Algorithmic Trading
17 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 implementationsLatest papers
EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading
In stage II, we construct a pool of diverse RL agents for different market trends, distinguished by return rates, where hundreds of RL agents are trained with different preferences of return rates and only a tiny fraction of them will be selected into the pool based on their profitability.
FinGPT: Democratizing Internet-scale Data for Financial Large Language Models
In light of this, we aim to democratize Internet-scale financial data for LLMs, which is an open challenge due to diverse data sources, low signal-to-noise ratio, and high time-validity.
FinGPT: Open-Source Financial Large Language Models
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.
Stock Trading Volume Prediction with Dual-Process Meta-Learning
Our method can model the common pattern behind different stocks with a meta-learner, while modeling the specific pattern for each stock across time spans with stock-dependent parameters.
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.
Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting
Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market.
A Modular Framework for Reinforcement Learning Optimal Execution
In this article, we develop a modular framework for the application of Reinforcement Learning to the problem of Optimal Trade Execution.
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach
The feasibility of making profitable trades on a single asset on stock exchanges based on patterns identification has long attracted researchers.
Exploration of Algorithmic Trading Strategies for the Bitcoin Market
This work brings an algorithmic trading approach to the Bitcoin market to exploit the variability in its price on a day-to-day basis through the classification of its direction.
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.