MVRV | Lyro RS📊 MVRV | Lyro RS is a powerful on-chain valuation tool designed to assess the relative market positioning of Bitcoin (BTC) or Ethereum (ETH) based on the Market Value to Realized Value (MVRV) ratio. It highlights potential undervaluation or overvaluation zones, helping traders and investors anticipate cyclical tops and bottoms.
✨ Key Features :
🔁 Dual Asset Support: Analyze either BTC or ETH with a single toggle.
📐 Dynamic MVRV Thresholds: Automatically calculates median-based bands at 50%, 64%, 125%, and 170%.
📊 Median Calculation: Period-based median MVRV for long-term trend context.
💡 Optional Smoothing: Use SMA to smooth MVRV for cleaner analysis.
🎯 Visual Threshold Alerts: Background and bar colors change based on MVRV position relative to thresholds.
⚠️ Built-in Alerts: Get notified when MVRV enters under- or overvalued territory.
📈 How It Works :
💰 MVRV Calculation: Uses data from IntoTheBlock and CoinMetrics to obtain real-time MVRV values.
🧠 Threshold Bands: Median MVRV is used as a baseline. Ratios like 50%, 64%, 125%, and 170% signal various levels of market extremes.
🎨 Visual Zones: Green zones for undervaluation and red zones for overvaluation, providing intuitive visual cues.
🛠️ Custom Highlights: Toggle individual threshold zones on/off for a cleaner view.
⚙️ Customization Options :
🔄 Switch between BTC or ETH for analysis.
📏 Adjust period length for median MVRV calculation.
🔧 Enable/disable threshold visibility (50%, 64%, 125%, 170%).
📉 Toggle smoothing to reduce noise in volatile markets.
📌 Use Cases :
🟢 Identify undervalued zones for long-term entry opportunities.
🔴 Spot potential overvaluation zones that may precede corrections.
🧭 Use in confluence with price action or macro indicators for better timing.
⚠️ Disclaimer :
This indicator is for educational purposes only. It should not be used in isolation for making trading or investment decisions. Always combine with price action, fundamentals, and proper risk management.
Statistics
Anchored Probability Cone by TenozenFirst of all, credit to @nasu_is_gaji for the open source code of Log-Normal Price Forecast! He teaches me alot on how to use polylines and inverse normal distribution from his indicator, so check it out!
What is this indicator all about?
This indicator draws a probability cone that visualizes possible future price ranges with varying levels of statistical confidence using Inverse Normal Distribution , anchored to the start of a selected timeframe (4h, W, M, etc.)
Feutures:
Anchored Cone: Forecasts begin at the first bar of each chosen higher timeframe, offering a consistent point for analysis.
Drift & Volatility-Based Forecast: Uses log returns to estimate market volatility (smoothed using VWMA) and incorporates a trend angle that users can set manually.
Probabilistic Price Bands: Displays price ranges with 5 customizable confidence levels (e.g., 30%, 68%, 87%, 99%, 99,9%).
Dynamic Updating: Recalculates and redraws the cone at the start of each new anchor period.
How to use:
Choose the Anchored Timeframe (PineScript only be able to forecast 500 bars in the future, so if it doesn't plot, try adjusting to a lower anchored period).
You can set the Model Length, 100 sample is the default. The higher the sample size, the higher the bias towards the overall volatility. So better set the sample size in a balanced manner.
If the market is inside the 30% conifidence zone (gray color), most likely the market is sideways. If it's outside the 30% confidence zone, that means it would tend to trend and reach the other probability levels.
Always follow the trend, don't ever try to trade mean reversions if you don't know what you're doing, as mean reversion trades are riskier.
That's all guys! I hope this indicator helps! If there's any suggestions, I'm open for it! Thanks and goodluck on your trading journey!
Nowein-Anchored VWAP with 1% Bands Anchored VWAP with ±1% Bands Starting at 9:00 AM
This indicator calculates an Anchored Volume Weighted Average Price (VWAP) starting precisely at 9:00 AM each trading day (customizable). It plots the VWAP line alongside two dynamic bands set at ±1% above and below the VWAP. The bands help visualize potential support and resistance zones relative to the intraday VWAP anchored at market open.
Key Features:
Anchors VWAP calculation to user-defined start time (default 9:00 AM)
Displays VWAP line in orange for easy tracking
Shows upper and lower dashed bands at ±1% of VWAP in green and red, respectively
Bands update dynamically with each new bar throughout the trading day
Designed for intraday charts (1-minute, 5-minute, etc.)
Use this tool to better assess intraday price action around VWAP and identify potential trading opportunities based on price deviations from the anchored VWAP.
Future Events TableTo show any type of Future Events Table.
Example shows "astronomical cycle reversal dates" in 2025
*no change, just on a clean chart
Anchored VWAP with Bands DebugAnchored VWAP with ±1% Bands Starting at 9:00 AM
This indicator calculates an Anchored Volume Weighted Average Price (VWAP) starting precisely at 9:00 AM each trading day (customizable). It plots the VWAP line alongside two dynamic bands set at ±1% above and below the VWAP. The bands help visualize potential support and resistance zones relative to the intraday VWAP anchored at market open.
Key Features:
Anchors VWAP calculation to user-defined start time (default 9:00 AM)
Displays VWAP line in orange for easy tracking
Shows upper and lower dashed bands at ±1% of VWAP in green and red, respectively
Bands update dynamically with each new bar throughout the trading day
Designed for intraday charts (1-minute, 5-minute, etc.)
Use this tool to better assess intraday price action around VWAP and identify potential trading opportunities based on price deviations from the anchored VWAP.
Adaptive Hurst Exponent Regime FilterAdaptive Hurst Exponent Regime Filter (AHERF)
█ OVERVIEW
The Adaptive Hurst Exponent Regime Filter (AHERF) is designed to identify the prevailing market regime—be it Trending, Mean-Reverting, or a Random Walk/Transition phase. While the Hurst Exponent is a well-known tool for this purpose, AHERF introduces a key innovation: an adaptive threshold . Instead of relying solely on the traditional fixed 0.5 Hurst value, this indicator's threshold dynamically adjusts based on current market volatility, aiming to provide more nuanced and responsive regime classifications.
This tool can assist traders in:
Gauging the current character of the market.
Tailoring trading strategies to the identified regime (e.g., deploying trend-following systems in Trending markets or mean-reversion tactics in Mean-Reverting conditions).
Filtering out trades that may be counterproductive to the dominant market behavior.
█ HOW IT WORKS
The indicator operates through the following key calculations:
1. Hurst Exponent Calculation:
The script computes an approximate Hurst Exponent (H). It utilizes log price changes as its input series.
The `calculateHurst` function implements a variance scaling approach:
It defines three sub-periods based on the main `Hurst Lookback Period`.
It calculates the standard deviation of the input series over these sub-periods.
The Hurst Exponent is then estimated from the slope of a log-log regression between the standard deviations and their respective sub-period lengths. A simplified calculation using the first and last sub-periods is performed: `H = (log(StdDev3) - log(StdDev1)) / (log(N3) - log(N1))`.
Theoretically, a Hurst Exponent:
H > 0.5 suggests persistence (trending behavior).
H < 0.5 suggests anti-persistence (mean-reverting behavior).
H ≈ 0.5 suggests a random walk (unpredictable movement).
Pine Script® Snippet (Hurst Calculation Call):
float logPriceChange = math.log(close) - math.log(close );
// ... ensure logPriceChange is not na on first bar ...
float hurstValue = calculateHurst(logPriceChange, hurstLookbackInput);
2. Volatility Proxy Calculation:
To enable the adaptive nature of the threshold, a volatility proxy is calculated.
Users can select the `Volatility Metric` to be either:
Average True Range (ATR), normalized by the closing price.
Standard Deviation (StdDev) of simple price returns.
This proxy quantifies the current degree of price activity or fluctuation in the market.
Pine Script® Snippet (Volatility Proxy Call):
float volatilityProxy = getVolatilityProxy(volatilityMetricInput, volatilityLookbackInput);
3. Adaptive Threshold Calculation:
This is the core of AHERF's adaptability. Instead of a static 0.5 line as the sole determinant, the script computes a dynamic threshold.
The adaptive threshold is calculated as: `0.5 + (Threshold Sensitivity * Volatility Proxy)`.
This means the threshold starts at the baseline 0.5 level and then adjusts upwards or downwards based on the current `volatilityProxy` scaled by the `Threshold Sensitivity (k)` input.
