
Entry Strategies
Strategy 1: High-Probability Scalps (Conservative)
When to Enter:
SCALP column shows BRIGHT GREEN "LONG" or BRIGHT ORANGE "SHORT"
Score is 5 or higher
Vol or Tape has yellow background (volume surge)
Example Long Setup:
SCALP = BRIGHT GREEN "LONG"
Score = 6L
Vol = Yellow background
Price AT Support (bright green Supp cell)
EMA, MACD, CVD, ΔCVD, Imbal all green
Entry: Enter immediately on next candle
Target: 0.5-1% move or resistance level
Stop: Below support or -0.3%
Hold Time: 2-10 minutes
Strategy 2: Momentum Scalps (Aggressive)
When to Enter:
Tape has yellow background (fast tape)
Vol has yellow background (volume surge)
ΔCVD is green (for longs) or red (for shorts)
Imbal shows strong imbalance in your direction
Score is 4+
Example Short Setup:
Tape & Vol = Yellow backgrounds
ΔCVD = Red, Imbal = Red
Price AT Resistance (bright orange)
Score = 5S
Entry: Enter immediately
Target: Quick 0.3-0.7% move
Stop: Tight -0.2%
Hold Time: 1-5 minutes
Strategy 3: Reversal Scalps (Mean Reversion)
When to Enter:
Stoch shows oversold (green) or overbought (red)
RSI confirms the extreme
Price is AT Support (for longs) or AT Resistance (for shorts)
ΔCVD and Imbal start reversing direction
Score is 4+
Example Long Setup:
Stoch = Green (oversold)
RSI = Green (oversold)
Supp = Bright green (at support)
ΔCVD turns green
Imbal turns green
Score = 4L or 5L
Entry: Wait for confirmation candle
Target: Move back to EMA9 or mid-range
Stop: Below the low
Hold Time: 3-8 minutes
Large Order Detection Usage
Diamond Signals
Green diamonds below bar = Large buy orders (institutional buying)
Red diamonds above bar = Large sell orders (institutional selling)
Size matters: Larger diamonds = larger order flow
How to Use with Dashboard
Confirmation Entries
Dashboard shows "LONG" signal
Green diamond appears
Enter immediately - institutions are buying
Divergence Alerts (CAUTION)
Dashboard shows "LONG" signal
RED diamond appears (institutions selling)
DO NOT ENTER - conflicting order flow
Cluster Patterns
Multiple green diamonds in row = Strong accumulation, stay long
Multiple red diamonds in row = Strong distribution, stay short
Alternating colors = Chop, avoid trading
Risk Management Rules
Position Sizing
Risk 0.5-1% of account per scalp
Maximum 3 concurrent positions
Reduce size after 2 consecutive losses
Stop Loss Guidelines
Tight stops: 0.2-0.3% for 1-2 min charts
Standard stops: 0.3-0.5% for 5 min charts
Always use stop loss - no exceptions
Place stops below support (longs) or above resistance (shorts)
Take Profit Targets
Target 1: 0.3-0.5% (take 50% off)
Target 2: 0.7-1% (take remaining 50%)
Move stop to breakeven after Target 1 hit
Trail stop if Score remains high
Time-Based Exits
Exit immediately if:
SCALP changes from LONG/SHORT to WAIT
Score drops below 3
Large diamond appears in opposite direction
Maximum hold time: 15 minutes (even if profitable)
Hard exit time: 30 minutes before market close
Trading Sessions
Best Times to Scalp
High-Liquidity Sessions
9:30-11:00 AM EST (Market open, highest volume)
2:00-3:30 PM EST (Afternoon session, good moves)
Avoid
11:30 AM-1:30 PM EST (Lunch, low volume)
Last 30 minutes (unpredictable, don't initiate new trades)
News releases (wait 5 minutes for volatility to settle)
Common Patterns & Setups
The Perfect Storm (Highest Probability)
Score = 6L or 7L
SCALP = BRIGHT GREEN
Vol + Tape = Yellow backgrounds
Green diamond appears
Price AT Support
Win rate: ~70-80%
The Fade Setup (Counter-Trend)
Price hits resistance (bright orange)
Stoch + RSI overbought (red)
Red diamond appears
CVD starts turning red
SCALP shows "SHORT"
Win rate: ~60-70%
The Breakout Continuation
Price breaks resistance (Res turns green)
EMA, MACD green
Vol surge (yellow)
Multiple green diamonds
SCALP = "LONG"
Win rate: ~65-75%
Warning Signs - DO NOT TRADE
Red Flags
❌ SCALP shows "WAIT"
❌ Score below 3
❌ Vol and Tape both gray (no volume)
❌ Conflicting signals (dashboard says LONG but red diamonds appearing)
❌ Alternating green/red circles (choppy market)
❌ Support and Resistance very close together (tight range)
Market Conditions to Avoid
Low volume periods
Major news releases (first 5 minutes after)
First 2 minutes after market open
Wide spreads
Consecutive losing trades (take a break after 2 losses)
Quick Reference Checklist
Before Taking ANY Trade:
☑ SCALP shows LONG or SHORT (not WAIT)
☑ Score is 4 or higher
☑ Vol or Tape shows activity
☑ No conflicting diamond signals
☑ Stop loss level identified
☑ Target profit level identified
☑ Not in restricted time periods
After Entering:
☑ Set stop loss immediately
☑ Set profit targets
☑ Watch SCALP column - exit if changes to WAIT
☑ Watch for opposite-colored diamonds
☑ Move stop to breakeven after first target
☑ Exit all by market close
Advanced Tips
Scalping Psychology
Be patient: Wait for Score 5+ setups
Be decisive: When signal appears, act immediately
Be disciplined: Follow your stop loss always
Be flexible: Exit quickly if dashboard reverses
Optimization
Backtest on your specific instrument
Adjust RSI/Stoch levels for your market
Fine-tune volume thresholds
Keep a trade journal to track which setups work best
Multi-Timeframe Confirmation
Use 5-min dashboard as "trend filter"
Take 1-min trades only in direction of 5-min SCALP signal
Increases win rate by ~10-15%
Troubleshooting
Q: Dashboard shows WAIT most of the time
Normal - scalping is about patience. Quality > Quantity
3-8 good setups per day is excellent
Q: Too many false signals
Increase minimum Score requirement to 5 or 6
Only trade with volume surge (yellow backgrounds)
Add large order detection confirmation
Q: Signals too slow
You may be on too high a timeframe
Try 1-minute chart for faster signals
Ensure real-time data feed is active
Q: Support/Resistance not updating
Normal - updates every 3 bars
If completely stuck, remove and re-add indicator
Summary
This scalping system works best when:
✅ Multiple indicators align (Score 5+)
✅ Volume and tape speed confirm the move
✅ Order flow (diamonds) confirms direction
✅ Price is at key levels (support/resistance)
✅ You manage risk strictly
✅ You exit before market close
The golden rule: When SCALP says WAIT, you WAIT. Discipline beats frequency.
ob-fvg-jorgechutofx📊 **4-Candle Pattern (OB + FVG + BOS)**
This indicator identifies a four-candle structural pattern combining **Order Block (OB)**, **Fair Value Gap (FVG)**, and **Break of Structure (BOS)**.
* **Candle 1:** reference level to be broken.
* **Candle 2:** potential **Order Block** (origin zone).
* **Candle 3:** confirms the **structure break**.
* **Candle 4:** forms the **FVG**, showing market imbalance.
Perfect for spotting **institutional entry zones** and validating **market inefficiencies** across any timeframe.
Adaptive AI Polar Oscillator [by Oberlunar]Adaptive AI Oscillator blends trading signals with two order-flow style oscillators and a lightweight online-learning model to keep it reactive, adaptive and computationally feasible.
What it is
A lightweight Multi Layer Perceptron (neural net) updates online on every bar, so it keeps adapting as conditions change.
An adaptive collector that fuses features like Price (close, ohlc4, etc...), a selectable (but not used in the original implementation) Moving Average (EMA/SMA/WMA/RMA/HMA/DEMA/TEMA), RSI, the classic volume datafeeds, plus two “OberPolar” oscillators computed above and below the current integral area price.
What you see
White line — the model’s denormalised forecast (in price units).
Colored price line — actual price, shown aqua when forecast ≥ price (“golden” bias) and red when forecast < price (“death” bias).
