Initial Balance Breakout Signals [LuxAlgo]The Initial Balance Breakout Signals help traders identify breakouts of the Initial Balance (IB) range.
The indicator includes automatic detection of IB or can use custom sessions, highlights top and bottom IB extensions, custom Fibonacci levels, and goes further with an IB forecast with two different modes.
🔶 USAGE
The initial balance is the price range made within the first hour of the trading session. It is an intraday concept based on the idea that high volume and volatility enter the market through institutional trading at the start of the session, setting the tone for the rest of the day.
The initial balance is useful for gauging market sentiment, or, in other words, the relationship between buyers and sellers.
Bullish sentiment: Price trades above the IB range.
Mixed sentiment: Price trades within the IB range.
Bearish sentiment: Price trades below the IB range.
The initial balance high and low are important levels that many traders use to gauge sentiment. There are two main ideas behind trading around the IB range.
IB Extreme Breakout: When the price breaks and holds the IB high or low, there is a high probability that the price will continue in that direction.
IB Extreme Rejection: When the price tries to break those levels but fails, there is a high probability that it will reach the opposite IB extreme.
This indicator is a complete Initial Balance toolset with custom sessions, breakout signals, IB extensions, Fibonacci retracements, and an IB forecast. All of these features will be explained in the following sections.
🔹 Custom Sessions and Signals
By default, sessions for Initial Balance and breakout signals are in Auto mode. This means that Initial Balance takes the first hour of the trading session and shows breakout signals for the rest of the session.
With this option, traders can use the tool for open range trading, making it highly versatile. The concept behind open range (OR) is the same as that of initial balance (IB), but in OR, the range is determined by the first minute, three or five minutes, or up to the first 30 minutes of the trading session.
As shown in the image above, the top chart uses the Auto feature for the IB and Breakouts sessions. The bottom chart has the Auto feature disabled to use custom sessions for both parameters. In this case, the first three minutes of the trading session are used, turning the tool into an Open Range trading indicator.
This chart shows another example of using custom sessions to display overnight NASDAQ futures sessions.
The left chart shows a custom session from the Tokyo open to the London open, and the right chart shows a custom session from the London open to the New York open.
The chart shows both the Asian and European sessions, their top and bottom extremes, and the breakout signals from those extremes.
🔹 Initial Balance Extensions
Traders can easily extend both extremes of the Initial Balance to display their preferred targets for breakouts. Enable or disable any of them and set the IB percentage to use for the extension.
As the chart shows, the percentage selected on the settings panel directly affects the displayed levels.
Setting 25 means the tool will use a quarter of the detected initial balance range for extensions beyond the IB extremes. Setting 100 means the full IB range will be used.
Traders can use these extensions as targets for breakout signals.
🔹 Fibonacci Levels
Traders can display default or custom Fibonacci levels on the IB range to trade retracements and assess the strength of market movements. Each level can be enabled or disabled and customized by level, color, and line style.
As we can see on the chart, after the IB was completed, prices were unable to fall below the 0.236 Fibonacci level. This indicates significant bullish pressure, so it is expected that prices will rise.
Traders can use these levels as guidelines to assess the strength of the side trying to penetrate the IB. In this case, the sellers were unable to move the market beyond the first level.
🔹 Initial Balance Forecast
The tool features two different forecasting methods for the current IB. By default, it takes the average of the last ten values and applies a multiplier of one.
IB Against Previous Open: averages the difference between IB extremes and the open of the previous session.
Filter by current day of the week: averages the difference between IB extremes and the open of the current session for the same day of the week.
This feature allows traders to see the difference between the current IB and the average of the last IBs. It makes it very easy to interpret: if the current IB is higher than the average, buyers are in control; if it is lower than the average, sellers are in control.
For example, on the left side of the chart, we can see that the last day was very bullish because the IB was completely above the forecasted value. This is the IB mean of the last ten trading days.
On the right, we can see that on Monday, September 15, the IB traded slightly higher but within the forecasted value of the IB mean of the last ten Mondays. In this case, it is within expectations.
🔶 SETTINGS
Display Last X IBs: Select how many IBs to display.
Initial Balance: Choose a custom session or enable the Auto feature.
Breakouts: Enable or disable breakouts. Choose custom session or enable the Auto feature.
🔹 Extensions
Top Extension: Enable or disable the top extension and choose the percentage of IB to use.
Bottom extension: Enable or disable the bottom extension and choose the percentage of IB to use.
🔹 Fibonacci Levels
Display Fibonacci: Enable or disable Fibonacci levels.
Reverse: Reverse Fibonacci levels.
Levels, Colors & Style
Display Labels: Enable or disable labels and choose text size.
🔹 Forecast
Display Forecast: Select the forecast method.
- IB Against Previous Open: Calculates the average difference between the IB high and low and the previous day's IB open price.
- Filter by Current Day of Week: Calculates the average difference between the IB high and low and the IB open price for the same day of the week.
Forecast Memory: The number of data points used to calculate the average.
Forecast Multiplier: This multiplier will be applied to the average. Bigger numbers will result in wider predicted ranges.
Forecast Colors: Choose from a variety of colors.
Forecast Style: Choose a line style.
🔹 Style
Initial Balance Colors
Extension Transparency: Choose the extension's transparency. 0 is solid, and 100 is fully transparent.
Indicadores y estrategias
Premier Stochastic Oscillator [LazyBear, V2]This script builds on the well-known Premier Stochastic Oscillator (PSO) originally introduced by LazyBear, and adds a Z-Score extension to provide statistical interpretation of momentum extremes.
Features
Premier Stochastic Core: A smoothed stochastic calculation that highlights bullish and bearish momentum phases.
Z-Score Mapping: The PSO values are standardized into Z-Scores (from –3 to +3), quantifying the degree of momentum stretch.
Positive / Negative Z-Scores:
Positive Z values suggest momentum strength that can align with accumulation or favorable buying conditions.
Negative Z values indicate stronger bearish pressure, often aligning with selling or distribution conditions.
On-Chart Label: The current Z-Score is displayed on the latest bar for quick reference.
How to Use
Momentum Confirmation: Use the oscillator to confirm whether bullish or bearish momentum is intensifying.
Overextended Conditions: Extreme Z-Scores (±2 or beyond) highlight statistically stretched conditions, often preceding reversions.
Strategic Integration: Best applied in confluence with trend tools or higher-timeframe filters; not a standalone trading signal.
Originality
Unlike the standard PSO, this version:
Adds a Z-Score framework for objective statistical scaling.
Provides real-time labeling of Z values for clarity.
Extends the classic oscillator into a tool for both momentum detection and mean-reversion context.
350DMA bands + Z-score (V2)This script extends the classic 350-day moving average (350DMA) by building dynamic valuation bands and a Z-Score framework to evaluate how far price deviates from its long-term mean.
Features
350DMA Anchor: Uses the 350-day simple moving average as the baseline reference.
Fixed Multipliers: Key bands plotted at ×0.625, ×1.0, ×1.6, ×2.0, and ×2.5 of the 350DMA — historically significant levels for cycle analysis.
Z-Score Mapping: Price is converted into a Z-Score on a scale from +2 (deep undervaluation) to –2 (extreme overvaluation), using log-space interpolation for accuracy.
Custom Display: HUD panel and on-chart label show the current Z-Score in real time.
Clamp Option: Users can toggle between raw Z values or capped values (±2).
How to Use
Valuation Context: The 350DMA is often considered a “fair value” anchor; large deviations identify cycles of under- or over-valuation.
Z-Score Insight:
Positive Z values suggest favorable accumulation zones where price is below long-term average.
Negative Z values highlight zones of stretched valuation, often associated with distribution or profit-taking.
Strategic Application: This is not a standalone trading system — it works best in confluence with other indicators, cycle models, or macro analysis.
Originality
Unlike a simple DMA overlay, this script:
Provides multiple cycle-based bands derived from the 350DMA.
Applies a logarithmic Z-Score mapping for more precise long-term scaling.
Adds an integrated HUD and labeling system for quick interpretation.
