Delta Reaction Zones [BOSWaves]Delta Reaction Zones - Cumulative Delta-Based Supply and Demand Identification with Flow-Weighted Zone Construction
Overview
Delta Reaction Zones is a volume flow-aware supply and demand detection system that identifies price levels where significant buying or selling pressure accumulated, constructing adaptive zones around cumulative delta extremes with intelligent flow composition analysis.
Instead of relying on traditional price-based support and resistance or fixed pivot structures, zone placement, thickness, and directional characterization are determined through delta accumulation patterns, volatility-adaptive sizing, and the proportional composition of positive versus negative volume flow.
This creates dynamic reaction boundaries that reflect actual order flow imbalances rather than arbitrary price levels - contracting during low volatility environments, expanding during elevated volatility periods, and incorporating flow composition statistics to reveal whether zones formed under buying or selling dominance.
Price is therefore evaluated relative to zones anchored at delta extremes rather than conventional technical levels.
Conceptual Framework
Delta Reaction Zones is founded on the principle that meaningful support and resistance emerge where cumulative volume flow reaches local extremes rather than where price alone forms patterns.
Traditional support and resistance methods identify turning points through price structure, which often ignores the underlying order flow dynamics that drive those reversals. This framework replaces price-centric logic with delta-driven zone construction informed by actual buying and selling pressure.
Three core principles guide the design:
Zone placement should correspond to cumulative delta extremes, not price pivots alone.
Zone thickness must adapt to current market volatility conditions.
Flow composition context reveals whether zones formed under accumulation or distribution.
This shifts supply and demand analysis from static price levels into adaptive, flow-anchored reaction boundaries.
Theoretical Foundation
The indicator combines delta proxy methodology, cumulative volume tracking, adaptive volatility measurement, and flow decomposition analysis.
A signed volume delta proxy estimates directional order flow on each bar, which accumulates into a running cumulative delta series. Pivot detection identifies local extremes in either cumulative delta or its rate of change, marking levels where flow momentum reached inflection points. Average True Range (ATR) provides volatility-responsive zone sizing, while impulse window analysis decomposes recent flow into positive and negative components with percentage weighting.
Four internal systems operate in tandem:
Delta Accumulation Engine : Computes smoothed signed volume and maintains cumulative delta tracking for directional flow measurement.
Pivot Detection System : Identifies significant turning points in cumulative delta or delta rate of change to anchor zone placement.
Adaptive Zone Construction : Scales zone thickness dynamically using ATR-based volatility measurement around pivot anchors.
Flow Composition Analysis : Calculates positive and negative flow percentages over a configurable impulse window to characterize zone formation context.
This design allows zones to reflect actual order flow behavior rather than reacting mechanically to price formations.
How It Works
Delta Reaction Zones evaluates price through a sequence of flow-aware processes:
Signed Volume Delta Calculation : Each bar's volume is directionally signed based on close-open relationship, creating a proxy for buying versus selling pressure.
Cumulative Delta Tracking : Signed volume accumulates into a running total, revealing sustained directional flow over time.
Pivot Identification : Local highs and lows in cumulative delta (or its rate of change) mark significant flow inflection points where zones anchor.
Volatility-Adaptive Sizing : ATR multiplier determines zone half-width, automatically adjusting thickness to current market conditions.
Flow Decomposition : Positive and negative volume components are separated and percentage-weighted over the impulse window to reveal dominant flow direction.
Intelligent Zone Merging : Overlapping zones of the same type automatically merge into broader reaction areas, with flow statistics blended proportionally.
Dynamic Extension and Visualization : Zones extend forward with gradient-filled composition segments showing buy versus sell flow proportions.
Breach Detection and Cleanup : Zones invalidate automatically when price closes beyond their boundaries, maintaining chart clarity.
Together, these elements form a continuously updating supply and demand framework anchored in order flow reality.
Interpretation
Delta Reaction Zones should be interpreted as flow-anchored supply and demand boundaries:
Support Zones (Green) : Form at cumulative delta lows, marking levels where selling exhaustion or buying accumulation occurred.
Resistance Zones (Red) : Establish at cumulative delta highs, identifying areas where buying exhaustion or selling distribution dominated.
Flow Composition Segments : Visual gradient within each zone reveals the buy/sell flow proportion during zone formation. The upper segment (red tint) represents negative (selling) flow percentage while the lower segment (green tint) represents positive (buying) flow percentage.
BUY FLOW / SELL FLOW / MIXED Labels : Indicate dominant flow character when one direction exceeds 60% of total impulse window activity.
Net Delta Statistics : Display cumulative flow totals (Δ) alongside percentage breakdowns for immediate context.
Zone Thickness : Reflects current volatility environment - wider zones in volatile conditions, tighter zones in calm markets.
Zone Merging : Multiple nearby pivots consolidate into broader reaction areas, weighted by their respective flow magnitudes.
Flow composition, volatility context, and delta magnitude outweigh isolated price reactions.
Signal Logic & Visual Cues
Delta Reaction Zones presents two primary interaction signals:
Support Reclaim (RC) : Green label appears when price crosses back above a support zone's midline after trading below it, suggesting renewed buying interest.
Resistance Re-enter (RE) : Red label displays when price crosses back below a resistance zone's midline after trading above it, indicating resumed selling pressure.
Alert generation covers zone creation and midline reclaim/re-entry events for systematic monitoring.
Strategy Integration
Delta Reaction Zones fits within order flow-informed and supply/demand trading approaches:
Flow-Anchored Entry Zones : Use zones as high-probability reaction areas where historical order flow imbalances occurred.
Composition-Based Bias : Favor trades aligning with dominant flow character - long setups near zones formed under buying dominance, short setups near selling-dominated zones.
Volatility-Aware Targeting : Expect wider reaction ranges when ATR expands zones, tighter ranges when ATR contracts them.
Merge-Informed Conviction : Broader merged zones represent multiple flow inflection points, potentially offering stronger support/resistance.
Midline Reclaim Validation : Use RC/RE signals as confirmation of zone respect rather than standalone entry triggers.
Multi-Timeframe Flow Context : Apply higher-timeframe delta zones to inform lower-timeframe entry precision.
Technical Implementation Details
Core Engine : Signed volume delta proxy with EMA smoothing
Accumulation Model : Persistent cumulative delta tracking with optional rate-of-change pivot detection
Zone Construction : ATR-scaled thickness around pivot anchors
Flow Analysis : Positive/negative decomposition over configurable impulse window
Visualization : Gradient-filled zones with embedded flow statistics and percentage segments
Signal Logic : Midline crossover detection with breach-based invalidation
Merge System : Proximity-based consolidation with weighted flow blending
Performance Profile : Optimized for real-time execution with configurable zone limits
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Micro-structure flow zones for scalping and short-term reversals
15 - 60 min : Intraday supply/demand identification with flow context
4H - Daily : Swing-level reaction zones with macro flow characterization
Suggested Baseline Configuration:
Delta Smoothing Length : 3
Pivot Length : 12
Pivot Source : Cumulative Delta
Impulse Window : 100
ATR Length : 14
ATR Multiplier : 0.35 (reduce for lower timeframes)
Maximum Zones : 8
Merge Overlapping Zones : Enabled
Merge Gap : 20 ticks
These suggested parameters should be used as a baseline; their effectiveness depends on the asset's volume profile, tick structure, and preferred zone density, so fine-tuning is expected for optimal performance.
Parameter Calibration Notes
Use the following adjustments to refine behavior without altering the core logic:
Zones appearing oversized : Reduce ATR Multiplier to tighten zone thickness, especially on lower timeframes.
Excessive zone clutter : Increase Pivot Length to demand stronger delta extremes before zone creation.
Unstable delta readings : Increase Delta Smoothing Length to reduce bar-to-bar noise in flow calculation.
Missing significant levels : Decrease Pivot Length or switch Pivot Source to "Cumulative Delta RoC" for flow acceleration sensitivity.
Flow percentages feel stale : Reduce Impulse Window Length to emphasize more recent buying/selling composition.
Too many merged zones : Decrease Merge Gap (ticks) or disable merging to preserve individual pivot zones.
Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions.
Performance Characteristics
High Effectiveness:
Markets with consistent volume and order flow characteristics
Instruments where delta proxy correlates well with actual tape reading
Mean-reversion strategies targeting flow exhaustion zones
Trend continuation entries at zones aligned with dominant flow direction
Reduced Effectiveness:
Extremely low volume environments where delta proxy becomes unreliable
News-driven or gapped markets with discontinuous flow
Highly manipulated or illiquid instruments with erratic volume patterns
Integration Guidelines
Confluence : Combine with BOSWaves structure, market profile, or traditional supply/demand analysis
Flow Respect : Trust zones formed with strong net delta magnitude and clear flow dominance
Context Awareness : Consider whether current market regime matches zone formation conditions
Merge Recognition : Treat merged zones as higher-conviction areas due to multiple flow inflections
Breach Discipline : Exit zone-based setups cleanly when price invalidates boundaries
Disclaimer
Delta Reaction Zones is a professional-grade order flow and supply/demand analysis tool. It uses a volume-based delta proxy that estimates directional pressure but does not access true order book data. Results depend on market conditions, volume reliability, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, volatility context, and comprehensive risk management.
Indikatoren und Strategien
Volume Cluster Profile [VCP] (Zeiierman)█ Overview
Volume Cluster Profile (Zeiierman) is a volume profile tool that builds cluster-enhanced volume-by-price maps for both the current market window and prior swing segments.
Instead of treating the profile as a raw histogram only, VCP detects the dominant volume peaks (clusters) inside the profile, then uses a Gaussian spread model to “radiate” those peaks into surrounding price bins. This produces a smoother, more context-aware profile that highlights where volume is most meaningfully concentrated, not just where it happened to print.
On top of the live profile, VCP automatically records historical swing profiles between pivots, wraps each segment for clarity, and can project the most recent segment’s High/Low Value extensions (VA/LV) forward to the current bar to keep key structure visible as price evolves.
█ How It Works
⚪ 1) Profile Construction (Volume-by-Price)
VCP builds a volume profile histogram over a chosen window (current lookback, or a swing segment):
Range Scan
The script finds the full min → max price range inside the window.
Bin the Range
That range is divided into a user-defined number of Price Bins (rows). More bins = finer detail, but heavier computation.
Accumulate Volume into Bins
For each bar inside the window, the script takes the bar’s close price, determines which price bin it belongs to, and adds the bar’s volume to that bin.
float step = (maxPrice - minPrice) / binsCount
for i = 0 to barsToUse - 1
int b = f_clamp(int(math.floor((close - minPrice) / step)), 0, binsCount - 1)
volBins += volume
Result: volBins becomes a standard volume-by-price histogram (close-based binning).
⚪ 2) Cluster Detection (Finding Dominant Peaks)
Once the raw histogram is built, VCP identifies cluster centers as the most meaningful volume “hills”:
Local Peak Test
A bin becomes a cluster candidate if its volume is greater than or equal to its immediate neighbors (left/right).
Filter Weak Peaks
Peaks must also be above a basic activity threshold (relative to the average bin volume) to avoid noise.
bool isPeak = v >= left and v >= right
if isPeak and v > avgVol
array.push(clusterIdxs, b)
Keep the Best Peaks Only
If too many peaks exist, the script keeps only the strongest ones, capped by: Max Cluster Centers
Result: clusterIdxs = the set of dominant profile peaks (cluster centers).
⚪ 3) Cluster Enhancement (Gaussian Spread Model)
This is what makes VCP different from a raw profile.
Instead of using volBins directly, the script builds an enhanced profile where each cluster center influences nearby price bins using a Gaussian curve:
Distance from each bin to each cluster center is computed in “bin units”
A Gaussian weight is applied so that bins near the center receive stronger influence, while bins farther away decay smoothly.
Cluster Spread (sigma) controls how wide this influence reaches: low sigma produces tight, sharp clusters, while high sigma results in wider, smoother structure zones.
enhanced += centerV * math.exp(-(dist*dist) / (2.0 * clusterSigma * clusterSigma))
volBinsAI := enhanced / szClFinal
Result: volBinsAI = the cluster-enhanced volume value for each bin.
In practice, VCP turns the profile into a structure map of dominant volume concentrations, rather than a simple “where volume printed” histogram.
⚪ 4) POC from the Enhanced Profile
After enhancement:
The bin with the highest volBinsAI becomes the POC (Point of Control)
POC is plotted at the midpoint price of that bin
if volBinsAI > maxVol
maxVol := volBinsAI , pocBin := b
So the POC reflects the cluster-enhanced profile rather than the raw histogram.
█ How to Use
⚪ Read Cluster Structure (Default = 2 Clusters)
By default, the Volume Cluster Profile (VCP) is configured to detect up to 2 dominant volume clusters within the profile. These clusters represent price zones where the market accepted trading activity, not just where volume printed randomly.
⚪ When TWO Clusters Appear
When VCP detects two distinct clusters, it usually indicates:
Two competing areas of value
Ongoing auction between higher and lower acceptance zones
Treat each cluster as an acceptance zone
Expect slower price action and rotation inside clusters
Expect faster movement in the low-volume space between clusters
Use cluster-to-cluster movement as:
rotation targets
range boundaries
acceptance vs rejection tests
Typical behavior:
Price enters a cluster → stalls, consolidates, rotates
Price rejects at cluster edge → moves toward the opposite cluster
⚪ When ONLY ONE Cluster Appears
If VCP detects only one cluster, or if two clusters visually merge into one:
Volume is no longer split
The market has formed a single dominant value area
Price consensus is strong
Treat the cluster as the primary value anchor
Expect pullbacks and reactions around this zone
Bias becomes directional:
Above the cluster → bullish context
Below the cluster → bearish context
Inside the cluster → balance/chop
This structure often appears during clean trends or stable equilibria.
⚪ VA/LV Extensions
VCP projects two zones from the end of the most recent swing segment:
VA extension = the segment’s highest enhanced-volume bin (dominant zone)
LV extension = the segment’s lowest enhanced-volume bin (thin/weak zone)
A breakout of the VA extension signals acceptance and potential continuation. A retest of the VA or LV extension is used to confirm acceptance or rejection, while rejection from either zone often leads to rotation back toward value.
