스크립트에서 "daily"에 대해 찾기
Daily Returns & STDWhat happened last time when xx increased by xx%? - Start collecting some stats!
You can choose the ticker and the timeframe you're interested in
RSI Oversold/UndersoldThe study script will place GREEN BUY arrows BELOW oversold conditions and RED SHORT arrows ABOVE overbought conditions. You can configure the period
Most RSI(14) indicators use a 14-period, I prefer a 5-period. The period, overbought and oversold periods are settings that can easily be changed by adding this study to your chart and clicking the "gear" icon next to the study inside your chart.
Daily SMAThis pine script on intraday chart is exactly the same SMA as built-in MovingAverage on a 1Day chart (with the same lengths)
ema200 filler═══════════════════════════════════════════════════════════════
TRADINGVIEW INDICATOR DESCRIPTION
═══════════════════════════════════════════════════════════════
TITLE: EMA 200 Filler - Visual Trend Indicator
SHORT DESCRIPTION:
Instantly see trend direction with color-coded shading between price and the 200 EMA. Green above = bullish, Red below = bearish.
═══════════════════════════════════════════════════════════════
MAIN DESCRIPTION
═══════════════════════════════════════════════════════════════
🎨 SEE THE TREND AT A GLANCE
This elegant indicator fills the space between price and the 200-period Exponential Moving Average (EMA) with color-coded shading, making trend direction instantly obvious without any analysis required.
✨ KEY FEATURES:
• Visual Trend Clarity - Green fill = bullish zone, Red fill = bearish zone
• EMA 200 Line - The institutional trader's favorite trend indicator
• Dynamic Shading - Fill automatically adjusts as price moves
• Clean Design - Semi-transparent fills won't clutter your chart
• Zero Configuration - Works perfectly right out of the box
• Universal Application - Works on any timeframe, any asset
📊 WHAT YOU SEE:
🟢 GREEN SHADED AREA
→ Price is ABOVE the 200 EMA
→ Bullish trend in effect
→ Look for LONG opportunities
🔴 RED SHADED AREA
→ Price is BELOW the 200 EMA
→ Bearish trend in effect
→ Look for SHORT opportunities
🔵 BLUE LINE = 200 EMA
→ The dividing line between bull and bear zones
→ Major support/resistance level
→ Institutional trend filter
💡 WHY THE 200 EMA MATTERS:
The 200-period EMA is one of the most widely watched technical indicators by:
✓ Institutional traders and hedge funds
✓ Day traders and swing traders
✓ Algorithmic trading systems
✓ Technical analysis professionals
When millions of traders watch the same level, it becomes a self-fulfilling prophecy - making it incredibly powerful for entries, exits, and stop placement.
🎯 TRADING APPLICATIONS:
✓ **Trend Filter** - Only take longs in green, shorts in red
✓ **Trend Confirmation** - Strong trends stay on one side for extended periods
✓ **Reversal Signals** - Watch for crossovers when price crosses the 200 EMA
✓ **Support/Resistance** - 200 EMA acts as dynamic support in uptrends, resistance in downtrends
✓ **Stay Out Zones** - Avoid trading when price chops around the 200 EMA (mixed colors)
📈 PERFECT FOR:
✓ Swing traders who need clear trend direction
✓ Day traders using the 200 EMA as a filter
✓ Beginners who want simple trend identification
✓ Multi-timeframe analysis (check higher timeframe trend)
✓ Anyone who wants cleaner charts with instant trend clarity
⚙️ WORKS WITH:
• All asset classes (stocks, forex, crypto, commodities, indices)
• All timeframes (1-minute to monthly charts)
• Combines perfectly with other indicators
• No special settings required - just add and trade
🌟 CLEAN & PROFESSIONAL:
• Semi-transparent fills (70% opacity) - won't hide candles or other indicators
• White price line for clear visibility
• Blue EMA line - industry standard color
• Minimalist design philosophy
🚀 INSTANT SETUP:
1. Add indicator to chart
2. Start trading with the trend
3. That's it - no configuration needed!
The simplest way to visualize trend direction. When you see green, think bullish. When you see red, think bearish. Trading doesn't get more straightforward than this.
═══════════════════════════════════════════════════════════════
CATEGORIES:
• Trend Analysis
• Moving Averages
• Overlays
TAGS:
ema, ema 200, moving average, trend indicator, trend filter, visual indicator, exponential moving average, 200 ema, trend following, color coded, bullish bearish
═══════════════════════════════════════════════════════════════
QUICK START GUIDE
═══════════════════════════════════════════════════════════════
🚀 QUICK START - EMA 200 Filler
──────────────────────────────────────────────────────────────
STEP 1: ADD TO CHART
──────────────────────────────────────────────────────────────
1. Open TradingView
2. Load any chart (stocks, forex, crypto - anything!)
3. Click "Indicators" button at top
4. Search: "EMA 200 Filler"
5. Click to add
You're done! No settings to adjust.
──────────────────────────────────────────────────────────────
STEP 2: UNDERSTAND THE COLORS
──────────────────────────────────────────────────────────────
The indicator fills the space between PRICE and the 200 EMA:
🟢 GREEN FILL = BULLISH ZONE
• Price is above the 200 EMA
• Uptrend is active
• Bias: Look for LONG entries only
🔴 RED FILL = BEARISH ZONE
• Price is below the 200 EMA
• Downtrend is active
• Bias: Look for SHORT entries only
🔵 BLUE LINE = 200 EMA
• The trend dividing line
• Acts as support in uptrends
• Acts as resistance in downtrends
──────────────────────────────────────────────────────────────
STEP 3: BASIC TRADING RULES
──────────────────────────────────────────────────────────────
📈 RULE #1: TRADE WITH THE COLOR
In GREEN zone:
→ Only look for LONG setups
→ Buy dips toward the 200 EMA
→ Avoid shorting against the trend
In RED zone:
→ Only look for SHORT setups
→ Sell rallies toward the 200 EMA
→ Avoid longing against the trend
──────────────────────────────────────────────────────────────
🎯 RULE #2: USE THE 200 EMA AS SUPPORT/RESISTANCE
In GREEN (uptrend):
→ 200 EMA acts as SUPPORT
→ Price bouncing off 200 EMA = buy opportunity
→ Price breaking BELOW 200 EMA = trend change warning
In RED (downtrend):
→ 200 EMA acts as RESISTANCE
→ Price rejecting at 200 EMA = sell opportunity
→ Price breaking ABOVE 200 EMA = trend change warning
──────────────────────────────────────────────────────────────
⚠️ RULE #3: AVOID THE CHOP ZONE
When price keeps crossing the 200 EMA (color changing frequently):
→ Market is RANGING, not trending
→ Stay out or reduce position size
→ Wait for a clear trend to establish
──────────────────────────────────────────────────────────────
STEP 4: TRADING STRATEGIES
──────────────────────────────────────────────────────────────
✅ STRATEGY #1: TREND FOLLOWING (PULLBACK ENTRIES)
Wait for GREEN zone (bullish trend):
1. Price pulls back toward the 200 EMA (blue line)
2. Look for bullish reversal candle near 200 EMA
3. Enter LONG
4. Stop below 200 EMA
5. Hold while in green zone
Example:
• Chart shows green shading
• Price dips to 200 EMA and bounces
• Enter long at bounce confirmation
• Stop 5-10 pips below 200 EMA
• Exit when price crosses back below 200 EMA (turns red)
──────────────────────────────────────────────────────────────
✅ STRATEGY #2: BREAKOUT TRADING (TREND CHANGE)
Watch for color change (crossover):
GREEN → RED (bearish reversal):
1. Price crosses below 200 EMA
2. Fill turns from green to red
3. Enter SHORT on next pullback to 200 EMA
4. Stop above 200 EMA
5. Ride the new downtrend
RED → GREEN (bullish reversal):
1. Price crosses above 200 EMA
2. Fill turns from red to green
3. Enter LONG on next pullback to 200 EMA
4. Stop below 200 EMA
5. Ride the new uptrend
──────────────────────────────────────────────────────────────
✅ STRATEGY #3: HIGHER TIMEFRAME FILTER
Use this indicator on a HIGHER timeframe as a filter:
Example for day trading:
• Add indicator to DAILY chart
• Check the color: Green or Red?
