Machine Learning and Big Data with kdb+/q

Name: Machine Learning and Big Data with kdb+/q
Brand: Wiley
Price: 63.99 EUR
Availability: OnlineOnly

Jan Novotny Paul A. Bilokon Aris Galiotos Frederic Deleze(Autor*in)

Wiley (Verlag)

1. Auflage

Erschienen am 12. November 2019

640 Seiten

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Beschreibung

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.

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Inhalt

Cover
Title Page
Copyright
Contents
Preface
History of Kdb+ and q
Motivation for this Book
Code Structure
Structure of the Book
Prerequisites
About the Authors
Part One Language Fundamentals
Chapter 1 Fundamentals of the q Programming Language
1.1 The (Not So Very) First Steps in q
1.2 Atoms and Lists
1.2.1 Casting Types
1.3 Basic Language Constructs
1.3.1 Assigning, Equality and Matching
1.3.2 Arithmetic Operations and Right-to-Left Evaluation: Introduction to q Philosophy
1.4 Basic Operators
1.5 Difference between Strings and Symbols
1.5.1 Enumeration
1.6 Matrices and Basic Linear Algebra in q
1.7 Launching the Session: Additional Options
1.8 Summary and How-To's
Chapter 2 Dictionaries and Tables: The q Fundamentals
2.1 Dictionary
2.2 Table
2.3 The Truth about Tables
2.4 Keyed Tables Are Dictionaries
2.5 From a Vector Language to an Algebraic Language
Chapter 3 Functions
3.1 Namespace
3.1.0.1 .quantQ. Namespace
3.2 The Six Adverbs
3.2.1 Each
3.2.1.1 Each
3.2.1.2 Each-left \:
3.2.1.3 Each-right /:
3.2.1.4 Cross Product /: \:
3.2.1.5 Each-both '
3.2.2 Each-prior ':
3.2.3 Compose (')
3.2.4 Over and Fold /
3.2.5 Scan
3.2.5.1 EMA: The Exponential Moving Average
3.2.6 Converge
3.2.6.1 Converge-repeat
3.2.6.2 Converge-iterate
3.3 Apply
3.3.1 @ (apply)
3.3.2 . (apply)
3.4 Protected Evaluations
3.5 Vector Operations
3.5.1 Aggregators
3.5.1.1 Simple Aggregators
3.5.1.2 Weighted Aggregators
3.5.2 Uniform Functions
3.5.2.1 Running Functions
3.5.2.2 Window Functions
3.6 Convention for User-Defined Functions
Chapter 4 Editors and Other Tools
4.1 Console
4.2 Jupyter Notebook
4.3 GUIs
4.3.1 qStudio
4.3.2 Q Insight Pad
4.4 IDEs: IntelliJ IDEA
4.5 Conclusion
Chapter 5 Debugging q Code
5.1 Introduction to Making It Wrong: Errors
5.1.1 Syntax Errors
5.1.2 Runtime Errors
5.1.2.1 The Type Error
5.1.2.2 Other Errors
5.2 Debugging the Code
5.3 Debugging Server-Side
Part Two Data Operations
Chapter 6 Splayed and Partitioned Tables
6.1 Introduction
6.2 Saving a Table as a Single Binary File
6.3 Splayed Tables
6.4 Partitioned Tables
6.5 Conclusion
Chapter 7 Joins
7.1 Comma Operator
7.2 Join Functions
7.2.1 ij
7.2.2 ej
7.2.3 lj
7.2.4 pj
7.2.5 upsert
7.2.6 uj
7.2.7 aj
7.2.8 aj0
7.2.8.1 The Next Valid Join
7.2.9 asof
7.2.10 wj
7.3 Advanced Example: Running TWAP
Chapter 8 Parallelisation
8.1 Parallel Vector Operations
8.2 Parallelisation over Processes
8.3 Map-Reduce
8.4 Advanced Topic: Parallel File/Directory Access
Chapter 9 Data Cleaning and Filtering
9.1 Predicate Filtering
9.1.1 The Where Clause
9.1.2 Aggregation Filtering
9.2 Data Cleaning, Normalising and APIs
Chapter 10 Parse Trees
10.1 Definition
10.1.1 Evaluation
10.1.2 Parse Tree Creation
10.1.3 Read-Only Evaluation
10.2 Functional Queries
10.2.1 Functional Select
10.2.2 Functional Exec
10.2.3 Functional Update
10.2.4 Functional Delete
Chapter 11 A Few Use Cases
11.1 Rolling VWAP
11.1.1 N Tick VWAP
11.1.2 Time Window VWAP
11.2 Weighted Mid for N Levels of an Order Book
