{"product_id":"professional-automated-trading-isbn-9781118129852","title":"Professional Automated Trading","description":"\u003cb\u003eAn insider's view of how to develop and operate an automated proprietary trading network\u003c\/b\u003e  \u003cp\u003eReflecting author Eugene Durenard's extensive experience in this field, \u003ci\u003eProfessional Automated Trading\u003c\/i\u003e offers valuable insights you won't find anywhere else. It reveals how a series of concepts and techniques coming from current research in artificial life and modern control theory can be applied to the design of effective trading systems that outperform the majority of published trading systems. It also skillfully provides you with essential information on the practical coding and implementation of a scalable systematic trading architecture.\u003c\/p\u003e \u003cp\u003eBased on years of practical experience in building successful research and infrastructure processes for purpose of trading at several frequencies, this book is designed to be a comprehensive guide for understanding the theory of design and the practice of implementation of an automated systematic trading process at an institutional scale.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eDiscusses several classical strategies and covers the design of efficient simulation engines for back and forward testing\u003c\/li\u003e \u003cli\u003eProvides insights on effectively implementing a series of distributed processes that should form the core of a robust and fault-tolerant automated systematic trading architecture\u003c\/li\u003e \u003cli\u003eAddresses trade execution optimization by studying market-pressure models and minimization of costs via applications of execution algorithms\u003c\/li\u003e \u003cli\u003eIntroduces a series of novel concepts from artificial life and modern control theory that enhance robustness of the systematic decision making—focusing on various aspects of adaptation and dynamic optimal model choice\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eEngaging and informative, \u003ci\u003eProprietary Automated Trading\u003c\/i\u003e covers the most important aspects of this endeavor and will put you in a better position to excel at it.\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1\u003c\/b\u003e \u003cb\u003eIntroduction to Systematic Trading 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Definition of Systematic Trading 2\u003c\/p\u003e \u003cp\u003e1.2 Philosophy of Trading 3\u003c\/p\u003e \u003cp\u003e1.2.1 Lessons from the Market 3\u003c\/p\u003e \u003cp\u003e1.2.2 Mechanism vs. Organism 5\u003c\/p\u003e \u003cp\u003e1.2.3 The Edge of Complexity 5\u003c\/p\u003e \u003cp\u003e1.2.4 Is Systematic Trading Reductionistic? 6\u003c\/p\u003e \u003cp\u003e1.2.5 Reaction vs. Proaction 6\u003c\/p\u003e \u003cp\u003e1.2.6 Arbitrage? 7\u003c\/p\u003e \u003cp\u003e1.2.7 Two Viable Paths 7\u003c\/p\u003e \u003cp\u003e1.3 The Business of Trading 7\u003c\/p\u003e \u003cp\u003e1.3.1 Profitability and Track Record 8\u003c\/p\u003e \u003cp\u003e1.3.2 The Product and Its Design 10\u003c\/p\u003e \u003cp\u003e1.3.3 The Trading Factory 12\u003c\/p\u003e \u003cp\u003e1.3.4 Marketing and Distribution 15\u003c\/p\u003e \u003cp\u003e1.3.5 Capital, Costs, and Critical Mass 16\u003c\/p\u003e \u003cp\u003e1.4 Psychology and Emotions 19\u003c\/p\u003e \u003cp\u003e1.4.1 Ups and Downs 19\u003c\/p\u003e \u003cp\u003e1.4.2 Peer Pressure and the Blame Game 20\u003c\/p\u003e \u003cp\u003e1.4.3 Trust: Continuity of Quality 20\u003c\/p\u003e \u003cp\u003e1.4.4 Learning from Each Other 21\u003c\/p\u003e \u003cp\u003e1.5 From Candlesticks in Kyoto to FPGAs in Chicago 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Strategy Design and Testing\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 A New Socioeconomic Paradigm 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Financial Theory vs. Market Reality 33\u003c\/p\u003e \u003cp\u003e2.1.1 