TSP Volume Change Big Small// Better Display of Volume change
// green candle : Big volume change
// red Candle : Small volume change
// Default for BTC m5
// Big volume are limited up to $limup% 5%
// Pump : Volume over $limgreen% 2%
// Flat : Very Small Volume under $limdo% 0.2%
// Adjust based on volatility / TF
// BTC/USD 1h : 4,2,0.25
Tìm kiếm tập lệnh với "btc走势预测"
Quote asset VolumeVolume expressed in quote asset units. For pair DOGE/BTC the volume is shown in BTC, instead of DOGE.
Values are imprecise, because each candle's price is calculated as (O+H+L+C)/4, instead of a weighted average one, which I couldn't obtain.
Noro's Trend MAs Strategy v1.8Trade strategy which uses only 2 MA.
The slow MA (blue) is used for definition of a trend
The fast MA (red) is used for an entrance to the transaction
For:
- For H1
- For crypto/fiat or crypto/crypto
- Good for "BTC/USD", "ETH/USD", "ETH/BTC"
Recomended:
Long = true (if it is profitable as a result of backtests)
Short = true (if it is profitable as a result of backtests)
Stops = false
Stop, % = any
OHLC4 = any
Use Fast MA = true
Fast MA Period = 5
Slow MA Period = 21
Bars Q = (2 for "bitcoin/fiat" or 1 for "crypto/fiat" or 0 for "crypto/crypto")
In the new version 1.8
- The second PriceChannel is added
- Profit became more
- Losses became less
- The unnecessary types of MA are removed
Bitcoin momentum correlation This is a pretty simple indicator, it measures the momentum of bitcoin as compared to usd,eur,eth,dash, and ltc, which you can see in all of the blue lines. If the red line is above zero then it means the overall value of btc is going up, opposite for down. The Ema_window controls how smooth the signal is. If you shorten the Ema_window parameter and open this on higher timeframe btc charts then the zero crossing gives pretty solid signals, despite being pretty choppy. A good way to interpret this is that if all the blue lines are moving in the same direction at once without disagreement, then the value of bitcoin has good momentum.
Mildly more technically:
Momentum is measured in the first derivative of an EMA for each ticker. To normalize the different values against each other they are all divided by their local maximums, which can be chosen in the parameter window, but shouldn't make a huge difference. All the checked values are then summed, as shown in the red line. To include a value into the red line simply keep it checked. Take a look at the script, it's kind of easy on the eyes.
It's pretty handy to look at, but doesn't seem too worthwhile to pursue much further. If someone wants much more out of the script then feel free to message me.
Remember rules #1 & #2
Don't lose money.
Happy trading
RSI+BSIThis script simply plots the current instruments RSI as well as Bitcoin's RSI from bitfinex. Helpful to identify when an alt is performing stronger than BTC or if BTC is dragging the alt down.
Volume Conversion IndicatorVolume Conversion Indicator
The volume conversion indicator is much like the in-built volume indicator. This particular volume indicator allows you to find out how much of something has been traded in a given timeframe.
This is done by multiplying volume by the average price at that point.
What does this mean?
Well, say, for example, you were watching DGB/BTC (DigiByte/Bitcoin). Instead of the volume being displayed in the amount of DGB traded, the amount of BTC traded is displayed instead.
Feel free to comment... Hope this helps :D
Indicator: Schaff Trend Cycle (STC)Another new indicator for TV community :)
STC detects up and down trends long before the MACD. It does this by using the same exponential moving averages (EMAs), but adds a cycle component to factor instrument cycle trends. STC gives more accuracy and reliability than the MACD.
More info: www.investopedia.com
Feel free to "Make mine" this chart and use the indicator in your charts. Appreciate any feedback on how effective this is for your instrument (I have tested this only with BTC).
For people trading BTC:
-------------------------------
Try 3/10 or 9/30 for MACD (fastLength/slowLength). They seem to catch the cycles better than the defaults. :)
$MTF Fractal Echo DetectorMIL:MTVFR FRACTAL ECHO DETECTOR by Timmy741
The first public multi-timeframe fractal convergence system that actually works.
Market makers don’t move price randomly.
They test the same fractal structure on lower timeframes first → then execute the real move on higher timeframes.
This indicator catches the “echo” — when 3–5 timeframes are printing fractals at almost the exact same price level.
That’s not coincidence. That’s preparation.
FEATURES
• 5 simultaneous timeframes (1min → 4H by default)
• Real Williams Fractal detection (configurable period)
• Dynamic echo tolerance & minimum TF alignment
• Visual S/R zones from every timeframe
• Bullish / Bearish echo convergence signals
• Strength meter (3/5, 4/5, 5/5 TF alignment)
• Zero repainting — uses proper lookahead=off
• Fully Pine v6 typed + optimized
USE CASE
When you see a 4/5 or 5/5 echo:
→ That level is being defended or attacked with intent
→ 80%+ chance the next real move comes from there
→ Trade the breakout or reversal at that exact fractal cluster
Works insane on:
• BTC / ETH (all timeframes)
• Nasdaq / SPX futures
• Forex majors (especially GBP & gold)
• 2025 small-cap rotation setups
100% Open Source • MPL 2.0 • Built by Timmy741 • December 2024
If you know about fractal echoes… you already know.
#fractal #mtf #echo #williamsfractal #multitimeframe #smartmoney #ict #smc #orderflow #convergence #timmy741 #snr #structure
DPX+ Command Structural Flow Engine (v6) - FinalDPX+ COMMAND STRUCTURAL FLOW ENGINE v6 — DARKPOOL EDITION
The most advanced auto-calibrated dark-pool absorption + structural flow detector ever released to the public.
100% Open Source • Zero repainting • Institutional-grade math • Built for commanders only.
WHAT THIS ACTUALLY IS
A real-time fusion of:
• Reynolds Number proxy (laminar → turbulent flow detection)
• Tsallis Δq non-extensive entropy (tension & phase transition predictor)
• DPX — proprietary Dark Pool Absorption Index (volume-weighted inefficiency)
All three are AUTO-CALIBRATED to the current market regime. No manual thresholds. Works on BTC, SPX, TSLA, 1m or monthly — same settings.
FEATURES
• Jet-black military HUD with live COMMAND output
• Lethal Entry signals when ALL 3 systems align (extremely rare, extremely high win rate)
• Visualizes laminar vs turbulent flow in real time
• DPX absorption/distribution zones with dynamic bands
• Structural break warnings before violent moves
• Zero input tweaking needed — fully adaptive
USE CASE
This is not a "buy/sell arrow" script.
This is a command-center structural flow monitor used by professionals who understand order flow phases:
→ Accumulation (dark pool buying dips)
→ Tension buildup (Δq spike)
→ Phase transition (laminar → turbulent)
→ Lethal structural convergence = high-conviction entry
WHEN THE HUD SAYS "**BUY** (Lethal Structural Convergence)" — you listen.
Tested and proven on:
• Crypto bear market bottoms
• 2022–2023 SPX distribution tops
• 2025 small-cap rotation
Fully open source because real edge isn’t in the code — it’s in understanding what the code is showing you.
If you know, you know.
#darkpool #orderflow #structural #dpx #reynolds #tsallis #institutional #smartmoney #accumulation #distribution #phasechange #ict #smc #commandcenter
Made with respect for the craft.
Drop a ♥ if this speaks to you.
Jiangnan_BTC_Compare将个别虚拟币走势与BTC的走势进行比较。打开个别币的K线，添加在下方的panel里添加本指标即可。Compare the price movement of individual cryptocurrencies with that of BTC.
Open the candlestick chart of the selected coin and simply add this indicator in the lower panel.
Crypto Signals & Overlays –29-11-2025Nebula Crypto Signals & Overlays
Nebula is a multi-timeframe trend and momentum indicator designed for high-cap crypto pairs (BTC, ETH, SOL, DOGE, etc.).
• Uses 21/50/200 EMAs + higher-timeframe EMA for trend filtering
• RSI and Bollinger Bands for momentum and squeeze detection
• Generates BUY/SELL labels on trend-side pullbacks
• ATR line as a dynamic stop/target guide, plus pivot-based support/resistance zones
• Background colors: green = bullish regime, red = bearish regime, yellow = low-volatility squeeze
Not financial advice. Always backtest and use proper risk management before trading live.
Extended SOPR Indicator — SSOPR Tops (A/B toggle)Extended SOPR Indicator — SSOPR Tops and Lows (A/B toggle)
Observation-only. Data: Glassnode SOPR.
Overview
This indicator extends the classical SOPR (Spent Output Profit Ratio) to improve readability and reduce noise on charts. SOPR measures whether coins moved on-chain were spent at a profit or at a loss. In brief: SOPR > 1 → spending at profit; SOPR < 1 → spending at loss. SSOPR (from "Smoothed SOPR") applies optional log transform (centers baseline at 0), smoothing (standard or adaptive), and adds structured signals: Z‑score lows (capitulation), buy zones , and top detection after prolonged elevation.
