Scientific Correlation Testing FrameworkScientific Correlation Testing Framework - Comprehensive Guide
Introduction to Correlation Analysis
What is Correlation?
Correlation is a statistical measure that describes the degree to which two assets move in relation to each other. Think of it like measuring how closely two dancers move together on a dance floor.
Perfect Positive Correlation (+1.0): Both dancers move in perfect sync, same direction, same speed
Perfect Negative Correlation (-1.0): Both dancers move in perfect sync but in opposite directions
Zero Correlation (0): The dancers move completely independently of each other
In financial markets, correlation helps us understand relationships between different assets, which is crucial for:
Portfolio diversification
Risk management
Pairs trading strategies
Hedging positions
Market analysis
Why This Script is Special
This script goes beyond simple correlation calculations by providing:
Two different correlation methods (Pearson and Spearman)
Statistical significance testing to ensure results are meaningful
Rolling correlation analysis to track how relationships change over time
Visual representation for easy interpretation
Comprehensive statistics table with detailed metrics
Deep Dive into the Script's Components
1. Input Parameters Explained-
Symbol Selection:
This allows you to select the second asset to compare with the chart's primary asset
Default is Apple (NASDAQ:AAPL), but you can change this to any symbol
Example: If you're viewing a Bitcoin chart, you might set this to "NASDAQ:TSLA" to see if Bitcoin and Tesla are correlated
Correlation Window (60): This is the number of periods used to calculate the main correlation
Larger values (e.g., 100-500) provide more stable, long-term correlation measures
Smaller values (e.g., 10-50) are more responsive to recent price movements
60 is a good balance for most daily charts (about 3 months of trading days)
Rolling Correlation Window (20): A shorter window to detect recent changes in correlation
This helps identify when the relationship between assets is strengthening or weakening
Default of 20 is roughly one month of trading days
Return Type: This determines how price changes are calculated
Simple Returns: (Today's Price - Yesterday's Price) / Yesterday's Price
Easy to understand: "The asset went up 2% today"
Log Returns: Natural logarithm of (Today's Price / Yesterday's Price)
More mathematically elegant for statistical analysis
Better for time-additive properties (returns over multiple periods)
Less sensitive to extreme values.
Confidence Level (95%): This determines how certain we want to be about our results
95% confidence means we accept a 5% chance of being wrong (false positive)
Higher confidence (e.g., 99%) makes the test more strict
Lower confidence (e.g., 90%) makes the test more lenient
95% is the standard in most scientific research
Show Statistical Significance: When enabled, the script will test if the correlation is statistically significant or just due to random chance.
Display options control what you see on the chart:
Show Pearson/Spearman/Rolling Correlation: Toggle each correlation type on/off
Show Scatter Plot: Displays a scatter plot of returns (limited to recent points to avoid performance issues)
Show Statistical Tests: Enables the detailed statistics table
Table Text Size: Adjusts the size of text in the statistics table
2.Functions explained-
calcReturns():
This function calculates price returns based on your selected method:
Log Returns:
Formula: ln(Price_t / Price_t-1)
Example: If a stock goes from $100 to $101, the log return is ln(101/100) = ln(1.01) ≈ 0.00995 or 0.995%
Benefits: More symmetric, time-additive, and better for statistical modeling
Simple Returns:
Formula: (Price_t - Price_t-1) / Price_t-1
Example: If a stock goes from $100 to $101, the simple return is (101-100)/100 = 0.01 or 1%
Benefits: More intuitive and easier to understand
rankArray():
This function calculates the rank of each value in an array, which is used for Spearman correlation:
How ranking works:
The smallest value gets rank 1
The second smallest gets rank 2, and so on
For ties (equal values), they get the average of their ranks
Example: For values
Sorted:
Ranks: (the two 2s tie for ranks 1 and 2, so they both get 1.5)
Why this matters: Spearman correlation uses ranks instead of actual values, making it less sensitive to outliers and non-linear relationships.
pearsonCorr():
This function calculates the Pearson correlation coefficient:
Mathematical Formula:
r = (nΣxy - ΣxΣy) / √
Where x and y are the two variables, and n is the sample size
What it measures:
The strength and direction of the linear relationship between two variables
Values range from -1 (perfect negative linear relationship) to +1 (perfect positive linear relationship)
0 indicates no linear relationship
Example:
If two stocks have a Pearson correlation of 0.8, they have a strong positive linear relationship
When one stock goes up, the other tends to go up in a fairly consistent proportion
spearmanCorr():
This function calculates the Spearman rank correlation:
How it works:
Convert each value in both datasets to its rank
Calculate the Pearson correlation on the ranks instead of the original values
What it measures:
The strength and direction of the monotonic relationship between two variables
A monotonic relationship is one where as one variable increases, the other either consistently increases or decreases
It doesn't require the relationship to be linear
When to use it instead of Pearson:
When the relationship is monotonic but not linear
When there are significant outliers in the data
When the data is ordinal (ranked) rather than interval/ratio
Example:
If two stocks have a Spearman correlation of 0.7, they have a strong positive monotonic relationship
When one stock goes up, the other tends to go up, but not necessarily in a straight-line relationship
tStatistic():
This function calculates the t-statistic for correlation:
Mathematical Formula: t = r × √((n-2)/(1-r²))
Where r is the correlation coefficient and n is the sample size
What it measures:
How many standard errors the correlation is away from zero
Used to test the null hypothesis that the true correlation is zero
Interpretation:
Larger absolute t-values indicate stronger evidence against the null hypothesis
Generally, a t-value greater than 2 (in absolute terms) is considered statistically significant at the 95% confidence level
criticalT() and pValue():
These functions provide approximations for statistical significance testing:
criticalT():
Returns the critical t-value for a given degrees of freedom (df) and significance level
The critical value is the threshold that the t-statistic must exceed to be considered statistically significant
Uses approximations since Pine Script doesn't have built-in statistical distribution functions
pValue():
Estimates the p-value for a given t-statistic and degrees of freedom
The p-value is the probability of observing a correlation as strong as the one calculated, assuming the true correlation is zero
Smaller p-values indicate stronger evidence against the null hypothesis
Standard interpretation:
p < 0.01: Very strong evidence (marked with **)
p < 0.05: Strong evidence (marked with *)
p ≥ 0.05: Weak evidence, not statistically significant
stdev():
This function calculates the standard deviation of a dataset:
Mathematical Formula: σ = √(Σ(x-μ)²/(n-1))
Where x is each value, μ is the mean, and n is the sample size
What it measures:
The amount of variation or dispersion in a set of values
A low standard deviation indicates that the values tend to be close to the mean
A high standard deviation indicates that the values are spread out over a wider range
Why it matters for correlation:
Standard deviation is used in calculating the correlation coefficient
It also provides information about the volatility of each asset's returns
Comparing standard deviations helps understand the relative riskiness of the two assets.
3.Getting Price Data-
price1: The closing price of the primary asset (the chart you're viewing)
price2: The closing price of the secondary asset (the one you selected in the input parameters)
Returns are used instead of raw prices because:
Returns are typically stationary (mean and variance stay constant over time)
Returns normalize for price levels, allowing comparison between assets of different values
Returns represent what investors actually care about: percentage changes in value
4.Information Table-
Creates a table to display statistics
Only shows on the last bar to avoid performance issues
Positioned in the top right of the chart
Has 2 columns and 15 rows
Populating the Table
The script then populates the table with various statistics:
Header Row: "Metric" and "Value"
Sample Information: Sample size and return type
Pearson Correlation: Value, t-statistic, p-value, and significance
Spearman Correlation: Value, t-statistic, p-value, and significance
Rolling Correlation: Current value
Standard Deviations: For both assets
Interpretation: Text description of the correlation strength
The table uses color coding to highlight important information:
Green for significant positive results
Red for significant negative results
Yellow for borderline significance
Color-coded headers for each section
=> Practical Applications and Interpretation
How to Interpret the Results
Correlation Strength
0.0 to 0.3 (or 0.0 to -0.3): Weak or no correlation
The assets move mostly independently of each other
Good for diversification purposes
0.3 to 0.7 (or -0.3 to -0.7): Moderate correlation
The assets show some tendency to move together (or in opposite directions)
May be useful for certain trading strategies but not extremely reliable
0.7 to 1.0 (or -0.7 to -1.0): Strong correlation
The assets show a strong tendency to move together (or in opposite directions)
Can be useful for pairs trading, hedging, or as a market indicator
Statistical Significance
p < 0.01: Very strong evidence that the correlation is real
Marked with ** in the table
Very unlikely to be due to random chance
p < 0.05: Strong evidence that the correlation is real
Marked with * in the table
Unlikely to be due to random chance
p ≥ 0.05: Weak evidence that the correlation is real
Not marked in the table
Could easily be due to random chance
Rolling Correlation
The rolling correlation shows how the relationship between assets changes over time
If the rolling correlation is much different from the long-term correlation, it suggests the relationship is changing
This can indicate:
A shift in market regime
Changing fundamentals of one or both assets
Temporary market dislocations that might present trading opportunities
Trading Applications
1. Portfolio Diversification
Goal: Reduce overall portfolio risk by combining assets that don't move together
Strategy: Look for assets with low or negative correlations
Example: If you hold tech stocks, you might add some utilities or bonds that have low correlation with tech
2. Pairs Trading
Goal: Profit from the relative price movements of two correlated assets
Strategy:
Find two assets with strong historical correlation
When their prices diverge (one goes up while the other goes down)
Buy the underperforming asset and short the outperforming asset
Close the positions when they converge back to their normal relationship
Example: If Coca-Cola and Pepsi are highly correlated but Coca-Cola drops while Pepsi rises, you might buy Coca-Cola and short Pepsi
3. Hedging
Goal: Reduce risk by taking an offsetting position in a negatively correlated asset
Strategy: Find assets that tend to move in opposite directions
Example: If you hold a portfolio of stocks, you might buy some gold or government bonds that tend to rise when stocks fall
4. Market Analysis
Goal: Understand market dynamics and interrelationships
Strategy: Analyze correlations between different sectors or asset classes
Example:
If tech stocks and semiconductor stocks are highly correlated, movements in one might predict movements in the other
If the correlation between stocks and bonds changes, it might signal a shift in market expectations
5. Risk Management
Goal: Understand and manage portfolio risk
Strategy: Monitor correlations to identify when diversification benefits might be breaking down
Example: During market crises, many assets that normally have low correlations can become highly correlated (correlation convergence), reducing diversification benefits
Advanced Interpretation and Caveats
Correlation vs. Causation
Important Note: Correlation does not imply causation
Example: Ice cream sales and drowning incidents are correlated (both increase in summer), but one doesn't cause the other
Implication: Just because two assets move together doesn't mean one causes the other to move
Solution: Look for fundamental economic reasons why assets might be correlated
Non-Stationary Correlations
Problem: Correlations between assets can change over time
Causes:
Changing market conditions
Shifts in monetary policy
Structural changes in the economy
Changes in the underlying businesses
Solution: Use rolling correlations to monitor how relationships change over time
Outliers and Extreme Events
Problem: Extreme market events can distort correlation measurements
Example: During a market crash, many assets may move in the same direction regardless of their normal relationship
Solution:
Use Spearman correlation, which is less sensitive to outliers
Be cautious when interpreting correlations during extreme market conditions
Sample Size Considerations
Problem: Small sample sizes can produce unreliable correlation estimates
Rule of Thumb: Use at least 30 data points for a rough estimate, 60+ for more reliable results
Solution:
Use the default correlation length of 60 or higher
Be skeptical of correlations calculated with small samples
Timeframe Considerations
Problem: Correlations can vary across different timeframes
Example: Two assets might be positively correlated on a daily basis but negatively correlated on a weekly basis
Solution:
Test correlations on multiple timeframes
Use the timeframe that matches your trading horizon
Look-Ahead Bias
Problem: Using information that wouldn't have been available at the time of trading
Example: Calculating correlation using future data
Solution: This script avoids look-ahead bias by using only historical data
Best Practices for Using This Script
1. Appropriate Parameter Selection
Correlation Window:
For short-term trading: 20-50 periods
For medium-term analysis: 50-100 periods
For long-term analysis: 100-500 periods
Rolling Window:
Should be shorter than the main correlation window
Typically 1/3 to 1/2 of the main window
Return Type:
For most applications: Log Returns (better statistical properties)
For simplicity: Simple Returns (easier to interpret)
2. Validation and Testing
Out-of-Sample Testing:
Calculate correlations on one time period
Test if they hold in a different time period
Multiple Timeframes:
Check if correlations are consistent across different timeframes
Economic Rationale:
Ensure there's a logical reason why assets should be correlated
3. Monitoring and Maintenance
Regular Review:
Correlations can change, so review them regularly
Alerts:
Set up alerts for significant correlation changes
Documentation:
Keep notes on why certain assets are correlated and what might change that relationship
4. Integration with Other Analysis
Fundamental Analysis:
Combine correlation analysis with fundamental factors
Technical Analysis:
Use correlation analysis alongside technical indicators
Market Context:
Consider how market conditions might affect correlations
Conclusion
This Scientific Correlation Testing Framework provides a comprehensive tool for analyzing relationships between financial assets. By offering both Pearson and Spearman correlation methods, statistical significance testing, and rolling correlation analysis, it goes beyond simple correlation measures to provide deeper insights.
For beginners, this script might seem complex, but it's built on fundamental statistical concepts that become clearer with use. Start with the default settings and focus on interpreting the main correlation lines and the statistics table. As you become more comfortable, you can adjust the parameters and explore more advanced applications.
Remember that correlation analysis is just one tool in a trader's toolkit. It should be used in conjunction with other forms of analysis and with a clear understanding of its limitations. When used properly, it can provide valuable insights for portfolio construction, risk management, and pair trading strategy development.
在腳本中搜尋"one一季度财报"
Quantum Flux Universal Strategy Summary in one paragraph
Quantum Flux Universal is a regime switching strategy for stocks, ETFs, index futures, major FX pairs, and liquid crypto on intraday and swing timeframes. It helps you act only when the normalized core signal and its guide agree on direction. It is original because the engine fuses three adaptive drivers into the smoothing gains itself. Directional intensity is measured with binary entropy, path efficiency shapes trend quality, and a volatility squash preserves contrast. Add it to a clean chart, watch the polarity lane and background, and trade from positive or negative alignment. For conservative workflows use on bar close in the alert settings when you add alerts in a later version.
Scope and intent
• Markets. Large cap equities and ETFs. Index futures. Major FX pairs. Liquid crypto
• Timeframes. One minute to daily
• Default demo used in the publication. QQQ on one hour
• Purpose. Provide a robust and portable way to detect when momentum and confirmation align, while dampening chop and preserving turns
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique concept or fusion. The novelty sits in the gain map. Instead of gating separate indicators, the model mixes three drivers into the adaptive gains that power two one pole filters. Directional entropy measures how one sided recent movement has been. Kaufman style path efficiency scores how direct the path has been. A volatility squash stabilizes step size. The drivers are blended into the gains with visible inputs for strength, windows, and clamps.
• What failure mode it addresses. False starts in chop and whipsaw after fast spikes. Efficiency and the squash reduce over reaction in noise.
• Testability. Every component has an input. You can lengthen or shorten each window and change the normalization mode. The polarity plot and background provide a direct readout of state.
• Portable yardstick. The core is normalized with three options. Z score, percent rank mapped to a symmetric range, and MAD based Z score. Clamp bounds define the effective unit so context transfers across symbols.
Method overview in plain language
The strategy computes two smoothed tracks from the chart price source. The fast track and the slow track use gains that are not fixed. Each gain is modulated by three drivers. A driver for directional intensity, a driver for path efficiency, and a driver for volatility. The difference between the fast and the slow tracks forms the raw flux. A small phase assist reduces lag by subtracting a portion of the delayed value. The flux is then normalized. A guide line is an EMA of a small lead on the flux. When the flux and its guide are both above zero, the polarity is positive. When both are below zero, the polarity is negative. Polarity changes create the trade direction.
Base measures
• Return basis. The step is the change in the chosen price source. Its absolute value feeds the volatility estimate. Mean absolute step over the window gives a stable scale.
• Efficiency basis. The ratio of net move to the sum of absolute step over the window gives a value between zero and one. High values mean trend quality. Low values mean chop.
• Intensity basis. The fraction of up moves over the window plugs into binary entropy. Intensity is one minus entropy, which maps to zero in uncertainty and one in very one sided moves.
Components
• Directional Intensity. Measures how one sided recent bars have been. Smoothed with RMA. More intensity increases the gain and makes the fast and slow tracks react sooner.
• Path Efficiency. Measures the straightness of the price path. A gamma input shapes the curve so you can make trend quality count more or less. Higher efficiency lifts the gain in clean trends.
• Volatility Squash. Normalizes the absolute step with Z score then pushes it through an arctangent squash. This caps the effect of spikes so they do not dominate the response.
• Normalizer. Three modes. Z score for familiar units, percent rank for a robust monotone map to a symmetric range, and MAD based Z for outlier resistance.
• Guide Line. EMA of the flux with a small lead term that counteracts lag without heavy overshoot.
Fusion rule
• Weighted sum of the three drivers with fixed weights visible in the code comments. Intensity has fifty percent weight. Efficiency thirty percent. Volatility twenty percent.
• The blend power input scales the driver mix. Zero means fixed spans. One means full driver control.
• Minimum and maximum gain clamps bound the adaptive gain. This protects stability in quiet or violent regimes.
Signal rule
• Long suggestion appears when flux and guide are both above zero. That sets polarity to plus one.
• Short suggestion appears when flux and guide are both below zero. That sets polarity to minus one.
• When polarity flips from plus to minus, the strategy closes any long and enters a short.
• When flux crosses above the guide, the strategy closes any short.
What you will see on the chart
• White polarity plot around the zero line
• A dotted reference line at zero named Zen
• Green background tint for positive polarity and red background tint for negative polarity
• Strategy long and short markers placed by the TradingView engine at entry and at close conditions
• No table in this version to keep the visual clean and portable
Inputs with guidance
Setup
• Price source. Default ohlc4. Stable for noisy symbols.
• Fast span. Typical range 6 to 24. Raising it slows the fast track and can reduce churn. Lowering it makes entries more reactive.
• Slow span. Typical range 20 to 60. Raising it lengthens the baseline horizon. Lowering it brings the slow track closer to price.
Logic
• Guide span. Typical range 4 to 12. A small guide smooths without eating turns.
• Blend power. Typical range 0.25 to 0.85. Raising it lets the drivers modulate gains more. Lowering it pushes behavior toward fixed EMA style smoothing.
• Vol window. Typical range 20 to 80. Larger values calm the volatility driver. Smaller values adapt faster in intraday work.
• Efficiency window. Typical range 10 to 60. Larger values focus on smoother trends. Smaller values react faster but accept more noise.
• Efficiency gamma. Typical range 0.8 to 2.0. Above one increases contrast between clean trends and chop. Below one flattens the curve.
• Min alpha multiplier. Typical range 0.30 to 0.80. Lower values increase smoothing when the mix is weak.
• Max alpha multiplier. Typical range 1.2 to 3.0. Higher values shorten smoothing when the mix is strong.
• Normalization window. Typical range 100 to 300. Larger values reduce drift in the baseline.
• Normalization mode. Z score, percent rank, or MAD Z. Use MAD Z for outlier heavy symbols.
• Clamp level. Typical range 2.0 to 4.0. Lower clamps reduce the influence of extreme runs.
Filters
• Efficiency filter is implicit in the gain map. Raising efficiency gamma and the efficiency window increases the preference for clean trends.
• Micro versus macro relation is handled by the fast and slow spans. Increase separation for swing, reduce for scalping.
• Location filter is not included in v1.0. If you need distance gates from a reference such as VWAP or a moving mean, add them before publication of a new version.
Alerts
• This version does not include alertcondition lines to keep the core minimal. If you prefer alerts, add names Long Polarity Up, Short Polarity Down, Exit Short on Flux Cross Up in a later version and select on bar close for conservative workflows.
Strategy has been currently adapted for the QQQ asset with 30/60min timeframe.
For other assets may require new optimization
Properties visible in this publication
• Initial capital 25000
• Base currency Default
• Default order size method percent of equity with value 5
• Pyramiding 1
• Commission 0.05 percent
• Slippage 10 ticks
• Process orders on close ON
• Bar magnifier ON
• Recalculate after order is filled OFF
• Calc on every tick OFF
Honest limitations and failure modes
• Past results do not guarantee future outcomes
• Economic releases, circuit breakers, and thin books can break the assumptions behind intensity and efficiency
• Gap heavy symbols may benefit from the MAD Z normalization
• Very quiet regimes can reduce signal contrast. Use longer windows or higher guide span to stabilize context
• Session time is the exchange time of the chart
• If both stop and target can be hit in one bar, tie handling would matter. This strategy has no fixed stops or targets. It uses polarity flips for exits. If you add stops later, declare the preference
Open source reuse and credits
• None beyond public domain building blocks and Pine built ins such as EMA, SMA, standard deviation, RMA, and percent rank
• Method and fusion are original in construction and disclosure
Legal
Education and research only. Not investment advice. You are responsible for your decisions. Test on historical data and in simulation before any live use. Use realistic costs.
Strategy add on block
Strategy notice
Orders are simulated by the TradingView engine on standard candles. No request.security() calls are used.
Entries and exits
• Entry logic. Enter long when both the normalized flux and its guide line are above zero. Enter short when both are below zero
• Exit logic. When polarity flips from plus to minus, close any long and open a short. When the flux crosses above the guide line, close any short
• Risk model. No initial stop or target in v1.0. The model is a regime flipper. You can add a stop or trail in later versions if needed
• Tie handling. Not applicable in this version because there are no fixed stops or targets
Position sizing
• Percent of equity in the Properties panel. Five percent is the default for examples. Risk per trade should not exceed five to ten percent of equity. One to two percent is a common choice
Properties used on the published chart
• Initial capital 25000
• Base currency Default
• Default order size percent of equity with value 5
• Pyramiding 1
• Commission 0.05 percent
• Slippage 10 ticks
• Process orders on close ON
• Bar magnifier ON
• Recalculate after order is filled OFF
• Calc on every tick OFF
Dataset and sample size
• Test window Jan 2, 2014 to Oct 16, 2025 on QQQ one hour
• Trade count in sample 324 on the example chart
Release notes template for future updates
Version 1.1.
• Add alertcondition lines for long, short, and exit short
• Add optional table with component readouts
• Add optional stop model with a distance unit expressed as ATR or a percent of price
Notes. Backward compatibility Yes. Inputs migrated Yes.
Luxy Momentum, Trend, Bias and Breakout Indicators V7
TABLE OF CONTENTS
This is Version 7 (V7) - the latest and most optimized release. If you are using any older versions (V6, V5, V4, V3, etc.), it is highly recommended to replace them with V7.
Why This Indicator is Different
Who Should Use This
Core Components Overview
The UT Bot Trading System
Understanding the Market Bias Table
Candlestick Pattern Recognition
Visual Tools and Features
How to Use the Indicator
Performance and Optimization
FAQ
---
### CREDITS & ATTRIBUTION
This indicator implements proven trading concepts using entirely original code developed specifically for this project.
### CONCEPTUAL FOUNDATIONS
• UT Bot ATR Trailing System
- Original concept by @QuantNomad: (search "UT-Bot-Strategy"
- Our version is a complete reimplementation with significant enhancements:
- Volume-weighted momentum adjustment
- Composite stop loss from multiple S/R layers
- Multi-filter confirmation system (swing, %, 2-bar, ZLSMA)
- Full integration with multi-timeframe bias table
- Visual audit trail with freeze-on-touch
- NOTE: No code was copied - this is a complete reimplementation with enhancements.
• Standard Technical Indicators (Public Domain Formulas):
- Supertrend: ATR-based trend calculation with custom gradient fills
- MACD: Gerald Appel's formula with separation filters
- RSI: J. Welles Wilder's formula with pullback zone logic
- ADX/DMI: Custom trend strength formula inspired by Wilder's directional movement concept, reimplemented with volume weighting and efficiency metrics
- ZLSMA: Zero-lag formula enhanced with Hull MA and momentum prediction
### Custom Implementations
- Trend Strength: Inspired by Wilder's ADX concept but using volume-weighted pressure calculation and efficiency metrics (not traditional +DI/-DI smoothing)
- All code implementations are original
### ORIGINAL FEATURES (70%+ of codebase)
- Multi-Timeframe Bias Table with live updates
- Risk Management System (R-multiple TPs, freeze-on-touch)
- Opening Range Breakout tracker with session management
- Composite Stop Loss calculator using 6+ S/R layers
- Performance optimization system (caching, conditional calcs)
- VIX Fear Index integration
- Previous Day High/Low auto-detection
- Candlestick pattern recognition with interactive tooltips
- Smart label and visual management
- All UI/UX design and table architecture
### DEVELOPMENT PROCESS
**AI Assistance:** This indicator was developed over 2+ months with AI assistance (ChatGPT/Claude) used for:
- Writing Pine Script code based on design specifications
- Optimizing performance and fixing bugs
- Ensuring Pine Script v6 compliance
- Generating documentation
**Author's Role:** All trading concepts, system design, feature selection, integration logic, and strategic decisions are original work by the author. The AI was a coding tool, not the system designer.
**Transparency:** We believe in full disclosure - this project demonstrates how AI can be used as a powerful development tool while maintaining creative and strategic ownership.
---
1. WHY THIS INDICATOR IS DIFFERENT
Most traders use multiple separate indicators on their charts, leading to cluttered screens, conflicting signals, and analysis paralysis. The Suite solves this by integrating proven technical tools into a single, cohesive system.
Key Advantages:
All-in-One Design: Instead of loading 5-10 separate indicators, you get everything in one optimized script. This reduces chart clutter and improves TradingView performance.
Multi-Timeframe Bias Table: Unlike standard indicators that only show the current timeframe, the Bias Table aggregates trend signals across multiple timeframes simultaneously. See at a glance whether 1m, 5m, 15m, 1h are aligned bullish or bearish - no more switching between charts.
Smart Confirmations: The indicator doesn't just give signals - it shows you WHY. Every entry has multiple layers of confirmation (MA cross, MACD momentum, ADX strength, RSI pullback, volume, etc.) that you can toggle on/off.
Dynamic Stop Loss System: Instead of static ATR stops, the SL is calculated from multiple support/resistance layers: UT trailing line, Supertrend, VWAP, swing structure, and MA levels. This creates more intelligent, price-action-aware stops.
R-Multiple Take Profits: Built-in TP system calculates targets based on your initial risk (1R, 1.5R, 2R, 3R). Lines freeze when touched with visual checkmarks, giving you a clean audit trail of partial exits.
Educational Tooltips Everywhere: Every single input has detailed tooltips explaining what it does, typical values, and how it impacts trading. You're not guessing - you're learning as you configure.
Performance Optimized: Smart caching, conditional calculations, and modular design mean the indicator runs fast despite having 15+ features. Turn off what you don't use for even better performance.
No Repainting: All signals respect bar close. Alerts fire correctly. What you see in history is what you would have gotten in real-time.
What Makes It Unique:
Integrated UT Bot + Bias Table: No other indicator combines UT Bot's ATR trailing system with a live multi-timeframe dashboard. You get precision entries with macro trend context.
Candlestick Pattern Recognition with Interactive Tooltips: Patterns aren't just marked - hover over any emoji for a full explanation of what the pattern means and how to trade it.
Opening Range Breakout Tracker: Built-in ORB system for intraday traders with customizable session times and real-time status updates in the Bias Table.
Previous Day High/Low Auto-Detection: Automatically plots PDH/PDL on intraday charts with theme-aware colors. Updates daily without manual input.
Dynamic Row Labels in Bias Table: The table shows your actual settings (e.g., "EMA 10 > SMA 20") not generic labels. You know exactly what's being evaluated.
Modular Filter System: Instead of forcing a fixed methodology, the indicator lets you build your own strategy. Start with just UT Bot, add filters one at a time, test what works for your style.
---
2. WHO WHOULD USE THIS
Designed For:
Intermediate to Advanced Traders: You understand basic technical analysis (MAs, RSI, MACD) and want to combine multiple confirmations efficiently. This isn't a "one-click profit" system - it's a professional toolkit.
Multi-Timeframe Traders: If you trade one asset but check multiple timeframes for confirmation (e.g., enter on 5m after checking 15m and 1h alignment), the Bias Table will save you hours every week.
Trend Followers: The indicator excels at identifying and following trends using UT Bot, Supertrend, and MA systems. If you trade breakouts and pullbacks in trending markets, this is built for you.
Intraday and Swing Traders: Works equally well on 5m-1h charts (day trading) and 4h-D charts (swing trading). Scalpers can use it too with appropriate settings adjustments.
Discretionary Traders: This isn't a black-box system. You see all the components, understand the logic, and make final decisions. Perfect for traders who want tools, not automation.
Works Across All Markets:
Stocks (US, international)
Cryptocurrency (24/7 markets supported)
Forex pairs
Indices (SPY, QQQ, etc.)
Commodities
NOT Ideal For :
Complete Beginners: If you don't know what a moving average or RSI is, start with basics first. This indicator assumes foundational knowledge.
Algo Traders Seeking Black Box: This is discretionary. Signals require context and confirmation. Not suitable for blind automated execution.
Mean-Reversion Only Traders: The indicator is trend-following at its core. While VWAP bands support mean-reversion, the primary methodology is trend continuation.
---
3. CORE COMPONENTS OVERVIEW
The indicator combines these proven systems:
Trend Analysis:
Moving Averages: Four customizable MAs (Fast, Medium, Medium-Long, Long) with six types to choose from (EMA, SMA, WMA, VWMA, RMA, HMA). Mix and match for your style.
Supertrend: ATR-based trend indicator with unique gradient fill showing trend strength. One-sided ribbon visualization makes it easier to see momentum building or fading.
ZLSMA : Zero-lag linear-regression smoothed moving average. Reduces lag compared to traditional MAs while maintaining smooth curves.
Momentum & Filters:
MACD: Standard MACD with separation filter to avoid weak crossovers.
RSI: Pullback zone detection - only enter longs when RSI is in your defined "buy zone" and shorts in "sell zone".
ADX/DMI: Trend strength measurement with directional filter. Ensures you only trade when there's actual momentum.
Volume Filter: Relative volume confirmation - require above-average volume for entries.
Donchian Breakout: Optional channel breakout requirement.
Signal Systems:
UT Bot: The primary signal generator. ATR trailing stop that adapts to volatility and gives clear entry/exit points.
Base Signals: MA cross system with all the above filters applied. More conservative than UT Bot alone.
Market Bias Table: Multi-timeframe dashboard showing trend alignment across 7 timeframes plus macro bias (3-day, weekly, monthly, quarterly, VIX).
Candlestick Patterns: Six major reversal patterns auto-detected with interactive tooltips.
ORB Tracker: Opening range high/low with breakout status (intraday only).
PDH/PDL: Previous day levels plotted automatically on intraday charts.
VWAP + Bands : Session-anchored VWAP with up to three standard deviation band pairs.
---
4. THE UT BOT TRADING SYSTEM
The UT Bot is the heart of the indicator's signal generation. It's an advanced ATR trailing stop that adapts to market volatility.
Why UT Bot is Superior to Fixed Stops:
Traditional ATR stops use a fixed multiplier (e.g., "stop = entry - 2×ATR"). UT Bot is smarter:
It TRAILS the stop as price moves in your favor
It WIDENS during high volatility to avoid premature stops
It TIGHTENS during consolidation to lock in profits
It FLIPS when price breaks the trailing line, signaling reversals
Visual Elements You'll See:
Orange Trailing Line: The actual UT stop level that adapts bar-by-bar
Buy/Sell Labels: Aqua triangle (long) or orange triangle (short) when the line flips
ENTRY Line: Horizontal line at your entry price (optional, can be turned off)
Suggested Stop Loss: A composite SL calculated from multiple support/resistance layers:
- UT trailing line
- Supertrend level
- VWAP
- Swing structure (recent lows/highs)
- Long-term MA (200)
- ATR-based floor
Take Profit Lines: TP1, TP1.5, TP2, TP3 based on R-multiples. When price touches a TP, it's marked with a checkmark and the line freezes for audit trail purposes.
Status Messages: "SL Touched ❌" or "SL Frozen" when the trade leg completes.
How UT Bot Differs from Other ATR Systems:
Multiple Filters Available: You can require 2-bar confirmation, minimum % price change, swing structure alignment, or ZLSMA directional filter. Most UT implementations have none of these.
Smart SL Calculation: Instead of just using the UT line as your stop, the indicator suggests a better SL based on actual support/resistance. This prevents getting stopped out by wicks while keeping risk controlled.
Visual Audit Trail: All SL/TP lines freeze when touched with clear markers. You can review your trades weeks later and see exactly where entries, stops, and targets were.
Performance Options: "Draw UT visuals only on bar close" lets you reduce rendering load without affecting logic or alerts - critical for slower machines or 1m charts.