Pine Script® Snippet (Adaptive Threshold Calculation):
float adaptiveThreshold = 0.5 + sensitivityInput * nz(volatilityProxy, 0.0);
4. Regime Identification:
The prevailing market regime is determined by comparing the `hurstValue` to this `adaptiveThreshold`, incorporating a `Threshold Buffer` to reduce noise and clearly delineate zones:
Trending: `hurstValue > adaptiveThreshold + bufferInput`
Mean-Reverting: `hurstValue < adaptiveThreshold - bufferInput`
Random/Transition: Otherwise (Hurst value is within the buffer zone around the adaptive threshold).
Pine Script® Snippet (Regime Determination Logic):
if not na(hurstValue) and not na(adaptiveThreshold)
if hurstValue > adaptiveThreshold + bufferInput
currentRegimeColor := TRENDING_COLOR
regimeText := "Trending"
else if hurstValue < adaptiveThreshold - bufferInput
currentRegimeColor := MEAN_REVERTING_COLOR
regimeText := "Mean-Reverting"
// else remains Random/Transition
█ HOW TO USE IT
Interpreting the Visuals:
Observe the plotted `Hurst Exponent (H)` line (White) relative to the `Adaptive Threshold` line (Orange).
The background color provides an immediate indication of the current regime: Green for Trending, Red for Mean-Reverting, and Gray for Random/Transition.
The fixed `0.5 Level` (Dashed Gray) is plotted for reference against traditional Hurst interpretation.
Labels "T", "M", and "R" appear below bars to signal new entries into Trending, Mean-Reverting, or Random/Transition regimes, respectively.
Inputs Customization:
Hurst Exponent Calculation
Hurst Lookback Period: Defines the number of bars used for the Hurst Exponent calculation. Longer periods generally yield smoother Hurst values, reflecting longer-term market memory. Shorter periods are more responsive.
Adaptive Threshold Settings
Volatility Metric: Choose "ATR" or "StdDev" to drive the adaptive threshold. Experiment to see which best suits the asset.
Volatility Lookback: The lookback period for the selected volatility metric.
Threshold Sensitivity (k): A crucial multiplier determining how strongly volatility influences the adaptive threshold. Higher values mean volatility has a greater impact, potentially widening or shifting the regime bands more significantly.
Threshold Buffer: Creates a neutral zone around the adaptive threshold. This helps prevent overly frequent regime shifts due_to minor Hurst fluctuations.
█ ORIGINALITY AND USEFULNESS
The AHERF indicator distinguishes itself by:
Implementing an adaptive threshold mechanism for Hurst Exponent analysis. This threshold dynamically responds to changes in market volatility, offering a more flexible approach than a fixed 0.5 reference, potentially leading to more contextually relevant regime detection.
Providing clear, at-a-glance visualization of market regimes through background coloring and distinct plot shapes.
Offering user-configurable parameters for both the Hurst calculation and the adaptive threshold components, allowing for tuning across various assets and timeframes.
Traders can leverage AHERF to better align their chosen strategies with the prevailing market character, potentially enhancing trade filtering and decision-making processes.
█ VISUALIZATION
The indicator plots the following in a separate pane:
Hurst Exponent (H): A white line representing the calculated Hurst value.
Adaptive Threshold: An orange line representing the dynamic threshold.
Fixed 0.5 Level: A dashed gray horizontal line for traditional Hurst reference.
Background Color: Changes based on the identified regime:
Green: Trending regime.
Red: Mean-Reverting regime.
Gray: Random/Transition regime.
Regime Entry Shapes: Plotted below the price bars (forced overlay for visibility):
"T" (Green Label): Signals entry into a Trending regime.
"M" (Teal Label): Signals entry into a Mean-Reverting regime.
"R" (Cyan Label): Signals entry into a Random/Transition regime.
█ ALERTS
The script provides alert conditions for changes in the market regime:
Regime Shift to Trending: Triggers when the Hurst Exponent crosses above the adaptive threshold into a Trending state.
Regime Shift to Mean-Reverting: Triggers when the Hurst Exponent crosses below the adaptive threshold into a Mean-Reverting state.
Regime Shift to Random/Transition: Triggers when the Hurst Exponent enters the Random/Transition zone around the adaptive threshold.
These can be configured directly from the TradingView alerts panel.
█ NOTES & DISCLAIMERS
The Hurst Exponent calculation is an approximation; various methods exist, each with its nuances.
The performance and relevance of the identified regimes can differ across financial instruments and timeframes. Parameter tuning is recommended.
This indicator is intended as a decision-support tool and should not be the sole basis for trading decisions. Always integrate its signals within a broader analytical framework.
Past performance of any trading system or indicator, including those derived from AHERF, is not indicative of future results.
█ CREDITS & LICENSE
Author: mastertop ( Twitter: x.com )
Color Palette: Uses the `MaterialPalette` library by MASTERTOP_ASTRAY.
This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
© mastertop, 2025
CANSLIM Từng Bước"CANSLIM Step-by-Step" Indicator Description for TradingView
CANSLIM Step-by-Step - Your Companion for Evaluating Stocks with the CANSLIM Methodology
Welcome, investors, to "CANSLIM Step-by-Step"! This indicator is designed to assist you in analyzing and evaluating stocks based on the seven core criteria of William J. O'Neil's renowned CANSLIM investment methodology.
Purpose of the Indicator:
This tool is not intended to provide fully automated buy/sell recommendations. Instead, it focuses on "digitizing" and visualizing each step in the CANSLIM evaluation process, helping you gain a more comprehensive and detailed overview of potential stocks.
Key Features:
Evaluation of 7 CANSLIM Criteria:
C (Current Quarterly Earnings): Allows manual input for the latest quarterly EPS growth (%) and positive EPS status. It also attempts to fetch data automatically (which may not be stable for all symbols) for comparison.
A (Annual Earnings & ROE): Prioritizes manual input for annual EPS growth rate (CAGR) and current ROE to ensure accuracy.
N (New Highs): Automatically analyzes price action from the chart to determine if the stock is near or making a new 52-week high.
S (Supply and Demand - Volume): Automatically analyzes current trading volume against its average to detect significant surges.
L (Leader or Laggard): You evaluate and input whether the stock is a market or industry leader.
I (Institutional Sponsorship): You evaluate and input the quality and quantity of significant institutional ownership.
M (Market Direction): Automatically analyzes the trend of a reference market index (e.g., VNINDEX) using moving averages.
Prioritized Manual Input for Financial Data: For criteria C and A, the indicator allows and encourages manual input to ensure the highest accuracy, given the inherent limitations of automatically accessing consistently updated financial data via Pine Script.
"Super Compact" Summary Table:
Clearly displays the status (Pass/Fail/N/A) of each criterion using color codes.
Provides specific values for each criterion (e.g., growth percentage, distance to 52-week high, volume ratio).
Aggregates a total score (out of 7) and a star rating (0 to 7 stars) for a quick overview of the stock's CANSLIM compliance.
Customizable Thresholds: You can adjust the evaluation thresholds for various criteria (e.g., minimum EPS growth %, minimum ROE %) to suit your risk appetite and personal standards.
How to Use Effectively:
Step 1: Select the stock symbol you wish to analyze.
Step 2: Open the indicator's settings:
Manually input your research findings for criteria C, A, L, and I.
Adjust thresholds and parameters for N, S, and M if needed.
Select the appropriate market index symbol for criterion M.
Step 3: Observe the summary table in the bottom-right corner of your screen for the overall assessment and detailed breakdown of each criterion.
"CANSLIM Step-by-Step" is a companion tool designed to help you systematize your stock evaluation process according to one of the most successful investment methodologies. Combine this indicator with your knowledge and experience to make informed investment decisions!
Commitment to Ongoing Development
We wish to share that the current "CANSLIM Step-by-Step" indicator is the initial version in our journey to build a more comprehensive CANSLIM stock evaluation support tool.
Our vision is to continuously develop and enhance this indicator with the following goals:
Increase Automation Capabilities: Explore solutions to automatically update certain basic financial data (for criteria C, A) more reliably and consistently, within the technical limits of Pine Script and available data sources.
Add Deeper Analytical Features: Such as visualizing changes in criteria over time, or comparisons with industry peers (where feasible).
Improve User Interface: Make data input and tracking even more intuitive and convenient.
