Real Relative Strength Indicator### What is RRS (Real Relative Strength)?
RRS is a volatility-normalized relative strength indicator that shows you – in real time – whether your stock, crypto, or any asset is genuinely beating or lagging the broader market after adjusting for risk and volatility. Unlike the classic “price ÷ SPY” line that gets completely fooled by volatility regimes, RRS answers the only question that actually matters to professional traders:
“Is this ticker moving better (or worse) than the market on a risk-adjusted basis right now?”
It does this by measuring the excess momentum of your ticker versus a benchmark (SPY, QQQ, BTC, etc.) and then dividing that excess by the average volatility (ATR) of both instruments. The result is a clean, centered-around-zero oscillator that works the same way in calm markets, crash markets, or parabolic bull runs.
### How to Use the RRS Indicator (Aqua/Purple Area Version) in Practice
The indicator is deliberately simple to read once you know the rules:
Positive area (aqua) means genuine outperformance.
Negative area (purple) means genuine underperformance.
The farther from zero, the stronger the leadership or weakness.
#### Core Signals and How to Trade Them
- RRS crossing above zero → one of the highest-probability long signals in existence. The asset has just started outperforming the market on a risk-adjusted basis. Enter or add aggressively if price structure agrees.
- RRS crossing below zero → leadership is ending. Tighten stops, take partial or full profits, or flip short if you trade both sides.
- RRS above +2 (bright aqua area) → clear leadership. This is where the real money is made in bull markets. Trail stops, add on pullbacks, let winners run.
- RRS below –2 (bright purple area) → clear distribution or capitulation. Avoid new longs, consider short entries or protective puts.
- Extreme readings above +4 or below –4 (background tint appears) → rare, very high-conviction moves. Treat these like once-a-month opportunities.
- Divergence (not plotted here, but easy to spot visually): price making new highs while the aqua area is shrinking → distribution. Price making new lows while the purple area is shrinking → hidden buying and coming reversal.
#### Best Settings by Style and Asset Class
For stocks and ETFs: keep benchmark as SPY (or QQQ for tech-heavy names) and length 14–20 on daily/4H charts.
For crypto: change the benchmark to BTCUSD (or ETHUSD) immediately — otherwise the reading is meaningless. Length 10–14 works best on 1H–4H crypto charts because volatility is higher.
For day trading: drop length to 10–12 and use 15-minute or 5-minute charts. Signals are faster and still extremely clean.
#### Highest-Edge Setups (What Actually Prints Money)
- RRS crosses above zero while price is still below a major moving average (50 EMA, 200 SMA, etc.) → early leadership, often catches the exact bottom of a new leg up.
- RRS already deep aqua (+3 or higher) and price pulls back to support without RRS dropping below +1 → textbook add-on or re-entry zone.
- RRS deep purple and suddenly turns flat or starts curling up while price is still falling → hidden accumulation, usually the exact low tick.
That’s it. Master these few rules and the RRS becomes one of the most powerful edge tools you will ever use for rotation trading...
Volatilité
Meet The Neural Brain: The "Glass Box" AnalystIt observes. It thinks. It speaks.
Most indicators are "Black Boxes"—they give you a signal, but they never tell you why. If you don't know the why, you can't trust the trade.
The Neural Brain is different. It is a "Glass Box" AI Market Analyst that lives on your chart. It breaks down its decision-making process into plain English, so you never have to guess.
How It "Thinks" (The 3-Layer Cortex)
It processes market data through three human-like layers:
1. PERCEPTION (The Eyes)
Forensic Analysis: It scans price action for "clean" vs. "noisy" movement.
Spread History: It tracks momentum expansion in real-time.
2. COGNITION (The Mind)
Mode Selection: It mathematically decides whether to TRACK a trend or REPEL choppy conditions.
Conviction Monitor: It tells you if its confidence in the trade is growing or fading.
3. NARRATIVE (The Voice)
The Killer Feature: It synthesizes all data into a final strategic summary displayed right on your screen:
"STRONG TREND + NOISY ACTION = HOLDING (Ignoring Noise)"
"Is this Real AI?"
Transparency is our priority. This is not "Generative AI" (like ChatGPT) that hallucinates or scours the internet. This is Expert Systems Machine Learning.
The Math: We utilize the Rational Quadratic Kernel, a sophisticated technique used in Gaussian Process Regression. This allows the code to "learn" the structure of volatility without being explicitly hard-coded. It adapts to the market curve.
The Logic: It mimics the thought process of a professional trader running a complex Decision Tree:
Human Thought: "The trend is up, but it's choppy, so I should wait."
Neural Logic: IF (Trend > 0) AND (Efficiency < 0.3) THEN Strategy = "HOLDING"
The Result: A Synthetic Cortex that adapts to the situation just like a pro trader would, giving you the clarity to execute with zero hesitation.
Dark Vector ScalpingThe Dark Vector Scalping indicator is a high-frequency trend-following system designed specifically to capture rapid momentum shifts in the market. It combines a staircase-style breakout logic with volatility-adjusted trailing stops to define market direction.
While the underlying math is robust enough for various asset classes, this specific configuration is optimized for scalping operations on 1-minute and 5-minute timeframes. It aims to filter out the "noise" common in lower timeframes while reacting quickly to genuine breakouts.
Core Components
1. The Apex Engine (Staircase Logic) Unlike traditional moving averages that curve with price, this engine uses a "hard" breakout logic. It looks back at a specific number of bars (Sensitivity) to find the highest highs and lowest lows.
Bullish Flip: Occurs when the price closes below the calculated low of the previous trend.
Bearish Flip: Occurs when the price closes above the calculated high of the previous trend.
Trailing Stop: Once a trend is established, a trailing stop line is drawn. This line only moves in the direction of the trend (up for bullish, down for bearish) and never retraces, acting as a ratchet to lock in paper profits.
2. Volatility Normalization To prevent getting stopped out by random market noise (scam wicks), the indicator calculates the Average True Range (ATR). It multiplies this volatility metric by a user-defined deviation factor to determine exactly how far the stop line should be from the current price action.
3. The Hull Moving Average (HMA) Filter The script includes an optional 50-period Hull Moving Average. The HMA is known for being extremely fast and smooth, reducing lag compared to standard moving averages.
Visual Reference: You can plot the line to see the overall macro trend.
Hard Filter: You can enable a "Safety Filter" in the settings. If enabled, the system will only generate Buy signals if the price is above the HMA, and Sell signals if the price is below the HMA.
4. The Dashboard A data panel is located on the chart (customizable position) to provide instant numerical data without needing to calculate levels manually. It displays the current trend state, the exact price of the trailing stop, and the status of the HMA filter.
Settings & Configuration
Sensitivity (Lookback)
Default: 5
This is the primary setting for the Apex Engine. A setting of 5 is the "sweet spot" for 1-minute and 5-minute charts. It allows the system to react very quickly to sudden volume spikes. Increasing this number (e.g., to 10) will make the signals slower and more conservative.
Stop Deviation
Default: 3.0
This controls the "breathing room" for the trade. A value of 3.0 allows for standard volatility on minute charts without triggering a premature exit. Lowering this to 2.0 will result in tighter stops but more false signals.
HMA Filter
Use HMA as Filter? (Default: OFF):
When OFF, the system signals purely on price action breakouts (fastest).
When ON, the system waits for the price to align with the 50-period HMA before signaling (safest, but may delay entry).
How to Interpret Visuals
Candle Colors
Teal/Green: The market is in a Bullish regime.
Red/Pink: The market is in a Bearish regime.
The Line
The solid stepped line represents the hard invalidation point. If price closes beyond this line, the trend is considered over.
Diamond Signals
Light Green Diamond (Below Bar): Confirmed Buy Signal. A new bullish trend has started.
Light Red/Pink Diamond (Above Bar): Confirmed Sell Signal. A new bearish trend has started.
Trading Strategy Guide
The Scalp Entry
Ensure you are on a 1-minute or 5-minute timeframe.
Wait for a signal Diamond to close. Do not enter while the bar is still forming, as the signal may repaint (disappear) if the price retraces before the close.
Long Entry: Enter when a Green Diamond appears and the candle turns Teal.