200WMA Overlay + Z (heatmap mapping)This script enhances the classic 200-week moving average (200WMA), a long-term market reference line, by adding Z-Score mapping and optional helper bands for extended cycle analysis.
Features
200WMA Anchor: Plots the true 200-week simple moving average on any chart, a widely followed metric for long-term Bitcoin and crypto cycles.
Helper Multiples: Optional overlay of key historical ratios (×0.625, ×1.6, ×2.0, ×2.5) often referenced as cycle support/resistance zones.
Z-Score Mapping: Translates the ratio of price to 200WMA into a Z-Score scale (from +2.5 to –2.5), offering a statistical perspective on whether the market is undervalued, neutral, or overheated relative to its long-term mean.
On-Chart Label: Current Z-Score displayed directly on the last bar for quick reference.
How to Use
Long-Term Valuation: The 200WMA serves as a “fair value” baseline; large deviations highlight extended phases of market sentiment.
Heatmap Context:
Positive Z values typically mark undervaluation or favorable accumulation zones.
Negative Z values highlight overvaluation or profit-taking / distribution zones.
Strategic View: Best used to contextualize long-term market cycles, not for short-term signals.
Confluence Approach: This indicator should not be used alone — combine it with other technical or fundamental tools for stronger decision-making.
Originality
Unlike a basic 200WMA overlay, this version:
Incorporates multi-band ratios for extended cycle mapping.
Introduces a custom Z-Score scale tied directly to price/WMA ratios.
Provides both visual structure and statistical interpretation on a single overlay.
Yearly VWAP with Z-Score V2This script extends the traditional Volume Weighted Average Price (VWAP) by applying it to yearly sessions (with a customizable start month) and combining it with a Z-Score framework to standardize price deviations from VWAP.
Features
Yearly VWAP: Automatically resets at the selected month, making it possible to align VWAP with fiscal or seasonal cycles (e.g., June–May).
Volatility-Weighted Bands: Standard deviation is calculated using volume-weighted price variance, creating adaptive upper and lower bands around VWAP.
Z-Score Calculation: Converts price distance from VWAP into standardized scores, ranging from +2.5 to –2.5. This enables statistical interpretation of whether price is trading at fair value, extended, or oversold relative to VWAP.
Custom Session Control: Input allows users to change the yearly anchor month.
On-Chart Display: VWAP and bands are plotted, with a live Z-Score label shown on the latest bar.
How to Use
Fair Value Reference: VWAP reflects the average price weighted by volume over the yearly session — a natural equilibrium point.
Overbought / Oversold Detection: Extreme Z-Score readings (±2 or beyond) highlight when price is stretched relative to VWAP.
Cycle Analysis: Resetting VWAP by custom months allows studying market behavior over fiscal years, seasons, or custom trading cycles.
Part of a Broader Toolkit: This script is not a standalone trading system. It works best when aggregated with other indicators, confluence factors, or a structured strategy.
Originality
Unlike a standard VWAP, this version:
Uses yearly anchoring with custom start month instead of session/day anchoring.
Adds volume-weighted standard deviation bands for statistical context.
Translates distance into a Z-Score scale for objective overbought/oversold assessment.
Positive Z-Score values indicate zones where price is positioned favorably for accumulation or potential buys, while negative values highlight areas more suitable for distribution or profit-taking — always best used in confluence with other tools rather than as a standalone signal
triple Keltner Channels with Z-Score V2This script expands on the classic Keltner Channel by plotting three adaptive volatility bands around an EMA baseline and introducing a dynamic Z-Score calculation to quantify price positioning within or beyond those bands.
Features
Three Keltner Channels:
Inner Channel at ×2 ATR
Outer Channel at ×3 ATR
Extended Channel at ×3.5 ATR
Customizable Inputs: EMA length, ATR length, and multipliers can be adjusted to suit different market conditions or asset volatility.
Z-Score Integration: Converts price location relative to the channels into standardized scores (from +2.5 to –2.5). Positive Z indicate a good value/zone to buy while negative one is the contrary (do not use it alone, use it with other indicators )
This provides a statistical lens for identifying overextended, neutral, or mean-reverting conditions.
Visual Clarity: Channel fills highlight volatility zones, while an on-chart label dynamically displays the current Z-Score.
How to Use
Overbought/Oversold Signals: Extreme Z-Score readings (±2 and beyond) suggest stretched conditions that often precede pullbacks or reversions.
Mean Reversion vs Breakout: Traders can assess whether price is likely to revert to the mean (EMA) or sustain momentum beyond outer bands.
Originality
Unlike a standard Keltner Channel, this one:
Uses three progressively wider ATR multiples for deeper volatility mapping.
Adds a Z-Score framework to statistically measure price displacement.
Provides a visual + numerical hybrid output (bands + live Z-Score label).
use only on 1W timeframe
Stiffness IndexStiffness Index Indicator
Overview
The Stiffness Index is a technical analysis indicator created by Markos Katsanos and first introduced in the November 2018 issue of Technical Analysis of Stocks & Commodities magazine. This indicator attempts to recognize strong price trends by counting the number of times price was above the 100-day moving average during the indicator period.
Core Philosophy
The premise is the fewer number of times price penetrates the MA, the stronger the trend. The philosophy behind this indicator is that traders should trade when the trend is at its strongest point - when the trend is at its "stiffest". Based on the observation that in strong long-lasting uptrends, price seldom penetrates the 100-bar simple moving average, this indicator helps assess the quality and strength of an uptrend.
How It Works
The Stiffness Index operates through several key components:
1. Moving Average Baseline: Uses a 100-period moving average as the primary reference level
2. Volatility Threshold: Includes a volatility threshold to eliminate minor movements - typically 0.2 standard deviations to reject minimal penetrations above the moving average
3. Counting Mechanism: Calculates the stiffness coefficient as the ratio of the number of times the price has closed above the moving average during the indicator period to the length of that period
4. Smoothing: Applies additional smoothing to the final result for cleaner signals
Key Components
Input Parameters
- Period 1 (100): The moving average period for the baseline calculation
- MA Method 1: Type of moving average for the baseline (SMA, EMA, SMMA, LWMA)
- Summation Period (60): The lookback period for counting closes above the moving average
- Period 2 (3): Smoothing period for the final signal line
- MA Method 2: Smoothing method for the signal line
- Threshold Level (80): Reference level for identifying strong trends
Visual Elements
- Blue Signal Line: The main stiffness reading showing trend strength
- Dotted Line: Adjustable threshold level for reference
Interpretation and Trading Applications
Signal Readings
- High Values (Above Threshold): Indicates a "stiff" trend where price consistently stays above the moving average with minimal penetrations
- Low Values (Below Threshold): Suggests a weaker trend with frequent penetrations of the moving average
- Original threshold levels mentioned in research range from 75-95
Trading Strategy
The original strategy suggests entering long positions when the stiffness reading reaches 90 or higher, with exits when the reading drops below 50. Some implementations use a threshold of 75 for entry confirmation.
Key Characteristics
- Designed primarily for stocks and instruments with upward bias
- Trades infrequently - typically about once per year when using strict parameters
- Best suited for trend-following strategies in strongly trending markets
Advantages
- Trend Quality Assessment: Quantifies the "stiffness" or quality of trends
- Volatility Filtering: Built-in volatility threshold reduces false signals from minor price movements
- Objective Measurement: Provides a numerical assessment of trend strength
- Customizable: Multiple parameters allow adaptation to different markets and timeframes
Best Practices
- Use in conjunction with baseline trend indicators for confirmation
- Most effective in markets with strong directional bias
- Consider the low frequency of signals when developing trading strategies
- May not be suitable for instruments that "twitch up and down" frequently
*Note: This indicator is specifically designed to identify and trade the strongest trending periods, which naturally results in fewer but potentially higher-quality trading opportunities.*
88-Key Piano Range - Musical Price Levels88-Key Piano Range - Musical Price Levels
Description:
Explore price analysis through musical harmony! This educational indicator maps price movements to the standard 88-key piano keyboard (A0 to C8), offering a creative way to visualize market ranges and explore harmonic price relationships with authentic keyboard-style background fills.
