I: INTRODUCTION Introduction Background What Is Online Portfolio Selection? Methodology Book Overview Problem Formulation Problem Settings Transaction Costs and Margin Buying Models Evaluation Summary II: Principles Benchmarks Buy-and-Hold Strategy Best Stock Strategy Constant Rebalanced Portfolios Follow the Winner Universal Portfolios Exponential Gradient Follow the Leader Follow the Regularized Leader Summary Follow the Loser Mean Reversion Anticorrelation Summary Pattern Matching Sample Selection Techniques Portfolio Optimization Techniques Combinations Summary Meta-Learning Aggregating Algorithms Fast Universalization Online Gradient and Newton Updates Follow the Leading History Summary III: Algorithms Correlation-Driven Nonparametric Learning Preliminaries Formulations Algorithms Analysis Summary Passive¿Aggressive Mean Reversion Preliminaries Formulations Algorithms Analysis Summary Confidence-Weighted Mean Reversion Preliminaries Formulations Algorithms Analysis Summary Online Moving Average Reversion Preliminaries Formulations Algorithms Analysis Summary IV: Empirical Studies Implementations The OLPS Platform Data Setups Performance Metrics Summary Empirical Results Experiment 1: Evaluation of Cumulative Wealth Experiment 2: Evaluation of Risk and Risk-Adjusted Return Experiment 3: Evaluation of Parameter Sensitivity Experiment 4: Evaluation of Practical Issues Experiment 5: Evaluation of Computational Time Experiment 6: Descriptive Analysis of Assets and Portfolios Summary Threats to Validity On Model Assumptions On Mean Reversion Assumptions On Theoretical Analysis On Back-Tests Summary V: Conclusion Conclusions Future Directions Appendix A: OLPS: A Toolbox for Online Portfolio Selection Introduction Framework and Interfaces Strategies Summary Appendix B: Proofs and Derivations Proof of CORN Derivations of PAMR Derivations of CWMR Derivation of OLMAR Appendix C: Supplementary Data and Portfolio Statistics Bibliography Index.