• Switch back to your trading timeframe (5m, 15m, etc.)
• Only take trades in the direction of daily trend
If daily = GREEN → Only take longs on lower timeframes
If daily = RED → Only take shorts on lower timeframes
This keeps you aligned with the bigger trend!
──────────────────────────────────────────────────────────────
STEP 5: REAL TRADING EXAMPLES
──────────────────────────────────────────────────────────────
📊 EXAMPLE #1: LONG ENTRY IN UPTREND
Chart: SPY on 1-hour timeframe
Indicator: Green fill (price above 200 EMA)
Setup:
• Price at 450, 200 EMA at 445
• Green shading shows bullish trend
• Price pulls back to 446 (near 200 EMA)
• Bullish hammer candle forms at 200 EMA
Trade:
→ Enter LONG at 446.50
→ Stop at 444.50 (below 200 EMA)
→ Target: Previous high at 452
→ Risk: 2 points | Reward: 5.50 points = 2.75:1 R/R
──────────────────────────────────────────────────────────────
📊 EXAMPLE #2: SHORT ENTRY IN DOWNTREND
Chart: EUR/USD on 4-hour timeframe
Indicator: Red fill (price below 200 EMA)
Setup:
• Price at 1.0850, 200 EMA at 1.0900
• Red shading shows bearish trend
• Price rallies to 1.0895 (near 200 EMA)
• Bearish rejection candle at 200 EMA
Trade:
→ Enter SHORT at 1.0890
→ Stop at 1.0910 (above 200 EMA)
→ Target: 1.0820 (recent support)
→ Risk: 20 pips | Reward: 70 pips = 3.5:1 R/R
──────────────────────────────────────────────────────────────
📊 EXAMPLE #3: AVOID THE CHOP
Chart: Bitcoin on 15-minute timeframe
Indicator: Color keeps changing (green/red/green/red)
Observation:
• Price crossed 200 EMA 4 times in 2 hours
• No clear trend established
• Whipsaw action
Action:
→ STAY OUT - wait for clear trend
→ Check higher timeframe for direction
→ Come back when one color dominates
──────────────────────────────────────────────────────────────
STEP 6: PRO TIPS
──────────────────────────────────────────────────────────────
💡 **Combine with Price Action**
Don't just enter because it's green - wait for bullish candle patterns (hammer, engulfing, etc.) at the 200 EMA for high-probability setups.
💡 **Respect the 200 EMA**
The longer price stays on one side, the stronger that side becomes. A stock green for months has strong bullish momentum.
💡 **Watch Volume at Crossovers**
When price crosses the 200 EMA with HIGH volume = strong signal
Low volume crossover = might be false breakout
💡 **Use Multiple Timeframes**
• Daily chart = overall trend direction
• 4H chart = swing trade setups
• 1H chart = day trade entries
Always align smaller timeframe trades with larger timeframe color!
💡 **Strongest Setups = Clean Trends**
Best trades happen when:
• Chart stays ONE color for extended period
• Price respects 200 EMA as support/resistance
• No frequent crossovers
──────────────────────────────────────────────────────────────
COMMON QUESTIONS
──────────────────────────────────────────────────────────────
❓ "What if price crosses the 200 EMA frequently?"
→ That's a ranging market. Stay out or trade smaller size. Wait for a clear trend.
❓ "Can I change the colors?"
→ Not in this version, but green/red is universal and intuitive.
❓ "Does this work on all timeframes?"
→ Yes! But longer timeframes (4H, Daily) tend to give cleaner signals.
❓ "Should I always use the 200 EMA?"
→ The 200 is the institutional standard. Stick with it for consistency.
❓ "What about the 50 or 20 EMA?"
→ You can add those separately. This indicator focuses on the proven 200 EMA.
──────────────────────────────────────────────────────────────
THE GOLDEN RULE
──────────────────────────────────────────────────────────────
🟢 GREEN = GO LONG (or stay long)
🔴 RED = GO SHORT (or stay short)
🔄 FREQUENT CHANGES = STAY OUT
It's that simple. The trend is your friend - this indicator just makes it impossible to miss!
──────────────────────────────────────────────────────────────
Happy Trading! 📈
──────────────────────────────────────────────────────────────
Wyckoff Dual WaveBased on the Wyckoff Method this indicator identifies small and large wave structures and generates trend following signals. It uses dual moving averages Small Wave MA and Large Wave MA to analyze wave cycles combined with volume confirmation KD stochastic and double top and bottom patterns for entry and exit signals.
Key featuresDual wave analysis for small and large timeframes. Customizable MA periods and trend confirmation rules including 3 Day Breakaway and Close Confirmation. Volume based signal validation and KD oscillator filters. Visual wave lines trend colors and clear buy and sell labels such as Long Short DBOT DTOP V BUY and V SELL.
Adjust MA periods PCT factors and signal toggles to fit different assets and timeframes. Ideal for Wyckoff focused traders seeking structured wave and trend analysis. Note For optimal use combine with price action and market context.
No suitable analytical tools for Wyckoff waves have been found so far, and most of the indicator’s concepts are derived from Wyckoff’s book The Tape Reading and Market Tactics.
The traditional Wyckoff Theory focuses on wave analysis and daily timeframe analysis, with waves based on percentage changes. This indicator is mainly designed for intraday trading. I developed it on 15-second charts, and it can also be applied to 1-minute, 3-minute, 5-minute and other short timeframes. For Wyckoff wave analysis on the daily timeframe, I will specifically develop a dedicated percentage-based daily Wyckoff wave tool in the future.
Wyckoff waves emphasize dynamic analysis. It is recommended to use this indicator together with support and resistance tools for dynamic analysis. Observe the price behavior when it touches support or resistance levels to decide the next trading move.
