统计优化与机器学习驱动的交易算法：第二版概览

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《算法交易方法：应用高级统计、优化与机器学习技术第二版》是一本由罗伯特·基斯尔博士编著的专业书籍，他拥有博士学位，担任Kissell Research Group的总裁，并在福特汉姆大学加贝利商学院担任兼职教员。本书专注于介绍在金融领域中运用算法交易（AlgoTrading）的先进方法，特别是如何结合统计学、优化理论和机器学习技术。该书的核心内容包括但不限于以下几个方面： 1. **算法交易基础**：章节首先概述了算法交易的基本概念，阐述其在金融市场中的作用，以及与传统交易方式的区别。它强调了自动化决策过程在高效执行交易策略、降低交易成本和捕捉高频市场机会等方面的优势。 2. **高级统计分析**：这部分深入探讨了如何利用统计学原理来理解和解析金融市场的数据，如时间序列分析、回归模型、概率论和假设检验等。通过这些工具，交易者可以对市场动态进行预测和风险评估。 3. **优化技术**：作者详细讲解了如何运用线性规划、整数编程、动态规划等优化方法设计和调整交易策略，以实现目标（如最大收益或最小风险）的最优解。这在构建复杂的交易规则和执行过程中至关重要。 4. **机器学习在量化交易中的应用**：这一部分是本书的重点，涵盖了监督学习（如预测模型）、无监督学习（如聚类和异常检测）、强化学习（如基于环境反馈的决策策略）等方法。读者将学习如何利用大数据训练模型，以识别模式、预测市场趋势并实时响应。 5. **实战案例与实证研究**：书中包含大量实际案例，展示如何将理论知识转化为实践操作，以及如何通过机器学习方法解决实际交易问题。同时，还会有针对不同市场环境的实证分析，以验证所学理论的有效性和实用性。 6. **版权和许可**：最后，关于版权的法律声明确保了学术出版物的知识产权保护，提醒读者在使用书中内容时必须获得出版商的书面许可。《算法交易方法：应用高级统计、优化与机器学习技术第二版》是一本深度剖析量化交易策略和工具的实用指南，为投资者、交易员和研究人员提供了深入理解现代金融市场和利用最新技术进行交易决策的宝贵资源。无论是初学者还是经验丰富的从业者，都能从中收获丰富知识和实践经验。

Derivation of the 1/3 Factor................................................................. 384

Timing Risk For a Basket of Stock ..................................................... 387

Comparison of Market Impact Estimates............................................. 388

Forecasting Covariance ........................................................................ 390

Efﬁcient Trading Frontier .................................................................... 391

Single Stock Trade Cost Objective Function........................ ............... 393

Portfolio Trade Cost Objective Function............................................. 394

Managing Portf olio Risk...................................................................... 395

Residual Risk Curve ............................................................................ 395

Minimum Trading Risk Quantity.............................. ........................... 397

Maximum Trad ing Opportunity............... ............................................ 398

When to Use These Criteria? ............................................................... 400

Program-Block Decomposition............................................................ 400

CHAPTER 16 Decoding and Reverse Engineering Broker Models with Machine

Learning Techniques ....................................................................

405

Introduction............................................................................................... 405

Pre-Trade of Pre-Trades............................................................................ 406

I-Star Model Approach ........................................................................ 407

Neural Network Model Approach ....................................................... 413

Portfolio Optimization .............................................................................. 416

What Should the Portfolio Manager Do? ............................................ 419

Deriving Portf olio Optimization Market Impact Models .................... 419

Example: Share Quantity Regression Model....................................... 420

Example: Trade Value Regression Model ........................................... 425

CHAPTER 17 Portfolio Construction with Transaction Cost Analysis ........................ 429

Introduction............................................................................................... 429

Portfolio Optimization and Constraints .................................................... 430

Transaction Costs in Portfolio Optimization ............................................ 434

Portfolio Management Process ................................................................. 437

Example: Efﬁcient Trading Frontier With and Without Short

Positions .............................................................................................. 437

Example: Maximizing Investor Ut ility ................................................ 438

Trading Decision Process ....................... .................................................. 439

What is the Appropriate Optimal Strategy to Use? ............................. 440

Unifying the Investment and Trading Theories........................................ 441

Which Execution Strategy Should the Trader Use? ............................ 443

Cost-Adjusted Frontier.............................................................................. 445

Determining the Appropriate Level of Risk Aversion ............................. 447

Best Execution Frontier ............................................................................ 448

Contents xv

Portfolio Construction with Transaction Costs......................................... 449

Quest for Best Execution Frontier ....................................................... 451

Example ....................................................... ............................................. 456

Important Findi ngs ............................................................................... 464

Conclusion ............... ................................................................................. 466

CHAPTER 18 Quantitative Analysis with TCA ...................................................... 469

Introduction ............................................................................................... 469

Quantitative Overlays........................................................................... 469

Market Impact Factor Scores ............................................................... 469

Cost Curves .......................................................................................... 469

Alpha Capture ...................................................................................... 470

Investment Capacity............................................................................. 470

Portfolio Optimization.......................................................................... 470

Backtesting .............................................. ............................................. 470

