CDVI – First Crypto Dominance Volatility Index by Armi GoldmanThe Crypto Dominance Volatility Index (CDVI) is the first volatility-based indicator designed specifically to analyze the stability and instability of dominance flows in the crypto market.
Instead of measuring price volatility, CDVI focuses on the volatility of market dominance itself — a structural driver behind capital rotation cycles such as Bitcoin Season, Altseason, accumulation zones, and macro cycle transitions.
CDVI transforms dominance changes into a clear volatility index that highlights compression, expansion, and regime shifts.
How it works
CDVI calculates the absolute or percentage-based realized volatility of your chosen dominance benchmark (BTC.D, TOTAL.D, or any dominance index available on TradingView).
The indicator then:
1. Smooths the volatility curve using adjustable parameters
2. Builds a long-term mean to identify regime structure
3. Computes percentile zones over a rolling lookback window
4. Highlights high-risk and low-risk dominance conditions using color-coded backgrounds
This creates a clean, noise-reduced volatility representation of the dominance market.
Why it looks like this
The CDVI curve is intentionally smooth and cyclical because dominance volatility behaves differently from price volatility:
• Dominance tends to trend slowly, then spike violently during rotation phases
• Periods of prolonged compression often occur before large macro moves
• Volatility bursts cluster during transitions (e.g. BTC → Alts, cycle tops, market-wide repricing)
The percentile zones (90% / 10%) give structural thresholds for extreme conditions.
Background color reveals when dominance volatility enters these extremes, creating visually clear “regime blocks.”
How to interpret CDVI
High CDVI (above the 90th percentile):
• Dominance instability
• Capital rotation phases are active
• Market is repricing sector allocations
• Often appears near Altseason tops or bottoms
• Signals caution for trend traders and opportunity for rotation traders
Low CDVI (below the 10th percentile):
• Compression and calm dominance
• Accumulation and structural balance
• Often precedes major expansions in Bitcoin or Alt markets
• Useful for anticipating cycle transitions before they break out
Long-term mean:
• Helps identify when the market is in a high-vol or low-vol regime
• Crossings around the mean often coincide with early cycle shifts
How to use CDVI in practice
1. Cycle Timing
Use CDVI to detect when the market moves from calm → expansion or expansion → exhaustion.
Low CDVI usually precedes major moves. High CDVI often marks transition turbulence.
2. BTC vs Altcoins Rotation
Combine CDVI with BTC.D / TOTAL2 / TOTAL3 to detect rotation windows.
High CDVI = dominance is unstable → rotations happen.
Low CDVI = dominance is stable → trending environment.
3. Risk Management
High CDVI suggests elevated structural risk (dominance shifting).
Low CDVI supports directional conviction.
4. Confluence with Price
When both price volatility and dominance volatility expand together → macro transition.
When price is volatile but CDVI is flat → noise, not structural change.
Who this indicator is for
• Cycle analysts
• Macro crypto traders
• BTC vs Alts rotation traders
• Portfolio allocators
• Long-term investors looking at structural market phases
CDVI is designed as a clean, structural tool for understanding volatility not of price — but of market power distribution.
Biến động
Trend Zones This tool helps you quickly understand the market’s direction and the strength of the most recent price move:
It identifies whether the market is in an uptrend, downtrend, or flat/sideways phase and clearly marks these conditions on the chart.
It can notify you when the trend changes, so you don’t have to constantly watch the screen.
Each alert includes:
The current closing price
The previous closing price
The difference between the two closes (how much price has moved in one bar)
This makes it easier to see not only what the trend is, but also how strong the latest price move is when the alert triggers.
ATR Risk Manager v5.2 [Auto-Extrapolate]If you ever had problems knowing how much contracts to use for a particular timeframe to keep your risk within acceptable levels, then this indicator should help. You just have to define your accepted risk based on ATR and also percetage of your drawdown, then the indicator will tell you how many contracts you should use. If the risk is too high, it will also tell you not to trade. This is only for futures NQ MNQ ES MES GC MGC CL MCL MYM and M2K.
Eggy Signal V2.1This script is a fully automated mechanical trading system designed to identify high-probability continuation setups based on significant market volatility expansions. It moves beyond simple crossovers or lagging indicators by analyzing price action structure and momentum velocity.
The algorithm detects specific "price disconnects" where aggressive buying volume has occurred, creating a high-value zone for potential re-entries. It waits for the market to efficiently rebalance and test these zones before signaling a trade, ensuring that you only engage with the market at discounted prices.
Key Features:
Algorithmic Zone Detection: The script automatically scans for significant volatility expansions. It uses an ATR (Average True Range) filter to ignore market noise and small fluctuations, focusing only on high-impact moves that indicate genuine institutional interest.
Smart Workflow (State Machine): Unlike standard indicators that spam signals, this tool uses a "State Machine" logic. It follows a strict discipline:
Phase 1 (Scan): Hunts for valid momentum zones.
Phase 2 (Wait): Projects a Limit Order setup (Entry, Stop Loss, Take Profit) and waits for the price to return (pullback).
Phase 3 (Active): Only activates the trade status if the price strictly touches the entry level.
Analysis Validation ("Missed Trade" Logic): A unique feature of this system is its ability to validate analysis even if no trade is taken. If the market respects the zone and hits the target without triggering your entry first, it marks the setup as "MISSED (Analysis OK)" in Green. This confirms the directional bias was correct, helping you build confidence in the algorithm without skewing your PnL.
Strict Risk Management: The system comes with a built-in, fixed Risk-to-Reward ratio (Default 1:2) to ensure positive expectancy over the long term.
How to Use:
Wait for the Setup: When a valid zone is detected, the script will draw the Entry (Blue), Stop Loss (Red), and Target (Green) lines. The status will read "WAITING".
Prepare Order: Place a Limit Order at the Blue line shown on the chart.
Execution:
If price touches the Blue line, the trade becomes "ACTIVE".
If price hits the Green line, it is a "WIN".
If price hits the Red line, it is a "LOSS".
Auto-Reset: Once a trade is concluded (Win/Loss) or invalidated, the drawings automatically clear to keep your chart clean for the next opportunity.
Settings:
Swing Length: Adjusts the sensitivity of the market structure detection.
Risk Reward: Define your target multiple (e.g., 1:2 or 1:1.5).
Minimum Zone Size (Volatility Filter): Filters out insignificant moves. Higher values = fewer but higher quality setups.