█ Settings
Cluster Volume Profile
Lookback Bars – how many recent bars build the current profile
Price Bins – profile resolution (more bins = more detail, heavier CPU)
Cluster Spread – Gaussian sigma; higher values widen/smooth cluster influence
Max Cluster Centers – cap on detected peaks used in enhancement
Historical Swing Cluster Volume Profile
Pivot Length – swing sensitivity (larger = fewer, broader segments)
Max Profiles – how many historical segments to retain
Profile Width – thickness of each historical profile
High & Low Value Area
Profile VA/LV – extend the last segment’s top-bin and low-bin zones forward
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Point of Control [BigBeluga]🔵 OVERVIEW
Point of Control identifies the exact price level with the highest traded volume over a selected lookback period.
This level—called the Point of Control (PoC) —marks where the greatest market participation occurred, representing a zone of highest volume.
The indicator helps traders visualize dominant volume concentrations, fair-value levels, and structural balance within recent price action.
🔵 CONCEPTS
Point of Control (PoC) — The single price level within the defined lookback range that has accumulated the most traded volume.
Volume Distribution Bins — The price range is divided into 25 equal bins, and volume is aggregated per bin to locate the maximum concentration.
Range Boundaries — The highest and lowest price within the lookback window are used to form the upper and lower reference limits.
PoC Channel — Optional upper and lower bands plotted around the main PoC to visualize a fair-value corridor.
Volume Intensity Mapping — Candle color dynamically shifts based on the candle’s position relative to the PoC channel, showing whether price is balanced or trending away from high-volume levels.
🔵 FEATURES
Configurable Lookback Range — Adjust how many bars (10–400) are used for calculating the PoC.
Precise PoC Calculation — Volume aggregation across 25 bins to identify the exact volume peak.
Dynamic Channel Visualization — PoC bands above and below the central level to indicate equilibrium tolerance.
Adaptive Candle Coloring —
- Neutral → price inside PoC channel. Gray
- Bullish → price above PoC channel. Blue 🔵
- Bearish → price below PoC channel. Orange 🟠
Automatic Volume Labeling — Displays total volume at the active PoC level for quick reference.
Directional Indicators — 🔵 or 🟠 markers appear when price shifts above or below the PoC channel.
Range Visualization — Plots the highest and lowest points of the active lookback window for contextual awareness.
Live Updating Logic — PoC recalculates automatically every 15 bars for efficient chart performance and accuracy.
🔵 HOW TO USE
Volume Anchoring — Use PoC as a reference for where the majority of volume occurred; price often reacts to or consolidates around this level.
Trend Confirmation — Sustained price movement away from PoC channel may signal developing directional imbalance.
Value Tracking — Watch the shifting of PoC across time to identify where fair value migrates during market evolution.
Equilibrium Mapping — When price hovers around PoC, the market is balanced; when it departs, a new value zone may form.
Combine With Volume Profiles — Use alongside profile tools for higher-resolution analysis of institutional activity.
🔵 CONCLUSION
Point of Control provides a pure, volume-centric view of market balance by pinpointing where most transactions occurred within any chosen range.
It delivers a clean and efficient visualization of fair value zones—helping traders track the heartbeat of market participation, recognize dominant liquidity areas, and stay aligned with where true market interest resides.
Expansion Setup: Entries & structure + AlertsThis is a specific market condition often called a Broadening Formation or an Expansion Move, where volatility increases enough to break both the previous structural low and then immediately break the previous structural high (or vice versa).
1. LL to HH: A New Lower Low is formed, followed immediately by a New Higher High.
2. HH to LL: A New Higher High is formed, followed immediately by a New Lower Low.
3. Entry Levels: When a setup is detected (LL ➔ HH or HH ➔ LL), the script now draws two specific entry lines extending forward:
The "Breaker" Level: The previous structure point that was broken. (Often a safe retest entry).
The 50% Retracement: The midpoint of the expansion move (The "Equilibrium" or "Discount" entry).
[codapro] Confirmed Supertrend Flags
Confirmed Supertrend Flags — Delayed Flip Confirmation
Description:
This script enhances the classic Supertrend by adding a confirmation delay after trend flips, helping traders filter noise and avoid premature entries in volatile environments.
Key Features:
ATR-based Supertrend stop level calculation
Confirmation logic: buy/sell flags appear only after N full bars confirm the new direction
Optional Supertrend stop line for visual tracking
Fully adjustable flag size, color, label, and placement
This is ideal for swing traders, trend followers, or anyone building a system that prefers confirmation over early guessing.
How It Works:
A trend flip is detected when price closes beyond the Supertrend stop level.
The indicator waits for a set number of bars to close in the new direction.
After confirmation, a visual flag is plotted: buy below bar, short above bar.
How to Extend with Risk Management:
While this script focuses on trend confirmation visualization, you can enhance your decision-making by combining it with risk rules:
Stop Loss: Set SL just beyond the last Supertrend level before confirmation
ATR-Based Sizing: Use the same ATR value to dynamically size your position based on volatility
Fixed % Rule: Risk a fixed % of capital per confirmed flip (e.g., 1–2%)
Time-Based Exit: Exit trades that don’t follow through within N bars post confirmation
Stack with Strategy: Use this confirmation logic to trigger entries in a separate strategy script where strategy.entry() and strategy.exit() can be defined with precise risk parameters
Want a full example of how to integrate that? Let me know and I’ll turn this into a plug-and-play strategy version.
Disclaimer:
This tool was developed as part of the codapro AI engine — a modular signal and automation layer for trading systems.
It is for educational and informational purposes only and is not financial advice. Always backtest and verify before live deployment.
Sentinel Market Structure [JOAT]
Sentinel Market Structure - Smart Money Structure Analysis
Introduction and Purpose
Sentinel Market Structure is an open-source overlay indicator that identifies swing highs/lows, tracks market structure (HH/HL/LH/LL), detects Break of Structure (BOS) and Change of Character (CHoCH) signals, and marks order blocks. The core problem this indicator solves is that retail traders often miss structural shifts that smart money traders use to identify trend changes.
This indicator addresses that by automatically tracking market structure and alerting traders to key structural breaks that often precede significant moves.
Why These Components Work Together
Each component provides different structural information:
1. Swing Detection - Identifies significant pivot highs and lows. These are the building blocks of market structure.
2. Structure Labels (HH/HL/LH/LL) - Classifies each swing relative to the previous swing. Higher Highs + Higher Lows = uptrend. Lower Highs + Lower Lows = downtrend.
3. Break of Structure (BOS) - Identifies when price breaks a swing level in the direction of the trend. This is a continuation signal.
4. Change of Character (CHoCH) - Identifies when price breaks a swing level against the trend. This is a potential reversal signal.
5. Order Blocks - Marks the last opposing candle before an impulse move. These zones often act as future support/resistance.
How the Detection Works
Swing Detection:
bool swingHighDetected = high == ta.highest(high, swingLength * 2 + 1)
bool swingLowDetected = low == ta.lowest(low, swingLength * 2 + 1)
BOS vs CHoCH Logic:
// BOS: Break in direction of trend (continuation)
bool bullishBOS = close > lastSwingHigh and marketTrend >= 0
// CHoCH: Break against trend (reversal signal)
bool bullishCHOCH = close > lastSwingHigh and marketTrend < 0
Order Block Detection:
bool bullOB = close < open and // Previous candle bearish
close > open and // Current candle bullish
close > high and // Breaking above
(high - low) > ta.atr(14) * 1.5 // Strong impulse
Signal Types
HH (Higher High) - Swing high above previous swing high (bullish structure)
HL (Higher Low) - Swing low above previous swing low (bullish structure)
LH (Lower High) - Swing high below previous swing high (bearish structure)
LL (Lower Low) - Swing low below previous swing low (bearish structure)
BOS↑/BOS↓ - Break of structure in trend direction (continuation)
CHoCH↑/CHoCH↓ - Change of character against trend (potential reversal)
Dashboard Information
Trend - Current market bias (BULLISH/BEARISH/NEUTRAL)
Swing High - Last swing high price with HH/LH label
Swing Low - Last swing low price with HL/LL label
Structure - Current structure state (HH+HL, LH+LL, etc.)
Price - Price position relative to structure
How to Use This Indicator
For Trend Following:
1. Identify trend using structure (HH+HL = uptrend, LH+LL = downtrend)
2. Enter on BOS signals in trend direction
3. Use swing levels for stop placement
For Reversal Trading:
1. Watch for CHoCH signals (break against trend)
2. Confirm with order block formation
3. Enter on retest of order block zone
For Risk Management:
1. Place stops beyond swing highs/lows
2. Use structure lines as trailing stop references
3. Exit when CHoCH signals against your position
Input Parameters
Swing Detection Length (5) - Bars on each side for pivot detection
Show Swing High/Low Points (true) - Toggle swing markers
Show BOS/CHoCH (true) - Toggle structural break signals
Show Structure Lines (true) - Toggle horizontal swing lines
Show Order Blocks (true) - Toggle order block zones
Zone Extension (50) - How far order block boxes extend
Timeframe Recommendations
15m-1H: Good for intraday structure analysis
4H-Daily: Best for swing trading structure
Lower timeframes require smaller swing detection length
Limitations
Swing detection has inherent lag (needs confirmation bars)
Not all BOS/CHoCH signals lead to continuation/reversal
Order block zones are simplified (not full ICT methodology)
Structure analysis is subjective - different traders see different swings
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes.
This indicator does not constitute financial advice. Market structure analysis does not guarantee trade outcomes. Always use proper risk management.
- Made with passion by officialjackofalltrades
Adjusted RSI - [JTCAPITAL]Adjusted RSI – is a modified and enhanced way to use the Relative Strength Index (RSI) combined with double normalization, adaptive exponential smoothing, and range compression to create a smoother, more readable, and more structurally consistent momentum oscillator for Trend-Following and momentum analysis.
This indicator is designed to solve several common RSI issues at once:
Excessive noise in raw RSI values
Inconsistent scaling across different market conditions
Difficulty identifying true momentum shifts versus random fluctuations
By re-centering, compressing, normalizing, and smoothing RSI data twice , this script produces a highly refined momentum curve that reacts smoothly while still respecting directional changes.
The indicator works by calculating in the following steps:
Raw RSI Calculation
The script begins by calculating a standard RSI using the selected RSI Length . This RSI is based on the closing price and measures relative strength by comparing average gains and losses over the defined period.
RSI Re-Centering
After the RSI is calculated, the script subtracts 50 from the RSI value.
This converts the RSI from its native scale into a centered oscillator ranging around 0 , making positive values bullish momentum and negative values bearish momentum.
Initial RSI Smoothing
The re-centered RSI is then smoothed using a Simple Moving Average (SMA) over the defined RSI Smoothing Length .
This step removes high-frequency noise and stabilizes short-term RSI fluctuations before further processing.
Range Compression (Clipping)
To prevent extreme outliers from dominating future calculations, the RSI values are clipped:
Values below -10 are forced to -10
Values above +10 are forced to +10
This creates a controlled and consistent RSI range, ensuring later normalization behaves reliably.
First Normalization (Min-Max Scaling)
The clipped RSI values are normalized over the selected Smoothing Length :
The lowest RSI value in the window is detected
The highest RSI value in the window is detected
Current RSI is scaled to a 0–100 range based on this dynamic range
This allows the indicator to adapt automatically to changing volatility and momentum environments.
First Adaptive Smoothing
The normalized RSI is then smoothed using a custom exponential smoothing formula controlled by the Smoothing Factor .
This smoothing behaves similarly to an EMA but allows explicit control over responsiveness.
Second Normalization
The smoothed values undergo a second min-max normalization over the same length.
This further stabilizes the oscillator and ensures consistent amplitude and structure, regardless of market regime.
Second Adaptive Smoothing
A second exponential smoothing pass is applied to the normalized data, further refining the curve and reducing residual noise.
Final Re-Centering
Finally, the indicator subtracts 50 from the smoothed normalized values, re-centering the oscillator around zero .
This produces the final Adjusted RSI line used for visualization and analysis.
Common interpretations for use include:
Bullish Momentum :
When the Adjusted RSI is above zero and rising, indicating strengthening bullish pressure.
Bearish Momentum :
When the Adjusted RSI is below zero and falling, indicating strengthening bearish pressure.
Momentum Shifts :
A change in slope (from falling to rising or vice versa) often signals an early momentum transition.
Divergences :
Differences between price direction and Adjusted RSI direction can highlight potential reversals.
Because the indicator is normalized and smoothed, it pairs exceptionally well with:
Trend filters (moving averages, trend lines)
Volatility filters
Higher-timeframe confirmation
Features and Parameters:
RSI Length
Defines the lookback period for the initial RSI calculation.
RSI Smoothing Length
Controls the SMA smoothing applied directly to the re-centered RSI.
Smoothing Length
Determines the lookback window used for both normalization passes.
Smoothing Factor
Controls the responsiveness of the adaptive exponential smoothing.
Lower values = smoother, slower reaction
Higher values = faster, more responsive reaction
Specifications:
Relative Strength Index (RSI)
RSI is a momentum oscillator that measures the speed and magnitude of recent price changes. By re-centering RSI around zero, the script converts it into a directional momentum oscillator that is easier to interpret for trend-following.
Simple Moving Average (SMA)
The SMA reduces short-term fluctuations in RSI, ensuring that only meaningful momentum changes proceed to later calculations.
Range Clipping
By limiting RSI values to a defined range, extreme spikes are prevented from skewing normalization. This keeps the indicator stable across different assets and timeframes.
Min-Max Normalization
Normalization rescales values into a fixed range (0–100), allowing momentum behavior to remain consistent regardless of volatility conditions.
Adaptive Exponential Smoothing
This smoothing technique gradually adjusts values toward new data based on the smoothing factor. It allows the indicator to remain smooth while still reacting to genuine momentum shifts.
