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Author: Yun Shen Publisher: ISBN: Category : Languages : en Pages : 8
Book Description
We propose a general framework of risk-averse reinforcement learning for algorithmic trading. Our approach is tested in an experiment based on 1.5 years of millisecond time-scale limit order data from NASDAQ, which contain the data around the 2010 flash crash. The results show that our algorithm outperforms the risk-neutral reinforcement learning algorithm by 1) keeping the trading cost at a substantially low level at the spot when the flash crash happened, and 2) significantly reducing the risk over the whole test period.
Author: Yun Shen Publisher: ISBN: Category : Languages : en Pages : 8
Book Description
We propose a general framework of risk-averse reinforcement learning for algorithmic trading. Our approach is tested in an experiment based on 1.5 years of millisecond time-scale limit order data from NASDAQ, which contain the data around the 2010 flash crash. The results show that our algorithm outperforms the risk-neutral reinforcement learning algorithm by 1) keeping the trading cost at a substantially low level at the spot when the flash crash happened, and 2) significantly reducing the risk over the whole test period.
Author: Ashwin Rao Publisher: CRC Press ISBN: 1000801101 Category : Mathematics Languages : en Pages : 658
Book Description
Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas — especially finance. Reinforcement Learning is emerging as a powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Its penetration in high-profile problems like self-driving cars, robotics, and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans. But when it comes getting educated in this area, there seems to be a reluctance to jump right in, because Reinforcement Learning appears to have acquired a reputation for being mysterious and technically challenging. This book strives to impart a lucid and insightful understanding of the topic by emphasizing the foundational mathematics and implementing models and algorithms in well-designed Python code, along with robust coverage of several financial trading problems that can be solved with Reinforcement Learning. This book has been created after years of iterative experimentation on the pedagogy of these topics while being taught to university students as well as industry practitioners. Features Focus on the foundational theory underpinning Reinforcement Learning and software design of the corresponding models and algorithms Suitable as a primary text for courses in Reinforcement Learning, but also as supplementary reading for applied/financial mathematics, programming, and other related courses Suitable for a professional audience of quantitative analysts or data scientists Blends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding To access the code base for this book, please go to: https://github.com/TikhonJelvis/RL-book
Author: Jason Test Publisher: ISBN: 9789918608157 Category : Computers Languages : en Pages : 284
Book Description
Escape the rat race now! Would you like to learn the Python Programming Language and machine learning in 7 days? Do you want to increase your trading thanks to Python and applied AI? If so, keep reading: this bundle book is for you! Today, thanks to computer programming and Python we can work with sophisticated machines that can study human behavior and identify underlying human behavioral patterns. Scientists can predict effectively what products and services consumers are interested in. You can also create various quantitative and algorithmic trading strategies using Python. Technology has become an asset in finance: financial institutions are now evolving to technology companies rather than only staying occupied with just the financial aspects. is getting increasingly challenging for traditional businesses to retain their customers without adopting one or more of the astonishing and cutting-edge technology explained in this book. LEARN MACHINE LEARNING FOR FINANCE will introduce you many selected tips and breaking down the basics of coding applied to finance. You will discover as a beginner the world of data science, machine learning and artificial intelligence with step-by-step guides that will guide you during the code-writing learning process. The following list is just a tiny fraction of what you will learn in this bundle STOCK MARKET INVESTING FOR BEGINNERS ✅ Options Trading Strategies that guarantee real results in all market conditions ✅ Top 7 endorsed indicators of a successful investment ✅ The Bull & Bear Game ✅ Learn about the 3 best charts patterns to fluctuations of stock prices OPTIONS TRADING FOR BEGINNERS ✅How Swing trading differs from Day trading in terms of risk-aversion ✅How your money should be invested and which trade is more profitable ✅Swing and Day trading proven indicators to learn investment timing ✅The secret DAY trading strategies leading to a gain of $ 9,000 per month and more than $100,000 per year. PYTHON CRASH COURSE ✅A Proven Method to Write your First Program in 7 Days ✅3 Common Mistakes to Avoid when You Start Coding ✅Importing Financial Data Into Python ✅7 Most effective Machine Learning Algorithms ✅ Build machine learning models for trading Even if you have never written a programming code before, you will quickly grasp the basics thanks to visual charts and guidelines for coding. Approached properly artificial intelligence, can provide significant benefits for the firm, its customers and wider society. Today is the best day to start programming like a pro and help your trading online! For those trading with leverage, looking for step-by-step process to take a controlled approach and manage risk, this bundle book is the answer If you really wish to LEARN MACHINE LEARNING FOR FINANCE and master its language, please click the BUY NOW button.
Author: Xin Guo Publisher: CRC Press ISBN: 1498706495 Category : Business & Economics Languages : en Pages : 357
Book Description
The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part covers market impact models, network models, multi-asset trading, machine learning techniques, and nonlinear filtering. The third part discusses electronic market making, liquidity, systemic risk, recent developments and debates on the subject.