24 minutes ago
Release Notes
With Alerts
⚠️ How to Activate Notifications (Mobile & PC)
Add the Indicator to your chart first.
On the right toolbar, click the Alerts icon (looks like a clock).
Click the Create Alert button (the + icon).
Condition: Change it from the symbol (e.g., XAUUSD) to Eggy Signal V2 (With Alerts).
Trigger: Select "Any alert() function call".
Important: You must select this option because the code uses dynamic alert() messages.
Notifications tab:
Check Notify in App (to get notifications on your phone).
Check Show Pop-up (to see it on your PC screen).
Alert Name: Give it a name (e.g., "Eggy Signal V2").
Click Create.
Eggy Signal V2This script is a fully automated mechanical trading system designed to identify high-probability continuation setups based on significant market volatility expansions. It moves beyond simple crossovers or lagging indicators by analyzing price action structure and momentum velocity.
The algorithm detects specific "price disconnects" where aggressive buying volume has occurred, creating a high-value zone for potential re-entries. It waits for the market to efficiently rebalance and test these zones before signaling a trade, ensuring that you only engage with the market at discounted prices.
Key Features:
Algorithmic Zone Detection: The script automatically scans for significant volatility expansions. It uses an ATR (Average True Range) filter to ignore market noise and small fluctuations, focusing only on high-impact moves that indicate genuine institutional interest.
Smart Workflow (State Machine): Unlike standard indicators that spam signals, this tool uses a "State Machine" logic. It follows a strict discipline:
Phase 1 (Scan): Hunts for valid momentum zones.
Phase 2 (Wait): Projects a Limit Order setup (Entry, Stop Loss, Take Profit) and waits for the price to return (pullback).
Phase 3 (Active): Only activates the trade status if the price strictly touches the entry level.
Analysis Validation ("Missed Trade" Logic): A unique feature of this system is its ability to validate analysis even if no trade is taken. If the market respects the zone and hits the target without triggering your entry first, it marks the setup as "MISSED (Analysis OK)" in Green. This confirms the directional bias was correct, helping you build confidence in the algorithm without skewing your PnL.
Strict Risk Management: The system comes with a built-in, fixed Risk-to-Reward ratio (Default 1:2) to ensure positive expectancy over the long term.
How to Use:
Wait for the Setup: When a valid zone is detected, the script will draw the Entry (Blue), Stop Loss (Red), and Target (Green) lines. The status will read "WAITING".
Prepare Order: Place a Limit Order at the Blue line shown on the chart.
Execution:
If price touches the Blue line, the trade becomes "ACTIVE".
If price hits the Green line, it is a "WIN".
If price hits the Red line, it is a "LOSS".
Auto-Reset: Once a trade is concluded (Win/Loss) or invalidated, the drawings automatically clear to keep your chart clean for the next opportunity.
Settings:
Swing Length: Adjusts the sensitivity of the market structure detection.
Risk Reward: Define your target multiple (e.g., 1:2 or 1:1.5).
Minimum Zone Size (Volatility Filter): Filters out insignificant moves. Higher values = fewer but higher quality setups.
Alpha Net Matrix ProAlpha Net Matrix Pro is an advanced momentum and volatility-based indicator that applies Gaussian smoothing and adaptive deviation bands to detect potential reversal zones and breakout points. It provides traders with dynamic visual cues that reflect real-time market behavior and price extremes.
投資の運勢※日本語説明文は英文の下にあります。
This indicator is a dashboard that simplifies the market’s current condition as a “fortune” by comprehensively evaluating the strength of multiple technical indicators. It allows you to check important analytical results at a glance without cluttering the chart with unnecessary lines.
🎯 How it works: Quantifying and integrating multiple indicators
At the core of this indicator is the process of quantifying four key aspects of the market—trend, momentum, volatility, and volume—assigning weights to each, and calculating an overall score.
How to use it
This indicator functions as a table (dashboard) displayed on your chart.
Check your “fortune for today” to get an overall view of the market’s current risk-reward profile.
Analyze the rows for each indicator to understand the factors behind the fortune.
For example: “The fortune is ‘moderately favorable,’ but volatility is very high (numerical value is large), which reduces the overall score due to its weighted impact.”
The table uses white text on a dark background, making it easy to read regardless of the chart’s color scheme.
⚙️ Customization (Settings Panel)
In the indicator’s settings panel, you can make the following key adjustments:
Type of Moving Average: Turning on use_ema allows the trend calculation to use EMA (Exponential Moving Average).
Weight Adjustment: You can adjust the weights of each indicator (e.g., w_trend, w_momentum) to modify the scoring logic according to your strategy (e.g., trend-focused, momentum-focused).
Use this “fortune chart” as a supplementary tool to objectively assess the current market conditions, rather than as the final decision-maker for trades.
---------------ここから日本語説明--------------------------
このインジケーターは、複数のテクニカル指標の強さを総合的に評価し、現在の市場の状況を**「運勢」**としてシンプルに表示するダッシュボードです。チャート上に邪魔なラインを表示せず、重要な分析結果をひと目で確認できます。
🎯 仕組み：複数の指標を数値化して統合
このインジケーターの核となるのは、市場の4つの主要な側面（トレンド、モメンタム、ボラティリティ、出来高）を数値化し、それぞれに重み付けをして総合スコアを算出する点です。
活用方法
このインジケーターは、チャートに表示される**テーブル（ダッシュボード）**として機能します。
「今日の運勢」を確認し、現在の市場のリスク・リワードの全体像を把握します。
各指標の行を見て、運勢の根拠となった要素を分析します。
例：「運勢が中吉だが、ボラティリティが非常に高い（数値が大きい）ため、重みが働いてスコアが抑えられている」といった分析が可能です。
テーブルは文字が白で背景が暗い色に統一されているため、どの背景色でも見やすくなっています。
⚙️ カスタマイズ（設定パネル）
インジケーターの設定画面で、以下の重要な調整が可能です。
移動平均線の種類: use_ema をONにすると、トレンド計算に**EMA（指数移動平均）**を使用できます。
重み調整: 各指標の w_trend, w_momentum などを調整することで、ご自身の戦略（例：トレンド重視、モメンタム重視）に合わせてスコアの算出ロジックを変更できます。
この「占いチャート」を、トレード判断の最終決定ではなく、現状の市場を客観的に評価する補助ツールとしてご活用ください。
TSO Lite v2 — Early Momentum Flip Signal (Free)✅ TSO Lite v2 — Momentum Ignition Signal (Free Version)
(Created by a Korean trader — structural momentum research)
Most indicators react late.