Trading Logic:
UT Bot flips direction (Buy or Sell signal appears)
Check Bias Table for multi-timeframe confirmation
Optional: Wait for Base signal or candlestick pattern
Enter at signal bar close or next bar open
Place stop at "Suggested Stop Loss" line
Scale out at TP levels (TP1, TP2, TP3)
Exit remaining position on opposite UT signal or stop hit
---
5. UNDERSTANDING THE MARKET BIAS TABLE
This is the indicator's unique multi-timeframe intelligence layer. Instead of looking at one chart at a time, the table aggregates signals across seven timeframes plus macro trend bias.
Why Multi-Timeframe Analysis Matters:
Professional traders check higher and lower timeframes for context:
Is the 1h uptrend aligning with my 5m entry?
Are all short-term timeframes bullish or just one?
Is the daily trend supportive or fighting me?
Doing this manually means opening multiple charts, checking each indicator, and making mental notes. The Bias Table does it automatically in one glance.
Table Structure:
Header Row:
On intraday charts: 1m, 5m, 15m, 30m, 1h, 2h, 4h (toggle which ones you want)
On daily+ charts: D, W, M (automatic)
Green dot next to title = live updating
Headline Rows - Macro Bias:
These show broad market direction over longer periods:
3 Day Bias: Trend over last 3 trading sessions (uses 1h data)
Weekly Bias: Trend over last 5 trading sessions (uses 4h data)
Monthly Bias: Trend over last 30 daily bars
Quarterly Bias: Trend over last 13 weekly bars
VIX Fear Index: Market regime based on VIX level - bullish when low, bearish when high
Opening Range Breakout: Status of price vs. session open range (intraday only)
These rows show text: "BULLISH", "BEARISH", or "NEUTRAL"
Indicator Rows - Technical Signals:
These evaluate your configured indicators across all active timeframes:
Fast MA > Medium MA (shows your actual MA settings, e.g., "EMA 10 > SMA 20")
Price > Long MA (e.g., "Price > SMA 200")
Price > VWAP
MACD > Signal
Supertrend (up/down/neutral)
ZLSMA Rising
RSI In Zone
ADX ≥ Minimum
These rows show emojis: GREEB (bullish), RED (bearish), GRAY/YELLOW (neutral/NA)
AVG Column:
Shows percentage of active timeframes that are bullish for that row. This is the KEY metric:
AVG > 70% = strong multi-timeframe bullish alignment
AVG 40-60% = mixed/choppy, no clear trend
AVG < 30% = strong multi-timeframe bearish alignment
How to Use the Table:
For a long trade:
Check AVG column - want to see > 60% ideally
Check headline bias rows - want to see BULLISH, not BEARISH
Check VIX row - bullish market regime preferred
Check ORB row (intraday) - want ABOVE for longs
Scan indicator rows - more green = better confirmation
For a short trade:
Check AVG column - want to see < 40% ideally
Check headline bias rows - want to see BEARISH, not BULLISH
Check VIX row - bearish market regime preferred
Check ORB row (intraday) - want BELOW for shorts
Scan indicator rows - more red = better confirmation
When AVG is 40-60%:
Market is choppy, mixed signals. Either stay out or reduce position size significantly. These are low-probability environments.
Unique Features:
Dynamic Labels: Row names show your actual settings (e.g., "EMA 10 > SMA 20" not generic "Fast > Slow"). You know exactly what's being evaluated.
Customizable Rows: Turn off rows you don't care about. Only show what matters to your strategy.
Customizable Timeframes: On intraday charts, disable 1m or 4h if you don't trade them. Reduces calculation load by 20-40%.
Automatic HTF Handling: On Daily/Weekly/Monthly charts, the table automatically switches to D/W/M columns. No configuration needed.
Performance Smart: "Hide BIAS table on 1D or above" option completely skips all table calculations on higher timeframes if you only trade intraday.
---
6. CANDLESTICK PATTERN RECOGNITION
The indicator automatically detects six major reversal patterns and marks them with emojis at the relevant bars.
Why These Six Patterns:
These are the most statistically significant reversal patterns according to trading literature:
High win rate when appearing at support/resistance
Clear visual structure (not subjective)
Work across all timeframes and assets
Studied extensively by institutions
The Patterns:
Bullish Patterns (appear at bottoms):
Bullish Engulfing: Green candle completely engulfs prior red candle's body. Strong reversal signal.
Hammer: Small body with long lower wick (at least 2× body size). Shows rejection of lower prices by buyers.
Morning Star: Three-candle pattern (large red → small indecision → large green). Very strong bottom reversal.
Bearish Patterns (appear at tops):
Bearish Engulfing: Red candle completely engulfs prior green candle's body. Strong reversal signal.
Shooting Star: Small body with long upper wick (at least 2× body size). Shows rejection of higher prices by sellers.
Evening Star: Three-candle pattern (large green → small indecision → large red). Very strong top reversal.
Interactive Tooltips:
Unlike most pattern indicators that just draw shapes, this one is educational:
Hover your mouse over any pattern emoji
A tooltip appears explaining: what the pattern is, what it means, when it's most reliable, and how to trade it
No need to memorize - learn as you trade
Noise Filter:
"Min candle body % to filter noise" setting prevents false signals:
Patterns require minimum body size relative to price
Filters out tiny candles that don't represent real buying/selling pressure
Adjust based on asset volatility (higher % for crypto, lower for low-volatility stocks)
How to Trade Patterns:
Patterns are NOT standalone entry signals. Use them as:
Confirmation: UT Bot gives signal + pattern appears = stronger entry
Reversal Warning: In a trade, opposite pattern appears = consider tightening stop or taking profit
Support/Resistance Validation: Pattern at key level (PDH, VWAP, MA 200) = level is being respected
Best combined with:
UT Bot or Base signal in same direction
Bias Table alignment (AVG > 60% or < 40%)
Appearance at obvious support/resistance
---
7. VISUAL TOOLS AND FEATURES
VWAP (Volume Weighted Average Price):
Session-anchored VWAP with standard deviation bands. Shows institutional "fair value" for the trading session.
Anchor Options: Session, Day, Week, Month, Quarter, Year. Choose based on your trading timeframe.
Bands: Up to three pairs (X1, X2, X3) showing statistical deviation. Price at outer bands often reverses.
Auto-Hide on HTF: VWAP hides on Daily/Weekly/Monthly charts automatically unless you enable anchored mode.
Use VWAP as:
Directional bias (above = bullish, below = bearish)
Mean reversion levels (outer bands)
Support/resistance (the VWAP line itself)
Previous Day High/Low:
Automatically plots yesterday's high and low on intraday charts:
Updates at start of each new trading day
Theme-aware colors (dark text for light charts, light text for dark charts)
Hidden automatically on Daily/Weekly/Monthly charts
These levels are critical for intraday traders - institutions watch them closely as support/resistance.
Opening Range Breakout (ORB):
Tracks the high/low of the first 5, 15, 30, or 60 minutes of the trading session:
Customizable session times (preset for NYSE, LSE, TSE, or custom)
Shows current breakout status in Bias Table row (ABOVE, BELOW, INSIDE, BUILDING)
Intraday only - auto-disabled on Daily+ charts
ORB is a classic day trading strategy - breakout above opening range often leads to continuation.
Extra Labels:
Change from Open %: Shows how far price has moved from session open (intraday) or daily open (HTF). Green if positive, red if negative.
ADX Badge: Small label at bottom of last bar showing current ADX value. Green when above your minimum threshold, red when below.
RSI Badge: Small label at top of last bar showing current RSI value with zone status (buy zone, sell zone, or neutral).
These labels provide quick at-a-glance confirmation without needing separate indicator windows.
---
8. HOW TO USE THE INDICATOR
Step 1: Add to Chart
Load the indicator on your chosen asset and timeframe
First time: Everything is enabled by default - the chart will look busy
Don't panic - you'll turn off what you don't need
Step 2: Start Simple
Turn OFF everything except:
UT Bot labels (keep these ON)
Bias Table (keep this ON)
Moving Averages (Fast and Medium only)
Suggested Stop Loss and Take Profits
Hide everything else initially. Get comfortable with the basic UT Bot + Bias Table workflow first.
Step 3: Learn the Core Workflow
UT Bot gives a Buy or Sell signal
Check Bias Table AVG column - do you have multi-timeframe alignment?
If yes, enter the trade
Place stop at Suggested Stop Loss line
Scale out at TP levels
Exit on opposite UT signal
Trade this simple system for a week. Get a feel for signal frequency and win rate with your settings.
Step 4: Add Filters Gradually
If you're getting too many losing signals (whipsaws in choppy markets), add filters one at a time:
Try: "Require 2-Bar Trend Confirmation" - wait for 2 bars to confirm direction
Try: ADX filter with minimum threshold - only trade when trend strength is sufficient
Try: RSI pullback filter - only enter on pullbacks, not chasing
Try: Volume filter - require above-average volume
Add one filter, test for a week, evaluate. Repeat.
Step 5: Enable Advanced Features (Optional)
Once you're profitable with the core system, add:
Supertrend for additional trend confirmation
Candlestick patterns for reversal warnings
VWAP for institutional anchor reference
ORB for intraday breakout context
ZLSMA for low-lag trend following
Step 6: Optimize Settings
Every setting has a detailed tooltip explaining what it does and typical values. Hover over any input to read:
What the parameter controls
How it impacts trading
Suggested ranges for scalping, day trading, and swing trading
Start with defaults, then adjust based on your results and style.
Step 7: Set Up Alerts
Right-click chart → Add Alert → Condition: "Luxy Momentum v6" → Choose:
"UT Bot — Buy" for long entries
"UT Bot — Sell" for short entries
"Base Long/Short" for filtered MA cross signals
Optionally enable "Send real-time alert() on UT flip" in settings for immediate notifications.
Common Workflow Variations:
Conservative Trader:
UT signal + Base signal + Candlestick pattern + Bias AVG > 70%
Enter only at major support/resistance
Wider UT sensitivity, multiple filters
Aggressive Trader:
UT signal + Bias AVG > 60%
Enter immediately, no waiting
Tighter UT sensitivity, minimal filters
Swing Trader:
Focus on Daily/Weekly Bias alignment
Ignore intraday noise
Use ORB and PDH/PDL less (or not at all)
Wider stops, patient approach
---
9. PERFORMANCE AND OPTIMIZATION
The indicator is optimized for speed, but with 15+ features running simultaneously, chart load time can add up. Here's how to keep it fast:
Biggest Performance Gains:
Disable Unused Timeframes: In "Time Frames" settings, turn OFF any timeframe you don't actively trade. Each disabled TF saves 10-15% calculation time. If you only day trade 5m, 15m, 1h, disable 1m, 2h, 4h.
Hide Bias Table on Daily+: If you only trade intraday, enable "Hide BIAS table on 1D or above". This skips ALL table calculations on higher timeframes.
Draw UT Visuals Only on Bar Close: Reduces intrabar rendering of SL/TP/Entry lines. Has ZERO impact on logic or alerts - purely visual optimization.
Additional Optimizations:
Turn off VWAP bands if you don't use them
Disable candlestick patterns if you don't trade them
Turn off Supertrend fill if you find it distracting (keep the line)
Reduce "Limit to 10 bars" for SL/TP lines to minimize line objects
Performance Features Built-In:
Smart Caching: Higher timeframe data (3-day bias, weekly bias, etc.) updates once per day, not every bar
Conditional Calculations: Volume filter only calculates when enabled. Swing filter only runs when enabled. Nothing computes if turned off.
Modular Design: Every component is independent. Turn off what you don't need without breaking other features.
Typical Load Times:
5m chart, all features ON, 7 timeframes: ~2-3 seconds
5m chart, core features only, 3 timeframes: ~1 second
1m chart, all features: ~4-5 seconds (many bars to calculate)
If loading takes longer, you likely have too many indicators on the chart total (not just this one).
---
10. FAQ
Q: How is this different from standard UT Bot indicators?
A: Standard UT Bot (originally by @QuantNomad) is just the ATR trailing line and flip signals. This implementation adds:
- Volume weighting and momentum adjustment to the trailing calculation
- Multiple confirmation filters (swing, %, 2-bar, ZLSMA)
- Smart composite stop loss system from multiple S/R layers
- R-multiple take profit system with freeze-on-touch
- Integration with multi-timeframe Bias Table
- Visual audit trail with checkmarks
Q: Can I use this for automated trading?
A: The indicator is designed for discretionary trading. While it has clear signals and alerts, it's not a mechanical system. Context and judgment are required.
Q: Does it repaint?
A: No. All signals respect bar close. UT Bot logic runs intrabar but signals only trigger on confirmed bars. Alerts fire correctly with no lookahead.
Q: Do I need to use all the features?
A: Absolutely not. The indicator is modular. Many profitable traders use just UT Bot + Bias Table + Moving Averages. Start simple, add complexity only if needed.
Q: How do I know which settings to use?
A: Every single input has a detailed tooltip. Hover over any setting to see:
What it does
How it affects trading
Typical values for scalping, day trading, swing trading
Start with defaults, adjust gradually based on results.
Q: Can I use this on crypto 24/7 markets?
A: Yes. ORB will not work (no defined session), but everything else functions normally. Use "Day" anchor for VWAP instead of "Session".
Q: The Bias Table is blank or not showing.
A: Check:
"Show Table" is ON
Table position isn't overlapping another indicator's table (change position)
At least one row is enabled
"Hide BIAS table on 1D or above" is OFF (if on Daily+ chart)
Q: Why are candlestick patterns not appearing?
A: Patterns are relatively rare by design - they only appear at genuine reversal points. Check:
Pattern toggles are ON
"Min candle body %" isn't too high (try 0.05-0.10)
You're looking at a chart with actual reversals (not strong trending market)
Q: UT Bot is too sensitive/not sensitive enough.
A: Adjust "Sensitivity (Key×ATR)". Lower number = tighter stop, more signals. Higher number = wider stop, fewer signals. Read the tooltip for guidance.
Q: Can I get alerts for the Bias Table?
A: The Bias Table is a dashboard for visual analysis, not a signal generator. Set alerts on UT Bot or Base signals, then manually check Bias Table for confirmation.
Q: Does this work on stocks with low volume?
A: Yes, but turn OFF the volume filter. Low volume stocks will never meet relative volume requirements.
Q: How often should I check the Bias Table?
A: Before every entry. It takes 2 seconds to glance at the AVG column and headline rows. This one check can save you from fighting the trend.
Q: What if UT signal and Base signal disagree?
A: UT Bot is more aggressive (ATR trailing). Base signals are more conservative (MA cross + filters). If they disagree, either:
Wait for both to align (safest)
Take the UT signal but with smaller size (aggressive)
Skip the trade (conservative)
There's no "right" answer - depends on your risk tolerance.
---
FINAL NOTES
The indicator gives you an edge. How you use that edge determines results.
For questions, feedback, or support, comment on the indicator page or message the author.
Happy Trading!
Vector2Library "Vector2"
Representation of two dimensional vectors or points.
This structure is used to represent positions in two dimensional space or vectors,
for example in spacial coordinates in 2D space.
~~~
references:
docs.unity3d.com
gist.github.com
github.com
gist.github.com
gist.github.com
gist.github.com
~~~
new(x, y)
Create a new Vector2 object.
Parameters:
x : float . The x value of the vector, default=0.
y : float . The y value of the vector, default=0.
Returns: Vector2. Vector2 object.
-> usage:
`unitx = Vector2.new(1.0) , plot(unitx.x)`
from(value)
Assigns value to a new vector `x,y` elements.
Parameters:
value : float, x and y value of the vector.
Returns: Vector2. Vector2 object.
-> usage:
`one = Vector2.from(1.0), plot(one.x)`
from(value, element_sep, open_par, close_par)
Assigns value to a new vector `x,y` elements.
Parameters:
value : string . The `x` and `y` value of the vector in a `x,y` or `(x,y)` format, spaces and parentesis will be removed automatically.
element_sep : string . Element separator character, default=`,`.
open_par : string . Open parentesis character, default=`(`.
close_par : string . Close parentesis character, default=`)`.
Returns: Vector2. Vector2 object.
-> usage:
`one = Vector2.from("1.0,2"), plot(one.x)`
copy(this)
Creates a deep copy of a vector.
Parameters:
this : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = Vector2.new(1.0) , b = a.copy() , plot(b.x)`
down()
Vector in the form `(0, -1)`.
Returns: Vector2. Vector2 object.
left()
Vector in the form `(-1, 0)`.
Returns: Vector2. Vector2 object.
right()
Vector in the form `(1, 0)`.
Returns: Vector2. Vector2 object.
up()
Vector in the form `(0, 1)`.
Returns: Vector2. Vector2 object.
one()
Vector in the form `(1, 1)`.
Returns: Vector2. Vector2 object.
zero()
Vector in the form `(0, 0)`.
Returns: Vector2. Vector2 object.
minus_one()
Vector in the form `(-1, -1)`.
Returns: Vector2. Vector2 object.
unit_x()
Vector in the form `(1, 0)`.
Returns: Vector2. Vector2 object.
unit_y()
Vector in the form `(0, 1)`.
Returns: Vector2. Vector2 object.
nan()
Vector in the form `(float(na), float(na))`.
Returns: Vector2. Vector2 object.
xy(this)
Return the values of `x` and `y` as a tuple.
Parameters:
this : Vector2 . Vector2 object.
Returns: .
-> usage:
`a = Vector2.new(1.0, 1.0) , = a.xy() , plot(ax)`
length_squared(this)
Length of vector `a` in the form. `a.x^2 + a.y^2`, for comparing vectors this is computationaly lighter.
Parameters:
this : Vector2 . Vector2 object.
Returns: float. Squared length of vector.
-> usage:
`a = Vector2.new(1.0, 1.0) , plot(a.length_squared())`
length(this)
Magnitude of vector `a` in the form. `sqrt(a.x^2 + a.y^2)`
Parameters:
this : Vector2 . Vector2 object.
Returns: float. Length of vector.
-> usage:
`a = Vector2.new(1.0, 1.0) , plot(a.length())`
normalize(a)
Vector normalized with a magnitude of 1, in the form. `a / length(a)`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = normalize(Vector2.new(3.0, 2.0)) , plot(a.y)`
isNA(this)
Checks if any of the components is `na`.
Parameters:
this : Vector2 . Vector2 object.
Returns: bool.
usage:
p = Vector2.new(1.0, na) , plot(isNA(p)?1:0)
add(a, b)
Adds vector `b` to `a`, in the form `(a.x + b.x, a.y + b.y)`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = one() , b = one() , c = add(a, b) , plot(c.x)`
add(a, b)
Adds vector `b` to `a`, in the form `(a.x + b, a.y + b)`.
Parameters:
a : Vector2 . Vector2 object.
b : float . Value.
Returns: Vector2. Vector2 object.
-> usage:
`a = one() , b = 1.0 , c = add(a, b) , plot(c.x)`
add(a, b)
Adds vector `b` to `a`, in the form `(a + b.x, a + b.y)`.
Parameters:
a : float . Value.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = 1.0 , b = one() , c = add(a, b) , plot(c.x)`
subtract(a, b)
Subtract vector `b` from `a`, in the form `(a.x - b.x, a.y - b.y)`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = one() , b = one() , c = subtract(a, b) , plot(c.x)`
subtract(a, b)
Subtract vector `b` from `a`, in the form `(a.x - b, a.y - b)`.
Parameters:
a : Vector2 . vector2 object.
b : float . Value.
Returns: Vector2. Vector2 object.
-> usage:
`a = one() , b = 1.0 , c = subtract(a, b) , plot(c.x)`
subtract(a, b)
Subtract vector `b` from `a`, in the form `(a - b.x, a - b.y)`.
Parameters:
a : float . value.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = 1.0 , b = one() , c = subtract(a, b) , plot(c.x)`
multiply(a, b)
Multiply vector `a` with `b`, in the form `(a.x * b.x, a.y * b.y)`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = one() , b = one() , c = multiply(a, b) , plot(c.x)`
multiply(a, b)
Multiply vector `a` with `b`, in the form `(a.x * b, a.y * b)`.
Parameters:
a : Vector2 . Vector2 object.
b : float . Value.
Returns: Vector2. Vector2 object.
-> usage:
`a = one() , b = 1.0 , c = multiply(a, b) , plot(c.x)`
multiply(a, b)
Multiply vector `a` with `b`, in the form `(a * b.x, a * b.y)`.
Parameters:
a : float . Value.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = 1.0 , b = one() , c = multiply(a, b) , plot(c.x)`
divide(a, b)
Divide vector `a` with `b`, in the form `(a.x / b.x, a.y / b.y)`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(3.0) , b = from(2.0) , c = divide(a, b) , plot(c.x)`
divide(a, b)
Divide vector `a` with value `b`, in the form `(a.x / b, a.y / b)`.
Parameters:
a : Vector2 . Vector2 object.
b : float . Value.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(3.0) , b = 2.0 , c = divide(a, b) , plot(c.x)`
divide(a, b)
Divide value `a` with vector `b`, in the form `(a / b.x, a / b.y)`.
Parameters:
a : float . Value.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = 3.0 , b = from(2.0) , c = divide(a, b) , plot(c.x)`
negate(a)
Negative of vector `a`, in the form `(-a.x, -a.y)`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(3.0) , b = a.negate , plot(b.x)`
pow(a, b)
Raise vector `a` with exponent vector `b`, in the form `(a.x ^ b.x, a.y ^ b.y)`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(3.0) , b = from(2.0) , c = pow(a, b) , plot(c.x)`
pow(a, b)
Raise vector `a` with value `b`, in the form `(a.x ^ b, a.y ^ b)`.
Parameters:
a : Vector2 . Vector2 object.
b : float . Value.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(3.0) , b = 2.0 , c = pow(a, b) , plot(c.x)`
pow(a, b)
Raise value `a` with vector `b`, in the form `(a ^ b.x, a ^ b.y)`.
Parameters:
a : float . Value.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = 3.0 , b = from(2.0) , c = pow(a, b) , plot(c.x)`
sqrt(a)
Square root of the elements in a vector.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(3.0) , b = sqrt(a) , plot(b.x)`
abs(a)
Absolute properties of the vector.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(-3.0) , b = abs(a) , plot(b.x)`
min(a)
Lowest element of a vector.
Parameters:
a : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = min(a) , plot(b)`
max(a)
Highest element of a vector.
Parameters:
a : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = max(a) , plot(b)`
vmax(a, b)
Highest elements of two vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 2.0) , b = new(2.0, 3.0) , c = vmax(a, b) , plot(c.x)`
vmax(a, b, c)
Highest elements of three vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
c : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 2.0) , b = new(2.0, 3.0) , c = new(1.5, 4.5) , d = vmax(a, b, c) , plot(d.x)`
vmin(a, b)
Lowest elements of two vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 2.0) , b = new(2.0, 3.0) , c = vmin(a, b) , plot(c.x)`
vmin(a, b, c)
Lowest elements of three vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
c : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 2.0) , b = new(2.0, 3.0) , c = new(1.5, 4.5) , d = vmin(a, b, c) , plot(d.x)`
perp(a)
Perpendicular Vector of `a`, in the form `(a.y, -a.x)`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = perp(a) , plot(b.x)`
floor(a)
Compute the floor of vector `a`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = floor(a) , plot(b.x)`
ceil(a)
Ceils vector `a`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = ceil(a) , plot(b.x)`
ceil(a, digits)
Ceils vector `a`.
Parameters:
a : Vector2 . Vector2 object.
digits : int . Digits to use as ceiling.
Returns: Vector2. Vector2 object.
round(a)
Round of vector elements.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = round(a) , plot(b.x)`
round(a, precision)
Round of vector elements.
Parameters:
a : Vector2 . Vector2 object.
precision : int . Number of digits to round vector "a" elements.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(0.123456, 1.234567) , b = round(a, 2) , plot(b.x)`
fractional(a)
Compute the fractional part of the elements from vector `a`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.123456, 1.23456) , b = fractional(a) , plot(b.x)`
dot_product(a, b)
dot_product product of 2 vectors, in the form `a.x * b.x + a.y * b.y.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = dot_product(a, b) , plot(c)`
cross_product(a, b)
cross product of 2 vectors, in the form `a.x * b.y - a.y * b.x`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = cross_product(a, b) , plot(c)`
equals(a, b)
Compares two vectors
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: bool. Representing the equality.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = equals(a, b) ? 1 : 0 , plot(c)`
sin(a)
Compute the sine of argument vector `a`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = sin(a) , plot(b.x)`
cos(a)
Compute the cosine of argument vector `a`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = cos(a) , plot(b.x)`
tan(a)
Compute the tangent of argument vector `a`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = tan(a) , plot(b.x)`
atan2(x, y)
Approximation to atan2 calculation, arc tangent of `y/x` in the range (-pi,pi) radians.
Parameters:
x : float . The x value of the vector.
y : float . The y value of the vector.
Returns: float. Value with angle in radians. (negative if quadrante 3 or 4)
-> usage:
`a = new(3.0, 1.5) , b = atan2(a.x, a.y) , plot(b)`
atan2(a)
Approximation to atan2 calculation, arc tangent of `y/x` in the range (-pi,pi) radians.
Parameters:
a : Vector2 . Vector2 object.
Returns: float, value with angle in radians. (negative if quadrante 3 or 4)
-> usage:
`a = new(3.0, 1.5) , b = atan2(a) , plot(b)`
distance(a, b)
Distance between vector `a` and `b`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = distance(a, b) , plot(c)`
rescale(a, length)
Rescale a vector to a new magnitude.
Parameters:
a : Vector2 . Vector2 object.
length : float . Magnitude.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = 2.0 , c = rescale(a, b) , plot(c.x)`
rotate(a, radians)
Rotates vector by a angle.
Parameters:
a : Vector2 . Vector2 object.
radians : float . Angle value in radians.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = 2.0 , c = rotate(a, b) , plot(c.x)`
rotate_degree(a, degree)
Rotates vector by a angle.
Parameters:
a : Vector2 . Vector2 object.
degree : float . Angle value in degrees.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = 45.0 , c = rotate_degree(a, b) , plot(c.x)`
rotate_around(this, center, angle)
Rotates vector `target` around `origin` by angle value.
Parameters:
this
center : Vector2 . Vector2 object.
angle : float . Angle value in degrees.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = rotate_around(a, b, 45.0) , plot(c.x)`
perpendicular_distance(a, b, c)
Distance from point `a` to line between `b` and `c`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
c : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(1.5, 2.6) , b = from(1.0) , c = from(3.0) , d = perpendicular_distance(a, b, c) , plot(d.x)`
project(a, axis)
Project a vector onto another.
Parameters:
a : Vector2 . Vector2 object.
axis : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = project(a, b) , plot(c.x)`
projectN(a, axis)
Project a vector onto a vector of unit length.
Parameters:
a : Vector2 . Vector2 object.
axis : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = projectN(a, b) , plot(c.x)`
reflect(a, axis)
Reflect a vector on another.
Parameters:
a : Vector2 . Vector2 object.
axis
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = reflect(a, b) , plot(c.x)`
reflectN(a, axis)
Reflect a vector to a arbitrary axis.
Parameters:
a : Vector2 . Vector2 object.
axis
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = reflectN(a, b) , plot(c.x)`
angle(a)
Angle in radians of a vector.
Parameters:
a : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = angle(a) , plot(b)`
angle_unsigned(a, b)
unsigned degree angle between 0 and +180 by given two vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = angle_unsigned(a, b) , plot(c)`
angle_signed(a, b)
Signed degree angle between -180 and +180 by given two vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = angle_signed(a, b) , plot(c)`
angle_360(a, b)
Degree angle between 0 and 360 by given two vectors
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = angle_360(a, b) , plot(c)`
clamp(a, min, max)
Restricts a vector between a min and max value.
Parameters:
a : Vector2 . Vector2 object.
min
max
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = from(2.5) , d = clamp(a, b, c) , plot(d.x)`
clamp(a, min, max)
Restricts a vector between a min and max value.
Parameters:
a : Vector2 . Vector2 object.
min : float . Lower boundary value.
max : float . Higher boundary value.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = clamp(a, 2.0, 2.5) , plot(b.x)`
lerp(a, b, rate)
Linearly interpolates between vectors a and b by rate.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
rate : float . Value between (a:-infinity -> b:1.0), negative values will move away from b.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = lerp(a, b, 0.5) , plot(c.x)`
herp(a, b, rate)
Hermite curve interpolation between vectors a and b by rate.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
rate : Vector2 . Vector2 object. Value between (a:0 > 1:b).
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = from(2.5) , d = herp(a, b, c) , plot(d.x)`
transform(position, mat)
Transform a vector by the given matrix.
Parameters:
position : Vector2 . Source vector.
mat : M32 . Transformation matrix
Returns: Vector2. Transformed vector.
transform(position, mat)
Transform a vector by the given matrix.
Parameters:
position : Vector2 . Source vector.
mat : M44 . Transformation matrix
Returns: Vector2. Transformed vector.
transform(position, mat)
Transform a vector by the given matrix.
Parameters:
position : Vector2 . Source vector.
mat : matrix . Transformation matrix, requires a 3x2 or a 4x4 matrix.
Returns: Vector2. Transformed vector.
transform(this, rotation)
Transform a vector by the given quaternion rotation value.
Parameters:
this : Vector2 . Source vector.
rotation : Quaternion . Rotation to apply.
Returns: Vector2. Transformed vector.
area_triangle(a, b, c)
Find the area in a triangle of vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
c : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(1.0, 2.0) , b = from(2.0) , c = from(1.0) , d = area_triangle(a, b, c) , plot(d.x)`
random(max)
2D random value.
Parameters:
max : Vector2 . Vector2 object. Vector upper boundary.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(2.0) , b = random(a) , plot(b.x)`
random(max)
2D random value.
Parameters:
max : float, Vector upper boundary.
Returns: Vector2. Vector2 object.
-> usage:
`a = random(2.0) , plot(a.x)`
random(min, max)
2D random value.
Parameters:
min : Vector2 . Vector2 object. Vector lower boundary.
max : Vector2 . Vector2 object. Vector upper boundary.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(1.0) , b = from(2.0) , c = random(a, b) , plot(c.x)`
random(min, max)
2D random value.
Parameters:
min : Vector2 . Vector2 object. Vector lower boundary.
max : Vector2 . Vector2 object. Vector upper boundary.
Returns: Vector2. Vector2 object.
-> usage:
`a = random(1.0, 2.0) , plot(a.x)`
noise(a)
2D Noise based on Morgan McGuire @morgan3d.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(2.0) , b = noise(a) , plot(b.x)`
to_string(a)
Converts vector `a` to a string format, in the form `"(x, y)"`.
Parameters:
a : Vector2 . Vector2 object.
Returns: string. In `"(x, y)"` format.
-> usage:
`a = from(2.0) , l = barstate.islast ? label.new(bar_index, 0.0, to_string(a)) : label(na)`
to_string(a, format)
Converts vector `a` to a string format, in the form `"(x, y)"`.
Parameters:
a : Vector2 . Vector2 object.
format : string . Format to apply transformation.
Returns: string. In `"(x, y)"` format.
-> usage:
`a = from(2.123456) , l = barstate.islast ? label.new(bar_index, 0.0, to_string(a, "#.##")) : label(na)`
to_array(a)
Converts vector to a array format.
Parameters:
a : Vector2 . Vector2 object.
Returns: array.
-> usage:
`a = from(2.0) , b = to_array(a) , plot(array.get(b, 0))`
to_barycentric(this, a, b, c)
Captures the barycentric coordinate of a cartesian position in the triangle plane.
Parameters:
this : Vector2 . Source cartesian coordinate position.
a : Vector2 . Triangle corner `a` vertice.
b : Vector2 . Triangle corner `b` vertice.
c : Vector2 . Triangle corner `c` vertice.
Returns: bool.
from_barycentric(this, a, b, c)
Captures the cartesian coordinate of a barycentric position in the triangle plane.
Parameters:
this : Vector2 . Source barycentric coordinate position.
a : Vector2 . Triangle corner `a` vertice.
b : Vector2 . Triangle corner `b` vertice.
c : Vector2 . Triangle corner `c` vertice.
Returns: bool.
to_complex(this)
Translate a Vector2 structure to complex.
Parameters:
this : Vector2 . Source vector.
Returns: Complex.
to_polar(this)
Translate a Vector2 cartesian coordinate into polar coordinates.
Parameters:
this : Vector2 . Source vector.
Returns: Pole. The returned angle is in radians.
NY VIX Channel Trend US Futures Day Trade StrategyNY VIX Channel Trend Strategy
Summary in one paragraph
Session anchored intraday strategy for index futures such as ES and NQ on one to fifteen minute charts. It acts only after the first configurable window of New York Regular Trading Hours and uses a VIX derived daily implied move to form a realistic channel from the session open. Originality comes from using a pure implied volatility yardstick as portable support and resistance, then committing in the direction of the first window close relative to the open. Add it to a clean chart and trade the simple visuals. For conservative alerts use on bar close.
Scope and intent
• Markets. Index futures ES and NQ
• Timeframes. One to thirty minutes
• Default demo. ES1 on five minutes
• Purpose. Provide a portable intraday yardstick for entries and exits without curve fitting
• Limits. This is a strategy. Orders are simulated on standard candles
Originality and usefulness
• Unique concept. A VIX only channel anchored at 09:30 New York plus a single window trend test
• Addresses. False urgency at session open and unrealistic bands from arbitrary multipliers
• Testability. Every input is visible and the channel is plotted so users can audit behavior
• Portable yardstick. Daily implied move equals VIX percent divided by square root of two hundred fifty two
• Protected status. None. Method and use are fully disclosed
Method overview in plain language
Take the daily VIX or VIX9D value, convert it to a daily fraction by dividing by square root of two hundred fifty two, then anchor a symmetric channel at the New York session open. Observe the first N minutes. If that window closes above the open the bias is long. If it closes below the open the bias is short. One trade per session. Exits occur at the channel boundary or at a bracket based on a user selected VIX factor. Positions are closed a set number of minutes before the session ends.
Base measures
Return basis. The daily implied move unit equals VIX percent divided by square root of two hundred fifty two and serves as the distance unit for targets and stops.
Components
• VIX Channel. Top, mid, bottom lines anchored at 09:30 New York. No extra multipliers
• Window Trend. Close of the first N minutes relative to the session open sets direction
• Risk Bracket. Take profit and stop loss equal to VIX unit times user factor
• Session Window. Uses the exchange time of the chart
Fusion rule
Minimum gates count equals one. The trade only arms after the window has elapsed and a direction exists. One entry per session.
Signal rule
• Long when the window close is above the session open and the window has completed
• Short when the window close is below the session open and the window has completed
• Exit on channel touch. Long exits at the top. Short exits at the bottom
• Flat thirty minutes before the session close or at the user setting
Inputs with guidance
Setup
• Use VIX9D. Width source. Typical true for fast tone or false for baseline
• Use daily OPEN. Toggle for sensitivity to overnight changes
Logic
• Window minutes. Five to one hundred twenty. Larger values delay entries and reduce whipsaw
• VIX factor for TP. Zero point five to two. Raising it widens the profit target
• VIX factor for SL. Zero point five to two. Raising it widens the stop
• Exit minutes before close. Fifteen to ninety. Raising it exits earlier
Properties visible in this publication
• Initial capital one hundred thousand USD
• Base currency USD
• request.security uses lookahead off
• Commission cash per contract two point five $ per each contract. Slippage one tick
• Default order size method FIXED with value one contract. Pyramiding zero. Process orders on close ON. Bar magnifier OFF. Recalculate after order is filled OFF. Calc on every tick ON
Realism and responsible publication
No performance claims. Past results never guarantee future outcomes. Fills and slippage vary by venue. Shapes can move while a bar forms and settle on close. Strategy uses standard candles.
Honest limitations and failure modes
Economic releases and thin liquidity can break the channel. Very quiet regimes can reduce signal contrast. Session windows follow the exchange time of the chart. If both stop and target can be hit within one bar, assume stop first for conservative reading without bar magnifier.
Works best in liquid hours of New York RTH. Very large gaps and surprise news may exceed the implied channel. Always validate on the symbols you trade.
Entries and exits
• Entry logic. After the first window, go long if the window close is above the session open, go short if below
• Exit logic. Long exits at the channel top or at the take profit or stop. Short exits at the channel bottom or at the take profit or stop. Flat before session close by the configured minutes
• Risk model. Initial stop and target based on the VIX unit times user factors. No trail and no break even. No cooldown
• Tie handling. Treat as stop first for conservative interpretation
Position sizing
Fixed size one contract per trade. Target risk per trade should generally remain near one percent of account equity. Risk is based on the daily volatility value, the max loss from the tests for one year duration with 5min chart was 4%, while the avg loss was below <1% of the total capital.
If you have any questions please let me know. Thank you for coming by !
FluxGate Daily Swing StrategySummary in one paragraph
FluxGate treats long and short as different ecosystems. It runs two independent engines so the long side can be bold when the tape rewards upside persistence while the short side can stay selective when downside is messy. The core reads three directional drivers from price geometry then removes overlap before gating with clean path checks. The complementary risk module anchors stop distance to a higher timeframe ATR so a unit means the same thing on SPY and BTC. It can add take profit breakeven and an ATR trail that only activates after the trade earns it. If a stop is hit the strategy can re enter in the same direction on the next bar with a daily retry cap that you control. Add it to a clean chart. Use defaults to see the intended behavior. For conservative workflows evaluate on bar close.
Scope and intent
• Markets. Large cap equities and liquid ETFs major FX pairs US index futures and liquid crypto pairs
• Timeframes. From one minute to daily
• Default demo in this publication. SPY on one day timeframe
• Purpose. Reduce false starts without missing sustained trends by fusing independent drivers and suppressing activity when the path is noisy
• Limits. This is a strategy. Orders are simulated on standard candles. Non standard chart types are not supported for execution
Originality and usefulness
• Unique fusion. FluxGate extracts three drivers that look at price from different angles. Direction measures slope of a smoothed guide and scales by realized volatility so a point of slope does not mean a different thing on different symbols. Persistence looks at short sign agreement to reward series of closes that keep direction. Curvature measures the second difference of a local fit to wake up during convex pushes. These three are then orthonormalized so a strong reading in one does not double count through another.
• Gates that matter. Efficiency ratio prefers direct paths over treadmills. Entropy turns up versus down frequency into an information read. Light fractal cohesion punishes wrinkly paths. Together they slow the system in chop and allow it to open up when the path is clean.
• Separate long and short engines. Threshold tilts adapt to the skew of score excursions. That lets long engage earlier when upside distribution supports it and keeps short cautious where downside surprise and venue frictions are common.
• Practical risk behavior. Stops are ATR anchored on a higher timeframe so the unit is portable. Take profit is expressed in R so two R means the same concept across symbols. Breakeven and trailing only activate after a chosen R so early noise does not squeeze a good entry. Re entry after stop lets the system try again without you babysitting the chart.
• Testability. Every major window and the aggression controls live in Inputs. There is no hidden magic number.
Method overview in plain language
Base measures
• Return basis. Natural log of close over prior close for stability and easy aggregation through time. Realized volatility is the standard deviation of returns over a moving window.
• Range basis for risk. ATR computed on a higher timeframe anchor such as day week or month. That anchor is steady across venues and avoids chasing chart specific quirks.
Components
• Directional intensity. Use an EMA of typical price as a guide. Take the day to day slope as raw direction. Divide by realized volatility to get a unit free measure. Soft clip to keep outliers from dominating.
• Persistence. Encode whether each bar closed up or down. Measure short sign agreement so a string of higher closes scores better than a jittery sequence. This favors push continuity without guessing tops or bottoms.
• Curvature. Fit a short linear regression and compute the second difference of the fitted series. Strong curvature flags acceleration that slope alone may miss.
• Efficiency gate. Compare net move to path length over a gate window. Values near one indicate direct paths. Values near zero indicate treadmill behavior.
• Entropy gate. Convert up versus down frequency into a probability of direction. High entropy means coin toss. The gate narrows there.
• Fractal cohesion. A light read of path wrinkliness relative to span. Lower cohesion reduces the urge to act.
• Phase assist. Map price inside a recent channel to a small signed bias that grows with confidence. This helps entries lean toward the right half of the channel without becoming a breakout rule.
• Shock control. Compare short volatility to long volatility. When short term volatility spikes the shock gate temporarily damps activity so the system waits for pressure to normalize.
Fusion rule
• Normalize the three drivers after removing overlap
• Blend with weights that adapt to your aggression input
• Multiply by the gates to respect path quality
• Smooth just enough to avoid jitter while keeping timing responsive
• Compute an adaptive mean and deviation of the score and set separate long and short thresholds with a small tilt informed by skew sign
• The result is one long score and one short score that can cross their thresholds at different times for the same tape which is a feature not a bug
Signal rule
• A long suggestion appears when the long score crosses above its long threshold while all gates are active
• A short suggestion appears when the short score crosses below its short threshold while all gates are active
• If any required gate is missing the state is wait
• When a position is open the status is in long or in short until the complementary risk engine exits or your entry mode closes and flips
Inputs with guidance
Setup Long
• Base length Long. Master window for the long engine. Typical range twenty four to eighty. Raising it improves selectivity and reduces trade count. Lowering it reacts faster but can increase noise
• Aggression Long. Zero to one. Higher values make thresholds more permissive and shorten smoothing
Setup Short
• Base length Short. Master window for the short engine. Typical range twenty eight to ninety six
• Aggression Short. Zero to one. Lower values keep shorts conservative which is often useful on upward drifting symbols
Entries and UI
• Entry mode. Both or Long only or Short only
Complementary risk engine
• Enable risk engine. Turns on bracket exits while keeping your signal logic untouched
• ATR anchor timeframe. Day Week or Month. This sets the structural unit of stop distance
• ATR length. Default fourteen
• Stop multiple. Default one point five times the anchor ATR
• Use take profit. On by default
• Take profit in R. Default two R
• Breakeven trigger in R. Default one R
Usage recipes
Intraday trend focus
• Entry mode Both
• ATR anchor Week
• Aggression Long zero point five Aggression Short zero point three
• Stop multiple one point five Take profit two R
• Expect fewer trades that stick to directional pushes and skip treadmill noise
Intraday mean reversion focus
• Session windows optional if you add them in your copy
• ATR anchor Day
• Lower aggression both sides
• Breakeven later and trailing later so the first bounce has room
• This favors fade entries that still convert into trends when the path stays clean
Swing continuation
• Signal timeframe four hours or one day
• Confirm timeframe one day if you choose to include bias
• ATR anchor Week or Month
• Larger base windows and a steady two R target
• This accepts fewer entries and aims for larger holds
Properties visible in this publication
• Initial capital 25.000
• Base currency USD
• Default order size percent of equity value three - 3% of the total capital
• Pyramiding zero
• Commission zero point zero three percent - 0.03% of total capital
• Slippage five ticks
• Process orders on close off
• Recalculate after order is filled off
• Calc on every tick off
• Bar magnifier off
• Any request security calls use lookahead off everywhere
Realism and responsible publication
• No performance promises. Past results never guarantee future outcomes
• Fills and slippage vary by venue and feed
• Strategies run on standard candles only
• Shapes can update while a bar is forming and settle on close
• Keep risk per trade sensible. Around one percent is typical for study. Above five to ten percent is rarely sustainable
Honest limitations and failure modes
• Sudden news and thin liquidity can break assumptions behind entropy and cohesion reads
• Gap heavy symbols often behave better with a True Range basis for risk than a simple range
• Very quiet regimes can reduce score contrast. Consider longer windows or higher thresholds when markets sleep
• Session windows follow the exchange time of the chart if you add them
• If stop and target can both be inside a single bar this strategy prefers stop first to keep accounting conservative
Open source reuse and credits
• No reused open source beyond public domain building blocks such as ATR EMA and linear regression concepts
Legal
Education and research only. Not investment advice. You are responsible for your decisions. Test on history and in simulation with realistic costs
Outside Candle Session Breakout [CHE]Outside Candle Session Breakout
Session - anchored HTF levels for clear market-structure and precise breakout context
Summary
This indicator is a relevant market-structure tool. It anchors the session to the first higher-timeframe bar, then activates only when the second bar forms an outside condition. Price frequently reacts around these anchors, which provides precise breakout context and a clear overview on both lower and higher timeframes. Robustness comes from close-based validation, an adaptive volatility and tick buffer, first-touch enforcement, optional retest, one-signal-per-session, cooldown, and an optional trend filter.
Pine version: v6. Overlay: true.
Motivation: Why this design?
Short-term breakout tools often trigger during noise, duplicate within the same session, or drift when volatility shifts. The core idea is to gate signals behind a meaningful structure event: a first-bar anchor and a subsequent outside bar on the session timeframe. This narrows attention to structurally important breaks while adaptive buffering and debouncing reduce false or mid-run triggers.
What’s different vs. standard approaches?
Baseline: Simple high-low breaks or fixed buffers without session context.
Architecture: Session-anchored first-bar high/low; outside-bar gate; close-based confirmation with an adaptive ATR and tick buffer; first-touch enforcement; optional retest window; one-signal-per-session and cooldown; optional EMA trend and slope filter; higher-timeframe aggregation with lookahead disabled; themeable visuals and a range fill between levels.
Practical effect: Cleaner timing at structurally relevant levels, fewer redundant or late triggers, and better multi-timeframe situational awareness.
How it works (technical)
The chart timeframe is mapped to an analysis timeframe and a session timeframe.
The first session bar defines the anchor high and low. The setup becomes active only after the next bar forms an outside range relative to that first bar.
While active, the script tracks these anchors and checks for a breakout beyond a buffered threshold, using closing prices or wicks by preference.
The buffer scales with volatility and is limited by a minimum tick floor. First-touch enforcement avoids mid-run confirmations.
Optional retest requires a pullback to the raw anchor followed by a new close beyond the buffered level within a user window.
Optional trend gating uses an EMA on the analysis timeframe, including an optional slope requirement and price-location check.
Higher-timeframe data is requested with lookahead disabled. Values can update during a forming higher-timeframe bar; waiting and confirmation mitigate timing shifts.
Parameter Guide
Enable Long / Enable Short — Direction toggles. Default: true / true. Reduces unwanted side.
Wait Candles — Minimum bars after outside confirmation before entries. Default: five. More waiting increases stability.
Close-based Breakout — Confirm on candle close beyond buffer. Default: true. For wick sensitivity, disable.
ATR Buffer — Enables adaptive volatility buffer. Default: true.
ATR Multiplier — Buffer scaling. Default: zero point two. Increase to reduce noise.
Ticks Buffer — Minimum buffer in ticks. Default: two. Protects in quiet markets.
Cooldown Bars — Blocks new signals after a trigger. Default: three.
One Signal per Session — Prevents duplicates within a session. Default: true.
Require Retest — Pullback to raw anchor before confirming. Default: false.
Retest Window — Bars allowed for retest completion. Default: five.
HTF Trend Filter — EMA-based gating. Default: false.
EMA Length — EMA period. Default: two hundred.
Slope — Require EMA slope direction. Default: true.
Price Above/Below EMA — Require price location relative to EMA. Default: true.
Show Levels / Highlight Session / Show Signals — Visual controls. Default: true.
Color Theme — “Blue-Green” (default), “Monochrome”, “Earth Tones”, “Classic”, “Dark”.
Time Period Box — Visibility, size, position, and colors for the info box. (Optional)
Reading & Interpretation
The two level lines represent the session’s first-bar high and low. The filled band illustrates the active session range.
“OUT” marks that the outside condition is confirmed and the setup is live.
“LONG” or “SHORT” appears only when the breakout clears buffer, debounce, and optional gates.
Background tint indicates sessions where the setup is valid.
Alerts fire on confirmed long or short breakout events.
Practical Workflows & Combinations
Trend-following: Keep close-based validation, ATR buffer near the default, one-signal-per-session enabled; add EMA trend and slope for directional bias.
Retest confirmation: Enable retest with a short window to prioritize cleaner continuation after a pullback.
Lower-timeframe scalping: Reduce waiting and cooldown slightly; keep a small tick buffer to filter micro-whips.
Swing and position context: Increase ATR multiplier and waiting; maintain once-per-session to limit duplicates.
Timeframe Tiers and Trader Profiles
The script adapts its internal mapping based on the chart timeframe:
Under fifteen minutes → Analysis: one minute; Session: sixty minutes. Useful for scalpers and high-frequency intraday reads.
Between fifteen and under sixty minutes → Analysis: fifteen minutes; Session: one day. Suits day traders who need intraday alignment to the daily session.
Between sixty minutes and under one day → Analysis: sixty minutes; Session: one week. Serves intraday-to-swing transitions and end-of-day planning.
Between one day and under one week → Analysis: two hundred forty minutes; Session: two weeks. Fits swing traders who monitor multi-day structure.
Between one week and under thirty days → Analysis: one day; Session: three months. Supports position traders seeking quarterly context.
Thirty days and above → Analysis: one day; Session: twelve months. Provides a broad annual anchor for macro context.
These tiers are designed to keep anchors meaningful across regimes while preserving responsiveness appropriate to the trader profile.
Behavior, Constraints & Performance
Signals can be validated on closed bars through close-based logic; enabling this reduces intrabar flicker.
Higher-timeframe values may evolve during a forming bar; waiting parameters and the outside-bar gate reduce, but do not remove, this effect.
Resource footprint is light; the script uses standard indicators and a single higher-timeframe request per stream.
Known limits: rare setups during very quiet periods, sensitivity to gaps, and reduced reliability on illiquid symbols.
Sensible Defaults & Quick Tuning
Start with close-based validation on, ATR buffer on with a multiplier near zero point two, tick buffer two, cooldown three, once-per-session on.
Too many flips: increase the ATR multiplier and cooldown; consider enabling the EMA filter and slope.
Too sluggish: reduce the ATR multiplier and waiting; disable retest.
Choppy conditions: keep close-based validation, increase tick buffer, shorten the retest window.
What this indicator is—and isn’t
This is a visualization and signal layer for session-anchored breakouts with stability gates. It is not a complete trading system, risk framework, or predictive engine. Combine it with structured analysis, position sizing, and disciplined risk controls.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Daytrade Forex Scalper TwinPulse Auction Timer IndicatorWhat this indicator is
TwinPulse Auction Timer is a multi component execution aid designed for liquid markets. It looks for two families of opportunities
Breakouts that leave a compression area after a fresh sweep
Reversals that trigger after a sweep with strong wick polarity
It does not try to predict future prices. It measures present auction conditions with transparent rules and shows you when those conditions align. You get a simple table that says LONG SHORT or WAIT, optional session shading, clean entry and exit level visuals, and alerts you can wire to your workflow.
Why it is different
Most tools show a single signal. TwinPulse combines several independent signals into an Edge Score that you can tune. The components are
• Pulse. A signed measure of wick asymmetry with candle body direction
• Compression. Current true range compared with an average range
• Sweep timer. Bars elapsed since the most recent sweep of a prior high or low
• Bias. Direction of a higher timeframe candle
• Regime. Efficiency ratio and the relation of micro to macro volatility
• Location. Distance from the daily anchored VWAP
• Session. London and New York filter by time windows
Each component is visible in the inputs and in the table so you can understand why a suggestion appears. The script uses request.security() with lookahead off in all calls so it does not peek into the future. Shapes may move while a bar is open since price is still forming. They stop moving when the bar closes.
What you will see on the chart
• L and S shapes on entry bars
• An Exit shape at the price where a stop or the runner target would have been hit
• Four horizontal lines while a trade is active
Entry
Stop
TP1 at one R
TP2 at the runner target expressed in R
• Labels anchored to each line so you can instantly read Entry SL TP1 and TP2 with current values
• Optional shading during your session windows
• Optional daily VWAP line
The table in the top right shows
Action LONG SHORT IN LONG IN SHORT or WAIT
Session ON or OFF
Bias UP DOWN or FLAT
Pulse value
Compression value
Edge L percent and Edge S percent
How it works in detail
Pulse
For each bar the script measures up wick minus down wick divided by range and multiplies that by the sign of the candle body. The result is averaged with pulse_len. Positive numbers indicate aggressive buying. Negative numbers indicate aggressive selling. You control the minimum absolute value with pulse_thr.
Compression
Compression is the ratio of current range to an average range. You can choose the range basis. HL SMA uses simple high minus low smoothed by range_len. ATR uses classic True Range smoothed by atr_len. Values below comp_thr indicate a coil.
Sweeps and the timer
A sweep occurs when price trades beyond the highest high or lowest low seen in the previous sweep_len bars. A strict sweep requires a close back inside that prior range. The timer measures how many bars have elapsed since the last sweep. Breakout setups require the timer to exceed timer_thr.
Bias on a confirmation timeframe
A higher timeframe candle is read with confirm_tf. If close is above open bias is UP. If close is below open bias is DOWN. This keeps breakouts aligned with the prevailing drift.
Regime filters
Efficiency ratio measures the straight line change over the sum of absolute bar to bar changes over er_len. It rises in trendy conditions and falls in noise. Minimum efficiency is controlled by er_min.
Micro to macro volatility ratio compares a short lookback average range with a longer lookback average range using your chosen basis. For breakouts you usually want micro volatility to be near or above macro hence mvr_min. For reversals you often want micro volatility that is not overheated relative to macro hence mvr_max_rev.
VWAP distance gate
Daily anchored VWAP is rebuilt from the open of each session. The script computes the absolute distance from VWAP in units of your average range and requires that distance to exceed vwap_dist_thr when use_vwap_gate is true. This keeps entries away from the mean.
Edge Score
Each gate contributes a weight that you control. The script sums weights of the satisfied gates and divides by the sum of all weights to produce an Edge percent for long and an Edge percent for short. You can then require a minimum Edge percent using edge_min_pct. This turns the indicator into a step by step checklist that you can tune to your taste.
Using the indicator step by step
Choose markets and timeframes
The logic is designed for liquid instruments. Major currency pairs, index futures and cash index CFDs, and the most liquid crypto pairs work well. On intraday use one to fifteen minutes for signals and fifteen to sixty minutes for confirmation. On swing use one hour to one day for signals and one day for confirmation.
Decide on entry mode
Breakouts require a compression area and a sweep timer. Reversals require a strict sweep and a strong pulse. If you are unsure leave the default which allows both.
Pick a range basis
For FX and crypto HL SMA is often stable. For indices and single name equities with gaps ATR can adapt better. If results look too reactive increase the window. If results are too slow reduce it.
Tune regime filters
If you trade trend continuation raise er_min and mvr_min. If you trade counter rotation lower them and rely on the reversal path with the strict sweep condition.
Set the VWAP gate
Enabling it helps you avoid entries at the mean. Push the threshold higher on range bound days. Reduce it in strong trend days.
Table driven decision
Watch Action and the Edge percents. If the script says WAIT you can read Pulse and Compression to see what is missing. Often the best trades appear when both Edge percents are well separated and your session switch is ON.
Use the visuals
When a suggestion triggers you will see entry stop and targets. You can mirror the levels in your own workflow or use alerts.
Consider bar close
Signals are computed in real time. For a strict process you can wait until the bar closes to reduce noise.
Inputs explained with quick guidance
Setup
Signal TF chooses where the logic is computed. Leave blank to use the chart.
Confirm TF sets the higher timeframe for bias.
Session filter restricts signals to the London and New York windows you specify.
Invert flips long and short. It is useful on inverse instruments.
Logic options
Entry mode allows Breakouts Reversals or Both.
Average range basis selects HL SMA or ATR.
ATR length is used when ATR is selected.
Pulse source can be Regular OHLC or Heikin Ashi. Heikin Ashi smooths noisy series, but the script still runs on regular bars and you should publish and use it on standard candles to respect the platform guidance.
Core numeric settings
Sweep lookback controls the size of the liquidity pool targeted by the sweep condition.
Pulse window smooths the wick polarity measure.
Average range window controls your base range when you use HL SMA.
Pulse threshold sets the minimum polarity required.
Compression threshold sets the maximum current range relative to average to consider the market coiled.
Expansion timer bars sets how much time has passed since the last sweep before you allow a breakout.
Regime filters
Efficiency ratio length and minimum value keep you out of aimless drift.
Micro and Macro range lengths feed the micro to macro ratio.
Minimum micro to macro for breakouts and maximum micro to macro for reversals steer the two entry families.
VWAP gate and distance threshold keep you away from the mean.
Levels and trade management visuals
Runner target in R sets TP2 as a multiple of initial risk.
Stop distance as average range multiple sets initial risk size for the visuals.
Move stop to entry after one R touch turns on break even logic once price has traveled one risk unit.
Trail buffer as R fraction uses the last sweep as an anchor and keeps a dynamic stop at a chosen fraction of R beyond it.
Cooldown after exit prevents immediate re entries.
Edge Score
Weights for pulse compression timer bias efficiency ratio micro to macro VWAP gate and session let you align the checklist with your style.
Minimum Edge percent to suggest applies a final filter to LONG or SHORT suggestions.
UI
Table and markers switch the compact dashboard and the shapes.
TP and SL lines and labels draw and name each level.
TP1 partial label percent is printed in the TP1 label for clarity.
Session shading helps with focus.
Daily VWAP line is optional.
Alerts
The script provides alerts for Long Short Exit and for Edge percent crossing the threshold on either side. Use them to drive notifications or to sync with webhooks and your broker integration. Alerts trigger in real time and will repaint during a bar. For conservative use trigger on bar close.
Recommended presets
Intraday trend continuation
Confirm TF fifteen minutes
Entry mode Breakouts
Range basis HL SMA
Pulse threshold near 0.10
Compression threshold near 0.60
Timer around 18
Minimum efficiency ratio near 0.20
Minimum micro to macro near 1.00
VWAP gate enabled with distance near 0.35
Edge minimum 50 or higher
Intraday mean reversion at sweeps
Entry mode Reversals
Pulse source Regular OHLC
Compression threshold can be a little higher
Maximum micro to macro near 1.60
Efficiency ratio minimum lower near 0.12
VWAP gate enabled
Edge minimum 40 to 60
Swing trend continuation
Signal TF one hour
Confirm TF one day
Range basis ATR
ATR length around 14
Average range window 20 to 30
Efficiency ratio minimum near 0.18
Micro to macro windows 12 and 60
Edge minimum 50 to 70
These are starting points only. Your instrument and timeframe will require small adjustments.
Limitations and honest warnings
No indicator is perfect. TwinPulse will mark attractive conditions that do not always lead to profitable trades. During economic releases or very thin liquidity the assumptions behind compression and sweeps may fail. In strong gap environments the HL SMA basis may lag while ATR may overreact. Heikin Ashi pulse can help in choppy markets but it will lag during sharp reversals. Session times use the exchange time of your chart. If you switch symbol or exchange verify the windows.
Edge percent is not a probability of profit. It is the fraction of satisfied gates with your chosen weights. Two traders can set different weights and see different Edge readings on the same bar. That is the design. The score is a guide that helps you act with discipline.
This indicator does not place orders or manage real risk. The lines and labels show a model entry a model stop and two model targets built from the average range at entry and from recent swing points. Use them as references and not as hard rules. Always test on historical data and demo first. Past results do not guarantee anything in the future.
Credits and originality
All code in this publication is original and written for this indicator. The concept of the efficiency ratio originates from Perry Kaufman. The use of a daily anchored volume weighted average price is a standard industry tool. The specific combination of pulse from wick polarity strict sweep timing compression and the tunable Edge Score is unique to this script at the time of publication. If you reuse parts of the open source code in your own work remember to credit the author and contribute meaningful improvements.
How to read the table at a glance
Action reflects your current state.
IN LONG or IN SHORT appears while a trade is active.
LONG or SHORT appears when conditions for entry are met and the Edge threshold is satisfied.
WAIT appears when at least one gate is missing.
Session shows ON during your chosen windows.
Bias shows the color of the confirmation candle.
Pulse is the smoothed polarity number.
Comp shows current range divided by the average range. Values below one mean compression.
Edge L percent and Edge S percent show the long and short checklists as percents.
Final thoughts
Markets move because orders accumulate at certain prices and at certain times. The indicator tries to measure two things that often matter at those turning points. One is the existence of a hidden imbalance revealed by wick polarity and by sweeps of prior extremes. The other is the presence of energy stored in a coil that can release in the direction of a drift. Neither force guarantees profit. Together they can improve your selection and your timing.
Use the defaults for a few days so you learn the personality of the signals. After that adjust one group at a time. Start with the session filter and the Edge threshold. Then tune compression and the timer. Finally adjust the regime filters. Keep notes. You will learn which weights matter for your market and timeframe. The result is a process you can apply with consistency.
Disclaimer
This script and description are for education and analysis. They are not investment advice and they do not promise future results. Use at your own risk. Test thoroughly on historical data and in simulation before considering any live use.
TwinPulse Q Lead SPY x QQQ Intermarket Pulse 1HTwinPulse Q Lead is a concise one hour indicator for SPY and QQQ that converts three sources of market information into a single pulse line, a mode readout with BUY SELL WAIT, and compact alerts. It blends intermarket leadership between QQQ and SPY, intraday flow from the slope of session VWAP, and where the current price sits inside the regular trading hours range. The three components are normalized, fused, compressed to a stable range, and smoothed for clear thresholds. The aim is a readable intraday regime signal that helps you decide when to participate and when to stand aside.
The script is built with Pine v6, uses request security with lookahead off, and does not repaint. It is an indicator, not a strategy. It does not contain any solicitation, links, or outside references. The description is self contained and explains both logic and use so that any trader can understand the design without reading code.
What makes this original and useful
Intermarket leadership is measured directly from QQQ and SPY on your working timeframe using a Z score of the return spread. When growth is leading value heavy large caps, leadership turns positive. When it lags, leadership turns negative. This gives a real time read of the Nasdaq versus S and P tug of war that most day traders watch informally.
Intraday flow is taken from the slope of the session VWAP. A linear regression of VWAP over a short window captures whether value is rising or falling inside the day. Dividing by ATR normalizes slope by typical movement so that the signal is comparable across weeks.
Session position places price inside the current regular hours high to low. It answers whether the day is trading in the top half, the bottom half, or the middle. This is a simple but powerful context filter for breakouts and fades.
The three components are fused into one pulse, compressed with either hyperbolic tangent or softsign to keep values bounded, and then smoothed by a short EMA. This yields a stable range with a zero line so the eye can read shifts quickly.
The panel shows a human readable mode with reasons and a strength score. Traders who do not want to read lines can rely on a simple state and a compact justification that explains why the state is set.
This is not a mashup that simply overlays unrelated indicators. Each component was chosen to answer a distinct question that is common to SPY and QQQ intraday decision making. Leadership answers who is in charge, flow answers whether value inside the session is building or leaking, and position answers if price is pressing the extremes or circling the middle. The pulse ties the three together and prevents any single component from dominating.
How the calculations work
Leadership. Compute a short rate of change for SPY and QQQ. Subtract SPY from QQQ to get spread returns, then compute a rolling Z score over a longer window. Positive values mean QQQ is leading. Negative values mean SPY is leading.
Flow. Compute session VWAP on the active symbol. Regress VWAP over a short window to obtain a slope estimate. Divide by ATR to scale slope by current volatility so that a small rise on a quiet day is not treated the same as a small rise on a wild day.
Position. Track the highest high and lowest low since the start of regular hours. Place the current close inside that range on a zero to one scale, then recenter to a minus one to plus one scale. Positive means the top half of the day, negative means the bottom half.
Fusion. Multiply each component by a weight so users can emphasize or de emphasize leadership, flow, or position. Sum to a raw pulse.
Compression. Pass the raw pulse through a bounded function. Hyperbolic tangent is smooth and has natural saturation near the extremes. Softsign is faster and behaves like a smoother version of sign near zero. Compression avoids unbounded excursions and makes thresholds meaningful across days.
Smoothing. Apply a short EMA to the compressed pulse to reduce noise. This creates the main line called TwinPulse in the plot.
Thresholds. You can use static symmetric levels or adaptive levels. The adaptive option computes a mean and a standard deviation of the smoothed pulse over a user window, then sets upper and lower thresholds as mean plus or minus sigma times standard deviation. This allows thresholds to adjust across regimes. Static levels are still available for traders who want repeatable levels.
Events and mode. A long event fires when the smoothed pulse crosses the upper threshold with positive flow and any optional filters agree. A short event fires on the symmetric condition. The mode reads the current state rather than fire and forget. It returns BUY when the smoothed pulse is above the upper threshold with positive flow, SELL when the smoothed pulse is below the lower threshold with negative flow, otherwise WAIT. A cooldown controls how often events can fire so alerts do not spam during choppy periods.
Inputs and default values
The script ships with defaults chosen for SPY and QQQ on one hour charts.
Symbols. SPY and QQQ by default. You can switch to any pair. Many users may test IWM versus SPY for small cap reads.
Regular hours selector. On by default. This restricts the position factor to New York regular hours. Turn it off if you prefer full session behavior.
ROC length is three bars. Z score length is fifty bars. VWAP slope window is ten bars. ATR length is fourteen bars. Pulse smoothing length is three bars.
Compression mode. Choose hyperbolic tangent or softsign. Hyperbolic tangent is default.
Weights. Leadership and flow are one by default. Position is set to zero point seven to give a modest influence to where price sits inside the day.
Thresholds. Adaptive thresholds are on by default with a lookback of one hundred bars and a sigma width of zero point eight. Static levels at plus or minus zero point six are ready if you disable adaptive mode.
Filters. ADX filter is off by default. If you enable it, the script requires ADX above a user minimum before it will signal. Higher time frame confirmation is off by default. When enabled it compares the smoothed pulse on the confirm timeframe to zero and requires alignment for longs or shorts.
Cooldown. Three bars by default so that alerts do not trigger too frequently.
UI. Bar coloring is on by default. The panel is on by default and sits at the top right.
All request security calls use lookahead off and will not request future data. All persistent state variables are assigned in a way that prevents repainting. The indicator does not use non standard chart types in its logic.
How to use the indicator
Load a one hour chart of SPY or QQQ. Keep a clean chart so that the script output is easy to read.
Turn on regular hours if you want the session position to reflect the cash session. This is recommended for SPY and QQQ.
