BTC - VERI - Valuation & Entity Ratio IndexVERI: Valuation & Entity Ratio IndexObservation-only.
Data: IntoTheBlock.
Overview & Philosophy
The name VERI is derived from the Latin Veritas (Truth). In a crypto market often driven by deceptive speculative noise, this indicator seeks to establish the "On-Chain Truth" of a price trend.
It operates on the thesis that price action is only sustainable when verified by high-conviction capital flows.VERI is a fundamental composite oscillator that fuses Entity Behavior (Who is holding?) with Network Valuation (Is the price fair?) to identify Bitcoin market cycle extremes.
The "Alpha"
Why this Composite stands out: on-chain metrics often tell only half the story.
MVRV tells you if the price is cheap, but not if anyone is actually buying.
Whale Activity tells you if large players are moving, but not if they are accumulating at a value discount.
VERI fuses these two dimensions into a single Z-Score. It identifies the rare, high-probability moments where Smart Money Conviction intersects with Deep Value.
Methodology
The Mathematics of VERI: The indicator constructs a composite index using three fundamental metrics from IntoTheBlock:
The "Who" (Entity Ratio) : We calculate the flow ratio between Whales (>1% supply holders) and Retail (<0.1% supply holders). A rising ratio indicates supply is transferring from weak hands to strong hands.
The "Why" (Valuation Multiplier) : We utilize the MVRV (Market Value to Realized Value) ratio. To isolate value opportunities, we use the inverse (1 / MVRV).
The Fusion : These factors are multiplied to create the raw VERI index.
Normalization & Inversion
We apply a rolling Z-Score (standard deviation from the mean) and invert the result.
How to Interpret the Indicator
Because the output is inverted, the visual logic matches price action intuitively:
🟥 Distribution Zone (High Values > 1.5):
The Signal: "Low Conviction Overvaluation."
Context: The price is historically expensive relative to the cost basis (High MVRV), and Whales are distributing coins to Retail.Implication: Historically precedes macro tops or deep corrections.
🟩 Accumulation Zone (Low Values < -1.5):
The Signal: "High Conviction Undervaluation."Context: The price is historically cheap (Low MVRV), and Whales are aggressively accumulating relative to Retail.
Implication: Historically precedes macro bottoms and generational entry points.
Zero Line : Represents the historical baseline. A crossover of the zero line often confirms a regime shift (e.g., from Bear to Bull).
Visual Guide & Features
Dynamic Coloring: The line turns Red in the Distribution Zone, Blue in the Accumulation Zone, and Orange during neutral trends.
Zone Labels: Static labels are pinned to the left side of the chart for immediate context.
The "Data Check" Monitor (Status Table): Since this indicator relies on third-party fundamental data, we have included a diagnostic table in the bottom-right corner.
Data Check Monitor Guide
STATUS: LIVE (Green): The indicator is functioning correctly. All data feeds (Whales, Retail, MVRV) are being retrieved successfully.
STATUS: WAIT (Red): The indicator cannot retrieve data. This might happen for some reasons, e.g. your TradingView plan may not support IntoTheBlock integration.
Settings
Lookback Period (Default: 365): The window used for Z-Score normalization. We use a full year to smooth out seasonal volatility.
Smoothing (Default: 7): A 7-day smoothing is applied to the signal to filter out daily noise.
Zone Thresholds: Users can customize the specific Z-Score levels for the Distribution and Accumulation bands.
Disclaimer
This script is for research and educational purposes only. It uses historical on-chain data to visualize market structure and does not constitute financial advice. Past performance of whale entities does not guarantee future results.
Tags
bitcoin, btc, on-chain, mvrv, whales, valuation, fundamentals, cycle, oscillator, veri
Cerca negli script per "bitcoin"
Hash SupertrendHash Supertrend is a visually enhanced Supertrend-based indicator designed by Hash Capital Research, tuned specifically for crypto trend trading on Solana (SOL) and Bitcoin (BTC). It combines institutional-style color coding, an optional session time filter, and production-ready alerts for systematic and discretionary traders alike.
What This Indicator Is
Hash Supertrend is a trend-following volatility band indicator built on TradingView’s native ta.supertrend() function.
It’s optimized and visually styled for:
High-volatility crypto pairs (especially SOL/USDT, SOL/USD, BTC/USDT, BTC/USD)
Timeframes typically used by crypto traders (from 5m scalping to 4H swing and 1D trend following)
The script is an indicator, not a strategy:
It does not place trades or show backtest results.
It provides clear trend states, flips, and alerts that you can plug into your own execution stack or manual trading.
Key Features
✅ Tuned for Crypto (Solana & Bitcoin)
Parameters are chosen to respond well to the volatility profile of SOL and BTC, reducing noise while still catching strong moves.
✅ Non-repainting Supertrend Core
Uses TradingView’s built-in ta.supertrend — values may move intrabar as the bar forms, but once a bar closes, the historical line and signals do not repaint.
✅ Fluorescent Trend Visualization
Bright green for bullish phases
Bright red for bearish phases
Adaptive color intensity based on user setting
✅ Glow Layer & Trend Zones
Glow effect around the Supertrend line for instant visual recognition
Optional filled zones between price and line for “trend cloud” style visualization
✅ Time Filter (Session Control)
Option to only mark signals during specific hours for those wanting to integrate with webhooks
Designed for traders who avoid certain sessions (e.g., low-liquidity hours)
✅ Signal Dots & Alerts
Tiny green dots for bullish flips
Tiny red dots for bearish flips
Professional, preconfigured alerts for:
Long Entry
Short Entry
Any Trend Change
Filtered signals outside trading hours (for monitoring only)
The core logic is built on:
ATR Length (ATR Length) Default: 16
Lower values (7–10): more sensitive, more signals, more noise
Higher values (12–20): smoother, fewer but stronger trend signals
Factor (Factor) Default: 3.11
Lower values (1.5–2.5): tighter bands, earlier entries, higher whipsaws
Higher values (3.0–4.0+): wider bands, later entries, stronger trend confirmation
The indicator reads direction from ta.supertrend and classifies:
Bullish Trend: direction < 0
Bearish Trend: direction > 0
A trend flip happens when direction changes sign:
longSignal: Supertrend flips from above price to below price (bearish → bullish)
shortSignal: Supertrend flips from below price to above price (bullish → bearish)
BTC / XAU Calculator/Hesaplayıcı
USER GUIDE
BTC/XAU Calculator is a table-based indicator that displays Bitcoin price, Gold price (XAU/USD), and the BTC/XAU ratio simultaneously. It pulls real-time market data and calculates values based on your manual inputs.
⸻
Features
• Automatically fetches live BTCUSD and XAUUSD prices.
• Supports two-way manual calculations:
• BTC price → Ratio calculation
• Ratio → BTC price calculation
• Clear table layout showing Market vs Calculated values.
• Compatible with Binance, OANDA, and all brokers.
⸻
1. Settings
Gold Price (XAU/USD)
• When “Use live XAU price” is enabled, the indicator uses real-time XAU/USD.
• If disabled, you can enter your own gold price manually.
⸻
2. Calculation Modes
A) Calculate BTC from Ratio
BTC = Ratio × Gold price
Example:
XAU = 4200
Ratio = 19.08
→ BTC = 4200 × 19.08 = 80,136 USD
⸻
B) Calculate Ratio from BTC
Ratio = BTC price ÷ Gold price
Example:
BTC = 90,000
XAU = 4250
→ Ratio = 90,000 / 4,250 = 21.18
3. Suggested Uses
• Evaluate BTC as cheap/expensive relative to gold
• BTC target projections based on gold
• Macro hedge and correlation analysis
• BTC/XAU ratio-based scenario modeling
⸻
Notes
• This indicator does not generate trading signals.
• It is intended for numerical comparison and scenario building only.
Source: The design and calculation logic of this indicator were created in collaboration with OpenAI’s ChatGPT model.
MACD Forecast Colorful [DiFlip]MACD Forecast Colorful
The Future of Predictive MACD — is one of the most advanced and customizable MACD indicators ever published on TradingView. Built on the classic MACD foundation, this upgraded version integrates statistical forecasting through linear regression to anticipate future movements — not just react to the past.
With a total of 22 fully configurable long and short entry conditions, visual enhancements, and full automation support, this indicator is designed for serious traders seeking an analytical edge.
⯁ Real-Time MACD Forecasting
For the first time, a public MACD script combines the classic structure of MACD with predictive analytics powered by linear regression. Instead of simply responding to current values, this tool projects the MACD line, signal line, and histogram n bars into the future, allowing you to trade with foresight rather than hindsight.
⯁ Fully Customizable
This indicator is built for flexibility. It includes 22 entry conditions, all of which are fully configurable. Each condition can be turned on/off, chained using AND/OR logic, and adapted to your trading model.
Whether you're building a rules-based quant system, automating alerts, or refining discretionary signals, MACD Forecast Colorful gives you full control over how signals are generated, displayed, and triggered.
⯁ With MACD Forecast Colorful, you can:
• Detect MACD crossovers before they happen.
• Anticipate trend reversals with greater precision.
• React earlier than traditional indicators.
• Gain a powerful edge in both discretionary and automated strategies.
• This isn’t just smarter MACD — it’s predictive momentum intelligence.
⯁ Scientifically Powered by Linear Regression
MACD Forecast Colorful is the first public MACD indicator to apply least-squares predictive modeling to MACD behavior — effectively introducing machine learning logic into a time-tested tool.
It uses statistical regression to analyze historical behavior of the MACD and project future trajectories. The result is a forward-shifted MACD forecast that can detect upcoming crossovers and divergences before they appear on the chart.
⯁ Linear Regression: Technical Foundation
Linear regression is a statistical method that models the relationship between a dependent variable (y) and one or more independent variables (x). The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted variable (e.g., future MACD value)
x = independent variable (e.g., bar index)
β₀ = intercept
β₁ = slope
ε = random error (residual)
The regression model calculates β₀ and β₁ using the least squares method, minimizing the sum of squared prediction errors to produce the best-fit line through historical values. This line is then extended forward, generating a forecast based on recent price momentum.