Listen to and Integrate Community Feedback: We highly value all user feedback, bug reports, and feature suggestions to make "CANSLIM Step-by-Step" an increasingly useful tool.
This is a dedicated project, and we are committed to continually working to make "CANSLIM Step-by-Step" an even more powerful assistant for investors following the CANSLIM philosophy.
Tick Value (Top-Center Fixed)this can be used by future traders some brokers and tradingview does not use tick value given by cme for some instruments , so this gives tick value which is used by tradingview chart
Granger Causality Flow IndicatorGranger Causality Flow Indicator (GC Flow)
█ OVERVIEW
The Granger Causality Flow Indicator (GC Flow) attempts to quantify the potential predictive relationship between two user-selected financial instruments (Symbol X and Symbol Y). In essence, it explores whether the past values of one series (e.g., Symbol X) can help explain the current value of another series (e.g., Symbol Y) better than Y's own past values alone.
This indicator provides a "Granger Causality Score" (GC Score) for both directions (X → Y and Y → X). A higher score suggests a stronger statistical linkage where one series may lead or influence the other. The indicator visualizes this "flow" of potential influence through background colors and on-chart text.
Important Note: "Granger Causality" does not imply true economic or fundamental causation. It is a statistical concept indicating predictive power or information flow. This implementation also involves simplifications (notably, using AR(1) models) due to the complexities of full Vector Autoregression (VAR) models in Pine Script®.
█ HOW IT WORKS
The indicator's methodology is based on comparing the performance of Autoregressive (AR) models:
1. Data Preprocessing:
Fetches historical close prices for two user-defined symbols (X and Y).
Optionally applies first-order differencing (`price - price `) to the series. Differencing is a common technique to achieve a proxy for stationarity, which is an underlying assumption for Granger Causality tests. Non-stationary series can lead to spurious correlations.
2. Autoregressive (AR) Models (Simplified to AR(1)):
Due to Pine Script's current limitations for complex multivariate time series models, this indicator uses simplified AR(1) models (where the current value is predicted by its immediately preceding value).
Restricted Model (for Y → Y): Predicts the target series (e.g., Y) using only its own past value (Y ).
`Y = c_R + a_R * Y + residuals_R`
The variance of `residuals_R` (Var_R) is calculated.
Unrestricted Model (Proxy for X → Y): To test if X Granger-causes Y, the indicator examines if the past values of X (X ) can explain the residuals from the restricted model of Y.
`residuals_R = c_UR' + b_UR * X + residuals_UR`
The variance of these final `residuals_UR` (Var_UR) is calculated.
The same process is repeated to test if Y Granger-causes X.
3. Granger Causality (GC) Score Calculation:
The GC Score quantifies the improvement in prediction from adding the other series' past values. It's calculated as:
`GC Score = 1 - (Var_UR / Var_R)`
A score closer to 1 suggests that the "causing" series significantly reduces the unexplained variance of the "target" series (i.e., Var_UR is much smaller than Var_R), indicating stronger Granger causality.
A score near 0 (or capped at 0 if Var_UR >= Var_R) suggests little to no improvement in prediction.
The score is calculated over a rolling `Calculation Window`.
Pine Script® Snippet (Conceptual GC Score Logic):
// Conceptual representation of GC Score calculation
// var_R: Variance of residuals when Y is predicted by Y
// var_UR: Variance of residuals when Y's AR(1) residuals are predicted by X
score = 0.0
if var_R > 1e-9 // Avoid division by zero
score := 1.0 - (var_UR / var_R)
score := score < 0 ? 0 : score // Ensure score is not negative
4. Determining Causal Flow:
The calculated GC Scores for X → Y and Y → X are compared against a user-defined `Significance Threshold for GC Score`.
If GC_X→Y > threshold AND GC_Y→X > threshold: Bidirectional flow.
If GC_X→Y > threshold only: X → Y flow.
If GC_Y→X > threshold only: Y → X flow.
Otherwise: No significant flow.
█ HOW TO USE IT
Interpreting the Visuals:
Background Color:
Green: Indicates X → Y (Symbol 1 potentially leads Symbol 2).
Orange: Indicates Y → X (Symbol 2 potentially leads Symbol 1).
Blue: Indicates Bidirectional influence.
Gray: No significant Granger causality detected based on the threshold.
Data Window Plots: The actual GC Scores for X → Y (blue) and Y → X (red) are plotted and visible in TradingView's Data Window. A dashed gray line shows your `Significance Threshold`.
On-Chart Table (Last Bar): Displays the currently detected causal direction text (e.g., "BTCUSDT → QQQ").
Potential Applications:
Intermarket Analysis: Explore potential lead-lag relationships between different asset classes (e.g., commodities and equities, bonds and currencies).
Pair Trading Components: Identify if one component of a potential pair tends to lead the other.
Confirmation Tool: Use alongside other analyses to see if a move in one asset might foreshadow a move in another.
Considerations:
Symbol Choice: Select symbols that have a plausible economic or market relationship.
Stationarity: Granger Causality tests ideally require stationary time series. The `Use Differencing` option is a simple proxy. True stationarity testing is complex. Non-stationary data can yield misleading results.
Lag Order (p): This indicator is fixed at p=1 due to Pine Script® limitations. In rigorous analysis, selecting the optimal lag order is crucial.
Calculation Window: Shorter windows are more responsive but may be noisier. Longer windows provide smoother scores but lag more.
Significance Threshold: Adjust this based on your desired sensitivity for detecting causal links. There's no universally "correct" threshold; it depends on the context and noise level of the series.
█ INPUTS
Symbol 1 (X): The first symbol in the analysis.
Symbol 2 (Y): The second symbol (considered the target when testing X → Y).
Use Differencing: If true, applies first-order differencing to both series as a proxy for stationarity.
Calculation Window (N): Lookback period for AR model coefficient estimation and variance calculations.
Lag Order (p): Currently fixed at 1. This defines the lag used (e.g., X , Y ) in the AR models.
Significance Threshold for GC Score: A value between 0.01 and 0.99. The calculated GC Score must exceed this to be considered significant.
█ VISUALIZATION
Background Color: Dynamically changes based on the detected Granger causal flow (Green for X → Y, Orange for Y → X, Blue for Bidirectional, Gray for None).
GC Scores (Data Window):
Blue Plot: GC Score for X → Y.
Red Plot: GC Score for Y → X.
Significance Threshold Line: A dashed gray horizontal line plotted at the level of your input threshold.
On-Chart Table: Displayed on the top-right (on the last bar), showing the current causal direction text.
█ ALERTS
The indicator can generate alerts for:
Emergence of X → Y causality.
Emergence of Y → X causality.
General change or cessation of a previously detected causal relationship.
█ IMPORTANT DISCLAIMERS & LIMITATIONS
Correlation vs. Causation: Granger causality measures predictive power, not true underlying economic causation. A strong GC Score doesn't prove one asset *causes* another to move, only that its past values improve predictions.
Stationarity Assumption: While differencing is offered, it's a simplified approach. Non-stationary data can lead to spurious (false) Granger causality detection.
Model Simplification (AR(1)): This script uses AR(1) models for simplicity. Real-world relationships can involve more complex dynamics and higher lag orders. The fixed lag of p=1 is a significant constraint.
Sensitivity to Parameters: Results can be sensitive to the chosen symbols, calculation window, differencing option, and significance threshold.
No Statistical Significance Testing (p-values): This indicator uses a direct threshold on the GC Score itself, not a formal statistical test (like an F-test producing p-values) typically found in econometric software.
Use this indicator as an exploratory tool within a broader analytical framework. Do not rely on it as a standalone basis for trading decisions.
█ CREDITS & LICENSE
Author: mastertop ( Twitter: x.com )
Version: 1.0 (Released: 2025-05-08)
This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
© mastertop, 2025
SOPR with Z-Score Table📊 Glassnode SOPR with Dynamic Z-Score Table
ℹ️ Powered by Glassnode On-Chain Metrics
📈 Description:
This indicator visualizes the Spent Output Profit Ratio (SOPR) for major cryptocurrencies — Bitcoin, Ethereum, and Litecoin — along with a dynamically normalized Z-Score. SOPR is a key on-chain metric that reflects whether coins moved on-chain are being sold at a profit or a loss.
🔍 SOPR is calculated using Glassnode’s entity-adjusted SOPR feed, and a custom SMA is applied to smooth the signal. The normalized Z-Score helps identify market sentiment extremes by scaling SOPR relative to its historical context.
