Mutanabby_AI | ONEUSDT_MR1
ONEUSDT Mean-Reversion Strategy | 74.68% Win Rate | 417% Net Profit
This is a long-only mean-reversion strategy designed specifically for ONEUSDT on the 1-hour timeframe. The core logic identifies oversold conditions following sharp declines and enters positions when selling pressure exhausts, capturing the subsequent recovery bounce.
Backtested Period: June 2019 – December 2025 (~6 years)
Performance Summary
| Metric | Value |
|--------|-------|
| Net Profit | +417.68% |
| Win Rate | 74.68% |
| Profit Factor | 4.019 |
| Total Trades | 237 |
| Sharpe Ratio | 0.364 |
| Sortino Ratio | 1.917 |
| Max Drawdown | 51.08% |
| Avg Win | +3.14% |
| Avg Loss | -2.30% |
| Buy & Hold Return | -80.44% |
Strategy Logic :
Entry Conditions (Long Only):
The strategy seeks confluence of three conditions that identify exhausted selling:
1. Prior Move Filter:*The price change from 5 bars ago to 3 bars ago must be ≥ -7% (ensures we're not entering during freefall)
2. Current Move Filter: The price change over the last 2 bars must be ≤ 0% (confirms momentum is stalling or reversing)
3. Three-Bar Decline: The price change from 5 bars ago to 3 bars ago must be ≤ -5% (confirms a significant recent drop occurred)
When all three conditions align, the strategy identifies a potential reversal point where sellers are exhausted.
Exit Conditions:
- Primary Exit: Close above the previous bar's high while the open of the previous bar is at or below the close from 9 bars ago (profit-taking on strength)
- Trailing Stop: 11x ATR trailing stop that locks in profits as price rises
Risk Management
- Position Sizing:Fixed position based on account equity divided by entry price
- Trailing Stop:11× ATR (14-period) provides wide enough room for crypto volatility while protecting gains
- Pyramiding:Up to 4 orders allowed (can scale into winning positions)
- **Commission:** 0.1% per trade (realistic exchange fees included)
Important Disclaimers
⚠️ This is NOT financial advice.
- Past performance does not guarantee future results
- Backtest results may contain look-ahead bias or curve-fitting
- Real trading involves slippage, liquidity issues, and execution delays
- This strategy is optimized for ONEUSDT specifically — results may differ on other pairs
- Always test before risking real capital
Recommended Usage
- Timeframe:*1H (as designed)
- Pair: ONEUSDT (Binance)
- Account Size: Ensure sufficient capital to survive max drawdown
Source Code
Feedback Welcome
I'm sharing this strategy freely for educational purposes. Please:
- Drop a comment with your backtesting results any you analysis
- Share any modifications that improve performance
- Let me know if you spot any issues in the logic
Happy trading
As a quant trader, do you think this strategy will survive in live trading?
Yes or No? And why?
I want to hear from you guys
在腳本中搜尋"curve"
ALT Risk Metric StrategyHere's a professional write-up for your ALT Risk Strategy script:
ALT/BTC Risk Strategy - Multi-Crypto DCA with Bitcoin Correlation Analysis
Overview
This strategy uses Bitcoin correlation as a risk indicator to time entries and exits for altcoins. By analyzing how your chosen altcoin performs relative to Bitcoin, the strategy identifies optimal accumulation periods (when alt/BTC is oversold) and profit-taking opportunities (when alt/BTC is overbought). Perfect for traders who want to outperform Bitcoin by strategically timing altcoin positions.
Key Innovation: Why Alt/BTC Matters
Most traders focus solely on USD price, but Alt/BTC ratios reveal true altcoin strength:
When Alt/BTC is low → Altcoin is undervalued relative to Bitcoin (buy opportunity)
When Alt/BTC is high → Altcoin has outperformed Bitcoin (take profits)
This approach captures the rotation between BTC and alts that drives crypto cycles
Key Features
📊 Advanced Technical Analysis
RSI (60% weight): Primary momentum indicator on weekly timeframe
Long-term MA Deviation (35% weight): Measures distance from 150-period baseline
MACD (5% weight): Minor confirmation signal
EMA Smoothing: Filters noise while maintaining responsiveness
All calculations performed on Alt/BTC pairs for superior market timing
💰 3-Tier DCA System
Level 1 (Risk ≤ 70): Conservative entry, base allocation
Level 2 (Risk ≤ 50): Increased allocation, strong opportunity
Level 3 (Risk ≤ 30): Maximum allocation, extreme undervaluation
Continuous buying: Executes every bar while below threshold for true DCA behavior
Cumulative sizing: L3 triggers = L1 + L2 + L3 amounts combined
📈 Smart Profit Management
Sequential selling: Must complete L1 before L2, L2 before L3
Percentage-based exits: Sell portions of position, not fixed amounts
Auto-reset on re-entry: New buy signals reset sell progression
Prevents premature full exits during volatile conditions
🤖 3Commas Automation
Pre-configured JSON webhooks for Custom Signal Bots
Multi-exchange support: Binance, Coinbase, Kraken, Bitfinex, Bybit
Flexible quote currency: USD, USDT, or BUSD
Dynamic order sizing: Automatically adjusts to your tier thresholds
Full webhook documentation compliance
🎨 Multi-Asset Support
Pre-configured for popular altcoins:
ETH (Ethereum)
SOL (Solana)
ADA (Cardano)
LINK (Chainlink)
UNI (Uniswap)
XRP (Ripple)
DOGE
RENDER
Custom option for any other crypto
How It Works
Risk Metric Calculation (0-100 scale):
Fetches weekly Alt/BTC price data for stability
Calculates RSI, MACD, and deviation from 150-period MA
Normalizes MACD to 0-100 range using 500-bar lookback
Combines weighted components: (MACD × 0.05) + (RSI × 0.60) + (Deviation × 0.35)
Applies 5-period EMA smoothing for cleaner signals
Color-Coded Risk Zones:
Green (0-30): Extreme buying opportunity - Alt heavily oversold vs BTC
Lime/Yellow (30-70): Accumulation range - favorable risk/reward
Orange (70-85): Caution zone - consider taking initial profits
Red/Maroon (85-100+): Euphoria zone - aggressive profit-taking
Entry Logic:
Buys execute every candle when risk is below threshold
As risk decreases, position sizing automatically scales up
Example: If risk drops from 60→25, you'll be buying at L1 rate until it hits 50, then L2 rate, then L3 rate
Exit Logic:
Sells only trigger when in profit AND risk exceeds thresholds
Sequential execution ensures partial profit-taking
If new buy signal occurs before all sells complete, sell levels reset to L1
Configuration Guide
Choosing Your Altcoin:
Select crypto from dropdown (or use CUSTOM for unlisted coins)
Pick your exchange
Choose quote currency (USD, USDT, BUSD)
Risk Metric Tuning:
Long Term MA (default 150): Higher = more extreme signals, Lower = more frequent
RSI Length (default 10): Lower = more volatile, Higher = smoother
Smoothing (default 5): Increase for less noise, decrease for faster reaction
Buy Settings (Aggressive DCA Example):
L1 Threshold: 70 | Amount: $5
L2 Threshold: 50 | Amount: $6
L3 Threshold: 30 | Amount: $7
Total L3 buy = $18 per candle when deeply oversold
Sell Settings (Balanced Exit Example):
L1: 70 threshold, 25% position
L2: 85 threshold, 35% position
L3: 100 threshold, 40% position (final exit)
3Commas Setup
Bot Configuration:
Create Custom Signal Bot in 3Commas
Set trading pair to your altcoin/USD (e.g., ETH/USD, SOL/USDT)
Order size: Select "Send in webhook, quote" to use strategy's dollar amounts
Copy Bot UUID and Secret Token
Script Configuration:
Paste credentials into 3Commas section inputs
Check "Enable 3Commas Alerts"
Save and apply to chart
TradingView Alert:
Create Alert → Condition: "alert() function calls only"
Webhook URL: api.3commas.io
Enable "Webhook URL" checkbox
Expiration: Open-ended
Strategy Advantages
✅ Outperform Bitcoin: Designed specifically to beat BTC by timing alt rotations
✅ Capture Alt Seasons: Automatically accumulates when alts lag, sells when they pump
✅ Risk-Adjusted Sizing: Buys more when cheaper (better risk/reward)
✅ Emotional Discipline: Systematic approach removes fear and FOMO
✅ Multi-Asset: Run same strategy across multiple altcoins simultaneously
✅ Proven Indicators: Combines RSI, MACD, and MA deviation - battle-tested tools
Backtesting Insights
Optimal Timeframes:
Daily chart: Best for backtesting and signal generation
Weekly data is fetched internally regardless of display timeframe
Historical Performance Characteristics:
Accumulates heavily during bear markets and BTC dominance periods
Captures explosive altcoin rallies when BTC stagnates
Sequential selling preserves capital during extended downtrends
Works best on established altcoins with multi-year history
Risk Considerations:
Requires capital reserves for extended accumulation periods
Some altcoins may never recover if fundamentals deteriorate
Past correlation patterns may not predict future performance
Always size positions according to personal risk tolerance
Visual Interface
Indicator Panel Displays:
Dynamic color line: Green→Lime→Yellow→Orange→Red as risk increases
Horizontal threshold lines: Dashed lines mark your buy/sell levels
Entry/Exit labels: Green labels for buys, Orange/Red/Maroon for sells
Real-time risk value: Numerical display on price scale
Customization:
All threshold lines are adjustable via inputs
Color scheme clearly differentiates buy zones (green spectrum) from sell zones (red spectrum)
Line weights emphasize most extreme thresholds (L3 buy and L3 sell)
Strategy Philosophy
This strategy is built on the principle that altcoins move in cycles relative to Bitcoin. During Bitcoin rallies, alts often bleed against BTC (high sell, accumulate). When Bitcoin consolidates, alts pump (take profits). By measuring risk on the Alt/BTC chart instead of USD price, we time these rotations with precision.
The 3-tier system ensures you're always averaging in at better prices and scaling out at better prices, maximizing your Bitcoin-denominated returns.
Advanced Tips
Multi-Bot Strategy:
Run this on 5-10 different altcoins simultaneously to:
Diversify correlation risk
Capture whichever alt is pumping
Smooth equity curve through rotation
Pairing with BTC Strategy:
Use alongside the BTC DCA Risk Strategy for complete portfolio coverage:
BTC strategy for core holdings
ALT strategies for alpha generation
Rebalance between them based on BTC dominance
Threshold Calibration:
Check 2-3 years of historical data for your chosen alt
Note where risk metric sat during major bottoms (set buy thresholds)
Note where it peaked during euphoria (set sell thresholds)
Adjust for your risk tolerance and holding period
Credits
Strategy Development & 3Commas Integration: Claude AI (Anthropic)
Technical Analysis Framework: RSI, MACD, Moving Average theory
Implementation: pommesUNDwurst
Disclaimer
This strategy is for educational purposes only. Cryptocurrency trading involves substantial risk of loss. Altcoins are especially volatile and many fail completely. The strategy assumes liquid markets and reliable Alt/BTC price data. Always do your own research, understand the fundamentals of any asset you trade, and never risk more than you can afford to lose. Past performance does not guarantee future results. The authors are not financial advisors and assume no liability for trading decisions.
Additional Warning: Using leverage or trading illiquid altcoins amplifies risk significantly. This strategy is designed for spot trading of established cryptocurrencies with deep liquidity.
Tags: Altcoin, Alt/BTC, DCA, Risk Metric, Dollar Cost Averaging, 3Commas, ETH, SOL, Crypto Rotation, Bitcoin Correlation, Automated Trading, Alt Season
Feel free to modify any sections to better match your style or add specific backtesting results you've observed! 🚀Claude is AI and can make mistakes. Please double-check responses. Sonnet 4.5
Macro-Sentiment (Macro_Serie 1:7)Part of a 7-indicator macro series. Combines yield curve dynamics, VIX structure, employment data (jobless claims, NFP), ISM manufacturing, US-Japan carry trade flows, and consumer sentiment into a single adaptive stress score. Color-coded regimes guide strategy from "Aggressive" to "Buy the Crash."