Double Normalization and Double Smoothing
Applying normalization and smoothing twice significantly improves structural stability. The result is a refined oscillator that filters noise without sacrificing trend awareness.
Why This Combination Works
By combining RSI with controlled compression, adaptive smoothing, and dynamic normalization, this indicator transforms raw momentum data into a highly structured and trend-aligned oscillator. The result is an RSI-based tool that:
Reduces noise
Adapts to volatility
Maintains consistent scaling
Highlights true momentum direction
This makes the Adjusted RSI particularly effective for swing trading, trend confirmation, and momentum-based strategies across all markets and timeframes.
Enjoy!
Adaptive Market Wave TheoryAdaptive Market Wave Theory
🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS
Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation.
The Wave Analysis Problem:
Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses:
1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition.
2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw.
3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management.
4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024.
5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve.
The AMWT Solution:
AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning:
• Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed.
• Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated.
• No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3."
• Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time.
• No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance.
The Core Insight:
"Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'"
🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS
Why Hidden Markov Models?
Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables.
This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations.
HMM Components:
1. Hidden States (S) : The unobservable market conditions
2. Observations (O) : What we can measure (price, volume, indicators)
3. Transition Matrix (A) : Probability of moving between states
4. Emission Matrix (B) : Probability of observations given each state
5. Initial Distribution (π) : Starting state probabilities
AMWT's Six Market States:
State 0: IMPULSE_UP
• Definition: Strong bullish momentum with high participation
• Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising
• Typical Duration: 5-20 bars depending on timeframe
• What It Means: Institutional buying pressure, trend acceleration phase
State 1: IMPULSE_DN
• Definition: Strong bearish momentum with high participation
• Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling
• Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster)
• What It Means: Institutional selling pressure, panic or distribution acceleration
State 2: CORRECTION
• Definition: Counter-trend consolidation with declining momentum
• Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing
• Typical Duration: 8-30 bars
• What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg
State 3: ACCUMULATION
• Definition: Base-building near lows where informed participants absorb supply
• Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range
• Typical Duration: 15-50 bars
• What It Means: Smart money buying from weak hands, preparing for markup phase
State 4: DISTRIBUTION
• Definition: Top-forming near highs where informed participants distribute holdings
• Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range
• Typical Duration: 15-50 bars
• What It Means: Smart money selling to late buyers, preparing for markdown phase
State 5: TRANSITION
• Definition: Regime change period with mixed signals and elevated uncertainty
• Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction
• Typical Duration: 5-15 bars
• What It Means: Market deciding next direction, dangerous for directional trades
The Transition Matrix:
The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online:
From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS
IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02
IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02
CORR 0.15 0.15 0.50 0.10 0.10 0.00
ACCUM 0.30 0.05 0.15 0.40 0.05 0.05
DIST 0.05 0.30 0.15 0.05 0.40 0.05
TRANS 0.20 0.20 0.20 0.15 0.15 0.10
Key Insights from Transition Probabilities:
• Impulse states are sticky (70% self-transition): Once trending, markets tend to continue
• Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain
• Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies
• Distribution strongly favors IMP_DN transition (30%): Topping leads to declines
The Viterbi Algorithm:
Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space.
Mathematical Formulation:
δ_t(j) = max_i × B_j(O_t)
Where:
δ_t(j) = probability of most likely path ending in state j at time t
A_ij = transition probability from state i to state j
B_j(O_t) = emission probability of observation O_t given state j
AMWT Implementation:
AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting:
• Current state estimate
• State confidence (probability of current state vs alternatives)
• State sequence for pattern detection
Online Learning (Baum-Welch Adaptation):
Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior:
f_onlineUpdateHMM(prev_state, curr_state, observation, decay) =>
// Update transition matrix
A *= decay
A += (1.0 - decay)
// Renormalize row
// Update emission matrix
B *= decay
B += (1.0 - decay)
// Renormalize row
The decay parameter (default 0.85) controls adaptation speed:
• Higher decay (0.95): Slower adaptation, more stable, better for consistent markets
• Lower decay (0.80): Faster adaptation, more reactive, better for regime changes
Why This Matters for Trading:
Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment :
"There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION."
This enables:
• Position sizing by confidence : 90% confidence = full size; 60% confidence = half size
• Risk management by transition probability : High correction probability = tighten stops
• Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in
🎰 THE 3-BANDIT CONSENSUS SYSTEM
The Multi-Agent Philosophy:
No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets.
AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions.
Agent 1: TREND AGENT
Philosophy : Markets trend. Follow the trend until it ends.
Analytical Components:
• EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish)
• MACD Histogram: Direction and rate of change
• Price Momentum: Close relative to ATR-normalized movement
• VWAP Position: Price above/below volume-weighted average price
Signal Generation:
Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP
→ Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4
Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1
→ Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3
Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP
→ Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4
Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1
→ Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3
When Trend Agent Excels:
• Trend days (IB extension >1.5x)
• Post-breakout continuation
• Institutional accumulation/distribution phases
When Trend Agent Fails:
• Range-bound markets (ADX <20)
• Chop zones after volatility spikes
• Reversal days at major levels
Agent 2: REVERSION AGENT
Philosophy: Markets revert to mean. Extreme readings reverse.
Analytical Components:
• Bollinger Band Position: Distance from bands, percent B
• RSI Extremes: Overbought (>70) and oversold (<30)
• Stochastic: %K/%D crossovers at extremes
• Band Squeeze: Bollinger Band width contraction
Signal Generation:
Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25
→ Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01
Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75
→ Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01
Squeeze Fire Bull: Band squeeze ending AND close > upper band
→ Signal: +1 (Long), Confidence: 0.65
Squeeze Fire Bear: Band squeeze ending AND close < lower band
→ Signal: -1 (Short), Confidence: 0.65
When Reversion Agent Excels:
• Rotation days (price stays within IB)
• Range-bound consolidation
• After extended moves without pullback
When Reversion Agent Fails:
• Strong trend days (RSI can stay overbought for days)
• Breakout moves
• News-driven directional moves
Agent 3: STRUCTURE AGENT
Philosophy: Market structure reveals institutional intent. Follow the smart money.
Analytical Components:
• Break of Structure (BOS): Price breaks prior swing high/low
• Change of Character (CHOCH): First break against prevailing trend
• Higher Highs/Higher Lows: Bullish structure
• Lower Highs/Lower Lows: Bearish structure
• Liquidity Sweeps: Stop runs that reverse
Signal Generation:
BOS Bull: Price breaks above prior swing high with momentum
→ Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bull: First higher low after downtrend, breaking structure
→ Signal: +1 (Long), Confidence: 0.75
BOS Bear: Price breaks below prior swing low with momentum
→ Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bear: First lower high after uptrend, breaking structure
→ Signal: -1 (Short), Confidence: 0.75
Liquidity Sweep Long: Price sweeps below swing low then reverses strongly
→ Signal: +1 (Long), Confidence: 0.80
Liquidity Sweep Short: Price sweeps above swing high then reverses strongly
→ Signal: -1 (Short), Confidence: 0.80
When Structure Agent Excels:
• After liquidity grabs (stop runs)
• At major swing points
• During institutional accumulation/distribution
When Structure Agent Fails:
• Choppy, structureless markets
• During news events (structure becomes noise)
• Very low timeframes (noise overwhelms structure)
Thompson Sampling: The Bandit Algorithm
With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives).
Thompson Sampling Solution:
Each agent maintains a Beta distribution representing its success/failure history:
Agent success rate modeled as Beta(α, β)
Where:
α = number of successful signals + 1
β = number of failed signals + 1
On Each Bar:
1. Sample from each agent's Beta distribution
2. Weight agent signals by sampled probabilities
3. Combine weighted signals into consensus
4. Update α/β based on trade outcomes
Mathematical Implementation:
// Beta sampling via Gamma ratio method
f_beta_sample(alpha, beta) =>
g1 = f_gamma_sample(alpha)
g2 = f_gamma_sample(beta)
g1 / (g1 + g2)
// Thompson Sampling selection
for each agent:
sampled_prob = f_beta_sample(agent.alpha, agent.beta)
weight = sampled_prob / sum(all_sampled_probs)
consensus += agent.signal × agent.confidence × weight
Why Thompson Sampling?
• Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution)
• Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff
• Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation
• Self-Correcting : Poor performers naturally get lower weights over time
Example Evolution:
Day 1 (Initial):
Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
After 50 Signals:
Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence)
Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming)
Structure Agent: Beta(32,19) → samples ~0.63 (outperforming)
Result: Structure Agent now receives highest weight in consensus
Consensus Requirements by Mode:
Aggressive Mode:
• Minimum 1/3 agents agreeing
• Consensus threshold: 45%
• Use case: More signals, higher risk tolerance
Balanced Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 55%
• Use case: Standard trading
Conservative Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 65%
• Use case: Higher quality, fewer signals
Institutional Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 75%
• Additional: Session quality >0.65, mode adjustment +0.10
• Use case: Highest quality signals only
🌀 INTELLIGENT CHOP DETECTION ENGINE
The Chop Problem:
Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike.
AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades.
Five-Factor Chop Analysis:
Factor 1: ADX Component (25% weight)
ADX (Average Directional Index) measures trend strength regardless of direction.
ADX < 15: Very weak trend (high chop score)
ADX 15-20: Weak trend (moderate chop score)
ADX 20-25: Developing trend (low chop score)
ADX > 25: Strong trend (minimal chop score)
adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100
Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop.
Factor 2: Choppiness Index (25% weight)
The Choppiness Index measures price efficiency using the ratio of ATR sum to price range:
CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n)
CI > 61.8: Choppy (range-bound, inefficient movement)
CI < 38.2: Trending (directional, efficient movement)
CI 38.2-61.8: Transitional
chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100
Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range).
Factor 3: Range Compression (20% weight)
Compares recent range to longer-term range, detecting volatility squeezes:
recent_range = Highest(20) - Lowest(20)
longer_range = Highest(50) - Lowest(50)
compression = 1 - (recent_range / longer_range)
compression > 0.5: Strong squeeze (potential breakout imminent)
compression < 0.2: No compression (normal volatility)
range_compression_score = compression × 100
Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet.
Factor 4: Channel Position (15% weight)
Tracks price position within the macro channel:
channel_position = (close - channel_low) / (channel_high - channel_low)
position 0.4-0.6: Center of channel (indecision zone)
position <0.2 or >0.8: Near extremes (potential reversal or breakout)
channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score
Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability.
Factor 5: Volume Quality (15% weight)
Assesses volume relative to average:
vol_ratio = volume / SMA(volume, 20)
vol_ratio < 0.7: Low volume (lack of conviction)
vol_ratio 0.7-1.3: Normal volume
vol_ratio > 1.3: High volume (conviction present)
volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0
Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall.
Combined Chop Intensity:
chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) +
(range_compression_score × 0.20) + (channel_chop × 0.15) +
(volume_chop × i_volumeChopWeight × 0.15)
Regime Classifications:
Based on chop intensity and component analysis:
• Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively
• Trending (20-35%): ADX >20, moderate directional bias, trade normally
• Transitioning (35-50%): Mixed signals, regime change possible, reduce size
• Mid-Range (50-60%): Price trapped in channel center, avoid new positions
• Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only
• Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout
• Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely
Signal Suppression:
When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification.
Chop Box Visualization:
When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions.
💧 LIQUIDITY ANCHORING SYSTEM
The Liquidity Concept:
Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing.
AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence.
Liquidity Event Types:
Type 1: Volume Spikes
Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x)
Interpretation: Sudden volume surge indicates institutional activity
• Near swing low + reversal: Likely accumulation
• Near swing high + reversal: Likely distribution
• With continuation: Institutional conviction in direction
Type 2: Stop Runs (Liquidity Sweeps)
Definition: Price briefly exceeds swing high/low then reverses within N bars
Detection:
• Price breaks above recent swing high (triggering buy stops)
• Then closes back below that high within 3 bars
• Signal: Bullish stop run complete, reversal likely
Or inverse for bearish:
• Price breaks below recent swing low (triggering sell stops)
• Then closes back above that low within 3 bars
• Signal: Bearish stop run complete, reversal likely
Type 3: Absorption Events
Definition: High volume with small candle body
Detection:
• Volume > 2x average
• Candle body < 30% of candle range
• Interpretation: Large orders being filled without moving price
• Implication: Accumulation (at lows) or distribution (at highs)
Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity)
BSL (Buy-Side Liquidity):
• Cluster of swing highs within ATR proximity
• Stop losses from shorts sit above these highs
• Breaking BSL triggers short covering (fuel for rally)
SSL (Sell-Side Liquidity):
• Cluster of swing lows within ATR proximity
• Stop losses from longs sit below these lows
• Breaking SSL triggers long liquidation (fuel for decline)
Liquidity Pool Mapping:
AMWT continuously scans for and maps liquidity pools:
// Detect swing highs/lows using pivot function
swing_high = ta.pivothigh(high, 5, 5)
swing_low = ta.pivotlow(low, 5, 5)
// Track recent swing points
if not na(swing_high)
bsl_levels.push(swing_high)
if not na(swing_low)
ssl_levels.push(swing_low)
// Display on chart with labels
Confluence Scoring Integration:
When signals fire near identified liquidity events, confluence scoring increases:
• Signal near volume spike: +10% confidence
• Signal after liquidity sweep: +15% confidence
• Signal at BSL/SSL pool: +10% confidence
• Signal aligned with absorption zone: +10% confidence
Why Liquidity Anchoring Matters:
Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst.
📊 SIGNAL GRADING SYSTEM
The Quality Problem:
Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence.
