The Book of Alternative Data: A Guide for Investors, Traders, and Risk Managers
Authors: Alexander Denev and Saeed Amen
The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management
Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject.
This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors—leading experts in financial modeling, machine learning, and quantitative research and analytics—employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book:
- Provides an integrated modeling approach to extract value from multiple types of datasets
- Treats the processes needed to make alternative data signals operational
- Helps investors and risk managers rethink how they engage with alternative datasets
- Features practical use case studies in many different financial markets and real-world techniques
- Describes how to avoid potential pitfalls and missteps in starting the alternative data journey
- Explains how to integrate information from different datasets to maximize informational value
The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users.
Machine Learning in Finance: From Theory to Practice
Authors: Matthew Dixon, Igor Halperin, and Paul Bilokon
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 derivative 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 Big Data with kdb+/q
Authors: Jan Novotny, Paul Bilokon, Aris Galiotos, and Frédéric Délèze
Upgrade your programming language to more effectively handle high-frequency data.
Machine Learning and Big Data with KDB+/Q offers quants, programmers and algorithmic traders a practical entry into the powerful but non-intuitive kdb+ database and q programming language. Ideally designed to handle the speed and volume of high-frequency financial data at sell- and buy-side institutions, these tools have become the de facto standard; this book provides the foundational knowledge practitioners need to work effectively with this rapidly-evolving approach to analytical trading.
The discussion follows the natural progression of working strategy development to allow hands-on learning in a familiar sphere, illustrating the contrast of efficiency and capability between the q language and other programming approaches. Rather than an all-encompassing “bible”-type reference, this book is designed with a focus on real-world practicality to help you quickly get up to speed and become productive with the language.
- Understand why kdb+/q is the ideal solution for high-frequency data
- Delve into “meat” of q programming to solve practical economic problems
- Perform everyday operations including basic regressions, cointegration, volatility estimation, modelling and more
- Learn advanced techniques from market impact and microstructure analyses to machine learning techniques including neural networks
The kdb+ database and its underlying programming language q offer unprecedented speed and capability. As trading algorithms and financial models grow ever more complex against the markets they seek to predict, they encompass an ever-larger swath of data – more variables, more metrics, more responsiveness and altogether more “moving parts.”
Traditional programming languages are increasingly failing to accommodate the growing speed and volume of data, and lack the necessary flexibility that cutting-edge financial modelling demands. Machine Learning and Big Data with KDB+/Q opens up the technology and flattens the learning curve to help you quickly adopt a more effective set of tools.
Novel Methods in Computational FInance
Chapter: Stochastic Filtering Methods in Electronic Trading
Authors: Paul Bilokon, James Gwinnutt, and Daniel Jones
Editors: Matthias Ehrhardt, Michael Günther, E. Jan W. ter Maten
Authors: Matthew Dixon, Igor Halperin, and Paul Bilokon
This book discusses the state-of-the-art and open problems in computational finance. It presents a collection of research outcomes and reviews of the work from the STRIKE project, an FP7 Marie Curie Initial Training Network (ITN) project in which academic partners trained early-stage researchers in close cooperation with a broader range of associated partners, including from the private sector.
The aim of the project was to arrive at a deeper understanding of complex (mostly nonlinear) financial models and to develop effective and robust numerical schemes for solving linear and nonlinear problems arising from the mathematical theory of pricing financial derivatives and related financial products. This was accomplished by means of financial modelling, mathematical analysis and numerical simulations, optimal control techniques and validation of models.
In recent years the computational complexity of mathematical models employed in financial mathematics has witnessed tremendous growth. Advanced numerical techniques are now essential to the majority of present-day applications in the financial industry.
Special attention is devoted to a uniform methodology for both testing the latest achievements and simultaneously educating young PhD students. Most of the mathematical codes are linked into a novel computational finance toolbox, which is provided in MATLAB and PYTHON with an open access license. The book offers a valuable guide for researchers in computational finance and related areas, e.g. energy markets, with an interest in industrial mathematics.
Trading Thalesians: What the Ancient World Can Teach Us about Trading Today
Author: Saeed Amen
What can the ancient world teach us about modern money markets? How can we use examples from the ancient world, philosophers and writers to better understand the markets? Just as historians such as Herodotus living in ancient Greece examined the past, can traders look to their past to learn something new?
In this exciting new book, Saeed Amen looks to the ancient world to help us better understand modern money markets, demonstrating what ancient philosophers can teach us about trading markets today, and showing readers how to maximize their returns.
Based on the rationale that if your primary objective is purely to make money from trading quickly, you can make decisions that perversely increase the likelihood of losing; this book demonstrates how successful trading can actually be achieved as a byproduct of good trading.
