NEWS FLASH... This book was just awarded Book of the Year by The Technical Analyst

In my decades of professional experience as a statistical consultant in the field of financial market trading, the single most important lesson that I've learned about trading is this: the quality of the indicators is vastly more important than the quality of the trading algorithm or predictive model. If you are sloppy about your indicator computation, no high-tech model or algorithm is going to bail you out. Garbage in, garbage out still rules.

This book presents numerous traditional and modern indicators that have been shown to carry significant predictive information. But it will do far more than just that. In addition to a wealth of useful indicators, you will see the following issues discussed:

There are simple tests that let you measure the potential information-carrying capacity of an indicator. If your proposed indicator fails this information-capacity test, you should consider revising it. This book describes simple transformations that raise the information-carrying capacity of your indicators and make them more useful for algorithmic trading.

You will learn how to locate the regions in your indicator's domain where maximum predictive power occurs so that you can focus on these important values.

You will learn how to compute statistically sound probabilities to help you decide whether the performance of an indicator is legitimate or just the product of random good luck.

Most traditional indicators examine one market at a time. But you will learn how examining pairs of markets, or even large collections of markets simultaneously, can provide valuable indicators that quantify complex inter-market relationships.

Govinda Khalsa devised a powerful indicator called the Follow-Through Index which reveals how likely it is that an existing trend will continue. This indicator is extremely useful to trend-following traders, but due to its complexity it is not widely employed. This book presents its essential theory and implementation in C++.

Gary Anderson developed a detailed and profound theory of market behavior that he calls The JANUS Factor. This theory enables computation of several powerful indicators that tell us, among other things, when trading opportunities are most likely to be profitable and when we should stay out of the market. This book provides the fundamental theory behind The JANUS Factor along with extensive C++ code.

Whether you compute a few indicators and trade by watching their plots on a computer screen, or do simple automated algorithmic trading, or employ sophisticated predictive models, this book provides tools that help you take your trading to a higher, more profitable level.

This book guides the reader through the construction of a Hauptwerk organ similar to the one built by the author. Detailed drawings and photographs, including all critical dimensions, are provided for a traditional compact console using stops and/or Novation LaunchPads. Issues with retrofitting a used console or using a desktop approach with touchscreens are also discussed. The book provides a MIDI tutorial, with special focus on how Hauptwerk uses MIDI messages to interact with your console electronics. Issues involving proper grounding and power distribution, vital topics often neglected, receive detailed coverage. Finally, an entire chapter is devoted to important Hauptwerk topics which the author believes are not thoroughly covered in the manual. These include things like optimal rank loading, adding output channels for solo stops, adopting a surround system for stereo organs, and simulating diatonic pipe chests. Anyone building a Hauptwerk organ will find a wealth of valuable material in this book.

This book provides the trading system developer with a powerful set of statistical tools for measuring vital aspects of performance that are ignored by most developers.

All algorithms include intuitive justification, basic theory, all relevant equations, and highly commented C++ code for complete programs that run in a Windows Command Console.

Reprogramming them in other languages should be easy, given the detailed explanations of each algorithm.

The following topics are covered:

Testing for overfitting at the earliest possible stage

Evaluating the luckiness-versus-skill of a fully developed system before deploying it

Testing the effectiveness and reliability of a trading system factory

Removing selection bias when screening a large number of indicators

Probability bounds for future mean returns

Bounding typical and catastrophic future drawdowns

Is the best indicator or model in a competition truly the best, or just the luckiest?

Which markets provide truly superior profits for your trading system?

What holding time for your system provides the best risk/return performance?

Build, test, and tune financial, insurance or other market trading systems using C++ algorithms and statistics. You've had an idea and have done some preliminary experiments, and it looks promising. Where do you go from here? Well, this book discusses and dissects this case study approach.

Seemingly good backtest performance isn't enough to justify trading real money. You need to perform rigorous statistical tests of the system's validity. Then, if basic tests confirm the quality of your idea, you need to tune your system, not just for best performance, but also for robust behavior in the face of inevitable market changes. Next, you need to quantify its expected future behavior, assessing how bad its real-life performance might actually be, and whether you can live with that. Finally, you need to find its theoretical performance limits so you know if its actual trades conform to this theoretical expectation, enabling you to dump the system if it does not liveup to expectations.

This book does not contain any sure-fire, guaranteed-riches trading systems. Those are a dime a dozen... But if you have a trading system, this book will provide you with a set of tools that will help you evaluate the potential value of your system, tweak it to improve its profitability, and monitor its on-going performance to detect deterioration before it fails catastrophically. Any serious market trader would do well to employ the methods described in this book.

What You Will Learn

• See how the 'spaghetti-on-the-wall' approach to trading system development can be done legitimately
• Detect overfitting early in development
• Estimate the probability that your system's backtest results could have been due to just good luck
• Regularize a predictive model so it automatically selects an optimal subset of indicator candidates
• Rapidly find the global optimum for any type of parameterized trading system
• Assess the ruggedness of your trading system against market changes
• Enhance the stationarity and information content of your proprietary indicators
• Nest one layer of walkforward analysis inside another layer to account for selection bias in complex trading systems
• Compute a lower bound on your system's mean future performance
• Bound expected periodic returns to detect on-going system deterioration before it becomes severe
• Estimate the probability of catastrophic drawdown

Who This Book Is For

Experienced C++ programmers, developers, and software engineers. Prior experience with rigorous statistical procedures to evaluate and maximize the quality of systems is recommended as well.