Confluence Components (100 points total):
1. Bandit Consensus Strength (25 points)
consensus_str = weighted average of agent confidences
score = consensus_str × 25
Example:
Trend Agent: +1 signal, 0.80 confidence, 0.35 weight
Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight
Structure Agent: +1 signal, 0.75 confidence, 0.40 weight
Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77
Score = 0.77 × 25 = 19.25 points
2. HMM State Confidence (15 points)
score = hmm_confidence × 15
Example:
HMM reports 82% probability of IMPULSE_UP
Score = 0.82 × 15 = 12.3 points
3. Session Quality (15 points)
Session quality varies by time:
• London/NY Overlap: 1.0 (15 points)
• New York Session: 0.95 (14.25 points)
• London Session: 0.70 (10.5 points)
• Asian Session: 0.40 (6 points)
• Off-Hours: 0.30 (4.5 points)
• Weekend: 0.10 (1.5 points)
4. Energy/Participation (10 points)
energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25
score = min(energy, 1.0) × 10
5. Volume Confirmation (10 points)
if volume > SMA(volume, 20) × 1.5:
score = 10
else if volume > SMA(volume, 20):
score = 5
else:
score = 0
6. Structure Alignment (10 points)
For long signals:
• Bullish structure (HH + HL): 10 points
• Higher low only: 6 points
• Neutral structure: 3 points
• Bearish structure: 0 points
Inverse for short signals
7. Trend Alignment (10 points)
For long signals:
• Price > EMA21 > EMA50: 10 points
• Price > EMA21: 6 points
• Neutral: 3 points
• Against trend: 0 points
8. Entry Trigger Quality (5 points)
• Strong trigger (multiple confirmations): 5 points
• Moderate trigger (single confirmation): 3 points
• Weak trigger (marginal): 1 point
Grade Scale:
Total Score → Grade
85-100 → A+ (Exceptional—all factors aligned)
70-84 → A (Strong—high probability)
55-69 → B (Acceptable—proceed with caution)
Below 55 → C (Marginal—filtered by default)
Grade-Based Signal Brightness:
Signal arrows on the chart have transparency based on grade:
• A+: Full brightness (alpha = 0)
• A: Slight fade (alpha = 15)
• B: Moderate fade (alpha = 35)
• C: Significant fade (alpha = 55)
This visual hierarchy helps traders instantly identify signal quality.
Minimum Grade Filter:
Configurable filter (default: C) sets the minimum grade for signal display:
• Set to "A" for only highest-quality signals
• Set to "B" for moderate selectivity
• Set to "C" for all signals (maximum quantity)
🕐 SESSION INTELLIGENCE
Why Sessions Matter:
Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality.
Session Definitions:
Asian Session (18:00-03:00 ET)
• Characteristics: Lower volatility, range-bound tendency, fewer institutional participants
• Quality Score: 0.40 (40% of peak quality)
• Strategy Implications: Fade extremes, expect ranges, smaller position sizes
• Best For: Mean-reversion setups, accumulation/distribution identification
London Session (03:00-12:00 ET)
• Characteristics: European institutional activity, volatility pickup, trend initiation
• Quality Score: 0.70 (70% of peak quality)
• Strategy Implications: Watch for trend development, breakouts more reliable
• Best For: Initial trend identification, structure breaks
New York Session (08:00-17:00 ET)
• Characteristics: Highest liquidity, US institutional activity, major moves
• Quality Score: 0.95 (95% of peak quality)
• Strategy Implications: Best environment for directional trades
• Best For: Trend continuation, momentum plays
London/NY Overlap (08:00-12:00 ET)
• Characteristics: Peak liquidity, both European and US participants active
• Quality Score: 1.0 (100%—maximum quality)
• Strategy Implications: Highest probability for successful breakouts and trends
• Best For: All signal types—this is prime time
Off-Hours
• Characteristics: Thin liquidity, erratic price action, gaps possible
• Quality Score: 0.30 (30% of peak quality)
• Strategy Implications: Avoid new positions, wider stops if holding
• Best For: Waiting
Smart Weekend Detection:
AMWT properly handles the Sunday evening futures open:
// Traditional (broken):
isWeekend = dayofweek == saturday OR dayofweek == sunday
// AMWT (correct):
anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime)
isWeekend = calendarWeekend AND NOT anySessionActive
This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend."
Session Transition Boosts:
Certain session transitions create trading opportunities:
• Asian → London transition: +15% confidence boost (volatility expansion likely)
• London → Overlap transition: +20% confidence boost (peak liquidity approaching)
• Overlap → NY-only transition: -10% confidence adjustment (liquidity declining)
• Any → Off-Hours transition: Signal suppression recommended
📈 TRADE MANAGEMENT SYSTEM
The Signal Spam Problem:
Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance.
Trade Lock Mechanism:
Once a signal fires, the system enters a "trade lock" state:
Trade Lock Duration: Configurable (default 30 bars)
Early Exit Conditions:
• TP3 hit (full target reached)
• Stop Loss hit (trade failed)
• Lock expiration (time-based exit)
During lock:
• No new signals of same type displayed
• Opposite signals can override (reversal)
• Trade status tracked in dashboard
Target Levels:
Each signal generates three profit targets based on ATR:
TP1 (Conservative Target)
• Default: 1.0 × ATR
• Purpose: Quick partial profit, reduce risk
• Action: Take 30-40% off position, move stop to breakeven
TP2 (Standard Target)
• Default: 2.5 × ATR
• Purpose: Main profit target
• Action: Take 40-50% off position, trail stop
TP3 (Extended Target)
• Default: 5.0 × ATR
• Purpose: Runner target for trend days
• Action: Close remaining position or continue trailing
Stop Loss:
• Default: 1.9 × ATR from entry
• Purpose: Define maximum risk
• Placement: Below recent swing low (longs) or above recent swing high (shorts)
Invalidation Level:
Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies:
invalidation = entry - (ATR × INVALIDATION_MULT × 1.5)
If price reaches invalidation, the current market interpretation is wrong—not just the trade.
Visual Trade Management:
During active trades, AMWT displays:
• Entry arrow with grade label (▲A+, ▼B, etc.)
• TP1, TP2, TP3 horizontal lines in green
• Stop Loss line in red
• Invalidation line in orange (dashed)
• Progress indicator in dashboard
Persistent Execution Markers:
When targets or stops are hit, permanent markers appear:
• TP hit: Green dot with "TP1"/"TP2"/"TP3" label
• SL hit: Red dot with "SL" label
These persist on the chart for review and statistics.
💰 PERFORMANCE TRACKING & STATISTICS
Tracked Metrics:
• Total Trades: Count of all signals that entered trade lock
• Winning Trades: Signals where at least TP1 was reached before SL
• Losing Trades: Signals where SL was hit before any TP
• Win Rate: Winning / Total × 100%
• Total R Profit: Sum of R-multiples from winning trades
• Total R Loss: Sum of R-multiples from losing trades
• Net R: Total R Profit - Total R Loss
Currency Conversion System:
AMWT can display P&L in multiple formats:
R-Multiple (Default)
• Shows risk-normalized returns
• "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades
• Best for comparing across different position sizes
Currency Conversion (USD/EUR/GBP/JPY/INR)
• Converts R-multiples to currency based on:
- Dollar Risk Per Trade (user input)
- Tick Value (user input)
- Selected currency
Example Configuration:
Dollar Risk Per Trade: $100
Display Currency: USD
If Net R = +4.2R
Display: Net P&L: +$420.00 | 78 trades
Ticks
• For futures traders who think in ticks
• Converts based on tick value input
Statistics Reset:
Two reset methods:
1. Toggle Reset
• Turn "Reset Statistics" toggle ON then OFF
• Clears all statistics immediately
2. Date-Based Reset
• Set "Reset After Date" (YYYY-MM-DD format)
• Only trades after this date are counted
• Useful for isolating recent performance
🎨 VISUAL FEATURES
Macro Channel:
Dynamic regression-based channel showing market boundaries:
• Upper/lower bounds calculated from swing pivot linear regression
• Adapts to current market structure
• Shows overall trend direction and potential reversal zones
Chop Boxes:
Semi-transparent overlay during high-chop periods:
• Purple/orange coloring indicates dangerous conditions
• Visual reminder to avoid new positions
Confluence Heat Zones:
Background shading indicating setup quality:
• Darker shading = higher confluence
• Lighter shading = lower confluence
• Helps identify optimal entry timing
EMA Ribbon:
Trend visualization via moving average fill:
• EMA 8/21/50 with gradient fill between
• Green fill when bullish aligned
• Red fill when bearish aligned
• Gray when neutral
Absorption Zone Boxes:
Marks potential accumulation/distribution areas:
• High volume + small body = absorption
• Boxes drawn at these levels
• Often act as support/resistance
Liquidity Pool Lines:
BSL/SSL levels with labels:
• Dashed lines at liquidity clusters
• "BSL" label above swing high clusters
• "SSL" label below swing low clusters
Six Professional Themes:
• Quantum: Deep purples and cyans (default)
• Cyberpunk: Neon pinks and blues
• Professional: Muted grays and greens
• Ocean: Blues and teals
• Matrix: Greens and blacks
• Ember: Oranges and reds
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Learning the System (Week 1)
Goal: Understand AMWT concepts and dashboard interpretation
Setup:
• Signal Mode: Balanced
• Display: All features enabled
• Grade Filter: C (see all signals)
Actions:
• Paper trade ONLY—no real money
• Observe HMM state transitions throughout the day
• Note when agents agree vs disagree
• Watch chop detection engage and disengage
• Track which grades produce winners vs losers
Key Learning Questions:
• How often do A+ signals win vs B signals? (Should see clear difference)
• Which agent tends to be right in current market? (Check dashboard)
• When does chop detection save you from bad trades?
• How do signals near liquidity events perform vs signals in vacuum?
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to your instrument and timeframe
Signal Mode Testing:
• Run 5 days on Aggressive mode (more signals)
• Run 5 days on Conservative mode (fewer signals)
• Compare: Which produces better risk-adjusted returns?
Grade Filter Testing:
• Track A+ only for 20 signals
• Track A and above for 20 signals
• Track B and above for 20 signals
• Compare win rates and expectancy
Chop Threshold Testing:
• Default (80%): Standard filtering
• Try 70%: More aggressive filtering
• Try 90%: Less filtering
• Which produces best results for your instrument?
Phase 3: Strategy Development (Weeks 3-4)
Goal: Develop personal trading rules based on system signals
Position Sizing by Grade:
• A+ grade: 100% position size
• A grade: 75% position size
• B grade: 50% position size
• C grade: 25% position size (or skip)
Session-Based Rules:
• London/NY Overlap: Take all A/A+ signals
• NY Session: Take all A+ signals, selective on A
• Asian Session: Only A+ signals with extra confirmation
• Off-Hours: No new positions
Chop Zone Rules:
• Chop >70%: Reduce position size 50%
• Chop >80%: No new positions
• Chop <50%: Full position size allowed
Phase 4: Live Micro-Sizing (Month 2)
Goal: Validate paper trading results with minimal risk
Setup:
• 10-20% of intended full position size
• Take ONLY A+ signals initially
• Follow trade management religiously
Tracking:
• Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus
• Calculate: Win rate by grade, by session, by chop level
• Compare to paper trading (should be within 15%)
Red Flags:
• Win rate diverges significantly from paper trading: Execution issues
• Consistent losses during certain sessions: Adjust session rules
• Losses cluster when specific agent dominates: Review that agent's logic
Phase 5: Scaling Up (Months 3-6)
Goal: Gradually increase to full position size
Progression:
• Month 3: 25-40% size (if micro-sizing profitable)
• Month 4: 40-60% size
• Month 5: 60-80% size
• Month 6: 80-100% size
Scale-Up Requirements:
• Minimum 30 trades at current size
• Win rate ≥50%
• Net R positive
• No revenge trading incidents
• Emotional control maintained
💡 DEVELOPMENT INSIGHTS
Why HMM Over Simple Indicators:
Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend.
The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP."
The Multi-Agent Evolution:
Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way.
The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection.
Chop Detection Revelation:
Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones.
Liquidity Anchoring Discovery:
Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals.
The Grade System Impact:
Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What AMWT Is NOT:
• NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty.
• NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment.
• NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis.
• NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate.
Core Assumptions:
1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure.
2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals.
3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns.
4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation.
Performs Best On:
• Liquid Futures : ES, NQ, MNQ, MES, CL, GC
• Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY
• Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap)
• Liquid Crypto : BTC, ETH on major exchanges
Performs Poorly On:
• Illiquid Instruments : Low volume stocks, exotic pairs
• Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal)
• Binary Event Days : Earnings, FDA approvals, court rulings
• Manipulated Markets : Penny stocks, low-cap altcoins
Known Weaknesses:
• Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable.
• Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance.
• Complexity : More parameters than simple indicators. Requires understanding to use effectively.
⚠️ RISK DISCLOSURE
Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses.
AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results.
Hidden Markov Model assumptions may not hold during:
• Major news events disrupting normal market behavior
• Flash crashes or circuit breaker events
• Low liquidity periods with erratic price action
• Algorithmic manipulation or spoofing
Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear.
Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing.
Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception.
By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes.
"Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable."
Initial Public Release
Core Engine:
• True Hidden Markov Model with online Baum-Welch learning
• Viterbi algorithm for optimal state sequence decoding
• 6-state market regime classification
Agent System:
• 3-Bandit consensus (Trend, Reversion, Structure)
• Thompson Sampling with true Beta distribution sampling
• Adaptive weight learning based on performance
Signal Generation:
• Quality-based confluence grading (A+/A/B/C)
• Four signal modes (Aggressive/Balanced/Conservative/Institutional)
• Grade-based visual brightness
Chop Detection:
• 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume)
• 7 regime classifications
• Configurable signal suppression threshold
Liquidity:
• Volume spike detection
• Stop run (liquidity sweep) identification
• BSL/SSL pool mapping
• Absorption zone detection
Trade Management:
• Trade lock with configurable duration
• TP1/TP2/TP3 targets
• ATR-based stop loss
• Persistent execution markers
Session Intelligence:
• Asian/London/NY/Overlap detection
• Smart weekend handling (Sunday futures open)
• Session quality scoring
Performance:
• Statistics tracking with reset functionality
• 7 currency display modes
• Win rate and Net R calculation
Visuals:
• Macro channel with linear regression
• Chop boxes
• EMA ribbon
• Liquidity pool lines
• 6 professional themes
Dashboards:
• Main Dashboard: Market State, Consensus, Trade Status, Statistics
📋 AMWT vs AMWT-PRO:
This version includes all core AMWT functionality:
✓ Full Hidden Markov Model state estimation
✓ 3-Bandit Thompson Sampling consensus system
✓ Complete 5-factor chop detection engine
✓ All four signal modes
✓ Full trade management with TP/SL tracking
✓ Main dashboard with complete statistics
✓ All visual features (channels, zones, pools)
✓ Identical signal generation to PRO
✓ Six professional themes
✓ Full alert system
The PRO version adds the AMWT Advisor panel—a secondary dashboard providing:
• Real-time Market Pulse situation assessment
• Agent Matrix visualization (individual agent votes)
• Structure analysis breakdown
• "Watch For" upcoming setups
• Action Command coaching
Both versions generate identical signals . The Advisor provides additional guidance for interpreting those signals.
Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
Hull DMI - MattesHull DMI - Mattes
A Directional Movement Index enhanced with Hull Moving Average smoothing for refined trend detection.
This indicator reimagines the classic Directional Movement Index (DMI) by incorporating Hull Moving Average (HMA) smoothing on high and low prices. It calculates the +DI and -DI components based on changes in these hulled values, then derives the ADX for trend strength. The core plot displays the difference between +DI and -DI, colored to indicate bullish (blue) or bearish (purple) dominance when ADX is rising. Additionally, it overlays colored candles on the price chart to visually represent the prevailing trend direction.
Key Features:
Hull-Smoothed Inputs: Applies HMA to highs and lows before computing directional changes, reducing noise and lag compared to standard DMI.
Customizable Lengths: Adjustable periods for HMA, DI, and ADX smoothing to suit various timeframes and assets.
Trend Visualization: Plots DI difference with dynamic coloring and overlays trend-colored candles for at-a-glance analysis.
Alert Conditions: Built-in alerts for long (bullish) and short (bearish) signals when conditions shift.
How It Differs from Standard DMI/ADX:
Unlike the traditional DMI, which uses raw price changes and true range, this version employs Hull Moving Averages on highs and lows for smoother, more responsive directional calculations. This minimizes whipsaws in choppy markets while preserving sensitivity to genuine trends. The ADX is integrated to filter signals, ensuring color changes and alerts only occur during strengthening trends, setting it apart from basic oscillator-based indicators. Why It's Useful:
Enhanced Trend Identification: The HMA smoothing provides clearer signals in volatile environments, helping traders spot emerging trends earlier.
Visual Clarity: Colored DI plot and candle overlays make it easy to interpret market bias without cluttering the chart.
Versatility: Suitable for stocks, forex, crypto, and more; excels in trend-following strategies or as a filter for other systems.
Risk Management Aid: By focusing on ADX-confirmed moves, it reduces false signals, potentially improving win rates in systematic trading.
This Hull DMI variant offers several practical advantages that can directly improve trading decisions and performance:
Reduced Lag with Smoother Signals: By applying Hull Moving Average smoothing to highs and lows, the indicator responds faster to genuine trend changes than the standard DMI while filtering out much of the noise that causes false signals in ranging or choppy markets. Traders get earlier entries into trending moves without excessive whipsaws.
Built-in Trend Strength Filter: The optional ADX confirmation (enabled by default) ensures bullish signals and blue coloring only activate when trend strength is increasing (ADX rising). This helps traders avoid entering long positions during weakening or sideways trends, focusing capital on higher-probability setups.
Clear Visual Bias at a Glance: The single oscillator line (+DI – -DI) centered on zero, combined with dynamic blue/purple coloring and full candle overlay on the price chart, instantly shows the dominant trend direction. No need to interpret multiple lines—traders can quickly assess market bias across multiple charts or timeframes.
Versatile Across Markets and Styles: Works effectively on stocks, forex, futures, and cryptocurrencies. Trend-following traders can use it standalone for entries/exits, swing traders can use it for bias confirmation, and scalpers/day traders benefit on lower timeframes due to the reduced lag.
Improved Risk Management: By prioritizing ADX-confirmed directional moves, the indicator naturally filters low-conviction setups. This can lead to higher win rates and better risk-reward ratios when used systematically, especially when combined with proper stop-loss placement below/above recent swings.
Easy Integration: Built-in alert conditions and simple long/short logic make it straightforward to incorporate into automated strategies, watchlists, or as a confirming filter alongside other indicators (e.g., moving averages, RSI, volume profile).
Customizable Sensitivity: Separate inputs for Hull length, DI period, and ADX smoothing allow traders to optimize the indicator for specific assets, volatility regimes, or personal trading horizons—making it adaptable rather than one-size-fits-all.
Signals & Interpretation
The oscillator plots the difference between +DI and -DI (positive = bullish dominance, negative = bearish).
Bullish Signal (Long): +DI crosses above -DI, and (if ADX confirmation enabled) ADX is rising — triggers blue coloring, candle overlay, and long alert.
Bearish Signal (Short): -DI crosses above +DI — triggers purple coloring, candle overlay, and short alert.
Zero line acts as neutrality; crossings indicate potential trend shifts.
Best used in trending markets; ADX rising filter helps avoid whipsaws.
// Example Usage in Strategy
strategy("Hull DMI Strategy Example", overlay=true)
if L
strategy.entry("Long", strategy.long)
if S
strategy.entry("Short", strategy.short)
Great Inventions Require great care
Disclaimer: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Past performance is not indicative of future results. Always backtest thoroughly on your specific assets and timeframes, and consult a qualified financial advisor before making trading decisions. The author assumes no responsibility for any losses incurred from its use.
Gann Square of Nine: Planetary Degrees█ Gann Square of Nine: Planetary Degrees maps planetary positions onto Gann's Square of Nine grid, tracking where pivot highs and lows accumulate by planetary degree. Use this indicator to identify recurring degree patterns on the So9, determine whether pivots cluster around cardinal, diagonal, or other significant angles, and project when the planet will return to those degrees.
Powered by the open-source BlueprintResearch Planetary Ephemeris library , which implements truncated VSOP87 (planets) and ELP2000 (Moon) series for high-accuracy celestial calculations entirely within Pine Script.
█ FEATURES
• Anchor Point System — Select any significant price pivot (high or low) as your reference point; all subsequent pivot tracking begins from this timestamp
• All 10 celestial bodies — Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, and Pluto
• Geocentric or Heliocentric views — Toggle between Earth-centered (traditional) and Sun-centered perspectives
• Interactive Square of Nine table — Visual grid displaying the Gann spiral pattern with highlighted pivot degrees
• Automatic pivot detection — Configurable bar sensitivity to identify price pivots (symmetric left/right)
• Pivot degree labeling — Each detected pivot displays the planet's ecliptic longitude (0-360°) at that moment
• Target degree alerts — Define specific So9 degrees to watch; triggers alerts when the planet crosses them
• Preset So9 angles — Quick selection of degrees along major So9 lines (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°)
• Custom degree input — Enter any degrees as comma-separated or newline-separated values
• Future degree projections — Scans up to 500 bars ahead and shows when the planet will reach each target degree
• Retrograde indicator — Shows ℞ symbol with red text when planets are in apparent retrograde motion
• So9 overlay tools — Plot 90° and 45° angle relationships from any entered degree
█ HOW IT WORKS
The Square of Nine Concept:
Gann's Square of Nine is a spiral grid where numbers flow outward from the center (1) in a square spiral pattern. Key angle relationships (0°, 45°, 90°, etc.) align along specific diagonals and cardinal lines. When planetary degrees land on the same So9 position as significant price pivots, it suggests potential support/resistance levels.
This Indicator:
1. User selects an "anchor" timestamp at a significant price pivot
2. The indicator calculates the selected planet's ecliptic longitude (0-360°) at each bar
3. Price pivots detected after the anchor are labeled with their planetary degrees
4. These degrees accumulate on the So9 grid, revealing patterns
5. Target degrees can be set to receive alerts when crossed
6. Future projections show when the planet will reach those target degrees
█ HOW TO USE
1. Click on the anchor timestamp input and select a significant high or low pivot on your chart
2. Choose "High" or "Low" pivot type based on your anchor point
3. Select your planet from the dropdown
4. Choose Geocentric (traditional) or Heliocentric view
5. The So9 table appears showing accumulated pivot degrees highlighted
6. Set target degrees using presets or custom input to receive crossing alerts
7. Future projections appear as vertical lines with date/time labels
8. Use the So9 overlay tools to visualize angle relationships from specific degrees
█ VISUAL GUIDE
So9 Table Colors:
• Anchor degree: White (⚓ symbol)
• Current planet position: Planet's assigned color with symbol
• Pivot Highs: Green background
• Pivot Lows: Red background
• Equal (both high and low): Orange background
• Diagonal crosses: Blue background
• Cardinal crosses: Red background
• Target degrees: Yellow highlight
Chart Labels:
• Pivot High labels appear above the price with the degree
• Pivot Low labels appear below the price with the degree
• Future projection lines: Yellow (upcoming) or Gray (already crossed since anchor)
█ SETTINGS OVERVIEW
1. Anchor Point — Set the starting pivot timestamp and type (High/Low)
2. Planet Selection — Choose celestial body and coordinate system
3. Target Degree Alerts — Configure which degrees to watch and receive alerts
4. Pivot Detection — Set bar sensitivity for pivot high/low detection and degree rounding precision
5. Visual Style — Customize colors and label sizes
6. So9 Grid Overlay — Enter a degree to visualize its angular relationships
7. So9 Table — Position, sizing, and color options for the grid
8. So9 Diagonals — Toggle and color the diagonal/cardinal cross highlights
█ LIMITATIONS & ACCURACY
This indicator uses optimized VSOP87 and ELP2000 series tailored for Pine Script performance. It delivers excellent accuracy for trading and analytical purposes.
Expected Accuracy:
• Sun, Moon, Mercury, Venus, Mars: Within 1-10 arcseconds
• Jupiter, Saturn: Within 10-30 arcseconds
• Uranus, Neptune: Within 1-2 arcminutes
• Pluto: Simplified Meeus method (valid 1900-2100)
Degree Resolution:
The So9 grid uses integer degrees (1-361). Planetary positions are rounded to the nearest whole degree for grid placement. Precise decimal degrees are retained for crossing calculations and alerts.
Crossing Detection:
Future projection lines and background highlights both point to the confirmation bar—the first bar where the crossing can be verified. Alerts also trigger on this bar. This ensures all visual elements align consistently: when the chart reaches a future projection line, that bar closes with the crossing confirmed and highlighted.
█ CREDITS
• Square of Nine grid visualization adapted from ThiagoSchmitz's "Gann Square of 9" (Feb 2023)
• Ephemeris calculations via BlueprintResearch/lib_ephemeris open-source library
Quantum Candle Scanner [JOAT]
Quantum Candle Scanner - Advanced Multi-Pattern Recognition System
Introduction and Purpose
Quantum Candle Scanner is an open-source overlay indicator that detects multiple candlestick patterns including engulfing patterns, kicker patterns, inside bar setups, momentum candles, and higher-high/lower-low sequences. The core problem this indicator solves is that traders often miss patterns because they're looking for only one type. Different patterns work better in different market conditions.
This indicator addresses that by scanning for five distinct pattern types simultaneously, giving traders a comprehensive view of price action signals.
Why These Five Pattern Types Work Together
Each pattern type identifies different market behavior:
1. Engulfing Patterns - Classic reversal signals where current candle completely engulfs the previous candle. Best for identifying potential turning points.
2. Kicker Patterns - Strong reversal signals with gap confirmation. The current candle opens beyond the previous candle's open with opposite direction. Best for identifying high-momentum reversals.
3. Inside Bar Patterns - Consolidation breakout signals where a candle's range is contained within the previous candle, followed by a breakout. Best for identifying compression before expansion.
4. Momentum Candles - Identifies the largest body candle over a lookback period. Best for spotting institutional activity.
5. HH/HL and LH/LL Sequences - Three-bar structure patterns showing trend continuation. Best for confirming trend direction.
How the Detection Works
Engulfing Pattern:
bool engulfBullBase = open <= math.min(close , open ) and
close >= math.max(close , open ) and
isBullish(0) and
getBodyPct(0) > bodyMinPct
Kicker Pattern:
bool kickerBull = isBearish(1) and isBullish(0) and
open > open and low > low and
getBodyPct(0) > 40 and getBodyPct(1) > 40
Inside Bar:
bool insideBarSetup = low < low and high > high
bool insideBarBull = insideBarSetup and isBullish(0)
HH/HL Sequence:
bool hhhlSeq = high > high and low > low and
high > high and low > low and
close > close
Optional Filters
ATR Filter - Only shows patterns where candle body exceeds ATR (strong candles only)
Body Minimum % - Requires minimum body percentage for engulfing patterns
Close Beyond Prior H/L - Requires engulfing candle to close beyond prior high/low
Dashboard Information
Engulfing - Total engulfing patterns detected
Kicker - Kicker pattern count
Inside Bar - Inside bar breakout count
HH/LL Seq - Structure sequence count
Total - Combined pattern count
How to Use This Indicator
For Reversal Trading:
1. Look for engulfing or kicker patterns at key support/resistance
2. Confirm with HH/HL or LH/LL sequence breaking
3. Enter with stop beyond the pattern
For Breakout Trading:
1. Identify inside bar setups (consolidation)
2. Enter on breakout candle in direction of break
3. Use the inside bar range for stop placement
For Trend Confirmation:
1. Use HH/HL sequences to confirm uptrend structure
2. Use LH/LL sequences to confirm downtrend structure
3. Momentum candles indicate institutional participation
Input Parameters
Detect Engulfing/Kicker/Inside Bar/Momentum/HHLL (all true) - Toggle each pattern type
Min Body % for Engulfing (0) - Minimum body percentage
ATR Filter (false) - Only show strong candles
Engulf Must Close Beyond Prior H/L (true) - Stricter engulfing definition
Compact Mode (false) - Shorter labels for cleaner charts
Timeframe Recommendations
1H-Daily: Best for reliable pattern detection
15m-30m: More patterns but higher noise
Use Compact Mode on lower timeframes
Limitations
Pattern detection is mechanical and does not consider context
Not all patterns lead to successful trades
Kicker patterns are rare but powerful
Inside bar breakouts can fail (false breakouts)
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes.