🎹 KEY FEATURES:
• Complete 88-Key Mapping - Full piano range from A0 to C8 mapped to your price range
• Piano-Style Visual Design - Clean background fills distinguishing white keys, black keys, and octaves
• Dual Anchor System - Set two time/price points to define your analytical range
• Flexible Display Options - Show all 88 keys, octaves only (C notes), or custom selections
• Harmonic Exploration - Explore consonant/dissonant key relationships based on music theory
• Real-time Price Note - See what musical note your current price represents
• Customizable Interface - Adjust colors, line widths, fills, and visual elements
🎵 EDUCATIONAL CONCEPTS:
• Octave Levels - C notes as harmonic reference points (similar to round numbers)
• Key Classifications - Natural notes (white keys) vs chromatic notes (black keys)
• Harmonic Intervals - Musical relationships applied to price analysis
• Creative Visualization - Alternative way to view price ranges and movements
⚙️ HOW TO USE:
1. Select Your Price Leg - Choose an upleg, downleg, or significant price movement to explore
2. Set Anchor A - Place at the start of your selected leg (swing low for upleg, swing high for downleg)
3. Set Anchor B - Place at the end of your selected leg (swing high for upleg, swing low for downleg)
4. Configure Display - Select all keys, octaves only, or enable background fills
5. Explore Harmonics - Enable harmony coloring to see musical relationships
6. Study Patterns - Observe how price movements align with musical intervals
🎼 CREATIVE APPLICATIONS:
• Experimental Analysis - Try a musical approach to leg analysis
• Educational Tool - Learn about mathematical relationships in both music and markets
• Alternative Perspective - View support/resistance through a musical lens
• Pattern Recognition - Explore if harmonic levels show interesting price behavior
• Fun Learning - Combine musical knowledge with trading concepts
📊 EXPERIMENTAL USE:
• Creative alternative to traditional Fibonacci levels
• Educational exploration of mathematical harmony in markets
• Interesting way to visualize price ranges and retracements
• Novel approach for musicians interested in trading concepts
Important Note: This is an educational and experimental tool that applies musical theory concepts to price analysis. It should be used for learning and exploration purposes alongside proven technical analysis methods. The musical relationships are mathematically based but not validated as reliable trading signals.
Options Max Pain Calculator [BackQuant]Options Max Pain Calculator
A visualization tool that models option expiry dynamics by calculating "max pain" levels, displaying synthetic open interest curves, gamma exposure profiles, and pin-risk zones to help identify where market makers have the least payout exposure.
What is Max Pain?
Max Pain is the theoretical expiration price where the total dollar value of outstanding options would be minimized. At this price level, option holders collectively experience maximum losses while option writers (typically market makers) have minimal payout obligations. This creates a natural gravitational pull as expiration approaches.
Core Features
Visual Analysis Components:
Max Pain Line: Horizontal line showing the calculated minimum pain level
Strike Level Grid: Major support and resistance levels at key option strikes
Pin Zone: Highlighted area around max pain where price may gravitate
Pain Heatmap: Color-coded visualization showing pain distribution across prices
Gamma Exposure Profile: Bar chart displaying net gamma at each strike level
Real-time Dashboard: Summary statistics and risk metrics
Synthetic Market Modeling**
Since Pine Script cannot access live options data, the indicator creates realistic synthetic open interest distributions based on configurable market parameters including volume patterns, put/call ratios, and market maker positioning.
How It Works
Strike Generation:
The tool creates a grid of option strikes centered around the current price. You can control the range, density, and whether strikes snap to realistic market increments.
Open Interest Modeling:
Using your inputs for average volume, put/call ratios, and market maker behavior, the indicator generates synthetic open interest that mirrors real market dynamics:
Higher volume at-the-money with decay as strikes move further out
Adjustable put/call bias to reflect current market sentiment
Market maker inventory effects and typical short-gamma positioning
Weekly options boost for near-term expirations
Pain Calculation:
For each potential expiry price, the tool calculates total option payouts:
Call options contribute pain when finishing in-the-money
Put options contribute pain when finishing in-the-money
The strike with minimum total pain becomes the Max Pain level
Gamma Analysis:
Net gamma exposure is calculated at each strike using standard option pricing models, showing where hedging flows may be most intense. Positive gamma creates price support while negative gamma can amplify moves.
Key Settings
Basic Configuration:
Number of Strikes: Controls grid density (recommended: 15-25)
Days to Expiration: Time until option expiry
Strike Range: Price range around current level (recommended: 8-15%)
Strike Increment: Spacing between strikes
Market Parameters:
Average Daily Volume: Baseline for synthetic open interest
Put/Call Volume Ratio: Market sentiment bias (>1.0 = bearish, <1.0 = bullish) It does not work if set to 1.0
Implied Volatility: Current option volatility estimate
Market Maker Factors: Dealer positioning and hedging intensity
Display Options:
Model Complexity: Simple (line only), Standard (+ zones), Advanced (+ heatmap/gamma)
Visual Elements: Toggle individual components on/off
Theme: Dark/Light mode
Update Frequency: Real-time or daily calculation
Reading the Display
Dashboard Table (Top Right):
Current Price vs Max Pain Level
Distance to Pain: Percentage gap (smaller = higher pin risk)
Pin Risk Assessment: HIGH/MEDIUM/LOW based on proximity and time
Days to Expiry and Strike Count
Model complexity level
Visual Elements:
Red Line: Max Pain level where payout is minimized
Colored Zone: Pin risk area around max pain
Dotted Lines: Major strike levels (green = support, orange = resistance)
Color Bar: Pain heatmap (blue = high pain, red = low pain/max pain zones)
Horizontal Bars: Gamma exposure (green = positive, red = negative)
Yellow Dotted Line: Gamma flip level where hedging behavior changes
Trading Applications
Expiration Pinning:
When price is near max pain with limited time remaining, there's increased probability of gravitating toward that level as market makers hedge their positions.
Support and Resistance:
High open interest strikes often act as magnets, with max pain representing the strongest gravitational pull.
Volatility Expectations:
Above gamma flip: Expect dampened volatility (long gamma environment)
Below gamma flip: Expect amplified moves (short gamma environment)
Risk Assessment:
The pin risk indicator helps gauge likelihood of price manipulation near expiry, with HIGH risk suggesting potential range-bound action.
Best Practices
Setup Recommendations
Start with Model Complexity set to "Standard"
Use realistic strike ranges (8-12% for most assets)
Set put/call ratio based on current market sentiment
Adjust implied volatility to match current levels
Interpretation Guidelines:
Small distance to pain + short time = high pin probability
Large gamma bars indicate key hedging levels to monitor
Heatmap intensity shows strength of pain concentration
Multiple nearby strikes can create wider pin zones
Update Strategy:
Use "Daily" updates for cleaner visuals during trading hours
Switch to "Every Bar" for real-time analysis near expiration
Monitor changes in max pain level as new options activity emerges
Important Disclaimers
This is a modeling tool using synthetic data, not live market information. While the calculations are mathematically sound and the modeling realistic, actual market dynamics involve numerous factors not captured in any single indicator.
Max pain represents theoretical minimum payout levels and suggests where natural market forces may create gravitational pull, but it does not guarantee price movement or predict exact expiration levels. Market gaps, news events, and changing volatility can override these dynamics.
Use this tool as additional context for your analysis, not as a standalone trading signal. The synthetic nature of the data makes it most valuable for understanding market structure and potential zones of interest rather than precise price prediction.
Technical Notes
The indicator uses established option pricing principles with simplified implementations optimized for Pine Script performance. Gamma calculations use standard financial models while pain calculations follow the industry-standard definition of minimized option payouts.
All visual elements use fixed positioning to prevent movement when scrolling charts, and the tool includes performance optimizations to handle real-time calculation without timeout errors.
Universal Gann Square & Cube LevelsUniversal Gann Square & Cube Levels - Dynamic Support/Resistance
Description:
📊 UNIVERSAL GANN LEVELS INDICATOR
This powerful indicator automatically plots Gann Square and Cube levels around the current stock price, providing dynamic support and resistance levels based on W.D. Gann's mathematical theories.
