Macroeconomic Artificial Neural Networks
This script was created by training 20 selected macroeconomic data to construct artificial neural networks on the S&P 500 index.
No technical analysis data were used.
The average error rate is 0.01.
In this respect, there is a strong relationship between the index and macroeconomic data.
Although it affects the whole world,I personally recommend using it under the following conditions: S&P 500 and related ETFs in 1W time-frame (TF = 1W SPX500USD, SP1!, SPY, SPX etc. )
Macroeconomic Parameters
Effective Federal Funds Rate (FEDFUNDS)
Initial Claims (ICSA)
Civilian Unemployment Rate (UNRATE)
10 Year Treasury Constant Maturity Rate (DGS10)
Gross Domestic Product , 1 Decimal (GDP)
Trade Weighted US Dollar Index : Major Currencies (DTWEXM)
Consumer Price Index For All Urban Consumers (CPIAUCSL)
M1 Money Stock (M1)
M2 Money Stock (M2)
2 - Year Treasury Constant Maturity Rate (DGS2)
30 Year Treasury Constant Maturity Rate (DGS30)
Industrial Production Index (INDPRO)
5-Year Treasury Constant Maturity Rate (FRED : DGS5)
Light Weight Vehicle Sales: Autos and Light Trucks (ALTSALES)
Civilian Employment Population Ratio (EMRATIO)
Capacity Utilization (TOTAL INDUSTRY) (TCU)
Average (Mean) Duration Of Unemployment (UEMPMEAN)
Manufacturing Employment Index (MAN_EMPL)
Manufacturers' New Orders (NEWORDER)
ISM Manufacturing Index (MAN : PMI)
Artificial Neural Network (ANN) Training Details :
Learning cycles: 16231
AutoSave cycles: 100
Grid
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 998
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Network
Input nodes connected: 19
Hidden layer 1 nodes: 2
Hidden layer 2 nodes: 0
Hidden layer 3 nodes: 0
Output nodes: 1
Controls
Learning rate: 0.1000
Momentum: 0.8000 (Optimized)
Target error: 0.0100
Training error: 0.010000
NOTE : Alerts added . The red histogram represents the bear market and the green histogram represents the bull market.
Bars subject to region changes are shown as background colors. (Teal = Bull , Maroon = Bear Market )
I hope it will be useful in your studies and analysis, regards.
Cari skrip untuk "美股标普500"
Damped Sine Wave Weighted FilterIntroduction
Remember that we can make filters by using convolution, that is summing the product between the input and the filter coefficients, the set of filter coefficients is sometime denoted "kernel", those coefficients can be a same value (simple moving average), a linear function (linearly weighted moving average), a gaussian function (gaussian filter), a polynomial function (lsma of degree p with p = order of the polynomial), you can make many types of kernels, note however that it is easy to fall into the redundancy trap.
Today a low-lag filter who weight the price with a damped sine wave is proposed, the filter characteristics are discussed below.
A Damped Sine Wave
A damped sine wave is a like a sine wave with the difference that the sine wave peak amplitude decay over time.
A damped sine wave
Used Kernel
We use a damped sine wave of period length as kernel.
The coefficients underweight older values which allow the filter to reduce lag.
Step Response
Because the filter has overshoot in the step response we can conclude that there are frequencies amplified in the passband, we could have reached to this conclusion by simply seeing the negative values in the kernel or the "zero-lag" effect on the closing price.
Enough ! We Want To See The Filter !
I should indeed stop bothering you with transient responses but its always good to see how the filter act on simpler signals before seeing it on the closing price. The filter has low-lag and can be used as input for other indicators
Filter with length = 100 as input for the rsi.
The bands trailing stop utility using rolling squared mean average error with length 500 using the filter of length 500 as input.
Approximating A Least Squares Moving Average
A least squares moving average has a linear kernel with certain values under 0, a lsma of length k can be approximated using the proposed filter using period p where p = k + k/4 .
Proposed filter (red) with length = 250 and lsma (blue) with length = 200.
Conclusions
The use of damping in filter design can provide extremely useful filters, in fact the ideal kernel, the sinc function, is also a damped sine wave.
VIX reversion-Buschi
English:
A significant intraday reversion (commonly used: 3 points) on a high (over 20 points) S&P 500 Volatility Index (VIX) can be a sign of a market bottom, because there is the assumption that some of the "big guys" liquidated their options / insurances because the worst is over.
This indicator shows these reversions (3 points as default) when the VIX was over 20 points. The character "R" is then shown directly over the daily column, the VIX need not to be loaded explicitly.
Deutsch:
Eine deutliche Intraday-Umkehr (3 Punkte im Normalfall) bei einem hohen (über 20 Punkte) S&P 500 Volatility Index (VIX) kann ein Zeichen für eine Bodenbildung im Markt sein, weil möglicherweise einige "große Jungs" ihre Optionen / Versicherungen auflösen, weil das schlimmste vorbei ist.
Dieser Indikator zeigt diese Umkehr (Standardwert: 3 Punkte), wenn der VIX vorher über 20 Punkte lag. Der Buchstabe "R" wird dabei direkt über dem Tagesbalken angezeigt, wobei der VIX nicht explizit geladen werden muss.
Relative Price StrengthThe strength of a stock relative to the S&P 500 is key part of most traders decision making process. Hence the default reference security is SPY, the most commonly trades S&P 500 ETF.
Most profitable traders buy stocks that are showing persistence intermediate strength verses the S&P as this has been shown to work. Hence the default period is 63 days or 3 months.
TICK Extremes IndicatorSimple TICK indicator, plots candles and HL2 line
Conditional green/red coloring for highs above 500, 900 and lows above 0, and for lows below -500, -900, and highs above 0
Probably best used for 1 - 5 min timeframes
Always open to suggestions if criteria needs tweaking or if something else would make it more useful or user-friendly!
Market direction and pullback based on S&P 500.A simple indicator based on www.swing-trade-stocks.com The link is also the guide for how to use it.
0 - nothing. If the indicator is showing 0 for a prolonged amount of time, it is likely the market is in "momentum mode" (referred to in the link above).
1 - indicates an uptrend based on SMA and EMA and also a place where a reversal to the upside is likely to occur. You should look only for long trades in the stock market when you see a spike upwards and S&P 500 is showing an obvious uptrend.
-1 - indicates a downtrend based on SMA and EMA and also a place where a reversal to the downside is likely to occur. You should look only for short trades in the stock market when you see a spike upwards and S&P 500 is showing an obvious uptrend.
Net XRP Margin PositionTotal XRP Longs minus XRP Shorts in order to give you the total outstanding XRP margin debt.
ie: If 500,000 XRP has been longed, and 400,000 XRP has been shorted, then 500,000 has been bought, and 400,000 sold, leaving us with 100,000 XRP (net) remaining to be sold to give us an overall neutral margin position.
That isn't to say that the net margin position must move towards zero, but it is a sensible reference point, and historical net values may provide useful insights into the current circumstances.
Net DASH Margin PositionTotal DASH Longs minus DASH Shorts in order to give you the total outstanding DASH margin debt.
ie: If 500,000 DASH has been longed, and 400,000 DASH has been shorted, then 500,000 has been bought, and 400,000 sold, leaving us with 100,000 DASH (net) remaining to be sold to give us an overall neutral margin position.
That isn't to say that the net margin position must move towards zero, but it is a sensible reference point, and historical net values may provide useful insights into the current circumstances.
(Anyone know what category this script should be in?)
Net NEO Margin PositionTotal NEO Longs minus NEO Shorts in order to give you the total outstanding NEO margin debt.
ie: If 500,000 NEO has been longed, and 400,000 NEO has been shorted, then 500,000 has been bought, and 400,000 sold, leaving us with 100,000 NEO (net) remaining to be sold to give us an overall neutral margin position.
That isn't to say that the net margin position must move towards zero, but it is a sensible reference point, and historical net values may provide useful insights into the current circumstances.
(Anyone know what category this script should be in?)
Everyday 0002 _ MAC 1st Trading Hour WalkoverThis is the second strategy for my Everyday project.
Like I wrote the last time - my goal is to create a new strategy everyday
for the rest of 2016 and post it here on TradingView.
I'm a complete beginner so this is my way of learning about coding strategies.
I'll give myself between 15 minutes and 2 hours to complete each creation.
This is basically a repetition of the first strategy I wrote - a Moving Average Crossover,
but I added a tiny thing.
I read that "Statistics have proven that the daily high or low is established within the first hour of trading on more than 70% of the time."
(source: )
My first Moving Average Crossover strategy, tested on VOLVB daily, got stoped out by the volatility
and because of this missed one nice bull run and a very nice bear run.
So I added this single line: if time("60", "1000-1600") regarding when to take exits:
if time("60", "1000-1600")
strategy.exit("Close Long", "Long", profit=2000, loss=500)
strategy.exit("Close Short", "Short", profit=2000, loss=500)
Sweden is UTC+2 so I guess UTC 1000 equals 12.00 in Stockholm. Not sure if this is correct, actually.
Anyway, I hope this means the strategy will only take exits based on price action which occur in the afternoon, when there is a higher probability of a lower volatility.
When I ran the new modified strategy on the same VOLVB daily it didn't get stoped out so easily.
On the other hand I'll have to test this on various stocks .
Reading and learning about how to properly test strategies is on my todo list - all tips on youtube videos or blogs
to read on this topic is very welcome!
Like I said the last time, I'm posting these strategies hoping to learn from the community - so any feedback, advice, or corrections is very much welcome and appreciated!
/pbergden
INDIVIDUAL ASSET BIAS DASHBOARD V3Strategy Name: Individual Asset Bias Dashboard V3
Author Concept: Multi-timeframe 3-pivot alignment bias monitor
Timeframe: Works on any chart, but bias is calculated on daily close vs higher timeframe pivots
Core Idea (3-Pivot Rule)
For each asset we compare the current daily closing level against three classic pivots from higher timeframes:
Previous Weekly pivot: (H+L+C)/3 of last completed week
Previous Monthly pivot: (H+L+C)/3 of last completed month
Previous 3-Monthly pivot: (H+L+C)/3 of last completed quarter
Bias Logic:
BULL → Price is above all three pivots
BEAR → Price is below all three pivots
MIXED → Price is in between (no clear alignment)
This is a clean, objective, and widely used institutional method to gauge short-term momentum alignment across multiple horizons.
Assets Tracke
SymbolMeaningSPX500S&P 500 IndexVIXVolatility IndexDXYUS Dollar IndexBTCUSDBitcoinXAUUSDGoldUSOILWTI Crude OilUS10Y10-Year US Treasury YieldUSDJPYJapanese Yen pair
Key Features
Real-time updating table in the bottom-left corner
Color coding: Lime = Bullish, Red = Bearish, Gray = Mixed
Optional "Change" column showing flips (▲/▼) when bias changes day-over-day
No repainting on closed daily bars (critical for reliability)
Compliant with TradingView rules (proper lookahead usage explained below)
Important Technical Notes (Why No Repainting)
lookahead = barmerge.lookahead_on is used only for higher-timeframe historical pivots → allowed and standard practice
Current price uses lookahead = barmerge.lookahead_off → reflects actual tradable daily close
Table only draws on barstate.islastconfirmedhistory or barstate.islast → prevents false signals on realtime bar
Limitations & Warnings
On intraday charts, the "current bias" updates with every tick using the running daily close
Bias can flip intraday before daily bar closes
On daily or higher charts, the dashboard is 100% confirmation-based and non-repainting
This is a bias filter, not a standalone trading system
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*
GRA v5 SNIPER# GRA v5 SNIPER - Documentation & Cheatsheet
## 🎯 Get Rich Aggressively v5 - SNIPER Edition
**Precision Futures Scalping | NQ • ES • YM • GC • BTC**
> **Philosophy:** *Quality over quantity. One sniper shot beats ten spray-and-pray attempts.*
---
## ⚡ QUICK CHEATSHEET
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ GRA v5 SNIPER - QUICK REFERENCE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🎯 SIGNAL REQUIREMENTS (ALL MUST BE TRUE): │
│ ═══════════════════════════════════════════ │
│ ✓ Tier → B minimum (20+ pts NQ) │
│ ✓ Volume → 1.5x+ average │
│ ✓ Delta → 60%+ dominance (buyers OR sellers) │
│ ✓ Body → 70%+ of candle range │
│ ✓ Range → 1.3x+ average candle size │
│ ✓ Wicks → Small opposite wick (<50% of body) │
│ ✓ CVD → Trending with signal direction │
│ ✓ Session → London (3-5am ET) OR NY (9:30-11:30am ET) │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 📊 TIER ACTIONS: │
│ ════════════════ │
│ S-TIER (100+ pts) → 🥇 HOLD position, ride the wave │
│ A-TIER (50-99 pts) → 🥈 SWING for 2-3 minutes │
│ B-TIER (20-49 pts) → 🥉 SCALP quick, 30-60 seconds │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🚨 ENTRY CHECKLIST: │
│ ═══════════════════ │
│ □ Signal appears (S🎯, A🎯, or B🎯) │
│ □ Table shows: Vol GREEN, Delta colored, Body GREEN │
│ □ CVD arrow matches direction (▲ for long, ▼ for short) │
│ □ Session active (LDN! or NY! in yellow) │
│ □ Enter at close of signal candle │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ⛔ DO NOT TRADE WHEN: │
│ ════════════════════ │
│ ✗ Session shows "---" (outside key hours) │
│ ✗ Vol shows RED (below 1.5x) │
│ ✗ Body shows RED (weak candle structure) │
│ ✗ Delta below 60% (no clear dominance) │
│ ✗ Multiple conflicting signals │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 📈 INSTRUMENT SETTINGS: │
│ ════════════════════════ │
│ NQ/ES (1-3 min): S=100, A=50, B=20 pts │
│ YM (1-5 min): S=100, A=50, B=25 pts │
│ GC (5-15 min): S=15, A=8, B=4 pts │
│ BTC (1-15 min): S=500, A=250, B=100 pts │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
