Hidden Markov Model [Extension] | FractalystWhat's the indicator's purpose and functionality?
The Hidden Markov Model is specifically designed to integrate with the Quantify Trading Model framework, serving as a probabilistic market regime identification system for institutional trading analysis.
Hidden Markov Models are particularly well-suited for market regime detection because they can model the unobservable (hidden) state of the market, capture probabilistic transitions between different states, and account for observable market data that each state generates.
The indicator uses Hidden Markov Model mathematics to automatically detect distinct market regimes such as low-volatility bull markets, high-volatility bear markets, or range-bound consolidation periods.
This approach provides real-time regime probabilities without requiring optimization periods that can lead to overfitting, enabling systematic trading based on genuine probabilistic market structure.
How does this extension work with the Quantify Trading Model?
The Hidden Markov Model | Fractalyst serves as a probabilistic state estimation engine for systematic market analysis.
Instead of relying on traditional technical indicators, this system automatically identifies market regimes using forward algorithm implementation with three-state probability calculation (bullish/neutral/bearish), Viterbi decoding process for determining most likely regime sequence without repainting, online parameter learning with adaptive emission probabilities based on market observations, and multi-feature analysis combining normalized returns, volatility comprehensive regime assessment.
The indicator outputs regime probabilities and confidence levels that can be used for systematic trading decisions, portfolio allocation, or risk management protocols.
Why doesn't this use optimization periods like other indicators?
The Hidden Markov Model | Fractalyst deliberately avoids optimization periods to prevent overfitting bias that destroys out-of-sample performance.
The system uses a fixed mathematical framework based on Hidden Markov Model theory rather than optimized parameters, probabilistic state estimation using forward algorithm calculations that work across all market conditions, online learning methodology with adaptive parameter updates based on real-time market observations, and regime persistence modeling using fixed transition probabilities with 70% diagonal bias for realistic regime behavior.
This approach ensures the regime detection signals remain robust across different market cycles without the performance degradation typical of over-optimized traditional indicators.
Can this extension be used independently for discretionary trading?
No, the Hidden Markov Model | Fractalyst is specifically engineered for systematic implementation within institutional trading frameworks.
The indicator is designed to provide regime filtering for systematic trading algorithms and risk management systems, enable automated backtesting through mathematical regime identification without subjective interpretation, and support institutional-level analysis when combined with systematic entry/exit models.
Using this indicator independently would miss the primary value proposition of systematic regime-based strategy optimization that institutional frameworks provide.
How do I integrate this with the Quantify Trading Model?
Integration enables institutional-grade systematic trading through advanced machine learning and statistical validation:
- Add both HMM Extension and Quantify Trading Model to your chart
- Select HMM Extension as the bias source using input.source()
- Quantify automatically uses the extension's bias signals for entry/exit analysis
- The built-in machine learning algorithms score optimal entry and exit levels based on trend intensity, and market structure patterns identified by the extension
The extension handles all bias detection complexity while Quantify focuses on optimal trade timing, position sizing, and risk management along with PineConnector automation
What markets and assets does the indicator Extension work best on?
The Hidden Markov Model | Fractalyst performs optimally on markets with sufficient price movement since the system relies on statistical analysis of returns, volatility, and momentum patterns for regime identification.
Recommended asset classes include major forex pairs (EURUSD, GBPUSD, USDJPY) with high liquidity and clear regime transitions, stock index futures (ES, NQ, YM) providing consistent regime behavior patterns, individual equities (large-cap stocks with sufficient volatility for regime detection), cryptocurrency markets (BTC, ETH with pronounced regime characteristics), and commodity futures (GC, CL showing distinct market cycles and regime transitions).
These markets provide sufficient statistical variation in returns and volatility patterns, ensuring the HMM system's mathematical framework can effectively distinguish between bullish, neutral, and bearish regime states.
Any timeframe from 15-minute to daily charts provides sufficient data points for regime calculation, with higher timeframes (4H, Daily) typically showing more stable regime identification with fewer false transitions, while lower timeframes (30m, 1H) provide more responsive regime detection but may show increased noise.
Acceptable Timeframes and Portfolio Integration:
- Any timeframe that can be evaluated within Quantify Trading Model's backtesting engine is acceptable for live trading implementation.
Legal Disclaimers and Risk Acknowledgments
Trading Risk Disclosure
The HMM Extension is provided for informational, educational, and systematic bias detection purposes only and should not be construed as financial, investment, or trading advice. The extension provides institutional analysis but does not guarantee profitable outcomes, accurate bias predictions, or positive investment returns.
Trading systems utilizing bias detection algorithms carry substantial risks including but not limited to total capital loss, incorrect bias identification, market regime changes, and adverse conditions that may invalidate analysis. The extension's performance depends on accurate data, TradingView infrastructure stability, and proper integration with Quantify Trading Model, any of which may experience data errors, technical failures, or service interruptions that could affect bias detection accuracy.
System Dependency Acknowledgment
The extension requires continuous operation of multiple interconnected systems: TradingView charts and real-time data feeds, accurate reporting from exchanges, Quantify Trading Model integration, and stable platform connectivity. Any interruption or malfunction in these systems may result in incorrect bias signals, missed transitions, or unexpected analytical behavior.
Users acknowledge that neither Fractalyst nor the creator has control over third-party data providers, exchange reporting accuracy, or TradingView platform stability, and cannot guarantee data accuracy, service availability, or analytical performance. Market microstructure changes, reporting delays, exchange outages, and technical factors may significantly affect bias detection accuracy compared to theoretical or backtested performance.
Intellectual Property Protection
The HMM Extension, including all proprietary algorithms, classification methodologies, three-state bias detection systems, and integration protocols, constitutes the exclusive intellectual property of Fractalyst. Unauthorized reproduction, reverse engineering, modification, or commercial exploitation of these proprietary technologies is strictly prohibited and may result in legal action.
Liability Limitation
By utilizing this extension, users acknowledge and agree that they assume full responsibility and liability for all trading decisions, financial outcomes, and potential losses resulting from reliance on the extension's bias detection signals. Fractalyst shall not be liable for any unfavorable outcomes, financial losses, missed opportunities, or damages resulting from the development, use, malfunction, or performance of this extension.
Past performance of bias detection accuracy, classification effectiveness, or integration with Quantify Trading Model does not guarantee future results. Trading outcomes depend on numerous factors including market regime changes, pattern evolution, institutional behavior shifts, and proper system configuration, all of which are beyond the control of Fractalyst.
User Responsibility Statement
Users are solely responsible for understanding the risks associated with algorithmic bias detection, properly configuring system parameters, maintaining appropriate risk management protocols, and regularly monitoring extension performance. Users should thoroughly validate the extension's bias signals through comprehensive backtesting before live implementation and should never base trading decisions solely on automated bias detection.
This extension is designed to provide systematic institutional flow analysis but does not replace the need for proper market understanding, risk management discipline, and comprehensive trading methodology. Users should maintain active oversight of bias detection accuracy and be prepared to implement manual overrides when market conditions invalidate analysis assumptions.
Terms of Service Acceptance
Continued use of the HMM Extension constitutes acceptance of these terms, acknowledgment of associated risks, and agreement to respect all intellectual property protections. Users assume full responsibility for compliance with applicable laws and regulations governing automated trading system usage in their jurisdiction.
Forecasting
Bitcoin Power Law Clock [LuxAlgo]The Bitcoin Power Law Clock is a unique representation of Bitcoin prices proposed by famous Bitcoin analyst and modeler Giovanni Santostasi.
It displays a clock-like figure with the Bitcoin price and average lines as spirals, as well as the 12, 3, 6, and 9 hour marks as key points in the cycle.
🔶 USAGE
Giovanni Santostasi, Ph.D., is the creator and discoverer of the Bitcoin Power Law Theory. He is passionate about Bitcoin and has 12 years of experience analyzing it and creating price models.
As we can see in the above chart, the tool is super intuitive. It displays a clock-like figure with the current Bitcoin price at 10:20 on a 12-hour scale.
This tool only works on the 1D INDEX:BTCUSD chart. The ticker and timeframe must be exact to ensure proper functionality.
According to the Bitcoin Power Law Theory, the key cycle points are marked at the extremes of the clock: 12, 3, 6, and 9 hours. According to the theory, the current Bitcoin prices are in a frenzied bull market on their way to the top of the cycle.
🔹 Enable/Disable Elements
All of the elements on the clock can be disabled. If you disable them all, only an empty space will remain.
The different charts above show various combinations. Traders can customize the tool to their needs.
🔹 Auto scale
The clock has an auto-scale feature that is enabled by default. Traders can adjust the size of the clock by disabling this feature and setting the size in the settings panel.
The image above shows different configurations of this feature.
🔶 SETTINGS
🔹 Price
Price: Enable/disable price spiral, select color, and enable/disable curved mode
Average: Enable/disable average spiral, select color, and enable/disable curved mode
🔹 Style
Auto scale: Enable/disable automatic scaling or set manual fixed scaling for the spirals
Lines width: Width of each spiral line
Text Size: Select text size for date tags and price scales
Prices: Enable/disable price scales on the x-axis
Handle: Enable/disable clock handle
Halvings: Enable/disable Halvings
Hours: Enable/disable hours and key cycle points
🔹 Time & Price Dashboard
Show Time & Price: Enable/disable time & price dashboard
Location: Dashboard location
Size: Dashboard size
M2 Growth Rate vs Borrowing RateHave you ever wondered how fast M2 is actually growing? Have you ever wanted to compare its percentage growth rate to the actual cost of borrowing? Are you also, like me, a giant nerd with too much time on your hands?
M2 Growth Rate vs Borrowing Rate
This Pine Script indicator analyzes the annualized growth rate of M2 money supply and compares it to key borrowing rates, providing insights into the relationship between money supply expansion and borrowing costs. Users can select between US M2 or a combined M2 (aggregating US, EU, China, Japan, and UK money supplies, adjusted for currency exchange rates). The M2 growth period is customizable, offering options from 1 month to 5 years for flexible analysis over different time horizons. The indicator fetches monthly data for US M2, EU M2, China M2, Japan M2, UK M2, and exchange rates (EURUSD, CNYUSD, JPYUSD, GBPUSD) to compute the combined M2 in USD terms.
It plots the annualized M2 growth rate alongside borrowing rates, including US 2-year and 10-year Treasury yields, corporate bond effective yield, high-yield bond effective yield, and 30-year US mortgage rates. Borrowing rates are color-coded for clarity: red if the rate exceeds the selected M2 growth rate, and green if below, highlighting relative dynamics. Displayed on a separate pane with a zero line for reference, the indicator includes labeled plots for easy identification.
This tool is designed for informational purposes, offering a visual framework to explore economic trends without providing trading signals or financial advice.
Bitcoin Power Law [LuxAlgo]The Bitcoin Power Law tool is a representation of Bitcoin prices first proposed by Giovanni Santostasi, Ph.D. It plots BTCUSD daily closes on a log10-log10 scale, and fits a linear regression channel to the data.
This channel helps traders visualise when the price is historically in a zone prone to tops or located within a discounted zone subject to future growth.
🔶 USAGE
Giovanni Santostasi, Ph.D. originated the Bitcoin Power-Law Theory; this implementation places it directly on a TradingView chart. The white line shows the daily closing price, while the cyan line is the best-fit regression.
A channel is constructed from the linear fit root mean squared error (RMSE), we can observe how price has repeatedly oscillated between each channel areas through every bull-bear cycle.
Excursions into the upper channel area can be followed by price surges and finishing on a top, whereas price touching the lower channel area coincides with a cycle low.
Users can change the channel areas multipliers, helping capture moves more precisely depending on the intended usage.
This tool only works on the daily BTCUSD chart. Ticker and timeframe must match exactly for the calculations to remain valid.
🔹 Linear Scale
Users can toggle on a linear scale for the time axis, in order to obtain a higher resolution of the price, (this will affect the linear regression channel fit, making it look poorer).
🔶 DETAILS
One of the advantages of the Power Law Theory proposed by Giovanni Santostasi is its ability to explain multiple behaviors of Bitcoin. We describe some key points below.
🔹 Power-Law Overview
A power law has the form y = A·xⁿ , and Bitcoin’s key variables follow this pattern across many orders of magnitude. Empirically, price rises roughly with t⁶, hash-rate with t¹² and the number of active addresses with t³.
When we plot these on log-log axes they appear as straight lines, revealing a scale-invariant system whose behaviour repeats proportionally as it grows.
🔹 Feedback-Loop Dynamics
Growth begins with new users, whose presence pushes the price higher via a Metcalfe-style square-law. A richer price pool funds more mining hardware; the Difficulty Adjustment immediately raises the hash-rate requirement, keeping profit margins razor-thin.
A higher hash rate secures the network, which in turn attracts the next wave of users. Because risk and Difficulty act as braking forces, user adoption advances as a power of three in time rather than an unchecked S-curve. This circular causality repeats without end, producing the familiar boom-and-bust cadence around the long-term power-law channel.
🔹 Scale Invariance & Predictions
Scale invariance means that enlarging the timeline in log-log space leaves the trajectory unchanged.
The same geometric proportions that described the first dollar of value can therefore extend to a projected million-dollar bitcoin, provided no catastrophic break occurs. Institutional ETF inflows supply fresh capital but do not bend the underlying slope; only a persistent deviation from the line would falsify the current model.
🔹 Implications
The theory assigns scarcity no direct role; iterative feedback and the Difficulty Adjustment are sufficient to govern Bitcoin’s expansion. Long-term valuation should focus on position within the power-law channel, while bubbles—sharp departures above trend that later revert—are expected punctuations of an otherwise steady climb.
Beyond about 2040, disruptive technological shifts could alter the parameters, but for the next order of magnitude the present slope remains the simplest, most robust guide.
Bitcoin behaves less like a traditional asset and more like a self-organising digital organism whose value, security, and adoption co-evolve according to immutable power-law rules.
🔶 SETTINGS
🔹 General
Start Calculation: Determine the start date used by the calculation, with any prior prices being ignored. (default - 15 Jul 2010)
Use Linear Scale for X-Axis: Convert the horizontal axis from log(time) to linear calendar time
🔹 Linear Regression
Show Regression Line: Enable/disable the central power-law trend line
Regression Line Color: Choose the colour of the regression line
Mult 1: Toggle line & fill, set multiplier (default +1), pick line colour and area fill colour
Mult 2: Toggle line & fill, set multiplier (default +0.5), pick line colour and area fill colour
Mult 3: Toggle line & fill, set multiplier (default -0.5), pick line colour and area fill colour
Mult 4: Toggle line & fill, set multiplier (default -1), pick line colour and area fill colour
🔹 Style
Price Line Color: Select the colour of the BTC price plot
Auto Color: Automatically choose the best contrast colour for the price line
Price Line Width: Set the thickness of the price line (1 – 5 px)
Show Halvings: Enable/disable dotted vertical lines at each Bitcoin halving
Halvings Color: Choose the colour of the halving lines
Intraweek Highs & Lows🔎 Track and analyze intraweek price extremes with full flexibility.
The indicator detects weekly highs or lows for any selected weekday and monitors when other days break those levels.
⚙️ Inputs
Select day
Pick which weekday’s extreme you want to monitor.
Find Low/High
Select whether you want to track Lows or Highs.
Use candle Wick/Body
Choose if extremes are calculated by full wick or candle body.
Cutoff date
Toggle the date-based filter and choose the starting date for event display.
RACZ-SIGNAL-V2.1RACZ-SIGNAL-V2.1 – Reactive Analytical Confluence Zones
Developed by: RACZ Trading
Indicator Type: Multi-Factor Confluence System
Overlay: Off (separate pane)
Purpose: Detect powerful trade opportunities through confluence of technical signals.
⸻
🔍 What is RACZ?
RACZ stands for Reactive Analytical Confluence Zones.
It’s a high-precision trading tool built for traders who rely on multi-signal confirmation, momentum alignment, and market structure awareness.
Rather than relying on a single technical metric, RACZ dynamically combines RSI, VWAP-RSI, Divergence, ADX, and Volume Analytics to produce a composite signal score from 0 to 12 — the higher the score, the stronger the signal.
⸻
🧠 How It Works – Core Components
1. RSI Analysis
• Detects momentum shifts.
• Compares RSI value to overbought (default: 67) and oversold (default: 33) thresholds.
• Adds points to Bullish or Bearish score.
2. VWAP-RSI
• Uses RSI based on VWAP (Volume Weighted Average Price).
• Adds weight to signals influenced by volume-adjusted price movement.
3. Divergence Detection
• Detects potential reversal zones.
• Bullish Divergence: RSI crosses up from low zone.
• Bearish Divergence: RSI crosses down from high zone.
• Strong confluence signal when present.
4. ADX Dynamic Strength Filter
• Custom-calculated ADX (trend strength indicator).
• Uses a dynamic threshold derived from SMA of ADX over a lookback period, scaled by a factor (default 0.9).
• Ensures signals are only validated in strong trend environments.
5. Volume Z-Score
• Detects anomalies in volume behavior.
• Z-score applied to 20-period volume average & deviation.
• Labels spikes, drops, high/low volume conditions.
⸻
📊 Signal Scoring Logic
Each component (RSI, VWAP-RSI, Divergence, ADX) can score up to 3 points each.
• Bullish Score: Total from bullish alignment of each factor.
• Bearish Score: Total from bearish alignment of each factor.
• Signal Power = max(bullish, bearish)
📈 Signal Interpretation
• BUY: Bullish Score > Bearish Score
• SELL: Bearish Score > Bullish Score
• NEUTRAL: Scores are equal
• Signal power is plotted on a 0–12 histogram:
• 0–5 = Weak
• 6–8 = Medium
• 9–12 = Strong (High Confluence Zone)
🖥️ Live Status Panel (Top-Right Corner)
This real-time panel helps you break down the signal:Component
Value Explanation: RSI / VWAP / DIV / ADX
Shows points contributing to signal
SIGNAL: Current market bias (BUY, SELL, NEUTRAL)
VOLUME: Volume classification (Spike, Drop, High, Low, Normal)
Color-coded for quick interpretation.
✅ How to Use
1. Look at Histogram: Bars ≥6 suggest valid setups, especially ≥9.
2. Confirm Panel Agreement: Check which components are supporting the signal.
3. Validate Volume: Unusual spikes/drops often precede strong moves.
4. Follow Direction: Use BUY/SELL signals aligned with signal power and trend.
⸻
⚙️ Customizable Inputs
• RSI period, overbought/oversold levels
• VWAP-RSI period
• ADX period and dynamic threshold settings
• Fully adjustable to fit any trading style
⸻
🚀 Why Choose RACZ?
• Clarity: Scores & signals derived from multiple tools, not just one.
• Confluence Logic: Designed for traders who look for confirmation across indicators.
• Speed: Real-time responsiveness to changing market dynamics.
• Volume Awareness: Integrated volume intelligence gives a deeper edge.
⸻
⚠️ Disclaimer
This indicator is intended strictly for educational and informational purposes only. It is not financial advice and should not be used to make actual investment decisions. Always conduct your own research or consult with a licensed financial advisor before trading or investing. Use of this script is at your own risk.
Yelober_Momentum_BreadthMI# Yelober_Momentum_BreadthMI: Market Breadth Indicator Analysis
## Overview
The Yelober_Momentum_BreadthMI is a comprehensive market breadth indicator designed to monitor market internals across NYSE and NASDAQ exchanges. It tracks several key metrics including up/down volume ratios, TICK readings, and trend momentum to provide traders with real-time insights into market direction, strength, and potential turning points.
## Indicator Components
This indicator displays a table with data for:
- NYSE breadth metrics
- NASDAQ breadth metrics
- NYSE TICK data and trends
- NASDAQ TICK (TICKQ) data and trends
## Table Columns and Interpretation
### Column 1: Market
Identifies the data source:
- **NYSE**: New York Stock Exchange data
- **NASDAQ**: NASDAQ exchange data
- **Tick**: NYSE TICK index
- **TickQ**: NASDAQ TICK index
### Column 2: Ratio
Shows the current ratio values with different calculations depending on the row:
- **For NYSE/NASDAQ rows**: Displays the up/down volume ratio
- Positive values (green): More up volume than down volume
- Negative values (red): More down volume than up volume
- The magnitude indicates the strength of the imbalance
- **For Tick/TickQ rows**: Shows the ratio of positive to negative ticks plus the current TICK reading in parentheses
- Format: "Ratio (Current TICK value)"
- Positive values (green): More stocks ticking up than down
- Negative values (red): More stocks ticking down than up
### Column 3: Trend
Displays the directional trend with both a symbol and value:
- **For NYSE/NASDAQ rows**: Shows the VOLD (volume difference) slope
- "↗": Rising trend (positive slope)
- "↘": Falling trend (negative slope)
- "→": Neutral/flat trend (minimal slope)
- **For Tick/TickQ rows**: Shows the slope of the ratio history
- Color-coding: Green for positive momentum, Red for negative momentum, Gray for neutral
The trend column is particularly important as it shows the current momentum of the market. The indicator applies specific thresholds for color-coding:
- NYSE: Green when normalized value > 2, Red when < -2
- NASDAQ: Green when normalized value > 3.5, Red when < -3.5
- TICK/TICKQ: Green when slope > 0.01, Red when slope < -0.01
## How to Use This Indicator
### Basic Interpretation
1. **Market Direction**: When multiple rows show green ratios and upward trends, it suggests strong bullish market internals. Conversely, red ratios and downward trends indicate bearish internals.
2. **Market Breadth**: The magnitude of the ratios indicates how broad-based the market movement is. Higher absolute values suggest stronger market breadth.