Why extend SOPR? (SSOPR vs classical SOPR)
• Noise reduction: Raw daily SOPR can whipsaw around its baseline. SSOPR uses smoothing and (optionally) adaptive smoothing so regimes are visible without overfitting.
• Better readability: The log transform shifts the break-even line to 0, making “profit territory” (above 0) and “loss territory” (below 0) visually intuitive on oscillators.
• Actionable context: Z‑score highlights extreme lows (capitulation risk), a simple buy-zone threshold marks potential accumulation, and a structured top pattern (with a time factor) helps frame distribution phases after sustained elevation.
What the script plots
• Smoothed SOPR (SSOPR): An orange line representing the smoothed SOPR (with optional log transform and optional adaptive smoothing).
• Top markers: A red triangle appears once at the onset of a confirmed top pattern.
• Background shading:
– Soft green: Buy zone when SSOPR falls below the “Buy Threshold.” (+ Z‑score capitulation zones (extreme lows)).
– Soft red: Top‑zone shading when the top criteria are met but before the single triangle fires.
Inputs & parameters
• Smoothing Length (default 14): Base window for smoothing SSOPR. Higher values = smoother, slower response.
• Apply Log Transform (default ON): Uses log(SOPR) so the baseline is 0 (log(1)=0). Above 0 → net profit regime; below 0 → net loss regime.
• Adaptive Smoothing (default OFF): Expands smoothing length as volatility rises using a standard deviation proxy; reduces whipsaws while preserving structure.
• Z‑score Threshold for Lows (default −2.5): Highlights capitulation zones when SSOPR deviates far below its rolling mean.
• SSOPR Buy Threshold (default −0.02): Simple rule-of-thumb level for potential accumulation context when below (log scale).
• SSOPR Top Threshold (default +0.005): Minimum elevation required for “profit territory” when assessing tops (log scale).
• Min Bars Above Threshold Before Top (default 50): Ensures prolonged elevation before calling a top.
• Lookback for Peak Detection (default 50): Window used to locate the recent high.
• Drop % from Peak to Confirm Top (default 5%): Confirms the start of distribution from a local high.
• Highlight Background : Toggles shaded zones.
Top detection (indicator-only)
A top fires when ALL of the following are true:
SSOPR spent at least Min Bars Above Threshold above the Top Threshold (sustained elevation).
The rising phase test passes (Option A or B; see below).
A drop from the local peak exceeds Drop % within the Lookback window.
The peak occurred in profit territory (SSOPR > Top Threshold).
To avoid repeated signals during the decline, the script emits the triangle once, at onset.
Rising‑phase switch: Option A vs Option B
• Option A — Up‑step ratio : Over the last A: Bars for Rising Check (default 50), it requires that at least A: Required Up‑Step Ratio (default 60%) of bars were rising (each bar compared to the previous). This favors gradual, persistent advances and filters out “choppy” lifts.
• Option B — Net slope : Compares current SSOPR to its value B: Bars Back for Net Slope ago (default 50). If higher, the series is considered rising. This is simpler and reacts faster in volatile phases but can admit brief pseudo‑trends.
Guidance : Prefer A for conservative confirmation in slow, persistent cycles; use B when trend moves are strong and you need timely detection.
Interpretation guide
• Regimes (log view): Above 0 → spending at profit; below 0 → spending at loss.
• Capitulation lows: When Z‑score < threshold, conditions often reflect forced/liquidity‑driven spending. Treat as context, not signals.
• Buy zone: SSOPR < Buy Threshold flags potential accumulation conditions (combine with price structure).
• Tops: After prolonged elevation, a confirmed top often coincides with profit‑taking/distribution phases.
Recommended timeframes
• Daily : Code optimized for daily timeframe.
Method summary
• SSOPR source: GLASSNODE:BTC_SOPR (via request.security ).
• Optional log transform: sopr → log(sopr) to normalize around 0.
• Smoothing: SMA over Smoothing Length , optionally adaptive using local volatility (std dev).
• Z‑score: (SSOPR − mean) / std dev, highlighting extreme lows.
• Top: Requires long elevation above Top Threshold , rising‑phase (A/B), and a subsequent drop > Drop % from recent high.
Limitations & notes
• SOPR reflects on‑chain movements; some activity occurs off‑chain (exchanges, internal transfers). Not all moves imply sale; aggregation makes it a usable proxy for profit/loss realization.
• Higher smoothing reduces noise but delays signals; adaptive smoothing can help but is still a trade‑off.
• Treat thresholds as context markers. They are not entry/exit signals by themselves.
• Use with price structure, volume, and other on‑chain indicators (e.g., realized price bands, dormancy/CDD) for confluence.
How to use (examples)
• Advance holding above 0 (log view): Retests of 0 from above that hold—while SSOPR remains elevated—often mark absorption; look for Top conditions only after sustained elevation and a confirmed drop from peak.
• Downtrend below 0: Rejections near 0 can align with continued loss realization; extreme Z‑score lows suggest capitulation risk—context for accumulation, not a blind buy.
Recommended settings
• Weekly: Log ON, Smoothing Length 14–30, Adaptive ON, Buy Threshold −0.02, Top Threshold +0.005, Rising Method A, Min Bars 50.
• Daily: Log ON, Smoothing Length 14–20, Adaptive OFF or ON (depending on noise), Rising Method B for timely slope checks.
Credits & references
• SOPR metric: Renato Shirakashi; documentation: Glassnode , CryptoQuant , overview: Bitbo .
Disclaimer
This script is for research/education on market behavior. It is not financial advice. Indicators provide context; decisions remain your responsibility.
Tags
bitcoin, btc, on‑chain, sopr, ssopr, glassnode, oscillator, regime, distribution, capitulation
SIDD Table Volume multiframe (Modified)🚀 SIDD Volume Table – The Most Powerful Multi-Timeframe Volume Dashboard
Designed by Siddhartha Mukherjee (SIDD)
Free for the community.
Get an unfair edge with the cleanest, fastest, and most accurate multi-timeframe volume analyzer available on TradingView. This tool reveals where buyers and sellers are truly active across multiple timeframes—helping you confirm trends, avoid traps, and enter with confidence.
🔥 Why Traders Love This Indicator
✅ 1. Multi-Timeframe Volume Domination
Instantly view Buy% / Sell% / Total Volume for:
1m • 5m • 15m • 1H • 4H • 1D • 1W
Choose any combination you want!
✅ 2. Advanced Buy/Sell Volume Logic
Not simple volume…
This tool breaks it into:
Buy Volume% (green dominance)
Sell Volume% (red dominance)
Using candle structure (H-L-C), giving far more accurate pressure detection.
✅ 3. Realtime Candle Countdown
Never guess when a candle will close again.
Get:
Seconds (1m)
MM:SS (5m/15m/1H)
DD:HH:MM:SS (4H, 1D, 1W)
Perfect for scalpers, swing traders, and index traders.
✅ 4. Beautiful & Customizable Dashboard
Choose position anywhere on screen
Auto size or choose Tiny → Huge
Color-coded Bias (Green Buyers, Red Sellers)
Clean layout built for modern charts
Your chart stays clean while your data stays powerful.
💡 What This Helps You Identify
Where buyers are gaining strength
Where sellers are dominating
Multi-timeframe alignment (the key to big moves)
Real reversal pressure
Volume divergence across timeframes
Trend confirmation before breakouts
Perfect for:
NIFTY / BANKNIFTY / Stocks / Crypto / FX / Commodities
🧠 Who Should Use This?
Intraday traders
Swing traders
Options traders
Futures traders
Crypto scalpers
Professional volume analysts
If volume matters to you → this indicator becomes a must-have.
🛠 Built with Precision
Non-repainting
Multi-TF aligned
Fast + lightweight arrays
Uses BTC/ETH feed to stabilize ticks
Zero chart clutter
❤️ Free for Everyone
This tool is released 100% free to help the community trade with clarity and confidence.
Leave a like ⭐, comment 💬, or follow if you want more such institutional-grade tools.
⚠️ Disclaimer
This is for educational/analytical use only.
Not financial advice. Trade at your own risk.
