Algorithmic trading and quantitative strategies:
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2020
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| Edition: | First edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xvi, 434 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9781498737166 1498737161 |
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MARC
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Record in the Search Index
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| adam_text | Contents Preface I 1 ier юге ion my my er II 2 Introduction to Trading Trading Fundamentals 1.1 A Brief History of Stock Trading ..................................................... 1.2 Market Structure and Trading Venues: A Review ............................. 1.2.1 Equity Markets Participants.................................................. 1.2.2 Watering Holes of Equity Markets......................................... 1.3 The Mechanics of Trading .................................................................. 1.3.1 How Double Auction Markets Work...................................... 1.3.2 The Open Auction.................................................................. 1.3.3 Continuous Trading ............................................................... 1.3.4 The Closing Auction............................................................... 1.4 Taxonomy of Data Used in Algorithmic Trading ............................ 1.4.1 Reference Data........................................................................ 1.4.2 Market Data ............................................................................ 1.4.3 Market Data Derived Statistics............................................... 1.4.4 Fundamental Data and Other Data Sets............................... 1.5 Market Microstructure: Economic Fundamentals of Trading .... 1.5.1 Liquidity and Market Making ............................................... Foundations: Basic Models and Empirics Univariate Time Series Models 2.1 Trades and Quotes Data and Their Aggregation: From Point Processes to Discrete Time Series
..................................................... 2.2 Trading Decisions as Short-Term Forecast Decisions ...................... 2.3 Stochastic Processes: Some Properties............................................... 2.4 Some Descriptive Tools and Their Properties .................................. 2.5 Time Series Models for Aggregated Data: Modeling the Mean ... 2.6 Key Steps for Model Building ............................................................ 2.7 Testing for Nonstationary (Unit Root) in ARIMA Models: To Difference or Not To............................................................................ 2.8 Forecasting for ARIMA Processes...................................................... 2.9 Stylized Models for Asset Returns..................................................... 2.10 Time Series Models for Aggregated Data: Modeling the Variance . . xi 1 3 3 7 7 8 13 13 14 19 24 26 26 35 39 42 44 45 51 53 54 56 57 61 63 70 77 78 82 84 v
Contents 2.11 Stylized Models for Variance of Asset Returns ................................ 90 2.12 Exercises .................................................................................................. 92 3 Multivariate TimeSeries Models 3.1 Multivariate Regression ...................................................................... 3.2 Dimension-Reduction Methods ......................................................... 3.3 Multiple Time Series Modeling ......................................................... 3.4 Co-Integration, Co-Movement and Commonality in Multiple Time Series .................................................................................................. 3.5 Applications in Finance ...................................................................... 3.6 Multivariate GARCH Models ............................................................ 3.7 Illustrative Examples............................................................................ 3.8 Exercises ............................................................................................... 4 106 110 112 114 122 Advanced Topics 125 4.1 State-Space Modeling ......................................................................... 125 4.2 Regime Switching and Change-Point Models ................................... 128 4.3 A Model for Volume-Volatility Relationships ................................... 131 4.4 Models for Point Processes................................................................... 134 4.4.1 Stylized Models for High Frequency Financial Data............. 136 4.4.2
Models for Multiple Assets: High Frequency Context .... 140 4.5 Analysis of Time Aggregated Data ................................................... 141 4.5.1 Realized Volatility and Econometric Models ...................... 141 4.5.2 Volatility and Price Bar Data................................................... 143 4.6 Analytics from Machine Learning Literature ................................... 146 4.6.1 Neural Networks...................................................................... 147 4.6.2 Reinforcement Learning......................................................... 150 4.6.3 Multiple Indicators and Boosting Methods............................. 152 4.7 Exercises ............................................................................................... 154 III 5 95 96 99 104 Trading Algorithms 157 Statistical Trading Strategiesand Back-Testing 159 5.1 Introduction to Trading Strategies՛. Origin and History ................... 159 5.2 Evaluation of Strategies: Various Measures ...................................... 160 5.3 Trading Rules for Time Aggregated Data ......................................... 161 5.3.1 Filter Rules............................................................................... 162 5.3.2 Moving Average Variants and Oscillators............................. 164 5.4 Patterns Discovery via Non-ParametricSmoothing Methods .... 166 5.5 A Decomposition Algorithm ............................................................ 168 5.6 Fair Value Models ............................................................................... 170 5.7