Why it helps
Combines heterogeneous information (trend, momentum, participation, regional buy/sell pressure) into a single adaptive forecast.
Online learning reduces regime staleness versus fixed-parameter indicators.
The aqua/red bias offers a quick, visual state for discretionary decisions.
How it works (intuitive)
Each AI input is standardised (z-score) with optional clamping to mitigate outliers.
A rolling window of recent values feeds a 2-layer AI to predict one step ahead.
After each bar closes, the model compares forecast vs. reality and nudges its weights (SGD with momentum, L2, optional gradient clipping).
The forecast is de-standardised back to price units and plotted as the white line.
Reading guide
Crossovers between forecast and price often mark potential bias flips.
Persistent aqua → model perceives supportive/positive conditions.
Persistent red → model perceives headwinds/negative conditions.
Complex Strategy — Oscillator Trendline Break
Connect the first pivot in the fading bias with the first pivot in the new bias, then trade the break of that line in the direction of the new bias.
Idea in one line
Use the Adaptive AI Oscillator (green = bullish bias, red = bearish). When bias flips, build a line across the oscillator pivots that “span” the transition; the break of that line times the entry.
Long setup (mirror for shorts)
Bias transition : a bearish (red) regime is ongoing, then the oscillator turns bullish (green).
Anchor pivots : take the first MIN in red just before/around the flip and the first MAX in green after the flip. Draw a trendline L through these two oscillator values (time–value line).
Trigger : enter LONG on the close that breaks above L —optional confirmations: price above your MA, non-decreasing volume, no immediate supply zone overhead.
Risk : stop below the last oscillator swing low or below a retest of L; first target at 1R–1.5R or at the opposite bias zone; trail under successive oscillator higher lows.
Short setup
Bias turns from green (bullish) to red (bearish).
Connect the first MAX in green to the first MIN in red → line L.
Enter SHORT on a close below L ; stop above the last oscillator swing high; symmetric targets/trailing.
Complex Strategy #2 — Bias-Pivot Breakout with Exit on Line Failure
Connect two pivots of the same bias to build a dynamic barrier; trade the breakout in the bias direction and exit when that line later fails.
Long play (mirror for shorts)
Build the line. During a green (bullish) phase, mark the first two local MAX of the oscillator. Connect them to form the yellow resistance line L (extend it right). If a new, clearer MAX appears before a break, re-anchor using the two most recent highs.
Entry trigger. Go LONG on a close above L (the “Break and LONG” in the image). Optional filters: price above your MA, rising volume, no immediate overhead level.
Risk. Initial stop: below the last oscillator swing low or below the retest of L . Position size for 1–2R baseline.
Exit. Close the long when the oscillator later breaks back below L (the “Break and LONG exit”), or on a bias flip to red, or at a fixed target/trailing under higher lows.
Short play (symmetric)
In a red phase, connect the first two local MIN to form support line L .
Enter SHORT on a close below L ; stop above the last oscillator swing high; exit on a break back above L or on a flip to green.
Notes
Require a minimum slope/spacing between pivots to avoid flat/noisy lines.
Re-anchor the line if fresher pivots emerge before a valid break.
Use with your regime filter (MA slope, higher-timeframe bias) to reduce whipsaws.
Complex Strategy #3 — Lateral Box & Zero-Slope Breakout
An easy way to understand sideways phases and the next price direction: draw two zero-slope lines (flat upper/lower bounds) across the oscillator’s lateral area; when a strong break occurs, trade in the direction of that break.
How to use it
Identify a lateral area on the oscillator (flat, low-variance region). Place a flat upper line on tops and a flat lower line on bottoms (slope ≈ 0).
Wait for a decisive break : close outside the band with expansion (range/true range rising, or a wide candle).
• Break up → bias for LONG .
• Break down → bias for SHORT .
Why it helps
Flat lines isolate congestion; the next impulsive move is often revealed by which side is broken with force.
It filters noise inside the range and focuses attention on the transition from balance → imbalance.
Practical filters (optional)
Require minimum bar body/ATR on the breakout candle to avoid false breaks .
Confirm with your regime filter (e.g., price above/below your MA) or a quick retest that holds.
Invalidate the signal if the price immediately returns inside the band on the next bar.
General Operational notes
If new pivots form before a break, re-anchor the line with the most recent qualifying pair (keeps the structure fresh).
Ignore very shallow lines (near-flat): require a minimum slope or angle to avoid noise.
Combine with your bias filter (e.g., MA slope/regime) to reduce false starts.
Limits & good practice
Adaptive models can react to noise; treat signals as context within a risk-managed plan.
No model predicts the future—this summarises evolving conditions compactly.
— Oberlunar 👁 ★
Supply and Demand Scanner Toolkit [TradingFinder]🔵 Introduction
The analytical system presented here is built upon a deep quantitative foundation designed to capture the dynamic behavior of supply and demand in live markets. At its core, it calculates continuously adaptive zones where institutional liquidity, volatility shifts, and momentum transitions converge. These zones are derived from a combination of a regression-based moving average, a long-period ATR, and Fibonacci expansion ratios, all working together to model real-time volatility, price momentum, and the underlying market imbalance.
In practice, this means that at any given moment, five primary bands and seven variable analytical zones are generated around price, representing different market states ranging from extreme overbought to extreme oversold.
Each band reacts dynamically to price volatility, recalibrating with every new candle, which allows the system to mirror the true, constantly changing structure of supply and demand. Every movement between these zones reflects a transition in the strength and dominance of buyers and sellers, a process referred to as volatility-driven price state transitions.
Traditional analytical models often rely on fixed or static indicators that cannot keep up with the rapid microstructural changes in modern markets. This system instead uses regression and smoothing logic to adapt on the fly. By combining a regression moving average with a smoothed moving average, the model calculates real-time trend direction, momentum flow, and trend strength.
When the regression average rises above the smoothed one, the system classifies the trend as bullish; when it falls below, bearish. This dual-layer structure not only helps confirm direction but also enables the automatic detection of critical structural shifts such as Break of Structure (BoS), Change of Character (CHoCH), and directional reversals.
Both the current trend (Live Trend) and projected future trend (Vision Trend) are calculated simultaneously across all available timeframes. This dual analysis allows traders to identify structural changes earlier and to recognize whether a trend is gaining or losing momentum.
In most conventional moving-average-based frameworks, trading signals are delayed because these models react to price rather than anticipate it. As a result, many buy or sell signals appear after the real move has already begun, leading to entries that contradict the current trend. This system eliminates that lag by employing a mean reversion trading model. Instead of waiting for crossovers, it observes how far price deviates from its statistical mean and reacts when that deviation begins to shrink, the moment when equilibrium forces reemerge.
This approach produces non-lagging, data-driven signals that appear at the exact moment price begins to revert toward balance. At the same time, traders can visually assess the market’s condition by observing the spacing, compression, or expansion of the dynamic bands, which represent volatility shifts and trend energy. Through this interaction, the trader can quickly gauge whether a trend is strengthening, losing power, or preparing for a reversal. In other words, the model provides both quantitative precision and intuitive visualization.
A unique visual element in this system is how candles are displayed during transitional states. When Live Trend and Vision Trend contradict each other, for instance, when the current trend is bullish but the projected trend turns bearish, candle bodies automatically appear as hollow.
These hollow candles act as visual alerts for zones of uncertainty or equilibrium between buyers and sellers, often preceding trend reversals, liquidity sweeps, or volatility compression phases. Traders quickly learn to interpret hollow candles as signals to pause, observe, or prepare for potential shifts rather than to act impulsively.
Signal generation in this model occurs when price reverts from extreme zones back toward neutrality. When price exits the strong overbought or strong oversold zones and reenters a milder area, the system produces a reversal signal that aligns with real-time market dynamics. To refine accuracy, these signals are confirmed through several filters, including momentum verification, volatility behavior, and smart money validation. This multi-layered signal logic significantly reduces false entries, helping traders avoid overreactions to temporary liquidity spikes and enhancing performance in volatility-driven markets.
On a broader level, the model supports full multi-timeframe analysis. It can analyze up to twenty symbols simultaneously, across multiple timeframes, to detect directional bias, correlation, and confluence. The result is a holistic map of market structure in real time, showing how each asset aligns or diverges from others and how lower timeframes fit into the macro trend. Variables such as Live Trend, Vision Trend, Directional Strength, and Zone Positioning combine to give a complete structural snapshot at any given moment.