Liquidation Cost................................................................................... 471

Sensitivity Analysis.............................................................................. 471

Are The Existing Models Useful Enough For Portfolio

Construction?....................................................................................... 472

Current State of Vendor Market Impact Models ................................. 473

Pretrade of Pretrades ................................................................................. 475

Applications ......................................................................................... 476

Example #1 .......................................................................................... 476

Example #2 .......................................................................................... 477

Example #3 .......................................................................................... 477

Example #4 .......................................................................................... 477

How Expensive is it to Trade? ................................................................. 478

Acquisition and Liquidation Costs....................................................... 481

Portfolio ManagementdScreening Techniques .................................. 484

Backtesting Strategies ............................................................................... 487

Market Impact Simulation ........................................................................ 490

Simulation Scenario ............................................................................. 491

Multi-Asset Class Investing...................................................................... 495

Investing in Beta Exposure and Other Factors .................................... 495

Example #5 .......................................................................................... 495

Multi-Asset Trading Costs........................................................................ 498

Global Equity Markets......................................................................... 500

Multi-Asset Classes................................. ............................................. 501

Market Impact Factor Scores.................................................................... 508

Current State of Market Impact Factor Scores .................................... 510

xvi Contents

Market Impact Factor Score Analysis ...................................................... 510

Alpha Capture Program ............................................................................ 512

Example #6 .......................................................................................... 513

Example #7 .......................................................................................... 514

Alpha Capture Curves.......................................................................... 516

CHAPTER 19 Machine Learning and Trade Schedule Optimization.......................... 519

Introduction............................................................................................... 519

Multiperiod Trade Schedule Optimization Problem................................. 520

Setting up the Problem......................................................................... 520

Trader’s Dilemma Objective Function................................................. 521

Nonlinear Optimization Convergence ...................................................... 523

Newton’s Method............... .................................................................. 525

Example #1 .......................................................................................... 526

Example #2 .......................................................................................... 527

Machine Learning ..................................................................................... 528

Neural Networks .................................................................................. 530

Neural Network Errors.......................................................... ............... 531

Machine Learning Training Exp eriment................................................... 531

Step I: Generating Simulated Trade Baskets ........................ ............... 532

Step II: Compile Stock and Basket Data Statistics.............................. 532

Step III: Solve the Multiperiod Trade Schedule Optimization

Problem ................................................................................ ............... 534

Step IV: Train the NNET..................................................................... 535

Step V. Calculate the Initial Parameter Values for the NNET............ 536

Principal Component Analysis............................................................. 536

Stepwise Regression Analysis.............................................................. 536

Neural Network Structure .................................................................... 539

Neural Network Error .......................................................................... 539

Performance Results ................................................................................. 540

Conclusions............................................................................................... 542

CHAPTER 20 TCA Analysis Using MATLAB, Excel, and Python ................................ 543

Introduction............................................................................................... 543

Transaction Cost Analysis Functions ....................................................... 545

Transaction Cost Model............................................................................ 547

MATLAB Functions................................................................................. 549

Excel and Python Functions ..................................................................... 549

TCA Report Examples.............................................................................. 550

Conclusion .............................. .................................................................. 558

Contents xvii

Preface

Any intelligent fool can make things bigger and more complex. It takes a touch of

genius — and a lot of courage to move in the opposite direction.

Albert Einstein

“Algorithmic Trading Methods: Applications using Advanced Statistics, Optimization, and

Machine Learning Techniques,” Second Edition is a sequel to “The Science of Algorithmic

Trading & Portfolio Management.” This book greatly expands the concepts, foundations,

methodology, and models from the ﬁrst edition, and it provides new insight into Algorithmic

Trading and Transaction Cost Analysis (TCA) using advanced mathematical techniques,

statistics, optimization, machine learning, neural networks, and predictive analytics.

Algorithmic Trading Methods provides traders, portfolio managers, analysts, students,

practitioners, and ﬁnancial executives with an overview of the electronic trading environment,

and insight into how algorithms can be utilized to improve execution quality, fund

performance, and portfolio construction.

We provide a discussion of the current state of the market and advanced modeling techniques

for trading algorithms, stock selection and portfolio construction.

This reference book will provide readers with:

 Insight into the new electronic trading environment.

 Overview of transaction cost analysis (TCA) and discussion of proper metrics for cost mea-

surement and performance evaluation.

 Description of the different types of trading algorithms: VWAP/TWAP, Arrival Price,

Implementation Shortfall, Liquidity Seeking, Dark Pols, Dynamic Pricing, Opportunistic,

and Portfolio Trading Algorithms.

 Proven market impact modeling and forecasting techniques.

 Trading costs across various asset classes: equities, futures, ﬁxed income, foreign exchange,

and commodities.

 Advanced forecasting techniques to estimate daily liquidity, monthly volumes, and ADV.

 An algorithmic decision-making framework to ensure consistency between investment and

trading objectives.

 An understanding of how machine learning techniques can be applied to algorithmic trading

and portfolio management.

 A best execution process to ensure funds are positioned to achieve their maximum level of

performance.

 A TCA library that allows investors to perform transaction cost analysis and develop algo-

rithmic trading models on their own desktop.

xix

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