High-frequency trading in a limit order book
High-frequency trading in a limit order book MARCO AVELLANEDA and SASHA STOIKOV* Mathematics, New York University, 251 Mercer Street, New York, NY 10012, USA (Received 24 April 2006; in final form 3 April 2007)
Code (1)
Tasks
FormVocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets
We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders. Based on the ABIDES limit order book sim…
Deep Reinforcement LearningManagementOpenAI GymQ-Learning+3Empirical analysis in limit order book modeling for Nikkei 225 Stocks with Cox-type intensities
In this paper, we build on the analysis of Muni Toke and Yoshida (2020) and conduct several empirical studies using high-frequency financial data. Muni Toke and Yoshida (2020) showed the consistency and asymptotic behavi…
Model SelectionA Markov model of a limit order book: thresholds, recurrence, and trading strategies
We analyze a tractable model of a limit order book on short time scales, where the dynamics are driven by stochastic fluctuations between supply and demand. We establish the existence of a limiting distribution for the h…
Maximizing Battery Storage Profits via High-Frequency Intraday Trading
Maximizing revenue for grid-scale battery energy storage systems in continuous intraday electricity markets requires strategies that are able to seize trading opportunities as soon as new information arrives. This paper …
Deep Learning Models Meet Financial Data Modalities
Algorithmic trading relies on extracting meaningful signals from diverse financial data sources, including candlestick charts, order statistics on put and canceled orders, traded volume data, limit order books, and news …
Algorithmic TradingDeep LearningPortfolio Optimization