```
---
## 📋 DETAILED DOCUMENTATION
### What Makes SNIPER Different?
The SNIPER edition eliminates 80%+ of signals compared to standard GRA. Every signal that passes through has been validated by **8 independent filters**:
| Filter | Standard GRA | SNIPER GRA | Why It Matters |
|--------|-------------|------------|----------------|
| Volume | 1.3x avg | **1.5x avg** | Institutional participation |
| Delta | 55% | **60%** | Clear buyer/seller control |
| Body Ratio | None | **70%+** | No dojis or spinners |
| Range | None | **1.3x avg** | Significant price movement |
| Wicks | None | **<50% body** | Conviction in direction |
| CVD | None | **Required** | Trend confirmation |
| B-Tier Min | 10 pts | **20 pts** | Filter noise |
| Session | Optional | **Required** | Institutional hours |
---
### Signal Anatomy
When you see a signal like `A🎯`, here's what passed validation:
```
Signal: A🎯 LONG at 21,450.00
Validation Breakdown:
├── Points: 67.5 pts ✓ (A-Tier = 50-99)
├── Volume: 2.1x avg ✓ (≥1.5x required)
├── Delta: 68% Buyers ✓ (≥60% required)
├── Body: 78% of range ✓ (≥70% required)
├── Range: 1.6x avg ✓ (≥1.3x required)
├── Wick: Upper 15% ✓ (<50% of body)
├── CVD: ▲ Rising ✓ (Matches LONG)
└── Session: NY! ✓ (Active session)
RESULT: VALID SNIPER SIGNAL
```
---
### Table Legend
| Field | Reading | Color Meaning |
|-------|---------|---------------|
| **Pts** | Point movement | Gold/Green/Yellow = Tiered |
| **Tier** | S/A/B/X | Gold/Green/Yellow/White |
| **Vol** | Volume ratio | 🟢 ≥1.5x, 🔴 <1.5x |
| **Delta** | Buy/Sell % | 🟢 Buy dom, 🔴 Sell dom, ⚪ Neutral |
| **Body** | Body % of range | 🟢 ≥70%, 🔴 <70% |
| **CVD** | Cumulative delta | ▲ Bullish trend, ▼ Bearish trend |
| **Sess** | Session status | 🟡 Active, ⚫ Inactive |
---
### Trading Rules
#### Entry Rules
1. **Wait for signal** - Don't anticipate
2. **Verify table** - All conditions GREEN
3. **Enter at candle close** - Not during formation
4. **Position size by tier:**
- S-Tier: Full size
- A-Tier: 75% size
- B-Tier: 50% size
#### Exit Rules
| Tier | Target | Max Hold Time |
|------|--------|---------------|
| S | Let it run | 5-10 minutes |
| A | 1:1.5 R:R | 2-3 minutes |
| B | 1:1 R:R | 30-60 seconds |
#### Stop Loss
- Place at **opposite end of signal candle**
- For S-Tier: Allow 50% retracement
- For B-Tier: Tight stop, quick exit
---
### Session Priority
```
LONDON OPEN (3:00-5:00 AM ET)
════════════════════════════
• Best for: GC, European indices
• Characteristics: Stop hunts, reversals
• Look for: Sweeps of Asian session levels
NY OPEN (9:30-11:30 AM ET)
════════════════════════════
• Best for: NQ, ES, YM
• Characteristics: High volume, trends
• Look for: Continuation after 10 AM
```
---
### Common Mistakes to Avoid
| Mistake | Why It's Bad | Solution |
|---------|-------------|----------|
| Trading outside sessions | Low volume = fake moves | Wait for LDN! or NY! |
| Ignoring weak body | Dojis reverse | Body must be 70%+ |
| Fighting CVD | Swimming upstream | CVD must confirm |
| Oversizing B-Tier | Small moves = small size | 50% max on B |
| Chasing missed signals | FOMO loses money | Wait for next setup |
---
### Alert Setup
Configure these alerts in TradingView:
| Alert | Priority | Action |
|-------|----------|--------|
| 🎯 S-TIER LONG/SHORT | 🔴 High | Drop everything, check chart |
| 🎯 A-TIER LONG/SHORT | 🟠 Medium | Evaluate within 30 seconds |
| 🎯 B-TIER LONG/SHORT | 🟢 Low | Quick glance if available |
| LONDON/NY OPEN | 🔵 Info | Prepare for action |
---
### Pine Script v6 Notes
This indicator uses Pine Script v6 features:
- `request.security_lower_tf()` for intrabar delta
- Type inference for cleaner code
- Array operations for CVD calculation
**Minimum TradingView Plan:** Pro (for intrabar data)
---
## 🏆 Golden Rule
> **"If you have to convince yourself it's a good signal, it's not a good signal."**
The SNIPER edition is designed so that when a signal appears, there's nothing to think about. If all conditions are met, you trade. If any condition fails, you wait.