Risk management is handled by an adaptive Trailing Stop Engine that continuously aligns with current volatility and price flow. It integrates pivot mapping with ATR-based calculations to dynamically adjust stop-loss levels as price evolves. The engine offers four adaptive modes, Grip, Flow, Drift, and Glide, each tailored to different levels of market volatility and trader risk tolerance. In visualization, the profit area between entry and stop-loss is shaded light green for long positions and light red for short positions. This design allows immediate recognition of active risk exposure and profit lock-in zones, all in real time.
Altogether, the combination of ATR Volatility Mapping, Fibonacci Band Calibration, Regression-Based Trend Engine, Dynamic Supply and Demand Equilibrium, Conflict Detection through Hollow Candles, Mean Reversion Signal Model, and Adaptive Trailing Stop forms a unified analytical system. It maps the market’s structure, identifies current and future trends, measures the real-time balance of buyers and sellers, and highlights optimal entry and exit points. The final result is higher analytical precision, improved risk control, and a clearer view of the true, data-defined market structure.
🔵 How to Use
Analyzing supply and demand in live financial markets is one of the most complex challenges traders face. Price rarely moves in a straight line; instead, it evolves through phases of expansion, compression, and redistribution. Many traders misinterpret these movements because the zones that appear strong or reactive at first glance often represent nothing more than temporary liquidity redistributions.
These areas, while visually convincing, may lose relevance quickly when volatility increases or when viewed from another timeframe. In high-volatility environments, traditional zone analysis becomes even more unreliable. Price may seem to respect a support or resistance level only to break through it a few candles later. This behavior creates false zones and misleading reversal points.
The key to filtering such movements lies in understanding the context, how volatility, momentum, and structural flow interact across different timeframes. A single timeframe can only tell part of the story. The market’s true structure emerges only when data is synchronized from macro to micro levels.
This is where multi-timeframe correlation becomes essential. Every timeframe offers a different lens through which supply and demand balance can be observed. For example, a trader might see a bullish setup on a 15-minute chart while the 4-hour chart is still showing a strong distribution phase. Without alignment between these layers, trades are easily positioned against the dominant liquidity flow. The model presented here solves this by processing all relevant timeframes simultaneously, allowing traders to see how short-term movements fit within higher-level structures.
Each market phase, whether accumulation, expansion, or reversion, carries a unique volatility fingerprint. The system tracks transitions in volatility regimes, momentum divergence, and structural breakouts to anticipate when a phase change is approaching. For instance, when volatility compresses and ATR readings narrow, it often signals an upcoming breakout or reversal. By monitoring these shifts in real time, the model helps the trader differentiate between liquidity grabs (temporary volatility spikes) and genuine structural changes.
Every supply-demand interaction within this system is adaptive rather than static. The zones continuously recalibrate based on live parameters such as price velocity, momentum distribution, and liquidity displacement. This adaptive structure ensures that the balance between buyers and sellers is represented accurately as market conditions evolve.
In practice, this allows the user to identify early signs of trend exhaustion, potential reversals, and continuation patterns long before traditional indicators would react.
In essence, successful supply and demand analysis requires moving beyond subjective interpretation toward data-driven decision-making.
Manual drawing of zones or relying solely on visual intuition can lead to inconsistent results, especially in fast-changing markets. By combining ATR-driven volatility mapping, mean reversion dynamics, and multi-timeframe alignment, this framework offers a clear, objective, and responsive model of how market forces actually operate. Each decision becomes grounded in measurable context, not assumptions.
The analytical interface is divided into two main sections : the visual chart framework and the scanner data table.
On the chart, five dynamic bands and seven analytical zones appear around price. These are calculated from ATR, regression moving average, and Fibonacci expansion ratios to define whether the market is overbought, oversold, or neutral. Each zone has distinct color coding, allowing traders to recognize the market state instantly without switching tools or indicators.
Price movement within these bands reveals more than just direction, it tells a story of volatility, liquidity flow, and market equilibrium. The upper zones typically indicate exhaustion of buying pressure, while lower zones highlight areas of overselling or potential recovery. The way price reacts near these boundaries can help determine whether a continuation or reversal is likely.
At the heart of the visualization are two layered trend components : Live Trend and Vision Trend.
The Live Trend shows the present market direction based on regression and smoothing logic, while the Vision Trend projects the probable future trajectory by analyzing slope deviation and momentum displacement. When these two align, the trader sees confirmation of market strength. When they diverge, candle bodies turn hollow, a simple yet powerful visual alert signaling hesitation, consolidation, or a possible turning point.
At the bottom of the interface, the Scanner Table organizes all analytical data into a structured display. Each row corresponds to a symbol and timeframe, showing the current Live Trend, Vision Trend, Directional Strength, Zone Position, and Signal Age. This table provides a real-time overview of all assets being tracked, showing which ones are trending, which are in reversal, and which are entering transition zones. By analyzing this table, traders can instantly identify correlation clusters, where multiple assets share the same trend direction, often a sign of broader market sentiment shifts.
The Scanner can simultaneously process multiple timeframes and up to twenty different assets, producing a panoramic market overview. This makes it easy to apply a top-down analytical workflow, starting with higher timeframe alignment, then drilling down into lower levels for execution. Instead of reacting to isolated signals, traders can see where confluence exists across structures and focus only on setups that align with overall market context.
The bands and their color coding make interpretation intuitive even for less experienced users. Darker shades correspond to extreme zones, typically where institutional orders are being absorbed or distributed, while lighter zones mark mild overbought or oversold conditions. When price transitions from an outer extreme zone into a milder region, a signal condition becomes active. At this point, traders can cross-check the event using momentum and volatility filters before acting.
The trailing stop section of the display adds another critical dimension to decision-making. It visualizes stop levels as continuously updating colored lines that follow price movement. These levels are calculated dynamically through pivot mapping and ATR-based sensitivity. The shaded area between the entry point and active stop loss (light green for buys, light red for sells) gives traders immediate insight into how much of the move is currently secured as profit and how much remains exposed. This simple visual cue transforms risk management from a static calculation into a living, responsive process.
All components of this analytical system are fully customizable. Users can adjust signal type, calculation periods, smoothing intensity, and band sensitivity to match their trading style. For example, a scalper might shorten ATR and MA periods to capture rapid fluctuations, while a swing trader might increase them for smoother and more stable readings. Because every element responds to live data, even small adjustments lead to meaningful changes in how the system behaves.
When combined with the scanner’s data table, these features enable a top-down analytical workflow, one where decisions are not made from isolated indicators but from a complete, multi-dimensional understanding of market structure. The result is a system that supports both reactive precision and proactive market awareness.
🟣 Long Signal
A long signal is generated when price begins to rebound from deeply oversold conditions. More precisely, when price enters the strong or extreme oversold zones and then returns into the mild oversold region, the system identifies the start of a mean reversion phase. This transition is not based on subjective interpretation but on mathematical deviation from equilibrium, meaning that selling pressure has been exhausted and liquidity begins to shift toward buyers.
Unlike delayed signals that depend on moving average crossovers or oscillators, this signal appears the moment price starts moving back toward balance. The model’s mean reversion logic detects when volatility contraction and momentum realignment coincide, producing a non-lagging entry condition.
In this situation, traders can visually confirm the setup by observing the spacing and curvature of the lower bands. When the lower volatility bands begin to flatten or curve upward while ATR readings stabilize, it indicates that the market is transitioning from distribution to accumulation.
The strength and quality of each long signal depend on the configuration of trend variables. When both Live Trend and Vision Trend are bullish, the probability of continuation is significantly higher. This alignment suggests that the market’s short-term momentum is supported by long-term structure. On the other hand, when the two trends contradict each other, which the chart highlights with hollow candles, it represents a temporary phase of indecision or conflicting forces.
In these moments, traders are encouraged to monitor volatility compression and observe whether the next few candles confirm a real breakout or revert back to range conditions.
Additional confirmation can be derived from observing the slope of the regression moving average and the magnitude of ATR fluctuations. A steeper upward slope combined with decreasing volatility indicates stronger bullish intent. In contrast, if ATR expands while price remains flat, it signals potential traps or fakeouts driven by short-term liquidity grabs.
Valid long signals often emerge near the end of volatility compression periods or immediately after liquidity sweeps around major lows. These are points where large players typically absorb remaining sell orders before initiating upward movement. Once the long condition triggers, the system automatically calculates the initial stop loss using a combination of recent pivots and ATR range. From that point, the Trailing Stop Engine dynamically adjusts as price rises, maintaining optimal distance from the entry point and locking in profits without restricting trade potential.