11.3 Consecutive Runs of a Rule
11.4 Real-Time Signals and Alerts
Part Three Data Science
Chapter 12 Basic Overview of Statistics
12.1 Histogram
12.2 First Moments
12.3 Hypothesis Testing
12.3.1 Normal p-values
12.3.2 Correlation
12.3.2.1 Implementation
12.3.3 t-test: One Sample
12.3.3.1 Implementation
12.3.4 t-test: Two Samples
12.3.4.1 Implementation
12.3.5 Sign Test
12.3.5.1 Implementation of the Test
12.3.5.2 Median Test
12.3.6 Wilcoxon Signed-Rank Test
12.3.7 Rank Correlation and Somers' D
12.3.7.1 Implementation
12.3.8 Multiple Hypothesis Testing
12.3.8.1 Bonferroni Correction
12.3.8.2 S?idák's Correction
12.3.8.3 Holm's Method
12.3.8.4 Example
Chapter 13 Linear Regression
13.1 Linear Regression
13.2 Ordinary Least Squares
13.3 The Geometric Representation of Linear Regression
13.3.1 Moore-Penrose Pseudoinverse
13.3.2 Adding Intercept
13.4 Implementation of the OLS
13.5 Significance of Parameters
13.6 How Good is the Fit: R2
13.6.1 Adjusted R-squared
13.7 Relationship with Maximum Likelihood Estimation and AIC with Small Sample Correction
13.8 Estimation Suite
13.9 Comparing Two Nested Models: Towards a Stopping Rule
13.9.1 Comparing Two General Models
13.10 In-/Out-of-Sample Operations
13.11 Cross-validation
13.12 Conclusion
Chapter 14 Time Series Econometrics
14.1 Autoregressive and Moving Average Processes
14.1.1 Introduction
14.1.2 AR(p) Process
14.1.2.1 Simulation
14.1.2.2 Estimation of AR(p) Parameters
14.1.2.3 Least Square Method
14.1.2.4 Example
14.1.2.5 Maximum Likelihood Estimator
14.1.2.6 Yule-Walker Technique
14.1.3 MA(q) Process
14.1.3.1 Estimation of MA(q) Parameters
14.1.3.2 Simulation
14.1.3.3 Example
14.1.4 ARMA(p,q) Process
14.1.4.1 Invertibility of the ARMA(p,q) Process
14.1.4.2 Hannan-Rissanen Algorithm: Two-Step Regression Estimation
14.1.4.3 Yule-Walker Estimation
14.1.4.4 Maximum Likelihood Estimation
14.1.4.5 Simulation
14.1.4.6 Forecast
14.1.5 ARIMA(p,d,q) Process
14.1.6 Code
14.1.6.1 Simulation
14.1.6.2 Estimation
14.1.6.3 Forecast
14.2 Stationarity and Granger Causality
14.2.1 Stationarity
14.2.2 Test of Stationarity - Dickey-Fuller and Augmented Dickey-Fuller Tests
14.2.3 Granger Causality
14.3 Vector Autoregression
14.3.1 VAR(p) Process
14.3.1.1 Notation
14.3.1.2 Estimator
14.3.1.3 Example
14.3.1.4 Code
14.3.2 VARX(p,q) Process
14.3.2.1 Estimator
14.3.2.2 Code
Chapter 15 Fourier Transform
15.1 Complex Numbers
15.1.1 Properties of Complex Numbers
15.2 Discrete Fourier Transform
15.3 Addendum: Quaternions
15.4 Addendum: Fractals
Chapter 16 Eigensystem and PCA
16.1 Theory
16.2 Algorithms
16.2.1 QR Decomposition
16.2.2 QR Algorithm for Eigenvalues
16.2.3 Inverse Iteration
16.3 Implementation of Eigensystem Calculation
16.3.1 QR Decomposition
16.3.2 Inverse Iteration
16.4 The Data Matrix and the Principal Component Analysis
16.4.1 The Data Matrix
16.4.2 PCA: The First Principal Component
16.4.3 Second Principal Component
16.4.4 Terminology and Explained Variance
16.4.5 Dimensionality Reduction
16.4.6 PCA Regression (PCR)
16.5 Implementation of PCA
16.6 Appendix: Determinant
16.6.1 Theory
16.6.2 Techniques to Calculate a Determinant
16.6.3 Implementation of the Determinant
Chapter 17 Outlier Detection
17.1 Local Outlier Factor
Chapter 18 Simulating Asset Prices
18.1 Stochastic Volatility Process with Price Jumps
18.2 Towards the Numerical Example
18.2.1 Numerical Preliminaries
