Delta Magnet Zone Extended – Selective HideLiquidity Zone Reversal — Description 🔍📊
This indicator automatically identifies liquidity zones where price previously grabbed orders, swept highs/lows, or created strong reaction points. Instead of plotting thin lines, this version converts those levels into zones, giving traders a clearer view of where the market has unfinished business and where future reactions are likely to occur.
These zones act as institutional magnets — areas where liquidity providers, algos, and larger players commonly enter or exit positions.
How It Works ⚙️💡
The script scans recent price action and detects local swing highs and lows. It then builds rectangular liquidity zones around these levels, extending them forward so you can see:
🟥 Bearish liquidity sweep zones
🟩 Bullish liquidity sweep zones
🔁 Areas where price previously failed, rejected, or consolidated
🎯 Potential reversal targets on both sides of the market
These zones update automatically as new structure forms, giving you an always-current map of market memory.
Why the 9-Day Look-Back Is Powerful (My Default) 📅✨
I personally keep the look-back set to 9 days by default because:
✔️ It captures the entire previous trading week
✔️ It maps out where SPY/QQQ/ES has already tapped liquidity
✔️ It shows the true zones institutions defended
✔️ It reveals where price is most likely to react again moving forward
Using a 9-day window gives you a clean, high-signal map of:
Last week’s highs & lows
Prior liquidity sweeps
Rejection zones
Imbalance cleanup levels
This keeps the chart minimal, powerful, and hyper-relevant to current order flow.
How Traders Use These Zones 🎯📈
Here are the most common ways traders use these liquidity zones:
1️⃣ Identify High-Probability Reversal Areas 🔄
Price often reacts strongly when returning to a past liquidity zone — especially if it previously swept stops there.
2️⃣ Confirm Breakouts or Failures 🚪➡️
Break above a bearish zone?
Momentum continuation is likely.
Reject inside a zone?
Reversal or range expansion often follows.
3️⃣ Set Targets & Stop Placement 🎯🛡️
Zones give logical:
Profit targets
Trend exhaustion points
Areas to avoid entering new trades
4️⃣ Time 0DTE Scalps With Precision ⚡
Liquidity zones tighten your expectations for:
Where SPY/QQQ will bounce
Where reversals start
Where liquidity magnets pull price by end of day
Why This Indicator Matters 🧠🔥
Liquidity drives markets.
Not indicators.
Not moving averages.
Not random levels.
This tool shows you where actual orders exist, where they were previously swept, and where institutions are most likely to step in again.
It gives you:
Cleaner charts
Higher confidence
Better strike selection
More precise entries
Stronger exits
All without noise.
Recherche dans les scripts pour "spy"
The Morning Map Out- V1.0The Morning Map Out (MMO) delivers the complete blueprint to your chart, automatically.
Every level is generated by our proprietary engine and then meticulously reviewed and curated by our team of professional traders each morning. This unique fusion of automation and expert oversight is our secret sauce.
Now, you have an on-demand map for every asset that matters. If TSLA is moving, you have its levels. If SPY is at a critical juncture, you have the blueprint. You will never fly blind again.
Core Coverage: SPY, QQQ, ES, NQ, NVDA, TSLA, AAPL, MSFT, AMZN & more.
This is your new daily edge.
Nexural OrderFlow MatrixNexural OrderFlow Matrix
### Professional Order Flow Analysis for Index Futures on TradingView
**Specifically Engineered for:** ES, NQ, YM, RTY, and other high-liquidity index futures
---
## Before You Read Any Further
I need to be upfront with you about something important.
**True order flow analysis—the kind used by institutional traders and prop firms—is not possible on TradingView.**
When professionals talk about order flow, they're referring to the raw tape: every single trade, the exact price, the exact size, and whether it was a buyer lifting the offer or a seller hitting the bid. That level of data simply doesn't exist in TradingView's infrastructure.
So why did I build this indicator? Because TradingView *does* provide meaningful volume delta data through their official functions, and when presented correctly, it can still give you a genuine edge in understanding buying and selling pressure—especially on **index futures** where liquidity is deep and the uptick/downtick methodology works best.
This indicator was specifically engineered with index futures traders in mind. The data sources, the color thresholds, the activity calculations—all of it is optimized for the characteristics of ES, NQ, YM, and RTY. It can work on other instruments, but index futures are where it shines.
I'm not here to oversell you. I'm here to give you the best tool possible within the platform's limitations—and to be completely transparent about what those limitations are.
---
## What This Indicator Actually Does
Nexural OrderFlow Matrix uses TradingView's most advanced volume analysis functions under the hood:
- `ta.requestUpAndDownVolume()` — Samples lower timeframe data to estimate volume on upticks vs downticks
- `ta.requestVolumeDelta()` — TradingView's official cumulative volume delta calculation
The indicator presents this data in two ways:
**1. The Matrix Table**
A heatmap grid aligned beneath each candle showing:
- **Volume** — Total bar volume with yellow/gold intensity gradient
- **Bar VWAP** — Volume-weighted average price within the bar
- **Delta** — Net difference between buying and selling volume
- **Delta %** — Delta as a percentage of total volume (the most important metric)
- **Bar Δ CVD** — How much cumulative volume delta changed this bar
- **Buy Volume** — Estimated volume on upticks
- **Sell Volume** — Estimated volume on downticks
**2. The Imbalance Bars**
A visual stacked bar chart showing the proportional split between buyers and sellers. Green on top represents buying volume, red on bottom represents selling volume. The split is proportional—so a 70/30 bar instantly shows you the imbalance without reading numbers.
**3. The Nexural Flow Meter**
A real-time panel showing:
- Current bias (BUYERS/SELLERS/NEUTRAL)
- Intensity classification (EXTREME/STRONG/MODERATE/WEAK)
- Imbalance ratio (e.g., "BUY 2.3:1")
- Live delta, volume, and VWAP readings
---
## The Color System
I spent considerable time on this because it matters.
Most indicators treat all bars equally. That's noise. In reality, a bar with 8% delta imbalance tells you almost nothing, while a bar with 65% imbalance is screaming information at you.
**The Activity Threshold System:**
- Bars below your threshold (default 25% delta) fade to muted gray tones
- As imbalance increases, colors transition from gray → muted color → vibrant color
- High-activity bars pop with bright greens and reds
- Low-activity bars fade into the background where they belong
**Volume uses a separate yellow/gold gradient:**
- Low volume: Faint, dark yellow-brown
- High volume: Rich, vibrant amber/gold
- This lets you instantly spot volume spikes without reading numbers
The result: your eye is naturally drawn to the bars that matter.
---
## Honest Accuracy Assessment
Based on extensive comparison testing against TradingView's own Volume Footprint and CVD indicators, this indicator achieves approximately **85-90% correlation** with official TradingView tools.
Let me put that in perspective:
| Platform | Data Source | Typical Accuracy |
|----------|-------------|------------------|
| Sierra Chart (Denali feed) | Actual bid/ask tape | 99%+ |
| Bookmap | Actual bid/ask tape | 99%+ |
| NinjaTrader + Kinetick | Tick-level data | 95-99% |
| Jigsaw Daytradr | Reconstructed tape | 95-99% |
| **TradingView (this indicator)** | **Aggregated LTF sampling** | **85-90%** |
| Generic volume indicators | Basic volume only | 50-60% |
We're at the ceiling of what TradingView can provide. The dual data source approach, official library functions, and lower timeframe sampling squeeze out every drop of accuracy the platform allows.
But if you're a dedicated tape reader who needs to see every lot hitting the book, this isn't the tool for that. No TradingView indicator is. That's not a criticism—it's just the reality of the platform's architecture.
---
## Where This Indicator Works Best
### Primary Use Case: Index Futures
This indicator was built specifically for index futures traders. These instruments have the characteristics that make order flow analysis most reliable:
**The Big Four:**
| Symbol | Name | Why It Works |
|--------|------|--------------|
| **ES** | E-mini S&P 500 | Deepest liquidity in the world, tight spreads, clean delta readings |
| **NQ** | E-mini NASDAQ-100 | Massive volume, excellent uptick/downtick correlation |
| **YM** | E-mini Dow | Strong institutional participation, reliable volume data |
| **RTY** | E-mini Russell 2000 | Good liquidity, solid delta accuracy |
Index futures are ideal because:
- **Deep liquidity** — Thousands of contracts per minute means meaningful sample sizes
- **Tight spreads** — Usually 1 tick, so bid/ask attribution is more accurate
- **Continuous trading** — No gaps during RTH, consistent data flow
- **Institutional participation** — Real order flow, not retail noise
- **Official CME volume** — Accurate, exchange-reported data
If you're trading ES, NQ, YM, or RTY on TradingView, this indicator will give you the most accurate order flow approximation the platform can provide.
---
### Secondary Use Cases
**Other Liquid Futures:**
- CL, GC, SI (commodities) — Work well but slightly less optimized
- 6E, 6B, 6J (currency futures) — Decent accuracy with good liquidity
**Large-Cap Stocks & ETFs:**
- SPY, QQQ, IWM
- AAPL, MSFT, NVDA, TSLA, AMD
- Any stock trading millions of shares daily
**Crypto (with caveats):**
- BTC, ETH on major exchanges
- Works best during active hours
- Quality varies by exchange data feed
**Best Timeframes:**
- 1-minute to 15-minute for active intraday trading
- The indicator automatically selects appropriate lower timeframe sampling
- Can work on higher timeframes but edge diminishes
---
## Where This Indicator Struggles
I could hide this section and let you figure it out the hard way. I'd rather just tell you.
**Low-Volume Stocks:**
If a stock trades 50,000 shares a day, the delta readings will be noisy and inconsistent. The uptick/downtick estimation needs sufficient trade activity to be meaningful.
**Wide-Spread Instruments:**
When spreads are 10+ cents wide, a trade at the ask doesn't necessarily indicate aggressive buying. The bid/ask classification becomes less reliable.
**Forex:**
TradingView shows broker-specific volume for forex, not actual market volume. Readings will vary wildly depending on your data provider. Use with extreme caution, or not at all.
**Pre-Market & After-Hours:**
Liquidity thins dramatically. Estimations become less reliable. I'd trust regular session data far more.
**Daily/Weekly/Monthly Charts:**
The aggregation becomes so smoothed that the edge largely disappears. This is designed for intraday analysis.
---
## How to Actually Use This
### Focus on Delta %, Not Raw Delta
Raw delta is influenced by overall volume. A 500-lot delta sounds significant until you realize the bar traded 50,000 lots—that's just 1% imbalance, which is noise.
Delta % normalizes this. Look for readings above ±30% to identify meaningful pressure. Above ±50% is strong. Above ±70% is extreme.
### Let the Colors Guide You
If a bar is gray, the market isn't showing its hand. Don't overanalyze it. When you see bright green or red cells, that's when something is happening.
### Confirm With Price Action
Order flow data is context, not a signal generator. A strong bullish delta at a key support level means something different than the same reading in the middle of nowhere.
Use this alongside your existing analysis—levels, structure, momentum—not as a replacement.
### Watch for Divergences
Price making new highs while delta turns negative? That's absorption—sellers stepping in but price hasn't reacted yet.
Price dropping but delta stays positive? Buyers are defending.
These divergences often precede reversals. They're where order flow analysis provides genuine edge.
### Adjust the Activity Threshold
The default is 25%. For volatile instruments like NQ futures, you might lower it to 20%. For calmer instruments, raise it to 30-35%. The goal is filtering noise while keeping meaningful signals visible.
---
## Understanding the Metrics
| Metric | What It Tells You |
|--------|-------------------|
| **Volume** | Total contracts/shares traded |
| **Delta** | Net buying minus selling volume |
| **Delta %** | How imbalanced the bar is (key metric) |
| **Bar Δ CVD** | Cumulative delta change for this bar |
| **Imbalance Ratio** | Buy:Sell ratio (e.g., 2.1:1 or 1:1.8) |
| **Bar VWAP** | Where most volume transacted within the bar |
| Delta % Range | Interpretation |
|---------------|----------------|
| 0-15% | Neutral, no clear pressure |
| 15-30% | Weak directional bias |
| 30-50% | Moderate pressure |
| 50-70% | Strong imbalance |
| 70%+ | Extreme one-sided flow |
| Color | Meaning |
|-------|---------|
| Gray | Low activity, likely noise |
| Muted Green | Mild buying pressure |
| Bright Green | Strong buying pressure |
| Muted Red | Mild selling pressure |
| Bright Red | Strong selling pressure |
| Yellow/Gold | Volume intensity (separate scale) |
---
## Settings Breakdown
**Display Settings:**
- *Show Matrix Table* — Toggle the data heatmap on/off
- *Show Imbalance Bars* — Toggle the stacked visual bars on/off
- *Row Height* — Adjust the matrix row sizing
- *Activity Threshold* — Delta % below which bars fade to gray
**Imbalance Bars:**
- *Bar Height* — Vertical size of the stacked bars
- *Show Volume Labels* — Display buy/sell volume numbers
- *Show Percentage* — Display buy/sell percentages
**Timeframe Mode:**
- *Auto* — Sensible defaults based on your chart timeframe
- *Aggressive* — Samples from lowest possible timeframe (more granular)
- *Conservative* — Samples from slightly higher timeframe (smoother)
- *Custom* — You choose the exact lower timeframe
**CVD Reset:**
- *Daily* — Standard for intraday trading
- *Weekly/Monthly* — Useful for swing analysis
- *None* — Running cumulative total
---
## A Note on Expectations
I built this to be the best possible order flow tool within TradingView's constraints. It uses every optimization available, presents data in a clean and functional way, and doesn't pretend to be something it's not.
But I want to be clear: if order flow is central to your strategy and you're making decisions based on tape reading, you should seriously consider platforms designed for that purpose. Sierra Chart, Bookmap, Jigsaw—these tools show you the actual order book and time & sales. The difference is substantial.
Think of Nexural OrderFlow Matrix as a bridge. It gives TradingView users access to order flow concepts with reasonable accuracy. For many traders, especially those combining multiple analysis methods, that's enough. For dedicated tape readers, it's a starting point that might inspire you to explore deeper tools.
---
## What You're Getting
- **Dual visualization modes** — Matrix table and/or Imbalance bars
- **Activity-based color system** — Noise fades, signals pop
- **Real-time Nexural Flow Meter** — Live imbalance readings
- **Flexible configuration** — Show what you need, hide what you don't
- **Honest accuracy** — 85-90% correlation with official TradingView data
- **Clean, professional presentation** — Designed for actual trading, not screenshots
---
## What You're Not Getting
- Raw tick data (TradingView limitation)
- Bid/ask tape attribution (TradingView limitation)
- Order book depth (TradingView limitation)
- 99% accuracy (impossible on this platform)
- Magic signals (this is a tool, not a strategy)
---
## Final Thoughts
Trading is hard enough without tools that overpromise and underdeliver. I'd rather give you something that works within its limitations and be honest about those limitations than sell you a fantasy.
Nexural OrderFlow Matrix does what it says. It presents TradingView's best volume delta data in a clear, heatmap format with intelligent color coding. It's accurate within the platform's constraints. It's clean, it's fast, and it doesn't clutter your chart with noise.
Use it wisely. Combine it with price action, levels, and your own market understanding. And if you ever feel limited by what TradingView offers, know that there are deeper tools waiting for you when you're ready.
Trade well.
*— Nexural Trading*
---
## Quick Reference Card
**Built For:** Index Futures (ES, NQ, YM, RTY)
**Also Works On:** CL, GC, SPY, QQQ, large-cap stocks
**Avoid On:** Low-volume stocks, forex, illiquid instruments
**Best Timeframes:** 1-min to 15-min intraday
**Key Metric:** Delta % (not raw delta)
**Accuracy:** ~85-90% vs TradingView official tools
**Edge:** Divergences between price and delta
---
*Nexural OrderFlow Matrix — Engineered for index futures. Maximum accuracy within TradingView's limits.*
FinPile Momentum📊 FinPile Momentum Indicator - User Guide
What Is This Indicator?**
A visual momentum histogram that sits below your price chart, giving you an instant read on whether momentum is bullish, bearish, or neutral. Designed for day traders who need to make fast decisions.
**The Basics: Grade System**
| Grade | Color | Score | What It Means | Action |
|-------|-------|-------|---------------|--------|
| **A+** | Bright Green | +60 to +100 | Everything aligned bullish | ✅ STRONG BUY |
| **A** | Green | +40 to +59 | Strong upward momentum | ✅ BUY |
| **B** | Light Green | +20 to +39 | Mild bullish momentum | ⚠️ MAYBE - be careful |
| **C** | Gray | -19 to +19 | No clear direction | ❌ NO TRADE - wait |
| **D** | Orange | -20 to -39 | Mild bearish momentum | ⚠️ Caution |
| **E** | Red | -40 to -59 | Bearish momentum | 🔴 AVOID longs |
| **F** | Dark Red | -60 to -100 | Strong downward momentum | 🔴 SHORT or stay out |
How to Read the Histogram**
A+ ──────── +60 ────────
A ──────── +40 ──────── ← GREEN ZONE = BUY
B ──────── +20 ────────
═════════ C ════════ 0 ═════════ ← GRAY = NO TRADE
D ──────── -20 ────────
E ──────── -40 ──────── ← RED ZONE = AVOID/SHORT
F ──────── -60 ────────
**Tall green bars above +40** = Strong momentum, look for long entries
**Bars near zero (gray)** = Choppy/no direction, stay out
**Tall red bars below -40** = Bearish momentum, avoid longs or short
### **Warning Symbols**
| Symbol | Meaning | What To Do |
|--------|---------|------------|
| ⚠️ | Exhaustion detected (climax top or bottom) | Expect potential reversal |
| ⚡ | Parabolic move | Too fast, pullback likely |
**The Info Table (Top Right)**
| Row | What It Shows |
|-----|---------------|
| **MOMENTUM** | Current grade (A+, A, B, C, D, E, F) |
| **Score** | Exact number (-100 to +100) |
| **Accel** | 🚀 ACCEL (speeding up) / 💨 DECEL (slowing down) / ➖ STEADY |
| **vs IWM/SPY** | 🟢 OUT (outperforming) / 🔴 UNDER (underperforming) |
| **Mode** | Current smoothing mode and EMA length |
**3 Smoothing Modes**
| Mode | Best For | How It Works |
|------|----------|--------------|
| **⚡ Quick & Clean** (Default) | Scalping, fast day trading | EMA(5) + threshold filter - responsive but no flickering |
| **🐢 Slow & Reliable** | Swing trading, patient traders | Longer lookback + EMA(8) - very smooth, fewer false signals |
| **🎯 Adaptive** | Volatile stocks, changing conditions | Adjusts EMA based on volatility - smart and automatic |
**How to change:** Settings → Smoothing → Smoothing Mode
---
### **Quick Decision Framework**
#### ✅ GO LONG when:
- Grade is **A+ or A** (green histogram above +40)
- Acceleration shows **🚀 ACCEL** (momentum increasing)
- vs IWM shows **🟢 OUT** (beating the market)
- No warning symbols (⚠️ or ⚡)
#### ❌ STAY OUT when:
- Grade is **C** (gray histogram near zero)
- Acceleration shows **💨 DECEL** while in a trade
- Score is bouncing between grades (indecision)
#### 🔴 GO SHORT or EXIT LONGS when:
- Grade is **E or F** (red histogram below -40)
- vs IWM shows **🔴 UNDER** (lagging market)
- Warning symbol ⚠️ appears at highs
---
### **Combining with Price Action**
| Momentum | Price Action | Decision |
|----------|--------------|----------|
| A/A+ rising | Breaking resistance | ✅ Strong buy |
| A/A+ but DECEL | At resistance | ⚠️ Wait for confirmation |
| B flat | Consolidating | ❌ No trade yet |
| C choppy | Ranging | ❌ Stay out |
| D/E falling | Breaking support | 🔴 Short or exit longs |
| F with ⚠️ | Capitulation low | 👀 Watch for bounce |
---
### **Settings Recommendations**
#### For Small Caps / Low Float:
```
Benchmark: IWM
Smoothing Mode: Adaptive
```
#### For Large Caps (AAPL, MSFT, etc.):
```
Benchmark: SPY
Smoothing Mode: Quick & Clean
```
#### For Volatile Meme Stocks:
```
Benchmark: IWM
Smoothing Mode: Adaptive
Adaptive High Vol EMA: 3
```
#### For Smoother Signals:
```
Smoothing Mode: Slow & Reliable
Slow Mode: Lookback Mult: 2.5
Slow Mode: EMA Length: 10
```
---
### **Pro Tips**
1. **Don't fight the color** - If histogram is red, don't go long hoping for reversal
2. **Watch for acceleration changes** - 🚀→💨 while price is rising = momentum fading, tighten stops
3. **Grade + Acceleration combo:**
- A + 🚀 ACCEL = Best setup
- A + 💨 DECEL = Momentum fading, be cautious
- C + 🚀 ACCEL = Potential breakout coming
4. **Use with the main indicator** - Momentum histogram for timing, main FinPile Institutional for levels and full analysis
5. **Background color** - When background turns green/red, momentum is strong (above +40 or below -40)
---
### **Example Trade**
```
You see:
┌─────────────────────────┐
│ MOMENTUM │ A │ ← Good grade
│ Score │ 52 │ ← Solid score
│ Accel │ 🚀 ACCEL │ ← Increasing!