For educational context, consider a situation where the market has been trending downward for several sessions, and the ATR value begins to decline, showing that volatility is compressing. As price touches the lower extreme zone and reverses into the mild oversold region while Live Trend starts turning positive, this creates an ideal long condition. A new cycle of expansion often begins right after such compression, and the system captures that early shift automatically.
🟣 Short Signal
A short signal represents the opposite scenario, a point where buying momentum weakens after a strong rally, and price begins to revert downward toward equilibrium. When price exits the strong or extreme overbought zones and moves into the mild overbought region, the model detects the start of a bearish mean reversion phase.
Here too, the signal appears without delay, as it is based on the real-time relationship between price and its volatility boundaries rather than on indicator crossovers.
The system identifies these short conditions when upward momentum shows visible fatigue in the volatility bands. The upper bands start to flatten or turn downward while the regression slope begins to lose angle. This is often accompanied by rising ATR readings, showing an expansion in volatility that reflects distribution rather than continuation.
The quality of the short signal is strongly influenced by the interaction between the two trend layers. When both Live Trend and Vision Trend point downward, the likelihood of sustained bearish continuation increases dramatically. However, if they diverge, candle bodies turn hollow, clearly marking zones of conflict or hesitation. These phases often coincide with the end of a bullish impulse wave and the start of an early correction.
A practical example can illustrate this clearly. Imagine a market that has been trending upward for several days with expanding volatility. When price pushes into the extreme overbought zone and starts pulling back into the mild region, the system interprets it as the first sign of distribution. If at the same time the regression moving average flattens and ATR begins to rise, it strongly suggests that institutional participants are taking profit. The generated short signal allows the trader to position early in anticipation of the downward reversion that follows.
The initial stop loss for short trades is calculated above the most recent pivot high, ensuring logical protection based on the structural context. From there, the Trailing Stop Engine automatically tracks the price movement downward, tightening stops as volatility decreases or expanding them during sharp swings to avoid premature exits.
The engine’s dynamic nature makes it suitable for both aggressive scalpers and patient swing traders. Scalpers can set the trailing sensitivity to “Grip” mode for tighter control, while swing traders can use “Glide” mode to capture larger portions of the trend.
Most short signals form right after volatility expansion or liquidity grabs around major highs, classic exhaustion areas where momentum divergence becomes evident. The combination of visual cues (upper band curvature, hollow candles, ATR spikes) provides traders with multiple layers of confirmation before taking action.
In both long and short scenarios, this analytical system replaces emotional decision-making with structured interpretation. By translating volatility, momentum, and price positioning into clear contextual patterns, it empowers the trader to see where reversals are forming in real time rather than guessing after the move has started.
🔵 Setting
🟣 Logical Setting
Channel Period : The main channel period that defines the base moving average used to calculate the central line of the bands. Higher values create a smoother and longer-term structure, while lower values increase short-term sensitivity and faster reactions.
Channel Coefficient Period : The ATR period used to measure volatility for determining the channel width. Higher values provide greater channel stability and reduce reactions to short-term market noise.
Channel Coefficient : The ATR sensitivity factor that defines the distance of the bands from the central average. A higher coefficient widens the bands and increases the probability of detecting overbought or oversold conditions earlier.
Band Smooth Period : The smoothing period applied to the bands to filter minor price noise. Lower values produce quicker reactions to price changes, while higher values create smoother and more stable lines.
Trend Period : The period used in the regression moving average calculation to identify overall trend direction. Shorter values highlight faster trend shifts, while longer values emphasize broader market trends.
Trend Smooth Period : The smoothing period for the regression trend to reduce volatility and confirm the dominant market direction. This setting helps to better distinguish between corrective and continuation phases.
Signals Gap : The time interval between generated signals to prevent consecutive signal clustering. A higher value strengthens the temporal filter and produces more selective and refined signals.
Bars to Calculate : Defines the number of historical candles used in calculations. Limiting this value optimizes script performance and reduces processing load, especially when multiple symbols or timeframes are analyzed simultaneously. Higher values increase analytical depth by including more historical data, while lower values improve responsiveness and reduce potential lag during live chart updates.
Trailing Stop : Enables or disables the dynamic trailing stop engine. When active, the system automatically adjusts stop loss levels based on live volatility and price structure, maintaining alignment with market flow and trend direction.
Trailing Stop Level : Defines the operational mode of the trailing stop engine with four adaptive styles: Grip, Flow, Drift, and Glide. Grip offers tight stop management for scalping and high precision setups, while Glide allows wider flexibility for swing or long-term trades.
Trailing Stop Noise Filter : Applies an additional filtering layer that smooths minor fluctuations and prevents unnecessary stop adjustments caused by short-term market noise or micro volatility.
🟣 Display Settings
Show Trend on Candles : Displays the current trend direction directly on price candles by applying dynamic color coding. When Live Trend and Vision Trend align bullish, candles appear in green tones, while bearish alignment displays in red. If the two trends conflict, candle bodies turn hollow, marking a Trend Conflict Zone that signals potential indecision or upcoming reversal. This feature provides instant visual confirmation of market direction without the need for external indicators
Table on Chart : Allows users to choose whether the analytical table appears directly over the chart or positioned below it. This gives full control over screen layout based on personal workspace preference and chart design.
Number of Symbols : Controls how many symbols are displayed in the screener table, adjustable from 10 up to 20 in steps of 2. This flexibility helps balance between detailed screening and visual clarity on different screen sizes.
Table Mode : Defines how the screener table is visually arranged.
Basic Mode : Displays all symbols in a single column for vertical readability.
Extended Mode : Arranges symbols side by side in pairs to create a more compact and space-efficient layout.
Table Size : Adjusts the visual scaling of the table. Available options include auto, tiny, small, normal, large, and huge, allowing traders to optimize table visibility based on their screen resolution and preferred chart density.
Table Position : Determines the exact placement of the screener table within the chart interface. Users can select from nine available alignments combining top, middle, and bottom vertically with left, center, and right horizontally.
🟣 Symbol Settings
Each of the 10 available symbol slots includes a full range of adjustable parameters for personalized analysis.
Symbol : Defines or selects the asset to be tracked in the screener, such as XAUUSD, BTCUSD, or EURUSD. This enables multi-asset scanning across different markets including forex, commodities, indices, and crypto.
Timeframe : Sets the specific timeframe for analysis for each selected symbol. Examples include 15 minutes, 1 hour (60), 4 hours (240), or 1 day (1D). This flexibility ensures precise control over how each asset is monitored within the multi-timeframe structure.
🟣 Alert Settings
Alert : Enables alerts for AAS.
Message Frequency : Determines the frequency of alerts. Options include 'All' (every function call), 'Once Per Bar' (first call within the bar), and 'Once Per Bar Close' (final script execution of the real-time bar). Default is 'Once per Bar'.
Show Alert Time by Time Zone : Configures the time zone for alert messages. Default is 'UTC'.
🔵 Conclusion
Understanding financial markets requires more than indicators, it demands a framework that captures the interaction of price, volatility, and structure in real time. This analytical system achieves that by combining mean reversion logic, volatility mapping, and dynamic supply and demand modeling into an adaptive, data-driven environment. Its computational bands and trend layers visualize market intent, showing when momentum is strengthening, fading, or preparing to shift.
Each signal, derived from statistical equilibrium rather than delayed indicators, reflects the exact moment when the balance between buyers and sellers changes. Variables like Live Trend, Vision Trend, Directional Strength, and ATR-based Volatility Context help traders assess signal quality and alignment across multiple timeframes. The system blends automation with human interpretation, preserving macro-to-micro consistency and enabling confident entries, exits, and stop management through its adaptive Trailing Stop Engine.
Every component, from color-coded zones to hollow candles, forms part of a broader narrative that teaches traders to read the market’s language instead of reacting to it. Built on self-correcting analysis, the framework continuously recalibrates with live data. By transforming volatility, liquidity, and price behavior into structured insight, it empowers traders to move from reaction to prediction, a living ecosystem that evolves with both the market and the trader.
