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husaam-atq/README.md

Hi, I'm Husaam 👋

MSc Data Science & Artificial Intelligence @ Queen Mary University of London
Fully Qualified Chartered Accountant (ICAS)
Interested in Quantitative Finance, Systematic Trading, and Machine Learning

About Me

  • Background in Financial Services (EY) working across banking, asset management, and private equity
  • Experience analysing complex financial datasets and evaluating risk in regulated environments
  • Currently developing quantitative models and trading strategies using Python, statistics, and machine learning
  • Strong interest in alpha generation, market microstructure, and systematic investing

Some Projects of Mine

🔹 Market Making Strategy (QMUL Hackathon)

  • Developed an ML-based market making strategy in a live simulated trading environment
  • Implemented fair value prediction, uncertainty-aware quoting, and inventory-based decision logic
  • Team ranked just outside the top 10 in a multi-round competition
  • Identified position sizing as the key driver of missed PnL in post-event analysis

🔹 Systematic Equity Factor Backtester

  • Built a research framework for testing cross-sectional equity factor strategies
  • Implemented long/short portfolio construction, transaction cost modelling, and turnover analysis
  • Designed pipeline to reflect realistic systematic investing workflows

🔹 Portfolio Risk Modelling (VaR & Monte Carlo)

  • Developed risk models using historical and parametric Value-at-Risk (99% confidence level)
  • Built Monte Carlo simulations using Geometric Brownian Motion
  • Analysed tail risk, return distributions, and portfolio volatility

Skills

Programming: Python (pandas, NumPy, matplotlib, scikit-learn, SciPy)
Analytical Methods: Statistical modelling, regression, hypothesis testing, backtesting
Finance: Portfolio analytics, risk modelling, valuation analysis
Tools: Jupyter, Google Colab, Excel, Git/GitHub

My Current Focus

  • Developing systematic trading strategies
  • IBM AI Agent Racing League
  • Exploring factor models and portfolio construction techniques
  • Applying machine learning to financial time series

📫 Contact

Email: husaam.ateeq@gmail.com
LinkedIn: https://www.linkedin.com/in/husaam-atq

Pinned Loading

  1. qmml-market-making-hackathon qmml-market-making-hackathon Public

    ML-based market making strategy developed for the QMUL Market Making Hackathon, including fair value modelling, uncertainty-aware quoting, and live decision logic.

  2. value-at-risk-analysis value-at-risk-analysis Public

    Portfolio risk estimation using historical and parametric Value-at-Risk

    Jupyter Notebook

  3. monte-carlo-simulation monte-carlo-simulation Public

    Monte Carlo stock price simulation using random drift and volatility.

    Jupyter Notebook

  4. systematic-equity-factor-backtester systematic-equity-factor-backtester Public

    Systematic equity factor backtesting framework with portfolio construction, transaction cost modelling, and performance analytics.

    Python

  5. systematic-portfolio-construction systematic-portfolio-construction Public

    Multi-factor portfolio construction engine with signal blending, volatility targeting, and systematic backtesting.

    Python

  6. BM25F_Search_Engine BM25F_Search_Engine Public

    Python