Part IV: Empirical studies
Part III: Algorithms
Confidence-weighted mean reversion
Passive–aggressive mean reversion
Online moving average reversion
Correlation-driven nonparametric learning
Part II: Principles
Benchmarks Follow the winner Follow the loser
Pattern matching–based
Part I: Introduction
Problem formulation
Empirical results reats of validityImplementations
Part V: Conclusion
Figure 1.1 Book organization.
Part I introduces the background, motivations, and basic definitions of the OLPS
problem. Specifically, Chapter 1 introduces the background of computational finance,
algorithmic trading, and machine learning and their connections to OLPS. Chapter 2
formally formulates the problem of OLPS as a scientific task.
Part II summarizes the main principles and algorithms of OLPS. In particular,
Chapter 3 introduces a family of strategies commonly known as the benchmark prin-
ciples for OLPS. Chapter 4 introduces the principle of “follow the winner,” which is
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commonly known as the strategies of exploring the “trend following” assumption for
investment. Chapter 5 introduces the principles of “follow the loser,” which is often
known as the strategies of exploring the “mean reversion” assumption for investment.
Chapter 6 introduces the principle of pattern matching for OLPS. Finally, Chapter 7
introduces the principle of meta-learning, which attempts to explore the combination
of multiple principles and strategies for OLPS.
Part III proposes four OLPS algorithms belonging to two categories, that is,
the pattern matching–based approach and follow the loser approach. The first algo-
rithm is a pattern-matching algorithm, “CORrelation-driven Nonparametric learning”
(CORN), in Chapter 8. The other three algorithms are mean reversion algorithms.
That is, we propose the “passive–aggressive mean reversion” (PAMR) algorithm
in Chapter 9, the “confidence-weighted mean reversion” (CWMR) algorithm in
Chapter 10, and the “online moving average reversion” (OLMAR) in Chapter 11.
Part IV presents our empirical studies. Chapter 12 introduces the method of empir-
ical studies, and Chapter 13 extensively evaluates the proposed algorithms on real
datasets and compares with a set of existing algorithms. Chapter 14 defends the
methodologies used in the model setting and empirical studies. Finally, Chapter 15
concludes the book with some future directions.
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