Deep Reinforcement Learning in High Frequency Trading

09/05/2018
by   Prakhar Ganesh, et al.
0

The ability to give a precise and fast prediction for the price movement of stocks is the key to profitability in High Frequency Trading. The main objective of this paper is to propose a novel way of modeling the high frequency trading problem using Deep Reinforcement Learning and to argue why Deep RL can have a lot of potential in the field of High Frequency Trading. We have analyzed the model's performance based on it's prediction accuracy as well as prediction speed across full-day trading simulations.

READ FULL TEXT
research
03/31/2020

Deep Probabilistic Modelling of Price Movements for High-Frequency Trading

In this paper we propose a deep recurrent architecture for the probabili...
research
01/18/2021

Deep Reinforcement Learning for Active High Frequency Trading

We introduce the first end-to-end Deep Reinforcement Learning (DRL) base...
research
04/29/2021

Quantum Quantitative Trading: High-Frequency Statistical Arbitrage Algorithm

Quantitative trading is an integral part of financial markets with high ...
research
09/08/2023

C++ Design Patterns for Low-latency Applications Including High-frequency Trading

This work aims to bridge the existing knowledge gap in the optimisation ...
research
06/17/2020

Learning a functional control for high-frequency finance

We use a deep neural network to generate controllers for optimal trading...
research
09/05/2017

Tensor Representation in High-Frequency Financial Data for Price Change Prediction

Nowadays, with the availability of massive amount of trade data collecte...
research
04/10/2021

Quantum Prisoner's Dilemma and High Frequency Trading on the Quantum Cloud

High-frequency trading (HFT) offers an excellent user case and a potenti...

Please sign up or login with your details

Forgot password? Click here to reset