Welcome to my collection of notebooks, code, and tools related to quantitative finance, forecasting, and analytics. This page links out to curated GitHub repositories and hosted notebooks used in research, prototyping, and experimentation.
Primary Repository
GitHub Repo: QuantAnalysisAssets
GitHub Pages Site: williamsryan.github.io/QuantAnalysisAssets
This repository contains: - Interactive Pluto and Jupyter notebooks - Julia and Python source code for modeling and data transformation - Guides and writeups covering statistical methods, forecasting pipelines, and performance evaluation
Featured Notebooks
Pitcher Strikeout Forecasting (MLB)
- File:
k_forecast.jl - Topics: Feature engineering, ridge regression, random forest, validation
- View: Notebook Link
This notebook demonstrates: - Statistical modeling of baseball performance data - Feature engineering techniques for time series forecasting - Model validation and performance evaluation - Practical application of machine learning to sports analytics
Technical Stack
The projects utilize a modern data science stack: - Julia for high-performance numerical computing - Python for data manipulation and visualization - Pluto.jl for interactive notebooks - MLJ.jl and scikit-learn for machine learning - DataFrames.jl and pandas for data handling
Research Areas
Current focus areas include: - Financial time series analysis and volatility modeling - Risk metrics and portfolio optimization - Forecasting methodologies and validation frameworks - Monte Carlo simulations for scenario analysis - Bayesian inference for uncertainty quantification
Coming Soon
- Financial time series toolkit (returns, volatility, risk metrics)
- Monte Carlo simulations for portfolio evaluation
- Bayesian inference models using Turing.jl
- Reproducible forecasting benchmarks
- Interactive dashboards for real-time analysis
Documentation
For full documentation, examples, and interactive notebooks, check out the QuantAnalysisAssets GitHub Pages.
Contributing
The repository welcomes contributions in the form of: - New analytical notebooks - Performance improvements to existing code - Documentation enhancements - Bug reports and feature requests