The purpose of the book is to provide a broad-based accessible introduction to three of the presently most important areas of computational finance, namely, option pricing, algorithmic trading and blockchain. This will provide a basic understanding required for a career in the finance industry and for doing more specialised courses in finance.

Interest in algorithmic trading is growing massively – it’s cheaper, faster and better to control than standard trading, it enables you to ‘pre-think’ the market, executing complex math in real time and take the required decisions based on the strategy defined. We are no longer limited by human ‘bandwidth’. The cost alone (estimated at 6 cents per share manual, 1 cent per share algorithmic) is a sufficient driver to power the growth of the industry. According to consultant firm, Aite Group LLC, high frequency trading firms alone account for 73% of all US equity trading volume, despite only representing approximately 2% of the total firms operating in the US markets. Algorithmic trading is becoming the industry lifeblood. But it is a secretive industry with few willing to share the secrets of their success. The book begins with a step-by-step guide to algorithmic trading, demystifying this complex subject and providing readers with a specific and usable algorithmic trading knowledge. It provides background information leading to more advanced work by outlining the current trading algorithms, the basics of their design, what they are, how they work, how they are used, their strengths, their weaknesses, where we are now and where we are going. The book then goes on to demonstrate a selection of detailed algorithms including their implementation in the markets. Using actual algorithms that have been used in live trading readers have access to real time trading functionality and can use the never before seen algorithms to trade their own accounts. The markets are complex adaptive systems exhibiting unpredictable behaviour. As the markets evolve algorithmic designers need to be constantly aware of any changes that may impact their work, so for the more adventurous reader there is also a section on how to design trading algorithms. All examples and algorithms are demonstrated in Excel on the accompanying CD ROM, including actual algorithmic examples which have been used in live trading.

The Algorithmic Trading Guide: How To Leverage Technology To Make Money In Finance Markets is a comprehensive guidebook for anyone interested in algorithmic trading, covering everything from basic concepts to advanced strategies and techniques. This book provides practical examples and case studies, demonstrating how to apply the concepts and techniques discussed in real-world trading scenarios. The book begins with an overview of algorithmic trading, its importance in financial markets, and the terminology and concepts related to it. It then moves on to cover popular trading strategies used in algorithmic trading and the installation and configuration of a trading platform. The book also delves into data analysis and visualization techniques, using Python and popular data analysis libraries, creating trading signals and indicators, and backtesting trading strategies using historical data. Readers will learn about building trading models using machine learning and reinforcement learning techniques, as well as backtesting and evaluating these models. Additionally, the book covers implementing trading strategies, developing trading algorithms using Python, and integrating these algorithms with a trading platform. It also explores market microstructure, high-frequency trading, and trading in different market conditions, as well as best practices for algorithmic trading and market microstructure. Risk management is a crucial aspect of algorithmic trading, and the book includes techniques for measuring and managing risk in trading strategies, using portfolio optimization techniques for risk management, and best practices for risk management in algorithmic trading. Finally, the book covers the regulatory landscape of algorithmic trading, compliance requirements, and best practices for complying with regulatory requirements in algorithmic trading. It also discusses future trends and challenges in algorithmic trading and regulation. The Algorithmic Trading Guide: How To Leverage Technology To Make Money In Finance Markets is an essential resource for traders and financial professionals looking to expand their knowledge and skills in the field of algorithmic trading. It is also suitable for novice traders just starting to explore algorithmic trading.

Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading. You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for algorithmic trading Learn how to retrieve financial data from public and proprietary data sources Explore vectorization for financial analytics with NumPy and pandas Master vectorized backtesting of different algorithmic trading strategies Generate market predictions by using machine learning and deep learning Tackle real-time processing of streaming data with socket programming tools Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms

Description: If you’ve ever been intrigued by the concept of algorithmic trading but felt overwhelmed by the complexity, “Algorithmic Trading: An Introductory Guide” is your ideal starting point. This book serves as your friendly introduction to the world of automated financial trading. Designed for individuals who are curious about algorithmic trading but don’t have an extensive background in the subject, this book demystifies the basics. It provides a clear and accessible entry point for those interested in understanding how algorithms can make trading decisions. Discover the fundamental principles of algorithmic trading and why it’s become a game-changer in financial markets. Explore how algorithms execute trades with incredible speed and remain free from the influence of human emotions. This introductory guide offers an overview that will satisfy your curiosity without overwhelming you with technical details. “Algorithmic Trading: An Introductory Guide” introduces various types of algorithmic trading strategies, shedding light on the strategies employed by professional traders. From market-making and arbitrage to trend-following and quantitative approaches, this book provides a broad understanding without diving deep into intricacies. Gain insights into the advantages and risks associated with algorithmic trading. Learn how it enhances efficiency and offers robust risk management while also understanding the potential challenges and pitfalls. While the book touches on data analysis, technical and fundamental analysis, and sentiment analysis, it does so in a manner that is easily digestible for beginners. You’ll get a sense of the analytical tools used in algorithmic trading without getting lost in the details. “Algorithmic Trading: An Introductory Guide” is the perfect starting point for those who have contemplated exploring this exciting field. It offers a taste of the world of algorithmic trading, providing you with the confidence to embark on your journey into this transformative realm of finance.

Dive into algo trading with step-by-step tutorials and expert insight Machine Trading is a practical guide to building your algorithmic trading business. Written by a recognized trader with major institution expertise, this book provides step-by-step instruction on quantitative trading and the latest technologies available even outside the Wall Street sphere. You'll discover the latest platforms that are becoming increasingly easy to use, gain access to new markets, and learn new quantitative strategies that are applicable to stocks, options, futures, currencies, and even bitcoins. The companion website provides downloadable software codes, and you'll learn to design your own proprietary tools using MATLAB. The author's experiences provide deep insight into both the business and human side of systematic trading and money management, and his evolution from proprietary trader to fund manager contains valuable lessons for investors at any level. Algorithmic trading is booming, and the theories, tools, technologies, and the markets themselves are evolving at a rapid pace. This book gets you up to speed, and walks you through the process of developing your own proprietary trading operation using the latest tools. Utilize the newer, easier algorithmic trading platforms Access markets previously unavailable to systematic traders Adopt new strategies for a variety of instruments Gain expert perspective into the human side of trading The strength of algorithmic trading is its versatility. It can be used in any strategy, including market-making, inter-market spreading, arbitrage, or pure speculation; decision-making and implementation can be augmented at any stage, or may operate completely automatically. Traders looking to step up their strategy need look no further than Machine Trading for clear instruction and expert solutions.

Today, algorithms steer and inform more than 75% of modern trades. These mathematical constructs play an intricate role in automating processes, predicting market trends, optimizing portfolios, and fortifying decision-making in the financial domain. In an era where algorithms underpin the very foundation of financial services, it is imperative to hold a deep understanding of the intricate web of computational finance. Algorithmic Approaches to Financial Technology: Forecasting, Trading, and Optimization takes a comprehensive approach, spotlighting the fusion of artificial intelligence(AI) and algorithms in financial operations. The chapters explore the expansive landscape of algorithmic applications, from scrutinizing market trends to managing risks. The emphasis extends to AI-driven personnel selection, implementing trusted financial services, crafting recommendation systems for financial platforms, and critical fraud detection. This book serves as a vital resource for researchers, students, and practitioners. Its core strength lies in discussing AI-based algorithms as a catalyst for evolving market trends. It provides algorithmic solutions for stock markets, portfolio optimization, and robust financial fraud detection mechanisms.