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
在脚本中搜索"volume profile"
Ultimate AI Trading System - BW + QIMLOverview
Ultimate AI Trading System - BW + QIML is an overlay indicator that integrates Bill Williams' Profitunity chaos theory framework—specifically the Alligator for trend detection, Awesome Oscillator (AO) for momentum acceleration, Fractals for breakout pivots, and Market Facilitation Index (MFI) for efficiency/volume confirmation—with a custom quantum-inspired machine learning (QIML) layer. This fusion creates a multi-tier signal hierarchy (ultra-high, high, medium confidence) for long/short entries, designed to mitigate false signals in chaotic markets by requiring cross-validation between qualitative pattern recognition (BW) and probabilistic state modeling (QIML). An AI enhancement filter blends additional features (e.g., Stoch RSI, MACD histogram) via a weighted hyperbolic tangent model for final confirmation. The result is a adaptive system that escalates signals based on alignment strength, with a dashboard displaying real-time scores and market phases, ideal for trend-following in volatile assets like forex pairs (EURUSD) or indices (SPX) on 1H–Daily timeframes.
Core Mechanics
The indicator operates via two synergistic engines, plus an AI filter, to generate non-repainting signals only on bar close:
Bill Williams Engine (Chaos Theory Foundation)
This draws from Williams' "Profitunity" philosophy, viewing markets as fractal-driven chaos where trends emerge from "sleeping" to "awakening" phases:
Alligator: Three smoothed moving averages (SMMA via RMA) on HL/2—Jaw (13-period, blue), Teeth (8-period, red), Lips (5-period, green). Bullish "open mouth" when Lips > Teeth > Jaw (price above lines); bearish inverse. Signals trend emergence; e.g., crossover above Jaw indicates chaos resolving into uptrend.
Awesome Oscillator (AO): Histogram of SMA(HL/2, 5) - SMA(HL/2, 34). Measures momentum divergence—rising green bars above zero = accelerating bulls; saucer patterns (three-bar lows) confirm shifts.
Fractals: Local pivots (2-bar left/right confirmation)—up-fractal (high > neighbors) as resistance breaks, down-fractal (low < neighbors) as support. Triggers on close crossing the most recent fractal price.
Market Facilitation Index (MFI): (High - Low) / Volume ratio. Filters efficiency: "Green" (MFI rising + volume up) confirms genuine moves; "Fake" (MFI up, volume down) warns traps; optional toggle to block signals without volume backing.
These create base conditions: e.g., long if Alligator bullish + AO positive + fractal breakout + MFI green.
Quantum-Inspired ML (QIML) Engine (Probabilistic Enhancement)
Inspired by quantum superposition (multiple market "states" co-existing until observed via price action) and tunneling (price "leaping" barriers in low-probability events), this layer quantifies BW's qualitative signals into confidence scores (0–100%):
Superposition State: Z-score normalized momentum differential (fast SMA(10) - slow SMA(20)) represents overlaid bull/bear potentials; scaled by volatility regime (ATR z-score) to dampen in high-vol (ATR >1.2x 20-period avg) or amplify in low-vol (<0.8x).
Probability Weighting: Squared normalized deviation from 20-SMA (as "quantum probability amplitude") weights deviations; e.g., |close - SMA| / max deviation over lookback, squared for non-linear emphasis on extremes.
Tunneling Breakouts: Volatility bands (±1.5x ATR around SMA); crossover = "tunneling" event adding 30% to score, modeling rare but decisive moves.
Confidence Calculation: Tanh-activated aggregation—buy score = tanh(momentum) * 0.5 + min(1, weight) * 0.2 + tunneling * 0.3; scaled 0–100% with vol adjustment (e.g., *0.8 in high vol). Threshold (default 70%) for signals; prevents simultaneous buy/sell by favoring stronger.
QIML complements BW by assigning probabilities to chaos patterns—e.g., Alligator open without momentum gets low score, filtering noise.
AI Enhancement Filter (Feature Fusion)
A simple weighted tanh model normalizes and blends four features over user lookback (default 20):
Momentum: Stoch RSI (RSI(14) stochastized) z-normalized (-1 to +1).
Trend: MACD(12,26,9) histogram normalized.
Volatility: ATR(14) normalized.
Context: (Close - Jaw) normalized for Alligator alignment.
Final score = 0.3momentum + 0.25trend + 0.15vol + 0.3context; tanh-applied for sigmoid-like bounding (-1 bear to +1 bull). Threshold (default 0.5) gates signals; e.g., >0.5 required for longs.
Signal Hierarchy & Integration
Ultra-High (Rare, Lime/Maroon labels): Full BW condition + QIML >85% + AI >0.7 (strict alignment for "quantum collapse" to trend).
High (Green/Red arrows): Mode-dependent—Conservative: BW + QIML; Aggressive: OR; Single modes: One engine only.
Medium (Faded circles): Partial (e.g., BW without QIML but QIML >50%) for scalps.
No overlaps; MFI/AI optional. Background tints market phase (green bull momentum low-vol, etc.).
Dashboard (bottom-right default): Rows for Alligator/AO/MFI status, AI score, QIML buy/sell %, final signal, and mode note.
Why This Adds Value & Originality
Standalone BW tools excel at chaos detection but lack probabilistic filtering, leading to whipsaws in ranging markets (e.g., Alligator "sleeps" indefinitely). Pure ML overlays often ignore fractal geometry, missing breakout nuances. This mashup justifies its integration by using QIML's superposition/tunneling to "quantize" BW signals—e.g., fractal breaks only fire if probability-weighted momentum aligns, reducing false positives by 30–50% in backtests on EURUSD 1H (user-verifiable via strategy tester). The AI layer fuses BW context (Jaw deviation) with standard oscillators, creating a "chaos-aware" score absent in generic hybrids. No equivalent script applies tanh-bounded quantum analogies to BW fractals with tiered modes and vol-regime damping; it condenses 4+ indicators into one, with ultra-signals for high-RR setups (e.g., scale into ultra on pullbacks).
How to Use
Setup: Overlay on chart. Start with Conservative mode + defaults (Jaw 13/Teeth 8/Lips 5; QIML lookback 20, threshold 70%; AI threshold 0.5). Enable MFI for volume assets; toggle ultra for rarer entries. Position dashboard as needed.
Interpret Signals:
Ultra: Large triangles—e.g., "ULTRA BUY" on Alligator open + AO saucer + fractal cross + QIML 90% (enter full size, trail via Teeth).
High: Standard arrows—Conservative requires dual confirmation; Aggressive suits scalps (e.g., BUY on QIML alone if BW neutral).
Medium: Small circles—probe with half-size (e.g., "B" if partial bull).
Dashboard: Green AO + 75% QIML buy = building case; "WAIT" if neutral.
Trading Example: On GBPUSD 4H, Alligator opens bull (Lips cross Teeth) + fractal break at 1.25 + QIML 72% (momentum z>0, low-vol amp) + AI 0.6 → High BUY. Stop below down-fractal; target 1:2 RR at upper band. In crypto (BTC 1H), shorten BW lengths (Jaw 10) + Aggressive mode for volatility.
Alerts: Set for ultra/high/medium; messages include ticker and type.
Best on trending/chaotic markets (avoid pure ranges); 1H+ for swings, 15M+ Aggressive for day trades. Pair with volume profiles for confluence.
Tips
Backtest modes: Conservative yields fewer (higher win-rate) signals; tune QIML vol sensitivity (0.8 low-vol assets like stocks, 1.5 crypto).
Customize: Disable Alligator display for clean charts; extend lookback in trends (QIML 40).
Optimization: Test AI weights (e.g., boost context to 0.4 for BW-heavy bias).
Limitations & Disclaimer
Signals confirm on close (1-bar lag); QIML/AI are rule-based heuristics, not trained neural nets—overfit risk in non-chaotic regimes (e.g., news spikes). BW assumes fractal persistence (fails in manipulations); MFI volume-dependent (weak on forex). No auto-exits—use ATR(14)*1.5 stops. Thresholds need per-asset tuning (e.g., lower 60% for high-vol). Max 10–20 signals/month in Conservative. Not financial advice; backtest thoroughly, risk ≤1% capital. Past performance ≠ future results. Share ideas in comments!
Malama's Heat MapOverview
Malama's Heat Map is an overlay indicator that visualizes historical liquidity as a dynamic heatmap aligned with the price chart, using volume as a proxy to map activity across time (X-axis) and price levels (Y-axis). It constructs a grid of up to 5000 cells via a matrix, distributing bar volume into discrete price bins to highlight concentration zones, creating a color-graded visualization from cool (low activity) to hot (high liquidity). This aids in identifying "Type II" fair value areas, support/resistance from past volume clusters, or potential imbalances without order book access. Built for v6 compatibility with efficiency in mind—computations run solely on the last bar, includes object limit enforcement, and offers two intra-bar volume distribution methods for flexible approximation.
Core Mechanics
The indicator generates a trailing heatmap through binning, accumulation, and box-based rendering:
Grid Setup: Configurable lookback (bars back, default 100) sets horizontal time span; bins (price divisions, default 50) define vertical resolution, limited to 5000 total cells to prevent errors. Bin height dynamically = max(mintick, (lookback high - low) / bins).
Y-Axis Stabilization: Anchors boundaries to the prior bar's high/low (if available) for a flicker-free view during live bar updates. All historical bar data (high/low/close/volume) is clipped to these bounds.
Volume Distribution Proxy:
Even: Divides bar volume equally across spanned bins (straightforward uniform spread).
POC Weighted (Inverse): Treats bar close as POC proxy; applies inverse distance weighting (1/(|bin - POC bin| + 1), normalized) to emphasize volume near the estimated control point, simulating clustered intra-bar trading.
Matrix Building: On last bar only, loops backward over lookback bars (newest right-aligned). For each, computes low/high bin indices, distributes volume per selected method into the matrix (columns=time, rows=price bins from low to high).
Scaling & Palette: Extracts max matrix value for relative normalization (0-1); maps to a 5-tier stepped color scheme (user-customizable: blue 90% transp. low → red 50% transp. high) for non-linear intensity.
Rendering: Clears old boxes, then iterates matrix to draw only non-zero cells as thin boxes: X spans one bar width (left=historical index from bar_index, right=next bar), Y fills bin height. Borderless for seamless heatmap effect.
The result is a right-leaning, chart-scrolling visualization emphasizing recent liquidity buildup.
Why This Adds Value & Originality
While session-based volume profiles exist, this heatmap captures ongoing multi-bar liquidity evolution ("Type II" style), revealing horizontal value areas or gaps dynamically. Originality shines in the custom inverse-weighting for POC realism (no ta.* dependencies), matrix-driven persistence for quick redraws, and stabilization to eliminate repaints—issues plaguing similar scripts. v6 adaptations (e.g., custom clamp, matrix recreation on input change) ensure broad compatibility without bloat. It condenses complex liquidity scanning into one tool: spot red "hot" bands as magnets, blue voids as FVGs. Unlike generic heatmaps, the proxy options and limit-aware design scale across timeframes/assets (e.g., forex vs. crypto), reducing the need for layered indicators.
How to Use
Setup: Apply as overlay. Defaults suit ~4-day 1H view; tune lookback/bins (e.g., 50x100 for intraday fine-detail, but watch 5000 cap—errors auto-flag excesses). Select "POC Weighted" for nuanced clustering, "Even" for simplicity. Customize palette (e.g., desaturate for dark themes).
Reading the Heatmap:
X-Axis (Time): Left=older (fainter context), right=recent focus; tracks evolving liquidity trails.
Y-Axis (Price): Bottom=range low, top=high; vertical density shows price-level attraction.
Colors: Faint blue (sparse volume, possible inefficiencies) → vivid red (dense activity, likely SR). Horizontal streaks = sustained value zones.
Trading Insights: Price wicking into red? Anticipate fills/reversals. Blue gaps post-break? Targets for retraces. Ideal on 5M–Daily; layer with candlesticks off for purity.
Example: In BTCUSD 4H, a yellow-red band at $60K from prior consolidation → treat as dynamic support for longs on dips.
Tips
Balance settings: High bins = sharper verticals but cap lookback (e.g., 80x60=4800 cells). Test on volatile pairs first.
"POC Weighted" excels in ranging markets; switch to "Even" for trending (avoids close-bias skew).
For deeper analysis, screenshot/export or pair with divergence tools; add manual alerts via box counts if extended.
Efficiency: Last-bar only keeps it snappy; refresh on input tweaks.
Limitations & Disclaimer
Visualization is historical/proxy-based—lagging by one bar, no forward projection or tick-level precision (close-as-POC is estimate). Clipping may trim outlier wicks; low-volume bars dilute globally. Stepped colors are relative (max scales per redraw), potentially compressing extremes. Exceeds 5000 cells? Runtime error halts—no fallback resize. Not real liquidity (volume ≠ depth); best as visual aid, not quantitative. Updates post-close only. Backtest zones on specific symbols—correlation ≠ causation. Not advice; trade responsibly. Ideas in comments!
Advanced Range Analyzer ProAdvanced Range Analyzer Pro – Adaptive Range Detection & Breakout Forecasting
Overview
Advanced Range Analyzer Pro is a comprehensive trading tool designed to help traders identify consolidations, evaluate their strength, and forecast potential breakout direction. By combining volatility-adjusted thresholds, volume distribution analysis, and historical breakout behavior, the indicator builds an adaptive framework for navigating sideways price action. Instead of treating ranges as noise, this system transforms them into opportunities for mean reversion or breakout trading.
How It Works
The indicator continuously scans price action to identify active range environments. Ranges are defined by volatility compression, repeated boundary interactions, and clustering of volume near equilibrium. Once detected, the indicator assigns a strength score (0–100), which quantifies how well-defined and compressed the consolidation is.
Breakout probabilities are then calculated by factoring in:
Relative time spent near the upper vs. lower range boundaries
Historical breakout tendencies for similar structures
Volume distribution inside the range
Momentum alignment using auxiliary filters (RSI/MACD)
This creates a live probability forecast that updates as price evolves. The tool also supports range memory, allowing traders to analyze the last completed range after a breakout has occurred. A dynamic strength meter is displayed directly above each consolidation range, providing real-time insight into range compression and breakout potential.
Signals and Breakouts
Advanced Range Analyzer Pro includes a structured set of visual tools to highlight actionable conditions:
Range Zones – Gradient-filled boxes highlight active consolidations.
Strength Meter – A live score displayed in the dashboard quantifies compression.
Breakout Labels – Probability percentages show bias toward bullish or bearish continuation.
Breakout Highlights – When a breakout occurs, the range is marked with directional confirmation.
Dashboard Table – Displays current status, strength, live/last range mode, and probabilities.
These elements update in real time, ensuring that traders always see the current state of consolidation and breakout risk.
Interpretation
Range Strength : High scores (70–100) indicate strong consolidations likely to resolve explosively, while low scores suggest weak or choppy ranges prone to false signals.
Breakout Probability : Directional bias greater than 60% suggests meaningful breakout pressure. Equal probabilities indicate balanced compression, favoring mean-reversion strategies.
Market Context : Ranges aligned with higher timeframe trends often resolve in the dominant direction, while counter-trend ranges may lead to reversals or liquidity sweeps.
Volatility Insight : Tight ranges with low ATR imply imminent expansion; wide ranges signal extended consolidation or distribution phases.
Strategy Integration
Advanced Range Analyzer Pro can be applied across multiple trading styles:
Breakout Trading : Enter on probability shifts above 60% with confirmation of volume or momentum.
Mean Reversion : Trade inside ranges with high strength scores by fading boundaries and targeting equilibrium.
Trend Continuation : Focus on ranges that form mid-trend, anticipating continuation after consolidation.
Liquidity Sweeps : Use failed breakouts at boundaries to capture reversals.
Multi-Timeframe : Apply on higher timeframes to frame market context, then execute on lower timeframes.
Advanced Techniques
Combine with volume profiles to identify areas of institutional positioning within ranges.
Track sequences of strong consolidations for trend development or exhaustion signals.
Use breakout probability shifts in conjunction with order flow or momentum indicators to refine entries.
Monitor expanding/contracting range widths to anticipate volatility cycles.
Custom parameters allow fine-tuning sensitivity for different assets (crypto, forex, equities) and trading styles (scalping, intraday, swing).
Inputs and Customization
Range Detection Sensitivity : Controls how strictly ranges are defined.
Strength Score Settings : Adjust weighting of compression, volume, and breakout memory.
Probability Forecasting : Enable/disable directional bias and thresholds.
Gradient & Fill Options : Customize range visualization colors and opacity.
Dashboard Display : Toggle live vs last range, info table size, and position.
Breakout Highlighting : Choose border/zone emphasis on breakout events.
Why Use Advanced Range Analyzer Pro
This indicator provides a data-driven approach to trading consolidation phases, one of the most common yet underutilized market states. By quantifying range strength, mapping probability forecasts, and visually presenting risk zones, it transforms uncertainty into clarity.
Whether you’re trading breakouts, fading ranges, or mapping higher timeframe context, Advanced Range Analyzer Pro delivers a structured, adaptive framework that integrates seamlessly into multiple strategies.
Ema With VoLume RangeEMA with Volume Range – Adaptive Trend, Trailing Stops & Volume Profile Zones
This sophisticated indicator integrates three powerful trading tools in a single overlay: a classic EMA200, precision ATR-based buy/sell signals, and a unique double-zone volume profile for deep market structure analysis. Ideal for swing traders, scalpers, and volume-driven investors seeking actionable, multi-dimensional price insights.
Core Features
EMA200 (Exponential Moving Average):
Plots a customizable EMA200 (blue line) for identifying primary trend direction and dynamic support/resistance.
Exponential smoothing is enabled by default for better tracking of recent price action.
ATR-Based Trailing Stop with Buy/Sell Signals:
Uses Average True Range (ATR) to set adaptive trailing stop levels that respond to current market volatility.
Buy and Sell signals (tiny green and red labels) trigger whenever price crosses the trailing stop for precise entries and exits.
All signals are alert-enabled for automated or semi-automated trading workflows.
Adjustable ATR multiplier and lookback for tuning responsiveness.
Dual Volume Range Zones & Profile Histogram:
Automatically highlights recent high/low price zones (upper and lower) using your lookback period and zone width settings.
Each zone is split into horizontal "bins," color-coded for buy/sell dominance and highlighting the Point of Control (POC)—the price with the most traded volume.
The indicator draws live volume histograms inside each zone, supplementing them with labels that show buy vs. sell volumes and POC statistics.
Adjustable bin count, transparency, colors, and histogram granularity to fit your visual preference.
Optional midlines and fair value drift line help visualize price equilibrium and value shifts over time.
How to Use
Trend Confirmation: Align trades with the EMA200—trade long above, short below, or wait for ATR-trailing stop triggers that coincide with the EMA bias.
Signal Generation: Use the ATR trailing stop Buy/Sell signals to spot shifts in volatility-adjusted direction early.
Volume Zone Analysis: Identify where the highest concentration of buy/sell activity occurred within the customizable upper/lower zones:
Use high volume bins and POC as magnets for price, support/resistance, or to confirm breakout/failure zones.
Leverage the fair value drift line and dynamic labels to detect changes in market sentiment and volume pressure.
Ema With Buy/Sell Signals Pro This advanced multi-tool indicator combines Exponential Moving Averages (EMAs), dynamic buy/sell signal logic, ATR-based trailing stops, and a custom volume profile heatmap, delivering a complete solution for identifying trend direction, momentum shifts, and high-activity price zones.
Core Components & Features
📊 1. Triple EMA Overlay
Plots 20, 50, and 200 EMA lines on the chart.
Visualizes short-term, medium-term, and long-term trend directions.
Acts as dynamic support/resistance levels and trend confirmation tools.
💡 2. Smart Buy/Sell Signal System (ATR-Based)
Utilizes an ATR Trailing Stop to detect trend reversals.
Generates Buy signals when price breaks above the ATR stop and confirms strength.
Generates Sell signals when price breaks below the ATR stop and confirms weakness.
Optionally triggers alerts on crossover signals to capture momentum moves early.
📈 3. ATR Extension Signal
Highlights strong momentum bursts using a price/ATR divergence logic.
Filters conditions where price is significantly extended from the 50 EMA.
Plots blue circles above bars to indicate potential breakout continuation.
🧮 4. Volume Profile Heatmap (Custom Coded)
Plots a horizontal Volume Distribution Profile over a customizable lookback window.
Visualizes buy vs sell volume density across price levels using colored boxes:
Green = Buy Dominant
Red = Sell Dominant
5. Fully Customizable Inputs
Adjustable EMAs, ATR period, multipliers, and signal sensitivity.
Fine-tune volume profile resolution, scale, and transparency.
Turn ON/OFF heatmap and lookback visualization for cleaner charts.
✅ Best Use-Cases
Trend-following strategies with reliable momentum confirmation.
Entry/exit signals based on volatility-adjusted stop loss logic.
Spotting key liquidity zones, support/resistance bands, and volume imbalances.
Works for intraday, swing, and position trading.
MojoPivots Breakout Signals [DonnieMojo]The MojoPivots Breakout Indicator is a precision-engineered tool designed for traders seeking high-probability breakout opportunities using dynamic pivot structures and real-time volume imbalances.
Built on DonnieMojo’s breakout framework, this indicator analyzes market structure via custom MR (Major Resistance) and MS (Major Support) levels, dynamically derived from intraday volume profiles and statistical price expansion. It intelligently tracks and visualizes potential breakout zones, key "line-in-the-sand" levels, and take-profit targets (TP1, TP2, TP3) based on volatility-adjusted zones.
🔑 Core Features:
Breakout Signal Detection
Identifies potential bullish and bearish breakouts when price breaches predefined resistance (MR1) or support (MS1) levels with confirmation from volume dynamics.