DeepFlow Zones SNIPER# DeepFlow Zones SNIPER - Documentation & Cheatsheet
## 🎯 DeepFlow Zones - SNIPER Edition
**Horizontal Limit Order Zones | Institutional FVG + Single Prints**
> **Philosophy:** *Only mark the zones where institutions MUST have orders. Everything else is noise.*
---
## ⚡ QUICK CHEATSHEET
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ DEEPFLOW ZONES SNIPER - QUICK REFERENCE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🎯 ZONE CREATION REQUIREMENTS (ALL MUST BE TRUE): │
│ ══════════════════════════════════════════════════ │
│ ✓ FVG exists → Gap between candle low and 2-bar-ago high │
│ ✓ Gap Size → At least 30% of ATR (significant gap) │
│ ✓ Impulse Candle → 1.8x average range + 65% body ratio │
│ ✓ Volume → 2.0x+ average on impulse candle │
│ ✓ Direction → Middle candle confirms gap direction │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 📊 ZONE TYPES: │
│ ══════════════ │
│ 🟢 BULLISH ZONE → Green box BELOW price (buy zone) │
│ 🔴 BEARISH ZONE → Red box ABOVE price (sell zone) │
│ ⚫ TESTED ZONE → Gray box (CE level touched) │
│ ⬛ BROKEN ZONE → Dark gray (price closed through) │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ⭐ SINGLE PRINT LINES: │
│ ══════════════════════ │
│ Requirements: │
│ • Range 1.8x+ average │
│ • Body 65%+ of range │
│ • Volume 2.0x+ average │
│ • Delta 60%+ confirms direction │
│ │
│ Usage: │
│ • Gold lines at HIGH and LOW of impulse candle │
│ • Price often returns to these levels │
│ • Use as support/resistance for entries │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🚨 ENTRY SIGNALS: │
│ ═══════════════════ │
│ BUY🎯 appears when: │
│ • Price is inside BULLISH zone │
│ • Delta shows 60%+ buy dominance │
│ • Volume is 1.5x+ average │
│ │
│ SELL🎯 appears when: │
│ • Price is inside BEARISH zone │
│ • Delta shows 60%+ sell dominance │
│ • Volume is 1.5x+ average │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 📐 ZONE ANATOMY: │
│ ═════════════════ │
│ │
│ BULLISH FVG ZONE: BEARISH FVG ZONE: │
│ │
│ Current Low ───────────────── ───────────────── 2-bar-ago Low │
│ ┌─────────────────────────┐ ┌─────────────────────────┐ │
│ │ █████ ZONE █████████████│ │ █████ ZONE █████████████│ │
│ │- - - CE (50%) - - - - - │ │- - - CE (50%) - - - - - │ │
│ │ ████████████████████████│ │ ████████████████████████│ │
│ └─────────────────────────┘ └─────────────────────────┘ │
│ 2-bar-ago High ────────────── ───────────────── Current High │
│ │
│ Entry: At or near CE line Entry: At or near CE line │
│ Stop: Below zone bottom Stop: Above zone top │
│ Target: 1:1 or 2:1 R:R Target: 1:1 or 2:1 R:R │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ⛔ ZONE IS INVALID WHEN: │
│ ═════════════════════════ │
│ ✗ Gap size < 30% of ATR (too small) │
│ ✗ No impulse candle (weak move) │
│ ✗ Volume < 2x average (retail move) │
│ ✗ Zone age > 50 bars (stale) │
│ ✗ Price already closed through zone │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
```
---
## 📋 DETAILED DOCUMENTATION
### What Makes SNIPER Zones Different?
Standard FVG indicators create zones everywhere. SNIPER zones only appear when there's **institutional footprint**:
| Filter | Standard FVG | SNIPER Zones | Why It Matters |
|--------|-------------|--------------|----------------|
| Gap Size | Any gap | **≥30% ATR** | Significant imbalance |
| Volume | Optional | **2.0x+ avg** | Institutional volume |
| Impulse | None | **1.8x range** | Real momentum |
| Body | None | **65%+ ratio** | Conviction candle |
| Max Zones | 20-50 | **10 max** | Only the best |
| Zone Life | 100 bars | **50 bars** | Fresh zones only |
---
### How Zones Are Created
```
BULLISH FVG FORMATION:
═══════════════════════
Bar 0 (2 bars ago): Bar 1 (Impulse): Bar 2 (Current):
┌─────┐ ┌─────┐ ┌─────┐
│ │ │█████│ │ │
│ │ HIGH ────── │█████│ │ │
│ │ │ │█████│ │ │
└─────┘ │ │█████│ │ │── LOW
│ └─────┘ └─────┘
│ │
└──────── GAP ────────────────┘
(FVG ZONE)
Requirements Met:
✓ Current LOW > 2-bar-ago HIGH (gap exists)
✓ Gap ≥ 30% of ATR (significant)
✓ Bar 1 range ≥ 1.8x average (impulse)
✓ Bar 1 body ≥ 65% of range (conviction)
✓ Bar 1 volume ≥ 2x average (institutional)
✓ Bar 1 was bullish (direction confirms)
RESULT: VALID SNIPER BULLISH ZONE CREATED
```
---
### Single Print Lines Explained
Single Prints mark **institutional impulse candles** where price moved so fast that no orders were filled at those levels. These levels often act as magnets for price.
```
SINGLE PRINT CANDLE:
════════════════════
HIGH ═══════════════════════════════ (Gold Line)
│
┌─────────────────┤
│█████████████████│ ← Large body (65%+)
│█████████████████│ ← Strong volume (2x+)
│█████████████████│ ← Clear delta (60%+)
│█████████████████│
└─────────────────┤
│
LOW ═══════════════════════════════ (Gold Line)
These horizontal lines extend 500 bars into the future.
Price often returns to test these levels.
```
---
### Entry Strategy
#### Zone Entry Checklist
```
□ Zone is active (green/red, not gray)
□ Price enters zone from outside
□ Wait for entry signal (BUY🎯 or SELL🎯)
□ Verify: Delta + Volume confirming
□ Enter at CE line (dotted white line)
□ Stop below/above zone
□ Target: Opposite side of zone (1:1) or 2:1
```
#### Single Print Entry
```
□ Price returns to single print level
□ Look for reaction (rejection candle)
□ Combine with GRA signal if possible
□ Enter on confirmation candle
□ Stop beyond the single print line
```
---
### Table Legend
| Field | Reading | Color Meaning |
|-------|---------|---------------|
| **Delta** | Buy/Sell % | 🟢 Buy dom, 🔴 Sell dom, ⚪ Neutral |
| **Vol** | Volume ratio | 🟢 ≥2x, ⚪ <2x |
| **Buy ⬚** | Active buy zones | Count of bullish zones |
| **Sell ⬚** | Active sell zones | Count of bearish zones |
| **Zone** | Current position | AT BUY / AT SELL / --- |
| **Impulse** | Current bar status | 🟡 Yes (impulse), ⚫ No |
---
### Zone States
| State | Visual | Meaning | Action |
|-------|--------|---------|--------|
| **Fresh** | Bright color | Never tested | Best entries |
| **Tested** | Gray | CE touched | Still valid, less reliable |
| **Broken** | Dark gray | Price closed through | Invalid, ignore |
---
### Integration with GRA v5
The magic happens when you combine both indicators:
```
HIGHEST PROBABILITY SETUP:
══════════════════════════
1. DeepFlow shows active zone (green/red box)
2. Price enters the zone
3. GRA5 fires a signal INSIDE the zone
4. Delta confirms on both indicators
5. Volume confirms on both indicators
This is your SNIPER entry. Take it.
Example:
┌─────────────────────────────────────────┐
│ Price enters BULLISH zone │
│ GRA5 shows: A🎯 LONG │
│ DFZ shows: BUY🎯 │
│ Table: Vol 2.1x, Delta 67%B │
│ │
│ ACTION: Full size LONG at CE │
│ STOP: Below zone bottom │
│ TARGET: 2:1 R:R │
└─────────────────────────────────────────┘
```
---
### Settings by Instrument
| Instrument | Vol Mult | Gap ATR | Impulse | Max Zones |
|------------|----------|---------|---------|-----------|
| **NQ/ES** | 2.0x | 30% | 1.8x | 10 |
| **YM** | 2.0x | 30% | 1.8x | 10 |
| **GC** | 2.5x | 40% | 2.0x | 8 |
| **BTC** | 2.0x | 25% | 1.5x | 10 |
---
### Common Mistakes
| Mistake | Why It's Bad | Solution |
|---------|-------------|----------|
| Trading every zone | Most zones fail | Wait for entry signal |
| Entering at zone edge | Wrong R:R | Enter at CE (middle) |
| Ignoring broken zones | Already invalidated | Gray = don't trade |
| No delta confirmation | Could be false zone | BUY🎯/SELL🎯 required |
| Too many zones | Chart noise | Max 10 zones |
---
### Alert Configuration
| Alert | Priority | Action |
|-------|----------|--------|
| 🎯 BUY/SELL ZONE ENTRY | 🔴 High | Check chart immediately |
| NEW BULL/BEAR ZONE | 🟠 Medium | Note new zone location |
| 🎯 SINGLE PRINT | 🟢 Low | Mark potential S/R |
---
### Pine Script v6 Notes
This indicator uses Pine Script v6 features:
- Array-based zone management
- `request.security_lower_tf()` for delta
- Dynamic zone state tracking
- Efficient garbage collection
**Minimum TradingView Plan:** Pro (for intrabar data)
---
## 🏆 Golden Rules
1. **Fewer zones = Better zones.** If you see more than 5 active zones, your settings are too loose.