📊 Features:
Selectable cryptocurrency: Bitcoin, Ethereum, or Litecoin
SOPR smoothed by user-defined SMA (default: 10 periods)
Upper & lower bounds (±4%) for SOPR, shown as red/green lines
Background highlighting when SOPR moves outside normal range
Normalized Z-Score scaled between –2 and +2
Live Z-Score display in a compact top-right table
🧮 Calculations:
SOPR data is sourced daily from Glassnode:
Bitcoin: XTVCBTC_SOPR
Ethereum: XTVCETH_SOPR
Litecoin: XTVCLTC_SOPR
Z-Score is calculated as:
SMA of SOPR over zscore_length periods
Standard deviation of SOPR
Z-Score = (SOPR – mean) / standard deviation
Z-Score is clamped between –2 and +2 for visual consistency
🎯 Interpretation:
SOPR > 1 implies coins are sold in profit
SOPR < 1 suggests coins are sold at a loss
When SOPR is significantly above or below its recent range (e.g., +4% or –4%), it may signal overheating or capitulation
The Z-Score contextualizes how extreme the current SOPR is relative to history
📌 Notes:
Best viewed on daily charts
Works across selected assets (BTC, ETH, LTC)
BPCO Z-ScoreBPCO Z-Score with Scaled Z-Value and Table
Description:
This custom indicator calculates the Z-Score of a specified financial instrument (using the closing price as a placeholder for the BPCO value), scales the Z-Score between -2 and +2 based on user-defined thresholds, and displays it in a table for easy reference.
The indicator uses a simple moving average (SMA) and standard deviation to calculate the original Z-Score, and then scales the Z-Score within a specified range (from -2 to +2) based on the upper and lower thresholds set by the user.
Additionally, the scaled Z-Score is displayed in a separate table on the right side of the chart, providing a clear, numerical value for users to track and interpret.
Key Features:
BPCO Z-Score: Calculates the Z-Score using a simple moving average and standard deviation over a user-defined window (default: 365 days). This provides a measure of how far the current price is from its historical average in terms of standard deviations.
Scaled Z-Score: The original Z-Score is then scaled between -2 and +2, based on the user-specified upper and lower thresholds. The thresholds default to 3.5 (upper) and -1.5 (lower), and can be adjusted as needed.
Threshold Bands: Horizontal lines are plotted on the chart to represent the upper and lower thresholds. These help visualize when the Z-Score crosses critical levels, indicating potential market overbought or oversold conditions.
Dynamic Table Display: The scaled Z-Score is shown in a dynamic table at the top-right of the chart, providing a convenient reference for traders. The table updates automatically as the Z-Score fluctuates.
How to Use:
Adjust Time Window: The "Z-Score Period (Days)" input allows you to adjust the time period used for calculating the moving average and standard deviation. By default, this is set to 365 days (1 year), but you can adjust this depending on your analysis needs.
Set Upper and Lower Thresholds: Use the "BPCO Upper Threshold" and "BPCO Lower Threshold" inputs to define the bands for your Z-Score. The default values are 3.5 for the upper band and -1.5 for the lower band, but you can adjust them based on your strategy.
Interpret the Z-Score: The Z-Score provides a standardized measure of how far the current price (or BPCO value) is from its historical mean, relative to the volatility. A value above the upper threshold (e.g., 3.5) may indicate overbought conditions, while a value below the lower threshold (e.g., -1.5) may indicate oversold conditions.
Use the Scaled Z-Score: The scaled Z-Score is calculated based on the original Z-Score, but it is constrained to a range between -2 and +2. When the BPCO value hits the upper threshold (3.5), the scaled Z-Score will be +2, and when it hits the lower threshold (-1.5), the scaled Z-Score will be -2. This gives you a clear, easy-to-read value to interpret the market's condition.
Data Sources:
BPCO Data: In this indicator, the BPCO value is represented by the closing price of the asset. The calculation of the Z-Score and scaled Z-Score is based on this price data, but you can modify it to incorporate other data streams as needed (e.g., specific economic indicators or custom metrics).
Indicator Calculation: The Z-Score is calculated using the following formulas:
Mean (SMA): A simple moving average of the BPCO (close price) over the selected period (365 days by default).
Standard Deviation (Std): The standard deviation of the BPCO (close price) over the same period.
Z-Score: (Current BPCO - Mean) / Standard Deviation
Scaled Z-Score: The Z-Score is normalized to fall within a specified range (from -2 to +2), based on the upper and lower threshold inputs.
Important Notes:
Customization: The indicator allows users to adjust the period (window) for calculating the Z-Score, as well as the upper and lower thresholds to suit different timeframes and trading strategies.
Visual Aids: Horizontal lines are drawn to represent the upper and lower threshold levels, making it easy to visualize when the Z-Score crosses critical levels.
Limitations: This indicator relies on historical price data (or BPCO) and assumes that the standard deviation and mean are representative of future price behavior. It does not account for potential market shifts or extreme events that may fall outside historical norms.
MVRVZ BTCMVRVZ BTC (Market Value to Realized Value Z-Score)
Description:
The MVRVZ BTC indicator provides insights into the relationship between the market value and realized value of Bitcoin, using the Market Value to Realized Value (MVRV) ratio, which is then adjusted using a Z-Score. This indicator highlights potential market extremes and helps in identifying overbought or oversold conditions, offering a unique perspective on Bitcoin's valuation.
How It Works:
MVRVZ is calculated by taking the difference between Bitcoin's Market Capitalization (MC) and Realized Capitalization (MCR), then dividing that by the Standard Deviation (Stdev) of the price over a specified period (usually 104 weeks).
The resulting value is plotted as the MVRVZ line, representing how far the market price deviates from its realized value.
Z-Score is then applied to the MVRVZ line, with the Z-Score bounded between +2 and -2, which allows it to be used within a consistent evaluation framework, regardless of how high or low the MVRVZ line goes. The Z-Score will reflect overbought or oversold conditions:
A Z-Score above +2 indicates the market is likely overbought (possible market top).
A Z-Score below -2 indicates the market is likely oversold (possible market bottom).
Values between -2 and +2 indicate more neutral market conditions.
How to Read the Indicator:
MVRVZ Line:
The MVRVZ line shows the relationship between market cap and realized cap. A higher value indicates the market is overvalued relative to the actual capital realized by holders.
The MVRVZ line can move above or below the top and bottom lines you define, which are adjustable according to your preferences. These lines act as trigger levels.
Top and Bottom Trigger Lines:
You can customize the Top Line and Bottom Line values to your preference.
When the MVRVZ line crosses the Top Line, the market might be considered overbought.
When the MVRVZ line crosses the Bottom Line, the market might be considered oversold.
SCDA Z-Score:
The Z-Score is displayed alongside the MVRVZ line and is bounded between -2 and +2. It scales proportionally based on the MVRVZ line's position relative to the top and bottom trigger lines.
The Z-Score ensures that even if the MVRVZ line moves beyond the trigger lines, the Z-Score will stay within the limits of -2 to +2, making it ideal for your custom evaluation system (SCDA).
Background Highlighting:
The background color changes when the MVRVZ line crosses key levels:
When the MVRVZ line exceeds the Top Trigger, the background turns red, indicating overbought conditions.
When the MVRVZ line falls below the Bottom Trigger, the background turns green, indicating oversold conditions.
Data Sources:
The data for the MVRVZ indicator is sourced from Glassnode and Coinmetrics, which provide the necessary values for:
BTC Market Cap (MC) – The total market capitalization of Bitcoin.
BTC Realized Market Cap (MCR) – The capitalization based on the price at which Bitcoin was last moved on the blockchain (realized value).
How to Use the Indicator:
Market Extremes:
Use the MVRVZ and Z-Score to spot potential market tops or bottoms.
A high Z-Score (above +2) suggests the market is overbought, while a low Z-Score (below -2) suggests the market is oversold.
Adjusting the Triggers:
Customize the Top and Bottom Trigger Lines to suit your trading strategy. These lines can act as dynamic reference points for when to take action based on the Z-Score or MVRVZ line crossing these levels.
Market Evaluation (SCDA Framework):
The bounded Z-Score (from -2 to +2) is tailored for your SCDA evaluation system, allowing you to assess market conditions based on consistent criteria, no matter how volatile the MVRVZ line becomes.
Conclusion:
The MVRVZ BTC indicator is a powerful tool for assessing the relative valuation of Bitcoin based on its market and realized capitalization. By combining it with the Z-Score, you get an easy-to-read, bounded evaluation system that highlights potential market extremes and helps you make informed decisions about Bitcoin's price behavior.
Zona Lateral Real (Slope + Rango)Script to detect sideways zones, which identifies the areas where the price enters a certain range in order to determine if there is consolidation or sideways movement.
Regime Scope | mad_tiger_slayerRegimeScope by mad_tiger_slayer
Adapt to the Market’s Mood. Trade in Sync with Regime Scope.
Overview
Regime Scope is an advanced multi-factor market regime identifier meticulously engineered to determine whether an asset is exhibiting trending behavior (Markup/Markdown phases) or mean-reverting dynamics (Sideways - Accumulation/Distribution). By integrating and synthesizing outputs from nine rigorously chosen statistical and volatility-based models, this tool offers a unified framework for assessing regime conditions with precision.
This indicator is best used in conjunction with other tools in your trading arsenal—serving not as a standalone signal generator, but as a high-value filter for confluence and strategic alignment. Whether you're trading breakouts, reversals, or mean-reversion setups, Regime Scope can elevate your system’s contextual awareness and execution timing.
How It Works – Part 1
Regime Scope calculates a composite "regime score" by normalizing and averaging a range of volatility and statistical measures. This score, which ranges between -1 and +1, indicates the likelihood of the market being in a trending versus mean-reverting state.
Values near +1 suggest a strong trending environment.
Values near -1 suggest strong mean-reversion (sideways, volatile) conditions.
Values between -0.30 and +0.30 are considered neutral and indicate choppy or range-bound market behavior.
When the average regime score crosses above the upper threshold, the asset likely enters a trending state.
When it crosses below the lower threshold, the market likely shifts to a volatile, mean-reverting state.
The histogram and dynamic background color provide an intuitive visual guide to the current regime.
How It Works – Part 2: Components
Each of the following sub-models has been carefully selected for its contribution to understanding price behavior. All components are normalized to create a consistent, unified score:
Phillips-Perron Test: Detects the presence of a unit root to infer stationarity and mean-reverting characteristics.
Hurst Exponent: Measures long-term memory in a time series to identify persistence or anti-persistence.
KPSS Test: Tests for level stationarity to contrast against unit-root behavior and validate trending assumptions.
GARCH Volatility: Captures volatility clustering and regime shifts in conditional variance.
Wavelet Transform: Decomposes price action into time-frequency space to extract non-linear and localized dynamics.
Half-Life of Mean Reversion: Estimates the speed at which price returns to its mean, enhancing the timing of reversion plays.
Augmented Dickey-Fuller (ADF) Test: Statistically verifies whether a series exhibits mean-reverting tendencies.
Garman-Klass-Yang-Zhang Volatility: A robust historical volatility measure using open-high-low-close data.
ADX (Average Directional Index): A classic technical tool for quantifying the strength of trend directionality.
How It Works – Part 3: Output Interpretation
All sub-models are normalized and synthesized into a single histogram plot shown in the lower chart panel.
+1.0 to +0.30: Indicates high probability of a directional, trending market.
-1.0 to -0.30: Indicates high probability of a sideways, mean-reverting regime.
-0.30 to +0.30: Suggests a neutral, uncertain market condition.
Transitions above or below these thresholds signal regime shifts.
Background shading adapts in real-time to visually reflect regime classification.
Features
Customizable thresholds to fine-tune sensitivity for regime classification.
Visual overlay positioning (choose from top-left, bottom-right, etc.).
Toggleable reference lines for regime thresholds.
Cross-timeframe consistency through dynamic normalization.
Each sub-model includes adjustable settings for personalized optimization.
Use Cases
Dynamically switch between trend-following and mean-reversion strategies.
Filter out choppy, low-probability zones by avoiding neutral regime periods.
Use regime score as confluence with entry/exit signals from other indicators.
Adapt strategies across timeframes—works well from scalping to swing trading.
Best used on timeframes ≥12H for macro regime context, but scalpers can benefit by using it on shorter windows with tuned parameters.
Scalping Use Case
Overlay the regime score on low timeframes (e.g., 1m–15m) and use it to avoid high chop zones or confirm breakout volume spikes during trending periods.
Long-Term Use Case
On 1D–1W charts, Regime Scope can filter false breakouts and confirm macro trend alignment for position trades or swing setups.
Tip
Combine Regime Scope with traditional technical tools like RSI, MACD, Bollinger Bands, or moving average crossovers to enhance strategic coherence.
For example, only act on breakout or trend-following signals when the regime score exceeds the upper threshold, confirming a high-trend environment.
Conversely, mean-reversion strategies like fading RSI extremes or trading Bollinger Band bounces work best when the regime score is in the lower range.
Aligning your tactical entries with the broader regime can significantly reduce false signals, enhance trade probability, and improve overall system robustness.
Annual Return to Margin\ Briefly\
\ Annual Return to Margin\ shows what annual yield (annualized %) an asset would generate at the current price by expiry, comparing that yield with the margin requirement.
\ What the indicator calculates\
Formula:
AR = close / tick\_size × tick\_value / margin / days\_to\_LTD × 365 × 100 %
where
close – Closing price of the current bar
tick\_size – Minimum price step (tick)
tick\_value – Monetary value of one tick in the contract currency
margin – Margin requirement
days\_to\_LTD – Number of calendar days until the last trading day (LTD)
365 – Conversion to an annual value |
\ Idea:\ we compare the price increase (in ticks × tick value) with the amount of capital “frozen” as margin, and annualize it.
\ Settings (available in the indicator panel)\
\ Margin\ — \ float\ – Margin requirement per contract.
\ Tick Size\ — \ float\ – Tick size (price step).
\ Tick Value\ — \ float\ – Monetary value of one tick.
\ LTD (Last Trading Day)\ — \ datetime\ – Date of the contract’s last trading day.
All fields can be changed on the fly — the indicator instantly recalculates the result for each bar.
AQPRO Block Force
📝 INTRODUCTION
AQPRO Block Force is a powerful trading tool designed to identify and track Orderblocks (OBs) in real-time based on Fair Value Gap (FVG) principles. This indicator employs quite strict yet effective FVG filtering criteria to ensure only significant OBs are displayed, avoiding minor inefficiencies or duplicates within the same impulse or corrective moves. Each OB adapts dynamically to price action and can be categorized as Classic, Strong, or Extreme, based on proprietary conditions and best ideas from SMC (Smart Money Concepts).
In addition to plotting Orderblocks, the indicator offers useful filtering systems like an Age Filter to ensure cleanliness of the OB data on the chart and prevent old, irrelevant OBs from obstructing the chart. Users can also enable MTF (Multi-Timeframe) functionality to view OBs from other timeframes, providing a comprehensive analysis across multiple levels of market structure. With extensive customization options, AQPRO Block Force allows traders to tailor the visuals and behavior to fit their specific trading preferences.
This indicator does not parse any instituotinal data, order books and other fancy financial sources for finding order blocks nor it uses them for confirmation purposes. Calculations algorithms of order blocks are based purely on current asset's price history.
IMPORTANT NOTE: in the sections below term 'quality' will be applied to orderblocks quite a number of times. By 'quality' in the context of orderblocks we mean the reaction of price upon the sweep of orderblock. Basically, if the price reverses after reaching the orderblock, this orderblock is considered to be of high quality. Definition for low -quality orderblock can be deducted by analogy.
🎯 PURPOSE OF USAGE
This indicator serves one and only purpose — help traders identify most lucrative institutional orderblocks on the chart in real time. Even though event of price reaching an orderblock cannot be considered as a sole signal in many trading strategies without proper confirmation, such event nevertheless is quite important in SMC-based trading, because when price sweeps OB it usually means, that a reversal will soon follow, but, of course, this is not the case every time.
Traders should not expect from this indicator detection of perfect orderblocks, which would surely revese the price on encounter, but they can expect is a time-proven algorithm of determing orderblocks that on average produces more high-quality orderblocks than simple similar tools from open-source libraries.
More in-depth advices on the usage will be given in the sections below, but for now let's summarise subgoals of the indicator:
Detecting orderblocks filtered through strict FVG validation rules to improve overall quality of orderblocks;
Classifying orderblocks as Classic, Strong, or Extreme based on wether or not classic orderblocks pass filtering conditions, which are based on crossing critical price levels and SMC principles like ChoCh (Change of Character);
Eliminating clutter and manage chart space with the Age Filter, removing old OBs outside a user-defined age range;
Utilizing MTF functionality to track significant OBs from other timeframes alongside current timeframe analysis;
Providing traders with customization options for indicator's visuals to help them organize information on the chart in a clean way.