3. **Momentum Shifts**: When trend arrows change direction or colors shift, it may signal a potential reversal or change in market momentum.
4. **Divergences**: Look for divergences between different markets (NYSE vs NASDAQ) or between ratios and trends, which can indicate potential market turning points.
### Advanced Usage
- **Volume Normalization**: The indicator includes options to normalize volume data (none, tens, thousands, millions, 10th millions) to handle different exchange scales.
- **Trend Averaging**: The slope calculation uses an averaging period (default: 5) to smooth out noise and identify more reliable trend signals.
## Examples for Interpretation
### Example 1: Strong Bullish Market
```
| Market | Ratio | Trend |
|--------|---------|-----------|
| NYSE | 1.75 | ↗ 2.85 |
| NASDAQ | 2.10 | ↗ 4.12 |
| Tick | 2.45 (485) | ↗ 0.05 |
| TickQ | 1.95 (320) | ↗ 0.03 |
```
**Interpretation**: All metrics are positive and trending upward (green), indicating a strong, broad-based rally. The high ratio values show significant bullish dominance. This suggests continuation of the upward move with good momentum.
### Example 2: Weakening Market
```
| Market | Ratio | Trend |
|--------|---------|-----------|
| NYSE | 0.45 | ↘ -1.50 |
| NASDAQ | 0.85 | → 0.30 |
| Tick | 0.95 (105) | ↘ -0.02 |
| TickQ | 1.20 (160) | → 0.00 |
```
**Interpretation**: The market is showing mixed signals with positive but low ratios, while NYSE and TICK trends are turning negative. NASDAQ shows neutral to slightly positive momentum. This divergence often occurs near market tops or during consolidation phases. Traders should be cautious and consider reducing position sizes.
### Example 3: Negative Market Turning Positive
```
| Market | Ratio | Trend |
|--------|---------|-----------|
| NYSE | -1.25 | ↗ 1.75 |
| NASDAQ | -0.95 | ↗ 2.80 |
| Tick | -1.35 (-250) | ↗ 0.04 |
| TickQ | -1.10 (-180) | ↗ 0.02 |
```
**Interpretation**: This is a potential bottoming pattern. Current ratios are still negative (red) showing overall negative breadth, but the trends are all positive (green arrows), indicating improving momentum. This divergence often occurs at market bottoms and could signal an upcoming reversal. Look for confirmation with price action before establishing long positions.
### Example 4: Mixed Market with Divergence
```
| Market | Ratio | Trend |
|--------|---------|-----------|
| NYSE | 1.45 | ↘ -2.25 |
| NASDAQ | -0.85 | ↘ -3.80 |
| Tick | 1.20 (230) | ↘ -0.03 |
| TickQ | -0.75 (-120) | ↘ -0.02 |
```
**Interpretation**: There's a significant divergence between NYSE (positive ratio) and NASDAQ (negative ratio), while all trends are negative. This suggests sector rotation or a market that's weakening but with certain segments still showing strength. Often seen during late-stage bull markets or in transitions between leadership groups. Consider reducing risk exposure and focusing on relative strength sectors.
## Practical Trading Applications
1. **Confirmation Tool**: Use this indicator to confirm price movements. Strong breadth readings in the direction of the price trend increase confidence in trade decisions.
2. **Early Warning System**: Watch for divergences between price and breadth metrics, which often precede market turns.
3. **Intraday Trading**: The real-time nature of TICK and volume data makes this indicator valuable for day traders to gauge intraday momentum shifts.
4. **Market Regime Identification**: Sustained readings can help identify whether the market is in a trend or chop regime, allowing for appropriate strategy selection.
This breadth indicator is most effective when used in conjunction with price action and other technical indicators rather than in isolation.
Trend Lines by CR86The basic construction algorithm:
1. The baseline trend line through the closing prices:
First, the best fit line (linear regression) is calculated for the closing prices for a given period.
The least squares method is used to find the optimal slope and intersection point.
2. Search for key deviation points:
For each bar in the period, the deviation of the maximum and minimum from the regression baseline is calculated.
The point with the maximum deviation of the maximum upward from the regression line (for the resistance line) is located
The point with the maximum deviation of the minimum is located down from the regression line (for the support line)
3. Optimizing the slope of the lines:
Lines with an optimized slope are drawn through the found key points.
The algorithm selects the slope so that the line best "bends around" the corresponding extremes (maxima for resistance, minima for support)
Numerical optimization is used to check the validity of the trend line.
4. The principle of validity:
For the support line: all points must be above or at the line level (with a tolerance of 1e-5)
For the resistance line: all points must be below or at the line level (with a tolerance of 1e-5)
Key Features
Adaptability: the lines automatically adjust to the actual price extremes
Mathematical precision: a rigorous mathematical approach with optimization is used
Logarithmic scaling: optional for dealing with highly volatile assets
The basic construction algorithm
1. The baseline trend line through the closing prices:
First, the best fit line (linear regression) is calculated for the closing prices for a given period.
The least squares method is used to find the optimal slope and intersection point.
2. Search for key deviation points:
For each bar in the period, the deviation of the maximum and minimum from the regression baseline is calculated.
The point with the maximum deviation of the maximum upward from the regression line (for the resistance line) is located
The point with the maximum deviation of the minimum is located down from the regression line (for the support line)
3. Optimizing the slope of the lines:
Lines with an optimized slope are drawn through the found key points.
The algorithm selects the slope so that the line best "bends around" the corresponding extremes (maxima for resistance, minima for support)
Numerical optimization is used to check the validity of the trend line.
4. The principle of validity:
For the support line: all points must be above or at the line level (with a tolerance of 1e-5)
For the resistance line: all points must be below or at the line level (with a tolerance of 1e-5)
Key Features
Adaptability: the lines automatically adjust to the actual price extremes
Mathematical precision: a rigorous mathematical approach with optimization is used
Logarithmic scaling: optional for dealing with highly volatile assets
***********************************************************************************************
Основной алгоритм построения:
1. Базовая линия тренда через цены закрытия:
Сначала вычисляется линия наилучшего соответствия (линейная регрессия) для цен закрытия за заданный период
Используется метод наименьших квадратов для нахождения оптимального наклона и точки пересечения
2. Поиск ключевых точек отклонения:
Для каждого бара в периоде вычисляется отклонение максимума и минимума от базовой линии регрессии
Находится точка с максимальным отклонением максимума вверх от линии регрессии (для линии сопротивления)
Находится точка с максимальным отклонением минимума вниз от линии регрессии (для линии поддержки)
3. Оптимизация наклона линий:
Через найденные ключевые точки проводятся линии с оптимизированным наклоном
Алгоритм подбирает такой наклон, чтобы линия наилучшим образом "огибала" соответствующие экстремумы (максимумы для сопротивления, минимумы для поддержки)
Используется численная оптимизация с проверкой валидности трендовой линии
4. Принцип валидности:
Для линии поддержки: все точки должны быть выше или на уровне линии (с допуском 1e-5)
Для линии сопротивления: все точки должны быть ниже или на уровне линии (с допуском 1e-5)
Ключевые особенности
Адаптивность: линии автоматически подстраиваются под фактические экстремумы цен
Математическая точность: используется строгий математический подход с оптимизацией
Логарифмическое масштабирование: опционально для работы с сильно волатильными активами
IU Market Rhythm WaveDESCRIPTION:
The IU Market Rhythm Wave is a multi-dimensional indicator designed to reveal the underlying rhythm and energy of the market. By analyzing price momentum, harmonic oscillations, volume behavior, and market breadth, it helps traders identify high-quality long and short wave signals. It also visualizes rhythm bands, wave strength zones, and harmonic levels to provide comprehensive context for decision-making.
This tool is best used on trending instruments where rhythm cycles and volume patterns create clear wave-based opportunities.
USER INPUTS:
Rhythm Cycle Length
Controls the main lookback period used to calculate price waves, harmonic oscillation, volume rhythm, and breath. A longer cycle smooths signals, while a shorter cycle makes them more responsive. Recommended range: 8 to 35.
Wave Signal Strength
Multiplies the standard deviation of rhythm to define dynamic breakout thresholds. A higher value results in fewer but stronger signals, filtering out minor fluctuations.
Harmonic Filter
Applies a sensitivity filter to the harmonic mean and standard deviation. It helps eliminate weak or noisy signals and ensures rhythm-based signals align with harmonic structure.
Show Wave Energy Zones
Toggles background color shading based on current rhythm conditions. Greenish zones indicate strong upward rhythm, red for strong downward rhythm, yellow for positive bias, and gray for weak or neutral zones.
Show Rhythm Bands
Enables the display of upper and lower rhythm bands derived from ATR and rhythm volatility. These bands act as dynamic price envelopes and potential support/resistance zones.
Wave Zone Opacity
Adjusts the transparency of background energy zones, allowing users to control how prominent these zones appear on the chart. Range: 60 to 90 for optimal visibility.
INDICATOR LOGIC:
The indicator combines multiple rhythmic components into a composite rhythm score:
1. Price Wave – Based on momentum (rate of price change) smoothed by a moving average.
2. Harmonic Oscillation – Measures how far price has deviated from a central harmonic average (HLC3).
3. Volume Rhythm – Uses volume’s deviation from its mean, standardized by its volatility.
4. Market Breath – Captures range expansion and closing strength relative to range.
These elements form the Raw Rhythm, which is further smoothed to produce the Market Rhythm. When the rhythm exceeds statistically calculated thresholds and other conditions like volume confirmation and harmonic proximity are met, wave signals are triggered.
Harmonic Fibonacci levels (0.236, 0.382, 0.618, 0.764) are also calculated every rhythm cycle to identify nearby structural price zones. Signals occurring near these levels are considered more reliable.
The Rhythm Bands use ATR and rhythm strength to define dynamic boundaries above and below price. Visual zones and arrows mark rhythm shifts and highlight the underlying energy of the market.
WHY IT IS UNIQUE:
This indicator goes beyond traditional oscillators or volume indicators by blending multiple market dimensions into one rhythmic framework. It adapts to volatility, applies harmonic structure awareness, and filters signals based on real-time market conditions. It offers:
* A unique rhythm-based view of price, volume, and volatility
* Dynamic, adaptive signal generation and zone coloring
* Visual analytics and contextual data in a summary table
* Signal filtering using harmonic alignment and market breath
Its real-time responsiveness and multi-layered logic make it suitable for intraday, swing, and positional traders.
HOW USER CAN BENEFIT FROM IT:
* Spot high-conviction long or short entries when rhythm, volume, and structure align
* Avoid low-quality trades during weak or noisy rhythm periods
* Use visual wave zones to gauge trend strength and rhythm direction
* Monitor harmonic proximity to enter or exit near key structural levels
* Apply rhythm bands for dynamic stop-loss and target setting
* Use rhythm direction arrows and analytics table to gain deeper market insight
DISCLAIMER:
This indicator is created for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset. All trading involves risk, and users should conduct their own analysis or consult with a qualified financial advisor before making any trading decisions. The creator is not responsible for any losses incurred through the use of this tool. Use at your own discretion.