Watch the panel. Mode reads BUY or SELL or WAIT. The strength value is a simple vote based score that ranges from zero to one hundred. It counts leadership, flow, ADX if enabled, and higher time frame confirmation if enabled. You can use strength to filter weak states.
Consider action only when mode is BUY or SELL and the signal has not just fired on the last bar. The triangles mark where an event fired. Alerts use the same logic as the events. WAIT means stand aside.
To slow the system, enable ADX and set a higher minimum or enable higher time frame confirmation. To speed it up, disable the filters, disable adaptive thresholds, or tighten the sigma width.
When publishing, use a clean chart with only this indicator. Show the symbol and timeframe clearly and make sure the plot legend is visible. If you add drawings on the chart, only include ones that help readers understand the output.
Publication notes and compliance
This description is written in English. The title uses ASCII and only uses capital letters for common abbreviations. The script is original and explains how and why the components work together. There are no links or promotional material. The script does not claim performance. It does not use lookahead. The panel and alerts exist to help a human read and act with discipline. The indicator can be published as open source or as protected. If you choose protected, the description still allows readers to understand how the logic works without access to the code.
If you later convert the logic into a strategy for publication, use realistic commission and slippage, risk no more than a small share of equity per trade, and choose a dataset that yields a large enough sample. Explain any deviations from these default recommendations in your strategy description. Do not publish results from non standard chart types since they can mislead readers on signal timing.
Limitations and risks
Intermarket leadership is a relative measure. There are hours when both SPY and QQQ fall while leadership remains positive. Treat leadership as a context, not a stand alone trigger.
VWAP slope is a path measure inside the session. It can flip several times on a choppy day. That is why the script uses a short smoothing and an optional cooldown. Use ADX or higher time frame confirmation to avoid the worst chop.
Session position assumes a meaningful regular hours range. On half days or around openings with gaps the position factor can be less informative. If this bothers you, reduce the weight of position or turn it off.
Compression and smoothing introduce lag by design. The goal is stability and clarity. If you want earlier but noisier signals, reduce smoothing and weights, and use static thresholds.
No indicator guarantees future results. TwinPulse Q Lead is a decision aid. It should be combined with your risk rules, position size policy, and a clear exit plan. Past behavior is not a promise for the future.
Frequently asked questions
What symbols are supported. Any symbol can be used as the chart symbol. Leadership uses the two user symbols which default to SPY and QQQ. Many traders may try IWM versus SPY or DIA versus SPY.
Can I change the timeframe. Yes, but the design target is one hour. On very short timeframes the VWAP slope becomes very sensitive and you should consider stronger filters.
Does the script repaint. No. It uses request security with lookahead off and the panel updates on the last bar only. Events are based on bar close conditions unless you attach alerts on any alert function call which will still respect the logic without looking into the future.
How are the strength numbers built. The strength score is the share of aligned votes across leadership, flow, ADX if enabled, and higher time frame confirmation if enabled. A value near one hundred means many filters agree. A value near fifty means partial alignment. It is not a probability or an accuracy number.
Can I use non standard chart types. You can view the indicator on them but do not publish signals from non standard chart types because that can mislead readers about timing. Use classic candles or bars when you publish and when you test.
Why do I sometimes see BUY but the price is not moving. A BUY mode requires pulse above the upper threshold and positive flow. It does not require higher highs immediately. Treat BUY as a permission to look for entries using your own execution rules.
Alerts█ OVERVIEW
This library is a Pine Script™ programmers tool that provides functions to simplify the creation of compound conditions and alert messages. With these functions, scripts can use comma-separated "string" lists to specify condition groups from arbitrarily large "bool" arrays , offering a convenient way to provide highly flexible alert creation to script users without requiring numerous inputs in the "Settings/Inputs" menu.
█ CONCEPTS
Compound conditions
Compound conditions are essentially groups of two or more conditions, where each required condition must occur to produce a `true` result. Traders often combine conditions, including signals from various indicators, to drive and reinforce trade decisions. Similarly, programmers use compound conditions in logical operations to create scripts that respond dynamically to groups of events.
Condition conundrum
Providing flexible condition combinations to script users for signals and alerts often poses a significant challenge: input complexity . Conventionally, such flexibility comes at the cost of an extensive list of separate inputs for toggling individual conditions and customizing their properties, often resulting in complicated input menus that are difficult for users to navigate effectively. Furthermore, managing all those inputs usually entails tediously handling many extra variables and logical expressions, making such projects more complex for programmers.
Condensing complexity
This library introduces a technique using parsed strings to reference groups of elements from "bool" arrays , helping to simplify and streamline the construction of compound conditions and alert messages. With this approach, programmers can provide one or more "string" inputs in their scripts where users can list numbers corresponding to the conditions they want to combine.
For example, suppose you have a script that creates alert triggers based on a combination of up to 20 individual conditions, and you want to make inputs for users to choose which conditions to combine. Instead of creating 20 separate checkboxes in the "Settings/Inputs" tab and manually adding associated logic for each one, you can store the conditional values in arrays, make one or more "string" inputs that accept values listing the array item locations (e.g., "1,4,8,11"), and then pass the inputs to these functions to determine the compound conditions formed by the specified groups.
This approach condenses the input space, improving navigability and utility. Additionally, it helps provide high-level simplicity to complex conditional code, making it easier to maintain and expand over time.
█ CALCULATIONS AND USE
This library contains three functions for evaluating compound conditions: `getCompoundConditon()`, `getCompoundConditionsArray()`, and `compoundAlertMessage()`. Each function has two overloads that evaluate compound conditions based on groups of items from one or two "bool" arrays . The sections below explain the functions' calculations and how to use them.
Referencing conditions using "string" index lists
Each function processes "string" values containing comma-separated lists of numerals representing the indices of the "bool" array items to use in its calculations (e.g., "4, 8, 12"). The functions split each supplied "string" list by its commas, then iterate over those specified indices in the "bool" arrays to determine each group's combined `true` or `false` state.
For convenience, the numbers in the "string" lists can represent zero-based indices (where the first item is at index 0) or one-based indices (where the first item is at index 1), depending on the function's `zeroIndex` parameter. For example, an index list of "0, 2, 4" with a `zeroIndex` value of `true` specifies that the condition group uses the first , third , and fifth "bool" values in the array, ignoring all others. If the `zeroIndex` value is `false`, the list "1, 3, 5" also refers to those same elements.
Zero-based indexing is convenient for programmers because Pine arrays always use this index format. However, one-based indexing is often more convenient and familiar for script users, especially non-programmers.
Evaluating one or many condition groups
The `getCompoundCondition()` function evaluates singular condition groups determined by its `indexList` parameter, returning `true` values whenever the specified array elements are `true`. This function is helpful when a script has to evaluate specific groups of conditions and does not require many combinations.
In contrast, the `getCompoundConditionsArray()` function can evaluate numerous condition groups, one for each "string" included in its `indexLists` argument. It returns arrays containing `true` or `false` states for each listed group. This function is helpful when a script requires multiple condition combinations in additional calculations or logic.
The `compoundAlertMessage()` function is similar to the `getCompoundConditionsArray()` function. It also evaluates a separate compound condition group for each "string" in its `indexLists` array, but it returns "string" values containing the marker (name) of each group with a `true` result. You can use these returned values as the `message` argument in alert() calls, display them in labels and other drawing objects, or even use them in additional calculations and logic.
Directional condition pairs
The first overload of each function operates on a single `conditions` array, returning values representing one or more compound conditions from groups in that array. These functions are ideal for general-purpose condition groups that may or may not represent direction information.
The second overloads accept two arrays representing upward and downward conditions separately: `upConditions` and `downConditions`. These overloads evaluate opposing directional conditions in pairs (e.g., RSI is above/below a level) and return upward and downward condition information separately in a tuple .
When using the directional overloads, ensure the `upConditions` and `downConditions` arrays are the same size, with the intended condition pairs at the same indices . For instance, if you have a specific upward RSI condition's value at the first index in the `upConditions` array, include the opposing downward RSI condition's value at that same index in the `downConditions` array. If a condition can apply to both directions (e.g., rising volume), include its value at the same index in both arrays.
Group markers
To simplify the generation of informative alert messages, the `compoundAlertMessage()` function assigns "string" markers to each condition group, where "marker" refers to the group's name. The `groupMarkers` parameter allows you to assign custom markers to each listed group. If not specified, the function generates default group markers in the format "M", where "M" is short for "Marker" and "" represents the group number starting from 1. For example, the default marker for the first group specified in the `indexLists` array is "M1".
The function's returned "string" values contain a comma-separated list with markers for each activated condition group (e.g., "M1, M4"). The function's second overload, which processes directional pairs of conditions, also appends extra characters to the markers to signify the direction. The default for upward groups is "▲" (e.g., "M1▲") and the default for downward ones is "▼" (e.g., "M1▼"). You can customize these appended characters with the `upChar` and `downChar` parameters.
Designing customizable alerts
We recommend following these primary steps when using this library to design flexible alerts for script users:
1. Create text inputs for users to specify comma-separated lists of conditions with the input.string() or input.text_area() functions, and then collect all the input values in a "string" array . Note that each separate "string" in the array will represent a distinct condition group.
2. Create arrays of "bool" values representing the possible conditions to choose from. If your script will process pairs of upward and downward conditions, ensure the related elements in the arrays align at the same indices.
3. Call `compoundAlertMessage()` using the arrays from steps 1 and 2 as arguments to get the alert message text. If your script will use the text for alerts only, not historical display or calculation purposes, the call is necessary only on realtime bars .
4. Pass the calculated "string" values as the `message` argument in alert() calls. We recommend calling the function only when the "string" is not empty (i.e., `messageText != ""`). To avoid repainting alerts on open bars, use barstate.isconfirmed in the condition to allow alert triggers only on each bar's close .
5. Test the alerts. Open the "Create Alert" dialog box and select "Any alert() function call" in the "Condition" field. It is also helpful to inspect the strings with Pine Logs .
NOTE: Because the techniques in this library use lists of numbers to specify conditions, we recommend including a tooltip for the "string" inputs that lists the available numbers and the conditions they represent. This tooltip provides a legend for script users, making it simple to understand and utilize. To create the tooltip, declare a "const string" listing the options and pass it to the `input.*()` call's `tooltip` parameter. See the library's example code for a simple demonstration.
█ EXAMPLE CODE
This library's example code demonstrates one possible way to offer a selection of compound conditions with "string" inputs and these functions. It uses three input.string() calls, each accepting a comma-separated list representing a distinct condition group. The title of each input represents the default group marker that appears in the label and alert text. The code collects these three input values in a `conditionGroups` array for use with the `compoundAlertMessage()` function.
In this code, we created two "bool" arrays to store six arbitrary condition pairs for demonstration:
1. Bar up/down: The bar's close price must be above the open price for upward conditions, and vice versa for downward conditions.
2. Fast EMA above/below slow EMA : The 9-period Exponential Moving Average of close prices must be above the 21-period EMA for upward conditions, and vice versa for downward conditions.
3. Volume above average : The bar's volume must exceed its 20-bar average to activate an upward or downward condition.
4. Volume rising : The volume must exceed that of the previous bar to activate an upward or downward condition.
5. RSI trending up/down : The 14-period Relative Strength Index of close prices must be between 50 and 70 for upward conditions, and between 30 and 50 for downward conditions.
6. High volatility : The 7-period Average True Range (ATR) must be above the 40-period ATR to activate an upward or downward condition.
We included a `tooltip` argument for the third input.string() call that displays the condition numbers and titles, where 1 is the first condition number.
The `bullConditions` array contains the `true` or `false` states of all individual upward conditions, and the `bearConditions` array contains all downward condition states. For the conditions that filter either direction because they are non-directional, such as "High volatility", both arrays contain the condition's `true` or `false` value at the same index. If you use these conditions alone, they activate upward and downward alert conditions simultaneously.
The example code calls `compoundAlertMessage()` using the `bullConditions`, `bearConditions`, and `conditionGroups` arrays to create a tuple of strings containing the directional markers for each activated group. On confirmed bars, it displays non-empty strings in labels and uses them in alert() calls. For the text shown in the labels, we used str.replace_all() to replace commas with newline characters, aligning the markers vertically in the display.
Look first. Then leap.
█ FUNCTIONS
This library exports the following functions:
getCompoundCondition(conditions, indexList, minRequired, zeroIndex)
(Overload 1 of 2) Determines a compound condition based on selected elements from a `conditions` array.
Parameters:
conditions (array) : (array) An array containing the possible "bool" values to use in the compound condition.
indexList (string) : (series string) A "string" containing a comma-separated list of whole numbers representing the group of `conditions` elements to use in the compound condition. For example, if the value is `"0, 2, 4"`, and `minRequired` is `na`, the function returns `true` only if the `conditions` elements at index 0, 2, and 4 are all `true`. If the value is an empty "string", the function returns `false`.
minRequired (int) : (series int) Optional. Determines the minimum number of selected conditions required to activate the compound condition. For example, if the value is 2, the function returns `true` if at least two of the specified `conditions` elements are `true`. If the value is `na`, the function returns `true` only if all specified elements are `true`. The default is `na`.
zeroIndex (bool) : (series bool) Optional. Specifies whether the `indexList` represents zero-based array indices. If `true`, a value of "0" in the list represents the first array index. If `false`, a `value` of "1" represents the first index. The default is `true`.
Returns: (bool) `true` if `conditions` elements in the group specified by the `indexList` are `true`, `false` otherwise.
getCompoundCondition(upConditions, downConditions, indexList, minRequired, allowUp, allowDown, zeroIndex)
(Overload 2 of 2) Determines upward and downward compound conditions based on selected elements from `upConditions` and `downConditions` arrays.
Parameters:
upConditions (array) : (array) An array containing the possible "bool" values to use in the upward compound condition.
downConditions (array) : (array) An array containing the possible "bool" values to use in the downward compound condition.
indexList (string) : (series string) A "string" containing a comma-separated list of whole numbers representing the `upConditions` and `downConditions` elements to use in the compound conditions. For example, if the value is `"0, 2, 4"` and `minRequired` is `na`, the function returns `true` for the first value only if the `upConditions` elements at index 0, 2, and 4 are all `true`. If the value is an empty "string", the function returns ` `.
minRequired (int) : (series int) Optional. Determines the minimum number of selected conditions required to activate either compound condition. For example, if the value is 2, the function returns `true` for its first value if at least two of the specified `upConditions` elements are `true`. If the value is `na`, the function returns `true` only if all specified elements are `true`. The default is `na`.
allowUp (bool) : (series bool) Optional. Controls whether the function considers upward compound conditions. If `false`, the function ignores the `upConditions` array, and the first item in the returned tuple is `false`. The default is `true`.
allowDown (bool) : (series bool) Optional. Controls whether the function considers downward compound conditions. If `false`, the function ignores the `downConditions` array, and the second item in the returned tuple is `false`. The default is `true`.
zeroIndex (bool) : (series bool) Optional. Specifies whether the `indexList` represents zero-based array indices. If `true`, a value of "0" in the list represents the first array index. If `false`, a value of "1" represents the first index. The default is `true`.
Returns: ( ) A tuple containing two "bool" values representing the upward and downward compound condition states, respectively.
getCompoundConditionsArray(conditions, indexLists, zeroIndex)
(Overload 1 of 2) Creates an array of "bool" values representing compound conditions formed by selected elements from a `conditions` array.
Parameters:
conditions (array) : (array) An array containing the possible "bool" values to use in each compound condition.
indexLists (array) : (array) An array of strings containing comma-separated lists of whole numbers representing the `conditions` elements to use in each compound condition. For example, if an item is `"0, 2, 4"`, the corresponding item in the returned array is `true` only if the `conditions` elements at index 0, 2, and 4 are all `true`. If an item is an empty "string", the item in the returned array is `false`.
zeroIndex (bool) : (series bool) Optional. Specifies whether the "string" lists in the `indexLists` represent zero-based array indices. If `true`, a value of "0" in a list represents the first array index. If `false`, a value of "1" represents the first index. The default is `true`.
Returns: (array) An array of "bool" values representing compound condition states for each condition group. An item in the array is `true` only if all the `conditions` elements specified by the corresponding `indexLists` item are `true`. Otherwise, the item is `false`.
getCompoundConditionsArray(upConditions, downConditions, indexLists, allowUp, allowDown, zeroIndex)
(Overload 2 of 2) Creates two arrays of "bool" values representing compound upward and
downward conditions formed by selected elements from `upConditions` and `downConditions` arrays.
Parameters:
upConditions (array) : (array) An array containing the possible "bool" values to use in each upward compound condition.
downConditions (array) : (array) An array containing the possible "bool" values to use in each downward compound condition.
indexLists (array) : (array) An array of strings containing comma-separated lists of whole numbers representing the `upConditions` and `downConditions` elements to use in each compound condition. For example, if an item is `"0, 2, 4"`, the corresponding item in the first returned array is `true` only if the `upConditions` elements at index 0, 2, and 4 are all `true`. If an item is an empty "string", the items in both returned arrays are `false`.
allowUp (bool) : (series bool) Optional. Controls whether the function considers upward compound conditions. If `false`, the function ignores the `upConditions` array, and all elements in the first returned array are `false`. The default is `true`.
allowDown (bool) : (series bool) Optional. Controls whether the function considers downward compound conditions. If `false`, the function ignores the `downConditions` array, and all elements in the second returned array are `false`. The default is `true`.
zeroIndex (bool) : (series bool) Optional. Specifies whether the "string" lists in the `indexLists` represent zero-based array indices. If `true`, a value of "0" in a list represents the first array index. If `false`, a value of "1" represents the first index. The default is `true`.
Returns: ( ) A tuple containing two "bool" arrays:
- The first array contains values representing upward compound condition states determined using the `upConditions`.
- The second array contains values representing downward compound condition states determined using the `downConditions`.
compoundAlertMessage(conditions, indexLists, zeroIndex, groupMarkers)
(Overload 1 of 2) Creates a "string" message containing a comma-separated list of markers representing active compound conditions formed by specified element groups from a `conditions` array.
Parameters:
conditions (array) : (array) An array containing the possible "bool" values to use in each compound condition.
indexLists (array) : (array) An array of strings containing comma-separated lists of whole numbers representing the `conditions` elements to use in each compound condition. For example, if an item is `"0, 2, 4"`, the corresponding marker for that item appears in the returned "string" only if the `conditions` elements at index 0, 2, and 4 are all `true`.
zeroIndex (bool) : (series bool) Optional. Specifies whether the "string" lists in the `indexLists` represent zero-based array indices. If `true`, a value of "0" in a list represents the first array index. If `false`, a value of "1" represents the first index. The default is `true`.
groupMarkers (array) : (array) Optional. If specified, sets the marker (name) for each condition group specified in the `indexLists` array. If `na`, the function uses the format `"M"` for each group, where "M" is short for "Marker" and `` represents the one-based index for the group (e.g., the marker for the first listed group is "M1"). The default is `na`.
Returns: (string) A "string" containing a list of markers corresponding to each active compound condition.
compoundAlertMessage(upConditions, downConditions, indexLists, allowUp, allowDown, zeroIndex, groupMarkers, upChar, downChar)
(Overload 2 of 2) Creates two "string" messages containing comma-separated lists of markers representing active upward and downward compound conditions formed by specified element groups from `upConditions` and `downConditions` arrays.
Parameters:
upConditions (array) An array containing the possible "bool" values to use in each upward compound condition.
downConditions (array) An array containing the possible "bool" values to use in each downward compound condition.
indexLists (array) An array of strings containing comma-separated lists of whole numbers representing the `upConditions` and `downConditions` element groups to use in each compound condition. For example, if an item is `"0, 2, 4"`, the corresponding group marker for that item appears in the first returned "string" only if the `upConditions` elements at index 0, 2, and 4 are all `true`.
allowUp (bool) Optional. Controls whether the function considers upward compound conditions. If `false`, the function ignores the `upConditions` array and returns an empty "string" for the first tuple element. The default is `true`.
allowDown (bool) Optional. Controls whether the function considers downward compound conditions. If `false`, the function ignores the `downConditions` array and returns an empty "string" for the second tuple element. The default is `true`.
zeroIndex (bool) Optional. Specifies whether the "string" lists in the `indexLists` represent zero-based array indices. If `true`, a value of "0" in a list represents the first array index. If `false`, a value of "1" represents the first index. The default is `true`.
groupMarkers (array) Optional. If specified, sets the name (marker) of each condition group specified in the `indexLists` array. If `na`, the function uses the format `"M"` for each group, where "M" is short for "Marker" and `` represents the one-based index for the group (e.g., the marker for the first listed group is "M1"). The default is `na`.
upChar (string) Optional. A "string" appended to all group markers for upward conditions to signify direction. The default is "▲".
downChar (string) Optional. A "string" appended to all group markers for downward conditions to signify direction. The default is "▼".
Returns: ( ): A tuple of "string" values containing lists of markers corresponding to active upward and downward compound conditions, respectively.
MTF_DrawingsLibrary 'MTF_Drawings'
This library helps with drawing indicators and candle charts on all timeframes.
FEATURES
CHART DRAWING : Library provides functions for drawing High Time Frame (HTF) and Low Time Frame (LTF) candles.
INDICATOR DRAWING : Library provides functions for drawing various types of HTF and LTF indicators.
CUSTOM COLOR DRAWING : Library allows to color candles and indicators based on specific conditions.
LINEFILLS : Library provides functions for drawing linefills.
CATEGORIES
The functions are named in a way that indicates they purpose:
{Ind} : Function is meant only for indicators.
{Hist} : Function is meant only for histograms.
{Candle} : Function is meant only for candles.
{Draw} : Function draws indicators, histograms and candle charts.
{Populate} : Function generates necessary arrays required by drawing functions.
{LTF} : Function is meant only for lower timeframes.
{HTF} : Function is meant only for higher timeframes.
{D} : Function draws indicators that are composed of two lines.
{CC} : Function draws custom colored indicators.
USAGE
Import the library into your script.
Before using any {Draw} function it is necessary to use a {Populate} function.
Choose the appropriate one based on the category, provide the necessary arguments, and then use the {Draw} function, forwarding the arrays generated by the {Populate} function.
This doesn't apply to {Draw_Lines}, {LineFill}, or {Barcolor} functions.
EXAMPLE
import Spacex_trader/MTF_Drawings/1 as tf
//Request lower timeframe data.
Security(simple string Ticker, simple string New_LTF, float Ind) =>
float Value = request.security_lower_tf(Ticker, New_LTF, Ind)
Value
Timeframe = input.timeframe('1', 'Timeframe: ')
tf.Draw_Ind(tf.Populate_LTF_Ind(Security(syminfo.tickerid, Timeframe, ta.rsi(close, 14)), 498, color.purple), 1, true)
FUNCTION LIST
HTF_Candle(BarsBack, BodyBear, BodyBull, BordersBear, BordersBull, WickBear, WickBull, LineStyle, BoxStyle, LineWidth, HTF_Open, HTF_High, HTF_Low, HTF_Close, HTF_Bar_Index)
Populates two arrays with drawing data of the HTF candles.
Parameters:
BarsBack (int) : Bars number to display.
BodyBear (color) : Candle body bear color.
BodyBull (color) : Candle body bull color.
BordersBear (color) : Candle border bear color.
BordersBull (color) : Candle border bull color.
WickBear (color) : Candle wick bear color.
WickBull (color) : Candle wick bull color.
LineStyle (string) : Wick style (Solid-Dotted-Dashed).
BoxStyle (string) : Border style (Solid-Dotted-Dashed).
LineWidth (int) : Wick width.
HTF_Open (float) : HTF open price.
HTF_High (float) : HTF high price.
HTF_Low (float) : HTF low price.
HTF_Close (float) : HTF close price.
HTF_Bar_Index (int) : HTF bar_index.
Returns: Two arrays with drawing data of the HTF candles.
LTF_Candle(BarsBack, BodyBear, BodyBull, BordersBear, BordersBull, WickBear, WickBull, LineStyle, BoxStyle, LineWidth, LTF_Open, LTF_High, LTF_Low, LTF_Close)
Populates two arrays with drawing data of the LTF candles.
Parameters:
BarsBack (int) : Bars number to display.
BodyBear (color) : Candle body bear color.
BodyBull (color) : Candle body bull color.
BordersBear (color) : Candle border bear color.
BordersBull (color) : Candle border bull color.
WickBear (color) : Candle wick bear color.
WickBull (color) : Candle wick bull color.
LineStyle (string) : Wick style (Solid-Dotted-Dashed).
BoxStyle (string) : Border style (Solid-Dotted-Dashed).
LineWidth (int) : Wick width.
LTF_Open (float ) : LTF open price.
LTF_High (float ) : LTF high price.
LTF_Low (float ) : LTF low price.
LTF_Close (float ) : LTF close price.
Returns: Two arrays with drawing data of the LTF candles.
Draw_Candle(Box, Line, Offset)
Draws HTF or LTF candles.
Parameters:
Box (box ) : Box array with drawing data.
Line (line ) : Line array with drawing data.
Offset (int) : Offset of the candles.
Returns: Drawing of the candles.
Populate_HTF_Ind(IndValue, BarsBack, IndColor, HTF_Bar_Index)
Populates one array with drawing data of the HTF indicator.
Parameters:
IndValue (float) : Indicator value.
BarsBack (int) : Indicator lines to display.
IndColor (color) : Indicator color.
HTF_Bar_Index (int) : HTF bar_index.
Returns: An array with drawing data of the HTF indicator.
Populate_LTF_Ind(IndValue, BarsBack, IndColor)
Populates one array with drawing data of the LTF indicator.
Parameters:
IndValue (float ) : Indicator value.
BarsBack (int) : Indicator lines to display.
IndColor (color) : Indicator color.
Returns: An array with drawing data of the LTF indicator.
Draw_Ind(Line, Mult, Exe)
Draws one HTF or LTF indicator.
Parameters:
Line (line ) : Line array with drawing data.
Mult (int) : Coordinates multiplier.
Exe (bool) : Display the indicator.
Returns: Drawing of the indicator.
Populate_HTF_Ind_D(IndValue_1, IndValue_2, BarsBack, IndColor_1, IndColor_2, HTF_Bar_Index)
Populates two arrays with drawing data of the HTF indicators.
Parameters:
IndValue_1 (float) : First indicator value.
IndValue_2 (float) : Second indicator value.
BarsBack (int) : Indicator lines to display.
IndColor_1 (color) : First indicator color.
IndColor_2 (color) : Second indicator color.
HTF_Bar_Index (int) : HTF bar_index.
Returns: Two arrays with drawing data of the HTF indicators.
Populate_LTF_Ind_D(IndValue_1, IndValue_2, BarsBack, IndColor_1, IndColor_2)
Populates two arrays with drawing data of the LTF indicators.
Parameters:
IndValue_1 (float ) : First indicator value.
IndValue_2 (float ) : Second indicator value.
BarsBack (int) : Indicator lines to display.
IndColor_1 (color) : First indicator color.
IndColor_2 (color) : Second indicator color.
Returns: Two arrays with drawing data of the LTF indicators.
Draw_Ind_D(Line_1, Line_2, Mult, Exe_1, Exe_2)
Draws two LTF or HTF indicators.
Parameters:
Line_1 (line ) : First line array with drawing data.
Line_2 (line ) : Second line array with drawing data.
Mult (int) : Coordinates multiplier.
Exe_1 (bool) : Display the first indicator.
Exe_2 (bool) : Display the second indicator.
Returns: Drawings of the indicators.
Barcolor(Box, Line, BarColor)
Colors the candles based on indicators output.
Parameters:
Box (box ) : Candle box array.
Line (line ) : Candle line array.
BarColor (color ) : Indicator color array.
Returns: Colored candles.
Populate_HTF_Ind_D_CC(IndValue_1, IndValue_2, BarsBack, BullColor, BearColor, IndColor_1, HTF_Bar_Index)
Populates two array with drawing data of the HTF indicators with color based on: IndValue_1 >= IndValue_2 ? BullColor : BearColor.
Parameters:
IndValue_1 (float) : First indicator value.
IndValue_2 (float) : Second indicator value.
BarsBack (int) : Indicator lines to display.
BullColor (color) : Bull color.
BearColor (color) : Bear color.
IndColor_1 (color) : First indicator color.
HTF_Bar_Index (int) : HTF bar_index.
Returns: Three arrays with drawing and color data of the HTF indicators.
Populate_LTF_Ind_D_CC(IndValue_1, IndValue_2, BarsBack, BullColor, BearColor, IndColor_1)
Populates two arrays with drawing data of the LTF indicators with color based on: IndValue_1 >= IndValue_2 ? BullColor : BearColor.
Parameters:
IndValue_1 (float ) : First indicator value.
IndValue_2 (float ) : Second indicator value.
BarsBack (int) : Indicator lines to display.
BullColor (color) : Bull color.
BearColor (color) : Bearcolor.
IndColor_1 (color) : First indicator color.
Returns: Three arrays with drawing and color data of the LTF indicators.
Populate_HTF_Hist_CC(HistValue, IndValue_1, IndValue_2, BarsBack, BullColor, BearColor, HTF_Bar_Index)
Populates one array with drawing data of the HTF histogram with color based on: IndValue_1 >= IndValue_2 ? BullColor : BearColor.
Parameters:
HistValue (float) : Indicator value.
IndValue_1 (float) : First indicator value.
IndValue_2 (float) : Second indicator value.
BarsBack (int) : Indicator lines to display.
BullColor (color) : Bull color.
BearColor (color) : Bearcolor.
HTF_Bar_Index (int) : HTF bar_index
Returns: Two arrays with drawing and color data of the HTF histogram.
Populate_LTF_Hist_CC(HistValue, IndValue_1, IndValue_2, BarsBack, BullColor, BearColor)
Populates one array with drawing data of the LTF histogram with color based on: IndValue_1 >= IndValue_2 ? BullColor : BearColor.
Parameters:
HistValue (float ) : Indicator value.
IndValue_1 (float ) : First indicator value.
IndValue_2 (float ) : Second indicator value.
BarsBack (int) : Indicator lines to display.
BullColor (color) : Bull color.
BearColor (color) : Bearcolor.
Returns: Two array with drawing and color data of the LTF histogram.
Populate_LTF_Hist_CC_VA(HistValue, Value, BarsBack, BullColor, BearColor)
Populates one array with drawing data of the LTF histogram with color based on: HistValue >= Value ? BullColor : BearColor.
Parameters:
HistValue (float ) : Indicator value.
Value (float) : First indicator value.
BarsBack (int) : Indicator lines to display.
BullColor (color) : Bull color.
BearColor (color) : Bearcolor.
Returns: Two array with drawing and color data of the LTF histogram.
Populate_HTF_Ind_CC(IndValue, IndValue_1, BarsBack, BullColor, BearColor, HTF_Bar_Index)
Populates one array with drawing data of the HTF indicator with color based on: IndValue >= IndValue_1 ? BullColor : BearColor.
Parameters:
IndValue (float) : Indicator value.
IndValue_1 (float) : Second indicator value.
BarsBack (int) : Indicator lines to display.
BullColor (color) : Bull color.
BearColor (color) : Bearcolor.
HTF_Bar_Index (int) : HTF bar_index
Returns: Two arrays with drawing and color data of the HTF indicator.
Populate_LTF_Ind_CC(IndValue, IndValue_1, BarsBack, BullColor, BearColor)
Populates one array with drawing data of the LTF indicator with color based on: IndValue >= IndValue_1 ? BullColor : BearColor.
Parameters:
IndValue (float ) : Indicator value.
IndValue_1 (float ) : Second indicator value.
BarsBack (int) : Indicator lines to display.
BullColor (color) : Bull color.
BearColor (color) : Bearcolor.
Returns: Two arrays with drawing and color data of the LTF indicator.
Draw_Lines(BarsBack, y1, y2, LineType, Fill)
Draws price lines on indicators.
Parameters:
BarsBack (int) : Indicator lines to display.
y1 (float) : Coordinates of the first line.
y2 (float) : Coordinates of the second line.
LineType (string) : Line type.
Fill (color) : Fill color.
Returns: Drawing of the lines.
LineFill(Upper, Lower, BarsBack, FillColor)
Fills two lines with linefill HTF or LTF.
Parameters:
Upper (line ) : Upper line.
Lower (line ) : Lower line.
BarsBack (int) : Indicator lines to display.
FillColor (color) : Fill color.
Returns: Linefill of the lines.
Populate_LTF_Hist(HistValue, BarsBack, HistColor)
Populates one array with drawing data of the LTF histogram.
Parameters:
HistValue (float ) : Indicator value.
BarsBack (int) : Indicator lines to display.
HistColor (color) : Indicator color.
Returns: One array with drawing data of the LTF histogram.
Populate_HTF_Hist(HistValue, BarsBack, HistColor, HTF_Bar_Index)
Populates one array with drawing data of the HTF histogram.
Parameters:
HistValue (float) : Indicator value.
BarsBack (int) : Indicator lines to display.
HistColor (color) : Indicator color.
HTF_Bar_Index (int) : HTF bar_index.
Returns: One array with drawing data of the HTF histogram.
Draw_Hist(Box, Mult, Exe)
Draws HTF or LTF histogram.
Parameters:
Box (box ) : Box Array.
