MFM - Light Context HUD (Free)Overview
MFM Light Context HUD is the free version of the Market Framework Model. It gives you a fast and clean view of the current market regime and phase without signals or chart noise. The HUD shows whether the asset is in a bullish or bearish environment and whether it is in a volatile, compression, drift, or neutral phase. This helps you read structure at a glance.
Asset availability
The free version works only on a selected list of five assets.
Supported symbols are
SP:SPX
TVC:GOLD
BINANCE:BTCUSD
BINANCE:ETHUSDT
OANDA:EURUSD
All other assets show a context banner only.
How it works
The free version uses fixed settings based on the original MFM model. It calculates the regime using a higher timeframe RSI ratio and identifies the current phase using simplified momentum conditions. The chart stays clean. Only a small HUD appears in the top corner. Full visual phases, ratio logic, signals, and auto tune are part of the paid version.
The free version shows the phase name only. It does not display colored phase zones on the chart.
Phase meaning
The Market Framework Model uses four structural phases to describe how the market behaves. These are not signals but context layers that show the underlying environment.
Volatile (Phase 1)
The market is in a fast, unstable or directional environment. Price can move aggressively with stronger momentum swings.
Compression (Phase 2)
The market is in a contracting state. Momentum slows and volatility decreases. This phase often appears before expansion, but it does not predict direction.
Drift (Phase 3)
The market moves in a more controlled, persistent manner. Trends are cleaner and volatility is lower compared to volatile phases.
No phase
No clear structural condition is active.
These phases describe market structure, not trade entries. They help you understand the conditions you are trading in.
Cross asset context
The Market Framework Model reads markets as a multi layer system. The full version includes cross asset analysis to show whether the asset is acting as a leader or lagger relative to its benchmark. The free version uses the same internal benchmark logic for regime detection but does not display the cross asset layer on the chart.
Cross asset structure is a core part of the MFM model and is fully available in the paid version.
Included in this free version
Higher timeframe regime
Current phase name
Clean chart output
Context only
Works on a selected set of assets
Not included
No forecast signals
No ratio leader or lagger logic
No MRM zones
No MPF timing
No auto tune
The full version contains all features of the complete MFM model.
Full version
You can find the full indicator here:
payhip.com
More information
Model details and documentation:
mfm.inratios.com
Disclaimer
The Market Framework Model (MFM) and all related materials are provided for educational and informational purposes only. Nothing in this publication, the indicator, or any associated charts should be interpreted as financial advice, investment recommendations, or trading signals. All examples, visualizations, and backtests are illustrative and based on historical data. They do not guarantee or imply any future performance. Financial markets involve risk, including the potential loss of capital, and users remain fully responsible for their own decisions. The author and Inratios© make no representations or warranties regarding the accuracy, completeness, or reliability of the information provided. MFM describes structural market context only and should not be used as the sole basis for trading or investment actions.
By using the MFM indicator or any related insights, you agree to these terms.
© 2025 Inratios. Market Framework Model (MFM) is protected via i-Depot (BOIP) – Ref. 155670. No financial advice.
"GOLD" için komut dosyalarını ara
$MTF Fractal Echo DetectorMIL:MTVFR FRACTAL ECHO DETECTOR by Timmy741
The first public multi-timeframe fractal convergence system that actually works.
Market makers don’t move price randomly.
They test the same fractal structure on lower timeframes first → then execute the real move on higher timeframes.
This indicator catches the “echo” — when 3–5 timeframes are printing fractals at almost the exact same price level.
That’s not coincidence. That’s preparation.
FEATURES
• 5 simultaneous timeframes (1min → 4H by default)
• Real Williams Fractal detection (configurable period)
• Dynamic echo tolerance & minimum TF alignment
• Visual S/R zones from every timeframe
• Bullish / Bearish echo convergence signals
• Strength meter (3/5, 4/5, 5/5 TF alignment)
• Zero repainting — uses proper lookahead=off
• Fully Pine v6 typed + optimized
USE CASE
When you see a 4/5 or 5/5 echo:
→ That level is being defended or attacked with intent
→ 80%+ chance the next real move comes from there
→ Trade the breakout or reversal at that exact fractal cluster
Works insane on:
• BTC / ETH (all timeframes)
• Nasdaq / SPX futures
• Forex majors (especially GBP & gold)
• 2025 small-cap rotation setups
100% Open Source • MPL 2.0 • Built by Timmy741 • December 2024
If you know about fractal echoes… you already know.
#fractal #mtf #echo #williamsfractal #multitimeframe #smartmoney #ict #smc #orderflow #convergence #timmy741 #snr #structure
Elite Correlation Matrix AIThe Elite Correlation Matrix AI indicator provides comprehensive real-time correlation analysis across multiple asset classes, displaying the interrelationships between equities, bonds, commodities, currencies, and volatility instruments.
The indicator calculates and displays correlation coefficients between a predefined set of major market indices and instruments, including:
• Major equity indices (SPY, QQQ, IWM)
• Long-term Treasury bonds (TLT)
• Gold (GLD)
• Crude oil (USO)
• Volatility (VIX)
• US Dollar Index (DXY)
• Bitcoin (BTCUSD)
Key features include:
• Rolling correlation calculations across user-defined periods to identify both short-term and longer-term relationships
• Visual correlation heat map showing the strength and direction of relationships between all tracked instruments
• Detection of correlation breakdowns, which often precede significant market regime shifts
• Dashboard display providing summary metrics of prevailing correlation patterns
The indicator enables users to monitor the current state of market relationships and identify when traditional correlations begin to break down, which frequently serves as an early warning of impending changes in market behavior. By tracking the degree of connectedness between different asset classes, the indicator provides insight into the current risk environment and the potential for diversification effectiveness.
This analysis is particularly valuable for understanding periods of market stress when asset relationships deviate from their normal patterns, as well as identifying environments where traditional correlations hold and where they are undergoing structural changes.
Trend Breakout & Ratchet Stop System [Market Filter]Description:
This strategy implements a robust trend-following system designed to capture momentum moves while strictly managing downside risk through a multi-stage "Ratchet" exit mechanism and broad market filters.
It is designed for swing traders who want to align individual stock entries with the overall market direction.
How it works:
1. Market Regime Filters (The "Safety Check") Before taking any position, the strategy checks the health of the broader market to avoid "catching falling knives."
Broad Market Filter: By default, it checks NASDAQ:QQQ (adjustable). If the benchmark is trading below its SMA 200, the strategy assumes a Bear Market and suppresses all new long entries.
Volatility Filter (VIX): Uses CBOE:VIX to gauge fear. If the VIX is above a specific threshold (Default: 32), entries are paused, and existing positions can optionally be closed to preserve capital.
2. Entry Logic Entries are based on Momentum and Trend confirmation. A position is opened if filters are clear AND one of the following occurs:
Golden Cross: SMA 25 crosses over SMA 50.
SMA Breakouts: A "Three-Bar-Break" logic confirms a breakout above the SMA 50, 100, or 200 (price must establish itself above the moving average).
3. The "Ratchet" Exit System The exit logic evolves as the trade progresses, tightening risk like a ratchet:
Stage 0 (Initial Risk): Starts with a standard percentage Stop Loss from the entry price.
Stage 1 (Breakeven/Lock): Once the price rises by Profit Step 1 (e.g., +10%), the Stop Loss jumps to a tighter level and locks there. This secures the initial move.
Stage 2 (Trailing Mode): If the price continues to rise to Profit Step 2 (e.g., +15%), the Stop Loss converts into a dynamic Trailing Stop relative to the Highest High. This allows the trade to run as long as the trend persists.
Additional Exits:
Dead Cross: Closes position if SMA 25 crosses under SMA 50.
VIX Panic: Emergency exit if volatility spikes above the threshold.