18.2.2 Implementing Stochastic Volatility Process with Jumps
18.3 Conclusion
Part Four Machine Learning
Chapter 19 Basic Principles of Machine Learning
19.1 Non-Numeric Features and Normalisation
19.1.1 Non-Numeric Features
19.1.1.1 Ordinal Features
19.1.1.2 Categorical Features
19.1.2 Normalisation
19.1.2.1 Normal Score
19.1.2.2 Range Scaling
19.2 Iteration: Constructing Machine Learning Algorithms
19.2.1 Iteration
19.2.2 Constructing Machine Learning Algorithms
Chapter 20 Linear Regression with Regularisation
20.1 Bias-Variance Trade-off
20.2 Regularisation
20.3 Ridge Regression
20.4 Implementation of the Ridge Regression
20.4.1 Optimisation of the Regularisation Parameter
20.5 Lasso Regression
20.6 Implementation of the Lasso Regression
Chapter 21 Nearest Neighbours
21.1 k-Nearest Neighbours Classifier
21.2 Prototype Clustering
21.3 Feature Selection: Local Nearest Neighbours Approach
21.3.1 Implementation
Chapter 22 Neural Networks
22.1 Theoretical Introduction
22.1.1 Calibration
22.1.1.1 Backpropagation
22.1.2 The Learning Rate Parameter
22.1.3 Initialisation
22.1.4 Overfitting
22.1.5 Dimension of the Hidden Layer(s)
22.2 Implementation of Neural Networks
22.2.1 Multivariate Encoder
22.2.2 Neurons
22.2.3 Training the Neural Network
22.3 Examples
22.3.1 Binary Classification
22.3.2 M-class Classification
22.3.3 Regression
22.4 Possible Suggestions
Chapter 23 AdaBoost with Stumps
23.1 Boosting
23.2 Decision Stumps
23.3 AdaBoost
23.4 Implementation of AdaBoost
23.5 Recommendation for Readers
Chapter 24 Trees
24.1 Introduction to Trees
24.2 Regression Trees
24.2.1 Cost-Complexity Pruning
24.3 Classification Tree
24.4 Miscellaneous
24.5 Implementation of Trees
Chapter 25 Forests
25.1 Bootstrap
25.2 Bagging
25.2.1 Out-of-Bag
25.3 Implementation
25.3.1 Prediction
25.3.2 Feature Selection
Chapter 26 Unsupervised Machine Learning: The Apriori Algorithm
26.1 Apriori Algorithm
26.2 Implementation of the Apriori Algorithm
Chapter 27 Processing Information
27.1 Information Retrieval
27.1.1 Corpus: Leonardo da Vinci
27.1.2 Frequency Counting
27.1.3 tf-idf
27.2 Information as Features
27.2.1 Sample: Simulated Proteins
27.2.2 Kernels and Metrics for Proteins
27.2.3 Implementation of Inner Products and Nearest Neighbours Principles
27.2.4 Further Topics
Chapter 28 Towards AI - Monte Carlo Tree Search
28.1 Multi-Armed Bandit Problem
28.1.1 Analytic Solutions
28.1.2 Greedy Algorithms
28.1.3 Confidence-Based Algorithms
28.1.4 Bayesian Algorithms
28.1.5 Online Gradient Descent Algorithms
28.1.6 Implementation of Some Learning Algorithms
28.2 Monte Carlo Tree Search
28.2.1 Selection Step
28.2.2 Expansion Step
28.2.3 Simulation Step
28.2.4 Back Propagation Step
28.2.5 Finishing the Algorithm
28.2.6 Remarks and Extensions
28.3 Monte Carlo Tree Search Implementation - Tic-tac-toe
28.3.1 Random Games
28.3.2 Towards the MCTS
28.3.3 Case Study
28.4 Monte Carlo Tree Search - Additional Comments
28.4.1 Policy and Value Networks
28.4.2 Reinforcement Learning
Chapter 29 Econophysics: The Agent-Based Computational Models
29.1 Agent-Based Modelling
29.1.1 Agent-Based Models in Society
29.1.2 Agent-Based Models in Finance
29.2 Ising Agent-Based Model for Financial Markets
29.2.1 Ising Model in Physics
29.2.2 Ising Model of Interacting Agents
29.2.3 Numerical Implementation
29.3 Conclusion
Chapter 30 Epilogue: Art
Bibliography
Index
EULA

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