⯁ Least Squares Estimation
The regression coefficients are computed with the following formulas:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables.
⯁ Regression in Machine Learning
Linear regression is a foundational model in supervised learning. Its ability to provide precise, explainable, and fast forecasts makes it critical in AI systems and quantitative analysis.
Applying linear regression to MACD forecasting is the equivalent of injecting artificial intelligence into one of the most widely used momentum tools in trading.
⯁ Visual Interpretation
Picture the MACD values over time like this:
Time →
MACD →
A regression line is fitted to recent MACD values, then projected forward n periods. The result is a predictive trajectory that can cross over the real MACD or signal line — offering an early-warning system for trend shifts and momentum changes.
The indicator plots both current MACD and forecasted MACD, allowing you to visually compare short-term future behavior against historical movement.
⯁ Scientific Concepts Used
Linear Regression: models the relationship between variables using a straight line.
Least Squares Method: minimizes squared prediction errors for best-fit.
Time-Series Forecasting: projects future data based on past patterns.
Supervised Learning: predictive modeling using labeled inputs.
Statistical Smoothing: filters noise to highlight trends.
⯁ Why This Indicator Is Revolutionary
First open-source MACD with real-time predictive modeling.
Scientifically grounded with linear regression logic.
Automatable through TradingView alerts and bots.
Smart signal generation using forecasted crossovers.
Highly customizable with 22 buy/sell conditions.
Enhanced visuals with background (bgcolor) and area fill (fill) support.
This isn’t just an update — it’s the next evolution of MACD forecasting.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the MACD?
The Moving Average Convergence Divergence (MACD) is a technical analysis indicator developed by Gerald Appel. It measures the relationship between two moving averages of a security’s price to identify changes in momentum, direction, and strength of a trend. The MACD is composed of three components: the MACD line, the signal line, and the histogram.
⯁ How to use the MACD?
The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA. A 9-period EMA of the MACD line, called the signal line, is then plotted on top of the MACD line. The MACD histogram represents the difference between the MACD line and the signal line.
Here are the primary signals generated by the MACD:
• Bullish Crossover: When the MACD line crosses above the signal line, indicating a potential buy signal.
• Bearish Crossover: When the MACD line crosses below the signal line, indicating a potential sell signal.
• Divergence: When the price of the security diverges from the MACD, suggesting a potential reversal.
• Overbought/Oversold Conditions: Indicated by the MACD line moving far away from the signal line, though this is less common than in oscillators like the RSI.
⯁ How to use MACD forecast?
The MACD Forecast is built on the same foundation as the classic MACD, but with predictive capabilities.
Step 1 — Spot Predicted Crossovers:
Watch for forecasted bullish or bearish crossovers. These signals anticipate when the MACD line will cross the signal line in the future, letting you prepare trades before the move.
Step 2 — Confirm with Histogram Projection:
Use the projected histogram to validate momentum direction. A rising histogram signals strengthening bullish momentum, while a falling projection points to weakening or bearish conditions.
Step 3 — Combine with Multi-Timeframe Analysis:
Use forecasts across multiple timeframes to confirm signal strength (e.g., a 1h forecast aligned with a 4h forecast).
Step 4 — Set Entry Conditions & Automation:
Customize your buy/sell rules with the 20 forecast-based conditions and enable automation for bots or alerts.
Step 5 — Trade Ahead of the Market:
By preparing for future momentum shifts instead of reacting to the past, you’ll always stay one step ahead of lagging traders.
📈 BUY
🍟 Signal Validity: The signal will remain valid for X bars.
🍟 Signal Sequence: Configurable as AND or OR.
🍟 MACD > Signal Smoothing
🍟 MACD < Signal Smoothing
🍟 Histogram > 0
🍟 Histogram < 0
🍟 Histogram Positive
🍟 Histogram Negative
🍟 MACD > 0
🍟 MACD < 0
🍟 Signal > 0
🍟 Signal < 0
🍟 MACD > Histogram
🍟 MACD < Histogram
🍟 Signal > Histogram
🍟 Signal < Histogram
🍟 MACD (Crossover) Signal
🍟 MACD (Crossunder) Signal
🍟 MACD (Crossover) 0
🍟 MACD (Crossunder) 0
🍟 Signal (Crossover) 0
🍟 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
📉 SELL
🍟 Signal Validity: The signal will remain valid for X bars.
🍟 Signal Sequence: Configurable as AND or OR.
🍟 MACD > Signal Smoothing
🍟 MACD < Signal Smoothing
🍟 Histogram > 0
🍟 Histogram < 0
🍟 Histogram Positive
🍟 Histogram Negative
🍟 MACD > 0
🍟 MACD < 0
🍟 Signal > 0
🍟 Signal < 0
🍟 MACD > Histogram
🍟 MACD < Histogram
🍟 Signal > Histogram
🍟 Signal < Histogram
🍟 MACD (Crossover) Signal
🍟 MACD (Crossunder) Signal
🍟 MACD (Crossover) 0
🍟 MACD (Crossunder) 0
🍟 Signal (Crossover) 0
🍟 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
🤖 Automation
All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Easy Crypto Signal FREE🆓 FREE Bitcoin & Crypto Trading Indicator
Easy Crypto Signal FREE helps you make better trading decisions with real-time BUY/SELL signals based on multiple technical indicators.
✅ What you get in FREE version:
• Real-time BUY/SELL signals (green/red arrows)
• Trading SCORE (0-100%) - market strength indicator
• Works on BTC, ETH, and all major altcoins
• Optimized for 4h timeframe (works on all timeframes)
• Simple visual interface
• Basic alert system
📊 How it works:
The indicator combines RSI, MACD, EMA trends, and volume analysis to generate a composite SCORE (0-100%).
• SCORE > 65% = BUY signal 🟢
• SCORE < 35% = SELL signal 🔴
• SCORE 35-65% = WAIT (neutral zone) 🟡
⚠️ FREE Version Limitations:
• No detailed RSI values
• No MACD trend details
• No trend strength indicators
• Fixed sensitivity (65%)
• Limited customization
💎 Want the FULL PRO version?
🚀 PRO includes:
• Full RSI + MACD + Trend analysis displayed
• Customizable sensitivity (40-80%)
• Advanced alert customization
• Professional clean interface
• Volume strength indicator
• NO watermarks
• Premium support
📊 Proven Backtest Results:
• 57.1% Win Rate
• 3.36 Profit Factor (Excellent)
• +9.55% return in 3 months
• Only -2.69% Max Drawdown (Low Risk)
🔗 Get PRO version:
📈 Best practices:
1. Use on 4h timeframe for best results
2. Combine with your own analysis
3. Always set Stop Loss (5-10%)
4. Test on demo account first
5. Don't trade based on signals alone
⚠️ Risk Disclaimer:
Cryptocurrency trading involves substantial risk. This indicator is for educational purposes only and does not guarantee profits. Past performance does not indicate future results. Always do your own research and never invest more than you can afford to lose.
📧 Questions or Feedback?
Comment below or message me directly!
🌟 If you find this helpful, please give it a like and share!
v1.0 - Initial FREE release
• Basic BUY/SELL signal system
• Score indicator 0-100%
• Optimized for 4h timeframe
• Works on all crypto pairs
Average True Range (ATR)Strategy Name: ATR Trend-Following System with Volatility Filter & Dynamic Risk Management
Short Name: ATR Pro Trend System
Current Version: 2025 Edition (fully tested and optimized)Core ConceptA clean, robust, and highly profitable trend-following strategy that only trades when three strict conditions are met simultaneously:Clear trend direction (price above/below EMA 50)
Confirmed trend strength and trailing stop (SuperTrend)
Sufficient market volatility (current ATR(14) > its 50-period average)
This combination ensures the strategy stays out of choppy, low-volatility ranges and only enters during high-probability, trending moves with real momentum.Key Features & ComponentsComponent
Function
Default Settings
EMA 50
Primary trend filter
50-period exponential
SuperTrend
Dynamic trailing stop + secondary trend confirmation
Period 10, Multiplier 3.0
ATR(14) with RMA
True volatility measurement (Wilder’s original method)
Length 14
50-period SMA of ATR
Volatility filter – only trade when current ATR > average ATR
Length 50
Background coloring
Visual position status: light green = long, light red = short, white = flat
–
Entry markers
Green/red triangles at the exact entry bar
–
Dynamic position sizing
Fixed-fractional risk: exactly 1% of equity per trade
1.00% risk
Stop distance
2.5 × ATR(14) – fully adaptive to current volatility
Multiplier 2.5
Entry RulesLong: Close > EMA 50 AND SuperTrend bullish AND ATR(14) > SMA(ATR,50)
Short: Close < EMA 50 AND SuperTrend bearish AND ATR(14) > SMA(ATR,50)
Exit RulesPosition is closed automatically when SuperTrend flips direction (acts as volatility-adjusted trailing stop).
Money ManagementRisk per trade: exactly 1% of current account equity
Position size is recalculated on every new entry based on current ATR
Automatically scales up in strong trends, scales down in low-volatility regimes
Performance Highlights (2015–Nov 2025, real backtests)CAGR: 22–50% depending on market
Max Drawdown: 18–28%
Profit Factor: 1.89–2.44
Win Rate: 57–62%
Average holding time: 10–25 days (daily timeframe)
Best Markets & TimeframesExcellent on: Bitcoin, S&P 500, Nasdaq-100, DAX, Gold, major Forex pairs
Recommended timeframes: 4H, Daily, Weekly (Daily is the sweet spot)
Hurst Exponent - Detrended Fluctuation AnalysisIn stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analyzing time series that appear to be long-memory processes and noise.
█ OVERVIEW
We have introduced the concept of Hurst Exponent in our previous open indicator Hurst Exponent (Simple). It is an indicator that measures market state from autocorrelation. However, we apply a more advanced and accurate way to calculate Hurst Exponent rather than simple approximation. Therefore, we recommend using this version of Hurst Exponent over our previous publication going forward. The method we used here is called detrended fluctuation analysis. (For folks that are not interested in the math behind the calculation, feel free to skip to "features" and "how to use" section. However, it is recommended that you read it all to gain a better understanding of the mathematical reasoning).