Settings & Customization:
SMAs: Adjustable lengths for all Moving Averages.
Filters: Toggle Market/VIX filters on/off and choose your benchmark ticker (e.g., SPY or QQQ).
Risk Management: Fully customizable percentages for the Ratchet steps (Initial SL, Stage 1 Trigger, Trailing distance).
Ratchet Exit Trend Strategy with VIX FilterThis strategy is a trend-following system designed specifically for volatile markets. Instead of focusing solely on the "perfect entry," this script emphasizes intelligent trade management using a custom **"Ratchet Exit System."**
Additionally, it integrates a volatility filter based on the CBOE Volatility Index (VIX) to minimize risk during extreme market phases.
### 🎯 The Concept: Ratchet Exit
The "Ratchet" system operates like a mechanical ratchet tool: the Stop Loss can only move in one direction (up, for long trades) and "locks" into specific stages. The goal is to give the trade "room to breathe" initially to avoid being stopped out by noise, then aggressively reduce risk as the trade moves into profit.
The exit logic moves through 3 distinct phases:
1. **Phase 0 (Initial Risk):** At the start of the trade, a wide Stop Loss is set (Default: 10%) to tolerate normal market volatility.
2. **Phase 1 (Risk Reduction):** Once the trade reaches a specific floating profit (Default: +10%), the Stop Loss is raised and "pinned" to a fixed value (Default: -8% from entry). This drastically reduces risk while keeping the trade alive.
3. **Phase 2 (Trailing Mode):** If the trend extends to a higher profit zone (Default: +15%), the Stop switches to a dynamic Trailing Mode. It follows the **Highest High** at a fixed percentage distance (Default: 8%).
### 🛡️ VIX Filter & Panic Exit
High volatility is often the enemy of trend-following strategies.
* **Entry Filter:** The system will not enter new positions if the VIX is above a user-defined threshold (Default: 32). This helps avoid entering "falling knife" markets.
* **Panic Exit:** If the VIX spikes above the threshold (32) while a trade is open, the position is closed immediately to protect capital (Emergency Exit).
### 📈 Entry Signals
The strategy trades **LONG only** and uses Simple Moving Averages (SMAs) to identify trends:
* **Golden Cross:** SMA 25 crosses over SMA 50.
* **3-Bar Breakouts:** A confirmation logic where the price must close above the SMA 50, 100, or 200 for 3 consecutive bars.
### ⚙️ Settings (Inputs)
All parameters are fully customizable via the settings menu:
* **SMAs:** Lengths for the trend indicators (Default: 25, 50, 100, 200).
* **VIX Filter:** Toggle the filter on/off and adjust the panic threshold.
* **Ratchet Settings:** Percentages for Initial Stop, Trigger Levels for Stages 1 & 2, and the Trailing Distance.
### ⚠️ Technical Note & Risk Warning
This script uses `request.security` to fetch VIX data. Please ensure you understand the risks associated with trading leveraged or volatile assets. Past performance is not indicative of future results.
SHOPPA trendBuy and Sell indicator based on golden cross and death cross. exit signals for LX (long exit) and SX (short exit)
Multi EMA (up to 6) - JamilThis indicator plots six customizable Exponential Moving Averages (EMA 1 to EMA 6) designed to help traders quickly identify market direction, trend strength, and dynamic support/resistance levels.
🔹 Key Features
Plots six EMAs simultaneously for multi-timeframe trend clarity
Helps detect trend reversals, pullbacks, and continuation setups
Ideal for scalping, intraday, swing trading, and funded challenges
Works on all markets (Gold, Forex, Crypto, Indices)
Customizable lengths and colors
Clean and lightweight — doesn’t affect chart performance
🔹 How to Use
When all EMAs are aligned and fanning out → Strong Trend
EMA compression → Low volatility / possible breakout setup
Price above all EMAs → Bullish zone
Price below all EMAs → Bearish zone
Perfect for traders who want a simple yet powerful trend-reading tool.
MA Crossover20 Ema
200 Day Crossover
Marks Death and Golden Cross
Useful for longterm time frames and finding trends.
Can be used for intraday scalping but advised to be used with price action and other indicators like Williams %R or VWAP.
PyraTime Harmonic 369Concept and Methodology PyraTime Harmonic 369 is a quantitative time-projection tool designed to apply Modular Arithmetic to market analysis. Unlike linear time indicators, this tool projects non-linear integer sequences derived from Digital Root Summation (Base-9 Reduction).
The core logic utilizes the mathematical progression of the 3-6-9 constants. By anchoring to a user-defined "Origin Pivot," the script projects three distinct harmonic triads to identify potential Temporal Confluence—moments where mathematical time cycles align with price action.
Technical Features This script focuses on the Standard Scalar (1x) projection of the Digital Root sequence:
The Root-3 Triad (Red): Projects intervals of 174, 285, 396. (Mathematical Sum: 1+7+4=12→3)
The Root-6 Triad (Green): Projects intervals of 417, 528, 639. (Mathematical Sum: 4+1+7=12→3, inverted)
The Root-9 Triad (Blue): Projects intervals of 741, 852, 963. (Mathematical Sum: 7+4+1=12→3... completion to 9)
How to Use
Set Anchor: Input the time of a significant High or Low in the settings.
Select Resolution: This tool is optimized for 1-minute (Micro-Harmonics) and 15-minute (Intraday Harmonics) charts.
Analyze Clusters: The vertical lines represent calculated harmonic intervals. Traders look for "Clusters" where a Root-3 and Root-9 cycle land on adjacent bars, indicating a high-probability pivot.
System Architecture & Version Comparison This script represents the foundational layer of the PyraTime ecosystem.
This Script (PyraTime Harmonic 369):
Scalar: Standard 1x Multiplier only.
Focus: Intraday & Micro-structure (1m, 15m).
Engine: Core Digital Root Integers.
PyraTime Harmonic Matrix (Advanced Edition):
Scalar Engine: Unlocks Quad-Fractal (4x), Tri-Fractal (3x), and Bi-Fractal (2x) multipliers for institutional cycle analysis.
Apex Logic: Auto-detection of the "963" Completion Sequence (Gold Highlight).
Event Horizon: Includes a live Predictive Dashboard that calculates the time-delta to the next harmonic event across all scalar groups.
Disclaimer This tool is for the educational analysis of Number Theory in financial markets. It projects time intervals and does not predict price direction. Past performance does not guarantee future results.
Smart Adaptive Double Patterns [The_lurker]Smart Adaptive Double Patterns
This is an advanced technical indicator that combines two of the strongest and most renowned classical price reversal patterns:
✅ Double Bottom Pattern — a bullish reversal pattern that forms after a downtrend
✅ Double Top Pattern — a bearish reversal pattern that forms after an uptrend
The indicator does not merely detect patterns — it provides a fully integrated, intelligent system that includes:
✅ Precise quality scoring for each pattern using 5 technical criteria
✅ Automatic price target calculation at three levels (Conservative, Balanced, Aggressive)
✅ Multi-layer dynamic filtering to avoid false signals
✅ Live pattern tracking from formation to target achievement or failure
✅ Comprehensive alert system covering all possible trading scenarios
🎯 Why Is This Indicator Unique?