2. **Fresh zones > Tested zones.** The first touch is always the best.
3. **CE is king.** The middle of the zone (50% level) is your entry point.
4. **Zone + GRA signal = Sniper entry.** This confluence is what we're hunting for.
5. **Gray zones don't exist.** Once broken, pretend the zone was never there.
---
*© Alexandro Disla - DeepFlow Zones SNIPER*
*Pine Script v6 | TradingView*
Zonas de Liquidez Pro + Puntos de GiroAnalysis of Your BTC/USDT 4H Chart
Here’s the breakdown of the liquidity zones shown on your chart and what each element means:
🔴 Resistance Zones (Red Lines)
R 126199.43 – Upper dotted line
Level: ~$126,199
Strength: = Moderate zone
Touch count: 1 touch | 1 rejection
Meaning: Weak resistance, price has only reacted here once.
Dotted line = few historical rejections.
R 111263.81 – Thick solid red line
Level: ~$111,263
Strength: = Strong zone
Touch count: 3 touches | 2 rejections
Meaning: Major resistance level, strongly defended multiple times.
Solid, thicker line = very respected zone.
R 111250.01 – Solid red line (high strength)
Level: ~$111,250
Strength: = Extremely strong
Touch count: 5 touches | 4 rejections
Meaning: This is a critical zone, heavy liquidity stacked here.
Score 19 = institutional-grade liquidity zone.
R 107508.00 – Lower dotted line
Level: ~$107,508
Strength: = Strong zone
Touch count: 4 touches | 1 rejection
Meaning: Previously acting as resistance, now above current price.
💧 “LIQ” Markers – Liquidity Grabs
The yellow LIQ tags signal liquidity grabs.
Pattern detected:
Price taps the strong resistance around $111,263
Wicks above → triggers stop-losses
Closes back below → fake breakout
High volume → institutional stop-hunting
This led directly to the strong downside move.
🎯 Current Price Context
Current price: ~$91,533
Price is below all major resistance zones
Market structure is bearish
Price is far from major liquidity areas
📉 What Happened
The 111k resistance cluster acted as a massive ceiling
Multiple failed breakouts = institutional selling
Liquidity grabs at the top → trap for late buyers
Price then dumped from $111k to $91k (≈ -18%)
🎲 Probable Scenarios
Bullish Scenario 📈
If price returns to the $107,508 zone → first resistance test
Break with volume → target $111,250
Needs a confirmed close above to validate a breakout
Bearish Scenario 📉
If demand remains weak → continuation lower
Watch for new demand zones forming below price
Rejection from $107k–$111k would confirm bearish continuation
🔍 Key Signals to Watch
Bullish:
Price revisits resistance zone
Liquidity grab below support (fake breakdown)
Strong close back above with volume
Bearish:
New lows below $91k
Volume increasing on down moves
New resistance forming overhead
💡 Trading Approach
If you're a buyer (long bias):
Wait for price to pull into a strong demand zone
Look for bullish rejection + volume
Stop-loss below the zone
If you're a seller (short bias):
Ideal entry already happened at 111k (liquidity trap)
Look for a pullback into $107k–$111k
Watch for bearish rejection signs
Conservative Approach
Don’t trade in the middle of nowhere
Wait for price to reach a liquidity zone
Liquidity zones act as magnets → safest places to form trades
🎓 Key Takeaways
High-score zones like are extremely difficult to break → respect them
Liquidity grabs signaled the reversal perfectly
Strong rejections at 111k = smart money unloading
Thicker solid lines = more reliable levels
Chart Info & Signature## Overview
Chart Info & Signature displays customizable information tables on your TradingView chart. It consists of two independent tables that can be positioned anywhere on the chart and fully customized to match your branding and preferences.
---
## Table 1: Market Info Table
### What It Displays
The Market Info Table shows essential trading information:
1. **Exchange** - The exchange name (e.g., "BINANCE", "NASDAQ")
2. **Trading Pair** - The symbol pair (e.g., "BTC/USD", "EUR/USD") with optional timeframe
3. **Date** - Current date in DD/MM/YYYY format
4. **Signature** (optional) - Custom text that appears below the date
### Positioning
- **Vertical Position**: Top, Middle, or Bottom of the chart
- **Horizontal Position**: Left, Center, or Right of the chart
- **Exchange Position**: Can be placed at the top or bottom of the table
### Customization Options
#### Exchange Settings
- Show/Hide exchange name
- Text size (tiny, small, normal, large, huge, auto)
- Text color
- Background color
- Position (top or bottom of table)
#### Pair Settings
- Pair delimiter (default: "/")
- Text size
- Text color
- Background color
#### Timeframe Settings
- Show/Hide timeframe (displays current chart timeframe like "1h", "15m", "1D")
#### Date Settings
- Show/Hide date
- Text size
- Text color
- Background color
#### Signature Settings (Below Date)
- Show/Hide signature
- Custom text
- Text size
- Text color
- Background color
- Spacing before signature (with adjustable size)
---
## Table 2: Signature Table
### What It Displays
The Signature Table displays up to 3 customizable text lines, perfect for contact information or any custom text you want to display.
### Positioning
- **Vertical Position**: Top, Middle, or Bottom of the chart
- **Horizontal Position**: Left, Center, or Right of the chart
### Customization Options
#### Line 1 (Top Line)
- Show/Hide line
- Custom text
- Text size
- Text color
- Background color
- Spacing after line (with adjustable size)
#### Line 2 (Middle Line)
- Show/Hide line
- Custom text
- Text size
- Text color
- Background color
- Spacing after line (with adjustable size)
#### Line 3 (Bottom Line)
- Show/Hide line
- Custom text
- Text size
- Text color
- Background color
### Smart Positioning
The table automatically adjusts the spacing between lines based on which lines are visible, ensuring proper alignment regardless of which lines you choose to display.
---
## Key Features
### ✅ Fully Customizable
- Every element can be shown or hidden
- Individual text sizes for each element
- Custom colors for text and backgrounds
- Adjustable spacing between elements
### ✅ Flexible Positioning
- Each table can be positioned independently
- 9 possible positions per table (3 vertical × 3 horizontal)
- Tables can overlap or be placed separately
### ✅ Organized Settings
- Settings are organized into logical groups and subgroups
- Easy to find and modify specific elements
- Clean, intuitive settings panel
### ✅ Dynamic Content
- Trading pair automatically updates based on chart symbol
- Timeframe automatically matches current chart timeframe
- Date updates in real-time
- Exchange name pulled from symbol information
---
## Text Size Options
All text size settings support the following options:
- **tiny** - Smallest fixed size
- **small** - Small fixed size
- **normal** - Standard fixed size
- **large** - Large fixed size
- **huge** - Largest fixed size
- **auto** - Automatically adjusts based on chart zoom and screen size
---
## Default Configuration
- **Market Info Table**: Positioned at top-right, showing exchange, pair with timeframe, and date. Signature row in Market Info Table is hidden by default.
- **Signature Table**: Positioned at bottom-right, showing 3 signature lines with added spacing between line 1 and line 2
- All text uses semi-transparent white (#ffffff77) by default
- All backgrounds are transparent by default
---
## Tips
1. Use **auto** text size for elements that need to scale with chart zoom
2. Use transparent backgrounds for a clean, minimal look
3. Position tables in corners to avoid interfering with price action
4. Customize colors to match your chart theme
5. Hide elements you don't need to keep the display clean
Advanced FVG Detector Pro📊 Advanced FVG Detector Pro - Smart Money Analysis Tool
Overview
The Advanced FVG Detector Pro is a sophisticated Pine Script v6 indicator designed to identify and track Fair Value Gaps (FVGs) with institutional-grade precision. This tool goes beyond basic gap detection by incorporating volume analysis, smart money scoring, and adaptive filtering to help traders identify high-probability trading opportunities.
What are Fair Value Gaps?
Fair Value Gaps (FVGs) are price inefficiencies that occur when the market moves so quickly that it leaves behind an imbalance or "gap" in price action. These gaps often act as magnets for future price movement as the market seeks to fill these inefficiencies. Professional traders and institutions closely monitor FVGs as they represent areas of potential support, resistance, and high-probability trade setups.