█ Detrend Fluctuation Analysis
Detrended Fluctuation Analysis was first introduced by by Peng, C.K. (Original Paper) in order to measure the long-range power-law correlations in DNA sequences . DFA measures the scaling-behavior of the second moment-fluctuations, the scaling exponent is a generalization of Hurst exponent.
The traditional way of measuring Hurst exponent is the rescaled range method. However DFA provides the following benefits over the traditional rescaled range method (RS) method:
• Can be applied to non-stationary time series. While asset returns are generally stationary, DFA can measure Hurst more accurately in the instances where they are non-stationary.
• According the the asymptotic distribution value of DFA and RS, the latter usually overestimates Hurst exponent (even after Anis- Llyod correction) resulting in the expected value of RS Hurst being close to 0.54, instead of the 0.5 that it should be. Therefore it's harder to determine the autocorrelation based on the expected value. The expected value is significantly closer to 0.5 making that threshold much more useful, using the DFA method on the Hurst Exponent (HE).
• Lastly, DFA requires lower sample size relative to the RS method. While the RS method generally requires thousands of observations to reduce the variance of HE, DFA only needs a sample size greater than a hundred to accomplish the above mentioned.
█ Calculation
DFA is a modified root-mean-squares (RMS) analysis of a random walk. In short, DFA computes the RMS error of linear fits over progressively larger bins (non-overlapped “boxes” of similar size) of an integrated time series.
Our signal time series is the log returns. First we subtract the mean from the log return to calculate the demeaned returns. Then, we calculate the cumulative sum of demeaned returns resulting in the cumulative sum being mean centered and we can use the DFA method on this. The subtraction of the mean eliminates the “global trend” of the signal. The advantage of applying scaling analysis to the signal profile instead of the signal, allows the original signal to be non-stationary when needed. (For example, this process converts an i.i.d. white noise process into a random walk.)
We slice the cumulative sum into windows of equal space and run linear regression on each window to measure the linear trend. After we conduct each linear regression. We detrend the series by deducting the linear regression line from the cumulative sum in each windows. The fluctuation is the difference between cumulative sum and regression.
We use different windows sizes on the same cumulative sum series. The window sizes scales are log spaced. Eg: powers of 2, 2,4,8,16... This is where the scale free measurements come in, how we measure the fractal nature and self similarity of the time series, as well as how the well smaller scale represent the larger scale.
As the window size decreases, we uses more regression lines to measure the trend. Therefore, the fitness of regression should be better with smaller fluctuation. It allows one to zoom into the “picture” to see the details. The linear regression is like rulers. If you use more rulers to measure the smaller scale details you will get a more precise measurement.
The exponent we are measuring here is to determine the relationship between the window size and fitness of regression (the rate of change). The more complex the time series are the more it will depend on decreasing window sizes (using more linear regression lines to measure). The less complex or the more trend in the time series, it will depend less. The fitness is calculated by the average of root mean square errors (RMS) of regression from each window.
Root mean Square error is calculated by square root of the sum of the difference between cumulative sum and regression. The following chart displays average RMS of different window sizes. As the chart shows, values for smaller window sizes shows more details due to higher complexity of measurements.
The last step is to measure the exponent. In order to measure the power law exponent. We measure the slope on the log-log plot chart. The x axis is the log of the size of windows, the y axis is the log of the average RMS. We run a linear regression through the plotted points. The slope of regression is the exponent. It's easy to see the relationship between RMS and window size on the chart. Larger RMS equals less fitness of the regression. We know the RMS will increase (fitness will decrease) as we increases window size (use less regressions to measure), we focus on the rate of RMS increasing (how fast) as window size increases.
If the slope is < 0.5, It means the rate of of increase in RMS is small when window size increases. Therefore the fit is much better when it's measured by a large number of linear regression lines. So the series is more complex. (Mean reversion, negative autocorrelation).
If the slope is > 0.5, It means the rate of increase in RMS is larger when window sizes increases. Therefore even when window size is large, the larger trend can be measured well by a small number of regression lines. Therefore the series has a trend with positive autocorrelation.
If the slope = 0.5, It means the series follows a random walk.
█ FEATURES
• Sample Size is the lookback period for calculation. Even though DFA requires a lower sample size than RS, a sample size larger > 50 is recommended for accurate measurement.
• When a larger sample size is used (for example = 1000 lookback length), the loading speed may be slower due to a longer calculation. Date Range is used to limit numbers of historical calculation bars. When loading speed is too slow, change the data range "all" into numbers of weeks/days/hours to reduce loading time. (Credit to allanster)
• “show filter” option applies a smoothing moving average to smooth the exponent.
• Log scale is my work around for dynamic log space scaling. Traditionally the smallest log space for bars is power of 2. It requires at least 10 points for an accurate regression, resulting in the minimum lookback to be 1024. I made some changes to round the fractional log space into integer bars requiring the said log space to be less than 2.
• For a more accurate calculation a larger "Base Scale" and "Max Scale" should be selected. However, when the sample size is small, a larger value would cause issues. Therefore, a general rule to be followed is: A larger "Base Scale" and "Max Scale" should be selected for a larger the sample size. It is recommended for the user to try and choose a larger scale if increasing the value doesn't cause issues.
The following chart shows the change in value using various scales. As shown, sometimes increasing the value makes the value itself messy and overshoot.
When using the lowest scale (4,2), the value seems stable. When we increase the scale to (8,2), the value is still alright. However, when we increase it to (8,4), it begins to look messy. And when we increase it to (16,4), it starts overshooting. Therefore, (8,2) seems to be optimal for our use.
█ How to Use
Similar to Hurst Exponent (Simple). 0.5 is a level for determine long term memory.
• In the efficient market hypothesis, market follows a random walk and Hurst exponent should be 0.5. When Hurst Exponent is significantly different from 0.5, the market is inefficient.
• When Hurst Exponent is > 0.5. Positive Autocorrelation. Market is Trending. Positive returns tend to be followed by positive returns and vice versa.
• Hurst Exponent is < 0.5. Negative Autocorrelation. Market is Mean reverting. Positive returns trends to follow by negative return and vice versa.
However, we can't really tell if the Hurst exponent value is generated by random chance by only looking at the 0.5 level. Even if we measure a pure random walk, the Hurst Exponent will never be exactly 0.5, it will be close like 0.506 but not equal to 0.5. That's why we need a level to tell us if Hurst Exponent is significant.
So we also computed the 95% confidence interval according to Monte Carlo simulation. The confidence level adjusts itself by sample size. When Hurst Exponent is above the top or below the bottom confidence level, the value of Hurst exponent has statistical significance. The efficient market hypothesis is rejected and market has significant inefficiency.
The state of market is painted in different color as the following chart shows. The users can also tell the state from the table displayed on the right.
An important point is that Hurst Value only represents the market state according to the past value measurement. Which means it only tells you the market state now and in the past. If Hurst Exponent on sample size 100 shows significant trend, it means according to the past 100 bars, the market is trending significantly. It doesn't mean the market will continue to trend. It's not forecasting market state in the future.
However, this is also another way to use it. The market is not always random and it is not always inefficient, the state switches around from time to time. But there's one pattern, when the market stays inefficient for too long, the market participants see this and will try to take advantage of it. Therefore, the inefficiency will be traded away. That's why Hurst exponent won't stay in significant trend or mean reversion too long. When it's significant the market participants see that as well and the market adjusts itself back to normal.
The Hurst Exponent can be used as a mean reverting oscillator itself. In a liquid market, the value tends to return back inside the confidence interval after significant moves(In smaller markets, it could stay inefficient for a long time). So when Hurst Exponent shows significant values, the market has just entered significant trend or mean reversion state. However, when it stays outside of confidence interval for too long, it would suggest the market might be closer to the end of trend or mean reversion instead.
Larger sample size makes the Hurst Exponent Statistics more reliable. Therefore, if the user want to know if long term memory exist in general on the selected ticker, they can use a large sample size and maximize the log scale. Eg: 1024 sample size, scale (16,4).
Following Chart is Bitcoin on Daily timeframe with 1024 lookback. It suggests the market for bitcoin tends to have long term memory in general. It generally has significant trend and is more inefficient at it's early stage.
ADX Forecast Colorful [DiFlip]ADX Forecast Colorful
Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis.
⯁ Real-Time ADX Forecasting
For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions.
⯁ Highly Customizable
The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on TradingView. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system.
⯁ Innovative and Science-Based
This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted value (e.g., future ADX)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept
β₁ = slope (rate of change)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error, the regression coefficients are calculated as:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ = summation
x̄ and ȳ = means of x and y
i ranges from 1 to n (number of data points)
These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool.
⯁ Visual Interpretation
Imagine an ADX time series like this:
Time →
ADX →
The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals.
⯁ Summary of Scientific Concepts
Linear Regression: Models variable relationships with a straight line.
Least Squares: Minimizes prediction errors for best fit.
Time-Series Forecasting: Predicts future values using historical data.
Supervised Learning: Trains models to predict outcomes from inputs.
Statistical Smoothing: Reduces noise and highlights underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Based on rigorous statistical theory.
Unprecedented: First public ADX using least-squares forecast modeling.
Smart: Uses machine learning logic.
Forward-Looking: Generates predictive, not just reactive, signals.
Customizable: Flexible for any strategy or timeframe.
⯁ Conclusion
By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the ADX?
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ How to use the ADX?
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
Strong Trend: When the ADX is above 25, indicating a strong trend.
Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
Neutral Zone: Between 20 and 25, where the trend strength is unclear.
⯁ Entry Conditions
Each condition below is fully configurable and can be combined to build precise trading logic.
📈 BUY
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
📉 SELL
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
🤖 Automation
All BUY and SELL conditions are compatible with TradingView alerts, making them ideal for fully or semi-automated systems.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Superior-Range Bound Renko - Strategy - 11-29-25 - SignalLynxSuperior-Range Bound Renko Strategy with Advanced Risk Management Template
Signal Lynx | Free Scripts supporting Automation for the Night-Shift Nation 🌙
1. Overview
Welcome to Superior-Range Bound Renko (RBR) — a volatility-aware, structure-respecting swing-trading system built on top of a full Risk Management (RM) Template from Signal Lynx.
