Probability-Based Adaptive Detection🙏🏻 PBAD (Probability-Based Adaptive Detection) : adaptive control tool for outliers || novelty detection, made for worst case data & processes, for the highest time complexity O(n^2) compared with the alternatives (would be explained in a sec). Thresholds are completely data driven and axiomatic, no need in provided hyperparameters, are not learned or optimized. The method accepts multiple weights, e.g. both temporal and volatility weights.
Method briefly explained (I can go deeper if any1 asks explicitly):
Performs weighted KDE on initial input data, finds KDE global maximum (mode), creates new “residuals” dataset by centering initial data around this value;
Performs weighted KDE on residuals, uses sigmoid based probability mass targets with increasing probability coverage to construct a set of non-disjoint High Density Intervals (also called HDR, HPD in Bayesian terms);
Uses these intervals to calculate analogs of centralized & standardized moments;
Uses these ^^ moments to construct a set of control thresholds. The scheme used in PBAD is not only based on a central threshold, or on neighboring ones, it utilizes all previous thresholds, gaining more information.
...
The most important part is to understand whether you really need PBAD. Because even tho it seems to be the best one given highest algocomplexity, irl it would work worse in cases when it’s not required by your data.
Here’s the menu (aka taxonomy omg) of methods you can use that would let you make the right choice:
Moment-Based Adaptive Detection (MBAD) :
Norm: L2
Time complexity: original O(n), successfully reduced to O(1) in online version
Use case: default, general purpose
Based on: method of moments (powers of residuals from mean)
Thresholds architecture: centralized
Quantile-Based Adaptive Detection (QBAD):
Norm: L1
Time complexity: O(nlogn)
Use case: either bad data Or process instability
Based on: quantile moments (dyadic percentiles of residuals from median)
Thresholds architecture: chained/recursive/sequential
Probability-Based Adaptive Detection (PBAD):
Norm: L0
Time complexity: O(n^2)
Use case: both bad data And process instability
Based on: probability moments (target probability masses of residuals from KDE mode)
Thresholds architecture: decentralized (for lack of a better name xd, the idea is that these thresholds gain information from the all other threshold and are Not exclusively based on the central or neighboring thresholds)
...
Examples of true use cases:
^^ an appropriate financial instrument to use PBAD
^^ and another one
...
Additional details about how to use it:
Keep the student5 kernel, it’s the best you can do. I added others mostly for comparisons and if you want to use the tool Not for its primary purpose (on a fine data)
“Calculate for N bars” and “Starting at bar N” options allow to reduce calculation period only on the N number of last bars or next bars from a chosen one. It's vital, because calculations here are heavy
Keep plotting offset at 1 (allows to visually compare current bar with the previous threshold values). This is the way it should be done on price data.
HLC3 is the optimal source input, unless you want to use your own better one point estimate of each datapoint (in the best case done by using PBAD itself on OHLC+ values).
In essence it should be used just like MBAD or QBAD, fade/push extensions and limit, fade/push/skip deviations & basis, or other strategies of your. Again, the only reason for 3 methods to exist is to be chosen for according data characteristics.
Btw:
This is the initial version, I don’t consider it perfected tbh, even tho it works as expected, however this method is very situational anyways.
In this script KDE function is modified to ensure the outcoming probabilities Do sum up to 1. I didn’t do this normalization in Weighted KDE Mode script , but there it’s not required since we just need a KDE global max.
see ya
∞
Statistics
Z-Score & StatsThis is an advanced indicator that measures price deviation from its mean using statistical z-scores, combined with multiple analytical features for trading signals.
Core Functionality-
Z-Score Calculation Engine:
The indicator uses a custom standardization function that calculates how many standard deviations the current price is from its rolling mean. Unlike simple moving averages, this provides a normalized view of price extremes. The calculation maintains a sliding window of data points, efficiently updating mean and variance values as new data arrives while removing old data points. This approach handles missing values gracefully and uses sample variance (rather than population variance) for more accurate statistical measurements.
Statistical Zones & Visual Framework:
The indicator creates a visual representation of statistical probability zones:
±1 Standard Deviation: Encompasses about 68% of normal price behavior (green zone)
±2 Standard Deviations: Covers approximately 95% of price movements (orange zone)
±3 Standard Deviations: Represents 99.7% probability range (red zone)
±3.5 and ±4 Thresholds: Extreme outlier levels that trigger special alerts
The z-score line changes color dynamically based on which zone it occupies, making it easy to identify the current market extremity at a glance.
Advanced Features:
Volume Contraction Analysis
The script monitors volume patterns to identify periods of reduced trading activity. It compares current volume against a moving average and flags when volume drops below a specified threshold (default 70%). Volume contraction often precedes significant price moves and is factored into the optimal entry detection system.
Momentum-Based Direction Model:
Rather than just showing current z-score levels, the indicator projects where the z-score is likely to move based on recent momentum. It calculates the rate of change in the z-score and extrapolates forward for a specified number of bars. This creates a directional arrow that indicates whether conditions are bullish (negative z-score with upward momentum) or bearish (positive z-score with downward momentum).
Divergence Detection System:
The script automatically identifies four types of divergences between price action and z-score behavior :-
Regular Bullish Divergence: Price makes lower lows while z-score makes higher lows, suggesting weakening downward pressure
Regular Bearish Divergence: Price makes higher highs while z-score makes lower highs, indicating exhaustion in the uptrend
Hidden Bullish Divergence: Price makes higher lows while z-score makes lower lows, confirming trend continuation in an uptrend
Hidden Bearish Divergence: Price makes lower highs while z-score makes higher highs, confirming downtrend continuation
The system uses pivot detection with configurable lookback periods and distance requirements, then draws connecting lines and labels directly on the chart when divergences occur.
Yearly Statistics Tracking:
The indicator maintains historical records of maximum z-score deviations over yearly periods (configurable bar count). This provides context by showing whether current extremes are unusual compared to typical annual ranges. The average yearly maximum helps traders understand if the current market is exhibiting normal volatility or exceptional conditions.
Mean Reversion Probability:
Based on the current z-score magnitude, the indicator calculates and displays the statistical probability that price will revert toward the mean. Higher absolute z-scores indicate stronger mean reversion probabilities, ranging from 38% at ±0.5 standard deviations to 99.7% at ±3 standard deviations.
Comprehensive Statistics Table:
A customizable on-chart table displays real-time statistics including:
Current z-score value with directional indicator
Predicted z-score based on momentum
Current year's maximum absolute z-score
Historical average yearly maximum
Mean reversion probability percentage
Zone status classification (Normal, Moderate, High, Extreme)
Directional bias (Bullish, Bearish, Neutral)
Active divergence status
Volume contraction status with ratio
Optimal setup detection (combining extreme z-scores with volume contraction)
Optimal Entry Setup Detection:
The most sophisticated feature identifies high-probability trading setups by combining multiple factors. An "Optimal Long" signal triggers when z-score reaches -3.5 or below AND volume is contracted. An "Optimal Short" signal appears when z-score exceeds +3.5 AND volume is contracted. This combination suggests extreme price deviation occurring on low volume, often preceding strong reversals.
Alert System:
The script includes a unified alert mechanism that triggers when z-score crosses specific thresholds:
Crossing above/below ±3.5 standard deviations (extreme levels)
Crossing above/below ±4 standard deviations (critical levels)
Alerts fire once per bar with confirmation (previous bar must be on opposite side of threshold) to avoid false signals.