This indicator does not constitute financial advice. Pattern detection does not guarantee trade outcomes. Always use proper risk management.
- Made with passion by officialjackofalltrades
Ping-Pong Fade (BB + Absorption Proxy)Ping-Pong Fade is a mean-reversion fade indicator designed to capture short-term reversals at statistically extreme price levels only when real participation and absorption behavior are present.
This script intentionally mashes up Bollinger Bands, volume expansion, and candle structure to filter out weak band touches and isolate defended extremes.
Why This Mashup Exists
Bollinger Band fades fail most often when:
Price is expanding with conviction
Breakouts are supported by strong directional bodies
There is no opposing liquidity at the extremes
This indicator solves that by requiring three independent confirmations before signaling a fade:
Statistical Extremity (Bollinger Bands)
Participation (Volume Expansion)
Absorption / Rejection (Candle Structure)
Only when all three align does the script trigger a signal.
Component Breakdown & How They Work Together
1. Bollinger Bands – Where price should react
Uses a standard SMA + standard deviation envelope
Defines upper and lower statistical extremes
Provides the location for potential fades, not the signal by itself
Bands answer where, not whether.
2. Volume Spike Filter – Who is involved
Compares current volume to a moving average
Requires volume to exceed a configurable multiple
Ensures the interaction at the band is meaningful, not illiquid noise
No volume = no real defense = no trade.
3. Candle Body % (Absorption Proxy) – How price is behaving
Measures candle body relative to full range
Small bodies at the band imply:
Heavy two-sided trading
Aggression being absorbed
Failure to close through the extreme
This acts as a practical proxy for order-flow absorption without requiring Level II or footprint data.
Big range + small body + high volume = pressure met with resistance.
Signal Logic (The “Ping-Pong” Effect)
🔽 Short Fade
Triggered when:
Price probes above the upper Bollinger Band
Volume spikes above normal
Candle shows a small body and fails to close strong at highs
Interpretation:
Buyers pushed price to an extreme, but were absorbed. Expect rotation back toward the mean.
🔼 Long Fade
Triggered when:
Price probes below the lower Bollinger Band
Volume spikes above normal
Candle shows a small body and fails to close strong at lows
Interpretation:
Sellers forced price down, but were absorbed. Expect a bounce toward the mean.
What This Indicator Is Best Used For
Intraday mean-reversion setups
Range-bound or rotational markets
Scalping and short-term fades near extremes
Confirmation layer alongside VWAP, structure, or HTF bias
What It Is Not
A breakout tool
A trend-following indicator
A standalone system without context
Core Philosophy
Extreme + Volume + Failure = Opportunity
Ping-Pong Fade is designed to show you when price tries to escape its range — and fails — allowing you to fade the move with structure and intent.
GK V2 Zero-Lag trend Ribbon GK Zero-Lag Trend Ribbon V2 is the same as version 1 but with a trend ribbon to help identify the trend. Designed to help keep traders aligned with clean market direction, it uses a Zero-Lag EMA based trend ribbon with adaptive volatility bands to clearly identify bullish and bearish trends wile filtering out noise with signal prints. The ribbon dynamically changes colour to show trend bias, and GK BUY / GK SELL only print on confirmed trend flip- one clean signal per trend no clutter
Apex Adaptive Trend Navigator [Pineify]Apex Adaptive Trend Navigator
The Apex Adaptive Trend Navigator is a comprehensive trend-following indicator that combines adaptive moving average technology, dynamic volatility bands, and market structure analysis into a single, cohesive trading tool. Designed for traders who want to identify trend direction with precision while filtering out market noise, this indicator adapts its sensitivity based on real-time market efficiency calculations.
Key Features
Adaptive Moving Average with efficiency-based smoothing factor
Dynamic ATR-based volatility bands that expand and contract with market conditions
Market Structure detection including BOS (Break of Structure) and CHoCH (Change of Character)
Real-time performance dashboard displaying trend status and efficiency metrics
Color-coded cloud visualization for intuitive trend identification
How It Works
The core of this indicator is built on an Adaptive Moving Average that uses a unique efficiency-based calculation method inspired by the Kaufman Adaptive Moving Average (KAMA) and TRAMA concepts. The efficiency ratio measures the directional movement of price relative to total price movement over the lookback period:
Efficiency = |Price Change over N periods| / Sum of |Individual Bar Changes|
This ratio ranges from 0 to 1, where values closer to 1 indicate a strong trending market with minimal noise, and values closer to 0 indicate choppy, sideways conditions. The smoothing factor is then squared to penalize noisy markets more aggressively, causing the adaptive line to flatten during consolidation and respond quickly during strong trends.
The Dynamic Volatility Bands are calculated using the Average True Range (ATR) multiplied by a user-defined factor. These bands create a channel around the adaptive moving average, helping traders visualize the current volatility regime and potential support/resistance zones.
Trading Ideas and Insights
When price stays above the adaptive line with the bullish cloud forming, consider this a confirmation of uptrend strength
The efficiency percentage in the dashboard indicates trend quality - higher values suggest more reliable trends
Watch for price interactions with the upper and lower bands as potential reversal or continuation zones
A flat adaptive line indicates consolidation - wait for a clear directional break before entering trades
How Multiple Indicators Work Together
This indicator integrates three complementary analytical approaches:
The Adaptive Moving Average serves as the trend backbone, providing a dynamic centerline that automatically adjusts to market conditions. Unlike fixed-period moving averages, it reduces lag during trends while minimizing whipsaws during ranging markets.
The ATR Volatility Bands work in conjunction with the adaptive MA to create a volatility envelope. When the adaptive line is trending and price remains within the cloud (between the MA and outer band), this confirms trend strength. Price breaking through the opposite band may signal exhaustion or reversal.
The Market Structure Analysis using swing point detection adds a Smart Money Concepts (SMC) layer. BOS signals indicate trend continuation when price breaks previous swing highs in uptrends or swing lows in downtrends. CHoCH signals warn of potential reversals when the structure shifts against the prevailing trend.
Unique Aspects
The squared efficiency factor creates a non-linear response that dramatically reduces noise sensitivity
Cloud fills only appear on the trend side, providing clear visual distinction between bullish and bearish regimes
The integrated dashboard eliminates the need to switch between multiple indicators for trend assessment
Pivot-based swing detection ensures accurate market structure identification
How to Use
Add the indicator to your chart and adjust the Lookback Period based on your trading timeframe (shorter for scalping, longer for swing trading)
Monitor the cloud color - green clouds indicate bullish conditions, red clouds indicate bearish conditions
Use the efficiency reading in the dashboard to gauge trend reliability before entering positions
Consider entries when price pulls back to the adaptive line during strong trends (high efficiency)
Use the volatility bands as dynamic take-profit or stop-loss reference levels
Customization
Lookback Period : Controls the sensitivity of trend detection and swing point identification (default: 20)
Volatility Multiplier : Adjusts the width of the ATR bands (default: 2.0)
Show Market Structure : Toggle visibility of BOS and CHoCH labels
Show Performance Dashboard : Toggle the trend status table
Color Settings : Customize bullish, bearish, and neutral colors to match your chart theme
Conclusion
The Apex Adaptive Trend Navigator offers traders a sophisticated yet intuitive approach to trend analysis. By combining adaptive smoothing technology with volatility measurement and market structure concepts, it provides multiple layers of confirmation for trading decisions. Whether you are a day trader seeking quick trend identification or a swing trader looking for reliable trend-following signals, this indicator adapts to your market conditions and trading style. The efficiency-based calculations ensure you always know not just the trend direction, but also the quality and reliability of that trend.
NQ Order Blocks (Smart Money)Shows the order blocks for different time frames
Detects Bullish Order Blocks (last down candle before impulsive up move)
Detects Bearish Order Blocks (last up candle before impulsive down move)
Plots rectangles (zones) on the chart
Works well on NQ 1m–15m and HTF confirmation
Lightweight & free
AI Signal Pro (MVP) | @GexProNew: AI Signal Preview (Free)
We’re excited to introduce a free AI-powered signal layer — now embedded directly in the Gamma Levels indicator — to help you spot high-conviction trade setups in real time.
This intelligent overlay analyzes gamma regime alignment, volume surges, and momentum shifts to surface only the highest-quality opportunities — no noise, no spam.
What You Get (Free)
Smart Trigger Logic: Only activates when Gamma Flip, Call Wall, and Put Support align with price action
Confidence Scoring: Clear % rating (e.g., "AI Confidence: 82%") so you know when to trust the signal
Actionable Levels: Auto-calculated entry, stop, and target based on gamma structure
Regime-Aware: Respects Positive/Negative Gamma environments — no counter-trend false signals
What’s Not Included (Free Version)
No LLM reasoning — no natural-language trade rationale
No real options data — uses price/volume proxies (not live OI or GEX)
No directional scoring — no Vanna/Charm-weighted conviction engine
No 0DTE or multi-expiry logic
Think of this as the “teaser” — a glimpse of what’s possible when AI meets institutional options flow.
Ready for the Full AI Engine?
→ Upgrade to GEX Pro and unlock:
Real-time options chain ingestion (OI, volume, strikes, expiries)
LLM-powered trade rationale — “Why this setup works now” in plain English
Institutional Conviction Score™ — 5-factor model (GEX slope, Vanna flow, vol skew, etc.)
0DTE-optimized signals with volume-priority gamma weighting
API access & trade ideas — auto-generated setups with risk metrics
Stop reacting. Start anticipating — where the smart money is positioned before the move.
Try GEX Pro — gexpro.asiaquant.com
Cody Order Block Finder with RegressionThe Cody Order Block Finder with Regression Channel is a comprehensive trading tool that combines order block identification with linear regression analysis. This dual-approach indicator is designed to help traders identify potential institutional order flow zones within the context of established market trends, providing a structured methodology for trade entry and exit decisions.
Free Telegram Trading Community t.me
Order Block Detection System
Identifies potential institutional buying and selling zones based on specific candle patterns
Configurable detection parameters including required subsequent candles and minimum percentage moves
Three visual color schemes (Dark, Bright, Neon) for different chart backgrounds
Options to display order blocks using wick ranges or body ranges
Advanced filtering capabilities including size-based and ATR-based filters
Historical display controls with options to show all order blocks or only the most recent instances
Linear Regression Channel
Customizable regression length from 1 to 5000 periods
Upper and lower deviation channels with adjustable multipliers
Pearson's R correlation coefficient display for trend strength assessment
Flexible extension options for channel lines (left, right, both, or none)
Visual channel fills between regression lines for clear trend identification
Technical Specifications
Detection Logic
Bullish order blocks: Identified by a bearish candle followed by a specified number of consecutive bullish candles with minimum percentage movement
Bearish order blocks: Identified by a bullish candle followed by a specified number of consecutive bearish candles with minimum percentage movement
Size validation through configurable percentage thresholds
Optional ATR filtering for volatility-adjusted order block identification
Visual Elements
Triangle markers indicating order block locations above or below relevant candles
Extended lines marking order block boundaries with configurable right-side extension
Informative labels displaying order block size percentages
Dynamic trend labels based on regression slope analysis
Adjustable transparency and coloring for all visual elements
Alert System
Context-aware alerts that only trigger in confirmed trends
Buy alerts: Bullish order blocks detected during uptrends (positive regression slope)
Sell alerts: Bearish order blocks detected during downtrends (negative regression slope)
Customizable alert messages with trend context information
Performance Optimization
Maximum 500 lines and 500 labels for system resource management
Efficient array-based line management for historical display controls
Conditional calculations to reduce processing overhead
Application for Traders
This indicator serves multiple trading methodologies:
Trend-following traders can use regression channels for trend identification
Institutional flow traders can identify potential order block zones
Swing traders can locate high-probability reversal areas
Risk management through size and volatility filtering
The combination of order block detection with regression trend analysis provides traders with a comprehensive tool for identifying potential trade setups that align with both institutional activity and broader market trends. The extensive customization options allow adaptation to various trading styles and market conditions.