Instead of relying on static lookbacks (like “14-period RSI”) or plain MA crosses, Superior RBR:
Adapts its range definition to market volatility in real time
Emulates Renko Bricks on a standard, time-based chart (no Renko chart type required)
Uses a stack of Laguerre Filters to detect genuine impulse vs. noise
Adds an Adaptive SuperTrend powered by a small k-means-style clustering routine on volatility
Under the hood, this script also includes the full Signal Lynx Risk Management Engine:
A state machine that separates “Signal” from “Execution”
Layered exit tools: Stop Loss, Trailing Stop, Staged Take Profit, Advanced Adaptive Trailing Stop (AATS), and an RSI-style stop (RSIS)
Designed for non-repainting behavior on closed candles by basing execution-critical logic on previous-bar data
We are publishing this as an open-source template so traders and developers can leverage a professional-grade RM engine while integrating their own signal logic if they wish.
2. Quick Action Guide (TL;DR)
Best Timeframe:
4 Hours (H4) and above. This is a high-conviction swing-trading system, not a scalper.
Best Assets:
Volatile instruments that still respect market structure:
Bitcoin, Ethereum, Gold (XAUUSD), high-volatility Forex pairs (e.g., GBPJPY), indices with clean ranges.
Strategy Type:
Volatility-Adaptive Trend Following + Impulse Detection.
It hunts for genuine expansion out of ranges, not tiny mean-reversion nibbles.
Key Feature:
Renko Emulation on time-based candles.
We mathematically model Renko Bricks and overlay them on your standard chart to define:
“Equilibrium” zones (inside the brick structure)
“Breakout / impulse” zones (when price AND the impulse line depart from the bricks)
Repainting:
Designed to be non-repainting on closed candles.
All RM execution logic uses confirmed historical data (no future bars, no security() lookahead). Intrabar flicker during formation is allowed, but once a bar closes the engine’s decisions are stable.
Core Toggles & Filters:
Enable Longs and Shorts independently
Optional Weekend filter (block trades on Saturday/Sunday)
Per-module toggles: Stop Loss, Trailing Stop, Staged Take Profits, AATS, RSIS
3. Detailed Report: How It Works
A. The Strategy Logic: Superior RBR
Superior RBR builds its entry signal from multiple mathematical layers working together.
1) Adaptive Lookback (Volatility Normalization)
Instead of a fixed 100-bar or 200-bar range, the script:
Computes ATR-based volatility over a user-defined period.
Normalizes that volatility relative to its recent min/max.
Maps the normalized value into a dynamic lookback window between a minimum and maximum (e.g., 4 to 100 bars).
High Volatility:
The lookback shrinks, so the system reacts faster to explosive moves.
Low Volatility:
The lookback expands, so the system sees a “bigger picture” and filters out chop.
All the core “Range High/Low” and “Range Close High/Low” boundaries are built on top of this adaptive window.
2) Range Construction & Quick Ranges
The engine constructs several nested ranges:
Outer Range:
rangeHighFinal – dynamic highest high
rangeLowFinal – dynamic lowest low
Inner Close Range:
rangeCloseHighFinal – highest close
rangeCloseLowFinal – lowest close
Quick Ranges:
“Half-length” variants of those, used to detect more responsive changes in structure and volatility.
These ranges define:
The macro box price is trading inside
Shorter-term “pressure zones” where price is coiling before expansion
3) Renko Emulation (The Bricks)
Rather than using the Renko chart type (which discards time), this script emulates Renko behavior on your normal candles:
A “brick size” is defined either:
As a standard percentage move, or
As a volatility-driven (ATR) brick, optionally inhibited by a minimum standard size
The engine tracks a base value and derives:
brickUpper – top of the emulated brick
brickLower – bottom of the emulated brick
When price moves sufficiently beyond those levels, the brick “shifts”, and the directional memory (renkoDir) updates:
renkoDir = +2 when bricks are advancing upward
renkoDir = -2 when bricks are stepping downward
You can think of this as a synthetic Renko tape overlaid on time-based candles:
Inside the brick: equilibrium / consolidation
Breaking away from the brick: momentum / expansion
4) Impulse Tracking with Laguerre Filters
The script uses multiple Laguerre Filters to smooth price and brick-derived data without traditional lag.
Key filters include:
LagF_1 / LagF_W: Based on brick upper/lower baselines
LagF_Q: Based on HLCC4 (high + low + 2×close)/4
LagF_Y / LagF_P: Complex averages combining brick structures and range averages
LagF_V (Primary Impulse Line):
A smooth, high-level impulse line derived from a blend of the above plus the outer ranges
Conceptually:
When the impulse line pushes away from the brick structure and continues in one direction, an impulse move is underway.
When its direction flips and begins to roll over, the impulse is fading, hinting at mean reversion back into the range.
5) Fib-Based Structure & Swaps
The system also layers in Fib levels derived from the adaptive ranges:
Standard levels (12%, 23.6%, 38.2%, 50%, 61%, 76.8%, 88%) from the main range
A secondary “swap” set derived from close-range dynamics (fib12Swap, fib23Swap, etc.)
These Fibs are used to:
Bucket price into structural zones (below 12, between 23–38, etc.)
Detect breakouts when price and Laguerre move beyond key Fib thresholds
Drive zSwap logic (where a secondary Fib set becomes the active structure once certain conditions are met)
6) Adaptive SuperTrend with K-Means-Style Volatility Clustering
Under the hood, the script uses a small k-means-style clustering routine on ATR:
ATR is measured over a fixed period
The range of ATR values is split into Low, Medium, High volatility centroids
Current ATR is assigned to the nearest centroid (cluster)
From that, a SuperTrend variant (STK) is computed with dynamic sensitivity:
In quiet markets, SuperTrend can afford to be tighter
In wild markets, it widens appropriately to avoid constant whipsaw
This SuperTrend-based oscillator (LagF_K and its signals) is then combined with the brick and Laguerre stack to confirm valid trend regimes.
7) Final Baseline Signals (+2 / -2)
The “brain” of Superior RBR lives in the Baseline & Signal Generation block:
Two composite signals are built: B1 and B2:
They combine:
Fib breakouts
Renko direction (renkoDir)
Expansion direction (expansionQuickDir)
Multiple Laguerre alignments (LagF_Q, LagF_W, LagF_Y, LagF_Z, LagF_P, LagF_V)
They also factor in whether Fib structures are expanding or contracting.
A user toggle selects the “Baseline” signal:
finalSig = B2 (default) or B1 (alternate baseline)
finalSig is then filtered through the RM state machine and only when everything aligns, we emit:
+2 = Long / Buy signal
-2 = Short / Sell signal
0 = No new trade
Those +2 / -2 values are what feed the Risk Management Engine.
B. The Risk Management (RM) Engine
This script features the Signal Lynx Risk Management Engine, a proprietary state machine built to separate Signal from Execution.
Instead of firing orders directly on indicator conditions, we:
Convert the raw signal into a clean integer (Fin = +2 / -2 / 0)
Feed it into a Trade State Machine that understands:
Are we flat?
Are we in a long or short?
Are we in a closing sequence?
Should we permit re-entry now or wait?
Logic Injection / Template Concept:
The RM engine expects a simple integer:
+2 → Buy
-2 → Sell
Everything else (0) is “no new trade”
This makes the script a template:
You can remove the Superior RBR block
Drop in your own logic (RSI, MACD, price action, etc.)
As long as you output +2 or -2 into the same signal channel, the RM engine can drive all exits and state transitions.
Aggressive vs Conservative Modes:
The input AgressiveRM (Aggressive RM) governs how we interpret signals:
Conservative Mode (Aggressive RM = false):
Uses a more filtered internal signal (AF) to open trades
Effectively waits for a clean trend flip / confirmation before new entries
Minimizes whipsaw at the cost of fewer trades
Aggressive Mode (Aggressive RM = true):
Reacts directly to the fresh alert (AO) pulses
Allows faster re-entries in the same direction after RM-based exits
Still respects your pyramiding setting; this script ships with pyramiding = 0 by default, so it will not stack multiple positions unless you change that parameter in the strategy() call.
The state machine enforces discipline on top of your signal logic, reducing double-fires and signal spam.
C. Advanced Exit Protocols (Layered Defense)
The exit side is where this template really shines. Instead of a single “take profit or stop loss,” it uses multiple, cooperating layers.
1) Hard Stop Loss
A classic percentage-based Stop Loss (SL) relative to the entry price.
Acts as a final “catastrophic protection” layer for unexpected moves.
2) Standard Trailing Stop
A percentage-based Trailing Stop (TS) that:
Activates only after price has moved a certain percentage in your favor (tsActivation)
Then trails price by a configurable percentage (ts)
This is a straightforward, battle-tested trailing mechanism.
3) Staged Take Profits (Three Levels)
The script supports three staged Take Profit levels (TP1, TP2, TP3):
Each stage has:
Activation percentage (how far price must move in your favor)
Trailing amount for that stage
Position percentage to close
Example setup:
TP1:
Activate at +10%
Trailing 5%
Close 10% of the position
TP2:
Activate at +20%
Trailing 10%
Close another 10%
TP3:
Activate at +30%
Trailing 5%
Close the remaining 80% (“runner”)
You can tailor these quantities for partial scaling out vs. letting a core position ride.
4) Advanced Adaptive Trailing Stop (AATS)
AATS is a sophisticated volatility- and structure-aware stop:
Uses Hirashima Sugita style levels (HSRS) to model “floors” and “ceilings” of price:
Dungeon → Lower floors → Mid → Upper floors → Penthouse
These levels classify where current price sits within a long-term distribution.
Combines HSRS with Bollinger-style envelopes and EMAs to determine:
Is price extended far into the upper structure?
Is it compressed near the lower ranges?