Candle Strength Analyzer by The Ultimate Bull Run# Candle Strength Analyzer
## 📊 Complete Beginner's Guide
---
### 🎯 What This Indicator Does
The **Candle Strength Analyzer** measures how "strong" or "weak" each candlestick is and displays a **score from 0 to 100** above or below every candle.
- **Green numbers** = Bullish (price went UP)
- **Red numbers** = Bearish (price went DOWN)
- **Gray numbers** = Doji (price barely moved)
**Higher score = Stronger candle = More reliable signal**
---
### 🕯️ Understanding Candlesticks (The Basics)
If you're new to trading, here's what a candlestick shows:
```
│ ← Upper Wick (prices that were rejected)
│
┌───┐
│ │ ← Body (the "real" price movement)
│ │ • Green/White body = Price went UP (Bullish)
│ │ • Red/Black body = Price went DOWN (Bearish)
└───┘
│
│ ← Lower Wick (prices that were rejected)
```
**Key Terms:**
- **Open**: The price when the candle started
- **Close**: The price when the candle ended
- **High**: The highest price during the candle
- **Low**: The lowest price during the candle
- **Body**: The rectangle between Open and Close
- **Wick/Shadow**: The thin lines above and below the body
---
## 📐 The 4 Components of Candle Strength
This indicator combines **4 measurements** to calculate the final strength score. Let's understand each one:
---
### 1️⃣ Body Ratio (30% of score)
**What it is:**
The percentage of the candle that is "body" versus "wicks."
**Formula:**
```
Body Ratio = Size of Body ÷ Total Candle Size × 100
```
**What it tells you:**
- **High Body Ratio (70-100%)**: Bulls or bears were in FULL control. The price moved in one direction and STAYED there. This is strong.
- **Low Body Ratio (0-30%)**: There was a fight. Price moved up AND down but ended up roughly where it started. This is weak/indecisive.
**Visual Example:**
```
Strong Candle (90% body): Weak Candle (20% body):
│ │
┌───┐ │
│ │ ┌─┴─┐
│ │ ← Mostly body │ │ ← Tiny body
│ │ └─┬─┘
└───┘ │
│ │
```
**How to interpret:**
| Body Ratio | Meaning |
|------------|---------|
| 90-100% | **Marubozu** - Extremely strong, full commitment |
| 70-90% | **Strong** - Clear winner (bulls or bears) |
| 40-70% | **Normal** - Typical market activity |
| 10-40% | **Weak** - Significant indecision |
| 0-10% | **Doji** - Complete indecision, no winner |
---
### 2️⃣ Close Position Score (25% of score)
**What it is:**
WHERE the candle closed within its range (high to low).
**What it tells you:**
- For a **bullish (green) candle**: Closing near the HIGH means buyers were still eager at the end = STRONG
- For a **bearish (red) candle**: Closing near the LOW means sellers were still eager at the end = STRONG
**Visual Example:**
```
Strong Bullish: Weak Bullish:
(closes near high) (closes near middle)
┌───┐ ← Close here │
│ │ ┌─┴─┐ ← Close here
│ │ │ │
│ │ │ │
└───┘ └───┘
│ │
```
**Why it matters:**
If price went UP but then sellers pushed it back down before the candle closed, that's a sign of weakness. The bulls couldn't hold their ground.
**How to interpret:**
| Close Position | For Bullish Candle | For Bearish Candle |
|----------------|-------------------|-------------------|
| 80-100% | Strong (near high) | Weak (near high) |
| 50-80% | Moderate | Moderate |
| 20-50% | Weak | Moderate |
| 0-20% | Very Weak (near low) | Strong (near low) |
---
### 3️⃣ Relative Volume - RVOL (25% of score)
**What is Volume?**
Volume is the NUMBER of shares/contracts traded during that candle. Think of it as "how many people participated."
**What is RVOL?**
RVOL compares TODAY'S volume to the AVERAGE volume.
**Formula:**
```
RVOL = Current Volume ÷ Average Volume (last 20 candles)
```
**What it tells you:**
- **RVOL = 1.0**: Normal activity (same as average)
- **RVOL = 2.0**: DOUBLE the normal activity (2x more traders involved)
- **RVOL = 0.5**: HALF the normal activity (fewer traders involved)
**Why it matters:**
A big price move with LOW volume is suspicious - it might not last.
A big price move with HIGH volume is confirmed - many traders agree.
**Think of it like voting:**
- High volume = Many people voted for this direction
- Low volume = Only a few people voted, decision might change
**How to interpret:**
| RVOL | Meaning | Signal Quality |
|------|---------|----------------|
| 2.0+ | Very High - Institutional activity likely | ⭐⭐⭐ Excellent |
| 1.5-2.0 | High - Significant interest | ⭐⭐ Good |
| 1.0-1.5 | Above Average | ⭐ Acceptable |
| 0.7-1.0 | Below Average | ⚠️ Caution |
| < 0.7 | Low - Lack of interest | ❌ Unreliable |
---
### 4️⃣ Size vs ATR (20% of score)
**What is ATR?**
ATR stands for "Average True Range." It measures how much the price TYPICALLY moves.
**What this component measures:**
How big is THIS candle compared to how big candles USUALLY are?
**Formula:**
```
ATR Ratio = This Candle's Size ÷ Average Candle Size (ATR)
```
**What it tells you:**
- **ATR Ratio = 2.0**: This candle is TWICE as big as normal = Significant move
- **ATR Ratio = 1.0**: This candle is normal sized
- **ATR Ratio = 0.5**: This candle is HALF the normal size = Minor move
**Why it matters:**
A 50-point move in a stock that normally moves 100 points is small.
A 50-point move in a stock that normally moves 20 points is HUGE.
Context matters!
**How to interpret:**
| ATR Ratio | Meaning |
|-----------|---------|
| 2.0+ | **Expansion** - Unusually large move, potential breakout |
| 1.5-2.0 | **Large** - Significant momentum |
| 1.0-1.5 | **Above Average** - Notable move |
| 0.5-1.0 | **Normal** - Typical movement |
| < 0.5 | **Small** - Insignificant, might be noise |
---
## 🧮 How the Final Score is Calculated
The indicator combines all 4 components with these weights:
```
Final Score = (Body Ratio × 30%) +
(Close Position × 25%) +
(RVOL Score × 25%) +
(Size Score × 20%)
```
**Result: A score from 0 to 100**
---
## 📊 Understanding the Strength Score
| Score | Classification | What It Means | Should You Trade It? |
|-------|---------------|---------------|---------------------|
| **70-100** | 🟢 STRONG | High conviction move, reliable signal | ✅ Yes - Good setup |
| **40-70** | 🟡 MODERATE | Average move, needs confirmation | ⚠️ Maybe - Add other indicators |
| **0-40** | 🔴 WEAK | Low conviction, unreliable | ❌ No - Wait for better setup |
---
## 🏷️ Special Pattern Markers
The indicator also detects special candlestick patterns:
### ⚡ Power Candle
**Requirements:**
- Body Ratio > 70% (strong body)
- RVOL > 1.5 (high volume)
- Close Position > 80% (closes near the extreme)