Practical Application:
This indicator is designed for mean reversion traders who seek statistically significant price extremes. The combination of z-score measurement, volume analysis, momentum projection, and divergence detection creates a multi-layered confirmation system. Traders can use extreme z-scores as potential reversal zones, while the direction model and divergence signals help time entries more precisely. The volume contraction filter adds an additional layer of confluence, identifying moments when reduced participation may precede explosive moves back toward the mean.
Chart Attached: NSE GMR Airports, EoD 12/12/25
DISCLAIMER: This information is provided for educational purposes only and should not be considered financial, investment, or trading advice.Happy Trading
Session ATR Progression Tracker📊 Session ATR Progression Tracker - SIYL Regression Trading Tool
Track how much of your instrument's 7-day Average True Range (ATR) has been covered during the current trading session. This indicator is specifically designed for regression traders who follow the "Stay In Your Lane" (SIYL) methodology, helping you identify when the probability of mean reversion significantly increases. If you are interested in more on that check out Rod Casselli and tradersdevgroup.com.
🎯 Key Features:
• Real-time ATR Coverage Percentage - See at a glance what percentage of the 7-day ATR has been covered in the current session
• SIYL-Optimized Thresholds - See at a glance when the instrument has achieved 80% and 100% ATR coverage, the proven thresholds where mean reversion probability increases (customizable)
• Flexible Session Modes:
- Daily: Resets at calendar day change
- Session: Uses exchange-defined trading sessions
- Custom Session: Set your exact session start/end times (perfect for futures traders and international markets)
• Visual Alerts - Color-coded display (gray → orange → red) and optional background highlighting
• Repositionable Display - Choose from 9 screen positions to avoid chart clutter
• Session Markers - Green triangles mark the start of each new session
• Detailed Stats - View current range, ATR value, session high/low, and session status
💡 Why Use This Indicator?
This tool is built around a proven concept: regression trading becomes significantly more effective once a session has achieved at least 80% of its 7-day ATR. At this threshold, the probability of price reverting to mean increases substantially, creating higher-probability trade setups for SIYL practitioners.
Benefits for regression traders:
- Identify optimal entry points when mean reversion probability is highest (≥80% ATR coverage)
- Avoid premature regression entries before adequate range has been established
- Recognize when daily moves have "earned their range" and are ripe for reversal
- Time fade-the-move and counter-trend strategies with statistical backing
- Improve win rates by trading only after proven probability thresholds are met
⚙️ Setup Instructions:
1. Add the indicator to your chart
2. Select your preferred "Reset Mode" (recommend "Custom Session" for futures/international markets)
3. If using Custom Session, enter your session times in 24-hour format (e.g., 0930-1600 for US stocks, 1700-1600 for CME futures)
4. Adjust alert thresholds if desired (default: 80% and 100% - proven SIYL thresholds)
5. Position the display where it's most visible on your chart
📈 Works Across All Markets:
Stocks • Futures • Forex • Indices • Crypto • Commodities
Perfect for regression traders, mean reversion specialists, and SIYL practitioners who want to trade with probability on their side by entering only after the session has "earned its range."
---
Tip: For futures contracts with overnight sessions that span calendar days (like MES, MNQ, MYM), use "Custom Session" mode with your exchange's official session times for accurate tracking.
EMA Slope Angle V2 Auto Threshold# EMA Slope Angle Indicator
## Overview
The EMA Slope Angle Indicator visualizes the Exponential Moving Average (EMA) slope as an angle in degrees, providing traders with a clear, quantitative measure of trend strength and direction. The indicator features **automatic threshold calculation based on Gaussian distribution**, making it adaptive to any market and timeframe.
## Key Features
### 🎯 **Automatic Threshold Calculation (NEW!)**
- **Gaussian Distribution-Based**: Automatically calculates optimal thresholds from the 50% interquartile range (IQR) of historical angle data
- **Asset-Adaptive**: Thresholds adjust to each instrument's unique volatility and price characteristics
- **No Manual Tuning Required**: Simply enable "Use Auto Thresholds" and let the indicator optimize itself
### 📊 **Dynamic EMA Coloring**
- **Color Intensity**: EMA line color intensity reflects slope strength
- **Visual Feedback**:
- Green shades for uptrends (darker = stronger)
- Red shades for downtrends (darker = stronger)
- Gray for flat/neutral conditions
### 📈 **Regime Detection**
- **Three Regimes**: RISING, FALLING, and FLAT
- **Smart Classification**: Based on statistical distribution of angles
- **Non-Repainting**: All calculations use confirmed bars only
### 🔔 **Trend-Shift Signals**
- **Visual Arrows**: Automatic signals when transitioning from FLAT to RISING/FALLING
- **Configurable**: Enable/disable signals as needed
- **Reliable**: Only triggers on significant regime changes
### 📋 **KPI Dashboard**
- **Real-Time Metrics**: Current angle, regime, and last signal
- **Auto-Threshold Display**: Shows calculated thresholds when auto-mode is active
- **Statistics**: Optional angle distribution statistics
- **Clean Layout**: Top-right corner, non-intrusive
### 📊 **Angle Statistics (Optional)**
- **Distribution Analysis**: Histogram of angle ranges
- **Dynamic Buckets**: Automatically adjusts to data distribution when auto-mode is enabled
- **Percentage Breakdown**: See how often each angle range occurs
## Settings
### Main Settings
- **EMA Length**: Period for the Exponential Moving Average (default: 50)
- **Slope Lookback Bars**: Number of bars to calculate slope over (default: 5)
### Angle Settings
- **Use Auto Thresholds**: Enable automatic threshold calculation (recommended!)
- **Analysis Period**: Number of bars to analyze for distribution (default: 500)
- **Manual Thresholds**: Flat, Rising, and Falling triggers (used when auto-mode is off)
- **Max Angle for Color Saturation**: Maximum angle for color intensity scaling
### Display Options
- **Colors**: Customize uptrend, downtrend, and flat colors
- **Show Signals**: Enable/disable trend-shift arrows
- **Show Statistics**: Display angle distribution table
- **Show Dashboard**: Toggle KPI dashboard visibility
## How It Works
### Angle Calculation
The indicator calculates the angle between the current EMA value and the EMA value N bars ago:
```
Angle = arctan((EMA_now - EMA_then) / lookback) × 180° / π
```
### Auto-Threshold Calculation
When enabled, the indicator:
1. Analyzes historical angle data over the specified period
2. Calculates mean and standard deviation
3. Determines thresholds based on the 50% interquartile range (IQR):
- **Flat Threshold**: ±0.674σ (middle 50% of data)
- **Rising Trigger**: 75th percentile (mean + 0.674σ)
- **Falling Trigger**: 25th percentile (mean - 0.674σ)
### Regime Classification
- **FLAT**: Angle within ±Flat Threshold
- **RISING**: Angle ≥ Rising Trigger
- **FALLING**: Angle ≤ Falling Trigger
## Use Cases
### Trend Following
- Identify strong trends (high angle values)
- Spot trend reversals (regime changes)
- Filter trades based on trend strength
### Range Trading
- Detect flat/consolidation periods
- Avoid trading during choppy markets
- Enter when regime shifts from FLAT to RISING/FALLING
### Multi-Timeframe Analysis
- Apply to different timeframes for confirmation
- Use higher timeframe for trend direction
- Use lower timeframe for entry timing
## Tips for Best Results
1. **Enable Auto-Thresholds**: Let the indicator adapt to your instrument
2. **Adjust Analysis Period**: Use more bars for stable markets, fewer for volatile ones
3. **Combine with Price Action**: Use regime changes as confirmation, not standalone signals
4. **Multi-Timeframe**: Check higher timeframes for trend context
5. **Backtest First**: Test settings on historical data before live trading
## Technical Details
- **Non-Repainting**: All calculations use `barstate.isconfirmed`
- **Pine Script v6**: Latest version for optimal performance
- **Efficient**: Minimal computational overhead
- **Customizable**: Extensive settings for fine-tuning
## Version History
**v2.0** (Current)
- Added automatic threshold calculation based on Gaussian distribution
- Dynamic bucket adjustment for statistics
- Enhanced dashboard with auto-threshold display
- Improved regime detection using IQR method
**v1.0**
- Initial release with manual thresholds
- Basic EMA coloring
- Trend-shift signals
- KPI dashboard
## Support
For questions, suggestions, or bug reports, please leave a comment or contact the author.
