Range Breakout [sgbpulse]Range Breakout
1. Overview
The "Range Breakout " indicator is a powerful tool designed to identify and visually display price ranges on your chart using pivot points. It dynamically draws two distinct boxes – an External Range and an Internal Range – helping traders pinpoint potential support and resistance zones. Beyond its visual representation, the indicator offers a comprehensive set of 12 unique breakout alerts, providing real-time notifications for significant price movements outside these defined ranges. Additionally, it integrates RSI and MFI metrics for momentum confirmation.
2. How It Works
The indicator operates by identifying pivot points based on user-defined "left" and "right" bar lengths. A high pivot is a bar with a specified number of lower highs both to its left and right, and similarly for a low pivot.
External Range: Calculated using longer pivot lengths (default: 15 bars left, 6 bars right). This range represents broader, more significant price consolidation areas.
Internal Range: Calculated using shorter pivot lengths (default: 4 bars left, 3 bars right). This range captures tighter, more immediate price consolidations within the broader trend.
The External Range will always be greater than or equal to the Internal Range, as it's based on a wider historical context. Both ranges are displayed as transparent boxes on your chart, dynamically adjusting as new pivots are formed.
3. Key Features and Settings
Customizable Pivot Lengths:
External Range (Left/Right Bars): Adjust sensitivity for identifying the broader price range. Longer lengths lead to more stable, but less frequent, range updates.
Internal Range (Left/Right Bars): Adjust sensitivity for the tighter, more immediate price range.
Tool Tips: Minimum 6 bars for the External Range, and minimum 2 bars for the Internal Range.
Customizable Range Colors: Easily change the background colors of the External and Internal Range boxes to match your chart's aesthetic.
Dynamic Range Display: The indicator automatically updates the range boxes as new pivot highs and lows are formed, always presenting the most current valid ranges.
RSI / MFI Settings:
Timeframe Source: Select the timeframe for RSI and MFI calculation.
- Chart: Calculation based on the current chart timeframe.
- Daily: Always calculated based on the daily ("D") timeframe, even if the chart is on a lower timeframe.
RSI Length: Period length for RSI calculation (default: 14).
RSI Overbought Level: Overbought level for RSI (default: 70.0).
RSI Oversold Level: Oversold level for RSI (default: 30.0).
MFI Length: Period length for MFI calculation (default: 14).
MFI Overbought Level: Overbought level for MFI (default: 80.0).
MFI Oversold Level: Oversold level for MFI (default: 20.0).
4. Synergy of Ranges & Breakout Strength
The interaction between the External and Internal Ranges provides deep insights into price movement and breakout strength:
Immediate Direction: The movement of the Internal Range (up or down) indicates the short-term directional bias within the broader framework of the External Range.
Strength Confirmation: A breakout of the External Range, followed by a breakout of the Internal Range, confirms the strength of the move and increases confidence in the breakout.
Strong Momentum ("Leaving" Ranges Behind): When price breaks out with exceptionally strong momentum, it continues to move aggressively and does not immediately form new pivots. In such situations, the existing ranges (External and Internal) remain in place while the candles "leave them behind." A "Full Candle" breakout, where the entire candle moves past both ranges, indicates a particularly powerful and decisive move.
Momentum (RSI / MFI) as Confirmation:
- RSI (Relative Strength Index): Measures the speed and change of price movements. Extreme values (above 70 or below 30) indicate overbought/oversold conditions respectively, confirming strong momentum in a breakout.
- MFI (Money Flow Index): Similar to RSI but incorporates volume. Extreme values (above 80 or below 20) indicate strong money flow in/out, reinforcing breakout confirmation.
- Importance of Confirmation: If a breakout occurs but momentum indicators do not confirm it (for example, an upside breakout while RSI is declining), this could signal weakness in the move and the risk of a false breakout (Fakeout).
5. Visuals
The indicator provides clear visual representations on the chart:
Range Boxes:
Two dynamic boxes are drawn on the chart: one for the External Range and one for the Internal Range.
These boxes update continuously, displaying the current range boundaries based on the latest pivots. They provide an immediate visual indication of support and resistance levels.
RSI/MFI Status Labels:
Small text labels appear to the right of the current bar, vertically centered.
They display the status of RSI and MFI: RSI OB (Overbought), RSI OS (Oversold), MFI OB, MFI OS, along with the exact value.
Important: The labels remain on the chart as long as the condition holds (indicator is above/below the level), unlike alerts which mark a singular crossover event.
Plotting of Key Values:
The indicator plots six invisible series on the chart, primarily to allow the user to view the exact numerical values of:
- The upper and lower bounds of the External Range (External High, External Low).
- The upper and lower bounds of the Internal Range (Internal High, Internal Low).
- The calculated RSI and MFI values (RSI, MFI).
These values are accessible for viewing through TradingView's Data Window and also via the Status Line when hovering over the relevant candle. This enables more precise quantitative analysis of range levels and momentum.
6. Comprehensive Breakout Alerts
The "Range Breakout " indicator provides 12 distinct alert conditions for breakouts, allowing you to select the required level of confirmation for each alert. All alerts are triggered only upon a fully confirmed bar close (barstate.isconfirmed) to minimize false signals and ensure reliability.
All breakout alerts are configured to detect a Crossover/Crossunder of the levels, meaning a specific event where the price moves from one side of the range to the other.
External Range Breakout UP
- Close: Price closes above the External Range.
- Real Body: The entire "real body" of the candle (min of open/close prices) closes above the External Range.
- Full Candle: The entire candle (the lowest point of the candle) closes above the External Range.
External Range Breakout DOWN
- Close: Price closes below the External Range.
- Real Body: The entire "real body" of the candle (max of open/close prices) closes below the External Range.
- Full Candle: The entire candle (the highest point of the candle) closes below the External Range.
Internal Range Breakout UP
- Close: Price closes above the Internal Range.
- Real Body: The "real body" of the candle closes above the Internal Range.
- Full Candle: The entire candle closes above the Internal Range.
Internal Range Breakout DOWN
- Close: Price closes below the Internal Range.
- Real Body: The "real body" of the candle closes below the Internal Range.
- Full Candle: The entire candle closes below the Internal Range.
7. Ideal Use Cases
This indicator is ideal for traders who:
Want to clearly identify and monitor price consolidation zones.
Seek confirmation for breakout strategies across various timeframes.
Require reliable and automated alerts for potential entry or exit points based on range expansion.
8. Complementary Indicator
For even more comprehensive market analysis, we highly recommend using this indicator in conjunction with Market Structure Support & Resistance External/Internal & BoS .
This powerful complementary indicator automatically and accurately identifies significant support and resistance levels by locating high and low pivot points, as well as key Pre-Market High/Low levels. Its strength lies in its dynamic adaptability to any timeframe and asset, providing precise and relevant real-time levels while maintaining a clean chart. It also identifies Break of Structure (BoS) to signal potential trend changes or continuations.
Using both indicators together provides a robust framework for identifying defined ranges and potential trend shifts, enabling more informed trading decisions.
View Market Structure Support & Resistance External/Internal & BoS Indicator
9. Important Note: Trading Risk
This indicator is intended for educational and informational purposes only and does not constitute investment advice or a recommendation for trading in any form whatsoever.
Trading in financial markets involves significant risk of capital loss. It is important to remember that past performance is not indicative of future results. All trading decisions are your sole responsibility. Never trade with money you cannot afford to lose.
Komut dosyalarını "bar" için ara
The Sequences of FibonacciThe Sequences of Fibonacci - Advanced Multi-Timeframe Confluence Analysis System
THEORETICAL FOUNDATION & MATHEMATICAL INNOVATION
The Sequences of Fibonacci represents a revolutionary approach to market analysis that synthesizes classical Fibonacci mathematics with modern adaptive signal processing. This indicator transcends traditional Fibonacci retracement tools by implementing a sophisticated multi-dimensional confluence detection system that reveals hidden market structure through mathematical precision.
Core Mathematical Framework
Dynamic Fibonacci Grid System:
Unlike static Fibonacci tools, this system calculates highest highs and lowest lows across true Fibonacci sequence periods (8, 13, 21, 34, 55 bars) creating a dynamic grid of mathematical support and resistance levels that adapt to market structure in real-time.
Multi-Dimensional Confluence Detection:
The engine employs advanced mathematical clustering algorithms to identify areas where multiple derived Fibonacci retracement levels (0.382, 0.500, 0.618) from different timeframe perspectives converge. These "Confluence Zones" are mathematically classified by strength:
- CRITICAL Zones: 8+ converging Fibonacci levels
- HIGH Zones: 6-7 converging levels
- MEDIUM Zones: 4-5 converging levels
- LOW Zones: 3+ converging levels
Adaptive Signal Processing Architecture:
The system implements adaptive Stochastic RSI calculations with dynamic overbought/oversold levels that adjust to recent market volatility rather than using fixed thresholds. This prevents false signals during changing market conditions.
COMPREHENSIVE FEATURE ARCHITECTURE
Quantum Field Visualization System
Dynamic Price Field Mathematics:
The Quantum Field creates adaptive price channels based on EMA center points and ATR-based amplitude calculations, influenced by the Unified Field metric. This visualization system helps traders understand:
- Expected price volatility ranges
- Potential overextension zones
- Mathematical pressure points in market structure
- Dynamic support/resistance boundaries
Field Amplitude Calculation:
Field Amplitude = ATR × (1 + |Unified Field| / 10)
The system generates three quantum levels:
- Q⁰ Level: 0.618 × Field Amplitude (Primary channel)
- Q¹ Level: 1.0 × Field Amplitude (Secondary boundary)
- Q² Level: 1.618 × Field Amplitude (Extreme extension)
Advanced Market Analysis Dashboard
Unified Field Analysis:
A composite metric combining:
- Price momentum (40% weighting)
- Volume momentum (30% weighting)
- Trend strength (30% weighting)
Market Resonance Calculation:
Measures price-volume correlation over 14 periods to identify harmony between price action and volume participation.
Signal Quality Assessment:
Synthesizes Unified Field, Market Resonance, and RSI positioning to provide real-time evaluation of setup potential.
Tiered Signal Generation Logic
Tier 1 Signals (Highest Conviction):
Require ALL conditions:
- Adaptive StochRSI setup (exiting dynamic OB/OS levels)
- Classic StochRSI divergence confirmation
- Strong reversal bar pattern (adaptive ATR-based sizing)
- Level rejection from Confluence Zone or Fibonacci level
- Supportive Unified Field context
Tier 2 Signals (Enhanced Opportunity Detection):
Generated when Tier 1 conditions aren't met but exceptional circumstances exist:
- Divergence candidate patterns (relaxed divergence requirements)
- Exceptionally strong reversal bars at critical levels
- Enhanced level rejection criteria
- Maintained context filtering
Intelligent Visualization Features
Fractal Matrix Grid:
Multi-layer visualization system displaying:
- Shadow Layer: Foundational support (width 5)
- Glow Layer: Core identification (width 3, white)
- Quantum Layer: Mathematical overlay (width 1, dotted)
Smart Labeling System:
Prevents overlap using ATR-based minimum spacing while providing:
- Fibonacci period identification
- Topological complexity classification (0, I, II, III)
- Exact price levels
- Strength indicators (○ ◐ ● ⚡)
Wick Pressure Analysis:
Dynamic visualization showing momentum direction through:
- Multi-beam projection lines
- Particle density effects
- Progressive transparency for natural flow
- Strength-based sizing adaptation
PRACTICAL TRADING IMPLEMENTATION
Signal Interpretation Framework
Entry Protocol:
1. Confluence Zone Approach: Monitor price approaching High/Critical confluence zones
2. Adaptive Setup Confirmation: Wait for StochRSI to exit adaptive OB/OS levels
3. Divergence Verification: Confirm classic or candidate divergence patterns
4. Reversal Bar Assessment: Validate strong rejection using adaptive ATR criteria
5. Context Evaluation: Ensure Unified Field provides supportive environment
Risk Management Integration:
- Stop Placement: Beyond rejected confluence zone or Fibonacci level
- Position Sizing: Based on signal tier and confluence strength
- Profit Targets: Next significant confluence zone or quantum field boundary
Adaptive Parameter System
Dynamic StochRSI Levels:
Unlike fixed 80/20 levels, the system calculates adaptive OB/OS based on recent StochRSI range:
- Adaptive OB: Recent minimum + (range × OB percentile)
- Adaptive OS: Recent minimum + (range × OS percentile)
- Lookback Period: Configurable 20-100 bars for range calculation
Intelligent ATR Adaptation:
Bar size requirements adjust to market volatility:
- High Volatility: Reduced multiplier (bars naturally larger)
- Low Volatility: Increased multiplier (ensuring significance)
- Base Multiplier: 0.6× ATR with adaptive scaling
Optimization Guidelines
Timeframe-Specific Settings:
Scalping (1-5 minutes):
- Fibonacci Rejection Sensitivity: 0.3-0.8
- Confluence Threshold: 2-3 levels
- StochRSI Lookback: 20-30 bars
Day Trading (15min-1H):
- Fibonacci Rejection Sensitivity: 0.5-1.2
- Confluence Threshold: 3-4 levels
- StochRSI Lookback: 40-60 bars
Swing Trading (4H-1D):
- Fibonacci Rejection Sensitivity: 1.0-2.0
- Confluence Threshold: 4-5 levels
- StochRSI Lookback: 60-80 bars
Asset-Specific Optimization:
Cryptocurrency:
- Higher rejection sensitivity (1.0-2.5) for volatile conditions
- Enable Tier 2 signals for increased opportunity detection
- Shorter adaptive lookbacks for rapid market changes
Forex Major Pairs:
- Moderate sensitivity (0.8-1.5) for stable trending
- Focus on Higher/Critical confluence zones
- Longer lookbacks for institutional flow detection
Stock Indices:
- Conservative sensitivity (0.5-1.0) for institutional participation
- Standard confluence thresholds
- Balanced adaptive parameters
IMPORTANT USAGE CONSIDERATIONS
Realistic Performance Expectations
This indicator provides probabilistic advantages based on mathematical confluence analysis, not guaranteed outcomes. Signal quality varies with market conditions, and proper risk management remains essential regardless of signal tier.