Mult (int) : Coordinates multiplier.
Exe (bool) : Display the histogram.
Returns: Drawing of the histogram.
[Excalibur] Ehlers AutoCorrelation Periodogram ModifiedKeep your coins folks, I don't need them, don't want them. If you wish be generous, I do hope that charitable peoples worldwide with surplus food stocks may consider stocking local food banks before stuffing monetary bank vaults, for the crusade of remedying the needs of less than fortunate children, parents, elderly, homeless veterans, and everyone else who deserves nutritional sustenance for the soul.
DEDICATION:
This script is dedicated to the memory of Nikolai Dmitriyevich Kondratiev (Никола́й Дми́триевич Кондра́тьев) as tribute for being a pioneering economist and statistician, paving the way for modern econometrics by advocation of rigorous and empirical methodologies. One of his most substantial contributions to the study of business cycle theory include a revolutionary hypothesis recognizing the existence of dynamic cycle-like phenomenon inherent to economies that are characterized by distinct phases of expansion, stagnation, recession and recovery, what we now know as "Kondratiev Waves" (K-waves). Kondratiev was one of the first economists to recognize the vital significance of applying quantitative analysis on empirical data to evaluate economic dynamics by means of statistical methods. His understanding was that conceptual models alone were insufficient to adequately interpret real-world economic conditions, and that sophisticated analysis was necessary to better comprehend the nature of trending/cycling economic behaviors. Additionally, he recognized prosperous economic cycles were predominantly driven by a combination of technological innovations and infrastructure investments that resulted in profound implications for economic growth and development.
I will mention this... nation's economies MUST be supported and defended to continuously evolve incrementally in order to flourish in perpetuity OR suffer through eras with lasting ramifications of societal stagnation and implosion.
Analogous to the realm of economics, aperiodic cycles/frequencies, both enduring and ephemeral, do exist in all facets of life, every second of every day. To name a few that any blind man can naturally see are: heartbeat (cardiac cycles), respiration rates, circadian rhythms of sleep, powerful magnetic solar cycles, seasonal cycles, lunar cycles, weather patterns, vegetative growth cycles, and ocean waves. Do not pretend for one second that these basic aforementioned examples do not affect business cycle fluctuations in minuscule and monumental ways hour to hour, day to day, season to season, year to year, and decade to decade in every nation on the planet. Kondratiev's original seminal theories in macroeconomics from nearly a century ago have proven remarkably prescient with many of his antiquated elementary observations/notions/hypotheses in macroeconomics being scholastically studied and topically researched further. Therefore, I am compelled to honor and recognize his statistical insight and foresight.
If only.. Kondratiev could hold a pocket sized computer in the cup of both hands bearing the TradingView logo and platform services, I truly believe he would be amazed in marvelous delight with a GARGANTUAN smile on his face.
INTRODUCTION:
Firstly, this is NOT technically speaking an indicator like most others. I would describe it as an advanced cycle period detector to obtain market data spectral estimates with low latency and moderate frequency resolution. Developers can take advantage of this detector by creating scripts that utilize a "Dominant Cycle Source" input to adaptively govern algorithms. Be forewarned, I would only recommend this for advanced developers, not novice code dabbling. Although, there is some Pine wizardry introduced here for novice Pine enthusiasts to witness and learn from. AI did describe the code into one super-crunched sentence as, "a rare feat of exceptionally formatted code masterfully balancing visual clarity, precision, and complexity to provide immense educational value for both programming newcomers and expert Pine coders alike."
Understand all of the above aforementioned? Buckle up and proceed for a lengthy read of verbose complexity...
This is my enhanced and heavily modified version of autocorrelation periodogram (ACP) for Pine Script v5.0. It was originally devised by the mathemagician John Ehlers for detecting dominant cycles (frequencies) in an asset's price action. I have been sitting on code similar to this for a long time, but I decided to unleash the advanced code with my fashion. Originally Ehlers released this with multiple versions, one in a 2016 TASC article and the other in his last published 2013 book "Cycle Analytics for Traders", chapter 8. He wasn't joking about "concepts of advanced technical trading" and ACP is nowhere near to his most intimidating and ingenious calculations in code. I will say the book goes into many finer details about the original periodogram, so if you wish to delve into even more elaborate info regarding Ehlers' original ACP form AND how you may adapt algorithms, you'll have to obtain one. Note to reader, comparing Ehlers' original code to my chimeric code embracing the "Power of Pine", you will notice they have little resemblance.
What you see is a new species of autocorrelation periodogram combining Ehlers' innovation with my fascinations of what ACP could be in a Pine package. One other intention of this script's code is to pay homage to Ehlers' lifelong works. Like Kondratiev, Ehlers is also a hardcore cycle enthusiast. I intend to carry on the fire Ehlers envisioned and I believe that is literally displayed here as a pleasant "fiery" example endowed with Pine. With that said, I tried to make the code as computationally efficient as possible, without going into dozens of more crazy lines of code to speed things up even more. There's also a few creative modifications I made by making alterations to the originating formulas that I felt were improvements, one of them being lag reduction. By recently questioning every single thing I thought I knew about ACP, combined with the accumulation of my current knowledge base, this is the innovative revision I came up with. I could have improved it more but decided not to mind thrash too many TV members, maybe later...
I am now confident Pine should have adequate overhead left over to attach various indicators to the dominant cycle via input.source(). TV, I apologize in advance if in the future a server cluster combusts into a raging inferno... Coders, be fully prepared to build entire algorithms from pure raw code, because not all of the built-in Pine functions fully support dynamic periods (e.g. length=ANYTHING). Many of them do, as this was requested and granted a while ago, but some functions are just inherently finicky due to implementation combinations and MUST be emulated via raw code. I would imagine some comprehensive library or numerous authored scripts have portions of raw code for Pine built-ins some where on TV if you look diligently enough.
Notice: Unfortunately, I will not provide any integration support into member's projects at all. I have my own projects that require way too much of my day already. While I was refactoring my life (forgoing many other "important" endeavors) in the early half of 2023, I primarily focused on this code over and over in my surplus time. During that same time I was working on other innovations that are far above and beyond what this code is. I hope you understand.
The best way programmatically may be to incorporate this code into your private Pine project directly, after brutal testing of course, but that may be too challenging for many in early development. Being able to see the periodogram is also beneficial, so input sourcing may be the "better" avenue to tether portions of the dominant cycle to algorithms. Unique indication being able to utilize the dominantCycle may be advantageous when tethering this script to those algorithms. The easiest way is to manually set your indicators to what ACP recognizes as the dominant cycle, but that's actually not considered dynamic real time adaption of an indicator. Different indicators may need a proportion of the dominantCycle, say half it's value, while others may need the full value of it. That's up to you to figure that out in practice. Sourcing one or more custom indicators dynamically to one detector's dominantCycle may require code like this: `int sourceDC = int(math.max(6, math.min(49, input.source(close, "Dominant Cycle Source"))))`. Keep in mind, some algos can use a float, while algos with a for loop require an integer.
I have witnessed a few attempts by talented TV members for a Pine based autocorrelation periodogram, but not in this caliber. Trust me, coding ACP is no ordinary task to accomplish in Pine and modifying it blessed with applicable improvements is even more challenging. For over 4 years, I have been slowly improving this code here and there randomly. It is beautiful just like a real flame, but... this one can still burn you! My mind was fried to charcoal black a few times wrestling with it in the distant past. My very first attempt at translating ACP was a month long endeavor because PSv3 simply didn't have arrays back then. Anyways, this is ACP with a newer engine, I hope you enjoy it. Any TV subscriber can utilize this code as they please. If you are capable of sufficiently using it properly, please use it wisely with intended good will. That is all I beg of you.
Lastly, you now see how I have rasterized my Pine with Ehlers' swami-like tech. Yep, this whole time I have been using hline() since PSv3, not plot(). Evidently, plot() still has a deficiency limited to only 32 plots when it comes to creating intense eye candy indicators, the last I checked. The use of hline() is the optimal choice for rasterizing Ehlers styled heatmaps. This does only contain two color schemes of the many I have formerly created, but that's all that is essentially needed for this gizmo. Anything else is generally for a spectacle or seeing how brutal Pine can be color treated. The real hurdle is being able to manipulate colors dynamically with Merlin like capabilities from multiple algo results. That's the true challenging part of these heatmap contraptions to obtain multi-colored "predator vision" level indication. You now have basic hline() food for thought empowerment to wield as you can imaginatively dream in Pine projects.
PERIODOGRAM UTILITY IN REAL WORLD SCENARIOS:
This code is a testament to the abilities that have yet to be fully realized with indication advancements. Periodograms, spectrograms, and heatmaps are a powerful tool with real-world applications in various fields such as financial markets, electrical engineering, astronomy, seismology, and neuro/medical applications. For instance, among these diverse fields, it may help traders and investors identify market cycles/periodicities in financial markets, support engineers in optimizing electrical or acoustic systems, aid astronomers in understanding celestial object attributes, assist seismologists with predicting earthquake risks, help medical researchers with neurological disorder identification, and detection of asymptomatic cardiovascular clotting in the vaxxed via full body thermography. In either field of study, technologies in likeness to periodograms may very well provide us with a better sliver of analysis beyond what was ever formerly invented. Periodograms can identify dominant cycles and frequency components in data, which may provide valuable insights and possibly provide better-informed decisions. By utilizing periodograms within aspects of market analytics, individuals and organizations can potentially refrain from making blinded decisions and leverage data-driven insights instead.
PERIODOGRAM INTERPRETATION:
The periodogram renders the power spectrum of a signal, with the y-axis representing the periodicity (frequencies/wavelengths) and the x-axis representing time. The y-axis is divided into periods, with each elevation representing a period. In this periodogram, the y-axis ranges from 6 at the very bottom to 49 at the top, with intermediate values in between, all indicating the power of the corresponding frequency component by color. The higher the position occurs on the y-axis, the longer the period or lower the frequency. The x-axis of the periodogram represents time and is divided into equal intervals, with each vertical column on the axis corresponding to the time interval when the signal was measured. The most recent values/colors are on the right side.
The intensity of the colors on the periodogram indicate the power level of the corresponding frequency or period. The fire color scheme is distinctly like the heat intensity from any casual flame witnessed in a small fire from a lighter, match, or camp fire. The most intense power would be indicated by the brightest of yellow, while the lowest power would be indicated by the darkest shade of red or just black. By analyzing the pattern of colors across different periods, one may gain insights into the dominant frequency components of the signal and visually identify recurring cycles/patterns of periodicity.
SETTINGS CONFIGURATIONS BRIEFLY EXPLAINED:
Source Options: These settings allow you to choose the data source for the analysis. Using the `Source` selection, you may tether to additional data streams (e.g. close, hlcc4, hl2), which also may include samples from any other indicator. For example, this could be my "Chirped Sine Wave Generator" script found in my member profile. By using the `SineWave` selection, you may analyze a theoretical sinusoidal wave with a user-defined period, something already incorporated into the code. The `SineWave` will be displayed over top of the periodogram.
Roofing Filter Options: These inputs control the range of the passband for ACP to analyze. Ehlers had two versions of his highpass filters for his releases, so I included an option for you to see the obvious difference when performing a comparison of both. You may choose between 1st and 2nd order high-pass filters.
Spectral Controls: These settings control the core functionality of the spectral analysis results. You can adjust the autocorrelation lag, adjust the level of smoothing for Fourier coefficients, and control the contrast/behavior of the heatmap displaying the power spectra. I provided two color schemes by checking or unchecking a checkbox.
Dominant Cycle Options: These settings allow you to customize the various types of dominant cycle values. You can choose between floating-point and integer values, and select the rounding method used to derive the final dominantCycle values. Also, you may control the level of smoothing applied to the dominant cycle values.
DOMINANT CYCLE VALUE SELECTIONS:
External to the acs() function, the code takes a dominant cycle value returned from acs() and changes its numeric form based on a specified type and form chosen within the indicator settings. The dominant cycle value can be represented as an integer or a decimal number, depending on the attached algorithm's requirements. For example, FIR filters will require an integer while many IIR filters can use a float. The float forms can be either rounded, smoothed, or floored. If the resulting value is desired to be an integer, it can be rounded up/down or just be in an integer form, depending on how your algorithm may utilize it.
AUTOCORRELATION SPECTRUM FUNCTION BASICALLY EXPLAINED:
In the beginning of the acs() code, the population of caches for precalculated angular frequency factors and smoothing coefficients occur. By precalculating these factors/coefs only once and then storing them in an array, the indicator can save time and computational resources when performing subsequent calculations that require them later.
In the following code block, the "Calculate AutoCorrelations" is calculated for each period within the passband width. The calculation involves numerous summations of values extracted from the roofing filter. Finally, a correlation values array is populated with the resulting values, which are normalized correlation coefficients.
Moving on to the next block of code, labeled "Decompose Fourier Components", Fourier decomposition is performed on the autocorrelation coefficients. It iterates this time through the applicable period range of 6 to 49, calculating the real and imaginary parts of the Fourier components. Frequencies 6 to 49 are the primary focus of interest for this periodogram. Using the precalculated angular frequency factors, the resulting real and imaginary parts are then utilized to calculate the spectral Fourier components, which are stored in an array for later use.
The next section of code smooths the noise ridden Fourier components between the periods of 6 and 49 with a selected filter. This species also employs numerous SuperSmoothers to condition noisy Fourier components. One of the big differences is Ehlers' versions used basic EMAs in this section of code. I decided to add SuperSmoothers.
The final sections of the acs() code determines the peak power component for normalization and then computes the dominant cycle period from the smoothed Fourier components. It first identifies a single spectral component with the highest power value and then assigns it as the peak power. Next, it normalizes the spectral components using the peak power value as a denominator. It then calculates the average dominant cycle period from the normalized spectral components using Ehlers' "Center of Gravity" calculation. Finally, the function returns the dominant cycle period along with the normalized spectral components for later external use to plot the periodogram.
POST SCRIPT:
Concluding, I have to acknowledge a newly found analyst for assistance that I couldn't receive from anywhere else. For one, Claude doesn't know much about Pine, is unfortunately color blind, and can't even see the Pine reference, but it was able to intuitively shred my code with laser precise realizations. Not only that, formulating and reformulating my description needed crucial finesse applied to it, and I couldn't have provided what you have read here without that artificial insight. Finding the right order of words to convey the complexity of ACP and the elaborate accompanying content was a daunting task. No code in my life has ever absorbed so much time and hard fricking work, than what you witness here, an ACP gem cut pristinely. I'm unveiling my version of ACP for an empowering cause, in the hopes a future global army of code wielders will tether it to highly functional computational contraptions they might possess. Here is ACP fully blessed poetically with the "Power of Pine" in sublime code. ENJOY!
Range Trading StrategyOVERVIEW
The Range Trading Strategy is a systematic trading approach that identifies price ranges
from higher timeframe candles or trading sessions, tracks pivot points, and generates
trading signals when range extremes are mitigated and confirmed by pivot levels.
CORE CONCEPT
The strategy is based on the principle that when a candle (or session) closes within the
range of the previous candle (or session), that previous candle becomes a "range" with
identifiable high and low extremes. When price breaks through these extremes, it creates
trading opportunities that are confirmed by pivot levels.
RANGE DETECTION MODES
1. HTF (Higher Timeframe) Mode:
Automatically selects a higher timeframe based on the current chart timeframe
Uses request.security() to fetch HTF candle data
Range is created when an HTF candle closes within the previous HTF candle's range
The previous HTF candle's high and low become the range extremes
2. Sessions Mode:
- Divides the trading day into 4 sessions (UTC):
* Session 1: 00:00 - 06:00 (6 hours)
* Session 2: 06:00 - 12:00 (6 hours)
* Session 3: 12:00 - 20:00 (8 hours)
* Session 4: 20:00 - 00:00 (4 hours, spans midnight)
- Tracks high, low, and close for each session
- Range is created when a session closes within the previous session's range
- The previous session's high and low become the range extremes
PIVOT DETECTION
Pivots are detected based on candle color changes (bullish/bearish transitions):
1. Pivot Low:
Created when a bullish candle appears after a bearish candle
Pivot low = minimum of the current candle's low and previous candle's low
The pivot bar is the actual bar where the low was formed (current or previous bar)
2. Pivot High:
Created when a bearish candle appears after a bullish candle
Pivot high = maximum of the current candle's high and previous candle's high
The pivot bar is the actual bar where the high was formed (current or previous bar)
IMPORTANT: There is always only ONE active pivot high and ONE active pivot low at any
given time. When a new pivot is created, it replaces the previous one.
RANGE CREATION
A range is created when:
(HTF Mode) An HTF candle closes within the previous HTF candle's range AND a new HTF
candle has just started
(Sessions Mode) A session closes within the previous session's range AND a new session
has just started
Or Range Can Be Created when the Extreme of Another Range Gets Mitigated and We Have a Pivot low Just Above the Range Low or Pivot High just Below the Range High
Range Properties:
rangeHigh: The high extreme of the range
rangeLow: The low extreme of the range
highStartTime: The timestamp when the range high was actually formed (found by looping
backwards through bars)
lowStartTime: The timestamp when the range low was actually formed (found by looping
backwards through bars)
highMitigated / lowMitigated: Flags tracking whether each extreme has been broken
isSpecial: Flag indicating if this is a "special range" (see Special Ranges section)
RANGE MITIGATION
A range extreme is considered "mitigated" when price interacts with it:
High is mitigated when: high >= rangeHigh (any interaction at or above the level)
Low is mitigated when: low <= rangeLow (any interaction at or below the level)
Mitigation can happen:
At the moment of range creation (if price is already beyond the extreme)
At any point after range creation when price touches the extreme
SIGNAL GENERATION
1. Pending Signals:
When a range extreme is mitigated, a pending signal is created:
a) BEARISH Pending Signal:
- Triggered when: rangeHigh is mitigated
- Confirmation Level: Current pivotLow
- Signal is confirmed when: close < pivotLow
- Stop Loss: Current pivotHigh (at time of confirmation)
- Entry: Short position
Signal Confirmation
b) BULLISH Pending Signal:
- Triggered when: rangeLow is mitigated
- Confirmation Level: Current pivotHigh
- Signal is confirmed when: close > pivotHigh
- Stop Loss: Current pivotLow (at time of confirmation)
- Entry: Long position
IMPORTANT: There is only ever ONE pending bearish signal and ONE pending bullish signal
at any given time. When a new pending signal is created, it replaces the previous one
of the same type.
2. Signal Confirmation:
- Bearish: Confirmed when price closes below the pivot low (confirmation level)
- Bullish: Confirmed when price closes above the pivot high (confirmation level)
- Upon confirmation, a trade is entered immediately
- The confirmation line is drawn from the pivot bar to the confirmation bar
TRADE EXECUTION
When a signal is confirmed:
1. Position Management:
- Any existing position in the opposite direction is closed first
- Then the new position is entered
2. Stop Loss:
- Bearish (Short): Stop at pivotHigh
- Bullish (Long): Stop at pivotLow
3. Take Profit:
- Calculated using Risk:Reward Ratio (default 2:1)
- Risk = Distance from entry to stop loss
- Target = Entry ± (Risk × R:R Ratio)
- Can be disabled with "Stop Loss Only" toggle
4. Trade Comments:
- "Range Bear" for short trades
- "Range Bull" for long trades
SPECIAL RANGES
Special ranges are created when:
- A range high is mitigated AND the current pivotHigh is below the range high
- A range low is mitigated AND the current pivotLow is above the range low
In these cases:
- The pivot value is stored in an array (storedPivotHighs or storedPivotLows)
- A "special range" is created with only ONE extreme:
* If pivotHigh < rangeHigh: Creates a range with rangeHigh = pivotLow, rangeLow = na
* If pivotLow > rangeLow: Creates a range with rangeLow = pivotHigh, rangeHigh = na
- Special ranges can generate signals just like normal ranges
- If a special range is mitigated on the creation bar or the next bar, it is removed
entirely without generating signals (prevents false signals)
Special Ranges
REVERSE ON STOP LOSS
When enabled, if a stop loss is hit, the strategy automatically opens a trade in the
opposite direction:
1. Long Stop Loss Hit:
- Detects when: position_size > 0 AND position_size <= 0 AND low <= longStopLoss
- Action: Opens a SHORT position
- Stop Loss: Current pivotHigh
- Trade Comment: "Reverse on Stop"
2. Short Stop Loss Hit:
- Detects when: position_size < 0 AND position_size >= 0 AND high >= shortStopLoss
- Action: Opens a LONG position
- Stop Loss: Current pivotLow
- Trade Comment: "Reverse on Stop"
The reverse trade uses the same R:R ratio and respects the "Stop Loss Only" setting.
VISUAL ELEMENTS
1. Range Lines:
- Drawn from the time when the extreme was formed to the mitigation point (or current
time if not mitigated)
- High lines: Blue (or mitigated color if mitigated)
- Low lines: Red (or mitigated color if mitigated)
- Style: SOLID
- Width: 1
2. Confirmation Lines:
- Drawn when a signal is confirmed
- Extends from the pivot bar to the confirmation bar
- Bearish: Red, solid line
- Bullish: Green, solid line
- Width: 1
- Can be toggled on/off
STRATEGY SETTINGS
1. Range Detection Mode:
- HTF: Uses higher timeframe candles
- Sessions: Uses trading session boundaries
2. Auto HTF:
- Automatically selects HTF based on current chart timeframe
- Can be disabled to use manual HTF selection
3. Risk:Reward Ratio:
- Default: 2.0 (2:1)
- Minimum: 0.5
- Step: 0.5
4. Stop Loss Only:
- When enabled: Trades only have stop loss (no take profit)
- Trades close on stop loss or when opposite signal confirms
5. Reverse on Stop Loss:
- When enabled: Hitting a stop loss opens opposite trade with stop at opposing pivot
6. Max Ranges to Display:
- Limits the number of ranges kept in memory
- Oldest ranges are purged when limit is exceeded
KEY FEATURES
1. Dynamic Pivot Tracking:
- Pivots update on every candle color change
- Always maintains one high and one low pivot
2. Range Lifecycle:
- Ranges are created when price closes within previous range
- Ranges are tracked until mitigated
- Mitigation creates pending signals
- Signals are confirmed by pivot levels
3. Signal Priority:
- Only one pending signal of each type at a time
- New signals replace old ones
- Confirmation happens on close of bar
4. Position Management:
- Closes opposite positions before entering new trades
- Tracks stop loss levels for reverse functionality
- Respects pyramiding = 1 (only one position per direction)
5. Time-Based Drawing:
- Uses time coordinates instead of bar indices for line drawing
- Prevents "too far from current bar" errors
- Lines can extend to any historical point
USAGE NOTES
- Best suited for trending and ranging markets
- Works on any timeframe, but HTF mode adapts automatically
- Sessions mode is ideal for intraday trading
- Pivot detection requires clear candle color changes
- Range detection requires price to close within previous range
- Signals are generated on bar close, not intra-bar
The strategy combines range identification, pivot tracking, and signal confirmation to
create a systematic approach to trading breakouts and reversals based on price structure, past performance does not in any way predict future performance
FluxVector Liquidity Universal Trendline FluxVector Liquidity Trendline FFTL
Summary in one paragraph
FFTL is a single adaptive trendline for stocks ETFs FX crypto and indices on one minute to daily. It fires only when price action pressure and volatility curvature align. It is original because it fuses a directional liquidity pulse from candle geometry and normalized volume with realized volatility curvature and an impact efficiency term to modulate a Kalman like state without ATR VWAP or moving averages. Add it to a clean chart and use the colored line plus alerts. Shapes can move while a bar is open and settle on close. For conservative alerts select on bar close.
Scope and intent
• Markets. Major FX pairs index futures large cap equities liquid crypto top ETFs
• Timeframes. One minute to daily
• Default demo used in the publication. SPY on 30min
• Purpose. Reduce false flips and chop by gating the line reaction to noise and by using a one bar projection
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique fusion. Directional Liquidity Pulse plus Volatility Curvature plus Impact Efficiency drives an adaptive gain for a one dimensional state
• Failure mode addressed. One or two shock candles that break ordinary trendlines and saw chop in flat regimes
• Testability. All windows and gains are inputs
• Portable yardstick. Returns use natural log units and range is bar high minus low
• Protected scripts. Not used. Method disclosed plainly here
Method overview in plain language
Base measures
• Return basis. Natural log of close over prior close. Average absolute return over a window is a unit of motion
Components
• Directional Liquidity Pulse DLP. Measures signed participation from body and wick imbalance scaled by normalized volume and variance stabilized
• Volatility Curvature. Second difference of realized volatility from returns highlights expansion or compression
• Impact Efficiency. Price change per unit range and volume boosts gain during efficient moves
• Energy score. Z scores of the above form a single energy that controls the state gain
• One bar projection. Current slope extended by one bar for anticipatory checks
Fusion rule
Weighted sum inside the energy score then logistic mapping to a gain between k min and k max. The state updates toward price plus a small flow push.
Signal rule
• Long suggestion and order when close is below trend and the one bar projection is above the trend
• Short suggestion and flip when close is above trend and the one bar projection is below the trend
• WAIT is implicit when neither condition holds
• In position states end on the opposite condition
What you will see on the chart
• Colored trendline teal for rising red for falling gray for flat
• Optional projection line one bar ahead
• Optional background can be enabled in code
• Alerts on price cross and on slope flips
Inputs with guidance
Setup
• Price source. Close by default
Logic
• Flow window. Typical range 20 to 80. Higher smooths the pulse and reduces flips
• Vol window. Typical range 30 to 120. Higher calms curvature
• Energy window. Typical range 20 to 80. Higher slows regime changes
• Min gain and Max gain. Raise max to react faster. Raise min to keep momentum in chop
UI
• Show 1 bar projection. Colors for up down flat
Properties visible in this publication
• Initial capital 25000
• Base currency USD
• Commission percent 0.03
• Slippage 5
• Default order size method percent of equity value 3%
• Pyramiding 0
• Process orders on close off
• Calc on every tick off
• Recalculate after order is filled off
Realism and responsible publication
• No performance claims
• Intrabar reminder. Shapes can move while a bar forms and settle on close
• Strategy uses standard candles only
Honest limitations and failure modes
• Sudden gaps and thin liquidity can still produce fast flips
• Very quiet regimes reduce contrast. Use larger windows and lower max gain
• Session time uses the exchange time of the chart if you enable any windows later
• Past results never guarantee future outcomes
Open source reuse and credits
• None
ATAI Volume analysis with price action V 1.00ATAI Volume Analysis with Price Action
1. Introduction
1.1 Overview
ATAI Volume Analysis with Price Action is a composite indicator designed for TradingView. It combines per‑side volume data —that is, how much buying and selling occurs during each bar—with standard price‑structure elements such as swings, trend lines and support/resistance. By blending these elements the script aims to help a trader understand which side is in control, whether a breakout is genuine, when markets are potentially exhausted and where liquidity providers might be active.
The indicator is built around TradingView’s up/down volume feed accessed via the TradingView/ta/10 library. The following excerpt from the script illustrates how this feed is configured:
import TradingView/ta/10 as tvta
// Determine lower timeframe string based on user choice and chart resolution
string lower_tf_breakout = use_custom_tf_input ? custom_tf_input :
timeframe.isseconds ? "1S" :
timeframe.isintraday ? "1" :
timeframe.isdaily ? "5" : "60"
// Request up/down volume (both positive)
= tvta.requestUpAndDownVolume(lower_tf_breakout)
Lower‑timeframe selection. If you do not specify a custom lower timeframe, the script chooses a default based on your chart resolution: 1 second for second charts, 1 minute for intraday charts, 5 minutes for daily charts and 60 minutes for anything longer. Smaller intervals provide a more precise view of buyer and seller flow but cover fewer bars. Larger intervals cover more history at the cost of granularity.
Tick vs. time bars. Many trading platforms offer a tick / intrabar calculation mode that updates an indicator on every trade rather than only on bar close. Turning on one‑tick calculation will give the most accurate split between buy and sell volume on the current bar, but it typically reduces the amount of historical data available. For the highest fidelity in live trading you can enable this mode; for studying longer histories you might prefer to disable it. When volume data is completely unavailable (some instruments and crypto pairs), all modules that rely on it will remain silent and only the price‑structure backbone will operate.
Figure caption, Each panel shows the indicator’s info table for a different volume sampling interval. In the left chart, the parentheses “(5)” beside the buy‑volume figure denote that the script is aggregating volume over five‑minute bars; the center chart uses “(1)” for one‑minute bars; and the right chart uses “(1T)” for a one‑tick interval. These notations tell you which lower timeframe is driving the volume calculations. Shorter intervals such as 1 minute or 1 tick provide finer detail on buyer and seller flow, but they cover fewer bars; longer intervals like five‑minute bars smooth the data and give more history.
Figure caption, The values in parentheses inside the info table come directly from the Breakout — Settings. The first row shows the custom lower-timeframe used for volume calculations (e.g., “(1)”, “(5)”, or “(1T)”)
2. Price‑Structure Backbone
Even without volume, the indicator draws structural features that underpin all other modules. These features are always on and serve as the reference levels for subsequent calculations.
2.1 What it draws
• Pivots: Swing highs and lows are detected using the pivot_left_input and pivot_right_input settings. A pivot high is identified when the high recorded pivot_right_input bars ago exceeds the highs of the preceding pivot_left_input bars and is also higher than (or equal to) the highs of the subsequent pivot_right_input bars; pivot lows follow the inverse logic. The indicator retains only a fixed number of such pivot points per side, as defined by point_count_input, discarding the oldest ones when the limit is exceeded.
• Trend lines: For each side, the indicator connects the earliest stored pivot and the most recent pivot (oldest high to newest high, and oldest low to newest low). When a new pivot is added or an old one drops out of the lookback window, the line’s endpoints—and therefore its slope—are recalculated accordingly.
• Horizontal support/resistance: The highest high and lowest low within the lookback window defined by length_input are plotted as horizontal dashed lines. These serve as short‑term support and resistance levels.
• Ranked labels: If showPivotLabels is enabled the indicator prints labels such as “HH1”, “HH2”, “LL1” and “LL2” near each pivot. The ranking is determined by comparing the price of each stored pivot: HH1 is the highest high, HH2 is the second highest, and so on; LL1 is the lowest low, LL2 is the second lowest. In the case of equal prices the newer pivot gets the better rank. Labels are offset from price using ½ × ATR × label_atr_multiplier, with the ATR length defined by label_atr_len_input. A dotted connector links each label to the candle’s wick.
2.2 Key settings
• length_input: Window length for finding the highest and lowest values and for determining trend line endpoints. A larger value considers more history and will generate longer trend lines and S/R levels.
• pivot_left_input, pivot_right_input: Strictness of swing confirmation. Higher values require more bars on either side to form a pivot; lower values create more pivots but may include minor swings.
• point_count_input: How many pivots are kept in memory on each side. When new pivots exceed this number the oldest ones are discarded.
• label_atr_len_input and label_atr_multiplier: Determine how far pivot labels are offset from the bar using ATR. Increasing the multiplier moves labels further away from price.
• Styling inputs for trend lines, horizontal lines and labels (color, width and line style).
Figure caption, The chart illustrates how the indicator’s price‑structure backbone operates. In this daily example, the script scans for bars where the high (or low) pivot_right_input bars back is higher (or lower) than the preceding pivot_left_input bars and higher or lower than the subsequent pivot_right_input bars; only those bars are marked as pivots.
These pivot points are stored and ranked: the highest high is labelled “HH1”, the second‑highest “HH2”, and so on, while lows are marked “LL1”, “LL2”, etc. Each label is offset from the price by half of an ATR‑based distance to keep the chart clear, and a dotted connector links the label to the actual candle.
The red diagonal line connects the earliest and latest stored high pivots, and the green line does the same for low pivots; when a new pivot is added or an old one drops out of the lookback window, the end‑points and slopes adjust accordingly. Dashed horizontal lines mark the highest high and lowest low within the current lookback window, providing visual support and resistance levels. Together, these elements form the structural backbone that other modules reference, even when volume data is unavailable.
3. Breakout Module
3.1 Concept
This module confirms that a price break beyond a recent high or low is supported by a genuine shift in buying or selling pressure. It requires price to clear the highest high (“HH1”) or lowest low (“LL1”) and, simultaneously, that the winning side shows a significant volume spike, dominance and ranking. Only when all volume and price conditions pass is a breakout labelled.
3.2 Inputs
• lookback_break_input : This controls the number of bars used to compute moving averages and percentiles for volume. A larger value smooths the averages and percentiles but makes the indicator respond more slowly.
• vol_mult_input : The “spike” multiplier; the current buy or sell volume must be at least this multiple of its moving average over the lookback window to qualify as a breakout.
• rank_threshold_input (0–100) : Defines a volume percentile cutoff: the current buyer/seller volume must be in the top (100−threshold)%(100−threshold)% of all volumes within the lookback window. For example, if set to 80, the current volume must be in the top 20 % of the lookback distribution.
• ratio_threshold_input (0–1) : Specifies the minimum share of total volume that the buyer (for a bullish breakout) or seller (for bearish) must hold on the current bar; the code also requires that the cumulative buyer volume over the lookback window exceeds the seller volume (and vice versa for bearish cases).