Back-Testing and Data Snooping: In-Sample and Out-of-Sample Performance Evaluation ...................................................................... 171 5.8 Pairs Trading......................................................................................... 174 5.8.1 Distance-Based Algorithms ................................................... 175 5.8.2 Co-Integration......................................................................... 176
Contents 5.9 5.10 5.11 5.12 5.13 5.14 vii 5.8.3 Some General Comments ..................................................... 5.8.4 Practical Considerations........................................................ Cross-Sectional Momentum Strategies............................................... Extraneous Signals: Trading Volume, Volatility,etc............................. 5.10.1 Filter Rules Based on Return and Volume............................ 5.10.2 An Illustrative Example ........................................................ Trading in Multiple Markets.............................................................. Other Topics: Trade Size, etc................................................................ Machine Learning Methods in Trading ............................................ Exercises ............................................................................................. 180 181 184 189 191 193 198 202 204 207 6 Dynamic Portfolio Management and Trading Strategies 6.1 Introduction to Modern Portfolio Theory ........................................ 6.1.1 Mean-Variance Portfolio Theory............................................ 6.1.2 Multifactor Models................................................................. 6.1.3 Tests Related to CAPM and APT ......................................... 6.1.4 An Illustrative Example ........................................................ 6.1.5 Implications for Investing ...................................................... 6.2 Statistical Underpinnings
................................................................... 6.2.1 Portfolio Allocation Using Regularization............................ 6.2.2 Portfolio Strategies: Some General Findings......................... 6.3 Dynamic Portfolio Selection................................................................ 6.4 Portfolio Tracking and Rebalancing .................................................... 6.5 Transaction Costs, Shorting and Liquidity Constraints ................... 6.6 Portfolio Trading Strategies ................................................................ 6.7 Exercises ............................................................................................... 7 News Analytics: From Market Attention and Sentiment to Trading 251 7.1 Introduction to News Analytics: Behavioral Finance and Investor Cognitive Biases ................................................................................... 251 7.2 Automated News Analysis and Market Sentiment............................ 256 7.3 News Analytics and Applications to Trading .................................... 258 7.4 Discussion / Future of Social Media and News in Algorithmic Trading................................................................................................... 267 IV 8 Execution Algorithms Modeling Trade Data 8.1 Normalizing Analytics........................................................................... 8.1.1 Order Size Normalization: ADV.............................................. 8.1.2 Time-Scale Normalization: Characteristic Time..................... 8.1.3 Intraday
Return Normalization: Mid-Quote Volatility .... 8.1.4 Other Microstructure Normalizations .................................. 8.1.5 Intraday Normalization: Profiles............................................ 8.1.6 Remainder (of the Day) Volume.............................................. 8.1.7 Auctions Volume..................................................................... 215 215 215 219 219 222 224 226 230 232 234 236 239 242 244 269 271 271 272 273 275 276 277 282 283
Contents viii 8.2 8.3 8.4 8.5 9 Microstructure Signals......................................................................... 283 Limit Order Book (LOB): Studying Its Dynamics............................. 286 8.3.1 LOB Construction and Key Descriptives................................ 287 8.3.2 Modeling LOB Dynamics...................................................... 289 8.3.3 Models Based on Hawkes Processes...................................... 295 Models for Hidden Liquidity................................................................ 306 Modeling LOB: Some Concluding Thoughts ................................... 310 Market Impact Models 313 9.1 Introduction............................................................................................ 313 9.2 What Is Market Impact? ...................................................................... 314 9.3 Modeling Transaction Costs (TC) .......................................................... 315 9.4 Historical Review of Market Impact Research................................... 318 9.5 Some Stylized Models ......................................................................... 320 9.6 Price Impact in the High Frequency Setting...................................... 323 9.7 Models Based on LOB......................................................................... 324 9.8 Empirical Estimation of Transaction Costs ...................................... 327 9.8.1 Review of Select Empirical Studies.......................................... 328 10 Execution Strategies 335 10.1 Execution Benchmarks: Practitioner’s