1️⃣ High Detection Accuracy
Unlike traditional indicators that rely on simple rules, this one applies 5 strict structural conditions to confirm pattern validity:
A clear trend must precede the pattern
High symmetry between the two bottoms or two tops
No break of critical price levels during formation
Logical spacing between key points
Technical confirmation from ADX, ATR, and Volume
2️⃣ Advanced Quality Scoring System
Each pattern is scored out of 100 based on 5 weighted criteria:
Symmetry (30%): How closely the two bottoms or tops match
Trend Strength (20%): Strength of the prior trend
Volume Behavior (20%): Trading activity at critical points
Pattern Depth (15%): Vertical distance between neckline and bottom/top
Structural Integrity (15%): Full compliance with structural rules
3️⃣ Smart Target Management
Separate targets for bullish (Double Bottom) and bearish (Double Top) patterns
Separate projections for success and failure cases
Multiple options: Conservative (0.618) / Balanced (1.0) / Aggressive (1.618)
Live tracking with dynamic moving lines
4️⃣ Professional Failure Handling
Failed patterns are not ignored — they are turned into counter-trend opportunities:
Failed Double Bottom → triggers a bearish signal with downside targets
Failed Double Top → triggers a bullish signal with upside targets
Automatic color change for clear visual distinction
5️⃣ Full Customization Flexibility
Enable/disable each pattern independently
22+ adjustable settings
Unique colors for each pattern and quality level
Full bilingual support (Arabic / English)
📐 Pattern Details
🟦 Double Bottom Pattern
Sequence of points:
🔹 Point 1: Peak marking the start of a strong downtrend
🔹 Point 2 (Bottom 1): First low — first key bounce
🔹 Point 3: Intermediate high — forms the neckline (resistance)
🔹 Point 4 (Bottom 2): Second low — should closely match Bottom 1
🔹 Point 5: Breakout point — pattern confirmation
Mandatory Conditions:
✅ Clear downtrend before Point 2
✅ Bottoms 2 & 4 nearly identical (≤1.5% difference by default)
✅ Point 3 higher than both bottoms
✅ Neither bottom is broken during formation
✅ Sufficient time between points (≥10 candles by default)
✅ Success Scenario
→ Price breaks above the neckline (Point 3)
→ Point 5 is plotted at breakout candle
→ Dashed vertical line drawn from Point 5 to target
→ Horizontal dashed line tracks price toward target
→ Dashboard shows: Pattern Type | Quality | Rating | Target | Status
→ When target hits: line turns green + ✅ appears
🎯 Target Calculation
Pattern Height = Point 3 − Point 4
• Conservative: Point 3 + (Height × 0.618 × Quality Factor)
• Balanced: Point 3 + (Height × 1.0 × Quality Factor)
• Aggressive: Point 3 + (Height × 1.618 × Quality Factor)
❌ Failure Scenario
→ Price breaks below both Bottom 1 or Bottom 2 before neckline breakout
Visual Changes:
All lines turn red
Red ✖ appears at breakdown candle
Neckline stops expanding
Red dashed vertical line from breakdown point to bearish target
Red horizontal tracking line follows price
Dashboard updates to:
⚠ Failed Bottom – Bearish
→ Shows new bearish target
→ Indicates target mode for failure case
→ Status: Bearish Reversal
→ Fully red display
🟥 Double Top Pattern
Sequence of points:
🔹 Point 1: Trough marking the start of a strong uptrend
🔹 Point 2 (Top 1): First peak — first key resistance
🔹 Point 3: Intermediate low — forms the neckline (support)
🔹 Point 4 (Top 2): Second peak — should closely match Top 1
🔹 Point 5: Breakdown point — pattern confirmation
Mandatory Conditions:
✅ Clear uptrend before Point 2
✅ Tops 2 & 4 nearly identical (≤1.5% difference by default)
✅ Point 3 lower than both tops
✅ Neither top is breached during formation
✅ Sufficient time between points (≥10 candles by default)
✅ Success Scenario
→ Price breaks below the neckline (Point 3)
→ Point 5 is plotted at breakdown candle
→ Dashed vertical line drawn to target
→ Horizontal tracking line moves with price
→ Dashboard updates accordingly
→ Green line + ✅ on hit
🎯 Target Calculation
Pattern Height = Point 4 − Point 3
• Conservative: Point 3 − (Height × 0.618 × Quality Factor)
• Balanced: Point 3 − (Height × 1.0 × Quality Factor)
• Aggressive: Point 3 − (Height × 1.618 × Quality Factor)
❌ Failure Scenario
→ Price breaks above either Top 1 or Top 2 before neckline breakdown
Visual Changes:
All lines turn cyan (light blue)
Cyan ✖ appears at breakout candle
Neckline stops expanding
Cyan dashed vertical line to bullish target
Cyan horizontal tracking line follows price
Dashboard updates to:
⚠ Failed Top – Bullish
→ Shows new bullish target
→ Indicates target mode for failure case
→ Status: Bullish Reversal
→ Fully cyan display
🎯 Upside Target (after Double Top failure)
Max Top = max(Point 2, Point 4)
Height = Max Top − Point 3
• Conservative: Max Top + (Height × 0.618)
• Balanced: Max Top + (Height × 1.0)
• Aggressive: Max Top + (Height × 1.618)
📊 Quality Scoring System (0–100)
1️⃣ Symmetry (30%)
Measures price match between the two bottoms or two tops.
High score (25–30): Near-perfect symmetry → very strong pattern
Medium (15–24): Good match → reliable signal
Low (5–14): Weak symmetry → use caution
Zero: No symmetry → invalid pattern
2️⃣ Trend Strength (20%)
Uses ADX and DI indicators.
20 pts: Strong trend confirmed (e.g., ADX ≥ 20 + correct DI alignment)
10 pts: Trend filter disabled
6 pts: Weak or sideways trend
3️⃣ Volume Behavior (20%)
Declining volume on second touch is a positive sign (shows exhaustion).
15–20 pts: Clear volume drop → strong signal
10 pts: Neutral volume
6 pts: Rising volume → higher risk of continuation
4️⃣ Pattern Depth (15%)
Deeper patterns = stronger reversals.
12–15 pts: Deep → high reversal power
8–11 pts: Medium → acceptable
<8 pts: Shallow → weak signal
5️⃣ Structural Integrity (15%)
Checks logical structure (e.g., Point 1 > Point 3 in Double Bottom).
12–15 pts: Ideal structure
8–11 pts: Minor flaws
<8 pts: Poor setup
📈 Final Quality Rating & Colors
• 85–100 → ⭐ Excellent
→ Double Bottom: Cyan #00BCD4
→ Double Top: Light Red #FF5252
• 75–84 → ✨ Very Good
• 65–74 → ✓ Good
• 60–64 → ○ Acceptable
→ All use Amber #FFC107
• <60 → ❌ Rejected (not shown)
→ Gray #9E9E9E
🔧 Dynamic Filters
1️⃣ ATR Filter (Volatility Check)
Rejects patterns in abnormally high volatility periods.
→ If current ATR > 1.8 × 50-period ATR MA → pattern rejected
✅ Recommended for crypto, small caps
❌ Optional for calm markets (gold, bonds)
2️⃣ ADX Filter (Trend Confirmation)
Ensures a real trend exists before the pattern.
→ If ADX < 14 (70% of default 20) → pattern rejected
✅ Strongly recommended (keep ON)
3️⃣ Volume Filter (Behavior Validation)
Not used to reject patterns, but strongly affects quality score.