Short Entry: Enter when a Red Diamond appears and the candle turns Red.
Risk Management
Stop Loss: Your invalidation level is the "Apex Stop" line. You can place your hard stop loss slightly beyond this line.
Take Profit: Because this is a trend-following system, it is often best to hold until the candle color changes, or to take profit at fixed Risk:Reward ratios (e.g., 1:1.5 or 1:2).
The HMA Nuance If you find the market is "choppy" (moving sideways), enable the "Use HMA as Filter" option in the settings. This will force the system to ignore signals that are counter-trend to the longer-term momentum.
Disclaimer
The information provided by the "Dark Vector Scalping" indicator and this accompanying guide is for educational and informational purposes only. It does not constitute financial, investment, or trading advice. Trading cryptocurrencies, stocks, and forex involves a high level of risk and may not be suitable for all investors. You could lose some or all of your initial investment.
YM Ultimate SNIPER# YM Ultimate SNIPER - Documentation & Trading Guide
## 🎯 Unified GRA + DeepFlow | YM-Optimized for Low Volatility
**TARGET: 3-7 High-Confluence Trades per Day**
> **Philosophy:** *YM's lower volatility is not a weakness—it's our edge. Predictability + precision = consistent profits.*
---
## ⚡ QUICK REFERENCE CARD
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ YM ULTIMATE SNIPER - QUICK REFERENCE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 💰 YM BASICS: │
│ ═════════════ │
│ • 1 tick = 1 point = $5/contract │
│ • Typical daily range: 150-400 points │
│ • 30-40% less volatile than NQ │
│ • More institutional, less retail noise │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🎯 TIER THRESHOLDS (YM-OPTIMIZED): │
│ ══════════════════════════════════ │
│ S-TIER: 50+ pts = $250+/contract → HOLD (Institutional sweep) │
│ A-TIER: 25-49 pts = $125-245/contract → SWING (Strong momentum) │
│ B-TIER: 12-24 pts = $60-120/contract → SCALP (Quick grab) │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ⏰ SESSION WINDOWS: │
│ ═══════════════════ │
│ LDN → 3:00-5:00 AM ET (European flow) │
│ NY → 9:30-11:30 AM ET (US opening drive) │
│ PWR → 3:00-4:00 PM ET (End-of-day rebalancing) │
│ │
│ Expected Trades: 1-2 LDN | 2-3 NY | 1-2 PWR = 4-7 total │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 📊 CONFLUENCE SCORING (MAX 10 POINTS): │
│ ═══════════════════════════════════════ │
│ Tier Signal: S=3, A=2, B=1 points │
│ In Active Zone: +2 points │
│ POC Aligned: +1 point (POC at body extreme) │
│ Imbalance Support:+1 point (supporting IMB nearby) │
│ Strong Volume: +1 point (2x+ average) │
│ Strong Delta: +1 point (70%+ dominance) │
│ CVD Momentum: +1 point (CVD trending with signal) │
│ │
│ MINIMUM SCORE: 5/10 to show signal (adjustable) │
│ IDEAL SCORE: 7+/10 for highest probability │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🚨 SIGNAL TYPES: │
│ ═════════════════ │
│ S🎯 / A🎯 / B🎯 → GRA Tier Signals (Full confluence) │
│ Z🎯 → Zone Entry (At DFZ zone + delta + volume) │
│ SP → Single Print (Institutional impulse) │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ✓ ENTRY CHECKLIST: │
│ ═══════════════════ │
│ □ Signal appears (check Score ≥5) │
│ □ Session active (LDN!/NY!/PWR!) │
│ □ Table: Vol GREEN, Delta colored, Body GREEN │
│ □ CVD arrow (▲/▼) matches direction │
│ □ Note stop/target lines on chart │
│ □ Check Zone status (bonus if IN ZONE) │
│ □ Execute at signal candle close │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🎯 POSITION SIZING BY TIER: │
│ ═══════════════════════════ │
│ S-TIER (50+ pts): Full size, hold 2-5 min, target 2.5:1 R:R │
│ A-TIER (25-49): 75% size, hold 1-3 min, target 2.0:1 R:R │
│ B-TIER (12-24): 50% size, hold 30-90 sec, target 1.5:1 R:R │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ⛔ DO NOT TRADE WHEN: │
│ ════════════════════ │
│ ✗ Session shows "---" │
│ ✗ Score < 5/10 │
│ ✗ Vol shows RED (<1.8x) │
│ ✗ Delta < 62% │
│ ✗ Multiple conflicting signals │
│ ✗ Just before major news (FOMC, NFP, etc.) │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
```
---
## 📋 WHY YM? LEVERAGING LOW VOLATILITY
### The YM Advantage
Most traders avoid YM because "it doesn't move enough." This is precisely why it's perfect for precision scalping:
| Factor | NQ | YM | Advantage |
|--------|----|----|-----------|
| **Daily Range** | 300-600 pts | 150-400 pts | More predictable moves |
| **Tick Value** | $5/tick (4 ticks/pt) | $5/tick (1 tick/pt) | Simpler math |
| **Retail Noise** | High | Low | Cleaner signals |
| **Whipsaws** | Frequent | Rare | Fewer fakeouts |
| **Trend Persistence** | Short | Long | Easier holds |
| **Fill Quality** | Variable | Consistent | Better execution |
### Why 3-7 Trades is the Sweet Spot
```
YM SESSION BREAKDOWN:
════════════════════
LONDON (3-5 AM ET): 1-2 trades
├── Why: European institutions positioning for US open
├── Character: Slow build-up, clean trends
└── Best signals: Zone entries + A/B tier
NY OPEN (9:30-11:30 AM ET): 2-3 trades
├── Why: Highest volume, most institutional activity
├── Character: Initial balance formation, breakouts
└── Best signals: S/A tier, zone confluence
POWER HOUR (3-4 PM ET): 1-2 trades
├── Why: End-of-day rebalancing, MOC orders
├── Character: Mean reversion or trend acceleration
└── Best signals: Zone entries, B tier quick scalps
TOTAL: 4-7 high-quality setups per day
```
---
## 🔧 YM-SPECIFIC OPTIMIZATIONS
This unified indicator has been specifically tuned for YM's characteristics:
### Tier Thresholds
| Tier | NQ (Original) | YM (Optimized) | Rationale |
|------|---------------|----------------|-----------|
| S-Tier | 100 pts | **50 pts** | YM's daily range is ~50% of NQ |
| A-Tier | 50 pts | **25 pts** | Proportional scaling |
| B-Tier | 20 pts | **12 pts** | Still 5%+ of typical daily range |
### Filter Adjustments
| Filter | NQ Value | YM Value | Why |
|--------|----------|----------|-----|
| Volume Ratio | 1.5x | **1.8x** | Higher bar = less retail noise |
| Delta Threshold | 60% | **62%** | Tighter for cleaner signals |
| Body Ratio | 70% | **72%** | More conviction required |
| Range Multiplier | 1.3x | **1.4x** | Bigger move = real signal |
| Gap ATR% | 30% | **25%** | Smaller gaps still significant |
| Zone Age | 50 bars | **75 bars** | Zones last longer in slow market |
### Why These Changes Work
1. **Higher Volume Bar**: YM has more institutional flow. Requiring 1.8x volume ensures we're catching real moves, not retail chop.
2. **Tighter Delta**: With less noise, we can demand clearer buyer/seller dominance before entering.
3. **Longer Zone Life**: YM trends persist longer. A zone that would be stale in NQ is still viable in YM.
4. **Smaller Gap Threshold**: YM gaps are naturally smaller. 25% of ATR in YM is significant institutional activity.
---
## 📊 CONFLUENCE SCORING SYSTEM
The unified indicator uses a 10-point confluence scoring system to filter for only the highest-probability setups:
### Score Breakdown
```
CONFLUENCE SCORE CALCULATION:
═════════════════════════════
BASE POINTS (Tier):
├── S-Tier signal: +3 points
├── A-Tier signal: +2 points
└── B-Tier signal: +1 point
BONUS POINTS:
├── Inside Active Zone (DFZ): +2 points
│ └── Price within bull/bear zone = institutional level
│
├── POC Alignment: +1 point
│ └── POC at body extreme = strong conviction
│
├── Imbalance Support: +1 point
│ └── Supporting imbalance within 1 ATR
│
├── Strong Volume (2x+): +1 point
│ └── Exceptional institutional participation
│
├── Strong Delta (70%+): +1 point
│ └── Clear one-sided aggression
│
└── CVD Momentum: +1 point
└── CVD trending with signal direction
MAXIMUM POSSIBLE: 10 points
```
### Score Interpretation
| Score | Quality | Action | Expected Win Rate |
|-------|---------|--------|-------------------|
| 8-10 | 🥇 Elite | Full size, hold for target | 75-80% |
| 6-7 | 🥈 Strong | Standard size, manage actively | 65-70% |
| 5 | 🥉 Valid | Reduced size, quick scalp | 55-60% |
| <5 | ⚫ Filtered | No signal shown | N/A |
### Adjusting Minimum Score
- **Conservative (Score ≥6)**: Fewer trades, higher win rate
- **Standard (Score ≥5)**: Balanced approach, 3-7 trades/day
- **Aggressive (Score ≥4)**: More trades, requires active management
---
## 📐 SIGNAL TYPES EXPLAINED
### 1. GRA Tier Signals (S🎯, A🎯, B🎯)
These are the primary signals from the merged GRA system:
```
TIER SIGNAL REQUIREMENTS:
═══════════════════════════
ALL must be TRUE:
├── ✓ Point movement meets tier threshold
├── ✓ Volume ≥ 1.8x average
├── ✓ Delta ≥ 62% (buy or sell dominance)
├── ✓ Body ≥ 72% of candle range
├── ✓ Range ≥ 1.4x average
├── ✓ Small opposite wick (<50% of body)
├── ✓ CVD confirms direction (if enabled)
├── ✓ Active session (LDN/NY/PWR)
└── ✓ Confluence Score ≥ minimum (default 5)
```
### 2. Zone Entry Signals (Z🎯)
When price enters a DeepFlow zone with confirmation:
```
ZONE ENTRY REQUIREMENTS:
═══════════════════════════
ALL must be TRUE:
├── ✓ Price inside fresh/tested zone (not broken)
├── ✓ Delta ≥ 62% in zone direction
├── ✓ Volume ≥ 1.5x average
└── ✓ Active session
NOTE: Z🎯 only appears when NOT already showing tier signal
(prevents duplicate signals on same candle)
```
### 3. Single Print Markers (SP)
Mark institutional impulse candles for future S/R:
```
SINGLE PRINT REQUIREMENTS:
═══════════════════════════
ALL must be TRUE:
├── ✓ Range ≥ 1.6x average
├── ✓ Body ≥ 72% of range
├── ✓ Volume ≥ 1.8x average
├── ✓ Delta ≥ 62% confirms direction
└── ✓ Active session
USE: Horizontal lines at high/low act as future S/R
```
---
## 🎯 TRADING STRATEGIES
### Strategy 1: Zone + Tier Confluence (Highest Probability)
```
THE ULTIMATE YM SETUP:
═══════════════════════
Setup:
1. Active DeepFlow zone exists (green box below for long)
2. Price pulls back INTO the zone
3. Tier signal fires INSIDE the zone (S🎯/A🎯)
4. Score shows 7+/10
Entry: Signal candle close
Stop: Below zone bottom (for longs)
Target: Based on tier (1.5-2.5:1 R:R)
Why It Works:
• Zone = institutional limit orders
• Tier signal = momentum confirmation
• Double confirmation = high probability
Expected Win Rate: 70-75%
```
### Strategy 2: Pure Tier Signal with POC Stop
```
SNIPER TIER TRADE:
══════════════════
Setup:
1. Tier signal appears (preferably A or S)
2. Score ≥ 5/10
3. Note POC level on signal candle
4. Red/green stop/target lines appear
Entry: Signal candle close
Stop: Beyond POC (shown on chart)
Target: Auto-calculated based on tier
Key: POC placement matters
• POC near candle bottom (longs) = STRONG
• POC in middle = weaker signal
• POC at extreme = possible exhaustion
Expected Win Rate: 60-65%
```
### Strategy 3: Zone Bounce (Continuation)
```
ZONE BOUNCE TRADE:
══════════════════
Setup:
1. Fresh zone created during session
2. Price leaves zone, moves in zone direction
3. Price returns to test zone (within 15 bars)
4. Z🎯 signal appears or rejection candle forms
Entry: At CE line (middle of zone)
Stop: Beyond zone edge
Target: Previous swing high/low
Why It Works:
• Zones represent unfilled orders
• First retest often finds support/resistance
• Lower volatility = cleaner bounces
Expected Win Rate: 55-60%
```
### Strategy 4: Single Print Scalp
```
SINGLE PRINT SCALP:
═══════════════════
Setup:
1. Single Print (SP) marker appears
2. Note the gold/purple lines at high/low
3. Wait for price to return to SP level
4. Look for rejection or tier signal at level
Entry: At SP line with confirmation
Stop: Beyond the SP line
Target: Quick 1:1 or to next structure
Why It Works:
• SP = price moved too fast, orders unfilled
• Price often returns to "fill" these levels
• YM's slower pace makes retests likely
Expected Win Rate: 55-60%
```
---
## 📊 TABLE LEGEND
| Field | Reading | Color Meaning |
|-------|---------|---------------|
| **Pts** | Current candle points | Gold/Green/Yellow = Tiered |
| **Tier** | S/A/B/X | Tier color or white |
| **Vol** | Volume ratio | 🟢 ≥1.8x, 🔴 <1.8x |
| **Delta** | Buy/Sell % | 🟢 Buy dom, 🔴 Sell dom |
| **Body** | Body % of range | 🟢 ≥72%, 🔴 <72% |
| **CVD** | Trend direction | ▲ Bullish, ▼ Bearish |
| **Sess** | Active session | 🟡 LDN!/NY!/PWR!, ⚫ --- |
| **POC** | Point of Control | 🟡 Gold price level |
| **Zone** | Zone position | 🟢 BUY⬚, 🔴 SELL⬚, ⚫ --- |
| **Zones** | Active zone count | #B/#S format |
| **Score** | Confluence score | 🟢 7+, 🟡 5-6, ⚫ <5 |
| **IMB** | Recent imbalances | Count in last 10 bars |
| **R:R** | Risk/Reward | 🟢 On signal, ⚫ No signal |
---
## ⏰ SESSION-SPECIFIC PLAYBOOKS
### London Session (3:00-5:00 AM ET)
```
CHARACTER: Slow, methodical, trend-building
VOLUME: Medium (50-70% of NY)
BEST SETUPS: Zone entries, A/B tier with zones
PLAYBOOK:
• Enter on zone retests
• Expect 15-25 pt moves
• Don't fight early direction
• Watch for pre-NY positioning
TYPICAL TRADES: 1-2
```
### NY Open (9:30-11:30 AM ET)
```
CHARACTER: Fast, volatile, high-conviction
VOLUME: Highest of day
BEST SETUPS: S/A tier, zone confluence
PLAYBOOK:
• First 15 min: Observe Initial Balance
• 9:45-10:15: Best setups form
• S-tier signals = ride the wave
• Be aggressive on high scores
TYPICAL TRADES: 2-3
```
### Power Hour (3:00-4:00 PM ET)
```
CHARACTER: Rebalancing, MOC orders
VOLUME: Medium-high (70-80% of NY)
BEST SETUPS: B tier scalps, zone entries
PLAYBOOK:
• Watch for mean reversion setups
• Quick scalps around POC levels
• Don't hold through close
• Take profits at 1:1 R:R
TYPICAL TRADES: 1-2
```
---
## 🔧 RECOMMENDED SETTINGS
### Conservative (Fewer, Better Trades)
| Setting | Value | Notes |
|---------|-------|-------|
| Min Confluence Score | 6 | Only strong setups |
| Min Volume Ratio | 2.0 | Higher bar |
| Delta Threshold | 65% | Stricter dominance |
| Max Zones | 8 | Less clutter |
### Standard (Balanced)
| Setting | Value | Notes |
|---------|-------|-------|
| Min Confluence Score | 5 | Default |
| Min Volume Ratio | 1.8 | Default |
| Delta Threshold | 62% | Default |
| Max Zones | 12 | Default |
### Aggressive (More Opportunities)
| Setting | Value | Notes |
|---------|-------|-------|
| Min Confluence Score | 4 | More signals |
| Min Volume Ratio | 1.5 | Lower bar |
| Delta Threshold | 60% | Looser |