Adaptive Reactions vs. Rigid Anticipations 33\u003c\/p\u003e \u003cp\u003e2.1.2 Accumulation vs. Divestment Games 37\u003c\/p\u003e \u003cp\u003e2.1.3 Phase Transitions under Leverage 38\u003c\/p\u003e \u003cp\u003e2.1.4 Derivatives: New Risks Do Not Project onto Old Hedges 40\u003c\/p\u003e \u003cp\u003e2.1.5 Socio-Political Dynamics and Feedbacks 41\u003c\/p\u003e \u003cp\u003e2.2 The Market Is a Complex Adaptive System 42\u003c\/p\u003e \u003cp\u003e2.2.1 Emergence 43\u003c\/p\u003e \u003cp\u003e2.2.2 Intelligence Is Not Always Necessary 44\u003c\/p\u003e \u003cp\u003e2.2.3 The Need to Adapt 45\u003c\/p\u003e \u003cp\u003e2.3 Origins of Robotics and Artificial Life 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Analogies between Systematic Trading and Robotics 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Models and Robots 49\u003c\/p\u003e \u003cp\u003e3.2 The Trading Robot 50\u003c\/p\u003e \u003cp\u003e3.3 Finite-State-Machine Representation of the Control System 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Implementation of Strategies as Distributed Agents 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Trading Agent 57\u003c\/p\u003e \u003cp\u003e4.2 Events 60\u003c\/p\u003e \u003cp\u003e4.3 Consuming Events 60\u003c\/p\u003e \u003cp\u003e4.4 Updating Agents 61\u003c\/p\u003e \u003cp\u003e4.5 Defining FSM Agents 63\u003c\/p\u003e \u003cp\u003e4.6 Implementing a Strategy 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Inter-Agent Communications 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Handling Communication Events 73\u003c\/p\u003e \u003cp\u003e5.2 Emitting Messages and Running Simulations 75\u003c\/p\u003e \u003cp\u003e5.3 Implementation Example 76\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Data Representation Techniques 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Data Relevance and Filtering of Information 83\u003c\/p\u003e \u003cp\u003e6.2 Price and Order Book Updates 84\u003c\/p\u003e \u003cp\u003e6.2.1 Elementary Price Events 85\u003c\/p\u003e \u003cp\u003e6.2.2 Order Book Data 85\u003c\/p\u003e \u003cp\u003e6.2.3 Tick Data: The Finest Grain 88\u003c\/p\u003e \u003cp\u003e6.3 Sampling: Clock Time vs. Event Time 89\u003c\/p\u003e \u003cp\u003e6.4 Compression 90\u003c\/p\u003e \u003cp\u003e6.4.1 Slicing Time into Bars and Candles 90\u003c\/p\u003e \u003cp\u003e6.4.2 Slicing Price into Boxes 96\u003c\/p\u003e \u003cp\u003e6.4.3 Market Distributions 97\u003c\/p\u003e \u003cp\u003e6.5 Representation 97\u003c\/p\u003e \u003cp\u003e6.5.1 Charts and Technical Analysis 99\u003c\/p\u003e \u003cp\u003e6.5.2 Translating Patterns into Symbols 101\u003c\/p\u003e \u003cp\u003e6.5.3 Translating News into Numbers 102\u003c\/p\u003e \u003cp\u003e6.5.4 Psychology of Data and Alerts 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Basic Trading Strategies 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Trend-Following 105\u003c\/p\u003e \u003cp\u003e7.1.1 Channel Breakout 106\u003c\/p\u003e \u003cp\u003e7.1.2 Moving Averages 106\u003c\/p\u003e \u003cp\u003e7.1.3 Swing Breakout 112\u003c\/p\u003e \u003cp\u003e7.2 Acceleration 114\u003c\/p\u003e \u003cp\u003e7.2.1 Trend Asymmetry 115\u003c\/p\u003e \u003cp\u003e7.2.2 The Shadow Index 116\u003c\/p\u003e \u003cp\u003e7.2.3 Trading Acceleration 117\u003c\/p\u003e \u003cp\u003e7.3 Mean-Reversion 118\u003c\/p\u003e \u003cp\u003e7.3.1 Swing Reversal 118\u003c\/p\u003e \u003cp\u003e7.3.2 Range Projection 120\u003c\/p\u003e \u003cp\u003e7.4 Intraday Patterns 122\u003c\/p\u003e \u003cp\u003e7.4.1 Openings 122\u003c\/p\u003e \u003cp\u003e7.4.2 Seasonality of Volatility 122\u003c\/p\u003e \u003cp\u003e7.5 News-Driven Strategies 124\u003c\/p\u003e \u003cp\u003e7.5.1 Expectations vs. Reality 124\u003c\/p\u003e \u003cp\u003e7.5.2 Ontology-Driven Strategies 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Architecture for Market-Making 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Traditional