The financial markets, which involve over \$90 trillion market capitals, attract the attention of innumerable profit-seeking investors globally. Recent explosion of reinforcement learning in financial trading (RLFT) research has shown stellar performance on many quantitative trading tasks. However, it is still challenging to deploy reinforcement learning (RL) methods into real-world financial markets due to the highly composite nature of this domain, which entails design choices and interactions between components that collect financial data, conduct feature engineering, build market environments, make investment decisions, evaluate model behaviors and offers user interfaces. Despite the availability of abundant financial data and advanced RL techniques, a remarkable gap still exists between the potential and realized utilization of RL in financial trading. In particular, orchestrating an RLFT project lifecycle poses challenges in engineering (i.e.

ABSTRACT Large language models (LLMs) have shown strong reasoning capabilities and are increasingly explored for financial trading. Existing LLM-based trading agents, however, largely focus on single-step prediction and lack integrated mechanisms for risk management, which reduces their effectiveness in volatile markets. We introduce FinRS, a risk-sensitive trading framework that combines hierarchical market analysis, dual-decision agents, and multi-timescale reward reflection to align trading actions with both return objectives and downside risk constraints. Experiments on multiple stocks and market conditions show that FinRS achieves superior profitability and stability compared to state-of-the-art methods. Index T erms-- Trading Agent, Risk-Sensitive, Real Markets 1. INTRODUCTION In recent years, large language models (LLMs) [1, 2] have demonstrated significant potential in financial trading.

The financial markets, which involve over \ 90 trillion market capitals, attract the attention of innumerable profit-seeking investors globally. Recent explosion of reinforcement learning in financial trading (RLFT) research has shown stellar performance on many quantitative trading tasks. However, it is still challenging to deploy reinforcement learning (RL) methods into real-world financial markets due to the highly composite nature of this domain, which entails design choices and interactions between components that collect financial data, conduct feature engineering, build market environments, make investment decisions, evaluate model behaviors and offers user interfaces. Despite the availability of abundant financial data and advanced RL techniques, a remarkable gap still exists between the potential and realized utilization of RL in financial trading. In particular, orchestrating an RLFT project lifecycle poses challenges in engineering (i.e.

In financial trading, factor models are widely used to price assets and capture excess returns from mispricing. Recently, we have witnessed the rise of variational autoencoder-based latent factor models, which learn latent factors self-adaptively. While these models focus on modeling overall market conditions, they often fail to effectively capture the temporal patterns of individual stocks. Additionally, representing multiple factors as single values simplifies the model but limits its ability to capture complex relationships and dependencies. As a result, the learned factors are of low quality and lack diversity, reducing their effectiveness and robustness across different trading periods. To address these issues, we propose a Spatio-Temporal factOR Model based on dual vector quantized variational autoencoders, named STORM, which extracts features of stocks from temporal and spatial perspectives, then fuses and aligns these features at the fine-grained and semantic level, and represents the factors as multi-dimensional embeddings. The discrete codebooks cluster similar factor embeddings, ensuring orthogonality and diversity, which helps distinguish between different factors and enables factor selection in financial trading. To show the performance of the proposed factor model, we apply it to two downstream experiments: portfolio management on two stock datasets and individual trading tasks on six specific stocks. The extensive experiments demonstrate STORM's flexibility in adapting to downstream tasks and superior performance over baseline models.

Trading is a highly competitive task that requires a combination of strategy, knowledge, and psychological fortitude. With the recent success of large language models(LLMs), it is appealing to apply the emerging intelligence of LLM agents in this competitive arena and understanding if they can outperform professional traders. In this survey, we provide a comprehensive review of the current research on using LLMs as agents in financial trading. We summarize the common architecture used in the agent, the data inputs, and the performance of LLM trading agents in backtesting as well as the challenges presented in these research. This survey aims to provide insights into the current state of LLM-based financial trading agents and outline future research directions in this field.

Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-frequency trading with various assets. While advanced AI techniques like deep learning and reinforcement learning are extensively utilized in finance, their application in financial trading tasks often faces challenges due to inadequate handling of multimodal data and limited generalizability across various tasks. To address these challenges, we present FinAgent, a multimodal foundational agent with tool augmentation for financial trading. FinAgent's market intelligence module processes a diverse range of data-numerical, textual, and visual-to accurately analyze the financial market. Its unique dual-level reflection module not only enables rapid adaptation to market dynamics but also incorporates a diversified memory retrieval system, enhancing the agent's ability to learn from historical data and improve decision-making processes. The agent's emphasis on reasoning for actions fosters trust in its financial decisions. Moreover, FinAgent integrates established trading strategies and expert insights, ensuring that its trading approaches are both data-driven and rooted in sound financial principles. With comprehensive experiments on 6 financial datasets, including stocks and Crypto, FinAgent significantly outperforms 9 state-of-the-art baselines in terms of 6 financial metrics with over 36% average improvement on profit. Specifically, a 92.27% return (a 84.39% relative improvement) is achieved on one dataset. Notably, FinAgent is the first advanced multimodal foundation agent designed for financial trading tasks.

Financial markets analysis has been and remains a topic of intense research interest since the seminal work of Markowitz [1] detailing his theory on portfolio choice, for which he was awarded the Nobel Prize in 1990. The rapid advancements of Machine Learning (ML) and, more specifically those made in the field of Deep Learning (DL) and Deep Reinforcement Learning (DRL), further fueled interest in the field. Financial markets analysts began using ML-based techniques and combining them with their own knowledge of the field [2]. As early as 1992, Neural Networks (NNs) were already being used for equity index futures trading [3]. More recently, DL research in financial market analysis has focused on high frequency trading, i.e., an algorithmic financial trading method where high speeds and large volumes are the main characteristics. The kind of data used in works that focus on this type of trading include Limit Order Book (LOB) data [4] as well as candle data for assets such as FOREX or Cryptocurrencies [5]. Candle data contain the Open, High, Low and Close prices for assets in a requested frequency, e.g., at the minute or hour level. Price forecasting is a first step towards solving the very complex task of portfolio management, and has proved to be a sufficiently difficult problem to tackle itself. One way to sufficiently solve it is by transforming the problem into one of classification, i.e., predicting the price movement instead of its actual value in the next step [4].

Artificial Intelligence is one of the greatest disruptions predicted to change the way that business operates over the near future. All kinds of industries have already adopted it in order to streamline everything from logistics to HR procedures, and it's no surprise that financial trading is one of them. After all, it's a field where many traditional trading techniques rely on the processing of a great deal of complex data, combined with economic, political and even consumer behavior considerations. Despite this, there remain a number of observers who believe that human intuition is an even more important element than simple number crunching. Even more significantly, they believe that it will never be fully replaced by machines, regardless of how well those machines "learn" to react in certain situations.

The New York Institute of Finance (NYIF) and Google Cloud have launched a new Machine Learning for Trading Specialisation available exclusively on the Coursera platform. The Specialisation helps learners leverage the latest AI and machine learning techniques for financial trading. Amid the Fourth Industrial Revolution, nearly 80 per cent of financial institutions cite machine learning as a core component of business strategy and 75 per cent of financial services firms report investing significantly in machine learning. The Machine Learning for Trading Specialisation equips professionals with key technical skills increasingly needed in the financial industry today. Composed of three courses in financial trading, machine learning, and artificial intelligence, the Specialisation features a blend of theoretical and applied learning.

The New York Institute of Finance (NYIF) and Google Cloud announced a new Machine Learning for Trading Specialization available exclusively on the Coursera platform. The Specialization helps learners leverage the latest AI and machine learning techniques for financial trading. Amid the Fourth Industrial Revolution, nearly 80 percent of financial institutions cite machine learning as a core component of business strategy and 75 percent of financial services firms report investing significantly in machine learning. The Machine Learning for Trading Specialization equips professionals with key technical skills increasingly needed in the financial industry today. Composed of three courses in financial trading, machine learning, and artificial intelligence, the Specialization features a blend of theoretical and applied learning.