🎯 KEY FEATURES:
✅ Auto-Adaptive: Works for ANY stock price (₹20 to ₹100,000+)
✅ Real-time Detection: Uses current close price automatically
✅ Dual Level System: Square levels (black) + Cube levels (red)
✅ Customizable Range: Adjust percentage range (5% to 50%)
✅ Clean Display: Toggle square/cube lines independently
✅ Universal Compatibility: Works on all timeframes and instruments
📈 HOW IT WORKS:
Square Levels (Black Lines): Based on perfect squares (n²) around current price
Cube Levels (Red Lines): Based on perfect cubes (n³) around current price
Smart Range: Automatically calculates relevant levels within your specified percentage range
Info Display: Shows current price and level counts
⚙️ SETTINGS:
Price Range %: Control how many levels appear (default: 15%)
Show Square Levels: Toggle black square lines on/off
Show Cube Levels: Toggle red cube lines on/off
🔥 PERFECT FOR:
Day traders seeking precise entry/exit points
Swing traders identifying key support/resistance zones
Gann theory practitioners and students
Multi-timeframe analysis across all instruments
💡 USAGE TIPS:
Use 10-20% range for active day trading
Use 30-50% range for swing trading analysis
Watch for price reactions at square/cube intersections
Combine with volume analysis for confirmation
🌟 WHY THIS INDICATOR?
Unlike fixed Gann calculators, this indicator dynamically adapts to ANY price level, making it truly universal for Indian stocks, crypto, forex, and commodities.
⚠️ DISCLAIMER:
This indicator is for educational and informational purposes only. It is not financial advice and should not be considered as a recommendation to buy or sell any security. Trading involves significant risk of loss and may not be suitable for all investors. Past performance does not guarantee future results. Always conduct your own research and consult with a qualified financial advisor before making any investment decisions. The developer assumes no responsibility for any trading losses incurred through the use of this indicator.
📋 COMPATIBILITY:
All TradingView plans
All timeframes (1m to 1M)
Stocks, Crypto, Forex, Commodities
Mobile and desktop platforms
StdDev Supertrend {CHIPA}StdDev Supertrend ~ C H I P A is a supertrend style trend engine that replaces ATR with standard deviation as the volatility core. It can operate on raw prices or log return volatility, with optional smoothing to control noise.
Key features include:
Supertrend trailing rails built from a stddev scaled envelope that flips the regime only when price closes through the opposite rail.
Returns-based mode that scales volatility by log returns for more consistent behavior across price regimes.
Optional smoothing on the volatility input to tune responsiveness versus stability.
Directional gap fill between price and the active trend line on the main chart; opacity adapts to the distance (vs ATR) so wide gaps read stronger and small gaps stay subtle.
Secondary pane view of the rails with the same adaptive fade, plus an optional candle overlay for context.
Clean alerts that fire once when state changes
Use cases: medium-term trend following, stop/flip systems, and visual regime confirmation when you prefer stddev-based distance over ATR.
Note: no walk-forward or robustness testing is implied; parameter choices and risk controls are on you.
TA█ TA Library
📊 OVERVIEW
TA is a Pine Script technical analysis library. This library provides 25+ moving averages and smoothing filters , from classic SMA/EMA to Kalman Filters and adaptive algorithms, implemented based on academic research.
🎯 Core Features
Academic Based - Algorithms follow original papers and formulas
Performance Optimized - Pre-calculated constants for faster response
Unified Interface - Consistent function design
Research Based - Integrates technical analysis research
🎯 CONCEPTS
Library Design Philosophy
This technical analysis library focuses on providing:
Academic Foundation
Algorithms based on published research papers and academic standards
Implementations that follow original mathematical formulations
Clear documentation with research references
Developer Experience
Unified interface design for consistent usage patterns
Pre-calculated constants for optimal performance
Comprehensive function collection to reduce development time
Single import statement for immediate access to all functions
Each indicator encapsulated as a simple function call - one line of code simplifies complexity
Technical Excellence
25+ carefully implemented moving averages and filters
Support for advanced algorithms like Kalman Filter and MAMA/FAMA
Optimized code structure for maintainability and reliability
Regular updates incorporating latest research developments
🚀 USING THIS LIBRARY
Import Library
//@version=6
import DCAUT/TA/1 as dta
indicator("Advanced Technical Analysis", overlay=true)
Basic Usage Example
// Classic moving average combination
ema20 = ta.ema(close, 20)
kama20 = dta.kama(close, 20)
plot(ema20, "EMA20", color.red, 2)
plot(kama20, "KAMA20", color.green, 2)
Advanced Trading System
// Adaptive moving average system
kama = dta.kama(close, 20, 2, 30)
= dta.mamaFama(close, 0.5, 0.05)
// Trend confirmation and entry signals
bullTrend = kama > kama and mamaValue > famaValue
bearTrend = kama < kama and mamaValue < famaValue
longSignal = ta.crossover(close, kama) and bullTrend
shortSignal = ta.crossunder(close, kama) and bearTrend
plot(kama, "KAMA", color.blue, 3)
plot(mamaValue, "MAMA", color.orange, 2)
plot(famaValue, "FAMA", color.purple, 2)
plotshape(longSignal, "Buy", shape.triangleup, location.belowbar, color.green)
plotshape(shortSignal, "Sell", shape.triangledown, location.abovebar, color.red)
📋 FUNCTIONS REFERENCE
ewma(source, alpha)
Calculates the Exponentially Weighted Moving Average with dynamic alpha parameter.
Parameters:
source (series float) : Series of values to process.
alpha (series float) : The smoothing parameter of the filter.
Returns: (float) The exponentially weighted moving average value.
dema(source, length)
Calculates the Double Exponential Moving Average (DEMA) of a given data series.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Double Exponential Moving Average value.
tema(source, length)
Calculates the Triple Exponential Moving Average (TEMA) of a given data series.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Triple Exponential Moving Average value.
zlema(source, length)
Calculates the Zero-Lag Exponential Moving Average (ZLEMA) of a given data series. This indicator attempts to eliminate the lag inherent in all moving averages.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Zero-Lag Exponential Moving Average value.
tma(source, length)
Calculates the Triangular Moving Average (TMA) of a given data series. TMA is a double-smoothed simple moving average that reduces noise.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Triangular Moving Average value.
frama(source, length)
Calculates the Fractal Adaptive Moving Average (FRAMA) of a given data series. FRAMA adapts its smoothing factor based on fractal geometry to reduce lag. Developed by John Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Fractal Adaptive Moving Average value.
kama(source, length, fastLength, slowLength)
Calculates Kaufman's Adaptive Moving Average (KAMA) of a given data series. KAMA adjusts its smoothing based on market efficiency ratio. Developed by Perry J. Kaufman.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the efficiency calculation.
fastLength (simple int) : Fast EMA length for smoothing calculation. Optional. Default is 2.
slowLength (simple int) : Slow EMA length for smoothing calculation. Optional. Default is 30.
Returns: (float) The calculated Kaufman's Adaptive Moving Average value.
t3(source, length, volumeFactor)
Calculates the Tilson Moving Average (T3) of a given data series. T3 is a triple-smoothed exponential moving average with improved lag characteristics. Developed by Tim Tillson.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
volumeFactor (simple float) : Volume factor affecting responsiveness. Optional. Default is 0.7.
Returns: (float) The calculated Tilson Moving Average value.
ultimateSmoother(source, length)
Calculates the Ultimate Smoother of a given data series. Uses advanced filtering techniques to reduce noise while maintaining responsiveness. Based on digital signal processing principles by John Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the smoothing calculation.
Returns: (float) The calculated Ultimate Smoother value.
kalmanFilter(source, processNoise, measurementNoise)
Calculates the Kalman Filter of a given data series. Optimal estimation algorithm that estimates true value from noisy observations. Based on the Kalman Filter algorithm developed by Rudolf Kalman (1960).