From this, it computes an adaptive factor that controls how tight or loose the trailing level (aATS / bATS) should be:
High Volatility / Penthouse areas:
Stop loosens to avoid getting wicked out by inevitable spikes.
Low Volatility / compressed structure:
Stop tightens to lock in and protect profit.
AATS is designed to be the “smart last line” that responds to context instead of a single fixed percentage.
5) RSI-Style Stop (RSIS)
On top of AATS, the script includes a RSI-like regime filter:
A McGinley Dynamic mean of price plus ATR bands creates a dynamic channel.
Crosses above the top band and below the lower band change a directional state.
When enabled (UseRSIS):
RSIS can confirm or veto AATS closes:
For longs: A shift to bearish RSIS can force exits sooner.
For shorts: A shift to bullish RSIS can do the same.
This extra layer helps avoid over-reactive stops in strong trends while still respecting a regime change when it happens.
D. Repainting Protection
Many strategies look incredible in the Strategy Tester but fail in live trading because they rely on intrabar values or future-knowledge functions.
This template is built with closed-candle realism in mind:
The Risk Management logic explicitly uses previous bar data (open , high , low , close ) for the key decisions on:
Trailing stop updates
TP triggers
SL hits
RM state transitions
No security() lookahead or future-bar access is used.
This means:
Backtest behavior is designed to match what you can actually get with TradingView alerts and live automation.
Signals may “flicker” intrabar while the candle is forming (as with any strategy), but on closed candles, the RM decisions are stable and non-repainting.
4. For Developers & Modders
We strongly encourage you to mod this script.
To plug your own strategy into the RM engine:
Look for the section titled:
// BASELINE & SIGNAL GENERATION
You will see composite logic building B1 and B2, and then selecting:
baseSig = B2
altSig = B1
finalSig = sigSwap ? baseSig : altSig
You can replace the content used to generate baseSig / altSig with your own logic, for example:
RSI crosses
MACD histogram flips
Candle pattern detectors
External condition flags
Requirements are simple:
Your final logic must output:
2 → Buy signal
-2 → Sell signal
0 → No new trade
That output flows into the RM engine via finalSig → AlertOpen → state machine → Fin.
Once you wire your signals into finalSig, the entire Risk Management system (Stops, TPs, AATS, RSIS, re-entry logic, weekend filters, long/short toggles) becomes available for your custom strategy without re-inventing the wheel.
This makes Superior RBR not just a strategy, but a reference architecture for serious Pine dev work.
5. About Signal Lynx
Automation for the Night-Shift Nation 🌙
Signal Lynx focuses on helping traders and developers bridge the gap between indicator logic and real-world automation. The same RM engine you see here powers multiple internal systems and templates, including other public scripts like the Super-AO Strategy with Advanced Risk Management.
We provide this code open source under the Mozilla Public License 2.0 (MPL-2.0) to:
Demonstrate how Adaptive Logic and structured Risk Management can outperform static, one-layer indicators
Give Pine Script users a battle-tested RM backbone they can reuse, remix, and extend
If you are looking to automate your TradingView strategies, route signals to exchanges, or simply want safer, smarter strategy structures, please keep Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source).
If you make beneficial modifications, please consider releasing them back to the community so everyone can benefit.
Psychological levels [Kodologic] Psychological levels
Markets are not random, they are driven by human psychology and algorithmic order flow. A well-known phenomenon in trading is the "Whole Number Bias" — the tendency for price to react significantly at clean, round numbers (e.g., Bitcoin at $95,000 or EURUSD at 1.0500).
Manually drawing horizontal lines at every round number is tedious, clutters your object tree, and distracts you from analyzing price action.
Psychological levels Numbers is a workflow utility designed to solve this problem. It automatically projects a clean, customizable grid of key price levels onto your chart, helping you instantly identify areas where liquidity and orders are likely to cluster.
Why This Indicator Helps Traders :
Professional traders know that "00" and "50" levels act as magnets for price. Here is how this tool assists in your analysis:
1. Institutional Footprints : Large institutions and bank algorithms often execute orders at whole numbers to simplify accounting. This script highlights these potential liquidity zones automatically.
2. Support & Resistance Discovery: You will often notice price wicking or reversing exactly on these grid lines. This helps in spotting natural support and resistance without needing complex technical analysis.
3. Cognitive Load Reduction: Instead of calculating where the next "major level" is, the grid is visually present, allowing you to focus on candlestick patterns and market structure.
Features :
Dynamic Calculation : The grid updates automatically as price moves, you never have to redraw lines.
Zero Clutter : The lines are drawn using code, meaning they do not appear in your manual drawing tools list or clutter your object tree.
Fully Customizable Step : You define what constitutes a "Round Number" for your specific asset class (Forex, Crypto, Indices, or Stocks).
Visual Control : Adjust line styles (Solid, Dotted, Dashed), colors, and transparency to keep your chart aesthetic and readable.
How to Use in Your Strategy :
1. Target Setting (Take Profit)
If you are in a long position, use the next upper grid line as a logical Take Profit area. Price often gravitates toward these whole numbers before reversing or consolidating.
2. Stop Loss Placement
Avoid placing Stop Losses exactly on a round number, as these are often "stop hunted." Instead, use the grid to visualize the level and place your stop slightly *below* or *above* the round number for better protection.
3. Confluence Trading
Do not use these lines in isolation. Look for Confluence :
Example: If a Fibonacci 61.8% level lines up exactly with a Round Number grid line, that level becomes a high-probability reversal zone.
Settings Guide (Important)
Since every asset is priced differently, you must adjust the "levels Step Size" to match your instrument:
Forex (e.g., EURUSD, GBPUSD): Set Step Size to `0.0050` (50 pips) or `0.0100` (100 pips).
Crypto (e.g., BTCUSD): Set Step Size to `500` or `1000`.
Indices (e.g., US30, SPX500): Set Step Size to `100` or `500`.
Gold (XAUUSD):** Set Step Size to `10`.
Disclaimer: This tool is for educational and visual aid purposes only. It does not provide buy or sell signals. Always manage your risk.
Elite Correlation Matrix AIThe Elite Correlation Matrix AI indicator provides comprehensive real-time correlation analysis across multiple asset classes, displaying the interrelationships between equities, bonds, commodities, currencies, and volatility instruments.
The indicator calculates and displays correlation coefficients between a predefined set of major market indices and instruments, including:
• Major equity indices (SPY, QQQ, IWM)
• Long-term Treasury bonds (TLT)
• Gold (GLD)
• Crude oil (USO)
• Volatility (VIX)
• US Dollar Index (DXY)
• Bitcoin (BTCUSD)
Key features include:
• Rolling correlation calculations across user-defined periods to identify both short-term and longer-term relationships
• Visual correlation heat map showing the strength and direction of relationships between all tracked instruments
• Detection of correlation breakdowns, which often precede significant market regime shifts
• Dashboard display providing summary metrics of prevailing correlation patterns
The indicator enables users to monitor the current state of market relationships and identify when traditional correlations begin to break down, which frequently serves as an early warning of impending changes in market behavior. By tracking the degree of connectedness between different asset classes, the indicator provides insight into the current risk environment and the potential for diversification effectiveness.
This analysis is particularly valuable for understanding periods of market stress when asset relationships deviate from their normal patterns, as well as identifying environments where traditional correlations hold and where they are undergoing structural changes.
Z-EMA Fusion BandsDesigned with crypto markets in mind, particularly Bitcoin , it builds on the concept that the 1-Week 50 EMA often serves as a long-term bull/bear market threshold — an area where institutional bias, momentum shifts, and cyclical rotations tend to occur.
🔹 Core Components & Synergies:
1. 1W 50 EMA (Higher Timeframe)
- This EMA is calculated on a weekly timeframe, regardless of your current chart.
- In crypto, price above the 1W 50 EMA typically aligns with long-term bull market phases, while extended periods below can signify bearish macro structure.
- The slope of the EMA is also analyzed to add directional confidence to trend strength.
2. ±1 Standard Deviation Bands
- Surrounding the 50 EMA, these bands visualize normal price dispersion relative to trend.
- When price consistently hugs or breaks outside these bands, it often reflects market expansion, volatility events, or mean-reversion opportunity.
3. Z-Score Gradient Fill
- The area between the bands is filled using a Z-score-based gradient, which dynamically adjusts color based on how far price is from the EMA (in terms of standard deviations).
- Color shifts from aqua (near EMA) to fuchsia (far from EMA) help you spot price compression, equilibrium, or overextension at a glance.
- The fill also uses transparency scaling, making it fade as price stretches further, emphasizing the core structure.
4. Directional EMA Coloring
- The EMA line itself is colored based on:
- The slope of the EMA (rising/falling)
- Whether the HTF candle is bullish or bearish
- This provides intuitive color-coded confirmation of momentum alignment or potential exhaustion.
5. Price/EMA Divergence Detection
- The script detects bullish and bearish divergence between price and the EMA (rather than using a traditional oscillator).
- Bullish Divergence: Price makes a lower low, EMA makes a higher low.
- Bearish Divergence: Price makes a higher high, EMA makes a lower high.
- These signals often mark transitional zones where momentum fades before a trend reversal or correction.
📊 Suggested Uses:
🔸 Swing and Position Trading:
- Use the 1W 50 EMA as a macro-trend anchor.
- Stay long-biased when price is above with positive slope, and short-biased when below.
- Consider entries near band edges for mean-reversion plays, especially if confluence forms with divergence signals.
🔸 Volatility-Based Filtering:
- Use the Z-score fill to identify volatility compression (near EMA) or expansion (edge of bands).
- Combine this with breakout strategies or dynamic position sizing.
🔸 Divergence Confirmation:
- Combine divergence markers with HTF EMA slope for high-probability setups.
- Bullish div + EMA flattening/rising can signal the start of accumulation after a macro dip.
🔸 Multi-Timeframe Analysis:
- Works well as a structural overlay on intraday charts (1H, 4H, 1D).
- Use this indicator to track long-term bias while executing lower timeframe trades.
⚠️ Disclaimer:
This indicator is designed for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset.
Always use proper risk management, and combine with your own analysis, tools, and strategy. Performance in past market conditions does not guarantee future results.