ronismc333 דור בן שימול: //+------------------------------------------------------------------+
//| SMC GBP PRO EA – FTMO Ready 30M עם חצים |
//+------------------------------------------------------------------+
#property strict
input double RiskPercent = 1.0;
input int RSIPeriod = 14;
input int StopLossPoints = 200;
input int TakeProfitPoints = 400;
input int MagicNumber = 202630;
input bool EnableAlerts = true;
int rsiHandle;
//+------------------------------------------------------------------+
int OnInit()
{
rsiHandle = iRSI(_Symbol, PERIOD_M30, RSIPeriod, PRICE_CLOSE);
Comment("SMC GBP PRO EA Status: CONNECTED Account: ", AccountNumber());
return(INIT_SUCCEEDED);
}
//+------------------------------------------------------------------+
void OnTick()
{
if(PositionsTotal() > 0)
{
UpdateStatus();
return;
}
double rsi ;
CopyBuffer(rsiHandle,0,0,1,rsi);
double high1 = iHigh(_Symbol, PERIOD_M30,1);
double low1 = iLow(_Symbol, PERIOD_M30,1);
double close1= iClose(_Symbol, PERIOD_M30,1);
double high2 = iHigh(_Symbol, PERIOD_M30,2);
double low2 = iLow(_Symbol, PERIOD_M30,2);
//==== HTF TREND (1H EMA50) ====
double emaHTF = iMA(_Symbol, PERIOD_H1, 50, 0, MODE_EMA, PRICE_CLOSE, 0);
double closeHTF = iClose(_Symbol, PERIOD_H1, 0);
bool htfBull = closeHTF > emaHTF;
bool htfBear = closeHTF < emaHTF;
//==== LIQUIDITY SWEEP ====
bool sweepBuy = low1 < low2 && close1 > low2;
bool sweepSell = high1 > high2 && close1 < high2;
//==== BOS ====
bool bosBuy = sweepBuy && close1 > high2;
bool bosSell = sweepSell && close1 < low2;
//==== BUY/SELL CONDITIONS ====
bool buy = bosBuy && rsi > 50 && htfBull;
bool sell = bosSell && rsi < 50 && htfBear;
double lot = CalculateLot(StopLossPoints, RiskPercent);
if(buy)
{
OpenTrade(ORDER_TYPE_BUY, lot, StopLossPoints, TakeProfitPoints, "BUY GBP");
DrawArrow("BUY", 0, low1 - 10*_Point, clrLime, "BUY GBP");
}
if(sell)
{
OpenTrade(ORDER_TYPE_SELL, lot, StopLossPoints, TakeProfitPoints, "SELL GBP");
DrawArrow("SELL", 0, high1 + 10*_Point, clrRed, "SELL GBP");
}
UpdateStatus();
}
//+------------------------------------------------------------------+
double CalculateLot(int slPoints, double riskPercent)
{
double riskMoney = AccountBalance() * riskPercent / 100.0;
double lot = riskMoney / (slPoints * _Point * 10);
lot = MathMax(lot,0.01);
return(NormalizeDouble(lot,2));
}
//+------------------------------------------------------------------+
void OpenTrade(ENUM_ORDER_TYPE type,double lot,int sl,int tp,string comment)
{
double price = (type==ORDER_TYPE_BUY) ? SymbolInfoDouble(_Symbol,SYMBOL_ASK)
: SymbolInfoDouble(_Symbol,SYMBOL_BID);
double slPrice = (type==ORDER_TYPE_BUY) ? price - sl*_Point
: price + sl*_Point;
double tpPrice = (type==ORDER_TYPE_BUY) ? price + tp*_Point
: price - tp*_Point;
MqlTradeRequest req;
MqlTradeResult res;
ZeroMemory(req);
req.action = TRADE_ACTION_DEAL;
req.symbol = _Symbol;
req.volume = lot;
req.type = type;
req.price = price;
req.sl = slPrice;
req.tp = tpPrice;
req.deviation= 20;
req.magic = MagicNumber;
req.comment = comment;
if(!OrderSend(req,res))
{
Print("Trade failed: ",res.retcode);
if(EnableAlerts) Alert("Trade failed: ",res.retcode);
}
else
{
if(EnableAlerts) Alert(comment," opened at ",price);
Print(comment," opened at ",price);
}
}
//+------------------------------------------------------------------+
void UpdateStatus()
{
string text = "SMC GBP PRO EA Status: CONNECTED Account: "+IntegerToString(AccountNumber());
if(PositionsTotal()>0) text += " Trade Open!";
Comment(text);
}
//+------------------------------------------------------------------+
void DrawArrow(string name, int shift, double price, color clr, string text)
{
string objName = name + IntegerToString(TimeCurrent());
if(ObjectFind(0,objName) >=0) ObjectDelete(0,objName);
ObjectCreate(0,objName,OBJ_ARROW,0,Time ,price);
ObjectSetInteger(0,objName,OBJPROP_COLOR,clr);
ObjectSetInteger(0,objName,OBJPROP_WIDTH,2);
ObjectSetInteger(0,objName,OBJPROP_ARROWCODE,233); // חץ
ObjectSetString(0,objName,OBJPROP_TEXT,text);
}
------------------------------------------------------------------+
//| SMC GBP PRO EA – FTMO 30M + TP/SL + Trailing Stop |
//+------------------------------------------------------------------+
#property strict
input double RiskPercent = 1.0;
input int RSIPeriod = 14;
input int StopLossPoints = 200;
input int TakeProfitPoints = 400;
input int MagicNumber = 202630;
input bool EnableAlerts = true;
int rsiHandle;
//+------------------------------------------------------------------+
int OnInit()
{
rsiHandle = iRSI(_Symbol, PERIOD_M30, RSIPeriod, PRICE_CLOSE);
Comment("SMC GBP PRO EA Status: CONNECTED Account: ", AccountNumber());
return(INIT_SUCCEEDED);
}
//+------------------------------------------------------------------+
void OnTick()
{
//
UpdateStatus();
// Trailing Stop
ManageTrailing();
if(PositionsTotal() > 0) return;
double rsi ;
CopyBuffer(rsiHandle,0,0,1,rsi);
double high1 = iHigh(_Symbol, PERIOD_M30,1);
double low1 = iLow(_Symbol, PERIOD_M30,1);
double close1= iClose(_Symbol, PERIOD_M30,1);
double high2 = iHigh(_Symbol, PERIOD_M30,2);
double low2 = iLow(_Symbol, PERIOD_M30,2);
//==== HTF TREND (1H EMA50) ====
double emaHTF = iMA(_Symbol, PERIOD_H1, 50, 0, MODE_EMA, PRICE_CLOSE, 0);
double closeHTF = iClose(_Symbol, PERIOD_H1, 0);
bool htfBull = closeHTF > emaHTF;
bool htfBear = closeHTF < emaHTF;
//==== LIQUIDITY SWEEP ====
bool sweepBuy = low1 < low2 && close1 > low2;
bool sweepSell = high1 > high2 && close1 < high2;
//==== BOS ====
bool bosBuy = sweepBuy && close1 > high2;
bool bosSell = sweepSell && close1 < low2;
//==== BUY/SELL CONDITIONS ====
bool buy = bosBuy && rsi > 50 && htfBull;
bool sell = bosSell && rsi < 50 && htfBear;
double lot = CalculateLot(StopLossPoints, RiskPercent);
if(buy)
{
OpenTrade(ORDER_TYPE_BUY, lot, StopLossPoints, TakeProfitPoints, "BUY GBP");
DrawArrow("BUY", 0, low1 - 10*_Point, clrLime, "BUY GBP");
}
if(sell)
{
OpenTrade(ORDER_TYPE_SELL, lot, StopLossPoints, TakeProfitPoints, "SELL GBP");
DrawArrow("SELL", 0, high1 + 10*_Point, clrRed, "SELL GBP");
}
}
//+------------------------------------------------------------------+
double CalculateLot(int slPoints, double riskPercent)
{
double riskMoney = AccountBalance() * riskPercent / 100.0;
double lot = riskMoney / (slPoints * _Point * 10);
lot = MathMax(lot,0.01);
return(NormalizeDouble(lot,2));
}
//+------------------------------------------------------------------+
void OpenTrade(ENUM_ORDER_TYPE type,double lot,int sl,int tp,string comment)
{
double price = (type==ORDER_TYPE_BUY) ? SymbolInfoDouble(_Symbol,SYMBOL_ASK)
: SymbolInfoDouble(_Symbol,SYMBOL_BID);
double slPrice = (type==ORDER_TYPE_BUY) ? price - sl*_Point
: price + sl*_Point;
double tpPrice = (type==ORDER_TYPE_BUY) ? price + tp*_Point
: price - tp*_Point;
MqlTradeRequest req;
MqlTradeResult res;
ZeroMemory(req);
req.action = TRADE_ACTION_DEAL;
req.symbol = _Symbol;
req.volume = lot;
req.type = type;
req.price = price;
req.sl = slPrice;
req.tp = tpPrice;
req.deviation= 20;
req.magic = MagicNumber;
req.comment = comment;
if(!OrderSend(req,res))
{
Print("Trade failed: ",res.retcode);
if(EnableAlerts) Alert("Trade failed: ",res.retcode);
}
else
{
if(EnableAlerts) Alert(comment," opened at ",price);
Print(comment," opened at ",price);
}
}
//+------------------------------------------------------------------+
void UpdateStatus()
{
string text = "SMC GBP PRO EA Status: CONNECTED Account: "+IntegerToString(AccountNumber());
if(PositionsTotal()>0) text += " Trade Open!";
Comment(text);
}
//+------------------------------------------------------------------+
void DrawArrow(string name, int shift, double price, color clr, string text)
{
string objName = name + IntegerToString(TimeCurrent());
if(ObjectFind(0,objName) >=0) ObjectDelete(0,objName);
ObjectCreate(0,objName,OBJ_ARROW,0,Time ,price);
ObjectSetInteger(0,objName,OBJPROP_COLOR,clr);
ObjectSetInteger(0,objName,OBJPROP_WIDTH,2);
ObjectSetInteger(0,objName,OBJPROP_ARROWCODE,233); // חץ
ObjectSetString(0,objName,OBJPROP_TEXT,text);
}
//+------------------------------------------------------------------+
void ManageTrailing()
{
for(int i=PositionsTotal()-1;i>=0;i--)
{
ulong ticket = PositionGetTicket(i);
if(PositionSelectByTicket(ticket))
{
double price = PositionGetDouble(POSITION_PRICE_OPEN);
double sl = PositionGetDouble(POSITION_SL);
double tp = PositionGetDouble(POSITION_TP);
ENUM_POSITION_TYPE type = (ENUM_POSITION_TYPE)PositionGetInteger(POSITION_TYPE);
double newSL = 0;
if(type == POSITION_TYPE_BUY)
{
double trail = SymbolInfoDouble(_Symbol,SYMBOL_BID) - StopLossPoints*_Point;
if(trail > sl) newSL = trail;
}
else if(type == POSITION_TYPE_SELL)
{
double trail = SymbolInfoDouble(_Symbol,SYMBOL_ASK) + StopLossPoints*_Point;
if(trail < sl) newSL = trail;
}
if(newSL != 0)
{
MqlTradeRequest req;
MqlTradeResult res;
ZeroMemory(req);
req.action = TRADE_ACTION_SLTP;
req.symbol = _Symbol;
req.position = ticket;
req.sl = newSL;
req.tp = tp;
OrderSend(req,res);
}
}
}
}
[codapro] Tenkan Cloud Signals
Cloud in the Skys — Tenkan Altitude Signals Above the Kumo
Description:
This is not your average Ichimoku script — this is “Cloud in the Skys”, a reimagined way to interpret the Tenkan line as an airplane navigating altitude around the Kumo cloud layer.
Visual Metaphor Explained:
Tenkan = Airplane
The fast-reacting Conversion Line becomes your flight path.
Cloud (Kumo) = Noise / Airspace
The Ichimoku cloud is your visual weather system. When the plane (Tenkan) is:
Above the cloud → Clear skies, likely breakout, nothing overhead
Inside the cloud → Turbulence zone, indecision, avoid trading
Below the cloud → Descending, seeing ground only, bearish sentiment
This script helps you see trend structure like a pilot sees airspace — visually, directionally, and with awareness of turbulence zones.
What It Includes:
Tenkan (Conversion) and Kijun (Base) line calculations
Full Kumo Cloud (Senkou A & B), with customizable displacement
Buy/Sell Flags based on Kijun crossing the forward-displaced Span B
Only plotted after a user-defined number of confirming closes
Full visual controls: cloud fill, line colors, flag display toggle
How to Use It:
Long Bias: When Tenkan rises above the cloud and Buy flag confirms — sky’s clear
Short Bias: When Tenkan descends and Sell flag confirms — plane is losing lift
Stay Out: If Tenkan is inside the cloud, wait — this is chop/noise
Pair this script with price action or volume confirmation for better clarity. Especially effective in trend-following or breakout strategies on mid-to-longer timeframes.
Disclaimer:
This tool was created using the CodaPro Pine Script indicator design system — a modular architecture for building visual signal overlays and automated alerts.
It is provided for educational and informational purposes only and is not financial advice. Always test thoroughly before using in live market conditions.
Liquidation Bubbles [OmegaTools]🔴🟢 Liquidation Bubbles — Advanced Volume & Price Stress Detector
Liquidation Bubbles is a professional-grade analytical tool designed to identify forced positioning events, stop-runs, and liquidation clusters by combining price displacement and volume imbalance into a single, statistically normalized framework.
This indicator is not a repainting signal tool and not a simple volume spike detector. It is a contextual market stress mapper, built to highlight areas where one-sided positioning becomes unstable and the probability of forced order execution (liquidations, stops, margin calls) materially increases.
---
## 🔬 Core Concept
Market liquidations do not occur randomly.
They emerge when price deviates aggressively from its volume-weighted equilibrium while volume itself becomes abnormal.
Liquidation Bubbles detects exactly this condition by:
* Estimating a **dynamic equilibrium price** using an *inverted volume-weighted moving average*
* Measuring **directional price stress** relative to that equilibrium
* Measuring **volume stress** relative to its own adaptive baseline
* Normalizing both into **Z-score–like metrics**
* Highlighting only **statistically extreme, asymmetric events**
The result is a clear visual map of stress points where market participants are most vulnerable.
---
⚙️ Methodology (How It Works)
1️⃣ Advanced Inverted VWMA (Equilibrium Engine)
The script uses a custom Advanced VWMA, where:
* High volume bars receive less weight
* Low volume bars receive more weight
This produces a **robust equilibrium level**, resistant to manipulation and volume bursts.
This equilibrium is used for **both price and volume normalization**, creating a consistent statistical framework.
---
2️⃣ Price Stress (Directional)
Price stress is calculated as:
* The **maximum deviation** between high/low and equilibrium
* Directionally signed (upside vs downside)
* Normalized by its own historical volatility
This allows the script to distinguish:
* Aggressive upside exhaustion
* Aggressive downside capitulation
---
3️⃣ Volume Stress
Volume stress is measured as:
* Deviation from volume equilibrium
* Normalized by historical volume dispersion
This filters out:
* Normal high-volume sessions
* Illiquid noise
And isolates abnormal participation imbalance.
---
4️⃣ Liquidation Logic
A liquidation event is flagged when:
* Both price stress and volume stress exceed adaptive thresholds
* The imbalance is directional and statistically extreme
Optional Combined Score Mode allows aggregation of price & volume stress into a single composite metric for smoother signals.
---
🔵 Bubble System (Signal Hierarchy)
The indicator plots **two tiers of bubbles**:
🟢🔴 Small Bubbles
* Early warning stress points
* Localized stop-runs
* Micro-liquidations
* Often precede reactions or short-term reversals
🟢🔴 Big Bubbles
* Full liquidation clusters
* Forced unwinds
* High probability exhaustion zones
* Frequently align with:
* Intraday extremes
* Range boundaries
* Reversal pivots
* Volatility expansions
Bubble color:
* **Green** → Downside liquidation (sell-side exhaustion)
* **Red** → Upside liquidation (buy-side exhaustion)
Bubble placement is **ATR-adjusted**, ensuring visual clarity without overlapping price.
