Application of reinforcement learning to the development of execution algorithms in the field of finance. These algorithms break CARE orders into CHILD orders and exectute them in the market.
Hands-on quantitative trading lab: implement classic papers in Python with Jupyter notebooks and complete documentation
Repository for CFA AI Investment Challenge competition, by Team 18 (QuantFin Exeter)
TWAP vs VWAP execution cost analysis on historical volume profiles
Research framework for optimal high-frequency market making with Avellaneda-Stoikov quoting, WRDS TAQ replay backtesting, queue-aware fills, volatility-adaptive spreads, and robust execution/P&L analysis.
Upgraded the intraday quant pipeline to institutional standards by implementing Almgren-Chriss slippage, Platt-calibrated ML ensembles, pre-market NLP, TWAP execution chunking, and automated real-time risk controls.
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