Dynamic Point of Control (POC)

The Dynamic Point of Control (POC) indicator provides traders and analysts with insightful information about price levels, volume distribution, and sentiment within a specified historical range.

Instant Updates: POC recalculates with every new bar, keeping you ahead of the game.
Market Bias: Assess market sentiment through bullish volume share.
Customization: Tailor inputs to match your unique trading strategy.
Chart Presence: See POC and related data graphically on your price chart.

How to Use:
Traders can use the Dynamic POC indicator to identify Point of Control price level, understand volume distribution, and gauge market sentiment. The indicator's visual cues and customizable parameters make it a valuable tool for technical analysis and decision-making.

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Minor bug fixes... (colorLine)

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Minor changes:

Removed smoothing feature.
MA (Moving Average) now shares the same input length with POC (Point of Control).

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Incorporated an indicator description as comments within the code to provide better context and understanding

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New Features:
Added data collection from lower timeframes, enhancing the precision and relevance of the POC calculations across different market conditions.

Functional Enhancements:
Core functionalities, including data collection and POC calculation, have been encapsulated into dedicated functions (collectData and getPoc). This modular approach improves code readability and scalability.

These updates streamline the Dynamic POC Indicator's architecture and expand its analytical capabilities to offer more precise insights with lower timeframe integration.

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Minor change in getPoc function

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getPoc()
Enhanced the logic to ensure that the index does not exceed the size of arrayPoc.

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Changes to getPoc() Function:

Proportion Logic Implementation: Added detailed volume allocation logic for full, partial, and complete slot overlaps.
Enhanced Index Bounds Validation: Improved index bounds validation for consistent calculations.
Reduced the frequency of POC calculations to avoid timeout errors

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Introduced Value Area High (VAH) and Value Area Low (VAL) levels for enhanced market analysis.
Refactored and optimized the main volume profile function for improved computational efficiency.

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Made minor adjustments and optimized the code to improve volume profile calculation speed.