**What it means:** The BEST possible signal. Everything aligns perfectly.
### Ⓜ️ Marubozu
**Requirements:**
- Body Ratio > 90% (almost no wicks)
**What it means:** Complete dominance by bulls or bears. Very strong continuation signal.
### ◆ High Volume Doji
**Requirements:**
- Doji candle (tiny body)
- High volume
**What it means:** Many traders are fighting, but no one won. Often signals a REVERSAL is coming.
---
## ⚙️ Settings Explained
### Volume Settings
| Setting | Default | What It Does |
|---------|---------|--------------|
| Volume Lookback Period | 20 | How many candles to average for "normal" volume |
| RVOL Threshold | 1.5 | What counts as "high" volume (1.5 = 50% above average) |
### ATR Settings
| Setting | Default | What It Does |
|---------|---------|--------------|
| ATR Period | 14 | How many candles to calculate average movement |
| ATR Multiplier | 1.5 | What counts as a "large" candle |
### Strength Thresholds
| Setting | Default | What It Does |
|---------|---------|--------------|
| Strong Candle Threshold | 70 | Score needed to be "strong" |
| Weak Candle Threshold | 30 | Score below this is "weak" |
### Label Filter (Important!)
TradingView limits indicators to **500 labels maximum**. Use filters to see more history:
| Filter Mode | Shows | Best For |
|-------------|-------|----------|
| All Candles | Every single candle | Short-term charts (5min, 15min) |
| Strong Only (70+) | Only strong candles | Longer history, key signals only |
| Moderate+ (40+) | Moderate and strong | Balance of detail and history |
| Custom Minimum | Your choice | Full control |
**Tip:** On daily charts, use "Strong Only" to see months of history instead of just a few weeks.
### Label Settings
| Setting | What It Does |
|---------|--------------|
| Label Size | tiny / small / normal / large |
| Show Decimal Places | Show "72.5" instead of "73" |
| Label Style | With background bubble OR just text |
---
## 📖 How to Read the Info Table
The table in the corner shows details for the CURRENT (most recent) candle:
| Row | Meaning |
|-----|---------|
| **Candle Strength** | The final score (0-100) |
| **Direction** | BULLISH / BEARISH / DOJI |
| **Body Ratio** | Percentage of candle that is body |
| **Close Position** | Where it closed (0-100) |
| **Upper Wick** | Size of upper wick as % |
| **Lower Wick** | Size of lower wick as % |
| **RVOL** | Current volume vs average (1.5x = 50% above average) |
| **Size/ATR** | Candle size vs average size |
| **Classification** | STRONG / MODERATE / WEAK |
| **Vol Confirmed** | Is volume above threshold? |
| **Pattern** | Special pattern detected |
---
## 🎓 How to Use This Indicator
### Step 1: Add to Chart
1. Open Pine Editor in TradingView
2. Paste the code
3. Click "Add to Chart"
### Step 2: Adjust Filter (if needed)
- If you see "max labels reached," change filter to "Strong Only (70+)"
- This lets you see more candles in history
### Step 3: Look for Strong Signals
Focus on candles with:
- ✅ Score **70+** (bright green or red)
- ✅ **RVOL > 1.5** (confirmed by volume)
- ✅ Special markers (⚡, M, ◆)
### Step 4: Avoid Weak Signals
Be careful with candles that have:
- ❌ Score **below 40** (muted colors)
- ❌ **RVOL < 1.0** (no volume confirmation)
- ❌ Large wicks (rejection happened)
---
## 💡 Trading Tips for Beginners
### ✅ DO:
1. **Wait for strong candles (70+)** before entering trades
2. **Confirm with volume** - Look for RVOL > 1.5
3. **Use at support/resistance levels** - Strong candles at key levels are more meaningful
4. **Combine with other indicators** - RSI, MACD, or moving averages
5. **Practice on demo first** - Learn to recognize strong vs weak candles
### ❌ DON'T:
1. **Trade every candle** - Not all candles are worth trading
2. **Ignore volume** - A strong candle with low volume is suspicious
3. **Fight the trend** - Strong bearish candles in an uptrend might just be pullbacks
4. **Over-leverage** - Even strong signals can fail
---
## 📝 Quick Reference Cheat Sheet
```
STRONG CANDLE CHECKLIST:
□ Score 70+
□ RVOL > 1.5
□ Body Ratio > 60%
□ Close Position > 75% (bullish) or < 25% (bearish)
□ At key support/resistance level
WEAK CANDLE WARNING SIGNS:
□ Score < 40
□ RVOL < 0.7
□ Large wicks (> 30%)
□ Doji pattern
□ Small candle (ATR Ratio < 0.5)
```
---
## ⚠️ Important Disclaimers
1. **No indicator is 100% accurate** - Always use stop losses
2. **Past performance ≠ future results** - Markets change
3. **This is a tool, not a strategy** - Combine with other analysis
4. **Practice first** - Use paper trading before real money
---
## 🔔 Alerts Available
Set alerts for:
- Strong Bullish Candle (with volume confirmation)
- Strong Bearish Candle (with volume confirmation)
- Power Candle detected
- Marubozu detected
- High Volume Doji detected
---
## ❓ FAQ
**Q: Why are some candles missing labels?**
A: TradingView limits indicators to 500 labels. Use filters to see more history.
**Q: The label colors are hard to see. Can I change them?**
A: Yes! Go to Settings → Colors and customize all colors.
**Q: Should I only trade strong candles?**
A: Strong candles are MORE reliable, but not guaranteed. Always use proper risk management.
**Q: What timeframe works best?**
A: Works on all timeframes. Higher timeframes (4H, Daily) tend to have more reliable signals.
**Q: Can I use this for crypto/forex/stocks?**
A: Yes! This indicator works on any market with candlestick data and volume.
---
## 📚 Glossary
| Term | Definition |
|------|------------|
| **Bullish** | Price is going UP / Buyers are winning |
| **Bearish** | Price is going DOWN / Sellers are winning |
| **Doji** | Candle where open and close are nearly equal (indecision) |
| **Marubozu** | Candle with no wicks (full body) |
| **RVOL** | Relative Volume - current volume vs average |
| **ATR** | Average True Range - typical price movement |
| **Wick/Shadow** | The thin lines above/below the candle body |
| **Support** | Price level where buyers tend to step in |
| **Resistance** | Price level where sellers tend to step in |
| **Breakout** | When price moves beyond support/resistance |
---
**Happy Trading! 📈**
*Remember: The best traders are patient traders. Wait for strong setups.*
Americana Crypto Retail FOMO IndexRetail FOMO Index
Overview
The Retail FOMO Index is a sentiment indicator designed to help identify when retail investors are piling into the crypto market — often a sign that the market may be approaching overheated conditions. Historically, periods of extreme retail enthusiasm have coincided with local and macro tops, making this a useful tool for gauging market temperature.