Triple ATR Adaptive MAs + VWAP Option + Clouds + Candle Trend V2Another one of my experiences ... combining things...
📘 Indicator Description – Triple ATR Adaptive Moving Averages with VWAP Influence
This indicator plots three adaptive moving averages whose behavior changes dynamically based on market volatility (ATR) and optionally VWAP deviation.
Because they adapt in real time to both volatility and VWAP pressure, their movement, slope, and reaction speed differ significantly from traditional moving averages.
🔶 1. ATR-Adaptive Moving Averages
Each of the three MAs uses a custom adaptive formula:
ATR (Average True Range) is measured over a chosen period.
Higher ATR → more volatility → the MA becomes more reactive and moves closer to price.
Lower ATR → stable market → the MA becomes smoother and slower.
This creates a volatility-aware smoothing factor, making the MA expand, contract, and respond to market conditions in ways a classic SMA, EMA, or HMA cannot.
🔷 2. Optional VWAP Influence
Each MA has an independent toggle allowing it to be influenced by VWAP.
When enabled:
The MA is gently “pulled” toward VWAP.
The strength of this attraction is determined by the VWAP Influence parameter (0–1).
This causes the moving averages to behave differently from normal MAs:
In trending markets, the ATR and price push the MA away from VWAP.
In mean-reverting or balanced conditions, VWAP pulls the MA back toward fair value.
The result is an MA that reflects both trend pressure and fair-value pressure.
🔶 3. Visual Behavior: Non-Traditional Movement
Because each MA is simultaneously influenced by volatility, trend magnitude, and VWAP deviation, their shape is often very distinct from normal moving averages.
They may:
Respond faster during high volatility
Flatten out earlier during consolidation
Curve toward VWAP when price becomes extended
Separate or compress depending on ATR strength
This is intentional and essential, since the goal is to show:
✔ Volatility expansion
✔ Trend exhaustion
✔ Overextended price relative to VWAP
✔ Dynamic trend confirmation
Rather than simply smoothing past price.
🔷 4. Three Independent Adaptive Lines
Each of the three moving averages has:
Its own ATR length
Its own sensitivity multiplier
Its own optional VWAP influence
Its own color and trail
This allows the user to combine:
a fast volatility-adaptive trend line
a mid-range adaptive baseline
a slow adaptive long-trend MA
All adapting independently to volatility and VWAP conditions.
🔶 5. Optional Candle Coloring
The indicator can color candles according to trend strength derived from the fast/slow MAs.
Stronger trends produce more vivid colors. Neutral or conflicting trends produce softer colors.
This adds a visual layer to identify:
Trend direction
Trend strength
Volatility state
Market compression
at a glance.
📌 Summary
This indicator does not behave like standard SMAs or EMAs because each line dynamically adapts to:
🔸 ATR (volatility)
🔸 VWAP (fair value)
This makes the indicator extremely responsive to market conditions while still reducing noise during stable phases.
It provides a more realistic, context-aware, and intelligent representation of price behavior compared to traditional moving averages.
VV Moving Average Convergence Divergence # VMACDv3 - Volume-Weighted MACD with A/D Divergence Detection
## Overview
**VMACDv3** (Volume-Weighted Moving Average Convergence Divergence Version 3) is a momentum indicator that applies volume-weighting to traditional MACD calculations on price, while using the Accumulation/Distribution (A/D) line for divergence detection. This hybrid approach combines volume-weighted price momentum with volume distribution analysis for comprehensive market insight.
## Key Features
- **Volume-Weighted Price MACD**: Traditional MACD calculation on price but weighted by volume for earlier signals
- **A/D Divergence Detection**: Identifies when A/D trend diverges from MACD momentum
- **Volume Strength Filtering**: Distinguishes high-volume confirmations from low-volume noise
- **Color-Coded Histogram**: 4-color system showing momentum direction and volume strength
- **Real-Time Alerts**: Background colors and alert conditions for bullish/bearish divergences
## Difference from ACCDv3
| Aspect | VMACDv3 | ACCDv3 |
|--------|---------|---------|
| **MACD Input** | **Price (Close)** | **A/D Line** |
| **Volume Weighting** | Applied to price | Applied to A/D line |
| **Primary Signal** | Volume-weighted price momentum | Volume distribution momentum |
| **Use Case** | Price momentum with volume confirmation | Volume flow and accumulation/distribution |
| **Sensitivity** | More responsive to price changes | More responsive to volume patterns |
| **Best For** | Trend following, breakouts | Volume analysis, smart money tracking |
**Key Insight**: VMACDv3 shows *where price is going* with volume weight, while ACCDv3 shows *where volume is accumulating/distributing*.
## Components
### 1. Volume-Weighted MACD on Price
Unlike standard MACD that uses simple price EMAs, VMACDv3 weights each price by its corresponding volume:
```
Fast Line = EMA(Price × Volume, 12) / EMA(Volume, 12)
Slow Line = EMA(Price × Volume, 26) / EMA(Volume, 26)
MACD = Fast Line - Slow Line
```
**Benefits of Volume Weighting**:
- High-volume price movements have greater impact
- Filters out low-volume noise and false moves
- Provides earlier trend change signals
- Better reflects institutional activity
### 2. Accumulation/Distribution (A/D) Line
Used for divergence detection, measuring buying/selling pressure:
```
A/D = Σ ((2 × Close - Low - High) / (High - Low)) × Volume
```
- **Rising A/D**: Accumulation (buying pressure)
- **Falling A/D**: Distribution (selling pressure)
- **Doji Handling**: When High = Low, contribution is zero
### 3. Signal Lines
- **MACD Line** (Blue, #2962FF): The fast-slow difference showing momentum
- **Signal Line** (Orange, #FF6D00): EMA or SMA smoothing of MACD
- **Zero Line**: Reference for bullish (above) vs bearish (below) bias
### 4. Histogram Color System
The histogram uses 4 distinct colors based on **direction** and **volume strength**:
| Condition | Color | Meaning |
|-----------|-------|---------|
| Rising + High Volume | **Dark Green** (#1B5E20) | Strong bullish momentum with volume confirmation |
| Rising + Low Volume | **Light Teal** (#26A69A) | Bullish momentum but weak volume (less reliable) |
| Falling + High Volume | **Dark Red** (#B71C1C) | Strong bearish momentum with volume confirmation |
| Falling + Low Volume | **Light Pink** (#FFCDD2) | Bearish momentum but weak volume (less reliable) |
Additional shading:
- **Light Cyan** (#B2DFDB): Positive but not rising (momentum stalling)
- **Bright Red** (#FF5252): Negative and accelerating down
### 5. Divergence Detection
VMACDv3 compares A/D trend against volume-weighted price MACD:
#### Bullish Divergence (Green Background)
- **Condition**: A/D is trending up BUT MACD is negative and trending down
- **Interpretation**: Volume is accumulating while price momentum appears weak
- **Signal**: Smart money accumulation, potential bullish reversal
- **Action**: Look for long entries, especially at support levels
#### Bearish Divergence (Red Background)
- **Condition**: A/D is trending down BUT MACD is positive and trending up
- **Interpretation**: Volume is distributing while price momentum appears strong
- **Signal**: Smart money distribution, potential bearish reversal
- **Action**: Consider exits, avoid new longs, watch for breakdown
## Parameters
| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| **Source** | Close | OHLC/HLC3/etc | Price source for MACD calculation |
| **Fast Length** | 12 | 1-50 | Period for fast EMA (shorter = more sensitive) |
| **Slow Length** | 26 | 1-100 | Period for slow EMA (longer = smoother) |
| **Signal Smoothing** | 9 | 1-50 | Period for signal line (MACD smoothing) |
| **Signal Line MA Type** | EMA | SMA/EMA | Moving average type for signal calculation |
| **Volume MA Length** | 20 | 5-100 | Period for volume average (strength filter) |
## Usage Guide
### Reading the Indicator
1. **MACD Lines (Blue & Orange)**
- **Blue Line (MACD)**: Volume-weighted price momentum
- **Orange Line (Signal)**: Smoothed trend of MACD
- **Crossovers**: Blue crosses above orange = bullish, below = bearish
- **Distance**: Wider gap = stronger momentum
- **Zero Line Position**: Above = bullish bias, below = bearish bias
2. **Histogram Colors**
- **Dark Green (#1B5E20)**: Strong bullish move with high volume - **most reliable buy signal**
- **Light Teal (#26A69A)**: Bullish but low volume - wait for confirmation
- **Dark Red (#B71C1C)**: Strong bearish move with high volume - **most reliable sell signal**
- **Light Pink (#FFCDD2)**: Bearish but low volume - may be temporary dip
3. **Background Divergence Alerts**
- **Green Background**: A/D accumulating while price weak - potential bottom
- **Red Background**: A/D distributing while price strong - potential top
- Most powerful at key support/resistance levels
### Trading Strategies
#### Strategy 1: Volume-Confirmed Trend Following
1. Wait for MACD to cross above zero line
2. Look for **dark green** histogram bars (high volume confirmation)
3. Enter long on second consecutive dark green bar
4. Hold while histogram remains green
5. Exit when histogram turns light green or red appears
6. Set stop below recent swing low
**Example**:
```
Price: 26,400 → 26,450 (rising)
MACD: -50 → +20 (crosses zero)
Histogram: Light teal → Dark green → Dark green
Volume: 50k → 75k → 90k (increasing)
```
#### Strategy 2: Divergence Reversal Trading
1. Identify divergence background (green = bullish, red = bearish)
2. Confirm with price structure (support/resistance, chart patterns)
3. Wait for MACD to cross signal line in divergence direction
4. Enter on first **dark colored** histogram bar after divergence
5. Set stop beyond divergence area
6. Target previous swing high/low
**Example - Bullish Divergence**:
```
Price: Making lower lows (26,350 → 26,300 → 26,250)
A/D: Rising (accumulation)
MACD: Below zero but starting to curve up
Background: Green shading appears
Entry: MACD crosses signal line + dark green bar
Stop: Below 26,230
Target: 26,450 (previous high)
```
#### Strategy 3: Momentum Scalping
1. Trade only in direction of MACD zero line (above = long, below = short)
2. Enter on dark colored bars only
3. Exit on first light colored bar or opposite color
4. Quick in and out (1-5 minute holds)
5. Tight stops (0.2-0.5% depending on instrument)
#### Strategy 4: Histogram Pattern Trading
**V-Bottom Reversal (Bullish)**:
- Red histogram bars start rising (becoming less negative)
- Forms "V" shape at the bottom
- Transitions to light red → light teal → **dark green**
- Entry: First dark green bar
- Signal: Momentum reversal with volume
**Λ-Top Reversal (Bearish)**:
- Green histogram bars start falling (becoming less positive)
- Forms inverted "V" at the top
- Transitions to light green → light pink → **dark red**
- Entry: First dark red bar
- Signal: Momentum exhaustion with volume
### Multi-Timeframe Analysis
**Recommended Approach**:
1. **Higher Timeframe (15m/1h)**: Identify overall trend direction
2. **Trading Timeframe (5m)**: Time entries using VMACDv3 signals
3. **Lower Timeframe (1m)**: Fine-tune entry prices
**Example Setup**:
```
15-minute: MACD above zero (bullish bias)
5-minute: Dark green histogram appears after pullback
1-minute: Enter on break of recent high with volume
```
### Volume Strength Interpretation
The volume filter compares current volume to 20-period average:
- **Volume > Average**: Dark colors (green/red) - high confidence signals
- **Volume < Average**: Light colors (teal/pink) - lower confidence signals
**Trading Rules**:
- ✓ **Aggressive**: Take all dark colored signals
- ✓ **Conservative**: Only take dark colors that follow 2+ light colors of same type
- ✗ **Avoid**: Trading light colored signals during high volatility
- ✗ **Avoid**: Ignoring volume context during news events
## Technical Details
### Volume-Weighted Calculation
```pine
// Volume-weighted fast EMA
fast_ma = ta.ema(src * volume, fast_length) / ta.ema(volume, fast_length)
// Volume-weighted slow EMA
slow_ma = ta.ema(src * volume, slow_length) / ta.ema(volume, slow_length)
// MACD is the difference
macd = fast_ma - slow_ma
// Signal line smoothing
signal = ta.ema(macd, signal_length) // or ta.sma() if SMA selected
// Histogram
hist = macd - signal
```
### Divergence Detection Logic
```pine
// A/D trending up if above its 5-period SMA
ad_trend = ad > ta.sma(ad, 5)
// MACD trending up if above zero
macd_trend = macd > 0
// Divergence when trends oppose each other
divergence = ad_trend != macd_trend
// Specific conditions for alerts
bullish_divergence = ad_trend and not macd_trend and macd < 0
bearish_divergence = not ad_trend and macd_trend and macd > 0
```
### Histogram Coloring Logic
```pine
hist_color = (hist >= 0
? (hist < hist
? (vol_strength ? #1B5E20 : #26A69A) // Rising: dark/light green
: #B2DFDB) // Positive but falling: cyan
: (hist < hist
? (vol_strength ? #B71C1C : #FFCDD2) // Rising (less negative): dark/light red
: #FF5252)) // Falling more: bright red
```
## Alerts
Built-in alert conditions for divergence detection:
### Bullish Divergence Alert
- **Trigger**: A/D trending up, MACD negative and trending down
- **Message**: "Bullish Divergence: A/D trending up but MACD trending down"
- **Use Case**: Potential reversal or continuation after pullback
- **Action**: Look for long entry setups
### Bearish Divergence Alert
- **Trigger**: A/D trending down, MACD positive and trending up
- **Message**: "Bearish Divergence: A/D trending down but MACD trending up"
- **Use Case**: Potential top or trend reversal
- **Action**: Consider exits or short entries
### Setting Up Alerts
1. Click "Create Alert" in TradingView
2. Condition: Select "VMACDv3"
3. Choose alert type: "Bullish Divergence" or "Bearish Divergence"
4. Configure: Email, SMS, webhook, or popup
5. Set frequency: "Once Per Bar Close" recommended
## Comparison Tables
### VMACDv3 vs Standard MACD
| Feature | Standard MACD | VMACDv3 |
|---------|---------------|---------|
| **Price Weighting** | Equal weight all bars | Volume-weighted |
| **Sensitivity** | Fixed | Adaptive to volume |
| **False Signals** | More during low volume | Fewer (volume filter) |
| **Divergence** | Price vs MACD | A/D vs MACD |
| **Volume Analysis** | None | Built-in |
| **Color System** | 2 colors | 4+ colors |
| **Best For** | Simple trend following | Volume-confirmed trading |
### VMACDv3 vs ACCDv3
| Aspect | VMACDv3 | ACCDv3 |
|--------|---------|--------|
| **Focus** | Price momentum | Volume distribution |
| **Reactivity** | Faster to price moves | Faster to volume shifts |
| **Best Markets** | Trending, breakouts | Accumulation/distribution phases |
| **Signal Type** | Where price + volume going | Where smart money positioning |
| **Divergence Meaning** | Volume vs price disagreement | A/D vs momentum disagreement |
| **Use Together?** | ✓ Yes, complementary | ✓ Yes, different perspectives |
## Example Trading Scenarios
### Scenario 1: Strong Bullish Breakout
```
Time: 9:30 AM (market open)
Price: Breaks above 26,400 resistance
MACD: Crosses above zero line
Histogram: Dark green bars (#1B5E20)
Volume: 2x average (150k vs 75k avg)
A/D: Rising (no divergence)
Action: Enter long at 26,405
Stop: 26,380 (below breakout)
Target 1: 26,450 (risk:reward 1:2)
Target 2: 26,500 (risk:reward 1:4)
Result: High probability setup with volume confirmation
```
### Scenario 2: False Breakout (Avoided)
```
Time: 2:30 PM (slow period)
Price: Breaks above 26,400 resistance
MACD: Slightly positive
Histogram: Light teal bars (#26A69A)
Volume: 0.5x average (40k vs 75k avg)
A/D: Flat/declining
Action: Avoid trade
Reason: Low volume, no conviction, potential false breakout
Outcome: Price reverses back below 26,400 within 10 minutes
Saved: Avoided losing trade due to volume filter
```
### Scenario 3: Bullish Divergence Bottom
```
Time: 11:00 AM
Price: Making lower lows (26,350 → 26,300 → 26,280)
MACD: Below zero but curving upward
Histogram: Red bars getting shorter (V-bottom forming)
Background: Green shading (divergence alert)
A/D: Rising despite price falling
Volume: Increasing on down bars
Setup:
1. Divergence appears at 26,280 (green background)
2. Wait for MACD to cross signal line
3. First dark green bar appears at 26,290
4. Enter long: 26,295 (next bar open)
5. Stop: 26,265 (below divergence low)
6. Target: 26,350 (previous swing high)
Result: +55 points (30 point risk, 1.8:1 reward)
Key: Divergence + volume confirmation = high probability reversal
```
### Scenario 4: Bearish Divergence Top
```
Time: 1:45 PM
Price: Making higher highs (26,500 → 26,520 → 26,540)
MACD: Positive but flattening
Histogram: Green bars getting shorter (Λ-top forming)
Background: Red shading (bearish divergence)
A/D: Declining despite rising price
Volume: Decreasing on up bars
Setup:
1. Bearish divergence at 26,540 (red background)
2. MACD crosses below signal line
3. First dark red bar appears at 26,535
4. Enter short: 26,530
5. Stop: 26,555 (above divergence high)
6. Target: 26,475 (support level)
Result: +55 points (25 point risk, 2.2:1 reward)
Key: Distribution while price rising = smart money exiting
```
### Scenario 5: V-Bottom Reversal
```
Downtrend in progress
MACD: Deep below zero (-150)
Histogram: Series of dark red bars
Pattern Development:
Bar 1: Dark red, hist = -80, falling
Bar 2: Dark red, hist = -95, falling
Bar 3: Dark red, hist = -100, falling (extreme)
Bar 4: Light pink, hist = -98, rising!
Bar 5: Light pink, hist = -90, rising
Bar 6: Light teal, hist = -75, rising (crosses to positive momentum)
Bar 7: Dark green, hist = -55, rising + volume
Action: Enter long on Bar 7
Reason: V-bottom confirmed with volume
Stop: Below Bar 3 low
Target: Zero line on histogram (mean reversion)
```
## Best Practices
### Entry Rules
✓ **Wait for dark colors**: High-volume confirmation is key
✓ **Confirm divergences**: Use with price support/resistance
✓ **Trade with zero line**: Long above, short below for best odds
✓ **Multiple timeframes**: Align 1m, 5m, 15m signals
✓ **Watch for patterns**: V-bottoms and Λ-tops are reliable
### Exit Rules
✓ **Partial profits**: Take 50% at first target
✓ **Trail stops**: Use histogram color changes
✓ **Respect signals**: Exit on opposite dark color
✓ **Time stops**: Close positions before major news
✓ **End of day**: Square up before close
### Avoid
✗ **Don't chase light colors**: Low volume = low confidence
✗ **Don't ignore divergence**: Early warning system
✗ **Don't overtrade**: Wait for clear setups
✗ **Don't fight the trend**: Zero line dictates bias
✗ **Don't skip stops**: Always use risk management
## Risk Management
### Position Sizing
- **Dark green/red signals**: 1-2% account risk
- **Light signals**: 0.5% account risk or skip
- **Divergence plays**: 1% account risk (higher uncertainty)
- **Multiple confirmations**: Up to 2% account risk
### Stop Loss Placement
- **Trend trades**: Below/above recent swing (20-30 points typical)
- **Breakout trades**: Below/above breakout level (15-25 points)
- **Divergence trades**: Beyond divergence extreme (25-40 points)
- **Scalp trades**: Tight stops at 10-15 points
### Profit Targets
- **Minimum**: 1.5:1 reward to risk ratio
- **Scalps**: 15-25 points (quick in/out)
- **Swing**: 50-100 points (hold through pullbacks)
- **Runners**: Trail with histogram color changes
## Timeframe Recommendations
| Timeframe | Trading Style | Typical Hold | Advantages | Challenges |
|-----------|---------------|--------------|------------|------------|
| **1-minute** | Scalping | 1-5 minutes | Fast profits, many setups | Noisy, high false signals |
| **5-minute** | Intraday | 15-60 minutes | Balance of speed/clarity | Still requires quick decisions |
| **15-minute** | Swing | 1-4 hours | Clearer trends, less noise | Fewer opportunities |
| **1-hour** | Position | 4-24 hours | Strong signals, less monitoring | Wider stops required |
**Recommendation**: Start with 5-minute for best balance of signal quality and opportunity frequency.
## Combining with Other Indicators
### VMACDv3 + ACCDv3
- **Use**: Confirm volume flow with price momentum
- **Signal**: Both showing dark green = highest conviction long
- **Divergence**: VMACDv3 bullish + ACCDv3 bearish = examine price action
### VMACDv3 + RSI
- **Use**: Overbought/oversold with momentum confirmation
- **Signal**: RSI < 30 + dark green VMACD = strong reversal
- **Caution**: RSI > 70 + light green VMACD = potential false breakout
### VMACDv3 + Elder Impulse
- **Use**: Bar coloring + histogram confirmation
- **Signal**: Green Elder bars + dark green VMACD = aligned momentum
- **Exit**: Blue Elder bars + light colors = momentum stalling
## Limitations
- **Requires volume data**: Will not work on instruments without volume feed
- **Lagging indicator**: MACD inherently follows price (2-3 bar delay)
- **Consolidation noise**: Generates false signals in tight ranges
- **Gap handling**: Large gaps can distort volume-weighted values
- **Not standalone**: Should combine with price action and support/resistance
## Troubleshooting
**Problem**: Too many light colored signals
**Solution**: Increase Volume MA Length to 30-40 for stricter filtering
**Problem**: Missing entries due to waiting for dark colors
**Solution**: Lower Volume MA Length to 10-15 for more signals (accept lower quality)
**Problem**: Divergences not appearing
**Solution**: Verify volume data available; check if A/D line is calculating
**Problem**: Histogram colors not changing
**Solution**: Ensure real-time data feed; refresh indicator
## Version History
- **v3**: Removed traditional MACD, using volume-weighted MACD on price with A/D divergence
- **v2**: Added A/D divergence detection, volume strength filtering, enhanced histogram colors
- **v1**: Basic volume-weighted MACD on price
## Related Indicators
**Companion Tools**:
- **ACCDv3**: Volume-weighted MACD on A/D line (distribution focus)
- **RSIv2**: RSI with A/D divergence detection
- **DMI**: Directional Movement Index with A/D divergence
- **Elder Impulse**: Bar coloring system using volume-weighted MACD
**Use Together**: VMACDv3 (momentum) + ACCDv3 (distribution) + Elder Impulse (bar colors) = complete volume-based trading system
---
*This indicator is for educational purposes. Past performance does not guarantee future results. Always practice proper risk management and never risk more than you can afford to lose.*
Open Interest RSI [BackQuant]Open Interest RSI
A multi-venue open interest oscillator that aggregates OI across major derivatives exchanges, converts it to coin or USD terms, and runs an RSI-style engine on that aggregated OI so you can track positioning pressure, crowding, and mean reversion in leverage flows, not just in price.
What this is
This tool is an RSI built on top of aggregated open interest instead of price. It pulls futures OI from several major exchanges, converts it into a unified unit (COIN or USD), sums it into a single synthetic OI candle, then applies RSI and smoothing to that combined series.
