7 papers with code • 0 benchmarks • 1 datasets
An algorithmic trading system is a software that is used for trading in the stock market.
This is the first in a series of arti-cles dealing with machine learning in asset management.
A system for trading the fixed volume of a financial instrument is proposed and experimentally tested; this is based on the asynchronous advantage actor-critic method with the use of several neural network architectures.
This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets.
In this study, we present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques to manipulate the input data stream in real time.
This research analyses high-frequency data of the cryptocurrency market in regards to intraday trading patterns related to algorithmic trading and its impact on the European cryptocurrency market.
Our method adapts Haar wavelets to the structure of the observed variables in order to detect the change points of the parameters consistently.