What It Measures
This indicator combines two real-time data sources to create a composite "FOMO score" scaled from 0 to 100:
Coinbase Premium (50% weight)
This measures the price difference between Bitcoin on Coinbase (USD) and Binance (USDT). When US retail investors are aggressively buying, Coinbase often trades at a premium to other exchanges. A rising premium suggests increased retail demand, while a negative premium can indicate selling pressure or reduced interest.
Coinbase Stock Volume (50% weight)
This tracks the trading volume of COIN (Coinbase's stock on NASDAQ) relative to its recent average. When retail interest in crypto surges, Coinbase stock volume tends to spike as both crypto traders and traditional investors react to market momentum. The indicator calculates a Z-score to identify when volume is unusually high or low compared to its norm.
Settings
The indicator includes adjustable parameters:
Weights: Adjust the balance between Coinbase Premium and COIN Volume if you find one component more relevant
Lookback Period: Controls how much historical data is used for normalization (default: 50 periods)
Smoothing: Reduces noise in the final output (default: 7 periods)
Threshold Levels: Customize the boundaries for each zone
Display Options: Toggle component lines and background fills on/off
Important Notes
The COIN volume data begins in April 2021 (Coinbase IPO), so the indicator does not have data prior to that date
The Coinbase Premium is calculated using BTC regardless of what asset chart you apply it to — BTC tends to be the leading indicator for broad retail sentiment
This indicator works best on daily, weekly, and monthly timeframes
This is a sentiment tool, not a trade signal — use it alongside your existing analysis to add context
SB Scanner (V2)⸻
📊 Stacey Burke Signal Day Scanner
This script is a multi-instrument signal day scanner scanner inspired by concepts taught by Stacey Burke. It is designed to help traders quickly identify contextual price behaviors across multiple futures, crypto, or index markets from a single chart.
The scanner evaluates a customizable list of symbols and displays results in a clean, configurable table on the chart.
⸻
🔍 What the Scanner Detects
For each symbol, the script analyzes signal days and highlights:
• FRD / FGD
First Red Days and First Green Days based on multi-day price behavior.
• 3-Day Breakout Sequences (3DL / 3DS)
Identifies consecutive directional expansion over three sessions.
• Inside / Outside Days (ID / OD)
Detects compression and expansion patterns in daily ranges.
• CIB (Close-In-Breakout)
Flags closes near the prior day’s range extremes.
• 3-Day Cycle Continuation
Highlights potential Day-3 directional continuation scenarios.
All pattern columns can be individually toggled on or off to keep the table focused on what matters most to you.
⸻
🧭 How It’s Meant to Be Used
This scanner is not a trade signal generator. It is a context and awareness tool intended to:
Help traders monitor multiple markets simultaneously
Quickly spot structural conditions worth deeper analysis
Support discretionary decision-making within a broader trading plan
It works best when combined with session context, key levels, and execution logic chosen by the trader.
⸻
🎨 Customization & Display
Adjustable table position (top/bottom, left/center/right)
Custom colors for:
Background
Text
Current chart symbol
Supports up to 24 symbols
Designed to be visually clean and easy on the eyes
⸻
⚠️ Disclaimer
This script is provided for educational and informational purposes only.
It does not constitute financial advice, investment recommendations, or trade signals.
All trading decisions and risk management remain the responsibility of the user.
⸻
Universal Po3 Profiler [Pro +] | cephxsUNIVERSAL Po3 PROFILER 🧪
All concepts used in the development of this indicator are open source and available to all on youtube.
Credits go out to Michael J. H. (ICT) as the one putting it out there for all.
A refined approach to multi-timeframe structural analysis. Clean visuals. Precise signals. Nothing more than what matters.
OVERVIEW
Built for traders who value clarity over complexity, this profiler distills institutional price delivery concepts into a visually cohesive overlay. It maps higher timeframe structure directly onto your chart while detecting key divergence patterns across correlated assets.
The design philosophy is intentional minimalism—every element earns its place on your chart. No redundant indicators. No visual noise. Just the structural context you need to frame your trading decisions.
📷 Full indicator overview showing HTF candles, CISD lines, and SMT divergences on a 5m YM chart
CORE FEATURES
🧪 Multi-Timeframe Profiling
Automatically profiles up to three higher timeframes based on your chart's resolution. The system intelligently selects appropriate HTF pairings—or lets you override with manual control. 0 stress, minimal input overload.
Auto Mode: Adapts HTF selection to your chart timeframe
Profiler Source: Choose which HTF defines your structural boundaries
Directional Bias: Filter profiling to bullish or bearish setups only
📷 Side-by-side comparison; Auto mode on 5m chart vs 15m chart showing different HTF selections
🔀 SMT DIVERGENCE DETECTION
Identifies Smart Money divergences between correlated assets in real-time. When the primary asset makes a structural move that correlated pairs fail to confirm by moving in-sync, the system flags potential reversals.
Tracks divergences across multiple HTF sessions
Supports both normal and inverse correlations
Independent chart and HTF candle visualization
Automatic invalidation when divergence resolves
Configurable asset pairs with correlation library integration that is updated regularly
📷 SMT divergence example, ES vs YM showing bearish divergence at session highs
📐 CISD DETECTION & FIBONACCI PROJECTIONS
Change in State of Delivery (CISD) marks the moment price starts to deliver in the opposing direction of where it previously went. The system identifies these pivotal transitions and projects optional Fibonacci extensions to map potential price targets.
Automatic stretch calculation from opposing candle sequences
Confirmation-based detection (not predictive)
Fibonacci projections with customizable levels (-1 to -4.5)
Size filtering to eliminate noise on smaller moves
Visual invalidation when structure breaks
📷 Confirmed CISD with Fibonacci projection levels (other features disabled to highlight)
📷 Multiple CISDs showing bullish and bearish examples simultaneously
🎯 MANIPULATION AREA PROFILING
Detects sweep-and-reversal sequences within HTF candle boundaries. The profiling system identifies when price sweeps the immediate previous candle's extreme then confirms directional commitment after a candle close.
How the manipulation area is calculated (it is not a magic level, it's a simple division : ) )
For C3 : is measured from the Open of C3 to the quadrant C.E of C2
For C4 : is measured from the Open of C4 to the Upper Quadrant (25/75) of C3.
All Based on openly-sourced ICT Concepts
Po3 area visualization on profiler HTF
Sweep line tracking at structural levels
C1/C2/C3 phase progression
Bias filtering for directional focus
📷 Manipulation area highlighting Po3 zone with sweep lines
HTF CANDLE VISUALIZATION
Projects higher timeframe candles to the right of your chart, providing structural context without cluttering price action. Full credit for the base logic used in building this goes to @fadizeidan.