Linear Regression Forecast (ADX Adaptive)Linear Regression Forecast (ADX Adaptive)
This indicator is a dynamic price projection tool that combines multiple linear regression forecasts into a single, adaptive forecast curve. By integrating trend strength via the ADX and directional bias, it aims to visualize how price might evolve in different market environments—from strong trends to mean-reverting conditions.
Core Concept:
This tool builds forward price projections based on a blend of linear regression models with varying lookback lengths (from 2 up to a user-defined max). It then adjusts those projections using two key mechanisms:
ADX-Weighted Forecast Blending
In trending conditions (high ADX), the model follows the raw forecast direction. In ranging markets (low ADX), the forecast flips or reverts, biasing toward mean-reversion. A logistic transformation of directional bias, controlled by a steepness parameter, determines how aggressively this blending reacts to price behavior.
Volatility Scaling
The forecast’s magnitude is scaled based on ADX and directional conviction. When trends are unclear (low ADX or neutral bias), the projection range expands to reflect greater uncertainty and volatility.
How It Works:
Regression Curve Generation
For each regression length from 2 to maxLength, a forward projection is calculated using least-squares linear regression on the selected price source. These forecasts are extrapolated into the future.
Directional Bias Calculation
The forecasted points are analyzed to determine a normalized bias value in the range -1 to +1, where +1 means strongly bullish, -1 means strongly bearish, and 0 means neutral.
Logistic Bias Transformation
The raw bias is passed through a logistic sigmoid function, with a user-defined steepness. This creates a probability-like weight that favors either following or reversing the forecast depending on market context.
ADX-Based Weighting
ADX determines the weighting between trend-following and mean-reversion modes. Below ADX 20, the model favors mean-reversion. Above 25, it favors trend-following. Between 20 and 25, it linearly blends the two.
Blended Forecast Curve
Each forecast point is blended between trend-following and mean-reverting values, scaled for volatility.
What You See:
Forecast Lines: Projected future price paths drawn in green or red depending on direction.
Bias Plot: A separate plot showing post-blend directional bias as a percentage, where +100 is strongly bullish and -100 is strongly bearish.
Neutral Line: A dashed horizontal line at 0 percent bias to indicate neutrality.
User Inputs:
-Max Regression Length
-Price Source
-Line Width
-Bias Steepness
-ADX Length and Smoothing
Use Cases:
Visualize expected price direction under different trend conditions
Adjust trading behavior depending on trending vs ranging markets
Combine with other tools for deeper analysis
Important Notes:
This indicator is for visualization and analysis only. It does not provide buy or sell signals and should not be used in isolation. It makes assumptions based on historical price action and should be interpreted with market context.
SNC Lite AssistantThis indicator only works on 5m or 15m.
It gives calls when a higher volume is detected than usual. And also tries to detect whales where high volume + cvd opposition happened.
Its mainly and especially built for Bitcoin. However, it also works fine on Nasdaq or indices, less likely to be talkative on commodities or forex.
It doesn't give TRADING SIGNALS, it only warns you about key areas or volumes or possible whale areas where price is moving fast and volume is increasing. If you don't have price action knowledge you will not understand if price will go up or down. It's better to watchout for few candles after calls or whales are detected so you may clearly see if price is shifting or most likely to continue. With help of whales, you can try to catch knife more precisely. Times when markets are less volatile and out of session times like weekends, assistant can go 'off' to create avoid false-positive signals on less volatile times.
It also informs you about if market is ranging, downtrend or uptrend in chatbox.
Black Box Trading Measuring Tool (BlackBox - BBT)Overview
The Black Box Trading Indicator is a comprehensive technical analysis tool that combines multiple trading concepts into a single, powerful indicator. It displays custom session ranges, Average Daily Range (ADR) projections, support/resistance levels, and order blocks to help traders identify key market levels and potential trading opportunities.
Key Features
1. Custom Session Ranges
Define and visualize any trading session with customizable start and end times
Automatically calculates session high, low, and midpoint
Displays quarter levels (25% and 75% of range)
Shows range projections at 100%, 150%, 200%, and 250% extensions
2. Average Daily Range (ADR) Analysis
Calculates and displays ADR for daily, weekly, monthly, and custom timeframes
Shows projected high and low targets based on ADR
Includes "hash" levels at 50% ADR from session midpoint
Visual range boxes highlight potential support/resistance zones
3. Market Structure Levels
Daily and weekly opening prices with dynamic coloring
Previous daily and weekly center mass (50% of previous period's range)
Real-time range statistics displayed in an information table
4. Order Block Detection
Automatically identifies bullish and bearish order blocks
Visual representation with customizable colors and transparency
Mitigation tracking to remove invalidated blocks
Alert system for price interaction with order blocks
Parameter Guide
Display Settings
Show Blocks
Enables/disables order block visualization
Useful for cleaner charts when focusing on other elements
Show Previous Daily/Weekly Center Mass
Displays the midpoint of the previous period's range
Helps identify potential support/resistance from prior price acceptance areas
Show Daily/Weekly Open
Shows opening prices with color coding (blue for bullish, orange for bearish)
Important reference points for intraday trading
Show ADR Targets
Displays projected highs and lows based on Average Daily Range
Essential for setting realistic profit targets and stop losses
Show Range Projection
Extends the session range by multiples (1x, 1.5x, 2x, 2.5x)
Helps identify potential price targets during trending moves
Show Average Daily Range
Displays the ADR statistics table
Shows current range metrics for multiple timeframes
Display range in pips
Converts range values to pips for forex traders
Provides standardized measurement across different instruments
ADR Configuration
ADR Days
Number of days to include in current ADR calculation
Default: 1 (shows today's developing range)
ADR Period
Lookback period for calculating average range
Default: 14 days (standard period for volatility measurement)
Custom Range
Select between 60-minute or 240-minute timeframes
Allows analysis of intermediate timeframes
Session Time Settings (EST)
Start Hour/Minute
Define when your custom session begins
Default: 19:00 EST (Asian session open)
End Hour/Minute
Define when your custom session ends
Default: 02:45 EST (London session approach)
Extend To Hour/Minute
How far to extend the horizontal lines
Default: 19:00 EST (full 24-hour extension)
Visual Customization
Color Settings
Top Color: Used for upper levels and bullish projections
Bottom Color: Used for lower levels and bearish projections
Range Outline Color: Main session range boundaries
Center Range Line Color: Session midpoint visualization
Line Settings
Range Outline Width: Thickness of range box borders
Session Line Width: Thickness of horizontal level lines
Line Styles: Choose between solid, dashed, or dotted
Text Settings
Text Color: Color for all labels
Text Size: AUTO, tiny, small, normal, or large
Order Block Settings
Sensitivity
Percentage threshold for order block detection (1-100)
Higher values = fewer but stronger blocks
Default: 25 (detects 25% price movements)
OB Mitigation Type
Close: Block is mitigated when price closes beyond it
Wick: Block is mitigated when price wicks beyond it
Color Configuration
Separate colors for bullish and bearish blocks
Border and background colors can be customized independently
Trading Applications
1. Session-Based Trading
Identify the initial balance (first hour of trading)
Trade breakouts from defined session ranges
Use range projections for profit targets
Monitor for range-bound vs trending conditions
2. ADR-Based Strategies
Set daily profit targets based on ADR projections
Identify overextended moves when price exceeds ADR
Use ADR levels for position sizing and risk management
Compare current range to average for volatility assessment
3. Support/Resistance Trading
Use previous period center mass as dynamic S/R
Trade bounces from daily/weekly opens
Combine multiple timeframe levels for confluence
Monitor order blocks for potential reversal zones
4. Order Block Trading
Enter trades when price returns to unmitigated blocks
Use blocks as stop loss placement guides
Look for confluence with other indicator levels
Monitor block mitigation for trend confirmation
Best Practices
1. Multi-Timeframe Analysis
Use higher timeframe blocks for major levels
Combine with lower timeframe entries
Monitor weekly levels on daily charts
2. Confluence Trading
Look for areas where multiple levels align
Combine order blocks with ADR targets
Use session ranges with center mass levels
3. Risk Management
Use ADR for realistic daily profit targets
Place stops beyond order blocks or range extremes
Size positions based on distance to key levels
4. Alert Usage
Set alerts for ADR target hits
Monitor order block interactions
Track range breakouts and hash level tests
Tips for Effective Use
Start Simple: Begin with basic session ranges and ADR before adding all features
Color Coding: Use consistent colors across your trading setup
Time Zones: Ensure session times match your trading schedule
Clean Charts: Toggle off unused features for clarity
Backtesting: Study how price respects these levels historically
Journaling: Document which levels work best for your traded instruments
Common Trading Scenarios
Range Trading
Enter longs at session low or lower projections
Enter shorts at session high or upper projections
Target the session midpoint or opposite extreme
Breakout Trading
Wait for clear breaks of session range
Use range width for measuring targets
Monitor ADR to gauge breakout potential
Trend Following
Use order blocks as pullback entries
Trail stops using range projections
Scale out at ADR targets
Reversal Trading
Look for price rejection at ADR extremes
Monitor order block mitigation failures
Use center mass as reversal confirmation
MTF Pivot Fib Speed Resistance FansOverview
This Pine Script indicator, titled "MTF Pivot Fib Speed Resistance Fans", is a multi-timeframe tool that automatically plots Fib Speed Resistance Fan lines based on pivot structures derived from higher timeframes. It mirrors the functionality of TradingView’s built-in “Fib Speed Resistance Fan” drawing tool, but in a dynamic, programmatic way. It uses pivot highs and lows to anchor fan projections, drawing forward-facing trend lines that align with well-known Fibonacci ratios and their extensions.
Pivot Detection Logic
The script identifies pivots by comparing the current bar’s high and low against the highest and lowest prices over a user-defined pivot period. This pivot detection occurs on a higher timeframe of your choice, giving a broader and more strategic view of price structure. The script tracks direction changes in the pivot trend and stores only the most recent few pivots to maintain clean and meaningful fan drawings.
Fan Direction Control
The user can select whether to draw fans for "Buys", "Sells", or "Both". The script only draws fan lines when a new directional move is detected based on the pivot structure and the selected bias. For example, in “Buys” mode, a rising pivot followed by another higher low will trigger upward fan projections.
Fib Speed Resistance Levels
Once two pivots are identified, the script draws multiple fan lines from the first pivot outward, at angles defined by a preset list of Fibonacci levels. These fan lines help visualize speed and strength of a price move.
The script also draws a horizontal line from the pivot for additional confluence at the base level (1.0).
Price Level Plotting
In addition to drawing fan lines, the indicator also plots their price levels on the right-hand price scale. This makes it easier for users to visually reference the projected support and resistance levels without needing to trace the lines manually across the chart.