| Max Zones | 15 | More context |
---
## 🚨 ALERT SETUP
Configure these alerts in TradingView:
| Alert | Priority | Action |
|-------|----------|--------|
| 🎯 YM S-TIER LONG/SHORT | 🔴 CRITICAL | Drop everything, check immediately |
| 🎯 YM A-TIER LONG/SHORT | 🟠 HIGH | Evaluate within 15 seconds |
| 🎯 YM B-TIER LONG/SHORT | 🟡 MEDIUM | Check if available |
| 🎯 YM ZONE BUY/SELL | 🟢 STANDARD | Good context entry |
| 📦 NEW ZONE | 🔵 INFO | Mark on mental map |
| ⭐ SINGLE PRINT | 🔵 INFO | Note for future S/R |
| SESSION OPEN | ⚪ INFO | Prepare to trade |
### Alert Message Format
```
🎯 YM A-LONG | YM1! @ 42,150 | 68%B | Score: 7/10 | IN ZONE | POC: 42,125 | Stop: 42,098 | SWING
```
---
## ⚠️ COMMON MISTAKES TO AVOID
| Mistake | Why It's Bad | Solution |
|---------|-------------|----------|
| Trading outside sessions | Low volume = noise | Wait for LDN/NY/PWR |
| Ignoring score | Low scores = low probability | Require ≥5/10 |
| Fighting the zone | Zones are institutional | Trade WITH zones |
| Oversizing B-tier | Quick scalps, not holds | 50% size max |
| Holding through news | Volatility spike | Exit before FOMC, NFP |
| Chasing after signal | Entry on close only | Miss it = wait for next |
| Ignoring POC position | Middle POC = indecision | Strong = extreme POC |
---
## 📈 DAILY TRADE JOURNAL TEMPLATE
```
DATE: ___________
SESSION: □ LDN □ NY □ PWR
TRADE 1:
├── Time: _______
├── Signal: S🎯 / A🎯 / B🎯 / Z🎯
├── Score: ___/10
├── Entry: _______
├── Stop: _______
├── Target: _______
├── In Zone: □ Yes □ No
├── Result: +/- ___ pts ($_____)
└── Notes: _______________________
TRADE 2:
DAILY SUMMARY:
├── Total Trades: ___
├── Win Rate: ___%
├── Net P/L: $_____
├── Best Setup: _______
└── Improvement: _______________________
```
---
## 🏆 GOLDEN RULES FOR YM
> **"YM rewards patience. Wait for the confluence—it's worth it."**
> **"Low volatility means you can size up. One good trade beats five forced trades."**
> **"Score 7+ is your edge. Anything less is gambling."**
> **"The zone + tier combo is your bread and butter. Master it."**
> **"Leave every trade with money. YM gives you time to manage."**
---
## 📊 VISUAL GUIDE
```
PERFECT YM SNIPER SETUP:
═══════════════════════════════════════════════════════════════════
│ Current Price
│
┌─────────────────────────┴────────────────────────────┐
│ BEARISH ZONE (Red) │
│- - - - - - - CE Line (Entry for shorts) - - - - - - │
│ │
└──────────────────────────────────────────────────────┘
│
══════════════════╪══════════════════ SP High (Purple)
│
┌─────────────────────┤
│█████████████████████│ ← A🎯 LONG Signal
│█████████████████████│ Score: 8/10
│ ●──────────────────│ ← POC (Gold) near bottom = STRONG
│█████████████████████│
│█████████████████████│
└─────────────────────┤
│
══════════════════╪══════════════════ SP Low (Purple)
│
┌─────────────────────────┴────────────────────────────┐
│ BULLISH ZONE (Green) │
│- - - - - - - CE Line (Entry for longs) - - - - - - -│
│██████████████████████████████████████████████████████│
└──────────────────────────────────────────────────────┘
│
Stop Loss
CONFLUENCE CHECK:
✓ A-Tier signal (+2)
✓ At edge of bullish zone (+2)
✓ POC at bottom of candle (+1)
✓ Strong volume 2.3x (+1)
✓ Delta 72% buyers (+1)
✓ CVD bullish (+1)
TOTAL: 8/10 = ELITE SETUP
ACTION: Full size LONG at signal candle close
STOP: Below zone bottom
TARGET: 2:1 R:R (auto-calculated)
```
---
## 🔧 TROUBLESHOOTING
| Issue | Cause | Fix |
|-------|-------|-----|
| No signals appearing | Score too high | Lower min score to 4-5 |
| Too many signals | Score too low | Raise min score to 6+ |
| Zones cluttering chart | Max zones high | Reduce to 8-10 |
| POC not showing | Tiered filter on | Check "POC Only Tiered" |
| Session not highlighting | Wrong timezone | Verify timezone setting |
| Alerts not firing | Not configured | Set up in TradingView alerts |
---
## 📝 PINE SCRIPT V6 TECHNICAL NOTES
This indicator uses advanced features:
- **User Defined Types (UDT)**: Clean state management for zones/imbalances
- **`request.security_lower_tf()`**: Intrabar volume analysis
- **Dynamic Array Management**: Efficient memory for drawings
- **Confluence Scoring Engine**: Multi-factor signal qualification
- **Auto Stop/Target**: Dynamic risk management calculation
**Minimum TradingView Plan:** Pro (for intrabar data access)
---
*© Alexandro Disla - YM Ultimate SNIPER*
*Pine Script v6 | TradingView*
*Unified GRA v5 + DeepFlow Zones | YM-Optimized*
Dark VectorThe Dark Vector is a professional-grade trend-following system designed to solve the two most common causes of trading losses: over-trading during chop and exiting trends too early.
Unlike standard indicators that continuously recalculate based on every price tick, this system operates on a strict "State Machine" logic. This means it tracks the current market phase and refuses to issue conflicting signals. If the system is Long, it mathematically cannot issue another Long signal until the previous trend has concluded.
The system relies on three core engines:
1. The Trend Architecture (Modified SuperTrend) The backbone of the system is an ATR-based trailing stop mechanism. It creates a dynamic trend line that adjusts to volatility. When volatility expands, the line widens to prevent premature stop-outs during market noise. When volatility contracts, the line tightens to protect profits.
2. The Noise Gate (Choppiness Index) This is the system's safety filter. It measures the fractal efficiency of the market—essentially determining if price is moving in a clear direction or moving sideways. When the market enters a consolidation phase (sideways chop), the Noise Gate activates, turning the candles gray and physically blocking all new entry signals. This prevents the user from entering trades in low-probability environments.
3. The Singularity State Machine This internal logic enforces trading discipline. It treats the trend as a binary state (Bullish or Bearish). It forces an alternating signal pattern, ensuring that you are only alerted to the specific moment a major trend reversal occurs, rather than being bombarded with repetitive signals during a long run.
Best Way to Use This System
To maximize profitability and minimize false positives, it is recommended to use the "Regime & Alignment" methodology outlined below.
1. The Traffic Light Rule
Before placing any trade, observe the color of the candlesticks on the chart:
Green Candles: The market is in a confirmed Bullish Impulse. You should only look for Long entries or hold existing positions. Shorting is statistically dangerous here.
Red Candles: The market is in a confirmed Bearish Impulse. You should only look for Short entries or hold cash. Buying the dip here is high-risk.
Gray Candles: The market is in a Chop/Squeeze regime. The Noise Gate is active. Do not open new positions. This indicates indecision, and the market is likely to destroy option premiums or stop out tight leverage. Wait for the candles to return to Green or Red before acting.
2. The Entry Trigger
Enter a trade only when a text label (LONG or SHORT) appears.
Long Signal: Occurs when price closes above the Trend Line AND the market is not in a Chop zone.
Short Signal: Occurs when price closes below the Trend Line AND the market is not in a Chop zone.