Author: Brindha Priyadarshini Jeyaraman Publisher: BPB Publications ISBN: 935551977X Category : Computers Languages : en Pages : 458
Book Description
Observe, optimize, and transform in finance KEY FEATURES ● Learn observability basics in finance. ● Monitor financial data with logs and alerts and improve data security. ● Identify the key metrics for financial oversight. ● Use new tech for financial observability. DESCRIPTION This book explains the role of observability in the finance sector, showing how new technologies can help monitor and manage financial systems more effectively. It outlines the use of real-time data monitoring, Machine Learning, and cloud computing to enhance the efficiency of financial operations and ensure they meet regulatory standards. The chapters guide you through the process of setting up systems to track financial activities accurately, analyze market trends, and predict future challenges to keep operations secure and competitive. It offers clear explanations of how these technologies can help finance professionals make better decisions and manage risks proactively. Designed for finance professionals looking to update their technical skills, this book provides practical guidance on adopting modern observability tools and practices. It will help you understand how to apply these technologies to increase transparency and strengthen the resilience of financial operations in a constantly evolving industry. WHAT YOU WILL LEARN ● Implement effective data monitoring strategies in finance. ● Use Machine Learning to enhance financial risk assessment. ● Develop robust compliance frameworks using observability tools. ● Apply real-time analytics for quicker financial decision-making. ● Integrate predictive analytics for forward-looking financial insights. ● Understand and deploy distributed tracing for financial operations. WHO THIS BOOK IS FOR This book is ideal for financial professionals seeking to deepen their understanding of observability. It is also suitable for IT specialists in finance who wish to advance their skills in modern observability tools and practices. TABLE OF CONTENTS 1. Introduction 2. The Fundamentals of Observability 3. Monitoring and Logging for Financial Data 4. Tracing and Correlation in Finance 5. Metrics and Key Performance Indicators for Finance 6. Real-time Monitoring and Alerting in Finance 7. Observability for Algorithmic Trading and Market Data 8. Compliance and Regulatory Considerations 9. Advanced Techniques: Machine Learning and Predictive Analytics 10. Organizational Culture and Collaboration 11. Case Studies and Best Practices Observability 12. The Future of Observability in Finance 13. The Horizon of Financial Observability
Author: Gordon Ritter Publisher: ISBN: Category : Languages : en Pages : 19
Book Description
In multi-period trading with realistic market impact, determining the dynamic trading strategy that optimizes expected utility of final wealth is a hard problem. In this paper we show that, with an appropriate choice of the reward function, reinforcement learning techniques (specifically, Q-learning) can successfully handle the risk-averse case. We provide a proof of concept in the form of a simulated market which permits a statistical arbitrage even with trading costs. The Q-learning agent finds and exploits this arbitrage.
Author: Robert Kissell Publisher: Academic Press ISBN: 0128156317 Category : Business & Economics Languages : en Pages : 614
Book Description
Algorithmic Trading Methods: Applications using Advanced Statistics, Optimization, and Machine Learning Techniques, Second Edition, is a sequel to The Science of Algorithmic Trading and Portfolio Management. This edition includes new chapters on algorithmic trading, advanced trading analytics, regression analysis, optimization, and advanced statistical methods. Increasing its focus on trading strategies and models, this edition includes new insights into the ever-changing financial environment, pre-trade and post-trade analysis, liquidation cost & risk analysis, and compliance and regulatory reporting requirements. Highlighting new investment techniques, this book includes material to assist in the best execution process, model validation, quality and assurance testing, limit order modeling, and smart order routing analysis. Includes advanced modeling techniques using machine learning, predictive analytics, and neural networks. The text provides readers with a suite of transaction cost analysis functions packaged as a TCA library. These programming tools are accessible via numerous software applications and programming languages. Provides insight into all necessary components of algorithmic trading including: transaction cost analysis, market impact estimation, risk modeling and optimization, and advanced examination of trading algorithms and corresponding data requirements Increased coverage of essential mathematics, probability and statistics, machine learning, predictive analytics, and neural networks, and applications to trading and finance Advanced multiperiod trade schedule optimization and portfolio construction techniques Techniques to decode broker-dealer and third-party vendor models Methods to incorporate TCA into proprietary alpha models and portfolio optimizers TCA library for numerous software applications and programming languages including: MATLAB, Excel Add-In, Python, Java, C/C++, .Net, Hadoop, and as standalone .EXE and .COM applications
Author: Leslie Pack Kaelbling Publisher: Springer Science & Business Media ISBN: 0792397053 Category : Computers Languages : en Pages : 286
Book Description
Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).
Author: Yun Shen
Author: Yun Shen
Author: Matthias Heger
Author: Ashwin Rao
Author: 林鼎鈞
Author: Jason Test
Author: Xin Guo
Author: Brindha Priyadarshini Jeyaraman
Author: Gordon Ritter
Author: Robert Kissell
Author: Leslie Pack Kaelbling