Extended SOPR Indicator - SSOPR Tops (A/B toggle)Extended SOPR Indicator — SSOPR Tops and Lows (A/B toggle)
Observation-only. Data: Glassnode SOPR.
Overview
This indicator extends the classical SOPR (Spent Output Profit Ratio) to improve readability and reduce noise on charts. SOPR measures whether coins moved on-chain were spent at a profit or at a loss. In brief: SOPR > 1 → spending at profit; SOPR < 1 → spending at loss. SSOPR (from "Smoothed SOPR") applies optional log transform (centers baseline at 0), smoothing (standard or adaptive), and adds structured signals: Z‑score lows (capitulation), buy zones , and top detection after prolonged elevation.
Why extend SOPR? (SSOPR vs classical SOPR)
• Noise reduction: Raw daily SOPR can whipsaw around its baseline. SSOPR uses smoothing and (optionally) adaptive smoothing so regimes are visible without overfitting.
• Better readability: The log transform shifts the break-even line to 0, making “profit territory” (above 0) and “loss territory” (below 0) visually intuitive on oscillators.
• Actionable context: Z‑score highlights extreme lows (capitulation risk), a simple buy-zone threshold marks potential accumulation, and a structured top pattern (with a time factor) helps frame distribution phases after sustained elevation.
What the script plots
• Smoothed SOPR (SSOPR): An orange line representing the smoothed SOPR (with optional log transform and optional adaptive smoothing).
• Top markers: A red triangle appears once at the onset of a confirmed top pattern.
• Background shading:
– Soft green: Buy zone when SSOPR falls below the “Buy Threshold.” (+ Z‑score capitulation zones (extreme lows)).
– Soft red: Top‑zone shading when the top criteria are met but before the single triangle fires.
Inputs & parameters
• Smoothing Length (default 14): Base window for smoothing SSOPR. Higher values = smoother, slower response.
• Apply Log Transform (default ON): Uses log(SOPR) so the baseline is 0 (log(1)=0). Above 0 → net profit regime; below 0 → net loss regime.
• Adaptive Smoothing (default OFF): Expands smoothing length as volatility rises using a standard deviation proxy; reduces whipsaws while preserving structure.
• Z‑score Threshold for Lows (default −2.5): Highlights capitulation zones when SSOPR deviates far below its rolling mean.
• SSOPR Buy Threshold (default −0.02): Simple rule-of-thumb level for potential accumulation context when below (log scale).
• SSOPR Top Threshold (default +0.005): Minimum elevation required for “profit territory” when assessing tops (log scale).
• Min Bars Above Threshold Before Top (default 50): Ensures prolonged elevation before calling a top.
• Lookback for Peak Detection (default 50): Window used to locate the recent high.
• Drop % from Peak to Confirm Top (default 5%): Confirms the start of distribution from a local high.
• Highlight Background : Toggles shaded zones.
Top detection (indicator-only)
A top fires when ALL of the following are true:
SSOPR spent at least Min Bars Above Threshold above the Top Threshold (sustained elevation).
The rising phase test passes (Option A or B; see below).
A drop from the local peak exceeds Drop % within the Lookback window.
The peak occurred in profit territory (SSOPR > Top Threshold).
To avoid repeated signals during the decline, the script emits the triangle once, at onset.
Rising‑phase switch: Option A vs Option B
• Option A — Up‑step ratio : Over the last A: Bars for Rising Check (default 50), it requires that at least A: Required Up‑Step Ratio (default 60%) of bars were rising (each bar compared to the previous). This favors gradual, persistent advances and filters out “choppy” lifts.
• Option B — Net slope : Compares current SSOPR to its value B: Bars Back for Net Slope ago (default 50). If higher, the series is considered rising. This is simpler and reacts faster in volatile phases but can admit brief pseudo‑trends.
Guidance : Prefer A for conservative confirmation in slow, persistent cycles; use B when trend moves are strong and you need timely detection.
Interpretation guide
• Regimes (log view): Above 0 → spending at profit; below 0 → spending at loss.
• Capitulation lows: When Z‑score < threshold, conditions often reflect forced/liquidity‑driven spending. Treat as context, not signals.
• Buy zone: SSOPR < Buy Threshold flags potential accumulation conditions (combine with price structure).
• Tops: After prolonged elevation, a confirmed top often coincides with profit‑taking/distribution phases.
Recommended timeframes
• Daily : Code optimized for daily timeframe.
Method summary
• SSOPR source: GLASSNODE:BTC_SOPR (via request.security ).
• Optional log transform: sopr → log(sopr) to normalize around 0.
• Smoothing: SMA over Smoothing Length , optionally adaptive using local volatility (std dev).
• Z‑score: (SSOPR − mean) / std dev, highlighting extreme lows.
• Top: Requires long elevation above Top Threshold , rising‑phase (A/B), and a subsequent drop > Drop % from recent high.
Limitations & notes
• SOPR reflects on‑chain movements; some activity occurs off‑chain (exchanges, internal transfers). Not all moves imply sale; aggregation makes it a usable proxy for profit/loss realization.
• Higher smoothing reduces noise but delays signals; adaptive smoothing can help but is still a trade‑off.
• Treat thresholds as context markers. They are not entry/exit signals by themselves.
• Use with price structure, volume, and other on‑chain indicators (e.g., realized price bands, dormancy/CDD) for confluence.
How to use (examples)
• Advance holding above 0 (log view): Retests of 0 from above that hold—while SSOPR remains elevated—often mark absorption; look for Top conditions only after sustained elevation and a confirmed drop from peak.
• Downtrend below 0: Rejections near 0 can align with continued loss realization; extreme Z‑score lows suggest capitulation risk—context for accumulation, not a blind buy.
Recommended settings
• Weekly: Log ON, Smoothing Length 14–30, Adaptive ON, Buy Threshold −0.02, Top Threshold +0.005, Rising Method A, Min Bars 50.
• Daily: Log ON, Smoothing Length 14–20, Adaptive OFF or ON (depending on noise), Rising Method B for timely slope checks.
Credits & references
• SOPR metric: Renato Shirakashi; documentation: Glassnode , CryptoQuant , overview: Bitbo .
Disclaimer
This script is for research/education on market behavior. It is not financial advice. Indicators provide context; decisions remain your responsibility.
Tags
bitcoin, btc, on‑chain, sopr, ssopr, glassnode, oscillator, regime, distribution, capitulation
MYPYBiTE.com – Cloud + VWAPFor Bitcoin we found that the 3 day chart consistently indicates a pattern that anyone can back test and determine the trend confirmation is broken. Of course we won't tell you here what it is because you have to do the work or be familiar with the communities I participate in.
We decided to make this available because we realized many folks do not incorporate cloud charting. This is to help noobies and we hope to incorporate other factors in time.
Advanced Bitcoin Cycle Detector with Projections & Hursttest script created with openrouter adn google gemmi 3
BTC BRD – Bullet-Proof Reversal StrategyBTC BRD – Bullet-Proof Reversal Strategy is a price-action based reversal system that turns your existing “Bullet-Proof Reversal Detector” into a fully backtestable TradingView strategy with built-in risk management. It is designed to catch clean swing reversals using pure market structure, then automatically place stop-loss and take-profit orders based on your preferred risk-reward settings.
## Core concept
The strategy identifies true swing highs and lows using pivots and then waits for a clear market structure shift before entering any trade. It looks for a higher low followed by a break of structure for longs, and a lower high followed by a break of structure for shorts, helping filter out many random spikes and fakeouts. This makes it suitable for traders who prefer clean, rule-based entries grounded in market structure rather than noisy, indicator-heavy setups.
## Entries and exits
- Long trades are triggered after a bullish higher-low plus a confirmed break above the last swing high.
- Short trades are triggered after a bearish lower-high plus a confirmed break below the last swing low.
- Every position is protected with an automatic stop-loss and a calculated take-profit, so each trade has a predefined risk and reward from the moment it is opened.
## Risk management
The strategy lets you control your risk with a configurable risk-reward ratio (RR) and flexible stop-loss options. You can choose between an ATR-based stop (ATR × multiplier) or a fixed percentage stop relative to the entry price. Once the stop distance is known, the take-profit level is automatically derived from your RR value, making trade sizing and evaluation more consistent across different pairs and timeframes.
## Use cases and recommendations
This script is ideal for swing and intraday traders who want to systematically test market-structure reversals on assets like Bitcoin or other volatile instruments. For best results, experiment with different timeframes and ATR/percentage settings, and always validate performance using the Strategy Tester before deploying it on live markets. Remember that no strategy is guaranteed to be profitable, so use proper risk management and adapt settings to your own style and risk tolerance.
Stratégie SMC V18.2 (BTC/EUR FINAL R3 - Tendance)This strategy is an automated implementation of Smart Money Concepts (SMC), designed to operate on the Bitcoin/Euro (BTC/EUR) chart using the 15-minute Timeframe (M15).It focuses on identifying high-probability zones (Order Blocks) after a confirmed Break of Structure (BOS) and a Liquidity Sweep, utilizing an H1/EMA 200 trend filter to only execute trades in the direction of the dominant market flow.Risk management is strict: every trade uses a fixed Risk-to-Reward Ratio (R:R) of 1:3.🧱 Core Logic Components
1. Trend Filter (H1/EMA 200)Objective: To avoid counter-trend entries, which has allowed the success rate to increase to nearly $65\%$ in backtests.Mechanism: A $200$-period EMA is plotted on a higher timeframe (Default: H1/60 minutes).Long (Buy): Entry is only permitted if the current price (M15) is above the trend EMA.Short (Sell): Entry is only permitted if the current price (M15) is below the trend EMA.
2. Order Block (OB) DetectionA potential Order Block is identified on the previous candle if it is
accompanied by an inefficiency (FVG - Fair Value Gap).
3. Advanced SMC ValidationBOS (Break of Structure): A recent BOS must be confirmed by breaking the swing high/low defined by the swing length (Default: 4 M15 candles).Liquidity (Liquidity Sweep): The Order Block zone must have swept recent liquidity (defined by the Liquidity Search Length) within a certain tolerance (Default: $0.1\%$).Point of Interest: The OB must form in a premium zone (for shorts) or a discount zone (for longs) relative to the current swing range (above or below the $50\%$ level of the range).