TSO Lite v2 shows the exact moment internal bullish momentum flips upward.
The green triangle is not decoration —
it’s the structural ignition point where upward momentum breaks above the internal zero-line.
👉 Zero-line breakout = internal momentum shift
👉 If the triangle appears, the shift is already underway
This signal is high-purity, valid only inside a bullish trend, and never repaints.
🔥 Why Lite v2 Feels Different
Structural momentum, not lagging averages
Valid only in bullish trend → naturally cleaner accuracy
No repainting
Detects transitions earlier than RSI / MACD
Minimal, focused, and fast
If the triangle shows → momentum is turning.
If it doesn’t → the market isn’t ready.
🟢 Essence of Lite v2
Green Triangle = first pulse of upward structural energy
You define the trend (MA, HTF regime, your own system).
Lite shows the ignition moment.
📊 Lite v2 vs PRO Engine (Information Only)
(No purchase pressure — simple comparison)
Feature Lite v2 (Free) TSO PRO (Full Engine)
Entry Triangles Green only (bullish) Green + Red (bidirectional)
Valid Condition Bull trend only Trend-aligned (bull/bear)
Structural Filtering ✗ ✓
Leading Momentum Engine Basic Multi-layer
Compression / Turning Zone ✗ ✓
Automation (Webhook) ✗ ✓
User Level Beginner Advanced / automation
Lite shows the moment momentum turns upward.
PRO interprets the entire structural engine.
⚠ Important
This indicator does not repaint.
PRO Flow formulas remain private for licensing and security.
Access to PRO is granted manually (invite-only).
🔑 TSO PRO Subscription (Optional - User Requested Links)
If you want the full structural engine:
• Monthly: tradesmith6.gumroad.com
• Yearly: tradesmith6.gumroad.com
(Yearly offers ~32% savings)
To activate access after purchase, send your TradingView username via DM.
Developer: Korean trader.
🇰🇷 TSO Lite v2 — 상승 모멘텀 점화 신호 (무료 버전)
(한국 트레이더 제작)
대부분의 지표는 늦게 반응합니다.
TSO Lite v2는 내부 상승 모멘텀이 전환되는 “그 순간”을 보여줍니다.
녹색 삼각형은 단순 신호가 아니라
내부 모멘텀이 0선을 돌파하는 상승 점화 지점입니다.
👉 0선 돌파 = 방향 전환 시작
👉 삼각형이 나타난 시점에는 이미 전환이 진행 중
이 신호는 상승 추세에서만 유효한 고순도 구조 신호이며,
한 번 표시되면 리페인트되지 않습니다.
🔥 Lite v2가 강력한 이유
평균값이 아닌 구조 기반 모멘텀 분석
상승 추세에서만 유효 → 신뢰도 향상
리페인트 없음
RSI/MACD보다 빠른 전환 감지
단순하면서도 강력한 상승 초기 신호
삼각형이 뜨면 → 모멘텀이 상승 전환
안 뜨면 → 시장은 아직 준비되지 않음
🟢 Lite v2의 핵심
녹색 삼각형 = 상승 구조 에너지의 첫 펄스
추세는 사용자가 정의합니다.
Lite는 “점화 순간”을 알려줍니다.
📊 Lite v2 vs PRO (정보 제공용)
기능 Lite v2 (무료) TSO PRO (전체 엔진)
진입 신호 녹색(상승전용) 녹/적(상승·하락)
신호 유효 조건 상승 추세 각 추세 정합 조건
구조 필터링 ✗ ✓
선행 모멘텀 엔진 기본 다층 구조
압축·턴닝존 ✗ ✓
자동매매 ✗ ✓
Lite는 상승 초기 모멘텀을 배우는 무료 버전,
PRO는 실전 구조 엔진입니다.
⚠ 중요 안내
이 지표는 리페인트 되지 않습니다.
PRO는 라이선스 보호를 위해 공식 공식(Formula)이 비공개로 유지됩니다.
PRO 접근은 인바이트 기반으로 수동 승인됩니다.
🔑 TSO PRO 구독 링크 (요청된 링크 삽입)
• 월간: tradesmith6.gumroad.com
• 연간: tradesmith6.gumroad.com
구매 후 TradingView ID를 DM으로 보내면 접근이 수동으로 부여됩니다.
개발자: 한국 트레이더
Dumb Money Flow - Retail Panic & FOMO# Dumb Money Flow (DMF) - Retail Panic & FOMO
## 🌊 Overview
**Dumb Money Flow (DMF)** is a powerful **contrarian indicator** designed to track the emotional state of the retail "herd." It identifies moments of extreme **Panic** (irrational selling) and **FOMO** (irrational buying) by analyzing on-chain data, volume anomalies, and price velocity.
In crypto markets, retail traders often buy the top (FOMO) and sell the bottom (Panic). This indicator helps you do the opposite: **Buy when the herd is fearful, and Sell when the herd is greedy.**
---
## 🧠 How It Works
The indicator combines multiple data points into a single **Sentiment Index** (0-100), normalized over a 90-day period to ensure it always uses the full range of the chart.
### 1. Panic Index (Bearish Sentiment)
Tracks signs of capitulation and fear. High values contribute to the **Panic Zone**.
* **Exchange Inflows:** Spikes in funds moving to exchanges (preparing to sell).
* **Volume Spikes:** High volume during price drops (panic selling).
* **Price Crash (ROC):** Rapid, emotional price drops over 3 days.
* **Volatility (ATR):** High market nervousness and instability.
### 2. FOMO Index (Bullish Sentiment)
Tracks signs of euphoria and greed. High values contribute to the **FOMO Zone**.
* **Exchange Outflows:** Funds moving to cold storage (HODLing/Greed).
* **Profitable Addresses:** When >90% of holders are in profit, tops often form.
* **Parabolic Rise:** Rapid, unsustainable price increases.
---
## 🎨 Visual Guide
The indicator uses a distinct color scheme to highlight extremes:
* **🟢 Dark Green Zone (> 80): Extreme FOMO**
* **Meaning:** The crowd is euphoric. Risk of a correction is high.
* **Action:** Consider taking profits or looking for short entries.