Understanding Adaptive Features:
- Adaptive parameters react to historical data, not future market conditions
- Dynamic levels adjust to past volatility patterns
- Signal quality reflects mathematical alignment probability, not certainty
Market Context Awareness:
- Strong trending markets may produce fewer reversal signals
- Range-bound conditions typically generate more confluence opportunities
- News events and fundamental factors can override technical analysis
Educational Value
Mathematical Concepts Introduced:
- Multi-dimensional confluence analysis
- Adaptive signal processing techniques
- Dynamic parameter optimization
- Mathematical field theory applications in trading
- Advanced Fibonacci sequence applications
Skill Development Benefits:
- Understanding market structure through mathematical lens
- Recognition of multi-timeframe confluence principles
- Appreciation for adaptive vs. static analysis methods
- Integration of classical Fibonacci with modern signal processing
UNIQUE INNOVATIONS
First-Ever Implementations
1. True Fibonacci Sequence Periods: First indicator using authentic Fibonacci numbers (8,13,21,34,55) for timeframe analysis
2. Mathematical Confluence Clustering: Advanced algorithm identifying true Fibonacci level convergence
3. Adaptive StochRSI Boundaries: Dynamic OB/OS levels replacing fixed thresholds
4. Tiered Signal Architecture: Democratic signal weighting with quality classification
5. Quantum Field Price Visualization: Mathematical field representation of price dynamics
Visualization Breakthroughs
- Multi-Layer Fibonacci Grid: Three-layer rendering with intelligent spacing
- Dynamic Confluence Zones: Strength-based color coding and sizing
- Adaptive Parameter Display: Real-time visualization of dynamic calculations
- Mathematical Field Effects: Quantum-inspired price channel visualization
- Progressive Transparency Systems: Natural visual flow without chart clutter
COMPREHENSIVE DASHBOARD SYSTEM
Multi-Size Display Options
Small Dashboard: Core metrics for mobile/limited screen space
Normal Dashboard: Balanced information density for standard desktop use
Large Dashboard: Complete analysis suite including adaptive parameter values
Real-Time Metrics Tracking
Market Analysis Section:
- Unified Field strength with visual meter
- Market Resonance percentage
- Signal Quality assessment with emoji indicators
- Market Bias classification (Bullish/Bearish/Neutral)
Confluence Intelligence:
- Total active zones count
- High/Critical zone identification
- Nearest zone distance and strength
- Price-to-zone ATR measurement
Adaptive Parameters (Large Dashboard):
- Current StochRSI OB/OS levels
- Active ATR multiplier for bar sizing
- Volatility ratio for adaptive scaling
- Real-time StochRSI positioning
TECHNICAL SPECIFICATIONS
Pine Script Version: v5 (Latest)
Calculation Method: Real-time with confirmed bar processing
Maximum Objects: 500 boxes, 500 lines, 500 labels
Dashboard Positions: 4 corner options with size selection
Visual Themes: Quantum, Holographic, Crystalline, Plasma
Alert Integration: Complete alert system for all signal types
Performance Optimizations:
- Efficient confluence zone calculation using advanced clustering
- Smart label spacing prevents overlap
- Progressive transparency for visual clarity
- Memory-optimized array management
EDUCATIONAL FRAMEWORK
Learning Progression
Beginner Level:
- Understanding Fibonacci sequence applications
- Recognition of confluence zone concepts
- Basic signal interpretation
- Dashboard metric comprehension
Intermediate Level:
- Adaptive parameter optimization
- Multi-timeframe confluence analysis
- Signal quality assessment techniques
- Risk management integration
Advanced Level:
- Mathematical field theory applications
- Custom parameter optimization strategies
- Market regime adaptation techniques
- Professional trading system integration
DEVELOPMENT ACKNOWLEDGMENT
Special acknowledgment to @AlgoTrader90 - the foundational concepts of this system came from him and we developed it through a collaborative discussions about multi-timeframe Fibonacci analysis. While the original framework came from AlgoTrader90's innovative approach, this implementation represents a complete evolution of the logic with enhanced mathematical precision, adaptive parameters, and sophisticated signal filtering to deliver meaningful, actionable trading signals.
CONCLUSION
The Sequences of Fibonacci represents a quantum leap in technical analysis, successfully merging classical Fibonacci mathematics with cutting-edge adaptive signal processing. Through sophisticated confluence detection, intelligent parameter adaptation, and comprehensive market analysis, this system provides traders with unprecedented insight into market structure and potential reversal points.
The mathematical foundation ensures lasting relevance while the adaptive features maintain effectiveness across changing market conditions. From the dynamic Fibonacci grid to the quantum field visualization, every component reflects a commitment to mathematical precision, visual elegance, and practical utility.
Whether you're a beginner seeking to understand market confluence or an advanced trader requiring sophisticated analytical tools, this system provides the mathematical framework for informed decision-making based on time-tested Fibonacci principles enhanced with modern computational techniques.
Trade with mathematical precision. Trade with the power of confluence. Trade with The Sequences of Fibonacci.
"Mathematics is the language with which God has written the universe. In markets, Fibonacci sequences reveal the hidden harmonies that govern price movement, and those who understand these mathematical relationships hold the key to anticipating market behavior."
* Galileo Galilei (adapted for modern markets)
— Dskyz, Trade with insight. Trade with anticipation.
Multifractal Forecast [ScorsoneEnterprises]Multifractal Forecast Indicator
The Multifractal Forecast is an indicator designed to model and forecast asset price movements using a multifractal framework. It uses concepts from fractal geometry and stochastic processes, specifically the Multifractal Model of Asset Returns (MMAR) and fractional Brownian motion (fBm), to generate price forecasts based on historical price data. The indicator visualizes potential future price paths as colored lines, providing traders with a probabilistic view of price trends over a specified trading time scale. Below is a detailed breakdown of the indicator’s functionality, inputs, calculations, and visualization.
Overview
Purpose: The indicator forecasts future price movements by simulating multiple price paths based on a multifractal model, which accounts for the complex, non-linear behavior of financial markets.
Key Concepts:
Multifractal Model of Asset Returns (MMAR): Models price movements as a multifractal process, capturing varying degrees of volatility and self-similarity across different time scales.
Fractional Brownian Motion (fBm): A generalization of Brownian motion that incorporates long-range dependence and self-similarity, controlled by the Hurst exponent.
Binomial Cascade: Used to model trading time, introducing heterogeneity in time scales to reflect market activity bursts.
Hurst Exponent: Measures the degree of long-term memory in the price series (persistence, randomness, or mean-reversion).
Rescaled Range (R/S) Analysis: Estimates the Hurst exponent to quantify the fractal nature of the price series.
Inputs
The indicator allows users to customize its behavior through several input parameters, each influencing the multifractal model and forecast generation:
Maximum Lag (max_lag):
Type: Integer
Default: 50
Minimum: 5
Purpose: Determines the maximum lag used in the rescaled range (R/S) analysis to calculate the Hurst exponent. A higher lag increases the sample size for Hurst estimation but may smooth out short-term dynamics.
2 to the n values in the Multifractal Model (n):
Type: Integer
Default: 4
Purpose: Defines the resolution of the multifractal model by setting the size of arrays used in calculations (N = 2^n). For example, n=4 results in N=16 data points. Larger n increases computational complexity and detail but may exceed Pine Script’s array size limits (capped at 100,000).
Multiplier for Binomial Cascade (m):
Type: Float
Default: 0.8
Purpose: Controls the asymmetry in the binomial cascade, which models trading time. The multiplier m (and its complement 2.0 - m) determines how mass is distributed across time scales. Values closer to 1 create more balanced cascades, while values further from 1 introduce more variability.
Length Scale for fBm (L):
Type: Float
Default: 100,000.0
Purpose: Scales the fractional Brownian motion output, affecting the amplitude of simulated price paths. Larger values increase the magnitude of forecasted price movements.
Cumulative Sum (cum):
Type: Integer (0 or 1)
Default: 1
Purpose: Toggles whether the fBm output is cumulatively summed (1=On, 0=Off). When enabled, the fBm series is accumulated to simulate a price path with memory, resembling a random walk with long-range dependence.
Trading Time Scale (T):
Type: Integer
Default: 5
Purpose: Defines the forecast horizon in bars (20 bars into the future). It also scales the binomial cascade’s output to align with the desired trading time frame.
Number of Simulations (num_simulations):
Type: Integer
Default: 5
Minimum: 1
Purpose: Specifies how many forecast paths are simulated and plotted. More simulations provide a broader range of possible price outcomes but increase computational load.
Core Calculations
The indicator combines several mathematical and statistical techniques to generate price forecasts. Below is a step-by-step explanation of its calculations:
Log Returns (lgr):
The indicator calculates log returns as math.log(close / close ) when both the current and previous close prices are positive. This measures the relative price change in a logarithmic scale, which is standard for financial time series analysis to stabilize variance.
Hurst Exponent Estimation (get_hurst_exponent):
Purpose: Estimates the Hurst exponent (H) to quantify the degree of long-term memory in the price series.
Method: Uses rescaled range (R/S) analysis:
For each lag from 2 to max_lag, the function calc_rescaled_range computes the rescaled range:
Calculate the mean of the log returns over the lag period.
Compute the cumulative deviation from the mean.
Find the range (max - min) of the cumulative deviation.
Divide the range by the standard deviation of the log returns to get the rescaled range.
The log of the rescaled range (log(R/S)) is regressed against the log of the lag (log(lag)) using the polyfit_slope function.
The slope of this regression is the Hurst exponent (H).
Interpretation:
H = 0.5: Random walk (no memory, like standard Brownian motion).
H > 0.5: Persistent behavior (trends tend to continue).
H < 0.5: Mean-reverting behavior (price tends to revert to the mean).
Fractional Brownian Motion (get_fbm):
Purpose: Generates a fractional Brownian motion series to model price movements with long-range dependence.
Inputs: n (array size 2^n), H (Hurst exponent), L (length scale), cum (cumulative sum toggle).
Method:
Computes covariance for fBm using the formula: 0.5 * (|i+1|^(2H) - 2 * |i|^(2H) + |i-1|^(2H)).
Uses Hosking’s method (referenced from Columbia University’s implementation) to generate fBm:
Initializes arrays for covariance (cov), intermediate calculations (phi, psi), and output.
Iteratively computes the fBm series by incorporating a random term scaled by the variance (v) and covariance structure.
Applies scaling based on L / N^H to adjust the amplitude.
Optionally applies cumulative summation if cum = 1 to produce a path with memory.
Output: An array of 2^n values representing the fBm series.
Binomial Cascade (get_binomial_cascade):
Purpose: Models trading time (theta) to account for non-uniform market activity (e.g., bursts of volatility).
Inputs: n (array size 2^n), m (multiplier), T (trading time scale).
Method:
Initializes an array of size 2^n with values of 1.0.
Iteratively applies a binomial cascade:
For each block (from 0 to n-1), splits the array into segments.
Randomly assigns a multiplier (m or 2.0 - m) to each segment, redistributing mass.
Normalizes the array by dividing by its sum and scales by T.
Checks for array size limits to prevent Pine Script errors.
Output: An array (theta) representing the trading time, which warps the fBm to reflect market activity.
Interpolation (interpolate_fbm):
Purpose: Maps the fBm series to the trading time scale to produce a forecast.
Method:
Computes the cumulative sum of theta and normalizes it to .
Interpolates the fBm series linearly based on the normalized trading time.
Ensures the output aligns with the trading time scale (T).
Output: An array of interpolated fBm values representing log returns over the forecast horizon.
Price Path Generation:
For each simulation (up to num_simulations):
Generates an fBm series using get_fbm.
Interpolates it with the trading time (theta) using interpolate_fbm.
Converts log returns to price levels:
Starts with the current close price.
For each step i in the forecast horizon (T), computes the price as prev_price * exp(log_return).
Output: An array of price levels for each simulation.
Visualization:
Trigger: Updates every T bars when the bar state is confirmed (barstate.isconfirmed).
Process:
Clears previous lines from line_array.
For each simulation, plots a line from the current bar’s close price to the forecasted price at bar_index + T.
Colors the line using a gradient (color.from_gradient) based on the final forecasted price relative to the minimum and maximum forecasted prices across all simulations (red for lower prices, teal for higher prices).
Output: Multiple colored lines on the chart, each representing a possible price path over the next T bars.
How It Works on the Chart
Initialization: On each bar, the indicator calculates the Hurst exponent (H) using historical log returns and prepares the trading time (theta) using the binomial cascade.
Forecast Generation: Every T bars, it generates num_simulations price paths:
Each path starts at the current close price.
Uses fBm to model log returns, warped by the trading time.
Converts log returns to price levels.
Plotting: Draws lines from the current bar to the forecasted price T bars ahead, with colors indicating relative price levels.
Dynamic Updates: The forecast updates every T bars, replacing old lines with new ones based on the latest price data and calculations.
Key Features
Multifractal Modeling: Captures complex market dynamics by combining fBm (long-range dependence) with a binomial cascade (non-uniform time).
Customizable Parameters: Allows users to adjust the forecast horizon, model resolution, scaling, and number of simulations.
Probabilistic Forecast: Multiple simulations provide a range of possible price outcomes, helping traders assess uncertainty.