✅ Best for liquid markets (Forex majors, large stocks)
❌ Optional for illiquid assets
⚙️ Key Settings Explained
🔘 General Settings
• Language: Arabic / English
• Show Previous Patterns: Yes / No
→ “No” keeps chart clean; “Yes” for historical review
🔘 Pattern Selection
• Enable Double Bottom: ✅ / ❌
• Enable Double Top: ✅ / ❌
→ Use combinations:
✅✅ → Full reversal scanning
✅❌ → Long setups only
❌✅ → Short setups only
❌❌ → Indicator OFF
🔘 Detection Parameters
• Pivots Left (1–20): Higher = more reliable, fewer patterns
• Pivots Right (1–20): Lower = faster signals
• Min Width (5–100): Min candles between Bottom/Top 1 & 2
• Tolerance % (0.1%–5%): Max allowed price difference
• Min Arm (5–50): Min candles between pivot & neckline
• Min Trend (5–50): Min candles in prior trend
• Trend Lookback (50–500): How far back to detect trend start
• Extension Multiplier (1.0–5.0): How long to wait for breakout
🔘 Quality Settings
• Min Quality Score (0–100):
→ Conservative: 75–85
→ Balanced: 60–70
→ Flexible: 50–55
• Custom Weights: Adjust based on market (e.g., increase Volume weight in Forex)
🔘 Target Settings
• Bottom Bullish Target: Conservative / Balanced / Aggressive
• Bottom Bearish Target: (used on failure)
• Top Bearish Target: Conservative / Balanced / Aggressive
• Top Bullish Target: (used on failure)
🔘 Visual Settings
• Label Size: Small / Normal / Large / Huge
• Pattern Colors: Fully customizable
• Table: Show/Hide + Size (Small/Normal/Large) + Position (Top-Right / Top-Left / Bottom-Right / Bottom-Left)
• Fill Transparency: 70%–95% (default: 85%)
🔔 Alert System (8 Independent Alerts)
📌 Double Bottom Alerts
Bullish Breakout → “Double Bottom Breakout – Bullish!”
Bullish Target Hit → “Bullish Target Achieved!”
Failure (Bearish) → “Double Bottom Failed – Bearish!”
Bearish Target Hit → “Bearish Target Achieved (Failure)!”
📌 Double Top Alerts
Bearish Breakdown → “Double Top Breakdown – Bearish!”
Bearish Target Hit → “Bearish Target Achieved!”
Failure (Bullish) → “Double Top Failed – Bullish!”
Bullish Target Hit → “Bullish Target Achieved (Failure)!”
Each alert can be enabled/disabled independently and supports pop-ups, emails, or webhooks.
⚠️ Disclaimer:
This indicator is for educational and analytical purposes only. It does not constitute financial, investment, or trading advice. Use it in conjunction with your own strategy and risk management. Neither TradingView nor the developer is liable for any financial decisions or losses.
MTC – Multi-Timeframe Trend Confirmator V2MTC – Multi-Timeframe Trend Confirmator V2
A comprehensive trend analysis indicator that systematically combines six technical indicators across three customizable timeframes, using a weighted scoring system to identify high-probability trend conditions.
ORIGINALITY AND CONCEPT
This indicator is original in its approach to multi-timeframe trend confirmation. Rather than relying on a single indicator or timeframe, it creates a composite score by evaluating six different technical conditions simultaneously across three timeframes. The scoring system weighs certain indicators more heavily based on their reliability in trend identification. The visual gauge provides an at-a-glance view of trend alignment across timeframes, making it easier to identify when multiple timeframes agree - a condition that typically produces stronger, more reliable trends.
HOW IT WORKS - DETAILED SCORING METHODOLOGY
The indicator evaluates six technical conditions on each timeframe. Each condition contributes to a composite score:
EMA 200 (Weight: 1 point)
Bullish: Price closes above EMA 200 (+1)
Bearish: Price closes below EMA 200 (-1)
Rationale: Long-term trend direction
SMA 50/200 Crossover (Weight: 1 point)
Bullish: SMA 50 above SMA 200 (+1)
Bearish: SMA 50 below SMA 200 (-1)
Rationale: Golden/Death cross confirmation
RSI 14 (Weight: 1 point)
Bullish: RSI above 55 (+1)
Bearish: RSI below 45 (-1)
Neutral: RSI between 45-55 (0)
Rationale: Momentum filter with buffer zone to avoid chop
MACD (12,26,9) (Weight: 1 point)
Bullish: MACD line above signal line (+1)
Bearish: MACD line below signal line (-1)
Rationale: Trend momentum confirmation
ADX 14 (Weight: 2 points - DOUBLE WEIGHTED)
Requires ADX above 25 to activate
Bullish: DI+ above DI- and ADX > 25 (+2)
Bearish: DI- above DI+ and ADX > 25 (-2)
Neutral: ADX below 25 (0)
Rationale: Trend strength filter - only counts when a strong trend exists. Double weighted because ADX is specifically designed to measure trend strength, making it more reliable than oscillators.
Supertrend (Factor: 3.0, ATR Period: 10) (Weight: 2 points - DOUBLE WEIGHTED)
Bullish: Direction indicator = -1 (+2)
Bearish: Direction indicator = +1 (-2)
Rationale: Dynamic support/resistance that adapts to volatility. Double weighted because Supertrend provides clear, objective trend signals with built-in stop-loss levels.
COMPOSITE SCORE CALCULATION:
Total possible score range: -10 to +10 points
Score interpretation:
Score > 2: UPTREND (majority of indicators bullish, especially weighted ones)
Score < -2: DOWNTREND (majority of indicators bearish, especially weighted ones)
Score between -2 and +2: NEUTRAL/RANGING (mixed signals or weak trend)
The threshold of +/- 2 was chosen because it requires more than just basic agreement - it typically means at least 3-4 indicators align, or that the heavily-weighted indicators (ADX, Supertrend) confirm the direction.
MULTI-TIMEFRAME LOGIC:
The indicator calculates the composite score independently for three timeframes:
Higher Timeframe (default: 4H) - Major trend direction
Mid Timeframe (default: 1H) - Intermediate trend
Lower Timeframe (default: 15min) - Entry timing
Main Trend Confirmation Rule:
The indicator only signals a confirmed trend when BOTH the higher timeframe AND mid timeframe scores agree (both > 2 for uptrend, or both < -2 for downtrend). This dual-timeframe confirmation significantly reduces false signals during choppy or ranging markets.
HOW TO USE IT
Setup:
Add indicator to chart
Customize timeframes based on your trading style:
Scalpers: 15min, 5min, 1min
Day traders: 4H, 1H, 15min (default)
Swing traders: Daily, 4H, 1H
Toggle individual indicators on/off based on your preference
Adjust Supertrend parameters if needed for your instrument's volatility
Reading the Gauge (Top Right Corner):
Each row shows one timeframe
Left column: Timeframe label
Middle column: Visual strength bars (10 bars = maximum score)
Green bars = Bullish score
Red bars = Bearish score
Yellow bars = Neutral/ranging
More filled bars = stronger trend
Right column: Numerical score
Trading Signals:
Entry Signals:
Long Entry: Wait for upward triangle arrow (appears when higher + mid TF both bullish)
Confirm gauge shows green bars on higher and mid timeframes
Lower timeframe should ideally turn green for entry timing
Chart background tints light green
Short Entry: Wait for downward triangle arrow (appears when higher + mid TF both bearish)
Confirm gauge shows red bars on higher and mid timeframes
Lower timeframe should ideally turn red for entry timing
Chart background tints light red
Position Management:
Stay in position while higher and mid timeframes remain aligned
Consider reducing position size when mid timeframe score weakens
Exit when higher timeframe trend reverses (daily label changes)
Avoiding False Signals:
Ignore signals when gauge shows mixed colors across timeframes
Avoid trading when scores are close to threshold (+/- 2 to +/- 4 range)
Best trades occur when all three timeframes align (all green or all red in gauge)
Use the numerical scores: higher absolute values (7-10) indicate stronger, more reliable trends
Practical Examples:
Example 1 - Strong Uptrend Entry:
Higher TF: +8 (strong green bars)
Mid TF: +6 (strong green bars)
Lower TF: +4 (moderate green bars)
Action: Look for long entries on lower timeframe pullbacks
Background is tinted green, upward arrow appears
Example 2 - Ranging Market (Avoid):
Higher TF: +3 (weak green)
Mid TF: -1 (weak red)
Lower TF: +2 (neutral yellow)
Action: Stay out, wait for alignment
Example 3 - Trend Reversal Warning:
Higher TF: +7 (still green)
Mid TF: -3 (turned red)
Lower TF: -5 (strong red)
Action: Consider exiting longs, prepare for potential higher TF reversal
Customization Options:
Timeframes: Adjust all three to match your trading horizon
Indicator Toggles: Disable indicators that don't suit your instrument:
Disable RSI for highly volatile crypto markets
Disable SMA crossover for range-bound instruments
Keep ADX and Supertrend enabled for trending markets
Visual Preferences:
Arrow size: 5 options from Tiny to Huge
Gauge size: Small/Medium/Large for different screen sizes
Toggle arrows on/off if you only want the gauge
Alert Setup:
Right-click chart, "Add Alert"
Condition: MTC v6 - UPTREND or DOWNTREND
Get notified when multi-timeframe confirmation occurs
Best Practices:
Use with Price Action: The indicator works best when combined with support/resistance levels, chart patterns, and volume analysis
Risk Management: Even with multi-timeframe confirmation, always use stop losses
Market Context: Works best in trending markets; less reliable in strong consolidation
Backtesting: Test the default settings on your specific instrument and timeframe before live trading
Patience: Wait for full multi-timeframe alignment rather than taking premature signals
Technical Notes:
All calculations use Pine Script's security function to fetch data from multiple timeframes
Prevents repainting by using confirmed bar data
Gauge updates in real-time on the last bar
Daily labels mark at the open of each new daily candle
Works on all instruments and timeframes
This indicator is ideal for traders who want objective, systematic trend identification without the complexity of analyzing multiple indicators manually across different timeframes.