Market-Making: The Specialists 127\u003c\/p\u003e \u003cp\u003e8.2 Conditional Market-Making: Open Outcry 128\u003c\/p\u003e \u003cp\u003e8.3 Electronic Market-Making 129\u003c\/p\u003e \u003cp\u003e8.4 Mixed Market-Making Model 131\u003c\/p\u003e \u003cp\u003e8.5 An Architecture for a Market-Making Desk 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Combining Strategies into Portfolios 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Aggregate Agents 139\u003c\/p\u003e \u003cp\u003e9.2 Optimal Portfolios 141\u003c\/p\u003e \u003cp\u003e9.3 Risk-Management of a Portfolio of Models 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Simulating Agent-Based Strategies 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 The Simulation Problem 146\u003c\/p\u003e \u003cp\u003e10.2 Modeling the Order Management System 147\u003c\/p\u003e \u003cp\u003e10.2.1 Orders and Algorithms 148\u003c\/p\u003e \u003cp\u003e10.2.2 Simulating Slippage 149\u003c\/p\u003e \u003cp\u003e10.2.3 Simulating Order Placement 151\u003c\/p\u003e \u003cp\u003e10.2.4 Simulating Order Execution 153\u003c\/p\u003e \u003cp\u003e10.2.5 A Model for the OMS 155\u003c\/p\u003e \u003cp\u003e10.2.6 Operating the OMS 156\u003c\/p\u003e \u003cp\u003e10.3 Running Simulations 158\u003c\/p\u003e \u003cp\u003e10.3.1 Setting Up a Back Test 158\u003c\/p\u003e \u003cp\u003e10.3.2 Setting Up a Forward Test 160\u003c\/p\u003e \u003cp\u003e10.4 Analysis of Results 162\u003c\/p\u003e \u003cp\u003e10.4.1 Continuous Statistics 163\u003c\/p\u003e \u003cp\u003e10.4.2 Per-Trade Statistics 164\u003c\/p\u003e \u003cp\u003e10.4.3 Parameter Search and Optimization 165\u003c\/p\u003e \u003cp\u003e10.5 Degrees of Over-Fitting 167\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Evolving Strategies\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Strategies for Adaptation 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Avenues for Adaptations 173\u003c\/p\u003e \u003cp\u003e11.2 The Cybernetics of Trading 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Feedback and Control 179\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Looking at Markets through Models 179\u003c\/p\u003e \u003cp\u003e12.1.1 Internal World 179\u003c\/p\u003e \u003cp\u003e12.1.2 Strategies as Generalized Filters 180\u003c\/p\u003e \u003cp\u003e12.1.3 Implicit Market Regimes 181\u003c\/p\u003e \u003cp\u003e12.1.4 Persistence of Regimes 183\u003c\/p\u003e \u003cp\u003e12.2 Fitness Feedback Control 184\u003c\/p\u003e \u003cp\u003e12.2.1 Measures of Fitness 186\u003c\/p\u003e \u003cp\u003e12.3 Robustness of Strategies 192\u003c\/p\u003e \u003cp\u003e12.4 Efficiency of Control 193\u003c\/p\u003e \u003cp\u003e12.4.1 Triggering Control 193\u003c\/p\u003e \u003cp\u003e12.4.2 Measuring Efficiency of Control 194\u003c\/p\u003e \u003cp\u003e12.4.3 Test Results 196\u003c\/p\u003e \u003cp\u003e12.4.4 Optimizing Control Parameters 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Simple Swarm Systems 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Switching Strategies 199\u003c\/p\u003e \u003cp\u003e13.1.1 Switching between Regimes 200\u003c\/p\u003e \u003cp\u003e13.1.2 Switching within the Same Regime 200\u003c\/p\u003e \u003cp\u003e13.1.3 Mechanics of Switching and Transaction Costs 205\u003c\/p\u003e \u003cp\u003e13.2 Strategy Neighborhoods 206\u003c\/p\u003e \u003cp\u003e13.3 Choice of a Simple Individual from a Population 208\u003c\/p\u003e \u003cp\u003e13.4 Additive Swarm System 210\u003c\/p\u003e \u003cp\u003e13.4.1 Example of an Additive Swarm 211\u003c\/p\u003e \u003cp\u003e13.5 Maximizing Swarm System 214\u003c\/p\u003e \u003cp\u003e13.5.1 Example of a Maximizing Swarm 215\u003c\/p\u003e \u003cp\u003e13.6 Global Performance Feedback Control 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Implementing Swarm Systems 219\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Setting Up the Swarm Strategy Set 220\u003c\/p\u003e \u003cp\u003e14.2 Running the Swarm 