Visual Clarity: Gradient-colored lines make it easy to distinguish bullish (teal) and bearish (red) forecasts.
Potential Use Cases
Trend Analysis: Identify potential price trends or reversals based on the direction and spread of forecast lines.
Risk Assessment: Evaluate the range of possible price outcomes to gauge market uncertainty.
Volatility Analysis: The Hurst exponent and binomial cascade provide insights into market persistence and volatility clustering.
Limitations
Computational Intensity: Large values of n or num_simulations may slow down execution or hit Pine Script’s array size limits.
Randomness: The binomial cascade and fBm rely on random terms (math.random), which may lead to variability between runs.
Assumptions: The model assumes log-normal price movements and fractal behavior, which may not always hold in extreme market conditions.
Adjusting Inputs:
Set max_lag based on the desired depth of historical analysis.
Adjust n for model resolution (start with 4–6 to avoid performance issues).
Tune m to control trading time variability (0.5–1.5 is typical).
Set L to scale the forecast amplitude (experiment with values like 10,000–1,000,000).
Choose T based on your trading horizon (20 for short-term, 50 for longer-term for example).
Select num_simulations for the number of forecast paths (5–10 is reasonable for visualization).
Interpret Output:
Teal lines suggest bullish scenarios, red lines suggest bearish scenarios.
A wide spread of lines indicates high uncertainty; convergence suggests a stronger trend.
Monitor Updates: Forecasts update every T bars, so check the chart periodically for new projections.
Chart Examples
This is a daily AMEX:SPY chart with default settings. We see the simulations being done every T bars and they provide a range for us to analyze with a few simulations still in the range.
On this intraday PEPPERSTONE:COCOA chart I modified the Length Scale for fBm, L, parameter to be 1000 from 100000. Adjusting the parameter as you switch between timeframes can give you more contextual simulations.
On BITSTAMP:ETHUSD I modified the L to be 1000000 to have a more contextual set of simulations with crypto's volatile nature.
With L at 100000 we see the range for NASDAQ:TLT is correctly simulated. The recent pop stays within the bounds of the highest simulation. Note this is a cherry picked example to show the power and potential of these simulations.
Technical Notes
Error Handling: The script includes checks for array size limits and division by zero (math.abs(denominator) > 1e-10, v := math.max(v, 1e-10)).
External Reference: The fBm implementation is based on Hosking’s method (www.columbia.edu), ensuring a robust algorithm.
Conclusion
The Multifractal Forecast is a powerful tool for traders seeking to model complex market dynamics using a multifractal framework. By combining fBm, binomial cascades, and Hurst exponent analysis, it generates probabilistic price forecasts that account for long-range dependence and non-uniform market activity. Its customizable inputs and clear visualizations make it suitable for both technical analysis and strategy development, though users should be mindful of its computational demands and parameter sensitivity. For optimal use, experiment with input settings and validate forecasts against other technical indicators or market conditions.
FvgTypes█ OVERVIEW
This library serves as a foundational module for Pine Script™ projects focused on Fair Value Gaps (FVGs). Its primary purpose is to define and centralize custom data structures (User-Defined Types - UDTs) and enumerations that are utilized across various components of an FVG analysis system. By providing standardized types for FVG characteristics and drawing configurations, it promotes code consistency, readability, and easier maintenance within a larger FVG indicator or strategy.
█ CONCEPTS
The library introduces several key data structures (User-Defined Types - UDTs) and an enumeration to organize Fair Value Gap (FVG) related data logically. These types are central to the functioning of FVG analysis tools built upon this library.
Timeframe Categorization (`tfType` Enum)
To manage and differentiate FVGs based on their timeframe of origin, the `tfType` enumeration is defined. It includes:
`LTF`: Low Timeframe (typically the current chart).
`MTF`: Medium Timeframe.
`HTF`: High Timeframe.
This allows for distinct logic and visual settings to be applied depending on the FVG's source timeframe.
FVG Data Encapsulation (`fvgObject` UDT)
The `fvgObject` is a comprehensive UDT designed to encapsulate all pertinent information and state for an individual Fair Value Gap throughout its lifecycle. Instead of listing every field, its conceptual structure can be understood as holding:
Core Definition: The FVG's fundamental price levels (top, bottom) and its formation time (`startTime`).
Classification Attributes: Characteristics such as its direction (`isBullish`) and whether it qualifies as a Large Volume FVG (`isLV`), along with its originating timeframe category (`tfType`).
Lifecycle State: Current status indicators including full mitigation (`isMitigated`, `mitigationTime`), partial fill levels (`currentTop`, `currentBottom`), midline interaction (`isMidlineTouched`), and overall visibility (`isVisible`).
Drawing Identifiers: References (`boxId`, `midLineId`, `mitLineLabelId`, etc.) to the actual graphical objects drawn on the chart to represent the FVG and its components.
Optimization Cache: Previous-bar state values (`prevIsMitigated`, `prevCurrentTop`, etc.) crucial for optimizing drawing updates by avoiding redundant operations.
This comprehensive structure facilitates easy access to all FVG-related information through a single object, reducing code complexity and improving manageability.
Drawing Configuration (`drawSettings` UDT)
The `drawSettings` UDT centralizes all user-configurable parameters that dictate the visual appearance of FVGs across different timeframes. It's typically populated from script inputs and conceptually groups settings for:
General Behavior: Global FVG classification toggles (e.g., `shouldClassifyLV`) and general display rules (e.g., `shouldHideMitigated`).
FVG Type Specific Colors: Colors for standard and Large Volume FVGs, both active and mitigated (e.g., `lvBullColor`, `mitigatedBearBoxColor`).
Timeframe-Specific Visuals (LTF, MTF, HTF): Detailed parameters for each timeframe category, covering FVG boxes (visibility, colors, extension, borders, labels), midlines (visibility, style, color), and mitigation lines (visibility, style, color, labels, persistence after mitigation).
Contextual Information: The current bar's time (`currentTime`) for accurate positioning of time-dependent drawing elements and timeframe display strings (`tfString`, `mtfTfString`, `htfTfString`).
This centralized approach allows for extensive customization of FVG visuals and simplifies the management of drawing parameters within the main script. Such centralization also enhances the maintainability of the visual aspects of the FVG system.
█ NOTES
User-Defined Types (UDTs): This library extensively uses UDTs (`fvgObject`, `drawSettings`) to group related data. This improves code organization and makes it easier to pass complex data between functions and libraries.
Mutability and Reference Behavior of UDTs: When UDT instances are passed to functions or methods in other libraries (like `fvgObjectLib`), those functions might modify the fields of the passed object if they are not explicitly designed to return new instances. This is because UDTs are passed by reference and are mutable in Pine Script™. Users should be aware of this standard behavior to prevent unintended side effects.
Optimization Fields: The `prev_*` fields in `fvgObject` are crucial for performance optimization in the drawing logic. They help avoid unnecessary redrawing of FVG elements if their state or relevant settings haven't changed.
No Direct Drawing Logic: `FvgTypes` itself does not contain any drawing logic. It solely defines the data structures. The actual drawing and manipulation of these objects are handled by other libraries (e.g., `fvgObjectLib`).
Centralized Definitions: By defining these types in a separate library, any changes to the structure of FVG data or settings can be made in one place, ensuring consistency across all dependent scripts and libraries.
█ EXPORTED TYPES
fvgObject
fvgObject Represents a Fair Value Gap (FVG) object.
Fields:
top (series float) : The top price level of the FVG.
bottom (series float) : The bottom price level of the FVG.
startTime (series int) : The start time (timestamp) of the bar where the FVG formed.
isBullish (series bool) : Indicates if the FVG is bullish (true) or bearish (false).
isLV (series bool) : Indicates if the FVG is a Large Volume FVG.
tfType (series tfType) : The timeframe type (LTF, MTF, HTF) to which this FVG belongs.
isMitigated (series bool) : Indicates if the FVG has been fully mitigated.
mitigationTime (series int) : The time (timestamp) when the FVG was mitigated.
isVisible (series bool) : The current visibility status of the FVG, typically managed by drawing logic based on filters.
isMidlineTouched (series bool) : Indicates if the price has touched the FVG's midline (50% level).
currentTop (series float) : The current top level of the FVG after partial fills.
currentBottom (series float) : The current bottom level of the FVG after partial fills.
boxId (series box) : The drawing ID for the main FVG box.
mitigatedBoxId (series box) : The drawing ID for the box representing the partially filled (mitigated) area.
midLineId (series line) : The drawing ID for the FVG's midline.
mitLineId (series line) : The drawing ID for the FVG's mitigation line.
boxLabelId (series label) : The drawing ID for the FVG box label.
mitLineLabelId (series label) : The drawing ID for the mitigation line label.
testedBoxId (series box) : The drawing ID for the box of a fully mitigated (tested) FVG, if kept visible.
keptMitLineId (series line) : The drawing ID for a mitigation line that is kept after full mitigation.
prevIsMitigated (series bool) : Stores the isMitigated state from the previous bar for optimization.
prevCurrentTop (series float) : Stores the currentTop value from the previous bar for optimization.
prevCurrentBottom (series float) : Stores the currentBottom value from the previous bar for optimization.
prevIsVisible (series bool) : Stores the visibility status from the previous bar for optimization (derived from isVisibleNow passed to updateDrawings).
prevIsMidlineTouched (series bool) : Stores the isMidlineTouched status from the previous bar for optimization.
drawSettings
drawSettings A structure containing settings for drawing FVGs.
Fields:
shouldClassifyLV (series bool) : Whether to classify FVGs as Large Volume (LV) based on ATR.
shouldHideMitigated (series bool) : Whether to hide FVG boxes once they are fully mitigated.
currentTime (series int) : The current bar's time, used for extending drawings.
lvBullColor (series color) : Color for Large Volume Bullish FVGs.
mitigatedLvBullColor (series color) : Color for mitigated Large Volume Bullish FVGs.
lvBearColor (series color) : Color for Large Volume Bearish FVGs.
mitigatedLvBearColor (series color) : Color for mitigated Large Volume Bearish FVGs.
shouldShowBoxes (series bool) : Whether to show FVG boxes for the LTF.
bullBoxColor (series color) : Color for LTF Bullish FVG boxes.
mitigatedBullBoxColor (series color) : Color for mitigated LTF Bullish FVG boxes.
bearBoxColor (series color) : Color for LTF Bearish FVG boxes.
mitigatedBearBoxColor (series color) : Color for mitigated LTF Bearish FVG boxes.
boxLengthBars (series int) : Length of LTF FVG boxes in bars (if not extended).
shouldExtendBoxes (series bool) : Whether to extend LTF FVG boxes to the right.
shouldShowCurrentTfBoxLabels (series bool) : Whether to show labels on LTF FVG boxes.
shouldShowBoxBorder (series bool) : Whether to show a border for LTF FVG boxes.
boxBorderWidth (series int) : Border width for LTF FVG boxes.
boxBorderStyle (series string) : Border style for LTF FVG boxes (e.g., line.style_solid).
boxBorderColor (series color) : Border color for LTF FVG boxes.
shouldShowMidpoint (series bool) : Whether to show the midline (50% level) for LTF FVGs.
midLineWidthInput (series int) : Width of the LTF FVG midline.
midpointLineStyleInput (series string) : Style of the LTF FVG midline.
midpointColorInput (series color) : Color of the LTF FVG midline.
shouldShowMitigationLine (series bool) : Whether to show the mitigation line for LTF FVGs.
(Line always extends if shown)
mitLineWidthInput (series int) : Width of the LTF FVG mitigation line.
mitigationLineStyleInput (series string) : Style of the LTF FVG mitigation line.
mitigationLineColorInput (series color) : Color of the LTF FVG mitigation line.
shouldShowCurrentTfMitLineLabels (series bool) : Whether to show labels on LTF FVG mitigation lines.
currentTfMitLineLabelOffsetX (series float) : The horizontal offset value for the LTF mitigation line's label.
shouldKeepMitigatedLines (series bool) : Whether to keep showing mitigation lines of fully mitigated LTF FVGs.
mitigatedMitLineColor (series color) : Color for kept mitigation lines of mitigated LTF FVGs.
tfString (series string) : Display string for the LTF (e.g., "Current TF").
shouldShowMtfBoxes (series bool) : Whether to show FVG boxes for the MTF.
mtfBullBoxColor (series color) : Color for MTF Bullish FVG boxes.
mtfMitigatedBullBoxColor (series color) : Color for mitigated MTF Bullish FVG boxes.
mtfBearBoxColor (series color) : Color for MTF Bearish FVG boxes.
mtfMitigatedBearBoxColor (series color) : Color for mitigated MTF Bearish FVG boxes.
mtfBoxLengthBars (series int) : Length of MTF FVG boxes in bars (if not extended).
shouldExtendMtfBoxes (series bool) : Whether to extend MTF FVG boxes to the right.
shouldShowMtfBoxLabels (series bool) : Whether to show labels on MTF FVG boxes.
shouldShowMtfBoxBorder (series bool) : Whether to show a border for MTF FVG boxes.
mtfBoxBorderWidth (series int) : Border width for MTF FVG boxes.
mtfBoxBorderStyle (series string) : Border style for MTF FVG boxes.
mtfBoxBorderColor (series color) : Border color for MTF FVG boxes.
shouldShowMtfMidpoint (series bool) : Whether to show the midline for MTF FVGs.
mtfMidLineWidthInput (series int) : Width of the MTF FVG midline.
mtfMidpointLineStyleInput (series string) : Style of the MTF FVG midline.
mtfMidpointColorInput (series color) : Color of the MTF FVG midline.
shouldShowMtfMitigationLine (series bool) : Whether to show the mitigation line for MTF FVGs.