You can then render that Open Interest RSI in different visual modes:
Clean line or colored line for classic oscillator-style reads.
Column-style oscillator for impulse and compression views.
Flag mode that fills between OI RSI and its EMA for trend/mean reversion blends. See:
Heatmap mode that paints the panel based on OI RSI extremes, ideal for scanning. See:
On top of that it includes:
Aggregated OI source selection (Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit).
Choice of OI units (COIN or USD).
Reference lines and OB/OS zones.
Extreme highlighting for either trend or mean reversion.
A vertical OI RSI meter that acts as a quick strength gauge.
Aggregated open interest source
Under the hood, the indicator builds a synthetic open interest candle by:
Looping over a list of supported exchanges: Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit.
Looping over multiple contract suffixes (such as USDT.P, USD.P, USDC.P, USD.PM) to capture different contract types on each venue.
Requesting OI candles from each venue + contract combination for the same underlying symbol.
Converting each OI stream into a common unit: In COIN mode, everything is normalized into coin-denominated OI. In USD mode, coin OI is multiplied by price to approximate notional OI.
Summing up open, high, low and close of OI across venues into a single aggregated OI candle.
If no valid OI is available for the current symbol across all sources, the script throws a clear runtime error so you know you are on an unsupported market.
This gives you a single, exchange-agnostic open interest curve instead of being tied to one venue. That aggregated OI is then passed into the RSI logic.
How the OI RSI is calculated
The RSI side is straightforward, but it is applied to the aggregated OI close:
Compute a base RSI of aggregated OI using the Calculation Period .
Apply a simple moving average of length Smoothing Period (SMA) to reduce noise in the raw OI RSI.
Optionally apply an EMA on top of the smoothed OI RSI as a moving average signal line.
Key parameters:
Calculation Period – base RSI length for OI.
Smoothing Period (SMA) – extra smoothing on the RSI value.
EMA Period – EMA length on the smoothed OI RSI.
The result is:
oi_rsi – raw RSI of aggregated OI.
oi_rsi_s – SMA-smoothed OI RSI.
ma – EMA of the smoothed OI RSI.
Thresholds and extremes
You control three core thresholds:
Mid Point – central reference level, typically 50.
Extreme Upper Threshold – high-level OI RSI edge (for example 80).
Extreme Lower Threshold – low-level OI RSI edge (for example 20).
These thresholds are used for:
Reference lines or OB/OS zone fills.
Heatmap gradient bounds.
Background highlighting of extremes.
The Extreme Highlighting mode controls how extremes are interpreted:
None – do nothing special in extreme regions.
Mean-Rev – background turns red on high OI RSI and green on low OI RSI, framing extremes as contrarian zones.
Trend – background turns green on high OI RSI and red on low OI RSI, framing extremes as participation zones aligned with the prevailing move.
Reference lines and OB/OS zones
You can choose:
None – clean plotting without guides.
Basic Reference Lines – mid, upper and lower thresholds as simple gray horizontals.
OB/OS Levels – filled zones between:
Upper OB: from the upper threshold to 100, colored with the short/overbought color.
Lower OS: from 0 to the lower threshold, colored with the long/oversold color.
These guides help visually anchor the OI RSI within "normal" versus "extreme" regions.
Plotting modes
The Plotting Type input controls how OI RSI is drawn. All modes share the same underlying OI and RSI logic, but emphasise different aspects of the signal.
1) Line mode
This is the classic oscillator representation:
Plots the smoothed OI RSI as a simple line using RSI Line Color and RSI Line Width .
Optionally plots the EMA overlay on the same panel.
Works well when you want standard RSI-style signals on leverage flows: crosses of the midline, divergences versus price, and so on.
2) Colored Line mode
In this mode:
The OI RSI is plotted as a line, but its color is dynamic.
If the smoothed OI RSI is above the mid point, it uses the Long/OB Color .
If it is below the mid point, it uses the Short/OS Color .
This creates an instant visual regime switch between "bullish positioning pressure" and "bearish positioning pressure", while retaining the feel of a traditional RSI line.
3) Oscillator mode
Oscillator mode renders OI RSI as vertical columns around the mid level:
The smoothed OI RSI is plotted as columns using plot.style_columns .
The histogram base is fixed at 50, so bars extend above and below the mid line.
Bar color is dynamic, using long or short colors depending on which side of the mid point the value sits.
This representation makes impulse and compression in OI flows more obvious. It is especially useful when you want to focus on how quickly OI RSI is expanding or contracting around its neutral level. See:
4) Flag mode
Flag mode turns OI RSI and its EMA into a two-line band with a filled area between them:
The smoothed OI RSI and its EMA are both plotted.
A fill is drawn between them.
The fill color flips between the long color and the short color depending on whether OI RSI is above or below its EMA.
Black outlines are added to both lines to make the band clear against any background.
This creates a "flag" style region where:
Green fills show OI RSI leading its EMA, suggesting positive positioning momentum.
Red fills show OI RSI trailing below its EMA, suggesting negative positioning momentum.
Crossovers of the two lines can be read as shifts in OI momentum regime.
Flag mode is useful if you want a more structural view that combines both the level and slope behaviour of OI RSI. See:
5) Heatmap mode
Heatmap mode recasts OI RSI as a single-row gradient instead of a line:
A single row at level 1 is plotted using column style.
The color is pulled from a gradient between the lower and upper thresholds: Near the lower threshold it approaches the short/oversold color and near the upper threshold it approaches the long/overbought color.
The EMA overlay and reference lines are disabled in this mode to keep the panel clean.
This is a very compact way to track OI RSI state at a glance, especially when stacking it alongside other indicators. See:
OI RSI vertical meter
Beyond the main plot, the script can draw a small "thermometer" table showing the current OI RSI position from 0 to 100:
The meter is a two-column table with a configurable number of rows.
Row colors form an inverted gradient: red at the top (100) and green at the bottom (0).
The script clamps OI RSI between 0 and 100 and maps it to a row index.
An arrow marker "▶" is drawn next to the row corresponding to the current OI RSI value.
0 and 100 labels are printed at the ends of the scale for orientation.
You control:
Show OI RSI Meter – turn the meter on or off.
OI RSI Blocks – number of vertical blocks (granularity).
OI RSI Meter Position – panel anchor (top/bottom, left/center/right).
The meter is particularly helpful if you keep the main plot in a small panel but still want an intuitive strength gauge.
How to read it as a market pressure gauge
Because this is an RSI built on aggregated open interest, its extremes and regimes speak to positioning pressure rather than price alone:
High OI RSI (near or above the upper threshold) indicates that open interest has been increasing aggressively relative to its recent history. This often coincides with crowded leverage and a buildup of directional pressure.
Low OI RSI (near or below the lower threshold) indicates aggressive de-leveraging or closing of positions, often associated with flushes, forced unwinds or post-liquidation clean-ups.
Values around the mid point indicate more balanced positioning flows.
You can combine this with price action:
Price up with rising OI RSI suggests fresh leverage joining the move, a more persistent trend.
Price up with falling OI RSI suggests shorts covering or longs taking profit, more fragile upside.
Price down with rising OI RSI suggests aggressive new shorts or levered selling.
Price down with falling OI RSI suggests de-leveraging and potential exhaustion of the move.
Trading applications
Trend confirmation on leverage flows
Use OI RSI to confirm or question a price trend:
In an uptrend, rising OI RSI with values above the mid point indicates supportive leverage flows.
In an uptrend, repeated failures to lift OI RSI above mid point or persistent weakness suggest less committed participation.
In a downtrend, strong OI RSI on the downside points to aggressive shorting.
Mean reversion in positioning
Use thresholds and the Mean-Rev highlight mode:
When OI RSI spends extended time above the upper threshold, the crowd is extended on one side. That can set up squeeze risk in the opposite direction.
When OI RSI has been pinned low, it suggests heavy de-leveraging. Once price stabilises, a re-risking phase is often not far away.
Background colours in Mean-Rev mode help visually identify these periods.
Regime mapping with plotting modes
Different plotting modes give different perspectives:
Heatmap mode for dashboard-style use where you just need to know "hot", "neutral" or "cold" on OI flows at a glance.
Oscillator mode for short term impulses and compression reads around the mid line. See:
Flag mode for blending level and trend of OI RSI into a single banded visual. See:
Settings overview
RSI group
Plotting Type – None, Line, Colored Line, Oscillator, Flag, Heatmap.
Calculation Period – base RSI length for OI.
Smoothing Period (SMA) – smoothing on RSI.
Moving Average group
Show EMA – toggle EMA overlay (not used in heatmap).
EMA Period – length of EMA on OI RSI.
EMA Color – colour of EMA line.
Thresholds group
Mid Point – central reference.
Extreme Upper Threshold and Extreme Lower Threshold – OB/OS thresholds.
Select Reference Lines – none, basic lines or OB/OS zone fills.
Extreme Highlighting – None, Mean-Rev, Trend.
Extra Plotting and UI
RSI Line Color and RSI Line Width .
Long/OB Color and Short/OS Color .
Show OI RSI Meter , OI RSI Blocks , OI RSI Meter Position .
Open Interest Source
OI Units – COIN or USD.
Exchange toggles: Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit.
Notes
This is a positioning and pressure tool, not a complete system. It:
Models aggregated futures open interest across multiple centralized exchanges.
Transforms that OI into an RSI-style oscillator for better comparability across regimes.
Offers several visual modes to match different workflows, from detailed analysis to compact dashboards.
Use it to understand how leverage and positioning are evolving behind the price, to gauge when the crowd is stretched, and to decide whether to lean with or against that pressure. Attach it to your existing signals, not in place of them.
Also, please check out @NoveltyTrade for the OI Aggregation logic & pulling the data source!
Here is the original script:
Interest Rate ExpectationsThis indicator shows how much rate cuts or hikes are currently priced into SOFR futures. You choose two SOFR contracts and the script converts each contract price into basis points relative to the current effective fed funds rate. This gives you a very clear view of how policy expectations shift over time.
You can switch between using a fixed EFFR value or pulling the live EFFR ticker. Colours for each line and label are fully adjustable. The script also includes an optional grid for the plus or minus 25, 50 and 75 basis point levels so the chart does not zoom out too far.
Labels appear at the end of both lines and display how many basis points of cuts or hikes are priced for each contract. A small reference box is added on the chart to remind you what each quarterly code represents. For example H is March and Z is December.
The background shading highlights changes in the timing of cuts. Green shading means the market is pushing cuts further out in time. Red shading means cuts are being pulled closer. This gives a simple and visual way to track how the curve reprices near term versus long term policy expectations.
This tool is useful for anyone tracking fed path repricing, front end volatility, macro catalysts or cross asset rate sensitivity.
Scalp Boost LONG✦ Overview
Scalp Boost LONG is a visual tool designed to highlight potential short-term upward impulses.
A signal is generated only when multiple market conditions align at the candle close, combining momentum dynamics, local probability shifts, and abnormal volume behavior.
The indicator does not repaint.
✦ Concept
The tool focuses on selective situations where the market shows signs of micro-breakout potential.
If all internal conditions are confirmed — a LONG event is displayed.
If not — the chart remains clean.
This builds a low-noise signal model, prioritizing quality over frequency.
✦ Signal Logic
The LONG signal requires confirmation of all core conditions:
• Local impulse dynamics
Identifies short-term acceleration suggesting a breakout from a compressed price structure.
• Probability beyond a statistical zone
Uses relative breakout probability instead of fixed levels, checking whether price exceeds expected local ranges.
• Abnormal volume activity
Highlights candles with monetary flow above a custom threshold, signaling increased market interest.
• Anti-overheat filter
Conditions avoiding exhausted or low-momentum phases where continuation is less likely.
Only when all filters are aligned a LONG marker appears.
✦ Visual Structure
The chart display is intentionally minimal:
• ROC Curve
Subdued line, showing short-term momentum without distraction.
• LONG Marker
Green triangle below the candle on confirmed events.