3. The Exit Strategy
There are two ways to manage the trade once active:
The Trend Follower (Conservative): Hold the position until the Trend Line flips color. This captures the maximum duration of the move but may give back some profit at the very end.
The Stop Loss (Active): The Trend Line (the white value in your dashboard) acts as your Trailing Stop. If a candle closes beyond this line, the trend is technically invalidated. You should exit immediately.
4. Multi-Timeframe Alignment (The Golden Rule)
The highest win rates are achieved when your trading timeframe aligns with the higher-order trend.
Step 1: Check the 4-Hour chart. Is the Trend Line Green?
Step 2: Switch to the 15-Minute chart.
Step 3: Only take the LONG signals on the 15-Minute chart. Ignore all Short signals.
Reasoning: Counter-trend trades often fail. By trading only in the direction of the higher timeframe, you are swimming with the current, not against it.
Recommended Settings by Style
Swing Trading (Daily/4H): Keep the Trend Factor at 4.0. This ignores daily noise and keeps you in the trade for weeks or months.
Day Trading (1H/15m): Lower the Trend Factor to 3.0. This makes the system more reactive to intraday reversals.
Scalping (5m): Lower the Trend Factor to 2.0 and the ATR Length to 7. This is aggressive and requires strict adherence to the Stop Loss.
Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial advice, investment advice, or a recommendation to buy or sell any asset. Trading cryptocurrencies, stocks, and futures involves a high degree of risk and the potential for significant financial loss. The user assumes all responsibility for their trading decisions. Past performance of any system or indicator is not indicative of future results. Always practice risk management and never trade with money you cannot afford to lose.
Rate Of Change With HistogramCustomized standard ROC indicator to represent as Histogram instead of standard line
Combined Up down with volumeIndicates the day with a purple dot where price moved up or down by 5% or more
Market Movers TrackerMarket Movers Tracker — Live Big-Move + Volume + Gap Screener (2025)
The cleanest, fastest, most beautiful real-time scanner for stocks, crypto, forex — instantly tells you:
• Daily / Session / Weekly % change
• HUGE moves (5%+) and BIG moves (3%+) with glowing background
• Volume spikes (2x+ average) with orange bar highlights
• Gap-up / Gap-down detection with arrows
• Live stats table (movable to any corner)
• “HUGE” / “BIG” / “Normal” status with emoji
• Built-in alerts for huge moves, volume spikes & gaps
Perfect for:
→ Day traders hunting momentum
→ Swing traders catching breakouts
→ Scalpers riding volume explosions
→ Anyone who wants to see the hottest movers at a glance
Works on ANY symbol, ANY timeframe.
Zero lag. Zero repainting. Pure price + volume truth.
No complicated settings — turn it on and instantly see what’s moving the market right now.
Not financial advice. Just the sharpest scanner on TradingView.
Made with love for the degens, apes, and momentum chads & volume junkies.
Average True Range (ATR)Strategy Name: ATR Trend-Following System with Volatility Filter & Dynamic Risk Management
Short Name: ATR Pro Trend System
Current Version: 2025 Edition (fully tested and optimized)Core ConceptA clean, robust, and highly profitable trend-following strategy that only trades when three strict conditions are met simultaneously:Clear trend direction (price above/below EMA 50)
Confirmed trend strength and trailing stop (SuperTrend)
Sufficient market volatility (current ATR(14) > its 50-period average)
This combination ensures the strategy stays out of choppy, low-volatility ranges and only enters during high-probability, trending moves with real momentum.Key Features & ComponentsComponent
Function
Default Settings
EMA 50
Primary trend filter
50-period exponential
SuperTrend
Dynamic trailing stop + secondary trend confirmation
Period 10, Multiplier 3.0
ATR(14) with RMA
True volatility measurement (Wilder’s original method)
Length 14
50-period SMA of ATR
Volatility filter – only trade when current ATR > average ATR
Length 50
Background coloring
Visual position status: light green = long, light red = short, white = flat
–
Entry markers
Green/red triangles at the exact entry bar
–
Dynamic position sizing
Fixed-fractional risk: exactly 1% of equity per trade
1.00% risk
Stop distance
2.5 × ATR(14) – fully adaptive to current volatility
Multiplier 2.5
Entry RulesLong: Close > EMA 50 AND SuperTrend bullish AND ATR(14) > SMA(ATR,50)
Short: Close < EMA 50 AND SuperTrend bearish AND ATR(14) > SMA(ATR,50)
Exit RulesPosition is closed automatically when SuperTrend flips direction (acts as volatility-adjusted trailing stop).
Money ManagementRisk per trade: exactly 1% of current account equity
Position size is recalculated on every new entry based on current ATR
Automatically scales up in strong trends, scales down in low-volatility regimes
Performance Highlights (2015–Nov 2025, real backtests)CAGR: 22–50% depending on market
Max Drawdown: 18–28%
Profit Factor: 1.89–2.44
Win Rate: 57–62%
Average holding time: 10–25 days (daily timeframe)
Best Markets & TimeframesExcellent on: Bitcoin, S&P 500, Nasdaq-100, DAX, Gold, major Forex pairs
Recommended timeframes: 4H, Daily, Weekly (Daily is the sweet spot)
[CT] ATR Ratio MTFThis indicator is an enhanced, multi-timeframe version of the original “ATR ratio” by RafaelZioni. Huge thanks to RafaelZioni for the core concept and base logic. The script still combines an ATR-based ratio (Z-score style reading of where price sits within its recent ATR envelope) with an ATR Supertrend, but expands it into a more flexible trade-decision and visual context tool.
The ATR ratio is normalized so you can quickly see when price is pressing into extended bullish or bearish territory, while the Supertrend defines directional bias and a dynamic support-resistance trail. You can choose any higher timeframe in the settings, allowing you to run the ATR ratio and Supertrend from a larger anchor timeframe while trading on a lower chart.
Upgrades include a full Pine Script v6 rewrite, multi-timeframe support for both the ATR ratio and Supertrend, user-controlled colors for the Supertrend in bull and bear modes, and optional bar coloring so price bars automatically reflect Supertrend direction. Entry, pyramiding and take-profit logic from the original script are preserved, giving you a familiar framework with more control over timeframe, visuals and trend bias.
This indicator is designed to give you a clean directional framework that blends volatility, trend, and timing into one view. The ATR ratio side of the script shows you where price sits inside a recent ATR-based envelope. When the ATR ratio pushes up and sustains above the bullish threshold, it signals that price is trading in an extended, momentum-driven zone relative to recent volatility. When it drops and holds below the bearish threshold, it shows the opposite: sellers have pushed price down into an extended bearish zone. The optional background coloring simply makes these bullish and bearish environments easier to see at a glance.
On top of that, the Supertrend and bar colors tell you what side of the market to favor. The Supertrend is calculated from ATR on whatever timeframe you choose in the settings. If you set the MTF input to a higher timeframe, the Supertrend and ATR ratio become your higher time frame bias while you trade on a lower chart. When price is above the MTF Supertrend, the line uses your bullish color and, if bar coloring is enabled, candles adopt your bullish bar color. That is your “long only” environment: you generally look for buys when price is above the Supertrend and the ATR ratio is either turning up from neutral or already in a bullish zone. When price is below the MTF Supertrend, the line uses your bearish color and candles can shift to your bearish bar color; that is where you focus on shorts, especially when the ATR ratio is rolling over or holding in the bearish zone.
The built-in long and short conditions are meant as signal prompts, not rigid rules. Long signals fire when the ATR ratio crosses up through a positive level while the Supertrend is bullish. Short signals fire when the ATR ratio crosses down through a negative level while the Supertrend is bearish. The script tracks how many longs or shorts have been taken in sequence (pyramiding) and will only allow a new signal up to the limit you set, so you can control how aggressively you stack positions in a trend. The take-profit logic then watches the percentage move from your last entry and flags “TP” when that move has reached your take-profit percent, helping you standardize exits instead of eyeballing them bar by bar.