Paid script
FU Candle Detector (Smart Money Concept) En Anglais🧠 Overall concept: “FU Candle” in Smart Money logic
In the context of Smart Money Concepts (SMC) or ICT (Inner Circle Trader), an FU Candle (also known as a “Fakeout Candle” or “Manipulation Candle”) is a candle that:
Creates an imbalance or a break (often above a swing high or below a swing low),
Attracts liquidity by trapping retail traders (liquidity grab),
Then abruptly reverses direction, revealing the hand of “Smart Money” (large institutions).
It therefore often marks:
The point of manipulation before an impulsive movement (reversal),
An area of interest for entering in the institutional direction (after the liquidity grab).
---
⚙️ How the “FU Candle Detector” script works
The script identifies these candlesticks by observing several typical criteria:
1. Detection of the manipulative candle (FU Candle)
Search for a candlestick that breaks a previous swing (significant high or low),
But closes in the opposite direction, often below/above the broken zone,
Thus indicating a fakeout.
Examples:
Bullish FU Candle: breaks a previous low, but closes bullish.
Bearish FU Candle: breaks a previous high, but closes bearish.
---
2. Visualization on the chart
The script generally displays:
🔴 Red markers for bearish FUs (Fake Breakout upwards),
🟢 Green markers for bullish FUs (Fake Breakout downwards),
🟦 Rectangles of areas of interest (often around the FU Candle Open),
📏 Horizontal lines on areas of imbalance (OB/FVG if integrated).
---
3. Possible additions depending on the version
Depending on the version you have received, the script can also:
Detect Fair Value Gaps (FVG) around FU Candles,
Mark Order Blocks (OB) associated with manipulation,
Add alerts when new FU Candles are detected,
Calculate the distance between the manipulation point and the price return,
Filter according to candle size, volume, or market structure (MSB/CHoCH).
---
🎯 Practical use
FU Candles are often used:
As confirmation of an imminent reversal,
To identify institutional entry zones (hidden Order Block),
To anticipate the direction of the next impulse after the liquidity hunt.
Typical entry example:
> Wait for the formation of an FU Candle + price return within the candle body = entry in the opposite direction to the false breakout.
📈 Recommended combinations
This detector is often combined with:
Structure Break Indicator (CHoCH / BOS)
Liquidity Pool Zones
Fair Value Gap Finder
Order Block Detector
This gives you a complete Smart Money Concept system, capable of mapping:
1. Where liquidity has been taken,
2. Where the price is rebalancing,
3. Where Smart Money is repositioning its orders.
Synapse Dynamics - Market Structure📊 SYNAPSE DYNAMICS - MARKET STRUCTURE INDICATOR
An educational tool for learning and practicing Smart Money Concepts (SMC) methodology through visual representation of institutional price action patterns.
═══════════════════════════════════════════════════
🎯 WHAT THIS INDICATOR DISPLAYS
This indicator visualizes Smart Money Concepts patterns on your chart:
- Order Blocks (OB) - Supply and demand zones based on institutional order flow theory. The indicator identifies these areas using price action criteria including the final opposing candle before a strong directional move.
- Breaker Blocks - Failed order blocks that may act as support/resistance. These occur when an order block is invalidated but price returns to the zone, potentially reversing its role.
- Fair Value Gaps (FVG) - Three-candle imbalance patterns where price gaps create inefficiencies. The indicator marks these zones for reference in analysis.
- Market Structure - Break of Structure (BOS) and Change of Character (CHoCH) patterns based on swing high/low breaks. These help identify potential trend continuation or reversal points.
- Reference Entry Signals - The indicator calculates potential entry zones with accompanying stop loss and take profit reference levels based on order block and FVG locations. These are for educational reference only.
- Higher Timeframe Context - Optional filter that displays the higher timeframe trend direction to provide additional market context.
- Information Panel - On-screen dashboard showing active reference signals, their status, and relevant price levels.
- Swing Point Mapping - Labels recent higher highs (HH), higher lows (HL), lower highs (LH), and lower lows (LL) based on configurable swing detection parameters.
═══════════════════════════════════════════════════
⚙️ HOW IT WORKS
The indicator uses the following methodology:
**Order Block Detection:** Identifies the last opposing candle before a strong directional move that breaks structure. Filters blocks by size to reduce noise.
**Market Structure Analysis:** Tracks swing points and identifies when price breaks previous highs/lows to determine BOS or CHoCH patterns.
**Fair Value Gap Identification:** Detects three-candle patterns where candle 1's high/low doesn't overlap with candle 3's low/high, creating an imbalance zone.
**Reference Signal Generation:** Combines order block proximity, FVG presence, and market structure breaks to suggest potential study areas. Optional HTF trend filter can be enabled.
**Timeframe Adaptation:** Automatically adjusts swing detection sensitivity based on the chart timeframe (using multipliers for intraday vs. higher timeframes).
═══════════════════════════════════════════════════
📚 EDUCATIONAL PURPOSE & IMPORTANT LIMITATIONS
**This indicator is designed as an educational tool for:**
- Learning Smart Money Concepts methodology
- Practicing pattern recognition
- Understanding institutional price action theories
- Analyzing market structure visually
**Critical Understanding:**
- All signals and levels are REFERENCE POINTS for study - not trading recommendations
- The indicator displays patterns based on historical price action - it cannot predict future movements
- Smart Money Concepts is a theoretical framework - market behavior varies
- Backtested or historical results shown do not guarantee future performance
- No indicator can account for all market variables, news events, or changing conditions
**Proper Use:**
This tool is meant to assist in learning technical analysis concepts. Users must develop their own analysis skills, risk management strategies, and trading plans. The displayed patterns require interpretation within broader market context.
═══════════════════════════════════════════════════
⚙️ CUSTOMIZATION OPTIONS
**Adjustable Parameters:**
- Order Block: Minimum size threshold, maximum count displayed
- Fair Value Gaps: Toggle visibility, maximum count
- Market Structure: Swing detection length, BOS/CHoCH display
- Signals: Entry/SL/TP calculation method, HTF filter toggle
- Visual Settings: Colors, line styles, label sizes, panel position
**Timeframe Compatibility:**
Works on all timeframes from 1-minute to monthly charts. The swing detection automatically scales based on timeframe.
═══════════════════════════════════════════════════
⚠️ DISCLAIMER
This indicator is for educational and informational purposes only. It does not constitute financial advice or trading recommendations. Trading involves substantial risk of loss. Past patterns and historical analysis do not indicate future results. Users are responsible for their own trading decisions and risk management. The author assumes no liability for trading losses.
═══════════════════════════════════════════════════
🔧 ALERT FUNCTIONALITY
Built-in alert conditions notify you when:
- New order blocks are detected
- Market structure changes occur (BOS/CHoCH)
- Reference entry signals appear
Configure alerts through TradingView's alert system.
Average Daily Session Range PRO [Capitalize Labs]Average Daily Session Range PRO
The Average Daily Session Range PRO (ADSR PRO) is a professional-grade analytical tool designed to quantify and visualize the probabilistic range behavior of intraday sessions.
It calculates directional range statistics using historical session data to show how far price typically moves up or down from the session open.
This helps traders understand session volatility profiles, range asymmetry, and probabilistic extensions relative to prior performance.
Key Features
Asymmetric Range Modeling: Separately tracks average upside and downside excursions from each session open, revealing directional bias and volatility imbalance.
Probability Engine Modes: Choose between Rolling Window (fixed-length lookback) and Exponential Decay (weighted historical memory) to control how recent or historic data influences probabilities.
Session-Aware Statistics: Calculates values independently for each defined session, allowing region-specific insights (e.g., Tokyo, London, New York).
Dynamic Range Table: Displays key metrics such as average up/down ticks, expected range extensions, and percentage probabilities.
Adaptive Display: Works across timeframes and instruments, automatically aligning with user-defined session start and end times.
Visual Clarity: Includes clean range markers and labels optimized for both backtesting and live-chart analysis.
Intended Use
ADSR PRO is a statistical reference indicator.
It does not generate buy/sell signals or predictive forecasts.
Its purpose is to help users observe historical session behavior and volatility tendencies to support their own discretionary analysis.
Credits
Developed by Capitalize Labs, specialists in quantitative and discretionary market research tools.
Risk Warning
This material is educational research only and does not constitute financial advice, investment recommendation, or a solicitation to buy or sell any instrument.
Foreign exchange and CFDs are complex, leveraged products that carry a high risk of rapid losses; leverage amplifies both gains and losses, and you should not trade with funds you cannot afford to lose.