• Candle Highlight
Soft background highlight on the signal bar.
• Volume Marker
Small red dot at the bottom of candles with abnormal monetary flow.
All visual elements appear only on candle close.
✦ Alerts
A clean event structure is available for notifications:
LONG Signal
This allows receiving alerts during chart analysis or in automated workflows while keeping full control over decision-making.
✦ Notes & Guidelines
This tool:
is not a trading system,
does not provide targets or stops,
may trigger against the dominant trend,
should be combined with the user’s own methodology.
Signals are rare by design.
Do not interpret each event as a trend continuation — it highlights conditions, not outcomes.
✦ Suggested Use
-(Non-mandatory ideas for advanced users)
-identifying potential micro-breakouts,
-timing entries around volume spikes,
-adding context to scalping models,
-filtering impulsive moves from noise.
-suitable for a 5-minute timeframe
The indicator can be helpful as a confirmation layer, not a standalone decision tool.
BTC STH Proxy vs Realized Price (RP) Ratio | STH : LTH📊 REALIZED PRICE MARKET SIGNAL
Indicator that builds a Short-Term Holder (STH) price proxy using a configurable moving average of Bitcoin’s market price and compares it to Bitcoin’s Realized Price (RP) derived from on-chain data.
Realized Price (RP) is calculated from CoinMetrics Realized Market Cap divided by Glassnode circulating supply.
STH Proxy is a user-defined moving average (EMA/SMA/WMA) of BTC price, designed to mimic the behavior of the true STH Realized Price.
Users can adjust the MA type, length, and RP smoothing to closely replicate the STH curve seen on Glassnode, Bitbo, and Bitcoin Magazine Pro.
Optionally, the indicator can display the STH/RP ratio, which highlights transitions between market phases.
This tool provides a simple but effective way to visualize short-term vs long-term holder cost-basis dynamics using only publicly accessible on-chain aggregates and price data.
----------
💡TLDR: An alt take on the Short-Term Holder Realized Price / Long-Term Holder Realized Price cross model | (STH/LTH cross)
- A mix of MAs are used to mimic STH.
- RP here used as a proxy for the long-term holder (LTH) cost basis.
- Bull/Bear signals are generated when the STH proxy crosses above or below RP.
⭐ Free to use • Leave feedback • Happy trading!
Probability Cone█ Overview:
Probability Cone is based on the Expected Move . While Expected Move only shows the historical value band on every bar, probability panel extend the period in the future and plot a cone or curve shape of the probable range. It plots the range from bar 1 all the way to bar 31.
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.
The area of probability range is based on an inverse normal cumulative distribution function. The inverse cumulative distribution gives the range of price for given input probability. People can adjust the range by adjusting the standard deviation in the settings. The probability of the entered standard deviation will be shown at the edges of the probability cone.
The shown 68% and 95% probabilities correspond to the full range between the two blue lines of the cone (68%) and the two purple lines of the cone (95%). The probabilities suggest the % of outcomes or data that are expected to lie within this range. It does not suggest the probability of reaching those price levels.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
█ 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.
You can learn more about each of the volatility models in out Historical Volatility Estimators indicator.
█ How to use
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 larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
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.
The shape of the cone will be skewed and have a directional bias when the length of mean is short. It might be more adaptive to the current price or trend, but more accurate estimation should use a longer period for the mean.
Using a short look back for mean will make the cone having a directional bias.
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
Time back settings shift the estimation period back by the input number. It's the origin of when the probability cone start to estimation it's range.
E.g., When time back = 5, the probability cone start its prediction interval estimation from 5 bars ago. So for time back = 5 , it estimates the probability range from 5 bars ago to X number of bars in the future, specified by the Forecast Period (max 1000).
█ 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.
The uncertainty in future bars makes the range wider. The overestimate effect of the body is partly neutralized when it's extended to future bars. We encourage people who use this indicator to further investigate the Historical Volatility Estimators , Fast Autocorrelation Estimator , Expected Move and especially the Linear Moments Indicator .
The probability is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between.
Linear Trajectory & Volume StructureThe Linear Trajectory & Volume Structure indicator is a comprehensive trend-following system designed to identify market direction, volatility-adjusted channels, and high-probability entry points. Unlike standard Moving Averages, this tool utilizes Linear Regression logic to calculate the "best fit" trajectory of price, encased within volatility bands (ATR) to filter out market noise.
It integrates three core analytical components into a single interface:
Trend Engine: A Linear Regression Curve to determine the mean trajectory.
Volume Verification: Filters signals to ensure price movement is backed by market participation.
Market Structure: Identifies previous high-volume supply and demand zones for support and resistance analysis.
2. Core Components and Logic
The Trajectory Engine
The backbone of the system is a Linear Regression calculation. This statistical method fits a straight line through recent price data points to determine the current slope and direction.
The Baseline: Represents the "fair value" or mean trajectory of the asset.
The Cloud: Calculated using Average True Range (ATR). It expands during high volatility and contracts during consolidation.
Trend Definition:
Bullish: Price breaks above the Upper Deviation Band.
Bearish: Price breaks below the Lower Deviation Band.
Neutral/Chop: Price remains inside the cloud.
Smart Volume Filter
The indicator includes a toggleable volume filter. When enabled, the script calculates a Simple Moving Average (SMA) of the volume.
High Volume: Current volume is greater than the Volume SMA.
Signal Validation: Reversal signals and structure zones are only generated if High Volume is present, reducing the likelihood of trading false breakouts on low liquidity.
Volume Structure (Smart Liquidity)
The script automatically plots Support (Demand) and Resistance (Supply) boxes based on pivot points.
Creation: A box is drawn only if a pivot high or low is formed with High Volume (if the volume filter is active).
Mitigation: The boxes extend to the right. If price breaks through a zone, the box turns gray to indicate the level has been breached.
3. Signal Guide
Trend Reversals (Buy/Sell Labels)
These are the primary signals indicating a potential change in the macro trend.
BUY Signal: Appears when price closes above the upper volatility band after previously being in a downtrend.
SELL Signal: Appears when price closes below the lower volatility band after previously being in an uptrend.
Pullbacks (Small Circles)
These are continuation signals, useful for adding to positions or entering an existing trend.
Long Pullback: The trend is Bullish, but price dips momentarily below the baseline (into the "discount" area) and closes back above it.
Short Pullback: The trend is Bearish, but price rallies momentarily above the baseline (into the "premium" area) and closes back below it.
4. Configuration and Settings
Trend Engine Settings
Trajectory Length: The lookback period for the Linear Regression. This is the most critical setting for tuning sensitivity.
Channel Multiplier: Controls the width of the cloud.
1.0: Aggressive. Results in narrower bands and earlier signals, but more false positives.
1.5: Balanced (Default).
2.0+: Conservative. Creates a wide channel, filtering out significant noise but delaying entry signals.
Signal Logic
Show Trend Reversals: Toggles the main Buy/Sell labels.
Show Pullbacks: Toggles the re-entry circle signals.
Smart Volume Filter: If checked, signals require above-average volume. Unchecking this yields more signals but removes the volume confirmation requirement.
Volume Structure
Show Smart Liquidity: Toggles the Support/Resistance boxes.
Structure Lookback: Defines how many bars constitute a pivot. Higher numbers identify only major market structures.
Max Active Zones: Limits the number of boxes on the chart to prevent clutter.
5. Timeframe Optimization Guide
To maximize the effectiveness of the Linear Trajectory, you must adjust the Trajectory Length input based on your trading style and timeframe.
Scalping (1-Minute to 5-Minute Charts)
Recommended Length: 20 to 30
Multiplier: 1.2 to 1.5
Logic: Fast-moving markets require a shorter lookback to react quickly to micro-trend changes.
Day Trading (15-Minute to 1-Hour Charts)
Recommended Length: 55 (Default)
Multiplier: 1.5
Logic: A balance between responsiveness and noise filtering. The default setting of 55 is standard for identifying intraday sessions.
Swing Trading (4-Hour to Daily Charts)
Recommended Length: 89 to 100
Multiplier: 1.8 to 2.0
Logic: Swing trading requires filtering out intraday noise. A longer length ensures you stay in the trade during minor retracements.
6. Dashboard (HUD) Interpretation
The Head-Up Display (HUD) provides a summary of the current market state without needing to analyze the chart visually.
Bias: Displays the current trend direction (BULLISH or BEARISH).
Momentum:
ACCELERATING: Price is moving away from the baseline (strong trend).
WEAKENING: Price is compressing toward the baseline (potential consolidation or reversal).
Volume: Indicates if the current candle's volume is HIGH or LOW relative to the average.
Disclaimer
*Trading cryptocurrencies, stocks, forex, and other financial instruments involves a high level of risk and may not be suitable for all investors. This indicator is a technical analysis tool provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of profit. Past performance of any trading system or methodology is not necessarily indicative of future results.
IDWM Master StructureExecutive Summary
The IDWM Master Structure is a Multi-Timeframe (MTF) trading tool designed to force discipline by aligning traders with the "Parent" trend. It functions by locking onto the "Completed Auction" of a higher timeframe candle (like a Daily or Weekly bar) and projecting that structure onto your lower timeframe chart. Its primary goal is to define the "Dealing Range"—the hard boundaries where value was previously established—so you don't get lost in the noise of smaller price movements.
1. The Principle of Completed Auctions (Hierarchy)
Most technical indicators curve dynamically with every price tick. This script acts differently because it relies on "Settled Arguments." A closed Daily candle represents a finished battle between buyers and sellers; the High and Low are the historical results of that battle.
To enforce this, the script automatically selects a "Parent" timeframe based on your view:
Scalping (charts below 15 minutes) uses the 4-Hour Auction.
Intraday trading (15 minutes to 4 Hours) uses the Daily Auction.
Swing trading (Daily chart) uses the Weekly Auction.
2. Liquidity Pools & The Sticky Range
The High and Low lines drawn by the indicator are not just support and resistance; they represent Liquidity Pools. In market theory, stop-losses (Sell Stops below Lows, Buy Stops above Highs) accumulate at these edges.
Smart money often pushes price just past these lines to grab this liquidity (a "Stop Hunt") before reversing direction. To account for this, the script uses a "Sticky Range" mechanism. It refuses to redraw the box simply because price touched the line. Instead, it uses an Average True Range (ATR) Buffer. A new structure is only formed if the candle closes decisively outside the range plus this volatility buffer. This ensures you are trading real breakouts, not liquidity sweeps.
3. Internal Range Mechanics (Premium vs. Discount)
Inside the Master Box, the script applies Equilibrium Theory to help with trade location.
The most important internal line is the Equilibrium (EQ), which marks the exact 50% point of the range.
Premium Zone (Above EQ): Price is mathematically "expensive" relative to the recent range. Algorithms generally look to establish Short positions here.
Discount Zone (Below EQ): Price is considered "cheap." Algorithms generally look to establish Long positions here.
It also plots the Master Open, which acts as a "Line in the Sand." If price is currently trading above the Master Open, the higher timeframe candle is Green (Bullish), suggesting longs have a higher probability. If below, the candle is Red (Bearish).
4. Wick Theory (Failed Auctions)
The script places special emphasis on the wicks of the Master Candle because a wick represents a "Failed Auction"—a price level the market tried to explore but ultimately rejected.
The indicator highlights the background of the wick area (from the High to the Body). On a retest, these zones often act as supply or demand blocks because the market remembers the previous failure.
It also calculates the "Consequent Encroachment," which is the 50% midpoint of the wick. The rule of thumb here is that if a candle body can close past 50% of a wick, the rejection is nullified, and price will likely travel to fill the entire wick.
5. Energy Expansion (Breakout Targets)
Market energy transfers from Consolidation (inside the box) to Expansion (the breakout). When the price finally breaks the "Sticky Range" (confirming via the ATR buffer), the script projects where that energy will go.
