Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio.

Key Features

• Design, train, and evaluate machine learning algorithms that underpin automated trading strategies
• Create a research and strategy development process to apply predictive modeling to trading decisions
• Leverage NLP and deep learning to extract tradeable signals from market and alternative data

Book Description

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 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.

“Algorithmic Trading is about timing the market using data and algorithms in order to improve your own trading performance, outcomes, and earnings. The wealth of techniques, algorithms, and models that are used for those purposes are presented comprehensively in this giant book and are also applicable to countless other predictive modeling applications and diverse use cases. That makes this an excellent machine learning book for all learners and users of predictive algorithms in data science and analytics.”

Dr Kirk Borne, Principal Data Scientist, Data Science Fellow, and Executive Advisor at Booz Allen Hamilton, and co-author of Ten Signs of Data Science Maturity

“This volume aims to present a broad yet technical treatment of (ML) algorithms used by financial practitioners and scholars alike. … the book fills a large void. … This encourages reproducibility as well as learning by doing, which is highly appreciated.” (Guillaume Coqueret, Quantitative Finance, October 15, 2020)

Stochastic Simulation and Applications in Finance with MATLAB Programs explains the fundamentals of Monte Carlo simulation techniques, their use in the numerical resolution of stochastic differential equations and their current applications in finance. Building on an integrated approach, it provides a pedagogical treatment of the need–to–know materials in risk management and financial engineering.

The book takes readers through the basic concepts, covering the most recent research and problems in the area, including: the quadratic re–sampling technique, the Least Squared Method, the dynamic programming and Stratified State Aggregation technique to price American options, the extreme value simulation technique to price exotic options and the retrieval of volatility method to estimate Greeks.   The authors also present modern term structure of interest rate models and pricing swaptions with the BGM market model, and give a full explanation of corporate securities valuation and credit risk based on the structural approach of Merton. Case studies on financial guarantees illustrate how to implement the simulation techniques in pricing and hedging.

“This book provides a very useful set of tools for those who are interested in the simulation method of asset pricing and its implementation with MatLab. It is pitched at just the right level for anyone who seeks to learn about this fascinating area of finance. The collection of specific topics thoughtfully selected by the authors, such as credit risk, loan guarantee and value–at–risk, is an additional nice feature, making it a great source of reference for researchers and practitioners. The book is a valuable contribution to the fast growing area of quantitative finance.”

–Tan Wang, Sauder School of Business, UBC

“This book is a good companion to text books on theory, so if you want to get straight to the meat of implementing the classical quantitative finance models here′s the answer.”

—Paul Wilmott, wilmott.com

“This powerful book is a comprehensive guide for Monte Carlo methods in finance. Every quant knows that one of the biggest issues in finance is to well understand the mathematical framework in order to translate it in programming code. Look at the chapter on Quasi Monte Carlo or the paragraph on variance reduction techniques and you will see that Huu Tue Huynh, Van Son Lai and Issouf Soumaré have done a very good job in order to provide a bridge between the complex mathematics used in finance and the programming implementation. Because it adopts both theoretical and practical point of views with a lot of applications, because it treats about some sophisticated financial problems (like Brownian bridges, jump processes, exotic options pricing or Longstaff–Schwartz methods) and because it is easy to understand, this handbook is valuable for academics, students and financial engineers who want to learn the computational aspects of simulations in finance.”

—Thierry Roncalli, Head of Investment Products and Strategies, SGAM Alternative Investments & Professor of Finance, University of Evry

Foundations of Computational Finance with MATLAB® is an introductory text for both finance professionals looking to branch out from the spreadsheet, and for programmers who wish to learn more about finance. As financial data grows in volume and complexity, its very nature has changed to the extent that traditional financial calculators and spreadsheet programs are simply no longer enough. Today’s analysts need more powerful data solutions with more customization and visualization capabilities, and MATLAB provides all of this and more in an easy-to-learn skillset.

