Portfolio

About Me

My work sits at the intersection of financial systems engineering, applied econometrics, and discretionary trading. On the systems side, I design and build proprietary software infrastructure in C++ (ACSIL) and Python — including a multi-component execution and risk-control architecture that bridges two trading platforms, enforces compliance rules across sessions and restarts, and provides real-time pre-trade validation. This work is informed by active discretionary trading in US Index Futures using order-flow and market-structure analysis, where the infrastructure I build is also what I trade through.

On the research side, I have co-authored empirical work on the causal dynamics between oil prices and macroeconomic uncertainty measures, using advanced econometric frameworks including ARDL bounds testing, VAR-based Granger causality, and long-run augmented VAR. The paper has been accepted for publication in the International Journal of Energy Economics and Policy (IJEEP) and was presented as a Strong Accept at the ERPBSS 2026 international conference proceeding at Middlesex University in Dubai.

Prior to this, I worked as a Crude Oil Market Research Analyst at Vici Energy in Dubai, covering crude and product flows across the Middle East, China, and Latin America — producing pricing reports, sanctions monitoring briefs, and benchmarking analysis for senior management.

My CV, completed projects, academic recommendation letters, and full certifications list are available below.

Technical Skills

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

Languages & Development Tools

• Python, C++ (ACSIL), Git, GitHub, CLI, LaTeX, STATA, EViews, Excel, Power BI

Platforms & Software

• Sierra Chart (ACSIL study development), MetaTrader 5 (MT5), Refinitiv Eikon (LSEG), S&P Global (Platts, Capital IQ), Kpler

Libraries & Frameworks

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

Econometrics & Quantitative Modelling

• Time Series Modelling (VAR, VECM, ARDL, ARIMA, GARCH), Cointegration & Bounds Testing (ARDL bounds test)
• Causality Analysis (Toda-Yamamoto, VAR-based Granger causality, Impulse Response, FEVD), Unit Root Testing (ADF, Phillips-Perron, KPSS, Zivot-Andrews structural break), Stability Diagnostics (CUSUM) - Statistical Validation (t-tests, chi-square, ANOVA, Tukey HSD), Probability Distributions (Normal, Binomial, Poisson), Data Pre-processing, EDA, Feature Engineering

Machine Learning

• Supervised: Linear & Logistic Regression, K-Nearest Neighbours, SVM, Decision Trees, Random Forest
• Unsupervised: K-Means Clustering
• Applied: ML for Trading (Classification, Regression, Mean Reversion, Event-Driven Strategies)

Trading & Market Structure

• Auction Market Theory (AMT), Market Profile / TPO Composites, Volume Profile (HVN, LVN, POC, Value Area)
• Order Flow Analysis (Cumulative Delta, Footprint Charts, DOM), VWAP
• Systems Design for Prop-Firm Compliance (risk enforcement, execution gating, state machines)

Publications

Title: The Causality between Oil Price, Policy & Financial Markets Uncertainty in the United States

Authors: Saimanish Prabhakar & Dr. Athanasia Kalaitzi

Journal: International Journal of Energy Economics and Policy (IJEEP) :- SJR: Q2, ABDC: B

Status: Accepted for publication (2025–2026)

• Examines the causal dynamics between WTI oil prices and uncertainty measures — economic policy uncertainty (EPU) and financial market uncertainty (VIX) — across supply, demand, inventory, and exchange rate channels using ARDL bounds testing, Toda-Yamamoto causality, and a full unit root battery (ADF, PP, KPSS, Zivot-Andrews) over a monthly sample spanning February 1990 to September 2024.
• Identifies recursive transmission loops where sustained demand-side shocks induce long-run supply stress, impacting oil prices via economic policy uncertainty as both a conduit and recipient of market dynamics — with implications for hedging strategy and policy design.
• Conference Presentation: Presented as ‘Strong Accept’ at the Eighth International Conference on Emerging Research Paradigms in Business and Social Sciences (ERPBSS 2026) under the ‘Geopolitics, Trade, and Economics’ track.

Completed Projects

Click the project title to visit the interactive dashboard where available.

Proprietary Trading Risk & Execution Engines

Technologies: C++ (ACSIL), Python (Streamlit)

Jun 2025 – Present

• Engineered a C++-based execution engine that translates analysis decisions into compliant order execution across two platforms, enforcing position-aware order semantics, dynamic unit sizing, and a state-gated arm-fire workflow — operating as a stateless enforcer that publishes trade events to disk and defers all account-level risk decisions to an external authority.
• Built a Python risk dashboard that automates cross-platform exposure translation, validates trades against a multi-tiered drawdown defence system, and writes sizing outputs consumed by the execution engine as an IPC bridge; paired with a standalone governor daemon that reconstructs account risk state from the execution engine’s trade-event stream on every cycle, computes cooldown and hard-lock decisions, and publishes verdicts atomically to disk — creating a crash-resilient, restart-safe enforcement layer that neither the execution engine nor the dashboard can bypass.

Options Pricing and Greeks Analysis

Libraries: numpy, pandas, matplotlib, streamlit, plotly, scipy

Jan – Feb 2025

• 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.

Options Strategy Payoff Calculator

Libraries: numpy, pandas, matplotlib, streamlit

Apr – May 2024

• Engineered an interactive options strategy profitability calculator enabling analysis of strategies including Strap, Bull Call Spread, Long Butterfly, and more, with dynamic visualisation of net-payoff tables and break-even points for risk-reward assessment.

Books

Books read in 2026 so far — the intellectual neighbourhoods I tend to wander through when not building systems or running models.

• Mind Over Markets — James Dalton, Robert Dalton, Eric Jones
• Markets and Momentum — James Dalton, Eric Jones, Robert Dalton
• Thinking in Bets — Annie Duke
• Fooled by Randomness — Nassim Nicholas Taleb
• Skin in the Game — Nassim Nicholas Taleb
• Alchemy — Rory Sutherland
• Meditations - Marcus Aurelius (Gregory Hays translation)

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)

• Options Trading Strategies in Python: Advanced
• 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)

• Introduction to Machine Learning for Trading
• Trading with Machine Learning: Classification and SVM
• Trading with Machine Learning: Regression
• Python for Machine Learning in Finance
• 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