v1.0.1

What’s Changed

New Features

• Customisation options: Base currency selection, regional factor datasets, and dark mode support
• Expanded Monte Carlo controls: Configurable starting wealth, withdrawal rate, target wealth, and simulation horizon
• Inflation-adjusted withdrawals for real (not nominal) spending analysis

Improvements

• Synthetic return diagnostics for validating generated data
• UI refinements: Improved sliders, clearer explanatory text, and layout scaling fixes
• Model portfolios: Included prebuilt portfolio templates

Full Changelog: core-functionality-v1.0.0...core-functionality-v1.0.1

v1.0.0

This release marks the first stable version of Quant Lab Alpha, a research-oriented Python toolkit for factor-based portfolio analysis, risk assessment, and long-horizon outcome simulation.
The core framework is complete and architecturally stable, providing an end-to-end workflow from data ingestion and factor regression through portfolio optimization, risk reporting, and Monte Carlo simulation.

Included Features

• Fama–French Five-Factor (FF5) regressions at asset and portfolio level
• Rolling factor exposure analysis over configurable windows
• Mean–variance (Markowitz) portfolio optimization with Ledoit–Wolf covariance shrinkage
• Portfolio- and factor-level risk reporting (drawdowns, VaR, CVaR)
• Correlation matrix visualization
• Monte Carlo retirement simulations using block bootstrap and FF5-fitted synthetic returns
• Stress testing via return shifts and volatility scaling
• Multiple withdrawal strategies (fixed, variable, guardrails, bucket)
• FX normalization for cross-currency portfolios
• Interactive Tkinter GUI for portfolio construction and analysis

Design Goals:
Quant Lab Alpha is intentionally focused on interpretability, modularity, and theoretical clarity.
Realism-enhancing features such as rebalancing, inflation adjustment, and leverage constraints are planned as opt-in extensions, not hardwired assumptions. The modelling is centered on USD as the base currency to align with the academic research factors.

Intended Use:
This project is intended for educational, research, and exploratory analysis.
It is not investment software and makes no claims of real-world performance.
Any decisions made based on this toolkit are the sole responsibility of the user.

Roadmap:
Future releases will introduce optional realism layers, enhanced stress testing, and extended data support without altering the core analytical engine.