Market conditions can change without notice, and news or illiquidity may cause gaps and slippage; stop-loss orders are not guaranteed.
The analysis presented does not take into account your objectives, financial situation, or risk tolerance.
Before acting, assess suitability in light of your circumstances and consider seeking advice from a licensed professional.
Past performance and back-tested or hypothetical scenarios are not reliable indicators of future results, and no outcome or level mentioned here is assured.
You are solely responsible for all trading decisions, including position sizing and risk management.
No external links, promotions, or contact details are provided, in line with TradingView House Rules.
Ant_JJun 5-Minute Day-Trading IndicatorThis invite-only indicator is designed for short-term BTC and crypto trading, focusing on precision during volatile data-driven markets and capital protection during sideways conditions.
It integrates Ichimoku-based structure mapping with volume asymmetry analysis and proprietary rule-based filters.
Unlike a traditional mashup, this system does not simply overlay multiple indicators.
It uses Ichimoku’s leading spans to classify structural bias (trend vs. neutral), then evaluates directional confirmation through candle displacement and volume pressure imbalance.
Only when both structure and momentum align is a directional label printed.
If the system detects indecision (flat or overlapping clouds with contracting volume), it enters a neutral state to avoid unnecessary exposure.
Key concept:
— Preventing bleed during non-trending phases
— Adaptive response around macro/volatility events (e.g., CPI, PMI)
— Rule-based execution to remove emotional decision-making
Usage notes:
— Intended for 5-minute intraday use
— Long/Short labels appear only on rule-confirmed entries
— No repainting / no backfill logic
— Analytical use only — not investment advice
Dynamic ~ CVDDynamic - CVD is a smart, time-adaptive version of the classic Cumulative Volume Delta (CVD) indicator, designed to help traders visualize market buying and selling pressure across all timeframes with minimal manual tweaking.
Overview
Cumulative Volume Delta tracks the difference between buying and selling volume during each bar. It reveals whether aggressive buyers or sellers dominate the market, offering deep insight into real-time market sentiment and underlying momentum.
This version of CVD automatically adjusts its EMA smoothing length based on your selected timeframe, ensuring optimal sensitivity and consistency across intraday, daily, weekly, and even monthly charts.
Features
Dynamic EMA Length — Automatically adapts smoothing parameters based on the chart timeframe:
1–59 min → 50
1–23 h → 21
Daily & Weekly → 100
Monthly → 10
CVD Visualization — Displays cumulative delta to show the ongoing buying/selling imbalance.
CVD‑EMA Curve — Offers a clear trend signal by comparing the CVD line with its EMA.
Adaptive Color Logic — EMA curve changes color dynamically:
Green when CVD > EMA (bullish pressure)
Gray when CVD < EMA (bearish pressure)
How to Use
Use Dynamic - CVD to gauge whether the market is accumulating (net buying) or distributing (net selling).
When CVD rises above its EMA, it often signals consistent buying pressure and potential bullish continuation.
When CVD stays below its EMA, it highlights sustained selling pressure and possible weakness.
The dynamic EMA makes it suitable for scalping, swing trading, and longer-term trend analysis—no need to manually adjust settings.
Best For
Traders looking to measure real buying/selling flow rather than price movement alone.
Market participants who want a plug‑and‑play CVD that stays accurate across all timeframes.
Anyone interested in volume‑based momentum confirmation tools.
Disclaimer
This script is provided for educational and analytical purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset. Past performance is not indicative of future results. Always perform your own analysis and consult a licensed financial advisor before making investment decisions. The author is not responsible for any financial losses or trading outcomes arising from the use of this indicator.
Liquidations Aggregated (Lite)Liquidations Aggregated (Lite)
The Liquidations Aggregated (Lite) script provides a unified cross-exchange visualization of short and long liquidation volumes, allowing traders to identify high-impact market events and sentiment reversals driven by forced position closures. It aggregates normalized liquidation data from Binance, Bybit, and OKX into a single coherent output, offering a consolidated perspective of derivative market stress across major venues.
Core Concept
Liquidations are involuntary closures of leveraged positions when margin requirements are breached. They represent points of structural orderflow imbalance, often triggering localized volatility spikes and price pivots. This indicator isolates and aggregates those liquidation volumes by direction (short vs. long), allowing traders to map where leveraged traders are being forced out and whether current market movement is driven by short covering or long capitulation.
Underlying Methodology
Each connected exchange provides liquidation feeds via standardized symbols (e.g., BTCUSDT.P_LQBUY or BTCUSD.P_LQSELL).
The script differentiates between:
Short Liquidations → Buy Volume: Forced covering of shorts, representing upward pressure.
Long Liquidations → Sell Volume: Forced selling of longs, representing downward pressure.
Bybit’s inverse data is normalized to align directional logic with Binance and OKX. Data is drawn through the request.security() function per symbol and per exchange, with per-exchange scaling adjustments applied to compensate for differences in reported nominal sizes (USD vs. coin-margined). The script is meant to match the calculation methods of professional-grade data sources (e.g., Velodata, Coinalyze). The value is denominated in the base currency at all times.
Computation Logic
Liquidation volumes are fetched separately for USD- and USDT-margined pairs on each exchange.
Exchange-specific magnitude adjustments are applied to account for nominal denomination differences.
Normalized liquidation buy and sell volumes are summed into two global aggregates:
combinedBuyVolumeLiquidationsShort → aggregated buy volume from forced short positions closes (Short Liquidations)
combinedSellVolumeLiquidationsLong → aggregated sell pressure from forced long position closes (Long Liquidations)
Final series are plotted as mirrored column charts around a zero baseline for direct comparison.
How to Use
Apply the script to any crypto perpetual futures symbol (e.g., BTCUSDT, ETHUSDT).
Observe teal bars (Buy Volume from Short Liquidations) for short squeezes and red bars (Sell Volume from Long Liquidations) for long wipes.
Strong teal spikes during downtrends often indicate aggressive short liquidations leading to short-term bounces.
Strong red spikes during uptrends often mark long unwinds that can trigger sharp retracements.
Sustained asymmetry in either direction suggests systemic imbalance across leveraged positioning.
Illuminati Zone🟣 Illuminati Zone — Hidden Power of the 11 PM NZ Candle
The Illuminati Zone reveals the hidden footprints of liquidity and market imbalance formed by the 11 PM New Zealand 15-minute candle — a time when global liquidity transitions between major sessions.
This candle often defines key intraday supply and demand boundaries, serving as a magnet for price and a pivot point for high-probability reversals or breakouts.
🧠 How it works
Automatically detects and marks the 11 PM NZ 15-minute candle each day.
Draws a translucent zone box between its high and low.
Extends two reference lines at +1 × range and –1 × range above and below the zone — ideal for spotting overextensions or liquidity sweeps.
Supports custom lookback, colors, and visual options.
💡 How to use it
Watch how price interacts with the zone — rejection often signals smart-money activity.
Use +1 and –1 levels as overextended zones for potential reversals or breakout retests.
Combine with your own confluence tools or volume analysis for precision entries.
⚙️ Customization Options
Target hour (NZ time)
Days back to display
Zone and line colors
Transparency and visual preferences
🔮 Pro Tip: Pair it with a volume or imbalance indicator for surgical-level precision in identifying where smart money positions are built or released.
Volume Delta [BigBeluga]🔵 OVERVIEW
The Volume Delta indicator visualizes the dominance between buying and selling volume within a given period. It calculates the percentage of bullish (buy) versus bearish (sell) volume, then color-codes the candles and provides a real-time dashboard comparing delta values across multiple currency pairs. This makes it a powerful tool for monitoring order-flow strength and intermarket relationships in real time.
🔵 CONCEPTS
Each bar’s buy volume is counted when the close is higher than the open.
Each bar’s sell volume is counted when the close is lower than the open.
volumeBuy = 0.
volumeSell = 0.
for i = 0 to period
if close > open
volumeBuy += volume
else
volumeSell += volume
The indicator sums both over a chosen period to calculate the ratio of buy-to-sell pressure.
Delta (%) = (Buy Volume ÷ (Buy Volume + Sell Volume)) × 100.
Gradient colors highlight whether buying or selling pressure dominates.
🔵 FEATURES
Calculates real-time Volume Delta for the selected chart or for multiple assets.
Colors candles dynamically based on the delta intensity (green = buy pressure, red = sell pressure).
Displays a dashboard table showing volume delta % for up to five instruments.