---
🔄 Cross-Market Volume Analysis
The script allows optional **external volume sourcing**, enabling:
* Futures volume applied to CFDs
* Index volume applied to ETFs
* Spot volume applied to derivatives
This is critical when:
* Your traded instrument has unreliable volume
* You want **institutional-grade confirmation**
---
🧠 How to Use Liquidation Bubbles
This indicator is **not meant to be traded alone**.
Best use cases:
* 🔹 Confluence with support & resistance
* 🔹 Contextual confirmation for reversals
* 🔹 Identifying fake breakouts
* 🔹 Liquidity sweep detection
* 🔹 Risk management (avoid entering into liquidation zones)
Ideal for:
* Futures
* Indices
* Crypto
* High-liquidity FX pairs
* Intraday & swing trading
---
🎯 Who This Tool Is For
Liquidation Bubbles is designed for:
* Advanced discretionary traders
* Order-flow & liquidity-based traders
* Macro & index traders
* Professionals seeking **context**, not signals
If you want **where the market is fragile**, not just where price moved — this tool was built for you.
---
📌 Key Characteristics
✔ Non-repainting
✔ Statistically normalized
✔ Adaptive to volatility
✔ Works on all timeframes
✔ Futures & crypto ready
✔ No lagging indicators
✔ No moving average crosses
---
Liquidation Bubbles does not predict the future.
It shows you where the market is most likely to break.
— OmegaTools
WoAlgo Premium v3.0
WoAlgo Premium v3.0 - Smart Money Analysis
Overview
** WoAlgo Premium v3.0 ** is an advanced technical analysis indicator designed for educational purposes. This tool combines Smart Money Concepts with multi-factor confluence analysis to help traders identify potential market opportunities across multiple timeframes.
The indicator integrates market structure analysis, order flow concepts, and technical momentum indicators into a comprehensive dashboard system. It is designed to assist traders in understanding institutional trading patterns and market dynamics through visual analysis tools.
### What It Does
This indicator provides:
**1. Smart Money Concepts Analysis**
- Market structure identification (Break of Structure and Change of Character patterns)
- Order block detection with volume confirmation
- Fair value gap recognition
- Liquidity zone mapping (equal highs and lows)
- Premium and discount zone calculations
**2. Multi-Factor Confluence Scoring**
The indicator calculates a proprietary confluence score (0-100) based on five key components:
- Price action analysis (30% weight)
- Volume confirmation (20% weight)
- Momentum indicators (25% weight)
- Trend strength measurement (15% weight)
- Money flow analysis (10% weight)
**3. Multi-Timeframe Analysis**
- Scans 5 different timeframes (5M, 15M, 1H, 4H, Daily)
- Calculates alignment percentage across timeframes
- Displays trend and structure status for each period
**4. Visual Dashboard System**
- Comprehensive main dashboard with 13 metrics
- Real-time screener table with 10 data columns
- Multi-timeframe scanner
- Performance tracking panel
### How It Works
**Market Structure Detection**
The indicator identifies key structural changes in price action:
- **BOS (Break of Structure)**: Indicates trend continuation when price breaks previous swing points
- **CHoCH (Change of Character)**: Signals potential trend reversal when market structure shifts
**Order Block Identification**
Order blocks are detected when:
- Significant volume appears at swing points
- Price shows strong directional movement from these levels
- Enhanced detection with extreme volume confirmation (OB++ markers)
**Fair Value Gap Recognition**
Gaps between candles are identified when:
- Price leaves inefficiencies in the market
- Three consecutive candles create a gap pattern
- Gap size exceeds minimum threshold based on ATR
**Confluence Calculation**
The system evaluates multiple technical factors:
1. **Price Position**: Relative to moving averages (EMA 20, 50, 200)
2. **Volume Analysis**: Standard deviation-based volume spikes
3. **Momentum**: RSI, MACD, Stochastic indicators
4. **Trend Strength**: ADX measurements
5. **Money Flow**: MFI indicator readings
Each factor contributes weighted points to create an overall confluence score that helps assess signal strength.
### Signal Types
**Confirmation Signals (▲ / ▼)**
Generated when:
- EMA crossovers occur (20/50 cross)
- Volume confirmation is present
- RSI is in appropriate zone
- Confluence score exceeds 50%
**Strong Signals (▲+ / ▼+)**
Higher-confidence signals requiring:
- Confluence score above 70%
- Extreme volume confirmation
- Alignment with 200 EMA trend
- MACD confirmation
- Bullish or bearish market structure
**Contrarian Signals (⚡)**
Reversal indicators appearing when:
- RSI reaches extreme levels (<30 or >70)
- Stochastic shows oversold/overbought conditions
- Price touches Bollinger Band extremes
- Potential divergence patterns emerge
**Reversal Zones**
Visual boxes highlighting areas where:
- Market structure conflicts with momentum
- High probability of directional change
- Key support/resistance levels interact
**Smart Trail**
Dynamic stop-loss indicator that:
- Adjusts based on ATR (Average True Range)
- Follows trend direction
- Updates automatically as price moves
- Provides risk management reference points
### Dashboard Components
**Main Dashboard (13 Metrics)**
1. **Confluence Score**: Current bull/bear percentage (0-100)
2. **Market Regime**: Trend classification (Strong Up/Down, Range, Squeeze)
3. **Signal Status**: Active buy/sell signal indication
4. **Structure State**: Current market structure (Bullish/Bearish/Neutral)
5. **Trend Strength**: ADX-based measurement
6. **RSI Level**: Momentum indicator with overbought/oversold zones
7. **MACD Direction**: Trend momentum confirmation
8. **Money Flow Index**: Smart money sentiment
9. **Volume Status**: Current volume relative to average
10. **Volatility Rating**: ATR percentage measurement
11. **ATR Value**: Average true range for position sizing
12. **MTF Alignment**: Multi-timeframe agreement percentage
**Screener Table (10 Columns)**
- Current symbol and timeframe
- Real-time price and percentage change
- Quality rating (star system)
- Active signal type
- Smart trail status
- Market structure state
- MACD direction
- Trend strength percentage
- Bollinger Band squeeze detection
**MTF Scanner (5 Timeframes)**
Displays for each timeframe:
- Trend direction indicator
- Market structure classification
- Visual confirmation with color coding
**Performance Metrics**
- Win rate percentage (simplified calculation)
- Total signals generated
- Current confluence score
- MTF alignment status
- Volatility level
### Settings and Customization
**Preset Styles**
Choose from predefined configurations:
- **Conservative**: Fewer, higher-quality signals
- **Moderate**: Balanced approach (recommended)
- **Aggressive**: More frequent signals
- **Scalper**: Short-term focused
- **Swing**: Longer-term oriented
- **Custom**: Full manual control
**Smart Money Concepts Controls**
- Toggle each feature independently
- Adjust swing length (3-50 periods)
- Enable/disable internal structure
- Control order block display
- Manage breaker block visibility
- Show/hide fair value gaps
- Display liquidity zones
- Premium/discount zone visualization
**Signal Configuration**
- Enable/disable confirmation signals
- Toggle strong signal markers
- Control contrarian signal display
- Show/hide reversal zones
- Smart trail activation
- Sensitivity adjustment (5-50)
**Visual Customization**
- Moving average display options
- MA period adjustments (Fast: 20, Slow: 50, Trend: 200)
- Support/resistance line toggle
- Dynamic S/R lookback period
- Candle coloring based on trend
- Color scheme customization
- Dashboard size options (Small/Normal/Large)
- Position placement (4 corners)
### How to Use
**Step 1: Initial Setup**
1. Add indicator to chart
2. Select appropriate preset or use Custom
3. Adjust timeframe to match trading style
4. Configure dashboard visibility preferences
**Step 2: Analysis Workflow**
1. Check MTF Scanner for timeframe alignment
2. Review Main Dashboard confluence score
3. Observe Market Regime classification
4. Identify active signals on chart
5. Confirm with Smart Money Concepts (order blocks, FVG, structure)
**Step 3: Trade Consideration**
Strong signals (▲+ / ▼+) require:
- Confluence score >70%
- MTF alignment >60%
- Confirmation from multiple dashboard metrics
- Support from Smart Money Concepts
- Appropriate volume levels
**Step 4: Risk Management**
- Use Smart Trail as dynamic stop-loss reference
- Consider ATR for position sizing
- Monitor volatility rating
- Respect support/resistance levels
- Combine with personal risk parameters
### Best Practices
**For Scalping (1M-5M timeframes)**
- Use Scalper preset
- Reduce swing length to 5-7
- Focus on strong signals only
- Monitor MTF alignment closely
- Quick entries near order blocks
**For Intraday Trading (15M-1H timeframes)**
- Use Moderate preset (recommended)
- Default swing length (10)
- Combine confirmation and strong signals
- Check MTF scanner before entry
- Use fair value gaps for entries
**For Swing Trading (4H-D timeframes)**
- Use Swing preset
- Increase swing length to 15-20
- Focus on strong signals
- Require high MTF alignment
- Patient approach with major structure levels
### Technical Specifications
**Indicators Used**
- Exponential Moving Averages (20, 50, 200)
- Hull Moving Average
- Relative Strength Index (14)
- MACD (12, 26, 9)
- Money Flow Index (14)
- Stochastic Oscillator (14, 3)
- ADX / DMI (14)
- Bollinger Bands (20, 2)
- ATR (14)
- Volume Analysis (SMA 20 with standard deviation)
**Calculation Methods**
- Swing detection using pivot high/low functions
- Volume confirmation via statistical analysis
- Multi-factor scoring with weighted components
- Dynamic support/resistance using highest/lowest functions
- Real-time MTF data via security() function
### Limitations and Considerations
**Important Notes**
1. This indicator is designed for educational and analytical purposes only
2. Historical performance does not guarantee future results
3. Signals should be confirmed with additional analysis
4. Market conditions vary and affect indicator performance
5. Not all signals will be profitable
6. Risk management is essential for all trading
**Known Limitations**
- Confluence scoring is algorithmic and not predictive
- MTF analysis requires sufficient historical data
- Effectiveness varies across different market conditions
- Sideways markets may produce conflicting signals
- High volatility can affect signal reliability
- Backtesting results shown are simplified calculations
**Not Suitable For**
- Automated trading without human oversight
- Sole basis for trading decisions
- Guaranteed profit expectations
- Inexperienced traders without proper education
- Trading without risk management plans
### Market Applicability
**Effective On**
- Trending markets (any direction)
- Clear structure formation periods
- Liquid instruments with consistent volume
- Multiple asset classes (forex, stocks, crypto, commodities)
- Various timeframes with appropriate settings
**Less Effective During**
- Extended ranging/choppy conditions
- Extremely low volume periods
- Major news events causing gaps
- Early market open with high spread
- Illiquid instruments with erratic price action
### Risk Disclaimer
**⚠️ IMPORTANT NOTICE**
This indicator is provided for **educational and informational purposes only**. It does not constitute financial advice, investment recommendations, or trading signals.
**Key Risk Factors:**
- Trading financial instruments involves substantial risk of loss
- Past performance does not indicate future results
- No indicator can predict market movements with certainty
- Users should conduct independent research and analysis
- Professional financial advice should be sought when appropriate
- Risk management and position sizing are critical to successful trading
- Users are solely responsible for their trading decisions
**Responsible Usage:**
- Combine with comprehensive market analysis
- Use appropriate stop-loss orders
- Never risk more than you can afford to lose
- Maintain realistic expectations
- Continue education on technical analysis principles
- Test thoroughly on demo accounts before live trading
- Understand all indicator features before using
### Educational Resources
**Understanding Smart Money Concepts**
Smart Money Concepts analyze how institutional traders and large market participants operate. Key principles include:
- Institutional order flow patterns
- Market structure changes
- Liquidity manipulation
- Supply and demand imbalances
- Order block formations
**Multi-Timeframe Analysis Theory**
Analyzing multiple timeframes helps:
- Identify overall market direction
- Improve entry timing
- Confirm trend strength
- Recognize consolidation periods
- Reduce conflicting signals
**Confluence Trading Approach**
Using multiple confirming factors:
- Increases signal reliability
- Reduces false signals
- Provides conviction for trades
- Helps with position sizing
- Improves risk-reward ratios
### Version History
**v3.0 (Current)**
- Multi-factor confluence scoring system
- Complete Smart Money Concepts implementation
- Real-time multi-timeframe analysis
- Four professional dashboard panels
- Enhanced order block detection
- Breaker block identification
- Premium/discount zone calculations
- Smart trail stop-loss system
- Customizable preset configurations
- Performance tracking metrics
**Development Philosophy**
This indicator was developed with focus on:
- Educational value for traders
- Transparent methodology
- Comprehensive feature set
- User-friendly interface
- Flexible customization options
### Technical Support
**For Questions About:**
- Indicator functionality
- Parameter optimization
- Signal interpretation
- Dashboard metrics
- Best practice recommendations
Please use TradingView's comment section below. The developer monitors comments and provides assistance to users learning to use the indicator effectively.
### Acknowledgments
This indicator implements concepts from:
- Smart Money Concepts trading methodology
- Multi-timeframe analysis techniques
- Technical indicator theory
- Market structure analysis principles
- Institutional order flow concepts
All implementations are original code and calculations based on established technical analysis principles.
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## ADDITIONAL INFORMATION SECTION
**Category**: Indicators
**Type**: Market Structure / Multi-Timeframe Analysis
**Complexity**: Intermediate to Advanced
**Open Source**: Code visible for transparency and education
**Pine Script Version**: v6
**Chart Overlay**: Yes
**Maximum Objects**: 500 boxes, 500 lines, 500 labels
ORB Asia London NYORB – Asia London NY in UTC time
Can adjust time settings to your own ORB strategy.