│ vs IWM │ 🟢 OUT │ ← Beating market
│ Mode │ ⚡ QUICK │
└─────────────────────────┘
Histogram: Tall green bar above +40 line
Decision: ✅ LONG - All signals aligned
```
---
### **Quick Reference Card**
```
🟢 GREEN (A+/A) + 🚀 ACCEL + 🟢 OUT = BUY
⚪ GRAY (C) = NO TRADE
🔴 RED (E/F) + 💨 DECEL + 🔴 UNDER = SHORT/EXIT
⚠️ WARNING = Expect reversal
Multi-Ticker Anchored CandlesMulti-Ticker Anchored Candles (MTAC) is a simple tool for overlaying up to 3 tickers onto the same chart. This is achieved by interpreting each symbol's OHLC data as percentages, then plotting their candle points relative to the main chart's open. This allows for a simple comparison of tickers to track performance or locate relationships between them.
> Background
The concept of multi-ticker analysis is not new, this type of analysis can be extremely helpful to get a gauge of the over all market, and it's sentiment. By analyzing more than one ticker at a time, relationships can often be observed between tickers as time progresses.
While seeing multiple charts on top of each other sounds like a good idea...each ticker has its own price scale, with some being only cents while others are thousands of dollars.
Directly overlaying these charts is not possible without modification to their sources.
By using a fixed point in time (Period Open) and percentage performance relative to that point for each ticker, we are able to directly overlay symbols regardless of their price scale differences.
The entire process used to make this indicator can be summed up into 2 keywords, "Scaling & Anchoring".
> Scaling
First, we start by determining a frame of reference for our analysis. The indicator uses timeframe inputs to determine sessions which are used, by default this is set to 1 day.
With this in place, we then determine our point of reference for scaling. While this could be any point in time, the most sensible for our application is the daily (or session) open.
Each symbol shares time, therefore, we can take a price point from a specified time (Opening Price) and use it to sync our analysis over each period.
Over the day, we track the percentage performance of each ticker's OHLC values relative to its daily open (% change from open).
Since each ticker's data is now tracked based on its opening price, all data is now using the same scale.
The scale is simply "% change from open".
> Anchoring
Now that we have our scaled data, we need to put it onto the chart.
Since each point of data is relative to it's daily open (anchor point), relatively speaking, all daily opens are now equal to each other.
By adding the scaled ticker data to the main chart's daily open, each of our resulting series will be properly scaled to the main chart's data based on percentages.
Congratulations, We have now accurately scaled multiple tickers onto one chart.
> Display
The indicator shows each requested ticker as different colored candlesticks plotted on top of the main chart.
Each ticker has an associated label in front of the current bar, each component of this label can be toggled on or off to allow only the desired information to be displayed.
To retain relevance, at the start of each session, a "Session Break" line is drawn, as well as the opening price for the session. These can also be toggled.
Note: The opening price is the opening price for ALL tickers, when a ticker crosses the open on the main chart, it is crossing its own opening price as well.
> Examples
In the chart below, we can see NYSE:MCD NASDAQ:WEN and NASDAQ:JACK overlaid on a NASDAQ:SBUX chart.
From this, we can see NASDAQ:JACK was the top gainer on the day. While this was the case, it also fell roughly 4% from its peak near lunchtime. Unlike the top gainer, we can see the other 3 tickers ended their day near their daily high.
In the explanations above, the daily timeframe is used since it is the default; however, the analysis is not constrained to only days. The anchoring period can be set to any timeframe period.
In the chart below, you can observe the Daily, Weekly, and Monthly anchored charts side-by-side.
This can be used on all tickers, timeframes, and markets. While a typical application may be comparing relevant assets... the script is not limited.
Below we have a chart tracking COMEX:GCV2026 , FX:EURUSD , and COINBASE:DOGEUSD on the AMEX:SPY chart.
While these tickers are not typically compared side-by-side, here it is simply a display of the capabilities of the script.
Enjoy!
Relative Performance Analyzer [AstrideUnicorn]Relative Performance Analyzer (RPA) is a performance analysis tool inspired by the data comparison features found in professional trading terminals. The RPA replicates the analytical approach used by portfolio managers and institutional analysts who routinely compare multiple securities or other types of data to identify relative strength opportunities, make allocation decisions, choose the most optimal investment from several alternatives, and much more.
Key Features:
Multi-Symbol Comparison: Track up to 5 different symbols simultaneously across any asset class or dataset
Two Performance Calculation Methods: Choose between percentage returns or risk-adjusted returns
Interactive Analysis: Drag the start date line on the chart or manually choose the start date in the settings
Professional Visualization: High-contrast color scheme designed for both dark and light chart themes
Live Performance Table: Real-time display of current return values sorted from the top to the worst performers
Practical Use Cases:
ETF Selection: Compare similar ETFs (e.g., SPY vs IVV vs VOO) to identify the most efficient investment
Sector Rotation: Analyze which sectors are showing relative strength for strategic allocation
Competitive Analysis: Compare companies within the same industry to identify leaders (e.g., APPLE vs SAMSUNG vs XIAOMI)
Cross-Asset Allocation: Evaluate performance across stocks, bonds, commodities, and currencies to guide portfolio rebalancing
Risk-Adjusted Decisions: Use risk-adjusted performance to find investments with the best returns per unit of risk
Example Scenarios:
Analyze whether tech stocks are outperforming the broader market by comparing XLK to SPY
Evaluate which emerging market ETF (EEM vs VWO) has provided better risk-adjusted returns over the past year
HOW DOES IT WORK
The indicator calculates and visualizes performance from a user-defined starting point using two methodologies:
Percentage Returns: Standard total return calculation showing percentage change from the start date
Risk-Adjusted Returns: Cumulative returns divided by the volatility (standard deviation), providing insight into the efficiency of performance. An expanding window is used to calculate the volatility, ensuring accurate risk-adjusted comparisons throughout the analysis period.
HOW TO USE
Setup Your Comparison: Enable up to 5 assets and input their symbols in the settings
Set Analysis Period: When you first launch the indicator, select the start date by clicking on the price chart. The vertical start date line will appear. Drag it on the chart or manually input a specific date to change the start date.
Choose Return Type: Select between percentage or risk-adjusted returns based on your analysis needs
Interpret Results
Use the real-time table for precise current values
SETTINGS
Assets 1-5: Toggle on/off and input symbols for comparison (stocks, ETFs, indices, forex, crypto, fundamental data, etc.)
Start Date: Set the initial point for return calculations (drag on chart or input manually)
Return Type: Choose between "Percentage" or "Risk-Adjusted" performance.
Market Energy & Direction DashboardMarket Energy & Direction Dashboard - Daytrading
Overview
A comprehensive real-time market internals dashboard that combines NYSE TICK, NYSE Advance-Decline (ADD) momentum, VIX direction, and relative volume into a single visual traffic light system with intelligent signal synthesis. Designed for active daytraders who need instant confirmation of market direction and energy based on momentum alignment across all major internals.
What It Does
This indicator synthesizes multiple market internals using directional momentum analysis rather than static thresholds to provide clear, actionable signals:
• Traffic Light System: Single glance confirmation of market state
o Bright Green: Maximum bullish - all internals aligned (TICK + ADD rising + VIX falling + volume)
o Bright Red: Maximum bearish - all internals aligned (TICK + ADD falling + VIX rising + volume)
o Yellow: Exhaustion warning - TICK at extremes, potential reversal imminent
o Moderate Colors: Partial alignment - some confirmation but not complete
o Gray: Choppy, neutral, or conflicting signals
• Real-Time Dashboard displays:
o Current TICK value with exhaustion warnings
o Current ADD with directional momentum indicator (↑ rising = breadth improving, ↓ falling = breadth deteriorating, ± compression)
o VIX level with directional indicator (↓ declining = bullish, ↑ rising = bearish, ± compression = neutral)
o Relative volume (current vs 20-period average)
o Composite status message synthesizing all data into clear directional summary
Key Features
✓ Momentum-based analysis - all indicators show direction/change, not just levels ✓ Intelligent signal hierarchy from "Maximum" to "Moderate" based on internal alignment ✓ ADD directional momentum - catches breadth shifts early, works in all market conditions ✓ VIX directional analysis - shows if fear is increasing, decreasing, or stagnant ✓ Color-coded traffic light for instant decision making ✓ Detects TICK/ADD divergences (conflicting signals = caution) ✓ Exhaustion warnings at extreme TICK levels (±1000+) ✓ Composite status messages - "Maximum Bull", "Strong Bull", "Moderate Bull", etc. ✓ Customizable thresholds for all parameters ✓ Moveable dashboard (9 position options) ✓ Built-in alerts for all signal strengths, exhaustion, and divergences
How To Use
Setup:
1. Add indicator to your main trading chart (SPY, ES, NQ, etc.)
2. Default settings work well for most traders, but you can customize:
o TICK Extreme Level (default 1000)
o ADD Compression Threshold (default 100 - detects when breadth is stagnant)
o VIX Elevated Level (default 20)
o VIX Compression Threshold (default 2% - detects low volatility)
o Volume Threshold (default 1.5x average)
3. Position dashboard wherever convenient on your chart
Reading The Signals:
Signal Hierarchy (Strongest to Weakest):
MAXIMUM SIGNALS ⭐ (Brightest colors - All 4 internals aligned)
• "✓ MAXIMUM BULL": TICK bullish + ADD rising (↑) + VIX falling (↓) + Volume elevated
o This is the holy grail setup - all momentum aligned, highest conviction longs
• "✓ MAXIMUM BEAR": TICK bearish + ADD falling (↓) + VIX rising (↑) + Volume elevated
o Perfect storm bearish - all momentum aligned, highest conviction shorts
STRONG SIGNALS (Bright colors - Core internals aligned)
• "✓ STRONG BULL": TICK bullish + ADD rising (↑)
o Strong confirmation even without VIX/volume - breadth supporting the move
• "✓ STRONG BEAR": TICK bearish + ADD falling (↓)
o Strong confirmation - both momentum and breadth deteriorating
MODERATE SIGNALS (Faded colors - Partial confirmation)
• "MODERATE BULL": TICK bullish but ADD not confirming direction
o Proceed with caution - momentum present but breadth questionable
• "MODERATE BEAR": TICK bearish but ADD not confirming direction
o Proceed with caution - selling but breadth not fully participating
WARNING SIGNALS
• "⚠ EXHAUSTION" (Yellow): TICK at ±1000+ extremes
o Potential reversal zone - prepare to fade or take profits
o Often marks blow-off tops or capitulation bottoms
NEUTRAL/AVOID
• "CHOPPY/NEUTRAL" (Gray): Conflicting signals or low conviction
o Stay out or reduce size significantly
Individual Indicator Interpretation:
TICK:
• Green: Bullish momentum (>+300)
• Red: Bearish momentum (<-300)
• Yellow: Exhaustion (±1000+)
• Gray: Neutral
ADD (Advance-Decline):
• Green (↑): Breadth improving - more stocks participating in the move
• Red (↓): Breadth deteriorating - fewer stocks participating
• Gray (±): Breadth stagnant - no clear participation trend
VIX:
• Green (↓): Fear declining - healthy environment for rallies
• Red (↑): Fear rising - risk-off mode, supports downward moves
• Gray (±): Volatility compression - often precedes explosive moves
Volume:
• Green: High conviction (>1.5x average)
• Gray: Low conviction
Trading Strategy:
1. Wait for "MAXIMUM" or "STRONG" signals for highest probability entries
o Maximum signals = go full size with confidence
o Strong signals = good conviction, normal position sizing
2. Confirm directional alignment:
o For longs: Want ADD ↑ (rising) and VIX ↓ (falling)
o For shorts: Want ADD ↓ (falling) and VIX ↑ (rising)
3. Use exhaustion warnings (yellow) to:
o Take profits on existing positions
o Prepare counter-trend entries
o Tighten stops
4. Avoid "MODERATE" signals unless you have strong conviction from other analysis
o These work best as confirmation for existing setups
o Not strong enough to initiate new positions alone
5. Never trade "CHOPPY/NEUTRAL" signals
o Gray means stay out - preserve capital
o Wait for clear alignment
6. Watch for divergences:
o Price making new highs but ADD ↓ (falling) = distribution warning
o Price making new lows but ADD ↑ (rising) = potential bottom
o Divergence alert will notify you
Best Practices:
• Use on 1-5 minute charts for daytrading
• Combine with your price action or technical setup (support/resistance, trendlines, patterns)
• The dashboard confirms when to take your setup, not what setup to take
• Most effective during regular market hours (9:30 AM - 4:00 PM ET) when volume is present
• The strongest edge comes from "MAXIMUM" signals - wait for these for best risk/reward
• Pay special attention to ADD direction - it's the most predictive breadth indicator
• VIX compression (gray ±) often signals upcoming volatility expansion - prepare for bigger moves
Customization Option
All thresholds are adjustable in settings:
• TICK Extreme: Higher = fewer exhaustion warnings (try 1200-1500 for less sensitivity)
• ADD Compression Threshold: Change detection sensitivity
o Default 100 = balanced
o Lower (50) = more sensitive to small breadth changes
o Higher (200-300) = only shows major breadth shifts
• VIX Elevated: Adjust for current volatility regime (15-25 typical range)
• VIX Compression Threshold:
o Default 2% = balanced
o Lower (0.5-1%) = catches subtle VIX changes
o Higher (3-5%) = only shows significant VIX moves
• Volume Threshold: Lower for quieter stocks/times, higher for more confirmation
Alerts Available
• Maximum Bullish: All 4 internals aligned bullish (TICK + ADD↑ + VIX↓ + Volume)
• Maximum Bearish: All 4 internals aligned bearish (TICK + ADD↓ + VIX↑ + Volume)
• Strong Bullish: TICK bullish + ADD rising
• Strong Bearish: TICK bearish + ADD falling
• Exhaustion Warning: TICK at extreme levels
• Divergence Warning: TICK and ADD directions conflicting
Understanding the Signal Synthesis
The indicator uses intelligent logic to combine all internals:
"MAXIMUM" Signals require:
• TICK direction (bullish/bearish)
• ADD momentum (rising/falling) in same direction
• VIX direction (falling for bulls, rising for bears)
• Volume elevated (>1.5x average)
"STRONG" Signals require:
• TICK direction (bullish/bearish)
• ADD momentum (rising/falling) in same direction
• (VIX and volume are bonuses but not required)
"MODERATE" Signals:
• TICK showing direction
• But ADD not confirming or contradicting
• Weakest actionable signal
This hierarchy ensures you know exactly how much conviction the market has behind any move.
Technical Details
• Pulls real-time data from NYSE TICK (USI:TICK), NYSE ADD (USI:ADD), and CBOE VIX
• ADD direction calculated using bar-to-bar change with compression detection
• VIX direction calculated using bar-to-bar percentage change
• Volume calculation uses 20-period simple moving average
• Dashboard updates every bar
• No repainting - all calculations based on closed bar data
Who This Is For
• Active daytraders of stocks, futures (ES/NQ), and options
• Scalpers needing quick directional confirmation with multiple internal alignment
• Swing traders looking to time intraday entries with maximum confluence
• Volatility traders who monitor VIX behavior
• Market makers and professionals who trade based on breadth and internals
• Anyone who monitors market internals but wants intelligent synthesis vs raw data
Tips For Success
Trading Philosophy:
• Quality over quantity - wait for "MAXIMUM" signals for best results
• One "MAXIMUM" signal trade is worth five "MODERATE" signal trades
• Gray/neutral is not a sign of missing opportunity - it's protecting your capital
Signal Confidence Levels:
1. MAXIMUM (95%+ confidence) - Trade these aggressively with full size
2. STRONG (80-85% confidence) - Trade these with normal position sizing
3. MODERATE (60-70% confidence) - Only if confirmed by strong technical setup
4. CHOPPY/NEUTRAL - Do not trade, wait for clarity
Advanced Techniques:
• Breadth divergences: Watch for price making new highs while ADD shows ↓ (falling) = major warning
• VIX/Price divergences: Rallies with rising VIX (↑) are usually false moves
• Volume confirmation: "MAXIMUM" signals with 2x+ volume are the absolute best
• Compression zones: When both ADD and VIX show compression (±), expect explosive breakout soon
• Sequential signals: Back-to-back "MAXIMUM" signals in same direction = strong trending day
Common Patterns:
• Opening surge with "MAXIMUM BULL" that shifts to "EXHAUSTION" (yellow) = fade the high
• Selloff with "MAXIMUM BEAR" followed by ADD ↑ (rising) divergence = potential reversal
• Choppy morning followed by "MAXIMUM" signal afternoon = best trending opportunity
Example Scenarios
Perfect Bull Entry:
• Bright green signal box
• TICK: +650
• ADD: +1200 (↑)
• VIX: 18.30 (↓)
• Volume: 2.3x
• Status: "✓ MAXIMUM BULL" → ALL SYSTEMS GO - Take aggressive long positions
Strong Bull (Good Confidence):
• Green signal box (slightly less bright)
• TICK: +500
• ADD: +800 (↑)
• VIX: 19.50 (±)
• Volume: 1.2x
• Status: "✓ STRONG BULL" → Good long setup - breadth confirming even without VIX/volume
Caution Bull (Moderate):
• Faded green signal box
• TICK: +400
• ADD: +900 (↓)
• VIX: 20.10 (↑)
• Volume: 0.9x
• Status: "MODERATE BULL" → CAUTION - TICK bullish but breadth deteriorating and VIX rising = weak rally
Exhaustion Warning:
• Yellow signal box
• TICK: +1350 ⚠
• ADD: +2100 (↑)
• VIX: 17.20 (↓)
• Volume: 1.8x
• Status: "⚠ EXHAUSTION" → Take profits or prepare to fade - TICK overextended despite good internals
Divergence Setup (Potential Reversal):
• Faded green signal
• TICK: +300
• ADD: +1800 (↓)
• VIX: 21.50 (↑)
• Volume: 1.6x
• Status: "MODERATE BULL" → WARNING - Price rallying but breadth collapsing and fear rising = distribution
Perfect Bear Entry:
• Bright red signal box
• TICK: -780
• ADD: -1600 (↓)
• VIX: 24.80 (↑)
• Volume: 2.5x
• Status: "✓ MAXIMUM BEAR" → Perfect short setup - all momentum bearish with conviction
Compression (Wait Mode):
• Gray signal box
• TICK: +50
• ADD: -200 (±)
• VIX: 16.40 (±)
• Volume: 0.7x
• Status: "CHOPPY/NEUTRAL" → STAY OUT - Volatility compression, no conviction, await breakout
Performance Optimization
Best Market Conditions:
• Works excellent in trending markets (up or down)
• Particularly powerful during high-volume sessions (first/last hours)
• "MAXIMUM" signals most reliable during 9:45-11:00 AM and 2:00-3:30 PM ET
Less Effective During:
• Lunch period (11:30 AM - 1:30 PM) - lower volume reduces signal quality
• Low-volatility environments - compression signals dominate
• Major news events in first 5 minutes - wait for internals to stabilize
Recommended Use Cases:
• Scalping: Trade only "MAXIMUM" signals for quick 5-15 minute moves
• Daytrading: Use "MAXIMUM" and "STRONG" signals for position entries
• Swing entries: Use "MAXIMUM" signals for optimal intraday entry timing
• Exit timing: Use "EXHAUSTION" (yellow) warnings to take profits
________________________________________
Pro Tip: Create a dedicated workspace with this indicator on SPY/ES/NQ charts. Set alerts for "MAXIMUM BULL", "MAXIMUM BEAR", and "EXHAUSTION" signals. Most professional traders only trade the "MAXIMUM" setups and ignore everything else - this alone can dramatically improve win rates.
Multi-Asset: Complete AnalysisDescription:
Comprehensive performance analysis tool for comparing multiple assets or custom weighted portfolios against benchmarks. Calculates Sharpe ratios across multiple timeframes (30d, 90d, 180d, 252d), total returns, CAGR, and normalized values starting from $100.