⚙️ SETTINGS OVERVIEW
This indicator's customization options allow you to fully control its functionality and visuals. Below is a breakdown of the settings grouped by the exact setting sections and parameters from the indicator:
🔑 Main Settings
Show OBs from current timeframe — toggles the display of OBs from the current timeframe on the chart;
Show classic OBs — enables or disables the display of Classic OBs;
Show strong OBs — enables or disables the display of Strong OBs, which meet the ChoCh-based filter criteria;
Show extreme OBs — enables or disables the display of Extreme OBs, which exceed proprietary price level risk thresholds.
⏳ Filter: Age
Use Age Filter — toggles the Age Filter, which removes old OBs based on their age;
Max Age — sets the maximum age of OBs to be displayed (in bars). OBs older than this value will be hidden;
Min Age — sets the minimum age of OBs to be displayed (in bars). OBs younger than this value will not be shown.
🌋 MTF Settings
Show MTF OBs — toggles the display of OBs from higher timeframes;
Timeframe — select the timeframe to use for MTF OB detection (e.g., 15m, 1h).
⏳ MTF / Filter: Age
Use Age Filter (MTF) — toggles the Age Filter for MTF OBs;
Max Age — sets the maximum age of MTF OBs to be displayed (in bars);
Min Age — sets the minimum age of MTF OBs to be displayed (in bars).
🎨 Visual Settings
Classic OB (Bullish) — sets the color for bullish Classic OBs;
Classic OB (Bearish) — sets the color for bearish Classic OBs;
Strong OB (Bullish) — sets the color for bullish Strong OBs;
Strong OB (Bearish) — sets the color for bearish Strong OBs;
Extreme OB (Bullish) — sets the color for bullish Extreme OBs;
Extreme OB (Bearish) — sets the color for bearish Extreme OBs.
📈 APPLICATION GUIDE
Application methodology of this indicator is pretty much the same as with any other indicator, whose purpose is to find and display orderblocks on the chart. However, before actually diving into the guide on application, we want to make a small step back to remind traders of the history of orderblocks as a concept, its limitations and benefits.
Orderblocks themselves are essentially just zones of potential institutional interest, which if reached are expected to reverse the price in the opposite direction. 'Potential' is a suitable remark for indicator's success probability, because, as was mentioned above, orderblocks don't guarantee price reversal regardless of quality of the indicator. This is the case for the simplest of reasons — orderblocks are based solely on price history and thus are to be considered a mathematical model , degree of success of which is never 100%, because all mathematical models abide by a "golden rule of trading" : past performance doesn't guarantee future results.
However, the extensive history of orderblocks clearly shows that this tool, despite being decades old, can still help traders produce market insights and improve any strategy's performance. Orderblocks can be used both as a primary source of signals and as confirmation tool, but from our experience they are better to be used as confirmation tool. Our indicator is not an exception in this matter and we advice any trader to use it mainly for confirmation purposes, because use-case of orderblocks as confirmation tools have much success stories on average than being used as primary signal source.
This being said, let's return to the application guide and start reviewing the indicator from the most basic step — how it will look like when you first load it on your chart:
This indicator consisis of 3 main logic blocks:
Orderblock evaluation;
MTF Orderblock evaluation;
Orderblock post-filtering.
The principles behind these logic blocks will be easy to understand for truly experiences traders, but we understand the need to explain them to a wider audience, so let's review each of these logic blocks below.
ORDERBLOCK EVALUATION
Principles behind our orderblock detection logic are as follows:
Find FVG (Fair Value Gap) .
Note: this indicator uses only three-candle FVGs and doesn't track FVGs with insidebars after third (farther) candle.
If you don't know what FVG means, we recommend researching this term in the Internet, but the basic explanation is this: FVG is the formation of candles, which are positioned in a way that there are an unclosed price area between 1st and 3rd candle.
Conditions:
bullish FVG = high of 3rd candle < low of 1st candle AND high of 3rd candle < close of 2nd candle AND high of 2nd candle < close of 1st candle AND low of 3rd candle < low of 2nd candle ;
bearish FVG = low of 3rd candle < high of 1st candle AND low of 3rd candle > close of 2nd candle AND low of 2nd candle > close of 1st candle AND high of 3rd candle > high of 2nd candle .
See visual showcase of valid & invalid bullish & bearish FVGs on the screenshot below:
As was shown on the screenshot above, the only correc t formation for FVGs are considered to be just like on pictures 1 and 2 (leftmost column of patterns) . Only these formations will take part in further determenings orderblocks.
Send FVGs through filtering conditions.
This is the truly important part. Without properly filtering FVGs we would get huge clusters of FVGs on the chart and they will not make sense to be reviewed, because there will be just too much of them and their quality will be very questionable .
Even though there is a quite number of ways to filter FVGs, we decided to go with the ones we deem actually useful. For this indicator we chose two methods, that work in tandem — 1) base candle's inside bar condition and 2) single appearance on current impulse/correction line. Let's review these conditions below and start with looking at the examples of them on the screenshot below:
Examples of 1st & 2nd conditions are displayed on the left and right charts respectively.
The filtering logic in 1st and 2nd is quite connected and further explanation should help you understand it just enough to start trading with our indicator.
Let's start with explaining the term 'base candle' and logic behind it. Base candle candle be explained quite shortly: it is the latest candle on the chart, whose high or low broke previous base candle's high or low respectively. The first candle in the time series of price data is by default considered the base candle. If any new candle after base candle doesn't overtake base candle's high or low (meaning, that this candle is inside the range of base candle), such candle is called an "inside bar" .
Inside bar's term is important to understand, because FVGs, which appear inside the inside bars are usually quite useless, because price doesn't react from them, so orderblocks with such FVGs are also of bad quality as well. Clear depiction of inside bar was provided in the screenshot of conditions above on the left chart, so we won't waste time making another example.
However, this is not it. Base candle, inside bars and a few other types of bars are all a part of SMC ideas and in the world of SMC there is a special term, that hold the most important place and is considered the cornerstone of SMC methodology — impulse/correction lines (valid pullbacks) . The average definition of impulse/correction lines is quite hard to understand for an average trader, but we can summarise like this:
Impulse/correction line is a line, that starts at the beginning of the sequence of base candles, each new candle of which consistently updates previous base candle's respective high/low.
We won't go into description of this principle because it is outside of scope of this indicator, but you can research this topic in the Internet by keywords ' impulse correction trading ' or 'valid pullback principles trading '. The general idea of usage of impulse/correction lines in the context of this indicator is that each such lines 'holds' inside at least one FVG and we need to find exactly the first FVG, while leaving all other FVGs behind, because they to be of worse quality on average.
Basically, by using translating these terms into conditions from example above, we have achieved a simple yet powerful filtering system. system for FVGs, which allows us to work with orderblocks of much higher quality than average open-source indicators.
If FVG passed filters, evaluate its OB.
When FVG is confirmed, we can start the evaluation of its orderblock. The evaluation of orderblocks consists of several checkpoints: 1) is orderblock beyond current ChoCh* AND/OR 2) is orderblock from extreme price levels, calculated by our proprietary risk system. Let's review these checkpoints below.
* ChoCh (Change of Character, fundamental SMC idea) — price level, which if broken by close of price can potentially cause a revesal of the trend to direction opposite to the the previous one. To learn more about ChoCh please research the term on the Internet, because this indicator uses its standard definition and explaining of this term goes beyond the scope of this indicator.
To determine if orderblock is beyond current ChoCh levels, we need to first determine where these levels are on the chart. ChoCh levels of this indicator are calculated with a very lite approach, which is based on pivot points.
You can see basic demonstration of ChoCh levels in action on the screenshot below:
IMPORTANT NOTE: pivot period for pivots points inside our indicator is by default equal to 5 and cannot be changed in settings at the moment of publication.
On the screenshot above you can clearly see that ChoCh levels are essentially highest/lowest pivot point levels in between certain range of bars, where price doesn't update its extremum. You can see on there screenshot a new type of line — BoS (Break of Structure). BoS is almost the same thing as ChoCh, but with one change: it is a confirmation of price updating its extremum in the same direction as it was before, while ChoCh updates price extremum in the direction opposite to which it was before .
