Algorithmic Trading
21 papers with code • 0 benchmarks • 1 datasets
An algorithmic trading system is a software that is used for trading in the stock market.
Benchmarks
These leaderboards are used to track progress in Algorithmic Trading
Libraries
Use these libraries to find Algorithmic Trading models and implementations
S&P 500 Intraday Data
Most implemented papers
A Wavelet Method for Panel Models with Jump Discontinuities in the Parameters
Our method adapts Haar wavelets to the structure of the observed variables in order to detect the change points of the parameters consistently.
Model-based gym environments for limit order book trading
This paper introduces \mbtgym, a Python module that provides a suite of gym environments for training reinforcement learning (RL) agents to solve such model-based trading problems.
FinGPT: Open-Source Financial Large Language Models
ai4finance-foundation/fingpt
•
•
While proprietary models like BloombergGPT have taken advantage of their unique data accumulation, such privileged access calls for an open-source alternative to democratize Internet-scale financial data.
Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies
This is the first in a series of arti-cles dealing with machine learning in asset management.
Using Reinforcement Learning in the Algorithmic Trading Problem
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.
An Application of Deep Reinforcement Learning to Algorithmic Trading
ThibautTheate/An-Application-of-Deep-Reinforcement-Learning-to-Algorithmic-Trading
•
•
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.
Rise of the Machines? Intraday High-Frequency Trading Patterns of Cryptocurrencies
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.
Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders
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.
Multi-Graph Tensor Networks
The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms.
Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review
Algorithmic stock trading has become a staple in today's financial market, the majority of trades being now fully automated.