Mapping to TradingView’s "Fib Speed Resistance Fan"
The expanded set of values used in this script is not arbitrary—they closely align with the default and extended levels available in TradingView's built-in "Fib Speed Resistance Fan" tool.
TradingView’s Fib Fan tool offers several levels by default, including traditional Fibonacci ratios like 0.382, 0.5, 0.618, and 1. However, if you right-click the tool and open its settings, you’ll find additional toggles for levels like 1.618, 2.000, 2.618, and even 4.000. These deeper levels are used to project stronger trend continuations beyond the standard retracement zones.
The inclusion of levels such as 0.25, 0.75, and 1.34 reflects configurations that are available when you manually add or customize levels in TradingView’s fan tool. While 1.34 is not a canonical Fibonacci ratio, it is often found in hybrid Gann/Fib methods and is included in some preset templates in TradingView’s drawing tool for advanced users.
By incorporating these levels directly into the Pine Script, the indicator faithfully reproduces the fan structure users would manually draw using TradingView’s graphical Fib Fan tool—but does so programmatically, dynamically, and with multi-timeframe control. This eliminates manual errors, allows for responsive updating, and adds custom visual tracking via the price scale.
These values are standardized within the context of TradingView's Fib Fan tool and not made up. This script automates what the manual drawing tool achieves, with added precision and flexibility.
RSI Divergence StrategyOverview
The RSI Divergence Strategy Indicator is a trading tool that uses the RSI and divergences created to generate high-probability buy and sell signals.
I have provided the best formula of numbers to use for BTC on a 30 minute timeframe.
You can change where on RSI you enter and exit both long or short trades. This way you can experiment on different tokens using different entry/exit points. Can use on multiple timeframes.
This strategy is designed to open and close long or short trades based on the levels you provide it. You can then check on the RSI where the best levels are for each token you want to trade and amend it as required to generate a profitable strategy.
How It Works
The RSI Divergence Strategy Indicator uses bear and bull divergences in conjuction with a level you have input on the RSI.
RSI for Overbought/Oversold:
• Input variables for entry and exit levels and when the entry levels combine with a bear or bull divergence signal, a trade is alerted.
RSI Divergence:
• Buy and sell signals are confirmed when the RSI creates bearish or bullish divergences and these divergences are in the same area as your levels you input for entry to short or long.
After 7 years of experience and testing I have calculated the exact numbers required and produced a formula to calculate the exact input variables for a 30 minute Bitcoin chart.
Key Features
1️⃣ Divergence Identification – Ensures trades are taken only when a bull or bear divergence has formed.
2️⃣ Overbought/Oversold Input Filtering – Set up your own variables on the RSI for different markets after identifying patterns on the RSI in relation to a bearish or bullish divergence.
3️⃣ Works on any chart – Suitable for all markets and timeframes once you input the correct variables for entry and exit levels.
How to Use
🟢 Basic Trading:
• Use on any timeframe.
• Enter trade only when alert has fired off. Close when it says to exit.
• Change entry and exit levels in the properties of the strategy indicator.
• Make entry and exit levels coincide with bearish or bullish divergences on the RSI.
Check the strategy tester to see backtesting so you know if the indicator is profitable or not for that market and timeframe as each crypto token is different and so is the timeframe you choose.
📢 Webhook Automation:
• Set up TradingView Alerts to auto-execute trades via Webhook-compatible platforms.
Key additions for divergence visualization:
Divergence Arrows:
Bullish divergence: Green label with white 'bull ' text
Bearish divergence: Red label with white 'bear' text
Positioned at the pivot point
Divergence Lines:
Connects consecutive RSI pivot points
Automatically drawn between consecutive pivot points
Enhanced RSI Coloring:
Overbought zone: Red
Oversold zone: Green
Neutral zone: Gray
The visualization helps you instantly spot:
Where divergences are forming on the RSI
The pattern of higher lows (bullish) or lower highs (bearish)
Contextual coloring of RSI relative to standard levels
All divergence markers appear at the correct historical pivot points, making it easy to visually confirm divergence patterns as they develop.
Strategy levels and background zones also shown to help visual look.
Why This Combination?
This indicator is just a simple RSI tool.
It is designed to filter out weak trades and only execute trades that have:
✅ RSI Divergence
✅ Overbought or Oversold Conditions
It does not calculate downtrends or bear markets so care is recommended taking long trades during these times.
Why It’s Worth Using?
📈 Open Source – Free to use and learn from.
📉 Long or Short Term Trading Style – Entry/Exit parameters options are designed for both short or long term trades allowing you to experiment until you find a profitable strategy for that market you want to trade.
📢 Seamless Webhook Automation – Execute trades automatically with TradingView alerts.
💲 Ready to trade smarter?
✅ Add the RSI Divergence Strategy Indicator to your TradingView chart.
DVPOOverview
The DVPO (Dynamic Volume Profile Oscillator) Strategy is a comprehensive and highly customizable trading tool designed for precision and control. It is built around a unique, volume-driven oscillator that identifies potential market entries by analyzing the relationship between price, volume, and volatility.
This strategy is not just another signal generator; it's a complete framework that includes dynamic entry logic, adaptive risk management (ATR Stop Loss and R:R-based Take Profit), and a powerful dashboard of 10+ optional confirmation filters to help you tailor the strategy to your specific instrument, timeframe, and trading style.
The Core Concept: The DVPO Oscillator
The heart of this strategy is the DVPO oscillator. Unlike standard oscillators like RSI or Stochastics, the DVPO's primary goal is to quantify how far the current price has deviated from its recent volume-weighted "fair value."
Here’s how it works conceptually:
Micro Volume Profile: The indicator first analyzes a recent period of bars (defined by Lookback Period) to build a mini-profile of price and volume.
Volume-Weighted Mean: From this profile, it calculates a volume-weighted average price (VWAP) and the average deviation from that mean. This establishes the central point of value for the recent period.
Deviation Measurement: The oscillator's value is derived from how far the current price is from this calculated mean, scaled by the observed price deviation and a user-defined Sensitivity. A value above the midline suggests the price is trading at a premium, while a value below suggests it's at a discount.
Adaptive Volatility Zones: Instead of using fixed overbought/oversold levels (e.g., 70/30), the DVPO calculates dynamic upper and lower zones using the standard deviation of the oscillator itself. These zones expand and contract based on recent market volatility.
An entry signal is triggered not just when the oscillator is "overbought" or "oversold," but when it breaks out of these adaptive volatility zones, signaling that a statistically significant price movement is underway.
📈 Long Entry Condition : The oscillator crosses above the dynamic upper zone.
📉 Short Entry Condition : The oscillator crosses below the dynamic lower zone.
Integrated Risk & Trade Management
A signal is useless without proper risk management. This strategy has professional-grade risk management built directly into its logic.
Stop Loss (ATR-Based): The Stop Loss is not a fixed percentage. It is calculated using the Average True Range (ATR), allowing it to adapt automatically to the market's current volatility. In volatile periods, the stop will be wider; in quiet periods, it will be tighter.
Take Profit (Risk/Reward Ratio): The Take Profit level is calculated based on a user-defined Risk/Reward Ratio. If you set a ratio of 2.0, the Take Profit target will be placed at twice the distance of the Stop Loss from your entry price.
Dynamic Position Sizing: The strategy can automatically calculate the trade quantity for you. It determines the position size based on your specified Capital Size and the % Risk Per Trade you are willing to accept, ensuring disciplined risk control on every trade.
The Filter Dashboard : Enhance Your Signal Quality
To help reduce false signals and adapt to different market conditions, the strategy includes a comprehensive dashboard of optional confirmation filters. An entry signal will only be executed if it aligns with all the filters you have activated.
Trend & Momentum Filters :
T3, VMA, & VWAP Trend Filters: Utilize a suite of advanced moving averages (T3, Variable Moving Average, and a session-based VWAP) to ensure your trades are aligned with the dominant trend.
ADX Filter: Confirms that the market has sufficient directional strength for a trend-following trade, helping to avoid entries during choppy conditions.
Kaufman Efficiency Filter: Uses the Kaufman Efficiency Ratio to measure market noise. It only allows trades when the market is trending efficiently.
Volume & Market State Filters :
Volume Flow (VFI): A sophisticated volume-based filter that confirms whether volume is supporting the price move.
TDFI (Trader's Dynamic Index): A market state indicator designed to identify when the market is primed for a strong, directional move.
Flat Market Detector: A unique filter that identifies and avoids trading in sideways or ranging markets where trend strategies typically underperform.
Trade Condition Filters :
Min TP / Max SL %: Filter out trades where the risk/reward profile doesn't meet your minimum requirements (e.g., ignore a trade if the ATR-based stop loss is more than 10% away from the price).
Session Filters: Allows you to enable or disable trading on specific days of the week and to set a Cooldown Period (a set number of bars to wait after a trade closes before looking for a new entry).
How To Use This Strategy
Start with the Core: Begin by configuring the DVPO Oscillator settings (Lookback Period, Sensitivity, Zone Width) and your Risk Management parameters (ATR Multiplier, RR Ratio, % Risk Per Trade). These form the foundation of the strategy.
Backtest and Observe: Use TradingView's Strategy Tester to see how the core signals perform on your chosen asset and timeframe.
Layer Filters Intelligently: Enable the confirmation filters one by one and re-run your backtest. Observe how each filter impacts performance (e.g., does the T3 filter increase profitability but reduce the number of trades?). The goal is to find the optimal balance between signal quality and frequency.
Visualize and Analyze: Use the Show Risk/Reward Area option to plot your entry, stop loss, and take profit levels directly on the chart for every trade, providing a clear visual representation of your trade plan.
Disclaimer: This strategy is provided for educational and analytical purposes only. Past performance is not indicative of future results. All trading involves risk, and you should conduct your own thorough backtesting and analysis before deploying any strategy in a live market.
Golden Crossover Momentum Check📊 Golden Cross Momentum Screener — Summary
🔍 What It Does
This indicator identifies Golden Cross events — where the 50 EMA crosses above the 200 EMA, signaling a potential long-term trend reversal — and evaluates the momentum strength to help determine whether price is likely to:
Surge immediately (Group B), or
Retrace first (Group A)
It uses 5 momentum-confirming conditions to score the quality of the breakout and display a single label on the chart with a classification.
✅ Momentum Conditions Validated
RSI > 60 and rising – Indicates bullish buying pressure
MACD Histogram > 0 and rising – Confirms increasing momentum
Volume > 2× 20-day average – Validates participation on the breakout
ADX > 25 – Measures trend strength
Price is >5% above 200 EMA – Confirms price extension above long-term trend
Each passing condition adds 1 point to the momentum score (0–5).