**Leave every trade with money. That's the goal.**
---
*© Alexandro Disla - Get Rich Aggressively v5 SNIPER*
*Pine Script v6 | TradingView*
Sideways & Breakout Detector + Forecast//@version=6
indicator("Sideways & Breakout Detector + Forecast", overlay=true, max_labels_count=500)
// Inputs
lengthATR = input.int(20, "ATR Länge")
lengthMA = input.int(50, "Trend MA Länge")
sqFactor = input.float(1.2, "Seitwärtsfaktor")
brkFactor = input.float(1.5, "Breakoutfaktor")
// ATR / Volatilität
atr = ta.atr(lengthATR)
atrSMA = ta.sma(atr, lengthATR)
// Basislinie / Trend
basis = ta.sma(close, lengthATR)
trendMA = ta.sma(close, lengthMA)
// Seitwärtsbedingung
isSideways = atr < atrSMA * sqFactor
// Breakouts
upperBreak = close > basis + atr * brkFactor
lowerBreak = close < basis - atr * brkFactor
// Vorhergesagter Ausbruch (Forecast)
// Wenn Seitwärtsphase + Kurs nahe obere oder untere Kanalgrenze
forecastBull = isSideways and (close > basis + 0.5 * atr)
forecastBear = isSideways and (close < basis - 0.5 * atr)
// Farben
barcolor(isSideways ? color.new(color.yellow, 40) : na)
barcolor(upperBreak ? color.green : na)
barcolor(lowerBreak ? color.red : na)
// Breakout-Bänder
plot(basis + atr * brkFactor, "Bull Break Zone", color=color.new(color.green, 60))
plot(basis - atr * brkFactor, "Bear Break Zone", color=color.new(color.red, 60))
// Labels (klein)
if isSideways
label.new(bar_index, close, "Seitwärts", color=color.yellow, style=label.style_label_center, size=size.tiny)
if upperBreak
label.new(bar_index, high, "Bull Breakout", color=color.green, style=label.style_label_up, size=size.tiny)
if lowerBreak
label.new(bar_index, low, "Bear Breakout", color=color.red, style=label.style_label_down, size=size.tiny)
// Vorhergesagte Ausbrüche markieren
plotshape(forecastBull, title="Forecast Bull", location=location.abovebar, color=color.new(color.green, 0), style=shape.triangleup, size=size.tiny)
plotshape(forecastBear, title="Forecast Bear", location=location.belowbar, color=color.new(color.red, 0), style=shape.triangledown, size=size.tiny)
// Alerts
alertcondition(isSideways, "Seitwärtsphase", "Der Markt läuft seitwärts.")
alertcondition(upperBreak, "Bull Breakout", "Ausbruch nach oben!")
alertcondition(lowerBreak, "Bear Breakout", "Ausbruch nach unten!")
alertcondition(forecastBull, "Forecast Bull", "Voraussichtlicher Bull-Ausbruch!")
alertcondition(forecastBear, "Forecast Bear", "Voraussichtlicher Bear-Ausbruch!")
Gould 10Y + 4Y patternDescription:
Overview This indicator is a comprehensive tool for macro-market analysis, designed to visualize historical market cycles on your chart. It combines Edson Gould’s famous Decennial Pattern with a Customizable 4-Year Cycle (e.g., 2002 base) to help traders identify long-term trends, potential market bottoms, and strong bullish years.
This tool is ideal for long-term investors and analysts looking for cyclical confluence on monthly or yearly timeframes (e.g., SPX, NDX).
Key Concepts
Edson Gould’s Decennial Pattern (10-Year Cycle)
Based on the theory that the stock market follows a psychological cycle determined by the last digit of the year.
5 (Strongest Bull): Historically the strongest performance years.
7 (Panic/Crash): Years often associated with market panic or crashes.
2 (Bottom/Buy): Years that often mark major lows.
Custom 4-Year Cycle (Target Year Strategy)
Identify recurring 4-year opportunities based on a user-defined base year.
Default Setting (Base 2002): Highlights years like 2002, 2006, 2010, 2014, 2018, 2022... which have historically been significant market bottoms or excellent buying opportunities.
When a "Target Year" arrives, the indicator highlights the background and displays a distinct Green "Target Year" Label.
Features
Real-time Dashboard: A table in the top-right corner displays the current year's status for both the 10-Year and 4-Year cycles, including a countdown to the next target year.
Dynamic Labels: Automatically marks every year on the chart with its Decennial status (e.g., "Strong Bull (5)", "Panic (7)").
Visual Highlighting:
Target Years: Distinct green background and labels for easy identification of the 4-year cycle.
Significant Decennial Years: Special small markers for years ending in 5 and 7.
Fully Customizable: You can change the base year for the 4-year cycle, toggle the dashboard, and adjust colors via the settings menu.
How to Use
Apply this indicator to high-timeframe charts (Weekly or Monthly) of major indices like S&P 500 or Nasdaq.
Look for confluence between the 10-Year Pattern (e.g., Year 6 - Bullish) and the 4-Year Cycle (Target Year) to confirm long-term bias.
Disclaimer This tool is for educational and research purposes only based on historical cycle theories. Past performance is not indicative of future results. Always manage your risk.
S&P 500 Breadth: Bull vs Bear (20DMA)Simple market breadth for S&P500 using percentage of stock above or below 20dma
🏛️ Inst. Value SuiteInstitutional Valuation Suite (IVS)
Executive Summary Traditional volatility indicators frequently exhibit limitations when applied to long-term secular growth assets. Because they calculate volatility in absolute currency units rather than percentage terms, standard deviation bands often distort or become obsolete during phases of exponential price expansion (e.g., significant capitalization shifts in Crypto or Growth Stocks).
The Institutional Valuation Suite addresses this latency by utilizing Geometric (Log-Normal) Standard Deviation. This methodology enables the model to adapt dynamically to the asset's price scale, providing statistically significant valuation zones regardless of price magnitude.
Operational Theory The model operates as a mean-reversion instrument, visualizing price action as a dynamic deviation from a "Fair Value" baseline. It quantifies statistical extremes to identify when an asset is overextended (Speculative Premium) or undervalued (Deep Discount) relative to historical volatility.