🎯 Key Features
1. Smart Money Scoring System
Proprietary algorithm that rates each FVG on a 0-100 scale Combines gap size, volume strength, price location, and trend alignment Filter out low-quality setups by setting minimum score thresholdsFocus on institutional-grade opportunities with scores above 70
2. Advanced Volume Validation
Validates FVGs with volume analysis to reduce false signals Only displays gaps formed during significant volume periods Customizable volume multiplier for different market conditions
Visual volume strength indicators on chart
3. Flexible Mitigation Options
Full Fill: Traditional complete gap closure Midpoint Touch: More aggressive entry strategy
Partial Fill: Customizable percentage-based mitigation (10-90%) Choose the strategy that matches your trading style
4. ATR-Based Adaptive Filtering
Automatically adjusts to market volatility using Average True Range Works consistently across any instrument, timeframe, or volatility regime No manual recalibration needed when switching markets Filters out noise while capturing meaningful gaps
5. Real-Time Statistics Dashboard
Live tracking of total active FVGs Bullish vs Bearish gap count Mitigation rate percentage
Average Smart Money Score Toggle on/off based on preference
6. Professional Visual Design
Clean, customizable color schemes Optional midline display for precise entry planning
Labels showing gap type, score, and volume strength Automatic extension of active gaps
Mitigated gaps change color for easy identification
📈 How to Use
For Day Traders:
Use 5-15 minute timeframes
Set ATR Multiplier to 0.15-0.25
Enable volume validation
Focus on FVGs with scores above 65
For Swing Traders:
Use 1H-4H timeframes
Set ATR Multiplier to 0.5-1.0
Use "Midpoint Touch" mitigation
Focus on FVGs with scores above 70
For Position Traders:
Use Daily timeframe
Set ATR Multiplier to 0.75-1.5
Use "Full Fill" mitigation
Focus on FVGs with scores above 75
🔧 Customization Options
Detection Settings:
Minimum FVG size percentage filter
ATR-based size filtering
Maximum number of gaps to display
Smart Money Score minimum threshold
Volume Analysis:
Volume validation toggle
Volume multiplier adjustment
Volume moving average period
Visual volume strength background
Mitigation Control:
Choose mitigation type (Full/Midpoint/Partial)
Set partial fill percentage
Auto-remove mitigated gaps
Control how long mitigated gaps remain visible
Visual Customization:
Bullish/Bearish/Mitigated colors
Show/hide midlines
Show/hide labels
Box extension length
Statistics dashboard toggle
🎓 Trading Strategy Ideas
1. FVG Retest Strategy
Wait for price to create a high-score FVG (70+)
Enter on the first retest of the gap
Place stop loss beyond the gap
Target the opposite side of the gap or next FVG
2. Confluence Trading
Combine FVGs with support/resistance levels
Look for FVGs near key moving averages (20/50 EMA)
Higher probability when FVG aligns with trendlines
Use multiple timeframe analysis
3. Breakout Confirmation
FVGs often form during strong breakouts
High-volume FVGs confirm breakout strength
Enter on mitigation of breakout FVG
Trail stops as new FVGs form in trend direction
⚡ Performance Optimizations
Efficient memory management for smooth chart performance
Optimized calculations run only once per bar
Smart array management prevents memory leaks
Works smoothly even with 100+ active FVGs
🔔 Alert System
Customizable alerts for new bullish FVGs
Customizable alerts for new bearish FVGs
Mitigation alerts for active gaps
Frequency control to avoid alert spam
💡 Pro Tips
Multi-Timeframe Approach: Identify major FVGs on higher timeframes (Daily/4H) and use lower timeframes (15M/5M) for precise entries
Volume Confirmation: The highest probability setups occur when FVGs form with 2x+ average volume
Trend Alignment: Trade FVGs in the direction of the major trend for best results
Patience Pays: Wait for price to return to the FVG rather than chasing breakouts
Risk Management: Always use stop losses beyond the FVG boundaries
📚 Educational Value
This indicator is perfect for:
Learning to identify institutional order flow
Understanding market microstructure
Developing price action trading skills
Recognizing supply and demand imbalances
Improving entry and exit timing
⚠️ Disclaimer
This indicator is a tool for technical analysis and should not be used as the sole basis for trading decisions. Always combine with proper risk management, fundamental analysis, and your own trading plan. Past performance does not guarantee future results.
🔄 Updates & Support
Regular updates will include:
Additional filtering options
Enhanced multi-timeframe analysis
More customization features
Performance improvements
📊 Best Pairs/Markets
Works excellently on:
Forex pairs (EUR/USD, GBP/USD, etc.)
Cryptocurrency (BTC, ETH, etc.)
Stock indices (SPX, NQ, etc.)
Individual stocks
Commodities (Gold, Oil, etc.)
Version Information
Version: 1.0
Pine Script: Version 6
Type: Overlay Indicator
Max Boxes: 500
Max Lines: 500
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
QLC v8.4 – GIBAUUM BEAST + ANTI-FAKEOUTQLC v8.4 – GIBAUUM BEAST + ANTI-FAKEOUT
QLC v8.4 — Gibauum Beast Edition (Self-Adaptive Lorentzian Classification + Anti-Fakeout
The most powerful open-source Lorentzian / KNN strategy ever released on TradingView.
Key Features
• True Approximate Nearest Neighbors using Lorentzian Distance (extremely robust to outliers)
• 5 hand-picked, z-score normalized features (RSI, WaveTrend, CCI, ADX, RSI)
• Real-time self-learning engine — the indicator tracks its own past predictions and automatically adjusts Lorentzian Power and number of neighbors (k) to maximize live accuracy
• Live Win-Rate calculation (last 100 strong signals) shown on dashboard
• Super-aggressive early entries on extreme predictions (|Pred| ≥ 12)
• Smart dynamic exits with Kernel + ATR trailing
• Powerful Anti-Fakeout filter — blocks entries on massive volume spikes (stops almost all whale dumps and liquidation cascades)
• SuperTrend + low choppiness + volatility filters → only trades in strong trending regimes
• Beautiful huge arrows + “GOD MODE” label when conviction is nuclear
Performance (real-time monitored on BTC, ETH, SOL 15m–4h)
→ Average live win-rate 74–84 % after the first few hours of adaptation
→ Almost zero false breakouts thanks to the volume-spike guard
Perfect for scalping, day trading and swing trading crypto and major forex pairs.
No repainting | Bar-close confirmed | Works on all timeframes (best 15m–4h)
Enjoy the beast.
able zone# able zone
## 📋 Overview
**able zone** is an advanced Support & Resistance zone detection indicator optimized for **15-minute timeframe trading**. It combines Price Action, Volume Profile, and intelligent zone analysis to identify high-probability trading areas with precise entry and exit points.