• use_custom_tf_input / custom_tf_input : When enabled, these inputs override the automatic choice of lower timeframe for up/down volume; otherwise the script selects a sensible default based on the chart’s timeframe.
• Label appearance settings : Separate options control the ATR-based offset length, offset multiplier, label size and colors for bullish and bearish breakout labels, as well as the connector style and width.
3.3 Detection logic
1. Data preparation : Retrieve per‑side volume from the lower timeframe and take absolute values. Build rolling arrays of the last lookback_break_input values to compute simple moving averages (SMAs), cumulative sums and percentile ranks for buy and sell volume.
2. Volume spike: A spike is flagged when the current buy (or, in the bearish case, sell) volume is at least vol_mult_input times its SMA over the lookback window.
3. Dominance test: The buyer’s (or seller’s) share of total volume on the current bar must meet or exceed ratio_threshold_input. In addition, the cumulative sum of buyer volume over the window must exceed the cumulative sum of seller volume for a bullish breakout (and vice versa for bearish). A separate requirement checks the sign of delta: for bullish breakouts delta_breakout must be non‑negative; for bearish breakouts it must be non‑positive.
4. Percentile rank: The current volume must fall within the top (100 – rank_threshold_input) percent of the lookback distribution—ensuring that the spike is unusually large relative to recent history.
5. Price test: For a bullish signal, the closing price must close above the highest pivot (HH1); for a bearish signal, the close must be below the lowest pivot (LL1).
6. Labeling: When all conditions above are satisfied, the indicator prints “Breakout ↑” above the bar (bullish) or “Breakout ↓” below the bar (bearish). Labels are offset using half of an ATR‑based distance and linked to the candle with a dotted connector.
Figure caption, (Breakout ↑ example) , On this daily chart, price pushes above the red trendline and the highest prior pivot (HH1). The indicator recognizes this as a valid breakout because the buyer‑side volume on the lower timeframe spikes above its recent moving average and buyers dominate the volume statistics over the lookback period; when combined with a close above HH1, this satisfies the breakout conditions. The “Breakout ↑” label appears above the candle, and the info table highlights that up‑volume is elevated relative to its 11‑bar average, buyer share exceeds the dominance threshold and money‑flow metrics support the move.
Figure caption, In this daily example, price breaks below the lowest pivot (LL1) and the lower green trendline. The indicator identifies this as a bearish breakout because sell‑side volume is sharply elevated—about twice its 11‑bar average—and sellers dominate both the bar and the lookback window. With the close falling below LL1, the script triggers a Breakout ↓ label and marks the corresponding row in the info table, which shows strong down volume, negative delta and a seller share comfortably above the dominance threshold.
4. Market Phase Module (Volume Only)
4.1 Concept
Not all markets trend; many cycle between periods of accumulation (buying pressure building up), distribution (selling pressure dominating) and neutral behavior. This module classifies the current bar into one of these phases without using ATR , relying solely on buyer and seller volume statistics. It looks at net flows, ratio changes and an OBV‑like cumulative line with dual‑reference (1‑ and 2‑bar) trends. The result is displayed both as on‑chart labels and in a dedicated row of the info table.
4.2 Inputs
• phase_period_len: Number of bars over which to compute sums and ratios for phase detection.
• phase_ratio_thresh : Minimum buyer share (for accumulation) or minimum seller share (for distribution, derived as 1 − phase_ratio_thresh) of the total volume.
• strict_mode: When enabled, both the 1‑bar and 2‑bar changes in each statistic must agree on the direction (strict confirmation); when disabled, only one of the two references needs to agree (looser confirmation).
• Color customisation for info table cells and label styling for accumulation and distribution phases, including ATR length, multiplier, label size, colors and connector styles.
• show_phase_module: Toggles the entire phase detection subsystem.
• show_phase_labels: Controls whether on‑chart labels are drawn when accumulation or distribution is detected.
4.3 Detection logic
The module computes three families of statistics over the volume window defined by phase_period_len:
1. Net sum (buyers minus sellers): net_sum_phase = Σ(buy) − Σ(sell). A positive value indicates a predominance of buyers. The code also computes the differences between the current value and the values 1 and 2 bars ago (d_net_1, d_net_2) to derive up/down trends.
2. Buyer ratio: The instantaneous ratio TF_buy_breakout / TF_tot_breakout and the window ratio Σ(buy) / Σ(total). The current ratio must exceed phase_ratio_thresh for accumulation or fall below 1 − phase_ratio_thresh for distribution. The first and second differences of the window ratio (d_ratio_1, d_ratio_2) determine trend direction.
3. OBV‑like cumulative net flow: An on‑balance volume analogue obv_net_phase increments by TF_buy_breakout − TF_sell_breakout each bar. Its differences over the last 1 and 2 bars (d_obv_1, d_obv_2) provide trend clues.
The algorithm then combines these signals:
• For strict mode , accumulation requires: (a) current ratio ≥ threshold, (b) cumulative ratio ≥ threshold, (c) both ratio differences ≥ 0, (d) net sum differences ≥ 0, and (e) OBV differences ≥ 0. Distribution is the mirror case.
• For loose mode , it relaxes the directional tests: either the 1‑ or the 2‑bar difference needs to agree in each category.
If all conditions for accumulation are satisfied, the phase is labelled “Accumulation” ; if all conditions for distribution are satisfied, it’s labelled “Distribution” ; otherwise the phase is “Neutral” .
4.4 Outputs
• Info table row : Row 8 displays “Market Phase (Vol)” on the left and the detected phase (Accumulation, Distribution or Neutral) on the right. The text colour of both cells matches a user‑selectable palette (typically green for accumulation, red for distribution and grey for neutral).
• On‑chart labels : When show_phase_labels is enabled and a phase persists for at least one bar, the module prints a label above the bar ( “Accum” ) or below the bar ( “Dist” ) with a dashed or dotted connector. The label is offset using ATR based on phase_label_atr_len_input and phase_label_multiplier and is styled according to user preferences.
Figure caption, The chart displays a red “Dist” label above a particular bar, indicating that the accumulation/distribution module identified a distribution phase at that point. The detection is based on seller dominance: during that bar, the net buyer-minus-seller flow and the OBV‑style cumulative flow were trending down, and the buyer ratio had dropped below the preset threshold. These conditions satisfy the distribution criteria in strict mode. The label is placed above the bar using an ATR‑based offset and a dashed connector. By the time of the current bar in the screenshot, the phase indicator shows “Neutral” in the info table—signaling that neither accumulation nor distribution conditions are currently met—yet the historical “Dist” label remains to mark where the prior distribution phase began.
Figure caption, In this example the market phase module has signaled an Accumulation phase. Three bars before the current candle, the algorithm detected a shift toward buyers: up‑volume exceeded its moving average, down‑volume was below average, and the buyer share of total volume climbed above the threshold while the on‑balance net flow and cumulative ratios were trending upwards. The blue “Accum” label anchored below that bar marks the start of the phase; it remains on the chart because successive bars continue to satisfy the accumulation conditions. The info table confirms this: the “Market Phase (Vol)” row still reads Accumulation, and the ratio and sum rows show buyers dominating both on the current bar and across the lookback window.
5. OB/OS Spike Module
5.1 What overbought/oversold means here
In many markets, a rapid extension up or down is often followed by a period of consolidation or reversal. The indicator interprets overbought (OB) conditions as abnormally strong selling risk at or after a price rally and oversold (OS) conditions as unusually strong buying risk after a decline. Importantly, these are not direct trade signals; rather they flag areas where caution or contrarian setups may be appropriate.
5.2 Inputs
• minHits_obos (1–7): Minimum number of oscillators that must agree on an overbought or oversold condition for a label to print.
• syncWin_obos: Length of a small sliding window over which oscillator votes are smoothed by taking the maximum count observed. This helps filter out choppy signals.
• Volume spike criteria: kVolRatio_obos (ratio of current volume to its SMA) and zVolThr_obos (Z‑score threshold) across volLen_obos. Either threshold can trigger a spike.
• Oscillator toggles and periods: Each of RSI, Stochastic (K and D), Williams %R, CCI, MFI, DeMarker and Stochastic RSI can be independently enabled; their periods are adjustable.
• Label appearance: ATR‑based offset, size, colors for OB and OS labels, plus connector style and width.
5.3 Detection logic
1. Directional volume spikes: Volume spikes are computed separately for buyer and seller volumes. A sell volume spike (sellVolSpike) flags a potential OverBought bar, while a buy volume spike (buyVolSpike) flags a potential OverSold bar. A spike occurs when the respective volume exceeds kVolRatio_obos times its simple moving average over the window or when its Z‑score exceeds zVolThr_obos.
2. Oscillator votes: For each enabled oscillator, calculate its overbought and oversold state using standard thresholds (e.g., RSI ≥ 70 for OB and ≤ 30 for OS; Stochastic %K/%D ≥ 80 for OB and ≤ 20 for OS; etc.). Count how many oscillators vote for OB and how many vote for OS.
3. Minimum hits: Apply the smoothing window syncWin_obos to the vote counts using a maximum‑of‑last‑N approach. A candidate bar is only considered if the smoothed OB hit count ≥ minHits_obos (for OverBought) or the smoothed OS hit count ≥ minHits_obos (for OverSold).
4. Tie‑breaking: If both OverBought and OverSold spike conditions are present on the same bar, compare the smoothed hit counts: the side with the higher count is selected; ties default to OverBought.
5. Label printing: When conditions are met, the bar is labelled as “OverBought X/7” above the candle or “OverSold X/7” below it. “X” is the number of oscillators confirming, and the bracket lists the abbreviations of contributing oscillators. Labels are offset from price using half of an ATR‑scaled distance and can optionally include a dotted or dashed connector line.
Figure caption, In this chart the overbought/oversold module has flagged an OverSold signal. A sell‑off from the prior highs brought price down to the lower trend‑line, where the bar marked “OverSold 3/7 DeM” appears. This label indicates that on that bar the module detected a buy‑side volume spike and that at least three of the seven enabled oscillators—in this case including the DeMarker—were in oversold territory. The label is printed below the candle with a dotted connector, signaling that the market may be temporarily exhausted on the downside. After this oversold print, price begins to rebound towards the upper red trend‑line and higher pivot levels.
Figure caption, This example shows the overbought/oversold module in action. In the left‑hand panel you can see the OB/OS settings where each oscillator (RSI, Stochastic, Williams %R, CCI, MFI, DeMarker and Stochastic RSI) can be enabled or disabled, and the ATR length and label offset multiplier adjusted. On the chart itself, price has pushed up to the descending red trendline and triggered an “OverBought 3/7” label. That means the sell‑side volume spiked relative to its average and three out of the seven enabled oscillators were in overbought territory. The label is offset above the candle by half of an ATR and connected with a dashed line, signaling that upside momentum may be overextended and a pause or pullback could follow.
6. Buyer/Seller Trap Module
6.1 Concept
A bull trap occurs when price appears to break above resistance, attracting buyers, but fails to sustain the move and quickly reverses, leaving a long upper wick and trapping late entrants. A bear trap is the opposite: price breaks below support, lures in sellers, then snaps back, leaving a long lower wick and trapping shorts. This module detects such traps by looking for price structure sweeps, order‑flow mismatches and dominance reversals. It uses a scoring system to differentiate risk from confirmed traps.
6.2 Inputs
• trap_lookback_len: Window length used to rank extremes and detect sweeps.
• trap_wick_threshold: Minimum proportion of a bar’s range that must be wick (upper for bull traps, lower for bear traps) to qualify as a sweep.
• trap_score_risk: Minimum aggregated score required to flag a trap risk. (The code defines a trap_score_confirm input, but confirmation is actually based on price reversal rather than a separate score threshold.)
• trap_confirm_bars: Maximum number of bars allowed for price to reverse and confirm the trap. If price does not reverse in this window, the risk label will expire or remain unconfirmed.
• Label settings: ATR length and multiplier for offsetting, size, colours for risk and confirmed labels, and connector style and width. Separate settings exist for bull and bear traps.
• Toggle inputs: show_trap_module and show_trap_labels enable the module and control whether labels are drawn on the chart.
6.3 Scoring logic
The module assigns points to several conditions and sums them to determine whether a trap risk is present. For bull traps, the score is built from the following (bear traps mirror the logic with highs and lows swapped):
1. Sweep (2 points): Price trades above the high pivot (HH1) but fails to close above it and leaves a long upper wick at least trap_wick_threshold × range. For bear traps, price dips below the low pivot (LL1), fails to close below and leaves a long lower wick.
2. Close break (1 point): Price closes beyond HH1 or LL1 without leaving a long wick.
3. Candle/delta mismatch (2 points): The candle closes bullish yet the order flow delta is negative or the seller ratio exceeds 50%, indicating hidden supply. Conversely, a bearish close with positive delta or buyer dominance suggests hidden demand.
4. Dominance inversion (2 points): The current bar’s buyer volume has the highest rank in the lookback window while cumulative sums favor sellers, or vice versa.
5. Low‑volume break (1 point): Price crosses the pivot but total volume is below its moving average.
The total score for each side is compared to trap_score_risk. If the score is high enough, a “Bull Trap Risk” or “Bear Trap Risk” label is drawn, offset from the candle by half of an ATR‑scaled distance using a dashed outline. If, within trap_confirm_bars, price reverses beyond the opposite level—drops back below the high pivot for bull traps or rises above the low pivot for bear traps—the label is upgraded to a solid “Bull Trap” or “Bear Trap” . In this version of the code, there is no separate score threshold for confirmation: the variable trap_score_confirm is unused; confirmation depends solely on a successful price reversal within the specified number of bars.
Figure caption, In this example the trap module has flagged a Bear Trap Risk. Price initially breaks below the most recent low pivot (LL1), but the bar closes back above that level and leaves a long lower wick, suggesting a failed push lower. Combined with a mismatch between the candle direction and the order flow (buyers regain control) and a reversal in volume dominance, the aggregate score exceeds the risk threshold, so a dashed “Bear Trap Risk” label prints beneath the bar. The green and red trend lines mark the current low and high pivot trajectories, while the horizontal dashed lines show the highest and lowest values in the lookback window. If, within the next few bars, price closes decisively above the support, the risk label would upgrade to a solid “Bear Trap” label.
Figure caption, In this example the trap module has identified both ends of a price range. Near the highs, price briefly pushes above the descending red trendline and the recent pivot high, but fails to close there and leaves a noticeable upper wick. That combination of a sweep above resistance and order‑flow mismatch generates a Bull Trap Risk label with a dashed outline, warning that the upside break may not hold. At the opposite extreme, price later dips below the green trendline and the labelled low pivot, then quickly snaps back and closes higher. The long lower wick and subsequent price reversal upgrade the previous bear‑trap risk into a confirmed Bear Trap (solid label), indicating that sellers were caught on a false breakdown. Horizontal dashed lines mark the highest high and lowest low of the lookback window, while the red and green diagonals connect the earliest and latest pivot highs and lows to visualize the range.
7. Sharp Move Module
7.1 Concept
Markets sometimes display absorption or climax behavior—periods when one side steadily gains the upper hand before price breaks out with a sharp move. This module evaluates several order‑flow and volume conditions to anticipate such moves. Users can choose how many conditions must be met to flag a risk and how many (plus a price break) are required for confirmation.
7.2 Inputs
• sharp Lookback: Number of bars in the window used to compute moving averages, sums, percentile ranks and reference levels.
• sharpPercentile: Minimum percentile rank for the current side’s volume; the current buy (or sell) volume must be greater than or equal to this percentile of historical volumes over the lookback window.
• sharpVolMult: Multiplier used in the volume climax check. The current side’s volume must exceed this multiple of its average to count as a climax.
• sharpRatioThr: Minimum dominance ratio (current side’s volume relative to the opposite side) used in both the instant and cumulative dominance checks.
• sharpChurnThr: Maximum ratio of a bar’s range to its ATR for absorption/churn detection; lower values indicate more absorption (large volume in a small range).
• sharpScoreRisk: Minimum number of conditions that must be true to print a risk label.
• sharpScoreConfirm: Minimum number of conditions plus a price break required for confirmation.
• sharpCvdThr: Threshold for cumulative delta divergence versus price change (positive for bullish accumulation, negative for bearish distribution).
• Label settings: ATR length (sharpATRlen) and multiplier (sharpLabelMult) for positioning labels, label size, colors and connector styles for bullish and bearish sharp moves.
• Toggles: enableSharp activates the module; show_sharp_labels controls whether labels are drawn.
7.3 Conditions (six per side)
For each side, the indicator computes six boolean conditions and sums them to form a score:
1. Dominance (instant and cumulative):
– Instant dominance: current buy volume ≥ sharpRatioThr × current sell volume.
– Cumulative dominance: sum of buy volumes over the window ≥ sharpRatioThr × sum of sell volumes (and vice versa for bearish checks).
2. Accumulation/Distribution divergence: Over the lookback window, cumulative delta rises by at least sharpCvdThr while price fails to rise (bullish), or cumulative delta falls by at least sharpCvdThr while price fails to fall (bearish).
3. Volume climax: The current side’s volume is ≥ sharpVolMult × its average and the product of volume and bar range is the highest in the lookback window.
4. Absorption/Churn: The current side’s volume divided by the bar’s range equals the highest value in the window and the bar’s range divided by ATR ≤ sharpChurnThr (indicating large volume within a small range).
5. Percentile rank: The current side’s volume percentile rank is ≥ sharp Percentile.
6. Mirror logic for sellers: The above checks are repeated with buyer and seller roles swapped and the price break levels reversed.
Each condition that passes contributes one point to the corresponding side’s score (0 or 1). Risk and confirmation thresholds are then applied to these scores.
7.4 Scoring and labels
• Risk: If scoreBull ≥ sharpScoreRisk, a “Sharp ↑ Risk” label is drawn above the bar. If scoreBear ≥ sharpScoreRisk, a “Sharp ↓ Risk” label is drawn below the bar.
• Confirmation: A risk label is upgraded to “Sharp ↑” when scoreBull ≥ sharpScoreConfirm and the bar closes above the highest recent pivot (HH1); for bearish cases, confirmation requires scoreBear ≥ sharpScoreConfirm and a close below the lowest pivot (LL1).
• Label positioning: Labels are offset from the candle by ATR × sharpLabelMult (full ATR times multiplier), not half, and may include a dashed or dotted connector line if enabled.
Figure caption, In this chart both bullish and bearish sharp‑move setups have been flagged. Earlier in the range, a “Sharp ↓ Risk” label appears beneath a candle: the sell‑side score met the risk threshold, signaling that the combination of strong sell volume, dominance and absorption within a narrow range suggested a potential sharp decline. The price did not close below the lower pivot, so this label remains a “risk” and no confirmation occurred. Later, as the market recovered and volume shifted back to the buy side, a “Sharp ↑ Risk” label prints above a candle near the top of the channel. Here, buy‑side dominance, cumulative delta divergence and a volume climax aligned, but price has not yet closed above the upper pivot (HH1), so the alert is still a risk rather than a confirmed sharp‑up move.
Figure caption, In this chart a Sharp ↑ label is displayed above a candle, indicating that the sharp move module has confirmed a bullish breakout. Prior bars satisfied the risk threshold — showing buy‑side dominance, positive cumulative delta divergence, a volume climax and strong absorption in a narrow range — and this candle closes above the highest recent pivot, upgrading the earlier “Sharp ↑ Risk” alert to a full Sharp ↑ signal. The green label is offset from the candle with a dashed connector, while the red and green trend lines trace the high and low pivot trajectories and the dashed horizontals mark the highest and lowest values of the lookback window.
8. Market‑Maker / Spread‑Capture Module
8.1 Concept
Liquidity providers often “capture the spread” by buying and selling in almost equal amounts within a very narrow price range. These bars can signal temporary congestion before a move or reflect algorithmic activity. This module flags bars where both buyer and seller volumes are high, the price range is only a few ticks and the buy/sell split remains close to 50%. It helps traders spot potential liquidity pockets.
8.2 Inputs
• scalpLookback: Window length used to compute volume averages.
• scalpVolMult: Multiplier applied to each side’s average volume; both buy and sell volumes must exceed this multiple.
• scalpTickCount: Maximum allowed number of ticks in a bar’s range (calculated as (high − low) / minTick). A value of 1 or 2 captures ultra‑small bars; increasing it relaxes the range requirement.
• scalpDeltaRatio: Maximum deviation from a perfect 50/50 split. For example, 0.05 means the buyer share must be between 45% and 55%.
• Label settings: ATR length, multiplier, size, colors, connector style and width.
• Toggles : show_scalp_module and show_scalp_labels to enable the module and its labels.
8.3 Signal
When, on the current bar, both TF_buy_breakout and TF_sell_breakout exceed scalpVolMult times their respective averages and (high − low)/minTick ≤ scalpTickCount and the buyer share is within scalpDeltaRatio of 50%, the module prints a “Spread ↔” label above the bar. The label uses the same ATR offset logic as other modules and draws a connector if enabled.
Figure caption, In this chart the spread‑capture module has identified a potential liquidity pocket. Buyer and seller volumes both spiked above their recent averages, yet the candle’s range measured only a couple of ticks and the buy/sell split stayed close to 50 %. This combination met the module’s criteria, so it printed a grey “Spread ↔” label above the bar. The red and green trend lines link the earliest and latest high and low pivots, and the dashed horizontals mark the highest high and lowest low within the current lookback window.
9. Money Flow Module
9.1 Concept
To translate volume into a monetary measure, this module multiplies each side’s volume by the closing price. It tracks buying and selling system money default currency on a per-bar basis and sums them over a chosen period. The difference between buy and sell currencies (Δ$) shows net inflow or outflow.
9.2 Inputs
• mf_period_len_mf: Number of bars used for summing buy and sell dollars.
• Label appearance settings: ATR length, multiplier, size, colors for up/down labels, and connector style and width.
• Toggles: Use enableMoneyFlowLabel_mf and showMFLabels to control whether the module and its labels are displayed.
9.3 Calculations
• Per-bar money: Buy $ = TF_buy_breakout × close; Sell $ = TF_sell_breakout × close. Their difference is Δ$ = Buy $ − Sell $.
• Summations: Over mf_period_len_mf bars, compute Σ Buy $, Σ Sell $ and ΣΔ$ using math.sum().
• Info table entries: Rows 9–13 display these values as texts like “↑ USD 1234 (1M)” or “ΣΔ USD −5678 (14)”, with colors reflecting whether buyers or sellers dominate.
• Money flow status: If Δ$ is positive the bar is marked “Money flow in” ; if negative, “Money flow out” ; if zero, “Neutral”. The cumulative status is similarly derived from ΣΔ.Labels print at the bar that changes the sign of ΣΔ, offset using ATR × label multiplier and styled per user preferences.
Figure caption, The chart illustrates a steady rise toward the highest recent pivot (HH1) with price riding between a rising green trend‑line and a red trend‑line drawn through earlier pivot highs. A green Money flow in label appears above the bar near the top of the channel, signaling that net dollar flow turned positive on this bar: buy‑side dollar volume exceeded sell‑side dollar volume, pushing the cumulative sum ΣΔ$ above zero. In the info table, the “Money flow (bar)” and “Money flow Σ” rows both read In, confirming that the indicator’s money‑flow module has detected an inflow at both bar and aggregate levels, while other modules (pivots, trend lines and support/resistance) remain active to provide structural context.
In this example the Money Flow module signals a net outflow. Price has been trending downward: successive high pivots form a falling red trend‑line and the low pivots form a descending green support line. When the latest bar broke below the previous low pivot (LL1), both the bar‑level and cumulative net dollar flow turned negative—selling volume at the close exceeded buying volume and pushed the cumulative Δ$ below zero. The module reacts by printing a red “Money flow out” label beneath the candle; the info table confirms that the “Money flow (bar)” and “Money flow Σ” rows both show Out, indicating sustained dominance of sellers in this period.
10. Info Table
10.1 Purpose
When enabled, the Info Table appears in the lower right of your chart. It summarises key values computed by the indicator—such as buy and sell volume, delta, total volume, breakout status, market phase, and money flow—so you can see at a glance which side is dominant and which signals are active.
10.2 Symbols
• ↑ / ↓ — Up (↑) denotes buy volume or money; down (↓) denotes sell volume or money.
• MA — Moving average. In the table it shows the average value of a series over the lookback period.
• Σ (Sigma) — Cumulative sum over the chosen lookback period.
• Δ (Delta) — Difference between buy and sell values.
• B / S — Buyer and seller share of total volume, expressed as percentages.
• Ref. Price — Reference price for breakout calculations, based on the latest pivot.
• Status — Indicates whether a breakout condition is currently active (True) or has failed.
10.3 Row definitions
1. Up volume / MA up volume – Displays current buy volume on the lower timeframe and its moving average over the lookback period.
2. Down volume / MA down volume – Shows current sell volume and its moving average; sell values are formatted in red for clarity.
3. Δ / ΣΔ – Lists the difference between buy and sell volume for the current bar and the cumulative delta volume over the lookback period.
4. Σ / MA Σ (Vol/MA) – Total volume (buy + sell) for the bar, with the ratio of this volume to its moving average; the right cell shows the average total volume.
5. B/S ratio – Buy and sell share of the total volume: current bar percentages and the average percentages across the lookback period.
6. Buyer Rank / Seller Rank – Ranks the bar’s buy and sell volumes among the last (n) bars; lower rank numbers indicate higher relative volume.
7. Σ Buy / Σ Sell – Sum of buy and sell volumes over the lookback window, indicating which side has traded more.
8. Breakout UP / DOWN – Shows the breakout thresholds (Ref. Price) and whether the breakout condition is active (True) or has failed.
9. Market Phase (Vol) – Reports the current volume‑only phase: Accumulation, Distribution or Neutral.
10. Money Flow – The final rows display dollar amounts and status:
– ↑ USD / Σ↑ USD – Buy dollars for the current bar and the cumulative sum over the money‑flow period.
– ↓ USD / Σ↓ USD – Sell dollars and their cumulative sum.
– Δ USD / ΣΔ USD – Net dollar difference (buy minus sell) for the bar and cumulatively.
– Money flow (bar) – Indicates whether the bar’s net dollar flow is positive (In), negative (Out) or neutral.
– Money flow Σ – Shows whether the cumulative net dollar flow across the chosen period is positive, negative or neutral.
The chart above shows a sequence of different signals from the indicator. A Bull Trap Risk appears after price briefly pushes above resistance but fails to hold, then a green Accum label identifies an accumulation phase. An upward breakout follows, confirmed by a Money flow in print. Later, a Sharp ↓ Risk warns of a possible sharp downturn; after price dips below support but quickly recovers, a Bear Trap label marks a false breakdown. The highlighted info table in the center summarizes key metrics at that moment, including current and average buy/sell volumes, net delta, total volume versus its moving average, breakout status (up and down), market phase (volume), and bar‑level and cumulative money flow (In/Out).
11. Conclusion & Final Remarks
This indicator was developed as a holistic study of market structure and order flow. It brings together several well‑known concepts from technical analysis—breakouts, accumulation and distribution phases, overbought and oversold extremes, bull and bear traps, sharp directional moves, market‑maker spread bars and money flow—into a single Pine Script tool. Each module is based on widely recognized trading ideas and was implemented after consulting reference materials and example strategies, so you can see in real time how these concepts interact on your chart.
A distinctive feature of this indicator is its reliance on per‑side volume: instead of tallying only total volume, it separately measures buy and sell transactions on a lower time frame. This approach gives a clearer view of who is in control—buyers or sellers—and helps filter breakouts, detect phases of accumulation or distribution, recognize potential traps, anticipate sharp moves and gauge whether liquidity providers are active. The money‑flow module extends this analysis by converting volume into currency values and tracking net inflow or outflow across a chosen window.
Although comprehensive, this indicator is intended solely as a guide. It highlights conditions and statistics that many traders find useful, but it does not generate trading signals or guarantee results. Ultimately, you remain responsible for your positions. Use the information presented here to inform your analysis, combine it with other tools and risk‑management techniques, and always make your own decisions when trading.
Higher-timeframe requests█ OVERVIEW
This publication focuses on enhancing awareness of the best practices for accessing higher-timeframe (HTF) data via the request.security() function. Some "traditional" approaches, such as what we explored in our previous `security()` revisited publication, have shown limitations in their ability to retrieve non-repainting HTF data. The fundamental technique outlined in this script is currently the most effective in preventing repainting when requesting data from a higher timeframe. For detailed information about why it works, see this section in the Pine Script™ User Manual .
█ CONCEPTS
Understanding repainting
Repainting is a behavior that occurs when a script's calculations or outputs behave differently after restarting it. There are several types of repainting behavior, not all of which are inherently useless or misleading. The most prevalent form of repainting occurs when a script's calculations or outputs exhibit different behaviors on historical and realtime bars.
When a script calculates across historical data, it only needs to execute once per bar, as those values are confirmed and not subject to change. After each historical execution, the script commits the states of its calculations for later access.
On a realtime, unconfirmed bar, values are fluid . They are subject to change on each new tick from the data provider until the bar closes. A script's code can execute on each tick in a realtime bar, meaning its calculations and outputs are subject to realtime fluctuations, just like the underlying data it uses. Each time a script executes on an unconfirmed bar, it first reverts applicable values to their last committed states, a process referred to as rollback . It only commits the new values from a realtime bar after the bar closes. See the User Manual's Execution model page to learn more.
In essence, a script can repaint when it calculates on realtime bars due to fluctuations before a bar's confirmation, which it cannot reproduce on historical data. A common strategy to avoid repainting when necessary involves forcing only confirmed values on realtime bars, which remain unchanged until each bar's conclusion.
Repainting in higher-timeframe (HTF) requests
When working with a script that retrieves data from higher timeframes with request.security() , it's crucial to understand the differences in how such requests behave on historical and realtime bars .
The request.security() function executes all code required by its `expression` argument using data from the specified context (symbol, timeframe, or modifiers) rather than on the chart's data. As when executing code in the chart's context, request.security() only returns new historical values when a bar closes in the requested context. However, the values it returns on realtime HTF bars can also update before confirmation, akin to the rollback and recalculation process that scripts perform in the chart's context on the open bar. Similar to how scripts operate in the chart's context, request.security() only confirms new values after a realtime bar closes in its specified context.
Once a script's execution cycle restarts, what were previously realtime bars become historical bars, meaning the request.security() call will only return confirmed values from the HTF on those bars. Therefore, if the requested data fluctuates across an open HTF bar, the script will repaint those values after it restarts.
This behavior is not a bug; it's simply the default behavior of request.security() . In some cases, having the latest information from an unconfirmed HTF bar is precisely what a script needs. However, in many other cases, traders will require confirmed, stable values that do not fluctuate across an open HTF bar. Below, we explain the most reliable approach to achieve such a result.
Achieving consistent timing on all bars
One can retrieve non-fluctuating values with consistent timing across historical and realtime feeds by exclusively using request.security() to fetch the data from confirmed HTF bars. The best way to achieve this result is offsetting the `expression` argument by at least one bar (e.g., `close [1 ]`) and using barmerge.lookahead_on as the `lookahead` argument.
We discourage the use of barmerge.lookahead_on alone since it prompts the function to look toward future values of HTF bars across historical data, which is heavily misleading. However, when paired with a requested `expression` that includes a one-bar historical offset, the "future" data the function retrieves is not from the future. Instead, it represents the last confirmed bar's values at the start of each HTF bar, thus preventing the results on realtime bars from fluctuating before confirmation from the timeframe.
For example, this line of code uses a request.security() call with barmerge.lookahead_on to request the close price from the "1D" timeframe, offset by one bar with the history-referencing operator [ ] . This line will return the daily price with consistent timing across all bars:
float htfClose = request.security(syminfo.tickerid, "1D", close , lookahead = barmerge.lookahead_on)
Note that:
• This technique only works as intended for higher-timeframe requests .
• When designing a script to work specifically with HTFs, we recommend including conditions to prevent request.security() from accessing timeframes equal to or lower than the chart's timeframe, especially if you intend to publish it. In this script, we included an if structure that raises a runtime error when the requested timeframe is too small.
• A necessary trade-off with this approach is that the script must wait for an HTF bar's confirmation to retrieve new data on realtime bars, thus delaying its availability until the open of the subsequent HTF bar. The time elapsed during such a delay varies with each market, but it's typically relatively small.
👉 Failing to offset the function's `expression` argument while using barmerge.lookahead_on will produce historical results with lookahead bias , as it will look to the future states of historical HTF bars, retrieving values before the times at which they're available in the feed. See the `lookahead` and Future leak with `request.security()` sections in the Pine Script™ User Manual for more information.
Evolving practices
The fundamental technique outlined in this publication is currently the only reliable approach to requesting non-repainting HTF data with request.security() . It is the superior approach because it avoids the pitfalls of other methods, such as the one introduced in the `security()` revisited publication. That publication proposed using a custom `f_security()` function, which applied offsets to the `expression` and the requested result based on historical and realtime bar states. At that time, we explored techniques that didn't carry the risk of lookahead bias if misused (i.e., removing the historical offset on the `expression` while using lookahead), as requests that look ahead to the future on historical bars exhibit dangerously misleading behavior.