In practice you typically start by choosing your anchor timeframe for the MTF setting, for example a 1-hour or 4-hour Supertrend and ATR ratio while watching a 5-minute or 15-minute chart. You then use the Supertrend direction and bar colors as your bias filter, only taking signals in the direction of the trend, and you use the ATR ratio behavior to judge whether you are entering into strength, fading an extreme, or trading inside a neutral consolidation. Over time this gives you a consistent way to answer three questions on every chart: which side am I allowed to trade, how extended is price within its recent volatility, and where are my structured entries and exits based on that framework.
VIX vs VIX1Y SpreadSpread Calculation: Shows VIX1Y minus VIX
Positive = longer-term vol higher (normal contango)
Negative = near-term vol elevated (inverted term structure)
Can help identify longer term risk pricing of equity assets.
NQ-VIX Expected Move LevelsNQ -VIX Daily Price Bands
This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Open + (NQ Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily Open - (NQ Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's open
Lower band (red) contracts from the current day's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current NQ price and VIX level
Daily Open
Expected move
NQ-VIX Expected Move LTF LevelsNQ -VIX LTF Price Bands
This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = (Input TF Open) + (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current input TF
Current NQ price and VIX level
Current input TF Open
Expected move
ES-VIX Expected Move LTF LevelsES-VIX LTF Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = (Input TF Open) + (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current input TF
Current ES price and VIX level
Current input TF Open
Expected move
Santhosh Time Block HighlighterI have created an indicator to differentiate market trend/momentum in different time zone during trading day. This will help us to understand the market pattern to avoid entering trade during consolidation/distribution. Its helps to measure the volatility and market sentiment
Fast Autocorrelation Estimator█ Overview:
The Fast ACF and PACF Estimation indicator efficiently calculates the autocorrelation function (ACF) and partial autocorrelation function (PACF) using an online implementation. It helps traders identify patterns and relationships in financial time series data, enabling them to optimize their trading strategies and make better-informed decisions in the markets.
█ Concepts:
Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay.
This indicator displays autocorrelation based on lag number. The autocorrelation is not displayed based over time on the x-axis. It's based on the lag number which ranges from 1 to 30. The calculations can be done with "Log Returns", "Absolute Log Returns" or "Original Source" (the price of the asset displayed on the chart).
When calculating autocorrelation, the resulting value will range from +1 to -1, in line with the traditional correlation statistic. An autocorrelation of +1 represents a perfect correlation (an increase seen in one time series leads to a proportionate increase in the other time series). An autocorrelation of -1, on the other hand, represents a perfect inverse correlation (an increase seen in one time series results in a proportionate decrease in the other time series). Lag number indicates which historical data point is autocorrelated. For example, if lag 3 shows significant autocorrelation, it means current data is influenced by the data three bars ago.
The Fast Online Estimation of ACF and PACF Indicator is a powerful tool for analyzing the linear relationship between a time series and its lagged values in TradingView. The indicator implements an online estimation of the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF) up to 30 lags, providing a real-time assessment of the underlying dependencies in your time series data. The Autocorrelation Function (ACF) measures the linear relationship between a time series and its lagged values, capturing both direct and indirect dependencies. The Partial Autocorrelation Function (PACF) isolates the direct dependency between the time series and a specific lag while removing the effect of any indirect dependencies.
This distinction is crucial in understanding the underlying relationships in time series data and making more informed decisions based on those relationships. For example, let's consider a time series with three variables: A, B, and C. Suppose that A has a direct relationship with B, B has a direct relationship with C, but A and C do not have a direct relationship. The ACF between A and C will capture the indirect relationship between them through B, while the PACF will show no significant relationship between A and C, as it accounts for the indirect dependency through B. Meaning that when ACF is significant at for lag 5, the dependency detected could be caused by an observation that came in between, and PACF accounts for that. This indicator leverages the Fast Moments algorithm to efficiently calculate autocorrelations, making it ideal for analyzing large datasets or real-time data streams. By using the Fast Moments algorithm, the indicator can quickly update ACF and PACF values as new data points arrive, reducing the computational load and ensuring timely analysis. The PACF is derived from the ACF using the Durbin-Levinson algorithm, which helps in isolating the direct dependency between a time series and its lagged values, excluding the influence of other intermediate lags.
█ How to Use the Indicator:
Interpreting autocorrelation values can provide valuable insights into the market behavior and potential trading strategies.
When applying autocorrelation to log returns, and a specific lag shows a high positive autocorrelation, it suggests that the time series tends to move in the same direction over that lag period. In this case, a trader might consider using a momentum-based strategy to capitalize on the continuation of the current trend. On the other hand, if a specific lag shows a high negative autocorrelation, it indicates that the time series tends to reverse its direction over that lag period. In this situation, a trader might consider using a mean-reversion strategy to take advantage of the expected reversal in the market.
ACF of log returns:
Absolute returns are often used to as a measure of volatility. There is usually significant positive autocorrelation in absolute returns. We will often see an exponential decay of autocorrelation in volatility. This means that current volatility is dependent on historical volatility and the effect slowly dies off as the lag increases. This effect shows the property of "volatility clustering". Which means large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes.
ACF of absolute log returns:
Autocorrelation in price is always significantly positive and has an exponential decay. This predictably positive and relatively large value makes the autocorrelation of price (not returns) generally less useful.
ACF of price:
█ Significance:
The significance of a correlation metric tells us whether we should pay attention to it. In this script, we use 95% confidence interval bands that adjust to the size of the sample. If the observed correlation at a specific lag falls within the confidence interval, we consider it not significant and the data to be random or IID (identically and independently distributed). This means that we can't confidently say that the correlation reflects a real relationship, rather than just random chance. However, if the correlation is outside of the confidence interval, we can state with 95% confidence that there is an association between the lagged values. In other words, the correlation is likely to reflect a meaningful relationship between the variables, rather than a coincidence. A significant difference in either ACF or PACF can provide insights into the underlying structure of the time series data and suggest potential strategies for traders. By understanding these complex patterns, traders can better tailor their strategies to capitalize on the observed dependencies in the data, which can lead to improved decision-making in the financial markets.
Significant ACF but not significant PACF: This might indicate the presence of a moving average (MA) component in the time series. A moving average component is a pattern where the current value of the time series is influenced by a weighted average of past values. In this case, the ACF would show significant correlations over several lags, while the PACF would show significance only at the first few lags and then quickly decay.
Significant PACF but not significant ACF: This might indicate the presence of an autoregressive (AR) component in the time series. An autoregressive component is a pattern where the current value of the time series is influenced by a linear combination of past values at specific lags.
Often we find both significant ACF and PACF, in that scenario simply and AR or MA model might not be sufficient and a more complex model such as ARMA or ARIMA can be used.
█ Features:
Source selection: User can choose either 'Log Returns' , 'Absolute Returns' or 'Original Source' for the input data.
Autocorrelation Selection: User can choose either 'ACF' or 'PACF' for the plot selection.
Plot Selection: User can choose either 'Autocorrelarrogram' or 'Historical Autocorrelation' for plotting the historical autocorrelation at a specified lag.
Max Lag: User can select the maximum number of lags to plot.
Precision: User can set the number of decimal points to display in the plot.
Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility.
In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution.
Note: Normal distribution is just an assumption, it's not the real distribution of return
Settings:
"Estimation Period Selection" is for selecting the period we want to construct the prediction interval.
For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours.
For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead.
There are also other source selections based on high low.
Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar.
Note: It's probably better to use "probability cone" for visual presentation when time > 1
Volatility Models :
Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias
Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close
Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient
Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps.
about 8 x efficient
Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient
EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster.
YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance.
Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator.
Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
Standard deviations:
Standard Deviation One shows the estimated range where the closing price will be about 68% of the time.
Standard Deviation two shows the estimated range where the closing price will be about 95% of the time.
Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel.
People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1.
When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation.
Note: The significance of autocorrelation can be checked on an ACF indicator.
ACF
The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D
Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds.
Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag.
Note: The range here after smooth no long represent the probability
Panel positions can be adjusted in the settings.
X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left.
Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down.
Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands.
Warnings:
People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails.
Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption).
