Portfolio

About Me

(Work in Progress)

For more information, please scroll through the page — my CV and academic recommendation letters are available at the bottom.

Technical Skills

• Please refer to ‘On-going certifications’ section for technical skills I am currently learning/developing.

Languages / Software

• Python, STATA, EViews, LaTeX, CLI, Git, GitHub, Kpler, Refinitiv Eikon, S&P Global (Platts, Capital IQ), Power BI, Power Automate, Excel, Google Cloud (Basic), Sierra Chart, MetaTrader5

Libraries / Frameworks

• pandas, NumPy, matplotlib, seaborn, scipy, statsmodels, scikit-learn, streamlit, plotly

Conceptual / Modelling

• Data Pre-processing, EDA, Feature Engineering, Probability Distributions (Normal, Binomial, Poisson – PMF/PDF/CDF), Statistical Validation (t-tests 1- & 2-sided, chi-square, ANOVA, Tukey HSD, binomial tests), Time Series Modeling (ARIMA, VAR, VECM, GARCH), Econometric Causality (Cointegration, Granger causality, Impulse Response, FEVD), Machine Learning (Regression (Linear, Logistic), K-Nearest Neighbors (Classifier, Regressor))

Publications (Working Papers)

“The Causality between Oil price, Financial Market uncertainty and Economic Policy Uncertainty in the United States”

Co-Authored by Saimanish Prabhakar and Dr. Athanasia Kalaitzi

2025-2026

Currently Under Review

Completed Projects

Click the project title to visit the interactive dashboard

Options Strategy Payoff Calculator

• Developed a robust web application that allows users to analyze a wide range of options trading strategies, including Long Call, Short Call, Long Put, Short Put, Bull Call Spread, Bear Put Spread, and more.
• Empowers users to easily input key parameters such as strike price, premium, and expiration prices. The application generates detailed net-payoff tables and dynamic graphs, aiding seasoned analysts and students alike in making informed decisions.
• Utilised popular libraries such as numpy, pandas, matplotlib, and streamlit to create a user-friendly interface. This enhances strategy interpretation, providing valuable insights into payoffs and break-even points for various options strategies.

Options Pricing and Greeks Analysis

• Developed an options pricing tool using Black-Scholes and Monte Carlo methods, comparing options price sensitivity to volatility, time to expiration, and strike price, with visualisations of Monte Carlo price paths and distributions.
• Implemented Greek analysis for both methods, and created multi-dimensional sensitivity plots for deeper insights into option pricing dynamics.

Pairs Trading Simulator

• Built a rudimentary pairs trading sim for an aluminium and lead asset pair with customisable parameters like z-score threshold, lookback period, lot sizes, stop loss, and take profit, enabling flexible strategy testing.

On-going Projects

(Work in Progress)

On-going Certifications

Career Path (50-150 hours -> with exams)

• Machine Learning / AI Engineer
• Data Scientist: Machine Learning Specialist
• Data Scientist: NLP Specialist
• Data Scientist: Inference Specialist
• Data Scientist: Analytics
• Data Engineer
• Fullstack Engineer

Skill Path (>20 hours)

• Analyze Data with SQL
• Analyze Data with R
• Feature Engineering
• Build a Machine Learning Model
• Intermediate Machine Learning
• Build Deep Learning Models with TensorFlow

Technical Courses (1-20 hours)

• Learn SQL
• Learn MongoDB
• kdb+/q Developer – Level 1
• kdb+/q Developer – Level 2
• kdb+/q Developer – Level 3
• Learn R
• Generative AI Models: Generating Data Using Generative Adversarial Networks (GANs)
• Intro to PyTorch and Neural Networks
• Creating AI Applications using Retrieval-Augmented Generation (RAG)
• Generative AI Models: Getting Started with Autoencoders
• Generative AI Models: Generating Data Using Variational Autoencoders
• Learn Image Classification with PyTorch

Algorithm Trading Courses (1-20 hours)

• Introduction to Machine Learning for Trading
• Trading with Machine Learning: Classification and SVM
• Options Trading Strategies in Python: Advanced
• Trading with Machine Learning: Regression
• Python for Machine Learning in Finance
• Mean Reversion Strategies in Python
• Backtesting Trading Strategies
• Event Driven Trading Strategies
• Financial Time Series Analysis for Trading
• Futures: Concepts & Strategies
• Systematic Options Trading
• Options Volatility Trading: Concepts and Strategies
• Data and Feature Engineering for Trading
• Decision Trees in Trading
• Natural Language Processing in Trading
• Unsupervised Learning in Trading
• Neural Networks in Trading
• Deep Reinforcement Learning in Trading
• Machine Learning for Options Trading
• Quantitative Portfolio Management
• Position sizing in Trading
• Factor Investing: Concepts and Strategies
• Portfolio Management using Machine Learning: Hierarchal Risk Parity
• AI for Portfolio Management: LSTM Networks
• News Sentiment Trading Strategies
• Momentum Trading Strategies
• Trading Alphas: Mining, Optimisation, and System Design
• Trading in Milliseconds: MFT Strategies and Setup

Completed Certifications

Skill Path (>20 hours)

• Data and Programming Foundations for AI
• Fundamental Math for Data Science
• Master Statistics with Python

Technical Courses (1-20 hours)

• Learn Python 3
• Learn the Command Line
• Learn Git and GitHub
• Level 4 Diploma in Investment Bank Sales and Trading
• Finance Accelerator Simulation Participant in Partnership with Morgan Stanley
• Getting Started with Refinitiv Eikon

Algorithm Trading Courses (1-20 hours)

• Python for Trading Basic
• Python for Trading
• Getting Started with Algorithmic Trading
• Statistical Arbitrage Trading
• Getting Market Data: Stocks, Crypto, News & Fundamentals
• Quantitative Trading Strategies and Models
• Options Trading Strategies in Python: Basic
• Options Trading Strategies in Python: Intermediate
• Trading using Options Sentiment Indicators
• Value Strategy in Forex
• Forex Trading using Python: Basic

Finance/Industry Experience Courses (1-10 hours)

• JP Morgan: Investment Banking Virtual Experience Program Participant
• JPMorgan Chase: Commercial Banking Virtual Experience Program Participant
• ACCA: Sustainability in Banking Virtual Experience Program Participant
• JPMorgan Chase: Markets (Sales & Trading) Virtual Experience Program Participant
• Fidelity International: Investment Management Virtual Experience Program Participant
• JP Morgan: Global Private Bank Virtual Experience Program
• Goldman Sachs: Excel Skills for Business Virtual Experience Program Participant
• JPMorgan Chase: Asset Management Virtual Experience Program Participant
• HSBC: Global Banking & Markets Virtual Experience
• Bank of America: Investment Banking Virtual Experience Program Participant
• Code and Standards Certifications on the CFA Institute Code of Ethics (Total of 22)

CV

Academic Recommendation Letters