Up to 3 HTF layers with independent candle counts
PSP (Precision Swing Point) divergence detection on candles
Clean, adjustable styling
Session labels with countdown timers
INPUTS
Directional Bias
Po3 Profiling Bias: Auto / Bullish / Bearish — filters manipulation area detection
CISD/SSMT Bias: Auto / Bullish / Bearish — filters divergence detection
HTF Configuration
Auto Mode: Enabled by default. Automatically configures HTF timeframes
HTF 1/2/3: Manual timeframe selection when Auto Mode disabled
Profiler Source: Which HTF defines profiling boundaries
SMT Settings
Enable SMT: Master toggle for divergence detection
Show on Chart: Render divergence lines on price chart
Show on HTF Candles: Render divergence lines on HTF candle visuals
Asset Selection: Configure correlated pairs and inversion settings
CISD Settings
Show CISD: Enable change in state detection
Fibonacci Projections: Enable extension level plotting
Level Configuration: Toggle individual fib levels (-1 through -4.5)
Profiler Status Bar
Position: 9 positions including center options
Styling: Size, bold toggle, custom colors
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RECOMMENDED USAGE
Start with Auto Mode enabled to learn the HTF relationships
Set your directional bias if you have a higher timeframe thesis
Watch for SMT divergences at session extremes
Use CISD confirmations to identify structural shifts
Reference Fibonacci projections for potential targets
Optimal Timeframes:
Scalping: 1m-3m charts (profiles to 15m-90m)
Intraday: 5m-15m charts (profiles to 1H-4H / Optionally 6H)
Swing: 1H-4H charts (profiles to Daily-Weekly)
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CORRELATED ASSETS
The SMT system works with major correlations including:
Index Futures: CME_MINI:ES1! , CME_MINI:NQ1! , CBOT_MINI:YM1!
Forex Majors: FOREXCOM:EURUSD , FOREXCOM:GBPUSD , TVC:DXY
Crypto: BINANCE:BTCUSDT , BINANCE:ETHUSDT
Crypto Futures: CME:BTC1! , CME:ETH1!
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NOTES
This version does not include alert conditions. Visual-first design.
It will be easier to understand if you have a preliminary knowledge of the concepts beforehand
This is not a learning instrument on it's own and could have bugs, Know it for yourself so you can identify bugs if any.
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DISCLAIMER
This indicator is provided for educational and analytical purposes only. It does not constitute financial advice, and no representation is made regarding future performance.
Trading involves substantial risk of loss. Always conduct your own analysis and use proper risk management. Past structural patterns do not guarantee future price behavior.
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CREDITS
Inspiration and HTF candle plotting boilerplate: @fadizeidan
Asset correlation library: fstarcapital
Development: cephxs & fstarcapital
---
CHANGELOG
v1.0: Initial Pro+ release — SMT divergences, CISD detection, multi-HTF Po3 profiling
ADR % RangesThis indicator is designed to visually represent percentage lines from the open of the day. The % amount is determined by X amount of the last days to create an average...or Average Daily Range (ADR).
1. ADR Percentage Lines: The core function of the script is to apply lines to the chart that represent specific percentage changes from the daily open. It first calculates the average over X amount of days and then displays two lines that are 1/3rd of that average. One line goes above the other line goes below. The other two lines are the full "range" of the average. These lines can act as boundaries or targets to know how an asset has moved recently. *Past performance is not indicative of current or future results.
The calculation for ADR is:
Step 1. Calculate Today's Range = DailyHigh - DailyLow
Step 2. Store this average after the day has completed
Step 3. Sum all day's ranges
Step 4. Divide by total number of days
Step 5. Draw on chart
2. Customizable Inputs: Users have the flexibility to customize the script through various inputs. This includes the option to display lines only for the current trading day (`todayonly`), and to select which lines are displayed. The user can also opt to show a table the displays the total range of previous days and the average range of those previous days.
3. No Secondary Timeframe: The ADR is computed based on whatever timeframe the chart is and does not reference secondary periods. Therefore the script cannot be used on charts greater than daily.
This script is can be used by all traders for any market. The trader might have to adjust the "X" number of days back to compute a historical average. Maybe they only want to know the average over the past week (5 days) or maybe the past month (20 days).
D_H_L_OIndicator Name: D_H_L_O
Primary Function:
This indicator is designed to display buying pressure, selling pressure, and other key metrics derived from the daily candle on a TradingView chart. It helps you analyze market momentum, buying and selling forces, and price spreads.
Features Overview:
Basic Calculations from Daily Candle:
dailyHigh, dailyLow, dailyOpen, dailyClose: Represent the high, low, open, and close prices of the daily candle.
dailySpread: The difference between the high and low prices of the daily candle.
Buying and Selling Pressure:
Buying Pressure (high_open): The difference between the daily high and the open price.
Selling Pressure (low_open): The absolute difference between the daily low and the open price (displayed as a negative value).
deltaVolume: The net difference between buying and selling pressure.
Color and Visuals:
Blue (buyingColor): Indicates buying pressure for green (bullish) days.
Orange (sellingColor): Indicates selling pressure for red (bearish) days.
Displays bars with transparency to distinguish buying and selling forces.
Neutral Reference Line:
A horizontal line at 0 for quick visual comparison of buying and selling forces.
Labels for Key Information:
Displays values of buying pressure, selling pressure, and daily candle spread directly on the chart at corresponding bar positions.
Includes the weekday name (currentWeekday) for additional time context.
Historical Statistics:
Highest and lowest values of buying and selling pressure across the dataset.
Average buying and selling pressure.
Displays statistical summaries (like maximum pressure values) as labels on the last bar of the chart.
Benefits:
Detailed Market Pressure Visualization: Provides a clear view of the forces driving market movement each day.
Historical Context: Helps analyze historical trends in buying and selling pressures over time.
Decision-Making Support: Use pressure metrics to gauge market momentum and assess potential trends.
How to Use:
Copy and paste the script into TradingView (create a new indicator using Pine Script v5).
Add the indicator to your chart on any timeframe to observe daily candle metrics.
Customize colors, transparency, or other parameters to suit your trading style.
This indicator is ideal for traders who want to analyze price momentum and make decisions based on daily market behavior.
Humble Student OB/OS Trifecta indicatorAfter reading Cam Hui's blog post about his "Trifecta" bottom spotting model I thought I'd try my hand at scripting it as an indicator. The results are pretty close to what he describes. Though the data TradingView feeds me doesn't seem to be identical to what he's using on StockCharts.com the results are close enough that I will call the effort a success worth publishing.
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.
MTF 20 SMA Table - DXY**MTF 20 SMA Table - Multi-Timeframe Trend Analysis Dashboard**
**Overview:**
This indicator provides a comprehensive multi-timeframe analysis dashboard that displays the relationship between price and the 20-period Simple Moving Average (SMA) across four key timeframes: 15-minute, 1-hour, 4-hour, and Daily. It's designed to help traders quickly identify trend alignment and potential trading opportunities across multiple timeframes at a glance. It's definitely not perfect but has helped me speed up my backtesting efforts as it's worked well for me eliminating flipping back and forth between timeframes excpet when I have confluence on the table, then I check the HTF.