The dashboard features visual progress bars for quick intermarket comparison.
An optional Delta Bar Panel shows the ratio of Buy/Sell volumes near the latest bar.
A floating label shows the exact Buy/Sell percentages.
Works across all symbols and timeframes for multi-asset delta tracking.
🔵 HOW TO USE
When Buy % > Sell % , it often signals bullish momentum or strong accumulation—but can also indicate over-excitement and a possible market top.
Market Tops
When Sell % > Buy % , it typically reflects bearish pressure or distribution—but may also occur near a market bottom where selling exhaustion forms.
Market Bottom
Use the Dashboard to compare volume flow across correlated assets (e.g., major Forex pairs or sector groups).
Combine readings with trend or volatility filters to confirm whether the imbalance aligns with broader directional conviction.
Treat the Delta Bar visualization as a real-time sentiment gauge—showing which side (buyers or sellers) dominates the current session.
🔵 CONCLUSION
Volume Delta transforms volume analysis into an intuitive directional signal.
By quantifying buy/sell pressure and displaying it as a percentage or color gradient, it provides traders with a clearer picture of real-time volume imbalance — whether within one market or across multiple correlated instruments.
Structure Pro+ 2.4 Structure Pro+ 2.4
Summary
Structure Pro+ 2.4 is a comprehensive, all-in-one indicator designed for traders who utilize Smart Money Concepts (SMC). It automates the detection of key market structure events, identifies high-probability trade signals, and incorporates time-based filters to focus on the most volatile trading sessions, helping you make informed decisions with precision and clarity.
This suite goes beyond simple lines on a chart by integrating Market Structure, Fair Value Gaps (FVGs), and institutional trading sessions into a single, powerful tool.
Core Features
📈 Automatic Market Structure
Break of Structure (BOS) & Change of Character (CHoCH): The indicator automatically identifies and labels significant breaks in market structure, allowing you to instantly recognize trend continuations (BOS) or potential reversals (CHoCH).
Customizable Pivot Detection: Fine-tune the sensitivity of the structure detection by adjusting the Left Bars and Right Bars settings to match your trading style and timeframe, from scalping to swing trading.
🎯 High-Probability Breakout Signals
Receive clear BUY and SELL signals based on a powerful confluence of events. A signal only appears when:
A BOS or CHoCH is confirmed.
The breakout move is validated by the creation of a recent Fair Value Gap (FVG), indicating strong momentum.
The signal occurs within a valid, high-volatility time session.
The breakout is confirmed on a closed candle to prevent fakeouts.
🔍 Key Liquidity & Imbalance Zones
Fair Value Gaps (FVGs): Automatically detects and displays FVG (Imbalance) zones on your chart, highlighting key areas of interest where the price may return.
Order Blocks (OBs): Optionally display the last order block before a structural break. The length of the OB box can be customized to keep your chart clean.
🕒 Time-Based Session Filters (Killzones)
Timing is everything. Structure Pro+ 2.4 provides fully customizable time filters to ensure you are only trading in optimal market conditions.
ICT Macro Sessions: Enable and customize standard ICT Macro "Killzone" sessions, which are displayed visually on your chart.
NASDAQ Open Session: A dedicated, customizable session filter for the high-volatility NASDAQ open.
Timezone Synchronization: Set your preferred timezone (America/New_York by default) to align all sessions perfectly, no matter where you are in the world.
⚙️ Full Customization & Alerts
Visuals: Take complete control over the look and feel of the indicator, including colors, line styles, and label sizes.
Alert System: A comprehensive alert system allows you to get notified for every key event:
Signal (BUY/SELL)
BOS or CHoCH
BOS/CHoCH with FVG Confluence
Start of a Macro Session
How to Use
Identify the Trend: Use the automatically plotted BOS and CHoCH labels to determine the current market bias on your chosen timeframe. An uptrend is defined by a series of bullish BOS, while a downtrend is defined by bearish BOS. A CHoCH signals a potential shift in this bias.
Wait for a Signal in a Valid Session: Be patient and wait for a BUY or SELL signal to appear on your chart. Ensure the signal occurs within one of the active, visually-drawn time sessions (Macros or NASDAQ Open) for the highest probability.
Confirm and Manage Risk: Use the signal as a primary point of confluence in your trading plan. For best results, combine it with your own analysis. Always practice proper risk management by setting a stop loss, typically below the low of the swing that caused a BUY signal or above the high of the swing that caused a SELL signal.
Disclaimer: This indicator is a tool designed to assist in trade analysis and should not be considered as financial advice. Trading involves substantial risk, and past performance is not indicative of future results. Always conduct your own research and risk assessment before entering any trade.
Devil Marks - Multi TimeframeA handy completely new script that shows Devil Marks for several time frames on the current time frame.
Devil Marks are where candles have no wick at one end of the candlestick. These levels are seen as areas that price needs to go back to at some point to re-balance the imbalance. These levels can add confluence to a trade idea.
A table is included that shows the closest devil mark for each time frame.
Devil Marks should show until that level is mitigated by price trading at that level.
Cumulative Volume Delta Profile and Heatmap [BackQuant]Cumulative Volume Delta Profile and Heatmap
A multi-view CVD workstation that measures buying vs selling pressure, renders a price-aligned CVD profile with Point of Control, paints an optional heatmap of delta intensity, and detects classical CVD divergences using pivot logic. Built for reading who is in control, where participation clustered, and when effort is failing to produce result.
What is CVD
Cumulative Volume Delta accumulates the difference between aggressive buys and aggressive sells over time. When CVD rises, buyers are lifting the offer more than sellers are hitting the bid. When CVD falls, the opposite is true. Plotting CVD alongside price helps you judge whether price moves are supported by real participation or are running on fumes.
Core Features
Visual Analysis Components
CVD Columns - Plot of cumulative delta, colored by side, for quick read of participation bias.
CVD Profile - Price-aligned histogram of CVD accumulation using user-set bins. Shows where net initiative clustered.
Split Buy and Sell CVD - Optional two-sided profile that separates positive and negative CVD into distinct wings.
POC - Point of Control - The price level with the highest absolute CVD accumulation, labeled and line-marked.
Heatmap - Semi-transparent blocks behind price that encode CVD intensity across the last N bars.
Divergence Engine - Pivot-based detection of Bearish and Bullish CVD divergences with optional lines and labels.
Stats Panel - Top level metrics: Total CVD, Buy and Sell totals with percentages, Delta Ratio, and current POC price.
How it works
Delta source and sampling
You select an Anchor Timeframe that defines the higher time aggregation for reading the trend of CVD.
The script pulls lower timeframe volume delta and aggregates it to the anchor window. You can let it auto-select the lower timeframe or force a custom one.
CVD is then accumulated bar by bar to form a running total. This plot shows the direction and persistence of initiative.
Profile construction
The recent price range is split into Profile Granularity bins.
As price traverses a bin, the current delta contribution is added to that bin.
If Split Buy and Sell CVD is enabled, positive CVD goes to the right wing and negative CVD to the left wing.
Widths are scaled by each side’s maximum so you can compare distribution shape at a glance.
The Point of Control is the bin with the highest absolute CVD. This marks where initiative concentrated the most.
Heatmap
For each bin, the script computes intensity as absolute CVD relative to the maximum bin value.
Color is derived from the side in control in that bin and shaded by intensity.
Heatmap Length sets how far back the panels extend, highlighting recurring participation zones.
Divergence model
You define pivot sensitivity with Pivot Left and Right .
Bearish divergence triggers when price confirms a higher high while CVD fails to make a higher high within a configurable Delta Tolerance .
Bullish divergence triggers when price confirms a lower low while CVD fails to make a lower low.
On trigger, optional link lines and labels are drawn at the pivots for immediate context.
Key Settings
Delta Source
Anchor Timeframe - Higher TF for the CVD narrative.
Custom Lower TF and Lower Timeframe - Force the sampling TF if desired.
Pivot Logic
Pivot Left and Right - Bars to each side for swing confirmation.
Delta Tolerance - Small allowance to avoid near-miss false positives.
CVD Profile
Show CVD Profile - Toggle profile rendering.
Split Buy and Sell CVD - Two-sided profile for clearer side attribution.
Show Heatmap - Project intensity panels behind price.
Show POC and POC Color - Mark the dominant CVD node.
Profile Granularity - Number of bins across the visible price range.
Profile Offset and Profile Width - Position and scale the profile.