---
**Disclaimer**: This indicator is for educational purposes only. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
**Keywords**: EMA, slope, angle, trend, automatic thresholds, Gaussian distribution, regime detection, non-repainting, adaptive
Expectativa de Juros (Fed)An indicator that measures future expectations for US interest rates, measured by the difference between the Fed's interest rate and pricing on the CME.
SigmaFlowSigmaFlow is a professional signal management connector designed to work with the SigmaFlow app. This indicator allows traders to structure trade setups (Entry, Stop Loss, TP1, TP2) on TradingView and send them into the SigmaFlow platform, where signals are managed, tracked, and delivered to Telegram.
Professional signal management — from TradingView to Telegram.
How SigmaFlow Works:
Sends trade data from TradingView to Telegram via the SigmaFlow platform.
SigmaFlow handles signal management, organization, history tracking, performance metrics, and Telegram delivery.
What It Does NOT Do:
Does not generate trading signals
Does not provide investment advice
Does not execute trades
Requirements:
TradingView plan with webhook alerts*
Active SigmaFlow account*
Disclaimer
SigmaFlow is a signal management and delivery tool only. All trade ideas are created manually by users. Trading involves risk and past performance does not guarantee future results.
Simple Candle Strategy# Candle Pattern Strategy - Pine Script V6
## Overview
A TradingView trading strategy script (Pine Script V6) that identifies candlestick patterns over a configurable lookback period and generates trading signals based on pattern recognition rules.
## Strategy Logic
The strategy analyzes the most recent N candlesticks (default: 5) and classifies their patterns into three categories, then generates buy/sell signals based on specific pattern combinations.
### Candlestick Pattern Classification
Each candlestick is classified as one of three types:
| Pattern | Definition | Formula |
|---------|-----------|---------|
| **Close at High** | Close price near the highest price of the candle | `(high - close) / (high - low) ≤ (1 - threshold)` |
| **Close at Low** | Close price near the lowest price of the candle | `(close - low) / (high - low) ≤ (1 - threshold)` |
| **Doji** | Opening and closing prices very close; long upper/lower wicks | `abs(close - open) / (high - low) ≤ threshold` |
### Trading Rules
| Condition | Action | Signal |
|-----------|--------|--------|
| Number of Doji candles ≥ 3 | **SKIP** - Market is too chaotic | No trade |
| "Close at High" count ≥ 2 + Last candle closes at high | **LONG** - Bullish confirmation | Buy Signal |
| "Close at Low" count ≥ 2 + Last candle closes at low | **SHORT** - Bearish confirmation | Sell Signal |
## Configuration Parameters
All parameters are adjustable in TradingView's "Settings/Inputs" tab:
| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| **K-line Lookback Period** | 5 | 3-20 | Number of candlesticks to analyze |
| **Doji Threshold** | 0.1 | 0.0-1.0 | Body size / Total range ratio for doji identification |
| **Doji Count Limit** | 3 | 1-10 | Number of dojis that triggers skip signal |
| **Close at High Proximity** | 0.9 | 0.5-1.0 | Required proximity to highest price (0.9 = 90%) |
| **Close at Low Proximity** | 0.9 | 0.5-1.0 | Required proximity to lowest price (0.9 = 90%) |
### Parameter Tuning Guide
#### Proximity Thresholds (Close at High/Low)
- **0.95 or higher**: Stricter - only very strong candles qualify
- **0.90 (default)**: Balanced - good for most market conditions
- **0.80 or lower**: Looser - catches more patterns, higher false signals
#### Doji Threshold
- **0.05-0.10**: Strict doji identification
- **0.10-0.15**: Standard doji detection
- **0.15+**: Includes near-doji patterns
#### Lookback Period
- **3-5 bars**: Fast, sensitive to recent patterns
- **5-10 bars**: Balanced approach
- **10-20 bars**: Slower, filters out noise
## Visual Indicators
### Chart Markers
- **Green Up Arrow** ▲: Long entry signal triggered
- **Red Down Arrow** ▼: Short entry signal triggered
- **Gray X**: Skip signal (too many dojis detected)
### Statistics Table
Located at top-right corner, displays real-time pattern counts:
- **Close at High**: Count of candles closing near the high
- **Close at Low**: Count of candles closing near the low
- **Doji**: Count of doji/near-doji patterns
### Signal Labels
- Green label: "✓ Long condition met" - below entry bar
- Red label: "✓ Short condition met" - above entry bar
- Gray label: "⊠ Too many dojis, skip" - trade skipped
## Risk Management
### Exit Strategy
The strategy includes built-in exit rules based on ATR (Average True Range):
- **Stop Loss**: ATR × 2
- **Take Profit**: ATR × 3
Example: If ATR is $10, stop loss is at -$20 and take profit is at +$30
### Position Sizing
Default: 100% of equity per trade (adjustable in strategy properties)
**Recommendation**: Reduce to 10-25% of equity for safer capital allocation
## How to Use
### 1. Copy the Script
1. Open TradingView
2. Go to Pine Script Editor
3. Create a new indicator
4. Copy the entire `candle_pattern_strategy.pine` content
5. Click "Add to Chart"
### 2. Apply to Chart
- Select your preferred timeframe (1m, 5m, 15m, 1h, 4h, 1d)
- Choose a trading symbol (stocks, forex, crypto, etc.)
- The strategy will generate signals on all historical bars and in real-time
### 3. Configure Parameters
1. Right-click the strategy on chart → "Settings"
2. Adjust parameters in the "Inputs" tab
3. Strategy will recalculate automatically
4. Backtest results appear in the Strategy Tester panel
### 4. Backtesting
1. Click "Strategy Tester" (bottom panel)
2. Set date range for historical testing
3. Review performance metrics:
- Win rate
- Profit factor
- Drawdown
- Total returns
## Key Features
✅ **Execution Model Compliant** - Follows official Pine Script V6 standards
✅ **Global Scope** - All historical references in global scope for consistency
✅ **Adjustable Sensitivity** - Fine-tune all pattern detection thresholds
✅ **Real-time Updates** - Works on both historical and real-time bars
✅ **Visual Feedback** - Clear signals with labels and statistics table
✅ **Risk Management** - Built-in ATR-based stop loss and take profit
✅ **No Repainting** - Signals remain consistent after bar closes
## Important Notes
### Before Trading Live
1. **Backtest thoroughly**: Test on at least 6-12 months of historical data
2. **Paper trading first**: Practice with simulated trades
3. **Optimize parameters**: Find the best settings for your trading instrument
4. **Manage risk**: Never risk more than 1-2% per trade
5. **Monitor performance**: Review trades regularly and adjust as needed
### Market Conditions
The strategy works best in:
- Trending markets with clear directional bias
- Range-bound markets with defined support/resistance
- Markets with moderate volatility
The strategy may underperform in:
- Highly choppy/noisy markets (many false signals)
- Markets with gaps or overnight gaps
- Low liquidity periods
### Limitations
- Works on chart timeframes only (not intrabar analysis)
- Requires at least 5 bars of history (configurable)
- Fixed exit rules may not suit all trading styles
- No trend filtering (will trade both directions)
## Technical Details
### Historical Buffer Management
The strategy declares maximum bars back to ensure enough historical data:
```pine
max_bars_back(close, 20)
max_bars_back(open, 20)
max_bars_back(high, 20)
max_bars_back(low, 20)
```
This prevents runtime errors when accessing historical candlestick data.