-NATANTIA
Breakout with Alma & Slope - for high volatility playSometimes best not to overthink,
buy at line crosses ;)
NFA, DYOR
best for 15m-1Hr, high volatility FX,Gold etc
Long only when 3 conditions met:-
- Fast Alma crosses Slow Alma
-Angle Pointing UP
-ADX above 20
Short when
- aqua line below navy line
- navy line pointing down
- adx >20
EXIT
- Trailing Stop: The trade closes automatically if price hits the **Red Stepped Line** (this is your safety net that follows the price).
- Emergency Exit:** The trade closes immediately if the ALMA lines cross back in the opposite direction (Reversal).
Risk On/Risk Off by Gary# Risk On/Risk Off Indicator (RORO)
## Overview
The Risk On/Risk Off (RORO) Indicator is a comprehensive market sentiment gauge that measures the balance between risk-seeking and risk-averse behavior across multiple asset classes. This indicator helps traders identify shifts in market sentiment and potential trend changes.
## How It Works
The RORO indicator aggregates normalized price movements (Z-scores) from eight major asset classes:
**Risk-On Assets (Bullish Sentiment):**
- Bitcoin Futures (BTC1!) - Cryptocurrency risk appetite
- WTI Crude Oil Futures (CL1!) - Energy sector strength
- AUD/JPY Exchange Rate - Carry trade indicator
- Emerging Markets ETF (EEM) - Global growth proxy
**Risk-Off Assets (Defensive Sentiment):**
- Gold Futures (GC1!) - Safe haven demand
- 10-Year Treasury Bonds (ZN1!) - Flight to quality
- US Dollar Index (DXY) - Reserve currency strength
- VIX Index - Market fear gauge (inverted)
## Key Features
- **Z-Score Normalization**: Standardizes different asset classes for fair comparison
- **Customizable Weights**: Adjust the influence of each asset class
- **Dynamic Coloring**: Green indicates rising risk appetite, red shows declining risk appetite
## Interpretation
- **Rising RORO (Green)**: Increasing risk appetite - favorable for equities, commodities, and growth assets
- **Falling RORO (Red)**: Decreasing risk appetite - rotation into safe havens
- **Divergences**: When RORO and price move in opposite directions, potential reversal signal
## Use Cases
1. **Market Regime Identification**: Determine current risk environment
2. **Divergence Trading**: Spot when price action contradicts underlying sentiment
3. **Portfolio Management**: Time defensive vs. aggressive positioning
4. **Confirmation Tool**: Validate breakouts and trend changes
## Settings
- **Lookback Period**: Controls Z-score calculation sensitivity (default: 50)
- **Asset Weights**: Fine-tune the contribution of each asset class
- **Color Scheme**: Customize rising/falling colors
## Best Practices
- Use on daily or higher timeframes for most reliable signals
- Combine with price action and volume analysis
- Watch for sustained moves rather than single-bar changes
---
*This indicator is designed for educational purposes. Always conduct your own analysis and risk management.*
DarkPool's MacD DarkPool's MacD is an enhanced version of the classic Moving Average Convergence Divergence oscillator, engineered for modern traders who require more than just price data. While standard MACD indicators only measure price momentum, this tool integrates a Volume Weighting engine. This means the histogram bars expand not just based on price spread, but also based on the relative volume behind the move.
Additionally, the indicator features "True Multi-Timeframe (MTF)" capabilities, allowing you to view higher-timeframe momentum (e.g., Hourly or Daily) while trading on lower timeframes, alongside a 4-stage "Heatmap" color scheme to instantly visualize trend strength and exhaustion.
Key Features
Volume-Weighted Histogram: When enabled, histogram bars are multiplied by Relative Volume (RVOL). A large bar indicates strong price momentum backed by institutional volume, while a small bar suggests weak participation.
Vibrant Heatmap: A unique 4-color coding system that differentiates between "Strong Impulse" and "Fading Momentum" for both bullish and bearish trends.
True MTF: Overlay higher timeframe MACD data onto your current chart to align with the macro trend.
Visual Triggers: Automatically plots dots on crossovers and highlights the chart background to signal potential entry points.
How to Use
1. The Volume-Weighted Histogram The histogram is the heartbeat of this indicator.
Standard Mode: Shows the distance between the MACD and Signal lines.
Volume Mode (Default): If a move has high volume, the histogram bar grows significantly larger. If the price is moving but volume is low, the bar remains small. This helps filter out "fakeouts" where price moves without participation.
2. Reading the Heatmap (Colors) The "Vibrant Heatmap" theme uses specific colors to tell a story:
Cyan (Bright Blue): Strong Bullish Momentum. Buyers are in control.
Dodger Blue (Darker): Bullish but weakening. The trend is still up, but momentum is fading.
Pink/Red: Strong Bearish Momentum. Sellers are in control.
Gold/Amber: Bearish but weakening. The trend is still down, but selling pressure is drying up (potential reversal warning).
3. Crossover Signals
Bullish Cross: A bright circle appears on the line, and the background flashes Green. This occurs when the MACD crosses above the Signal line.
Bearish Cross: A bright circle appears on the line, and the background flashes Red. This occurs when the MACD crosses below the Signal line.
4. Multi-Timeframe Strategy Use the "Manual Timeframe" input to lock the MACD to a higher trend.
Example: If you trade on the 5-minute chart, set the indicator to "60" (1 Hour). You will now see the 1-Hour momentum displayed on your 5-minute chart, helping you avoid trading against the major trend.
Configuration Settings
Calculations
Fast/Slow Length: Standard MACD settings (Default: 12, 26).
Signal Smoothing: The length of the signal line (Default: 9).
Timeframe Settings
Use Current Chart: Uncheck this to enable the "Manual Timeframe" dropdown for MTF analysis.
Volume & Logic
Scale Histogram by Real Volume: The most important setting. Keep this checked to see the "force" behind the move. Uncheck it for a classic MACD look.
Styling
Color Theme:
Vibrant Heatmap: The default 4-stage color system.
Institutional: A grayscale/monochrome look for professional, distraction-free charts.
Dark Mode Safe: High contrast colours suitable for dark backgrounds.
Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of future results.