Profile Position - Right, Left, or Current bar alignment.
Visuals
Bullish Div Color and Bearish Div Color - Colors for divergence artifacts.
Show Divergence Lines and Labels - Visualize pivots and annotations.
Plot CVD - Column plot of total CVD.
Show Statistics and Position - Toggle and place the summary table.
Reading the display
CVD columns
Rising CVD confirms buyers are in control. Falling CVD confirms sellers.
Flat or choppy CVD during wide price moves hints at passive or exhausted participation.
CVD profile wings
Thick right wing near a price zone implies heavy buy initiative accumulated there.
Thick left wing implies heavy sell initiative.
POC marks the strongest initiative node. Expect reactions on first touch and rotations around this level when the tape is balanced.
Heatmap
Brighter blocks indicate stronger historical net initiative at that price.
Stacked bright bands form CVD high volume nodes. These often behave like magnets or shelves for future trade.
Divergences
Bearish - Price prints a higher high while CVD fails to do so. Effort is not producing result. Potential fade or pause.
Bullish - Price prints a lower low while CVD fails to do so. Capitulation lacks initiative. Potential bounce or reversal.
Stats panel
Total CVD - Net initiative over the window.
Buy and Sell volume with percentages - Side composition.
Delta Ratio - Buy over Sell. Values above 1 favor buyers, below 1 favor sellers.
POC Price - Current control node for plan and risk.
Workflows
Trend following
Choose an Anchor Timeframe that matches your holding period.
Trade in the direction of CVD slope while price holds above a bullish POC or below a bearish POC.
Use pullbacks to CVD nodes on your profile as entry locations.
Trend weakens when price makes new highs but CVD stalls, or new lows while CVD recovers.
Mean reversion
Look for divergences at or near prior CVD nodes, especially the POC.
Fade tests into thick wings when the side that dominated there now fails to push CVD further.
Target rotations back toward the POC or the opposite wing edge.
Liquidity and execution map
Treat strong wings and heatmap bands as probable passive interest zones.
Expect pauses, partial fills, or flips at these shelves.
Stops make sense beyond the far edge of the active wing supporting your idea.
Alerts included
CVD Bearish Divergence and CVD Bullish Divergence.
Price Cross Above POC and Price Cross Below POC.
Extreme Buy Imbalance and Extreme Sell Imbalance from Delta Ratio.
CVD Turn Bullish and CVD Turn Bearish when net CVD crosses zero.
Price Near POC proximity alert.
Best practices
Use a higher Anchor Timeframe to stabilize the CVD story and a sensible Profile Granularity so wings are readable without clutter.
Keep Split mode on when you want to separate initiative attribution. Turn it off when you prefer a single net profile.
Tune Pivot Left and Right by instrument to avoid overfitting. Larger values find swing divergences. Smaller values find micro fades.
If volume is thin or synthetic for the symbol, CVD will be less reliable. The script will warn if volume is zero.
Trading applications
Context - Confirm or question breakouts with CVD slope.
Location - Build entries at CVD nodes and POC.
Timing - Use divergence and POC crosses for triggers.
Risk - Place stops beyond the opposite wing or outside the POC shelf.
Important notes and limits
This is a price and volume based study. It does not access off-book or venue-level order flow.
CVD profiles are built from the data available on your chart and the chosen lower timeframe sampling.
Like all volume tools, readings can distort during roll periods, holidays, or feed anomalies. Validate on your instrument.
Technical notes
Delta is aggregated from a lower timeframe into an Anchor Timeframe narrative.
Profile bins update in real time. Splitting by side scales each wing independently so both are readable in the same panel.
Divergences are confirmed using standard pivot definitions with user-set tolerances.
All profile drawing uses fixed X offsets so panels and POC do not swim when you scroll.
Quick start
Anchor Timeframe = Daily for intraday context.
Split Buy and Sell CVD = On.
Profile Granularity = 100 to 200, Profile Position = Right, Width to taste.
Pivot Left and Right around 8 to 12 to start, then adapt.
Turn on Heatmap for a fast map of interest bands.
Bottom line
CVD tells you who is doing the lifting. The profile shows where they did it. Divergences tell you when effort stops paying. Put them together and you get a clear read on control, location, and timing for both trend and mean reversion.
Delta Histogram - OnlyFlowThis script plots a histogram of delta proxies (approximations of buying vs. selling pressure) using available chart data. Because TradingView does not provide bid/ask tape data, delta is estimated with several selectable methods:
Uptick/Down-tick (proxy): volume signed by close direction.
Body-weighted Volume: weights volume by candle body relative to its range.
VWAP-slope Volume: signs volume by changes in the typical price (HLC3).
Features
Cumulative or per-bar mode: reset daily or by custom session hours.
Normalization options: Z-score, percentile scaling, or raw values; with percentile clipping for stable colors and axis scaling.
Visualization: color-coded positive/negative bars, optional zero line and ± bands, adjustable opacity scaling.
Readout Panel: shows the latest delta values and their normalized equivalents on the chart edge.
Alerts: triggers when normalized delta exceeds positive or negative thresholds, highlighting potential spikes in pressure.
Usage
Switch between delta modes to explore different perspectives on order-flow imbalance. Cumulative mode shows whether pressure builds over a session, while per-bar mode highlights bar-to-bar shifts. Normalization helps identify relative extremes in context rather than raw values.
ICT Suspension Block [tncylyv]ICT Suspension Block
Overview
This indicator identifies and highlights the "ICT Suspension Block," a specific three-candle pattern that signifies a potential area of support or resistance. It is designed to find temporary pauses or "suspensions" in price delivery, creating zones where the market may later return.
This tool is highly customizable, allowing you to focus on specific market conditions, sessions, and biases.
What is an ICT Suspension Block?
The Suspension Block is a nuanced 3-bar pattern that captures a very specific type of price action imbalance. Unlike a standard Fair Value Gap (FVG), it does not require a literal price gap. Instead, it's defined by the relationship between the opens and closes of three consecutive candles, all moving in the same direction.
• Bullish Suspension Block (+ SB) Conditions:
1. All three candles in the pattern must be bullish (close > open).
2. The close of the first candle must be below the open of the second candle.
3. The close of the second candle must be below the open of the third candle.
The resulting zone is drawn from the close of the first candle to the open of the third candle.
• Bearish Suspension Block (- SB) Conditions:
1. All three candles in the pattern must be bearish (close < open).
2. The close of the first candle must be above the open of the second candle.
3. The close of the second candle must be above the open of the third candle.
The resulting zone is drawn from the close of the first candle to the open of the third candle.
How to Use It
Suspension Blocks can be powerful tools when integrated into a broader trading strategy. They represent areas where price moved aggressively, leaving behind an inefficiently traded zone that the market may need to revisit.
• Potential Support & Resistance: A Bullish Suspension Block can act as a potential support level on a retest. Conversely, a Bearish Suspension Block can act as potential resistance.
• Entry Confluence: Look for price to retrace into a previously formed Suspension Block. The zone can provide a high-probability area to look for entries, especially when combined with other confluences like order blocks, breaker blocks, or higher timeframe market structure.
• Context is Key: The validity of a Suspension Block often depends on the market context. A block formed during a strong, impulsive move is typically more significant than one formed during choppy, consolidative price action.
Features & Settings
This indicator is designed to be flexible and adapt to your specific trading style.
• Show Blocks:
o Both: Display both Bullish and Bearish blocks.
o Bullish Only: Focus exclusively on potential support zones. Ideal for an uptrending market or when you have a bullish bias.
o Bearish Only: Focus exclusively on potential resistance zones. Ideal for a downtrending market or when you have a bearish bias.
• Max # of Blocks to Show:
o Avoid chart clutter by only displaying the most recent N blocks. This ensures you are always focused on the latest and most relevant zones.
• Time Filter (RTH Session):
o Enable Only Detect Inside RTH? to filter out patterns that form in low-volume, after-hours sessions. This helps ensure the zones you see were created during periods of significant market participation.
o The RTH session times and timezone are fully customizable.
• Customization:
o Adjust the colors for Bullish and Bearish blocks to match your chart's theme.
o Modify the text size of the + SB and - SB labels for better visibility.
________________________________________
Disclaimer: This indicator is a tool for market analysis and should not be used as a standalone trading signal. Always use proper risk management and combine this tool with your own comprehensive trading strategy. Past performance is not indicative of future results.