This book walks you through the basics, and then shows you how to stretch your new skills to create customized solutions. Part I demonstrates MATLAB’s capabilities as they apply to traditional finance concepts, and PART II shows you how to create interactive and reusable code, link with external data sources, communicate graphically, and more.

From the Back Cover

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples.

The book begins with the first step in data science: importing and wrangling data, which in the investment context means importing asset prices, converting to returns, and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation, skewness, kurtosis, and their rolling histories. The third section focuses on portfolio theory, analyzing the Sharpe Ratio, CAPM, and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks, the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards.

Introduction to Computational Economics Using Fortran is the essential guide to conducting economic research on a computer. Aimed at students of all levels of education as well as advanced economic researchers, it facilitates the first steps into writing programs using Fortran.

Introduction to Computational Economics Using Fortran assumes no prior experience as it introduces the reader to this programming language. It shows the reader how to apply the most important numerical methods conducted by computational economists using the toolbox that accompanies this text. It offers various examples from economics and finance organized in self-contained chapters that speak to a diverse range of levels and academic backgrounds. Each topic is supported by an explanation of the theoretical background, a demonstration of how to implement the problem on the computer, and a discussion of simulation results. Readers can work through various exercises that promote practical experience and deepen their economic and technical insights.

This textbook is accompanied by a website from which readers can download all program codes as well as a numerical toolbox, and receive technical information on how to install Fortran on their computer.

The book collects updated posts from Goutham’s blog and the transcripts of the screencasts that Luigi is publishing on YouTube.

The posts and screencasts use Jupyter notebooks to demonstrate the QuantLib library. Together, they provide a sort of cookbook that showcases features of the library by means of working examples and provides guidance to its use.

Among other content, the book also includes notebooks that reproduce the results from the often-cited Ametrano and Bianchetti paper, Everything You Always Wanted to Know About Multiple Interest Rate Curve Bootstrapping but Were Afraid to Ask.

If you’re interested in the architecture of QuantLib and want to know how to extend it, you might want to look at Implementing QuantLib, too.

This book is a report on the design and implementation of QuantLib, alike in spirit—but, hopefully, with less frightening results—to the “How I did it” book prominently featured in Mel Brooks’ Young Frankenstein (in this case, of course, it would be “how we did it”). If you are, or want to be, a QuantLib user, you will find here useful information on the design of the library that might not be readily apparent when reading the code. If you’re working in quantitative finance, even if not using QuantLib, you can still read it as a field report on the design of a financial library. You will find that it covers issues that you might also face, as well as some possible solutions and their rationale. Based on your constraints, it is possible—even likely—that you will choose other solutions; but you might profit from this discussion just the same.The book is primarily aimed at users wanting to extend the library with their own instruments or models; if you desire to do so, the description of the available class hierarchies and frameworks will provide you with information about the hooks you need to integrate your code with QuantLib and take advantage of its facilities. If you’re not this kind of user, don’t give up on the book yet; you can find useful information too. However, you might want to look at the QuantLib Python Cookbook instead; it’s available in all electronic formats from Leanpub.

This book will introduce you to the key mathematical models used to price financial derivatives, as well as the implementation of main numerical models used to solve them. In particular, equity, currency, interest rates, and credit derivatives are discussed. In the first part of the book, the main mathematical models used in the world of financial derivatives are discussed. Next, the numerical methods used to solve the mathematical models are presented. Finally, both the mathematical models and the numerical methods are used to solve some concrete problems in equity, forex, interest rate, and credit derivatives.

The models used include the Black-Scholes and Garman-Kohlhagen models, the LIBOR market model, structural and intensity credit models. The numerical methods described are Monte Carlo simulation (for single and multiple assets), Binomial Trees, and Finite Difference Methods. You will find implementation of concrete problems including European Call, Equity Basket, Currency European Call, FX Barrier Option, Interest Rate Swap, Bankruptcy, and Credit Default Swap in C++.