Key Features:
Build custom weighted portfolios (default: 50/30/20 allocation)
Compare against SPY, QQQ, and other benchmarks
Dynamic risk-free rate from ^IRX or manual input
Multi-period Sharpe ratio analysis to validate strategy consistency
Total return and annualized return (CAGR) metrics
All assets normalized to common start date for accurate comparison
Toggle individual assets on/off for cleaner chart viewing
Use Case:
Perfect for evaluating whether your custom portfolio allocation justifies its complexity versus simply buying SPY/QQQ. If your risk-adjusted returns (Sharpe) and absolute returns aren't beating the benchmarks, you're overcomplicating your strategy.
Ideal for: Portfolio managers, factor investors, and anyone building custom allocations who need proof their strategy actually works.
Lorentzian Harmonic Flow - Adaptive ML⚡ LORENTZIAN HARMONIC FLOW — ADAPTIVE ML COMPLETE SYSTEM
THEORETICAL FOUNDATION: TEMPORAL RELATIVITY MEETS MACHINE LEARNING
The Lorentzian Harmonic Flow Adaptive ML system represents a paradigm shift in technical analysis by addressing a fundamental limitation that plagues traditional indicators: they assume time flows uniformly across all market conditions. In reality, markets experience time compression during volatile breakouts and time dilation during consolidation. A 50-period moving average calculated during a quiet overnight session captures vastly different market information than the same calculation during a high-volume news event.
This indicator solves this problem through Lorentzian spacetime modeling , borrowed directly from Einstein's special relativity. By calculating a dynamic gamma factor (γ) that measures market velocity relative to a volatility-based "speed of light," every calculation adapts its effective lookback period to the market's intrinsic clock. Combined with a dual-memory architecture, multi-regime detection, and Bayesian strategy selection, this creates a system that genuinely learns which approaches work in which market conditions.
CRITICAL DISTINCTION: TRUE ADAPTIVE LEARNING VS STATIC CLASSIFICATION
Before diving into the system architecture, it's essential to understand how this indicator fundamentally differs from traditional "Lorentzian" implementations, particularly the well-known Lorentzian Classification indicator.
THE ORIGINAL LORENTZIAN CLASSIFICATION APPROACH:
The pioneering Lorentzian Classification indicator (Jdehorty, 2022) introduced the financial community to Lorentzian distance metrics for pattern matching. However, it used offline training methodology :
• External Training: Required Python scripts or external ML tools to train the model on historical data
• Static Model: Once trained, the model parameters remained fixed
• No Real-Time Learning: The indicator classified patterns but didn't learn from outcomes
• Look-Ahead Bias Risk: Offline training could inadvertently use future data
• Manual Retraining: To adapt to new market conditions, users had to retrain externally and reload parameters
This was groundbreaking for bringing ML concepts to Pine Script, but it wasn't truly adaptive. The model was a snapshot—trained once, deployed, static.
THIS SYSTEM: TRUE ONLINE LEARNING
The Lorentzian Harmonic Flow Adaptive ML system represents a complete architectural departure :
✅ FULLY SELF-CONTAINED:
• Zero External Dependencies: No Python scripts, no external training tools, no data exports
• 100% Pine Script: Entire learning pipeline executes within TradingView
• One-Click Deployment: Load indicator, it begins learning immediately
• No Manual Configuration: System builds its own training data in real-time
✅ GENUINE FORWARD-WALK LEARNING:
• Real-Time Adaptation: Every trade outcome updates the model
• Forward-Only Logic: System uses only past confirmed data—zero look-ahead bias
• Continuous Evolution: Parameters adapt bar-by-bar based on rolling performance
• Regime-Specific Memory: Learns which patterns work in which conditions independently
✅ GETS BETTER WITH TIME:
• Week 1: Bootstrap mode—gathering initial data across regimes
• Month 2-3: Statistical significance emerges, parameter adaptation begins
• Month 4+: Mature learning, regime-specific optimization, confident selection
• Year 2+: Deep pattern library, proven parameter sets, robust to regime shifts
✅ NO RETRAINING REQUIRED:
• Automatic Adaptation: When market structure changes, system detects via performance degradation
• Memory Refresh: Old patterns naturally decay, new patterns replace them
• Parameter Evolution: Thresholds and multipliers adjust to current conditions
• Regime Awareness: If new regime emerges, enters bootstrap mode automatically
THE FUNDAMENTAL DIFFERENCE:
Traditional Lorentzian Classification:
"Here are patterns from the past. Current state matches pattern X, which historically preceded move Y. Signal fired."
→ Static knowledge, fixed rules, periodic retraining required
LHF Adaptive ML:
"In Trending Bull regime, Strategy B has 58% win rate and 1.4 Sharpe over last 30 trades. In High Vol Range, Strategy C performs better with 61% win rate and 1.8 Sharpe. Current state is Trending Bull, so I select Strategy B. If Strategy B starts failing, I'll adapt parameters or switch strategies. I'm learning which patterns matter in which contexts, and I improve every trade."
→ Dynamic learning, contextual adaptation, self-improving system
WHY THIS MATTERS:
Markets are non-stationary. A model trained on 2023 data may fail in 2024 when Fed policy shifts, volatility regime changes, or market structure evolves. Static models require constant human intervention—retraining, re-optimization, parameter updates.
This system learns continuously . It doesn't need you to tell it when markets changed. It discovers regime shifts through performance feedback, adapts parameters accordingly, and rebuilds its pattern library organically. The system running in Month 12 is fundamentally smarter than the system in Month 1—not because you retrained it, but because it learned from 1,000+ real outcomes.
This is the difference between pattern recognition (static ML) and reinforcement learning (adaptive ML). One classifies, the other learns and improves.
PART 1: LORENTZIAN TEMPORAL DYNAMICS
Markets don't experience time uniformly. During explosive volatility, price can compress weeks of movement into minutes. During consolidation, time dilates. Traditional indicators ignore this, using fixed periods regardless of market state.
The Lorentzian approach models market time using the Lorentz factor from special relativity:
γ = 1 / √(1 - v²/c²)
Where:
• v (velocity): Trend momentum normalized by ATR, calculated as (close - close ) / (N × ATR)
• c (speed limit): Realized volatility + volatility bursts, multiplied by c_multiplier parameter
• γ (gamma): Time dilation factor that compresses or expands effective lookback periods
When trend velocity approaches the volatility "speed limit," gamma spikes above 1.0, compressing time. Every calculation length becomes: base_period / γ. This creates shorter, more responsive periods during explosive moves and longer, more stable periods during quiet consolidation.
The system raises gamma to an optional power (gamma_power parameter) for fine control over compression strength, then applies this temporal scaling to every calculation in the indicator. This isn't metaphor—it's quantitative adaptation to the market's intrinsic clock.
PART 2: LORENTZIAN KERNEL SMOOTHING
Traditional moving averages use uniform weights (SMA) or exponential decay (EMA). The Lorentzian kernel uses heavy-tailed weighting:
K(distance, γ) = 1 / (1 + (distance/γ)²)
This Cauchy-like distribution gives more influence to recent extremes than Gaussian assumptions suggest, capturing the fat-tailed nature of financial returns. For any calculation requiring smoothing, the system loops through historical bars, computes Lorentzian kernel weights based on temporal distance and current gamma, then produces weighted averages.
This creates adaptive smoothing that responds to local volatility structure rather than imposing rigid assumptions about price distribution.
PART 3: HARMONIC FLOW (Multi-Timeframe Momentum)
The core directional signal comes from Harmonic Flow (HFL) , which blends three gamma-compressed Lorentzian smooths:
• Short Horizon: base_period × short_ratio / γ (default: 34 × 0.5 / γ ≈ 17 bars, faster with high γ)
• Mid Horizon: base_period × mid_ratio / γ (default: 34 × 1.0 / γ ≈ 34 bars, anchor timeframe)
• Long Horizon: base_period × long_ratio / γ (default: 34 × 2.5 / γ ≈ 85 bars, structural trend)
Each produces a Lorentzian-weighted smooth, converted to a z-score (distance from smooth normalized by ATR). These z-scores are then weighted-averaged:
HFL = (w_short × z_short + w_mid × z_mid + w_long × z_long) / (w_short + w_mid + w_long)
Default weights (0.45, 0.35, 0.20) favor recent momentum while respecting longer structure. Scalpers can increase short weight; swing traders can emphasize long weight. The result is a directional momentum indicator that captures multi-timeframe flow in compressed time.
From HFL, the system derives:
• Flow Velocity: HFL - HFL (momentum acceleration)
• Flow Acceleration: Second derivative (turning points)
• Temporal Compression Index (TCI): base_period / compressed_length (shows how much time is compressed)
PART 4: DUAL MEMORY ARCHITECTURE
Markets have memory—current conditions resonate with past regimes. But memory operates on two timescales, inspiring this indicator's dual-memory design:
SHORT-TERM MEMORY (STM):
• Capacity: 100 patterns (configurable 50-200)
• Decay Rate: 0.980 (50% weight after ~35 bars)
• Update Frequency: Every 10 bars
• Purpose: Capture current regime's tactical patterns
• Storage: Recent market states with 10-bar forward outcomes
• Analogy: Hippocampus (rapid encoding, fast fade)
LONG-TERM MEMORY (LTM):
• Capacity: 512 patterns (configurable 256-1024)
• Decay Rate: 0.997 (50% weight after ~230 bars)
• Quality Gate: Only high-quality patterns admitted (adaptive threshold per regime)
• Purpose: Strategic pattern library validated across regimes
• Storage: Validated patterns from weeks/months of history
• Analogy: Neocortex (slow consolidation, persistent storage)
Each memory stores 6-dimensional feature vectors:
1. HFL (harmonic flow strength)
2. Flow Velocity (momentum)
3. Flow Acceleration (turning points)
4. Volatility (realized vol EMA)
5. Entropy (market uncertainty)
6. Gamma (time compression state)
Plus the actual outcome (10-bar forward return).
K-NEAREST NEIGHBORS (KNN) PATTERN MATCHING:
When evaluating current market state, the system queries both memories using Lorentzian distance :
distance = Σ (1 - K(|feature_current - feature_memory|, γ))
This calculates similarity across all 6 dimensions using the same Lorentzian kernel, weighted by current gamma. The system finds K nearest neighbors (default: 8), weights each by:
• Similarity: Lorentzian kernel distance
• Age: Exponential decay based on bars since pattern
• Regime: Only patterns from similar regimes count
The weighted average of these neighbors' outcomes becomes the prediction. High-confidence predictions require both high similarity and agreement between multiple neighbors.
REGIME-AWARE BLENDING:
STM and LTM predictions are blended adaptively:
• High Vol Range regime: Trust STM 70% (recent matters in chaos)
• Trending regimes: Trust LTM 70% (structure matters in trends)
• Normal regimes: 50/50 blend
Agreement metric: When STM and LTM strongly disagree, the system flags low confidence—often indicating regime transition or novel market conditions requiring caution.
PART 5: FIVE-REGIME MARKET CLASSIFICATION
Traditional regime detection stops at "trending vs ranging." This system detects five distinct market states using linear regression slope and volatility analysis:
REGIME 0: TRENDING BULL ↗
• Detection: LR slope > trend_threshold (default: 0.3)
• Characteristics: Sustained positive HFL, elevated gamma, low entropy
• Best Strategy: B (Flow Momentum)
• Trading Behavior: Follow momentum, trail stops, pyramid winners
REGIME 1: TRENDING BEAR ↘
• Detection: LR slope < -trend_threshold
• Characteristics: Sustained negative HFL, elevated gamma, low entropy
• Best Strategy: B (Flow Momentum)
• Trading Behavior: Follow momentum short, aggressive exits on reversal
REGIME 2: HIGH VOL RANGE ↔
• Detection: |slope| < threshold AND vol_ratio > vol_expansion_threshold (default: 1.5)
• Characteristics: Oscillating HFL, high gamma spikes, high entropy
• Best Strategies: A (Squeeze Breakout) or C (Memory Pattern)
• Trading Behavior: Fade extremes, tight stops, quick profits
REGIME 3: LOW VOL RANGE —
• Detection: |slope| < threshold AND vol_ratio < vol_expansion_threshold
• Characteristics: Low HFL magnitude, gamma ≈ 1, squeeze conditions
• Best Strategy: A (Squeeze Breakout)
• Trading Behavior: Wait for breakout, wide stops on breakout entry
REGIME 4: TRANSITION ⚡
• Detection: Trend reversal OR volatility spike > 1.5× threshold
• Characteristics: Erratic gamma, high entropy, conflicting signals
• Best Strategy: None (often unfavorable)
• Trading Behavior: Stand aside, wait for clarity
Each regime gets a confidence score (0-1) measuring how clearly defined it is. Low confidence indicates messy, ambiguous conditions.
PART 6: THREE INDEPENDENT TRADING STRATEGIES
Rather than one signal logic, the system implements three distinct approaches:
STRATEGY A: SQUEEZE BREAKOUT
• Logic: Bollinger Bands squeeze release + HFL direction + flow velocity confirmation
• Calculation: Compares BB width to Keltner Channel width; fires when BB expands beyond KC
• Strength Score: 70 + compression_strength × 0.3 (tighter squeeze = higher score)
• Best Regimes: Low Vol Range (3), Transition exit (4→0 or 4→1)
• Pattern: Volatility contraction → directional expansion
• Philosophy: Calm before the storm; compression precedes explosion
STRATEGY B: LORENTZIAN FLOW MOMENTUM
• Logic: Strong HFL (×flow_mult) + positive velocity + gamma > 1.1 + NOT squeezing
• Calculation: |HFL × flow_mult| > 0.12, velocity confirms direction, gamma shows acceleration
• Strength Score: |HFL × flow_mult| × 80 + gamma × 10
• Best Regimes: Trending Bull (0), Trending Bear (1)
• Pattern: Established momentum → acceleration in compressed time
• Philosophy: Trend is friend when spacetime curves
STRATEGY C: MEMORY PATTERN MATCHING
• Logic: Dual KNN prediction > threshold + high confidence + agreement + HFL confirms
• Calculation: |memory_pred| > 0.005, memory_conf > 1.0, agreement > 0.5, HFL direction matches
• Strength Score: |prediction| × 800 × agreement
• Best Regimes: High Vol Range (2), sometimes others with sufficient pattern library
• Pattern: Historical similarity → outcome resonance
• Philosophy: Markets rhyme; learn from validated patterns
Each strategy generates independent strength scores. In multi-strategy mode (enabled by default), the system selects one strategy per regime based on risk-adjusted performance. In weighted mode (multi-strategy disabled), all three fire simultaneously with configurable weights.
PART 7: ADAPTIVE LEARNING & BAYESIAN SELECTION
This is where machine learning meets trading. The system maintains 15 independent performance matrices :
3 strategies × 5 regimes = 15 tracking systems
For each combination, it tracks:
• Trade Count: Number of completed trades
• Win Count: Profitable outcomes
• Total Return: Sum of percentage returns
• Squared Returns: For variance/Sharpe calculation
• Equity Curve: Virtual P&L assuming 10% risk per trade
• Peak Equity: All-time high for drawdown calculation
• Max Drawdown: Peak-to-trough decline
RISK-ADJUSTED SCORING:
For current regime, the system scores each strategy:
Sharpe Ratio: (mean_return / std_dev) × √252
Calmar Ratio: total_return / max_drawdown
Win Rate: wins / trades
Combined Score = 0.6 × Sharpe + 0.3 × Calmar + 0.1 × Win_Rate
The strategy with highest score is selected. This is similar to Thompson Sampling (multi-armed bandits) but uses deterministic selection rather than probabilistic sampling due to Pine Script limitations.
BOOTSTRAP MODE (Critical for Understanding):
For the first min_regime_samples trades (default: 10) in each regime:
• Status: "🔥 BOOTSTRAP (X/10)" displayed in dashboard
• Behavior: All signals allowed (gathering data)
• Regime Filter: Disabled (can't judge with insufficient data)
• Purpose: Avoid cold-start problem, build statistical foundation
After reaching threshold:
• Status: "✅ FAVORABLE" (score > 0.5) or "⚠️ UNFAVORABLE" (score ≤ 0.5)
• Behavior: Only trade favorable regimes (if enable_regime_filter = true)
• Learning: Parameters adapt based on outcomes
This solves a critical problem: you can't know which strategy works in a regime without data, but you can't get data without trading. Bootstrap mode gathers initial data safely, then switches to selective mode once statistical confidence emerges.
PARAMETER ADAPTATION (Per Regime):
Three parameters adapt independently for each regime based on outcomes:
1. SIGNAL QUALITY THRESHOLD (30-90):
• Starts: base_quality_threshold (default: 60)
• Adaptation:
Win Rate < 45% → RAISE threshold by learning_rate × 10 (be pickier)
Win Rate > 55% → LOWER threshold by learning_rate × 5 (take more)
• Effect: System becomes more selective in losing regimes, more aggressive in winning regimes
2. LTM QUALITY GATE (0.2-0.8):
• Starts: 0.4 (if adaptive gate enabled)
• Adaptation:
Sharpe < 0.5 → RAISE gate by learning_rate (demand better patterns)
Sharpe > 1.5 → LOWER gate by learning_rate × 0.5 (accept more patterns)
• Effect: LTM fills with high-quality patterns from winning regimes
3. FLOW MULTIPLIER (0.5-2.0):
• Starts: 1.0
• Adaptation:
Strong win (+2%+) → MULTIPLY by (1 + learning_rate × 0.1)
Strong loss (-2%+) → MULTIPLY by (1 - learning_rate × 0.1)
• Effect: Amplifies signal strength in profitable regimes, dampens in unprofitable
Each regime evolves independently. Trending Bull might develop threshold=55, gate=0.35, mult=1.3 while High Vol Range develops threshold=70, gate=0.50, mult=0.9.
PART 8: SHADOW PORTFOLIO VALIDATION
To validate learning objectively, the system runs three virtual portfolios :
Shadow Portfolio A: Trades only Strategy A signals
Shadow Portfolio B: Trades only Strategy B signals
Shadow Portfolio C: Trades only Strategy C signals
When any signal fires:
1. Open virtual position for corresponding strategy
2. On exit, calculate P&L (10% risk per trade)
3. Update equity, win count, profit factor
Dashboard displays:
• Equity: Current virtual balance (starts $10,000)
• Win%: Overall win rate across all regimes
• PF: Profit Factor (gross_profit / gross_loss)
This transparency shows which strategies actually perform, validates the selection logic, and prevents overfitting. If Shadow C shows $12,500 equity while A and B show $9,800, it confirms Strategy C's edge.
PART 9: HISTORICAL PRE-TRAINING
The system includes historical pre-training to avoid cold-start:
On Chart Load (if enabled):
1. Scan past pretrain_bars (default: 200)
2. Calculate historical HFL, gamma, velocity, acceleration, volatility, entropy
3. Compute 10-bar forward returns as outcomes
4. Populate STM with recent patterns
5. Populate LTM with high-quality patterns (quality > 0.4)
Effect:
• Without pre-training: Memories empty, no predictions for weeks, pure bootstrap
• With pre-training: System starts with pattern library, predictions from day one
Pre-training uses only past data (no future peeking) and fills memories with validated outcomes. This dramatically accelerates learning without compromising integrity.