* **🔴 Dark Burgundy Zone (< 20): Extreme Panic**
* **Meaning:** The crowd is capitulating. Prices may be oversold.
* **Action:** Look for buying opportunities (catching the knife with confirmation).
* **🔵 Light Blue Line:**
* The smoothed moving average of the sentiment, helpful for seeing the trend direction.
---
## 🛠️ How to Use (Trading Strategies)
### 1. Contrarian Reversals (The Primary Strategy)
* **Buy Signal:** Wait for the line to drop deep into the **Burgundy Panic Zone (< 20)** and then start curling up. This indicates that the worst of the selling pressure is over.
* **Sell Signal:** Wait for the line to spike into the **Green FOMO Zone (> 80)** and then start curling down. This suggests buying exhaustion.
### 2. Divergences
* **Bullish Divergence:** Price makes a **Lower Low**, but the DMF Indicator makes a **Higher Low** (less panic on the second drop). This is a strong reversal signal.
* **Bearish Divergence:** Price makes a **Higher High**, but the DMF Indicator makes a **Lower High** (less FOMO/buying power on the second peak).
### 3. Trend Confirmation (Midline Cross)
* **Crossing 50 Up:** Sentiment is shifting from Fear to Greed (Bullish).
* **Crossing 50 Down:** Sentiment is shifting from Greed to Fear (Bearish).
---
## ⚙️ Settings
* **Data Source:** Defaults to `INTOTHEBLOCK` for on-chain data.
* **Crypto Asset:** Auto-detects BTC/ETH, but can be forced.
* **Normalization Period:** Default 90 days. Determines the "window" for defining what is considered "Extreme" relative to recent history.
* **Weights:** You can customize how much each factor (Volume, Inflows, Price) contributes to the index.
---
**Disclaimer:** This indicator is for educational purposes only. "Dumb Money" analysis is a probability tool, not a crystal ball. Always manage your risk.
**Indicator by:** @iCD_creator
**Version:** 1.0
**Pine Script™ Version:** 6
---
## Updates & Support
For questions, suggestions, or bug reports, please comment below or message the author.
**Like this indicator? Leave a 👍 and share your feedback!**
Smart Accumulation Pro – US SmallCap Edition v2
Smart Accumulation Pro v2 — US SmallCap Edition
Institutional Footprint and Structural Behavior Engine
Overview
Smart Accumulation Pro v2 detects structural behavior, internal liquidity shifts, and multi-phase accumulation footprints that are not visible through momentum or volatility indicators. The engine focuses on underlying institutional habits rather than reacting to price alone.
ULTRA — High-Threshold Structural Trigger
ULTRA appears only when multiple internal phases align simultaneously. It is not a momentum spike or volume anomaly. It represents compression pressure, phase readiness, and structural alignment. ULTRA does not repaint. When this signal appears, internal liquidity has already transitioned into an acceleration phase.
PRE — Early Structural Drift (Not a Buy Signal)
PRE should not be interpreted as a buy signal. It indicates gradual accumulation or controlled liquidity positioning. PRE usually appears during stable or quiet phases but rarely appears during panic drops or disorderly downtrends.
ACC — Transitional Footprint Signal
ACC identifies late-stage structural footprints. It is not intended as a standalone buy trigger. ACC highlights that structural preparation is underway, but direction and timing require user validation. ACC often precedes larger institutional behavior.
Philosophy
This engine does not attempt to cover every market pattern. It focuses on the highest-probability institutional habits. Exit timing, risk management, and execution remain user responsibility. The tool minimizes noise and emphasizes rare, high-impact structural zones.
Preset Modes
1) Conservative
For ETFs or stable large-cap instruments. Minimal noise and lower signal frequency.
2) Normal
Optimized for US mid-cap and small-cap behavior. Balanced and recommended as the default mode.
3) Aggressive
For volatile or thematic instruments. Higher frequency, higher risk.
Usage Notes
This indicator does not provide financial advice. It highlights structural conditions that often precede institutional movement. Execution and risk decisions depend on the user.
License Notice
Unauthorized copying, redistribution, or sharing is prohibited. Invite-Only access requires your TradingView username. One purchase equals one user license.
------------------------------------------------------------
Korean Summary (한국어 요약본)
------------------------------------------------------------
Smart Accumulation Pro v2는 세력의 습관, 유동성 이동, 압축 단계 등의 “보이지 않는 내부 구조”를 추적하는 지표다. 기존 모멘텀 기반 지표로는 포착되지 않는 패턴을 분석한다.
ULTRA 신호는 여러 내부 단계가 동시에 정렬될 때만 등장하는 극히 희귀한 트리거다. 페인팅이 없으며, 신호가 뜰 때 이미 내부 구조는 가속 단계에 진입한 상태다.
PRE는 매수 신호가 아니다. 세력이 서서히 움직이기 시작하거나 유동성을 재정렬할 때 나타나는 미세한 초기 흔적이다.
ACC는 본격 움직임 전에 나타나는 마지막 흔적이다. 단독 매수 신호가 아니며, 이후 더 큰 구조적 변화로 이어질 가능성을 나타내는 정도로 해석해야 한다.
이 지표는 모든 패턴을 잡지 않는다. 세력이 반복적으로 사용해 온 고확률 구조만 좁게 추적한다. 출구 전략과 리스크 관리는 사용자의 몫이다.
프리셋은 Conservative, Normal, Aggressive의 3가지 모드로 구성되며, 각각 안정형·균형형·변동성형 종목에 맞춰 설계되었다.
본 지표는 금융 조언을 제공하지 않으며, 무단 공유 또는 재배포는 금지된다. Invite-Only 기반이며 1인 1라이선스 방식이다.
TSO PRO v2 Entry – Hybrid Flow Engine
TSO PRO v2 Entry – Hybrid Flow Engine
is an invite-only entry system created by a Korean trader and system developer,
specialized in structural momentum flow and transition timing.
This indicator is built on a dual-engine architecture:
✔ Lite Flow — Confirmed Entry Engine
Lite Flow uses TSO’s proprietary Flow Dynamics and zero-line structural shifts
—not moving averages or conventional indicators—
to detect the moment momentum actually turns in one direction.
It displays green (long) / red (short) triangular entry markers
only when internal flow confirms a real directional transition.
Traders may apply their own trend framework
(market structure, regime logic, price action, etc.),
and use Lite Flow entries as clean visual timing hints within that framework.
