Woonghyeon's passion project repository

Welcome to the DSLA Residency, the DAO testbed of DSLA Protocol!

Risk matrix vuetify component

This project employs ML algorithms for risk management to accurately predict credit defaults.

Vigilante Vixen has learned that there were many security vulnerabilities from their technical, behavioral, law, and human resources aspects. Despite us not being directly involved in offshore financial services or the legal profession, technology roles have a considerable amount of opportunity to review this case and implement security regulations

creating an ecommerce api using laravel

Excel/Python application of stochastic methods for financial analysis

Backtesting Algo-Trading Strategies, FinTech Analysis & Portfolio Optimization: NVDA, AMD, INTC, MSI vs S&P 500 Benchmark

Non-Emergency Medical Transportation(NEMT) software solution implementation plan is a business need assessment, requirement analysis, resource allocation techniques, IT solution design, IT support services, business continuity plans, and ethical considerations for the new user interface solution.

Volatility Intervention and Trade Adjustment Layer (VITAL)

Cybersecurity Tasks Repository: A collection of tasks and exercises showcasing practical applications in network security and risk management. Includes tools like a Packet Sniffer, scripts for accessing network packets, and a Risk Calculator to assess security risks. Ideal for hands-on learning and skill development in cybersecurity.

Variable Scale Evacuation Model, a tool for the design of risk mitigation strategies

🌎Tidyrisk Terraform stack

Portfolios. Made Better.

Efficient simulation in the CreditMetrics model with Julia

Taxonomy derived from APRA CPG 235 - Managing Data Risk (September 2013)

Portfolio project: Machine learning automation project for a lending company. Automated the calculation of fees that make each new transaction/customer profitable by predicting the expected financial loss based on probability of default, loss given default, and exposure at default risk models.

This project performs a Monte Carlo simulation to estimate the future value of a stock portfolio based on historical data. By calculating mean returns and the covariance matrix, it simulates potential portfolio performance over time, helping investors understand possible outcomes and risks. It includes risk assessment metrics and portfolio optimiz.

Project as part of the course Introduction to risk modeling and management at ETH Zurich, held in spring 2024

Risk Assessment Library