PART 10: COMPREHENSIVE INPUT SYSTEM
The indicator provides 50+ inputs organized into logical groups. Here are the key parameters and their market-specific guidance:
🧠 ADAPTIVE LEARNING SYSTEM:
Enable Adaptive Learning (true/false):
• Function: Master switch for regime-specific strategy selection and parameter adaptation
• Enabled: System learns which strategies work in which regimes (recommended)
• Disabled: All strategies fire simultaneously with fixed weights (simpler, less adaptive)
• Recommendation: Keep enabled for all markets; system needs 2-3 months to mature
Learning Rate (0.01-0.20):
• Function: Speed of parameter adaptation based on outcomes
• Stocks/ETFs: 0.03-0.05 (slower, more stable)
• Crypto: 0.05-0.08 (faster, adapts to volatility)
• Forex: 0.04-0.06 (moderate)
• Timeframes:
1-5min scalping: 0.08-0.10 (rapid adaptation)
15min-1H day trading: 0.05-0.07 (balanced)
4H-Daily swing: 0.03-0.05 (conservative)
• Tradeoff: Higher = responsive but may overfit; Lower = stable but slower to adapt
Min Samples Per Regime (5-30):
• Function: Trades required before exiting bootstrap mode
• Active trading (>5 signals/day): 8-10 trades
• Moderate (1-5 signals/day): 10-15 trades
• Swing (few signals/week): 5-8 trades
• Logic: Bootstrap mode until this threshold; then uses Sharpe/Calmar for regime filtering
• Tradeoff: Lower = faster exit (risky, less data); Higher = more validation (safer, slower)
🌍 REGIME DETECTION:
Regime Lookback Period (20-200):
• Function: Bars used for linear regression to classify regime
• By Timeframe:
1-5min: 30-50 bars (~2-4 hour context)
15min: 40-60 bars (daily context)
1H: 50-100 bars (weekly context)
4H: 100-150 bars (monthly context)
Daily: 50-75 bars (quarterly context)
• By Market:
Crypto: 40-60 (faster regime changes)
Forex: 50-75 (moderate stability)
Stocks: 60-100 (slower structural trends)
• Tradeoff: Shorter = more regime switches (reactive); Longer = fewer switches (stable)
Trend Strength Threshold (0.1-0.8):
• Function: Minimum normalized LR slope to classify as trending vs ranging
• Lower (0.1-0.2): More markets classified as trending
• Higher (0.4-0.6): Only strong trends qualify
• Recommendations:
Choppy markets (BTC, small caps): 0.25-0.35
Smooth trends (major FX pairs): 0.30-0.40
Strong trends (indices during bull): 0.20-0.30
• Effect: Controls sensitivity of trending vs ranging classification
Vol Expansion Factor (1.2-3.0):
• Function: Volatility ratio to classify high-vol regimes (current_vol / avg_vol)
• By Asset:
Bitcoin: 1.4-1.6 (frequent vol spikes)
Altcoins: 1.3-1.5 (very volatile)
Major FX (EUR/USD): 1.6-2.0 (stable baseline)
Stocks (SPY): 1.5-1.8 (moderate)
Penny stocks: 1.3-1.4 (always volatile)
• Impact: Higher = fewer "High Vol Range" classifications; Lower = more sensitive to volatility spikes
🎯 SIGNAL GENERATION:
Base Quality Threshold (30-90):
• Function: Starting signal strength requirement (adapts per regime)
• THIS IS YOUR MAIN SIGNAL FREQUENCY CONTROL
• Conservative (70-80): Fewer, higher-quality signals
• Balanced (55-65): Moderate signal flow
• Aggressive (40-50): More signals, more noise
• By Trading Style:
Scalping (1-5min): 50-60
Day trading (15min-1H): 60-70
Swing (4H-Daily): 65-75
• Adaptive Behavior: System raises this in losing regimes (pickier), lowers in winning regimes (take more)
Min Confidence (0.1-0.9):
• Function: Minimum confidence score to fire signal
• Calculation: (Signal_Strength / 100) × Regime_Confidence
• Recommendations:
High-frequency (scalping): 0.2-0.3 (permissive)
Day trading: 0.3-0.4 (balanced)
Swing/position: 0.4-0.6 (selective)
• Interaction: During Transition regime (low regime confidence), even strong signals may fail confidence check; creates natural regime filtering
Only Trade Favorable Regimes (true/false):
• Function: Block signals in unfavorable regimes (where all strategies have negative risk-adjusted scores)
• Enabled (Recommended): Only trades when best strategy has positive Sharpe in current regime; auto-disables during bootstrap; protects capital
• Disabled: Always allows signals regardless of historical performance; use for manual regime assessment
• Bootstrap: Auto-allows trading until min_regime_samples reached, then switches to performance-based filtering
Min Bars Between Signals (1-20):
• Function: Prevents signal spam by enforcing minimum spacing
• By Timeframe:
1min: 3-5 bars (3-5 minutes)
5min: 3-6 bars (15-30 minutes)
15min: 4-8 bars (1-2 hours)
1H: 5-10 bars (5-10 hours)
4H: 3-6 bars (12-24 hours)
Daily: 2-5 bars (2-5 days)
• Logic: After signal fires, no new signals for X bars
• Tradeoff: Lower = more reactive (may overtrade); Higher = more patient (may miss reversals)
🌀 LORENTZIAN CORE:
Base Period (10-100):
• Function: Core time period for flow calculation (gets compressed by gamma)
• THIS IS YOUR PRIMARY TIMEFRAME KNOB
• By Timeframe:
1-5min scalping: 20-30 (fast response)
15min-1H day: 30-40 (balanced)
4H swing: 40-55 (smooth)
Daily position: 50-75 (very smooth)
• By Market Character:
Choppy (crypto, small caps): 25-35 (faster)
Smooth (major FX, indices): 35-50 (moderate)
Slow (bonds, utilities): 45-65 (slower)
• Gamma Effect: Actual length = base_period / gamma; High gamma compresses to ~20 bars, low gamma expands to ~50 bars
• Default 34 (Fibonacci) works well across most assets
Velocity Period (5-50):
• Function: Window for trend velocity calculation: (price_now - price ) / (N × ATR)
• By Timeframe:
1-5min scalping: 8-12 (fast momentum)
15min-1H day: 12-18 (balanced)
4H swing: 14-21 (smooth trend)
Daily: 18-30 (structural trend)
• By Market:
Crypto (fast moves): 10-14
Stocks (moderate): 14-20
Forex (smooth): 18-25
• Impact: Feeds into gamma calculation (v/c ratio); shorter = more sensitive to velocity spikes → higher gamma
• Relationship: Typically vel_period ≈ base_period / 2 to 2/3
Speed-of-Market (c) (0.5-3.0):
• Function: "Speed limit" for gamma calculation: c = realized_vol + vol_burst × c_multiplier
• By Asset Volatility:
High vol (BTC, TSLA): 1.0-1.3 (lower c = more compression)
Medium vol (SPY, EUR/USD): 1.3-1.6 (balanced)
Low vol (bonds, utilities): 1.6-2.5 (higher c = less compression)
• What It Does:
Lower c → velocity hits "speed limit" sooner → higher gamma → more compression
Higher c → velocity rarely hits limit → gamma stays near 1 → less adaptation
• Effect on Signals: More compression (low c) = faster regime detection, more responsive; Less compression (high c) = smoother, less adaptive
• Tuning: Start at 1.4; if gamma always ~1.0, lower to 1.0-1.2; if gamma spikes >5 often, raise to 1.6-2.0
Gamma Power (0.5-2.0):
• Function: Exponent applied to gamma: final_gamma = gamma^power
• Compression Strength:
0.5-0.8: Softens compression (gamma 4 → 2)
1.0: Linear (gamma 4 → 4)
1.2-2.0: Amplifies compression (gamma 4 → 16)
• Use Cases:
Reduce power (<1.0) if adaptive lengths swing too wildly or getting whipsawed
Increase power (>1.0) for more aggressive regime adaptation in fast markets
• Most users should leave at 1.0; only adjust if gamma behavior needs tuning
Max Kernel Lookback (20-200):
• Function: Computational limit for Lorentzian smoothing (performance control)
• Recommendations:
Fast PC / simple chart: 80-100
Slow PC / complex chart: 40-60
Mobile / lots of indicators: 30-50
• Impact: Each kernel smoothing loops through this many bars; higher = more accurate but slower
• Default 60 balances accuracy and speed; lower to 40-50 if indicator is slow
🎼 HARMONIC FLOW:
Short Horizon (0.2-1.0):
• Function: Fast timeframe multiplier: short_length = base_period × short_ratio / gamma
• Default: 0.5 (captures 2× faster flow than base)
• By Style:
Scalping: 0.3-0.4 (very fast)
Day trading: 0.4-0.6 (moderate)
Swing: 0.5-0.7 (balanced)
• Effect: Lower = more weight on micro-moves; Higher = smooths out fast fluctuations
Mid Horizon (0.5-2.0):
• Function: Medium timeframe multiplier: mid_length = base_period × mid_ratio / gamma
• Default: 1.0 (equals base_period, anchor timeframe)
• Usually keep at 1.0 unless specific strategy needs fine-tuning
Long Horizon (1.0-5.0):
• Function: Slow timeframe multiplier: long_length = base_period × long_ratio / gamma
• Default: 2.5 (captures trend/structure)
• By Style:
Scalping: 1.5-2.0 (less long-term influence)
Day trading: 2.0-3.0 (balanced)
Swing: 2.5-4.0 (strong trend component)
• Effect: Higher = more emphasis on larger structure; Lower = more reactive to recent price action
Short Weight (0-1):
Mid Weight (0-1):
Long Weight (0-1):
• Function: Relative importance in HFL calculation (should sum to 1.0)
• Defaults: Short: 0.45, Mid: 0.35, Long: 0.20 (day trading balanced)
• Preset Configurations:
SCALPING (fast response):
Short: 0.60, Mid: 0.30, Long: 0.10
DAY TRADING (balanced):
Short: 0.45, Mid: 0.35, Long: 0.20
SWING (trend-following):
Short: 0.25, Mid: 0.35, Long: 0.40
• Effect: More short weight = responsive but noisier; More long weight = smoother but laggier
🧠 DUAL MEMORY SYSTEM:
Enable Pattern Memory (true/false):
• Function: Master switch for KNN pattern matching via dual memory
• Enabled (Recommended): Strategy C (Memory Pattern) can fire; memory predictions influence all strategies; prediction arcs shown; heatmaps available
• Disabled: Only Strategy A and B available; faster performance (less computation); pure technical analysis (no pattern matching)
• Keep enabled for full system capabilities; disable only if CPU-constrained or testing pure flow signals
STM Size (50-200):
• Function: Short-Term Memory capacity (recent pattern storage)
• Characteristics: Fast decay (0.980), captures current regime, updates every 10 bars, tactical pattern matching
• Sizing:
Active markets (crypto): 80-120
Moderate (stocks): 100-150
Slow (bonds): 50-100
• By Timeframe:
1-15min: 60-100 (captures few hours of patterns)
1H: 80-120 (captures days)
4H-Daily: 100-150 (captures weeks/months)
• Tradeoff: More = better recent pattern coverage; Less = faster computation
• Default 100 is solid for most use cases
LTM Size (256-1024):
• Function: Long-Term Memory capacity (validated pattern storage)
• Characteristics: Slow decay (0.997), only high-quality patterns (gated), regime-specific recall, strategic pattern library
• Sizing:
Fast PC: 512-768
Medium PC: 384-512
Slow PC/Mobile: 256-384
• By Data Needs:
High-frequency (lots of patterns): 512-1024
Moderate activity: 384-512
Low-frequency (swing): 256-384
• Performance Impact: Each KNN search loops through entire LTM; 512 = good balance of coverage and speed; if slow, drop to 256-384
• Fills over weeks/months with validated patterns
STM Decay (0.95-0.995):
• Function: Short-Term Memory age decay rate: age_weight = decay^bars_since_pattern
• Decay Rates:
0.950: Aggressive fade (50% weight after 14 bars)
0.970: Moderate fade (50% after 23 bars)
0.980: Balanced (50% after 35 bars)
0.990: Slow fade (50% after 69 bars)
• By Timeframe:
1-5min: 0.95-0.97 (fast markets, old patterns irrelevant)
15min-1H: 0.97-0.98 (balanced)
4H-Daily: 0.98-0.99 (slower decay)
• Philosophy: STM should emphasize RECENT patterns; lower decay = only very recent matters; 0.980 works well for most cases
LTM Decay (0.99-0.999):
• Function: Long-Term Memory age decay rate
• Decay Rates:
0.990: 50% weight after 69 bars
0.995: 50% weight after 138 bars
0.997: 50% weight after 231 bars
0.999: 50% weight after 693 bars
• Philosophy: LTM should retain value for LONG periods; pattern from 6 months ago might still matter
• Usage:
Fast-changing markets: 0.990-0.995
Stable markets: 0.995-0.998
Structural patterns: 0.998-0.999
• Warning: Be careful with very high decay (>0.998); market structure changes, old patterns may mislead
• 0.997 balances long-term memory with regime evolution
K Neighbors (3-21):
• Function: Number of similar patterns to query in KNN search
• By Sample Size:
Small dataset (<100 patterns): 3-5
Medium dataset (100-300): 5-8
Large dataset (300-1000): 8-13
Very large (>1000): 13-21
• Tradeoff:
Fewer K (3-5): More reactive to closest matches; noisier; outlier-sensitive; better when patterns very distinct
More K (13-21): Smoother, more stable predictions; may dilute strong signals; better when patterns overlap
• Rule of Thumb: K ≈ √(memory_size) / 3; For STM=100, LTM=512: K ≈ 8-10 ideal
Adaptive Quality Gate (true/false):
• Function: Adapts LTM entry threshold per regime based on Sharpe ratio
• Enabled: Quality gate adapts: Low Sharpe → RAISE gate (demand better patterns); High Sharpe → LOWER gate (accept more patterns); each regime has independent gate
• Disabled: Fixed quality gate (0.4 default) for all regimes
• Recommended: Keep ENABLED; helps LTM focus on proven pattern types per regime; prevents weak patterns from polluting memory
🎯 MULTI-STRATEGY SYSTEM:
Enable Strategy Learning (true/false):
• Function: Core learning feature for regime-specific strategy selection
• Enabled: Tracks 3 strategies × 5 regimes = 15 performance matrices; selects best strategy per regime via Sharpe/Calmar/WinRate; adaptive strategy switching
• Disabled: All strategies fire simultaneously (weighted combination); no regime-specific selection; simpler but less adaptive
• Recommended: ENABLED (this is the core of the adaptive system); disable only for testing or simplification
Strategy A Weight (0-1):
• Function: Weight for Strategy A (Squeeze Breakout) when multi-strategy disabled
• Characteristics: Fires on Bollinger squeeze release; best in Low Vol Range, Transition; compression → expansion pattern
• When Multi-Strategy OFF: Default 0.33 (equal weight); increase to 0.4-0.5 for choppy ranges with breakouts; decrease to 0.2-0.3 for trending markets
• When Multi-Strategy ON: This is ignored (system auto-selects based on performance)
Strategy B Weight (0-1):
• Function: Weight for Strategy B (Lorentzian Flow) when multi-strategy disabled
• Characteristics: Fires on strong HFL + velocity + gamma; best in Trending Bull/Bear; momentum → acceleration pattern
• When Multi-Strategy OFF: Default 0.33; increase to 0.4-0.5 for trending markets; decrease to 0.2-0.3 for choppy/ranging markets
• When Multi-Strategy ON: Ignored (auto-selected)
Strategy C Weight (0-1):
• Function: Weight for Strategy C (Memory Pattern) when multi-strategy disabled
• Characteristics: Fires when dual KNN predicts strong move; best in High Vol Range; requires memory system enabled + sufficient data
• When Multi-Strategy OFF: Default 0.34; increase to 0.4-0.6 if strong pattern repetition and LTM has >200 patterns; decrease to 0.2-0.3 if new to system; set to 0.0 if memory disabled
• When Multi-Strategy ON: Ignored (auto-selected)
📚 PRE-TRAINING:
Historical Pre-Training (true/false):
• Function: Bootstrap feature that fills memory on chart load
• Enabled: Scans past bars to populate STM/LTM before live trading; calculates historical outcomes (10-bar forward returns); builds initial pattern library; system starts with context, not blank slate
• Disabled: Memories only populate in real-time; takes weeks to build pattern library
• Recommended: ENABLED (critical for avoiding "cold start" problem); disable only for testing clean learning
Training Bars (50-500):
• Function: How many historical bars to scan on load (limited by available history)
• Recommendations:
1-5min charts: 200-300 (few hours of history)
15min-1H: 200-400 (days/weeks)
4H: 300-500 (months)
Daily: 200-400 (years)
• Performance:
100 bars: ~1 second
300 bars: ~2-3 seconds
500 bars: ~4-5 seconds
• Sweet Spot: 200-300 (enough patterns without slow load)
• If chart loads slowly: Reduce to 100-150
🎨 VISUALIZATION:
Show Regime Background (true/false):
• Function: Color-code background by current regime
• Colors: Trending Bull (green tint), Trending Bear (red tint), High Vol Range (orange tint), Low Vol Range (blue tint), Transition (purple tint)
• Helps visually track regime changes
Show Flow Bands (true/false):
• Function: Plot upper/lower bands based on HFL strength
• Shows dynamic support/resistance zones; green fill = bullish flow; red fill = bearish flow
• Useful for visual trend confirmation
Show Confidence Meter (true/false):
• Function: Plot signal confidence (0-100) in separate pane
• Calculation: (Signal_Strength / 100) × Regime_Confidence
• Gold line = current confidence; dashed line = minimum threshold
• Signals fire when confidence exceeds threshold
Show Prediction Arc (true/false):
• Function: Dashed line projecting expected price move based on memory prediction
• NOT a price target - a probability vector; steep arc = strong expected move; flat arc = weak/uncertain prediction
• Green = bullish prediction; red = bearish prediction
Show Signals (true/false):
• Function: Triangle markers at entry points
• ▲ Green = Long signal; ▼ Red = Short signal
• Markers show on bar close (non-repainting)
🏆 DASHBOARD:
Show Dashboard (true/false):
• Function: Main info panel showing all system metrics
• Sections: Lorentzian Core, Regime, Dual Memory, Adaptive Parameters, Regime Performance, Shadow Portfolios, Current Signal Status
• Essential for understanding system state
Dashboard Position: Top Left, Top Right, Bottom Left, Bottom Right
Individual Section Toggles:
• System Stats: Lorentzian Core section (Gamma, v/c, HFL, TCI)
• Memory Stats: Dual Memory section (STM/LTM predictions, agreement)
• Shadow Portfolios: Shadow Portfolio table (equity, win%, PF)
• Adaptive Params: Adaptive Parameters section (threshold, quality gate, flow mult)
🔥 HEATMAPS:
Show Dual Heatmaps (true/false):
• Function: Visual pattern density maps for STM and LTM
• Layout: X-axis = pattern age (left=recent, right=old); Y-axis = outcome direction (top=bearish, bottom=bullish); Color intensity = pattern count; Color hue = bullish (green) vs bearish (red)
• Warning: Can clutter chart; disable if not using
Heatmap Position: Screen position for heatmaps (STM at selected position, LTM offset)
Resolution (5-15):
• Function: Grid resolution (bins)
• Higher = more detailed but smaller cells; Lower = clearer but less granular
• 10 is good balance; reduce to 6-8 if hard to read
PART 11: DASHBOARD METRICS EXPLAINED
The comprehensive dashboard provides real-time transparency into every aspect of the adaptive system:
⚡ LORENTZIAN CORE SECTION:
Gamma (γ):
• Range: 1.0 to ~10.0 (capped)
• Interpretation:
γ ≈ 1.0-1.2: Normal market time, low velocity
γ = 1.5-2.5: Moderate compression, trending
γ = 3.0-5.0: High compression, explosive moves
γ > 5.0: Extreme compression, parabolic volatility
• Usage: High gamma = system operating in compressed time; expect shorter effective periods and faster adaptation
v/c (Velocity / Speed Limit):
• Range: 0.0 to 0.999 (approaches but never reaches 1.0)
• Interpretation:
v/c < 0.3: Slow market, low momentum
v/c = 0.4-0.7: Moderate trending
v/c > 0.7: Approaching "speed limit," high velocity
v/c > 0.9: Parabolic move, system at limit
• Color Coding: Red (>0.7), Gold (0.4-0.7), Green (<0.4)
• Usage: High v/c warns of extreme conditions where trend may exhaust
HFL (Harmonic Flow):
• Range: Typically -3.0 to +3.0 (can exceed in extremes)
• Interpretation:
HFL > 0: Bullish flow
HFL < 0: Bearish flow
|HFL| > 0.5: Strong directional bias
|HFL| < 0.2: Weak, indecisive
• Color: Green (positive), Red (negative)
• Usage: Primary directional indicator; strategies often require HFL confirmation
TCI (Temporal Compression Index):
• Calculation: base_period / compressed_length
• Interpretation:
TCI ≈ 1.0: No compression, normal time
TCI = 1.5-2.5: Moderate compression
TCI > 3.0: Significant compression
• Usage: Shows how much time is being compressed; mirrors gamma but more intuitive
╔═══ REGIME SECTION ═══╗
Current:
• Display: Regime name with icon (Trending Bull ↗, Trending Bear ↘, High Vol Range ↔, Low Vol Range —, Transition ⚡)
• Color: Gold for visibility
• Usage: Know which regime you're in; check regime performance to see expected strategy behavior
Confidence:
• Range: 0-100%
• Interpretation:
>70%: Very clear regime definition
40-70%: Moderate clarity
<40%: Ambiguous, mixed conditions
• Color: Green (>70%), Gold (40-70%), Red (<40%)
• Usage: High confidence = trust regime classification; low confidence = regime may be transitioning
Mode:
• States:
🔥 BOOTSTRAP (X/10): Still gathering data for this regime
✅ FAVORABLE: Best strategy has positive risk-adjusted score (>0.5)
⚠️ UNFAVORABLE: All strategies have negative scores (≤0.5)
• Color: Orange (bootstrap), Green (favorable), Red (unfavorable)
• Critical Importance: This tells you whether the system will trade or stand aside (if regime filter enabled)
╔═══ DUAL MEMORY KNN SECTION ═══╗
STM (Size):
• Display: Number of patterns currently in STM (0 to stm_size)
• Interpretation: Should fill to capacity within hours/days; if not filling, check that memory is enabled
STM Pred:
• Range: Typically -0.05 to +0.05 (representing -5% to +5% expected 10-bar move)
• Color: Green (positive), Red (negative)
• Usage: STM's prediction based on recent patterns; emphasis on current regime
LTM (Size):
• Display: Number of patterns in LTM (0 to ltm_size)
• Interpretation: Fills slowly (weeks/months); only validated high-quality patterns; check quality gate if not filling
LTM Pred:
• Range: Similar to STM pred
• Color: Green (positive), Red (negative)
• Usage: LTM's prediction based on long-term validated patterns; more strategic than tactical
Agreement:
• Display:
✅ XX%: Strong agreement (>70%) - both memories aligned
⚠️ XX%: Moderate agreement (40-70%) - some disagreement
❌ XX%: Conflict (<40%) - memories strongly disagree
• Color: Green (>70%), Gold (40-70%), Red (<40%)
• Critical Usage: Low agreement often precedes regime change or signals novel conditions; Strategy C won't fire with low agreement
╔═══ ADAPTIVE PARAMS SECTION ═══╗
Threshold:
• Display: Current regime's signal quality threshold (30-90)
• Interpretation: Higher = pickier; lower = more permissive
• Watch For: If steadily rising in a regime, system is struggling (low win rate); if falling, system is confident