View...................................... 336 10.2 Evolution of Execution Strategies ........................................................ 344 10.3 Layers of an Execution Strategy ......................................................... 348 10.3.1 Scheduling Layer...................................................................... 348 10.3.2 Order Placement.......................................................................... 352 10.3.3 Order Routing ............................................................................. 353 10.4 Formal Description of Some Execution Models................................ 359 10.4.1 First Generation Algorithms................................................... 359 10.4.2 Second Generation Algorithms................................................ 361 10.5 Multiple Exchanges: Smart Order Routing Algorithm...................... 367 10.6 Execution Algorithms for Multiple Assets........................................... 371 10.7 Extending the Algorithms to Other Asset Classes.............................375 V Technology Considerations 381 11 The Technology Stack 383 11.1 From Client Instruction to Trade Reconciliation................................ 383 11.2 Algorithmic Trading Infrastructure ................................................... 387 11.3 HFT Infrastructure................................................................................. 394 11.4 ATS Infrastructure ................................................................................... 395 11.4.1 Regulatory
Considerations..........................................................395 11.4.2 Matching Engine...................................................................... 397 11.4.3 Client Tiering and Other Rules............................................... 397
Contents ix 12 The Research Stack 12.1 Data Infrastructure.............................................................................. 12.2 Calibration Infrastructure .................................................................. 12.3 Simulation Environment..................................................................... 12.4 TCA Environment .............................................................................. 12.5 Conclusion ........................................................................................... 401 401 403 404 408 410 Bibliography 411 Subject Index 433
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| spelling | Velu, Raja P. Verfasser (DE-588)170357392 aut Algorithmic trading and quantitative strategies Raja Velu (Department of Finance, Whitman School of Management, Syracuse University) Maxence Hardy (eTrading Quantitative Research, J.P. Morgan), Daniel Nehren (Statistical Modeling & Development, Barclays) First edition Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2020 xvi, 434 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Wertpapierhandelssystem (DE-588)4510686-1 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Portfolio Selection (DE-588)4046834-3 gnd rswk-swf Wertpapierhandel (DE-588)4189707-9 gnd rswk-swf Finance / Data processing Finance / Computer programs Investment analysis Electronic trading of securities Wertpapierhandel (DE-588)4189707-9 s Wertpapierhandelssystem (DE-588)4510686-1 s Algorithmus (DE-588)4001183-5 s Portfolio Selection (DE-588)4046834-3 s b DE-604 Hardy, Maxence Verfasser (DE-588)1235693724 aut Nehren, Daniel Verfasser (DE-588)1235693775 aut Erscheint auch als Online-Ausgabe 978-0-4291-8394-2 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031596584&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Velu, Raja P. Hardy, Maxence Nehren, Daniel Algorithmic trading and quantitative strategies Wertpapierhandelssystem (DE-588)4510686-1 gnd Algorithmus (DE-588)4001183-5 gnd Portfolio Selection (DE-588)4046834-3 gnd Wertpapierhandel (DE-588)4189707-9 gnd |
| subject_GND | (DE-588)4510686-1 (DE-588)4001183-5 (DE-588)4046834-3 (DE-588)4189707-9 |
| title | Algorithmic trading and quantitative strategies |
| title_auth | Algorithmic trading and quantitative strategies |
| title_exact_search | Algorithmic trading and quantitative strategies |
| title_full | Algorithmic trading and quantitative strategies Raja Velu (Department of Finance, Whitman School of Management, Syracuse University) Maxence Hardy (eTrading Quantitative Research, J.P. Morgan), Daniel Nehren (Statistical Modeling & Development, Barclays) |
| title_fullStr | Algorithmic trading and quantitative strategies Raja Velu (Department of Finance, Whitman School of Management, Syracuse University) Maxence Hardy (eTrading Quantitative Research, J.P. Morgan), Daniel Nehren (Statistical Modeling & Development, Barclays) |
| title_full_unstemmed | Algorithmic trading and quantitative strategies Raja Velu (Department of Finance, Whitman School of Management, Syracuse University) Maxence Hardy (eTrading Quantitative Research, J.P. Morgan), Daniel Nehren (Statistical Modeling & Development, Barclays) |
| title_short | Algorithmic trading and quantitative strategies |
| title_sort | algorithmic trading and quantitative strategies |
| topic | Wertpapierhandelssystem (DE-588)4510686-1 gnd Algorithmus (DE-588)4001183-5 gnd Portfolio Selection (DE-588)4046834-3 gnd Wertpapierhandel (DE-588)4189707-9 gnd |
| topic_facet | Wertpapierhandelssystem Algorithmus Portfolio Selection Wertpapierhandel |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031596584&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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