Imagine looking at a picture on a printed page or computer screen, adjusting your eyes in a manner that most people can learn easily, and suddenly having objects pop out at you in vivid 3D. Many people have already experienced this with the Magic Eye and related posters that were massively popular in the 90's. But what is not so well known is that this single-image stereogram technology has come a long way since those early days. The big breakthrough came when algorithms were discovered that could map textures onto the surface of single-image stereograms. I believe this is the only available book that delves deeply into stereogram algorithms, including highly documented C++ source code. These algorithms can be used to great effect by artists to create works of art that are far beyond the crude stereograms of yesteryear. Perhaps even more importantly, the ability to display depth maps in clear stereo using only a single printed image can be invaluable for scientific presentations. This book is an essential resource for anyone writing programs for stereogram generation.

This book serves two purposes. First, it teaches the importance of using sophisticated yet accessible statistical methods to evaluate a trading system before it is put to real-world use. In order to accommodate readers having limited mathematical background, these techniques are illustrated with step-by-step examples using actual market data, and all examples are explained in plain language. Second, this book shows how the free program TSSB (Trading System Synthesis & Boosting) can be used to develop and test trading systems. The machine learning and statistical algorithms available in TSSB go far beyond those available in other off-the-shelf development software. Intelligent use of these state-of-the-art techniques greatly improves the likelihood of obtaining a trading system whose impressive backtest results continue when the system is put to use in a trading account. Among other things, this book will teach the reader how to: Estimate future performance with rigorous algorithms Evaluate the influence of good luck in backtests Detect overfitting before deploying your system Estimate performance bias due to model fitting and selection of seemingly superior systems Use state-of-the-art ensembles of models to form consensus trade decisions Build optimal portfolios of trading systems and rigorously test their expected performance Search thousands of markets to find subsets that are especially predictable Create trading systems that specialize in specific market regimes such as trending/flat or high/low volatility More information on the TSSB program can be found at TSSBsoftware dot com.

Tragedy inside mankind's first outpost on Mars triggers political intrigue, betrayal, murder, and the ultimate triumph of courage over greed.

Glenn Station broadcasts a desperate call for help and then goes silent. A rescue crew sets out on the long journey to Mars, but mysterious accidents and injuries throw the operation into chaos.

The pilot, John Hellman, soon concludes that someone on Earth has planted a traitor in their midst. As panic mounts among his shipmates, he struggles to identify the deadly phantom force that is terrorizing his crew.

Back on Earth, John's wife Marie heads up the government lab studying peculiar artifacts from the red planet. When she uncovers the frightening truth behind the Glenn Station disaster, she realizes that the future of humanity may be in her hands.

But the danger she now faces is far different from what she ever expected. A cabal of nationalistic zealots has taken control of the mission to Mars, and they are determined to destroy anyone who threatens their hold on power. Marie's only hope is to match wits with the player at the heart of the game, a ruthless renegade who will do whatever it takes to win.

John and Marie Hellman are a world apart, yet together they are fighting the deadliest threat that mankind has ever seen.

Discover hidden relationships among the variables in your data, and learn how to exploit these relationships. This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code.
Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program. The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work.
What You'll Learn

• Use Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data
  
• Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data
  
• Work with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methods
  
• See how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data
  
• Plot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high
  

Who This Book Is For

Anyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.

Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application.
Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models. This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics.
All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Manyof these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program.

What You'll Learn

• Compute entropy to detect problematic predictors
  
• Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing
  
• Carry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling
  
• Harness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising
  
• Use Monte-Carlo permutation methods to assessthe role of good luck in performance results
  
• Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions
  

Who This Book is For
Anyone who creates prediction or classification models will find a wealth of useful algorithms in this book. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.

Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards.
The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. As such, you'll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting.
All theroutines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines.

What You Will Learn

• Employ deep learning using C++ and CUDA C
  
• Work with supervised feedforward networks
  
• Implement restricted Boltzmann machines
  
• Use generative samplings
  
• Discover why these are important
  

Who This Book Is For
Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.

Discover the essential building blocks of a common and powerful form of deep belief net: the autoencoder. You'll take this topic beyond current usage by extending it to the complex domain for signal and image processing applications. Deep Belief Nets in C++ and CUDA C: Volume 2 also covers several algorithms for preprocessing time series and image data. These algorithms focus on the creation of complex-domain predictors that are suitable for input to a complex-domain autoencoder. Finally, you'll learn a method for embedding class information in the input layer of a restricted Boltzmann machine. This facilitates generative display of samples from individual classes rather than the entire data distribution. The ability to see the features that the model has learned for each class separately can be invaluable.
At each step this book provides you with intuitive motivation, a summary of the most important equations relevant to the topic, and highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards.