Parameters:
source (series float) : Series of values to process.
processNoise (simple float) : Process noise variance (Q). Controls adaptation speed. Optional. Default is 0.05.
measurementNoise (simple float) : Measurement noise variance (R). Controls smoothing. Optional. Default is 1.0.
Returns: (float) The calculated Kalman Filter value.
mcginleyDynamic(source, length)
Calculates the McGinley Dynamic of a given data series. McGinley Dynamic is an adaptive moving average that adjusts to market speed changes. Developed by John R. McGinley Jr.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the dynamic calculation.
Returns: (float) The calculated McGinley Dynamic value.
mama(source, fastLimit, slowLimit)
Calculates the Mesa Adaptive Moving Average (MAMA) of a given data series. MAMA uses Hilbert Transform Discriminator to adapt to market cycles dynamically. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5.
slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05.
Returns: (float) The calculated Mesa Adaptive Moving Average value.
fama(source, fastLimit, slowLimit)
Calculates the Following Adaptive Moving Average (FAMA) of a given data series. FAMA follows MAMA with reduced responsiveness for crossover signals. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5.
slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05.
Returns: (float) The calculated Following Adaptive Moving Average value.
mamaFama(source, fastLimit, slowLimit)
Calculates Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA).
Parameters:
source (series float) : Series of values to process.
fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5.
slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05.
Returns: ( ) Tuple containing values.
laguerreFilter(source, length, gamma, order)
Calculates the standard N-order Laguerre Filter of a given data series. Standard Laguerre Filter uses uniform weighting across all polynomial terms. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Length for UltimateSmoother preprocessing.
gamma (simple float) : Feedback coefficient (0-1). Lower values reduce lag. Optional. Default is 0.8.
order (simple int) : The order of the Laguerre filter (1-10). Higher order increases lag. Optional. Default is 8.
Returns: (float) The calculated standard Laguerre Filter value.
laguerreBinomialFilter(source, length, gamma)
Calculates the Laguerre Binomial Filter of a given data series. Uses 6-pole feedback with binomial weighting coefficients. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Length for UltimateSmoother preprocessing.
gamma (simple float) : Feedback coefficient (0-1). Lower values reduce lag. Optional. Default is 0.5.
Returns: (float) The calculated Laguerre Binomial Filter value.
superSmoother(source, length)
Calculates the Super Smoother of a given data series. SuperSmoother is a second-order Butterworth filter from aerospace technology. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Period for the filter calculation.
Returns: (float) The calculated Super Smoother value.
rangeFilter(source, length, multiplier)
Calculates the Range Filter of a given data series. Range Filter reduces noise by filtering price movements within a dynamic range.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the average range calculation.
multiplier (simple float) : Multiplier for the smooth range. Higher values increase filtering. Optional. Default is 2.618.
Returns: ( ) Tuple containing filtered value, trend direction, upper band, and lower band.
qqe(source, rsiLength, rsiSmooth, qqeFactor)
Calculates the Quantitative Qualitative Estimation (QQE) of a given data series. QQE is an improved RSI that reduces noise and provides smoother signals. Developed by Igor Livshin.
Parameters:
source (series float) : Series of values to process.
rsiLength (simple int) : Number of bars for the RSI calculation. Optional. Default is 14.
rsiSmooth (simple int) : Number of bars for smoothing the RSI. Optional. Default is 5.
qqeFactor (simple float) : QQE factor for volatility band width. Optional. Default is 4.236.
Returns: ( ) Tuple containing smoothed RSI and QQE trend line.
sslChannel(source, length)
Calculates the Semaphore Signal Level (SSL) Channel of a given data series. SSL Channel provides clear trend signals using moving averages of high and low prices.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: ( ) Tuple containing SSL Up and SSL Down lines.
ma(source, length, maType)
Calculates a Moving Average based on the specified type. Universal interface supporting all moving average algorithms.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
maType (simple MaType) : Type of moving average to calculate. Optional. Default is SMA.
Returns: (float) The calculated moving average value based on the specified type.
atr(length, maType)
Calculates the Average True Range (ATR) using the specified moving average type. Developed by J. Welles Wilder Jr.
Parameters:
length (simple int) : Number of bars for the ATR calculation.
maType (simple MaType) : Type of moving average to use for smoothing. Optional. Default is RMA.
Returns: (float) The calculated Average True Range value.
macd(source, fastLength, slowLength, signalLength, maType, signalMaType)
Calculates the Moving Average Convergence Divergence (MACD) with customizable MA types. Developed by Gerald Appel.
Parameters:
source (series float) : Series of values to process.
fastLength (simple int) : Period for the fast moving average.
slowLength (simple int) : Period for the slow moving average.
signalLength (simple int) : Period for the signal line moving average.
maType (simple MaType) : Type of moving average for main MACD calculation. Optional. Default is EMA.
signalMaType (simple MaType) : Type of moving average for signal line calculation. Optional. Default is EMA.
Returns: ( ) Tuple containing MACD line, signal line, and histogram values.
dmao(source, fastLength, slowLength, maType)
Calculates the Dual Moving Average Oscillator (DMAO) of a given data series. Uses the same algorithm as the Percentage Price Oscillator (PPO), but can be applied to any data series.
Parameters:
source (series float) : Series of values to process.
fastLength (simple int) : Period for the fast moving average.
slowLength (simple int) : Period for the slow moving average.
maType (simple MaType) : Type of moving average to use for both calculations. Optional. Default is EMA.
Returns: (float) The calculated Dual Moving Average Oscillator value as a percentage.
continuationIndex(source, length, gamma, order)
Calculates the Continuation Index of a given data series. The index represents the Inverse Fisher Transform of the normalized difference between an UltimateSmoother and an N-order Laguerre filter. Developed by John F. Ehlers, published in TASC 2025.09.
Parameters:
source (series float) : Series of values to process.
length (simple int) : The calculation length.
gamma (simple float) : Controls the phase response of the Laguerre filter. Optional. Default is 0.8.
order (simple int) : The order of the Laguerre filter (1-10). Optional. Default is 8.
Returns: (float) The calculated Continuation Index value.
📚 RELEASE NOTES
v1.0 (2025.09.24)
✅ 25+ technical analysis functions
✅ Complete adaptive moving average series (KAMA, FRAMA, MAMA/FAMA)
✅ Advanced signal processing filters (Kalman, Laguerre, SuperSmoother, UltimateSmoother)
✅ Performance optimized with pre-calculated constants and efficient algorithms
✅ Unified function interface design following TradingView best practices
✅ Comprehensive moving average collection (DEMA, TEMA, ZLEMA, T3, etc.)
✅ Volatility and trend detection tools (QQE, SSL Channel, Range Filter)
✅ Continuation Index - Latest research from TASC 2025.09
✅ MACD and ATR calculations supporting multiple moving average types
✅ Dual Moving Average Oscillator (DMAO) for arbitrary data series analysis
All Levels This script draws key price levels on your chart, including:
• Previous Day (PD): High, Low, Close
• Day Before Yesterday (DBY): High, Low, Close
• Pre-Market (PM): High and Low
• Today’s levels: High, Low, Open, Close
• Current bar levels: High, Low, Open, Close
Each level is displayed as a horizontal line with a label showing the level value.
It works on any timeframe, including 1-minute charts, and automatically updates as new bars form.
⸻
2. Features
1. Custom Colors
Each type of level has its own color, declared as a const color. For example:
• Previous Day High = red
• Today’s Close = gold
• Pre-Market High = fuchsia
2. Right-Extending Lines
All horizontal levels extend to the right, so you always see them on the chart.
3. Persistent Labels
Every line has a label at the right side showing its name and price. For example:
• PDH 422
• TODL 415.5
4. Dynamic Updates
The script updates automatically whenever a new bar forms, so levels stay accurate.
5. Session-Based Pre-Market
You can define the pre-market session (default “04:00–09:30 EST”). The script calculates the high and low of this session only.
6. Checkbox Inputs
You can enable/disable entire groups of levels:
• Previous Day
• Day Before Yesterday
• Pre-Market
• Today
• Current bar
Trend Pro V2 [CRYPTIK1]Introduction: What is Trend Pro V2?