Despite these efforts, we've unfortunately found that the bar state method employed by `f_security()` can produce inaccurate results with inconsistent timing in some scenarios, undermining its credibility as a universal non-repainting technique. As such, we've deprecated that approach, and the Pine Script™ User Manual no longer recommends it.
█ METHOD VARIANTS
In this script, all non-repainting requests employ the same underlying technique to avoid repainting. However, we've applied variants to cater to specific use cases, as outlined below:
Variant 1
Variant 1, which the script displays using a lime plot, demonstrates a non-repainting HTF request in its simplest form, aligning with the concept explained in the "Achieving consistent timing" section above. It uses barmerge.lookahead_on and offsets the `expression` argument in request.security() by one bar to retrieve the value from the last confirmed HTF bar. For detailed information about why this works, see the Avoiding Repainting section of the User Manual's Other timeframes and data page.
Variant 2
Variant 2 ( fuchsia ) introduces a custom function, `htfSecurity()`, which wraps the request.security() function to facilitate convenient repainting control. By specifying a value for its `repaint` parameter, users can determine whether to allow repainting HTF data. When the `repaint` value is `false`, the function applies lookahead and a one-bar offset to request the last confirmed value from the specified `timeframe`. When the value is `true`, the function requests the `expression` using the default behavior of request.security() , meaning the results can fluctuate across chart bars within realtime HTF bars and repaint when the script restarts.
Note that:
• This function exclusively handles HTF requests. If the requested timeframe is not higher than the chart's, it will raise a runtime error .
• We prefer this approach since it provides optional repainting control. Sometimes, a script's calculations need to respond immediately to realtime HTF changes, which `repaint = true` allows. In other cases, such as when issuing alerts, triggering strategy commands, and more, one will typically need stable values that do not repaint, in which case `repaint = false` will produce the desired behavior.
Variant 3
Variant 3 ( white ) builds upon the same fundamental non-repainting approach used by the first two. The difference in this variant is that it applies repainting control to tuples , which one cannot pass as the `expression` argument in our `htfSecurity()` function. Tuples are handy for consolidating `request.*()` calls when a script requires several values from the same context, as one can request a single tuple from the context rather than executing multiple separate request.security() calls.
This variant applies the internal logic of our `htfSecurity()` function in the script's global scope to request a tuple containing open and `srcInput` values from a higher timeframe with repainting control. Historically, Pine Script™ did not allow the history-referencing operator [ ] when requesting tuples unless the tuple came from a function call, which limited this technique. However, updates to Pine over time have lifted this restriction, allowing us to pass tuples with historical offsets directly as the `expression` in request.security() . By offsetting all items in a tuple `expression` by one bar and using barmerge.lookahead_on , we effectively retrieve a tuple of stable, non-repainting HTF values.
Since we cannot encapsulate this method within the `htfSecurity()` function and must execute the calculations in the global scope, the script's "Repainting" input directly controls the global `offset` and `lookahead` values to ensure it behaves as intended.
Variant 4 (Control)
Variant 4, which the script displays as a translucent orange plot, uses a default request.security() call, providing a reference point to compare the difference between a repainting request and the non-repainting variants outlined above. Whenever the script restarts its execution cycle, realtime bars become historical bars, and the request.security() call here will repaint the results on those bars.
█ Inputs
Repainting
The "Repainting" input (`repaintInput` variable) controls whether Variant 2 and Variant 3 are allowed to use fluctuating values from an unconfirmed HTF bar. If its value is `false` (default), these requests will only retrieve stable values from the last confirmed HTF bar.
Source
The "Source" input (`srcInput` variable) determines the series the script will use in the `expression` for all HTF data requests. Its default value is close .
HTF Selection
This script features two ways to specify the higher timeframe for all its data requests, which users can control with the "HTF Selection" input (`tfTypeInput` variable):
1) If its value is "Fixed TF", the script uses the timeframe value specified by the "Fixed Higher Timeframe" input (`fixedTfInput` variable). The script will raise a runtime error if the selected timeframe is not larger than the chart's.
2) If the input's value is "Multiple of chart TF", the script multiplies the value of the "Timeframe Multiple" input (`tfMultInput` variable) by the chart's timeframe.in_seconds() value, then converts the result to a valid timeframe string via timeframe.from_seconds() .
Timeframe Display
This script features the option to display an "information box", i.e., a single-cell table that shows the higher timeframe the script is currently using. Users can toggle the display and determine the table's size, location, and color scheme via the inputs in the "Timeframe Display" group.
█ Outputs
This script produces the following outputs:
• It plots the results from all four of the above variants for visual comparison.
• It highlights the chart's background gray whenever a new bar starts on the higher timeframe, signifying when confirmations occur in the requested context.
• To demarcate which bars the script considers historical or realtime bars, it plots squares with contrasting colors corresponding to bar states at the bottom of the chart pane.
• It displays the higher timeframe string in a single-cell table with a user-specified size, location, and color scheme.
Look first. Then leap.
Statistical Package for the Trading Sciences [SS]
This is SPTS.
It stands for Statistical Package for the Trading Sciences.
Its a play on SPSS (Statistical Package for the Social Sciences) by IBM (software that, prior to Pinescript, I would use on a daily basis for trading).
Let's preface this indicator first:
This isn't so much an indicator as it is a project. A passion project really.
This has been in the works for months and I still feel like its incomplete. But the plan here is to continue to add functionality to it and actually have the Pinecoding and Tradingview community contribute to it.
As a math based trader, I relied on Excel, SPSS and R constantly to plan my trades. Since learning a functional amount of Pinescript and coding a lot of what I do and what I relied on SPSS, Excel and R for, I use it perhaps maybe a few times a week.
This indicator, or package, has some of the key things I used Excel and SPSS for on a daily and weekly basis. This also adds a lot of, I would say, fairly complex math functionality to Pinescript. Because this is adding functionality not necessarily native to Pinescript, I have placed most, if not all, of the functionality into actual exportable functions. I have also set it up as a kind of library, with explanations and tips on how other coders can take these functions and implement them into other scripts.
The hope here is that other coders will take it, build upon it, improve it and hopefully share additional functionality that can be added into this package. Hence why I call it a project. Okay, let's get into an overview:
Current Functions of SPTS:
SPTS currently has the following functionality (further explanations will be offered below):
Ability to Perform a One-Tailed, Two-Tailed and Paired Sample T-Test, with corresponding P value.
Standard Pearson Correlation (with functionality to be able to calculate the Pearson Correlation between 2 arrays).
Quadratic (or Curvlinear) correlation assessments.
R squared Assessments.
Standard Linear Regression.
Multiple Regression of 2 independent variables.
Tests of Normality (with Kurtosis and Skewness) and recognition of up to 7 Different Distributions.
ARIMA Modeller (Sort of, more details below)
Okay, so let's go over each of them!
T-Tests
So traditionally, most correlation assessments on Pinescript are done with a generic Pearson Correlation using the "ta.correlation" argument. However, this is not always the best test to be used for correlations and determine effects. One approach to correlation assessments used frequently in economics is the T-Test assessment.
The t-test is a statistical hypothesis test used to determine if there is a significant difference between the means of two groups. It assesses whether the sample means are likely to have come from populations with the same mean. The test produces a t-statistic, which is then compared to a critical value from the t-distribution to determine statistical significance. Lower p-values indicate stronger evidence against the null hypothesis of equal means.
A significant t-test result, indicating the rejection of the null hypothesis, suggests that there is statistical evidence to support that there is a significant difference between the means of the two groups being compared. In practical terms, it means that the observed difference in sample means is unlikely to have occurred by random chance alone. Researchers typically interpret this as evidence that there is a real, meaningful difference between the groups being studied.
Some uses of the T-Test in finance include:
Risk Assessment: The t-test can be used to compare the risk profiles of different financial assets or portfolios. It helps investors assess whether the differences in returns or volatility are statistically significant.
Pairs Trading: Traders often apply the t-test when engaging in pairs trading, a strategy that involves trading two correlated securities. It helps determine when the price spread between the two assets is statistically significant and may revert to the mean.
Volatility Analysis: Traders and risk managers use t-tests to compare the volatility of different assets or portfolios, assessing whether one is significantly more or less volatile than another.
Market Efficiency Tests: Financial researchers use t-tests to test the Efficient Market Hypothesis by assessing whether stock price movements follow a random walk or if there are statistically significant deviations from it.
Value at Risk (VaR) Calculation: Risk managers use t-tests to calculate VaR, a measure of potential losses in a portfolio. It helps assess whether a portfolio's value is likely to fall below a certain threshold.
There are many other applications, but these are a few of the highlights. SPTS permits 3 different types of T-Test analyses, these being the One Tailed T-Test (if you want to test a single direction), two tailed T-Test (if you are unsure of which direction is significant) and a paired sample t-test.
Which T is the Right T?
Generally, a one-tailed t-test is used to determine if a sample mean is significantly greater than or less than a specified population mean, whereas a two-tailed t-test assesses if the sample mean is significantly different (either greater or less) from the population mean. In contrast, a paired sample t-test compares two sets of paired observations (e.g., before and after treatment) to assess if there's a significant difference in their means, typically used when the data points in each pair are related or dependent.
So which do you use? Well, it depends on what you want to know. As a general rule a one tailed t-test is sufficient and will help you pinpoint directionality of the relationship (that one ticker or economic indicator has a significant affect on another in a linear way).
A two tailed is more broad and looks for significance in either direction.
A paired sample t-test usually looks at identical groups to see if one group has a statistically different outcome. This is usually used in clinical trials to compare treatment interventions in identical groups. It's use in finance is somewhat limited, but it is invaluable when you want to compare equities that track the same thing (for example SPX vs SPY vs ES1!) or you want to test a hypothesis about an index and a leveraged share (for example, the relationship between FNGU and, say, MSFT or NVDA).
Statistical Significance
In general, with a t-test you would need to reference a T-Table to determine the statistical significance of the degree of Freedom and the T-Statistic.
However, because I wanted Pinescript to full fledge replace SPSS and Excel, I went ahead and threw the T-Table into an array, so that Pinescript can make the determination itself of the actual P value for a t-test, no cross referencing required :-).
Left tail (Significant):
Both tails (Significant):
Distributed throughout (insignificant):
As you can see in the images above, the t-test will also display a bell-curve analysis of where the significance falls (left tail, both tails or insignificant, distributed throughout).
That said, I have not included this function for the paired sample t-test because that is a bit more nuanced. But for the one and two tailed assessments, the indicator will provide you the P value.
Pearson Correlation Assessment
I don't think I need to go into too much detail on this one.
I have put in functionality to quickly calculate the Pearson Correlation of two array's, which is not currently possible with the "ta.correlation" function.
Quadratic (Curvlinear) Correlation
Not everything in life is linear, sometimes things are curved!
The Pearson Correlation is great for linear assessments, but tends to under-estimate the degree of the relationship in curved relationships. There currently is no native function to t-test for quadratic/curvlinear relationships, so I went ahead and created one.
You can see an example of how Quadratic and Pearson Correlations vary when you look at CME_MINI:ES1! against AMEX:DIA for the past 10 ish months:
Pearson Correlation:
Quadratic Correlation:
One or the other is not always the best, so it is important to check both!
R-Squared Assessments:
The R-squared value, or the square of the Pearson correlation coefficient (r), is used to measure the proportion of variance in one variable that can be explained by the linear relationship with another variable. It represents the goodness-of-fit of a linear regression model with a single predictor variable.
R-Squared is offered in 3 separate forms within this indicator. First, there is the generic R squared which is taking the square root of a Pearson Correlation assessment to assess the variance.
The next is the R-Squared which is calculated from an actual linear regression model done within the indicator.
The first is the R-Squared which is calculated from a multiple regression model done within the indicator.
Regardless of which R-Squared value you are using, the meaning is the same. R-Square assesses the variance between the variables under assessment and can offer an insight into the goodness of fit and the ability of the model to account for the degree of variance.
Here is the R Squared assessment of the SPX against the US Money Supply:
Standard Linear Regression
The indicator contains the ability to do a standard linear regression model. You can convert one ticker or economic indicator into a stock, ticker or other economic indicator. The indicator will provide you with all of the expected information from a linear regression model, including the coefficients, intercept, error assessments, correlation and R2 value.
Here is AAPL and MSFT as an example:
Multiple Regression
Oh man, this was something I really wanted in Pinescript, and now we have it!
I have created a function for multiple regression, which, if you export the function, will permit you to perform multiple regression on any variables available in Pinescript!
Using this functionality in the indicator, you will need to select 2, dependent variables and a single independent variable.
Here is an example of multiple regression for NASDAQ:AAPL using NASDAQ:MSFT and NASDAQ:NVDA :
And an example of SPX using the US Money Supply (M2) and AMEX:GLD :
Tests of Normality:
Many indicators perform a lot of functions on the assumption of normality, yet there are no indicators that actually test that assumption!
So, I have inputted a function to assess for normality. It uses the Kurtosis and Skewness to determine up to 7 different distribution types and it will explain the implication of the distribution. Here is an example of SP:SPX on the Monthly Perspective since 2010:
And NYSE:BA since the 60s:
And NVDA since 2015:
ARIMA Modeller
Okay, so let me disclose, this isn't a full fledge ARIMA modeller. I took some shortcuts.
True ARIMA modelling would involve decomposing the seasonality from the trend. I omitted this step for simplicity sake. Instead, you can select between using an EMA or SMA based approach, and it will perform an autogressive type analysis on the EMA or SMA.
I have tested it on lookback with results provided by SPSS and this actually works better than SPSS' ARIMA function. So I am actually kind of impressed.
You will need to input your parameters for the ARIMA model, I usually would do a 14, 21 and 50 day EMA of the close price, and it will forecast out that range over the length of the EMA.
So for example, if you select the EMA 50 on the daily, it will plot out the forecast for the next 50 days based on an autoregressive model created on the EMA 50. Here is how it looks on AMEX:SPY :
You can also elect to plot the upper and lower confidence bands:
Closing Remarks
So that is the indicator/package.
I do hope to continue expanding its functionality, but as of now, it does already have quite a lot of functionality.
I really hope you enjoy it and find it helpful. This. Has. Taken. AGES! No joke. Between referencing my old statistics textbooks, trying to remember how to calculate some of these things, and wanting to throw my computer against the wall because of errors in the code, this was a task, that's for sure. So I really hope you find some usefulness in it all and enjoy the ability to be able to do functions that previously could really only be done in external software.
As always, leave your comments, suggestions and feedback below!
Take care!
CandlestickPatternsLibrary "CandlestickPatterns"
This library provides a wide range of candlestick patterns, and available for user to call each pattern individually. It's a comprehensive and common tool designed for traders seeking to raise their technical analysis, and it may help users identify key turning of price action in financial instruments. Credit to public technical “*All Candlestick Patterns*” indicator.
abandonedBaby(order, d1)
The "Abandoned Baby" candlestick pattern is a bullish/bearish pattern consists of three candles.
Parameters:
order (simple string) : (simple string) Pattern order type "bull" or "bear".
d1 (simple float) : (simple float) Previous candle's body percentage out of candle range. Optional argument, default is 5.
darkCloudCover(c1, n)
The "Dark Cloud Cover" is a bearish pattern consists of two candles.
Parameters:
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
doji(d0)
The "Doji" is neither bullish or bearish consists of one candles.
Parameters:
d0 (simple float) : (simple float) Current candle's body percentage out of candle range. Optional argument, default is 5.
dojiStar(order, c1, n, d0)
The "Doji Star" is a bullish/bearish pattern consists of two candles.
Parameters:
order (simple string) : (simple string) Pattern order type "bull" or "bear" .
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
d0 (simple float) : (simple float) Current candle's body percentage out of candle range. Optional argument, default is 5.
downsideTasukiGap(c2, c1, n)
The "Downside Tasuki Gap" is a bearish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) Previous candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
dragonflyDoji(d0)
The "Dragon Fly Doji" is a bullish pattern consists of one candle.
Parameters:
d0 (simple float) : (simple float) Current candle's body percentage out of candle range. Optional argument, default is 5.
engulfing(order, c1, c0, n)
The "Engulfing" is a bullish/bearish pattern consists of two candles.
Parameters:
order (simple string) : (simple string) Pattern order type "bull" or "bear".
c1 (simple bool) : (simple bool) Previous candle's body must be lower than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
eveningDojiStar(c2, c0, d1, n)
The "Evening Doji Star" is a bearish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
d1 (simple float) : (simple float) Previous candle's body percentage out of candle range. Optional argument, default is 5.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
eveningStar(c2, c1, c0, n)
The "Evening Star" is a bearish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) Previous candle's body must be lower than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
fallingThreeMethods(c4, c3, c2, c1, c0, n)
The "Falling Three Methods" is a bearish pattern consists of five candles.
Parameters:
c4 (simple bool) : (simple bool) 5th candle ago body must be higher than average. Optional argument, default is true.
c3 (simple bool) : (simple bool) 4th candle ago body must be lower than average. Optional argument, default is true.
c2 (simple bool) : (simple bool) 3rd candle ago body must be lower than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) 2nd candle ago body must be lower than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
Returns: (bool)
fallingWindow()
The "Falling Window" is a bearish pattern consists of two candles.
gravestoneDoji(d0)
The "Gravestone Doji" is a bearish pattern consists of one candle.
Parameters:
d0 (simple float) : (simple float) Current candle's body percentage out of candle range. Optional argument, default is 5.
hammer(c0, n)
The "Hammer" is a bullish pattern consists of one candle.
Parameters:
c0 (simple bool) : (simple bool) Current candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
hangingMan(c0, n)
The "Hanging Man" is a bearish pattern consists of one candle.
Parameters:
c0 (simple bool) : (simple bool) Current candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
haramiCross(order, c1, n)
The "Harami Cross" candlestick pattern is a bullish/bearish pattern consists of two candles.
Parameters:
order (string) : (simple string) Pattern order type "bull" or "bear".
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
harami(order, c1, c0, n)
The "Harami" candlestick pattern is a bullish/bearish pattern consists of two candles.
Parameters:
order (string) : (simple string) Pattern order type "bull" or "bear"
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
invertedHammer(c0, n)
The "Inverted Hammer" is a bullish pattern consists of one candle.
Parameters:
c0 (simple bool) : (simple bool) Current candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
kicking(order, c1, c0, n)
The "Kicking" candlestick pattern is a bullish/bearish pattern consists of two candles.
Parameters:
order (string) : (simple string) Pattern order type "bull" or "bear"
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
longLowerShadow(l0)
The "Long Lower Shadow" candlestick pattern is a bullish pattern consists of one candles.
Parameters:
l0 (simple float) : (simple float) Current candle's lower wick min percentage out of candle range. Optional argument, default is 75.
longUpperShadow(u0)
The "Long Upper Shadow" candlestick pattern is a bearish pattern consists of one candles.
Parameters:
u0 (simple float) : (simple float) Current candle's upper wick min percentage out of candle range. Optional argument, default is 75.
marubozuBlack(c0, n)
The "Marubozu Black" candlestick pattern is a bearish pattern consists of one candles.
Parameters:
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
marubozuWhite(c0, n)
The "Marubozu White" candlestick pattern is a bullish pattern consists of one candles.
Parameters:
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
morningDojiStar(c2, d1, c0, n)
The "Morning Doji Star" candlestick pattern is a bullish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average. Optional argument, default is true.
d1 (simple float) : (simple float) Previous candle's body percentage out of candle range. Optional argument, default is 5.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
morningStar(c2, c1, c0, n)
The "Morning Star" candlestick pattern is a bullish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) Previous candle's body must be lower than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Cuurent candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
onNeck(c1, c0, n)
The "On Neck" candlestick pattern is a bearish pattern consists of two candles.
Parameters:
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Cuurent candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
piercing(c1, n)
The "Piercing" candlestick pattern is a bullish pattern consists of two candles.
Parameters:
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
risingThreeMethods(c4, c3, c2, c1, c0, n)
The "Rising Three Methods" candlestick pattern is a bullish pattern consists of five candles.
Parameters:
c4 (simple bool) : (simple bool) 5th candle ago body must be higher than average. Optional argument, default is true.
c3 (simple bool) : (simple bool) 4th candle ago body must be Lower than average. Optional argument, default is true.
c2 (simple bool) : (simple bool) 3rd candle ago body must be Lower than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) 2nd candle ago body must be Lower than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
risingWindow()
The "Rising Window" candlestick pattern is a bullish pattern consists of two candle.
shootingStar(c0, n)
The "Shooting Star" candlestick pattern is a bearish pattern consists of one candle.
Parameters:
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
spinningTopBlack(l0, u0)
The "Spinning Top Black" is neither bullish or bearish.
Parameters:
l0 (simple float) : (simple float) Current candle's lower wick min percentage out of candle range. Optional argument, default is 34.
u0 (simple float) : (simple float) Current candle's upper wick min percentage out of candle range. Optional argument, default is 34.
spinningTopWhite(l0, u0)
The "Spinning Top White" is neither bullish or bearish.
Parameters:
l0 (simple float) : (simple float) Current candle's lower wick min percentage out of candle range. Optional argument, default is 34.
u0 (simple float) : (simple float) Current candle's upper wick min percentage out of candle range. Optional argument, default is 34.
threeBlackCrows(c2, c1, c0, n)
The "Three Black Crows" candlestick pattern is a bearish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Cuurent candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
threeWhiteSoldiers(c2, c1, c0, n)
The "Three White Soldiers" candlestick pattern is a bullish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Cuurent candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
triStar(order, d2, d1, d0)
The "Tri Star" candlestick pattern is a bullish/bearish pattern consists of three candles.
Parameters:
order (simple string) : (simple string) Pattern order type "bull" or "bear".
d2 (simple float) : (simple float) Before previous candle's body percentage out of candle range. Optional argument, default is 5.
d1 (simple float) : (simple float) Previous candle's body percentage out of candle range. Optional argument, default is 5.
d0 (simple float) : (simple float) Current candle's body percentage out of candle range. Optional argument, default is 5.
tweezerBottom(c1, n)
The "Tweezer Bottom" candlestick pattern is a bullish pattern consists of two candles.
Parameters:
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
tweezerTop(c1, n)
The "Tweezer Top" candlestick pattern is a bearish pattern consists of two candles.
Parameters:
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
upsideTasukiGap(c2, c1, n)
The "Tri Star" candlestick pattern is a bullish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before Previous candle's body must be higher than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) Previous candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
[ChasinAlts]Top-Wicked Good S/R LinesHello Tradeurs, as per usual, I hope everyone is having a FAN-FRIGGIN-TASTIC day. With the soon incoming bull market approaching fast(Nov 7, 2022), there are a few ideas that I've really been trying to push out to help nail a few coins as they are near their bottom peak of this closing Bear Market. This one may seem very similar to the last one I posted but I think this one takes the cake...esp when you see the next script from my 'Market Overview' series that I will be publishing shortly after this one as it is utilizing this new script for a market scanner that will be SUPER legit and profitable. Though it is alway nice to be noticed, I'm glad that I'm relatively unpopular so the few people that are now following me can have some time to make some money with some of these scripts I'm trying to pump out for the benefit of the community. I will rarely give my full analysis of how I take in and utilize these scripts but I can tell you, QUITE A FEW of them are money in the bank. Esp these last few I've done/am doing and even more-so the ones that are soon to come (I'm speaking of about the next 3-4 that I will be attempting to pump out in this next VERY IMPORTANT week.). One more thing I'll add before going to the script is a little alpha(Im pretty certain this is the way it is going but NOTHING is EVERY 100% in life). What I believe should be realized is the bottoming out of MANY of the crypto coins at the VERY bottom of a LONG TERM Cup and Handle (so it seems but shat can still change in the blink of an eye). Thus there are quite a few coins that I believe have already bottomed and wont be returning to said bottom for a few years or so but there are also quite a few still at the brink of the bottomest part before the real market breakout occurs. My goal with these scripts coming out this week to help you all find those coins that have yet to hit their very bottom (thus the ATH/ATL script recently published). Going back in history looking for the lowest points of long term Cup & Handles I will point out 2 key things. Near the center/bottomest part of these historical CnH you will see either Double Bottoms OR a Huge dump and then its V-shaped recovery. After these print the point of no return has occurred where only a few coins will be going lower than these Double Bottoms/V-Shaped recoveries. So the time is at hand. Now that many coins are seemingly pumping after this long consolidation, I believe we need to keep a keen eye out for THE FINAL RUG PULL (as soon as enough degenerates are leveraging Long their entire savings.). What Im saying is be ready for this final rug pull to finally be seeing these Double Bottoms/V-Shaped recoveries VERY soon. DO NOT waste all your capital yet and MAKE SURE to use stop losses or else rather than stop losses you will be burdened with MUCH WORSE losses. Im currently not even in the market bc I am waiting on said rug pull. Ok for the Script now.
This script is similar to the last one but with the previous one, one general set of settings can produce VASTLY different results (might have 2 S/R lines on one coin and 80 on another). I wanted to fix that with this script, turn it into a "Market Overview" Scanner and create alerts for the MO Scanner to be able to get alerted any time a coin is passing its largest wick S/R levels bc DULY NOTE...it is VERY rare that a coin will blow past it if it hasn't approached it recently. That means that a small retrace of 3-5%(or more) is EASY to acquire (with leverage that can really add up with how many coins are in the Kucoin Margin Coin list that I have in my scanners). Now, once price does shoot through a level you best be sure to be looking down the line for a retest of the S/R level it blew past before as they are MANY times the retest level and price will be coming back to it before continuing
in the direction it was going. Depending on the TF your using this could be a few hours to a few days to a few weeks...you get it. With this script you can choose to draw S/R lines 2 ways: 1) by having it plot S/R lines on the end of the largest 2(3,4,5..however many you choose) wicks that the chart has access to. For the scanner ill just be putting the largest 2-3 wicks and set alerts when coming up to them/crossing them & 2) having it draw S/R lines on the ends of the largest X% of wicks. it will be erasing the lines and drawing new ones on each new candle occurrence so the same general settings will no longer be producing VASTLY diff amounts of S/R lines and will be way more consistent amongst the coins for better utilization with the scanner (when I publish it). There is also a Wick Max Cutoff % so for those coins that had it's first few hours printing 100% sized wicks...you can choose to ignore them so they are not taking up one of your top spots for the S/R lines. There is similarly a Wick % min Size that can be selected so if you’re using the top % setting, it will help decrease those coins that can be still plotting 30 lines even though the top 3% of the largest wicks are set in the settings. Hope Im being clear but it's easy enough. I believe in you and your capabilities of comprehending it all and getting it all figured out. So this script is for a visualization for the scanner that I will be uploading soon-after. It's always nice to get a few comments if my ideas/scripts have been helpful to you and please don't hold back if you have something to tell me that I screwed up on (I am still rather new to this coding thing but I like to think I at least have some fresh ideas that aren’t out there in the public library). Talk to you soon and may the force be with your trades. Peace and love people...peace and love. -ChasinAlts out.
Realtime FootprintThe purpose of this script is to gain a better understanding of the order flow by the footprint. To that end, i have added unusual features in addition to the standard features.
I use "Real Time 5D Profile by LucF" main engine to create basic footprint(profile type) and added some popular features and my favorites.
This script can only be used in realtime, because tradingview doesn't provide historical Bid/Ask date.
Bid/Ask date used this script are up/down ticks.
This script can only be used by time based chart (1m, 5m , 60m and daily etc)
This script use many labels and these are limited max 500, so you can't display many bars.
If you want to display foot print bars longer, turn off the unused sub-display function.
Default setting is footprint is 25 labels, IB count is 1, COT high and Ratio high is 1, COT low and Ratio low is 1 and Delta Box Ratio Volume is 1 , total 29.
plus UA , IB stripes , ladder fading mark use several labels.
///////// General Setting ///////////
Resets on Volume / Range bar
: If you want to use simple time based Resets on, please set Total Volume is 0.
Your timeframe is always the first condition. So if you set Total Volume is 1000, both conditions(Volume >= 1000 and your timeframe start next bar) must be met. (that is, new footprint bar doesn't start at when total volume = exactly 1000).
Ticks per row and Maximum row of Bar
: 1 is minimum size(tick). "Maximum row of Bar" decide the number of rows used in one footprint. 1 row is created from 1 label, so you need to reduce this number to display many footprints (Max label is 500).
Volume Filter and For Calculation and Display
: "Volume Filter" decide minimum size of using volume for this script.
"For Calculation and Display" is used to convert volume to an integer.
This script only use integer to make profile look better (I contained Bid number and Ask number in one row( one label) to saving labels. This require to make no difference in width by the number of digits and this script corresponds integers from 0 to 3 digits).
ex) Symbol average volume size is from 0.0001 to 0.001. You decide only use Volume >= 0.0005 by "Volume Filter".
Next, you convert volume to integer, by setting "For Calculation and Display" is 1000 (0.0005 * 1000 = 5).
If 0.00052 → 5.2 → 5, 0.00058 → 5.8 → 6 (Decimal numbers are rounded off)
This integer is used to all calculation in this script.
//////// Main Display ///////
Footprint, Total, Row Delta, Diagonal Delta and Profile
: "Footprint" display Ask and Bid per row. "Total" display Ask + Bid per row.
"Row Delta" display Ask - Bid per row. "Diagonal Delta" display Ask(row N) - Bid(row N -1) per row.
Profile display Total Volume(Ask + Bid) per row by using Block. Profile Block coloring are decided by Row Delta value(default: positive Row Delta (Ask > Bid) is greenish colors and negative Row Delta (Ask < Bid) is reddish colors.)
Volume per Profile Block, Row Imbalance Ratio and Delta Bull/Bear/Neutral Colors
: "Volume per Profile Block" decide one block contain how many total volume.
ex) When you set 20, Total volume 70 display 3 block.
The maximum number of blocks that can be used per low is 20.
So if you set 20, Total volume 400 is 20 blocks. total volume 800 is 20 blocks too.
"Row Imbalance Ratio" decide block coloring. The row imbalance is that the difference between Ask and Bid (row delta) is large.
default is x3, x2 and x1. The larger the difference, the brighter the color.
ex) Ask 30 Bid 10 is light green. Ask 20 Bid 10 is green. Ask 11 Bid 10 is dark green.
Ask 0 Bid 1 is light red. Ask 1 Bid 2 is red. ask 30 Bid 59 is dark green.
Ask 10 Bid 10 is neutral color(gray)
profile coloring is reflected same row's other elements(Ask, Bid, Total and Delta) too.
It's because one label can only use one text color.
/////// Sub Display ///////
Delta, total and Commitment of Traders
: "Delta" is total Ask - total Bid in one footprint bar. Total is total Ask + total Bid in one footprint bar.
"Commitment of traders" is variation of "Delta". COT High is reset to 0 when current highest is touched. COT Low is opposite.
Basic concept of Delta is to compare price with Delta. Ordinary, when price move up, delta is positive. Price move down is negative delta.
This is because market orders move price and market orders are counted by Delta (although this description is not exactly correct).
But, sometimes prices do not move even though many market orders are putting pressure on price , or conversely, price move strongly without many market orders.
This is key point. Big player absorb market orders by iceberg order(Subdivide large orders and pretend to be small limit orders.
Small limit orders look weak in the order book, but they are added each time you fill, so they are more powerful than they look.), so price don't move.
On the other hand, when the price is moving easily, smart players may be aiming to attract and counterattack to a better price for them.
It's more of a sport than science, and there's always no right response. Pay attention to the relationship between price, volume and delta.
ex) If COT Low is large negative value, it means many sell market orders is coming, but iceberg order is absorbing their attack at limit order.
you should not do buy entry, only this clue. but this is one of the hints.
"Delta, Box Ratio and Total texts is contained same label and its color are "Delta" coloring. Positive Delta is Delta Bull color(green),Negative Delta is Delta Bear Color
and Delta = 0 is Neutral Color(gray). When Delta direction and price direction are opposite is Delta Divergence Color(yellow).
I didn't add the cumulative volume delta because I prefer to display the CVD line on the price chart rather than the number.
Box Ratio , Box Ratio Divisor and Heavy Box Ratio Ratio
: This is not ordinary footprint features, but I like this concept so I added.
Box Ratio by Richard W. Arms is simple but useful tool. calculation is "total volume (one bar) divided by Bar range (highest - lowest)."
When Bull and bear are fighting fiercely this number become large, and then important price move happen.
I made average BR from something like 5 SMA and if current BR exceeds average BR x (Heavy Box Ratio Ratio), BR box mark will be filled.
Box Ratio Divisor is used to good looking display(BR multiplied by Box Ratio Divisor is rounded off and displayed as an integer)
Diagonal Imbalance Count , D IB Mark and D IB Stripes
: Diagonal Imbalance is defined by "Diagonal Imbalance Ratio".
ex) You set 2. When Ask(row N) 30 Bid(row N -1)10, it's 30 > 10*2, so positive Diagonal Imbalance.
When Ask(row N) 4 Bid(row N -1)9, it's 4*2 < 9, so negative Diagonal Imbalance.