• Default: Starts at base_quality_threshold (usually 60)
Quality:
• Display: Current regime's LTM quality gate (0.2-0.8)
• Interpretation: Minimum quality score for pattern to enter LTM
• Watch For: If rising, system demanding higher-quality patterns; if falling, accepting more diverse patterns
• Default: Starts at 0.4
Flow Mult:
• Display: Current regime's flow multiplier (0.5-2.0)
• Interpretation: Amplifies or dampens HFL for Strategy B
• Watch For: If >1.2, system found strong edge in flow signals; if <0.8, flow signals underperforming
• Default: Starts at 1.0
Learning:
• Display: ✅ ON or ❌ OFF
• Shows whether adaptive learning is enabled
• Color: Green (on), Red (off)
╔═══ REGIME PERFORMANCE SECTION ═══╗
This table shows ONLY the current regime's statistics:
S (Strategy):
• Display: A, B, or C
• Color: Gold if selected strategy; gray if not
• Shows which strategies have data in this regime
Trades:
• Display: Number of completed trades for this pair
• Interpretation: Blank or low numbers mean bootstrap mode; >10 means statistical significance building
Win%:
• Display: Win rate percentage
• Color: Green (>55%), White (45-55%), Red (<45%)
• Interpretation: 52%+ is good; 58%+ is excellent; <45% means struggling
• Note: Short-term variance is normal; judge after 20+ trades
Sharpe:
• Display: Annualized Sharpe ratio
• Color: Green (>1.0), White (0-1.0), Red (<0)
• Interpretation:
>2.0: Exceptional (rare)
1.0-2.0: Good
0.5-1.0: Acceptable
0-0.5: Marginal
<0: Losing
• Usage: Primary metric for strategy selection (60% weight in score)
╔═══ SHADOW PORTFOLIOS SECTION ═══╗
Shows virtual equity tracking across ALL regimes (not just current):
S (Strategy):
• Display: A, B, or C
• Color: Gold if currently selected strategy; gray otherwise
Equity:
• Display: Current virtual balance (starts $10,000)
• Color: Green (>$10,000), White ($9,500-$10,000), Red (<$9,500)
• Interpretation: Which strategy is actually making virtual money across all conditions
• Note: 10% risk per trade assumed
Win%:
• Display: Overall win rate across all regimes
• Color: Green (>55%), White (45-55%), Red (<45%)
• Interpretation: Aggregate performance; strategy may do well in some regimes and poorly in others
PF (Profit Factor):
• Display: Gross profit / gross loss
• Color: Green (>1.5), White (1.0-1.5), Red (<1.0)
• Interpretation:
>2.0: Excellent
1.5-2.0: Good
1.2-1.5: Acceptable
1.0-1.2: Marginal
<1.0: Losing
• Usage: Confirms win rate; high PF with moderate win rate means winners >> losers
╔═══ STATUS BAR ═══╗
Display States:
• 🟢 LONG: Currently in long position (green background)
• 🔴 SHORT: Currently in short position (red background)
• ⬆️ LONG SIGNAL: Long signal present but not yet confirmed (waiting for bar close)
• ⬇️ SHORT SIGNAL: Short signal present but not yet confirmed
• ⚪ NEUTRAL: No position, no signal (white background)
Usage: Immediate visual confirmation of system state; check before manually entering/exiting
PART 12: VISUAL ELEMENT INTERPRETATION
REGIME BACKGROUND COLORS:
Green Tint: Trending Bull regime - expect Strategy B (Flow) to dominate; focus on long momentum
Red Tint: Trending Bear regime - expect Strategy B (Flow) shorts; focus on short momentum
Orange Tint: High Vol Range - expect Strategy A (Squeeze) or C (Memory); trade breakouts or patterns
Blue Tint: Low Vol Range - expect Strategy A (Squeeze); wait for compression release
Purple Tint: Transition regime - often unfavorable; system may stand aside; high uncertainty
Usage: Quick visual regime identification without reading dashboard
FLOW BANDS:
Upper Band: close + HFL × ATR × 1.5
Lower Band: close - HFL × ATR × 1.5
Green Fill: HFL positive (bullish flow); bands act as dynamic support/resistance in uptrend
Red Fill: HFL negative (bearish flow); bands act as dynamic resistance/support in downtrend
Usage:
• Bullish flow: Price bouncing off lower band = trend continuation; breaking below = possible reversal
• Bearish flow: Price rejecting upper band = trend continuation; breaking above = possible reversal
CONFIDENCE METER (Separate Pane):
Gold Line: Current signal confidence (0-100)
Dashed Line: Minimum confidence threshold
Interpretation:
• Line above threshold: Signal likely to fire if strength sufficient
• Line below threshold: Even if signal logic met, won't fire (insufficient confidence)
• Gradual rise: Signal building strength
• Sharp spike: Sudden conviction (check if sustainable)
Usage: Real-time signal probability; helps anticipate upcoming entries
PREDICTION ARC:
Dashed Line: Projects from current close to expected price 8 bars forward
Green Arc: Bullish memory prediction
Red Arc: Bearish memory prediction
Steep Arc: High conviction (strong expected move)
Flat Arc: Low conviction (weak/uncertain move)
Important: NOT a price target; this is a probability vector based on KNN outcomes; actual price may differ
Usage: Directional bias from pattern matching; confirms or contradicts flow signals
SIGNAL MARKERS:
▲ Green Triangle (below bar):
• Long signal confirmed on bar close
• Entry on next bar open
• Non-repainting (appears after bar closes)
▼ Red Triangle (above bar):
• Short signal confirmed on bar close
• Entry on next bar open
• Non-repainting
Size: Tiny (unobtrusive)
Text: "L" or "S" in marker
Usage: Historical signal record; alerts should fire on these; verify against dashboard status
DUAL HEATMAPS (If Enabled):
STM HEATMAP:
• X-axis: Pattern age (left = recent, right = older, typically 0-50 bars)
• Y-axis: Outcome direction (top = bearish outcomes, bottom = bullish outcomes)
• Color Intensity: Brightness = pattern count in that cell
• Color Hue: Green tint (bullish), Red tint (bearish), Gray (neutral)
Interpretation:
• Dense bottom-left: Many recent bullish patterns (bullish regime)
• Dense top-left: Many recent bearish patterns (bearish regime)
• Scattered: Mixed outcomes, ranging regime
• Empty areas: Few patterns (low data)
LTM HEATMAP:
• Similar layout but X-axis spans wider age range (0-500+ bars)
• Shows long-term pattern distribution
• Denser = more validated patterns
Comparison Usage:
• If STM and LTM heatmaps look similar: Current regime matches historical patterns (high agreement)
• If STM bottom-heavy but LTM top-heavy: Recent bullish activity contradicts historical bearish patterns (low agreement, transition signal)
PART 13: DEVELOPMENT STORY
The creation of the Lorentzian Harmonic Flow Adaptive ML system represents over six months of intensive research, mathematical exploration, and iterative refinement. What began as a theoretical investigation into applying special relativity to market time evolved into a complete adaptive learning framework.
THE CHALLENGE:
The fundamental problem was this: markets don't experience time uniformly, yet every indicator treats a 50-period calculation the same whether markets are exploding or sleeping. Traditional adaptive indicators adjust parameters based on volatility, but this is reactive—by the time you measure high volatility, the explosive move is over. What was needed was a framework that measured the market's intrinsic velocity relative to its own structural limits, then compressed time itself proportionally.
THE LORENTZIAN INSIGHT:
Einstein's special relativity provides exactly this framework through the Lorentz factor. When an object approaches the speed of light, time dilates—but from the object's reference frame, it experiences time compression. By treating price velocity as analogous to relativistic velocity and volatility structure as the "speed limit," we could calculate a gamma factor that compressed lookback periods during explosive moves.
The mathematics were straightforward in theory but devilishly complex in implementation. Pine Script has no native support for dynamically-sized arrays or recursive functions, forcing creative workarounds. The Lorentzian kernel smoothing required nested loops through historical bars, calculating kernel weights on the fly—a computational nightmare. Early versions crashed or produced bizarre artifacts (negative gamma values, infinite loops during volatility spikes).
Optimization took weeks. Limiting kernel lookback to 60 bars while still maintaining smoothing quality. Pre-calculating gamma once per bar and reusing it across all calculations. Caching intermediate results. The final implementation balances mathematical purity with computational reality.
THE MEMORY ARCHITECTURE:
With temporal compression working, the next challenge was pattern memory. Simple moving average systems have no memory—they forget yesterday's patterns immediately. But markets are non-stationary; what worked last month may not work today. The solution: dual-memory architecture inspired by cognitive neuroscience.
Short-Term Memory (STM) would capture tactical patterns—the hippocampus of the system. Fast encoding, fast decay, always current. Long-Term Memory (LTM) would store validated strategic patterns—the neocortex. Slow consolidation, persistent storage, regime-spanning wisdom.
The KNN implementation nearly broke me. Calculating Lorentzian distance across 6 dimensions for 500+ patterns per query, applying age decay, filtering by regime, finding K nearest neighbors without native sorting functions—all while maintaining sub-second execution. The breakthrough came from realizing we could use destructive sorting (marking found neighbors as "infinite distance") rather than maintaining separate data structures.
Pre-training was another beast. To populate memory with historical patterns, the system needed to scan hundreds of past bars, calculate forward outcomes, and insert patterns—all on chart load without timing out. The solution: cap at 200 bars, optimize loops, pre-calculate features. Now it works seamlessly.
THE REGIME DETECTION:
Five-regime classification emerged from empirical observation. Traditional trending/ranging dichotomy missed too much nuance. Markets have at least four distinct states: trending up, trending down, volatile range, quiet range—plus a chaotic transition state. Linear regression slope quantifies trend; volatility ratio quantifies expansion; combining them creates five natural clusters.
But classification is useless without regime-specific learning. That meant tracking 15 separate performance matrices (3 strategies × 5 regimes), computing Sharpe ratios and Calmar ratios for sparse data, implementing Bayesian-like strategy selection. The bootstrap mode logic alone took dozens of iterations—too strict and you never get data, too permissive and you blow up accounts during learning.
THE ADAPTIVE LAYER:
Parameter adaptation was conceptually elegant but practically treacherous. Each regime needed independent thresholds, quality gates, and multipliers that adapted based on outcomes. But naive gradient descent caused oscillations—win a few trades, lower threshold, take worse signals, lose trades, raise threshold, miss good signals. The solution: exponential smoothing via learning rate (α) and separate scoring for selection vs adaptation.
Shadow portfolios provided objective validation. By running virtual accounts for all strategies simultaneously, we could see which would have won even when not selected. This caught numerous bugs where selection logic was sound but execution was flawed, or vice versa.
THE DASHBOARD & VISUALIZATION:
A learning system is useless if users can't understand what it's doing. The dashboard went through five complete redesigns. Early versions were information dumps—too much data, no hierarchy, impossible to scan. The final version uses visual hierarchy (section headers, color coding, strategic whitespace) and progressive disclosure (show current regime first, then performance, then parameters).
The dual heatmaps were a late addition but proved invaluable for pattern visualization. Seeing STM cluster in one corner while LTM distributed broadly immediately signals regime novelty. Traders grasp this visually faster than reading disagreement percentages.
THE TESTING GAUNTLET:
Testing adaptive systems is uniquely challenging. Static backtest results mean nothing—the system should improve over time. Early "tests" showed abysmal performance because bootstrap periods were included. The breakthrough: measure pre-learning baseline vs post-learning performance. A system going from 48% win rate (first 50 trades) to 56% win rate (trades 100-200) is succeeding even if absolute performance seems modest.
Edge cases broke everything repeatedly. What happens when a regime never appears in historical data? When all strategies fail simultaneously? When memory fills with only bearish patterns during a bull run? Each required careful handling—bootstrap modes, forced diversification, quality gates.
THE DOCUMENTATION:
This isn't an indicator you throw on a chart with default settings and trade immediately. It's a learning system that requires understanding. The input tooltips alone contain over 10,000 words of guidance—market-specific recommendations, timeframe-specific settings, tradeoff explanations. Every parameter needed not just a description but a philosophical justification and practical tuning guide.
The code comments span 500+ lines explaining theory, implementation decisions, edge cases. Future maintainers (including myself in six months) need to understand not just what the code does but why certain approaches were chosen over alternatives.
WHAT ALMOST DIDN'T WORK:
The entire project nearly collapsed twice. First, when initial Lorentzian smoothing produced complete noise—hours of debugging revealed a simple indexing error where I was accessing instead of in the kernel loop. One character, entire system broken.
Second, when memory predictions showed zero correlation with outcomes. Turned out the KNN distance metric was dominated by the gamma dimension (values 1-10) drowning out normalized features (values -1 to 1). Solution: apply kernel transformation to all dimensions, not just final distance. Obvious in retrospect, maddening at the time.
THE PHILOSOPHY:
This system embodies a specific philosophy: markets are learnable but non-stationary. No single strategy works forever, but regime-specific patterns persist. Time isn't uniform, memory isn't perfect, prediction isn't possible—but probabilistic edges exist for those willing to track them rigorously.
It rejects the premise that indicators should give universal advice. Instead, it says: "In this regime, based on similar past states, Strategy B has a 58% win rate and 1.4 Sharpe. Strategy A has 45% and 0.2 Sharpe. I recommend B. But we're still in bootstrap for Strategy C, so I'm gathering data. Check back in 5 trades."
That humility—knowing what it knows and what it doesn't—is what makes it robust.
PART 14: PROFESSIONAL USAGE PROTOCOL
PHASE 1: DEPLOYMENT (Week 1-4)
Initial Setup:
1. Load indicator on primary trading chart with default settings
2. Verify historical pre-training enabled (should see ~200 patterns in STM/LTM on first load)
3. Enable all dashboard sections for maximum transparency
4. Set alerts but DO NOT trade real money
Observation Checklist:
• Dashboard Validation:
✓ Lorentzian Core shows reasonable gamma (1-5 range, not stuck at 1.0 or spiking to 10)
✓ HFL oscillates with price action (not flat or random)
✓ Regime classifications make intuitive sense
✓ Confidence scores vary appropriately
• Memory System:
✓ STM fills within first few hours/days of real-time bars
✓ LTM grows gradually (few patterns per day, quality-gated)
✓ Predictions show directional bias (not always 0.0)
✓ Agreement metric fluctuates with regime changes
• Bootstrap Tracking:
✓ Dashboard shows "🔥 BOOTSTRAP (X/10)" for each regime
✓ Trade counts increment on regime-specific signals
✓ Different regimes reach threshold at different rates
Paper Trading:
• Take EVERY signal (ignore unfavorable warnings during bootstrap)
• Log each trade: entry price, regime, selected strategy, outcome
• Calculate your actual P&L assuming proper risk management (1-2% risk per trade)
• Do NOT judge system performance yet—focus on understanding behavior
Troubleshooting:
• No signals for days:
- Check base_quality_threshold (try lowering to 50-55)
- Verify enable_regime_filter not blocking all regimes
- Confirm signal confidence threshold not too high (try 0.25)
• Signals every bar:
- Raise base_quality_threshold to 65-70
- Increase min_bars_between to 8-10
- Check if gamma spiking excessively (raise c_multiplier)
• Memory not filling:
- Confirm enable_memory = true
- Verify historical pre-training completed (check STM size after load)
- May need to wait 10 bars for first real-time update
PHASE 2: VALIDATION (Week 5-12)
Statistical Emergence:
By week 5-8, most regimes should exit bootstrap. Look for:
✓ Regime Performance Clarity:
- At least 2-3 strategies showing positive Sharpe in their favored regimes
- Clear separation (Strategy B strong in Trending, Strategy A strong in Low Vol Range, etc.)
- Win rates stabilizing around 50-60% for winning strategies
✓ Shadow Portfolio Divergence:
- Virtual portfolios showing clear winners ($10K → $11K+) and losers ($10K → $9K-)
- Profit factors >1.3 for top strategy
- System selection aligning with best shadow portfolio
✓ Parameter Adaptation:
- Thresholds varying per regime (not stuck at initial values)
- Quality gates adapting (some regimes higher, some lower)
- Flow multipliers showing regime-specific optimization
Validation Questions:
1. Do patterns make intuitive sense?
- Strategy B (Flow) dominating Trending Bull/Bear? ✓ Expected
- Strategy A (Squeeze) succeeding in Low Vol Range? ✓ Expected
- Strategy C (Memory) working in High Vol Range? ✓ Expected
- Random strategy winning everywhere? ✗ Problem
2. Is unfavorable filtering working?
- Regimes with negative Sharpe showing "⚠️ UNFAVORABLE"? ✓ System protecting capital
- Transition regime often unfavorable? ✓ Expected
- All regimes perpetually unfavorable? ✗ Settings too strict or asset unsuitable
3. Are memories agreeing appropriately?
- High agreement during stable regimes? ✓ Expected
- Low agreement during transitions? ✓ Expected (novel conditions)
- Perpetual conflict? ✗ Check memory sizes or decay rates
Fine-Tuning (If Needed):
Too Many Signals in Losing Regimes:
→ Increase learning_rate to 0.07-0.08 (faster adaptation)
→ Raise base_quality_threshold by 5-10 points
→ Enable regime filter if disabled
Missing Profitable Setups:
→ Lower base_quality_threshold by 5-10 points
→ Reduce min_confidence to 0.25-0.30
→ Check if bootstrap mode blocking trades (let it complete)
Excessive Parameter Swings:
→ Reduce learning_rate to 0.03-0.04
→ Increase min_regime_samples to 15-20 (more data before adaptation)
Memory Disagreement Too Frequent:
→ Increase LTM size to 768-1024 (broader pattern library)
→ Lower adaptive_quality_gate requirement (allow more patterns)
→ Increase K neighbors to 10-12 (smoother predictions)
PHASE 3: LIVE TRADING (Month 4+)
Pre-Launch Checklist:
1. ✓ At least 3 regimes show positive Sharpe (>0.8)
2. ✓ Top shadow portfolio shows >53% win rate and >1.3 profit factor
3. ✓ Parameters have stabilized (not changing more than 10% per month)
4. ✓ You understand every dashboard metric and can explain regime/strategy behavior
5. ✓ You have proper risk management plan independent of this system
Position Sizing:
Conservative (Recommended for Month 4-6):
• Risk per trade: 0.5-1.0% of account
• Max concurrent positions: 1-2
• Total exposure: 10-25% of intended full size
Moderate (Month 7-12):
• Risk per trade: 1.0-1.5% of account
• Max concurrent positions: 2-3
• Total exposure: 25-50% of intended size
Full Scale (Year 2+):
• Risk per trade: 1.5-2.0% of account
• Max concurrent positions: 3-5
• Total exposure: 100% (still following risk limits)
Entry Execution:
On Signal Confirmation:
1. Verify dashboard shows signal type (▲ LONG or ▼ SHORT)
2. Check regime mode (avoid if "⚠️ UNFAVORABLE" unless testing)
3. Note selected strategy (A/B/C) and its regime Sharpe
4. Verify memory agreement if Strategy C selected (want >60%)
Entry Method:
• Market entry: Next bar open after signal (for exact backtest replication)
• Limit entry: Slight improvement (2-3 ticks) if confident in direction
Stop Loss Placement:
• Strategy A (Squeeze): Beyond opposite band or recent swing point
• Strategy B (Flow): 1.5-2.0 ATR from entry against direction
• Strategy C (Memory): Based on predicted move magnitude (tighter if pred > 2%)
Exit Management:
System Exit Signals:
• Opposite signal fires: Immediate exit, potential reversal entry
• 20 bars no exit signal: System implies position stale, consider exiting
• Regime changes to unfavorable: Tighten stop, consider partial exit
Manual Exit Conditions:
• Stop loss hit: Take loss, log for validation (system expects some losses)
• Profit target hit: If using fixed targets (2-3R typical)
• Major news event: Flatten during high-impact news (system can't predict these)
Warning Signs (Exit Criteria):
🚨 Stop Trading If:
1. All regimes show negative Sharpe for 4+ weeks (market structure changed)
2. Your results >20% worse than shadow portfolios (execution problem)
3. Parameters hitting extremes (thresholds >85 or <35 across all regimes)
4. Memory agreement <30% for extended periods (unprecedented conditions)
5. Account drawdown >20% (risk management failure, system or otherwise)
⚠️ Reduce Size If:
1. Win rate drops 10%+ from peak (temporary regime shift)
2. Selected strategy underperforming another by >30% (selection lag)
3. Consecutive losses >5 (variance or problem, reduce until clarity)
4. Major market regime change (Fed policy shift, war, etc. - let system re-adapt)
PART 15: THEORETICAL IMPLICATIONS & LIMITATIONS
WHAT THIS SYSTEM REPRESENTS:
Contextual Bandits:
The regime-specific strategy selection implements a contextual multi-armed bandit problem. Each strategy is an "arm," each regime is a "context," and we select arms to maximize expected reward given context. This is reinforcement learning applied to trading.