Key Features
1. Log-Normal Volatility Engine
Geometric Mode (Default): Calculates volatility in percentage terms. This is the requisite setting for assets exhibiting logarithmic growth, such as Cryptocurrencies and Technology equities.
Arithmetic Mode: Retains linear calculation methods for Forex pairs or range-bound assets where traditional standard deviation is preferred.
2. Valuation Heatmap
Visualizes valuation metrics directly onto price candles to mitigate subjective interpretation bias.
GREEN: Deep Value / Accumulation Zone (<−0.5σ).
ORANGE: Overvaluation / Premium Zone (>2.0σ).
RED: Speculative Anomaly Zone (>3.0σ).
3. Mean Reversion Signals
VALUE RECLAIM: Triggers when price re-enters the lower deviation band from below. This confirms support validation and filters out premature entries during high-momentum drawdowns.
TOP EXIT: Triggers when price breaks down from the upper speculative zone, signaling a potential trend exhaustion.
4. Statistical Dashboard
Displays a real-time Z-Score to quantify the standard deviations the current price is from its baseline.
>3.0: Statistical Anomaly (upper bound).
<−0.5: Statistical Discount (lower bound).
Configuration & Parameters
Per your requirements, the suggested code tooltips for your inputs are listed below.
Cycle Length
Determines the lookback period used to calculate the Fair Value baseline.
Crypto Macro: 200 (Approx. 4 Years).
Altcoins: 100 (Approx. 2 Years).
Equities (S&P 500): 50 (1 Year Trend).
Intraday: Set "Timeframe Lock" to "Chart".
Tooltip Text: "Sets the lookback period for the baseline calculation. Recommended: 200 for Crypto Macro, 50 for Equities, or adjust based on the asset's specific volatility cycle."
Timeframe Lock
Allows the user to fix the calculation to a specific timeframe or allow it to float with the chart.
Tooltip Text: "Locks the calculation to a specific timeframe (e.g., Daily, Weekly) to ensure baseline consistency when zooming into lower timeframes."
Technical Integrity
This indicator employs strict strict offset logic (barmerge.lookahead_on) to ensure historical data integrity. The signals rendered on historical bars are mathematically identical to those that would have appeared in a real-time environment, ensuring backtesting reliability.
Disclaimer: This script provides statistical analysis based on historical volatility metrics and does not constitute financial advice.
Fibonacci Degree System This Pine Script creates a sophisticated technical analysis tool that combines Fibonacci retracements with a degree-based cycle system. Here's a comprehensive breakdown:
Core Concept
The indicator maps price movements onto a 360-degree circular framework, treating market cycles like geometric angles. It creates a visual "mesh" where Fibonacci ratios intersect in both price (horizontal) and time (vertical) dimensions.
How It Works
1. Finding Reference Points
The script looks back over a specified period (default 100 bars) to identify:
Highest High: The peak price point
Lowest Low: The trough price point
Time Locations: Exactly which bars these extremes occurred on
These two points form the boundaries of your analysis window.
2. Creating the Fibonacci Grid
Horizontal Lines (Price Levels):
The script divides the price range between high and low into seven key Fibonacci ratios:
0% (Low) - Bottom boundary in red
23.6% - Minor retracement in orange
38.2% - Shallow retracement in yellow
50% - Midpoint in lime green
61.8% - Golden ratio in aqua (most significant)
78.6% - Deep retracement in blue
100% (High) - Top boundary in purple
Each line represents a potential support/resistance level where price might react.
Vertical Lines (Time Cycles):
The same Fibonacci ratios are applied to the time dimension between the high and low bars. If your high and low are 50 bars apart, vertical lines appear at:
Bar 0 (0%)
Bar 12 (23.6%)
Bar 19 (38.2%)
Bar 25 (50%)
Bar 31 (61.8%)
Bar 39 (78.6%)
Bar 50 (100%)
These suggest when price might make significant moves.
3. The Degree Mapping System
The innovative feature maps the time progression to degrees:
0° = Start point (0% time)
85° = 23.6% through the cycle
138° = 38.2% through the cycle
180° = Midpoint (50%)
222° = 61.8% through the cycle (golden angle)
283° = 78.6% through the cycle
360° = Complete cycle (100%)
This treats market movements as circular patterns, similar to how planets orbit or pendulums swing.
Visual Output
When you apply this indicator, you'll see:
A rectangular mesh extending beyond your high-low range (by 150% default)
Color-coded horizontal lines showing price Fibonacci levels
Matching vertical lines showing time Fibonacci intervals
Price labels on the right showing percentage levels
Degree labels at the bottom showing the angular position in the cycle
Intersection points creating a grid of potentially significant price-time coordinates
Trading Application
Traders use this to identify:
Support/Resistance Zones: Where horizontal and vertical lines intersect
Time Targets: When price might reverse (at vertical Fibonacci times)
Cycle Completion: When approaching 360°, a new cycle may begin
Harmonic Patterns: Geometric relationships between price and time
Customization Features
The script offers extensive control:
Lookback period: Adjust cycle length (10-500 bars)
Mesh extension: How far to project the grid forward
Visual toggles: Show/hide horizontal lines, vertical lines, labels
Styling: Line thickness, style (solid/dashed/dotted), colors
Label positioning: Fine-tune text placement for readability
The intersection at 61.8% time and 61.8% price at 222° becomes a key target zone.
This tool essentially converts the abstract concept of market cycles into a concrete, visual geometric framework that traders can analyze and act upon.
DISCLAIMER: This information is provided for educational purposes only and should not be considered financial, investment, or trading advice.
No guarantee of profits: Past performance and theoretical models do not guarantee future results. Trading and investing involve substantial risk of loss.
Not a recommendation: This script illustration does not constitute a recommendation to buy, sell, or hold any financial instrument.
Do your own research: Always conduct thorough independent research and consider consulting with a qualified financial advisor before making any trading decisions.
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Multi-Timeframe EMA & SMA Scanner - Price Level LabelsOverview
A powerful multi-timeframe moving average scanner that displays EMA and SMA levels from up to 8 different timeframes simultaneously on your chart. Perfect for identifying key support/resistance levels, confluence zones, and multi-timeframe trend analysis.