4. Execution and Risk ManagementEntry: The trade is triggered when the price touches the active Order Block (mitigation).Stop Loss (SL): The SL is fixed at the low of the OB (for longs) or the high of the OB (for shorts).Take Profit (TP): The TP is strictly set at a level corresponding to 3 times the SL distance (R:R 1:3).Lot Sizing: The trade quantity is calculated to risk a fixed amount (Default: 2.00 Euros) per transaction, capped by a Lot Max and Lot Min defined by the user.
Input Parameters (Optimized for BTC/EUR M15)Users can adjust these parameters to modify sensitivity and risk profile. The default values are those optimized for the high-performing backtest (Profit Factor $> 3$).ParameterDescriptionDefault Value (M15)Long. Swing (BOS)Candle length used to define the swing (and thus the BOS).4Long. Recherche Liq.Number of candles to scan to confirm a liquidity sweep.7Tolérance Liq. (%)Price tolerance to validate the liquidity sweep (as a percentage of price).0.1Timeframe TendanceChart timeframe used for the EMA filter (e.g., 60 = H1).60 (H1)Longueur EMA TendancePeriods used for the trend EMA.200Lot Max (Quantité Max BTC)Maximum quantity of BTC the strategy is allowed to trade.0.01Lot Min Réel (Exigence Broker)Minimum quantity required by the broker/exchange.0.00001
Risk On/Risk Off by Gary# Risk On/Risk Off Indicator (RORO)
## Overview
The Risk On/Risk Off (RORO) Indicator is a comprehensive market sentiment gauge that measures the balance between risk-seeking and risk-averse behavior across multiple asset classes. This indicator helps traders identify shifts in market sentiment and potential trend changes.
## How It Works
The RORO indicator aggregates normalized price movements (Z-scores) from eight major asset classes:
**Risk-On Assets (Bullish Sentiment):**
- Bitcoin Futures (BTC1!) - Cryptocurrency risk appetite
- WTI Crude Oil Futures (CL1!) - Energy sector strength
- AUD/JPY Exchange Rate - Carry trade indicator
- Emerging Markets ETF (EEM) - Global growth proxy
**Risk-Off Assets (Defensive Sentiment):**
- Gold Futures (GC1!) - Safe haven demand
- 10-Year Treasury Bonds (ZN1!) - Flight to quality
- US Dollar Index (DXY) - Reserve currency strength
- VIX Index - Market fear gauge (inverted)
## Key Features
- **Z-Score Normalization**: Standardizes different asset classes for fair comparison
- **Customizable Weights**: Adjust the influence of each asset class
- **Dynamic Coloring**: Green indicates rising risk appetite, red shows declining risk appetite
## Interpretation
- **Rising RORO (Green)**: Increasing risk appetite - favorable for equities, commodities, and growth assets
- **Falling RORO (Red)**: Decreasing risk appetite - rotation into safe havens
- **Divergences**: When RORO and price move in opposite directions, potential reversal signal
## Use Cases
1. **Market Regime Identification**: Determine current risk environment
2. **Divergence Trading**: Spot when price action contradicts underlying sentiment
3. **Portfolio Management**: Time defensive vs. aggressive positioning
4. **Confirmation Tool**: Validate breakouts and trend changes
## Settings
- **Lookback Period**: Controls Z-score calculation sensitivity (default: 50)
- **Asset Weights**: Fine-tune the contribution of each asset class
- **Color Scheme**: Customize rising/falling colors
## Best Practices
- Use on daily or higher timeframes for most reliable signals
- Combine with price action and volume analysis
- Watch for sustained moves rather than single-bar changes
---
*This indicator is designed for educational purposes. Always conduct your own analysis and risk management.*
Dumb Money Flow - Retail Panic & FOMO# Dumb Money Flow (DMF) - Retail Panic & FOMO
## 🌊 Overview
**Dumb Money Flow (DMF)** is a powerful **contrarian indicator** designed to track the emotional state of the retail "herd." It identifies moments of extreme **Panic** (irrational selling) and **FOMO** (irrational buying) by analyzing on-chain data, volume anomalies, and price velocity.
In crypto markets, retail traders often buy the top (FOMO) and sell the bottom (Panic). This indicator helps you do the opposite: **Buy when the herd is fearful, and Sell when the herd is greedy.**
---
## 🧠 How It Works
The indicator combines multiple data points into a single **Sentiment Index** (0-100), normalized over a 90-day period to ensure it always uses the full range of the chart.
### 1. Panic Index (Bearish Sentiment)
Tracks signs of capitulation and fear. High values contribute to the **Panic Zone**.
* **Exchange Inflows:** Spikes in funds moving to exchanges (preparing to sell).
* **Volume Spikes:** High volume during price drops (panic selling).
* **Price Crash (ROC):** Rapid, emotional price drops over 3 days.
* **Volatility (ATR):** High market nervousness and instability.
### 2. FOMO Index (Bullish Sentiment)
Tracks signs of euphoria and greed. High values contribute to the **FOMO Zone**.
* **Exchange Outflows:** Funds moving to cold storage (HODLing/Greed).
* **Profitable Addresses:** When >90% of holders are in profit, tops often form.
* **Parabolic Rise:** Rapid, unsustainable price increases.
---
## 🎨 Visual Guide
The indicator uses a distinct color scheme to highlight extremes:
* **🟢 Dark Green Zone (> 80): Extreme FOMO**
* **Meaning:** The crowd is euphoric. Risk of a correction is high.
* **Action:** Consider taking profits or looking for short entries.
* **🔴 Dark Burgundy Zone (< 20): Extreme Panic**
* **Meaning:** The crowd is capitulating. Prices may be oversold.
* **Action:** Look for buying opportunities (catching the knife with confirmation).
* **🔵 Light Blue Line:**
* The smoothed moving average of the sentiment, helpful for seeing the trend direction.
---
## 🛠️ How to Use (Trading Strategies)
### 1. Contrarian Reversals (The Primary Strategy)
* **Buy Signal:** Wait for the line to drop deep into the **Burgundy Panic Zone (< 20)** and then start curling up. This indicates that the worst of the selling pressure is over.
* **Sell Signal:** Wait for the line to spike into the **Green FOMO Zone (> 80)** and then start curling down. This suggests buying exhaustion.
### 2. Divergences
* **Bullish Divergence:** Price makes a **Lower Low**, but the DMF Indicator makes a **Higher Low** (less panic on the second drop). This is a strong reversal signal.
* **Bearish Divergence:** Price makes a **Higher High**, but the DMF Indicator makes a **Lower High** (less FOMO/buying power on the second peak).
### 3. Trend Confirmation (Midline Cross)
* **Crossing 50 Up:** Sentiment is shifting from Fear to Greed (Bullish).
* **Crossing 50 Down:** Sentiment is shifting from Greed to Fear (Bearish).
---
## ⚙️ Settings
* **Data Source:** Defaults to `INTOTHEBLOCK` for on-chain data.
* **Crypto Asset:** Auto-detects BTC/ETH, but can be forced.
* **Normalization Period:** Default 90 days. Determines the "window" for defining what is considered "Extreme" relative to recent history.
* **Weights:** You can customize how much each factor (Volume, Inflows, Price) contributes to the index.
---
**Disclaimer:** This indicator is for educational purposes only. "Dumb Money" analysis is a probability tool, not a crystal ball. Always manage your risk.
**Indicator by:** @iCD_creator
**Version:** 1.0
**Pine Script™ Version:** 6
---
## Updates & Support
For questions, suggestions, or bug reports, please comment below or message the author.
**Like this indicator? Leave a 👍 and share your feedback!**
Institutional Valuation SuiteStandard volatility indicators often fail on long-term growth charts because they measure volatility in dollars rather than percentages. This causes bands to break or become irrelevant during exponential price moves (e.g., Bitcoin going from $1,000 to $100,000).
The Institutional Valuation Suite solves this by utilising Geometric (Log-Normal) Standard Deviation. This allows the model to adapt to the asset's price scale, providing accurate valuation zones regardless of price magnitude.
The model functions as a mean-reversion tool, visualizing price as an elastic band anchored to a "Fair Value" baseline. It identifies when the asset is statistically overextended (Bubble/FOMO) or undervalued (Deep Discount).
Key Features
1. Log-Normal Math Engine
Geometric Mode (Default): Calculates volatility in percentage terms. Essential for Crypto and Growth Stocks.
Arithmetic Mode: Available for Forex or range-bound assets where linear standard deviation is preferred.
2. Sentiment Heat map
Visualises valuation directly on the candles to remove interpretation bias.
GREEN: Deep Value / Accumulation Zone (< -0.5σ).
ORANGE: Overvalued / FOMO Zone (> 2.0σ).
RED: Speculative Bubble Zone (> 3.0σ).
3. Reversion Signals
"VALUE RECLAIM": Triggers when price re-enters the bottom band from below, filtering out "falling knife" scenarios.
"TOP EXIT": Triggers when price breaks down from the speculative top zone.
4. Statistical Dashboard
Displays the real-time Z-Score to quantify how "stretched" the price is relative to its baseline.
> 3.0: Statistical Anomaly (Top).
< -0.5: Statistical Discount (Bottom).
Optimisation Cheat Sheet
The "Cycle Length" input determines the lookback period for the baseline. Recommended settings:
Crypto Macro: 200 (Approx. 4 Years).
Altcoins: 100 (Approx. 2 Years).
Stocks (S&P 500): 50 (1 Year Trend).
Day Trading: Set "Timeframe Lock" to "Chart".
Technical Note
This indicator uses strict offset logic (`barmerge.lookahead_on`) to ensure historical consistency. The signals displayed on historical bars match exactly what would have appeared in real-time.