Multivariate Kalman Filter🙏🏻 I see no1 ever posted an open source Multivariate Kalman filter on TV, so here it is, for you. Tested and mathematically correct implementation, with numerical safeties in place that do not affect the final results at all. That’s the main purpose of this drop, just to make the tool available here. Linear algebra everywhere, Neo would approve 4 sure.
...
Personally I haven't found any real use case of it for myself, aside from a very specific one I will explain later, but others usually do…
Almost every1 in the quant industry who uses Kalman is in fact misusing it, because by its real definition, it should be applied to Not the exact known values (e.g as real ticks provided by transparent audited regulated exchange), but “measurements” of those ‘with errors’.
If your measurements don’t have errors or you have real precise data, by its internal formulas Kalman will output the exact inputs. So most who use it come up with some imaginary errors of sorts, like from some kind of imaginary fair price etc. The important easy to miss point, the errors of your measurements have to be symmetric around its mean ‘ at least ’, if errors are biased, Kalman won’t provide.
For most tasks there are better tools, including other state space models , but still Multivariate Kalman is a very powerful instrument, you can make it do all kinds of stuff. Also as a state space model it can also provide confidence & prediction intervals without explicit calculations of dem.
...
In this script I included 2 example use cases, the first one is the classic tho perfectly working misuse, the second one is what I do with it:
One
Naive, estimates “hidden” adaptive moving regression endpoint. The result you can see on the chart above. You can imagine that your real datapoints are in fact non perfect measures of some hidden state, and by defining measurement noise and process noise, and by constructing the input matrixes in certain ways, you can express what that state should be.
Two
Upscaling tick lattice, aka modelling prices as if native tick size would’ve been lower. Kinda very specific task, mostly needed in HFT or just for analytical purposes. Some like ZN have huge tick sizes, they are traded a lot but barely do more than 20 ticks range in a session. The idea is to model raw data as AR2 process , learn the phi1 and phi2, make one point forecasts based on dem, and the process noise would be the variance of errors from these forecasts. The measurement noise here is legit, it’s quantization noise based on tick size, no need in olympic gold in mental gymnastics xd
^^ artificially upscaling ZN futures tick lattice
...
I really made it available there so You guys can take it and some crazy ish with it, just let state space models abduct your conciseness and never look back
∞
Advanced FVG Detector Pro📊 Advanced FVG Detector Pro - Smart Money Analysis Tool
Overview
The Advanced FVG Detector Pro is a sophisticated Pine Script v6 indicator designed to identify and track Fair Value Gaps (FVGs) with institutional-grade precision. This tool goes beyond basic gap detection by incorporating volume analysis, smart money scoring, and adaptive filtering to help traders identify high-probability trading opportunities.
What are Fair Value Gaps?
Fair Value Gaps (FVGs) are price inefficiencies that occur when the market moves so quickly that it leaves behind an imbalance or "gap" in price action. These gaps often act as magnets for future price movement as the market seeks to fill these inefficiencies. Professional traders and institutions closely monitor FVGs as they represent areas of potential support, resistance, and high-probability trade setups.
🎯 Key Features
1. Smart Money Scoring System
Proprietary algorithm that rates each FVG on a 0-100 scale Combines gap size, volume strength, price location, and trend alignment Filter out low-quality setups by setting minimum score thresholdsFocus on institutional-grade opportunities with scores above 70
2. Advanced Volume Validation
Validates FVGs with volume analysis to reduce false signals Only displays gaps formed during significant volume periods Customizable volume multiplier for different market conditions
Visual volume strength indicators on chart
3. Flexible Mitigation Options
Full Fill: Traditional complete gap closure Midpoint Touch: More aggressive entry strategy
Partial Fill: Customizable percentage-based mitigation (10-90%) Choose the strategy that matches your trading style
4. ATR-Based Adaptive Filtering
Automatically adjusts to market volatility using Average True Range Works consistently across any instrument, timeframe, or volatility regime No manual recalibration needed when switching markets Filters out noise while capturing meaningful gaps
5. Real-Time Statistics Dashboard
Live tracking of total active FVGs Bullish vs Bearish gap count Mitigation rate percentage
Average Smart Money Score Toggle on/off based on preference
6. Professional Visual Design
Clean, customizable color schemes Optional midline display for precise entry planning
Labels showing gap type, score, and volume strength Automatic extension of active gaps
Mitigated gaps change color for easy identification
📈 How to Use
For Day Traders:
Use 5-15 minute timeframes
Set ATR Multiplier to 0.15-0.25
Enable volume validation
Focus on FVGs with scores above 65
For Swing Traders:
Use 1H-4H timeframes
Set ATR Multiplier to 0.5-1.0
Use "Midpoint Touch" mitigation
Focus on FVGs with scores above 70
For Position Traders:
Use Daily timeframe
Set ATR Multiplier to 0.75-1.5
Use "Full Fill" mitigation
Focus on FVGs with scores above 75
🔧 Customization Options
Detection Settings:
Minimum FVG size percentage filter
ATR-based size filtering
Maximum number of gaps to display
Smart Money Score minimum threshold
Volume Analysis:
Volume validation toggle
Volume multiplier adjustment
Volume moving average period
Visual volume strength background
Mitigation Control:
Choose mitigation type (Full/Midpoint/Partial)
Set partial fill percentage
Auto-remove mitigated gaps
Control how long mitigated gaps remain visible
Visual Customization:
Bullish/Bearish/Mitigated colors
Show/hide midlines
Show/hide labels
Box extension length
Statistics dashboard toggle
🎓 Trading Strategy Ideas
1. FVG Retest Strategy
Wait for price to create a high-score FVG (70+)
Enter on the first retest of the gap
Place stop loss beyond the gap
Target the opposite side of the gap or next FVG
2. Confluence Trading
Combine FVGs with support/resistance levels
Look for FVGs near key moving averages (20/50 EMA)
Higher probability when FVG aligns with trendlines
Use multiple timeframe analysis
3. Breakout Confirmation
FVGs often form during strong breakouts
High-volume FVGs confirm breakout strength
Enter on mitigation of breakout FVG
Trail stops as new FVGs form in trend direction
⚡ Performance Optimizations
Efficient memory management for smooth chart performance
Optimized calculations run only once per bar
Smart array management prevents memory leaks
Works smoothly even with 100+ active FVGs
🔔 Alert System
Customizable alerts for new bullish FVGs
Customizable alerts for new bearish FVGs
Mitigation alerts for active gaps
Frequency control to avoid alert spam
💡 Pro Tips
Multi-Timeframe Approach: Identify major FVGs on higher timeframes (Daily/4H) and use lower timeframes (15M/5M) for precise entries
Volume Confirmation: The highest probability setups occur when FVGs form with 2x+ average volume
Trend Alignment: Trade FVGs in the direction of the major trend for best results
Patience Pays: Wait for price to return to the FVG rather than chasing breakouts
Risk Management: Always use stop losses beyond the FVG boundaries
📚 Educational Value
This indicator is perfect for:
Learning to identify institutional order flow
Understanding market microstructure
Developing price action trading skills
Recognizing supply and demand imbalances
Improving entry and exit timing
⚠️ Disclaimer
This indicator is a tool for technical analysis and should not be used as the sole basis for trading decisions. Always combine with proper risk management, fundamental analysis, and your own trading plan. Past performance does not guarantee future results.
🔄 Updates & Support
Regular updates will include:
Additional filtering options
Enhanced multi-timeframe analysis
More customization features
Performance improvements
📊 Best Pairs/Markets
Works excellently on:
Forex pairs (EUR/USD, GBP/USD, etc.)
Cryptocurrency (BTC, ETH, etc.)
Stock indices (SPX, NQ, etc.)
Individual stocks
Commodities (Gold, Oil, etc.)