Key Features
📊 Multi-Timeframe Analysis
Monitor up to 8 different timeframes simultaneously (5m, 10m, 15m, 30m, 1H, 4H, 1D, 1W)
Each timeframe can be independently enabled/disabled
Fully customizable timeframe selection
📈 Comprehensive Moving Averages
5 configurable EMA periods (default: 8, 21, 50, 100, 200)
2 configurable SMA periods (default: 200, 400)
All periods are fully customizable to match your trading strategy
🎯 Smart Price Level Labels
Labels positioned at actual price levels (not in a list)
Color-coded labels for easy identification
Dynamic text color: Green when price is above, Red when below
Compact notation: E8-5m means EMA 8 on 5-minute timeframe
Adjustable label offset from current price
📉 Optional Horizontal Lines
Dotted reference lines at each MA level
Color-matched to corresponding MA type
Can be toggled on/off independently
📋 Comprehensive Data Table
Shows all MA values organized by timeframe
Displays percentage distance from current price
Trend indicator (Strong Up/Up/Neutral/Down/Strong Down)
EMA alignment status (Bullish/Bearish/Mixed)
Color-coded cells for quick visual analysis
🎨 Full Customization
Individual color settings for each MA type
Adjustable table size (Tiny/Small/Normal/Large)
Choose table position (Left/Right)
Toggle any MA or timeframe on/off
🔔 Built-in Alerts
Golden Cross detection (EMA 50 crosses above EMA 200)
Death Cross detection (EMA 50 crosses below EMA 200)
Price crossing major EMAs
Available for multiple timeframes
How to Use
For Day Traders:
Enable lower timeframes (5m, 10m, 15m, 30m)
Focus on faster EMAs (8, 21, 50)
Watch for confluence zones where multiple timeframe MAs cluster
For Swing Traders:
Enable higher timeframes (1H, 4H, 1D)
Use all EMAs plus SMAs for broader perspective
Look for alignment across timeframes for high-probability setups
For Position Traders:
Focus on daily and weekly timeframes
Emphasize 100, 200 EMAs and 200, 400 SMAs
Use for long-term trend confirmation
Understanding the Labels
Label Format: E8-5m 45250.50
E8 = EMA with period 8
5m = 5-minute timeframe
45250.50 = Current price level
Green text = Price is currently above this level (potential support)
Red text = Price is currently below this level (potential resistance)
For SMAs: S200-1D 44500.00
S200 = SMA with period 200
1D = Daily timeframe
Trading Applications
Support/Resistance Identification
MAs act as dynamic support and resistance levels
Multiple timeframe MAs create stronger zones
Confluence Trading
When multiple MAs from different timeframes cluster together, it creates high-probability zones
These areas often result in strong reactions
Trend Analysis
Check the Alignment column: Bullish alignment = all EMAs in ascending order
Trend column shows overall price position relative to all MAs
Entry/Exit Timing
Use lower timeframe MAs for precise entries
Use higher timeframe MAs for trend direction and exits
Settings Guide
Timeframes Section:
Select and enable/disable up to 8 timeframes
Default: 5m, 10m, 15m, 30m, 1H, 4H, 1D, 1W
MA Periods Section:
Customize all EMA and SMA periods
Default EMAs: 8, 21, 50, 100, 200
Default SMAs: 200, 400
Display Section:
Toggle price labels and horizontal lines
Adjust label offset (distance from right edge)
Show/hide data table
Choose table position and size
Colors Section:
Customize colors for each MA type
Each MA has independent color control
Pro Tips
✅ Start with default settings and adjust based on your trading style
✅ Disable timeframes/MAs you don't use to reduce chart clutter
✅ Use the data table for quick overview, labels for precise levels
✅ Look for "confluence clusters" where multiple MAs from different timeframes align
✅ Green labels = potential support, Red labels = potential resistance
✅ Set alerts on key crossovers for automated notifications
Technical Specifications
Pine Script v6
Overlay indicator (displays on main chart)
Maximum 500 labels supported
Real-time updates on each bar close
Compatible with all instruments and timeframes
Perfect For:
Day traders seeking multi-timeframe confirmation
Swing traders looking for high-probability setups
Position traders monitoring long-term trends
Anyone using moving averages as part of their strategy
Note: This indicator does not provide buy/sell signals. It's a tool for analysis and should be used in conjunction with your trading strategy and risk management rules.
Futures Momentum Scanner – jyoti//@version=5
indicator("Futures Momentum Scanner – Avvu Edition", overlay=false, max_lines_count=500)
//------------------------------
// USER INPUTS
//------------------------------
rsiLen = input.int(14, "RSI Length")
macdFast = input.int(12, "MACD Fast")
macdSlow = input.int(26, "MACD Slow")
macdSignal = input.int(9, "MACD Signal")
stLength = input.int(10, "Supertrend Length")
stMult = input.float(3.0, "Supertrend Multiplier")
//------------------------------
// SUPER TREND
//------------------------------
= ta.supertrend(stMult, stLength)
trendUp = stDirection == 1
//------------------------------
// RSI
//------------------------------
rsi = ta.rsi(close, rsiLen)
rsiBull = rsi > 50 and rsi < 65
//------------------------------
// MACD
//------------------------------
= ta.macd(close, macdFast, macdSlow, macdSignal)
macdBull = macd > signal and macd > 0
//------------------------------
// MOVING AVERAGE TREND
//------------------------------
ema20 = ta.ema(close, 20)
ema50 = ta.ema(close, 50)
ema200 = ta.ema(close, 200)
trendStack = ema20 > ema50 and ema50 > ema200
//------------------------------
// BREAKOUT LOGIC
//------------------------------
prevHigh = ta.highest(high, 20)
breakout = close > prevHigh
//------------------------------
// FINAL SCANNER LOGIC
//------------------------------
bullishCandidate = trendUp and rsiBull and macdBull and trendStack and breakout
//------------------------------
// TABLE OUTPUT FOR SCANNER FEEL
//------------------------------
var table t = table.new(position.top_right, 1, 1)
if barstate.islast
msg = bullishCandidate ? "✔ BUY Candidate" : "– Not a Setup"
table.cell(t, 0, 0, msg, bgcolor=bullishCandidate ? color.new(color.green, 0) : color.new(color.red, 70))
//------------------------------
// ALERT
//------------------------------
alertcondition(bullishCandidate, title="Scanner Trigger", message="This stock meets Avvu's futures scanner criteria!")
