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.
TWAP excecution Algorithm
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.
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
Reproducible benchmark for intraday order execution on real crypto LOB. Classical vs deep RL vs LLM. Result: no method beats a simple classical schedule (BTC + ETH).
Mini BestEx: equity tick/minute ingestion -> partitioned Postgres -> TWAP/VWAP execution simulation -> Transaction Cost Analysis dashboard.
Cash-and-carry basis trading on Binance coin-margined futures: opportunity scanner, TWAP execution and 2021 historical replay. Built with Nayt Technologies.
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.
Execution research platform: Maslov LOB simulator, HMM/KMeans regime detection, VWAP/POV scheduling, and a paired-significance harness with shuffled-label negative controls. Finds no measurable benefit from regime conditioning on this data.
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