### Pattern Detection Algorithm
```
For each bar in lookback period:
1. Calculate (high - close) / (high - low) → close_to_high_ratio
2. If close_to_high_ratio ≤ (1 - threshold) → count as "Close at High"
3. Calculate (close - low) / (high - low) → close_to_low_ratio
4. If close_to_low_ratio ≤ (1 - threshold) → count as "Close at Low"
5. Calculate abs(close - open) / (high - low) → body_ratio
6. If body_ratio ≤ doji_threshold → count as "Doji"
Signal Generation:
7. If doji_count ≥ cross_count_limit → SKIP_SIGNAL
8. If close_at_high_count ≥ 2 AND last_close_at_high → LONG_SIGNAL
9. If close_at_low_count ≥ 2 AND last_close_at_low → SHORT_SIGNAL
```
## Example Scenarios
### Scenario 1: Bullish Signal
```
Last 5 bars pattern:
Bar 1: Closes at high (95%) ✓
Bar 2: Closes at high (92%) ✓
Bar 3: Closes at mid (50%)
Bar 4: Closes at low (10%)
Bar 5: Closes at high (96%) ✓ (last bar)
Result:
- Close at high count: 3 (≥ 2) ✓
- Last closes at high: ✓
- Doji count: 0 (< 3) ✓
→ LONG SIGNAL ✓
```
### Scenario 2: Skip Signal
```
Last 5 bars pattern:
Bar 1: Doji pattern ✓
Bar 2: Doji pattern ✓
Bar 3: Closes at mid
Bar 4: Doji pattern ✓
Bar 5: Closes at high
Result:
- Doji count: 3 (≥ 3)
→ SKIP SIGNAL - Market too chaotic
```
## Performance Optimization
### Tips for Better Results
1. **Use Higher Timeframes**: 15m or higher reduces false signals
2. **Combine with Indicators**: Add volume or trend filters
3. **Seasonal Adjustment**: Different parameters for different seasons
4. **Instrument Selection**: Test on liquid, high-volume instruments
5. **Regular Rebalancing**: Adjust parameters quarterly based on performance
## Troubleshooting
### No Signals Generated
- Check if lookback period is too large
- Verify proximity thresholds aren't too strict (try 0.85 instead of 0.95)
- Ensure doji limit allows for trading (try 4-5 instead of 3)
### Too Many False Signals
- Increase proximity thresholds to 0.95+
- Reduce lookback period to 3-4 bars
- Increase doji limit to 3-4
- Test on higher timeframes
### Strategy Tester Shows Losses
- Review individual trades to identify patterns
- Adjust stop loss and take profit ratios
- Change lookback period and thresholds
- Test on different market conditions
## References
- (www.tradingview.com)
- (www.tradingview.com)
- (www.investopedia.com)
- (www.investopedia.com)
## Disclaimer
**This strategy is provided for educational and research purposes only.**
- Not financial advice
- Past performance does not guarantee future results
- Always conduct thorough backtesting before live trading
- Trading involves significant risk of loss
- Use proper risk management and position sizing
## License
Created: December 15, 2025
Version: 1.0
---
**For updates and modifications, refer to the accompanying documentation files.**
BTC - Bitcoin Strategic Dashboard by RM Title: BTC - Bitcoin Strategic Dashboard | RM
Overview & Philosophy
The Bitcoin Strategic Dashboard is a comprehensive analytics tool designed to provide deeper market context beyond simple price action.
While a standard chart displays price history, this dashboard focuses on the structural health of the market. It aims to answer clearer questions: Is the asset statistically overextended? Is the current volatility compressed or expanding? How is Bitcoin currently correlating with traditional equity markets?
This script aggregates key data points—Performance, Risk, Valuation, and Macro Correlations—into a single, organized table. It is designed to be a quiet, high-density reference tool that sits unobtrusively in the corner of your screen, helping to contextualize daily price movements without cluttering your workspace.
Methodology & Module Breakdown
The dashboard is divided into 5 strategic modules. Here is exactly how to read them, how they are calculated, and how to interpret the data.
1. PERFORMANCE
This section answers: "Is Bitcoin actually beating the traditional market, and by how much?"
BTC Return : The raw percentage growth of Bitcoin.
Timeframes: 1-Year (Tactical Trend) and 4-Year (The Halving Cycle).
Alpha (vs SPX / Gold):
Meaning : "Alpha" measures true outperformance. It tells you how much better your capital worked in Bitcoin compared to the S&P 500 (Stocks) or Gold.
Calculation : We use a Relative Growth Ratio. Instead of simple subtraction, we calculate the growth factor of BTC divided by the growth factor of the Benchmark.
Interpretation :
Green: Bitcoin is outperforming. It is the superior vehicle for capital.
Red: Bitcoin is underperforming traditional assets (Opportunity Cost is high).
2. RISK PROFILE
This section answers: "How dangerous is the market right now?"
Drawdown (DD):
Meaning : The percentage loss from the 1-Year High.
Interpretation : Deep Drawdowns (e.g., > -50%) historically signal generational buying opportunities (Deep Red). Small Drawdowns (< -5%) signal we are near "Discovery Mode" (Blue/Green).
Sharpe Ratio:
Meaning : The industry standard for "Risk-Adjusted Return." It asks: "Is the profit worth the stress?"
Timeframe : Annualized over 365 Days.
Interpretation :
> 1.0: Good. The return justifies the risk.
> 2.0: Excellent. (Dark Green).
< 0.0: Bad. You are taking risk for negative returns.
Sortino Ratio:
Meaning : Similar to Sharpe, but it only counts downside volatility as "risk." Bitcoin often rallies aggressively (Good Volatility); Sortino ignores the upside "risk" and focuses only on minimizing losses.
Volatility (Vol) & Rank:
Meaning : How violently the price is moving.
Calculation : We compare the current 30-Day Volatility against the last 4 Years of volatility history (Rank 0-100).
Interpretation (The Squeeze Strategy) :
BLUE (Cold / <25%): Volatility is historically low. The market is "compressed." Big moves often follow these periods.
RED (Hot / >75%): Volatility is extreme. High risk of mean reversion or panic.
3. VALUATION & MOMENTUM
This section answers: "Is Bitcoin cheap or expensive?"
Mayer Multiple (MM):
Meaning: A "Godfather" of Bitcoin ratios.
Calculation : Current Price divided by the 200-Day Moving Average.
Interpretation :
< 0.8 (Blue): Historically "Cheap."
1.0: Fair Value (Price = Trend).
> 2.4 (Red): Speculative Bubble territory.
RSI (Relative Strength Index):
Timeframe : 14 Days.
Interpretation : >70 suggests the market is overheated (Red). <30 suggests oversold conditions (Blue).
Trend (ADX) :
Meaning : The Average Directional Index measures the strength of a trend, not the direction.
Interpretation : Values >25 (Green) indicate a strong trend is present. Values <20 (Gray) indicate a choppy/sideways market (no trend).
vs 200W (Macro):
Meaning : The distance to the 200-Week Moving Average.
