Dominic Ktori

Dominic Ktori London, UK | - |-| linkedin Education Physics: Integrated Masters with Honours Sep 2017- Jun 2022 University of Hull • Hull Grade: First Class degree with Honours: 83%. Awarded: MPhys Finalist of The Year, for “excellent performance.” Experience Quantitative Researcher Aug 2024 - Present Quant Trading Consultancy London • Designed and implemented MFT arbitrage algorithm strategies for Crypto Spot & Futures. Backtested on 8 years of historical data, incorporating latency, slippage, and transaction fees. • Developed smart contracts enhancing security on the Ethereum blockchain for DeFi trading strategies. • Built robust SQL data pipelines to feed algorithmic trading models. • Developed Tableau client-facing dashboards and presentations. Data Analyst May 2023 - Aug 2024 Malaberg • London Successfully traded over £1.4 million in revenue at a 49% ROI. • Developed and executed trading strategies leveraging game theory principles, such as exploiting competitor downtime, increasing weekly ROI by up to 50%. • Performed lasso linear regression on internal metrics to optimize strategies, reducing costs by up to 30%. • Utilized Power BI and Excel to analyze datasets, delivering audience insights and financial reports. AI and Algorithms Research Internship May 2023 - Aug 2023 University of Hull Hull • Successfully created a fault isolation system for commercial buildings using an LSTM neural network. • Utilised Pandas, NumPy for data wrangling and applied principal component analysis for dimensionality reduction. Deployed models on Google Cloud for scalable processing and evaluation. • Projects & Research Jane Street Market Data Forecasting Kaggle Competition Nov 2024 - Jan 2025 • Ensembled 7 LightGBM neural networks to forecast key features from Jane Street’s anonymized market data. • Designed and implemented an autoencoder to reduce dataset dimensionality, enhancing model performance by 60%. • Utilised Polars, Panda and Numpy for data wraggling and Optuna for automated fine-tuning of LightGBM hyperparameters. Statistical Arbitrage: Neural Network Cointegration Strategy • Developed an LSTM-driven cointegration statistical arbitrage strategy in Python, utilizing TensorFlow and Keras. • Backtested on 5 years of historical market data, achieving an annualized return of 17% & Sharpe ratio of 2.4. • Optimized model by fine-tuning features, reducing false signals by 13% • Implemented risk management protocols, including stop-loss mechanisms, limiting maximum drawdown to 6%. Machine Learning Exoplanet Research Project • Research project to predict exoplanet properties, using Bayesian model selection across linear, polynomial, and exponential regression: Achieved 90.6% predictive accuracy. Skills Summary: : Python (NumPy, Pandas, Matplotlib, TensorFlow, Keras), Jupyter, SQL, Power BI, Tableau, Excel, Machine learning: Neural Networks, Supervised/Unsupervised Learning, Regression Analysis, Git, Github, LaTeX.