📈 How to Use
Watch for a Golden Cross signal (triangle appears below candle)
If momentum score ≥ 4, the script labels the setup as:
"🚀 Surge Likely (Group B)" — consider immediate breakout entries
If score is 2–3, labeled:
"🔄 Pullback Likely (Group A)" — expect retest/consolidation before continuation
If score < 2, labeled:
"❌ No Momentum Confirmed" — avoid or wait for confirmation
IU Liquidity Flow TrackerDESCRIPTION
The IU Liquidity Flow Tracker is a powerful market analysis tool designed to visualize hidden buying and selling activity by analyzing price action, volume behavior, market pressure, and depth. It provides a composite view of liquidity dynamics to help traders identify accumulation, distribution, and neutral phases with high clarity.
This indicator is ideal for traders who want to gauge the flow of market participants and make informed entry/exit decisions based on the underlying liquidity structure.
USER INPUTS:
* Flow Analysis Period: Length used for analyzing price spread and volume flow.
* Pressure Sensitivity: Adjusts the sensitivity of threshold detection for flow classification.
* Flow Smoothing: Controls the smoothing applied to raw flow data.
* Market Depth Analysis: Sets the depth range for rejection and wick analysis.
* Colors: Customize colors for accumulation, distribution, neutral zones, and pressure visualization.
INDICATOR LOGIC:
The IU Liquidity Flow Tracker uses a multi-factor model to evaluate market behavior:
1. Liquidity Pressure: Combines price spread, price efficiency, and volume imbalance.
2. Flow Direction: Weighted momentum using short, medium, and long-term price changes adjusted for volume.
3. Market Depth: Wick-based rejection scoring to estimate buying/selling aggressiveness at price extremes.
4. Composite Flow Index: Blended value of flow direction, pressure, and depth—smoothed for clarity.
5. Dynamic Thresholds: Automatically adjusts based on volatility to classify the market into:
* Accumulation: Strong buying signals.
* Distribution: Strong selling signals.
* Neutral: No significant flow dominance.
6. Entry Signals: Long/Short signals are generated when flow state shifts, supported by momentum, volume surge, and depth strength.
WHY IT IS UNIQUE:
Unlike typical indicators that rely solely on price or volume, this tool combines spread behavior, volume polarity, momentum weighting, and price rejection zones into a single visual interface. It dynamically adjusts sensitivity based on market volatility, helping avoid false signals during sideways or low-volume periods.
It is not based on any traditional indicator (RSI, MACD, etc.), making it ideal for traders looking for an original and data-driven market read.
HOW USER CAN BENEFIT FROM IT:
* Understand Market Context: Know whether the market is being accumulated, distributed, or ranging.
* Improve Entries/Exits: Use flow transitions combined with volume confirmation for high-probability setups.
* Spot Institutional Activity: Detect subtle shifts in liquidity that precede major price moves.
* Reduce Whipsaws: Dynamic thresholds and multi-factor confirmation help filter noise.
* Use with Any Style: Whether you're a swing trader, day trader, or scalper, this tool adapts to different timeframes and strategies.
DISCLAIMER:
This indicator is created for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset. All trading involves risk, and users should conduct their own analysis or consult with a qualified financial advisor before making any trading decisions. The creator is not responsible for any losses incurred through the use of this tool. Use at your own discretion.
Liquidation Heatmap ║ BullVision 🧠Overview
The Liquidation Heatmap ║ BullVision 💥 is a high-precision visualization tool engineered to highlight probable liquidation levels in crypto markets. It leverages multi-exchange Open Interest data, real-time volume dynamics, and structure-aware volatility signals to reveal where leveraged traders are most at risk of forced position closures.
📖 What Are Liquidations?
In leveraged derivatives markets, a liquidation occurs when a trader’s margin becomes insufficient to maintain their position, triggering an automatic force-close by the exchange. These events are typically clustered around price levels where large volumes of overleveraged positions accumulate. When breached, they often result in sharp, aggressive price movements — also known as liquidation cascades.
This indicator is designed to detect and project such high-risk zones before they trigger, giving traders an edge in visualizing hidden pressure points in the market.
🧠 How It Works
The core engine aggregates real-time ∆OI (Open Interest delta) data from multiple major exchanges and applies a layered filtering system that considers:
Relative Open Interest shifts, normalized against an adaptive moving average baseline
Volume acceleration patterns, compared to a rolling historical benchmark
Market structure context, identifying meaningful directional breaks and failed retests
Leverage-tier modeling, using probabilistic distance rules to simulate where liquidations from 5x to 100x positions would be triggered
Each qualifying liquidation level is rendered using dynamic gradient lines and optional glow-enhanced zone visuals. The display adapts in real time to structural confirmation, volatility regime, and liquidity depth.
Exemple of Liquidation cascades
Exemple of Liquidation rejection
🔍 Key Features
🔗 Multi-Exchange OI Aggregation: Binance, OKX, BitMEX, Kraken (toggleable)
📊 Leverage-Tier Mapping: 5x, 10x, 25x, 50x, 100x projections
🎨 Gradient Zones: Custom color ramps reflect level significance
🧱 Structure-Sensitive Filtering: Noise reduction via multi-condition confirmation logic
🧠 Contextual Directional Bias: Zones filtered based on recent bullish/bearish transitions
⚙️ Fully Customizable: User-defined intensity thresholds, color palette, and range filtering
🧩 Why It’s Worth Paying For
This is not a mashup of public indicators. The script introduces an original, multi-layered architecture combining real-time Open Interest dynamics, structural analysis, and custom liquidation modeling.
Unlike speculative support/resistance plots or volume-only heatmaps, this tool is built to:
Detect liquidation zones before they cascade
React dynamically to market shifts
Filter noise through structural confirmation
Retain historical zones for visual learning and backtesting
✅ Compliance & Originality
This script was developed entirely in-house with original detection logic. No reused open-source components are included. Data requests are made through TradingView’s native .P_OI feeds, and all calculations, signal conditions, and visual logic were coded from scratch for this script.
⚠️ Risk Disclaimer & Access Policy
This script is a visual risk-awareness tool, not a signal generator or financial advice mechanism. No guarantee is made regarding future price action, liquidation triggers, or trading performance.
Use at your own discretion, with proper position sizing, risk management, and awareness of the market's inherent uncertainty.
🔒 Why This Script Is Invite-Only and Closed-Source
To protect its proprietary detection engine, this script is both closed-source and invite-only. The algorithm uses original methods to:
Aggregate real-time Open Interest delta across exchanges
Simulate leverage-based liquidation zones
Dynamically filter zones using structure and volatility layers
Opening the source would expose core detection logic to copycats or misuse. Likewise, access is limited to ensure the tool is used responsibly by serious traders and not distributed or repackaged unethically.
This model preserves the script’s quality, originality, and intended value.
Stop Hunt Indicator ║ BullVision 🧠 Overview
The Stop Hunt Indicator (SmartTrap Radar) is an original tool designed to identify potential liquidity traps caused by institutional stop hunts. It visually maps out historically significant levels where price has repeatedly reversed or rejected — and dynamically detects real-time sweep patterns based on volume, structure, and candle rejection behavior.
This script does not repurpose existing public indicators, nor does it use default TradingView built-ins such as RSI, MACD, or MAs. Its core logic is fully proprietary and was developed from scratch to support discretionary and data-driven traders in visualizing volatility risks and manipulation zones.
🔍 What the Indicator Does
This indicator identifies and visualizes potential stop hunt zones using:
Historical structure analysis: Swing highs/lows are identified via a configurable lookback period.
Liquidity level tracking: Once detected, levels are monitored for touches, age, and volume strength.
Proprietary scoring model: Each level receives a real-time significance score based on:
Age (how long the level has held)
Number of rejections (touches)
Relative volume strength
Proximity to current price
The glow intensity of plotted levels is dynamically mapped based on this score. Bright glow = higher institutional interest probability.
⚙️ Stop Hunt Detection Logic
A stop hunt is flagged when all of the following are met:
Price sweeps through a high/low beyond a user-defined penetration threshold
Wick rejection occurs (i.e., candle closes back inside the level)
Volume spikes above the average in a recent window
The script automatically:
Detects bullish stop hunts (below support) and bearish ones (above resistance)
Marks detected sweeps on-chart with optional 🔰/🚨 signals
Adjusts glow visuals based on score even after the sweep occurs
These sweeps often precede local reversals or high-volatility zones — this is not predictive, but rather a reactive mapping of market manipulation behavior.
📌 Why This Is Not Just Another Liquidity Tool
Unlike typical liquidity heatmaps or S/R indicators, this script includes:
A proprietary significance score instead of fixed rules
Multi-layer glow rendering to reflect level importance visually
Real-time scoring updates as new volume and touches occur
Combined volume × rejection × structure logic to validate stop hunts
Fully customizable detection logic (lookback, wick %, volume filters, max bars, etc.)
This indicator provides a specialized view focused solely on visualizing trap setups — not generic trend signals.
🧪 Usage Recommendations
To get started:
Add the indicator to your chart (volume-enabled instruments only)
Customize detection:
Lookback Period for structure
Penetration % for how far price must sweep
Volume Spike Multiplier
Wick rejection strength
Enable/disable features:
Glow effects
Hunt markers
Score labels
Volume highlights
Watch for:
🔰 Bullish Sweeps (below support)
🚨 Bearish Sweeps (above resistance)
Bright glowing zones = high-liquidity targets
This tool can be used for both confluence and risk assessment, especially around high-impact sessions, liquidation events, or range extremes.
📊 Volume Dependency Notice
⚠️ This indicator requires real volume data to function correctly. On instruments without volume (e.g., synthetic pairs), certain features like spike detection and scoring will be disabled or inaccurate.
🔐 Closed-Source Disclosure
This script is published as invite-only to protect its proprietary scoring, glow mapping, and detection logic. While the full implementation remains confidential, this description outlines all key mechanics and configurable logic for user transparency.
Mavericks ORBMavericks ORB – Opening Range Breakout Zones
Overview:
Mavericks ORB is a fully customizable Opening Range Breakout (ORB) indicator designed for serious intraday traders. It dynamically plots the ORB range for your chosen session and timeframe (5 min, 15 min, or any custom range), projects powerful price zones above and below the range, and automatically includes key midpoints—giving you actionable levels for breakouts, reversals, and dynamic support/resistance.
How It Works:
Configurable Session & Duration:
Choose any session start time and range length (e.g., 5 or 15 minutes) to define your personal ORB window.
Automatic Range Detection:
The indicator marks the high, low, and midpoint of the ORB range as soon as your defined period completes.
Dynamic Zones & Midpoints:
Three replicated price zones are projected both above and below the initial ORB, each calculated using the original ORB’s range and evenly spaced. Each zone includes its own midpoint for nuanced trade management and target planning.
Pre-Market Levels:
Tracks pre-market high and low (with fully customizable colors), giving you crucial context as the regular session opens.
Session Range Visualization:
Highlights the defined trading session with an adjustable background color for easy visual tracking.
Real-Time Info Table:
Displays a summary of all key levels—ORB range, highs, lows, and pre-market levels—right on your chart.