Interpretation : This line is historically the "Cycle Bottom" or "Absolute Support" for Bitcoin. Being close to it (or below it) is rare and often marks cycle lows.
4. MACRO CORRELATIONS
This section answers: "Is Bitcoin moving on its own, or just following the Stock Market?"
vs TradFi (SPX):
Timeframe : 90-Day Correlation Coefficient.
Interpretation :
High Positive (Red): BTC is just acting like a tech stock. No "Safe Haven" status.
Negative/Zero (Green): BTC is "decoupled." It is moving independently of Wall Street.
vs DXY (US Dollar):
Interpretation : Bitcoin usually moves inverse to the Dollar.
Negative (Green): Normal healthy behavior.
Positive (Red): Warning signal. If both DXY and BTC rise, something is breaking in the system.
5. HISTORICAL LEDGER
A Year-by-Year breakdown of returns.
Feature : You can toggle the comparison column in the settings to compare Bitcoin against either S&P 500 or Gold.
Usage : Helps visualize the cyclical nature of returns (e.g., the 4-year cycle pattern of Green-Green-Green-Red).
How to Read the Visuals (Heatmap)
The dashboard uses a standardized Bloomberg-style heatmap to let you assess the market state in milliseconds:
🟢 Green: Profit / Good Performance / Positive Alpha.
🔴 Red: Loss / Overheating / High Risk.
🔵 Blue: "Cold" / Cheap / Low Volatility (Potential Buy Zones).
🟠 Orange: Warning / High Drawdown.
⚫ Gray/Black: Neutral or Fair Value.
Settings & Customization
Visuals: Change the text size (Tiny, Small, Normal) to fit your screen resolution.
Modules: You can toggle individual sections on/off to save screen space.
Calculation: Switch the Historical Benchmark between "S&P 500" and "Gold" depending on your thesis.
Disclaimer
This script is for research and educational purposes only. The metrics provided (Sharpe, Sortino, Mayer Multiple) are derived from historical data and do not guarantee future performance. "Cheap" (Low Mayer Multiple) does not mean the price cannot go lower. Always manage your own risk.
Tags
bitcoin, btc, bloomberg, terminal, dashboard, onchain, mayer multiple, sharpe ratio, volatility, alpha, risk management, Rob Maths
Improved Candle Strategy (without daily squared)# Candle Pattern Trading Strategy
## Core Logic
Analyzes the last 5 candlesticks to identify "close at high" and "close at low" patterns, generating long/short signals.
## Trading Conditions
- **Long**: ≥2 bars closed at high in past 5 bars + current bar closes at high → Open long
- **Short**: ≥2 bars closed at low in past 5 bars + current bar closes at low → Open short
- **Filter**: If ≥3 doji patterns detected, skip trading
## Risk Management
- Stop Loss: Based on entry bar's high/low
- Take Profit: Risk × 2x multiplier
- Cooldown: No trading for 2 bars after entry
- Session Filter: No trading for first 5 bars after market open
## Configurable Parameters
- Lookback period, doji threshold, close proximity ratio, TP/SL ratio, cooldown bars, etc.
**Use Cases**: 1-minute and higher timeframes on stocks/futures
Index Construction Tool🙏🏻 The most natural mathematical way to construct an index || portfolio, based on contraharmonic mean || contraharmonic weighting. If you currently traded assets do not satisfy you, why not make your own ones?
Contraharmonic mean is literally a weighted mean where each value is weighted by itself.
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Now let me explain to you why contraharmonic weighting is really so fundamental in two ways: observation how the industry (prolly unknowably) converged to this method, and the real mathematical explanation why things are this way.
How it works in the industry.
In indexes like TVC:SPX or TVC:DJI the individual components (stocks) are weighted by market capitalization. This market cap is made of two components: number of shares outstanding and the actual price of the stock. While the number of shares holds the same over really long periods of time and changes rarely by corporate actions , the prices change all the time, so market cap is in fact almost purely based on prices itself. So when they weight index legs by market cap, it really means they weight it by stock prices. That’s the observation: even tho I never dem saying they do contraharmonic weighting, that’s what happens in reality.
Natural explanation
Now the main part: how the universe works. If you build a logical sequence of how information ‘gradually’ combines, you have this:
Suppose you have the one last datapoint of each of 4 different assets;
The next logical step is to combine these datapoints somehow in pairs. Pairs are created only as ratios , this reveals relationships between components, this is the only step where these fundamental operations are meaningful, they lose meaning with 3+ components. This way we will have 16 pairs: 4 of them would be 1s, 6 real ratios, and 6 more inverted ratios of these;
Then the next logical step is to combine all the pairs (not the initial single assets) all together. Naturally this is done via matrices, by constructing a 4x4 design matrix where each cell will be one of these 16 pairs. That matrix will have ones in the main diagonal (because these would be smth like ES/ES, NQ/NQ etc). Other cells will be actual ratios, like ES/NQ, RTY/YM etc;
Then the native way to compress and summarize all this structure is to do eigendecomposition . The only eigenvector that would be meaningful in this case is the principal eigenvector, and its loadings would be what we were hunting for. We can multiply each asset datapoint by corresponding loading, sum them up and have one single index value, what we were aiming for;
Now the main catch: turns out using these principal eigenvector loadings mathematically is Exactly the same as simply calculating contraharmonic weights of those 4 initial assets. We’re done here.
For the sceptics, no other way of constructing the design matrix other than with ratios would result in another type of a defined mean. Filling that design matrix with ratios Is the only way to obtain a meaningful defined mean, that would also work with negative numbers. I’m skipping a couple of details there tbh, but they don’t really matter (we don’t need log-space, and anyways the idea holds even then). But the core idea is this: only contraharmonic mean emerges there, no other mean ever does.
Finally, how to use the thing:
Good news we don't use contraharmonic mean itself because we need an internals of it: actual weights of components that make this contraharmonic mean, (so we can follow it with our position sizes). This actually allows us to also use these weights but not for addition, but for subtraction. So, the script has 2 modes (examples would follow):
Addition: the main one, allows you to make indexes, portfolios, baskets, groups, whatever you call it. The script will simply sum the weighted legs;
Subtraction: allows you to make spreads, residual spreads etc. Important: the script will subtract all the symbols From the first one. So if the first we have 3 symbols: YM, ES, RTY, the script will do YM - ES - RTY, weights would be applied to each.
At the top tight corner of the script you will see a lil table with symbols and corresponding weights you wanna trade: these are ‘already’ adjusted for point value of each leg, you don’t need to do anything, only scale them all together to meet your risk profile.
Symbols have to be added the way the default ones are added, one line : one symbol.
Pls explore the script’s Style setting:
You can pick a visualization method you like ! including overlays on the main chart pane !
Script also outputs inferred volume delta, inferred volume and inferred tick count calculated with the same method. You can use them in further calculations.
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Examples of how you can use it
^^ Purple dotted line: overlay from ICT script, turned on in Style settings, the contraharmonic mean itself calculated from the same assets that are on the chart: CME_MINI:RTY1! , CME_MINI:ES1! , CME_MINI:NQ1! , CBOT_MINI:YM1!