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Swarm Systems with Learning 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Reinforcement Learning 224\u003c\/p\u003e \u003cp\u003e15.2 Swarm Efficiency 224\u003c\/p\u003e \u003cp\u003e15.3 Behavior Exploitation by the Swarm 225\u003c\/p\u003e \u003cp\u003e15.4 Exploring New Behaviors 227\u003c\/p\u003e \u003cp\u003e15.5 Lamark among the Machines 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Optimizing Execution\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 Analysis of Trading Costs 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 No Free Lunch 231\u003c\/p\u003e \u003cp\u003e16.2 Slippage 232\u003c\/p\u003e \u003cp\u003e16.3 Intraday Seasonality of Liquidity 233\u003c\/p\u003e \u003cp\u003e16.4 Models of Market Impact 234\u003c\/p\u003e \u003cp\u003e16.4.1 Reaction to Aggression 235\u003c\/p\u003e \u003cp\u003e16.4.2 Limits to Openness 235\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17 Estimating Algorithmic Execution Tools 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Basic Algorithmic Execution Tools 237\u003c\/p\u003e \u003cp\u003e17.2 Estimation of Algorithmic Execution Methodologies 240\u003c\/p\u003e \u003cp\u003e17.2.1 A Simulation Engine for Algos 240\u003c\/p\u003e \u003cp\u003e17.2.2 Using Execution Algo Results in Model Estimation 241\u003c\/p\u003e \u003cp\u003e17.2.3 Joint Testing of Models and Algos 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Four Practical Implementation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18 Overview of a Scalable Architecture 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 ECNs and Translation 247\u003c\/p\u003e \u003cp\u003e18.2 Aggregation and Disaggregation 249\u003c\/p\u003e \u003cp\u003e18.3 Order Management 250\u003c\/p\u003e \u003cp\u003e18.4 Controls 250\u003c\/p\u003e \u003cp\u003e18.5 Decisions 251\u003c\/p\u003e \u003cp\u003e18.6 Middle and Back Office 251\u003c\/p\u003e \u003cp\u003e18.7 Recovery 252\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19 Principal Design Patterns 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Language-Agnostic Domain Model 253\u003c\/p\u003e \u003cp\u003e19.2 Solving Tasks in Adapted Languages 254\u003c\/p\u003e \u003cp\u003e19.3 Communicating between Components 257\u003c\/p\u003e \u003cp\u003e19.3.1 Messaging Bus 258\u003c\/p\u003e \u003cp\u003e19.3.2 Remote Procedure Calls 259\u003c\/p\u003e \u003cp\u003e19.4 Distributed Computing and Modularity 260\u003c\/p\u003e \u003cp\u003e19.5 Parallel Processing 262\u003c\/p\u003e \u003cp\u003e19.6 Garbage Collection and Memory Control 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20 Data Persistence 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Business-Critical Data 265\u003c\/p\u003e \u003cp\u003e20.2 Object Persistence and Cached Memory 267\u003c\/p\u003e \u003cp\u003e20.3 Databases and Their Usage 269\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21 Fault Tolerance and Recovery Mechanisms 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Situations of Stress 273\u003c\/p\u003e \u003cp\u003e21.1.1 Communication Breakdown 273\u003c\/p\u003e \u003cp\u003e21.1.2 External Systems Breakdown 274\u003c\/p\u003e \u003cp\u003e21.1.3 Trades Busted at the ECN Level 275\u003c\/p\u003e \u003cp\u003e21.1.4 Give-Up Errors Causing Credit Line Problems 276\u003c\/p\u003e \u003cp\u003e21.1.5 Internal Systems Breakdown 277\u003c\/p\u003e \u003cp\u003e21.1.6 Planned Maintenance and Upgrades 277\u003c\/p\u003e \u003cp\u003e21.2 A Jam of Logs Is Better Than a Logjam of Errors 277\u003c\/p\u003e \u003cp\u003e21.3 Virtual Machine and Network Monitoring 278\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 22 Computational Efficiency 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 CPU Spikes 281\u003c\/p\u003e \u003cp\u003e22.2 Recursive Computation of Model Signals and Performance 282\u003c\/p\u003e \u003cp\u003e22.3 Numeric Efficiency 