✔ PRO Flow — Hidden Leading Filter
PRO Flow analyzes internal momentum before Lite Flow triggers.
It does not show signals on the chart.
Instead, it filters out weak/false entries,
refines internal flow conditions, and adjusts background zones.
Only high-quality Lite Flow entries remain visible.
This Hybrid structure significantly reduces false breakouts
and provides confidence during live trading.
🔼 New in v2 — Long/Short Entry Arrows
Green triangle → Long timing hint during upward flow conditions
Red triangle → Short timing hint during downward flow conditions
TSO does not force a trend definition.
Each trader uses their own method,
and Lite Flow simply reveals the moment internal momentum supports that direction.
🔧 Features
Dual-engine Hybrid system (Lite Flow + PRO Flow)
Leading momentum filter (hidden PRO Flow)
High-precision non-repainting entry arrows
Background zones that reflect internal flow strength
Automation-ready structure (Webhook compatible)
PRO Flow logic fully protected (security locked)
⚠ Important
This indicator does not repaint.
PRO Flow formulas remain private for licensing and security.
Access is granted manually (invite-only).
🔑 TSO PRO Subscription
• Monthly: tradesmith6.gumroad.com
• Yearly: tradesmith6.gumroad.com
For access activation, send a DM to the developer (Korean trader).
🇰🇷 한국어 설명
TSO PRO v2 Entry – Hybrid Flow Engine은
한국 트레이더이자 시스템 개발자가 제작한
듀얼 엔진 기반의 고정밀 진입 시스템입니다.
시장 내부의 흐름(Flow Dynamics)과
구조적 모멘텀 전환에 초점을 두어 설계되었습니다.
✔ Lite Flow — 확정 진입 신호 엔진
Lite Flow는 이동평균이나 기존 지표가 아닌
TSO 고유의 Flow Dynamics + 0선 구조 전환을 기반으로
모멘텀이 실제로 특정 방향으로 전환되는 순간을 포착합니다.
이때 차트에 녹색(롱) / 빨강(숏) 삼각형이 표시됩니다.
사용자는 자신의 추세 판단 방식
(시장 구조, 레짐 분석, 캔들 패턴 등)에 맞춰
Lite Flow 신호를 직관적인 진입 타이밍 힌트로 활용할 수 있습니다.
✔ PRO Flow — 선행 필터(비공개 엔진)
Lite Flow보다 먼저 내부 흐름을 분석하여
약한 신호·거짓 돌파를 자동으로 제거합니다.
PRO Flow는 차트에 신호를 표시하지 않으며,
배경 흐름·필터링·구조적 조건을 조절하는
선행 보정 엔진입니다.
Hybrid 구조로 인해
Lite Flow에서 실제 가치 있는 진입만 남아
정확성과 안정성이 크게 향상됩니다.
🔼 v2 신규 기능 — 상승/하락 삼각형 진입 신호 강화
녹색 삼각형 → 상승 흐름 조건에서의 롱 진입 힌트
빨강 삼각형 → 하락 흐름 조건에서의 숏 진입 힌트
TSO는 특정 추세 기준을 강제하지 않습니다.
Lite Flow는 단지 내부 모멘텀이 해당 방향을 지지하는 순간을
시각적으로 알려줍니다.
🎯 주요 기능
Lite Flow + PRO Flow 듀얼 엔진
PRO Flow 기반 선행 모멘텀 필터
고정밀·비리페인트 진입 신호
배경 조건으로 흐름 강도 표시
Webhook 기반 자동매매 구조 지원
PRO Flow 공식 로직 완전 보호(비공개)
⚠ 주의사항
이 지표는 리페인트되지 않습니다.
PRO Flow 로직은 보안·라이선스 사유로 비공개입니다.
접근 권한은 수동 승인 방식입니다.
🔑 TSO PRO 구독 안내
• 월간: tradesmith6.gumroad.com
• 연간: tradesmith6.gumroad.com
접근 및 승인 요청은 DM으로 메시지를 보내주세요.
(한국 트레이더가 직접 승인 처리합니다.)
Displacement Intelligence Channel (DIC) @darshaksscThe Displacement Intelligence Channel (DIC) is a clean, minimal, non-repainting analytical tool designed to help traders observe how price behaves around its dynamic equilibrium.
It does not generate buy/sell signals, does not predict future price movement, and should not be interpreted as financial advice.
All calculations are based strictly on confirmed historical bars.
⭐ What This Indicator Does
Price constantly fluctuates between expansion (large moves) and compression (small moves).
The DIC analyzes these changes through:
Displacement (how far price moves per bar)
ATR response (how volatility reacts over time)
Dynamic width calculation (channel widens or tightens as volatility changes)
EMA-based core midline (a smooth equilibrium reference)
The result is a smart two-line channel that adapts to market conditions without cluttering the chart.
This is NOT a fair value gap, moving average ribbon, or premium/discount model.
It is a purely mathematical displacement-ATR engine.
⭐ How It Works
The indicator builds three elements:
1. Intelligence Midline
A smooth EMA that acts as the channel’s core “equilibrium.”
It gives a stable reference of where price is gravitating during the current session or trend.
2. Adaptive Upper Boundary
Calculated using displacement + ATR.
When volatility increases, the channel expands outward.
When volatility compresses, the channel tightens.
3. Adaptive Lower Boundary
Mirrors the upper boundary.
Also expands and contracts based on market conditions.
All lines update only on confirmed bar closes, keeping the script non-repainting.
⭐ What to Look For (Purely Analytical)
This indicator does not imply trend continuation, reversal, or breakout.
Instead, here’s what traders typically observe:
1. Price Reactions Around the Midline
Price often oscillates around the midline during equilibrium phases.
Strong deviation from the midline highlights expansion or momentum phases.
2. Channel Expansion / Contraction
Wider channel → increased volatility, displacement, and uncertainty
Tighter channel → compression and calm conditions
Traders may use this for context only — not for decision-making.
3. Respect of Channel Boundary
When market structure respects the upper/lower channel lines, it simply indicates volatility boundaries, not overbought/oversold conditions.
⭐ How to Add This Indicator
Open TradingView
Select any chart
Click Indicators → Invite-Only Scripts / My Scripts
Choose “Displacement Intelligence Channel (DIC)”
The channel will appear automatically on the chart
⭐ Recommended Settings (Optional)
These settings do not change signals (because the indicator has none).