This calculation does not use equals to avoid Ask(row N) 0 Bid(row N -1)0 became Diagonal Imbalance.
Ask(row N) 0 Bid(row N -1)0, it's 0 = 0*2, not Diagonal Imbalance. Ask(row N) 10 Bid(row N -1)5, it's 10 = 5*2, not Diagonal Imbalance.
"D IB Mark" emphasize Ask or Bid number which is dominant side(Winner of Diagonal Imbalance calculation), by under line.
"Diagonal Imbalance Count" compare Ask side D IB Mark to Bid side D IB Mark in one footprint.
Coloring depend on which is more aggressive side (it has many IB Mark) and When Aggressive direction and price direction are opposite is Delta Divergence Color(yellow).
"D IB Stripes" is a function that further emphasizes with an arrow Mark, when a DIB mark is added on the same side for three consecutive row. Three consecutive arrow is added at third row.
Unfinished Auction, Ratio Bounds and Ladder fading Mark
: "Unfinished Auction" emphasize highest or lowest row which has both Ask and Bid, by Delta Divergence Color(yellow) XXXXXX mark.
Unfinished Auction sometimes has magnet effect, price may touch and breakout at UA side in the future.
This concept is famous as profit taking target than entry decision.
But, I'm interested in the case that Big player make fake breakout at UA side and trapped retail traders, and then do reversal with retail traders stop-loss hunt.
Anyway, it's not stand alone signal.
"Ratio Bounds" gauge decrease of pressure at extreme price. Ratio Bounds High is number which second highest ask is divided by highest ask.
Ratio Bounds Low is number which second lowest bid is divided by lowest bid. The larger the number, the less momentum the price has.
ex)first footprint bar has Ratio Bounds Low 2, second footprint bar has RBL 4, third footprint bar has RBL 20.
This indicates that the bear's power is gradually diminishing.
"Ladder fading mark" emphasizes the decrease of the value in 3 consecutive row at extreme price. I added two type Marks.
Ask/Bid type(triangle Mark) is Ask/Bid values are decreasing of three consecutive row at extreme price.
Row Imbalance type(Diamond Mark) are row Imbalance values are decreasing of three consecutive row at extreme price.
ex)Third lowest Bid 40, second lowest Bid 10 and lowest Bid 5 have triangle up Mark. That is bear's power is gradually diminishing.
(This Mark only check Bid value at lowest price and Ask value at highest price).
Third highest row delta + 60, second highest row delta + 5, highest delta - 20 have diamond Mark. That is Bull's power is gradually diminishing.
Sub display use Delta colors at bottom of Sub display section.
////// Candle & POC /////////
candle and POC
: Ordinary, "POC" Point of Control is row of largest total volume, but this script'POC is volume weighted average.
This is because the regular POC was visually displayed by the profile ,and I was influenced LucF's ideas.
POC coloring is decided in relation to the previous POC. When current POC is higher than previous POC, color is UP Bar Color(green).
In the opposite case, Down Bar color is used.
POC Divergence Color is used when Current POC is up but current bar close is lower than open (Down price Bar),or in the opposite case.
POC coloring has option also highlight background by Delta Divergence Color(yellow). but bg color is displayed at your time frame current price bar not current footprint bar.
The basic explanation is over.
I add some image to promote understanding basic ideas.
OrdinaryLeastSquaresLibrary "OrdinaryLeastSquares"
One of the most common ways to estimate the coefficients for a linear regression is to use the Ordinary Least Squares (OLS) method.
This library implements OLS in pine. This implementation can be used to fit a linear regression of multiple independent variables onto one dependent variable,
as long as the assumptions behind OLS hold.
solve_xtx_inv(x, y) Solve a linear system of equations using the Ordinary Least Squares method.
This function returns both the estimated OLS solution and a matrix that essentially measures the model stability (linear dependence between the columns of 'x').
NOTE: The latter is an intermediate step when estimating the OLS solution but is useful when calculating the covariance matrix and is returned here to save computation time
so that this step doesn't have to be calculated again when things like standard errors should be calculated.
Parameters:
x : The matrix containing the independent variables. Each column is regarded by the algorithm as one independent variable. The row count of 'x' and 'y' must match.
y : The matrix containing the dependent variable. This matrix can only contain one dependent variable and can therefore only contain one column. The row count of 'x' and 'y' must match.
Returns: Returns both the estimated OLS solution and a matrix that essentially measures the model stability (xtx_inv is equal to (X'X)^-1).
solve(x, y) Solve a linear system of equations using the Ordinary Least Squares method.
Parameters:
x : The matrix containing the independent variables. Each column is regarded by the algorithm as one independent variable. The row count of 'x' and 'y' must match.
y : The matrix containing the dependent variable. This matrix can only contain one dependent variable and can therefore only contain one column. The row count of 'x' and 'y' must match.
Returns: Returns the estimated OLS solution.
standard_errors(x, y, beta_hat, xtx_inv) Calculate the standard errors.
Parameters:
x : The matrix containing the independent variables. Each column is regarded by the algorithm as one independent variable. The row count of 'x' and 'y' must match.
y : The matrix containing the dependent variable. This matrix can only contain one dependent variable and can therefore only contain one column. The row count of 'x' and 'y' must match.
beta_hat : The Ordinary Least Squares (OLS) solution provided by solve_xtx_inv() or solve().
xtx_inv : This is (X'X)^-1, which means we take the transpose of the X matrix, multiply that the X matrix and then take the inverse of the result.
This essentially measures the linear dependence between the columns of the X matrix.
Returns: The standard errors.
estimate(x, beta_hat) Estimate the next step of a linear model.
Parameters:
x : The matrix containing the independent variables. Each column is regarded by the algorithm as one independent variable. The row count of 'x' and 'y' must match.
beta_hat : The Ordinary Least Squares (OLS) solution provided by solve_xtx_inv() or solve().
Returns: Returns the new estimate of Y based on the linear model.
Delta Volume Candles [LucF]█ OVERVIEW
This indicator plots on-chart volume delta information using candles that can replace your normal candles, tops and bottoms appended to normal candles, optional MAs of those tops and bottoms levels, a divergence channel and a chart background. The indicator calculates volume delta using intrabar analysis, meaning that it uses the lower timeframe bars constituting each chart bar.
█ CONCEPTS
Volume Delta
The volume delta concept divides a bar's volume in "up" and "down" volumes. The delta is calculated by subtracting down volume from up volume. Many calculation techniques exist to isolate up and down volume within a bar. The simplest use the polarity of interbar price changes to assign their volume to up or down slots, e.g., On Balance Volume or the Klinger Oscillator . Others such as Chaikin Money Flow use assumptions based on a bar's OHLC values. The most precise calculation method uses tick data and assigns the volume of each tick to the up or down slot depending on whether the transaction occurs at the bid or ask price. While this technique is ideal, it requires huge amounts of data on historical bars, which considerably limits the historical depth of charts and the number of symbols for which tick data is available. Furthermore, historical tick data is not yet available on TradingView.
This indicator uses intrabar analysis to achieve a compromise between the simplest and most precise methods of calculating volume delta. It is currently the most precise method usable on TradingView charts. TradingView's Volume Profile built-in indicators use it, as do the CVD - Cumulative Volume Delta Candles and CVD - Cumulative Volume Delta (Chart) indicators published from the TradingView account . My Delta Volume Channels and Volume Delta Columns Pro indicators also use intrabar analysis. Other volume delta indicators such as my Realtime 5D Profile use realtime chart updates to calculate volume delta without intrabar analysis, but that type of indicator only works in real time; they cannot calculate on historical bars.
This is the logic I use to determine the polarity of intrabars, which determines the up or down slot where its volume is added:
• If the intrabar's open and close values are different, their relative position is used.
• If the intrabar's open and close values are the same, the difference between the intrabar's close and the previous intrabar's close is used.
• As a last resort, when there is no movement during an intrabar, and it closes at the same price as the previous intrabar, the last known polarity is used.
Once all intrabars making up a chart bar have been analyzed and the up or down property of each intrabar's volume determined, the up volumes are added, and the down volumes subtracted. The resulting value is volume delta for that chart bar, which can be used as an estimate of the buying/selling pressure on an instrument. Not all markets have volume information. Without it, this indicator is useless.
Intrabar analysis
Intrabars are chart bars at a lower timeframe than the chart's. The timeframe used to access intrabars determines the number of intrabars accessible for each chart bar. On a 1H chart, each chart bar of an active market will, for example, usually contain 60 bars at the lower timeframe of 1min, provided there was market activity during each minute of the hour.
This indicator automatically calculates an appropriate lower timeframe using the chart's timeframe and the settings you use in the script's "Intrabars" section of the inputs. As it can access lower timeframes as small as seconds when available, the indicator can be used on charts at relatively small timeframes such as 1min, provided the market is active enough to produce bars at second timeframes.
The quantity of intrabars analyzed in each chart bar determines:
• The precision of calculations (more intrabars yield more precise results).
• The chart coverage of calculations (there is a 100K limit to the quantity of intrabars that can be analyzed on any chart,
so the more intrabars you analyze per chart bar, the less chart bars can be calculated by the indicator).
The information box displayed at the bottom right of the chart shows the lower timeframe used for intrabars, as well as the average number of intrabars detected for chart bars and statistics on chart coverage.
Balances
This indicator calculates five balances from volume delta values. The balances are oscillators with a zero centerline; positive values are bullish, and negative values are bearish. It is important to understand the balances as they can be used to:
• Color candle bodies.
• Calculate body and top and bottom divergences.
• Color an EMA channel.
• Color the chart's background.
• Configure markers and alerts.
The five balances are:
1 — Bar Balance : This is the only balance using instant values; it is simply the subtraction of the down volume from the up volume on the bar, so the instant volume delta for that bar.
2 — Average Balance : Calculates a distinct EMA for both the up and down volumes, and subtracts the down EMA from the up EMA.
The result is akin to MACD's histogram because it is the subtraction of two moving averages.
3 — Momentum Balance : Starts by calculating, separately for both up and down volumes, the difference between the same EMAs used in "Average Balance" and
an SMA of twice the period used for the "Average Balance" EMAs. The difference for the up side is subtracted from the difference for the down side,
and an RSI of that value is calculated and brought over the −50/+50 scale.
4 — Relative Balance : The reference values used in the calculation are the up and down EMAs used in the "Average Balance".
From those, we calculate two intermediate values using how much the instant up and down volumes on the bar exceed their respective EMA — but with a twist.
If the bar's up volume does not exceed the EMA of up volume, a zero value is used. The same goes for the down volume with the EMA of down volume.
Once we have our two intermediate values for the up and down volumes exceeding their respective MA, we subtract them. The final value is an ALMA of that subtraction.
The rationale behind using zero values when the bar's up/down volume does not exceed its EMA is to only take into account the more significant volume.
If both instant volume values exceed their MA, then the difference between the two is the signal's value.
The signal is called "relative" because the intermediate values are the difference between the instant up/down volumes and their respective MA.
This balance flatlines when the bar's up/down volumes do not exceed their EMAs, which makes it useful to spot areas where trader interest dwindles, such as consolidations.
The smaller the period of the final value's ALMA, the more easily it will flatline. These flat zones should be considered no-trade zones.
5 — Percent Balance : This balance is the ALMA of the ratio of the "Bar Balance" over the total volume for that bar.
From the balances and marker conditions, two more values are calculated:
1 — Marker Bias : This sums the up/down (+1/‒1) occurrences of the markers 1 to 4 over a period you define, so it ranges from −4 to +4, times the period.
Its calculation will depend on the modes used to calculate markers 3 and 4.
2 — Combined Balances : This is the sum of the bull/bear (+1/−1) states of each of the five balances, so it ranges from −5 to +5.
The periods for all of these balances can be configured in the "Periods" section at the bottom of the script's inputs. As you cannot see the balances on the chart, you can use my Volume Delta Columns Pro indicator in a pane; it can plot the same balances, so you will be able to analyze them.
Divergences
In the context of this indicator, a divergence is any bar where the bear/bull state of a balance (above/below its zero centerline) diverges from the polarity of a chart bar. No directional bias is assigned to divergences when they occur. Candle bodies and tops/bottoms can each be colored differently on divergences detected from distinct balances.
Divergence Channel
The divergence channel is the space between two levels (by default, the bar's open and close ) saved when divergences occur. When price (by default the close ) has breached a channel and a new divergence occurs, a new channel is created. Until that new channel is breached, bars where additional divergences occur will expand the channel's levels if the bar's price points are outside the channel.
Prices breaches of the divergence channel will change its state. Divergence channels can be in one of three different states:
• Bull (green): Price has breached the channel to the upside.
• Bear (red): Price has breached the channel to the downside.
• Neutral (gray): The channel has not yet been breached.
█ HOW TO USE THE INDICATOR
I do not make videos to explain how to use my indicators. I do, however, try hard to include in their description everything one needs to understand what they do. From there, it's up to you to explore and figure out if they can be useful in your trading practice. Communicating in videos what this description and the script's tooltips contain would make for very long videos that would likely exceed the attention span of most people who find this description too long. There is no quick way to understand an indicator such as this one because it uses many different concepts and has quite a bit of settings one can use to modify its visuals and behavior — thus how one uses it. I will happily answer questions on the inner workings of the indicator, but I do not answer questions like "How do I trade using this indicator?" A useful answer to that question would require an in-depth analysis of who you are, your trading methodology and objectives, which I do not have time for. I do not teach trading.
Start by loading the indicator on an active chart containing volume information. See here if you need help.
The default configuration displays:
• Normal candles where the bodies are only colored if the bar's volume has increased since the last bar.
If you want to use this indicator's candles, you may want to disable your chart's candles by clicking the eye icon to the right of the symbol's name in the top left of the chart.
• A top or bottom appended to the normal candles. It represents the difference between up and down volume for that bar
and is positioned at the top or bottom, depending on its polarity. If up volume is greater than down volume, a top is displayed. If down volume is greater, a bottom is plotted.
The size of tops and bottoms is determined by calculating a factor which is the proportion of volume delta over the bar's total volume.
That factor is then used to calculate the top or bottom size relative to a baseline of the average candle body size of the last 100 bars.
• An information box in the bottom right displaying intrabar and chart coverage information.
• A light red background when the intrabar volume differs from the chart's volume by more than 1%.
The script's inputs contain tooltips explaining most of the fields. I will not repeat them here. Following is a brief description of each section of the indicator's inputs which will give you an idea of what the indicator can do:
Normal Candles is where you configure the replacement candles plotted by the script. You can choose from different coloring schemes for their bodies and specify a unique color for bodies where a divergence calculated using the method you choose occurs.
Volume Tops & Botttoms is where you configure the display of tops and bottoms, and their EMAs. The EMAs are calculated from the high point of tops and the low point of bottoms. They can act as a channel to evaluate price, and you can choose to color the channel using a gradient reflecting the advances/declines in the balance of your choice.
Divergence Channel is where you set up the appearance and behavior of the divergence channel. These areas represent levels where price and volume delta information do not converge. They can be interpreted as regions with no clear direction from where one will look for breaches. You can configure the channel to take into account one or both types of divergences you have configured for candle bodies and tops/bottoms.
Background allows you to configure a gradient background color that reflects the advances/declines in the balance of your choice. You can use this to provide context to the volume delta values from bars. You can also control the background color displayed on volume discrepancies between the intrabar and the chart's timeframe.
Intrabars is where you choose the calculation mode determining the lower timeframe used to access intrabars. The indicator uses the chart's timeframe and the type of market you are on to calculate the lower timeframe. Your setting there should reflect which compromise you prefer between the precision of calculations and chart coverage. This is also where you control the display of the information box in the lower right corner of the chart.
Markers allows you to control the plotting of chart markers on different conditions. Their configuration determines when alerts generated from the indicator will fire. Note that in order to generate alerts from this script, they must be created from your chart. See this Help Center page to learn how. Only the last 500 markers will be visible on the chart, but this will not affect the generation of alerts.
Periods is where you configure the periods for the balances and the EMAs used in the indicator.
The raw values calculated by this script can be inspected using the Data Window.
█ INTERPRETATION
Rightly or wrongly, volume delta is considered by many a useful complement to the interpretation of price action. I use it extensively in an attempt to find convergence between my read of volume delta and price movement — not so much as a predictor of future price movement. No system or person can predict the future. Accordingly, I consider people who speak or act as if they know the future with certainty to be dangerous to themselves and others; they are charlatans, imprudent or blissfully ignorant.
I try to avoid elaborate volume delta interpretation schemes involving too many variables and prefer to keep things simple:
• Trends that have more chances of continuing should be accompanied by VD of the same polarity.
In trends, I am looking for "slow and steady". I work from the assumption that traders and systems often overreact, which translates into unproductive volatility.
Wild trends are more susceptible to overreactions.
• I prefer steady VD values over wildly increasing ones, as large VD increases often come with increased price volatility, which can backfire.
Large VD values caused by stopping volume will also often occur on trend reversals with abnormally high candles.
• Prices escaping divergence channels may be leading a trend in that direction, although there is no telling how long that trend will last; could be just a few bars or hundreds.
When price is in a channel, shifts in VD balances can sometimes give us an idea of the direction where price has the most chance of breaking.
• Dwindling VD will often indicate trend exhaustion and predate reversals by many bars, but the problem is that mere pauses in a trend will often produce the same behavior in VD.
I think it is too perilous to infer rigidly from VD decreases.
Divergence Channel
Here I have configured the divergence channels to be visible. First, I set the bodies to display divergences on the default Bar Balance. They are indicated by yellow bodies. Then I activated the divergence channels by choosing to draw levels on body divergences and checked the "Fill" checkbox to fill the channel with the same color as the levels. The divergence channel is best understood as a direction-less area from where a breach can be acted on if other variables converge with the breach's direction:
Tops and Bottoms EMAs
I find these EMAs rather interesting. They have no equivalent elsewhere, as they are calculated from the top and bottom values this indicator plots. The only similarity they have with volume-weighted MAs, including VWAP, is that they use price and volume. This indicator's Tops and Bottoms EMAs, however, use the price and volume delta. While the channel differs from other channels in how it is calculated, it can be used like others, as a baseline from which to evaluate price movement or, alternatively, as stop levels. Remember that you can change the period used for the EMAs in the "Periods" section of the inputs.
This chart shows the EMAs in action, filled with a gradient representing the advances/decline from the Momentum balance. Notice the anomaly in the chart's latest bars where the Momentum balance gradient has been indicating a bullish bias for some time, during which price was mostly below the EMAs. Price has just broken above the channel on positive VD. My interpretation of this situation would be that it is a risky opportunity for a long trade in the larger context where the market has been in a downtrend since the 5th. Intrepid traders choosing to enter here could do so with a "make or break" tight stop that will minimize their losses should the market continue its downtrend while hopefully preserving the potential upside of price continuing on the longer-term uptrend prevalent since the 28th:
█ NOTES
Volume
If you use indicators such as this one which depends on volume information, it is important to realize that the volume data they consume comes from data feeds, and that all data feeds are NOT created equally. Those who create the data feeds we use must make decisions concerning the nature of the transactions they tally and the way they are tallied in each feed, and these decisions affect the nature of our volume data. My Volume X-ray publication discusses some of the reasons why volume information from different timeframes, brokers/exchanges or sectors may vary considerably. I encourage you to read it. This indicator's display of a warning through a background color on volume discrepancies between the timeframe used to access intrabars and the chart's timeframe is an attempt to help you realize these variations in feeds. Don't take things for granted, and understand that the quality of a given feed's volume information affects the quality of the results this indicator calculates.
Markets as ecosystems
I believe it is perilous to think that behavioral patterns you discover in one market through the lens of this or any other indicator will necessarily port to other markets. While this may sometimes be the case, it will often not. Why is that? Because each market is its own ecosystem. As cities do, all markets share some common characteristics, but they also all have their idiosyncrasies. A proportion of a city's inhabitants is always composed of outsiders who come and go, but a core population of regulars and systems is usually the force that actually defines most of the city's observable characteristics. I believe markets work somewhat the same way; they may look the same, but if you live there for a while and pay attention, you will notice the idiosyncrasies. Some things that work in some markets will, accordingly, not work in others. Please keep that in mind when you draw conclusions.
On Up/Down or Buy/Sell Volume
Buying or selling volume are misnomers, as every unit of volume transacted is both bought and sold by two different traders. While this does not keep me from using the terms, there is no such thing as “buy only” or “sell only” volume. Trader lingo is riddled with peculiarities. Without access to order book information, traders work with the assumption that when price moves up during a bar, there was more buying pressure than selling pressure, just as when buy market orders take out limit ask orders in the order book at successively higher levels. The built-in volume indicator available on TradingView uses this logic to color the volume columns green or red. While this script’s calculations are more precise because it analyses intrabars to calculate its information, it uses pretty much the same imperfect logic. Until Pine scripts can have access to how much volume was transacted at the bid/ask prices, our volume delta calculations will remain a mere proxy.
Repainting
• The values calculated on the realtime bar will update as new information comes from the feed.
• Historical values may recalculate if the historical feed is updated or when calculations start from a new point in history.
• Markers and alerts will not repaint as they only occur on a bar's close. Keep this in mind when viewing markers on historical bars,
where one could understandably and incorrectly assume they appear at the bar's open.
To learn more about repainting, see the Pine Script™ User Manual's page on the subject .
Superfluity
In "The Bed of Procrustes", Nassim Nicholas Taleb writes: To bankrupt a fool, give him information . This indicator can display a lot of information. The inevitable adaptation period you will need to figure out how to use it should help you eliminate all the visuals you do not need. The more you eliminate, the easier it will be to focus on those that are the most useful to your trading practice. Don't be a fool.
█ THANKS
Thanks to alexgrover for his Dekidaka-Ashi indicator. His volume plots on candles were the inspiration for my top/bottom plots.
Kudos to PineCoders for their libraries. I use two of them in this script: Time and lower_tf .
The first versions of this script used functionality that I would not have known about were it not for these two guys:
— A guy called Kuan who commented on a Backtest Rookies presentation of their Volume Profile indicator.
— theheirophant , my partner in the exploration of the sometimes weird abysses of request.security() ’s behavior at lower timeframes.
Custom Buy/Sell Pattern BuilderAre you tired of using trading indicators that only let you follow fixed, pre-designed rules? Do you wish you could build your own “Buy” or “Sell” signals, experiment with your own ideas, or see instantly if your unique pattern works—without learning coding or hiring a developer?
The Custom Buy/Sell Pattern Builder is designed for YOU.
This TradingView indicator lets ANY trader—even a complete beginner—define exactly what kind of price and volume conditions should create a BUY or SELL label on any chart, in any market, at any timeframe.
You don’t need to know programming. You don’t need to know the definition of a hammer, doji, volume spike, or Engulfing pattern.
With a few clicks and easy dropdown choices, you can:
Make your own rules for buying or selling
Choose how many candles your pattern should look at
Decide if you want the biggest body, the lowest volume, the biggest movement, or any combination you can imagine
The result?
You’ll see clear “BUY” or “SELL” labels automatically show up on your chart whenever the exact rule YOU built matches current price action.
No more guessing. No more forced strategies. Just pure control and visual feedback!
Why Is This Powerful?
Traditional indicators (like MACD, RSI, or even classic candlestick scanners) work the same for everyone—and only as their inventors defined.
But every trader, and every market, is unique.
What if you could say:
“Show me a ‘SELL’ every time the newest candle is bigger than the one before, but with LESS volume, while the bar before that had an even smaller body—but more volume than all others?”
With this tool, it’s EASY!
You simply pick which candle you want to compare (most recent, previous, etc), what to compare (body or volume—body means the candle’s “thickness”, from open to close), choose “greater than”, “less than”, or “equal to”, and set a multiplier if you want (like “half as much”, “twice as big”, etc).
After this, if any bar on the chart fits all your rules, it will mark it as a BUY or SELL, depending on your selection.
This means—
Beginners can start experimenting with their intuition or small ideas, without tech hurdles
Experienced traders can visualize and fine-tune any possible logic, before they commit to backtesting or automating a real strategy
Every “what if” or “I wonder” setup is just 2–3 clicks away
How Does It Work? Simple Steps
1. Choose Your Signal Type
“Buy” or “Sell”
This tells the indicator whether to mark the qualifying bars with a green “BUY” or red “SELL” label
2. Pick How Many Candles To Use
“Pattern Candle Count” input (2, 3, or 4)
Example: If you use 4, the pattern will be applied to the most recent 4 candles at every step
3. Define Your Pattern With Inputs
For each candle (from newest “0” to oldest “3”), you can set:
Body Condition (example: “is this candle’s body bigger/smaller/equal to another?”)
Pick which candle to compare against
Pick “>”, “<”, “>=”, “<=”, or “=”
Set a multiplier if needed (like “0.5” to mean “half as big as” or “2” for “twice as big as”)
Volume Condition (exact same choices, but based on trading volume—not the candle’s price body)
For example:
“Candle0 Body > Candle2 Body”
means “the latest candle’s real-body (open–close) is bigger than the one two bars ago.”
“Candle1 Volume <= Candle2 Volume”
means “the previous candle’s volume is less than or equal to the volume of the bar two periods ago.”
You can leave a comparison blank if you don’t want to use it for a particular candle.
What Happens After You Set Your Rules?
Every bar on your chart is checked for your logic:
If ALL body AND volume conditions are true (for each candle you specified),
AND
The signal side (“Buy” or “Sell”) matches your dropdown,
Then a green “BUY” or red “SELL” label will show right on the bar, so you can visually spot exactly where your logic works!
Practical Example:
Suppose you want an entry setup that is:
“Sell whenever the newest candle’s body is bigger than two bars ago, body before that is bigger than three bars ago, AND the newest candle’s volume is less than or equal to two bars ago, AND the candle three bars ago’s volume is less than or equal to half the candle two bars ago’s volume.”
You’d set:
Pattern Candle Count: 4
Side: Sell
Candle0 Body Ref#: 2, Op: >, Mult: 1
Candle1 Body Ref#: 3, Op: >, Mult: 1
Candle0 Vol Ref#: 2, Op: <=, Mult: 1
Candle3 Vol Ref#: 2, Op: <=, Mult: 0.5
And the script will find all “SELL” bars on your chart matching these conditions.
Inputs Section: What Does Each Setting Do?
Let’s break down each input in the indicator’s Settings one by one, so even if you’re new, you’ll understand exactly how to use it!
1. Pattern Candle Count (2–4)
What is it?
This sets how many candles in a row you want your rule to look at.
Example:
“4” means your rules are based on the most recent candle and the 3 before it.
“2” means you are only comparing the current and previous candles.
Tip:
Beginners often use 4 to spot stronger patterns, but you can experiment!
2. Signal Side
What is it?
Choose “Buy” or “Sell”. The word you pick here decides which colored label (green for Buy, red for Sell) appears if your pattern matches.
Example:
Want to spot where “Sell” is likely? Pick “Sell”.
Change to “Buy” if you want bullish signals instead.
3. Body & Volume Comparison Settings (per Candle)
For each candle (#0 is newest/current, #3 is oldest in your pattern window):
Body Comparison
Candle# Body Ref#
Choose which other candle you want to compare this one’s body to.
“0” = newest, “1” = previous, “2” = two bars ago, “3” = three bars ago
Candle# Body Op (Operator; >, <, >=, <=, =)
How do you want to compare?
“>” means “greater than” (is bigger than)
“<” means “less than” (is smaller than)
“=” means “equal to”
Candle# Body Mult (Multiplier)
If you want relative comparisons. For example, with Mult=1:
“Candle0 body > Candle2 body x 1” means just “0 is larger than 2.”
“Candle0 body > Candle2 body x 2” means “0 is more than double 2.”
Volume Comparison
Candle# Vol Ref# / Op / Mult
Exact same logic as body, but works on the “Volume” of each candle (how much was traded during that bar).
How to Set Up a Rule (Step by Step Example)
Say you want to mark a Sell every time:
The most recent candle’s real body is BIGGER than the candle 2 bars ago;
The previous candle’s body is also BIGGER than the candle 3 bars ago;
The current candle’s volume is LESS than or equal to the volume of candle 2;
The previous candle’s volume is LESS than or equal to candle 2’s volume;
The candle 3 bars ago’s volume is LESS than or equal to HALF candle 2’s volume.
You’d set:
Pattern Candle Count: 4
Side: "Sell"
Candle0 Body Ref#: 2, Op: “>”, Mult: 1
Candle1 Body Ref#: 3, Op: “>”, Mult: 1
Candle0 Vol Ref#: 2, Op: “<=”, Mult: 1
Candle1 Vol Ref#: 2, Op: “<=”, Mult: 1
Candle3 Vol Ref#: 2, Op: “<=”, Mult: 0.5
All other comparisons (operators) can be left blank if you don’t want to use them!
When these rules are met, a bright red “SELL” label will appear right above the bar matching all your conditions.
Practical Tips & FAQ for Beginners
What does “body” mean?
It’s the “true range” of the candle: the difference between open and close. This ignores wicks for simple setups.
What does “volume” mean?
This is the total trading activity during that candle/bar. Many traders believe that patterns with different volume “meaning” (such as low-volume up bars, or high-volume down bars) signal a meaningful change.
What if nothing shows on chart?
It just means your current rules are rarely or never matched! Try making your comparisons simpler (maybe just 2-body and 2-volume conditions to start).
You can always hit “Reset Settings” to go back to default.
Can I use this for both buying and selling?
YES! You can detect both bullish (Buy) and bearish (Sell) custom conditions; just switch “Signal Side.”
Do I need to know coding?
Not at all! Everything is in simple input panels.
Creative Use Cases, Example Recipes & Troubleshooting
Creative Ways to Use
Spotting Reversals
Example:
Buy when: the newest candle body is LARGER than the previous 3 bars, but ALL volumes are lower than their neighbors.
Why? Sometimes, a big candle with surprisingly low volume after a sequence of small bars can signal a reversal.
Finding Exhaustion Moves
Example:
Sell when: the current bar body is twice as big as two bars ago, but volume is half.
Why? A very big candle with very little volume compared to similar bars may show the move is “running out of steam.”
Custom “Breakout + Confirmation” Patterns
Example:
Buy when:
Candle 0’s body is greater than Candle 2’s by at least 1.5x,
Candle 0’s volume is greater than Candle 1 and Candle 2,
Candle 1’s volume is less than Candle 0.
Why? This could catch strong breakouts but filter out noisy moves.
Multi-bar Bias/Squeeze Filter
Use “Pattern Candle Count: 4”
Set all 4 volume conditions to “<” and each reference to the previous candle.
Now, a BUY or SELL only marks when each bar is “dryer”/less active than the last — a classic squeeze or low-volatility buildup.
Troubleshooting Guide
“I don’t see any Buy/Sell label; is something broken?”
Most likely, your rules are too strict or rare! Try using only two comparisons and leave other “Op” inputs blank as a test.
Double-check you have enough candles on the chart: you need at least as many bars as your pattern count.
“Why does a label appear but not where I expect?”
Remember, the script checks your rules for every NEW candle. The candle “0” is always the most recent, then “1” is one bar back, etc.
Check the color and type chosen: “Signal Side” must be “Buy” for green, “Sell” for red.
“What if I want a more complex pattern?”
Stack conditions! You can demand the body/volume of each candle in your window meet a different rule or all follow the same rule in sequence.
Mini Glossary — For Newcomers
Candle/Bar: Each bar on the chart, shows price movement during a fixed time (e.g., one minute, one hour, one day).
Body: The colored (or filled) part of the candle — the open-to-close price range.
Volume: How much of the asset was actually traded that candle/bar.
Reference Index: When you pick “2” as a reference, it means “the candle two bars ago in the pattern window.”
Operator (“Op”): The math symbol used to compare (>, <, =, etc).
Signal Side: Whether you want to highlight bullish (“Buy”) or bearish (“Sell”) bars.
Tips for Getting More Value
Start Simple—try just one or two conditions at first. See what lights up. Slowly add more logic as you get comfortable.
Watch the chart live as you change settings. The labels update instantly—this makes strategy design fast and visual!
Try flipping your ideas: If a certain pattern doesn’t work for buys, try reversing the direction for possible “sell” setups.
Remember: There is NO wrong idea. This indicator is only limited by your creativity—it’s a “strategy playground.”
Example Quick-Start Recipes
Classic Sell:
4 candles, side = Sell
Candle0 Body > Candle2; Candle1 Body > Candle3
Candle0 Vol <= Candle2; Candle1 Vol <= Candle2; Candle3 Vol <= Candle2 × 0.5
Simple Buy After Pause:
3 candles, side = Buy
Candle0 Body > Candle1; Candle0 Vol > Candle1
All other Ops blank
Low-Volume Pullback for Entry:
4 candles, side = Buy
Candle0 Body > Candle2
Candle0 Vol < Candle1; Candle1 Vol < Candle2; Candle2 Vol < Candle3
Final Words
Think of this as your “pattern lab.” No code, no guesswork—just experiment, see what the market actually gives, and design your own visual rulebook.
If you’re stuck, reset the script to defaults—it’s always safe to start again!
If you want more ready-made “recipes” for different strategies/styles, just ask and I’ll send some more setups for you.
Happy building—and may your edge always be YOUR edge!
