[ENH] Add CVXCLA backend integration for faster performance and interactive Plotly support

[ayushraj09]

Summary

Fixes NumPy and Pandas compatibility issues and adds optional interactive Plotly support for efficient frontier plotting. Fully backward compatible.

Fixes

• NumPy ≥1.26 compatibility: Resolved scalar conversion TypeError in CLA (_compute_w, _compute_lambda) using safe .item() extraction.
• Pandas compatibility: Replaced deprecated squeeze=True in pd.read_csv() with .squeeze().

Enhancements

• Added interactive=True option to plot_efficient_frontier() for Plotly-based interactive plots.
• Matplotlib remains default (no breaking changes).

Dependencies

• Added plotly
• Added packaging
• Ignored uv.lock

Testing

• All tests pass (CLA, plotting, efficient frontier).

[ayushraj09]

@tschm @fkiraly
Can you review this PR ?

[tschm]

Honestly, the cla algorithm in this package is extremely slow. You better replace it with a modern implementation, as given in https://github.com/cvxgrp/cvxcla

[tschm]

For a benchmark comparison, see https://github.com/tschm/eqd/blob/main/PresentationEQD.pdf

[ayushraj09]

Can you please check the url for benchmark comparison?

[tschm]

It's a link into a pdf document. A presentation I gave 2 years ago. Slide 5 or so, contains the benchmark

[ayushraj09]

Summary

This PR integrates cvxcla library as an optional high-performance backend for the Critical Line Algorithm (CLA) for portfolio optimization while maintaining full backward compatibility. Additionally, it enhances plotting capabilities with consistent showfig parameter handling for both matplotlib and Plotly plots.

Performance Enhancement

CVXCLA Backend Integration

• Added optional use_cvxcla=True parameter to CLA constructor
• Faster execution for max_sharpe(), min_volatility(), and efficient_frontier() operations
• Automatic fallback to original implementation when cvxcla is not available
• Zero breaking changes - all existing code continues to work without modification

Usage

# Before (original implementation)
cla = CLA(expected_returns, cov_matrix)

# After (high-performance backend) 
cla = CLA(expected_returns, cov_matrix, use_cvxcla=True)

Technical Implementation

Backend Architecture

• Implemented backend abstraction layer with seamless API compatibility
• Added dynamic covariance matrix updates with automatic engine recreation
• Preserved all existing method signatures and return formats
• Comprehensive error handling with graceful degradation

Enhanced Plotting System

• Fixed showfig parameter handling for interactive Plotly plots
• Enhanced plot_efficient_frontier() with consistent behavior across matplotlib/Plotly
• Interactive plots now properly display when showfig=True

Testing & Validation

Comprehensive Test Coverage

• Added tests/test_cvxcla_backend.py with 5 focused backend parity tests
• All existing tests pass (312+ tests) ensuring no regressions

Performance Verification

# Both backends produce identical results
Original:  Expected return: 29.9%, Volatility: 21.8%, Sharpe: 1.38
CVXCLA:    Expected return: 29.9%, Volatility: 21.8%, Sharpe: 1.38

Dependencies & Compatibility

New Dependencies

• cvxcla>=1.5.1 - High-performance CLA implementation (optional)
• plotly - Interactive plotting capabilities
• packaging - Dependency management utilities

Compatibility Fixes

• Updated pandas frequency notation: "M" → "ME" for modern pandas compatibility in test files
• Ensured Python 3.12+ support across all components

Files Changed

Core Implementation

• pypfopt/cla.py - Backend integration and dynamic engine management
• pypfopt/plotting.py - Enhanced showfig parameter handling for interactive plots
• pyproject.toml - Updated dependencies (cvxcla, plotly, packaging)

[ayushraj09]

@tschm Thanks for the benchmark comparison which I found in your github: https://github.com/tschm/eqd_markowitz/blob/main/PresentationEQDweb.pdf (corrected url i was asking for :-|)

Please review the current PR with changes that add cvxcla integration.

[tschm]

Interesting that you keep both implementations of the cla algorithm in the package. I would remove the previous implementation and point to the cvxvla repo where both implementations are present. Less code is better than more code. Glad you kept the original API so cvxcla is an easy replacement

[ayushraj09]

@tschm Is any there anything else that needs to be done for merging this PR?

[tschm]

@ayushraj09 Many thanks for those contributions. I have no write access to the repo. I would recommend imho to replace the existing cla implementation rather than accumulating code