Full Customization:
Adjust all colors, enable/disable session range shading, show/hide labels, and tweak all session settings to fit your trading style.
Key Features:
Select any ORB start time and duration (fully customizable)
Plots ORB High, Low, and Midpoint in real time
Automatically projects 3 zones above and 3 zones below, each with its own midpoint
Pre-market high/low detection and labeling
Configurable session shading for visual clarity
At-a-glance info table with all major levels
Multiple color customizations for all zones and lines
Ready-to-use alert conditions for session and pre-market events
How to Use:
Set your preferred ORB start time and duration (e.g., 9:30 AM, 5 min for US equities).
Watch as the ORB forms and updates in real time.
Once complete, the high, low, and midpoint are plotted.
Monitor the projected zones above and below.
Use these for breakouts, targets, or support/resistance.
Reference the info table for all levels and pre-market context.
Customize as you go: Adjust colors, shading, and session settings to your needs.
Who is this for?
Intraday traders who trade the opening range breakout strategy (stocks, futures, forex, crypto)
Price action traders who want clean, actionable levels
Anyone looking for a reliable, highly visual ORB framework on TradingView
Short Description (for TradingView):
Mavericks ORB is a customizable Opening Range Breakout indicator that plots your session’s high, low, midpoint, and projects three dynamic zones above and below the range including midpoints for powerful trade planning. Includes pre-market levels, session highlights, and a real-time info table. Perfect for intraday price action traders.
What Makes Mavericks ORB Unique?
Flexible: Works with any timeframe or session.
Visual: Clean, uncluttered, and fully customizable.
Strategic: Automatic zone and midpoint projection, not just lines.
Practical: At-a-glance info table and real pre-market context.
Alert-ready: Triggers for session and pre-market events.
If you want to include any tips or a personal note (some script publishers do), you could add:
Tip: Use the midpoints for partial profit-taking or to gauge momentum strength. Adjust your ORB window for different asset classes or volatility environments.
Mariam Ichimoku DashboardPurpose
The Mariam Ichimoku Dashboard is designed to simplify the Ichimoku trading system for both beginners and experienced traders. It provides a complete view of trend direction, strength, momentum, and key signals all in one compact dashboard on your chart. This tool helps traders make faster and more confident decisions without having to interpret every Ichimoku element manually.
How It Works
1. Trend Strength Score
Calculates a score from -5 to +5 based on Ichimoku components.
A high positive score means strong bullish momentum.
A low negative score shows strong bearish conditions.
A near-zero score indicates a sideways or unclear market.
2. Future Cloud Bias
Looks 26 candles ahead to determine if the future cloud is bullish or bearish.
This helps identify the longer-term directional bias of the market.
3. Flat Kijun / Flat Senkou B
Detects flat zones in the Kijun or Senkou B lines.
These flat areas act as strong support or resistance and can attract price.
4. TK Cross
Identifies Tenkan-Kijun crosses:
Bullish Cross means Tenkan crosses above Kijun
Bearish Cross means Tenkan crosses below Kijun
5. Last TK Cross Info
Shows whether the last TK cross was bullish or bearish and how many candles ago it happened.
Helps track trend development and timing.
6. Chikou Span Position
Checks if the Chikou Span is above, below, or inside past price.
Above means bullish momentum
Below means bearish momentum
Inside means mixed or indecisive
7. Near-Term Forecast (Breakout)
Warns when price is near the edge of the cloud, preparing for a potential breakout.
Useful for anticipating price moves.
8. Price Breakout
Shows if price has recently broken above or below the cloud.
This can confirm the start of a new trend.
9. Future Kumo Twist
Detects upcoming twists in the cloud, which often signal potential trend reversals.
10. Ichimoku Confluence
Measures how many key Ichimoku signals are in agreement.
The more signals align, the stronger the trend confirmation.
11. Price in or Near the Cloud
Displays if the price is inside the cloud, which often indicates low clarity or a choppy market.
12. Cloud Thickness
Shows whether the cloud is thin or thick.
Thick clouds provide stronger support or resistance.
Thin clouds may allow easier breakouts.
13. Recommendation
Gives a simple trading suggestion based on all major signals.
Strong Buy, Strong Sell, or Hold.
Helps simplify decision-making at a glance.
Features
All major Ichimoku signals summarized in one panel
Real-time trend strength scoring
Detects flat zones, crosses, cloud twists, and breakouts
Visual alerts for trend alignment and signal confluence
Compact, clean design
Built with simplicity in mind for beginner traders
Tips
Best used on 15-minute to 1-hour charts for short-term trading
Avoid entering trades when price is inside the cloud because the market is often indecisive
Wait for alignment between trend score, TK cross, cloud bias, and confluence
Use the dashboard to support your trading strategy, not replace it
Enable alerts for major confluence or upcoming Kumo twists
Futures vs CFD Price Display
🎯 Trading the same asset in CFDs and Futures but tired of switching charts to compare prices? This is your indicator!
Stop the constant chart hopping! This live price comparison shows you instantly where the better conditions are.
✨ What you get:
Bidirectional: Works in both Futures AND CFD charts
Live prices: Real-time comparison of both markets
Spread calculation: Automatic difference in points and percentage
Fully customizable: Colors, position, size to your liking
Professional design: Clean display with symbol header
🎯 Perfect for:
Gold traders (Futures vs CFD)
Arbitrage strategies
Spread monitoring
Multi-broker comparisons
⚙️ Customization:
3 sizes (Small/Normal/Large) for all screens
4 positions available
Individual color schemes
Toggle features on/off
💡 Simply enter the symbol and keep both markets in sight!
Notice: "Co-developed with Claude AI (Anthropic) - because even AI needs to pay the server bills! 😄"
Fair Value Trend Model [SiDec]ABSTRACT
This pine script introduces the Fair Value Trend Model, an on-chart indicator for TradingView that constructs a continuously updating "fair-value" estimate of an asset's price via a logarithmic regression on historical data. Specifically, this model has been applied to Bitcoin (BTC) to fully grasp its fair value in the cryptocurrency market. Symmetric channel bands, defined by fixed percentage offsets around this central fair-value curve, provide a visual band within which normal price fluctuations may occur. Additionally, a short-term projection extends both the fair-value trend and its channel bands forward by a user-specified number of bars.
INTRODUCTION
Technical analysts frequently seek to identify an underlying equilibrium or "fair value" about which prices oscillate. Traditional approaches-moving averages, linear regressions in price-time space, or midlines-capture linear trends but often misrepresent the exponential or power-law growth patterns observable in many financial markets. The Fair Value Trend Model addresses this by performing an ordinary least squares (OLS) regression in log-space, fitting ln(Price) against ln(Days since inception). In practice, the primary application has been to Bitcoin, aiming to fully capture Bitcoin's underlying value dynamics.
The result is a curved trend line in regular (price-time) coordinates, reflecting Bitcoin's long-term compounding characteristics. Surrounding this fair-value curve, symmetric bands at user-specified percentage deviations serve as dynamic support and resistance levels. A simple linear projection extends both the central fair-value and its bands into the immediate future, providing traders with a heuristic for short-term trend continuation.
This exposition details:
Data transformation: converting bar timestamps into days since first bar, then applying natural logarithms to both time and price.
Regression mechanics: incremental (or rolling-window) accumulation of sums to compute the log-space fit parameters.
Fair-value reconstruction: exponentiation of the regression output to yield a price-space estimate.
Channel-band definition: establishing ±X% offsets around the fair-value curve and rendering them visually.
Forecasting methodology: projecting both the fair-value trend and channel bands by extrapolating the most recent incremental change in price-space.
Interpretation: how traders can leverage this model for trend identification, mean-reversion setups, and breakout analysis, particularly in Bitcoin trading.
Analysing the macro cycle on Bitcoin's monthly timeframe illustrates how the fair-value curve aligns with multi-year structural turning points.
DATA TRANSFORMATION AND NOTATION
1. Timestamp Baseline (t0)
Let t0 = timestamp of the very first bar on the chart (in milliseconds). Each subsequent bar has a timestamp ti, where ti ≥ t0.
2. Days Since Inception (d(t))
Define the “days since first bar” as
d(t) = max(1, (t − t0) / 86400000.0)
Here, 86400000.0 represents the number of milliseconds in one day (1,000 ms × 60 seconds × 60 minutes × 24 hours). The lower bound of 1 ensures that we never compute ln(0).
3. Logarithmic Coordinates:
Given the bar’s closing price P(t), define:
xi = ln( d(ti) )
yi = ln( P(ti) )
Thus, each data point is transformed to (xi, yi) in log‐space.
REGRESSION FORMULATION
We assume a log‐linear relationship:
yi = a + b·xi + εi
where εi is the residual error at bar i. Ordinary least squares (OLS) fitting minimizes the sum of squared residuals over N data points. Define the following accumulated sums:
Sx = Σ for i = 1 to N
Sy = Σ for i = 1 to N
Sxy = Σ for i = 1 to N
Sx2 = Σ for i = 1 to N
N = number of data points
The OLS estimates for b (slope) and a (intercept) are:
b = ( N·Sxy − Sx·Sy ) / ( N·Sx2 − (Sx)^2 )
a = ( Sy − b·Sx ) / N
All‐Time Versus Rolling‐Window Mode:
All-Time Mode:
Each new bar increments N by 1.
Update Sx ← Sx + xN, Sy ← Sy + yN, Sxy ← Sxy + xN·yN, Sx2 ← Sx2 + xN^2.
Recompute a and b using the formulas above on the entire dataset.
Rolling-Window Mode:
Fix a window length W. Maintain two arrays holding the most recent W values of {xi} and {yi}.
On each new bar N:
Append (xN, yN) to the arrays; add xN, yN, xN·yN, xN^2 to the sums Sx, Sy, Sxy, Sx2.
If the arrays’ length exceeds W, remove the oldest point (xN−W, yN−W) and subtract its contributions from the sums.
Update N_roll = min(N, W).
Compute b and a using N_roll, Sx, Sy, Sxy, Sx2 as above.
This incremental approach requires only O(1) operations per bar instead of recomputing sums from scratch, making it computationally efficient for long time series.
FAIR‐VALUE RECONSTRUCTION
Once coefficients (a, b) are obtained, the regressed log‐price at time t is:
ŷ(t) = a + b·ln( d(t) )
Mapping back to price space yields the “fair‐value”:
F(t) = exp( ŷ(t) )
= exp( a + b·ln( d(t) ) )
= exp(a) · ^b
In other words, F(t) is a power‐law function of “days since inception,” with exponent b and scale factor C = exp(a). Special cases:
If b = 1, F(t) = C · d(t), which is an exponential function in original time.
If b > 1, the fair‐value grows super‐linearly (accelerating compounding).
If 0 < b < 1, it grows sub‐linearly.
If b < 0, the fair‐value declines over time.