They only adjust sensitivity:
Center EMA Length (default 34)
Smoother or faster midline
Displacement Lookback (default 21)
Controls how much recent displacement affects width
ATR Lookback (default 21)
Governs how volatility is interpreted
Min/Max Multipliers
Limits how tight or wide the channel can expand
Adjust them cautiously for different timeframes or asset classes.
⭐ Important Notes
This tool is non-repainting
It does not use future data
It does not repaint previous channel widths
It follows TradingView House Rules
It contains no signals, no alerts, and no predictions
The DIC is designed for visual context only and should be used as an analytical overlay, not as a stand-alone decision tool.
⭐ Disclaimer
This script is strictly for informational and educational purposes only.
It does not provide or imply any trading signals, financial advice, or expected outcomes.
Always do your own research and consult a licensed financial professional before making trading decisions.
1MN Profitcosmos Gold Scalping📈 Profitcosmos Gold Scalping Indicator (1MN)
The Profitcosmos Gold Scalping Indicator is a high-precision scalping system designed specifically for XAUUSD (Gold) on the 1-minute timeframe. It blends ATR-based trend logic with smart session filtering to detect only the most actionable trading opportunities during high-liquidity market hours.
This indicator is built for traders who demand clean entries, structured risk management, and disciplined execution.
✅ Core Features
🔹 ATR Dynamic Stop System
Uses adaptive volatility-based trailing logic to detect strong directional moves.
🔹 Session-Based Trading Only
Trades are filtered to execute exclusively during high-probability sessions:
London Session
New York Session
Asian Session
🔹 Visual Trade Guidance
Every signal automatically draws:
✅ Entry level
🔴 Stop Loss (Swing-based)
🟢 Take Profit (3R risk-reward)
🔹 Clear BUY / SELL Markers
BUY below candle (arrow pointing up)
SELL above candle (arrow pointing down)
No confusion. No overtrading. Only precision.
🔹 Optional Heikin Ashi Mode
Smooth price data for cleaner trend detection.
🎯 How To Trade (Rules)
✅ Trade BUY signals only when price is trending up
✅ Trade SELL signals only when price is trending down
✅ Respect the Stop Loss and Take Profit levels
✅ Never revenge trade
✅ Focus on quality over quantity
🛡 Risk Management
Each signal follows a 3:1 reward-to-risk ratio, ensuring long-term profitability when combined with discipline and consistency.
⚠️ Disclaimer
This indicator is not financial advice. Trading involves risk. Use proper money management and test strategies on demo accounts before trading live capital.
TR-ATR-DATR+MAs shows the Range of selected Candle + 3 Moving Averages
True Range
Avg True Range
Daily Range
Bollinger Bands HTF Hardcoded (Len 20 / Dev 2) [CHE]Bollinger Bands HTF Hardcoded (Len 20 / Dev 2) — Higher-timeframe BB emulation with bucket-based length scaling and on-chart diagnostics
Summary
This indicator emulates higher-timeframe Bollinger Bands directly on the current chart by scaling a fixed base length (20) via a timeframe-to-bucket multiplier map. It avoids cross-timeframe requests and instead applies the “HTF feel” by using a longer effective lookback on lower timeframes. Bands use the classic deviation of 2 and the original color scheme (Basis blue, Upper red, Lower green, blue fill). An on-chart table reports the resolved bucket, multiplier, and effective length.
Pine version: v6
Overlay: true
Primary outputs: Basis (SMA), Upper/Lower bands, background fill, optional info table
Motivation: Why this design?
Cross-timeframe Bollinger Bands typically rely on `request.security`, which can introduce complexity, mixed-bar alignment issues, and potential repaint paths depending on how users consume signals intrabar. This design offers a deterministic alternative: a single-series calculation on the chart timeframe, with a hardcoded “HTF emulation” achieved by scaling the BB length according to coarse higher-timeframe buckets. The result is a smoother, slower band structure on low timeframes without external timeframe calls.
What’s different vs. standard approaches?
Baseline: Standard Bollinger Bands with a fixed user length on the current timeframe, or true HTF bands via `request.security`.
Architecture differences:
Fixed base parameters: Length = 20, Deviation = 2.
Bucket mapping derived from the chart timeframe (or manually overridden).
No `request.security`; all computations occur on the current series.
Effective length is “20 × multiplier”, where multiplier approximates aggregation into the chosen bucket.
Diagnostics table for transparency (bucket, multiplier, resolved length, bandwidth).
Practical effect: On lower timeframes, the effective length becomes much larger, behaving like a higher-timeframe Bollinger structure (smoother basis and wider stability), while remaining purely local to the chart series.
How it works (technical)
The script first resolves a target bucket (“Auto” or a manual selection such as 60/240/1D/…/12M). It then computes a multiplier that approximates how many current bars fit into that bucket (e.g., 1m→60m uses mult≈60, 5m→60m uses mult≈12). The effective Bollinger length becomes:
`bb_len = 20 mult` (clamped to at least 1)
Using the effective length, it calculates:
`basis = ta.sma(src, bb_len)`
`dev = 2 ta.stdev(src, bb_len)`
`upper = basis + dev`
`lower = basis - dev`
A “bandwidth” diagnostic is also computed as `(upper-lower) / basis` (guarded against division by zero) and shown in the table as a percentage. A persistent table object is created/deleted based on the visibility toggle and updated only on the last bar for performance.
Parameter Guide
Source — Input series for the bands — Default: Close
Use close for classic behavior; smoother sources reduce responsiveness.
Bucket — HTF bucket selection — Default: Auto
Auto derives a bucket from the chart timeframe; manual selection forces the intended target bucket.
Offset — Plot offset — Default: 0
Shifts plots forward/back for visual alignment, displayed in the data window.
Table X / Table Y — Table anchor — Default: Right / Top
Places the diagnostics table in one of nine anchor points.
Table Size — Table text size — Default: Normal
Use small on dense charts, large for presentations.
Dark Mode — Table theme — Default: Enabled
Switches table palette for readability against chart background.
Show Table — Toggle diagnostics table — Default: Enabled
Disable for a cleaner chart.
Reading & Interpretation
Basis (blue): The moving average centerline of the bands (SMA of effective length).
Upper (red) / Lower (green): ±2 standard deviations around the basis using the same effective length.