^^ precious metals residual spread ( COMEX:GC1! COMEX:SI1! NYMEX:PL1! )
^^ CBOT:ZC1! vs CBOT:ZW1! grain spread
^^ BDI (Bid Dope Index), constructed from: NYSE:MO , NYSE:TPB , NYSE:DGX , NASDAQ:JAZZ , NYSE:IIPR , NASDAQ:CRON , OTC:CURLF , OTC:TCNNF
^^ NYMEX:CL1! & ICEEUR:BRN1! basket
^^ resulting index price, inferred volume delta, inferred volume and inferred tick count of CME_MINI:NQ1! vs CME_MINI:ES1! spread
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Synthetic assets is the whole new Universe you can jump into and never look back, if this is your way
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EM Levelsstdv levels for you using VIX and VXN for ES and NQ so hopefully it helps you try it out and have fun
Session HeatmapIntraday Seasonality
Overview
Analyzes historical patterns by time of day. Identifies when volatility, volume, and open interest changes tend to be highest or lowest.
Features
Multiple Metrics: TR (volatility), Volume, and Open Interest changes
Flexible Grouping: View patterns by weekday or month to spot day-of-week or seasonal effects
Heatmap Visualization: Blue (low) to Red (high) color scale for quick pattern recognition
Percentile Mode: Reduces outlier impact by using 5th-95th percentile range
Timezone Support: Display in UTC alongside your local time
Metrics Explained
TR: Volatility - when markets move most
Volume: Liquidity - when participation is highest
OI Increase: When new positions are opened
OI Decrease: When positions are closed
OI Net: Net open interest change
Usage
Set your timezone and preferred slot size (30min/1H)
Choose a date range (relative or custom)
Select a metric to analyze
Use "Group By" to see weekday or monthly patterns
Switch to Percentile color scale if outliers dominate
Notes
Chart timeframe should be equal to or smaller than Slot Size
OI metrics require Binance Perpetual symbols
DST is not automatically adjusted; consider seasonal shifts for US/EU sessions
Vertical Time LinesVertical Time Lines is an indicator that draws vertical lines at specific times of each day on the price chart.
⚙️ Main Features
Up to 5 independent time lines
Precise hour and minute editing (HH:MM)
Individual enable/disable option per line
Customizable line color and style
Works on any asset and any timeframe
📝 Note
Due to Pine Script limitations, the lines are drawn using UTC time, not the time zone configured on the chart.
Lines are generated only when a candle exists exactly at the configured minute. If candles for the specified hours and minutes are not visible on the chart, the lines will not be displayed.
SCOTTGO - RSI Divergence IndicatorRSI Divergence Indicator
This indicator combines the Relative Strength Index (RSI) with an automatic divergence detection system.
It is designed to help traders spot potential trend changes by:
Color-Coded RSI: The main RSI line dynamically changes color (e.g., green/red) above and below a user-defined threshold (default 50) to highlight strong or weak momentum instantly.
Divergence Signals: It automatically identifies and plots four types of RSI divergences (Regular Bullish, Hidden Bullish, Regular Bearish, and Hidden Bearish) between the price and the oscillator.
Custom Alerts: Includes alerts for all divergence types so you can be notified when a new signal is found.
This tool helps visualize momentum shifts and potential reversals in the market.
SCOTTGO - Buy Sell Volume📊 SCOTTGO - Buy Sell Volume Bars - Delta - Up Down Volume Bars
This indicator disaggregates the total volume traded on each bar into estimated Buying Volume and Selling Volume to visualize market pressure and dominance directly in a dedicated sub-pane.
Key Features:
Volume Disaggregation: Uses a standard formula to estimate how much of a bar's total volume was associated with upward (buying) pressure and how much was associated with downward (selling) pressure.
Visual Clarity: Plots the Buy Volume (teal, upward) and Sell Volume (red, downward) as separate columns against a transparent total volume background, allowing for quick assessment of pressure balance.
Real-Time Badge: A dynamic badge is fixed to the corner of the chart (default: Top Right) providing a numeric summary of the latest bar:
Buy %: Percentage of the bar's total volume estimated as Buying Volume.
Sell %: Percentage of the bar's total volume estimated as Selling Volume.
Delta %: The magnitude of the volume difference (Delta) as a percentage of total volume, indicating the strength of the dominant side.
Dominance Indicator: The background color of the badge changes dynamically to immediately signal whether Buying (customizable color, default: Teal) or Selling (customizable color, default: Red) pressure was dominant on the current bar.
Usage:
Traders can use this tool to identify periods of heavy accumulation (high Buy Volume) or distribution (high Sell Volume), providing insight into the conviction behind price movements.
Day of WeekDay of Week is an indicator that runs in a separate panel and colors the panel background according to the day of the week.
Main Features
Colors the background of the lower panel based on the day of the week
Includes all days, from Monday to Sunday
Customizable colors
Time Offset Correction
TradingView calculates the day of the week using the exchange’s timezone, which can cause visual inconsistencies on certain symbols.
To address this, the indicator includes a configurable time offset that allows the user to synchronize the calculated day with the day displayed on the chart.
By simply adjusting the Time Offset (hours) parameter, the background will align correctly with the visible chart calendar.
RO H1 Signal CandleMarks specific H1 signal candles based on Bucharest (RO) time.
Designed for clean backtesting and time-based analysis.
Displays a small marker on selected hourly candles only.
USDT Market Cap Change [Alpha Extract]A sophisticated stablecoin market analysis tool that tracks USDT market capitalization changes across daily and 60-day periods with statistical normalization and gradient intensity visualization. Utilizing z-score methodology for overbought/oversold detection and dynamic color gradients reflecting change magnitude, this indicator delivers institutional-grade market liquidity assessment through stablecoin flow analysis. The system's dual-timeframe approach combined with statistical normalization provides comprehensive market sentiment measurement based on capital inflows and outflows from the dominant stablecoin.
🔶 Advanced Market Cap Tracking Framework
Implements daily USDT market capitalization monitoring with dual-period change calculations measuring both 1-day and 60-day net capital flows. The system retrieves real-time CRYPTOCAP:USDT data on daily timeframe resolution, calculating absolute dollar changes to quantify stablecoin supply expansion or contraction as primary market liquidity indicator.
// Core Market Cap Analysis
USDT = request.security("CRYPTOCAP:USDT", "D", close)
USDT_60D_Change = USDT - USDT
USDT_1D_Change = USDT - USDT
🔶 Dynamic Gradient Intensity System
Features sophisticated color gradient engine that intensifies visual representation based on change magnitude relative to recent extremes. The system normalizes current 60-day change against configurable lookback period maximum, applying gradient strength calculation to transition colors from neutral tones through progressively intense blues (negative) or reds (positive) based on flow direction and magnitude.
🔶 Statistical Z-Score Normalization Engine
Implements comprehensive z-score calculation framework that normalizes 60-day market cap changes using rolling mean and standard deviation for objective overbought/oversold determination. The system applies statistical normalization over configurable periods, enabling cross-temporal comparison and threshold-based regime identification independent of absolute market cap levels.
// Z-Score Normalization
Change_Mean = ta.sma(USDT_60D_Change, Normalization_Length)
Change_StdDev = ta.stdev(USDT_60D_Change, Normalization_Length)
Z_Score = Change_StdDev > 0 ? (USDT_60D_Change - Change_Mean) / Change_StdDev : 0.0
🔶 Multi-Tier Threshold Detection System
Provides four-level regime classification including standard overbought (+1.5σ), standard oversold (-1.5σ), extreme overbought (+2.5σ), and extreme oversold (-2.5σ) thresholds with configurable adjustment. The system identifies market liquidity extremes when stablecoin inflows or outflows reach statistically significant levels, indicating potential market turning points or trend exhaustion.