What You'll Learn

• Code for deep learning, neural networks, and AI using C++ and CUDA C
  
• Carry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and more
  
• Use the Fourier Transform for image preprocessing
  
• Implement autoencoding via activation in the complex domain
  
• Work with algorithms for CUDA gradient computation
  
• Use the DEEP operating manual
  

Who This Book Is For
Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.

Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a 'thought process' that is capable of learning abstract concepts built from simpler primitives. These models are especially useful for image processing applications.

At each step Deep Belief Nets in C++ and CUDA C: Volume 3 presents intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the executable CONVNET program which implements these algorithms, are available for free download.

What You Will Learn

• Discover convolutional nets and how to use them
  
• Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs
  
• Master the various programming algorithms required
  
• Carry out multi-threaded gradient computations and memory allocations for this threading
  
• Work with CUDA code implementations of all core computations, including layer activations and gradient calculations
  
• Make use of the CONVNET program and manual to explore convolutional nets and case studies
  

Who This Book Is For
Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.

This book describes and provides instructions for four programs that can be downloaded for free, including complete source code, from my website. These programs are:

Vortex - This program creates an image of a vortex plunging into an abyss. A black-and-white checkerboard is distorted in such a way as to give the appearance of a hole into which objects can be sucked. If this pattern is transferred to a large floor covering, examples of which can be seen for sale on the internet, the effect can be striking.

Blocks - All you need is a quantity of three slightly differently shaped and colored parallelograms, easily mass produced, to create an impressive 3D illusion. I have seen woodworkers making and selling beautiful cutting boards in this pattern. A little careful veneer work can produce gorgeous covers for wood boxes. This program provides the exact shapes needed, and lets the artist visualize what the end product would look like. The size and shape of the illusory cubes, as well as the viewing angle, can be adjusted by the artist for maximum creative control.

Julia - Programs for visualizing Mandelbrot and Julia sets were a dime a dozen in the 90's, and are still somewhat available on the internet. My program goes beyond the common versions in two ways. First, it uses a very advanced algorithm that can produce only binary (such as black and white) images of these sets, but it does so with extreme detail, reaching resolutions far beyond what can be obtained with the usual 'dwell escape' algorithm. Second, it generalizes Mandelbrot and Julia sets to the quaternion (four dimensional) domain, lets the uses define a 3D cross sectional object from the 4D set, and then it uses ray tracing to display this cross section with white or colored lights arrayed in any of several user-defined positions. Julia sets in four dimensions have a strange, almost creepy otherworldly appearance and can make for highly unusual decorative patterns.

Lin - Aristid Lindenmayer devised a mathematical language that can be used to describe the growth and structure of plants. Just a few lines of Lindenmayer code can serve to rigorously describe trees, bushes, flowers, and even alien-looking plant-like creations. The Lin program supports three levels of Lindenmayer systems:1) Simple edge replacement algorithms produce snowflake curves, Koch curves, dragon curves, Sierpinski gaskets, and so forth. This primitive but often lovely family of curves makes fabulous decorative ornamentation.2) Two-dimensional axial generation produces flat but interesting plant-like objects, with strikingly realistic branching patterns. These simple plant representations make perfect focal points for handcrafted items.3) Three-dimensional axial generation can produce shockingly realistic plants and flowers, reminiscent of plant sketches made by professional artists and illustrators. The Lin program allows for an almost unlimited number of productions, up to ten different colors for different plant parts, and even up to three different versions of each production, with versions randomly selected according to user-specified probabilities. This lets the artist create entire fields of plants, all of the same 'species', but no two exactly alike.

All of these programs run under 64-bit Windows. The source code compiles with Microsoft Visual C++ 2019 and probably most other modern C++ compilers. The executables as well as complete source code can be downloaded for free from my website, TimothyMasters.info. This book provides detailed explanations of the algorithms as well as tutorial examples.

Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables.

As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. You'll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are:

• Forward selection component analysis
• Local feature selection
• Linking features and a target with a hidden Markov model
• Improvements on traditional stepwise selection
• Nominal-to-ordinal conversion

All algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code.

The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it.

What You Will Learn

• Combine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set.
• Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods.
• Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets.
• Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input.

Who This Book Is For

Intermediate to advanced data science programmers and analysts.

Civilizations on both ends of the galaxy want them, dead or alive.

Mallory Milton Smith, small-time freight hauler and intergalactic cab driver, takes a job transporting a trio of ugly, smelly Ixinians (Ickies to him) to their home on the far side of the galaxy. Unfortunately, what should have been a simple journey turns into a breathless game of cat-and-mouse when a brazen fighter pilot with her own agenda enters the picture and takes command.

With Nikita Marikolova, an elite Special Forces pilot at the helm, Mallory and an Ixinian criss-cross the galaxy, fleeing from the Earth Navy, the Ixinian Navy, mercenaries, and the commander of Nikita's Special Forces unit. Alone and unaided, Mallory and Nikita must deliver their passenger to the Ixinian home world if they are to avert intergalactic war.