Fill (blue tint): Visual band zone to quickly see compression/expansion.
Interpretation staples:
Price riding the upper band suggests strong bullish pressure; riding the lower band suggests strong bearish pressure.
Band expansion indicates rising volatility; contraction indicates volatility compression.
Mean reversion setups often key off the basis and re-entries from outside bands, while breakout/trend setups often key off sustained band rides.
Diagnostics table:
HTF Tag: Human-readable label showing the current timeframe → bucket mapping.
Bucket: The resolved target bucket (Auto result or manual selection).
Multiplier: The integer factor applied to the base length.
Len/Dev: Shows base length (20) and the effective length result plus deviation (2).
Bandwidth: Normalized width of the band (percent), useful for spotting squeezes.
Practical Workflows & Combinations
HTF context on LTF charts: Use this as “slow structure” bands on 1m–15m charts without requesting HTF data.
Squeeze detection: Watch bandwidth shrink to historically low levels, then look for break/hold outside bands.
Trend filtering: Favor long bias when price stays above the basis and repeatedly respects it; favor short bias when below.
Confluence: Combine with market structure (swing highs/lows), volume tools, or a trend filter (e.g., a longer MA) for confirmation.
Behavior, Constraints & Performance
Repaint/confirmation: No cross-timeframe requests. Values can still evolve intrabar and settle on close, as with any indicator computed on live bars.
History requirements: Very large effective lengths need sufficient historical bars; expect a warm-up period after loading or switching symbols/timeframes.
Known limits: Because the method approximates HTF behavior by scaling lookback, it is not identical to true HTF Bollinger Bands computed on aggregated candles. In particular, volatility and mean can differ slightly versus a real HTF series.
Sensible Defaults & Quick Tuning
Default workflow:
Bucket: Auto
Source: Close
Table: On (until you trust the mapping), then optionally off
If bands feel too slow on your timeframe: choose a smaller bucket (e.g., 60 instead of 240).
If bands feel too reactive/noisy: choose a larger bucket (e.g., 1D or 3D).
If chart looks cluttered: hide the table; keep only the bands and fill.
What this indicator is—and isn’t
This is a Bollinger Band visualization layer that emulates higher-timeframe “slowness” via deterministic length scaling. It is not a complete trading system and does not include entries, exits, sizing, or risk management. Use it as context alongside your execution rules and protective stops.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino.
ProCrypto OI Candles — by ruben_procryptoThis indicator visualizes aggregated Open Interest (OI) from multiple futures exchanges (Binance, Bybit, OKX).
It plots OI as colored candles (blue for increasing OI, orange for decreasing OI), combined with a smoothed OI line for clearer trend reading.
Key Features:
Multiple exchange support (Binance / Bybit / OKX)
Aggregated OI calculation
OI candlesticks with custom opacity
Smoothed OI trend line
Optional OI Delta bars
Adjustable smoothing length, range offset, and lookback settings
Works on all timeframes
What it helps with:
Spotting liquidity traps
Identifying fake pumps / fake dumps
Detecting aggressive long/short positioning
Reading funding cycles and OI expansions
Tracking market strength/weakness behind price movements
OI is one of the most powerful tools for understanding leverage behavior and true market intent.
This script gives a clear, clean, real-time view of OI so traders can see where momentum is actually coming from.
Built for traders who use liquidity, leverage, OI shifts, and momentum to understand price movement more accurately.
Created by @ruben_procrypto.
TriPrimeTriPrime is a multi-layer momentum-distance engine designed to capture structural trend behavior and directional transitions.
The system decomposes market displacement into three response-speed layers, representing different structural components of trend development:
Alpha – fast-response distance
Beta – medium-response distance
Gamma – slow-response distance
Together, the three layers reveal:
• Trend rising vs. trend falling cycles
• Multi-speed directional alignment
• Early-stage rotation signals
• Trend continuation and weakening phases
Bright colors indicate a rising trend.
Soft colors indicate a falling trend.
A synchronized-movement alert is included, highlighting moments when all three layers rise or fall together — conditions commonly associated with highly clear market direction.
TriPrime is designed for professional trading workflows, multi-layer momentum analysis, and structural trend validation.
TriPrime 是一套多层动能-距离分析引擎，用于捕捉结构性趋势、方向变化与趋势阶段特征。
系统将市场位移拆分为三个不同反应速度的层级，代表趋势结构中的多速度特性：
Alpha — 快速反应距离
Beta — 中速反应距离
Gamma — 慢速反应距离
三层结构可揭示：
• 趋势上升 / 趋势下降周期
• 多速度趋势一致性
• 趋势早期方向旋转信号
• 趋势延续与趋势衰减阶段
亮色代表趋势上升。
柔色代表趋势下降。
系统包含同步提醒
用于标记三层同时趋势上升或趋势下降的时刻 —— 通常对应趋势方向非常明确的行情阶段。
TriPrime 适用于专业交易流程、多层动能研究与趋势结构验证。
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.
Hyper Squeeze Sniper (Dual Side: Long + Short)Hyper Squeeze Sniper (Dual Side Strategy)
This script is a comprehensive Volatility Breakout System designed to identify and trade explosive price moves following periods of consolidation. It combines the classical "Squeeze" theory with Linear Regression Momentum, Volume Analysis, and an ATR-based Trailing Stop to filter false signals and manage risk effectively.
The script operates on a logic of "Compression -> Explosion -> Trend Following" suitable for both Long and Short positions.
🛠 Detailed Methodology (How it works)
1. The Squeeze Detection (Consolidation) The core concept relies on the relationship between Bollinger Bands (BB) and Keltner Channels (KC).
Condition: When the Bollinger Bands (Standard Deviation) contract and fall inside the Keltner Channels (ATR based), it indicates a period of extremely low volatility (The Squeeze).
Visual: The background turns Gray to indicate "Do Not Trade / Wait Mode".
2. Momentum Confirmation (Linear Regression) Instead of using standard lagging indicators, this script utilizes Linear Regression of the price deviation to determine the direction of the breakout.
If the Linear Regression Slope > 0, the bias is Bullish.
If the Linear Regression Slope < 0, the bias is Bearish.
3. Volume Validation To avoid fake breakouts, a Volume Spike filter is applied. A signal is only valid if the current volume exceeds its moving average by a defined multiplier (Default x1.2).
4. Risk Management: ATR Trailing Stop Once a trade is entered, the script calculates a dynamic Trailing Stop based on the Average True Range (ATR).
