Inspired by many specialized ETFs. This project offers optimization and modeling tools for investment strategies. Leveraging Efficient Frontier modeling, Fama French factor models, Monte Carlo simulation, and 15 risk metrics (Sharpe ratio, VaR) w/ python libraries like pandas, yfinance, and PyPortfolioOpt.
A glimpse of the system that was used @ Futures First. This system utilized data analytics, quantitative analysis, and account management. It involved keeping an eagle eye on fixed income products w/ live Excel dashboards. Being prepared for a range of outcomes w/ risk scenario analysis. Changing the game w/ performance analysis, financial metrics, and stats. Applied ML and probability models to identify and predict market states, optimizing portfolio and risk assessment. Providing defense to account management w/ a resilient capital allocation system.
Data Analytics | Risk Management | Fixed Income (Interest rate, bond) | Futures Trading System | Markov Model
Pairs Watch web app is a quantitative finance tool that helps users analyze potential pairs trading opportunities. In this project we try to check linear relationship between Nike (NKE) and Adidas (ADDY). Then we regress one time series on the other to get the cointegration vector, and perform ADF test on the residuals to check for stationarity. If stationary, it means the stocks are cointegrated, and the residuals represent a mean-reverting spread. Now we can use the residuals of this relationship to generate signals for mean reversion.
Checking Assumptions of OLS regression for Fama French 3-Factor Model. Steps involved in Model validation and Hyperparameter tuning in Random Forest Regression.
Exploratory Data Analysis (EDA) w/ SQL, Tableau, Python