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.
Volume-Weighted Average Price (VWAP) benchmark engine calculating cumulative execution quality and institutional slippage metrics.
Volume-Weighted Average Price (VWAP) benchmark engine calculating cumulative execution quality and institutional slippage metrics.
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.
Research-grade limit order book and market microstructure laboratory for simulation, historical L2 validation, execution algorithms, reinforcement learning, and quantitative research.
Repository for CFA AI Investment Challenge competition, by Team 18 (QuantFin Exeter)
Hands-on quantitative trading lab: implement classic papers in Python with Jupyter notebooks and complete documentation
TWAP vs VWAP execution cost analysis on historical volume profiles
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.
Event-driven market microstructure and execution research platform: C++20 limit-order-book core, Python research layer, and a TCA stack that refuses to report a number it cannot justify.
C++17 regime-switching execution and risk framework: TSRV and MedRV volatility, Lee-Mykland jump tests, a Gaussian HMM regime detector, HAR-RV-J forecasts, and a Hawkes-process circuit breaker, with an event-driven backtester.
Deterministic execution simulator for trading algorithms (TWAP/VWAP/POV/IS/adaptive) with exact P&L cost decomposition. OCaml + Bonsai.
Mini BestEx: equity tick/minute ingestion -> partitioned Postgres -> TWAP/VWAP execution simulation -> Transaction Cost Analysis dashboard.
Causal regime-aware optimal execution research with reproducible simulations, walk-forward prediction scoring, and a paper-only Alpaca market-data bridge with fail-closed risk controls.
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).
C++20 event-driven platform that records and reconstructs Kalshi L2 order books, measures short-horizon price formation, and compares execution strategies under explicit latency, fee, partial-fill, and queue-position assumptions.
Cash-and-carry basis trading on Binance coin-margined futures: opportunity scanner, TWAP execution and 2021 historical replay. Built with Nayt Technologies.
Deep RL agent for optimal trade execution in crypto markets — PPO-LSTM and RL² meta-learning, evaluated against TWAP and VWAP on Bybit L2 data
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