Why do these levels matter when evaluating the orderblocks? Orderblocks, which are located beyond current BoS/ChoCh levels, are of much higher quality on average than average orderblocks and they are called Strong Orderblocks .
On the chart such orderblocks are marked with 'Strong OB' label inside the body of an orderblock.
You can see the examples of Strong OBs on the screenshot below:
That was the explanation of the 1st orderblock evaluation criteria. Now let's talk about the 2nd one.
Our 2nd evaluation criteria for orderblocks is a test on whether or price is behind specific price level, which is calculated by our proprietary risk system, which is based on fundamental of statistics, such as 'standard deviation' and etc.
This criteria allows us to catch orderblocks, which are located at quite extreme price levels, and mark them on trader's chart explicitly. Orderblocks, which are above our custom price levels, are called Extreme Orderblocks an are marked with 'Extreme OB' label inside orderblock's body.
You can see the example of Extreme OB on the screenshot below:
That was the explanation of the 2nd evaluation criteria of the orderblock.
If an orderblock doesn't pass any of these two criterias, it is considered a classic orderblock. These orderblock are most common ones and have the lowest success rate among other types of orderblocks, listed above. Such orderblocks are marked with 'OB' label inside the orderblock's body.
You can see the examples of classic OB on the screenshot below:
This is it for orderblock evaluation logic. After doing all these steps, all orderblocks that we found are collected and displayed on the chart with their bodies and label marks.
What happens after the detection of the orderblocks?
All active orderblocks are being tracked in real time and their statuses are being updated as well (Strong orderblock can become Extreme orderblock and vice versa) . By an active orderblock we mean an orderblock, which wasn't swept by price's high or low. Bodies of active orderblocks are prolonged to the next candle on each new candle.
If an orderblock was swept, indicator will stop prolonging this orderblock and will mark it as swept on the chart with almost hollow body and dashed border line of the orderblock's body. Also swept orderblocks lose their name label, so you won't see any text in the orderblock after it was swept, but you will see its colour.
You can see the example of an active & swept orderblocks on the screenshot below:
This functionality helps distinguish active orderblocks from swept ones (inactive) and make more informed decisions.
MTF OB EVALUATION
Principles of MTF OBs evaluation are exactly the same as they are for current timeframe's OBs.
MTF OBs are displayed on the chart in same way as other OBs, but with one little change: to the right side of MTF OB's status will be postfix of the timeframe, from which this OB came from. Timeframe for MTF OBs can be chosen by user in the settings of the indicator.
MTF OBs also preserve their statuses (Strong, Extreme and Classic) when displayed on the current timeframe, so you won't stack of mistakenly marked MTF OBs as Extreme just because they are far away from the price.
You can see the example of MTF OBs on the screenshot below:
Also MTF OBs when swept lose only their name label, but the timeframe postfix will still be there, so you could distinguish MTF OBs from OBs of the current timeframe.
See the example of swept MTF OBs below:
Overall MTF orderblocks is a very useful to get a sense of where the higher timeframe liquidity reside and then adjust your strategy accordingly. Taking your trades from the place of high liquidity, like orderblocks, doesn't guarantee certain solid price reaction, but it definitely provides a trader with much a greater change of 1) catching a decent price move 2) not losing money white trading against institutional players.
As was stated above, we recommend using this tool as a confirmation system for your main trading strategy, because its usage as primary source of signals in the long-run is not viable, judging from historical backtest results and general public opinions of traders.
ORDERBLOCK POST-FILTERING
To enhance filtering capabilities of this indicator even further, we decided to add two filters, which would help reduce the amount of bad and untradeable orderblocks. These two filters are 1) age filter and 2) cancellation filter. Let's review both of them below.
Talking about the age filter , this filter was designed to help get rid of old orderblocks, which clutter the chart with visual noise and make it harder to find valueable orderblocks. This filter has to parameters: min age and max age . What does age mean in the context of an orderblock? It is the distance between OB's left border's bar and current bar. If this distance is between min age and max age values, such orderblock is considered valid and age filter passes it for further evaluation, but this distance is too short or too long, age filter deletes this orderblock from the chart.
You can the example of an orderblock which didn't pass age filter requirements and was deleted from the chart on the screenshot below:
It is important to mention that the missing orderblock from the right chart will be appear on the chart right when its age will exceed min age parameter of age filter.
The principle of work for max age parameter can be deducted by analogy: if the orderblock's age in bigger than max age value of age filter, this orderblock will be deleted from the chart .
For MTF OBs we decided to their own age filter, so that it won't abide by current timeframe's restrictions, because MTF OBs are usually much older than OB from current timeframe, so they would deleted a lot of time before they even appear on the chart, if they would abide by the age filter of current timeframe.
Default parameters of age filter are "max age = 500" and "min age = 0" . "Min age = 0" means that there is restrictions on the minimum age of orderblocks and they will appear on the chart as soon as the indicator validates them.
That was the explanation of the age filter.
Talking about the cancellation filter , this filter was intended to spot orderblocks which were extremely untradable and visually alert traders about them on the chart. In this indicator this filter works like this: for each orderblock cancellation filter creates a special price level and checks if it was broken by the close of price.
This special price level consists of the farthest border. of the orderblock ( top border for bearish OBs and bottom border for bullish OBs) and a certain threshold, which is added to the farthest border. This threshold is based on the current ATR value of the asset. This filter helps detect the orderblocks which should not be considered for trading, because price has already went too far beyond the liquidity of this orderblock.
Orderblocks, which are spotted by this filter, are marked with '❌' emoji on the price history.
You can see the example of an orderblock which was spotted by the cancellation filter in the screenshot below:
This filter is applied to both current timeframe and MTF timeframe and is NOT configurable in the settings.
🔔 ALERTS
This indicator employs alerts for an event when new signal occurs on the current timeframe or on MTF timeframe. While creating the alert below 'Condition' field choose 'any alert() function call'.
When this alert is triggered, it will generate this kind of message:
// Alerts for current timeframe
string msg_template = "EXCHANGE:ASSET, TIMEFRAME: BULLISH_OR_BEARISH OB at SWEPT_OB_BORDER_PRICE was reached."
string msg_example = "BINANCE:BTCUSDT, 15m: bearish OB at 170000.00 was reached."
// Alerts for MTF timeframe
string msg_template_mtf = "EXCHANGE:ASSET, TIMEFRAME: BULLISH_OR_BEARISH MTF OB at SWEPT_OB_BORDER_PRICE was reached."
string msg_example_mtf = "BINANCE:BTCUSDT, 15m: bearish MTF OB at 170000.00 was reached."
📌 NOTES
These OBs work on any timeframe, but we would advise to to use on higher timeframes, starting from at least 15m, because liquidity from higher timeframe tends to be much valuable when deciding which orderblock to take for a trade;
Use these OBs as a confirmation tool for your main strategy and refrain from using them as primary signal source. Traders, which use SMC-based strategies, will benefit from these orderblocks the most;
We recommend trading only with Strong and Extreme orderblocks, because they are proved to be of much greater quality than classic orderblocks and they work quite well in mid-term and long-term trading strategies. Classic orderblocs can be used for short-term trading strategies, but even in this case these OBs cannot be blindly trusted;
We strongly advise against take for a trading orderblocks, which were spotted by cancellation filter, because they are considered to be voided of liquidity;
Don't forget that you can toggle different types of OBs, MTF settings and visual settings in the settings of the indicator and fine-tune them to your liking.
🏁 AFTERWORD
AQPRO Block Force is an indicator which designed with idea of helping trading save time on automatically detecting valuable orderblocks on the chart, evaluate their strength and filter out bad orderblocks. These employ the best principles of SMC, including FVGs, valid pullbacks and etc. FVGs play the key role in validating the existence of a particular orderblock and work in tandem with valid pullback to determine the maximum amount of true FVGs even in the most cluttered impulse/correction moves of the price. Our filters — Age Filter and Cancellation Filter — enhance the quality of the orderblocks by allowing only the newest and liquid orderblocks to appear on the chart. Additional MTF functionality allow trader to see orderblocks from other timeframe, which can be chosen in the settings, and get a sense of where the global liquidity resides. This indicator will be a useful confirmation tool to any trading strategy, but the SMC traders will surely get the most benefits out of it.
ℹ️ If you have questions about this or any other our indicator, please leave it in the comments.
ATGRIDBOT OPTIMIZER Hello Trader..
