Optimizing cybersecurity incident response decisions using deep reinforcement learning

The main purpose of this paper is to explore and investigate the role of deep reinforcement learning (DRL) in optimizing the post-alert incident response process in security incident and event management (SIEM) systems. Although machine learning is used at multiple levels of SIEM systems, the last mile decision process is often ignored. Few papers reported efforts regarding the use of DRL to improve the post-alert decision and incident response processes. All the reported efforts applied only shallow (traditional) machine learning approaches to solve the problem. This paper explores the possibility of solving the problem using DRL approaches. The main attraction of DRL models is their ability to make accurate decisions based on live streams of data without the need for prior training, and they proved to be very successful in other fields of applications. Using standard datasets, a number of experiments have been conducted using different DRL configurations The results showed that DRL models can provide highly accurate decisions without the need for prior training

Tutorial: “An Introduction To Sizing And Operations of Energy Systems with GBOML”

7. Affordable and clean energy13. Climate action9. Industry, innovation and infrastructur

Deep Reinforcement Learning for Active High Frequency Trading

We introduce the first end-to-end Deep Reinforcement Learning (DRL) based framework for active high frequency trading. We train DRL agents to trade one unit of Intel Corporation stock by employing the Proximal Policy Optimization algorithm. The training is performed on three contiguous months of high frequency Limit Order Book data, of which the last month constitutes the validation data. In order to maximise the signal to noise ratio in the training data, we compose the latter by only selecting training samples with largest price changes. The test is then carried out on the following month of data. Hyperparameters are tuned using the Sequential Model Based Optimization technique. We consider three different state characterizations, which differ in their LOB-based meta-features. Analysing the agents' performances on test data, we argue that the agents are able to create a dynamic representation of the underlying environment. They identify occasional regularities present in the data and exploit them to create long-term profitable trading strategies. Indeed, agents learn trading strategies able to produce stable positive returns in spite of the highly stochastic and non-stationary environment.Comment: 9 pages, 4 figure

Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models

This study enhances a Deep Q-Network (DQN) trading model by incorporating advanced techniques like Prioritized Experience Replay, Regularized Q-Learning, Noisy Networks, Dueling, and Double DQN. Extensive tests on assets like BTC/USD and AAPL demonstrate superior performance compared to the original model, with marked increases in returns and Sharpe Ratio, indicating improved risk-adjusted rewards. Notably, convolutional neural network (CNN) architectures, both 1D and 2D, significantly boost returns, suggesting their effectiveness in market trend analysis. Across instruments, these enhancements have yielded stable and high gains, eclipsing the baseline and highlighting the potential of CNNs in trading systems. The study suggests that applying sophisticated deep learning within reinforcement learning can greatly enhance automated trading, urging further exploration into advanced methods for broader financial applicability. The findings advocate for the continued evolution of AI in finance.Comment: 16 pages, 9 figure

Investigation into a Practical Application of Reinforcement Learning for the Stock Market

A major problem of the financial industry is the ability to adapt their trading strategies at the same rate the market evolves. This paper proposes a solution using existing Reinforcement Learning libraries to help find new strategies at a practical scale. Using a wide domain of ticker symbols, an algorithm is trained in an environment that better represents reality. The supplied decision-making algorithm is tested using recorded data from the U.S stock market from 2000 through 2022. The results of this research show that existing techniques are statistically better than making decisions at random. With this result, this research shows how a practical application of Reinforcement Learning is possible through the inclusion of many more ticker symbols than previous research has done before. However, there is still work to be done to achieve acceptable returns. Potential applications of this research include informing human traders or creating automated traders

Funcionamiento del trading algorítmico en los mercados de capitales

Trabajo final (Licenciatura en Administración con orientación en Finanzas)Propósito: este trabajo tiene como finalidad exponer información acerca del trading algorítmico y su relación con el mercado de capitales, el análisis técnico y fundamental, los activos financieros y sus derivados para todo aquel interesado en interiorizarse en el mundo de las finanzas. Metodología: se realizó una revisión sistemática de literatura, relevando 572 artículos acerca del trading algorítmico, publicados en el periodo 2015-2022. En la búsqueda se aplicaron criterios de exclusión, quedando un total de 29 artículos. Su análisis pertinente permitió contestar las preguntas de investigación y desarrollar la temática elegida. Además, se efectuaron entrevistas semi-estructuradas a personas trabajando en la operatoria de trading. Conclusiones: El trading algorítmico posee ventajas excepcionales sobre el trading discrecional. Entre ellas se destaca la capacidad de procesamiento superior que tiene una computadora que simplifica toda operación y reduce los tiempos empleados y por otro lado, elimina el lado emocional de la toma de decisiones del proceso de inversión. Limitaciones: En el protocolo de investigación se estableció la condición de seleccionar solo artículos de libre acceso y aceptar únicamente los artículos que hayan sido redactados en inglés o el español. Los idiomas de los textos que fueron dejados de lado son francés, alemán, portugués y ucraniano. Originalidad-Valor: El valor del trabajo radica en que se aborda una temática novedosa en el campo de las finanzas por medio de dos metodologías que aportan por un lado información de calidad y con respaldo científico y por otro lado la experiencia y conocimientos de los profesionales entrevistados que actualmente trabajan con esta herramienta.Fil: Castro, Francisco Javier. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Gervasoni, Lucía Florencia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Giannelli, Agostina Belén. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Vogel Dotta, María Sol. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina

A novel financial trading system based on reinforcement learning and technical analysis applied on the Tehran securities exchange market

Stock trading is a significant decision-making problem in asset management. This study introduces a financial trading system (FTS) that leverages artificial intelligence (AI) techniques to automate buy and sell orders specifically in Iran's stock market. Due to limited availability of labeled data in financial markets, the FTS utilizes reinforcement learning (RL), a subset of AI, for training. The model incorporates technical analysis and a constrained policy to enhance decision-making capabilities. The proposed algorithm is applied to the Tehran Securities Exchange, evaluating its efficiency across 45 periods using three different stock market indices. Performance comparisons are made against common strategies such as buy and hold, randomly selected actions, and maintaining the initial stock portfolio, with and without transaction costs. The results indicate that the FTS outperforms these methods, exhibiting excellent performance metrics including Sharp ratio, PP, PF, and MDD. Consequently, the findings suggest that the FTS serves as a valuable asset management tool in the Iranian financial market

Risk-Sensitive Policy with Distributional Reinforcement Learning

peer reviewedClassical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk associated with the actions taken, which may be critical in certain applications. To address that issue, the present research work introduces a novel methodology based on distributional RL to derive sequential decision-making policies that are sensitive to the risk, the latter being modelled by the tail of the return probability distribution. The core idea is to replace the Q function generally standing at the core of learning schemes in RL by another function, taking into account both the expected return and the risk. Named the risk-based utility function U, it can be extracted from the random return distribution Z naturally learnt by any distributional RL algorithm. This enables the spanning of the complete potential trade-off between risk minimisation and expected return maximisation, in contrast to fully risk-averse methodologies. Fundamentally, this research yields a truly practical and accessible solution for learning risk-sensitive policies with minimal modification to the distributional RL algorithm, with an emphasis on the interpretability of the resulting decision-making process

Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm

Algorithms and the Foundations of Software technolog

Stock Market Prediction via Deep Learning Techniques: A Survey

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction