Quality Money Management: Process Engineering and Best Practices for Systematic Trading and Investment

Kumiega, Andrew; Van Vliet, Benjamin

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Table of contents
  • Cover
  • Contentsv
  • Prefacevii
  • Chapter 1 Introduction1
  • 1.1. A Brief History of the Quality Revolution2
  • 1.2. A Brief History of Finance and Engineering3
  • 1.3. Quality and Trading/Money Management4
  • 1.4. Managing the Process of Trading/Investment System Development4
  • 1.5. Operational Risk7
  • 1.6. Project Risk7
  • 1.7. Buy versus Build8
  • 1.8. A Quality Approach to Development9
  • 1.9. Who Is This Book For?11
  • 1.10. THE KEY: Design Your Own Process12
  • 1.11. Summary12
  • Chapter 2 Key Concepts and Definitions of Terms15
  • 2.1. Benchmarking15
  • 2.2. Best Practices17
  • 2.3. Capital17
  • 2.4. Conformance17
  • 2.5. Continuous Improvement, or Kaizen17
  • 2.6. Customer, Client, and Investor18
  • 2.7. Corrective Action18
  • 2.8. Delphi Techniques18
  • 2.9. Design and Development19
  • 2.10. Document19
  • 2.11. Financial Engineering19
  • 2.12. Gate20
  • 2.13. Innovation20
  • 2.14. Life Cycle21
  • 2.15. Modeling Software21
  • 2.16. Preventive Action21
  • 2.17. Process or System21
  • 2.18. Process Approach to Management22
  • 2.19. Product22
  • 2.20. Product Realization22
  • 2.21. Product Team23
  • 2.22. Quality23
  • 2.23. Quality Money Management23
  • 2.24. Quality Planning24
  • 2.25. Software Quality Attributes24
  • 2.26. Stable or Stationary System24
  • 2.27. Standard25
  • 2.28. Statistical Process Control25
  • 2.29. Timeboxing25
  • 2.30. Top Management25
  • 2.31. Trading/Investing25
  • 2.32. Trading/Investment System26
  • 2.33. Trading/Investment System Maturity Model26
  • 2.34. Value Stream and Value-Stream Mapping27
  • 2.35. Variation27
  • 2.36. Vendor27
  • 2.37. Summary27
  • Chapter 3 Overview of the Trading/Investment System Development Methodology29
  • 3.1. The Money Document30
  • 3.2. Waterfall Methodology30
  • 3.3. Spiral Methodology31
  • 3.4. Stage-Gate® Methodology31
  • 3.5. Six Sigma, Lean, and Agile Development33
  • 3.6. Trading/Investment System Development Methodology33
  • 3.7. Design and Document Trading/Investment Strategy (Chapter 7)36
  • 3.8. Gate 1 (Chapter 12)38
  • 3.9. Backtest (Chapter 13)38
  • 3.10. Gate 2 (Chapter 18)40
  • 3.11. Implement (Chapter 19)40
  • 3.12. Gate 3 (Chapter 24)42
  • 3.13. Manage Portfolio and Risk (Chapter 25)42
  • 3.14. Repeat the Entire Waterfall Process for Continuous Improvement (Kaizen) (Chapter 30)43
  • Chapter 4 Managing Design and Development45
  • 4.1. Trading and Money Management Firms45
  • 4.2. Portfolios of Trading/Investment Systems46
  • 4.3. The Role of Top Management49
  • 4.4. The Role of the Product Team53
  • 4.5. The Fuzzy Front End56
  • 4.6. Summary59
  • Chapter 5 Types of Trading Systems61
  • 5.1. Trigger Systems61
  • 5.2. Filter Systems62
  • 5.3. Signal Strength Systems62
  • 5.4. Example Trigger System: Statistical Arbitrage63
  • 5.5. Example Filter System: Buy Write64
  • 5.6. Example Signal Strength System: Multifactor Long–Short65
  • 5.7. Conclusion65
  • Chapter 6 STAGE 0: The Money Document67
  • 6.1. Business Description69
  • 6.2. Market Analysis71
  • 6.3. Request Seed Capital74
  • 6.4. Conclusion77
  • STAGE I: Design and Document Trading/Investment Strategy79
  • Chapter 7 STAGE 1: Overview81
  • 7.1. LOOP 1: Analyze Competing Strategies83
  • 7.2. LOOP 2: Develop New Strategies84
  • 7.3. LOOP 3: Consolidate Trading/Investment System Design84
  • 7.4. Outputs/Deliverables85
  • 7.5. Summary85
  • Chapter 8 Describe Trading/Investment Idea87
  • 8.1. Writing Descriptions Effectively90
  • 8.2. Logic Leaks91
  • 8.3. STEP 1, LOOP 1: Review Money Document Description92
  • 8.4. STEP 1, LOOP 2: Review Knowledge and Plan New Research93
  • 8.5. STEP 1, LOOP 3: Consolidate Trading System Details (All Equations and Logic Rules)94
  • 8.6. Summary94
  • Chapter 9 Research Quantitative Methods97
  • 9.1. Benchmarking Quantitative Methods98
  • 9.2. STEP 2, LOOP 1: Research Similar/Competing Systems99
  • 9.3. STEP 2, LOOP 2: Research New Methods101
  • 9.4. STEP 2, LOOP 3: Consolidate Trading/Investment System Design102
  • 9.5. Summary103
  • Chapter 10 Prototype in Modeling Software105
  • 10.1. Rapid Development106
  • 10.2. Prototyping Components107
  • 10.3. Spreadsheet Modeling111
  • 10.4. STEP 3, LOOP 1: Prototype Known Calculations112
  • 10.5. STEP 3, LOOP 2: Prototype New Calculations113
  • 10.6. STEP 3, LOOP 3: Build Consolidated Prototype113
  • 10.7. Summary114
  • Chapter 11 Check Performance115
  • 11.1. Walkthroughs and Inspections115
  • 11.2. Spreadsheet Testing116
  • 11.3. STEP 4, LOOP 1: White Box Test Formulas118
  • 11.4. STEP 4, LOOP 2: Perform Gray Box Testing with Inputs and Outputs119
  • 11.5. STEP 4, LOOP 3: Perform Black Box Testing, Documenting Results Using Inputs and Outputs119
  • 11.6. Trading the Prototype120
  • 11.7. Performance Runs and Control121
  • 11.8. Summary121
  • Chapter 12 Gate 1123
  • 12.1. Running a Successful Gate Meeting124
  • 12.2. Inputs/Deliverables127
  • 12.3. Summary127
  • STAGE II: Backtest129
  • Chapter 13 STAGE 2: Overview131
  • 13.1. Converting Prototypes to Coded Models132
  • 13.2. Information Management and Database Design133
  • 13.3. LOOP 1: Quality Assurance Testing134
  • 13.4. LOOP 2: Optimize Signals135
  • 13.5. LOOP 3: Generate Simulated Track Record135
  • 13.6. Outputs/Deliverables136
  • 13.7. Summary136
  • Chapter 14 Gather Historical Data139
  • 14.1. Purchasing Data from Vendors140
  • 14.2. STEP 1, LOOP 1: Survey Data Needs and Vendors140
  • 14.3. STEP 1, LOOP 2: Purchase Data144
  • 14.4. STEP 1, LOOP 3: Document Data Maps and Results Tables146
  • 14.5. Summary146
  • Chapter 15 Develop Cleaning Algorithms149
  • 15.1. STEP 2, LOOP 1: Identify Required Cleaning Activities and Algorithms150
  • 15.2. STEP 2, LOOP 2: Clean and Adjust for Known Issues155
  • 15.3. STEP 2, LOOP 3: Document Cleaning Algorithms155
  • 15.4. Summary156
  • Chapter 16 Perform In-Sample/Out-of-Sample Tests157
  • 16.1. STEP 3, LOOP 1: Define Testing Methodology158
  • 16.2. STEP 3, LOOP 2: Perform In-Sample Test for Large Sample160
  • 16.3. STEP 3, LOOP 3: Perform Out-of-Sample Test162
  • 16.4. Summary163
  • Chapter 17 Check Performance and Shadow Trade165
  • 17.1. STEP 4, LOOP 1: Perform Regression Test to Validate Algorithms and Benchmark166
  • 17.2. STEP 4, LOOP 2: Perform Regression Test of In-Sample Results against Prototype167
  • 17.3. STEP 4, LOOP 3: Perform Regression Test of OS Test Results against IS Results and SPC Outputs167
  • 17.4. Summary168
  • Chapter 18 Gate 2169
  • 18.1. Portfolio Review171
  • 18.2. Inputs/Deliverables172
  • 18.3. Summary172
  • STAGE III: Implement175
  • Chapter 19 STAGE 3: Overview177
  • 19.1. Development Team178
  • 19.2. Real-Time Systems180
  • 19.3. LOOP 1: Program and Test Trading/Investment Algorithms180
  • 19.4. LOOP 2: Build and Test Interfaces181