Welcome to Trend Pro V2! This analysis tool give you at-a-glance understanding of the market's direction. In a noisy market, the single most important factor is the dominant trend. Trend Pro V2 filters out this noise by focusing on one core principle: trading with the primary momentum.
Instead of cluttering your chart with confusing signals, this indicator provides a clean, visual representation of the trend, helping you make more confident and informed trading decisions.
The dashboard provides a simple, color-coded view of the trend across multiple timeframes.
The Core Concept: The Power of Confluence
The strength of any trading decision comes from confluence—when multiple factors align. Trend Pro V2 is built on this idea. It uses a long-term moving average (200-period EMA by default) to define the primary trend on your current chart and then pulls in data from three higher timeframes to confirm whether the broader market agrees.
When your current timeframe and the higher timeframes are all aligned, you have a state of "confluence," which represents a higher-probability environment for trend-following trades.
Key Features
1. The Dynamic Trend MA:
The main moving average on your chart acts as your primary guide. Its color dynamically changes to give you an instant read on the market.
Teal MA: The price is in a confirmed uptrend (trading above the MA).
Pink MA: The price is in a confirmed downtrend (trading below the MA).
The moving average changes color to instantly show you if the trend is bullish (teal) or bearish (pink).
2. The Multi-Timeframe (MTF) Trend Dashboard:
Located discreetly in the bottom-right corner, this dashboard is your window into the broader market sentiment. It shows you the trend status on three customizable higher timeframes.
Teal Box: The trend is UP on that timeframe.
Pink Box: The trend is DOWN on that timeframe.
Gray Box: The price is neutral or at the MA on that timeframe.
How to Use Trend Pro V2: A Simple Framework
Step 1: Identify the Primary Trend
Look at the color of the MA on your chart. This is your starting point. If it's teal, you should generally be looking for long opportunities. If it's pink, you should be looking for short opportunities.
Step 2: Check for Confluence
Glance at the MTF Trend Dashboard.
Strong Confluence (High-Probability): If your main chart shows an uptrend (Teal MA) and the dashboard shows all teal boxes, the market is in a strong, unified uptrend. This is a high-probability environment to be a buyer on dips.
Weak or No Confluence (Caution Zone): If your main chart shows an uptrend, but the dashboard shows pink or gray boxes, it signals disagreement among the timeframes. This is a sign of market indecision and a lower-probability environment. It's often best to wait for alignment.
Here, the daily trend is down, but the MTF dashboard shows the weekly trend is still up—a classic sign of weak confluence and a reason for caution.
Best Practices & Settings
Timeframe Synergy: For best results, use Trend Pro on a lower timeframe and set your dashboard to higher timeframes. For example, if you trade on the 1-hour chart, set your MTF dashboard to the 4-hour, 1-day, and 1-week.
Use as a Confirmation Tool: Trend Pro V2 is designed as a foundational layer for your analysis. First, confirm the trend, then use your preferred entry method (e.g., support/resistance, chart patterns) to time your trade.
This is a tool for the community, so feel free to explore the open-source code, adapt it, and build upon it. Happy trading!
For your consideration @TradingView
Market Internals Dashboard (Table) v5 - FixedHas a Dashboard for Market Internals and 3 Indices, very helpful
Multi Momentum 10/21/42/63 — Histogram + 2xSMAMY MM INDICATOR INDIRED BY KARADI
It averages four rate-of-change snapshots of price, all anchored at today’s close.
If “Show as %” is on, the value is multiplied by 100.
Each term is a simple momentum/ROC over a different lookback.
Combining 10, 21, 42, 63 bars blends short, medium, and intermediate horizons into one number.
Positive MM → average upward pressure across those horizons; negative MM → average downward pressure.
Why those lengths?
They roughly stack into ~2× progression (10→21≈2×10, 21→42=2×21, 63≈1.5×42). That creates a “multi-scale” momentum that’s less noisy than a single fast ROC but more responsive than a long ROC alone.
How to read the panel
Gray histogram = raw Multi-Momentum value each bar.
SMA Fast/Slow lines (defaults 12 & 26 over the MM values) = smoothing of the histogram to show the trend of momentum itself.
Typical signals
Zero-line context:
Above 0 → bullish momentum regime on average.
Below 0 → bearish regime.
Crosses of SMA Fast & Slow: momentum trend shifts (fast above slow = improving momentum; fast below slow = deteriorating).
Histogram vs SMA lines: widening distance suggests strengthening momentum; narrowing suggests momentum is fading.
Divergences: price makes a new high/low but MM doesn’t → potential exhaustion.
Compared to a classic ROC
A single ROC(20) is very sensitive to that one window.
MM averages several windows, smoothing idiosyncrasies (e.g., a one-off spike 21 bars ago) and reducing “lookback luck.”
Settings & customization
Lookbacks (10/21/42/63): you can tweak for your asset/timeframe; the idea is to mix short→medium horizons.
Percent vs raw ratio: percent is easier to compare across symbols.
SMA lengths: shorter = more reactive but choppier; longer = smoother but slower.
Practical tips
Use regime + signal: trade longs primarily when MM>0 and fast SMA>slow SMA; consider shorts when MM<0 and fast
cd_bsl_ssl_CxGeneral
This indicator is designed to show the levels where stop-loss orders from buyers and sellers are most likely clustered.
Swing levels formed on the aligned higher time frame (HTF) are displayed on the chart as Buy Side Liquidity (BSL) and Sell Side Liquidity (SSL).
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Menu & Usage
• HTF Selection:
o In “Auto” mode, the HTF is selected automatically.
o In “Manual” mode, the user can choose the HTF themselves.
• Bar Control:
By adjusting the bar control value, the user can define the number of bars required for a valid BSL or SSL sweep.
This option helps keep the number of alerts under control.
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I’d be happy to hear your feedback and suggestions.
Happy trading! 🎉
Simple Turnover (Enhanced v2)📊 Simple Turnover (Enhanced)
🔹 Overview
The Simple Turnover Indicator calculates a stock’s turnover by combining both price and volume, and then compares it against quarterly highs. This helps traders quickly gauge whether market participation in a move is strong enough to confirm a breakout, or weak and likely to be false.
Unlike volume alone, turnover considers both traded volume and price level, giving a truer reflection of capital flow in/out of a stock.
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🔹 Formulae Used
1. Average Price (SMA)
AvgPrice=SMA(Close,n)
2. Average Volume (SMA)
AvgVol=SMA(Volume,n)
3. Turnover (Raw)
Turnover raw=AvgPrice × AvgVol
4. Unit Adjustment
• If Millions → Turnover = Turnover raw × 10^−6
• If Crores → Turnover = Turnover raw × 10^−7
• If Raw → Turnover = Turnover raw
5. Quarterly High Turnover (qHigh)
Within each calendar quarter (Jan–Mar, Apr–Jun, Jul–Sep, Oct–Dec), we track the maximum turnover seen:
qHigh=max (Turnover within current quarter)
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🔹 Visualization
• Bars → Color follows price candle:
o Green if Close ≥ Open
o Red if Close < Open
• Blue Line → Rolling Quarterly High Turnover (qHigh)
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🔹 Strategy Use Case
The Simple Turnover Indicator is most effective for confirming true vs false breakouts.
• A true breakout should be supported by increasing turnover, showing real capital backing the move.
• A false breakout often occurs with weak or declining turnover, suggesting lack of conviction.
📌 Example Strategy (3H timeframe):
1. Identify a demand zone using your preferred supply-demand indicator.
2. From this demand zone, monitor turnover bars.
3. A potential long entry is validated when:
o The current turnover bar is at least 20% higher than the previous one or two bars.
o Example setting: SMA length = 5 (i.e., turnover = 5-bar average close × 5-bar average volume).
4. This confirms strong participation in the move, increasing probability of a sustained breakout.
________________________________________
🔹 Disclaimer
⚠️ This indicator/strategy does not guarantee 100% accurate results.
It is intended to improve the probability of identifying true breakouts.