Smart Take-Profit System
Targets are automatically mapped to MR2–MR4 and MS2–MS4, offering structured TP zones based on standard deviation thresholds.
Delta Zone Visuals
Color-coded fills display real-time buyer/seller dominance in each zone using an imbalance-weighted volume model.
VPOC "Sand Line"
The Volume Point of Control is plotted to show the session's key battle line for trend continuation or rejection.
Statistical Performance Panel
Live breakout stats with hit-rate bars (TP1/TP2/TP3) help you evaluate performance and adjust trade management.
🧪 Usage Tips:
Timeframe Sync: The default detection logic is based on 15-minute candles, but pivot zones are calculated from higher timeframes (2H by default). Adjust these in the settings to suit your strategy.
Entry Trigger: Wait for price to close below MS1 or above MR1 and breach it on the next bar to confirm a breakout signal.
TP Scaling: Use TP1 for conservative exits or scale out progressively at TP2 and TP3 for extended moves.
Volume Confirmation: Delta zone fills (green/red) help validate whether breakout levels are supported by buyer/seller strength — fade low-delta signals with caution.
Combine with Trend Filters: Enhance results by using MojoPivots alongside trend indicators like EMAs, ADX, or macro S/R.
Dynamic VWAP: Fair Value & Divergence SuiteDynamic VWAP: Fair Value & Divergence Suite
Dynamic VWAP: Fair Value & Divergence Suite is a comprehensive tool for tracking contextual valuation, overextension, and potential reversal signals in trending markets. Unlike traditional VWAP that anchors to the start of a session or a fixed period, this indicator dynamically resets the VWAP anchor to the most recent swing low. This design allows you to monitor how far price has extended from the most recent significant low, helping identify zones of potential profit-taking or reversion.
Deviation bands (standard deviations above the anchored VWAP) provide a clear visual framework to assess whether price is in a fair value zone (±1σ), moderately extended (+2σ), or in zones of extreme extension (+3σ to +5σ). The indicator also highlights contextual divergence signals, including slope deceleration, weak-volume retests, and deviation failures—giving you actionable confluence around potential reversal points.
Because the anchor updates dynamically, this tool is particularly well suited for trend-following assets like BTC or stocks in sustained moves, where price rarely returns to deep negative deviation zones. For this reason, the indicator focuses on upside extension rather than symmetrical reversion to a long-term mean.
🎯 Key Features
✅ Dynamic Swing Low Anchoring
Continuously re-anchors VWAP to the most recent swing low based on your chosen lookback period.
Provides context for trend progression and overextension relative to structural lows.
✅ Standard Deviation Bands
Plots up to +5σ deviation bands to visualize levels of overextension.
Extended bands (+3σ to +5σ) can be toggled for simplicity.
✅ Conditional Zone Fills
Colored background fills show when price is inside each valuation zone.
Helps you immediately see if price is in fair value, moderately extended, or highly stretched territory.
✅ Divergence Detection
VWAP Slope Divergence: Flags when price makes a higher high but VWAP slope decelerates.
Low Volume Retest: Highlights weak re-tests of VWAP on low volume.
Deviation Failure: Identifies when price reverts back inside +1σ after closing beyond +3σ.
✅ Volume Fallback
If volume is unavailable, uses high-low range as a proxy.
✅ Highly Customizable
Adjust lookbacks, show/hide extended bands, toggle fills, and enable or disable divergences.
🛠️ How to Use
Identify Buy and Sell Zones
Price in the fair value band (±1σ) suggests equilibrium.
Reaching +2σ to +3σ signals increasing overextension and potential areas to take profits.
+4σ to +5σ zones can be used to watch for exhaustion or mean-reversion setups.
Monitor Divergence Signals
Use slope divergence and deviation failures to look for confluence with overextension.
Low volume retests can flag rallies lacking conviction.
Adapt Swing Lookback
30–50 bars: Faster re-anchoring for swing trading.
75–100 bars: More stable anchors for longer-term trends.
🧭 Best Practices
Combine the anchored VWAP with higher timeframe structure.
Confirm signals with other tools (momentum, volume profiles, or trend filters).
Use extended deviation zones as context, not as standalone signals.
⚠️ Disclaimer
This script is for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any security or asset. Always do your own research and consult a qualified financial professional before making any trading decisions. Past performance does not guarantee future results.
ZenAlgo - AvengerThe ZenAlgo - Avenger indicator provides a multi-layered view of market behavior by combining volume delta analytics, trend-following EMAs, average price comparison, and price-volume profiling into a unified overlay. It is designed to visually assist traders in identifying areas of interest, momentum shifts, and potential reversals using cumulative data from both spot and perpetual markets.
Volume Delta Calculation
This indicator computes delta as the difference between estimated buy and sell volumes using volume data from multiple centralized exchanges. It distinguishes between spot and perpetual volumes, combining them into total volume.
To estimate buying and selling volume from raw volume data, candle structure is broken down into body and wicks. The body is interpreted as the core directional movement (buy/sell), while the wicks are treated as uncertain or counteraction. This segmentation helps infer the likely share of buying and selling within each bar.
The delta is calculated per bar and then aggregated over a lookback period (default 14 bars) to generate a cumulative delta. This approach provides a smoothed value of volume pressure trends over time.
A moving average is applied to the delta values (using selectable MA types like EMA or SMA) to define signal crossovers and suppress noise.
Delta Visualization
To contextualize delta within price action, the delta is scaled dynamically (by ATR or user-defined value) and plotted as a band around the closing price. Positive delta expands upward from price, negative delta downward. This provides a visual overlay that reflects net market pressure in context with price movement.
In cases of extreme delta (threshold set at 80% of recent maximum), the indicator marks spike bars using symbols to indicate significant directional pressure.
Identification of Noteworthy Conditions
The indicator highlights points on the chart where specific conditions are met based on the interaction between volume delta and its moving average. These conditions may align with moments of market pressure imbalance and directional movement, but they are not to be interpreted as trade signals in isolation.
Instead, these chart markers serve as visual flags for potential interest. They are intended to draw the user’s attention to scenarios where:
The delta crosses above or below its moving average, suggesting a potential shift in volume pressure.
The cumulative delta supports the direction of this crossover.
Optional filters can further restrict these markings to periods where:
The short-term trend (as inferred from EMA slope) supports the direction.
Volume is elevated relative to a recent average.
A user-defined cooldown period prevents multiple markings within short succession to avoid clutter.
It is essential to underscore that these markers do not constitute buy or sell advice . Their role is diagnostic , helping the trader to identify potential moments of interest which should be analyzed in conjunction with broader context, such as trend structure, price action, support/resistance levels, or external market data.
EMA Structure
Six EMAs with fixed lengths (13 to 56) are plotted and colored dynamically based on the most recent crossover between the fastest and slowest (EMA1 and EMA6). These EMAs help visualize short- to mid-term trends. The crossover itself is marked with symbols, with vertical offset based on ATR to maintain chart readability.
Average Line (AVG)
The indicator also calculates an average price based on a fixed window (100 bars). This is not a standard moving average but rather a raw average of recent prices stored in a circular buffer. The average is plotted, and its relative distance to the current price is labeled as a percentage. This feature serves as a simplified representation of fair value or mean reversion anchor.
EMA6 vs AVG Cross
Another layer of point of interest detection involves EMA6 crossing the AVG line. This crossover is only considered valid if EMA6 shows slope consistency in the crossing direction. These events are marked using symbols and offset vertically to avoid overlapping price action.
Divergence Detection
The script detects both regular and hidden divergences between price and delta:
Regular divergences are defined when price makes a higher high or lower low, while delta fails to confirm (makes a lower high or higher low).
Hidden divergences occur when price retraces (lower high or higher low), but delta moves against this retracement, indicating underlying strength or weakness.
Divergence points are labeled with "R" (regular) or "H" (hidden) and appear at local pivot highs or lows. The number of visible divergence labels can be limited for chart clarity.
POC and nPOC Calculations
The script includes a simplified volume profile implementation, calculating:
POC (Point of Control): the price level with the highest volume for the given period.
nPOC (non-tested POC): historical POCs that have not yet been revisited by price.
Price levels are bucketed into rows (user-defined), and volume per bucket is tracked to identify the POC. Upon a new period (e.g., day, week), a horizontal POC line is drawn. Once tested by price, the line’s appearance changes (color fades, label shrinks), helping users distinguish between untouched and touched levels.
Limits are enforced on the number of retained POCs and their maximum distance from current bars to optimize performance and chart readability.
Exchange Aggregation
Volume data is aggregated across major exchanges. This ensures that the delta calculation captures a broader market picture beyond a single venue, reducing exchange-specific noise.
How to Interpret Values
Delta Band: Wide bands indicate strong directional imbalance. Narrow bands suggest indecision or low volume.
EMA Crossover Symbols: Appear on directional shifts in moving averages. Multiple EMAs reinforcing the same slope typically indicate stronger trend.
AVG Line: Represents average price over recent history. Large deviations can indicate overextension or potential mean reversion.
Divergences: Regular ones may point to weakening momentum; hidden ones can suggest continuation despite corrective price action.
POC / nPOC: Key volume-based support/resistance levels. Untested nPOCs can act as magnets for price retests.
How to Best Use This Indicator
Use in conjunction with trend context (e.g., higher timeframe EMAs) to avoid counter-trend indications.
Treat delta spikes as caution zones—especially if they occur at known support/resistance.
Watch for divergences as early warning signs before price reverses.
Use POC/nPOC as target levels, especially if aligned with delta signals.
Apply volume and trend filters to reduce noise on shorter timeframes.
Added Value
Multi-exchange volume aggregation makes the delta calculation more robust.
Real-time cumulative delta overlaid directly on the price chart provides immediate context.
Points of interest on chart are conservative and filterable, intended to reduce false positives.
The combination of delta, trend-following EMAs, fair value line, and volume profile data is rarely found in one overlay script.
POC/nPOC visualization based on real traded volume helps identify high-interest zones for future price interaction.
Why Is It Worth Paying For
While free alternatives may provide partial insights (e.g., basic delta or single EMA crossovers), this indicator integrates multiple domains—delta, divergence, average price, trend overlays, and profile levels—into a coherent, optimized chart tool. The value lies not just in having these tools, but in how they are synchronized and visualized.
Furthermore, sourcing and synchronizing volume data from multiple exchanges for delta estimation is not straightforward in Pine Script and adds to the indicator's complexity and utility.
Disclaimers and Limitations
Delta estimation is based on candle structure and assumes wick/body distribution reflects buyer/seller activity, which may not always be precise.
Multi-exchange volume data relies on availability via TradingView’s request.security() function; if exchange data is missing or delayed, results may be incomplete.
Divergences do not guarantee reversals—should be used as part of a broader analysis framework.
On illiquid instruments or exotic pairs, the value of delta and volume-based analytics may be reduced due to unreliable volume.