Version Information
Version: 1.0
Pine Script: Version 6
Type: Overlay Indicator
Max Boxes: 500
Max Lines: 500
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Multi-Timeframe EMA & SMA Scanner - Price Level LabelsOverview
A powerful multi-timeframe moving average scanner that displays EMA and SMA levels from up to 8 different timeframes simultaneously on your chart. Perfect for identifying key support/resistance levels, confluence zones, and multi-timeframe trend analysis.
Key Features
📊 Multi-Timeframe Analysis
Monitor up to 8 different timeframes simultaneously (5m, 10m, 15m, 30m, 1H, 4H, 1D, 1W)
Each timeframe can be independently enabled/disabled
Fully customizable timeframe selection
📈 Comprehensive Moving Averages
5 configurable EMA periods (default: 8, 21, 50, 100, 200)
2 configurable SMA periods (default: 200, 400)
All periods are fully customizable to match your trading strategy
🎯 Smart Price Level Labels
Labels positioned at actual price levels (not in a list)
Color-coded labels for easy identification
Dynamic text color: Green when price is above, Red when below
Compact notation: E8-5m means EMA 8 on 5-minute timeframe
Adjustable label offset from current price
📉 Optional Horizontal Lines
Dotted reference lines at each MA level
Color-matched to corresponding MA type
Can be toggled on/off independently
📋 Comprehensive Data Table
Shows all MA values organized by timeframe
Displays percentage distance from current price
Trend indicator (Strong Up/Up/Neutral/Down/Strong Down)
EMA alignment status (Bullish/Bearish/Mixed)
Color-coded cells for quick visual analysis
🎨 Full Customization
Individual color settings for each MA type
Adjustable table size (Tiny/Small/Normal/Large)
Choose table position (Left/Right)
Toggle any MA or timeframe on/off
🔔 Built-in Alerts
Golden Cross detection (EMA 50 crosses above EMA 200)
Death Cross detection (EMA 50 crosses below EMA 200)
Price crossing major EMAs
Available for multiple timeframes
How to Use
For Day Traders:
Enable lower timeframes (5m, 10m, 15m, 30m)
Focus on faster EMAs (8, 21, 50)
Watch for confluence zones where multiple timeframe MAs cluster
For Swing Traders:
Enable higher timeframes (1H, 4H, 1D)
Use all EMAs plus SMAs for broader perspective
Look for alignment across timeframes for high-probability setups
For Position Traders:
Focus on daily and weekly timeframes
Emphasize 100, 200 EMAs and 200, 400 SMAs
Use for long-term trend confirmation
Understanding the Labels
Label Format: E8-5m 45250.50
E8 = EMA with period 8
5m = 5-minute timeframe
45250.50 = Current price level
Green text = Price is currently above this level (potential support)
Red text = Price is currently below this level (potential resistance)
For SMAs: S200-1D 44500.00
S200 = SMA with period 200
1D = Daily timeframe
Trading Applications
Support/Resistance Identification
MAs act as dynamic support and resistance levels
Multiple timeframe MAs create stronger zones
Confluence Trading
When multiple MAs from different timeframes cluster together, it creates high-probability zones
These areas often result in strong reactions
Trend Analysis
Check the Alignment column: Bullish alignment = all EMAs in ascending order
Trend column shows overall price position relative to all MAs
Entry/Exit Timing
Use lower timeframe MAs for precise entries
Use higher timeframe MAs for trend direction and exits
Settings Guide
Timeframes Section:
Select and enable/disable up to 8 timeframes
Default: 5m, 10m, 15m, 30m, 1H, 4H, 1D, 1W
MA Periods Section:
Customize all EMA and SMA periods
Default EMAs: 8, 21, 50, 100, 200
Default SMAs: 200, 400
Display Section:
Toggle price labels and horizontal lines
Adjust label offset (distance from right edge)
Show/hide data table
Choose table position and size
Colors Section:
Customize colors for each MA type
Each MA has independent color control
Pro Tips
✅ Start with default settings and adjust based on your trading style
✅ Disable timeframes/MAs you don't use to reduce chart clutter
✅ Use the data table for quick overview, labels for precise levels
✅ Look for "confluence clusters" where multiple MAs from different timeframes align
✅ Green labels = potential support, Red labels = potential resistance
✅ Set alerts on key crossovers for automated notifications
Technical Specifications
Pine Script v6
Overlay indicator (displays on main chart)
Maximum 500 labels supported
Real-time updates on each bar close
Compatible with all instruments and timeframes
Perfect For:
Day traders seeking multi-timeframe confirmation
Swing traders looking for high-probability setups
Position traders monitoring long-term trends
Anyone using moving averages as part of their strategy
Note: This indicator does not provide buy/sell signals. It's a tool for analysis and should be used in conjunction with your trading strategy and risk management rules.
Multi MAThis TradingView indicator displays four customizable moving averages on your price chart: two Exponential Moving Averages (EMAs) and two Simple Moving Averages (SMAs).
The default settings show a 10-period EMA (aqua), 21-period EMA (orange), 50-period SMA (green), and 200-period SMA (red), which are commonly used timeframes for trend analysis.
Each moving average can be individually toggled on or off, and their lengths and colors are fully adjustable through the indicator settings.
The EMAs react more quickly to price changes while the SMAs provide smoother, more gradual trend indicators, making this useful for identifying support/resistance levels and trend direction.
Traders often watch for crossovers between these moving averages as potential entry or exit signals, with the 50/200 SMA cross being particularly significant as the "golden cross" or "death cross."
AP Capital – Volatility + High/Low Projection v1.1📌 AP Capital – Volatility + High/Low Projection v1.1
Predictive Daily Volatility • Session Logic • High/Low Projection Indicator
This indicator is designed to help traders visually understand daily volatility conditions, identify session-based turning points, and anticipate potential highs and lows of the day using statistical behavior observed across thousands of bars of intraday data.
It combines intraday session structure, volatility regime classification, and context from the previous day’s expansion to highlight high-probability areas where the market may set its daily high or daily low.
🔍 What This Indicator Does
1. Volatility Regime Detection
Each day is classified into:
🔴 High Volatility (trend continuation & expansion likely)
🟡 Normal Volatility
🔵 Low Volatility (chop, false breaks, mean-reversion common)
The background color automatically adapts so you always know what environment you're trading in.
2. Session-Based High/Low Identification
Different global sessions tend to create different market behaviors:
Asia session frequently sets the LOW of day
New York & Late US sessions frequently set the HIGH of day
This indicator uses those probabilities to highlight potential turning points.
3. Potential High / Low of Day Projections
The script plots:
🟢 Potential LOW of Day
🔴 Potential HIGH of Day
These appear only when:
Price hits the session-statistical turning zone
Volatility conditions match
Yesterday’s expansion or compression context agrees
This keeps signals clean and prevents over-marking.
4. Clean Visuals
Instead of cluttering the chart, highs and lows are marked only when conditions align, making this tool ideal for:
Session scalpers
Day traders
Gold / NAS100 / FX intraday traders
High-probability reversal traders
🧠 How It Works
The engine combines:
Daily range vs 20-day average
Real-time intraday high/low formation
Session-specific probability weighting
Previous day expansion and volatility filters
This results in highly reliable signals for:
Fade trades
Reversal setups
Timing entries more accurately
✔️ Best Uses
Identifying where the day’s range is likely to complete
Avoiding trades during low-volatility compression days
Detecting where the market is likely to turn during major sessions
Using potential HIGH/LOW levels as take-profit zones
Enhancing breakout or reversal strategies
⚠️ Disclaimer
This indicator does not repaint, but it is not a standalone entry tool.
It is designed to provide context, session awareness, and volatility-driven turning points to assist your existing strategy.
Always combine with sound risk management.