285\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 23 Connectivity to Electronic Commerce Networks 291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Adaptors 291\u003c\/p\u003e \u003cp\u003e23.2 The Translation Layer 292\u003c\/p\u003e \u003cp\u003e23.2.1 Orders: FIX 292\u003c\/p\u003e \u003cp\u003e23.2.2 Specific ECNs 293\u003c\/p\u003e \u003cp\u003e23.2.3 Price Sources: FAST 293\u003c\/p\u003e \u003cp\u003e23.3 Dealing with Latency 294\u003c\/p\u003e \u003cp\u003e23.3.1 External Constraints and Co-Location 294\u003c\/p\u003e \u003cp\u003e23.3.2 Avoid Being Short the Latency Option 295\u003c\/p\u003e \u003cp\u003e23.3.3 Synchronization under Constraints 296\u003c\/p\u003e \u003cp\u003e23.3.4 Improving Internal Latency 297\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 24 The Aggregation and Disaggregation Layer 299\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Quotes Filtering and Book Aggregation 300\u003c\/p\u003e \u003cp\u003e24.1.1 Filtering Quotes 300\u003c\/p\u003e \u003cp\u003e24.1.2 Synthetic Order Book 301\u003c\/p\u003e \u003cp\u003e24.2 Orders Aggregation and Fills Disaggregation 301\u003c\/p\u003e \u003cp\u003e24.2.1 Aggregating Positions and Orders 301\u003c\/p\u003e \u003cp\u003e24.2.2 Fills Disaggregation 303\u003c\/p\u003e \u003cp\u003e24.2.3 Book Transfers and Middle Office 303\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 25 The OMS Layer 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Order Management as a Recursive Controller 305\u003c\/p\u003e \u003cp\u003e25.1.1 Management of Positions 307\u003c\/p\u003e \u003cp\u003e25.1.2 Management of Resting Orders 307\u003c\/p\u003e \u003cp\u003e25.1.3 Algorithmic Orders 308\u003c\/p\u003e \u003cp\u003e25.2 Control under Stress 309\u003c\/p\u003e \u003cp\u003e25.3 Designing a Flexible OMS 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 26 The Human Control Layer 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Dashboard and Smart Scheduler 311\u003c\/p\u003e \u003cp\u003e26.1.1 Parameter Control 311\u003c\/p\u003e \u003cp\u003e26.1.2 Scheduled Flattening of Exposure 312\u003c\/p\u003e \u003cp\u003e26.2 Manual Orders Aggregator 313\u003c\/p\u003e \u003cp\u003e26.2.1 Representing a Trader by an Agent 313\u003c\/p\u003e \u003cp\u003e26.2.2 Writing a Trading Screen 314\u003c\/p\u003e \u003cp\u003e26.2.3 Monitoring Aggregated Streams 314\u003c\/p\u003e \u003cp\u003e26.3 Position and P \u0026amp; L Monitor 314\u003c\/p\u003e \u003cp\u003e26.3.1 Real-Time Exposure Monitor 315\u003c\/p\u003e \u003cp\u003e26.3.2 Displaying Equity Curves 315\u003c\/p\u003e \u003cp\u003e26.3.3 Online Trade Statistics and Fitnesses 315\u003c\/p\u003e \u003cp\u003e26.3.4 Trades Visualization Module 317\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 27 The Risk Management Layer 319\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e27.1 Risky Business 319\u003c\/p\u003e \u003cp\u003e27.2 Automated Risk Management 320\u003c\/p\u003e \u003cp\u003e27.3 Manual Risk Control and the Panic Button 320\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 28 The Core Engine Layer 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28.1 Architecture 323\u003c\/p\u003e \u003cp\u003e28.2 Simulation and Recovery 325\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 29 Some Practical Implementation Aspects 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e29.1 Architecture for Build and Patch Releases 327\u003c\/p\u003e \u003cp\u003e29.1.1 Testing of Code before a Release 327\u003c\/p\u003e \u003cp\u003e29.1.2 Versioning of Code and Builds 328\u003c\/p\u003e \u003cp\u003e29.1.3 Persistence of State during Version Releases 328\u003c\/p\u003e \u003cp\u003e29.2 Hardware Considerations 329\u003c\/p\u003e \u003cp\u003e29.2.1 Bottleneck Analysis 329\u003c\/p\u003e \u003cp\u003e29.2.2 The Edge of Technology 330\u003c\/p\u003e \u003cp\u003eAppendix Auxiliary LISP Functions 333\u003c\/p\u003e \u003cp\u003eBibliography 