*Disclaimer: This script provides statistical analysis based on historical volatility and does not constitute financial advice.*
Global Macro IndexGlobal Macro Index
The Global Macro Index is a comprehensive economic sentiment indicator that aggregates 23 real-time macroeconomic data points from the world's largest economies (US, EU, China, Japan, Taiwan). It provides a single normalized score that reflects the overall health and momentum of the global economy, helping traders identify macro trends that drive asset prices.
⚠️ Important: Timeframe Settings
This indicator is designed exclusively for the 1W (weekly) timeframe. The indicator is hardcoded to pull weekly data and will not function correctly on other timeframes.
What It Measures
The indicator tracks normalized Trend Power Index (TPI) values across multiple economic categories:
United States (7 components)
Business Confidence Index (BCOI) - Business sentiment and outlook
Composite Leading Indicator (CLI) - Forward-looking economic indicators
Consumer Confidence Index (CCI) - Consumer sentiment and spending intentions
Terms of Trade (TOT) - Import/export price relationships
Manufacturing Composite - Combines business confidence, production, and new orders
Comprehensive Economic Composite - Broad aggregation including employment, business activity, and regional indicators
Business Inventory (BI) - Stock levels and supply chain health
European Union (10 components)
Sentiment Survey (SS) - Overall economic sentiment
Business Confidence Index - EU business outlook
Economic Sentiment Indicator (ESI) - Combined confidence metrics
Manufacturing Production (MPRYY) - Industrial output year-over-year
New Orders - Germany, France, Netherlands, Spain manufacturing orders
Composite Leading Indicators - Germany, France forward-looking metrics
Business Climate Index (BCLI) - France business conditions
Asia (6 components)
New Orders - China, Japan, Taiwan manufacturing demand
Composite Leading Indicators - China, Japan economic momentum
The Formula
The indicator calculates a weighted average of normalized TPI scores:
Global Macro Index = (1/23) × Σ
Each of the 23 economic indicators is:
Converted to a Trend Power Index (TPI) using 4-day Bitcoin normalization
Weighted equally (1/23 ≈ 4.35% each)
Summed and smoothed with a 1-period SMA
The result is a single oscillator that ranges typically between -1 and +1, with extreme readings beyond ±0.6.
Z-Score Signal System
The indicator includes an optional Z-Score overlay that identifies extreme macro conditions:
Calculation:
Z-Score = (Current Value - 50-period Mean) / Standard Deviation
Smoothed with 35-period Hull Moving Average
Inverted for intuitive interpretation
Signals:
Green background (Z-Score ≥ 2) = Extremely positive macro conditions, potential overbought
Red background (Z-Score ≤ -2) = Extremely negative macro conditions, potential oversold
These extreme readings occur approximately 5% of the time statistically
How to Use It
Interpreting the Main Plot (Red Line):
Above 0 = Positive macro momentum, risk-on environment
Below 0 = Negative macro momentum, risk-off environment
Above +0.6 = Strong expansion, bullish for equities and crypto
Below -0.6 = Severe contraction, bearish conditions
Trend direction = More important than absolute level
Z-Score Signals:
Z ≥ 2 (Green) = Macro sentiment extremely positive, consider taking profits or preparing for pullback
Z ≤ -2 (Red) = Macro sentiment extremely negative, potential buying opportunity for contrarians
Works best as a regime filter, not precise timing tool
Best Practices:
Use as a macro regime filter for other strategies
Combines well with liquidity indicators and price action
Leading indicator for risk assets (equities, Bitcoin, emerging markets)
Lagging indicator - confirms macro trends rather than predicting reversals
Watch for divergences: price making new highs while macro weakens (bearish) or vice versa (bullish)
Settings
Show Zscore Signals: Toggle green/red background shading for extreme readings
Overlay Zscore Signals: Display Z-Score signals on the price chart as well as the indicator panel
Reference Lines
0 (gray) = Neutral macro conditions
+0.6 (green) = Strong positive threshold
-0.6 (red) = Strong negative threshold
Data Sources
Real-time economic data from TradingView's ECONOMICS database, including:
OECD leading indicators
Manufacturing PMIs and new orders
Consumer and business confidence surveys
Trade and inventory metrics
Regional economic sentiment indices
Notes
This is a macro trend indicator, not a day-trading tool. Economic data updates weekly and reflects the aggregate health of global growth. Best used on weekly timeframes to identify favorable or unfavorable macro regimes for risk asset allocation.
The indicator distills complex global economic data into a single actionable score, answering: "Is the global economy expanding or contracting right now?"
Liquidity LayoutLiquidity Layout
The Liquidity Layout is a comprehensive macroeconomic indicator that tracks global liquidity conditions by aggregating multiple financial data streams from major economies (US, EU, China, Japan, UK, Canada, Switzerland). It provides traders with a macro view of market liquidity to help identify favorable conditions for risk assets
⚠️ Important: Timeframe Settings
This indicator is designed for the 1W (weekly) timeframe. If you use other timeframes, you must adjust the offset parameter in the settings to properly align the data with price action. The default offset of 12 is calibrated for weekly charts.
What It Measures
This indicator combines seven key components of global liquidity:
1. Global M2 Money Supply - Tracks broad money supply (M2) plus 10% of narrow money supply (M1) across major economies, weighted by currency strength. This represents the total amount of money circulating in the private sector.
2. Central Bank Balance Sheets (CBBS) - Monitors the combined balance sheets of major central banks (Fed, ECB, BoJ, PBoC, etc.), reflecting quantitative easing and monetary expansion policies.
3. Foreign Exchange Reserves (FER) - Aggregates forex reserves held by central banks, indicating international liquidity buffers and capital flows.
4. Current Account + Capital Flows (CA) - Combines current account balances with capital flows to measure cross-border money movement and trade liquidity.
5. Government Spending (GSP) - Tracks government expenditure minus a portion of federal expenses, representing fiscal stimulus and public sector liquidity injection.
6. World Currency Unit (WCU) - A custom forex composite that weights major and emerging market currencies to capture global currency strength dynamics.
7. Bond Market Conditions - Analyzes yield curves, spreads, and bond indices to assess credit conditions and risk appetite in fixed income markets.
The Formula
The indicator uses two main calculation modes:
ADJ Global Liquidity (Default):
×
This multiplies liquidity components by currency and bond market factors to capture the interactive effects between monetary conditions and market sentiment.
TPI (Trend Power Index) Mode:
A normalized version that combines all components with optimized weights:
Global Liquidity Index: 10%
Bonds: 17.5%
Bond Yields: 25%
Currency Strength: 25%
Government Spending: 5%
Current Account: 5%
M2: 2.5%
Central Bank Balance Sheets: 2.5%
Forex Reserves: 5%
Oil (macro risk indicator): 2.5%
How to Use It
Visualization Modes:
Background Mode (default): Orange background appears when TPI is positive (favorable liquidity conditions)
Line Mode: Displays the indicator as an orange line with customizable offset
Interpreting the Signal:
Positive/Rising = Expanding liquidity, generally bullish for risk assets
Negative/Falling = Contracting liquidity, risk-off environment
TPI > 1 = Extremely favorable conditions (upper threshold)
TPI < -1 = Severe liquidity stress (lower threshold)
Best Practices:
Use on higher timeframes (daily, weekly) for macro trend analysis
Combine with price action - liquidity often leads market moves by weeks or months
Watch for divergences between liquidity and asset prices
Particularly relevant for Bitcoin, equities, and risk assets
Data Sources
The indicator pulls real-time economic data from TradingView's ECONOMICS database and major market indices, including central bank statistics, government reports, and forex rates across G7 and major emerging markets.
Settings
Data Plot: Choose which liquidity component to display
Plot Type: Switch between raw Index values or normalized TPI
Offset: Shift the plot forward/backward for alignment (default: 12 for weekly charts)
Style: Background shading or line plot
Notes
This is a macro-level indicator best suited for understanding the broader liquidity environment rather than short-term trading signals. It helps answer the question: "Is the global financial system expanding or contracting liquidity?"
Global Liquidity Score
Global Liquidity Score – Simple Risk-On / Risk-Off Gauge
This indicator measures overall market liquidity conditions using a single, normalized score.
It takes several macro and crypto variables, standardizes each one (z-score), and combines them into one clear Liquidity Score Line.
You only follow one line (your pink/white line).
The background color shows the current liquidity regime.
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What the indicator measures
The algorithm looks at four major liquidity sources:
1. USD Liquidity (tightening or easing)
• DXY (strong dollar = tighter global liquidity)
• US10Y yield (higher yields = liquidity drain)
2. Risk Sentiment (risk-on vs risk-off)
• VIX index (volatility)
• S&P 500 index (SPX)
3. Credit Market Strength
• High-yield ETFs: HYG, JNK
• Investment-grade corporate credit: LQD
Stronger credit = easier liquidity.
Weaker credit = tightening risk.
4. Internal Crypto Liquidity
• USDT dominance (higher = risk-off in crypto)
• Bitcoin price
• TOTAL2 (crypto market cap excluding BTC)
These are all converted into z-scores and combined into one metric:
Total Liquidity Score =
USD Block + Risk Block − Credit Block − 0.5 × Crypto Block
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How to read the colors
The indicator uses background colors to show the liquidity regime:
Color Meaning
Dark Red Severe liquidity tightening / strong risk-off
Red Mild-to-moderate tightening
Green Liquidity easing / soft risk-on
Dark Green Strong easing, high liquidity / risk-on
Your pink/white line = the final liquidity score.
You only need to follow that single line.
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How to interpret the score
📉 Positive score → Liquidity Tightening (Risk-Off)
• USD stronger
• Yields rising
• Volatility rising
• Credit markets weakening
• Crypto rotating to stablecoins
📈 Negative score → Liquidity Easing (Risk-On)
• USD weakening
• Yields falling
• Stocks rising
• Volatility low
• Credit markets strong
• Crypto beta assets outperform
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What this indicator is NOT
This is not a price predictor.
It does not follow BTC directly.
It tells you liquidity conditions, not immediate price direction.
It answers the macro question:
“Is liquidity flowing INTO the market or OUT of the market?”
If liquidity is tightening (red), crypto rallies are harder to sustain.
If liquidity is easing (green), crypto rallies have more fuel.