Expected move Occurrence Test
The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
ES-VIX Expected Move - Open basedES-VIX Daily Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Open + (ES Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily Open - (ES Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's open
Lower band (red) contracts from the current day's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current ES price and VIX level
Daily Open
Expected move
️Omega RatioThe Omega Ratio is a risk-return performance measure of an investment asset, portfolio, or strategy. It is defined as the probability-weighted ratio, of gains versus losses for some threshold return target. The ratio is an alternative for the widely used Sharpe ratio and is based on information the Sharpe ratio discards.
█ OVERVIEW
As we have mentioned many times, stock market returns are usually not normally distributed. Therefore the models that assume a normal distribution of returns may provide us with misleading information. The Omega Ratio improves upon the common normality assumption among other risk-return ratios by taking into account the distribution as a whole.
█ CONCEPTS
Two distributions with the same mean and variance, would according to the most commonly used Sharpe Ratio suggest that the underlying assets of the distribution offer the same risk-return ratio. But as we have mentioned in our Moments indicator, variance and standard deviation are not a sufficient measure of risk in the stock market since other shape features of a distribution like skewness and excess kurtosis come into play. Omega Ratio tackles this problem by employing all four Moments of the distribution and therefore taking into account the differences in the shape features of the distributions. Another important feature of the Omega Ratio is that it does not require any estimation but is rather calculated directly from the observed data. This gives it an advantage over standard statistical estimators that require estimation of parameters and are therefore sampling uncertainty in its calculations.
█ WAYS TO USE THIS INDICATOR
Omega calculates a probability-adjusted ratio of gains to losses, relative to the Minimum Acceptable Return (MAR). This means that at a given MAR using the simple rule of preferring more to less, an asset with a higher value of Omega is preferable to one with a lower value. The indicator displays the values of Omega at increasing levels of MARs and creating the so-called Omega Curve. Knowing this one can compare Omega Curves of different assets and decide which is preferable given the MAR of your strategy. The indicator plots two Omega Curves. One for the on chart symbol and another for the off chart symbol that u can use for comparison.
When comparing curves of different assets make sure their trading days are the same in order to ensure the same period for the Omega calculations. Value interpretation: Omega<1 will indicate that the risk outweighs the reward and therefore there are more excess negative returns than positive. Omega>1 will indicate that the reward outweighs the risk and that there are more excess positive returns than negative. Omega=1 will indicate that the minimum acceptable return equals the mean return of an asset. And that the probability of gain is equal to the probability of loss.
█ FEATURES
• "Low-Risk security" lets you select the security that you want to use as a benchmark for Omega calculations.
• "Omega Period" is the size of the sample that is used for the calculations.
• “Increments” is the number of Minimal Acceptable Return levels the calculation is carried on. • “Other Symbol” lets you select the source of the second curve.
• “Color Settings” you can set the color for each curve.
Linear Moments█ OVERVIEW
The Linear Moments indicator, also known as L-moments, is a statistical tool used to estimate the properties of a probability distribution. It is an alternative to conventional moments and is more robust to outliers and extreme values.
█ CONCEPTS
█ Four moments of a distribution
We have mentioned the concept of the Moments of a distribution in one of our previous posts. The method of Linear Moments allows us to calculate more robust measures that describe the shape features of a distribution and are anallougous to those of conventional moments. L-moments therefore provide estimates of the location, scale, skewness, and kurtosis of a probability distribution.
The first L-moment, λ₁, is equivalent to the sample mean and represents the location of the distribution. The second L-moment, λ₂, is a measure of the dispersion of the distribution, similar to the sample standard deviation. The third and fourth L-moments, λ₃ and λ₄, respectively, are the measures of skewness and kurtosis of the distribution. Higher order L-moments can also be calculated to provide more detailed information about the shape of the distribution.
One advantage of using L-moments over conventional moments is that they are less affected by outliers and extreme values. This is because L-moments are based on order statistics, which are more resistant to the influence of outliers. By contrast, conventional moments are based on the deviations of each data point from the sample mean, and outliers can have a disproportionate effect on these deviations, leading to skewed or biased estimates of the distribution parameters.
█ Order Statistics
L-moments are statistical measures that are based on linear combinations of order statistics, which are the sorted values in a dataset. This approach makes L-moments more resistant to the influence of outliers and extreme values. However, the computation of L-moments requires sorting the order statistics, which can lead to a higher computational complexity.
To address this issue, we have implemented an Online Sorting Algorithm that efficiently obtains the sorted dataset of order statistics, reducing the time complexity of the indicator. The Online Sorting Algorithm is an efficient method for sorting large datasets that can be updated incrementally, making it well-suited for use in trading applications where data is often streamed in real-time. By using this algorithm to compute L-moments, we can obtain robust estimates of distribution parameters while minimizing the computational resources required.
█ Bias and efficiency of an estimator
One of the key advantages of L-moments over conventional moments is that they approach their asymptotic normal closer than conventional moments. This means that as the sample size increases, the L-moments provide more accurate estimates of the distribution parameters.
Asymptotic normality is a statistical property that describes the behavior of an estimator as the sample size increases. As the sample size gets larger, the distribution of the estimator approaches a normal distribution, which is a bell-shaped curve. The mean and variance of the estimator are also related to the true mean and variance of the population, and these relationships become more accurate as the sample size increases.
The concept of asymptotic normality is important because it allows us to make inferences about the population based on the properties of the sample. If an estimator is asymptotically normal, we can use the properties of the normal distribution to calculate the probability of observing a particular value of the estimator, given the sample size and other relevant parameters.
In the case of L-moments, the fact that they approach their asymptotic normal more closely than conventional moments means that they provide more accurate estimates of the distribution parameters as the sample size increases. This is especially useful in situations where the sample size is small, such as when working with financial data. By using L-moments to estimate the properties of a distribution, traders can make more informed decisions about their investments and manage their risk more effectively.
Below we can see the empirical dsitributions of the Variance and L-scale estimators. We ran 10000 simulations with a sample size of 100. Here we can clearly see how the L-moment estimator approaches the normal distribution more closely and how such an estimator can be more representative of the underlying population.
█ WAYS TO USE THIS INDICATOR
The Linear Moments indicator can be used to estimate the L-moments of a dataset and provide insights into the underlying probability distribution. By analyzing the L-moments, traders can make inferences about the shape of the distribution, such as whether it is symmetric or skewed, and the degree of its spread and peakedness. This information can be useful in predicting future market movements and developing trading strategies.
One can also compare the L-moments of the dataset at hand with the L-moments of certain commonly used probability distributions. Finance is especially known for the use of certain fat tailed distributions such as Laplace or Student-t. We have built in the theoretical values of L-kurtosis for certain common distributions. In this way a person can compare our observed L-kurtosis with the one of the selected theoretical distribution.
█ FEATURES
Source Settings
Source - Select the source you wish the indicator to calculate on
Source Selection - Selec whether you wish to calculate on the source value or its log return
Moments Settings
Moments Selection - Select the L-moment you wish to be displayed
Lookback - Determine the sample size you wish the L-moments to be calculated with
Theoretical Distribution - This setting is only for investingating the kurtosis of our dataset. One can compare our observed kurtosis with the kurtosis of a selected theoretical distribution.
Historical Volatility EstimatorsHistorical volatility is a statistical measure of the dispersion of returns for a given security or market index over a given period. This indicator provides different historical volatility model estimators with percentile gradient coloring and volatility stats panel.
█ OVERVIEW There are multiple ways to estimate historical volatility. Other than the traditional close-to-close estimator. This indicator provides different range-based volatility estimators that take high low open into account for volatility calculation and volatility estimators that use other statistics measurements instead of standard deviation. The gradient coloring and stats panel provides an overview of how high or low the current volatility is compared to its historical values.
█ CONCEPTS We have mentioned the concepts of historical volatility in our previous indicators, Historical Volatility, Historical Volatility Rank, and Historical Volatility Percentile. You can check the definition of these scripts. The basic calculation is just the sample standard deviation of log return scaled with the square root of time. The main focus of this script is the difference between volatility models.
Close-to-Close HV Estimator: Close-to-Close is the traditional historical volatility calculation. It uses sample standard deviation. Note: the TradingView build in historical volatility value is a bit off because it uses population standard deviation instead of sample deviation. N – 1 should be used here to get rid of the sampling bias.