(Line always extends if shown)
mtfMitLineWidthInput (series int) : Width of the MTF FVG mitigation line.
mtfMitigationLineStyleInput (series string) : Style of the MTF FVG mitigation line.
mtfMitigationLineColorInput (series color) : Color of the MTF FVG mitigation line.
shouldShowMtfMitLineLabels (series bool) : Whether to show labels on MTF FVG mitigation lines.
mtfMitLineLabelOffsetX (series float) : The horizontal offset value for the MTF mitigation line's label.
shouldKeepMtfMitigatedLines (series bool) : Whether to keep showing mitigation lines of fully mitigated MTF FVGs.
mtfMitigatedMitLineColor (series color) : Color for kept mitigation lines of mitigated MTF FVGs.
mtfTfString (series string) : Display string for the MTF (e.g., "MTF").
shouldShowHtfBoxes (series bool) : Whether to show FVG boxes for the HTF.
htfBullBoxColor (series color) : Color for HTF Bullish FVG boxes.
htfMitigatedBullBoxColor (series color) : Color for mitigated HTF Bullish FVG boxes.
htfBearBoxColor (series color) : Color for HTF Bearish FVG boxes.
htfMitigatedBearBoxColor (series color) : Color for mitigated HTF Bearish FVG boxes.
htfBoxLengthBars (series int) : Length of HTF FVG boxes in bars (if not extended).
shouldExtendHtfBoxes (series bool) : Whether to extend HTF FVG boxes to the right.
shouldShowHtfBoxLabels (series bool) : Whether to show labels on HTF FVG boxes.
shouldShowHtfBoxBorder (series bool) : Whether to show a border for HTF FVG boxes.
htfBoxBorderWidth (series int) : Border width for HTF FVG boxes.
htfBoxBorderStyle (series string) : Border style for HTF FVG boxes.
htfBoxBorderColor (series color) : Border color for HTF FVG boxes.
shouldShowHtfMidpoint (series bool) : Whether to show the midline for HTF FVGs.
htfMidLineWidthInput (series int) : Width of the HTF FVG midline.
htfMidpointLineStyleInput (series string) : Style of the HTF FVG midline.
htfMidpointColorInput (series color) : Color of the HTF FVG midline.
shouldShowHtfMitigationLine (series bool) : Whether to show the mitigation line for HTF FVGs.
(Line always extends if shown)
htfMitLineWidthInput (series int) : Width of the HTF FVG mitigation line.
htfMitigationLineStyleInput (series string) : Style of the HTF FVG mitigation line.
htfMitigationLineColorInput (series color) : Color of the HTF FVG mitigation line.
shouldShowHtfMitLineLabels (series bool) : Whether to show labels on HTF FVG mitigation lines.
htfMitLineLabelOffsetX (series float) : The horizontal offset value for the HTF mitigation line's label.
shouldKeepHtfMitigatedLines (series bool) : Whether to keep showing mitigation lines of fully mitigated HTF FVGs.
htfMitigatedMitLineColor (series color) : Color for kept mitigation lines of mitigated HTF FVGs.
htfTfString (series string) : Display string for the HTF (e.g., "HTF").
Institutional Volume Profile# Institutional Volume Profile (IVP) - Advanced Volume Analysis Indicator
## Overview
The Institutional Volume Profile (IVP) is a sophisticated technical analysis tool that combines traditional volume profile analysis with institutional volume detection algorithms. This indicator helps traders identify key price levels where significant institutional activity has occurred, providing insights into market structure and potential support/resistance zones.
## Key Features
### 🎯 Volume Profile Analysis
- **Point of Control (POC)**: Identifies the price level with the highest volume activity
- **Value Area**: Highlights the price range containing a specified percentage (default 70%) of total volume
- **Multi-Row Distribution**: Displays volume distribution across 10-50 price levels for detailed analysis
- **Customizable Period**: Analyze volume profiles over 10-500 bars
### 🏛️ Institutional Volume Detection
- **Pocket Pivot Volume (PPV)**: Detects bullish institutional buying when up-volume exceeds recent down-volume peaks
- **Pivot Negative Volume (PNV)**: Identifies bearish institutional selling when down-volume exceeds recent up-volume peaks
- **Accumulation Detection**: Spots potential accumulation phases with high volume and narrow price ranges
- **Distribution Analysis**: Identifies distribution patterns with high volume but minimal price movement
### 🎨 Visual Customization Options
- **Multiple Color Schemes**: Heat Map, Institutional, Monochrome, and Rainbow themes
- **Bar Styles**: Solid, Gradient, Outlined, and 3D Effect rendering
- **Volume Intensity Display**: Visual intensity based on volume magnitude
- **Flexible Positioning**: Left or right side profile placement
- **Current Price Highlighting**: Real-time price level indication
### 📊 Advanced Visual Features
- **Volume Labels**: Display volume amounts at key price levels
- **Gradient Effects**: Multi-step gradient rendering for enhanced visibility
- **3D Styling**: Shadow effects for professional appearance
- **Opacity Control**: Adjustable transparency (10-100%)
- **Border Customization**: Configurable border width and styling
## How It Works
### Volume Distribution Algorithm
The indicator analyzes each bar within the specified period and distributes its volume proportionally across the price levels it touches. This creates an accurate representation of where trading activity has been concentrated.
### Institutional Detection Logic
- **PPV Trigger**: Current up-bar volume > highest down-volume in lookback period + above volume MA
- **PNV Trigger**: Current down-bar volume > highest up-volume in lookback period + above volume MA
- **Accumulation**: High volume + narrow range + bullish close
- **Distribution**: Very high volume + minimal price movement
### Value Area Calculation
Starting from the POC, the algorithm expands both upward and downward, adding volume until reaching the specified percentage of total volume (default 70%).
## Configuration Parameters
### Profile Settings
- **Profile Period**: 10-500 bars (default: 50)
- **Number of Rows**: 10-50 levels (default: 24)
- **Profile Width**: 10-100% of screen (default: 30%)
- **Value Area %**: 50-90% (default: 70%)
### Institutional Analysis
- **PPV Lookback Days**: 5-20 periods (default: 10)
- **Volume MA Length**: 10-200 periods (default: 50)
- **Institutional Threshold**: 1.0-2.0x multiplier (default: 1.2)
### Visual Controls
- **Bar Style**: Solid, Gradient, Outlined, 3D Effect
- **Color Scheme**: Heat Map, Institutional, Monochrome, Rainbow
- **Profile Position**: Left or Right side
- **Opacity**: 10-100%
- **Show Labels**: Volume amount display toggle
## Interpretation Guide
### Volume Profile Elements
- **Thick Horizontal Bars**: High volume nodes (strong support/resistance)
- **Thin Horizontal Bars**: Low volume nodes (weak levels)
- **White Line (POC)**: Strongest support/resistance level
- **Blue Highlighted Area**: Value Area (fair value zone)
### Institutional Signals
- **Blue Triangles (PPV)**: Bullish institutional buying detected
- **Orange Triangles (PNV)**: Bearish institutional selling detected
- **Color-Coded Bars**: Different colors indicate institutional activity types
### Color Scheme Meanings
- **Heat Map**: Red (high volume) → Orange → Yellow → Gray (low volume)
- **Institutional**: Blue (PPV), Orange (PNV), Aqua (Accumulation), Yellow (Distribution)
- **Monochrome**: Grayscale intensity based on volume
- **Rainbow**: Color-coded by price level position
## Trading Applications
### Support and Resistance
- POC acts as dynamic support/resistance
- High volume nodes indicate strong price levels
- Low volume areas suggest potential breakout zones
### Institutional Activity
- PPV above Value Area: Strong bullish signal
- PNV below Value Area: Strong bearish signal
- Accumulation patterns: Potential upward breakouts
- Distribution patterns: Potential downward pressure
### Market Structure Analysis
- Value Area defines fair value range
- Profile shape indicates market sentiment
- Volume gaps suggest potential price targets
## Alert Conditions
- PPV Detection at current price level
- PNV Detection at current price level
- PPV above Value Area (strong bullish)
- PNV below Value Area (strong bearish)
## Best Practices
1. Use multiple timeframes for confirmation
2. Combine with price action analysis
3. Pay attention to volume context (above/below average)
4. Monitor institutional signals near key levels
5. Consider overall market conditions
## Technical Notes
- Maximum 500 boxes and 100 labels for optimal performance
- Real-time calculations update on each bar close
- Historical analysis uses complete bar data
- Compatible with all TradingView chart types and timeframes
---
*This indicator is designed for educational and informational purposes. Always combine with other analysis methods and risk management strategies.*
Test OHLCV LibraryThis indicator, "Test OHLCV Library," serves as a practical example of how to use the OHLCVData library to fetch historical candle data from a specific timeframe (like 4H) in a way that is largely impervious to the chart's currently selected time frame.
Here's a breakdown of its purpose and how it addresses request.security limitations:
Indicator Purpose:
The main goal of this indicator is to demonstrate and verify that the OHLCVData library can reliably provide confirmed historical OHLCV data for a user-specified timeframe (e.g., 4H), and that a collection of these data points (the last 10 completed candles) remains consistent even when the user switches the chart's time frame (e.g., from 5-second to Daily).
It does this by:
Importing the OHLCVData library.
Using the library's getTimeframeData function on every bar of the chart.
Checking the isTargetBarClosed flag returned by the library to identify the exact moment a candle in the target timeframe (e.g., 4H) has closed.
When isTargetBarClosed is true, it captures the confirmed OHLCV data provided by the library for that moment and stores it in a persistent var array.
It maintains a list of the last 10 captured historical 4H candle opens in this array.
It displays these last 10 confirmed opens in a table.
It uses the isAdjustedToChartTF flag from the library to show a warning if the chart's time frame is higher than the target timeframe, indicating that the data fetched by request.security is being aligned to that higher resolution.
Circumventing request.security Limitations:
The primary limitation of request.security that this setup addresses is the challenge of getting a consistent, non-repainting collection of historical data points from a different timeframe when the chart's time frame is changed.
The Problem: Standard request.security calls, while capable of fetching data from other timeframes, align that data to the bars of the current chart. When you switch the chart's time frame, the set of chart bars changes, and the way the requested data aligns to these new bars changes. If you simply collected data on every chart bar where request.security returned a non-na value, the resulting collection would differ depending on the chart's resolution. Furthermore, using request.security without lookahead=barmerge.lookahead_off or an offset ( ) can lead to repainting on historical bars, where values change as the script recalculates.
How the Library/Indicator Setup Helps:
Confirmed Data: The OHLCVData library uses lookahead=barmerge.lookahead_off and, more importantly, provides the isTargetBarClosed flag. This flag is calculated using a reliable method (checking for a change in the target timeframe's time series) that accurately identifies the precise chart bar corresponding to the completion of a candle in the target timeframe (e.g., a 4H candle), regardless of the chart's time frame.
Precise Capture: The indicator only captures and stores the OHLCV data into its var array when this isTargetBarClosed flag is true. This means it's capturing the confirmed, finalized data for the target timeframe candle at the exact moment it closes.
Persistent Storage: The var array in the indicator persists its contents across the bars of the chart's history. As the script runs through the historical bars, it selectively adds confirmed 4H candle data points to this array only when the trigger is met.
Impervious Collection: Because the array is populated based on the completion of the target timeframe candles (detected reliably by the library) rather than simply collecting data on every chart bar, the final contents of the array (the list of the last 10 confirmed 4H opens) will be the same regardless of the chart's time frame. The table then displays this static collection.
In essence, this setup doesn't change how request.security fundamentally works or aligns data to the chart's bars. Instead, it uses the capabilities of request.security (fetching data from another timeframe) and Pine Script's execution model (bar-by-bar processing, var persistence) in a specific way, guided by the library's logic, to build a historical collection of data points that represent the target timeframe's candles and are independent of the chart's display resolution.
Casa_SessionsLibrary "Casa_Sessions"
Advanced trading session management library that enhances TradingView's default functionality:
Key Features:
- Accurate session detection for futures markets
- Custom session hour definitions
- Drop-in replacements for standard TradingView session functions
- Flexible session map customization
- Full control over trading windows and market hours
Perfect for traders who need precise session timing, especially when working
with futures markets or custom trading schedules.
SetSessionTimes(session_type_input, custom_session_times_input, syminfo_type, syminfo_root, syminfo_timezone)
Parameters:
session_type_input (simple string) : Input string for session selection:
- 'Custom': User-defined session times
- 'FX-Tokyo': Tokyo forex session
- 'FX-London': London forex session
- 'FX-New York': NY forex session
- 'Overnight Session (ON)': After-hours trading
- 'Day Session (RTH)': Regular trading hours
custom_session_times_input (simple string) : Session parameter for custom time windows
Only used when session_type_input is 'Custom'
syminfo_type (simple string)
syminfo_root (simple string)
syminfo_timezone (simple string)
Returns:
session_times: Trading hours for selected session
session_timezone: Market timezone (relevant for forex)
getSessionMap()
Get futures trading session hours map
Keys are formatted as 'symbol:session', examples:
- 'ES:market' - Regular trading hours (RTH)
- 'ES:overnight' - Extended trading hours (ETH)
- 'NQ:market' - NASDAQ futures RTH
- 'CL:overnight' - Crude Oil futures ETH
Returns: Map
Key: Symbol:session identifier
Value: Session hours in format "HH:MM-HH:MM"
getSessionString(session, symbol, sessionMap)
Returns a session string representing the session hours (and days) for the requested symbol (or the chart's symbol if the symbol value is not provided). If the session string is not found in the collection, it will return a blank string.
Parameters:
session (string) : A string representing the session hour being requested. One of: market (regular trading hours), overnight (extended/electronic trading hours), postmarket (after-hours), premarket
symbol (string) : The symbol to check. Optional. Defaults to chart symbol.