Experience Replay:
The dual-memory architecture mirrors DeepMind's DQN breakthrough. STM = recent experience buffer; LTM = validated experience replay. This prevents catastrophic forgetting while enabling rapid adaptation—a key challenge in neural network training.
Meta-Learning:
The system learns how to learn. Parameter adaptation adjusts the system's own sensitivity and selectivity based on outcomes. This is "learning to learn"—optimizing the optimization process itself.
Non-Stationary Optimization:
Traditional backtesting assumes stationarity (past patterns persist). This system assumes non-stationarity and continuously adapts. The goal isn't finding "the best parameters" but tracking the moving optimum.
Regime-Conditional Policies:
Rather than a single strategy for all conditions, this implements regime-specific policies. This is contextual decision-making—environment state determines action selection.
FINAL WISDOM:
"The market is a complex adaptive system. To trade it successfully, one must also adapt. This indicator provides the framework—memory, learning, regime awareness—but wisdom comes from understanding when to trade, when to stand aside, and when to defer to conditions the system hasn't yet learned. The edge isn't in the algorithm alone; it's in the partnership between mathematical rigor and human judgment."
— Inspired by the intersection of Einstein's relativity, Kahneman's behavioral economics, and decades of quantitative trading research
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Qullamaggie Trading System ProQullamaggie Trading System
OVERVIEW
The Qullamaggie Trading System is a comprehensive indicator that implements Kristjan Qullamaggie's legendary "3 Timeless Setups" methodology. This professional-grade tool is designed for swing traders who want to identify high-probability momentum breakouts, episodic pivots, and parabolic short opportunities with institutional-level precision.
"The goal is not to trade often, but to trade well." - Qullamaggie
KEY FEATURES
1. Three Core Qullamaggie Setups
🚀 Momentum Breakouts
Identifies stocks with 30-100%+ prior moves in the last 60 days
Detects tight consolidation patterns (2-8 weeks) with higher lows
Confirms breakouts with volume expansion (1.5x+ average)
Validates "surfing" behavior on 10-day and 20-day moving averages
Never buys below 50MA (configurable Qullamaggie rule)
⚡ Episodic Pivots (EP)
Detects gap-ups of 10%+ on massive volume (2x+ average)
Identifies earnings-driven EPs with special labeling
Confirms strong closes above the gap open
Highlights fundamental catalysts
🔻 Parabolic Shorts
Identifies overextended stocks (3+ consecutive up days)
RSI overbought threshold (75+)
30%+ extension from recent lows
Perfect for counter-trend shorting opportunities
2. Advanced Pattern Recognition
🟡 Coiling/VCP Detection (Gold Dots)
Identifies Volatility Contraction Patterns (VCP)
Shows when price is tight (<10% range) and volume is drying up
Indicates pre-breakout accumulation phase
Hover tooltip shows: Range %, Volume ratio, Which MA it's surfing
💎 Relative Strength New Highs (Blue Dots)
Tracks when RS line vs SPY/QQQ makes a new 50-day high
Identifies true market leaders BEFORE they breakout
Customizable benchmark (SPY, QQQ, or any index)
Hover tooltip shows: RS status and what it means
🟣 Pocket Pivots (Purple Dots)
Detects institutional accumulation inside the base
Volume > Highest down-volume of last 10 days
Bullish sign per Qullamaggie methodology
Hover tooltip shows: Current volume vs down-volume comparison
3. 5-Star Setup Quality Rating System
Based on deep research of Qullamaggie's methodology, the indicator rates every setup:
⭐⭐⭐⭐⭐ (5-Star) - Exceptional Quality
Prior move 50%+ in 60 days
Above 50MA ✓
RS at new high (market leader)
Range < 3% (extremely tight)
Volume dry → expansion pattern
Perfect MA alignment (10>20>50>200)
Clean setup (not choppy)
⭐⭐⭐⭐ (4-Star) - Strong Quality
Prior move 30%+
Above 50MA ✓
Strong RS
Range < 5%
Good volume pattern
⭐⭐⭐ (3-Star) - Decent Quality
Prior move 20%+
Basic requirements met
Scoring Algorithm
Prior Move: 1.5 pts
Above 50MA: 1.0 pt
RS New High: 1.0 pt
Tightness: 1.0 pt
Volume Pattern: 1.0 pt
MA Alignment: 0.5 pt
Clean Setup: 0.5 pt
4. Professional "Pro Desk" Dashboard
A sleek, glassmorphism-style dashboard displays:
Prior Move (60d): Shows % move in last 60 days (green if >30%)
Above 50MA: YES ✓ or NO ✗ (Qullamaggie's core rule)
Setup Quality: ⭐⭐⭐⭐⭐ rating with color coding
ADR (20): Average Daily Range for volatility assessment
Trend: BULLISH 🟢 or MIXED 🟡 based on MA stack
RS vs Index: NEW HIGH 💎, STRONG 💪, or WEAK 📉
Status: BREAKOUT 🚀, EPISODIC ⚡, COILING 🕸️, PARABOLIC 🔻, or WAITING ⏳
Volume: EXPANSION 🔊, DRY UP 🔇, or NORMAL
Range (10d): Current 10-day range percentage
4 Premium Themes:
Deep Space (default)
Bloomberg
Clean Light
Midnight
5. Qullamaggie Filters (Configurable)
All core Qullamaggie rules are configurable:
✅ Require Prior Move (default: ON)
Min Prior Move %: 30% (adjustable)
Lookback: 60 days (adjustable)
✅ Require Above 50MA (default: ON)
Qullamaggie rarely buys below 50MA
✅ Volume Expansion (default: 1.5x)
Adjustable multiplier
✅ Consolidation Range (default: 10%)
Max allowed range for tight consolidation
6. Visual Elements
Moving Averages
10-day EMA (Cyan) - Primary trailing stop
20-day SMA (Purple) - Secondary support
50-day SMA (Orange) - Key Qullamaggie filter
200-day SMA (Grey) - Long-term trend
Dynamic coloring: Fades when MA is declining
Signal Labels
BO (Green) - Breakout confirmed
EP (Blue) - Episodic Pivot
EP (Earn) (Blue) - Earnings-driven EP
P-Short (Red) - Parabolic Short setup
Consolidation Boxes
Golden dotted boxes show active consolidation zones
Updates in real-time as price tightens
Trailing Stop Line
Visual 10-EMA crosshair when price is trending
Helps manage trades per Qullamaggie's rules
7. Comprehensive Alert System
6 customizable alerts:
Breakout Alert - When all criteria are met
EP Alert - Episodic Pivot detected
Parabolic Short Alert - Short setup triggered
Coil Alert - Price coiling (anticipation phase)
RS New High Alert - Relative strength breakout
Below 50MA Alert - EXIT signal when price drops below 50MA
🎓 HOW TO USE
For Breakout Trading:
Look for Gold Coil Dots (●) appearing near 10/20MA
Wait for Green "BO" label with volume expansion
Check Setup Quality: Only trade ⭐⭐⭐⭐ or ⭐⭐⭐⭐⭐ setups
Verify Above 50MA = YES ✓
Confirm Prior Move > 30%
Enter on breakout, stop at low of day (or 10EMA)
For Episodic Pivots:
Look for Blue "EP" or "EP (Earn)" labels
Earnings-driven EPs are highest quality
Enter at open or ORH (Opening Range High)
Stop at low of gap day
For Market Leaders:
Watch for Blue RS Dots (●) above price
These appear when stock outperforms the index
Often precedes major breakouts
Combine with Coil Dots for "Power Play" setups
NeuraEdge ORB Professional Opening Range Breakout Indicator-15m🚀 NeuraEdge ORB - Professional Opening Range Breakout Indicator
We're excited to release this clean, effective Opening Range Breakout (ORB) indicator for the trading community. The 15-minute ORB is one of the most time-tested intraday strategies, and we've built this tool to make it simple and actionable.
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📖 WHAT IS THE 15-MINUTE ORB STRATEGY?
The Opening Range Breakout strategy captures the initial price range established in the first 15 minutes of market open (9:30-9:45 AM ET). This range often sets the tone for the trading day, as institutional order flow and overnight gap reactions play out during this window.
The concept is simple:
- Mark the HIGH and LOW of the first 15 minutes
- Trade the breakout when price breaks above or below this range
- Use the opposite side of the range as your stop loss
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⚙️ HOW TO USE THIS INDICATOR
1️⃣ SETUP
• Apply to SPY, QQQ, IWM, or any liquid stock/ETF
• Recommended timeframes: 1-minute or 5-minute charts
• The indicator automatically detects the 9:30-9:45 AM ET session
2️⃣ WAIT FOR THE RANGE
• A blue box will form showing the Opening Range
• Wait for the 15-minute period to complete (marked "✅ COMPLETE" in dashboard)
• Note the range size - larger ranges often mean stronger moves
3️⃣ TRADE THE BREAKOUT
• 🟢 LONG: When price closes above the Opening Range High
• 🔴 SHORT: When price closes below the Opening Range Low
• Signals appear automatically with entry, stop loss (SL), and take profit (TP) levels
4️⃣ MANAGE YOUR TRADE
• Stop Loss: Placed at opposite side of range (default) or midpoint
• Take Profit: Based on your selected Risk:Reward ratio
• The indicator tracks win rate automatically
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🎯 ENTRY TYPES
BREAKOUT MODE (Default)
- Enters immediately when price breaks the range
- More signals, catches the initial move
- Best for: Trending days, high momentum
RETEST MODE
- Waits for price to break out, then pull back to the range
- Fewer signals, better entry price
- Best for: Choppy days, tighter stops
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📊 SETTINGS EXPLAINED
Display Settings:
- Show Signals - Toggle buy/sell signals
- Show Opening Range Box - Visual box around the 15-min range
- Show Dashboard - Information panel with status and stats
Opening Range Settings:
- Opening Range Minutes - Default 15, adjustable 5-60
- Stop Trading After - Prevents late-day trades (default 3PM ET)
Entry Settings:
- Entry Type - Breakout or Retest
- Require Volume Confirmation - Only signals on above-average volume
- Require FVG Confluence - Adds Fair Value Gap filter for extra confirmation
Risk Management:
- Stop Loss Placement - Opposite Side / Midpoint / ATR Based
- Risk:Reward Ratio - Set your target (1.5 recommended)
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💡 TIPS FOR BEST RESULTS
✅ DO:
- Trade liquid instruments (SPY, QQQ, major stocks)
- Use 1-5 minute charts for better entry precision
- Respect the stop loss - the range defines your risk
- Pay attention to range size (0.5-1.5x ATR is ideal)
- Be patient - only 1-2 setups per day
❌ AVOID:
- Trading both directions on the same day
- Taking trades after 2-3 PM ET
- Very small ranges (likely to get chopped out)
- Low volume breakouts (often fail)
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📈 DASHBOARD INFORMATION
The dashboard shows:
- OR Status - Forming / Complete / Waiting
- OR High/Low - The range levels
- Range Size - In points and ATR multiples
- Breakout Direction - Long / Short / None
- Volume Status - High or Normal
- Win Rate - Tracked automatically
- W/L Record - Wins and losses count
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🔔 ALERTS INCLUDED
- Opening Range Complete - Notifies when the 15-min range is set
- ORB Long Signal - Buy signal triggered
- ORB Short Signal - Sell signal triggered
- Breakout Up/Down - Range broken (even without signal)
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⚠️ DISCLAIMER
This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose. This is not financial advice.
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We hope you find this indicator valuable in your trading journey!
💬 Questions or feedback? Leave a comment below.
🌐 Check out our full Indicator Suite: www.neura-edge.com
📧 Support: support@neura-edge.com
Happy Trading!
Curvature Tensor Pivots - HIVECurvature Tensor Pivots - HIVE
I. CORE CONCEPT & ORIGINALITY
Curvature Tensor Pivots - HIVE is an advanced, multi-dimensional pivot detection system that combines differential geometry, reinforcement learning, and statistical physics to identify high-probability reversal zones before they fully form. Unlike traditional pivot indicators that rely on simple price comparisons or lagging moving averages, this system models price action as a smooth curve in geometric space and calculates its mathematical curvature (how sharply the price trajectory is "bending") to detect pivots with scientific precision.
What Makes This Original:
Differential Geometry Engine: The script calculates first and second derivatives of price using Kalman-filtered trajectory analysis, then computes true mathematical curvature (κ) using the classical formula: κ = |y''| / (1 + y'²)^(3/2). This approach treats price as a physical phenomenon rather than discrete data points.
Ghost Vertex Prediction: A proprietary algorithm that detects pivots 1-3 bars BEFORE they complete by identifying when velocity approaches zero while acceleration is high—this is the mathematical definition of a turning point.
Multi-Armed Bandit AI: Four distinct pivot detection strategies (Fast, Balanced, Strict, Tensor) run simultaneously in shadow portfolios. A Thompson Sampling reinforcement learning algorithm continuously evaluates which strategy performs best in current market conditions and automatically selects it.
Hive Consensus System: When 3 or 4 of the parallel strategies agree on the same price zone, the system generates "confluence zones"—areas of institutional-grade probability.
Dynamic Volatility Scaling (DVS): All parameters auto-adjust based on current ATR relative to historical average, making the indicator adaptive across all timeframes and instruments without manual re-optimization.
II. HOW THE COMPONENTS WORK TOGETHER
This is NOT a simple mashup —each subsystem feeds data into the others in a closed-loop learning architecture:
The Processing Pipeline:
Step 1: Geometric Foundation
Raw price is normalized against a 50-period SMA to create a trajectory baseline
A Zero-Lag EMA smooths the trajectory while preserving edge response
Kalman filter removes noise while maintaining signal integrity
Step 2: Calculus Layer
First derivative (y') measures velocity of price movement
Second derivative (y'') measures acceleration (rate of velocity change)
Curvature (κ) is calculated from these derivatives, representing how sharply price is turning
Step 3: Statistical Validation
Z-Score measures how many standard deviations current price deviates from the Kalman-filtered "true price"
Only pivots with Z-Score > threshold (default 1.2) are considered statistically significant
This filters out noise and micro-fluctuations
Step 4: Tensor Construction
Curvature is combined with volatility (ATR-based) and momentum (ROC-based) to create a multidimensional "tensor score"
This tensor represents the geometric stress in the price field
High tensor magnitude = high probability of structural failure (reversal)
Step 5: AI Decision Layer
All 4 bandit strategies evaluate current conditions using different sensitivity thresholds
Each strategy maintains a virtual portfolio that trades its signals in real-time
Thompson Sampling algorithm updates Bayesian priors (alpha/beta distributions) based on each strategy's Sharpe ratio, win rate, and drawdown
The highest-performing strategy's signals are displayed to the user
Step 6: Confluence Aggregation
When multiple strategies agree on the same price zone, that zone is highlighted as a confluence area. These represent "hive mind" consensus—the strongest setups
Why This Integration Matters:
Traditional indicators either detect pivots too late (lagging) or generate too many false signals (noisy). By requiring geometric confirmation (curvature), statistical significance (Z-Score), multi-strategy agreement (hive voting), and performance validation (RL feedback) , this system achieves institutional-grade precision. The reinforcement learning layer ensures the system adapts as market regimes change, rather than degrading over time like static algorithms.
III. DETAILED METHODOLOGY
A. Curvature Calculation (Differential Geometry)
The system models price as a parametric curve where:
x-axis = time (bar index)
y-axis = normalized price
The curvature at any point represents how quickly the direction of the tangent line is changing. High curvature = sharp turn = potential pivot.
Implementation:
Lookback window (default 8 bars) defines the local curve segment
Smoothing (default 5 bars) applies adaptive EMA to reduce tick noise
Curvature is normalized to 0-1 scale using local statistical bounds (mean ± 2 standard deviations)
B. Ghost Vertex (Predictive Pivot Detection)
Classical pivot detection waits for price to form a swing high/low and confirm. Ghost Vertex uses calculus to predict the turning point:
Conditions for Ghost Pivot:
Velocity (y') ≈ 0 (price rate of change approaching zero)
Acceleration (y'') ≠ 0 (change is decelerating/accelerating)
Z-Score > threshold (statistically abnormal position)
This allows detection 1-3 bars before the actual high/low prints, providing an early entry edge.
C. Multi-Armed Bandit Reinforcement Learning
The system runs 4 parallel "bandits" (agents), each with different detection sensitivity:
Bandit Strategies:
Fast: Low curvature threshold (0.1), low Z-Score requirement (1.0) → High frequency, more signals
Balanced: Standard thresholds (0.2 curvature, 1.5 Z-Score) → Moderate frequency
Strict: High thresholds (0.4 curvature, 2.0 Z-Score) → Low frequency, high conviction
Tensor: Requires tensor magnitude > 0.5 → Geometric-weighted detection
Learning Algorithm (Thompson Sampling):
Each bandit maintains a Beta distribution with parameters (α, β)
After each trade outcome, α is incremented for wins, β for losses
Selection probability is proportional to sampled success rate from the distribution
This naturally balances exploration (trying underperformed strategies) vs exploitation (using best strategy)
Performance Metrics Tracked:
Equity curve for each shadow portfolio
Win rate percentage
Sharpe ratio (risk-adjusted returns)
Maximum drawdown
Total trades executed
The system displays all metrics in real-time on the dashboard so users can see which strategy is currently "winning."
D. Dynamic Volatility Scaling (DVS)
Markets cycle between high volatility (trending, news-driven) and low volatility (ranging, quiet). Static parameters fail when regime changes.
DVS Solution:
Measures current ATR(30) / close as normalized volatility
Compares to 100-bar SMA of normalized volatility
Ratio > 1 = high volatility → lengthen lookbacks, raise thresholds (prevent noise)
Ratio < 1 = low volatility → shorten lookbacks, lower thresholds (maintain sensitivity)
This single feature is why the indicator works on 1-minute crypto charts AND daily stock charts without parameter changes.
E. Confluence Zone Detection
The script divides the recent price range (200 bars) into 200 discrete zones. On each bar:
Each of the 4 bandits votes on potential pivot zones
Votes accumulate in a histogram array
Zones with ≥ 3 votes (75% agreement) are drawn as colored boxes
Red boxes = resistance confluence, Green boxes = support confluence
These zones act as magnet levels where price often returns multiple times.