Get_rich_aggressively_v5# 🚀 GET RICH AGGRESSIVELY v5 - TIER SYSTEM
### Precision Futures Scalping | NQ • ES • YM • GC • BTC
### *Leave Every Trade With Money*
---
## 📋 QUICK CHEATSHEET
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ GRA v5 SIGNAL REQUIREMENTS │
├─────────────────────────────────────────────────────────────────────────────┤
│ ✓ TIER MET Points ≥ 10 (B), ≥ 50 (A), ≥ 100 (S) │
│ ✓ VOLUME ≥ 1.3x average │
│ ✓ DELTA ≥ 55% dominance (buyers OR sellers) │
│ ✓ DIRECTION Candle color = Delta direction │
│ ✓ SESSION In London (3-5AM) or NY (9:30-11:30AM) if filter ON │
├─────────────────────────────────────────────────────────────────────────────┤
│ TIER ACTIONS │
├─────────────────────────────────────────────────────────────────────────────┤
│ 🥇 S-TIER (100+ pts) │ HOLD LONGER │ Big institutional move │
│ 🥈 A-TIER (50-99 pts) │ HOLD A BIT │ Medium move, trail to BE │
│ 🥉 B-TIER (10-49 pts) │ CLOSE QUICK │ Scalp 5-10 pts, exit fast │
│ ❌ NO TIER (< 10 pts) │ NO TRADE │ Not enough conviction │
├─────────────────────────────────────────────────────────────────────────────┤
│ SESSION PRIORITY │
├─────────────────────────────────────────────────────────────────────────────┤
│ 🔵 LONDON OPEN 03:00-05:00 ET │ IB forms 03:00-04:00 │
│ 🟢 NY OPEN 09:30-11:30 ET │ IB forms 09:30-10:30 │
│ 📊 IB BREAKOUT Close beyond IB + Impulse + 1.3x Vol = HIGH CONVICTION│
├─────────────────────────────────────────────────────────────────────────────┤
│ VOLUME PROFILE ZONES │
├─────────────────────────────────────────────────────────────────────────────┤
│ 🔵 HVN (Blue BG) High volume = Support/Resistance, expect consolidation │
│ 🟡 LVN (Yellow BG) Low volume = Breakout acceleration, fast moves │
│ 🟣 POC Point of Control = Institutional fair value │
│ 🟣 VAH/VAL Value Area edges = S/R zones │
├─────────────────────────────────────────────────────────────────────────────┤
│ MARKET STATE DECODER │
├─────────────────────────────────────────────────────────────────────────────┤
│ TREND UP │ Price > EMA20 + CVD rising │ Trade WITH the trend │
│ TREND DN │ Price < EMA20 + CVD falling │ Trade WITH the trend │
│ RETRACE │ Price/CVD diverging │ Pullback, prepare for entry │
│ RANGE │ No clear direction │ Reduce size or skip │
├─────────────────────────────────────────────────────────────────────────────┤
│ 💎 HIGH CONVICTION UPGRADE │
├─────────────────────────────────────────────────────────────────────────────┤
│ Purple diamond (◆) appears when: │
│ • Strong delta (≥65%) + Strong volume (≥2x) + Market in imbalance │
│ → Consider upgrading tier (B→A, A→S) for position sizing │
└─────────────────────────────────────────────────────────────────────────────┘
```
---
## 🎯 THE TIER SYSTEM
The tier system classifies candles by **point movement** to determine trade management:
| Tier | Points | Action | Expected R:R |
|:----:|:------:|:------:|:------------:|
| 🥇 **S-TIER** | 100+ | HOLD LONGER | 2:1+ |
| 🥈 **A-TIER** | 50-99 | HOLD A BIT | 1.5:1 |
| 🥉 **B-TIER** | 10-49 | CLOSE QUICK | 1:1 |
| ❌ **NO TIER** | < 10 | NO TRADE | — |
---
## ✅ SIGNAL REQUIREMENTS
**ALL conditions must be TRUE for a signal:**
```
SIGNAL = TIER + VOLUME + DELTA + DIRECTION + SESSION
☐ Points ≥ 10 (minimum B-tier)
☐ Volume ≥ 1.3x average
☐ Delta dominance ≥ 55%
☐ Candle direction = Delta direction
☐ In session (if filter ON)
ANY FALSE = NO SIGNAL = NO TRADE
```
---
## 📊 VOLUME DOMINANCE ANALYSIS
This is the **core edge** of GRA v5. We use intrabar analysis to determine who is in control:
```
VOLUME ANALYSIS BREAKDOWN
Total Volume = Buy Volume + Sell Volume
Buy Volume: Who pushed price UP within the bar
Sell Volume: Who pushed price DOWN within the bar
Delta = Buy Volume - Sell Volume
Buy Dominance = Buy Volume / Total Volume
Sell Dominance = Sell Volume / Total Volume
≥ 55% = ONE SIDE IN CONTROL
≥ 65% = STRONG DOMINANCE (high conviction)
```
**Direction Confirmation Matrix:**
| Candle | Delta | Signal |
|:-------|:------|:-------|
| 🟢 Bullish | 55%+ Buyers | ✅ LONG |
| 🟢 Bullish | 55%+ Sellers | ❌ Trap |
| 🔴 Bearish | 55%+ Sellers | ✅ SHORT |
| 🔴 Bearish | 55%+ Buyers | ❌ Trap |
---
## 🕐 SESSION CONTEXT
### Initial Balance (IB) Framework
The **first hour** of each session establishes the IB range. Institutions use this for the day's framework.