🔶 Dual-Timeframe Flow Visualization
Features layered area plots displaying both 60-day strategic flows and 1-day tactical movements with distinct color coding for instant flow direction assessment. The system overlays short-term daily changes on longer-term 60-day trends, enabling traders to identify divergences between tactical and strategic capital flows into or out of stablecoin reserves.
🔶 Gradient Color Psychology Framework
Implements intuitive color scheme where red gradients indicate capital inflow (bullish for crypto as USDT supply expands for buying) and blue gradients show capital outflow (bearish as USDT is redeemed). The intensity progression from pale to vivid colors communicates flow magnitude, with extreme colors signaling statistically significant liquidity events requiring attention.
🔶 Background Zone Highlighting System
Provides subtle background coloring when z-score breaches overbought or oversold thresholds, creating visual alerts without obscuring primary data. The system applies translucent red backgrounds during overbought conditions and blue during oversold states, enabling instant regime recognition across chart timeframes.
🔶 Configurable Normalization Architecture
Features adjustable gradient lookback and statistical normalization periods enabling optimization across different market cycles and trading timeframes. The system allows traders to calibrate sensitivity by modifying the window used for maximum change detection (gradient) and mean/standard deviation calculation (z-score), adapting to volatile or stable market regimes.
🔶 Market Liquidity Interpretation Framework
Tracks USDT supply changes as proxy for overall cryptocurrency market liquidity conditions, where expanding market cap indicates fresh capital entering crypto markets and contracting cap suggests capital flight. The system provides leading indicator properties as large stablecoin inflows often precede major market rallies while outflows may signal distribution phases.
🔶 Why Choose USDT Market Cap Change ?
This indicator delivers sophisticated stablecoin flow analysis through statistical normalization and gradient visualization of USDT market capitalization changes. Unlike traditional market sentiment indicators that rely on price action alone, this tool measures actual capital flows through the dominant stablecoin, providing objective assessment of market liquidity conditions. The combination of dual-timeframe tracking, z-score normalization for overbought/oversold detection, and intensity-based gradient coloring makes it essential for traders seeking macro-level market assessment and regime change detection across cryptocurrency markets. The indicator excels at identifying liquidity extremes that often precede major market reversals or trend accelerations.
Magical Thirteen Turns - The Greedy SnakeThe number 9 appears:
Meaning: Warning signal. The rise may encounter resistance and a cautious pullback is about to begin.
Operation: Consider reducing your holdings (selling a portion) to lock in profits and avoid experiencing wild fluctuations.
The number 13 appears:
Meaning: Strong sell signal. The upward momentum is likely to be exhausted, which is also known as "bull exhaustion".
Operation: It is recommended to liquidate your positions or significantly reduce them. Short sell (if you are trading contracts).
Sideways Zone Breakout 📘 Sideways Zone Breakout – Indicator Description
Sideways Zone Breakout is a visual market-structure indicator designed to identify low-volatility consolidation zones and highlight potential breakout opportunities when price exits these zones.
This indicator focuses on detecting periods where price trades within a tight range, often referred to as sideways or consolidation phases, and visually marks these zones directly on the chart for clarity.
🔍 Core Concept
Markets often spend time moving sideways before making a directional move.
This indicator aims to:
Detect price compression
Visually highlight the sideways zone
Signal when price breaks above or below the zone boundaries
Instead of predicting direction, it simply reacts to range expansion after consolidation.
⚙️ How the Indicator Works
1️⃣ Sideways Zone Detection
The indicator looks back over a user-defined number of candles
It calculates the highest high and lowest low within that window
If the total price range remains within a defined percentage of the current price, the market is considered sideways
This helps filter out trending and highly volatile conditions.
2️⃣ Visual Zone Representation
When a sideways condition is detected:
A clear price zone is drawn between the recent high and low
The zone is displayed using a soft gradient fill for better visibility
Outer borders are added to enhance zone clarity without cluttering the chart
This makes consolidation areas easy to spot at a glance.
3️⃣ Breakout Identification
Once a sideways zone is active:
A bullish breakout is marked when price closes above the upper boundary
A bearish breakout is marked when price closes below the lower boundary
Directional arrows and labels are plotted directly on the chart to indicate these events.
📊 Visual Elements Included
Sideways consolidation zones with gradient fill
Upper and lower zone boundaries
Buy and Sell arrows on breakout
Optional text labels for clear interpretation
All visuals are designed to remain lightweight and readable on any chart theme.
🔧 User Inputs
Sideways Lookback (candles): Controls how many past candles are used to define the range
Max Range % (tightness): Determines how tight the range must be to qualify as sideways
Adjusting these inputs allows users to adapt the indicator to different instruments and timeframes.
📈 Usage Guidelines
Can be applied to any market or timeframe
Works well as a context or confirmation tool
Best used alongside volume, trend, or risk management tools
Signals should be validated with proper trade planning
⚠️ Disclaimer
This indicator is provided as open-source for educational and analytical purposes only.
It does not generate trade recommendations or guarantee outcomes.
Market conditions vary, and users are responsible for their own trading decisions.
Pair Creation🙏🏻 The one and only pair construction tech you need, unlike others:
Applies one consistent operation to all the data features (not only prices). Then, the script outputs these, so you can apply other calculations on these outputs.
calculates a very fast and native volatility based hedge ratio, that also takes into account point value (think SPY vs ES) so you can easily use it in position sizing
Has built-in forward pricing aka cost of carry model , so you can de-drift pairs from cost of carry, discover spot price of oil based on futures, and ofc find arbitrage opportunities
Also allows to make a pair as a product of 2 series, useful for triangular arbitrage
This script can make a pair in 2 ways:
Ratio, by dividing leg 1 by leg 2
Product, by multiplying leg 1 by leg 2
The real mathematically right way to construct a pair is a ratio/product (Spreads are in fact = 2 legged portfolio, but I ain't told ya that ok). Why? Because a pair of 2 entities has a mathematically unique beauty, it allows direct comparisons and relationship analysis, smth you can't do directly with 3 and more components.
Multiplication (think inversions like (EURUSD -> USDEUR), and use cases for triangular arbitrage) is useful sometimes too.
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Quickguide:
First, "Legs" are pair components: make a pair of related assets. Don’t be guided exclusively by clustering, cointegrations, mutual information etc. Common sense and exogenous info can easily made them all Forward pricing model: is useful when u work with spot vs futures pairs. Otherwise: put financing, storage and yield all on zeros, this way u will turn it off and have a pure ratio/product of 2 legs.
Look at the 2 numbers on the script’s status line: the first one would always be 1), and the second one is a variable.
First number (always 1) is multiplier for your position size on leg 1
The second number is the multiplier for your position size on leg 2 in the opposite direction.
If both legs are related, trading your sizes with these multipliers makes you do statistical arbitrage -> trading ~ volatility in risk free mode, while the relationship between the assets is still in place.
Also guys srsly, nobody ‘ever’ made a universal law that somewhy somehow for whatever secret conspiracy reason one shall only trade pairs in mean reverting style xd. You can do whatever you want:
Tilt hedge ratio significantly based on relative strength of legs
Trade the pair in momentum style
Ignore hedge ratio all together
And more and more, the limit is your imagination, e.g.:
Anticipate hedge ratio changes based on exogenous info and act accordingly
Scalp a pair just like any other asset
Make a pair out of 2 pairs
Like I mean it, whatever you desire
About forward pricing model:
It’s applied only to leg 2;
Direct: takes spot price and finds out implied futures price
Inverse: takes futures price and finds out implied spot price (try on oil)
Pls read online how to choose parameters, it’s open access reliable info
About the hedge ratio I use:
You prolly noticed the way I prefer to use inferred volumes vs the “real” ones. In pairs it’s especially meaningful, because real volumes lose sense in pair creation. And while volumes are closely tied to volatility, the inferred volumes ‘Are’ volatility irl (and later can be converted to currency space by using point value, allowing direct comparisons symbol vs symbol).