**How It Works:**
The indicator creates a table overlay on your chart showing three critical metrics for each timeframe:
1. **Price vs SMA (Row 1):** Shows whether price is currently above (bullish) or below (bearish) the 20 SMA
- Green = Price Above SMA
- Red = Price Below SMA
2. **SMA Direction (Row 2):** Indicates the trend direction of the SMA itself over a lookback period
- Green (↗ Rising) = Uptrend
- Red (↘ Falling) = Downtrend
- Gray (→ Flat) = Ranging/Consolidation
3. **Strength (Row 3):** Displays the distance between current price and the SMA in pips
- Purple background = Strong move (>50 pips away)
- Orange background = Moderate move (20-50 pips)
- Gray background = Weak/consolidating (<20 pips)
- Text color: Green for positive distance, Red for negative
**Key Features:**
- **Customizable Table Position:** Place the table anywhere on your chart (9 position options)
- **Adjustable SMA Lengths:** Modify the SMA period for each timeframe independently (default: 20)
- **Direction Lookback Settings:** Fine-tune how far back the indicator looks to determine SMA direction for each timeframe
- **Flat Threshold:** Set the pip threshold for determining when an SMA is "flat" vs trending (default: 5 pips)
- **DXY Optimized:** Calculations are calibrated for the US Dollar Index (1 pip = 0.01)
**Best Use Cases:**
1. **Trend Alignment:** Identify when multiple timeframes align in the same direction for higher probability trades
2. **Divergence Spotting:** Detect when lower timeframes diverge from higher timeframes (potential reversals)
3. **Entry Timing:** Use lower timeframe signals while higher timeframes confirm overall trend
4. **Strength Assessment:** Gauge how extended price is from the mean (SMA) to avoid overextended entries
**Settings Guide:**
- **SMA Settings Group:** Adjust the SMA period for each timeframe (15M, 1H, 4H, Daily)
- **SMA Direction Group:** Control lookback periods to determine trend direction
- 15M: Default 5 candles
- 1H: Default 10 candles
- 4H: Default 15 candles
- Daily: Default 20 candles
- **Flat Threshold:** Set sensitivity for "flat" detection (lower = more sensitive to ranging markets)
**Trading Strategy Examples:**
1. **Trend Following:** Look for all timeframes showing the same direction (all green or all red)
2. **Pullback Trading:** When Daily/4H are green but 15M/1H show red, wait for lower timeframes to flip green for entry
3. **Ranging Markets:** When multiple SMAs show "flat", consider range-bound strategies
**Important Notes:**
- This is a reference tool only, not a standalone trading system
- Always use proper risk management and combine with other analysis methods
- Best suited for trending instruments like indices and major forex pairs
- Calculations are optimized for DXY but can be used on other instruments (pip calculations may need adjustment)
**Credits:**
Feel free to modify and improve this code! Suggestions for enhancements are welcome in the comments.
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**Installation Instructions:**
1. Add the indicator to your TradingView chart
2. Adjust the table position via settings to avoid overlap with price action
3. Customize SMA lengths and lookback periods to match your trading style
4. Monitor the table for timeframe alignment and trend confirmation
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This indicator is published as open source for the community to learn from and improve upon. Happy trading! 📈
BTC Backwardation SearcherThis Pine Script code is a custom indicator named "BTC Backwardation Searcher" designed for the TradingView platform. The indicator aims to identify and visualize the price difference between two Bitcoin futures contracts: CME:BTC1! and CME:BTC2!.
Here's a breakdown of the code:
1. The script fetches the daily close prices of CME:BTC1! and CME:BTC2! using the security() function.
2. It calculates the percentage price difference between the two contracts using the formula: (btc1Price - btc2Price) / btc2Price * 100.
3. The script also calculates the price difference for the previous two days (2 days ago and 3 days ago) using the same formula.
4. Two conditions are defined:
(1) dailyGreenCondition: If the price difference is greater than or equal to 0.3% for three
consecutive days, including the current day and the previous two days.
(2) dailyRedCondition(commented): If the price difference is less than or equal to -1% for three consecutive days, including the current day and the previous two days.
(I commented it out because I don't think it's useful.)
5. The plotshape() function is used to display green triangles on the chart when the dailyGreenCondition is met, and red triangles when the dailyRedCondition is met. These triangles are displayed on the daily, weekly, and monthly timeframes.
The purpose of this indicator is to help traders identify potential trading opportunities based on the price difference between the two Bitcoin futures contracts. The green triangles suggest a bullish scenario where CME:BTC1! is significantly higher than CME:BTC2!, while the red triangles indicate a bearish scenario where CME:BTC2! is significantly lower than CME:BTC1!.
However, it's important to note that this indicator should be used in conjunction with other technical analysis tools and fundamental analysis. Traders should also consider their risk tolerance, investment goals, and market conditions before making any trading decisions based on this indicator.
VSA MTF Dashboard OXEVSA Multi-Timeframe Dashboard
The VSA Multi-Timeframe Dashboard is a professional Volume Spread Analysis (VSA) scanner that detects institutional trading patterns across Daily, H4, and H1 timeframes simultaneously. It identifies when "smart money" (banks, hedge funds, institutions) is accumulating, distributing, or manipulating price, giving you an edge to trade with—not against—the professionals.
Price spread (high to low range)
Volume (trading activity)
Closing price (where the battle ended)
Core Principle: By reading volume and price action together, you can see what smart money is doing before retail traders catch on.The 7 VSA Patterns Detected
🟢 BULLISH PATTERNS (Buy Signals)PatternWhat It Looks LikeWhat It MeansWeightStopping VolumeDown bar + Ultra high volume + Close near highSmart money absorbing panic selling at lows. Strong reversal signal.+10SpringPrice makes new low, then closes back inside rangeLiquidity sweep below support. Bear trap - institutions buying cheap.+9No SupplyDown bar + Low volume + Narrow spreadNo selling pressure from professionals. Supply dried up.+8
🔴 BEARISH PATTERNS (Sell Signals)PatternWhat It Looks LikeWhat It MeansWeightUpthrustPrice makes new high, then closes back inside rangeLiquidity sweep above resistance. Bull trap - institutions selling high.-9No DemandUp bar + Low volume + Narrow spreadNo buying interest from professionals. Weakness at tops.-6
🟡 CONTEXT-DEPENDENT PATTERNSPatternWhat It Looks LikeWhat It MeansWeightClimactic ActionExtreme volume + Wide spreadExhaustion move. Buying climax = bearish. Selling climax = bullish.±7-8Effort vs ResultHigh volume + Narrow spreadSmart money absorption. High effort, little result = hidden weakness/strength.±7How to Read the DashboardTop Section: Current Market State┌──────────────────────────────┐