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
inSession(session, sessionMap, barsBack)
Returns true if the current symbol is currently in the session parameters defined by sessionString.
Parameters:
session (string) : A string representing the session hour being requested. One of: market (regular trading hours), overnight (extended/electronic trading hours), postmarket (after-hours), premarket
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
barsBack (int) : Private. Only used by futures to check islastbar. Optional. The default is 0.
ismarket(sessionMap)
Returns true if the current bar is a part of the regular trading hours (i.e. market hours), false otherwise. Works for futures (TradingView's methods do not).
Parameters:
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: bool
isfirstbar()
Returns true if the current bar is the first bar of the day's session, false otherwise. If extended session information is used, only returns true on the first bar of the pre-market bars. Works for futures (TradingView's methods do not).
Returns: bool
islastbar()
Returns true if the current bar is the last bar of the day's session, false otherwise. If extended session information is used, only returns true on the last bar of the post-market bars. Works for futures (TradingView's methods do not).
Returns: bool
ispremarket(sessionMap)
Returns true if the current bar is a part of the pre-market, false otherwise. On non-intraday charts always returns false. Works for futures (TradingView's methods do not).
Parameters:
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: bool
ispostmarket(sessionMap)
Returns true if the current bar is a part of the post-market, false otherwise. On non-intraday charts always returns false. Works for futures (TradingView's methods do not).
Parameters:
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: bool
isfirstbar_regular(sessionMap)
Returns true on the first regular session bar of the day, false otherwise. The result is the same whether extended session information is used or not. Works for futures (TradingView's methods do not).
Parameters:
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: bool
islastbar_regular(sessionMap)
Returns true on the last regular session bar of the day, false otherwise. The result is the same whether extended session information is used or not. Works for futures (TradingView's methods do not).
Parameters:
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: bool
isovernight(sessionMap)
Returns true if the current bar is a part of the pre-market or post-market, false otherwise. On non-intraday charts always returns false.
Parameters:
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: bool
getSessionHighAndLow(session, sessionMap)
Returns a tuple containing the high and low print during the specified session.
Parameters:
session (string) : The session for which to get the high & low prints. Defaults to market.
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: A tuple containing
getSessionHigh(session, sessionMap)
Convenience function to return the session high. Necessary if you want to call this function from within a request.security expression where you can't return a tuple.
Parameters:
session (string) : The session for which to get the high & low prints. Defaults to market.
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: The high of the session
getSessionLow(session, sessionMap)
Convenience function to return the session low. Necessary if you want to call this function from within a request.security expression where you can't return a tuple.
Parameters:
session (string) : The session for which to get the high & low prints. Defaults to market.
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: The low of the session
Volume Flow Indicator Signals | iSolani
Volume Flow Indicator Signals | iSolani: Decoding Trend Momentum with Volume Precision
In markets where trends are fueled by institutional participation, discerning genuine momentum from false moves is critical. The Volume Flow Indicator Signals | iSolani cuts through this noise by synthesizing price action with volume dynamics, generating high-confidence signals when capital flows align with directional bias. This tool reimagines traditional volume analysis by incorporating volatility-adjusted thresholds and dual-layer smoothing, offering traders a laser-focused approach to trend identification.
Core Methodology
The indicator employs a multi-stage calculation to quantify volume-driven momentum:
Volatility-Adjusted Filter: Measures price changes via log returns, scaling significance using a 30-bar standard deviation multiplied by user-defined sensitivity (default: 2x).
Volume Normalization: Caps extreme volume spikes at 3x the 50-bar moving average, preventing distortion from anomalous trades.
Directional Volume Flow: Assigns positive/negative values to volume based on whether price movement exceeds volatility-derived thresholds.
Dual Smoothing: Applies consecutive SMA (3-bar) and EMA (14-bar) to create the Volume Flow Indicator (VFI) and its signal line, filtering out transient fluctuations.
Breaking New Ground
This implementation introduces three key innovations:
Adaptive Noise Gates: Unlike static volume oscillators, the sensitivity coefficient dynamically adjusts to market volatility, reducing false signals during choppy conditions.
Institutional Volume Capping: The vcoef parameter limits the influence of outlier volume spikes, focusing on sustained institutional activity rather than one-off trades.
Non-Repainting Signals: Generates single-per-trend labels (buy below bars, sell above) to avoid chart clutter while maintaining visual clarity.
Engine Under the Hood
The script executes through five systematic stages:
Data Preparation: Computes HLC3 typical price and its logarithmic rate of change.
Threshold Calculation: Derives dynamic cutoff levels using 30-period volatility scaled by user sensitivity.
Volume Processing: Filters raw volume through a 50-bar SMA, capping extremes at 3x average.
VFI Construction: Sums directional volume flow over 50 bars, smoothed with a 3-bar SMA.
Signal Generation: Triggers alerts when VFI crosses zero, confirmed by a 14-bar EMA crossover.
Standard Configuration
Optimized defaults balance responsiveness and reliability:
Volume MA: 50-bar smoothing window
Sensitivity: 2.0 (doubles volatility threshold)
Signal Smoothing: 14-bar EMA
Volume Cap: 3x average (hidden parameter)
VFI Smoothing: Enabled (3-bar SMA)
By fusing adaptive volume filtering with price confirmation logic, the Volume Flow Indicator Signals | iSolani transforms raw market data into institutional-grade trend signals. Its ability to mute choppy price action while amplifying high-conviction volume moves makes it particularly effective for spotting early trend reversals in equities, forex, and futures markets.
CHAKRA RISS ENGULFING CANDLESTICK STRATEGYChakra RISS Engulfing Candlestick Strategy
Type: Technical Indicator & Strategy
Platform: TradingView
Script Version: Pine Script v6
Overview:
The Chakra RISS Engulfing Candlestick Strategy combines a momentum-based approach using the Relative Strength Index (RSI) with Engulfing Candlestick Patterns to generate buy and sell signals. The strategy filters trades based on price movement relative to a 50-period Simple Moving Average (SMA), making it a trend-following strategy.
The indicator uses color-coded bars to visually represent market conditions, helping traders easily identify bullish and bearish trends. The strategy is designed to be dynamic, adapting to changing market conditions and filtering out noise using key technical indicators.
How It Works:
RSI-Based Color Conditions:
Green Bars: When the RSI crosses above a specified UpLevel (default: 50), indicating a bullish momentum and signaling potential buy conditions.
Red Bars: When the RSI crosses below a specified DownLevel (default: 50), indicating a bearish momentum and signaling potential sell conditions.
Buy Signal:
Triggered when the following conditions are met:
RSI crosses from below the UpLevel (default: 50) to above it, signaling increasing bullish momentum.
The close price is above the 50-period Simple Moving Average (SMA), confirming an uptrend.
The Buy Signal is plotted below the bar with a green arrow and a "BUY" label.
Sell Signal:
Triggered when the following conditions are met:
RSI crosses from above the DownLevel (default: 50) to below it, signaling increasing bearish momentum.
The close price is below the 50-period Simple Moving Average (SMA), confirming a downtrend.
The Sell Signal is plotted above the bar with a red arrow and a "SELL" label.
Stop Loss and Take Profit:
For long trades (buy signals), the stop loss is placed below the previous bar's low, and the take profit is set at 3% above the entry price.
For short trades (sell signals), the stop loss is placed above the previous bar's high, and the take profit is set at 3% below the entry price.
Dynamic Bar Coloring:
The bar colors change dynamically based on RSI levels:
Green Bars: Indicating a potential uptrend (bullish).
Red Bars: Indicating a potential downtrend (bearish).
These visual cues help traders quickly identify market trends and potential reversals.
Trend Filtering:
The 50-period Simple Moving Average (SMA) is used to filter trades based on the overall market trend:
Buy signals are only considered when the price is above the moving average, indicating an uptrend.
Sell signals are only considered when the price is below the moving average, indicating a downtrend.
Alerting System:
Alerts can be set for both buy and sell signals. These alerts notify traders in real-time when potential trades are generated, allowing them to act promptly.
Alerts can be configured to send notifications through email, SMS, or a webhook for integration with other services like IFTTT or Zapier.
Key Features:
RSI and Moving Average-Based Signals: Combines RSI with a moving average for more accurate trade signals.
Stop Loss and Take Profit: Dynamic risk management with custom stop loss and take profit levels based on previous high and low prices.
Buy and Sell Alerts: Provides real-time alerts when a buy or sell signal is triggered.
Trend Confirmation: Uses the 50-period Simple Moving Average to filter signals and confirm the direction of the trend.
Visual Bar Color Changes: Makes it easy to identify bullish or bearish trends with color-coded bars.
Usage:
This strategy is suitable for traders who prefer a trend-following approach and want to combine momentum indicators (RSI) with price action (Engulfing Candlestick patterns). It is particularly useful in volatile markets where quick identification of trend changes can lead to profitable trades.
Best Used For: Day trading, swing trading, and trend-following strategies.
Timeframes: Works well on various timeframes, from 1-minute charts for scalping to daily charts for swing trading.
Markets: Can be applied to any market with sufficient liquidity (stocks, forex, crypto, etc.).
Settings:
UpLevel: The RSI level above which the market is considered bullish (default: 50).
DownLevel: The RSI level below which the market is considered bearish (default: 50).
SMA Length: The period of the Simple Moving Average used to filter trades (default: 50).
Risk Management: Customizable stop loss and take profit settings based on price action (default: 3% above/below the entry price).
Wick Strategy AnalyzerOverview
This indicator analyzes candle wick patterns and evaluates their outcomes over a user-definable range (default is 1 year). Labels are rendered on the chart to mark events that meet the specified wick condition.
Features
Customizable Bar Range - users can specify the range of bars to include in the analysis. Default is 365 bars back from the most recent bar (bar 0)
Visual Indicators - labels are rendered to mark conditions & outcomes.
Wick Condition Met - an Orange label below the wick candle displaying the wick’s percentage size.
Outcome Labels - rendered above the candle after wick condition met candles
P (Green): Pass
F (Red): Fail
N (Navy): Neutral
I (Blue): Indicates the current candle has not yet closed, so the outcome is undetermined.
Input Parameters
Wick Threshold - minimum wick size required to qualify as a wick condition.
Success Margin - Defines the margin for classifying outcomes as Pass, Fail, or Neutral. E.g., a success margin of 0.01 requires the next candle's close to exceed the wick candle's close by 1% in order to be a Pass.
Bar Offset Start - starting offset from the last bar for analysis. A value of -1 will include all bars.
Bar Offset End - ending offset from the last bar for analysis. Bars outside this range are excluded.
Example Scenario
Goal: Analyze how candles with a wick size of at least 3.5% perform within a success margin of 1% over the past 540 days.
Setup:
Set Wick Threshold to 0.035
Set Success Margin to 0.01
Set Bar Range Start to 0
Set Bar Range End to 540.
Expected Output
Candles with a wick of at least 3.5% are labeled.
Outcome labels (P, F, or N) indicate performance.
ToolsPosLibrary "ToolsPos"
Library for general purpose position helpers
new_pos(state, price, when, index)
Returns new PosInfo object
Parameters:
state (series PosState) : Position state
price (float) : float Entry price
when (int) : int Entry bar time UNIX. Default: time
index (int) : int Entry bar index. Default: bar_index
Returns: PosInfo
new_tp(pos, price, when, index, info)
Returns PosInfo object with new take profit info object
Parameters:
pos (PosInfo) : PosInfo object
price (float) : float Entry price
when (int) : int Entry bar time UNIX. Default: time
index (int) : int Entry bar index. Default: bar_index
info (Info type from aybarsm/Tools/14) : Info holder object. Default: na
Returns: PosInfo
new_re(pos, price, when, index, info)
Returns PosInfo object with new re-entry info object
Parameters:
pos (PosInfo) : PosInfo object
price (float) : float Entry price
when (int) : int Entry bar time UNIX. Default: time
index (int) : int Entry bar index. Default: bar_index
info (Info type from aybarsm/Tools/14) : Info holder object. Default: na
Returns: PosInfo
PosTPInfo
PosTPInfo - Position Take Profit info object
Fields:
price (series float) : float Take profit price
when (series int) : int Take profit bar time UNIX. Default: time
index (series int) : int Take profit bar index. Default: bar_index
info (Info type from aybarsm/Tools/14) : Info holder object
PosREInfo
PosREInfo - Position Re-Entry info object
Fields:
price (series float) : float Re-entry price
when (series int) : int Re-entry bar time UNIX. Default: time
index (series int) : int Take profit bar index. Default: bar_index
info (Info type from aybarsm/Tools/14) : Info holder object
PosInfo
PosInfo - Position info object
Fields:
state (series PosState) : Position state
price (series float) : float Entry price
when (series int) : int Entry bar time UNIX. Default: time
index (series int) : int Entry bar index. Default: bar_index
tp (array) : PosTPInfo Take profit info. Default: na
re (array) : PosREInfo Re-entry info. Default: na
info (Info type from aybarsm/Tools/14) : Info holder object
Mean Price
^^ Plotting switched to Line.
This method of financial time series (aka bars) downsampling is literally, naturally, and thankfully the best you can do in terms of maximizing info gain. You can finally chill and feed it to your studies & eyes, and probably use nothing else anymore.
(HL2 and occ3 also have use cases, but other aggregation methods? Not really, even if they do, the use cases are ‘very’ specific). Tho in order to understand why, you gotta read the following wall, or just believe me telling you, ‘I put it on my momma’.
The true story about trading volumes and why this is all a big misdirection
Actually, you don’t need to be a quant to get there. All you gotta do is stop blindly following other people’s contextual (at best) solutions, eg OC2 aggregation xD, and start using your own brain to figure things out.