Exponential Action Map (EAM)### **Exponential Action Map (EAM) – Description and Differences from VPVR**
The Exponential Action Map (EAM) indicator is a Pine Script-based volume profile indicator that offers **a weighted representation of buying and selling activity**. Unlike the standard **Volume Profile Visible Range (VPVR)**, which simply shows traded volume at various price levels, the EAM provides the following additional features:
1. **Exponential Weighting**:
- Instead of treating the volume of all considered bars equally, the EAM uses a **decay factor** to gradually diminish the significance of older data. This allows **more recent price movements to have greater influence**, making it particularly useful for short-term analysis.
2. **Exponential Stealth Move (ESM)**:
- In addition to buy and sell volume, the EAM calculates and displays the **Exponential Stealth Move (ESM)**.
- This measures the relative price movement compared to volume and highlights areas where **significant price changes occur with low volume**, which may indicate institutional activity or strong momentum.
- The ESM visualization is not present in VPVR, making it a distinct and valuable feature.
3. **Visualization Methodology**:
- Instead of simple histograms like in VPVR, volume is represented by **dynamic boxes** that encompass Buy (EBA), Sell (ESA), and Stealth Move (ESM) activities.
- The size and color of these boxes are **customizable**, allowing for clear differentiation between various volume types.
4. **Flexibility & Configuration**:
- Users can adjust parameters such as **Number of Bars, Decay Factor, Bar Width, and Maximum History Data**.
- The ability to **toggle historical data visibility** offers a **tailored view** that VPVR does not provide.
**Conclusion:** The EAM extends the classic volume profile (VPVR) by introducing **time-weighted volume analysis and detection of Stealth Moves (ESM)**. This not only highlights price levels with high trading volume but also reveals **price movements with low liquidity**, which can potentially indicate institutional interest.
Multiple AVWAP [OmegaTools]The Multiple AVWAP indicator is a sophisticated trading tool designed for professional traders who require precision in volume-weighted price tracking. This indicator allows for the deployment of multiple Anchored Volume Weighted Average Price (AVWAP) calculations simultaneously, offering deep insights into price movements, dynamic support and resistance levels, and trend structures across multiple timeframes.
This indicator caters to both institutional and retail traders by integrating flexible anchoring methods, multi-timeframe adaptability, and enhanced visualization features. It also includes deviation bands for statistical analysis, making it a comprehensive volume-based trading solution.
Key Features & Functionalities
1. Multiple AVWAP Configurations
Users can configure up to four distinct AVWAP calculations to track different market conditions.
Supports various anchoring methods:
Fixed: A traditional AVWAP that starts from a defined historical point.
Perpetual: A rolling VWAP that continuously adjusts over time.
Extension: An extension-based AVWAP that projects from past calculations.
High Volume: Anchors AVWAP to the highest volume bar within a specified period.
None: Option to disable AVWAP calculation if not required.
2. Advanced Deviation Bands
Implements standard deviation bands (1st and 2nd deviation) to provide a statistical measure of price dispersion from the AVWAP.
Serves as a dynamic method for identifying overbought and oversold conditions relative to VWAP pricing.
Deviation bands are customizable in terms of visibility, color, and transparency.
3. Multi-Timeframe Support
Users can assign different timeframes to each AVWAP calculation for macro and micro analysis.
Helps in identifying long-term institutional trading levels alongside short-term intraday trends.
4. Z-Score Normalization Mode
Option to standardize oscillator values based on AVWAP deviations.
Converts price movements into a statistical Z-score, allowing traders to measure price strength in a normalized range.
Helps in detecting extreme price dislocations and mean-reversion opportunities.
5. Customizable Visual & Aesthetic Settings
Fully customizable line colors, transparency, and thickness to enhance clarity.
Users can modify AVWAP and deviation band colors to distinguish between different levels.
Configurable display options to match personal trading preferences.
6. Oscillator Mode for Trend & Momentum Analysis
The indicator converts price deviations into an oscillator format, displaying AVWAP strength and weakness dynamically.
This provides traders with a momentum-based perspective on volume-weighted price movements.
User Guide & Implementation
1. Configuring AVWAPs for Optimal Use
Choose the mode for each AVWAP instance:
Fixed (set historical point)
Perpetual (rolling, continuously updated AVWAP)
Extension (projection from past AVWAP levels)
High Volume (anchored to highest volume bar)
None (disables the AVWAP line)
Adjust the length settings to fine-tune calculation sensitivity.
2. Utilizing Deviation Bands for Market Context
Activate deviation bands to see statistical boundaries of price action.
Monitor +1 / -1 and +2 / -2 standard deviation levels for extended price movements.
Consider price action outside of deviation bands as potential mean-reversion signals.
3. Multi-Timeframe Analysis for Institutional-Level Insights
Assign different timeframes to each AVWAP to compare:
Daily VWAP (institutional trading levels)
Weekly VWAP (swing trading trends)
Intraday VWAPs (short-term momentum shifts)
Helps identify where institutional liquidity is positioned relative to price.
4. Activating the Oscillator for Momentum & Bias Confirmation
The oscillator converts AVWAP deviations into a normalized value.
Use overbought/oversold levels to determine strength and potential reversals.
Combine with other indicators (RSI, MACD) for confluence-based trading decisions.
Trading Applications & Strategies
5. Trend Confirmation & Institutional VWAP Tracking
If price consistently holds above the primary AVWAP, it signals a bullish trend.
If price remains below AVWAP, it indicates selling pressure and a bearish trend.
Monitor retests of AVWAP levels for potential trend continuation or reversal.
6. Dynamic Support & Resistance Levels
AVWAP lines act as dynamic floating support and resistance zones.
Price bouncing off AVWAP suggests continuation, whereas breakdowns indicate a shift in momentum.
Look for confluence with high-volume zones for stronger trade signals.
7. Mean Reversion & Statistical Edge Trading
Prices that deviate beyond +2 or -2 standard deviations often revert toward AVWAP.
Mean reversion traders can fade extended moves and target AVWAP re-tests.
Helps in identifying exhaustion points in trending markets.
8. Institutional Liquidity & Volume Footprints
Institutions often execute large trades near VWAP zones, causing price reactions.
Tracking multi-timeframe AVWAP levels allows traders to anticipate key liquidity areas.
Use higher timeframe AVWAPs as macro support/resistance for swing trading setups.
9. Enhancing Momentum Trading with AVWAP Oscillator
The oscillator provides a momentum-based measure of AVWAP deviations.
Helps in confirming entry and exit timing for trend-following trades.
Useful for pairing with stochastic oscillators, MACD, or RSI to validate trade decisions.
Best Practices & Trading Tips
Use in Conjunction with Volume Analysis: Combine with volume profiles, OBV, or CVD for increased accuracy.
Adjust Timeframes Based on Trading Style: Scalpers can focus on short-term AVWAP, while swing traders benefit from weekly/daily AVWAP tracking.
Backtest Different AVWAP Configurations: Experiment with different anchoring methods and lookback periods to optimize trade performance.
Monitor Institutional Order Flow: Identify key VWAP zones where institutional traders may be active.
Use with Other Technical Indicators: Enhance trading confidence by integrating with moving averages, Bollinger Bands, or Fibonacci retracements.
Final Thoughts & Disclaimer
The Multiple AVWAP indicator provides a comprehensive approach to volume-weighted price tracking, making it ideal for professional traders. While this tool enhances market clarity and trade decision-making, it should be used as part of a well-rounded trading strategy with risk management principles in place.
This indicator is provided for informational and educational purposes only. Trading involves risk, and past performance is not indicative of future results. Always conduct your own analysis and due diligence before executing trades.
OmegaTools - Enhancing Market Clarity with Precision Indicators
Simplified Market ProfileVolume Bins: This script divides the price range into num_bins equal price levels. Each bin holds the cumulative volume for that price range.
Profile Length: The number of past bars that the profile considers for building the volume histogram.
Bin Size: The price range between bins is determined by dividing the difference between the highest and lowest prices over the specified range.
Volume Calculation: The script iterates over each bar within the specified range, determining which price bin the bar’s volume should be added to.
Plotting: The script visualizes the volume profile as lines plotted horizontally at different price levels, with thickness proportional to the volume traded at that level.
CVD - Cumulative Volume Delta (Chart)█ OVERVIEW
This indicator displays cumulative volume delta (CVD) as an on-chart oscillator. It uses intrabar analysis to obtain more precise volume delta information compared to methods that only use the chart's timeframe.
The core concepts in this script come from our first CVD indicator , which displays CVD values as plot candles in a separate indicator pane. In this script, CVD values are scaled according to price ranges and represented on the main chart pane.
█ CONCEPTS
Bar polarity
Bar polarity refers to the position of the close price relative to the open price. In other words, bar polarity is the direction of price change.
Intrabars
Intrabars are chart bars at a lower timeframe than the chart's. Each 1H chart bar of a 24x7 market will, for example, usually contain 60 bars at the lower timeframe of 1min, provided there was market activity during each minute of the hour. Mining information from intrabars can be useful in that it offers traders visibility on the activity inside a chart bar.
Lower timeframes (LTFs)
A lower timeframe is a timeframe that is smaller than the chart's timeframe. This script utilizes a LTF to analyze intrabars, or price changes within a chart bar. The lower the LTF, the more intrabars are analyzed, but the less chart bars can display information due to the limited number of intrabars that can be analyzed.
Volume delta
Volume delta is a measure that separates volume into "up" and "down" parts, then takes the difference to estimate the net demand for the asset. This approach gives traders a more detailed insight when analyzing volume and market sentiment. There are several methods for determining whether an asset's volume belongs in the "up" or "down" category. Some indicators, such as On Balance Volume and the Klinger Oscillator , use the change in price between bars to assign volume values to the appropriate category. Others, such as Chaikin Money Flow , make assumptions based on open, high, low, and close prices. The most accurate method involves using tick data to determine whether each transaction occurred at the bid or ask price and assigning the volume value to the appropriate category accordingly. However, this method requires a large amount of data on historical bars, which can limit the historical depth of charts and the number of symbols for which tick data is available.
In the context where historical tick data is not yet available on TradingView, intrabar analysis is the most precise technique to calculate volume delta on historical bars on our charts. This indicator uses intrabar analysis to achieve a compromise between simplicity and accuracy in calculating volume delta on historical bars. Our Volume Profile indicators use it as well. Other volume delta indicators in our Community Scripts , such as the Realtime 5D Profile , use real-time chart updates to achieve more precise volume delta calculations. However, these indicators aren't suitable for analyzing historical bars since they only work for real-time analysis.
This is the logic we use to assign intrabar volume to the "up" or "down" category:
• If the intrabar's open and close values are different, their relative position is used.
• If the intrabar's open and close values are the same, the difference between the intrabar's close and the previous intrabar's close is used.
• As a last resort, when there is no movement during an intrabar and it closes at the same price as the previous intrabar, the last known polarity is used.
Once all intrabars comprising a chart bar are analyzed, we calculate the net difference between "up" and "down" intrabar volume to produce the volume delta for the chart bar.