This hedge ratio is a good example of how discovering the real nature of entities beats making 100s of inventions, why domain knowledge and proper feature engineering beats difficult bulky models, neural networks etc. How simple data understanding & operations on it is all you need.
This script simply does this:
Takes inferred volume delta of both assets, makes a ratio, normalizes it by tick sizes and points values of both legs, calculates a typical value of this series.
That’s it, no step 2, we’re done. No Kalman filters, no TLS regression, no vine copulas, or whatever new fancy keywords you can come up with etc.
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^^ comparing real ES prices vs theoretical ones by forward-pricing model. Financing: 0.04, yield 0.0175
^^ EURUSD, 6E futures with theoretical futures price calculated with interest rate differential 0.02 (4% USD - 2% EUR interest rates)
^^4 different pairs (RTY/ES, YM/ES, NQ/ES, ES/ZN) each with different plot style (pick one you like in script's Style settings)
^^ YM/RTY pair, each plot represents ratio of different features: ratio of prices, ratio of inferred volume deltas, ratio of inferred volumes, ratio of inferred tick counts (also can be turned on/off in Style settings)
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How can u upgrade it and make a step forward yourself:
On tradingview missing values are automatically fixed by backfilling, and this never becomes a thing until you hit high frequency data. You can do better and use Kalman filter for filling missing values.
Script contains the functions I use everywhere to calculate inferred volume delta, inferred volume, and inferred tick count.
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Trinity Real Move Detector DashboardRelease Notes (critical)
1. This code "will" require tweaks for different timeframes to the multiplier, do not assume the data in the table is accurate, cross check it with the Trinity Real Move Detector or another ATR tool, to validate the values in the table and ensure you have set the correct values.
2. I mention this below. But please understand that pine code has a limitation in the number of security calls (40 request.security() calls per script). This code is on the limit of that threshold and I would encourage developers to see if they can find a way around this to improve the script and release further updates.
What do we have...
The Trinity Real Move Detector Dashboard is a powerful TradingView indicator designed to scan multiple assets at once and show when each one has genuine short-term volatility "energy" — the kind that makes directional options trades (especially 0DTE or short-dated) have a high probability of follow-through, and can be used for swing trading as well. It combines a simple ATR-based volatility filter with a SuperTrend-style bias to tell you not only if the market is "awake" but also in which direction the momentum is leaning.
At its core, the indicator calculates the current ATR on your chosen timeframe and compares it to a user-defined percentage of the asset's daily ATR. When the short-term ATR spikes above that threshold, it signals "enough energy" — meaning the underlying is moving with real force rather than choppy noise. The SuperTrend logic then determines bullish or bearish bias, so the status shows "BULLISH ENERGY" (green) or "BEARISH ENERGY" (red) when energy is on, or "WAIT" when it's not. It also counts how many bars the energy has been active and shows the current ATR vs threshold for quick visual confirmation.
The dashboard displays all this in a clean table with columns for Symbol, Multiplier, Current ATR, Threshold, Status, Bars Active, and Bias (UP/DOWN). It's perfect for 3-minute charts but works on any timeframe — just adjust the multiplier based on the hints in the settings.
Editing symbols and multipliers is straightforward and user-friendly. In the indicator settings, you'll see numbered inputs like "1. Symbol - NVDA" and "1. Multiplier". To change an asset, simply type the new ticker in the symbol field (e.g., replace "NVDA" with "TSLA", "AVGO", or "ADAUSD"). You can also adjust the multiplier for each asset individually in the corresponding "Multiplier" field to make it more or less sensitive — lower numbers give more signals, higher numbers give stricter, higher-quality ones. This lets you customize the dashboard to your watchlist without any coding. For example, if you switch to a 4-hour chart or a slower-moving stock like AVGO, you may need to raise the multiplier (e.g., to 0.3–0.4) to avoid false "bullish" signals during minor bounces in a larger downtrend.
One important note about the multiplier and timeframes: the default values are optimized for fast intraday charts (like 3-minute or 5-minute). On higher timeframes (15-minute, 1-hour, 4-hour, or daily), the SuperTrend bias can be too sensitive with low multipliers (1.0 default in the code), leading to situations like the AVGO 4-hour example — where price is clearly downtrending, but the dashboard shows "BULLISH ENERGY" because the tight bands flip on small bounces. To fix this, you need to manually increase the multiplier for that asset (or all assets) in the settings. For 4-hour or daily charts, 0.25–0.35 is often better to match smoother SuperTrend indicators like Trinity. Always test on your timeframe and asset — crypto usually needs slightly lower multipliers than stocks due to higher volatility.
TradingView has a hard limit of 40 request.security() calls per script. Each asset in the dashboard requires several calls (current ATR, daily ATR, SuperTrend components, etc.), so with the full ATR-based bias, you can safely monitor about 6–8 assets before hitting the limit. Adding more symbols increases the number of calls and will trigger the "too many securities" error. This is a platform restriction to prevent excessive server load, and there's no official way around it in a single script. Some advanced coders use tricks like caching or lower-timeframe requests to squeeze in a few more, but for reliability, sticking to 6–8 assets is recommended. If you need more, the common workaround is to create two separate indicators (e.g., one for stocks, one for crypto) and add both to the same chart.
Overall, this dashboard gives you a professional-grade multi-asset scanner that filters out low-energy noise and highlights real momentum opportunities across stocks and crypto — all in one glance. It's especially valuable for options traders who want to avoid theta decay on weak moves and only strike when the market has true fuel. By tweaking the per-symbol multipliers in the settings, you can perfectly adapt it to any timeframe or asset behavior, avoiding issues like the AVGO false bullish signal on higher timeframes.
Golden Volume Lines📌 Golden Volume — Lines (Golden Team)
Golden Volume — Lines is an advanced volume-based indicator that detects Ultra High Volume candles using a statistical percentile model, then automatically draws and tracks key price levels derived from those candles.
The indicator highlights where real market interest and liquidity appear and shows how price reacts when those levels are broken.
🔍 How It Works
Volume Measurement
Choose between:
Units (raw volume)
Money (Volume × Average Price)
Average price can be calculated using HL2 or OHLC4.
Percentile-Based Classification
Volume is classified into:
Medium
High
Ultra High Volume
Thresholds are calculated using a rolling percentile window.
Ultra Volume candles are colored orange.
Dynamic High & Low Levels
For every Ultra Volume candle:
A High and Low dotted line is drawn.
Lines extend to the right until price breaks them.
Smart Line Break Detection (Wick-Based)
A line is considered broken when price wicks through it.
When a break occurs:
🟧 Orange line → broken by an Ultra Volume candle
⚪ White line → broken by a normal candle
The line stops exactly at the breaking candle.
🔔 Alerts
Alert on Ultra High Volume candles
Alert when a High or Low line is broken
Separate alerts for:
Break by Ultra Volume candle
Break by Normal candle
🎯 Use Cases
Breakout & continuation confirmation
Liquidity sweep detection
Volume-validated support & resistance
Market reaction after extreme participation
⚙️ Key Inputs
Volume display mode (Units / Money)
Percentile thresholds
Lookback window size
Maximum number of active Ultra levels
Optional dynamic alerts
⚠️ Disclaimer
This indicator is a volume and market structure tool, not a standalone trading system.
Always use proper risk management and additional confirmation.