  • 19.5. LOOP 3: Create Network Architecture181
  • 19.6. Outputs/Deliverables182
  • 19.7. Summary182
  • Chapter 20 Plan and Document Technology Specifications183
  • 20.1. Software Requirements Specification184
  • 20.2. Collaborative Planning186
  • 20.3. Buy versus Build187
  • 20.4. Hitting Deadlines188
  • 20.5. LOOP 1: Document Software Requirements189
  • 20.6. LOOP 2: Document Interface Requirements190
  • 20.7. LOOP 3: Document Hardware and Network Requirements190
  • 20.8. Summary190
  • Chapter 21 Design System Architecture193
  • 21.1. Software Tiers194
  • 21.2. Design Process195
  • 21.3. Software Architecture Document199
  • 21.4. High Frequency Trading Systems200
  • 21.5. LOOP 1: Design Business Rules Packages200
  • 21.6. LOOP 2: Design Interface Packages201
  • 21.7. LOOP 3: Design Network Architecture202
  • 21.8. Summary202
  • Chapter 22 Build and Document the System205
  • 22.1. Programming Considerations206
  • 22.2. Database Connections208
  • 22.3. Refactoring208
  • 22.4. LOOP 1: Program Business Rules Packages209
  • 22.5. LOOP 2: Program Interface Packages210
  • 22.6. LOOP 3: Buy/Build Network Infrastructure Components211
  • 22.7. Summary212
  • Chapter 23 Check Performance and Probationary Trade213
  • 23.1. Quality Assurance214
  • 23.2. Loop 1: Test against Black Box Results from Stage 2215
  • 23.3. Loop 2: Test Data and Graphical User Interfaces216
  • 23.4. Loop 3: Perform Final Audit and Probationary Trade216
  • 23.5. User Documentation218
  • 23.6. Probationary Trading218
  • 23.7. Summary218
  • Chapter 24 Gate 3221
  • 24.1. Process Improvement and Benchmarking222
  • 24.2. Deliverables/Inputs224
  • 24.3. Summary225
  • STAGE IV: Manage Portfolio and Risk227
  • Chapter 25 STAGE 4: Overview229
  • 25.1. History of Risk and Return230
  • 25.2. A Brief History of Quality Control232
  • 25.3. Combining Performance and Quality Control232
  • 25.4. LOOP 1: Assess Single Performance233
  • 25.5. LOOP 2: Measure Performance Attribution233
  • 25.6. LOOP 3: Assess Value at Risk234
  • 25.7. Deliverables234
  • 25.8. Summary234
  • Chapter 26 Plan Performance and Risk Processes237
  • 26.1. LOOP 1: Specify Single Performance Controls239
  • 26.2. LOOP 2: Define Benchmarks and Attribution Controls239
  • 26.3. LOOP 3: Choose VaR Methodology242
  • 26.4. Summary243
  • Chapter 27 Define Performance Controls245
  • 27.1. LOOP 1: Benchmark Single Performance Calculations246
  • 27.2. LOOP 2: Benchmark Attribution Calculations246
  • 27.3. LOOP 3: Benchmark VaR Calculations and Software248
  • 27.4. Summary249
  • Chapter 28 Perform SPC Analysis251
  • 28.1. A Brief History of SPC252
  • 28.2. Process View253
  • 28.3. SPC Control Charts254
  • 28.4. LOOP 1: Perform SPC Analysis on Single Metrics261
  • 28.5. LOOP 2: Perform SPC on Attribution Metrics261
  • 28.6. LOOP 3: Perform SPC on VaR Metrics261
  • 28.7. Summary261
  • Chapter 29 Determine Causes of Variation263
  • 29.1. Failure Mode and Effects Analysis264
  • 29.2. The Five Whys264
  • 29.3. Fishbone Diagram264
  • 29.4. Pareto Analysis265
  • 29.5. Design of Experiments266
  • 29.6. Analysis of Variance267
  • 29.7. Sources of Assignable Causes267
  • 29.8. LOOP 1: Determine Causes of Variation in Single Performance268
  • 29.9. LOOP 2: Determine Causes of Variation in Attribution268
  • 29.10. LOOP 3: Determine Causes of Variation in VaR269
  • 29.11. Summary270
  • Chapter 30 Kaizen: Continuous Improvement271
  • 30.1. Continuous Improvement Processes271
  • 30.2. ISO 9000272