```
SESSION WINDOWS (Eastern Time):
LONDON:
├── IB Period: 03:00 - 04:00 ← Range established
├── Trade Window: 03:00 - 05:00 ← Best signals
└── Extension Targets: 1.5x, 2.0x
NY:
├── IB Period: 09:30 - 10:30 ← Range established
├── Trade Window: 09:30 - 11:30 ← Best signals
└── Extension Targets: 1.5x, 2.0x
```
### IB Breakout Signals
```
L▲ / L▼ = London IB Breakout (Blue)
N▲ / N▼ = NY IB Breakout (Orange)
Confirmation Required:
☐ Close beyond IB level (not just wick)
☐ Impulse candle (body > 60% of range)
☐ Volume > 1.3x average
```
**IB Statistics:**
- 97% of days break either IB high or low
- 1.5x extension = first profit target
- 2.0x extension = full range target
- ~66% of London sessions sweep Asian high/low first
---
## 📈 VIRTUAL VOLUME PROFILE ZONES
GRA v5 calculates volume profile zones **without drawing the profile**, giving you the key levels:
### Zone Types
| Zone | Background | Meaning | Action |
|:-----|:-----------|:--------|:-------|
| **HVN** | 🔵 Blue | High Volume Node | S/R zone, expect consolidation |
| **LVN** | 🟡 Yellow | Low Volume Node | Breakout zone, fast acceleration |
| **POC** | 🟣 Purple dots | Point of Control | Institutional fair value |
| **VAH/VAL** | 🟣 Purple lines | Value Area edges | S/R boundaries |
### How to Use
```
ENTERING A TRADE:
At HVN:
├── Expect price to consolidate
├── Look for rejection/absorption
└── Better for reversals
At LVN:
├── Expect fast price movement
├── Don't fight the direction
└── Better for breakouts
Near POC:
├── Institutional fair value
├── Strong magnet effect
└── Watch for volume at POC
```
---
## 🔄 MARKET STATE DETECTION
GRA v5 classifies the market into four states using **CVD + Price Action**:
```
CVD Direction
↑ Rising ↓ Falling
┌─────────────┬─────────────┐
Price > EMA20 │ TREND UP │ RETRACE │
│ (Go Long) │ (Pullback) │
├─────────────┼─────────────┤
Price < EMA20 │ RETRACE │ TREND DN │
│ (Pullback) │ (Go Short) │
└─────────────┴─────────────┘
```
| State | Meaning | Action |
|:------|:--------|:-------|
| **TREND UP** | Buyers in control | Trade long, follow signals |
| **TREND DN** | Sellers in control | Trade short, follow signals |
| **RETRACE** | Pullback against trend | Prepare for continuation entry |
| **RANGE** | No clear direction | Reduce size or wait |
---
## 💎 HIGH CONVICTION UPGRADES
When extra conditions align, GRA v5 marks the signal with a **purple diamond**:
```
HIGH CONVICTION = Base Signal + Strong Delta (65%+) + Strong Volume (2x+) + Imbalance State
```
**Action:** Consider upgrading tier for position sizing:
- B-Tier → A-Tier management
- A-Tier → S-Tier management
---
## 📋 TRADING BY TIER
### 🥇 S-TIER (100+ points)
| | |
|:--|:--|
| **Entry** | Candle close |
| **Target** | IB extension / Next S/R |
| **Management** | HOLD LONGER |
**Rules:**
- Watch next candle - continues? HOLD
- Same tier same direction? ADD
- Opposite tier signal? EXIT on close
- Never close early unless reversal signal
### 🥈 A-TIER (50-99 points)
| | |
|:--|:--|
| **Entry** | Candle close |
| **Target** | 1.5x initial risk minimum |
| **Management** | HOLD A BIT |
**Rules:**
- Target 1.5:1 R:R minimum
- Trail to breakeven after 1:1
- If stalls, take profit
- Upgrade to S-tier management if high conviction
### 🥉 B-TIER (10-49 points)
| | |
|:--|:--|
| **Entry** | Candle close |
| **Target** | 5-10 points MAX |
| **Management** | CLOSE QUICK |
**Rules:**
- Exit in 1-3 candles
- DO NOT hold for more
- Any doubt = EXIT
- Quick scalp mentality
---
## ⚙️ SETTINGS BY INSTRUMENT
| Setting | NQ/ES | YM | GC | BTC |
|:--------|:-----:|:--:|:--:|:---:|
| **Timeframe** | 1-5 min | 1-5 min | 5-15 min | 1-15 min |
| **S-Tier** | 100 pts | 100 pts | 15 pts | 500 pts |
| **A-Tier** | 50 pts | 50 pts | 8 pts | 250 pts |
| **B-Tier** | 10 pts | 15 pts | 3 pts | 50 pts |
| **Min Volume** | 1.3x | 1.3x | 1.5x | 1.3x |
| **Delta %** | 55% | 55% | 58% | 55% |
| **Best Time** | 9:30-11:30 ET | 9:30-11:30 ET | 3-5AM & 8:30-10:30 ET | 24/7 |
---
## 📊 TABLE LEGEND
The info panel displays real-time market data:
| Row | Shows | Colors |
|:----|:------|:-------|
| **Pts** | Candle points | Gold/Green/Yellow by tier |
| **Tier** | S/A/B/X | Gold/Green/Yellow/White |
| **Vol** | Volume ratio | Yellow (2x+) / Green (1.3x+) / Red |
| **Delta** | Buy/Sell % | Green (buy) / Red (sell) / White |
| **CVD** | Direction | Green ▲ / Red ▼ |
| **State** | Market state | Green/Red/Orange/Gray |
| **Sess** | Session | Yellow if active |
| **Zone** | VP zone | Blue/Yellow/Purple |
| **Sig** | Signal | Green/Red if active |
---
## 🔔 ALERTS
| Alert | When | Action |
|:------|:-----|:-------|
| **S-TIER LONG/SHORT** | S-tier signal | Hold longer |
| **A-TIER LONG/SHORT** | A-tier signal | Hold a bit |
| **B-TIER LONG/SHORT** | B-tier signal | Close quick |
| **LON IB BREAK UP/DN** | London IB breakout | Major session move |
| **NY IB BREAK UP/DN** | NY IB breakout | Major session move |
| **HIGH CONVICTION** | Upgraded signal | Consider larger size |
| **LONDON/NY OPEN** | Session start | Get ready |
---
## 💰 THE GOLDEN RULE
> ### **LEAVE EVERY TRADE WITH MONEY**
>
> | Situation | Rule |
> |:----------|:-----|
> | B-Tier | Small win > Small loss |
> | A-Tier | Trail to BE, lock profit |
> | S-Tier | Let it run to target |
> | No Signal | NO TRADE |
> | Wrong Side | EXIT immediately |
>
> **Capital preserved = Trade tomorrow**
---
## ⚠️ DISCLAIMER
> Risk management is **YOUR** responsibility.
> Never risk more than 1-2% per trade.
> Paper trade until you understand the signals.
> Past performance ≠ future results.
---
### Get Rich. Stay Rich. Trade Aggressively. 🚀
**Get Rich Aggressively v5**
*Precision Futures Scalping*