█ FEATURES
CVD resets
The "cumulative" part of the indicator's name stems from the fact that calculations accumulate during a period of time. By periodically resetting the volume delta accumulation, we can analyze the progression of volume delta across manageable chunks, which is often more useful than looking at volume delta accumulated from the beginning of a chart's history.
You can configure the reset period using the "CVD Resets" input, which offers the following selections:
• None : Calculations do not reset.
• On a fixed higher timeframe : Calculations reset on the higher timeframe you select in the "Fixed higher timeframe" field.
• At a fixed time that you specify.
• At the beginning of the regular session .
• On trend changes : Calculations reset on the direction change of either the Aroon indicator, Parabolic SAR , or Supertrend .
• On a stepped higher timeframe : Calculations reset on a higher timeframe automatically stepped using the chart's timeframe and following these rules:
Chart TF HTF
< 1min 1H
< 3H 1D
<= 12H 1W
< 1W 1M
>= 1W 1Y
Specifying intrabar precision
Ten options are included in the script to control the number of intrabars used per chart bar for calculations. The greater the number of intrabars per chart bar, the fewer chart bars can be analyzed.
The first five options allow users to specify the approximate amount of chart bars to be covered:
• Least Precise (Most chart bars) : Covers all chart bars by dividing the current timeframe by four.
This ensures the highest level of intrabar precision while achieving complete coverage for the dataset.
• Less Precise (Some chart bars) & More Precise (Less chart bars) : These options calculate a stepped LTF in relation to the current chart's timeframe.
• Very precise (2min intrabars) : Uses the second highest quantity of intrabars possible with the 2min LTF.
• Most precise (1min intrabars) : Uses the maximum quantity of intrabars possible with the 1min LTF.
The stepped lower timeframe for "Less Precise" and "More Precise" options is calculated from the current chart's timeframe as follows:
Chart Timeframe Lower Timeframe
Less Precise More Precise
< 1hr 1min 1min
< 1D 15min 1min
< 1W 2hr 30min
> 1W 1D 60min
The last five options allow users to specify an approximate fixed number of intrabars to analyze per chart bar. The available choices are 12, 24, 50, 100, and 250. The script will calculate the LTF which most closely approximates the specified number of intrabars per chart bar. Keep in mind that due to factors such as the length of a ticker's sessions and rounding of the LTF, it is not always possible to produce the exact number specified. However, the script will do its best to get as close to the value as possible.
As there is a limit to the number of intrabars that can be analyzed by a script, a tradeoff occurs between the number of intrabars analyzed per chart bar and the chart bars for which calculations are possible.
Display
This script displays raw or cumulative volume delta values on the chart as either line or histogram oscillator zones scaled according to the price chart, allowing traders to visualize volume activity on each bar or cumulatively over time. The indicator's background shows where CVD resets occur, demarcating the beginning of new zones. The vertical axis of each oscillator zone is scaled relative to the one with the highest price range, and the oscillator values are scaled relative to the highest volume delta. A vertical offset is applied to each oscillator zone so that the highest oscillator value aligns with the lowest price. This method ensures an accurate, intuitive visual comparison of volume activity within zones, as the scale is consistent across the chart, and oscillator values sit below prices. The vertical scale of oscillator zones can be adjusted using the "Zone Height" input in the script settings.
This script displays labels at the highest and lowest oscillator values in each zone, which can be enabled using the "Hi/Lo Labels" input in the "Visuals" section of the script settings. Additionally, the oscillator's value on a chart bar is displayed as a tooltip when a user hovers over the bar, which can be enabled using the "Value Tooltips" input.
Divergences occur when the polarity of volume delta does not match that of the chart bar. The script displays divergences as bar colors and background colors that can be enabled using the "Color bars on divergences" and "Color background on divergences" inputs.
An information box in the lower-left corner of the indicator displays the HTF used for resets, the LTF used for intrabars, the average quantity of intrabars per chart bar, and the number of chart bars for which there is LTF data. This is enabled using the "Show information box" input in the "Visuals" section of the script settings.
FOR Pine Script™ CODERS
• This script utilizes `ltf()` and `ltfStats()` from the lower_tf library.
The `ltf()` function determines the appropriate lower timeframe from the selected calculation mode and chart timeframe, and returns it in a format that can be used with request.security_lower_tf() .
The `ltfStats()` function, on the other hand, is used to compute and display statistical information about the lower timeframe in an information box.
• The script utilizes display.data_window and display.status_line to restrict the display of certain plots.
These new built-ins allow coders to fine-tune where a script’s plot values are displayed.
• The newly added session.isfirstbar_regular built-in allows for resetting the CVD segments at the start of the regular session.
• The VisibleChart library developed by our resident PineCoders team leverages the chart.left_visible_bar_time and chart.right_visible_bar_time variables to optimize the performance of this script.
These variables identify the opening time of the leftmost and rightmost visible bars on the chart, allowing the script to recalculate and draw objects only within the range of visible bars as the user scrolls.
This functionality also enables the scaling of the oscillator zones.
These variables are just a couple of the many new built-ins available in the chart.* namespace.
For more information, check out this blog post or look them up by typing "chart." in the Pine Script™ Reference Manual .
• Our ta library has undergone significant updates recently, including the incorporation of the `aroon()` indicator used as a method for resetting CVD segments within this script.
Revisit the library to see more of the newly added content!
Look first. Then leap.
Delta Volume Channels [LucF]█ OVERVIEW
This indicator displays on-chart visuals aimed at making the most of delta volume information. It can color bars and display two channels: one for delta volume, another calculated from the price levels of bars where delta volume divergences occur. Markers and alerts can also be configured using key conditions, and filtered in many different ways. The indicator caters to traders who prefer chart visuals over raw values. It will work on historical bars and in real time, using intrabar analysis to calculate delta volume in both conditions.
█ CONCEPTS
Delta Volume
The volume delta concept divides a bar's volume in "up" and "down" volumes. The delta is calculated by subtracting down volume from up volume. Many calculation techniques exist to isolate up and down volume within a bar. The simplest techniques use the polarity of interbar price changes to assign their volume to up or down slots, e.g., On Balance Volume or the Klinger Oscillator . Others such as Chaikin Money Flow use assumptions based on a bar's OHLC values. The most precise calculation method uses tick data and assigns the volume of each tick to the up or down slot depending on whether the transaction occurs at the bid or ask price. While this technique is ideal, it requires huge amounts of data on historical bars, which usually limits the historical depth of charts and the number of symbols for which tick data is available.
This indicator uses intrabar analysis to achieve a compromise between the simplest and most precise methods of calculating volume delta. In the context where historical tick data is not yet available on TradingView, intrabar analysis is the most precise technique to calculate volume delta on historical bars on our charts. TradingView's Volume Profile built-in indicators use it, as do the CVD - Cumulative Volume Delta Candles and CVD - Cumulative Volume Delta (Chart) indicators published from the TradingView account . My Volume Delta Columns Pro indicator also uses intrabar analysis. Other volume delta indicators such as my Realtime 5D Profile use realtime chart updates to achieve more precise volume delta calculations. Indicators of that type cannot be used on historical bars however; they only work in real time.
This is the logic I use to assign intrabar volume to up or down slots:
• If the intrabar's open and close values are different, their relative position is used.
• If the intrabar's open and close values are the same, the difference between the intrabar's close and the previous intrabar's close is used.
• As a last resort, when there is no movement during an intrabar and it closes at the same price as the previous intrabar, the last known polarity is used.
Once all intrabars making up a chart bar have been analyzed and the up or down property of each intrabar's volume determined, the up volumes are added and the down volumes subtracted. The resulting value is volume delta for that chart bar, which can be used as an estimate of the buying/selling pressure on an instrument.
Delta Volume Percent (DV%)
This value is the proportion that delta volume represents of the total intrabar volume in the chart bar. Note that on some symbols/timeframes, the total intrabar volume may differ from the chart's volume for a bar, but that will not affect our calculations since we use the total intrabar volume.
Delta Volume Channel
The DV channel is the space between two moving averages: the reference line and a DV%-weighted version of that reference. The reference line is a moving average of a type, source and length which you select. The DV%-weighted line uses the same settings, but it averages the DV%-weighted price source.
The weight applied to the source of the reference line is calculated from two values, which are multiplied: DV% and the relative size of the bar's volume in relation to previous bars. The effect of this is that DV% values on bars with higher total volume will carry greater weight than those with lesser volume.
The DV channel can be in one of four states, each having its corresponding color:
• Bull (teal): The DV%-weighted line is above the reference line.
• Strong bull (lime): The bull condition is fulfilled and the bar's close is above the reference line and both the reference and the DV%-weighted lines are rising.
• Bear (maroon): The DV%-weighted line is below the reference line.
• Strong bear (pink): The bear condition is fulfilled and the bar's close is below the reference line and both the reference and the DV%-weighted lines are falling.
Divergences
In the context of this indicator, a divergence is any bar where the slope of the reference line does not match that of the DV%-weighted line. No directional bias is assigned to divergences when they occur.
Divergence Channel
The divergence channel is the space between two levels (by default, the bar's low and high ) saved when divergences occur. When price has breached a channel and a new divergence occurs, a new channel is created. Until that new channel is breached, bars where additional divergences occur will expand the channel's levels if the bar's price points are outside the channel.
Prices breaches of the divergence channel will change its state. Divergence channels can be in one of five different states:
• Bull (teal): Price has breached the channel to the upside.
• Strong bull (lime): The bull condition is fulfilled and the DV channel is in the strong bull state.
• Bear (maroon): Price has breached the channel to the downside.
• Strong bear (pink): The bear condition is fulfilled and the DV channel is in the strong bear state.
• Neutral (gray): The channel has not been breached.
█ HOW TO USE THE INDICATOR
Load the indicator on an active chart (see here if you don't know how).
The default configuration displays:
• The DV channel, without the reference or DV%-weighted lines.
• The Divergence channel, without its level lines.
• Bar colors using the state of the DV channel.
The default settings use an Arnaud-Legoux moving average on the close and a length of 20 bars. The DV%-weighted version of it uses a combination of DV% and relative volume to calculate the ultimate weight applied to the reference. The DV%-weighted line is capped to 5 standard deviations of the reference. The lower timeframe used to access intrabars automatically adjusts to the chart's timeframe and achieves optimal balance between the number of intrabars inspected in each chart bar, and the number of chart bars covered by the script's calculations.
The Divergence channel's levels are determined using the high and low of the bars where divergences occur. Breaches of the channel require a bar's low to move above the top of the channel, and the bar's high to move below the channel's bottom.
No markers appear on the chart; if you want to create alerts from this script, you will need first to define the conditions that will trigger the markers, then create the alert, which will trigger on those same conditions.
To learn more about how to use this indicator, you must understand the concepts it uses and the information it displays, which requires reading this description. There are no videos to explain it.
█ FEATURES
The script's inputs are divided in four sections: "DV channel", "Divergence channel", "Other Visuals" and "Marker/Alert Conditions". The first setting is the selection method used to determine the intrabar precision, i.e., how many lower timeframe bars (intrabars) are examined in each chart bar. The more intrabars you analyze, the more precise the calculation of DV% results will be, but the less chart coverage can be covered by the script's calculations.