  • 30.3. Ford 8D Problem Solving Process273
  • 30.4. Continuous Improvement and Innovation273
  • 30.5. In Conclusion274
  • Endnotes279
  • Index287
  • A287
  • B287
  • C288
  • D288
  • E289
  • F289
  • G290
  • H290
  • I290
  • K290
  • L290
  • M291
  • N291
  • O291
  • P291
  • Q292
  • R293
  • S293
  • T294
  • U295
  • V295
  • W295
  • X295
Book details
  • Vendor Elsevier S & T
  • SKU 9780123725493
  • ISBN-13 9780080559919
  • Author Kumiega, Andrew; Van Vliet, Benjamin
  • Category Business & Economics
  • Subject Finance

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The financial markets industry is at the same crossroads as the automotive industry in the late 1970s. Margins are collapsing and customization is rapidly increasing. The automotive industry turned to quality and its no coincidence that in the money management industry many of the spectacular failures have been due largely to problems in quality control. The financial industry in on the verge of a quality revolution.
New and old firms alike are creating new investment vehicles and new strategies that are radically changing the nature of the industry. To compete, mutual funds, hedge fund industries, banks and proprietary trading firms are being forced to quicklyy research, test and implement trade selection and execution systems. And, just as in the early stages of factory automation, quality suffers and leads to defects. Many financial firms fall short of quality, lacking processes and methodologies for proper development and evaluation of trading and investment systems.
Authors Kumiega and Van Vliet present a new step-by-step methodology for such development. Their methodology (called K|V) has been presented in numerous journal articles and at academic and industry conferences and is rapidly being accepted as the preferred business process for the institutional trading and hedge fund industries for development, presentation, and evaluation of trading and investment systems. The K|V model for trading system development combines new product development, project management and software development methodologies into one robust system. After four stages, the methodology requires repeating the entire waterfall for continuous improvement.
The discussion quality and its applications to the front office is presented using lessons learned by the authors after using the methodology in the real world. As a result, it is flexible and modifiable to fit various projects in finance in different types of firms. Their methodology works equally well for short-term trading systems, longer-term portfolio management or mutual fund style investment strategies as well as more sophisticated ones employing derivative instruments in hedge funds.
Additionally, readers will be able to quickly modify the standard K|V methodology to meet their unique needs and to quickly build other quantitatively drive applications for finance. At the beginning and the end of the book the authors pose a key question: Are you willing to change and embrace quality for the 21st century or are willing to accept extinction?
The real gem in this book is that the concepts give the reader a road map to avoid extinction.

* Presents a robust process engineering framework for developing and evaluating trading and investment systems
* Best practices along the step-by-step process will mitigate project risk, model risk, and ensure data quality.
* Includes a quality model for backtesting and managing market risk of working systems.