6 books
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A Primer to Generative AI with Python
by Deepak K. Kanungo

There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilistic ML also lets you systematically encode personal, empirical, and institutional knowledge into ML models.

Whether they're based on academic theories or ML strategies, all financial models are subject to modeling errors that can be mitigated but not eliminated. Probabilistic ML systems treat uncertainties and errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates. This makes for realistic financial inferences and predictions that are useful for decision-making and risk management.

Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. By moving away from flawed statistical methodologies and a restrictive conventional view of probability as a limiting frequency, you’ll move toward an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This book shows you how.

From Idea to Cloud Deployment
by Yves Hilpisch

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
Mastering Data-Driven Finance
by Yves Hilpisch

The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics.

Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.

Over 80 powerful recipes for effective financial data analysis
by Eryk Lewinson

Python is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions.

You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses.

Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.

What you will learn

  • Preprocess, analyze, and visualize financial data
  • Explore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning models
  • Uncover advanced time series forecasting algorithms such as Meta's Prophet
  • Use Monte Carlo simulations for derivatives valuation and risk assessment
  • Explore volatility modeling using univariate and multivariate GARCH models
  • Investigate various approaches to asset allocation
  • Learn how to approach ML-projects using an example of default prediction
  • Explore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphet

Who this book is for

This book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems.

Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.

How to Build Your Own Algorithmic Trading Business
by Ernest P. Chan

In the newly revised Second Edition of Quantitative Trading: How to Build Your Own Algorithmic Trading Business, quant trading expert Dr. Ernest P. Chan shows you how to apply both time-tested and novel quantitative trading strategies to develop or improve your own trading firm.

You'll discover new case studies and updated information on the application of cutting-edge machine learning investment techniques, as well as:

  • Updated back tests on a variety of trading strategies, with included Python and R code examples
  • A new technique on optimizing parameters with changing market regimes using machine learning.
  • A guide to selecting the best traders and advisors to manage your money

Perfect for independent retail traders seeking to start their own quantitative trading business, or investors looking to invest in such traders, this new edition of Quantitative Trading will also earn a place in the libraries of individual investors interested in exploring a career at a major financial institution.

by Perry J. Kaufman

Professional and individual traders haverelied on Trading Systems and Methods for over three decades. Acclaimed trading systems expert Perry Kaufman provides complete, authoritative information on proven indicators, programs, systems, and algorithms. Now in its sixth edition, this respected book continues to provide readers with the knowledge required to develop or select the trading programs best suited for their needs. In-depth discussions of basic mathematical and statistical concepts instruct readers on how much data to use, how to create an index, how to determine probabilities, and how best to test your ideas. These technical tools and indicators help readers identify trends, momentum, and patterns, while an analytical framework enables comparisons of systematic methods and techniques.

This updated, fully-revised edition offers new examples using stocks, ETFs and futures, and provides expanded coverage of arbitrage, high frequency trading, and sophisticated risk management models. More programs and strategies have been added, such as Artificial Intelligence techniques and Game Theory approaches to trading. Offering a complete array of practical, user-ready tools, this invaluable resource:

  • Offers comprehensive revisions and additional mathematical and statistical tools, trading systems, and examples of current market situations
  • Explains basic mathematical and statistical concepts with accompanying code
  • Includes new Excel spreadsheets with genetic algorithms, TradeStation code, MetaStock code, and more
  • Provides access to a companion website packed with supplemental materials

Trading Systems and Methods is an indispensable reference on trading systems, as well as system design and methods for professional and individual active traders, money managers, trading systems developers.