│ VSA Scanner │
├────┬──────────┬─────┬────────┤
│ TF │ Pattern │ Dir │ Pts │
├────┼──────────┼─────┼────────┤
│ D │ Upthrust │ ↓ │ -27 │ ← Daily trend
│ H4 │ No Supply│ ↑ │ +16 │ ← 4-hour trend
│ H1 │ Spring │ ↑ │ +9 │ ← 1-hour trend
├────┴──────────┴─────┴────────┤
│ ↑ 52% MODERATE BULLISH │ ← OVERALL BIAS
└──────────────────────────────┘Reading the signals:
TF (Timeframe): D = Daily, H4 = 4-hour, H1 = 1-hour
Pattern: Which VSA pattern is detected
Dir (Direction): ↑ = Bullish, ↓ = Bearish
Pts (Points): Weighted score (Daily = 3x, H4 = 2x, H1 = 1x)
Bottom Row = Aggregate Score:
0-50%: WEAK bias
50-75%: MODERATE bias
75-100%: STRONG bias
Bottom Section: Pattern ReferenceQuick reference guide showing all 7 patterns, their detection criteria, bias, and meaning. Always visible for learning.Trading Guidelines✅ HIGH PROBABILITY SETUPS1. Strong Confluence (75%+ Score)
All 3 timeframes aligned in same direction
Action: Aggressive entry in signal direction
Example: Daily Spring + H4 No Supply + H1 Spring = 85% BULLISH → BUY
2. HTF Dominance
Daily and H4 agree, H1 disagrees
Action: Trade with Daily/H4 bias (higher timeframes win)
Example: Daily/H4 bearish, H1 bullish → Wait for H1 to flip bearish, then SELL
3. Spring/Upthrust on Daily
Strongest reversal signals (liquidity sweeps)
Action: Major reversal trade opportunity
Example: Daily Spring after downtrend = significant bottom forming
⚠️ CAUTION ZONES1. Mixed Signals (30-50% Score)
Timeframes conflict
Action: WAIT for alignment or reduce position size
Example: Daily bullish, H4 bearish, H1 bullish = choppy, avoid
2. No Patterns Detected
All timeframes show "-"
Action: Market consolidating, wait for setup
3. Weak Bias (Below 50%)
Low conviction signals
Action: Scalp only or sit out
❌ AVOID
Trading against Daily timeframe (Daily always wins long-term)
Entering during mixed signals
Ignoring No Demand/No Supply (early distribution/accumulation warnings)
Indicator SettingsEssential Settings:SettingDefaultRecommendationDashboard PositionTop RightAdjust to avoid blocking chartLight ModeONTurn OFF if using dark chartsColor CandlesONKeep ON for visual pattern recognitionShow Candle LabelsOFFTurn ON if learning (shows UT, SPR, etc.)Volume Average Length20Don't change unless very experiencedATR Length14Standard setting, leave as isBest PracticesFor Swing Trading (Daily/H4):
Focus on Daily and H4 patterns (ignore H1)
Enter when both align
Use H4 Spring/Upthrust for precise entries
Target: Major support/resistance zones
For Day Trading (H4/H1):
Check Daily bias first (trade WITH it)
Use H4 for trend, H1 for entries
Enter on H1 Spring/Upthrust in direction of H4
Target: Intraday highs/lows
For Scalping (H1 only):
Only trade when H1 shows 70%+ score
Quick entries on Spring/Upthrust
Tight stops (10-15 pips on XAUUSD)
Target: 2:1 risk/reward minimum
Common QuestionsQ: Why does the score change when I switch timeframes?
A: The "bars ago" metric counts in your current chart timeframe. The pattern and bias remain the same, just the time reference changes. Focus on the pattern name and direction, not bars ago.Q: Can patterns repaint?
A: NO. Patterns only confirm after bar close. The dashboard shows live but patterns are stable.Q: What if Daily is bearish but H1 is bullish?
A: Daily ALWAYS wins. The H1 bullish move is likely a pullback in a bearish trend. Wait for H1 to flip bearish for best entries.Q: Should I trade every signal?
A: NO. Only trade when:
Score is 70%+ (strong conviction)
Multiple timeframes align
Pattern makes sense with overall trend
Q: How often do patterns appear?
A: Variable. You might see 2-5 signals per week on Daily, more frequently on H1. Quality over quantity.Quick Reference CardBULLISH SIGNALS TO BUY:
✅ Stopping Volume (strongest)
✅ Spring (liquidity grab)
✅ No Supply (weakness gone)
✅ Score: 70%+ BULLISH
BEARISH SIGNALS TO SELL:
✅ Upthrust (liquidity grab)
✅ No Demand (strength gone)
✅ Climactic Buying (exhaustion)
✅ Score: 70%+ BEARISH
STAY OUT:
❌ Mixed signals (30-50%)
❌ No patterns detected
❌ Timeframes conflicting
Example Trade SetupsPerfect Long Setup:
Daily: Spring ↑ +27 (Liquidity sweep)
H4: No Supply ↑ +16 (No sellers)
H1: Stopping Vol ↑ +10 (Absorption)
Score: 88% STRONG BULLISH
Action: BUY aggressively, target major resistancePerfect Short Setup:
Daily: Upthrust ↓ -27 (Liquidity trap)
H4: No Demand ↓ -12 (No buyers)
H1: Upthrust ↓ -9 (Fake breakout)
Score: 80% STRONG BEARISH
Action: SELL aggressively, target major supportAvoid This Setup:
Daily: No Supply ↑ +24 (Bullish)
H4: Upthrust ↓ -16 (Bearish)
H1: No Demand ↓ -6 (Bearish)
Score: 3% WEAK BULLISH (Mixed!)
Action: WAIT - Conflicting signals
HTF Current/Average RangeThe "HTF(Higher Timeframe) Current/Average Range" indicator calculates and displays the current and average price ranges across multiple timeframes, including daily, weekly, monthly, 4 hour, and user-defined custom timeframes.
Users can customize the lookback period, table size, timeframe, and font color; with the indicator efficiently updating on the final bar to optimize performance.
When the current range surpasses the average range for a given timeframe, the corresponding table cell is highlighted in green, indicating potential maximum price expansion and signaling the possibility of an impending retracement or consolidation.
For day trading strategies, the daily average range can serve as a guide, allowing traders to hold positions until the current daily range approaches or meets the average range, at which point exiting the trade may be considered.
For scalping strategies, the 15min and 5min average range can be utilized to determine optimal holding periods for fast trades.
Other strategies:
Intraday Trading - 1h and 4h Average Range
Swing Trading - Monthly Average Range
Short-term Trading - Weekly Average Range
Also using these statistics in accordance with Power 3 ICT concepts, will assist in holding trades to their statistical average range of the chosen HTF candle.
CODE
The core functionality lies in the data retrieval and table population sections.
The request.security function (e.g., = request.security(syminfo.tickerid, "D", , lookahead = barmerge.lookahead_off)) retrieves high and low prices from specified timeframes without lookahead bias, ensuring accurate historical data.
These values are used to compute current ranges and average ranges (ta.sma(high - low, avgLength)), which are then displayed in a dynamically generated table starting at (if barstate.islast) using table.new, with conditional green highlighting when the current range is greater than average range, providing a clear visual cue for volatility analysis.
HMA & D1 crossover FX (Study)Can work on other Forex pairs if change settings: Period
This example tuned for AUDUSD (FX Version)
Enter new order on HMA ( Hull Moving Average ) and D1 ( Daily Candle) crossovers, Exit orders as basket when profit = Your Target Profit
This study version built for users of Alerts. Crossover of HMA and DailyCandle1 (and/or DailyCandle1 cross DailyCandle2) (also possible Price cross HMA)