The actual success of the strategy will depend on price action, market momentum, and prevailing market conditions.
Always use this as a supporting tool along with broader trading analysis and risk management.
Institutional Levels (CNN) - [PhenLabs]📊Institutional Levels (Convolutional Neural Network-inspired)
Version : PineScript™v6
📌Description
The CNN-IL Institutional Levels indicator represents a breakthrough in automated zone detection technology, combining convolutional neural network principles with advanced statistical modeling. This sophisticated tool identifies high-probability institutional trading zones by analyzing pivot patterns, volume dynamics, and price behavior using machine learning algorithms.
The indicator employs a proprietary 9-factor logistic regression model that calculates real-time reaction probabilities for each detected zone. By incorporating CNN-inspired filtering techniques and dynamic zone management, it provides traders with unprecedented accuracy in identifying where institutional money is likely to react to price action.
🚀Points of Innovation
● CNN-Inspired Pivot Analysis - Advanced binning system using convolutional neural network principles for superior pattern recognition
● Real-Time Probability Engine - Live reaction probability calculations using 9-factor logistic regression model
● Dynamic Zone Intelligence - Automatic zone merging using Intersection over Union (IoU) algorithms
● Volume-Weighted Scoring - Time-of-day volume Z-score analysis for enhanced zone strength assessment
● Adaptive Decay System - Intelligent zone lifecycle management based on touch frequency and recency
● Multi-Filter Architecture - Optional gradient, smoothing, and Difference of Gaussians (DoG) convolution filters
🔧Core Components
● Pivot Detection Engine - Advanced pivot identification with configurable left/right bars and ATR-normalized strength calculations
● Neural Network Binning - Price level clustering using CNN-inspired algorithms with ATR-based bin sizing
● Logistic Regression Model - 9-factor probability calculation including distance, width, volume, VWAP deviation, and trend analysis
● Zone Management System - Intelligent creation, merging, and decay algorithms for optimal zone lifecycle control
● Visualization Layer - Dynamic line drawing with opacity-based scoring and optional zone fills
🔥Key Features
● High-Probability Zone Detection - Automatically identifies institutional levels with reaction probabilities above configurable thresholds
● Real-Time Probability Scoring - Live calculation of zone reaction likelihood using advanced statistical modeling
● Session-Aware Analysis - Optional filtering to specific trading sessions for enhanced accuracy during active market hours
● Customizable Parameters - Full control over lookback periods, zone sensitivity, merge thresholds, and probability models
● Performance Optimized - Efficient processing with controlled update frequencies and pivot processing limits
● Non-Repainting Mode - Strict mode available for backtesting accuracy and live trading reliability
🎨Visualization
● Dynamic Zone Lines - Color-coded support and resistance levels with opacity reflecting zone strength and confidence scores
● Probability Labels - Real-time display of reaction probabilities, touch counts, and historical hit rates for active zones
● Zone Fills - Optional semi-transparent zone highlighting for enhanced visual clarity and immediate pattern recognition
● Adaptive Styling - Automatic color and opacity adjustments based on zone scoring and statistical significance
📖Usage Guidelines
● Lookback Bars - Default 500, Range 100-1000, Controls the historical data window for pivot analysis and zone calculation
● Pivot Left/Right - Default 3, Range 1-10, Defines the pivot detection sensitivity and confirmation requirements
● Bin Size ATR units - Default 0.25, Range 0.1-2.0, Controls price level clustering granularity for zone creation
● Base Zone Half-Width ATR units - Default 0.25, Range 0.1-1.0, Sets the minimum zone width in ATR units for institutional level boundaries
● Zone Merge IoU Threshold - Default 0.5, Range 0.1-0.9, Intersection over Union threshold for automatic zone merging algorithms
● Max Active Zones - Default 5, Range 3-20, Maximum number of zones displayed simultaneously to prevent chart clutter
● Probability Threshold for Labels - Default 0.6, Range 0.3-0.9, Minimum reaction probability required for zone label display and alerts
● Distance Weight w1 - Controls influence of price distance from zone center on reaction probability
● Width Weight w2 - Adjusts impact of zone width on probability calculations
● Volume Weight w3 - Modifies volume Z-score influence on zone strength assessment
● VWAP Weight w4 - Controls VWAP deviation impact on institutional level significance
● Touch Count Weight w5 - Adjusts influence of historical zone interactions on probability scoring
● Hit Rate Weight w6 - Controls prior success rate impact on future reaction likelihood predictions
● Wick Penetration Weight w7 - Modifies wick penetration analysis influence on probability calculations
● Trend Weight w8 - Adjusts trend context impact using ADX analysis for directional bias assessment
✅Best Use Cases
● Swing Trading Entries - Enter positions at high-probability institutional zones with 60%+ reaction scores
● Scalping Opportunities - Quick entries and exits around frequently tested institutional levels
● Risk Management - Use zones as dynamic stop-loss and take-profit levels based on institutional behavior
● Market Structure Analysis - Identify key institutional levels that define current market structure and sentiment
● Confluence Trading - Combine with other technical indicators for high-probability trade setups
● Session-Based Strategies - Focus analysis during high-volume sessions for maximum effectiveness
⚠️Limitations
● Historical Pattern Dependency - Algorithm effectiveness relies on historical patterns that may not repeat in changing market conditions
● Computational Intensity - Complex calculations may impact chart performance on lower-end devices or with multiple indicators
● Probability Estimates - Reaction probabilities are statistical estimates and do not guarantee actual market outcomes
● Session Sensitivity - Performance may vary significantly between different market sessions and volatility regimes
● Parameter Sensitivity - Results can be highly dependent on input parameters requiring optimization for different instruments
💡What Makes This Unique
● CNN Architecture - First indicator to apply convolutional neural network principles to institutional-level detection
● Real-Time ML Scoring - Live machine learning probability calculations for each zone interaction
● Advanced Zone Management - Sophisticated algorithms for zone lifecycle management and automatic optimization
● Statistical Rigor - Comprehensive 9-factor logistic regression model with extensive backtesting validation
● Performance Optimization - Efficient processing algorithms designed for real-time trading applications
🔬How It Works
● Multi-timeframe pivot identification - Uses configurable sensitivity parameters for advanced pivot detection
● ATR-normalized strength calculations - Standardizes pivot significance across different volatility regimes
● Volume Z-score integration - Enhanced pivot weighting based on time-of-day volume patterns
● Price level clustering - Neural network binning algorithms with ATR-based sizing for zone creation
● Recency decay applications - Weights recent pivots more heavily than historical data for relevance
● Statistical filtering - Eliminates low-significance price levels and reduces market noise
● Dynamic zone generation - Creates zones from statistically significant pivot clusters with minimum support thresholds
● IoU-based merging algorithms - Combines overlapping zones while maintaining accuracy using Intersection over Union
● Adaptive decay systems - Automatic removal of outdated or low-performing zones for optimal performance
● 9-factor logistic regression - Incorporates distance, width, volume, VWAP, touch history, and trend analysis
● Real-time scoring updates - Zone interaction calculations with configurable threshold filtering
● Optional CNN filters - Gradient detection, smoothing, and Difference of Gaussians processing for enhanced accuracy
💡Note
This indicator represents advanced quantitative analysis and should be used by traders familiar with statistical modeling concepts. The probability scores are mathematical estimates based on historical patterns and should be combined with proper risk management and additional technical analysis for optimal trading decisions.
Irrationality Index by CRYPTO_ADA_BTC"The market can be irrational longer than you can stay solvent" ~ John Maynard Keynes
This indicator, the Irrationality Index, measures how far the current market price has deviated from a smoothed estimate of its "fair value," normalized for recent volatility. It provides traders with a visual sense of when the market may be behaving irrationally, without giving direct buy or sell signals.
How it works:
1. Fair Value Calculation
The indicator estimates a "fair value" for the asset using a combination of a long-term EMA (exponential moving average) and a linear regression trend over a configurable period. This fair value serves as a smoothed baseline for price, balancing trend-following and mean-reversion.