DV Channel
Here, you control the visibility and colors of the reference line, its weighted version, and the DV channel between them.
You also specify what type of moving average you want to use as a reference line, its source and length. This acts as the DV channel's baseline. The DV%-weighted line is also a moving average of the same type and length as the reference line, except that it will be calculated from the DV%-weighted source used in the reference line. By default, the DV%-weighted line is capped to five standard deviations of the reference line. You can change that value here. This section is also where you can disable the relative volume component of the weight.
Divergence Channel
This is where you control the appearance of the divergence channel and the key price values used in determining the channel's levels and breaching conditions. These choices have an impact on the behavior of the channel. More generous level prices like the default low and high selection will produce more conservative channels, as will the default choice for breach prices.
In this section, you can also enable a mode where an attempt is made to estimate the channel's bias before price breaches the channel. When it is enabled, successive increases/decreases of the channel's top and bottom levels are counted as new divergences occur. When one count is greater than the other, a bull/bear bias is inferred from it.
Other Visuals
You specify here:
• The method used to color chart bars, if you choose to do so.
• The display of a mark appearing above or below bars when a divergence occurs.
• If you want raw values to appear in tooltips when you hover above chart bars. The default setting does not display them, which makes the script faster.
• If you want to display an information box which by default appears in the lower left of the chart.
It shows which lower timeframe is used for intrabars, and the average number of intrabars per chart bar.
Marker/Alert Conditions
Here, you specify the conditions that will trigger up or down markers. The trigger conditions can include a combination of state transitions of the DV and the divergence channels. The triggering conditions can be filtered using a variety of conditions.
Configuring the marker conditions is necessary before creating an alert from this script, as the alert will use the marker conditions to trigger.
Markers only appear on bar closes, so they will not repaint. Keep in mind, when looking at markers on historical bars, that they are positioned on the bar when it closes — NOT when it opens.
Raw values
The raw values calculated by this script can be inspected using a tooltip and the Data Window. The tooltip is visible when you hover over the top of chart bars. It will display on the last 500 bars of the chart, and shows the values of DV, DV%, the combined weight, and the intermediary values used to calculate them.
█ INTERPRETATION
The aim of the DV channel is to provide a visual representation of the buying/selling pressure calculated using delta volume. The simplest characteristic of the channel is its bull/bear state. One can then distinguish between its bull and strong bull states, as transitions from strong bull to bull states will generally happen when buyers are losing steam. While one should not infer a reversal from such transitions, they can be a good place to tighten stops. Only time will tell if a reversal will occur. One or more divergences will often occur before reversals.
The nature of the divergence channel's design makes it particularly adept at identifying consolidation areas if its settings are kept on the conservative side. A gray divergence channel should usually be considered a no-trade zone. More adventurous traders can use the DV channel to orient their trade entries if they accept the risk of trading in a neutral divergence channel, which by definition will not have been breached by price.
If your charts are already busy with other stuff you want to hold on to, you could consider using only the chart bar coloring component of this indicator:
At its simplest, one way to use this indicator would be to look for overlaps of the strong bull/bear colors in both the DV channel and a divergence channel, as these identify points where price is breaching the divergence channel when buy/sell pressure is consistent with the direction of the breach. I have highlighted all those points in the chart below. Not all of them would have produced profitable trades, but nothing is perfect in the markets. Also, keep in mind that the circles identify the visual you would be looking for — not the trade's entry level.
█ LIMITATIONS
• The script will not work on symbols where no volume is available. An error will appear when that is the case.
• Because a maximum of 100K intrabars can be analyzed by a script, a compromise is necessary between the number of intrabars analyzed per chart bar
and chart coverage. The more intrabars you analyze per chart bar, the less coverage you will obtain.
The setting of the "Intrabar precision" field in the "DV channel" section of the script's inputs
is where you control how the lower timeframe is calculated from the chart's timeframe.
█ NOTES
Volume Quality
If you use volume, it's important to understand its nature and quality, as it varies with sectors and instruments. My Volume X-ray indicator is one way you can appraise the quality of an instrument's intraday volume.
For Pine Script™ Coders
• This script uses the new overload of the fill() function which now makes it possible to do vertical gradients in Pine. I use it for both channels displayed by this script.
• I use the new arguments for plot() 's `display` parameter to control where the script plots some of its values,
namely those I only want to appear in the script's status line and in the Data Window.
• I wrote my script using the revised recommendations in the Style Guide from the Pine v5 User Manual.
█ THANKS
To PineCoders . I have used their lower_tf library in this script, to manage the calculation of the LTF and intrabar stats, and their Time library to convert a timeframe in seconds to a printable form for its display in the Information box.
To TradingView's Pine Script™ team. Their innovations and improvements, big and small, constantly expand the boundaries of the language. What this script does would not have been possible just a few months back.
And finally, thanks to all the users of my scripts who take the time to comment on my publications and suggest improvements. I do not reply to all but I do read your comments and do my best to implement your suggestions with the limited time that I have.
Bitcoin Stalemate IndicatorThe Bitcoin Stalemate Indicator examines periods in the market defined by a combination of high volume and low price volatility. These periods are a bit like a tug-of-war with both sides applying a lot of force but the rope moving very little. Periods of high volume and low volatility suggest both sides of the trade are stuck in a stalemate. This indicator may be useful in identifying psychologically important price levels.
The mechanics of the indicator are fairly simple: the indicator takes the volume and divides it by the candle’s size over it’s close for that same period.
volume / ((high - low) / close)
Candles that move very little but with high volume will produce higher reads and vice versa. Finally a smoothing average is applied to clean up the noise.
Volume profiles from the top 6 exchanges are averaged in order to avoid a single exchange’s popularity acting as an overriding factor. Single exchanges can be isolated but are of lesser use. Heat map functionality is only active when all exchanges are selected.
Market Profile Fixed ViewSome instruments does not provide any volume information, therefore, as a fixed volume profile user, I needed a fixed market profile indicator to use the same principles, regardless of whether the volumes are available or not.
This script draws a market profile histogram corresponding to price variations within a specific duration, you only need to specify Start and End date/time values to see the histogram on your chart.
Details
Two lines corresponding to highest/lowest prices are displayed around the histogram
The redline corresponds to the POC (point of control)
Options
Start calculation
End calculation
Bars number (histogram resolution, currently locked to a max value of 50 bars)
Display side/Width (allows to modify size of bars, to the left or to the right)
Bars/Borders/POC Color customization
Notes
This script will probably be updated (to add VAH/VAL zones, and maybe other options). However, some common market profile attributes have not been implemented yet since I don't really use them)
TIL Volume by Price SRTrading Indicator Lab's Volume by Price SR is a volume-based indicator for TradingView that reveals the strongest (and weakest) support and resistance levels in the chart among 12 price zones within a given period.
How It Works
The Volume by Price indicator uses a spectrum of blue to red colors to differentiate the strength of the volume within a price range for each bar. Think of it as a running volume profile with 12 price zones.
For each bar, the indicator calculates the rank of each price zone from the one that has the least number of volume to the highest within a given length of bars. Price zones that have less volume count are assigned colors that are closer to blue while price zones that have higher volume appear red. The indicator also marks the highest and lowest price levels in the rank with a red and blue dot which correspond to the same color code. The indicator repeats this in the next bar up to the last until it creates a stream of 12 lines that visually represent the gradual shift of volume strength in the price axis.
How to Use
The Volume by Price SR indicator is simple and can be used primarily to gauge support and resistance. Red lines represent price levels where there is a history of higher volume within the period, which also act as good support/resistance levels where price is more likely to be tested or bounce off.
As it can also be seen as a running volume profile indicator, the red and blue dots in each bar can be considered as high volume nodes (HVN) and low volume nodes (LVN) respectively. Though the calculation of the volume profile is continuous, the HVN and LVN dots can often appear consecutively or in a series within a single price level. The price tends to linger around or test lines that has the red dot (HVN). Meanwhile price rarely cross lines with the blue dot (LVN) or not spend as much time in these areas compared to other levels.
The height of the 12 price zones is determined by the difference between the highest high and lowest low of the period which can be useful in visualizing the chart's dynamic price range.
Inputs
- Length - sets the length of the period the indicator calculates for each bar
- Line Thickness - sets the thickness of the 12 lines all at once
- Dot Size - sets the size of the HVN and LVN dots
Multi Time Frame Trend, Volume and Momentum ProfileWHAT DOES THIS INDICATOR DO?
I created this indicator to address some of the significant inconveniences when analyzing a security, such as continually switching between different time frames to determine the trend and potential pullbacks, adding volume or volume-derived indicators, and finally, something that would help me determine the strength of the trend (maybe two additional indicators here). So I decided to code this all-in-one indicator that you can add multiple times to your chart depending on the settings you want to use, or just optimize the parameters for the particular asset and then switch between the options.
As the name suggests, it consists of three main sections - Trend , Volume , and Momentum . You have complete control over the parameters, including the Time Frames you want to use for each one (they can be different). So, let me explain each section in more detail.
HOW DOES THE INDICATOR WORK?
1. Trend Settings
In order to determine the trend, you need to set up two Moving Averages. You have a wide choice here - SMA, EMA, WMA, RMA, HMA, DEMA, TEMA, VWMA, and ALMA. Since the indicator does not plot the moving averages on the chart, I strongly suggest using this indicator along with the free "Trend Indicator for Directional Trading(main)" , which you can find in the Public Library. Once you set up the Trend Resolution, the Types of MAs, and their lengths, the indicator will generate a histogram of their convergences and divergences.
The change in colors should help you more easily determine the trend:
a) Bright Green - bull trend and price trending up (a good place to open long)
b) Dark Green - bull trend and price trending down (stay flat or open a long position with great caution)
c) Bright Red - bear trend and price trending down (a good place to open short)
d) Dark Red - bear trend and price trending up (stay flat or open a short position with great caution)
e) In addition, you can change the color palette to reflect the bull/bear trend momentum by scrolling to the bottom and selecting "Color Based on Bull/Bear Momentum", but I will discuss this in more detail below.
This part of the indicator is useful for opening a trade in the direction of the trend or for spotting a potential divergence. Both cases are illustrated below.
2. Volume Settings
The calculations for this part of the indicator are partially taken from "Multi Time Frame Effective Volume Profile" . I will quickly outline the specifics here, but if you want a more thorough understanding of how it works, please check the description of the MTF Effective Volume Profile indicator .
You have three elements with the following default settings - Resolution (5-min), Lookback (100), and Average (1). This means that the indicator will analyze the last one hundred 5-min bars and will plot a sum of only those that are at least 1 times bigger than the average. Those that are smaller than the average will be left out from the calculation. What you get is a trend line showing you accumulation/distribution based on modified volume parameters.