CHANNEL‐BAND DEFINITION
To visualise a “normal” range around the fair‐value curve F(t), we define two channel bands at fixed percentage offsets:
1. Upper Channel Band
U(t) = F(t) · (1 + α_upper)
where α_upper = (Channel Band Upper %) / 100.
2. Lower Channel Band
L(t) = F(t) · (1 − α_lower)
where α_lower = (Channel Band Lower %) / 100.
For example, default values of 50% imply α_upper = α_lower = 0.50, so:
U(t) = 1.50 · F(t)
L(t) = 0.50 · F(t)
When “Show FV Channel Bands” is enabled, both U(t) and L(t) are plotted in a neutral grey, and a semi‐transparent fill is drawn between them to emphasise the channel region.
SHORT‐TERM FORECAST PROJECTION
To extend both the fair‐value and its channel bands M bars into the future, the model uses a simple constant‐increment extrapolation in price space. The procedure is:
1. Compute Recent Increments
Let
F_prev = F( t_{N−1} )
F_curr = F( t_N )
Then define the per‐bar change in fair‐value:
ΔF = F_curr − F_prev
Similarly, for channel bands:
U_prev = U( t_{N−1} ), U_curr = U( t_N ), ΔU = U_curr − U_prev
L_prev = L( t_{N−1} ), L_curr = L( t_N ), ΔL = L_curr − L_prev
2. Forecasted Values After M Bars
Assuming the same per‐bar increments continue:
F_future = F_curr + M · ΔF
U_future = U_curr + M · ΔU
L_future = L_curr + M · ΔL
These forecasted values produce dashed lines on the chart:
A dashed segment from (bar_N, F_curr) to (bar_{N+M}, F_future).
Dashed segments from (bar_N, U_curr) to (bar_{N+M}, U_future), and from (bar_N, L_curr) to (bar_{N+M}, L_future).
Forecasted channel bands are rendered in a subdued grey to distinguish them from the current solid bands. Because this method does not re‐estimate regression coefficients for future t > t_N, it serves as a quick visual heuristic of trend continuation rather than a precise statistical forecast.
MATHEMATICAL SUMMARY
Summarising all key formulas:
1. Days Since Inception
d(t_i) = max( 1, ( t_i − t0 ) / 86400000.0 )
x_i = ln( d(t_i) )
y_i = ln( P(t_i) )
2. Regression Summations (for i = 1..N)
Sx = Σ
Sy = Σ
Sxy = Σ
Sx2 = Σ
N = number of data points (or N_roll if using rolling‐window)
3. OLS Estimator
b = ( N · Sxy − Sx · Sy ) / ( N · Sx2 − (Sx)^2 )
a = ( Sy − b · Sx ) / N
4. Fair‐Value Computation
ŷ(t) = a + b · ln( d(t) )
F(t) = exp( ŷ(t) ) = exp(a) · ^b
5. Channel Bands
U(t) = F(t) · (1 + α_upper)
L(t) = F(t) · (1 − α_lower)
with α_upper = (Channel Band Upper %) / 100, α_lower = (Channel Band Lower %) / 100.
6. Forecast Projection
ΔF = F_curr − F_prev
F_future = F_curr + M · ΔF
ΔU = U_curr − U_prev
U_future = U_curr + M · ΔU
ΔL = L_curr − L_prev
L_future = L_curr + M · ΔL
IMPLEMENTATION CONSIDERATIONS
1. Time Precision
Timestamps are recorded in milliseconds. Dividing by 86400000.0 yields days with fractional precision.
For the very first bar, d(t) = 1 ensures x = ln(1) = 0, avoiding an undefined logarithm.
2. Incremental Versus Sliding Summation
All‐Time Mode: Uses persistent scalar variables (Sx, Sy, Sxy, Sx2, N). On each new bar, add the latest x and y contributions to the sums.
Rolling‐Window Mode: Employs fixed‐length arrays for {x_i} and {y_i}. On each bar, append (x_N, y_N) and update sums; if array length exceeds W, remove the oldest element and subtract its contribution from the sums. This maintains exact sums over the most recent W data points without recomputing from scratch.
3. Numerical Robustness
If the denominator N·Sx2 − (Sx)^2 equals zero (e.g., all x_i identical, as when only one day has passed), then set b = 0 and a = Sy / N. This produces a constant fair‐value F(t) = exp(a).
Enforcing d(t) ≥ 1 avoids attempts to compute ln(0).
4. Plotting Strategy
The fair‐value line F(t) is plotted on each new bar. Its color depends on whether the current price P(t) is above or below F(t): a “bullish” color (e.g., green) when P(t) ≥ F(t), and a “bearish” color (e.g., red) when P(t) < F(t).
The channel bands U(t) and L(t) are plotted in a neutral grey when enabled; otherwise they are set to “not available” (no plot).
A semi‐transparent fill is drawn between U(t) and L(t). Because the fill function is executed at global scope, it is automatically suppressed if either U(t) or L(t) is not plotted (na).
5. Forecast Line Management
Each projection line (for F, U, and L) is created via a persistent line object. On successive bars, the code updates the endpoints of the same line rather than creating a new one each time, preserving chart clarity.
If forecasting is disabled, any existing projection lines are deleted to avoid cluttering the chart.
INTERPRETATION AND APPLICATIONS
1. Trend Identification
The fair‐value curve F(t) represents the best‐fit long‐term trend under the assumption that ln(Price) scales linearly with ln(Days since inception). By capturing power‐law or exponential patterns, it can more accurately reflect underlying compounding behavior than simple linear regressions.
When actual price P(t) lies above U(t), it may be considered “overextended” relative to its long‐term trend; when price falls below L(t), it may be deemed “oversold.” These conditions can signal potential mean‐reversion or breakout opportunities.
2. Mean‐Reversion and Breakout Signals
If price re‐enters the channel after touching or slightly breaching L(t), some traders interpret this as a mean‐reversion bounce and consider initiating a long position.
Conversely, a sustained move above U(t) can indicate strong upward momentum and a possible bullish breakout. Traders often seek confirmation (e.g., price remaining above U(t) for multiple bars, rising volume, or corroborating momentum indicators) before acting.
3. Rolling Versus All‐Time Usage
All‐Time Mode: Captures the entire dataset since inception, focusing on structural, long‐term trends. It is less sensitive to short‐term noise or volatility spikes.
Rolling‐Window Mode: Restricts the regression to the most recent W bars, making the fair‐value curve more responsive to changing market regimes, sudden volatility expansions, or fundamental shifts. Traders who wish to align the model with local behaviour often choose W so that it approximates a market cycle length (e.g., 100–200 bars on a daily chart).
4. Channel Percentage Selection
A wider band (e.g., ±50 %) accommodates larger price swings, reducing the frequency of breaches but potentially delaying actionable signals.
A narrower band (e.g., ±10 %) yields more frequent “overbought/oversold” alerts but may produce more false signals during normal volatility. It is advisable to calibrate the channel width to the asset’s historical volatility regime.
5. Forecast Cautions
The short‐term projection assumes that the last single‐bar increment ΔF remains constant for M bars. In reality, trend acceleration or deceleration can occur, rendering the linear forecast inaccurate.
As such, the forecast serves as a visual guide rather than a statistically rigorous prediction. It is best used in conjunction with other momentum, volume, or volatility indicators to confirm trend continuation or reversal.
LIMITATIONS AND CONSIDERATIONS
1. Power‐Law Assumption
By fitting ln(P) against ln(d), the model posits that P(t) ≈ C · ^b. Real markets may deviate from a pure power‐law, especially around significant news events or structural regime changes. Temporary misalignment can occur.
2. Fixed Channel Width
Markets exhibit heteroskedasticity: volatility can expand or contract unpredictably. A static ±X % band does not adapt to changing volatility. During high‐volatility periods, a fixed ±50 % may prove too narrow and be breached frequently; in unusually calm periods, it may be excessively broad, masking meaningful variations.
3. Endpoint Sensitivity
Regression‐based indicators often display greater curvature near the most recent data, especially under rolling‐window mode. This can create sudden “jumps” in F(t) when new bars arrive, potentially confusing users who expect smoother behaviour.
4. Forecast Simplification
The projection does not re‐estimate regression slope b for future times. It only extends the most recent single‐bar change. Consequently, it should be regarded as an indicative extension rather than a precise forecast.
PRACTICAL IMPLEMENTATION ON TRADINGVIEW
1 Adding the Indicator
In TradingView’s “Indicators” dialog, search for Fair Value Trend Model or visit my profile, under "scripts" add it to your chart.
Add it to any chart (e.g., BTCUSD, AAPL, EURUSD) to see real‐time computation.
2. Configuring Inputs
Show Forecast Line: Toggle on or off the dashed projection of the fair‐value.
Forecast Bars: Choose M, the number of bars to extend into the future (default is often 30).
Forecast Line Colour: Select a high‐contrast colour (e.g., yellow).
Bullish FV Colour / Bearish FV Colour: Define the colour of the fair‐value line when price is above (e.g., green) or below it (e.g., red).
Show FV Channel Bands: Enable to display the grey channel bands around the fair‐value.
Channel Band Upper % / Channel Band Lower %: Set α_upper and α_lower as desired (defaults of 50 % create a ±50 % envelope).
Use Rolling Window?: Choose whether to restrict the regression to recent data.
Window Bars: If rolling mode is enabled, designate W, the number of bars to include.
3. Visual Output
The central curve F(t) appears on the price chart, coloured green when P(t) ≥ F(t) and red when P(t) < F(t).
If channel bands are enabled, the chart shows two grey lines U(t) and L(t) and a subtle shading between them.
If forecasting is active, dashed extensions of F(t), U(t), and L(t) appear, projecting forward by M bars in neutral hues.
CONCLUSION
The Fair Value Trend Model furnishes traders with a mathematically principled estimate of an asset’s equilibrium price curve by fitting a log‐linear regression to historical data. Its channel bands delineate a normal corridor of fluctuation based on fixed percentage offsets, while an optional short‐term projection offers a visual approximation of trend continuation.
By operating in log‐space, the model effectively captures exponential or power‐law growth patterns that linear methods overlook. Rolling‐window capability enables responsiveness to regime shifts, whereas all‐time mode highlights broader structural trends. Nonetheless, users should remain mindful of the model’s assumptions—particularly the power‐law form and fixed band percentages—and employ the forecast projection as a supplemental guide rather than a standalone predictor.
When combined with complementary indicators (e.g., volatility measures, momentum oscillators, volume analysis) and robust risk management, the Fair Value Trend Model can enhance market timing, mean‐reversion identification, and breakout detection across diverse trading environments.
REFERENCES
Draper, N. R., & Smith, H. (1998). Applied Regression Analysis (3rd ed.). Wiley.
Tsay, R. S. (2014). Introductory Time Series with R (2nd ed.). Springer.
Hull, J. C. (2017). Options, Futures, and Other Derivatives (10th ed.). Pearson.
These references provide background on regression, time-series analysis, and financial modeling.