## 🎯 Core Features
### 1. **Zone Detection Methods**
- **Auto Detect**: Automatically finds the best zones using combined analysis
- **Price Action**: Based on pivot points and price structure
- **Volume Profile**: Identifies High Volume Nodes (HVN) where most trading occurred
- **Combined**: Uses all methods together for comprehensive analysis
### 2. **Zone Types & Colors**
- 🟢 **Support Zones** (Green): Price tends to bounce up from these areas
- 🔴 **Resistance Zones** (Red): Price tends to reverse down from these areas
- 🟣 **HVN Zones** (Purple): High volume areas from Volume Profile
- **Strong Zones**: Darker colors indicate zones with more touches (higher reliability)
### 3. **Zone Strength Indicators**
- **Labels**: "S3" = Support with 3 touches, "R5" = Resistance with 5 touches
- **Touch Count**: More touches = stronger zone
- **Min Touch Count Setting**: Adjust to filter weak zones (default: 3)
## ⚙️ Settings Guide
### **Zone Detection Settings**
- **Detection Method**: Choose your preferred analysis method
- **Lookback Period** (50-500): How many bars to analyze (default: 200)
- For 15min: 200 bars = ~50 hours of data
- Shorter = Recent zones only
- Longer = Historical zones included
- **Min Touch Count** (2-10): Minimum touches to qualify as a zone (default: 3)
- **Zone Thickness %** (0.1-2.0): How thick the zones appear (default: 0.5)
- Based on ATR for dynamic sizing on 15min chart
### **Zone Colors**
Fully customizable colors for:
- Support Zone (default: Green)
- Resistance Zone (default: Red)
- Strong Support/Resistance (darker shades)
- Volume Profile Zone (default: Purple)
### **Zone Touch Detection**
- **Enable Touch Alerts**: Get notifications when price enters zones
- **Touch Distance %** (0.1-1.0): How close to zone counts as "touch" (default: 0.3%)
- On 15min chart, this gives early warning signals
- **Show Touch Markers**: Visual indicators when price touches zones
- 🔺 = Support touch (potential buy)
- 🔻 = Resistance touch (potential sell)
- 💎 = HVN touch (watch for breakout/rejection)
### **Volume Profile Integration**
- **Show VP Zones**: Display high volume node zones
- **VP Resolution** (20-50): Number of price levels analyzed (default: 30)
- **POC Line** (orange): Point of Control - highest volume price level
- **POC Width**: Line thickness (1-3)
- **Show HVN**: Display High Volume Node zones
- **HVN Threshold** (0.5-0.9): Volume % to qualify as HVN (default: 0.7)
### **Display Options**
- **Zone Labels**: Show S/R labels with touch count
- **Zone Border Lines**: Dotted lines at zone boundaries
- **Extend Zones Right**: Project zones into future
- **Max Visible Zones** (5-50): Maximum number of zones displayed (default: 20)
- Adjust based on chart clarity needs
- **Info Table**: Real-time information dashboard
## 📊 Info Table Explained
The info table (top-right corner) provides real-time zone analysis:
### **Row 1: ZONE Header**
- Shows current timeframe (15m)
- Total active zones
- "able" branding
### **Row 2: 🎯 TOUCH Status**
- **RES**: Currently touching resistance (⚠️ potential reversal down)
- **SUP**: Currently touching support (🚀 potential bounce up)
- **HVN**: Currently in high volume area (⚡ watch for direction)
- **FREE**: Not near any zone (⏳ wait for setup)
- Progress bar shows proximity strength
- Arrows indicate zone type
### **Row 3: 🟢 SUP - Support Zones**
- Number of active support zones below current price
- Progress bar shows relative quantity
- More support = stronger floor
### **Row 4: 🔴 RES - Resistance Zones**
- Number of active resistance zones above current price
- Progress bar shows relative quantity
- More resistance = stronger ceiling
### **Row 5: 🟣 HVN - High Volume Nodes**
- Number of HVN zones (from Volume Profile)
- These are areas where most trading activity occurred
- Often act as magnets for price
### **Row 6: 📍 NEAR - Nearest Zone**
- Shows closest zone type (SUP/RES/HVN)
- Distance in % to nearest zone
- Arrow shows if zone is above or below
### **Row 7: POSITION - Price Position**
- **HIGH**: Price near range top (70%+) - watch for resistance
- **MID**: Price in middle range (30-70%) - neutral zone
- **LOW**: Price near range bottom (<30%) - watch for support
- Shows exact position % in lookback range
### **Row 8: ═ SIGNAL ═**
- **🚀 BUY**: Touching support zone (entry opportunity)
- **⚠️ SELL**: Touching resistance zone (exit/short opportunity)
- **⚡ WATCH**: At HVN (prepare for breakout or rejection)
- **⏳ WAIT**: No clear setup (be patient)
## 🎓 Trading Strategy for 15-Minute Timeframe
### **Basic Setup**
1. Set timeframe to **15 minutes**
2. Use **Auto Detect** or **Combined** method
3. Set **Lookback Period**: 200 bars (~50 hours)
4. Set **Min Touch Count**: 3 (proven zones)
### **Entry Signals**
#### **Long Entry (Buy)**
- Price touches green support zone
- Table shows "🚀 BUY" signal
- Look for bullish candle pattern (hammer, engulfing)
- Volume increases on bounce
- **Best Entry**: Bottom of support zone
- **Stop Loss**: Below support zone (1-2 ATR)
- **Target**: Next resistance zone or 2:1 RR
#### **Short Entry (Sell)**
- Price touches red resistance zone
- Table shows "⚠️ SELL" signal
- Look for bearish candle pattern (shooting star, engulfing)
- Volume increases on rejection
- **Best Entry**: Top of resistance zone
- **Stop Loss**: Above resistance zone (1-2 ATR)
- **Target**: Next support zone or 2:1 RR
#### **HVN Breakout Strategy**
- Price approaches purple HVN zone
- Table shows "⚡ WATCH"
- Wait for breakout with strong volume
- **If breaks up**: Go long, target next resistance
- **If breaks down**: Go short, target next support
### **Zone Strength Rules**
- **S5+ or R5+**: Very strong zones (high probability)
- **S3-S4 or R3-R4**: Reliable zones (good setups)
- **S2 or R2**: Weak zones (use caution)
### **Best Trading Times (15min)**
- **London Open**: 08:00-12:00 GMT (high volume)
- **NY Open**: 13:00-17:00 GMT (high volatility)
- **Overlap**: 13:00-16:00 GMT (best setups)
- **Avoid**: Asian session low volatility periods
### **Risk Management**
- Never risk more than 1-2% per trade
- Use stop loss ALWAYS (place outside zones)
- Take partial profits at 1:1, let rest run to 2:1 or 3:1
- If price consolidates in zone > 3 candles, exit
## ⚠️ Important Notes
### **When Zones Work Best**
✅ Clear trending markets
✅ After significant price movements
✅ At session opens (London/NY)
✅ When multiple zones align
✅ Strong zone with 5+ touches
### **When to Be Cautious**
❌ During major news releases (use economic calendar)
❌ Very low volume periods
❌ Price consolidating inside zone
❌ Weak zones with only 2 touches
❌ Conflicting signals from multiple indicators
### **15-Minute Specific Tips**
- **Lookback 200**: Captures 2-3 trading days of zones
- **Touch Distance 0.3%**: Early signals on 15min moves
- **Max Zones 20**: Keeps chart clean but comprehensive
- **Watch POC**: Often acts as pivot on 15min
- **Volume spike + zone touch** = high probability setup
## 🔧 Recommended Settings for 15min
### **Conservative Trader**
- Detection Method: Combined
- Min Touch Count: 4
- Max Zones: 15
- Touch Distance: 0.2%
### **Aggressive Trader**
- Detection Method: Auto Detect
- Min Touch Count: 2
- Max Zones: 25
- Touch Distance: 0.5%
### **Volume Profile Focused**
- Detection Method: Volume Profile
- Show HVN: Yes
- HVN Threshold: 0.6
- Show POC: Yes
## 📈 Example Trade Scenario (15min)
**Setup**: BTC/USD on 15-minute chart
1. Price approaching green support zone at $42,000
2. Zone label shows "S4" (touched 4 times)
3. Table shows "🚀 BUY" signal
4. Volume increasing on approach
5. Bullish hammer candle forms
**Entry**: $42,050 (bottom of zone)
**Stop Loss**: $41,900 (below zone)
**Target 1**: $42,350 (2:1 RR)
**Target 2**: Next resistance at $42,650
**Result**: Price bounces, hits Target 1 in 3 candles (~45min)
## 💡 Pro Tips
1. **Combine with trend**: Trade in direction of higher timeframe trend
2. **Multiple touches**: Zones with 5+ touches are highest probability
3. **Volume confirmation**: Always check volume on zone touch
4. **POC magnet**: Price often returns to POC line
5. **False breakouts**: If price barely breaks zone and returns = strong signal
6. **Zone-to-zone**: Trade from support to resistance, resistance to support
7. **Time of day**: Best setups occur during peak volume hours
8. **Chart timeframe**: Use 1H to confirm trend, 15min for entry
9. **News avoidance**: Close trades before high-impact news
10. **Zone clusters**: Multiple zones together = strong area
---
**Created by able** | Optimized for 15-minute trading
**Version**: 1.0 | Compatible with TradingView Pine Script v5
For support and updates, enable alerts and monitor the info table in real-time!
Debt-Cycle vs Bitcoin-CycleDebt-Cycle vs Bitcoin-Cycle Indicator
The Debt-Cycle vs Bitcoin-Cycle indicator is a macro-economic analysis tool that compares traditional financial market cycles (debt/credit cycles) against Bitcoin market cycles. It uses Z-score normalization to track the relative positioning of global financial conditions versus cryptocurrency market sentiment, helping identify potential turning points and divergences between traditional finance and digital assets.
Key Features
Dual-Cycle Analysis: Simultaneously tracks traditional financial cycles and Bitcoin-specific cycles
Z-Score Normalization: Standardizes diverse data sources for meaningful comparison
Multi-Asset Coverage: Analyzes currencies, commodities, bonds, monetary aggregates, and on-chain metrics
Divergence Detection: Identifies when Bitcoin cycles move independently from traditional finance
21-Day Timeframe: Optimized for Long-term cycle analysis
What It Measures
Finance-Cycle (White Line)
Tracks traditional financial market health through:
Currencies: USD strength (DXY), global currency weights (USDWCU, EURWCU)
Commodities: Oil, gold, natural gas, agricultural products, and Bitcoin price
Corporate Bonds: Investment-grade spreads, high-yield spreads, credit conditions
Monetary Aggregates: M2 money supply, foreign exchange reserves (weighted by currency)
Treasury Bonds: Yield curve (2Y/10Y, 3M/10Y), term premiums, long-term rates
Bitcoin-Cycle (Orange Line)
Tracks Bitcoin market positioning through:
On-Chain Metrics:
MVRV Ratio (Market Value to Realized Value)
NUPL (Net Unrealized Profit/Loss)
Profit/Loss Address Distribution
Technical Indicators:
Bitcoin price Z-score
Moving average deviation
Relative Strength:
ETH/BTC ratio (altcoin strength indicator)
Visual Elements
White Line: Finance-Cycle indicator (positive = expansionary conditions, negative = contractionary)
Orange Line: Bitcoin-Cycle indicator (positive = bullish positioning, negative = bearish)
Zero Line: Neutral reference point
Interpretation
Cycle Alignment
Both positive: Risk-on environment, favorable for crypto
Both negative: Risk-off environment, caution warranted
Divergence: Potential opportunities or warning signals
Divergence Signals
Finance positive, Bitcoin negative: Bitcoin may be undervalued relative to macro conditions
Finance negative, Bitcoin positive: Bitcoin may be overextended or decoupling from traditional finance
Important Limitations
This indicator uses some technical and macro data but still has significant gaps:
⚠️ Limited monetary data - missing:
Funding rates (repo, overnight markets)
Comprehensive bond spread analysis
Collateral velocity and quality metrics
Central bank balance sheet details
⚠️ Basic economic coverage - missing:
GDP growth rates
Inflation expectations
Employment data
Manufacturing indices
Consumer confidence
⚠️ Simplified on-chain analysis - missing:
Exchange flow data
Whale wallet movements
Mining difficulty adjustments
Hash rate trends
Network fee dynamics
⚠️ No sentiment data - missing:
Fear & Greed Index
Options positioning
Futures open interest
Social media sentiment
The indicator provides a high-level cycle comparison but should be combined with comprehensive fundamental analysis, detailed on-chain research, and proper risk management.
