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-grade limit order book and market microstructure laboratory for simulation, historical L2 validation, execution algorithms, reinforcement learning, and quantitative research.
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.
Repository for CFA AI Investment Challenge competition, by Team 18 (QuantFin Exeter)
Smart order router for crypto exchanges: best price across Binance, Bybit, OKX and HTX after fees, seven pre-trade risk checks, price-protected IOC child orders, execution algorithms, a RAFT-replicated kill switch, research and a copilot. Tested with real money. UIT CS14 final-year project.
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
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.
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.
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.
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).
Deterministic execution simulator for trading algorithms (TWAP/VWAP/POV/IS/adaptive) with exact P&L cost decomposition. OCaml + Bonsai.
Post-trade transaction cost analysis on a simulator that records the counterfactual price path, so every TCA estimate can be checked against the truth.
Mini BestEx: equity tick/minute ingestion -> partitioned Postgres -> TWAP/VWAP execution simulation -> Transaction Cost Analysis dashboard.
Quantitative market microstructure laboratory modeling limit order book hydrodynamics, Kyle-Obizhaeva transient price impact, and high-frequency risk surfaces under multidimensional Hawkes excitation.
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.
Cash-and-carry basis trading on Binance coin-margined futures: opportunity scanner, TWAP execution and 2021 historical replay. Built with Nayt Technologies.
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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