Nine Challenges in Modern Algorithmic Trading and Controls

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1. Apparenté Pas pertinent

   Optimal position-building strategies in Competition

   Neil A. Chriss · 2024 · Trading et microstructure des marchés · Résumé IA disponible

   Résumé scientifique

   This paper develops a mathematical framework for building a position in a stock over a fixed period of time while in competition with one or more other traders doing the same thing. We develop a game-theoretic framework that takes place in the space of trading strategies where action sets are trading strategies and traders try to devise best-response strategies to their adversaries. In this setup trading is guided by a desire to minimize the total cost of trading arising from a mixture of temporary and permanent market impact caused by the aggregate level of trading including the trader and the competition. We describe a notion of equilibrium strategies, show that they exist and provide clos…

2. Apparenté Pas pertinent

   An Application of Deep Reinforcement Learning to Algorithmic Trading

   Thibaut Théate, Damien Ernst · 2020 · Expert Systems with Applications, Volume 173, 1 July 2021, 114632 · Résumé IA disponible

   Résumé scientifique

   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. It proposes a novel DRL trading strategy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets. Denominated the Trading Deep Q-Network algorithm (TDQN), this new trading strategy is inspired from the popular DQN algorithm and significantly adapted to the specific algorithmic trading problem at hand. The training of the resulting reinforcement learning (RL) agent is entirely based on the generat…

3. Apparenté Pas pertinent

   Deep Reinforcement Learning for Quantitative Trading

   Maochun Xu, Zixun Lan, Zheng Tao et al. · 2023 · Trading et microstructure des marchés · Résumé IA disponible

   Résumé scientifique

   Artificial Intelligence (AI) and Machine Learning (ML) are transforming the domain of Quantitative Trading (QT) through the deployment of advanced algorithms capable of sifting through extensive financial datasets to pinpoint lucrative investment openings. AI-driven models, particularly those employing ML techniques such as deep learning and reinforcement learning, have shown great prowess in predicting market trends and executing trades at a speed and accuracy that far surpass human capabilities. Its capacity to automate critical tasks, such as discerning market conditions and executing trading strategies, has been pivotal. However, persistent challenges exist in current QT methods, especia…

4. Apparenté Pas pertinent

   TradingAgents: Multi-Agents LLM Financial Trading Framework

   Yijia Xiao, Edward Sun, Di Luo et al. · 2024 · Trading et microstructure des marchés · Résumé IA disponible

   Résumé scientifique

   Significant progress has been made in automated problem-solving using societies of agents powered by large language models (LLMs). In finance, efforts have largely focused on single-agent systems handling specific tasks or multi-agent frameworks independently gathering data. However, multi-agent systems' potential to replicate real-world trading firms' collaborative dynamics remains underexplored. TradingAgents proposes a novel stock trading framework inspired by trading firms, featuring LLM-powered agents in specialized roles such as fundamental analysts, sentiment analysts, technical analysts, and traders with varied risk profiles. The framework includes Bull and Bear researcher agents ass…

5. Apparenté Pas pertinent

   Quantitative Trading using Deep Q Learning

   Soumyadip Sarkar · 2023 · Trading et microstructure des marchés · Résumé IA disponible

   Résumé scientifique

   Reinforcement learning (RL) is a branch of machine learning that has been used in a variety of applications such as robotics, game playing, and autonomous systems. In recent years, there has been growing interest in applying RL to quantitative trading, where the goal is to make profitable trades in financial markets. This paper explores the use of RL in quantitative trading and presents a case study of a RL-based trading algorithm. The results show that RL can be a powerful tool for quantitative trading, and that it has the potential to outperform traditional trading algorithms. The use of reinforcement learning in quantitative trading represents a promising area of research that can potenti…

6. Apparenté Pas pertinent

   Optimal Trade Execution with Uncertain Volume Target

   Julien Vaes, Raphael Hauser · 2018 · Trading et microstructure des marchés · Résumé IA disponible

   Résumé scientifique

   In the seminal paper on optimal execution of portfolio transactions, Almgren and Chriss (2001) define the optimal trading strategy to liquidate a fixed volume of a single security under price uncertainty. Yet there exist situations, such as in the power market, in which the volume to be traded can only be estimated and becomes more accurate when approaching a specified delivery time. During the course of execution, a trader should then constantly adapt their trading strategy to meet their fluctuating volume target. In this paper, we develop a model that accounts for volume uncertainty and we show that a risk-averse trader has benefit in delaying their trades. More precisely, we argue that th…

7. Apparenté Pas pertinent

   FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance

   Xiao-Yang Liu, Hongyang Yang, Jiechao Gao et al. · 2021 · ACM International Conference on AI in Finance, 2021 · Résumé IA disponible

   Résumé scientifique

   Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to obtain an agent that automatically positions to win in the market, namely \textit{to decide where to trade, at what price} and \textit{what quantity}, due to the error-prone programming and arduous debugging. In this paper, we present the first open-source framework \textit{FinRL} as a full pipeline to help quantitative traders overcome the steep learning curve. FinRL is featured with simplicity, applicability and extensibility under the key principles, \textit{full-stack framework, customization, reproducibility} an…

8. Apparenté Pas pertinent

   Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach

   Shuo Sun, Rundong Wang, Bo An · 2022 · Trading et microstructure des marchés · Résumé IA disponible

   Résumé scientifique

   Quantitative investment is a fundamental financial task that highly relies on accurate stock prediction and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, we observe that the performance of existing DL methods is sensitive to random seeds and network initialization. To design more profitable DL methods, we analyze this phenomenon and find two major limitations of existing works. First, there is a noticeable gap between accurate financial predictions and profitable investment strategies. Second, investment decisions are made based on only one individual predi…