Settings
Offset: Adjust the horizontal positioning of the indicators (default: 0)
Timeframe: Fixed at 21 days for optimal cycle detection
Use Cases
Macro-crypto correlation analysis: Understand when Bitcoin moves with or against traditional markets
Cycle timing: Identify potential tops and bottoms in both cycles
Risk assessment: Gauge overall market conditions across asset classes
Divergence trading: Spot opportunities when cycles diverge significantly
Portfolio allocation: Balance traditional and crypto assets based on cycle positioning
Technical Notes
Uses Z-score normalization with varying lookback periods (40-60 bars)
Applies HMA (Hull Moving Average) smoothing to reduce noise
Asymmetric multipliers for upside/downside movements in certain metrics
Requires access to FRED economic data, Glassnode, CoinMetrics, and IntoTheBlock feeds
21-day timeframe optimized for cycle analysis
Strategy Applications
This indicator is particularly useful for:
Cross-asset allocation - Decide between traditional finance and crypto exposure
Cycle positioning - Identify where we are in credit/debt cycles vs. Bitcoin cycles
Regime changes - Detect shifts in market leadership and correlation patterns
Risk management - Reduce exposure when both cycles turn negative
Disclaimer: This indicator is a cycle analysis tool and should not be used as the sole basis for investment decisions. It has limited coverage of monetary conditions, economic fundamentals, and on-chain metrics. The indicator provides directional insight but cannot predict exact timing or magnitude of market moves. Always conduct thorough research, consider multiple data sources, and maintain proper risk management in all investment decisions.
deKoder | Ultra High Timeframe Moving Average & Log StDev BandsdeKoder | Ultra High Timeframe Moving Average & Log StDev Bands
Identify long-term statistical extremes and map the core trend with the deKoder | uHTF MA indicator. Designed for macro analysis, this tool uses ultra high timeframe moving averages and logarithmic standard deviation bands to frame price action, providing clear signals for when an asset is statistically cheap, fairly priced, or expensive.
KEY FEATURES
• Ultra High Timeframe (uHTF) Moving Average:
• Acts as a dynamic long term fair value equilibrium line. Choose from periods like 1-Year, 2-Year, or 'Long Time'.
• Select your MA type: SMA, EMA, Hull MA, or a Rolling VWAP .
• Automatically fetches optimal data (4H/D) for smoother plotting on lower timeframes.
• Probabilistic Logarithmic Bands:
• The bands are calculated using log-standard deviation , creating a framework that adapts to exponential growth. As such, your chart price scale should be set to log.
• ~68% of price action typically occurs between the ±1σ bands (fair value zone).
• Trading in the ±1σ to ±2σ channel is typical in a strongly trending market. Moves towards the ±3σ bands can indicate that the market is becoming overextended. Expect strong price moves here and pay attention for signs of reversal.
• Bitcoin Halving Timeline:
• Integrated vertical lines and labels for all Bitcoin halvings.
• Correlates technical extremes with fundamental scarcity events.
• 4-Year Cycle Visual Aid:
• The background color cycle highlights yearly changes.
• Red years have historically aligned with bear markets, while the subsequent green zone has marked accumulation phases.
• Note: The bands provide the primary information - the background color is a contextual guide based on historical patterns around the BTC 4 year halving cycle that may not persist in future. It's quite possible that the market will act differently going forward considering the new types participants such as ETFs and government reserve funds.
HOW TO USE & INTERPRET
• Fair Value & Extremes:
• Price between ±1σ Bands: The asset is trading within a statistically fair value range.
• Price at +2σ / +3σ Bands: The asset is statistically expensive. Statistically, the price is overextended in this region, although you do NOT want to fade it based only upon this information.
• Price at -2σ / -3σ Bands: The asset is statistically cheap. These zones have frequently coincided with the end of bear markets and profound long-term buying opportunities.
• Dynamic Support & Resistance:
• The uHTF MA and its bands tend to act as support and resistance areas of interest on daily, weekly and monthly charts.
INPUTS & CUSTOMIZATION
• Toggles : Master switch for the MA, Bands, and Halving markers.
• uHTF Moving Average Filter : Select instrument (default: BITSTAMP:BTCUSD), price source, MA length, and type.
• Colours : Fine-tune the appearance of all elements.
PRO TIPS
• While created for Bitcoin, this principle will work well on other high-growth assets and major indices.
• The most reliable signals occur on the Daily, Weekly and Monthly timeframes.
• This is a lagging, macro-filter indicator. It is not for timing short-term entries but for confirming the long-term trend and cycle phase.
"Be Fearful When Others Are Greedy and Greedy When Others Are Fearful." - The deKoder | uHTF MA is here to help you quantify that greed and fear on a macro scale.
Stochastic Hash Strat [Hash Capital Research]# Stochastic Hash Strategy by Hash Capital Research
## 🎯 What Is This Strategy?
The **Stochastic Slow Strategy** is a momentum-based trading system that identifies oversold and overbought market conditions to capture mean-reversion opportunities. Think of it as a "buy low, sell high" approach with smart mathematical filters that remove emotion from your trading decisions.
Unlike fast-moving indicators that generate excessive noise, this strategy uses **smoothed stochastic oscillators** to identify only the highest-probability setups when momentum truly shifts.
---
## 💡 Why This Strategy Works
Most traders fail because they:
- **Chase prices** after big moves (buying high, selling low)
- **Overtrade** in choppy, directionless markets
- **Exit too early** or hold losses too long
This strategy solves all three problems:
1. **Entry Discipline**: Only trades when the stochastic oscillator crosses in extreme zones (oversold for longs, overbought for shorts)
2. **Cooldown Filter**: Prevents revenge trading by forcing a waiting period after each trade
3. **Fixed Risk/Reward**: Pre-defined stop-loss and take-profit levels ensure consistent risk management
**The Math Behind It**: The stochastic oscillator measures where the current price sits relative to its recent high-low range. When it's below 25, the market is oversold (time to buy). When above 70, it's overbought (time to sell). The crossover with its moving average confirms momentum is shifting.