This part of the indicator is useful for spotting exhaustions and increased buying/selling volume that is opposite to the price trend. As you will see in the picture below, in frame 1 the selling pressure is decreasing, while buying volume is increasing. At one point supply dries out and the bulls take control, thus reverting the price. In frame 2, however, you can see that the higher high is not met with nearly as much buying volume as in the previous peak, showing that the bulls are exhausted and maybe a trend change will follow or at the very least that the bull trend will take a break.
3. Momentum Settings
The final part is an RSI smoothed through a Moving Average with the addition of some minor optimizations. Thus, the parameters you have to configure here aside from the resolution are the RSI length, the moving average that will be used, and its length. Out of the three, this is the most lagging component, but it's also the most accurate one. I must mention that due to the modified nature of this RSI, overbought and oversold levels carry less weight to the trading signals. Rather, pay attention to the change of colors, as they do so when the RSI changes direction based on preset parameters. The picture below shows such instances.
4. Additional Settings
This section consists of 4 elements:
a) Length of Trend - filters out the noise and gives a signal only when the trend becomes more established
b) ADX Threshold - filters out trading ranges and indecision zones when it's not recommended to open a trade
c) Select Analysis - choose what part of the indicator you want to see from a drop-down menu
d) Color Based on Bull/Bear Momentum - a global setting that will override the preset coloring of each indicator and will replace it with colors based on bull/bear strength and momentum - green for bulls, red for bears, and gray for non-trading zones.
The last part of this indicator is a combination of all of the above and is called a Points-Based System . It generates 3 rows of dots that go light green when bull criteria are met, orange when bear criteria are met, or gray when it's neither of the two. When you get a column of 3 green dots you get a buy signal. Similarly, a column of 3 orange dots gives you a sell signal. Grey zones are non-tradeable. It goes without saying that the frequency and quality of the signals you get will almost entirely depend on your settings, so feel free to experiment and adjust the indicator to catch the best moves for the given security.
In terms of indicator adjustments, I have left almost every part open to configuration. That is 15 parameters and 35 adjustable colors.
HOW MUCH DOES THE INDICATOR COST ?
As much as I would like to offer it for free (as some of my other ones), a great deal of work, trading logic, and testing have gone into creating this indicator. More than a few hundred iterations and a few dozen branches were required to reach the end result which is a precise combination of usefulness, simplicity, and practicality. Furthermore, this indicator will continue to be updated and user-requested features that improve its performance will be added.
Disclaimer: The purpose of all indicators is to indicate potential setups, which may lead to profitable results. No indicator is perfect and certainly, no indicator has a 100% success rate. They are subject to flaws, wrongful interpretation, bugs, etc. This indicator makes no exception. It must be used with a sound money management plan that puts the main emphasis on protecting your capital. Please, do not rely solely on any single indicator to make trading decisions instead of you. Indicators are storytellers, not fortune tellers. They help you see the bigger picture, not the future.
To find out more about how to gain access to this indicator, please use the provided information below or just message me. Thank you for your time.
Bar Balance [LucF]Bar Balance extracts the number of up, down and neutral intrabars contained in each chart bar, revealing information on the strength of price movement. It can display stacked columns representing raw up/down/neutral intrabar counts, or an up/down balance line which can be calculated and visualized in many different ways.
WARNING: This is an analysis tool that works on historical bars only. It does not show any realtime information, and thus cannot be used to issue alerts or for automated trading. When realtime bars elapse, the indicator will require a browser refresh, a change to its Inputs or to the chart's timeframe/symbol to recalculate and display information on those elapsed bars. Once a trader understands this, the indicator can be used advantageously to make discretionary trading decisions.
Traders used to work with my Delta Volume Columns Pro will feel right at home in this indicator's Inputs . It has lots of options, allowing it to be used in many different ways. If you value the bar balance information this indicator mines, I hope you will find the time required to master the use of Bar Balance well worth the investment.
█ OVERVIEW
The indicator has two modes: Columns and Line .
Columns
• In Columns mode you can display stacked Up/Down/Neutral columns.
• The "Up" section represents the count of intrabars where `close > open`, "Down" where `close < open` and "Neutral" where `close = open`.
• The Up section always appears above the centerline, the Down section below. The Neutral section overlaps the centerline, split halfway above and below it.
The Up and Down sections start where the Neutral section ends, when there is one.
• The Up and Down sections can be colored independently using 7 different methods.
• The signal line plotted in Line mode can also be displayed in Columns mode.
Line
• Displays a single balance line using a zero centerline.
• A variable number of independent methods can be used to calculate the line (6), determine its color (5), and color the fill (5).
You can thus evaluate the state of 3 different components with this single line.
• A "Divergence Levels" feature will use the line to automatically draw expanding levels on divergence events.
Features available in both modes
• The color of all components can be selected from 15 base colors, with 16 gradient levels used for each base color in the indicator's gradients.
• A zero line can show a 6-state aggregate value of the three main volume balance modes.
• The background can be colored using any of 5 different methods.
• Chart bars can be colored using 5 different methods.
• Divergence and large neutral count ratio events can be shown in either Columns or Line mode, calculated in one of 4 different methods.
• Markers on 6 different conditions can be displayed.
█ CONCEPTS
Intrabar inspection
Intrabar inspection means the indicator looks at lower timeframe bars ( intrabars ) making up a given chart bar to gather its information. If your chart is on a 1-hour timeframe and the intrabar resolution determined by the indicator is 5 minutes, then 12 intrabars will be analyzed for each chart bar and the count of up/down/neutral intrabars among those will be tallied.
Bar Balances and calculation methods
The indicator uses a variety of methods to evaluate bar balance and to derive other calculations from them:
1. Balance on Bar : Uses the relative importance of instant Up and Down counts on the bar.
2. Balance Averages : Uses the difference between the EMAs of Up and Down counts.
3. Balance Momentum : Starts by calculating, separately for both Up and Down counts, the difference between the same EMAs used in Balance Averages and an SMA of double the period used for the EMAs. These differences are then aggregated and finally, a bounded momentum of that aggregate is calculated using RSI.
4. Markers Bias : It sums the bull/bear occurrences of the four previous markers over a user-defined period (the default is 14).
5. Combined Balances : This is the aggregate of the instant bull/bear bias of the three main bar balances.
6. Dual Up/Down Averages : This is a display mode showing the EMA calculated for each of the Up and Down counts.
Interpretation of neutral intrabars
What do neutral intrabars mean? When price does not change during a bar, it can be because there is simply no interest in the market, or because of a perfect balance between buyers and sellers. The latter being more improbable, Bar Balance assumes that neutral bars reveal a lack of interest, which entails uncertainty. That is the reason why the option is provided to interpret ratios of neutral intrabars greater than 50% as divergences. It is also the rationale behind the option to dampen signal lines on the inverse ratio of neutral intrabars, so that zero intrabars do not affect the signal, and progressively larger proportions of neutral intrabars will reduce the signal's amplitude, as the balance calcs using the up/down counts lose significance. The impact of the dampening will vary with markets. Weaker markets such as cryptos will often contain greater numbers of neutral intrabars, so dampening the Line in that sector will have a greater impact than in more liquid markets.
█ FEATURES
1 — Columns
• While the size of the Up/Down columns always represents their respective importance on the bar, their coloring mode is independent. The default setup uses a standard coloring mode where the Up/Down columns over/under the zero line are always in the bull/bear color with a higher intensity for the winning side. Six other coloring modes allow you to pack more information in the columns. When choosing to color the top columns using a bull/bear gradient on Balance Averages, for example, you will end up with bull/bear colored tops. In order for the color of the bottom columns to continue to show the instant bar balance, you can then choose the "Up/Down Ratio on Bar — Dual Solid Colors" coloring mode to make those bars the color of the winning side for that bar.
• Line mode shows only the line, but Columns mode allows displaying the line along with it. If the scale of the line is different than that of the scale of the columns, the line will often appear flat. Traders may find even a flat line useful as its bull/bear colors will be easily distinguishable.
2 — Line
• The default setup for Line mode uses a calculation on "Balance Momentum", with a fill on the longer-term "Balance Averages" and a line color based on the "Markers Bias". With the background set on "Line vs Divergence Levels" and the zero line on the hard-coded "Combined Bar Balances", you have access to five distinct sources of information at a glance, to which you can add divergences, divergences levels and chart bar coloring. This provides powerful potential in displaying bar balance information.
• When no columns are displayed, Line mode can show the full scale of whichever line you choose to calculate because the columns' scale no longer interferes with the line's scale.
• Note that when "Balance on Bar" is selected, the Neutral count is also displayed as a ratio of the balance line. This is the only instance where the Neutral count is displayed in Line mode.
• The "Dual Up/Down Averages" is an exception as it displays two lines: one average for the Up counts and another for the Down counts. This mode will be most useful when Columns are also displayed, as it provides a reference for the top and bottom columns.
3 — Zero Line
The zero line can be colored using two methods, both based on the Combined Balances, i.e., the aggregate of the instant bull/bear bias of the three main bar balances.
• In "Six-state Dual Color Gradient" mode, a dot appears on every bar. Its color reflects the bull/bear state of the Combined Balances, and the dot's brightness reflects the tally of balance biases.
• In "Dual Solid Colors (All Bull/All Bear Only)" a dot only appears when all three balances are either bullish or bearish. The resulting pattern is identical to that of Marker 1.
4 — Divergences
• Divergences are displayed as a small circle at the top of the scale. Four different types of divergence events can be detected. Divergences occur whenever the bull/bear bias of the method used diverges with the bar's price direction.
• An option allows you to include in divergence events instances where the count of neutral intrabars exceeds 50% of the total intrabar count.
• The divergence levels are dynamic levels that automatically build from the line's values on divergence events. On consecutive divergences, the levels will expand, creating a channel. This implementation of the divergence levels corresponds to my view that divergences indicate anomalies, hesitations, points of uncertainty if you will. It excludes any association of a pre-determined bullish/bearish bias to divergences. Accordingly, the levels merely take note of divergence events and mark those points in time with levels. Traders then have a reference point from which they can evaluate further movement. The bull/bear/neutral colors used to plot the levels are also congruent with this view in that they are determined by price's position relative to the levels, which is how I think divergences can be put to the most effective use.
5 — Background
• The background can show a bull/bear gradient on four different calculations. You can adjust its brightness to make its visual importance proportional to how you use it in your analysis.
6 — Chart bars
• Chart bars can be colored using five different methods.
• You have the option of emptying the body of bars where volume does not increase, as does my TLD indicator, the idea behind this being that movement on bars where volume does not increase is less relevant.
7 — Intrabar Resolution
You can choose between three modes. Two of them are automatic and one is manual:
a) Fast, Longer history, Auto-Steps (~12 intrabars) : Optimized for speed and deeper history. Uses an average minimum of 12 intrabars.
b) More Precise, Shorter History Auto-Steps (~24 intrabars) : Uses finer intrabar resolution. It is slower and provides less history. Uses an average minimum of 24 intrabars.
c) Fixed : Uses the fixed resolution of your choice.
Auto-Steps calculations vary for 24/7 and conventional markets in order to achieve the proper target of minimum intrabars.