There are some option in the script, let see one by one:
Market Direction: To specify the direction of the position: Long only, Short only, and Neutral
Contrat (Lot) Number: It specifies the size of the position to be opened when the trend reverses (position changes).
Test by backward bar count: If selected, it indicates how many backward bars will be taken into account in the back test process. If not selected, Start Date is taken into account.
Show Table: shows the results on the screen.
In this way, you will find the best parameter values according to the number of bars or date you entered.
Here the top 5 parameters are show along with total profit, profitable position, loss position and parameter values.
Remember that as the number of bars and dates in backtests increases, the calculation time will increase.
DISCLAIMER: No sharing, copying, reselling, modifying, or any other forms of use are authorized for our documents, script / strategy, and the information published with them. This informational planning script / strategy is strictly for individual use and educational purposes only. This is not financial or investment advice. Investments are always made at your own risk and are based on your personal judgement. I am not responsible for any losses you may incur. Please invest wisely.
P1 & P2 Helper by Brighter DataThis script draws the current high & low on the chart for multiple timeframes in P1/P2 format: P1 is either the highest or lowest point of the timeframe, whichever came first. P2 is whichever came second.
For example, on the daily timeframe if the daily low is marked out as P1 and the daily high is P2, it means that the daily low was put in before the daily high. This mapping of highs/lows is used as support for the BD dashboard and its statistics.
ATS DELTABAR V5.0This indicator is called DeltaBar, which reflects the actual momentum of buyers and sellers while filtering out market noise. It helps you identify the true starting and ending points of trends.
By analyzing the Delta Net Volume chart, you can anticipate shifts in market sentiment and gain a strategic edge. For instance:
Resonance Breakouts – When price and DeltaBar break key levels simultaneously
Divergences – Visible and hidden bullish/bearish divergences signaling potential reversals
DeltaBar provides scientific, high-precision trading signals, turning raw data into actionable intelligence for smarter decisions.
这个指标叫deltabar，反映了买卖双方的实际力量的走势图，过滤了市场噪音，帮助您识别真正的趋势起点与终点。
通过观察Delta净量走势图，您可以提前感知市场情绪的变化，抢占先机，比如价格和deltabar形成共振突破，形成了顶底背离和隐性的顶底背离。
Delta净量走势图都能为您提供更科学、更精准的交易策略。
ATS Net Volume V5.0This is a fully quantitative auxiliary chart indicator. Values above the zero line represent net inflow status and the magnitude of net inflow, while values below the zero line indicate net outflow status and the magnitude of net outflow. Changes in net volume often signal trend reversals and emerging opportunities. This fully quantitative indicator serves as a powerful tool to help you identify these critical signals. By precisely visualizing the dynamic changes in net volume, it provides clear insight into the battle between bullish and bearish forces.
这是基于全量化的副图指标，零轴以上代表净流入状态和净流入的数值，零轴以下代表净流出状态和净流出的数值。净量的变化往往预示着趋势的转折与机遇的来临。这个全量化的指标正是帮助您捕捉这些关键信号的有力工具。通过精准呈现净量的动态变化，它让您清晰看到多空力量的博弈
Capital Flow StrengthCapital Flow Strength Indicator Guide
This is a comprehensive technical indicator that measures capital flow into or out of an asset, combined with volume analysis. Here's how to use it effectively:
Basic Understanding
The indicator shows capital flow strength on a scale from -100 to +100
Positive values (green) indicate money flowing into the asset
Negative values (red) indicate money flowing out
The blue/gray volume bars show relative volume compared to recent average
Key Components
Capital Flow Line
Green line above zero: Buying pressure dominates
Red line below zero: Selling pressure dominates
Crossing zero: Potential shift in market sentiment
Reference Lines
0 line: Neutral balance between buyers and sellers
+50 line: Strong buying pressure
-50 line: Strong selling pressure
Volume Strength Bars
Blue bars: Volume exceeding threshold (currently 1.5x average)
Gray bars: Normal volume levels
Taller bars: Higher relative volume
Trading Applications
Entry Signals
Strong buying setup: Capital flow above +50 with blue volume bars
Strong selling setup: Capital flow below -50 with blue volume bars
Confirmation Tool
Use with price action and other indicators for confirmation
Strong readings are more reliable when volume is higher than average
Divergence Analysis
Bullish divergence: Price making lower lows but capital flow making higher lows
Bearish divergence: Price making higher highs but capital flow making lower highs
Customization Options
Length (14): Adjust the calculation period
Volume Threshold (1.5): Modify sensitivity to volume spikes
Alert Conditions
The indicator has two built-in alerts:
"Strong Capital Inflow" - triggers when flow > 50 with high volume
"Strong Capital Outflow" - triggers when flow < -50 with high volume
These alerts can help you identify significant buying or selling pressure as it emerges.
Correlation Drift📈 Correlation Drift
The Correlation Drift indicator is designed to detect shifts in market momentum by analyzing the relationship between correlation and price lag. It combines the principles of correlation analysis and lag factor measurement to provide a unique perspective on trend alignment and momentum shifts.
🔍 Core Concept:
The indicator calculates the Correlation vs PLF Ratio, which measures the alignment between an asset’s price movement and a chosen benchmark (e.g., BTCUSD). This ratio reflects how well the asset’s momentum matches the market trend while accounting for price lag.
📊 How It Works:
Correlation Calculation:
The script calculates the correlation between the asset and the selected benchmark over a specified period.
A higher correlation indicates that the asset’s price movements are in sync with the benchmark.
Price Lag Factor (PLF) Calculation:
The PLF measures the difference between long-term and short-term price momentum, dynamically scaled by recent volatility.
It highlights potential overextensions or lags in the asset’s price movements.
Combining Correlation and PLF:
The Correlation vs PLF Ratio combines these metrics to detect momentum shifts relative to the trend.
The result is a dynamic, smoothed histogram that visualizes whether the asset is leading or lagging behind the trend.
💡 How to Interpret:
Positive Values (Green/Aqua Bars):
Indicates bullish alignment with the trend.
Aqua: Rising bullish momentum, suggesting continuation.
Teal: Decreasing bullish momentum, signaling caution.
Negative Values (Purple/Fuchsia Bars):
Indicates bearish divergence from the trend.
Fuchsia: Falling bearish momentum, indicating increasing pressure.
Purple: Rising bearish momentum, suggesting potential reversal.
Clipping for Readability:
Values are clipped between -3 and +3 to prevent outliers from compressing the histogram.
This ensures clear visualization of typical momentum shifts while still marking extreme cases.
🚀 Best Practices:
Use Correlation Drift as a confirmation tool in conjunction with trend indicators (e.g., moving averages) to identify momentum alignment or divergence.
Look for transitions from positive to negative (or vice versa) as signals of potential trend shifts.
Combine with volume analysis to strengthen confidence in breakout or breakdown signals.
⚠️ Key Features:
Customizable Settings: Adjust the correlation length, PLF length, and smoothing factor to fine-tune the indicator for different market conditions.
Visual Gradient: The histogram changes color based on the strength and direction of the ratio, making it easy to identify shifts at a glance.
Zero Line Reference: Clearly distinguishes between bullish and bearish momentum zones.
🔧 Recommended Settings:
Correlation Length: 14 (for short to medium-term analysis)
PLF Length: 50 (to smooth out noise while capturing trend shifts)
Smoothing Factor: 3 (for enhanced clarity without excessive lag)
Benchmark Symbol: BTCUSD (or another relevant market indicator)
By providing a quantitative measure of trend alignment while accounting for price lag, the Correlation Drift indicator helps traders make more informed decisions during periods of momentum change. Whether you are trading crypto, forex, or equities, this tool can be a powerful addition to your momentum-based trading strategies.
⚠️ Disclaimer:
The Correlation Drift indicator is a technical analysis tool designed to aid in identifying potential shifts in market momentum and trend alignment. It is intended for informational and educational purposes only and should not be considered as financial advice or a recommendation to buy, sell, or hold any financial instrument.
Trading financial instruments, including cryptocurrencies, involves significant risk and may result in the loss of your capital. Past performance is not indicative of future results. Always conduct thorough research and seek advice from a certified financial professional before making any trading decisions.
The developer (RWCS_LTD) is not responsible for any trading losses or adverse outcomes resulting from the use of this indicator. Users are encouraged to test and validate the indicator in a simulated environment before applying it to live trading. Use at your own risk.