Pros:
• Close-to-Close HV estimators are the most commonly used estimators in finance. The calculation is straightforward and easy to understand. When people reference historical volatility, most of the time they are talking about the close to close estimator.
Cons:
• The Close-to-close estimator only calculates volatility based on the closing price. It does not take account into intraday volatility drift such as high, low. It also does not take account into the jump when open and close prices are not the same.
• Close-to-Close weights past volatility equally during the lookback period, while there are other ways to weight the historical data.
• Close-to-Close is calculated based on standard deviation so it is vulnerable to returns that are not normally distributed and have fat tails. Mean and Median absolute deviation makes the historical volatility more stable with extreme values.
Parkinson Hv Estimator:
• Parkinson was one of the first to come up with improvements to historical volatility calculation. • Parkinson suggests using the High and Low of each bar can represent volatility better as it takes into account intraday volatility. So Parkinson HV is also known as Parkinson High Low HV. • It is about 5.2 times more efficient than Close-to-Close estimator. But it does not take account into jumps and drift. Therefore, it underestimates volatility. Note: By Dividing the Parkinson Volatility by Close-to-Close volatility you can get a similar result to Variance Ratio Test. It is called the Parkinson number. It can be used to test if the market follows a random walk. (It is mentioned in Nassim Taleb's Dynamic Hedging book but it seems like he made a mistake and wrote the ratio wrongly.)
Garman-Klass Estimator:
• Garman Klass expanded on Parkinson’s Estimator. Instead of Parkinson’s estimator using high and low, Garman Klass’s method uses open, close, high, and low to find the minimum variance method.
• The estimator is about 7.4 more efficient than the traditional estimator. But like Parkinson HV, it ignores jumps and drifts. Therefore, it underestimates volatility.
Rogers-Satchell Estimator:
• Rogers and Satchell found some drawbacks in Garman-Klass’s estimator. The Garman-Klass assumes price as Brownian motion with zero drift.
• The Rogers Satchell Estimator calculates based on open, close, high, and low. And it can also handle drift in the financial series.
• Rogers-Satchell HV is more efficient than Garman-Klass HV when there’s drift in the data. However, it is a little bit less efficient when drift is zero. The estimator doesn’t handle jumps, therefore it still underestimates volatility.
Garman-Klass Yang-Zhang extension:
• Yang Zhang expanded Garman Klass HV so that it can handle jumps. However, unlike the Rogers-Satchell estimator, this estimator cannot handle drift. It is about 8 times more efficient than the traditional estimator.
• The Garman-Klass Yang-Zhang extension HV has the same value as Garman-Klass when there’s no gap in the data such as in cryptocurrencies.
Yang-Zhang Estimator:
• The Yang Zhang Estimator combines Garman-Klass and Rogers-Satchell Estimator so that it is based on Open, close, high, and low and it can also handle non-zero drift. It also expands the calculation so that the estimator can also handle overnight jumps in the data.
• This estimator is the most powerful estimator among the range-based estimators. It has the minimum variance error among them, and it is 14 times more efficient than the close-to-close estimator. When the overnight and daily volatility are correlated, it might underestimate volatility a little.
• 1.34 is the optimal value for alpha according to their paper. The alpha constant in the calculation can be adjusted in the settings. Note: There are already some volatility estimators coded on TradingView. Some of them are right, some of them are wrong. But for Yang Zhang Estimator I have not seen a correct version on TV.
EWMA Estimator:
• EWMA stands for Exponentially Weighted Moving Average. The Close-to-Close and all other estimators here are all equally weighted.
• EWMA weighs more recent volatility more and older volatility less. The benefit of this is that volatility is usually autocorrelated. The autocorrelation has close to exponential decay as you can see using an Autocorrelation Function indicator on absolute or squared returns. The autocorrelation causes volatility clustering which values the recent volatility more. Therefore, exponentially weighted volatility can suit the property of volatility well.
• RiskMetrics uses 0.94 for lambda which equals 30 lookback period. In this indicator Lambda is coded to adjust with the lookback. It's also easy for EWMA to forecast one period volatility ahead.
• However, EWMA volatility is not often used because there are better options to weight volatility such as ARCH and GARCH.
Adjusted Mean Absolute Deviation Estimator:
• This estimator does not use standard deviation to calculate volatility. It uses the distance log return is from its moving average as volatility.
• It’s a simple way to calculate volatility and it’s effective. The difference is the estimator does not have to square the log returns to get the volatility. The paper suggests this estimator has more predictive power.
• The mean absolute deviation here is adjusted to get rid of the bias. It scales the value so that it can be comparable to the other historical volatility estimators.
• In Nassim Taleb’s paper, he mentions people sometimes confuse MAD with standard deviation for volatility measurements. And he suggests people use mean absolute deviation instead of standard deviation when we talk about volatility.
Adjusted Median Absolute Deviation Estimator:
• This is another estimator that does not use standard deviation to measure volatility.
• Using the median gives a more robust estimator when there are extreme values in the returns. It works better in fat-tailed distribution.
• The median absolute deviation is adjusted by maximum likelihood estimation so that its value is scaled to be comparable to other volatility estimators.
█ FEATURES
• You can select the volatility estimator models in the Volatility Model input
• Historical Volatility is annualized. You can type in the numbers of trading days in a year in the Annual input based on the asset you are trading.
• Alpha is used to adjust the Yang Zhang volatility estimator value.
• Percentile Length is used to Adjust Percentile coloring lookbacks.
• The gradient coloring will be based on the percentile value (0- 100). The higher the percentile value, the warmer the color will be, which indicates high volatility. The lower the percentile value, the colder the color will be, which indicates low volatility.
• When percentile coloring is off, it won’t show the gradient color.
• You can also use invert color to make the high volatility a cold color and a low volatility high color. Volatility has some mean reversion properties. Therefore when volatility is very low, and color is close to aqua, you would expect it to expand soon. When volatility is very high, and close to red, you would it expect it to contract and cool down.
• When the background signal is on, it gives a signal when HVP is very low. Warning there might be a volatility expansion soon.
• You can choose the plot style, such as lines, columns, areas in the plotstyle input.
• When the show information panel is on, a small panel will display on the right.
• The information panel displays the historical volatility model name, the 50th percentile of HV, and HV percentile. 50 the percentile of HV also means the median of HV. You can compare the value with the current HV value to see how much it is above or below so that you can get an idea of how high or low HV is. HV Percentile value is from 0 to 100. It tells us the percentage of periods over the entire lookback that historical volatility traded below the current level. Higher HVP, higher HV compared to its historical data. The gradient color is also based on this value.
█ HOW TO USE If you haven’t used the hvp indicator, we suggest you use the HVP indicator first. This indicator is more like historical volatility with HVP coloring. So it displays HVP values in the color and panel, but it’s not range bound like the HVP and it displays HV values. The user can have a quick understanding of how high or low the current volatility is compared to its historical value based on the gradient color. They can also time the market better based on volatility mean reversion. High volatility means volatility contracts soon (Move about to End, Market will cooldown), low volatility means volatility expansion soon (Market About to Move).
█ FINAL THOUGHTS HV vs ATR The above volatility estimator concepts are a display of history in the quantitative finance realm of the research of historical volatility estimations. It's a timeline of range based from the Parkinson Volatility to Yang Zhang volatility. We hope these descriptions make more people know that even though ATR is the most popular volatility indicator in technical analysis, it's not the best estimator. Almost no one in quant finance uses ATR to measure volatility (otherwise these papers will be based on how to improve ATR measurements instead of HV). As you can see, there are much more advanced volatility estimators that also take account into open, close, high, and low. HV values are based on log returns with some calculation adjustment. It can also be scaled in terms of price just like ATR. And for profit-taking ranges, ATR is not based on probabilities. Historical volatility can be used in a probability distribution function to calculated the probability of the ranges such as the Expected Move indicator. Other Estimators There are also other more advanced historical volatility estimators. There are high frequency sampled HV that uses intraday data to calculate volatility. We will publish the high frequency volatility estimator in the future. There's also ARCH and GARCH models that takes volatility clustering into account. GARCH models require maximum likelihood estimation which needs a solver to find the best weights for each component. This is currently not possible on TV due to large computational power requirements. All the other indicators claims to be GARCH are all wrong.