IV. HOW TO USE THIS INDICATOR
For Scalpers (1m - 5m timeframes):
Settings: Use "Aggressive" or "Adaptive" pivot mode, Curvature Window 5-8, Min Pivot Strength 50-60
Entry Signal: Triangle marker appears (🔺 for longs, 🔻 for shorts)
Confirmation: Check that Hive Sentiment on dashboard agrees (3+ votes)
Stop Loss: Use the dotted volatility-adjusted target line in reverse (if pivot is at 100 with target at 110, stop is ~95)
Take Profit: Use the projected target line (default 3× ATR)
Advanced: Wait for confluence zone formation, then enter on retest of the zone
For Day Traders (15m - 1H timeframes):
Settings: Use "Adaptive" mode (default settings work well)
Entry Signal: Pivot marker + Hive Consensus alert
Confirmation: Check dashboard—ensure selected bandit has Sharpe > 1.5 and Win% > 55%
Filter: Only take pivots with Pivot Strength > 70 (shown in dashboard)
Risk Management: Monitor the Live Position Tracker—if your selected bandit is holding a position, consider that as market structure context
Exit: Either use target lines OR exit when opposite pivot appears
For Swing Traders (4H - Daily timeframes):
Settings: Use "Conservative" mode, Curvature Window 12-20, Min Bars Between Pivots 15-30
Focus on Confluence: Only trade when 4/4 bandits agree (unanimous hive consensus)
Entry: Set limit orders at confluence zones rather than market orders at pivot signals
Confirmation: Look for breakout diamonds (◆) after pivot—these signal momentum continuation
Risk Management: Use wider stops (base stop loss % = 3-5%)
Dashboard Interpretation:
Top Section (Real-Time Metrics):
κ (Curv): Current curvature. >0.6 = active pivot forming
Tensor: Geometric stress. Positive = bullish bias, Negative = bearish bias
Z-Score: Statistical deviation. >2.0 or <-2.0 = extreme outlier (strong signal)
Bandit Performance Table:
α/β: Bayesian parameters. Higher α = more wins in history
Win%: Self-explanatory. >60% is excellent
Sharpe: Risk-adjusted returns. >2.0 is institutional-grade
Status: Shows which strategy is currently selected
Live Position Tracker:
Shows if the selected bandit's shadow portfolio is currently holding a position
Displays entry price and real-time P&L
Use this as "what the AI would do" confirmation
Hive Sentiment:
Shows vote distribution across all 4 bandits
"BULLISH" with 3+ green votes = high-conviction long setup
"BEARISH" with 3+ red votes = high-conviction short setup
Alert Setup:
The script includes 6 alert conditions:
"AI High Pivot" = Selected bandit signals short
"AI Low Pivot" = Selected bandit signals long
"Hive Consensus BUY" = 3+ bandits agree on long
"Hive Consensus SELL" = 3+ bandits agree on short
"Breakout Up" = Resistance breakout (continuation long)
"Breakdown Down" = Support breakdown (continuation short)
Recommended Alert Strategy:
Set "Hive Consensus" alerts for high-conviction setups
Use "AI Pivot" alerts for active monitoring during your trading session
Use breakout alerts for momentum/trend-following entries
V. PARAMETER OPTIMIZATION GUIDE
Core Geometry Parameters:
Curvature Window (default 8):
Lower (3-5): Detects micro-structure, best for scalping volatile pairs (crypto, forex majors)
Higher (12-20): Detects macro-structure, best for swing trading stocks/indices
Rule of thumb: Set to ~0.5% of your typical trade duration in bars
Curvature Smoothing (default 5):
Increase if you see too many false pivots (noisy instrument)
Decrease if pivots lag (missing entries by 2-3 bars)
Inflection Threshold (default 0.20):
This is advanced. Lower = more inflection zones highlighted
Useful for identifying order blocks and liquidity voids
Most users can leave default
Pivot Detection Parameters:
Pivot Sensitivity Mode:
Aggressive: Use in low-volatility range-bound markets
Normal: General purpose
Adaptive: Recommended—auto-adjusts via DVS
Conservative: Use in choppy, whipsaw conditions or for swing trading
Min Bars Between Pivots (default 8):
THIS IS CRITICAL for visual clarity
If chart looks cluttered, increase to 12-15
If missing pivots, decrease to 5-6
Match to your timeframe: 1m charts use 3-5, Daily charts use 20+
Min Z-Score (default 1.2):
Statistical filter. Higher = fewer but stronger signals
During news events (NFP, FOMC), increase to 2.0+
In calm markets, 1.0 works well
Min Pivot Strength (default 60):
Composite quality score (0-100)
80+ = institutional-grade pivots only
50-70 = balanced
Below 50 = will show weak setups (not recommended)
RL & DVS Parameters:
Enable DVS (default ON):
Leave enabled unless you want to manually tune for a specific market condition
This is the "secret sauce" for cross-timeframe performance
DVS Sensitivity (default 1.0):
Increase to 1.5-2.0 for extremely volatile instruments (meme stocks, altcoins)
Decrease to 0.5-0.7 for stable instruments (utilities, bonds)
RL Algorithm (default Thompson Sampling):
Thompson Sampling: Best for non-stationary markets (recommended)
UCB1: Best for stable, mean-reverting markets
Epsilon-Greedy: For testing only
Contextual: Advanced—uses market regime as context
Risk Parameters:
Base Stop Loss % (default 2.0):
Set to 1.5-2× your instrument's average ATR as a percentage
Example: If SPY ATR = $3 and price = $450, ATR% = 0.67%, so use 1.5-2.0%
Base Take Profit % (default 4.0):
Aim for 2:1 reward/risk ratio minimum
For mean-reversion strategies, use 1.5-2.0%
For trend-following, use 3-5%
VI. UNDERSTANDING THE UNDERLYING CONCEPTS
Why Differential Geometry?
Traditional technical analysis treats price as discrete data points. Differential geometry models price as a continuous manifold —a smooth surface that can be analyzed using calculus. This allows us to ask: "At what rate is the trend changing?" rather than just "Is price going up or down?"
The curvature metric captures something fundamental: inflection points in market psychology . When buyers exhaust and sellers take over (or vice versa), the price trajectory must curve. By measuring this curvature mathematically, we detect these psychological shifts with precision.
Why Reinforcement Learning?
Markets are non-stationary —statistical properties change over time. A strategy that works in Q1 may fail in Q3. Traditional indicators have fixed parameters and degrade over time.
The multi-armed bandit framework solves this by:
Running multiple strategies in parallel (diversification)
Continuously measuring performance (feedback loop)
Automatically shifting capital to what's working (adaptation)
This is how professional hedge funds operate—they don't use one strategy, they use ensembles with dynamic allocation.
Why Kalman Filtering?
Raw price contains two components: signal (true movement) and noise (random fluctuations). Kalman filters are the gold standard in aerospace and robotics for extracting signal from noisy sensors.
By applying this to price data, we get a "clean" trajectory to measure curvature against. This prevents false pivots from bid-ask bounce or single-print anomalies.
Why Z-Score Validation?
Not all high-curvature points are tradeable. A sharp turn in a ranging market might just be noise. Z-Score ensures that pivots occur at statistically abnormal price levels —places where price has deviated significantly from its Kalman-filtered "fair value."
This filters out 70-80% of false signals while preserving true reversal points.
VII. COMMON USE CASES & STRATEGIES
Strategy 1: Confluence Zone Reversal Trading
Wait for confluence zone to form (red or green box)
Wait for price to approach zone
Enter when pivot marker appears WITHIN the confluence zone
Stop: Beyond the zone
Target: Opposite confluence zone or 3× ATR
Strategy 2: Hive Consensus Scalping
Set alert for "Hive Consensus BUY/SELL"
When alert fires, check dashboard—ensure 3-4 votes
Enter immediately (market order or 1-tick limit)
Stop: Tight, 1-1.5× ATR
Target: 2× ATR or opposite pivot signal
Strategy 3: Bandit-Following Swing Trading
On Daily timeframe, monitor which bandit has best Sharpe ratio over 30+ days
Take ONLY that bandit's signals (ignore others)
Enter on pivot, hold until opposite pivot or target line
Position size based on bandit's current win rate (higher win% = larger position)
Strategy 4: Breakout Confirmation
Identify key support/resistance level manually
Wait for pivot to form AT that level
If price breaks level and diamond breakout marker appears, enter in breakout direction
This combines support/resistance with geometric confirmation
Strategy 5: Inflection Zone Limit Orders
Enable "Show Inflection Zones"
Place limit buy orders at bottom of purple zones
Place limit sell orders at top of purple zones
These zones represent structural change points where price often pauses
VIII. WHAT THIS INDICATOR DOES NOT DO
To set proper expectations:
This is NOT:
A "holy grail" with 100% win rate
A strategy that works without risk management
A replacement for understanding market fundamentals
A signal copier (you must interpret context)
This DOES NOT:
Predict black swan events
Account for fundamental news (you must avoid trading during major news if not experienced)
Work well in extremely low liquidity conditions (penny stocks, microcap crypto)
Generate signals during consolidation (by design—prevents whipsaw)
Best Performance:
Liquid instruments (SPY, ES, NQ, EUR/USD, BTC/USD, etc.)
Clear trend or range conditions (struggles in choppy transition periods)
Timeframes 5m and above (1m can work but requires experience)
IX. PERFORMANCE EXPECTATIONS
Based on shadow portfolio backtesting across multiple instruments:
Conservative Mode:
Signal frequency: 2-5 per week (Daily charts)
Expected win rate: 60-70%
Average RRR: 2.5:1
Adaptive Mode:
Signal frequency: 5-15 per day (15m charts)
Expected win rate: 55-65%
Average RRR: 2:1
Aggressive Mode:
Signal frequency: 20-40 per day (5m charts)
Expected win rate: 50-60%
Average RRR: 1.5:1
Note: These are statistical expectations. Individual results depend on execution, risk management, and market conditions.
X. PRIVACY & INVITE-ONLY NATURE
This script is invite-only to:
Maintain signal quality (prevent market impact from mass adoption)
Provide dedicated support to users
Continuously improve the algorithm based on user feedback
Ensure users understand the complexity before deploying real capital
The script is closed-source to protect proprietary research in:
Ghost Vertex prediction mathematics
Tensor construction methodology
Bandit reward function design
DVS scaling algorithms
XI. FINAL RECOMMENDATIONS
Before Trading Live:
Paper trade for minimum 2 weeks to understand signal timing
Start with ONE timeframe and master it before adding others
Monitor the dashboard —if selected bandit Sharpe drops below 1.0, reduce size
Use confluence and hive consensus for highest-quality setups
Respect the Min Bars Between Pivots setting —this prevents overtrading
Risk Management Rules:
Never risk more than 1-2% of account per trade
If 3 consecutive losses occur, stop trading and review (possible regime change)
Use the shadow portfolio as a guide—if ALL bandits are losing, market is in transition
Combine with other analysis (order flow, volume profile) for best results
Continuous Learning:
The RL system improves over time, but only if you:
Keep the indicator running (it learns from bar data)
Don't constantly change parameters (confuses the learning)
Let it accumulate at least 50 samples before judging performance
Review the dashboard weekly to see which bandits are adapting
CONCLUSION
Curvature Tensor Pivots - HIVE represents a fusion of advanced mathematics, machine learning, and practical trading experience. It is designed for serious traders who want institutional-grade tools and understand that edge comes from superior methodology, not magic formulas.
The system's strength lies in its adaptive intelligence —it doesn't just detect pivots, it learns which detection method works best right now, in this market, under these conditions. The hive consensus mechanism provides confidence, the geometric foundation provides precision, and the reinforcement learning provides evolution.
Use it wisely, manage risk properly, and let the mathematics work for you.
Disclaimer: This indicator is a tool for analysis and does not constitute financial advice. Past performance of shadow portfolios does not guarantee future results. Trading involves substantial risk of loss. Always perform your own due diligence and never trade with capital you cannot afford to lose.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
TernTable: Crypto SectorsTernTables:CryptoSecs
This was hung on my Sector ETFs script to see if I could filter some noise from crypto by applying a GICS (Global Industry Classification Standard) style sector model to the crypto markets.
Crypto classification is certainly a little more nuanced so not completely straightforward.
It was designed to filter a researched and organised view of generally recognised cryptocurrency sectors and their confirmed constituent components.
The main purpose was a shot at displaying live crypto market data on my chart with instantaneous visual analysis, using leader laggard colour logic for performance indication, plus bullish bearish colour logic using the header for instant visual sector strength analysis.
This was never going to be an exhaustive tool of course and amazingly only or two of the sector lists wont fit on your laptop screen without zooming but it’s UI versatility both in custom display and custom threshold functionality is very effective. Viewing a coin on your watchlist with its sector overlayed in the chart brings the optional visual alert function into consideration. All basic but all effective and all customisable
Can't ignore the educational value either it’s teaching by osmosis what the sectors do and which coins go where clues to why.
As an after thought - I added a live stock market filter for 20 sector-specific ETFs like SPY, QQQ, XLV, XLF, allowing the comparison of the live performance of traditional financial sectors to live crypto sector data without leaving your chart.
Not certain how often it will need to be updated and any feedback re the legitimacy and accuracy of its components is kindly welcomed it is up to date at date of publishing.
It’s pretty easy to use, here is a list what you're getting with sector classifications with brief descriptions
CMC 20
CoinMarketCap Top 20: the largest cryptos by market cap. Great starting point to see what the overall market is doing
ETFs
All major U.S.-listed Bitcoin & Ethereum ETFs. Lets you compare crypto performance directly with traditional finance
Layer 0
Foundational interoperability protocols (Polkadot, Cosmos, ICP, etc.). These are the “bridges” that allow different blockchains to communicate
Layer 1
Independent base-layer blockchains that run their own consensus and security (Bitcoin, Ethereum, Solana, Cardano, TON, etc.).
Layer 2
Scaling networks built on top of Layer 1s to increase speed and lower fees (Arbitrum, Optimism, Base, Polygon, zk-rollups, etc.)
Layer 3
Application-specific chains or rollups designed for one purpose (gaming chains, DeFi-specific, social, etc.)
Web3
The “ownership internet”: gaming tokens, NFTs, metaverse land, music/streaming platforms, social tokens, and creator-economy projects
DeFi
Decentralised Finance: lending platforms, decentralized exchanges, derivatives, yield aggregators, and insurance protocols
Decentralised Storage
Blockchain-based alternatives to AWS/Google Cloud (Filecoin, Arweave, Storj, etc.)
Oracles
Data providers that feed off-chain information (prices, weather, sports results) into smart contracts
Privacy
Privacy coins and protocols that obfuscate transaction details (Monero, Zcash, Beam, etc.)
Yield & Lending
Protocols focused purely on lending, borrowing, and yield generation
DEX
Pure decentralized exchanges (Uniswap, SushiSwap, Jupiter, GMX, etc.)
DAO
Governance tokens of major decentralized autonomous organizations (Maker, Lido, Aave, ENS, etc.)
Infrastructure / Middleware
The picks-and-shovels layer: node services, RPC providers, indexing, cross-chain bridges, etc
Real World Assets (RWA)
Tokenised traditional assets: treasuries, real estate, private credit, stablecoins backed by real-world collateral
Restaking & Liquid Restaking
EigenLayer ecosystem and liquid-restaking tokens (eigen, ether.fi, Pendle, etc.). Currently the fastest-growing narrative
Traditional Sector ETFs
Classic U.S. sector ETFs (SPY, QQQ, XLF, XLE, XLV, XLY, etc.). Extra layer of analysis by comparing live stock market conditions with livecrypto market conditions
A list of the UI Toggles
* Sector Dropdown
• Select Sector: Choose the sector to display (e.g., CMC 20, Layer 1, DeFi, etc.)
* Custom Tickers
• Enter Tickers: Input custom coin tickers (e.g., BTCUSD, ETHUSD) to track specific assets
* Show % Change Row
• Toggle On/Off: Display the % change row for each sector/coin
* Show Current Price Row
• Toggle On/Off: Display the current price for each sector/coin
* Show Price-Diff Row
• Toggle On/Off: Display the price difference (current price - previous day's price)
* Show Spacer Row
• Toggle On/Off: Add a spacer row between data rows for clarity
* Table Position
• Select Position: Choose the position of the data table on your chart (Top Left, Top Right, etc.)
Visual Options:
* Show Sector Name
• Toggle On/Off: Display the sector name pane label on chart
* Custom Bull/Bear Threshold
• Toggle On/Off: Set a custom threshold for bullish/bearish sector performance
• Threshold (%): Set the percentage threshold (e.g., 50%) for bullish/bearish classification
* Show Live % in Header
• Toggle On/Off: Display the live percentage change in the table header
* Dynamic Decimal Formatting
• Toggle On/Off: Enable dynamic formatting for numbers display.
* Sort by % Change
• Toggle On/Off: Sort sectors by % change in performance
* Enable Alerts
• Toggle On/Off: Enable alerts based on performance thresholds
* Alert Threshold (%)
• Set Threshold: Define the percentage threshold (e.g.,70%) for triggering alerts
* Cooldown (bars)
• Toggle On/Off: Enable cooldown to prevent alerts from triggering too quickly
• Cooldown Duration: Set the cooldown period in bars (e.g., 10 bars)
* % Threshold Mode
• Toggle On/Off: Enable % Threshold Mode to filter sectors based on a percentage change threshold
• Threshold %: Set the percentage for filtering sectors (e.g., only show sectors with > 5% change)
A lot of toggles probably left once favourites are set but this UI interface does allow experimentation with the utility of channelling raw live data through custom designed filters. Just saying !
I need to include this of course
This indicator provides sector-based organisation and real-time performance visualisation for cryptocurrencies. It is not intended to predict price movements or guarantee outcomes. Crypto assets carry significant risk, including loss of capital. Past performance does not guarantee future results. All data and sector classifications are best-effort and may be incomplete, inaccurate, or outdated. Nothing in this script should be interpreted as financial advice. You are solely responsible for your own trading decisions.
That’s it really, I am currently pleased with how this indicator turned out, if you have a crypto trading toolkit put this in it.
Distribution Day Grading [Blk0ut]Distribution Day Grading
This script is designed to give traders and investors a fast, objective, and modern read on market health by analyzing distribution days, and stall days, two forms of institutional selling that often begin to appear before trend weakness, failed breakouts, and sharp corrections.
The goal of this script isn’t to predict tops or bottoms, but instead, it measures the character of the tape in a way that’s simple, visual, and immediately actionable.
While distribution analysis has existed for decades, my implementation is, I think, a little more adaptive. Traditional rules for identifying distribution days, coming from CANSLIM methodology, were built for markets which had lower volatility, different liquidity profiles, and slower institutional rotation. This script updates the traditional method with modernized thresholds, recency-weighted decay, stall-day logic, and dynamic presets tuned uniquely for the personality of each major U.S. index (you can change the values yourself as well).
The results are displayed as a compact letter-grade that quantitatively reflects a measure of how much institutional supply has been hitting the market, as well as how recently. This helps determine whether conditions are supportive of breakouts, mean reversion trades, aggressive trend trades, or whether caution and lighter sizing are warranted.
__________________________________________________________________________________
How It Works
The script evaluates each bar for two conditions:
1. Distribution Day
A bar qualifies as distribution when:
- Price closes down beyond a threshold (default 0.30%, adjustable)
- Volume is higher than the prior session (optional toggle)
Distribution days typically represent active institutional selling .
2. Stall Day
A softer form of supply:
-Price remains flat to slightly negative within a small threshold
-Close < open
-Volume higher than prior day
Stall days represent a passive distribution or hidden supply .
Each distribution day is counted as 1 unit by the script, each stall day as 0.5 units.
Recency Weighting
The script applies an optional half-life decay so that fresh distribution matters more than old distribution. This mimics the “aging out” effect that professional traders use, but does it in a smoother, more mathematically consistent way.
The script then produces:
A weighted distribution score
A raw distribution + stall count
A letter grade from A → F
Let's talk about the letters...
_________________________________________________________________________________
Letter Grade Meaning
A — Very Healthy Tape
Minimal institutional selling.
Breakouts behave better, momentum holds, pullbacks are shallow, upside targets are hit more consistently.
B — Healthy / Slight Caution
Some isolated supply but nothing structural.
Conditions remain favorable for trend trades, pullbacks, and breakout continuation.
C — Mixed / Caution Warranted
Distribution is building.
Breakouts begin to fail faster, candles widen, rotation becomes unstable, and risk/reward compresses.
D — Weak / Risk Elevated
Institutional selling is becoming persistent.
Failed breakouts, sharp reversals, and failed rallies become more common. Position sizing should tighten.
F — Clear Deterioration
Broad, repeated institutional distribution.
This is where major tops, deeper pullbacks, and corrections often begin to form underneath the surface.
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Index-Tuned Presets (Auto Mode)
Market structure varies dramatically across indices.
To address this, the script includes auto-detect presets for:
SPY / SPX equivalents
QQQ / NASDAQ-100 equivalents
IWM / Russell 2000 equivalents
DIA / Dow 30 equivalents
Each preset contains optimized values based on volatility, liquidity, noise, and institutional behavior:
SPY / SPX
Low noise, deep liquidity → classic thresholds work well.
Distribution thresholds remain conservative.
QQQ
Higher volatility → requires a slightly larger down-percentage filter to avoid false signals.
IWM
Noisiest of the major indices → requires much stricter thresholds to filter out junk signals.
DIA
Slowest-moving index → tighter conditions catch real distribution earlier.
The script automatically detects which symbol family you’re viewing and loads the appropriate preset unless manual overrides are enabled.