Every individual trade (basically an imprint on 1D price space that emerges when market orders hit the order book) has several features like: price, time, volume, AND direction (Up if a market buy order hits the asks, Down if a market sell order hits the bids). Now, the last two features—volume and direction—can be effectively combined into one (by multiplying volume by 1 or -1), and this is probably how every order matching engine should output data. If we’re not considering size/direction, we’re leaving data behind. Moreover, trades aren’t just one-price dots all the time. One trade can consume liquidity on several levels of the order book, so a single trade can be several ticks big on the price axis.
You may think now that there are no zero-volume ticks. Well, yes and no. It depends on how you design an exchange and whether you allow intra-spread trades/mid-spread trades (now try to Google it). Intra-spread trades could happen if implemented when a matching engine receives both buy and sell orders at the same microsecond period. This way, you can match the orders with each other at a better price for both parties without even hitting the book and consuming liquidity. Also, if orders have different sizes, the remaining part of the bigger order can be sent to the order book. Basically, this type of trade can be treated as an OTC trade, having zero volume because we never actually hit the book—there’s no imprint. Another reason why it makes sense is when we think about volume as an impact or imbalance act, and how the medium (order book in our case) responds to it, providing information. OTC and mid-spread trades are not aggressive sells or buys; they’re neutral ticks, so to say. However huge they are, sometimes many blocks on NYSE, they don’t move the price because there’s no impact on the medium (again, which is the order book)—they’re not providing information.
... Now, we need to aggregate these trades into, let’s say, 1-hour bars (remember that a trade can have either positive or negative volume). We either don’t want to do it, or we don’t have this kind of information. What we can do is take already aggregated OHLC bars and extract all the info from them. Given the market is fractal, bars & trades gotta have the same set of features:
- Highest & lowest ticks (high & low) <- by price;
- First & last ticks (open & close) <- by time;
- Biggest and smallest ticks <- by volume.*
*e.g., in the array ,
2323: biggest trade,
-1212: smallest trade.
Now, in our world, somehow nobody started to care about the biggest and smallest trades and their inclusion in OHLC data, while this is actually natural. It’s the same way as it’s done with high & low and open & close: we choose the minimum and maximum value of a given feature/axis within the aggregation period.
So, we don’t have these 2 values: biggest and smallest ticks. The best we can do is infer them, and given the fact the biggest and smallest ticks can be located with the same probability everywhere, all we can do is predict them in the middle of the bar, both in time and price axes. That’s why you can see two HL2’s in each of the 3 formulas in the code.
So, summed up absolute volumes that you see in almost every trading platform are actually just a derivative metric, something that I call Type 2 time series in my own (proprietary ‘for now’) methods. It doesn’t have much to do with market orders hitting the non-uniform medium (aka order book); it’s more like a statistic. Still wanna use VWAP? Ok, but you gotta understand you’re weighting Type 1 (natural) time series by Type 2 (synthetic) ones.
How to combine all the data in the right way (khmm khhm ‘order’)
Now, since we have 6 values for each bar, let’s see what information we have about them, what we don’t have, and what we can do about it:
- Open and close: we got both when and where (time (order) and price);
- High and low: we got where, but we don’t know when;
- Biggest & smallest trades: we know shit, we infer it the way it was described before.'
By using the location of the close & open prices relative to the high & low prices, we can make educated guesses about whether high or low was made first in a given bar. It’s not perfect, but it’s ultimately all we can do—this is the very last bit of info we can extract from the data we have.
There are 2 methods for inferring volume delta (which I call simply volume) that are presented everywhere, even here on TradingView. Funny thing is, this is actually 2 parts of the 1 method. I wonder how many folks see through it xD. The same method can be used for both inferring volume delta AND making educated guesses whether high or low was made first.
Imagine and/or find the cases on your charts to understand faster:
* Close > open means we have an up bar and probably the volume is positive, and probably high was made later than low.
* Close < open means we have a down bar and probably the volume is negative, and probably low was made later than high.
Now that’s the point when you see that these 2 mentioned methods are actually parts of the 1 method:
If close = open, we still have another clue: distance from open/close pair to high (HC), and distance from open/close pair to low (LC):
* HC < LC, probably high was made later.
* HC > LC, probably low was made later.
And only if close = open and HC = LC, only in this case we have no clue whether high or low was made earlier within a bar. We simply don’t have any more information to even guess. This bar is called a neutral bar.
At this point, we have both time (order) and price info for each of our 6 values. Now, we have to solve another weighted average problem, and that’s it. We’ll weight prices according to the order we’ve guessed. In the neutral bar case, open has a weight of 1, close has a weight of 3, and both high and low have weights of 2 since we can’t infer which one was made first. In all cases, biggest and smallest ticks are modeled with HL2 and weighted like they’re located in the middle of the bar in a time sense.
P.S.: I’ve also included a "robust" method where all the bars are treated like neutral ones. I’ve used it before; obviously, it has lesser info gain -> works a bit worse.
simple swing indicator-KTRNSE:NIFTY
1. Pivot High/Low as Lines:
Purpose: Identifies local peaks (pivot highs) and troughs (pivot lows) in price and draws horizontal lines at these levels.
How it Works:
A pivot high occurs when the price is higher than the surrounding bars (based on the pivotLength parameter).
A pivot low occurs when the price is lower than the surrounding bars.
These pivots are drawn as horizontal lines at the price level of the pivot.
Visualization:
Pivot High: A red horizontal line is drawn at the price level of the pivot high.
Pivot Low: A green horizontal line is drawn at the price level of the pivot low.
Example:
Imagine the price is trending up, and at some point, it forms a peak. The script identifies this peak as a pivot high and draws a red line at the price of that peak. Similarly, if the price forms a trough, the script will draw a green line at the low point.
2. Moving Averages (20-day and 50-day):
Purpose: Plots the 20-day and 50-day simple moving averages (SMA) on the chart.
How it Works:
The 20-day SMA smooths the closing price over the last 20 days.
The 50-day SMA smooths the closing price over the last 50 days.
These lines provide an overview of short-term and long-term price trends.
Visualization:
20-day SMA: A blue line showing the 20-day moving average.
50-day SMA: An orange line showing the 50-day moving average.
Example:
When the price is above both moving averages, it indicates an uptrend. If the price crosses below these averages, it might signal a downtrend.
3. Supertrend:
Purpose: The Supertrend is an indicator based on the Average True Range (ATR) and is used to track the market trend.
How it Works:
When the market is in an uptrend, the Supertrend line will be green.
When the market is in a downtrend, the Supertrend line will be red.
Visualization:
Uptrend: The Supertrend line will be plotted in green.
Downtrend: The Supertrend line will be plotted in red.
Example:
If the price is above the Supertrend, the market is considered to be in an uptrend, and if the price is below the Supertrend, the market is in a downtrend.
4. Momentum (Rate of Change):
Purpose: Measures the rate at which the price changes over a set period, showing if the momentum is positive or negative.
How it Works:
The Rate of Change (ROC) measures how much the price has changed over a certain number of periods (e.g., 14).
Positive ROC indicates upward momentum, and negative ROC indicates downward momentum.
Visualization:
Positive ROC: A purple line is plotted above the zero line.
Negative ROC: A purple line is plotted below the zero line.
Example:
If the ROC line is above zero, it means the price is increasing, suggesting bullish momentum. If the ROC is below zero, it indicates bearish momentum.
5. Volume:
Purpose: Displays the volume of traded assets, giving insight into the strength of price movements.
How it Works:
The script will color the volume bars based on whether the price closed higher or lower than the previous bar.
Green bars indicate bullish volume (closing price higher than the previous bar), and red bars indicate bearish volume (closing price lower than the previous bar).
Visualization:
Bullish Volume: Green volume bars when the price closes higher.
Bearish Volume: Red volume bars when the price closes lower.
Example:
If you see a green volume bar, it suggests that the market is participating in an uptrend, and the price has closed higher than the previous period. Red bars indicate a downtrend or selling pressure.
6. MACD (Moving Average Convergence Divergence):
Purpose: The MACD is a trend-following momentum indicator that shows the relationship between two moving averages of the price.
How it Works:
The MACD Line is the difference between the 12-period EMA (Exponential Moving Average) and the 26-period EMA.
The Signal Line is the 9-period EMA of the MACD Line.
The MACD Histogram shows the difference between the MACD line and the Signal line.
Visualization:
MACD Line: A blue line representing the difference between the 12-period and 26-period EMAs.
Signal Line: An orange line representing the 9-period EMA of the MACD line.
MACD Histogram: A red or green histogram that shows the difference between the MACD line and the Signal line.
Example:
When the MACD line crosses above the Signal line, it’s considered a bullish signal. When the MACD line crosses below the Signal line, it’s considered a bearish signal.
Full Chart Example:
Imagine you're looking at a price chart with all the indicators:
Pivot High/Low Lines are drawn as red and green horizontal lines.
20-day and 50-day SMAs are plotted as blue and orange lines, respectively.
Supertrend shows a green or red line indicating the trend.
Momentum (ROC) is shown as a purple line oscillating around zero.
Volume bars are green or red based on whether the close is higher or lower.
MACD appears as a blue line and orange line, with a red or green histogram showing the MACD vs. Signal line difference.
How the Indicators Work Together:
Trend Confirmation: If the price is above the Supertrend line and both SMAs are trending up, it indicates a strong bullish trend.
Momentum: If the ROC is positive and the MACD line is above the Signal line, it further confirms bullish momentum.
Volume: Increasing volume, especially with green bars, suggests that the trend is being supported by active participation.
By using these combined indicators, you can get a comprehensive view of the market's trend, momentum, and potential reversal points (via pivot highs and lows).
[AWC] Vector -AYNETThis Pine Script code is a custom indicator designed for TradingView. Its purpose is to visualize the opening and closing prices of a specific timeframe (e.g., weekly, daily, or monthly) by drawing lines between these price points whenever a new bar forms in the specified timeframe. Below is a detailed explanation from a scientific perspective:
1. Input Parameters
The code includes user-defined inputs to customize its functionality:
tf1: This input defines the timeframe (e.g., 'W' for weekly, 'D' for daily). It determines the periodicity for analyzing price data.
icol: This input specifies the color of the lines drawn on the chart. Users can select from predefined options such as black, red, or blue.
2. Color Assignment
A switch statement maps the user’s color selection (icol) to the corresponding color object in Pine Script. This mapping ensures that the drawn lines adhere to the user's preference.
3. New Bar Detection
The script uses the ta.change(time(tf1)) function to determine when a new bar forms in the specified timeframe (tf1):
ta.change checks if the timestamp of the current bar differs from the previous one within the selected timeframe.
If the value changes, it indicates that a new bar has formed, and further calculations are triggered.
4. Data Request
The script employs request.security to fetch price data from the specified timeframe:
o1: Retrieves the opening price of the previous bar.
c1: Calculates the average price (high, low, close) of the previous bar using the hlc3 formula.
These values represent the key price levels for visualizing the line.
5. Line Drawing
When a new bar is detected:
The script uses line.new to create a line connecting the previous bar's opening price (o1) and the closing price (c1).
The line’s properties are defined as follows:
x1, y1: The starting point corresponds to the opening price at the previous bar index.
x2, y2: The endpoint corresponds to the closing price at the current bar index.
color: Uses the user-defined color (col).
style: The line style is set to line.style_arrow_right.
Additionally, the lines are stored in an array (lines) for later reference, enabling potential modifications or deletions.
6. Visual Outcome
The script visually represents price movements over the specified timeframe:
Each line connects the opening and closing price of a completed bar in the given timeframe.
The lines are drawn dynamically, updating whenever a new bar forms.
Scientific Context
This script applies concepts of time series analysis and visualization in financial data:
Time Segmentation: By isolating specific timeframes (e.g., weekly), the script provides a focused analysis of price behavior.
Price Dynamics: Connecting opening and closing prices highlights key price transitions within each period.
User Customization: The inclusion of inputs allows for adaptable use, accommodating different analytical preferences.
Applications
Trend Analysis: Identifies how price evolves between opening and closing levels across periods.
Market Behavior Comparison: Facilitates the observation of patterns or anomalies in price transitions over time.
Technical Indicators: Serves as a supplementary tool for decision-making in trading strategies.
If further enhancements or customizations are needed, let me know! 😊
Parabolic (Brachistochrone) Curve IndicatorOverview of the Script
The script is designed to plot an approximation of the Brachistochrone curve between two points on a TradingView chart. The Brachistochrone curve represents the path of fastest descent under gravity between two points not aligned vertically. In physics, this curve is a segment of a cycloid.
Understanding the Brachistochrone Curve
Definition: The Brachistochrone curve is the curve along which a particle will descend from one point to another in the least time under gravity, without friction.
Mathematical Representation: The solution to the Brachistochrone problem is a cycloid, which is the curve traced by a point on the rim of a circular wheel as it rolls along a straight line.
Relevance to Trading: While the Brachistochrone curve originates from physics, plotting it on a price-time chart can offer a unique visual representation of the fastest possible movement between two price levels.
How the Script Works
Inputs
Start and End Bars:
startBar: The number of bars back from the current bar to define the starting point.
endBar: The number of bars back from the current bar to define the ending point.
Curve Customization:
numPoints: The number of points used to plot the curve (affects smoothness).
curveColor: The color of the curve.
curveWidth: The width of the curve lines.
Labels:
showTimeLabels: A toggle to display labels along the curve for reference.
Calculations
Determine Start and End Points:
The script calculates the coordinates (x_start, y_start, x_end, y_end) of the start and end points based on the specified bar offsets.
x_start and x_end correspond to bar indices (time).
y_start and y_end correspond to price levels.
