35 citations · 225 across the 65 of their papers we have counts for
5 papers · 1 filter
Learning to simulate realistic limit order book markets from data as a World Agent
Andrea Coletta, Aymeric Moulin, Svitlana Vyetrenko +1
Multi-agent market simulators usually require careful calibration to emulate real markets, which includes the number and the type of agents. Poorly calibrated simulators can lead t…
The Importance of Low Latency to Order Book Imbalance Trading Strategies
David Byrd, Sruthi Palaparthi, Maria Hybinette +1
There is a pervasive assumption that low latency access to an exchange is a key factor in the profitability of many high-frequency trading strategies. This belief is evidenced by t…
Get Real: Realism Metrics for Robust Limit Order Book Market Simulations
Svitlana Vyetrenko, David Byrd, Nick Petosa +4
Machine learning (especially reinforcement learning) methods for trading are increasingly reliant on simulation for agent training and testing. Furthermore, simulation is important…
Intra-day Equity Price Prediction using Deep Learning as a Measure of Market Efficiency
David Byrd, Tucker Hybinette Balch
In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices. In this paper we show tha…
How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?
Tucker Hybinette Balch, Mahmoud Mahfouz, Joshua Lockhart +2
We show how a multi-agent simulator can support two important but distinct methods for assessing a trading strategy: Market Replay and Interactive Agent-Based Simulation (IABS). Ou…