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How to Interpret This Indicator
Grade A–B:
Breakouts have higher odds of clean continuation
Mean reversion is smoother
Position sizing can be more assertive
Grade C:
Start tightening risk
Focus on A- setups, not B- or C- risk ideas
Grade D–F:
Expect lower win rates
Expect breakout failures
Favor countertrend plays or reduced exposure
Take faster profits
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This indicator should help traders prevent themselves from fighting the tape or sizing aggressively when the underlying environment is deteriorating through:
- Modernized distribution logic, not the 1990s thresholds
- Recency-weighted decay instead of the old 5-week “aging out”
- Stall-day detection for subtle institutional supply
- Auto-presets tuned per index, adjusting thresholds to match volatility and liquidity
- Unified letter-grade scoring for visual clarity
- Independent application for any trading style, it helps with trend, momentum, mean reversion, and options
_________________________________________________________________________________
Keep in mind: This script is provided strictly for educational and informational purposes.
Nothing in this indicator constitutes financial advice, trading advice, investment guidance, or a recommendation to buy or sell any security, option, cryptocurrency, or financial instrument.
No indicator should ever be used as the sole basis for a trading or investment decision.
Markets carry risk. Past performance does not predict future results.
Always perform your own analysis, use proper risk management, and consult a licensed professional if you need advice specific to your financial situation.
Happy Trading!
Blk0uts
RED-E Index and ETF ConverterThis indicator provides real-time conversion between major US stock market indices and their corresponding ETFs, displaying current prices, calculated conversions, and market sentiment in an easy-to-read dashboard format.
WHAT IT DOES:
Tracks three major index-ETF pairs and shows bi-directional conversions:
SPX (S&P 500 Index) ↔ SPY (SPDR S&P 500 ETF)
NDX (NASDAQ-100 Index) ↔ QQQ (Invesco QQQ ETF)
RUT (Russell 2000 Index) ↔ IWM (iShares Russell 2000 ETF)
HOW IT WORKS:
The script uses request.security() to fetch real-time price data from each instrument and applies standard conversion ratios:
SPX to SPY: ~1:10 ratio
NDX to QQQ: ~1:40 ratio
RUT to IWM: ~1:10 ratio
Market sentiment is determined by comparing current price to previous bar, displaying BULLISH (green ▲), BEARISH (red ▼), or NEUTRAL (gray ●).
KEY FEATURES:
Real-time price tracking for all six instruments
Bi-directional conversion calculations
Visual sentiment indicators based on price movement
Customizable dashboard position
Adjustable font sizes
Toggle individual index pairs on/off
Color-coded sections
Clean professional table layout
USAGE:
Add the indicator to any chart. The dashboard will display in the bottom left corner by default. Use the settings to:
Change dashboard position
Adjust font size
Show/hide specific index-ETF pairs
Customize sentiment colors
This tool is useful for traders who:
Trade both indices and ETFs
Want to quickly compare index vs ETF pricing
Monitor multiple market segments simultaneously
Need at-a-glance sentiment across major indices
Note: Conversion ratios are approximate and based on standard tracking ratios. Actual ETF prices may vary slightly due to tracking error, fees, and market conditions.
Disclaimer: This indicator is for educational and informational purposes only. It does not constitute financial advice. The creator is not a financial advisor, and users should consult with a licensed financial professional before making any investment decisions. Use at your own risk.
Liquidity Sweeps 2.0 – MGTrading Professional Liquidity Sweep Engine with Volume, MACD, Trend, SMT Divergence & Rolling VWAP
Liquidity Sweeps 2.0 is a **complete precision-based liquidity detection framework** built for traders who follow smart money concepts, sweep-based entries, and algorithmic price behavior.
This tool detects **high-probability buy/sell sweeps**, confirms them with market structure, Volume, MACD engine, Trend filtering, SMT divergence, and overlays a Rolling VWAP to track accumulation & distribution behavior.
It is designed for futures, indices, forex, crypto, and options traders.
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🔥 **MAIN FEATURES**
✅ **1. True Liquidity Sweep Detection**
Automatically detects when price:
* Sweeps a prior high (Sell Sweep)
* Sweeps a prior low (Buy Sweep)
* Rejects and closes back inside the previous range
This helps identify real **stop hunts**, **liquidity grabs**, and **reversal moments**.
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✅ **2. Volume Spike Confirmation (Smart Filter) **
Sweeps are only confirmed when volume exceeds the dynamic SMA threshold.
This filters out weak sweeps and keeps only **high-quality liquidity grabs**.
✅ **3. MACD Engine Confirmation**
Advanced MACD rules confirm sweeps by:
* MACD direction
* MACD signal agreement
* MACD histogram alignment
This dramatically increases accuracy and removes fake sweeps that occur in weak trends.
Optional **MACD Divergence Detection** reveals trend exhaustion before major reversals.
✅ **4. EMA Trend Filter (9/21) **
Avoid fighting the trend with the optional trend filter:
* Only buy sweeps in uptrend
* Only sell sweeps in downtrend
Great for scalpers and day traders who want trend alignment.
✅ **5. SMT Divergence (Relative Strength vs Reference Symbol) **
Enable SMT to detect when:
* Your chart sweeps a high/low
* But the reference symbol DOES NOT
This creates **displacement**, a powerful reversal signal used by institutional traders.
The symbol is selectable (ES, NQ, SPY, QQQ, BTC.D, DXY, etc.).
✅ **6. Rolling VWAP (RVWAP)**
A more dynamic version of VWAP using:
* Time-based rolling windows
* Volume-weighted price
* Automatic or adjustable window size
* Color change based on slope
Excellent for tracking **accumulation**, **distribution**, and **algorithmic trend bias**.
✅ **7. Clean Mode**
Only show “confirmed” sweeps with a ✓
Ideal for traders who want a simple and clean chart.
# 📊 **LABELS & COLOR CODING**
* 🟥 **Sweep Sell**
* 🟩 **Sweep Buy**
* 🟧 **MACD Bearish Divergence**
* 🟩 **MACD Bullish Divergence**
* 🟦/🟧 **RVWAP Up/Down Trend**
* “✓” means the sweep passed all confirmations.
# 🎯 **WHO IS THIS FOR?**
✔ Futures Traders (ES, NQ, YM, RTY, CL, GC)
✔ Options Traders (SPX, SPY, QQQ)
✔ Forex Traders
✔ Crypto Traders
✔ Smart Money / ICT Style Traders
✔ Liquidity Sweep Traders
✔ Scalpers, Day Traders, Swing Traders
If you trade **liquidity**, **sweeps**, **SMT**, **divergence**, or **VWAP-based behavior**, this indicator is designed for you.
# 🧠 **HOW TO USE IT**
1. Wait for a sweep label to appear at a liquidity pool.
2. Confirm with volume + MACD + trend (if enabled).
3. Watch RVWAP for bias (accumulation vs distribution).
4. Enter on the imbalance/FVG, retrace, or structure break.
5. Use SMT divergence as a premium confirmation.
This tool does NOT repaint after the bar closes.
Signals only appear when conditions are confirmed.
# 📦 **SETTINGS OVERVIEW**
* Lookback window for sweeps: (7 - 13)
* Volume spike threshold
* MACD lengths & filters
* Trend filter (EMA 9/21)
* SMT reference symbol
* RVWAP window + colors
RVWAP Line Width: (2) & Minimum Window Bars: (5)
* Label placement & visual adjustments
* Clean mode
Everything is fully customizable.
⚠️ **DISCLAIMER**
This indicator is for educational purposes only.
It does not guarantee profits.
Always backtest, practice proper risk management, and trade responsibly.
❤️ **If this helped you, leave a like & comment! **
Your support motivates further updates, improvements, and new tools.
The Sentiment Indicator - Ultimate Hybrid v2(image shown of chart is not the coloured candles - message for a screenshot)
The Sentiment Indicator – Ultimate Hybrid v2
Most indicators react. This one anticipates.
Using dual-timeframe sentiment normalization, it blends institutional money flow, market participation, global risk appetite, and adaptive momentum into one real-time composite score — then colours your candles from blood red (panic) to deep green (conviction).
What You See
Dark Green Candles = Institutional buying confirmed
(Filtered by volume, flow, and participation — no fakeouts)
Early Warning Flash = Short-term sentiment collapsing while price still high
→ Your edge to exit or hedge before the drop
Dynamic Thresholds = Levels shift with market regime — never static
Trend Boost Engine = Rewards sustained moves, punishes chop
Built for Real Traders
Works on SPY, QQQ, IWM, stocks, futures, crypto
No repainting | No lookahead | Fully transparent logic
Top-right dashboard shows every layer in real time
Dark Green Gate™ blocks false strength signals
How It Works (The Edge)
We analyse 12 major assets and over 20 institutional-grade metrics — including:
Smart money flow (volume-weighted, momentum-adjusted)
Market breadth (% of stocks above key MAs)
Global risk-on/risk-off signals (equities, bonds, commodities, EM)
VIX regime penalties
Up/down volume panic ratio
All fused into one adaptive composite score using dual-timeframe normalization (long-term stability + short-term sensitivity).
Key Inputs You Control
Primary Lookback (default: 40) – Core sensitivity
Early Warning Threshold (default: -15) – Catch tops early
Money Flow Weight (default: 50%) – Prioritize volume action
Dark Green Gate™ – ON/OFF – Blocks false strength signals
Exclusive Features
Early Warning System™ – Flashes when short-term sentiment collapses while price is still high
Dynamic Thresholds – Auto-adjust to current market volatility
Trend Boost Engine – Rewards sustained moves above the 150-day MA
Dark Green Gate™ – Requires volume + flow + price confirmation for top-tier signals
Works Everywhere
SPY, QQQ, IWM, ES, NQ, stocks, crypto
Work on daily, weekly
Real-time dashboard with every layer visible
Stop reacting. Start anticipating.
Index Weighted Returns [SS]This is the index weighted return indicator.
It supports a few ETFs, including:
SPY/SPX
QQQ/NDX
ARKK
SMH
UFO
XBI
QTUM
What it does is it takes the top, approximately 40, of the most heavily weighted tickers on the ETF, monitors their returns using the request security function, and then uses their weight to calculate the synthetic returns of the ETF of interest.
For example, in the chart we have SMH.
The indicator is looking at the top weighted tickers of SMH, calculating their returns, adjusting it for their individual weight on SMH and then predicting the expected return of SMH based on the weighing and holding's returns themselves.
How to Use it
The indicator is pretty straight forward, you select which ever index you are on and your desired timeframe (you can do as low as 30-Minutes or as high as monthly or quarterly).
The indicator will then retrieve the top holdings for that ticker, their corresponding weights and calculate the expected daily return based on the weight and return of these tickers.
It will plot this return for you on the chart.
Other Options
There is an optional table for you to view the actual weight, ticker composition and period returns for each of the top x tickers for an index. You can simply toggle "Show Table" in the settings menu, and it will show you the list of all tickers included, their period returns and their weight on the ETF.
Tips for Use
Works well to see when an index may be over the actual top weighted tickers, implying a pullback/sell, or under. For example:
SPY today fell well below its top tickers and is currently rallying back up to the expected close range.
You can see in the primary chart, SMH fell below and returned to its balance, being at the expected close range based on its component tickers.
That is the indicator!
Its simple but powerful!
Hope you enjoy and as always, safe trades!
Zarattini Intra-day Threshold Bands (ZITB)This indicator implements the intraday threshold band methodology described in the research paper by Carlo Zarattini et al.
Overview:
Plots intraday threshold bands based on daily open/close levels.
Supports visualization of BaseUp/BaseDown levels and Threshold Upper/Lower bands.
Optional shading between threshold bands for easier interpretation.
Usage Notes / Limitations:
Originally studied on SPY (US equities), this implementation is adapted for NSE intraday market timing, specifically the NIFTY50 index.
Internally, 2-minute candles are used if the chart timeframe is less than 2 minutes.
Values may be inaccurate if the chart timeframe is more than 1 day.
Lookback days are auto-capped to avoid exceeding TradingView’s 5000-bar limit.
The indicator automatically aligns intraday bars across multiple days to compute average deltas.
For better returns, it is recommended to use this indicator in conjunction with VWAP and a volatility-based position sizing mechanism.
Can be used as a reference for Open Range Breakout (ORB) strategies.
Customizations:
Toggle plotting of base levels and thresholds.
Toggle shading between thresholds.
Line colors and styles can be adjusted in the Style tab.
Intended for educational and research purposes only.
This indicator implements the approach described in the research paper by Zarattini et al.
Note: This implementation is designed for the NSE NIFTY50 index. While Zarattini’s original study was conducted on SPY, this version adapts the methodology for the Indian market.
Methodology Explanation
This indicator is primarily designed for Open Range Breakout (ORB) strategies.
Base Levels
BaseUp = Maximum of today’s open and previous day’s close
BaseDown = Minimum of today’s open and previous day’s close
Delta Calculation
For the past 14 trading days (lookbackDays), the delta for each intraday candle is calculated as the ab
solute difference from the close of the first candle of that day.
Average Delta
For a given intraday time/candle today, deltaAvg is computed as the average of the deltas at the same time across the previous 14 days.
Threshold Bands
ThresholdUp = BaseUp + deltaAvg
ThresholdDown = BaseDown − deltaAvg
Signals
Spot price moving above ThresholdUp → Long signal
Spot price moving below ThresholdDown → Short signal
Tip: For better returns, combine this indicator with VWAP and a volatility-based position sizing mechanism.
Trading Toolkit - Comprehensive AnalysisTrading Toolkit – Comprehensive Analysis
A unified trading analysis toolkit with four sections:
📊 Company Info
Fundamentals, market cap, sector, and earnings countdown.
📅 Performance
Date‑range analysis with key metrics.
🎯 Market Sentiment
CNN‑style Fear & Greed Index (7 components) + 150‑SMA positioning.
🛡️ Risk Levels
ATR/MAD‑based stop‑loss and take‑profit calculations.
Key Features
CNN‑style Fear & Greed approximation using:
Momentum: S&P 500 vs 125‑DMA
Price Strength: NYSE 52‑week highs vs lows
Market Breadth: McClellan Volume Summation (Up/Down volume)
Put/Call Ratio: 5‑day average (inverted)
Volatility: VIX vs 50‑DMA (inverted)
Safe‑Haven Demand: 20‑day SPY–IEF return spread
Junk‑Bond Demand: HY vs IG credit spread (inverted)
Normalization: z‑score → percentile (0–100) with ±3 clipping.
CNN‑aligned thresholds:
Extreme Fear: 0–24 | Fear: 25–44 | Neutral: 45–54 | Greed: 55–74 | Extreme Greed: 75+.
Risk tools: ATR & MAD volatility measures with configurable multipliers.
Flexible layout: vertical or side‑by‑side columns.
Data Sources
S&P 500: CBOE:SPX or AMEX:SPY
NYSE: INDEX:HIGN, INDEX:LOWN, USI:UVOL, USI:DVOL
Options: USI:PCC (Total PCR), fallback INDEX:CPCS (Equity PCR)
Volatility: CBOE:VIX
Treasuries: NASDAQ:IEF
Credit Spreads: FRED:BAMLH0A0HYM2, FRED:BAMLC0A0CM
Risk Management
ATR risk bands: 🟢 ≤3%, 🟡 3–6%, ⚪ 6–10%, 🟠 10–15%, 🔴 >15%
MAD‑based stop‑loss and take‑profit calculations.
Author: Daniel Dahan
(AI Generated, Merged & enhanced version with CNN‑style Fear & Greed)
RSI Colored by Relative StrengthThis indicator enhances the traditional RSI by combining it with Relative Strength (RS) — the ratio of an asset’s price to a chosen benchmark (e.g., SPY, QQQ, BTCUSD) — to create a more accurate, powerful, and dynamic momentum confirmation tool.
Instead of relying solely on RSI’s internal momentum, this version color-codes RSI values and backgrounds based on whether the asset is outperforming, underperforming, or neutral relative to the benchmark, not only identifying the RSI value, but color codes it in relation to the overall market to give more accurate confirmations.
• RS > 1 → The asset is outperforming the benchmark (relative strength).
• RS < 1 → The asset is underperforming.
• RS ≈ 1 → Neutral or moving in sync with the benchmark.
Gradient background zones:
• Green tones = outperformance (RS > 1).
• Red tones = underperformance (RS < 1).
• Gray neutral band = parity (RS ≈ 1).
Intensity adjusts dynamically based on how far RS deviates from 1, giving an at-a-glance view of market leadership strength.
• Color-coded RSI line: Green when RS > 1, red when RS < 1.
• Optional markers and labels show confirmed RS+RSI crossovers with smart spacing to prevent clutter.
• Alerts included for bullish and bearish RS+RSI alignment events.
How to Use
1. Add your preferred benchmark symbol (default: SPY).
2. Move this indicator into the same pane as your RSI (No need to overlay, does so automatically) and can also be used standalone.
3. Watch for:
• Green RSI & background: Significant momentum strength (asset trending upward and outpacing the market).
• Red RSI & background: False or insignificant momentum (asset lagging).
• Gray zone: neutral phase — consolidation or rotation period.
Use this as a trend-confirmation filter rather than a signal generator.
For example:
• Confirm and refine breakout entries when RS > 1 (RSI support = stronger conviction).
• Take profits when RSI weakens and RS slips below 1.
Market Sentiment Suite: PCCE + VIX + Signals📊 Market Sentiment Suite: PCCE + VIX + Signals
Identify fear, greed, and turning points in the market.
This script combines the CBOE Put/Call Ratio (PCCE) with the VIX volatility index percentile to visualize crowd sentiment and highlight potential market tops and bottoms.
🔍 Key Features
Dual-indicator design: PCCE + normalized VIX percentile
Color-coded zones for Greed (<0.6) and Fear (>1.2)
Automatic alert signals when sentiment reaches extremes
Live sentiment table displaying real-time PCCE and VIX data
Works seamlessly on SPX, SPY, QQQ, or any major index
🧠 How to Use
When PCCE > 1.2 and VIX percentile > 80%, fear is extreme → possible market bottom
When PCCE < 0.6 and VIX percentile < 20%, greed is extreme → possible market top
Perfect for contrarian traders, sentiment analysts, and swing traders
✨ Best Timeframe: Daily
⚙️ Markets: SPX / SPY / QQQ / Global Indexes
📈 Type: Contrarian Sentiment Indicator
Real Relative Strength Breakout & BreakdownReal Relative Strength Breakout & Breakdown Indicator
What It Does
Identifies high-probability trading setups by combining:
Technical Breakouts/Breakdowns - Price breaking support/resistance zones
Real Relative Strength (RRS) - Volatility-adjusted performance vs benchmark (SPY)
Key Insight: The strongest signals occur when price action contradicts market direction—breakouts during market weakness or breakdowns during market strength show exceptional buying/selling pressure.
Real Relative Strength (RRS) Calculation
RRS measures outperformance/underperformance on a volatility-adjusted basis:
Power Index = (Benchmark Price Move) / (Benchmark ATR)
RRS = (Stock Price Move - Power Index × Stock ATR) / Stock ATR
RRS (smoothed) = 3-period SMA of RRS
Interpretation:
RRS > 0 = Relative Strength (outperforming)
RRS < 0 = Relative Weakness (underperforming)
Signal Types
🟢 Large Green Triangle (Premium Long)
Condition: Breakout + RRS > 0
Meaning: Stock breaking resistance WHILE outperforming benchmark
Best when: Market is weak but stock breaks out anyway = exceptional strength
Use: High-conviction long entries
🔵 Small Blue Triangle (Standard Breakout)
Condition: Breakout + RRS ≤ 0
Meaning: Breaking resistance but underperforming benchmark
Typical: "Rising tide lifts all boats" scenario during market rally
Use: Lower conviction—may just be following market
🟠 Large Orange Triangle (Premium Short)
Condition: Breakdown + RRS < 0
Meaning: Stock breaking support WHILE underperforming benchmark
Best when: Market is strong but stock breaks down anyway = severe weakness
Use: High-conviction short entries
🔴 Small Red Triangle (Standard Breakdown)
Condition: Breakdown + RRS ≥ 0
Meaning: Breaking support but outperforming benchmark
Typical: Stock falling less than market during selloff
Use: Lower conviction—may recover when market does
Why Large Triangles Matter
Large signals show divergence = genuine institutional flow:
Stock breaking out while market falls → Aggressive buying despite headwinds
Stock breaking down while market rallies → Aggressive selling despite tailwinds
These setups reveal where real conviction lies, not just momentum-following behavior.
Quick Settings
RRS: 12-period lookback, 3-bar smoothing, vs SPY
Breakouts: 5-period pivots, 200-bar lookback, 3% zone width, 2 minimum tests