- Long: The stop line trails below the price and never moves down.
- Short: The stop line trails above the price and never moves up.
- Exit: The position is closed immediately when the price breaches this volatility-based safety line.
How to Use
1. Wait: Look for the Gray Background. This is the accumulation phase.
2. Entry:
LONG: Wait for a Green Triangle ▲ (Price breaks Upper BB + Vol Spike + Bullish Momentum).
SHORT: Wait for a Red Triangle ▼ (Price breaks Lower BB + Vol Spike + Bearish Momentum).
3. Exit: Close the position when the "X" mark appears or when candles cross the trailing safety line.
Settings
- BB Length/Mult: Adjust the sensitivity of the squeeze detection.
- Vol Spike Factor: Increase this to filter out low-volume breakouts.
- ATR Period/Mult: Adjust the trailing stop distance (Higher = Wider stop for swing trading).
HC HighCrew Volume Intelligence Surge TrackerThis indicator measures coordinated market activity by comparing live volume flow across multiple timeframes against its normalized baseline.
It detects when institutional participation increases beyond historical averages, signaling either a breakout ignition, sustained trend pressure, or liquidity cooling.
Each timeframe is classified by surge intensity, and the system aggregates those readings into a unified “market energy” output that reveals whether participation is concentrated, fading, or fragmented.
The goal is to help traders differentiate between real accumulation and low-resistance drift, improving timing on breakouts or exits.
Use cases: breakout validation, liquidity-flow analysis, volume confirmation with trend bias.
Low Volatility Breakout + TP/SL Levels█ OVERVIEW
"Low Volatility Breakout + TP/SL Levels" is a breakout indicator designed to detect and trade breakouts from periods of low volatility (consolidation). Unlike classic strategies based on fixed support/resistance levels, this indicator dynamically identifies consolidations characterized by small candle bodies and only generates a signal when the breakout occurs with a large, decisive candle. It also automatically plots 3 Take Profit levels and a Stop Loss (with two calculation modes), making it a complete breakout trading tool.
█ CONCEPTS
The strongest market moves most often start after a prolonged period of very low volatility — when candles become small and the market "falls asleep". The indicator first detects such consolidations (small bodies for at least X bars), draws a box around them, and then waits for a breakout with a candle significantly larger than the average. Additional filters (e.g., the box height cannot exceed the average candle body by too much) eliminate false consolidations and volatility traps. Immediately after the breakout, TP1, TP2, TP3, and SL levels are plotted.
█ FEATURES
Dynamic detection of low-volatility consolidations
- candles with small bodies (< average body × consolidationMultiplier)
- minimum number of bars in consolidation: confirmBars (default 5)
Automatic drawing of consolidation boxes
- green (bullish) or red (bearish) with transparent background (85)
- adjustable border thickness (border_width 1–5)
- box height filter (boxHeightMultiplier, default 6.0 × average body) – removes overly stretched/false consolidations
Breakout conditions
- current candle must be larger than average body × threshold (default 1.5)
- must be the largest candle in the entire consolidation
- must close above the highest high (long) or below the lowest low (short)
Breakout signals
- small green triangles below the bar (long)
- small red triangles above the bar (short)
Automatic Take Profit and Stop Loss levels (drawn 5 bars forward)
- two calculation modes:
• Candle Multiplier – based on average true range (high-low) over tp_sl_length period
• Percentage – fixed percentage from breakout close price (percentages must be manually adjusted to the asset and timeframe)
- 3 TP levels (default 2×, 3×, 4× or 2%, 3%, 4%)
- 1 SL level (default 2× or 1.5%)
Live TP/SL price table (top-right corner)
- displays exact current values of SL, TP1, TP2, TP3 immediately after each new signal
- colors identical to drawn lines (red background for SL, green for TP levels)
- updates automatically with every new breakout
Built-in alerts
- “Bullish Breakout Alert” and “Bearish Breakout Alert”
█ HOW TO USE
Add the indicator to your TradingView chart → Indicators → search “Low Volatility Breakout + TP/SL Levels”.
After each valid breakout you will immediately see:
- the colored box
- signal triangle
- horizontal TP/SL lines
- updated table in the top-right corner showing precise price levels for the current trade
Key settings to adjust:
Consolidation Settings
- Volatility Window (length) – period for average body calculation (default 20)
- Consolidation Multiplier – how small bodies must be to count as consolidation (default 2.0)
- Breakout Multiplier – minimum size of breakout candle (default 1.5)
- Box Height Multiplier – maximum allowed box height (default 6.0)
- Min Consolidation Bars – minimum bars required (default 5)
Risk Management Settings
- Choose TP/SL mode: Candle Multiplier or Percentage
- Adjust TP1–3 and SL multipliers/percentages to match your risk management style
Signal interpretation:
- Green triangle below bar + green box + green TP levels in table = long signal
- Red triangle above bar + red box + red SL level in table = short signal
- Boxes remain on chart until broken — they highlight accumulation/distribution zones
█ APPLICATIONS
- Trading breakouts from consolidation on all markets and timeframes
- Recommended to trade in the direction of the higher-timeframe trend or with additional confirmations (e.g., key level breaks). Aggressive mode (trading both directions) is also possible — provided box and TP/SL settings are properly optimized
- Experiment with different TP/SL ratios — higher reward-to-risk setups (e.g., SL 1×, TP3 6–8×) with lower win rate are often more profitable in the long run
- Strongly encourage testing various box parameters (consolidationMultiplier, boxHeightMultiplier, confirmBars) — small changes can dramatically affect signal frequency and quality
█ NOTES
Always test and optimize parameters for the specific instrument and timeframe.
HighCrew Sniper Entry/Exit This system uses a multi-timeframe momentum-forecast model that detects pressure shifts before standard confirmation signals trigger.
It calculates real-time Force, Speed, Power, and Acceleration values derived from live RSI and price-velocity behavior, then adapts dynamically between lower (scalp) and higher (swing) intervals.
When acceleration and power converge, the system identifies early directional intent and prints a bias signal for traders to confirm entry or manage exits.
The framework continuously self-adjusts its thresholds based on volatility and relative strength to maintain precision during fast market changes.
Use cases: intraday scalping, micro-trend reversal timing, swing-bias validation.
Disclaimer: Algorithmic forecasts only; practice proper risk management.