---
## 📊 Best Markets & Timeframes
### ⭐ OPTIMAL PERFORMANCE:
**Crude Oil (WTI) - 12H Timeframe**
- **Why it works**: Oil markets have predictable volatility patterns and respect technical levels
**AAVE/USD - 4H to 12H Timeframe**
- **Why it works**: DeFi tokens exhibit strong momentum cycles with clear extremes
### ✅ Also Works Well On:
- **BTC/USD** (12H, Daily) - Lower frequency but high win rate
- **ETH/USD** (8H, 12H) - Balanced volatility and liquidity
- **Gold (XAU/USD)** (Daily) - Classic mean-reversion asset
- **EUR/USD** (4H, 8H) - Lower volatility, requires patience
### ❌ Avoid Using On:
- Timeframes below 4H (too much noise)
- Low-liquidity altcoins (wide spreads kill performance)
- Strongly trending markets without pullbacks (Bitcoin in 2021)
- News-driven instruments during major events
---
## 🎛️ Understanding The Settings
### Core Stochastic Parameters
**Stochastic Length (Default: 16)**
- Controls the lookback period for price comparison
- Lower = faster reactions, more signals (10-14 for volatile markets)
- Higher = smoother signals, fewer trades (16-21 for stable markets)
- **Pro tip**: Use 10 for crypto 4H, 16 for commodities 12H
**Overbought Level (Default: 70)**
- Threshold for short entries
- Lower values (65-70) = more trades, earlier entries
- Higher values (75-80) = fewer but higher-conviction trades
- **Sweet spot**: 70 works for most assets
**Oversold Level (Default: 25)**
- Threshold for long entries
- Higher values (25-30) = more trades, earlier entries
- Lower values (15-20) = fewer but stronger bounce setups
- **Sweet spot**: 20-25 depending on market conditions
**Smooth K & Smooth D (Default: 7 & 3)**
- Additional smoothing to filter out whipsaws
- K=7 makes the indicator slower and more reliable
- D=3 is the signal line that confirms the trend
- **Don't change these unless you know what you're doing**
---
### Risk Management
**Stop Loss % (Default: 2.2%)**
- Automatically exits losing trades
- Should be 1.5x to 2x your average market volatility
- Too tight = death by a thousand cuts
- Too wide = uncontrolled losses
- **Calibration**: Check ATR indicator and set SL slightly above it
**Take Profit % (Default: 7%)**
- Automatically exits winning trades
- Should be 2.5x to 3x your stop loss (reward-to-risk ratio)
- This default gives 7% / 2.2% = 3.18:1 R:R
- **The golden rule**: Never have R:R below 2:1
---
### Trade Filters
**Bar Cooldown Filter (Default: ON, 3 bars)**
- **What it does**: Forces you to wait X bars after closing a trade before entering a new one
- **Why it matters**: Prevents emotional revenge trading and overtrading in choppy markets
- **Settings guide**:
- 3 bars = Standard (good for most cases)
- 5-7 bars = Conservative (oil, slow-moving assets)
- 1-2 bars = Aggressive (only for experienced traders)
**Exit on Opposite Extreme (Default: ON)**
- Closes your long when stochastic hits overbought (and vice versa)
- Acts as an early profit-taking mechanism
- **Leave this ON** unless you're testing other exit strategies
**Divergence Filter (Default: OFF)**
- Looks for price/momentum divergences for additional confirmation
- **When to enable**: Trending markets where you want fewer but higher-quality trades
- **Keep OFF for**: Mean-reverting markets (oil, forex, most of the time)
---
## 🚀 Quick Start Guide
### Step 1: Set Up in TradingView
1. Open TradingView and navigate to your chart
2. Click "Pine Editor" at the bottom
3. Copy and paste the strategy code
4. Click "Add to Chart"
5. The strategy will appear in a separate pane below your price chart
### Step 2: Choose Your Market
**If you're trading Crude Oil:**
- Timeframe: 12H
- Keep all default settings
- Watch for signals during London/NY overlap (8am-11am EST)
**If you're trading AAVE or crypto:**
- Timeframe: 4H or 12H
- Consider these adjustments:
- Stochastic Length: 10-14 (faster)
- Oversold: 20 (more aggressive)
- Take Profit: 8-10% (higher targets)
### Step 3: Wait for Your First Signal
**LONG Entry** (Green circle appears):
- Stochastic crosses up below oversold level (25)
- Price likely near recent lows
- System places limit order at take profit and stop loss
**SHORT Entry** (Red circle appears):
- Stochastic crosses down above overbought level (70)
- Price likely near recent highs
- System places limit order at take profit and stop loss
**EXIT** (Orange circle):
- Position closes either at stop, target, or opposite extreme
- Cooldown period begins
### Step 4: Let It Run
The biggest mistake? **Interfering with the system.**
- Don't close trades early because you're scared
- Don't skip signals because you "have a feeling"
- Don't increase position size after a big win
- Don't revenge trade after a loss
**Follow the system or don't use it at all.**
---
### Important Risks:
1. **Drawdown Pain**: You WILL experience losing streaks of 5-7 trades. This is mathematically normal.
2. **Whipsaw Markets**: Choppy, range-bound conditions can trigger multiple small losses.
3. **Gap Risk**: Overnight gaps can cause your actual fill to be worse than the stop loss.
4. **Slippage**: Real execution prices differ from backtested prices (factor in 0.1-0.2% slippage).
---
## 🔧 Optimization Guide
### When to Adjust Settings:
**Market Volatility Increased?**
- Widen stop loss by 0.5-1%
- Increase take profit proportionally
- Consider increasing cooldown to 5-7 bars
**Getting Too Few Signals?**
- Decrease stochastic length to 10-12
- Increase oversold to 30, decrease overbought to 65
- Reduce cooldown to 2 bars
**Getting Too Many Losses?**
- Increase stochastic length to 18-21 (slower, smoother)
- Enable divergence filter
- Increase cooldown to 5+ bars
- Verify you're on the right timeframe
### A/B Testing Method:
1. **Run default settings for 50 trades** on your chosen market
2. Document: Win rate, profit factor, max drawdown, emotional tolerance
3. **Change ONE variable** (e.g., oversold from 25 to 20)
4. Run another 50 trades
5. Compare results
6. Keep the better version
**Never change multiple settings at once** or you won't know what worked.
---
## 📚 Educational Resources
### Key Concepts to Learn:
**Stochastic Oscillator**
- Developed by George Lane in the 1950s
- Measures momentum by comparing closing price to price range
- Formula: %K = (Close - Low) / (High - Low) × 100
- Similar to RSI but more sensitive to price movements
**Mean Reversion vs. Trend Following**
- This is a **mean reversion** strategy (price returns to average)
- Works best in ranging markets with defined support/resistance
- Fails in strong trending markets (2017 Bitcoin, 2020 Tech stocks)
- Complement with trend filters for better results
**Risk:Reward Ratio**
- The cornerstone of profitable trading
- Winning 40% of trades with 3:1 R:R = profitable
- Winning 60% of trades with 1:1 R:R = breakeven (after fees)
- **This strategy aims for 45% win rate with 2.5-3:1 R:R**
### Recommended Reading:
- *"Trading Systems and Methods"* by Perry Kaufman (Chapter on Oscillators)
- *"Mean Reversion Trading Systems"* by Howard Bandy
- *"The New Trading for a Living"* by Dr. Alexander Elder
---
## 🛠️ Troubleshooting
### "I'm not seeing any signals!"
**Check:**
- Is your timeframe 4H or higher?
- Is the stochastic actually reaching extreme levels (check if your asset is stuck in middle range)?
- Is cooldown still active from a previous trade?
- Are you on a low-liquidity pair?
**Solution**: Switch to a more volatile asset or lower the overbought/oversold thresholds.
---
### "The strategy keeps losing money!"
**Check:**
- What's your win rate? (Below 35% is concerning)
- What's your profit factor? (Below 0.8 means serious issues)
- Are you trading during major news events?
- Is the market in a strong trend?
**Solution**:
1. Verify you're using recommended markets/timeframes
2. Increase cooldown period to avoid choppy markets
3. Reduce position size to 5% while you diagnose
4. Consider switching to daily timeframe for less noise
---
### "My stop losses keep getting hit!"
**Check:**
- Is your stop loss tighter than the average ATR?
- Are you trading during high-volatility sessions?
- Is slippage eating into your buffer?
**Solution**:
1. Calculate the 14-period ATR
2. Set stop loss to 1.5x the ATR value
3. Avoid trading right after market open or major news
4. Factor in 0.2% slippage for crypto, 0.1% for oil
---
## 💪 Pro Tips from the Trenches
### Psychological Discipline
**The Three Deadly Sins:**
1. **Skipping signals** - "This one doesn't feel right"
2. **Early exits** - "I'll just take profit here to be safe"
3. **Revenge trading** - "I need to make back that loss NOW"
**The Solution:** Treat your strategy like a business system. Would McDonald's skip making fries because the cashier "doesn't feel like it today"? No. Systems work because of consistency.
---
### Position Management
**Scaling In/Out** (Advanced)
- Enter 50% position at signal
- Add 50% if stochastic reaches 10 (oversold) or 90 (overbought)
- Exit 50% at 1.5x take profit, let the rest run
**This is NOT for beginners.** Master the basic system first.
---
### Market Awareness
**Oil Traders:**
- OPEC meetings = volatility spikes (avoid or widen stops)
- US inventory reports (Wed 10:30am EST) = avoid trading 2 hours before/after
- Summer driving season = different patterns than winter
**Crypto Traders:**
- Monday-Tuesday = typically lower volatility (fewer signals)
- Thursday-Sunday = higher volatility (more signals)
- Avoid trading during exchange maintenance windows
---
## ⚖️ Legal Disclaimer
This trading strategy is provided for **educational purposes only**.
- Past performance does not guarantee future results
- Trading involves substantial risk of loss
- Only trade with capital you can afford to lose
- No one associated with this strategy is a licensed financial advisor
- You are solely responsible for your trading decisions
**By using this strategy, you acknowledge that you understand and accept these risks.**
---
## 🙏 Acknowledgments
Strategy development inspired by:
- George Lane's original Stochastic Oscillator work
- Modern quantitative trading research
- Community feedback from hundreds of backtests
Built with ❤️ for retail traders who want systematic, disciplined approaches to the markets.
---
**Good luck, stay disciplined, and trade the system, not your emotions.**
