Accelerating machine learning for trading using programmable switches

Accelerating machine learning for trading using programmable switches

High-frequency trading (HFT) employs cutting-edge hardware for rapid decision-making and order execution but often relies on simpler algorithms that may miss deeper market trends. Conversely, lower-frequency algorithmic trading uses machine learning (ML) for better market predictions but higher late...

Bibliografske podrobnosti
Main Authors:	Hong, X, Zheng, C, Zohren, S, Zilberman, N
Format:	Conference item
Jezik:	English
Izdano:	IOS Press 2024

Podobne knjige/članki

LOBIN: in-network machine learning for limit order books
od: Hong, X, et al.
Izdano: (2023)

Linnet: limit order books within switches
od: Hong, X, et al.
Izdano: (2022)

In-network machine learning using programmable network devices: a survey
od: Zheng, C, et al.
Izdano: (2023)

MTPSA: Multi-tenant programmable switches
od: Stoyanov, R, et al.
Izdano: (2020)

Iisy: hybrid in-network classification using programmable switches
od: Zheng, C, et al.
Izdano: (2024)

INDDoS+: secure DDoS detection mechanism in programmable switches
od: Ding, D, et al.
Izdano: (2024)

In-network machine learning for real-time transaction fraud detection
od: Hong, X, et al.
Izdano: (2024)

JAX-LOB: a GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading
od: Frey, SY, et al.
Izdano: (2023)

Planter: seeding trees within switches
od: Zheng, C, et al.
Izdano: (2021)

Asynchronous Deep Double Dueling Q-learning for trading-signal execution in limit order book markets
od: Peer Nagy, et al.
Izdano: (2023-09-01)

Slow momentum with fast reversion: a trading strategy using deep learning and changepoint detection
od: Wood, K, et al.
Izdano: (2022)

Planter: rapid prototyping of in-network machine learning inference
od: Zheng, C, et al.
Izdano: (2024)

Towards in-network machine learning on programmable network devices
od: Zheng, C
Izdano: (2024)

Machine intelligence accelerated design of conductive MXene aerogels with programmable properties
od: Snehi Shrestha, et al.
Izdano: (2024-06-01)

A deep learning approach to programmable RNA switches
od: Angenent-Mari, Nicolaas M, et al.
Izdano: (2021)

A deep learning approach to programmable RNA switches
od: Nicolaas M. Angenent-Mari, et al.
Izdano: (2020-10-01)

Federated in-network machine learning for privacy-preserving IoT traffic analysis
od: Zang, M, et al.
Izdano: (2024)

Network-accelerated cluster scheduler
od: Stoyanov, R, et al.
Izdano: (2022)

Accelerating the design of lattice structures using machine learning
od: Aldair E. Gongora, et al.
Izdano: (2024-06-01)

MEMe: An accurate maximum entropy method for efficient approximations in large-scale machine learning
od: Granziol, D, et al.
Izdano: (2019)

Machine learning in pair trading
od: Wang, Guanlan
Izdano: (2022)

Machine learning strategies for trading
od: Lim, Wee
Izdano: (2016)

Machine learning for algorithmic trading
od: Kondratieva T, et al.
Izdano: (2020-01-01)

Metacognitive support accelerates computer assisted learning for novice programmer
od: Mohd Rum, Siti Nurulain, et al.
Izdano: (2017)

Machine learning and deep learning methods for exchange-traded fund trading
od: Jeremia, Alexander
Izdano: (2023)

Explainable Machine-Learning Predictions for Peak Ground Acceleration
od: Rui Sun, et al.
Izdano: (2023-04-01)

Optimal portfolio and trading strategy using machine learning
od: Ouazzane, Karim, et al.
Izdano: (2024)

International agricultural trade forecasting using machine learning
od: Munisamy Gopinath, et al.
Izdano: (2021-01-01)

Trading latency for compute in the network
od: Bressana, P, et al.
Izdano: (2020)

Trade Ease With Machine Learning and AWS
od: Kamurthi Ravi Teja, et al.
Izdano: (2023-01-01)

Machine Learning-Enhanced Pairs Trading
od: Eli Hadad, et al.
Izdano: (2024-06-01)

Challenging the Stateless Quo of Programmable Switches
od: Gebara, Nadeen, et al.
Izdano: (2022)

dRMT: Disaggregated Programmable Switching
od: Chole, Sharad, et al.
Izdano: (2021)

Trade security : why a trade recovery programme is vital
od: See, Chak Mun
Izdano: (2008)

Acceleration of Boltzmann Collision Integral Calculation Using Machine Learning
od: Ian Holloway, et al.
Izdano: (2021-06-01)

Mitigating Microbursts by Packet Recirculation in Programmable Switch
od: Ping-Hsien Huang, et al.
Izdano: (2024-01-01)

Stock prediction and trading using low-shot machine learning
od: Tee, Chee Zhang
Izdano: (2020)

Accelerated discovery of eutectic compositionally complex alloys by generative machine learning
od: Z. Q. Chen, et al.
Izdano: (2024-09-01)

Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials
od: Sungwoo Kang, et al.
Izdano: (2022-05-01)

Accelerating Giant-impact Simulations with Machine Learning
od: Caleb Lammers, et al.
Izdano: (2024-01-01)