It uses the height of the previous range to calculate Fibonacci extensions. Specifically, it targets the 1.618 Extension, often called the "Golden Ratio." This is a statistically significant level where expansion moves tend to exhaust themselves and reverse.
6. Safety Protocol: Live Detection
A dashboard monitors the state of the parent candle. If the text turns Magenta with a warning symbol, it means the Higher Timeframe candle is "Live" (still forming).
Trading off a live structure is considered higher risk because the "Auction" isn't finished—the High or Low can still shift. The safest approach is to trade when the dashboard indicates a standard, locked, historical structure.
Long Only EMA Strategy (9/20 with 200 EMA Filter)Details:
This strategy is built around a very simple idea: follow the primary trend and enter only when momentum supports it.
It uses three EMAs on a standard candlestick chart:
1. 9‑period EMA – short‑term momentum
2. 20‑period EMA – medium‑term structure
3. 200‑period EMA – long‑term trend filter
The strategy is ** long‑only ** and is mainly designed for swing trading and positional trading.
It avoids counter‑trend trades by taking entries only when price is trading ** above the 200 EMA **, which is commonly used as a long‑term trend reference.
The rules are deliberately kept simple so that they are easy to understand, modify, and test on different markets and timeframes.
---
Key Features
1. **Trend‑Filtered Entries**
- Fresh long positions are considered only when:
- The 9 EMA crosses above the 20 EMA
- The closing price is **above** the 200 EMA
- This attempts to combine short‑term momentum with a higher‑timeframe trend filter.
2. **Clean Exit Logic**
- The long position is exited when the closing price crosses **below** the 20 EMA.
- This creates an objective, rule‑based way to trail the trade as long as the medium‑term structure remains intact.
3. **Long‑Only, No Short Selling**
- The script intentionally ignores short setups.
- This makes it suitable for markets or accounts where short selling is restricted, or for traders who prefer to participate only on the long side of the market.
4. **Simple Visuals**
- All three EMAs are plotted directly on the chart:
- 9 EMA (fast)
- 20 EMA (medium)
- 200 EMA (trend)
- Trade entries and exits are handled by TradingView’s strategy engine, so users can see results in the Strategy Tester as well as directly on the chart.
5. **Backtest‑Friendly Structure**
- Uses TradingView’s built‑in `strategy()` framework.
- Can be applied to different symbols, timeframes, and markets (equities, indices, crypto, etc.).
- Works on standard candlestick charts, which are supported by TradingView’s backtesting engine.
6. **Configurable in Code**
- The EMA periods are defined in the code and can be easily adjusted.
- Users can tailor the parameters to fit their own style (for example, faster EMAs for intraday trading, slower EMAs for positional trades).
---
How to Use
1. **Add the Strategy to Your Chart**
1. Open any symbol and select a **standard candlestick chart**.
2. Apply the strategy from your “My Scripts” section.
3. Make sure it is enabled so that the trades and results appear.
2. **Select Timeframe**
- The logic can be tested on various timeframes:
- Higher timeframes (1H, 4H, 1D) for swing and positional setups.
- Lower timeframes (5m, 15m) for more active trading, if desired.
- Users should experiment and see where the strategy behaves more consistently for their chosen market.
3. **Read the Signals**
- **Entry:**
- A long trade is opened when the 9 EMA crosses above the 20 EMA while the closing price is above the 200 EMA.
- **Exit:**
- The open long position is closed when the closing price crosses below the 20 EMA.
- All orders are generated automatically once the strategy is attached to the chart.
4. **Use the Strategy Tester**
- Go to the **Strategy Tester** tab in TradingView.
- Check:
- Net profit / drawdown
- Win rate and average trade
- List of trades and the equity curve
- Change the date range and timeframe to see how stable the results are over different periods.
5. **Adjust Parameters if Needed**
- Advanced users can open the code and experiment with:
- EMA lengths (for example 8/21 with 200, or 10/30 with 200)
- Risk sizing and capital settings within the `strategy()` call
- Any changes should be thoroughly re‑tested before considering real‑world application.
---
Practical Applications
1. **Swing Trading on Daily Charts**
- Can be applied to stocks, indices, or ETFs on the daily timeframe.
- The 200 EMA acts as a trend filter to stay aligned with the broad direction, while the 9/20 crossover helps catch medium‑term swings inside that trend.
2. **Positional Trades on Higher Timeframes**
- On 4H or 1D charts, this approach can help in holding trades for several days to weeks.
- The exit rule based on the 20 EMA crossing helps avoid emotional decisions and provides a rules‑based way to trail the trend.
3. **Trend‑Following Filter**
- Even if used purely as a filter, the 200 EMA condition can help traders:
- Avoid taking long trades when the market is in a clear downtrend.
- Focus only on instruments that are trading above their long‑term average.
4. **Educational Use**
- The script is intentionally kept straightforward so that newer users can:
- Learn how a moving average crossover strategy works.
- See how to combine a short‑term signal with a long‑term filter.
- Understand how TradingView’s strategy engine handles entries and exits.
5. **Basis for Further Development**
- This can serve as a starting point for more advanced systems.
- Traders can extend it by adding:
- Additional filters (RSI, volume, volatility filters, time‑of‑day filters, etc.)
- Risk management rules (fixed stop loss, take profit, trailing stops).
- The current version is kept minimal on purpose, so modifications are easy to implement and test.
---
Important Notes & Disclaimer
1. This strategy is provided **for testing, research, and educational purposes only**.
2. It is ** not ** a recommendation to buy or sell any financial instrument.
3. Past performance on historical data does not guarantee similar results in live markets.
4. Markets are risky and trading can lead to financial loss; users should always do their own research, manage risk appropriately, and consult a qualified financial professional if needed.
5. Before using any strategy with real capital, it is strongly advised to:
- Forward test it on a demo / paper trading account.
- Check how it behaves during different market phases (trending, sideways, high‑volatility conditions).
You are free to modify the parameters and logic to better align it with your own trading style and risk tolerance.
Équilibre du Sentiment – Multi-Périodes (v6)
English
A unique and advanced sentiment indicator based on the harmonic mean of highs and lows over nested rolling windows.
How it works:
The neutral sentiment point is reached when positive sentiment equals negative sentiment, which corresponds to the situation where the percentage between the price and the minimum is equal to the percentage between the maximum and the price.
For each chosen period N, the script calculates N different "neutral feeling" values:
- One using the last 1 bar
- One using the last 2 bars
- …
- One using the last N bars
It then extracts the exact median of these N values using a sorted insertion method (no approximation).
This produces an extremely smooth, non-repainting equilibrium line that represents the true "central sentiment" of the market over the selected lookback.
Features:
- Up to 3 independent periods (365, 52, 26 by default – fully customizable)
- Optional background coloring (green/red) when price is above/below the main curve
- Clean labels on the last bar showing the current value for each active period
- Zero repainting – fully compatible with strategies and alerts
- Highly responsive even with very long periods (up to 3500 bars)
Great for:
- Identifying long-term fair value / equilibrium zones
- Building mean-reversion or breakout systems
Pure Pine Script® v6 – no external libraries, no security calls, no repainting-free.
SMC Statistical Liquidity Walls [PhenLabs]📊 SMC Statistical Liquidity Walls
Version: PineScript™ v6
📌 Description
The SMC Statistical Liquidity Walls indicator is designed to visualize market volatility and potential reversal zones using advanced statistical modeling. Unlike traditional Bollinger Bands that use simple lines, this script utilizes an “Inverted Sigmoid” opacity function to create a “fog of war” effect. This visualizes the density of liquidity: the further price moves from the equilibrium (mean), the “harder” the liquidity wall becomes.
This tool solves the problem of over-trading in low-probability areas. By automatically mapping “Premium” (Resistance) and “Discount” (Support) zones based on Standard Deviation (SD), traders can instantly see when price is overextended. The result is a clean, intuitive overlay that helps you identify high-probability mean reversion setups without cluttering your chart with manual drawings.
🚀 Points of Innovation
Inverted Sigmoid Logic: A custom mathematical function maps Standard Deviation to opacity, creating a realistic “wall” density effect rather than linear gradients.
Dynamic “Solidity”: The indicator is transparent at the center (Equilibrium) and becomes visually solid at the edges, mimicking physical resistance.
Separated Directional Bias: distinct Red (Premium) and Green (Discount) coding helps SMC traders instantly recognize expensive vs. cheap pricing.
Smart “Safe” Deviation: Includes fallback logic to handle calculation errors if deviation hits zero, ensuring the indicator never crashes during data gaps.
🔧 Core Components
Basis Calculation: Uses a Simple Moving Average (SMA) to determine the market’s equilibrium point.
Standard Deviation Zones: Calculates 1SD, 2SD, and 3SD levels to define the statistical extremes of price action.
Sigmoid Alpha Calculation: Converts the SD distance into a transparency value (0-100) to drive the visual gradient.
🔥 Key Features
Automated Premium/Discount Zones: Red zones indicate overbought (Premium) areas; Green zones indicate oversold (Discount) areas.
Customizable Density: Users can adjust the “Steepness” and “Midpoint” of the sigmoid curve to control how fast the walls become solid.
Integrated Alerts: Built-in alert conditions trigger when price hits the “Solid” wall (2SD or higher), perfect for automated trading or notifications.
Visual Clarity: The center of the chart remains clear (high transparency) to keep focus on price action where it matters most.
🎨 Visualization
Equilibrium Line: A gray line representing the mean price.
Gradient Fills: The space between bands fills with color that increases in opacity as it moves outward.
Premium Wall: Upper zones fade from transparent red to solid red.
Discount Wall: Lower zones fade from transparent green to solid green.
📖 Usage Guidelines
Range Period: Default 20. Controls the lookback period for the SMA and Standard Deviation calculation.
Source: Default Close. The price data used for calculations.
Center Transparency: Default 100 (Clear). Controls how transparent the middle of the chart is.
Edge Transparency: Default 45 (Solid). Controls the opacity of the outermost liquidity wall.
Wall Steepness: Default 2.5. Adjusts how aggressively the gradient transitions from clear to solid.
Wall Start Point: Default 1.5 SD. The deviation level where the gradient shift begins to accelerate.
✅ Best Use Cases
Mean Reversion Trading: Enter trades when price hits the solid 2SD or 3SD wall and shows rejection wicks.
Take Profit Targets: Use the Equilibrium (Gray Line) as a logical first target for reversal trades.
Trend Filtering: Do not initiate new long positions when price is deep inside the Red (Premium) wall.
⚠️ Limitations
Lagging Nature: As a statistical tool based on Moving Averages, the walls react to past price data and may lag during sudden volatility spikes.
Trending Markets: In strong parabolic trends, price can “ride” the bands for extended periods; mean reversion should be used with caution in these conditions.
💡 What Makes This Unique
Physics-Based Visualization: We treat liquidity as a physical barrier that gets denser the deeper you push, rather than just a static line on a chart.
🔬 How It Works
Step 1: The script calculates the mean (SMA) and the Standard Deviation (SD) of the source price.
Step 2: It defines three zones above and below the mean (1SD, 2SD, 3SD).
Step 3: The custom `get_inverted_sigmoid` function calculates an Alpha (transparency) value based on the SD distance.
Step 4: Plot fills are colored dynamically, creating a seamless gradient that hardens at the extremes to visualize the “Liquidity Wall.”
💡 Note
For best results, combine this indicator with Price Action confirmation (such as pin bars or engulfing candles) when price touches the solid walls.
MACD Forecast Colorful [DiFlip]MACD Forecast Colorful
The Future of Predictive MACD — is one of the most advanced and customizable MACD indicators ever published on TradingView. Built on the classic MACD foundation, this upgraded version integrates statistical forecasting through linear regression to anticipate future movements — not just react to the past.
With a total of 22 fully configurable long and short entry conditions, visual enhancements, and full automation support, this indicator is designed for serious traders seeking an analytical edge.