You can choose to view the intrabar resolution currently used to calculate delta volume. It is the default.
The proper selection of the intrabar resolution is important. It must achieve maximal granularity to produce precise results while not unduly slowing down calculations, or worse, causing runtime errors.
8 — Markers
Six markers are available:
1. Combined Balances Agreement : All three Bar Balances are either bullish or bearish.
2. Up or Down % Agrees With Bar : An up marker will appear when the percentage of up intrabars in an up chart bar is greater than the specified percentage. Conditions mirror to down bars.
3. Divergence confirmations By Price : One of the four types of balance calculations can be used to detect divergences with price. Confirmations occur when the bar following the divergence confirms the balance bias. Note that the divergence events used here do not include neutral intrabar events.
4. Balance Transitions : Bull/bear transitions of the selected balance.
5. Markers Bias Transitions : Bull/bear transitions of the Markers Bias.
6. Divergence Confirmations By Line : Marks points where the line first breaches a divergence level.
Markers appear when the condition is detected, without delay. Since nothing is plotted in realtime, markers do not appear on the realtime bar.
9 — Settings
• Two modes can be selected to dampen the line on the ratio of neutral intrabars.
• A distinct weight can be attributed to the count of the latter half of intrabars, on the assumption that later intrabars may be more important in determining the outcome of chart bars.
• Allows control over the periods of the different moving averages used in calculations.
• The default periods used for the various calculations define the following hierarchy from slow to fast:
Balance Averages: 50,
Balance Momentum: 20,
Dual Up/Down Averages: 20,
Marker Bias: 10.
█ LIMITATIONS
• This script uses a special characteristic of the `security()` function allowing the inspection of intrabars—which is not officially supported by TradingView.
• The method used does not work on the realtime bar—only on historical bars.
• The indicator only works on some chart resolutions: 3, 5, 10, 15 and 30 minutes, 1, 2, 4, 6, and 12 hours, 1 day, 1 week and 1 month. The script’s code can be modified to run on other resolutions, but chart resolutions must be divisible by the lower resolution used for intrabars and the stepping mechanism could require adaptation.
• When using the "Line vs Divergence Levels — Dual Color Gradient" color mode to fill the line, background or chart bars, keep in mind that a line calculation mode must be defined for it to work, as it determines gradients on the movement of the line relative to divergence levels. If the line is hidden, it will not work.
• When the difference between the chart’s resolution and the intrabar resolution is too great, runtime errors will occur. The Auto-Steps selection mechanisms should avoid this.
• Alerts do not work reliably when `security()` is used at intrabar resolutions. Accordingly, no alerts are configured in the indicator.
• The color model used in the indicator provides for fancy visuals that come at a price; when you change values in Inputs , it can take 20 seconds for the changes to materialize. Luckily, once your color setup is complete, the color model does not have a large performance impact, as in normal operation the `security()` calls will become the most important factor in determining response time. Also, once in a while a runtime error will occur when you change inputs. Just making another change will usually bring the indicator back up.
█ RAMBLINGS
Is this thing useful?
I'll let you decide. Bar Balance acts somewhat like an X-Ray on bars. The intrabars it analyzes are no secret; one can simply change the chart's resolution to see the same intrabars the indicator uses. What the indicator brings to traders is the precise count of up/down/neutral intrabars and, more importantly, the calculations it derives from them to present the information in a way that can make it easier to use in trading decisions.
How reliable is Bar Balance information?
By the same token that an up bar does not guarantee that more up bars will follow, future price movements cannot be inferred from the mere count of up/down/neutral intrabars. Price movement during any chart bar for which, let's say, 12 intrabars are analyzed, could be due to only one of those intrabars. One can thus easily see how only relying on bar balance information could be very misleading. The rationale behind Bar Balance is that when the information mined for multiple chart bars is aggregated, it can provide insight into the history behind chart bars, and thus some bias as to the strength of movements. An up chart bar where 11/12 intrabars are also up is assumed to be stronger than the same up bar where only 2/12 intrabars are up. This logic is not bulletproof, and sometimes Bar Balance will stray. Also, keep in mind that balance lines do not represent price momentum as RSI would. Bar Balance calculations have no idea where price is. Their perspective, like that of any historian, is very limited, constrained that it is to the narrow universe of up/down/neutral intrabar counts. You will thus see instances where price is moving up while Balance Momentum, for example, is moving down. When Bar Balance performs as intended, this indicates that the rally is weakening, which does necessarily imply that price will reverse. Occasionally, price will merrily continue to advance on weakening strength.
Divergences
Most of the divergence detection methods used here rely on a difference between the bias of a calculation involving a multi-bar average and a given bar's price direction. When using "Bar Balance on Bar" however, only the bar's balance and price movement are used. This is the default mode.
As usual, divergences are points of interest because they reveal imbalances, which may or may not become turning points. I do not share the overwhelming enthusiasm traders have for the purported ability of bullish/bearish divergences to indicate imminent reversals.
Superfluity
In "The Bed of Procrustes", Nassim Nicholas Taleb writes: To bankrupt a fool, give him information . Bar Balance can display lots of information. While learning to use a new indicator inevitably requires an adaptation period where we put it through its paces and try out all its options, once you have become used to Bar Balance and decide to adopt it, rigorously eliminate the components you don't use and configure the remaining ones so their visual prominence reflects their relative importance in your analysis. I tried to provide flexible options for traders to control this indicator's visuals for that exact reason—not for window dressing.
█ NOTES
For traders
• To avoid misleading traders who don't read script descriptions, the indicator shows nothing in the realtime bar.
• The Data Window shows key values for the indicator.
• All gradients used in this indicator determine their brightness intensities using advances/declines in the signal—not their relative position in a fixed scale.
• Note that because of the way gradients are optimized internally, changing their brightness will sometimes require bringing down the value a few steps before you see an impact.
• Because this indicator does not use volume, it will work on all markets.
For coders
• For those interested in gradients, this script uses an advanced version of the Advance/Decline gradient function from the PineCoders Color Gradient (16 colors) Framework . It allows more precise control over the range, steps and min/max values of the gradients.
• I use the PineCoders Coding Conventions for Pine to write my scripts.
• I used functions modified from the PineCoders MTF Selection Framework for the selection of timeframes.
█ THANKS TO:
— alexgrover who helped me think through the dampening method used to attenuate signal lines on high ratios of neutral intrabars.
— A guy called Kuan who commented on a Backtest Rookies presentation of their Volume Profile indicator . The technique I use to inspect intrabars is derived from Kuan's code.
— theheirophant , my partner in the exploration of the sometimes weird abysses of `security()`’s behavior at intrabar resolutions.
— midtownsk8rguy , my brilliant companion in mining the depths of Pine graphics. He is also the co-author of the PineCoders Color Gradient Frameworks .
NR-VP-Period with VAH/VAL V.1.0Description
This indicator combines several useful trading tools into one package so you don’t need to load multiple scripts on your chart. It includes a built-in lot size calculator, session high-low zones, a custom volume profile with VPOC, VAH and VAL, previous-day high/low levels, pivot points and inside-bar detection. Each feature has its own on/off switch so you can keep the chart as clean or detailed as you want.
1. Lot Size Calculator
The script calculates position size based on your entry price, stop loss, account balance and risk percentage. It identifies whether the setup is a buy or sell and displays the results in a compact table on the chart, including SL distance in pips, risk amount and the final lot size.
2. Session High-Low Boxes
It draws high, low and mid lines for three intraday sessions: Asia, Midnight and London. Each session creates a dynamic box on the chart with optional extended lines to highlight future reaction levels. All colors and time windows can be customized.
3. Volume Profile with VPOC / VAH / VAL
The script calculates a multi-day volume profile at a custom resolution. It shows the VPOC line, the highest and lowest prices within the profile range, and the value area boundaries (VAH and VAL) based on your chosen percentage. Optional horizontal volume bars can be added for extra clarity. All elements can be toggled on or off.
4. Daily High and Low
It plots the previous day’s high and low with fully adjustable colors and line width. The levels update automatically and extend across the chart.
5. Pivot Points
The indicator detects automatic swing highs and lows (pivot points) using a configurable left/right length. Each pivot is marked with a small label and an extended dotted line.
6. Inside Bar Highlights
The script includes an inside bar detection system so you can visually track potential breakout or compression zones.
RSI ✶ YSTCThis is a Bonus Indicator from YSTC's Volume Profile Tools.
Relative Strength Index (RSI)
A momentum based oscillator which is used to measure the speed (velocity) as well as the change (magnitude) of directional price movements.
What Different about this RSI by YSTC.
You get Support and Resistance lines for RSI which are 20, 30, 40, 50, 60, 70, 80. as shown below.
It can also show RSI Candles as shown below.
For those who want all types of MA with MA Cross can play with this indicator. Below is MA Cross of 9, 21.
And for NEW user with untrained eyes who cant yet detect Divergence this indicator Saves you the trouble of finding.
Below is Regular Bullish and Bearish Divergence. Linewidth 2.
Below is Hidden Bullish and Bearish Divergence. Linewidth 1.
You can add this script to your chart by clicking "Add to favorites" button.
Have Questions ?
Contact: +91 9637070868.
Name: Yogesh Patil (YS Trading Coach).
Time: Monday to Saturday (10:00 AM - 06:00 PM).
Visit our website - YS Trading Coach .
FREE Self Study Yourself Course: Trading with Price Action Volume .
Free Stock Market Introduction Available here .
Paid Course: Trading with Price Action Volume
Paid Volume Profile Tools available here.
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.
Trading Blueprint v7 Pro — VWAP-CVD, cPOC Trend MomentumTBv7 Pro is the advanced release of the Trading Blueprint framework — engineered for institutional-style intraday analysis that fuses VWAP location, CVD orderflow, composite profile bias, and momentum curvature into one cohesive system.
Core Framework
VWAP Structure → Adaptive mean anchored to session VWAP with ±1σ / ±2σ deviation envelopes for dynamic equilibrium detection.
vPOC per bar by ruckard ()
Anchored Volume Profile by DGT ()
CVD Orderflow Divergence → Smoothed delta histogram with fractal pivots identifying hidden absorption and exhaustion (patterns (Bull / Bear Div). Cumulative Volume Delta by AustrianTradingMachine )
cPOC Integration (2-Day Composite) by poopsnag (me :)→ Confirms true acceptance or rejection zones across sessions for precision bias alignment.
TMI (Trend Momentum Indicator by TradingRiot()) → Quantifies slope + mean crossover strength, providing actionable momentum confirmation (bullish / bearish support / divergence).
Bias Dashboard → Displays VWAP bias, numerical score, and dynamic color feedback for at-a-glance trade orientation.
Usage Context
Designed for professionals trading 15 m execution inside 1 h / 4 h context. Ideal for VWAP-cPOC location setups, reversion / continuation scalps, and orderflow confirmation using cumulative delta behavior.
🔧 Modules such as RSI / AO are pre-wired and easily activated for full Trading Blueprint confluence mapping.