Calculate Differences and Parameters:
Horizontal and Vertical Differences:
delta_x = x_end - x_start
delta_y = y_end - y_start
Ensure Descending Motion:
If the end point is higher than the start point (i.e., delta_y is positive), the script swaps the start and end points to ensure the curve represents a descent.
Cycloid Parameters:
Angle (theta): Calculated using theta = atan(delta_y / delta_x), representing the inclination of the curve.
Radius (R): The radius of the generating circle for the cycloid, calculated with R = delta_x / (π * cos(theta)).
Generate Points Along the Cycloid:
Parameter t: Varies from 0 to t_end, where t_end is set to π to represent half a cycloid (a common segment for the Brachistochrone).
Cycloid Equations:
Horizontal Component (x_t): x_t = R * (t - sin(t))
Vertical Component (y_t): y_t = R * (1 - cos(t))
Adjust Coordinates:
The script adjusts the cycloid coordinates to align with the chart's axes:
x_plot = x_start + x_t * cos(theta)
y_plot = y_start + y_t * sin(theta)
The x_plot values are converted to integer bar indices to match the chart's x-axis.
Plotting the Curve
Drawing Lines:
The script connects consecutive points using lines to form the curve.
It uses the line.new function, specifying the start and end coordinates of each line segment.
Adding Labels (Optional):
If showTimeLabels is enabled, the script places labels at intervals along the curve to indicate progress or parameter values.
Adjustments for Accurate Visualization
Handling Ascending Paths:
To adhere to the physical definition of the Brachistochrone curve, the script ensures that the ending point is below the starting point in terms of price.
If not, it swaps the points to represent a descending path.
Parameter Constraints:
The script ensures that calculations involving trigonometric functions remain within valid ranges to prevent mathematical errors (e.g., division by zero or invalid arguments for acos).
Scaling Considerations:
Adjustments are made to account for the differences in scaling between time (x-axis) and price (y-axis) on the chart.
The script maps spatial coordinates to the chart's axes appropriately.
Limitations and Considerations
Theoretical Nature:
The Brachistochrone curve is a theoretical concept from physics and doesn't necessarily predict actual price movements in financial markets.
Chart Scaling:
The visual appearance of the curve may be affected by the chart's scaling settings. Users may need to adjust the chart's zoom or scale to view the curve properly.
Data Range:
The start and end bars must be within the range of available data on the chart. If the specified bars are out of range, the script may not plot the curve.
Computational Limits:
TradingView imposes limits on the number of drawing objects (lines, labels) that can be displayed. The script accounts for this, but extremely high numPoints values may lead to performance issues.
Usage Instructions
Adding the Indicator:
The script is added to the chart as a custom indicator in TradingView's Pine Script Editor.
Configuring Inputs:
Start and End Bars: Users specify the bar offsets for the start and end points. It's important that the end point is below the start point in price to represent a descent.
Curve Customization: Users can adjust the number of points for smoothness and customize the curve's color and width.
Labels: Users can choose to display or hide labels along the curve.
Observing the Curve:
After configuring the inputs, the curve will be plotted between the two specified points.
Users can observe the curve to understand the theoretical fastest descent between the two price levels.
Potential Applications
Educational Tool:
The script serves as a visual aid to understand the properties of the Brachistochrone curve and cycloid.
Analytical Insights:
While not predictive, the curve might inspire new ways of thinking about price movements, momentum, or acceleration in markets.
Visualization:
It provides a unique way to visualize the relationship between time and price over a specific interval.
Conclusion
The script effectively adapts the mathematical concept of the Brachistochrone curve to a financial chart by carefully mapping spatial coordinates to time and price axes. By accounting for the unique characteristics of TradingView charts and implementing necessary mathematical adjustments, the script plots the curve between two user-defined points, offering a novel and educational visualization.
Prometheus Fractal-Based TrendThe Fractal-Based Trend indicator is a tool that uses fractals to try and detect which direction an underlying will continue to go.
Calculation:
A bullish fractal occurs when the current bar's high is lower than the previous bar high, and the previous bar's high is higher than both the high from two bars ago and the high from three bars ago.
A bearish fractal happens when the current bar's low is higher than the previous bar's low, and the previous bar's low is lower than both the low from two bars ago and the low from three bars ago.
When a bullish or bearish fractal forms, the corresponding value stored is the previous bar high for a bearish fractal or the previous bar's low for a bullish fractal.
The trade scenarios are when these fractals occur, a green or red label being plotted on the chart for whatever direction it predicts.
Trade examples:
We see on this daily chart of AMEX:SPY that the fractals represent the potential for a directional trade that can last a few days. The more volatile a chart is the more of these fractals we can see.
We see on this 5 minute chart for NASDAQ:TSLA there is way more activity, there are more sporadic candles on a lower time frame, so we can see more anomalies in the price action.
We see this to be true for BITSTAMP:BTCUSD even on a daily time frame, since it is very volatile. There are a lot of these labels plotted.
This is the perspective we aim to provide. We encourage traders to not follow indicators blindly. No indicator is 100% accurate. This one can give you a different perspective of price strength with volatility. We encourage any comments about desired updates or criticism!
Kurutoga Histogram with HTF and LTF
Kurutoga Histogram:
The Kurutoga Histogram is a technical analysis indicator designed to measure price divergence from the 50% level of a recent price range. By calculating how far the current price is from the midpoint of a selected base length of candles, the histogram provides insight into the momentum, strength, and potential reversals in the market. Additionally, it can be applied across multiple timeframes to provide a comprehensive view of both short- and long-term market dynamics.
Key Components:
Base Length:
The base length is the number of candles (bars) over which the high and low prices are observed. The default base length is typically 14 periods, but it can be adjusted according to the trader's preference.
This base length defines the range from which the 50% level, or midpoint, is calculated.
50% Level (Midpoint):
The midpoint is the average of the highest high and the lowest low over the selected base length. This 50% level acts as an equilibrium point around which the price fluctuates.
Formula:
Midpoint = (Highest High + Lowest Low) / 2
The price’s distance from this midpoint is an indicator of how strong the current trend or divergence is.
Price Divergence:
The main calculation of the histogram is the difference between the current closing price and the midpoint of the price range.
Formula:
Divergence = Close Price − Midpoint
A positive divergence (price above the midpoint) indicates bullish strength, while a negative divergence (price below the midpoint) indicates bearish strength.
Multi-Timeframe Analysis:
The Kurutoga Histogram can be applied to both the current timeframe and a higher timeframe (HTF), allowing traders to gauge price movement in both short-term and long-term contexts.
By comparing the histograms of multiple timeframes, traders can determine if there is alignment (confluence) between trends, which can strengthen trade signals or provide additional confirmation.
Color-Coded Histogram:
Blue Bars (Positive Divergence): Represent that the price is above the 50% level, indicating bullish momentum. Taller blue bars suggest stronger upward momentum, while shrinking bars suggest weakening strength.
Red Bars (Negative Divergence): Represent that the price is below the 50% level, indicating bearish momentum. Taller red bars suggest stronger downward momentum, while shrinking bars suggest a potential reversal or consolidation.
The histogram’s color intensity and transparency can be adjusted to enhance the visual effect, distinguishing between current timeframe (LTF) and higher timeframe (HTF) divergence.
Interpretation:
Bullish Signals: When the histogram bars are blue and growing, the price is gaining momentum above the midpoint of its recent range. This could signal an ongoing uptrend.
Bearish Signals: When the histogram bars are red and growing, the price is gaining momentum below the midpoint, signaling an ongoing downtrend.
Momentum Shifts: When the histogram bars shrink in size (whether blue or red), it could indicate that the current trend is losing strength and may reverse or enter consolidation.
Neutral or Sideways Movement: When the histogram bars hover around zero, it means the price is trading near the midpoint of its recent range, often signaling a lack of strong momentum in either direction.
Multi-Timeframe Confluence:
When the current timeframe (LTF) histogram aligns with the higher timeframe (HTF) histogram (e.g., both are showing strong bullish or bearish divergence), it may provide stronger confirmation of the trend's strength.
Divergence between timeframes (e.g., bullish on LTF but bearish on HTF) may suggest that price movements on lower timeframes are not yet reflected in the broader trend, signaling caution.
Applications:
Trend Identification: The Kurutoga Histogram is highly useful for detecting when the price is trending away from its equilibrium point, providing insight into the strength of ongoing trends.
Momentum Analysis: By measuring the divergence from the 50% level, the histogram helps traders identify when momentum is increasing or decreasing.
Reversal Detection: Shrinking histogram bars can signal weakening momentum, which often precedes trend reversals.
Consolidation and Breakouts: When the histogram remains near zero for an extended period, it suggests consolidation, which often precedes a breakout in either direction.
Advantages:
Clear Visuals: The use of a color-coded histogram makes it easy to visually assess whether the market is gaining bullish or bearish momentum.
Multi-Timeframe Utility: The ability to compare current timeframe signals with higher timeframe signals adds an extra layer of confirmation, reducing false signals.
Dynamic Adjustment: By adjusting the base length, traders can fine-tune the sensitivity of the indicator to match different markets or trading styles.
Limitations:
Lagging Indicator: Like most divergence indicators, the Kurutoga Histogram may lag slightly behind actual price movements, especially during fast, volatile markets.
Requires Confirmation: This indicator works best when used in conjunction with other technical tools like moving averages, support/resistance levels, or volume indicators, to avoid relying on divergence alone.
Conclusion:
The Kurutoga Histogram is a versatile and visually intuitive tool for measuring price divergence from a key equilibrium point, helping traders to assess the strength of trends and identify potential reversal points. Its use across multiple timeframes provides deeper insights, making it a valuable addition to any trading strategy that emphasizes momentum and trend following.
Volume Density AnalysisVolume Density Analysis
Overview
The "Volume Density Analysis" indicator is designed to provide traders with insights into volume dynamics relative to price movements. By analyzing the density of volume against price spread, this indicator helps identify potential reversal points and extreme volume conditions, enhancing decision-making in trading strategies.
Key Features
Volume Density Calculation: The indicator computes the density of volume by dividing the total volume by the price spread (high - low) for each bar. This allows for a more nuanced understanding of volume activity in relation to price movements.
Extremum Detection: Users can specify the number of bars to consider when identifying extreme volume conditions, allowing for tailored analysis based on market behavior.
Reversal Bar Conditions: The indicator includes options to determine if low or high volume bars must coincide with reversal patterns, providing additional context for potential trade signals.
Dynamic Coloring*: The histogram displays colored bars based on specific conditions:
Blue Bars: Indicate the lowest and highest volume density within a specified range, highlighting significant volume extremes.
Gray Bars: Represent lower or higher volume density that meets reversal criteria.
Green and Red Bars: Indicate bullish or bearish reversal signals based on historical density patterns.
User Inputs
nl: Number of previous lower bars to consider for comparison (default is 8).
nh: Number of previous higher bars to consider for comparison (default is 8).
ext: Number of bars for detecting extremum volume (default is 30).
LReversalBar: Boolean option to determine if low volume bar spread must indicate a reversal.
HReversalBar: Boolean option to determine if high volume bar spread must indicate a reversal.
Suggested Timeframes
M15: Without reversal considerations, use `nl=3`, `nh=3`, and `ext=20`.
M5: Without reversal considerations, use `nl=4`, `nh=4`, and `ext=35`.
M1: Use `nl=8`, `nh=8`, and `ext=58` for more detailed analysis.
Rempi Volume
Greetings, dear traders. I present to your attention the concept of a Rempi Volume indicator + info table.
Rempi Volume displays volume in a color palette, where:
gray color - very weak volume,
blue color - weak volume,
green color - normal volume,
orange color - high volume,
red color - very high volume,
purple color - ultra high volume
The indicator also supports the function of displaying a moving average, the default is 20.
The indicator can color bars on the main price chart, depending on how much volume is currently inside the bar.
The Rempi Volume indicator table has the following information for the trader:
Current Bar -information about the current bar: its volume in real time, as well as the percentage of buyers and sellers.
Previous Bar - information about the previous bar: its volume, as well as the percentage of buyers and sellers. (data is updated at bar close)
10 Bar Volume Comparison - data on the volume of buyers or sellers for the previous 10 bars on the chart.
Volume Change - changing the amount of volume between the current and previous bar, in real time.
Average Volume - average trading volume for the current day.
Market Volatility - market volatility and recommendations.
Current Trend - current trend on the market.
RSI - RSI indicator and recommendations.
/////////////////////////////////////////////////////////////
Приветствую вас уважаемые трейдеры. Вашему вниманию представляю концепт индикатора объемов Rempi Volume + информативная таблица.
Rempi Volume отображает объем в цветовой палитре , где:
серый цвет - очень слабый объем,
голубой цвет - слабый объем,
зеленый цвет - нормальный объем,
оранжевый цвет - высокий объем,
красный цвет - очень высокий объем,
фиолетовый цвет - ультра высокий объем
Также индикатор поддерживает функцию отображения скользящей средней, по умолчанию равна 20.
Индикатор может окрашивать бары на основном графике цены, в зависимости ,какой объем в данный момент внутри бара.
Таблица индикатора Rempi Volume имеет следующую информацию для трейдера:
Current Bar - информация о текущем баре: его объем в режиме реального времени, а также процентное соотношение покупателей и продавцов.
Previous Bar - информация о предыдущем баре: его объем , а также процентное соотношение покупателей и продавцов. ( данные обновляются на закрытии бара )
10 Bar Volume Comparison - данные об объеме покупателей или продавцов за предыдущие 10 баров на графике.
Volume Change - изменение количества объема между текущим и предыдущим баром,в режиме реального времени.
Average Volume - средний объем торгов за текущий день.
Market Volatility - волатильность рынка и рекомендации.
Current Trend - текущее направление рынка.
RSI - показатель RSI и рекомендации.
lib_no_delayLibrary "lib_no_delay"
This library contains modifications to standard functions that return na before reaching the bar of their 'length' parameter.
That is because they do not compromise speed at current time for correct results in the past. This is good for live trading in short timeframes but killing applications on Monthly / Weekly timeframes if instruments, like in crypto, do not have extensive history (why would you even trade the monthly on a meme coin ... not my decision).
Also, some functions rely on source (value at previous bar), which is not available on bar 1 and therefore cascading to a na value up to the last bar ... which in turn leads to a non displaying indicator and waste of time debugging this)
Anyway ... there you go, let me know if I should add more functions.
sma(source, length)
Parameters:
source (float) : Series of values to process.
length (simple int) : Number of bars (length).
Returns: Simple moving average of source for length bars back.
ema(source, length)
Parameters:
source (float) : Series of values to process.
length (simple int) : Number of bars (length).
Returns: (float) The exponentially weighted moving average of the source.
rma(source, length)
Parameters:
source (float) : Series of values to process.
length (simple int) : Number of bars (length).
Returns: Exponential moving average of source with alpha = 1 / length.
atr(length)
Function atr (average true range) returns the RMA of true range. True range is max(high - low, abs(high - close ), abs(low - close )). This adapted version extends ta.atr to start without delay at first bar and deliver usable data instead of na by averaging ta.tr(true) via manual SMA.
Parameters:
length (simple int) : Number of bars back (length).
Returns: Average true range.
rsi(source, length)
Relative strength index. It is calculated using the ta.rma() of upward and downward changes of source over the last length bars. This adapted version extends ta.rsi to start without delay at first bar and deliver usable data instead of na.
Parameters:
source (float) : Series of values to process.
length (simple int) : Number of bars back (length).
Returns: Relative Strength Index.
SessionLibrary "Session"
Helper functions for trading sessions. TradingView doesn't provide correct data when
calling some of the convenience methods like session.ismarket when you are looking at futures charts. This library corrects those mistakes by providing functions with the same names as the TradingView default properties. that reference a custom defined set of session hours for futures. It also provides a way for consumers to customize the map values by calling getSessionMap() and then overwriting (or adding) custom session definitions.
getSessionMap()
Returns a map of the futures rth & eth session hours. The map is keyed with symbol:session format (eg. ES:market or ES:overnight).
Returns: A map of futures symbols and their associated session hours.
getSessionString(session, symbol, sessionMap)
Returns a session string representing the session hours (and days) for the requested symbol (or the chart's symbol if the symbol value is not provided). If the session string is not found in the collection, it will return a blank string.
Parameters:
session (string) : A string representing the session hour being requested. One of: market (regular trading hours), overnight (extended/electronic trading hours), postmarket (after-hours), premarket
symbol (string) : The symbol to check. Optional. Defaults to chart symbol.
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
inSession(session, sessionMap, barsBack)
Returns true if the current symbol is currently in the session parameters defined by sessionString.
Parameters:
session (string) : A string representing the session hour being requested. One of: market (regular trading hours), overnight (extended/electronic trading hours), postmarket (after-hours), premarket
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
barsBack (int) : Private. Only used by futures to check islastbar. Optional. The default is 0.
ismarket(sessionMap)
Returns true if the current bar is a part of the regular trading hours (i.e. market hours), false otherwise. Works for futures (TradingView's methods do not).
Parameters:
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: bool
isfirstbar()
Returns true if the current bar is the first bar of the day's session, false otherwise. If extended session information is used, only returns true on the first bar of the pre-market bars. Works for futures (TradingView's methods do not).
Returns: bool
islastbar()
Returns true if the current bar is the last bar of the day's session, false otherwise. If extended session information is used, only returns true on the last bar of the post-market bars. Works for futures (TradingView's methods do not).
Returns: bool
ispremarket(sessionMap)
Returns true if the current bar is a part of the pre-market, false otherwise. On non-intraday charts always returns false. Works for futures (TradingView's methods do not).
Parameters:
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: bool
ispostmarket(sessionMap)
Returns true if the current bar is a part of the post-market, false otherwise. On non-intraday charts always returns false. Works for futures (TradingView's methods do not).
Parameters:
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: bool
isfirstbar_regular(sessionMap)
Returns true on the first regular session bar of the day, false otherwise. The result is the same whether extended session information is used or not. Works for futures (TradingView's methods do not).
Parameters:
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: bool
islastbar_regular(sessionMap)
Returns true on the last regular session bar of the day, false otherwise. The result is the same whether extended session information is used or not. Works for futures (TradingView's methods do not).
Parameters:
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: bool
isovernight(sessionMap)
Returns true if the current bar is a part of the pre-market or post-market, false otherwise. On non-intraday charts always returns false.
Parameters:
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: bool
getSessionHighAndLow(session, sessionMap)
Returns a tuple containing the high and low print during the specified session.
Parameters:
session (string) : The session for which to get the high & low prints. Defaults to market.
sessionMap (map) : The map of futures session hours. Optional. Uses default if not provided.
Returns: A tuple containing
Average Candle Range [UkutaLabs]█ OVERVIEW
The Average Candle Range is a powerful indicator that compares the size of the current bar to past bars. This comparison can be used in a wide variety of trading strategies, allowing traders to understand at a glance the relative size of each candle.
█ USAGE
As each candlestick forms, two bars will be plotted on the indicator. The grey bar represents the total range of the candle from the high to the low, and the second bar represents the body of the bar from the open to the close. Depending on whether the bar is bullish or bearish, the second bar will be colored green or red respectively.
Two averages will also be drawn over these bars that represent the average size of the two bar types over a period that is specified by the user. These averages can be toggled in the indicator settings.
█ SETTINGS
Configuration
• Period: Determines how many bars to use in the calculation of the averages.
• Show Bar Average: Determines whether or not the average for the full bar size is displayed.
• Show Body Average: Determines whether or not the average for the body is displayed.
Alert Sender Library [TradingFinder]Library "AlertSenderLibrary_TradingFinder"
🔵 Introduction
The "Alert Sender Library" is a management and production program for "Alert Messages" that enables the creation of unique messages for any type of signal generated by indicators or strategies.
These messages include the direction of the signal, symbol, time frame, the date and time the condition was triggered, prices related to the signal, and a personal message from you. To make better and more optimal use of this "library", you should carefully study " Key Features" and "How to Use".
🔵 Key Features
Automatic Detection of Appropriate Type :
Using two parameters, "AlertType" and "DetectionType", which you must enter at the beginning into the "AlertSender" function, the type of the alert message is determined.
For example, if you select one of the "DetectionType"s such as "Order Block Signal", "Signal", and "Setup", your alert type will be chosen based on "Long" and "Short". Whether it's "Long" or "Short" depends on the "AlertType" you have set to either "Bullish" or "Bearish".
Automatic Symbol Detection :
Whenever you add an alert for a specific symbol, if you want the name of that symbol to be in your message text, you must manually write the name of the symbol in your message. One of the capabilities of the "Alert Sender" is the automatic detection of the symbol and adding it to the message text.
Automatic Time Frame Detection :
When adding your alert, the "Alert Sender" detects the time frame of the symbol you intend to add the alert for and adds it to the text. This feature is very practical and can prevent traders from making mistakes.
For example, a trader might add alerts for a specific symbol using a specific indicator in different time frames, taking the main signal in the 1-hour time frame and only a confirmation signal in the 15-minute time frame. This feature helps to identify in which time frame the signal is set.
Detection of Date and Time When the Signal is Triggered :
You can have the date and time at the moment the message is sent. This feature has various uses. For example, if you use the Webhook URL feature to send messages to a Telegram channel, there might be issues with alert delivery on your server, causing delays, and you might receive the message when it has lost its validity.
With this feature, you can match the sending time of the message from TradingView with the receipt time in your messenger and detect if there is a delay in message delivery.
Important :
You can also set the Time Zone you wish to receive the date and time based on.
Display of "Key Prices" :
Key prices can vary based on the type of signals. For example, when the "DetectionType" is in "Order Block Signal" mode, the key prices are the "Distal" and "Proximal" prices. Or if the "DetectionType" is in "Setup" mode, the key prices are "Entry", "Stop Loss", and "Take Profit".
Receipt of Personal "Messages" :
You can enter your personal message using "input.string" or "input.text_area" in addition to the messages that are automatically created.
Beautiful and Functional Display of Messages :
The titles of messages sent by "AlertSender" are displayed using related emojis to prevent mistakes due to visual errors, enhancing beauty.
🔵 How to Use
🟣 Familiarity with Function and Parameters
AlertSender(Condition, Alert, AlertName, AlertType, DetectionType, SetupData, Frequency, UTC, MoreInfo, Message, o, h, l, c, Entry, TP, SL, Distal, Proximal)
Parameters:
- Condition (bool)
- Alert (string)
- AlertName (string)
- AlertType (string)
- DetectionType (string)
- SetupData (string)
- Frequency (string)
- UTC (string)
- MoreInfo (string)
- Message (string)
- o (float)
- h (float)
- l (float)
- c (float)
- Entry (float)
- TP (float)
- SL (float)
- Distal (float)
- Proximal (float)
To add "Alert Sender Library", you must first add the following code to your script.
import TFlab/AlertSenderLibrary_TradingFinder/1
🟣 Parameters
"Condition" : This parameter is a Boolean. You need to set it based on the condition that, when met (or fired), you want to receive an alert. The output should be either "true" or "false".
"Alert" : This parameter accepts one of two inputs, "On" or "Off". If set to "On", the alarm is active; if "Off", the alarm is deactivated. This input is useful when you have numerous alerts in an indicator or strategy and need to activate only a few of them. "Alert" is a string parameter.
Alert = input.string('On', 'Alert', , 'If you turn on the Alert, you can receive alerts and notifications after setting the "Alert".', group = 'Alert')
"AlertName" : This is a string parameter where you can enter the name you choose for your alert.
AlertName = input.string('Order Blocks Finder ', 'Alert Name', group = 'Alert')
"AlertType" : The inputs for this parameter are "Bullish" or "Bearish". If the condition selected in the "Condition" parameter is of a bullish bias, you should set this parameter to "Bullish", and if the condition is of a bearish bias, it should be set to "Bearish". "AlertType" is a string parameter.
"DetectionType" : This parameter's predefined inputs include "Order Block Signal", "Signal", "Setup", and "Analysis". You may provide other inputs, but some functionalities, like "Key Price", might be lost. "DetectionType" is a string parameter.
"SetupData" :
If "DetectionType" is set to "Setup", you must specify "SetupData" as either "Basic" or "Full". In "Basic" mode, only the "Entry" price needs to be defined in the function, and "TP" (Take Profit) and "SL" (Stop Loss) can be any number or NA. In "Full" mode, you need to define "Entry", "SL", and "TP". "Setup" is a string parameter.
"Frequency" : This string parameter defines the announcement frequency. Choices include: "All" (activates the alert every time the function is called), "Once Per Bar" (activates the alert only on the first call within the bar), and "Once Per Bar Close" (the alert is activated only by a call at the last script execution of the real-time bar upon closing). The default setting is "Once per Bar".
Frequency = input.string('Once Per Bar', 'Message Frequency', , 'The triggering frequency. Possible values are: All (all function calls trigger the alert), Once Per Bar (the first function call during the bar triggers the alert), Per Bar Close (the function call triggers the alert only when it occurs during the last script iteration of the real-time bar, when it closes). The default is alert.freq_once_per_bar.', group = 'Alert')
"UTC" : With this parameter, you can set the Time Zone for the date and time of the alert's dispatch. "UTC" is a string parameter and can be set as "UTC-4", "UTC+1", "UTC+9", or any other Time Zone.
UTC = input.string('UTC', 'Show Alert time by Time Zone', group = 'Alert')
"MoreInfo" : This parameter can take one of two inputs, "On" or "Off", which are strings. Additional information, including "Time" and "Key Price", is included. If set to "On", this information is received; if "Off", it is not displayed in the sent message.
MoreInfo = input.string('On', 'Display More Info', , group = 'Alert')
"Message" : This parameter captures the user's personal message through an input and displays it at the end of the sent message. It is a string input.
MessageBull = input.text_area('Long Position', 'Long Signal Message', group = 'Alert') MessageBear = input.text_area('Short Position', 'Short Signal Message', group = 'Alert')
"o" (Open Price): A floating-point number representing the opening price of the candle. This input is necessary when the "DetectionType" is set to "Signal". Otherwise, it can be any number or "na".
"h" (High Price): A float variable for the highest price of the candle. Required when "DetectionType" is "Signal"; in other cases, any number or "na" is acceptable.
"l" (Low Price): A float representing the lowest price of the candle. This field must be filled if "DetectionType" is "Signal". If not, it can be any number or "na".
"c" (Close Price): A floating-point variable indicating the closing price of the candle. Needed for "Signal" type detections; otherwise, it can take any value or "na".
"Entry" : A float variable indicating the entry price into a trading setup. This is relevant when "DetectionType" is in "Setup" mode. In other scenarios, it can be any number or "na". It denotes the price at which the trade setup is entered.
"TP" (Take Profit): A float that is necessary when "DetectionType" is "Setup" and "SetupData" is "Full". Otherwise, it can be any number or "na". It signifies the price target for taking profits in a trading setup.
"SL" (Stop Loss): A float required when "DetectionType" is "Setup" and "SetupData" is "Full". It can be any number or "na" in other cases. This value represents the price at which a stop loss is set to limit losses.
"Distal" : A float important for "Order Block Signal" detection. It can be any number or "na" if not in use. This variable indicates the price reaching the distal line of an order block.
"Proximal" : A float needed for "Order Block Signal" detection mode. It can take any value or "na" otherwise. It marks the price reaching the proximal line of an order block.