⯁ Real-Time MACD Forecasting
For the first time, a public MACD script combines the classic structure of MACD with predictive analytics powered by linear regression. Instead of simply responding to current values, this tool projects the MACD line, signal line, and histogram n bars into the future, allowing you to trade with foresight rather than hindsight.
⯁ Fully Customizable
This indicator is built for flexibility. It includes 22 entry conditions, all of which are fully configurable. Each condition can be turned on/off, chained using AND/OR logic, and adapted to your trading model.
Whether you're building a rules-based quant system, automating alerts, or refining discretionary signals, MACD Forecast Colorful gives you full control over how signals are generated, displayed, and triggered.
⯁ With MACD Forecast Colorful, you can:
• Detect MACD crossovers before they happen.
• Anticipate trend reversals with greater precision.
• React earlier than traditional indicators.
• Gain a powerful edge in both discretionary and automated strategies.
• This isn’t just smarter MACD — it’s predictive momentum intelligence.
⯁ Scientifically Powered by Linear Regression
MACD Forecast Colorful is the first public MACD indicator to apply least-squares predictive modeling to MACD behavior — effectively introducing machine learning logic into a time-tested tool.
It uses statistical regression to analyze historical behavior of the MACD and project future trajectories. The result is a forward-shifted MACD forecast that can detect upcoming crossovers and divergences before they appear on the chart.
⯁ Linear Regression: Technical Foundation
Linear regression is a statistical method that models the relationship between a dependent variable (y) and one or more independent variables (x). The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted variable (e.g., future MACD value)
x = independent variable (e.g., bar index)
β₀ = intercept
β₁ = slope
ε = random error (residual)
The regression model calculates β₀ and β₁ using the least squares method, minimizing the sum of squared prediction errors to produce the best-fit line through historical values. This line is then extended forward, generating a forecast based on recent price momentum.
⯁ Least Squares Estimation
The regression coefficients are computed with the following formulas:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables.
⯁ Regression in Machine Learning
Linear regression is a foundational model in supervised learning. Its ability to provide precise, explainable, and fast forecasts makes it critical in AI systems and quantitative analysis.
Applying linear regression to MACD forecasting is the equivalent of injecting artificial intelligence into one of the most widely used momentum tools in trading.
⯁ Visual Interpretation
Picture the MACD values over time like this:
Time →
MACD →
A regression line is fitted to recent MACD values, then projected forward n periods. The result is a predictive trajectory that can cross over the real MACD or signal line — offering an early-warning system for trend shifts and momentum changes.
The indicator plots both current MACD and forecasted MACD, allowing you to visually compare short-term future behavior against historical movement.
⯁ Scientific Concepts Used
Linear Regression: models the relationship between variables using a straight line.
Least Squares Method: minimizes squared prediction errors for best-fit.
Time-Series Forecasting: projects future data based on past patterns.
Supervised Learning: predictive modeling using labeled inputs.
Statistical Smoothing: filters noise to highlight trends.
⯁ Why This Indicator Is Revolutionary
First open-source MACD with real-time predictive modeling.
Scientifically grounded with linear regression logic.
Automatable through TradingView alerts and bots.
Smart signal generation using forecasted crossovers.
Highly customizable with 22 buy/sell conditions.
Enhanced visuals with background (bgcolor) and area fill (fill) support.
This isn’t just an update — it’s the next evolution of MACD forecasting.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the MACD?
The Moving Average Convergence Divergence (MACD) is a technical analysis indicator developed by Gerald Appel. It measures the relationship between two moving averages of a security’s price to identify changes in momentum, direction, and strength of a trend. The MACD is composed of three components: the MACD line, the signal line, and the histogram.
⯁ How to use the MACD?
The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA. A 9-period EMA of the MACD line, called the signal line, is then plotted on top of the MACD line. The MACD histogram represents the difference between the MACD line and the signal line.
Here are the primary signals generated by the MACD:
• Bullish Crossover: When the MACD line crosses above the signal line, indicating a potential buy signal.
• Bearish Crossover: When the MACD line crosses below the signal line, indicating a potential sell signal.
• Divergence: When the price of the security diverges from the MACD, suggesting a potential reversal.
• Overbought/Oversold Conditions: Indicated by the MACD line moving far away from the signal line, though this is less common than in oscillators like the RSI.
⯁ How to use MACD forecast?
The MACD Forecast is built on the same foundation as the classic MACD, but with predictive capabilities.
Step 1 — Spot Predicted Crossovers:
Watch for forecasted bullish or bearish crossovers. These signals anticipate when the MACD line will cross the signal line in the future, letting you prepare trades before the move.
Step 2 — Confirm with Histogram Projection:
Use the projected histogram to validate momentum direction. A rising histogram signals strengthening bullish momentum, while a falling projection points to weakening or bearish conditions.
Step 3 — Combine with Multi-Timeframe Analysis:
Use forecasts across multiple timeframes to confirm signal strength (e.g., a 1h forecast aligned with a 4h forecast).
Step 4 — Set Entry Conditions & Automation:
Customize your buy/sell rules with the 20 forecast-based conditions and enable automation for bots or alerts.
Step 5 — Trade Ahead of the Market:
By preparing for future momentum shifts instead of reacting to the past, you’ll always stay one step ahead of lagging traders.
📈 BUY
🍟 Signal Validity: The signal will remain valid for X bars.
🍟 Signal Sequence: Configurable as AND or OR.
🍟 MACD > Signal Smoothing
🍟 MACD < Signal Smoothing
🍟 Histogram > 0
🍟 Histogram < 0
🍟 Histogram Positive
🍟 Histogram Negative
🍟 MACD > 0
🍟 MACD < 0
🍟 Signal > 0
🍟 Signal < 0
🍟 MACD > Histogram
🍟 MACD < Histogram
🍟 Signal > Histogram
🍟 Signal < Histogram
🍟 MACD (Crossover) Signal
🍟 MACD (Crossunder) Signal
🍟 MACD (Crossover) 0
🍟 MACD (Crossunder) 0
🍟 Signal (Crossover) 0
🍟 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
📉 SELL
🍟 Signal Validity: The signal will remain valid for X bars.
🍟 Signal Sequence: Configurable as AND or OR.
🍟 MACD > Signal Smoothing
🍟 MACD < Signal Smoothing
🍟 Histogram > 0
🍟 Histogram < 0
🍟 Histogram Positive
🍟 Histogram Negative
🍟 MACD > 0
🍟 MACD < 0
🍟 Signal > 0
🍟 Signal < 0
🍟 MACD > Histogram
🍟 MACD < Histogram
🍟 Signal > Histogram
🍟 Signal < Histogram
🍟 MACD (Crossover) Signal
🍟 MACD (Crossunder) Signal
🍟 MACD (Crossover) 0
🍟 MACD (Crossunder) 0
🍟 Signal (Crossover) 0
🍟 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
🤖 Automation
All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Trinity CCI Pro PlusWhat It Is
Trinity CCI Pro Plus is an innovative overlay indicator that reimagines the classic Commodity Channel Index (CCI) by plotting its levels directly on the price chart. No more separate oscillator panel—instead, you get dynamic price-based bands and lines for instant momentum insights.
What You See on the Chart
Orange line: The CCI zero line (20-period SMA of typical price, hlc3)—acts as the baseline.
Aqua line: Dynamic upper band at CCI = +100 (overbought threshold).
Purple line: Dynamic lower band at CCI = -100 (oversold threshold).
Optional thick purple line: The extra SMA of CCI (14-period smooth) scaled back to price—serves as a signal line for crossovers.
Optional outer zones: ±200 bands (aqua/purple extensions) for extreme momentum levels, often added as dotted or filled areas to spot blow-off tops/bottoms.
Key Differences from Regular CCI
Standard CCI lives in a lower pane with fixed horizontal lines at +100, 0, and -100, forcing you to split your focus. This version overlays everything on price: the bands curve with market volatility, the zero line becomes a moving average, and the extra SMA/signal line integrates seamlessly for price-action trading. Plus, it naturally supports outer ±200 zones without extra coding, making extremes visually pop.
How Traders Use It
Momentum breakouts: Buy when price closes above the +100 aqua band (or +200 for aggressive entries); sell below -100 purple (or -200).
Mean reversion: Fade touches on the bands—take profits if price rejects the +100/-100 levels, or watch for exhaustion at ±200.
Trend bias: Price above orange zero = bullish filter; below = bearish. Use the extra SMA for confirmation (e.g., price crossing above it signals upside).
Crossover signals: Price vs. the thick purple SMA line—bullish above, bearish below—pairs perfectly with band breaks.
Range trading: Treat ±100 bands as dynamic support/resistance; outer ±200 zones highlight potential breakout setups.
This setup shines in trending markets (e.g., stocks or forex on 1H/daily charts), turning CCI into a one-glance channel system. Start with the defaults, add the ±200 and extra SMA via simple code tweaks, and backtest for your style—it's versatile and reduces screen clutter dramatically.
More Info
The 20 period MA is the original and still the most common setting for CCI, and it is exactly what the creator of the CCI, Donald Lambert, published it in 1980 with these exact parameters:
Length: 20 periods
Constant: 0.015 (to make CCI fall between +100 and –100 about 70–80 % of the time)
Typical Price: hlc3 (or sometimes (high + low + close)/3)
Deviation measure: Mean Deviation (not standard deviation)
So the “Trinity CCI Pro Plus” you are using is 100 % faithful to Lambert’s original design when the length is set to 20.
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.
Session Opening Range Breakout (ORBO)This strategy automates a classic Opening Range Breakout (ORBO) approach: it builds a price range for the first minutes after the market opens, then looks for strong breakouts above or below that range to catch early directional moves.
Concept
The idea behind ORBO is simple:
The first minutes after the session open are often highly informative.
Price forms an “opening range” that acts as a mini support/resistance zone.
A clean breakout beyond this zone can lead to high-momentum moves.
This script turns that logic into a fully backtestable strategy in TradingView.
How the strategy works
Opening Range Session
Default session: 09:30–09:50 (exchange time)
During this window, the script tracks:
orHigh → highest high within the session
orLow → lowest low within the session
This forms your Opening Range for the day.
Breakout Logic (after the window ends)
Once the defined session ends:
Long Entry:
If the close crosses above the Opening Range High (orHigh),
→ strategy.entry("OR Long", strategy.long) is triggered.
Short Entry:
If the close crosses below the Opening Range Low (orLow),
→ strategy.entry("OR Short", strategy.short) is triggered.
Only one opening range per day is considered, which keeps the logic clean and easy to interpret.
Daily Reset
At the start of a new trading day, the script resets:
orHigh := na
orLow := na
A fresh Opening Range is then built using the next session’s 09:30–09:50 candles.
This ensures entries are always based on today’s structure, not yesterday’s.
Visuals & Inputs
Inputs:
Opening range session → default: "0930-0950"
Show OR levels → toggle visibility of OR High / Low lines
Fill range body → optional shaded zone between OR High and OR Low
Chart visuals:
A green line marks the Opening Range High.
A red line marks the Opening Range Low.
Optional yellow fill highlights the entire OR zone.
Background shading during the session shows when the range is currently being built.
These visuals make it easy to see:
Where the OR sits relative to current price
How clean / noisy the breakout was
How often price respects or rejects the opening zone
Backtesting & Optimization
Because this is written as a strategy():
You can use TradingView’s Strategy Tester to view:
Win rate
Net profit
Drawdown
Profit factor
Equity curve
Ideas to experiment with:
Change the session window (e.g., 09:15–09:45, 10:00–10:30)
Apply to different:
Markets: indices, FX, crypto, stocks
Timeframes: 1m / 5m / 15m
Add your own:
Stop Loss & Take Profit levels
Time filters (only trade certain days / times)
Volatility filters (e.g., ATR, range size thresholds)
Higher-timeframe trend filter (e.g., only take longs above 200 EMA)
Bitcoin Power Law Zones (Dunk)Introduction When viewed on a standard linear chart, Bitcoin’s long-term price action can appear chaotic and exponential. However, when analyzed through the lens of physics and network growth models, a distinct structure emerges.