Debt-Cycle vs Bitcoin-CycleDebt-Cycle vs Bitcoin-Cycle Indicator
The Debt-Cycle vs Bitcoin-Cycle indicator is a macro-economic analysis tool that compares traditional financial market cycles (debt/credit cycles) against Bitcoin market cycles. It uses Z-score normalization to track the relative positioning of global financial conditions versus cryptocurrency market sentiment, helping identify potential turning points and divergences between traditional finance and digital assets.
Key Features
Dual-Cycle Analysis: Simultaneously tracks traditional financial cycles and Bitcoin-specific cycles
Z-Score Normalization: Standardizes diverse data sources for meaningful comparison
Multi-Asset Coverage: Analyzes currencies, commodities, bonds, monetary aggregates, and on-chain metrics
Divergence Detection: Identifies when Bitcoin cycles move independently from traditional finance
21-Day Timeframe: Optimized for Long-term cycle analysis
What It Measures
Finance-Cycle (White Line)
Tracks traditional financial market health through:
Currencies: USD strength (DXY), global currency weights (USDWCU, EURWCU)
Commodities: Oil, gold, natural gas, agricultural products, and Bitcoin price
Corporate Bonds: Investment-grade spreads, high-yield spreads, credit conditions
Monetary Aggregates: M2 money supply, foreign exchange reserves (weighted by currency)
Treasury Bonds: Yield curve (2Y/10Y, 3M/10Y), term premiums, long-term rates
Bitcoin-Cycle (Orange Line)
Tracks Bitcoin market positioning through:
On-Chain Metrics:
MVRV Ratio (Market Value to Realized Value)
NUPL (Net Unrealized Profit/Loss)
Profit/Loss Address Distribution
Technical Indicators:
Bitcoin price Z-score
Moving average deviation
Relative Strength:
ETH/BTC ratio (altcoin strength indicator)
Visual Elements
White Line: Finance-Cycle indicator (positive = expansionary conditions, negative = contractionary)
Orange Line: Bitcoin-Cycle indicator (positive = bullish positioning, negative = bearish)
Zero Line: Neutral reference point
Interpretation
Cycle Alignment
Both positive: Risk-on environment, favorable for crypto
Both negative: Risk-off environment, caution warranted
Divergence: Potential opportunities or warning signals
Divergence Signals
Finance positive, Bitcoin negative: Bitcoin may be undervalued relative to macro conditions
Finance negative, Bitcoin positive: Bitcoin may be overextended or decoupling from traditional finance
Important Limitations
This indicator uses some technical and macro data but still has significant gaps:
⚠️ Limited monetary data - missing:
Funding rates (repo, overnight markets)
Comprehensive bond spread analysis
Collateral velocity and quality metrics
Central bank balance sheet details
⚠️ Basic economic coverage - missing:
GDP growth rates
Inflation expectations
Employment data
Manufacturing indices
Consumer confidence
⚠️ Simplified on-chain analysis - missing:
Exchange flow data
Whale wallet movements
Mining difficulty adjustments
Hash rate trends
Network fee dynamics
⚠️ No sentiment data - missing:
Fear & Greed Index
Options positioning
Futures open interest
Social media sentiment
The indicator provides a high-level cycle comparison but should be combined with comprehensive fundamental analysis, detailed on-chain research, and proper risk management.
Settings
Offset: Adjust the horizontal positioning of the indicators (default: 0)
Timeframe: Fixed at 21 days for optimal cycle detection
Use Cases
Macro-crypto correlation analysis: Understand when Bitcoin moves with or against traditional markets
Cycle timing: Identify potential tops and bottoms in both cycles
Risk assessment: Gauge overall market conditions across asset classes
Divergence trading: Spot opportunities when cycles diverge significantly
Portfolio allocation: Balance traditional and crypto assets based on cycle positioning
Technical Notes
Uses Z-score normalization with varying lookback periods (40-60 bars)
Applies HMA (Hull Moving Average) smoothing to reduce noise
Asymmetric multipliers for upside/downside movements in certain metrics
Requires access to FRED economic data, Glassnode, CoinMetrics, and IntoTheBlock feeds
21-day timeframe optimized for cycle analysis
Strategy Applications
This indicator is particularly useful for:
Cross-asset allocation - Decide between traditional finance and crypto exposure
Cycle positioning - Identify where we are in credit/debt cycles vs. Bitcoin cycles
Regime changes - Detect shifts in market leadership and correlation patterns
Risk management - Reduce exposure when both cycles turn negative
Disclaimer: This indicator is a cycle analysis tool and should not be used as the sole basis for investment decisions. It has limited coverage of monetary conditions, economic fundamentals, and on-chain metrics. The indicator provides directional insight but cannot predict exact timing or magnitude of market moves. Always conduct thorough research, consider multiple data sources, and maintain proper risk management in all investment decisions.
Technology Stocks RSPSTechnology Stocks RSPS Indicator - TradingView Description
Overview
The Technology Stocks RSPS (Relative Strength Portfolio System) indicator is a sophisticated portfolio allocation tool designed specifically for technology sector stocks. It calculates relative strength positions and provides dynamic allocation recommendations based on technical price momentum analysis.
Key Features
- Relative Strength Analysis: Compares 15 major technology stocks and the XLK sector ETF
against each other and gold as a baseline
- Dynamic Portfolio Allocation: Automatically calculates optimal position sizes based on relative
performance
- Visual Portfolio Performance: Tracks cumulative portfolio returns with color-coded
performance indicators
- Customizable Table Display: Shows real-time allocation percentages and optional cash values
for each position
- Technical Momentum Filtering: Uses normalized indicators to identify strength and filter out
weak positions
Included Assets
Sector ETF: XLK
Major Tech Stocks: AAPL, MSFT, NVDA, AVGO, CRM, ORCL, CSCO, ADBE, ACN, AMD, IBM, INTC, NOW, TXN
Benchmark: Gold (TVC:GOLD)
How It Works
The indicator calculates a relative strength score for each asset by comparing it against:
Gold (baseline commodity)
All other technology stocks in the pool
The XLK sector ETF
Assets with positive relative strength receive portfolio allocations proportional to their strength scores. Weak or negative performers are automatically filtered out (allocated 0%).
Visual Elements
Red Area: Aggregate strength of major technology stocks
Navy Blue Area: Overall technical positioning index (TPI)
Performance Line: Cumulative portfolio return (blue = cash-heavy, red = equity-heavy)
Allocation Table: Bottom-left display showing current recommended positions
Important Limitations
This indicator primarily uses technical data and has significant limitations:
❌ No fundamental economic data (ISM, CLI, etc.)
❌ Limited monetary data - missing critical components:
comprehensive monetary data
Funding rates
Detailed bond spreads analysis
Collateral data
❌ No sentiment indicators
❌ No options flow or derivatives data
❌ No earnings or valuation metrics
The indicator focuses purely on price-based relative strength and technical momentum. Users should combine this tool with fundamental analysis, economic data, and proper risk management for complete investment decisions.
Settings
Plot Table: Toggle allocation table visibility
Use Cash: Enable to display dollar amounts based on portfolio size
Cash Amount: Set your total portfolio value for cash allocation calculations
Use Cases
Sector rotation within technology stocks
Relative strength-based portfolio rebalancing
Technical momentum screening for tech sector
Dynamic position sizing based on price trends
Technical Notes
The script avoids for-loops to reduce calculation errors and noise
Uses semi-individual calculations for each asset
Requires the Unicorpus/NormalizedIndicators/1 library for normalized momentum calculations
Maximum lookback: 100 bars
Disclaimer: This indicator is a technical tool only and should not be used as the sole basis for investment decisions. It does not incorporate fundamental, economic, or comprehensive monetary data. Always conduct thorough research and consider your risk tolerance before making investment decisions.