2. Volatility-Adjusted Z-Score
The deviation between price and fair value is measured in standard deviations of recent log returns:
Z = (log(price) - log(fairValue)) / volatility
This standardization accounts for different volatility environments, allowing comparison across assets.
3. Irrationality Score (0–100)
The Z-score is transformed using a logistic mapping into a 0–100 scale:
- 50 → price near fair value (rational zone)
- >75 → high irrationality, price stretched above fair value
- >90 → extreme irrationality, unsustainable extremes
- <25 → high irrationality, price stretched below fair value
- <10 → extreme bearish irrationality
4. Price vs Fair Value (% deviation)
The indicator plots the percentage difference between price and fair value:
pctDiff = (price - fairValue) / fairValue * 100
- Positive values → Percentage above fair value (optimistic / overvalued)
- Negative values → Percentage below fair value (pessimistic / undervalued)
Visuals:
- Irrationality (%) Line (0–100) shows irrationality level.
- Background Colors: Yellow= high bullish irrationality, Green= extreme bullish irrationality, Orange= high bearish irrationality, Red= extreme bearish irrationality.
- Price - FairValue (%) plot: price deviation vs fair value (%), Colored green above 0 and red below 0.
- Label: display actual price, estimated fair value, and Z-score for the latest bar.
- Alerts: configurable thresholds for high and extreme irrationality.
How to read it:
- 50 → Market trading near fair value.
- >75 / >90 → Price may be irrationally high; risk of pullback increases.
- <25 / <10 → Price may be irrationally low; potential rebound zones, but trends can continue.
- Price - FairValue (%) plot → visual guide for % price stretch relative to fair value.
Notes / Warnings:
- Measures relative deviation, not fundamental value!
- High irrationality scores do not automatically indicate trades; markets can remain can be irrational longer than you can stay solvent .
- Best used with other tools: momentum, volume, divergence, and multi-timeframe analysis.
RMA EMA Crossover | MisinkoMasterThe RMA EMA Crossover (REMAC) is a trend-following overlay indicator designed to detect shifts in market momentum using the interaction between a smoothed RMA (Relative Moving Average) and its EMA (Exponential Moving Average) counterpart.
This combination provides fast, adaptive signals while reducing noise, making it suitable for a wide range of markets and timeframes.
🔎 Methodology
RMA Calculation
The Relative Moving Average (RMA) is calculated over the user-defined length.
RMA is a type of smoothed moving average that reacts more gradually than a standard EMA, providing a stable baseline.
EMA of RMA
An Exponential Moving Average (EMA) is then applied to the RMA, creating a dual-layer moving average system.
This combination amplifies trend signals while reducing false crossovers.
Trend Detection (Crossover Logic)
Bullish Signal (Trend Up) → When RMA crosses above EMA.
Bearish Signal (Trend Down) → When EMA crosses above RMA.
This simple crossover system identifies the direction of momentum shifts efficiently.
📈 Visualization
RMA and EMA are plotted directly on the chart.
Colors adapt dynamically to the current trend:
Cyan / Green hues → RMA above EMA (bullish momentum).
Magenta / Red hues → EMA above RMA (bearish momentum).
Filled areas between the two lines highlight zones of trend alignment or divergence, making it easier to spot reversals at a glance.
⚡ Features
Adjustable length parameter for RMA and EMA.
Overlay format allows for direct integration with price charts.
Visual trend scoring via color and fill for rapid assessment.
Works well across all asset classes: crypto, forex, stocks, indices.
✅ Use Cases
Trend Following → Stay on the right side of the market by following momentum shifts.
Reversal Detection → Crossovers highlight early trend changes.
Filter for Trading Systems → Use as a confirmation overlay for other indicators or strategies.
Visual Market Insight → Filled zones provide immediate context for trend strength.
NY Anchored VWAP and Auto SMAThis NY Anchored VWAP and Auto SMA script is a powerful combination of two of the most popular technical indicators, designed to help you identify the intraday trend and potential shifts in market momentum. It stands out by automatically adjusting to current volatility, providing more adaptive and reliable signals than standard moving averages.
How It Works
This script combines a New York session-anchored VWAP with a dynamic Simple Moving Average (SMA) that automatically adjusts its length based on market volatility.
New York Anchored VWAP: The VWAP (Volume-Weighted Average Price) resets at the beginning of the New York trading session. This allows it to accurately track the average price paid by traders for the day, providing a key benchmark for identifying whether the price is trading at a premium or a discount relative to the volume-driven trend. The color of the VWAP line itself changes to indicate its slope: green for an upward trend and red for a downward trend.
Auto SMA: The script calculates a Simple Moving Average (SMA) but with a twist. It uses the Average True Range (ATR) to measure market volatility. When volatility is high, the SMA's lookback period automatically shortens to make it more responsive to price changes. Conversely, when volatility is low, the lookback period lengthens to smooth out the data and reduce noise. This dynamic adjustment helps the SMA stay relevant in all market conditions.
Key Features
Adaptive Lookback: The Auto SMA dynamically adjusts to market volatility, providing more responsive signals during volatile periods and smoother, more reliable signals during calm periods.
Color-Coded VWAP: The VWAP line changes color to instantly show the direction of the trend, making it easy to see at a glance if the average price is rising or falling.
Automated Alerts: The script provides automated alerts for when the VWAP crosses above or below the Auto SMA, signaling potential bullish or bearish momentum shifts.
Customizable Settings: You can hide the VWAP on daily or higher timeframes and change the source for the VWAP calculation to suit your specific trading style.
This tool is perfect for intraday and swing traders who want a more intelligent and adaptive way to measure trend direction and identify potential trading opportunities.
Volume ClusteringThis Volume Clustering script is a powerful tool for analyzing intraday trading dynamics by combining two key metrics: volume Z-Score and Cumulative Volume Delta (CVD). By categorizing market activity into distinct clusters, it helps you identify high-conviction trading opportunities and understand underlying market pressure.
How It Works
The script operates on a simple, yet effective, premise: it classifies each trading bar based on its statistical significance (volume Z-Score) and buying/selling pressure (CVD).
Volume Z-Score
The volume Z-Score measures how far the current bar's volume is from its average, helping to identify periods of unusually high or low volume. This metric is a powerful way to spot when institutional or large players might be entering the market. A high Z-Score suggests a significant event is taking place, regardless of direction.
Cumulative Volume Delta (CVD)
CVD tracks the net buying and selling pressure across different timeframes. The script uses a lower timeframe (e.g., 1-minute) and anchors it to a higher timeframe (e.g., 1-day) to capture intraday pressure. A positive CVD indicates more buying pressure, while a negative CVD suggests more selling pressure.
Cluster Categories
The script analyzes the confluence of these two metrics to assign a cluster to each bar, providing actionable insights. The clusters are color-coded and labeled to make them easy to interpret:
🟢 High Conviction Bullish: Unusually high volume (high Z-Score) combined with significant buying pressure (high CVD). This cluster suggests strong bullish momentum.
🔴 High Conviction Bearish: Unusually high volume (high Z-Score) coupled with significant selling pressure (low CVD). This cluster suggests strong bearish momentum.
🟡 Low Conviction/Noise: Low to moderate volume and mixed buying/selling pressure. This represents periods of indecision or consolidation, where market noise is more prevalent.
🟣 Other Clusters: The script also identifies other combinations, such as high volume with moderate CVD, or low volume with high CVD, which can provide additional context for understanding market dynamics.
Key Features & Customization
The script offers several customizable settings to tailor the analysis to your specific trading style:
Z-Score Lookback Length: Adjust the lookback period for calculating the average volume. A shorter period focuses on recent volume trends, while a longer period provides a broader context.
CVD Anchor & Lower Timeframe: Define the timeframes used for CVD calculation. You can anchor the analysis to a daily or weekly timeframe while using a lower timeframe (e.g., 1-minute) to capture granular intraday pressure.
High/Low Volume Mode: Toggle between "High Volume" mode (which uses 90th and 10th percentiles for clustering) and "Low Volume" mode (which uses 75th and 25th percentiles). This allows you to choose whether to focus on extreme events or more subtle shifts in market sentiment.