Relating concepts from the ancient world, such as water and risk, diversified knowledge, Herodotus and historical bias to the modern world money markets, Amen demonstrates that by focusing on goals that go beyond making money, lateral thinking, targeting risk adjusted returns, and keeping drawdowns in check, investors will indirectly make more money in the long run.
Investors might be fooled by randomness on occasion, but luck can never be derided as an important factor, which helps investors succeed. Instead repeated success in investing capital over an extended period seems to be less a product of randomness, but instead a product of a profound understanding of markets.
As part of the Machine Learning and Big Data with kdb+/q book project authored by Jan Novotny, Paul Bilokon, Aris Galiotos, and Frédéric Délèze, we have contributed to the quantQ library—a project managed my Jan Novotny (hanssmail on GitHub).
The library has since been extended beyond the book and implements:
- mathematical functions
- dynamic time warp
- deep neural networks
- stochastic optimization
- support vector machines
- Poisson regression
tsa: The Thalesians' Time Series Analysis Library (TSA)
Installation: pip install thalesians.tsa
The Thalesians time series library is a heterogeneous collection of tools for facilitating efficient
- data analysis and, more broadly,
- data science; and
- machine learning.
The originating developes’ primary applications are
- quantitative finance and economics;
- electronic trading, especially,
- algorithmic trading, especially,
- algorithmic market making;
- high-frequency finance;
- financial alpha generation;
- client analysis;
- risk analysis;
- financial strategy backtesting.
However, since data science and machine learning are universal, it is hoped that this code will be useful in other areas. Therefore we are looking for contributors with the above backgrounds as well as
- computer science,
- engineering, especially mechanical, electrical, electronic, marine, aeronautical, and aerospace,
- science, especially biochemistry and genetics, and
Currently, the following functionality is implemented and is being expanded:
- stochastic filtering, including Kalman and particle filtering approaches,
- stochastic processes, including mean-reverting (Ornstein-Uhlenbeck) processes,
- Gauss-Markov processes,
- stochastic simulation, including Euler-Maruyama scheme,
- interprocess communication via “pypes”,
- online statistics,
- visualisation, including interactive visualisation for Jupyter,
- pre-, post-condition, and invariant checking,
- utilities for dealing with Pandas dataframes, especially large ones,
- native Python, NumPy, and Pandas type conversions,
- interoperability with kdb+/q.
Nanosecond-precision temporal types for Java.
In modern-day neocybernetics applications, such as high-frequency electronic/algorithmic trading, high-frequency time series econometrics, robotics, and others, timestamps of necessity have nanosecond precision.
This library implements nanosecond-precision temporal types for such applications.
We would like to thank all those former and current colleagues from whom we have learned so much about Java programming.
We welcome requests for collaboration on maintaining this library and taking it further.
Comments, bug fixes, and new ideas are most welcome.
The LaThalesians library comprises a heterogeneous collection of LaTeX packages, which facilitate the type-setting of the Thalesians’ work in mathematics, computer science, and finance. It was originally developed by Paul Bilokon to support his academic and professional work and the library’s scope still reflects some of his personal biases, viz.:
- mathematical finance,
- scientific computing,
- stochastic analysis,
- probability theory,
- domain theory,
- computability theory.
It is hoped that, as more people get involved in the development and maintenance of this library, its scope will become more balanced and will more faithfully reflect the diverse activities of the Thalesians.
Rather than being structured as a single package, LaThalesians is a suite of packages, each package name starting with lathalesians-. Thus the modules may be used individually, depending on the user’s specific needs. Modularity is one of the design principles that guided us in this library’s development.
Another one is simplicity: tasks that occur often in our research and development work should be made easy. The syntax should be straightforward and easy to remember. LaTeX commands that we type in often should be brief.
But not too brief: they should still be unambiguous and easy to remember. Readability and clarity are also important to us. Finding the right balance between simplicity and readability is an art more than a science.
We are believers in domain-specific languages. Therefore we often define LaTeX commands to represent the concepts from a particular research area rather than typesetting instructions. This is done at the cost of introducing more words into our language. We believe that these are the very words that we need. Let’s express what things are, rather than what they should look like.
Finally, we believe that truth and beauty should go hand in hand. Both should be present in the content. Form should do justice to the content’s truth and present it in a way that is beautiful. We don’t claim that we have achieved this in LaThalesians. However, this is indeed our striving, our intention. We will be very much obliged for any recommendations on how to make the library’s output more aesthetically pleasing.
We, the Thalesians, would be very much obliged to you for your contributions to this library. It is far from perfect now. In many ways it is quite defective. Please help us make it both useful and beautiful.