This indicator implements the Bitcoin Power Law , a mathematical model that suggests Bitcoin’s price evolves in a straight line when plotted against time on a "log-log" scale. By calculating parallel bands around this regression line, we create a "Rainbow" of valuation zones that help investors visualize whether the asset is historically overheated, undervalued, or sitting at fair value.
The Math Behind the Model The Power Law dictates that price scales with time according to the formula: Price = A * (days since genesis)^b
This script uses the specific parameters popularized by recent physics-based analyses of the network: Slope (b): 5.78 (Representing the scaling law of the network adoption). Amplitude (A): 1.45 x 10^-17 (The intercept coefficient).
While simple moving averages react to price, this model is predictive based on time and network growth physics, providing a long-term "gravity" center for the asset.
Guide to the Valuation Zones
Upper Bands (Red/Orange): Extr. Overvalued, High Premium, Overvalued. Historically, these zones have marked cycle peaks where price moved too far, too fast ahead of the network's steady growth. The Baseline (Black Line): Fair Value. The mathematical mean of the Power Law. Price has historically oscillated around this line, treating it as a center of gravity. Lower Bands (Green/Blue): Undervalued, Discount, Deep Discount. These zones represent periods where the market price has historically lagged behind the network's intrinsic value, often marking accumulation phases.
Note: The lowest theoretical tiers ("Bitcoin Dead") have been trimmed from this chart to focus on relevant historical support levels.
How to Use Logarithmic Scale: You MUST set your chart to "Log" scale (bottom right of the TradingView window) for this indicator to function correctly. On a linear chart, the bands will appear to curve upwards aggressively; on a Log chart, they will appear as smooth, parallel channels. Timeframe: This is a macro-economic indicator. It is best viewed on Daily or Weekly timeframes. Overlay Labels: The indicator includes dynamic labels on the right-side axis, allowing you to instantly see the current price requirements for each valuation zone without manually tracing lines.
Credits This script is based on the Power Law theory popularized by Giovanni Santostasi and the original Corridor concepts by Harold Christopher Burger .
Disclaimer This tool is for educational and informational purposes only. It visualizes historical mathematical trends and does not constitute financial advice. Past performance of a model is not indicative of future results.
Further Reading
www.hcburger.com
giovannisantostasi.medium.com
ADX Forecast Colorful [DiFlip]ADX Forecast Colorful
Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis.
⯁ Real-Time ADX Forecasting
For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions.
⯁ Highly Customizable
The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on TradingView. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system.
⯁ Innovative and Science-Based
This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted value (e.g., future ADX)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept
β₁ = slope (rate of change)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error, the regression coefficients are calculated as:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ = summation
x̄ and ȳ = means of x and y
i ranges from 1 to n (number of data points)
These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool.
⯁ Visual Interpretation
Imagine an ADX time series like this:
Time →
ADX →
The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals.
⯁ Summary of Scientific Concepts
Linear Regression: Models variable relationships with a straight line.
Least Squares: Minimizes prediction errors for best fit.
Time-Series Forecasting: Predicts future values using historical data.
Supervised Learning: Trains models to predict outcomes from inputs.
Statistical Smoothing: Reduces noise and highlights underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Based on rigorous statistical theory.
Unprecedented: First public ADX using least-squares forecast modeling.
Smart: Uses machine learning logic.
Forward-Looking: Generates predictive, not just reactive, signals.
Customizable: Flexible for any strategy or timeframe.
⯁ Conclusion
By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the ADX?
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ How to use the ADX?
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
Strong Trend: When the ADX is above 25, indicating a strong trend.
Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
Neutral Zone: Between 20 and 25, where the trend strength is unclear.
⯁ Entry Conditions
Each condition below is fully configurable and can be combined to build precise trading logic.
📈 BUY
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
📉 SELL
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
🤖 Automation
All BUY and SELL conditions are compatible with TradingView alerts, making them ideal for fully or semi-automated systems.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Elite Federal Reserve AIThe Elite Federal Reserve AI indicator provides an analytical framework focused on monitoring economic and market conditions that influence Federal Reserve policy decisions. The indicator examines key relationships and rate-of-change metrics across multiple proxies for monetary policy drivers.
The indicator tracks and analyzes:
• Yield curve dynamics through rate-of-change measurements in short and intermediate-term Treasury yields
• Inflation expectations via TIPS breakeven rate momentum
• Dollar strength and its rate of change over specified periods
• Financial market stress indicators including volatility and sector performance metrics
• Breadth measures through small capitalization stock performance
The indicator calculates momentum and rate-of-change values across these variables to identify shifts in the economic and financial conditions that serve as primary inputs to Federal Reserve decision-making. By monitoring the velocity of change in these key relationships, the indicator provides insight into the changing balance between inflationary pressures, growth expectations, financial stability concerns, and currency dynamics.
This approach focuses on the observable market-based indicators that reflect the underlying economic conditions the Federal Reserve considers in its policy formulation, enabling users to assess the prevailing policy environment through the lens of these critical market relationships and their momentum characteristics.
RSI Forecast Colorful [DiFlip]RSI Forecast Colorful
Introducing one of the most complete RSI indicators available — a highly customizable analytical tool that integrates advanced prediction capabilities. RSI Forecast Colorful is an evolution of the classic RSI, designed to anticipate potential future RSI movements using linear regression. Instead of simply reacting to historical data, this indicator provides a statistical projection of the RSI’s future behavior, offering a forward-looking view of market conditions.
⯁ Real-Time RSI Forecasting
For the first time, a public RSI indicator integrates linear regression (least squares method) to forecast the RSI’s future behavior. This innovative approach allows traders to anticipate market movements based on historical trends. By applying Linear Regression to the RSI, the indicator displays a projected trendline n periods ahead, helping traders make more informed buy or sell decisions.
⯁ Highly Customizable
The indicator is fully adaptable to any trading style. Dozens of parameters can be optimized to match your system. All 28 long and short entry conditions are selectable and configurable, allowing the construction of quantitative, statistical, and automated trading models. Full control over signals ensures precise alignment with your strategy.
⯁ Innovative and Science-Based
This is the first public RSI indicator to apply least-squares predictive modeling to RSI calculations. Technically, it incorporates machine-learning logic into a classic indicator. Using Linear Regression embeds strong statistical foundations into RSI forecasting, making this tool especially valuable for traders seeking quantitative and analytical advantages.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental statistical method that models the relationship between a dependent variable y and one or more independent variables x. The general formula for simple linear regression is:
y = β₀ + β₁x + ε
where:
y = predicted variable (e.g., future RSI value)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept (value of y when x = 0)
β₁ = slope (rate of change of y relative to x)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the RSI projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error between predicted and observed values, we use the formulas:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational component of supervised learning. Its simplicity and precision in numerical prediction make it essential in AI, predictive algorithms, and time-series forecasting. Applying regression to RSI is akin to embedding artificial intelligence inside a classic indicator, adding a new analytical dimension.
⯁ Visual Interpretation
Imagine a time series of RSI values like this:
Time →
RSI →
The regression line smooths these historical values and projects itself n periods forward, creating a predictive trajectory. This projected RSI line can cross the actual RSI, generating sophisticated entry and exit signals. In summary, the RSI Forecast Colorful indicator provides both the current RSI and the forecasted RSI, allowing comparison between past and future trend behavior.
⯁ Summary of Scientific Concepts Used
Linear Regression: Models relationships between variables using a straight line.
Least Squares: Minimizes squared prediction errors for optimal fit.
Time-Series Forecasting: Predicts future values from historical patterns.
Supervised Learning: Predictive modeling based on known output values.
Statistical Smoothing: Reduces noise to highlight underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Built on statistical and mathematical theory.
First of its kind: The first public RSI with least-squares predictive modeling.
Intelligent: Incorporates machine-learning logic into RSI interpretation.
Forward-looking: Generates predictive, not just reactive, signals.
Customizable: Exceptionally flexible for any strategic framework.
⯁ Conclusion
By combining RSI and linear regression, the RSI Forecast Colorful allows traders to predict market momentum rather than simply follow it. It's not just another indicator: it's a scientific advancement in technical analysis technology. Offering 28 configurable entry conditions and advanced signals, this open-source indicator paves the way for innovative quantitative systems.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What Is RSI?
The RSI (Relative Strength Index) is a technical indicator developed by J. Welles Wilder. It measures the velocity and magnitude of recent price movements to identify overbought and oversold conditions. The RSI ranges from 0 to 100 and is commonly used to identify potential reversals and evaluate trend strength.
⯁ How RSI Works
RSI is calculated from average gains and losses over a set period (commonly 14 bars) and plotted on a 0–100 scale. It consists of three key zones:
Overbought: RSI above 70 may signal an overbought market.
Oversold: RSI below 30 may signal an oversold market.
Neutral Zone: RSI between 30 and 70, indicating no extreme condition.
These zones help identify potential price reversals and confirm trend strength.
⯁ Entry Conditions
All conditions below are fully customizable and allow detailed control over entry signal creation.
📈 BUY
🧲 Signal Validity: Signal remains valid for X bars.
🧲 Signal Logic: Configurable using AND or OR.
🧲 RSI > Upper
🧲 RSI < Upper
🧲 RSI > Lower
🧲 RSI < Lower
🧲 RSI > Middle
🧲 RSI < Middle
🧲 RSI > MA
🧲 RSI < MA
🧲 MA > Upper
🧲 MA < Upper
🧲 MA > Lower
🧲 MA < Lower
🧲 RSI (Crossover) Upper
🧲 RSI (Crossunder) Upper
🧲 RSI (Crossover) Lower
🧲 RSI (Crossunder) Lower
🧲 RSI (Crossover) Middle
🧲 RSI (Crossunder) Middle
🧲 RSI (Crossover) MA
🧲 RSI (Crossunder) MA
🧲 MA (Crossover)Upper
🧲 MA (Crossunder)Upper
🧲 MA (Crossover) Lower
🧲 MA (Crossunder) Lower
🧲 RSI Bullish Divergence
🧲 RSI Bearish Divergence
🔮 RSI (Crossover) Forecast MA
🔮 RSI (Crossunder) Forecast MA
📉 SELL
🧲 Signal Validity: Signal remains valid for X bars.
🧲 Signal Logic: Configurable using AND or OR.
🧲 RSI > Upper
🧲 RSI < Upper
🧲 RSI > Lower
🧲 RSI < Lower
🧲 RSI > Middle
🧲 RSI < Middle
🧲 RSI > MA
🧲 RSI < MA
🧲 MA > Upper
🧲 MA < Upper
🧲 MA > Lower
🧲 MA < Lower
🧲 RSI (Crossover) Upper
🧲 RSI (Crossunder) Upper
🧲 RSI (Crossover) Lower
🧲 RSI (Crossunder) Lower
🧲 RSI (Crossover) Middle
🧲 RSI (Crossunder) Middle
🧲 RSI (Crossover) MA
🧲 RSI (Crossunder) MA
🧲 MA (Crossover)Upper
🧲 MA (Crossunder)Upper
🧲 MA (Crossover) Lower
🧲 MA (Crossunder) Lower
🧲 RSI Bullish Divergence
🧲 RSI Bearish Divergence
🔮 RSI (Crossover) Forecast MA
🔮 RSI (Crossunder) Forecast MA
🤖 Automation
All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies.
⯁ Unique Features
Linear Regression Forecast
Signal Validity: Keep signals active for X bars
Signal Logic: AND/OR configuration
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Chart Labels: BUY/SELL markers above price
Automation & Alerts: BUY/SELL
Background Colors: bgcolor
Fill Colors: fill
Linear Regression Forecast
Signal Validity: Keep signals active for X bars
Signal Logic: AND/OR configuration
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Chart Labels: BUY/SELL markers above price
Automation & Alerts: BUY/SELL
Background Colors: bgcolor
Fill Colors: fill
