Deep Reinforcement Learning for Active High Frequency Trading

Briola, Antonio; Turiel, Jeremy; Marcaccioli, Riccardo; Aste, Tomaso

doi:10.48550/arxiv.2101.07107

Cited by 12 publications

(15 citation statements)

References 29 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In subsequent continuation of this work, we can apply MBO data to various financial applications including market-making or trade execution. Further, the work of Briola et al (2021) applies Reinforcement Learning (RL) algorithms to high-frequency trading, and it would be interesting to test the effectiveness of using MBO data within a RL framework.…”

Section: Appendix a Complete Search Space For Hyperparametersmentioning

confidence: 99%

Deep Learning for Market by Order Data

Zhang¹,

Lim²,

Zohren³

2021

Preprint

View full text Add to dashboard Cite

Market by order (MBO) data -a detailed feed of individual trade instructions for a given stock on an exchange -is arguably one of the most granular sources of microstructure information. While limit order books (LOBs) are implicitly derived from it, MBO data is largely neglected by current academic literature which focuses primarily on LOB modelling. In this paper, we demonstrate the utility of MBO data for forecasting high-frequency price movements, providing an orthogonal source of information to LOB snapshots. We provide the first predictive analysis on MBO data by carefully introducing the data structure and presenting a specific normalisation scheme to consider level information in order books and to allow model training with multiple instruments. Through forecasting experiments using deep neural networks, we show that while MBO-driven and LOB-driven models individually provide similar performance, ensembles of the two can lead to improvements in forecasting accuracy -indicating that MBO data is additive to LOB-based features.

show abstract

Section: Appendix a Complete Search Space For Hyperparametersmentioning

confidence: 99%

Deep Learning for Market by Order Data

Zhang¹,

Lim²,

Zohren³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The main source of complexity comes from the intricate interaction of heterogeneous actors following various strategies designed to impact at different time scales. They are highly stochastic environments with a low signal to noise ratio, dominated by strong non-stationary dynamics and characterized by feedback loops and non-linear effects [2,3]. Despite their complexity, financial systems are governed by rather a stable and partially identified framework of rules [4].…”

Section: Introductionmentioning

confidence: 99%

Dependency structures in cryptocurrency market from high to low frequency

Briola¹,

Aste²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange. We study how the structure of the Minimum Spanning Tree (MST) and the Triangulated Maximally Filtered Graph (TMFG) evolve across time horizons from high (15 seconds) to low (1 day) frequency time resolutions. For each horizon, we test the stability, statistical significance and economic meaningfulness of the networks. Results give a deep insight into the evolutionary process of the time dependent hierarchical organization of the system under analysis. A decrease in correlation between pairs of cryptocurrencies is observed for finer time sampling resolutions. A growing structure emerges for coarser ones, highlighting multiple changes in the hierarchical reference role played by mainstream cryptocurrencies. This effect is studied both in its inter-and intrasector realizations.

show abstract

“…The main source of complexity comes from the intricate interaction of heterogeneous actors following various strategies designed to impact at different time scales. They are highly stochastic environments with a low signal to noise ratio, dominated by strong non-stationary dynamics and characterized by feedback loops and non-linear effects [ 2 , 3 , 4 ]. Despite their complexity, financial systems are governed by a rather stable and partially identified framework of rules [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Dependency Structures in Cryptocurrency Market from High to Low Frequency

Briola

Aste

2022

Entropy

View full text Add to dashboard Cite

We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange. We study how the structure of the Minimum Spanning Tree (MST) and the Triangulated Maximally Filtered Graph (TMFG) evolve from high (15 s) to low (1 day) frequency time resolutions. For each horizon, we test the stability, statistical significance and economic meaningfulness of the networks. Results give a deep insight into the evolutionary process of the time dependent hierarchical organization of the system under analysis. A decrease in correlation between pairs of cryptocurrencies is observed for finer time sampling resolutions. A growing structure emerges for coarser ones, highlighting multiple changes in the hierarchical reference role played by mainstream cryptocurrencies. This effect is studied both in its pairwise realizations and intra-sector ones.

show abstract

Deep Reinforcement Learning for Active High Frequency Trading

Cited by 12 publications

References 29 publications

Deep Learning for Market by Order Data

Deep Learning for Market by Order Data

Dependency structures in cryptocurrency market from high to low frequency

Dependency Structures in Cryptocurrency Market from High to Low Frequency

Contact Info

Product

Resources

About