341\u003c\/p\u003e \u003cp\u003eIndex 351\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eEUGENE A. DURENARD\u003c\/b\u003e is CEO of DTC Ltd in Bermuda, a firm that specializes in applying systematic techniques to leveraged and real money trading across a wide variety of asset classes. DTC has been acting over the years as investment advisor and manager for a range of institutions including banks, hedge funds, and proprietary trading groups. DTC is currently advising Capital G, the largest private bank in Bermuda, where Eugene acts as Head of Research and Product Development. Eugene is a partner and CIO of ERA Capital Partners, a Chicago-based quantitative systematic Proprietary Trading Group.     \u003c\/p\u003e\u003cp\u003eTrading is a science based on a variety of fields, from mathematics to physics. It is also an art based on knowing and respecting the markets and, equally important, knowing oneself. But most of all, trading is a business that hinges on a carefully understood discipline and process of seeking reward in the face of risk. \u003c\/p\u003e\u003cp\u003eNo one is more familiar with this than author Eugene Durenard. A recognized expert in automated trading—who has long incorporated aspects of artificial life and robotics systems research into his trading—he knows what it takes to create systematic trading strategies that are adaptive and opportunistic. Now, with this new book, he shares those valuable insights with you. Whether you're part of a professional prop trading desk or an ambitious individual trader, this practical guide will put you in a better position to successfully navigate today's competitive markets. \u003c\/p\u003e\u003cp\u003eDivided into four comprehensive parts, \u003ci\u003eProfessional Automated Trading: Theory and Practice\u003c\/i\u003e opens with an introductory chapter that sets the stage for modern systematic trading. It critically examines the merits of systematic trading from a philosophical and psychological perspective, as well as focuses on it as a business activity. \u003c\/p\u003e\u003cp\u003eWith this information in hand, Durenard moves on to skillfully cover the theory, practice, and technologies needed to excel at this endeavor. \u003c\/p\u003e\u003cul\u003e \u003cli\u003e\n\u003cb\u003ePart I\u003c\/b\u003e introduces the basic conceptual and programmatic framework for the design of trading strategies as trading agents—exploring data representation, indicators, basic model types, and techniques to test them\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003ePart II\u003c\/b\u003e introduces innovative concepts designed to tackle adaptation of trading strategies to changing market conditions. Those include Swarm Systems that help navigate the higher complexity of the markets at lower timescales\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003ePart III\u003c\/b\u003e focuses on the important aspect of trading costs and slippage—discussing the analysis of the intraday bid-offer and volume seasonality in major markets and detailing several algorithmic execution strategies designed to help reduce market impact\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003ePart IV\u003c\/b\u003e presents the practical implementation of a real-time, low-latency automated trading infrastructure designed to support a Swarm of Trading Agents operating on a collection of Electronic Commerce Networks (ECNs)\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eEngaging and accessible, \u003ci\u003eProfessional Automated Trading\u003c\/i\u003e contains the information you need to make the most of automated trading in free markets.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989864923365,"sku":"NP9781118129852","price":100.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118129852.jpg?v=1761785723","url":"https:\/\/k12savings.com\/products\/professional-automated-trading-isbn-9781118129852","provider":"K12savings","version":"1.0","type":"link"}