AI TRADER LIBRARY Artificial Intelligence in Finance: A Python-Based Guide The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you’ll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you’ll be able to replicate all results and figures presented in the book. Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI). Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice. Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets. Identify and exploit economic inefficiencies through backtesting and algorithmic trading–the automated execution of trading strategies. Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about Advances in Financial Machine Learning Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance. Machine Learning for Algorithmic Trading: Predictive models to extract signals from the market and alternative data for systematic trading strategies with Python The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with the market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha-factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. Python for Algorithmic Trading: From Idea to Cloud Deployment 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 Machine Learning in Finance: From Theory to Practice This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete-time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance, and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivatives modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility, and fixed-income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment, and wealth management. Python code examples are provided to support the readers’ understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher’s perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance. Machine Learning and Data Science Blueprints for Finance Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management. Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies. Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction. Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management. Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management. NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations.
