Optimize Lorentzian Classification
Optimizes the Lorentzian Classification Pine Script indicator for performance and clarity.
Optimize the provided TradingView Pine Script 'Machine Learning: Lorentzian Classification' indicator for improved performance and clarity without altering its core functionality or output. Analyze the script linked at https://www.tradingview.com/script/WhBzgfDu-Machine-Learning-Lorentzian-Classification/ along with its usage example at https://www.tradingview.com/chart/94fQVPwT/ and the Lorentzian classification documentation at https://ai-edge.io/docs/category/lorentzian-classification.
Carefully review the script's structure, organization, data management, and computational efficiency. Provide suggestions or modifications that maintain the algorithm's logic, especially the Approximate Nearest Neighbors (ANN) algorithm using Lorentzian Distance, the kernel regression filters, and signal generation logic. Avoid removing any essential features, but target improvement in areas such as:
- Reducing computational overhead, especially in loops and data structure usage.
- Enhancing readability through clearer naming or structured segmentation.
- Improving resource utilization, including array management and variable scopes.
- Preserving user inputs and configuration options.
Explain any optimization steps with reasoning before implementation. Engage in stepwise reasoning to identify bottlenecks or redundant operations. When suggesting changes, provide code snippets referencing affected sections. Confirm that alert conditions, plotting, and backtesting functionality remain intact and effective.
# Steps
1. Analyze array operations and suggest memory or performance improvements.
2. Examine loops, particularly the Approximate Nearest Neighbors search, for computational efficiency gains.
3. Review feature calculations and consider caching or simplified computations.
4. Assess filtering and conditional logic for redundancy or clarity.
5. Propose improved or clarified variable naming conventions if applicable.
6. Verify plot and alert calls maintain their naming and parameters.
7. Confirm backtesting and trade statistics computations are consistent and optimized.
8. Summarize all changes with clear explanations.
# Output Format
Produce a detailed optimization report followed by the optimized Pine Script code. The report should detail:
- Identification of bottlenecks or inefficiencies.
- Step-by-step reasoning for each optimization.
- Before-and-after code excerpts where helpful.
Then output the full optimized script preserving comments and documentation but implementing the improvements. Format the script to maintain readability, consistent indentation, and style.
# Notes
- The solution should hold to the original script's functionality, no new features are to be added.
- Optimization should not break alert or plotting mechanics.
- Preserve all user input structures and default settings.
- Do not remove explanatory comments; condense only if redundant.
# References
- Original script: https://www.tradingview.com/script/WhBzgfDu-Machine-Learning-Lorentzian-Classification/
- Example chart: https://www.tradingview.com/chart/94fQVPwT/
- Documentation: https://ai-edge.io/docs/category/lorentzian-classification
Your response should begin reasoning about the script's organization and point out optimization targets before rewriting the code textually in full without additional commentary.
Created: 7/9/2025
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