لایب سان

Author(s): Raymond S. T. Lee

Publisher: Springer Singapore, Year: 2020

ISBN: 978-981-32-9795-1,978-981-32-9796-8

5.8 Finite Difference Method (FDM) . . . . . . . . . . . . . . . . . . . . . . . 100 5.9 Finite Difference Method (FDM) to Evaluate QF Wave Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 5.10 Numerical Evaluation of k XAUUSDj Using FDM . . . . . . . . . . . . . 102 5.11 Numerical Computation of Quantum Energy Levels En . . . . . . . 104 5.12 Numerical Computation of Quantum Finance Energy Levels (MATLAB Version) . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.13 Depressed Cubic Equation Solver—Gerolamo Cardano . . . . . . . 107 5.14 Cardano’s Method for Calculating QFEL . . . . . . . . . . . . . . . . . 108 5.15 Numerical Computation of Quantum Energy Levels (MQL Version) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 5.16 Numerical Algorithm to Calculate QPL Using MQL . . . . . . . . . 110 5.17 QPLs for XAUUSD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 5.18 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 6 Quantum Trading and Hedging Strategy . . . . . . . . . . . . . . . . . . . . 119 6.1 Financial Trading and Hedging Strategies in a Nutshell . . . . . . 120 6.2 Traditional Trading and Hedging Strategies . . . . . . . . . . . . . . . 121 6.3 Latest R&D on Trading and Hedging Strategies . . . . . . . . . . . . 122 6.4 Technical Analysis and Program Trading . . . . . . . . . . . . . . . . . 123 6.5 Technical Analytical Tools and Indicators . . . . . . . . . . . . . . . . 124 6.6 Seven Major Financial Trading and Hedging Algorithms . . . . . 125 6.6.1 M1.1 Trend Trading Strategy—Key Patterns . . . . . . . 126 6.6.2 M1.2 Trend Trading Strategy—MA Crossing . . . . . . . 127 6.6.3 M1.3 Trend Trading Strategy—Signal Lines Crossing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 6.6.4 M2.1 Breakout Trading Strategy—Key Patterns . . . . . 129 6.6.5 M2.2 Breakout Trading Strategy—S & R Lines . . . . . 130 6.6.6 M3.1 Reversal Trading Strategy—Key Reversal Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 6.6.7 M3.2 Reversal Trading Strategy—S & R Lines . . . . . 132 6.6.8 M3.3 Reversal Trading Strategy—Technical Indicator RSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 6.6.9 M4.1 Channel Trading Strategy—Trend Channel Trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 6.6.10 M4.2 Channel Trading Strategy—S & R Channel Trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 6.6.11 M4.3 Channel Trading Strategy—Bollinger Bands . . . 136 6.6.12 M5 Averaging Trading Strategy . . . . . . . . . . . . . . . . 137 6.6.13 M6 Stop-Loss Trading Strategy . . . . . . . . . . . . . . . . . 139 6.6.14 M7.1 Hedge Trading Strategy—Single-Market Single-Product (SM-SP) Hedge . . . . . . . . . . . . . . . . . 140 xx Contents 6.6.15 M7.2 Hedge Trading Strategy—Single-Platform Multi-product (SP-MP) Hedge . . . . . . . . . . . . . . . . . . 140 6.6.16 M7.3 Hedge Trading Strategy—Multi-platform Multi-product (MP-MP) Hedge . . . . . . . . . . . . . . . . . 142 6.7 Quantum Trading and Hedging Strategy . . . . . . . . . . . . . . . . . . 143 6.7.1 Basic Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 6.7.2 Quantum Price Levels Versus S & R Analysis . . . . . . 144 6.7.3 Quantum Finance Forecast Center—Daily Forecast and QPLs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 6.7.4 Basic Quantum Trading and Hedging Strategies . . . . . 147 6.7.5 QT#1.1 Inexperienced Investors—Quantum Forecast Oscillation Trading Strategy (QFOTS) . . . . . 147 6.7.6 QT#1.2 Inexperienced Investors—Quantum Forecast Oscillation Trading with Averaging Strategy (QFOTAS) . . . . . . . . . . . . . . . . . . . . . . . . . 148 6.7.7 QT#2.1 Experienced Traders—Quantum Forecast Breakout Trading Strategy (QFBTS) . . . . . . . . . . . . . 150 6.7.8 QT#2.2 Experienced Traders—Quantum Forecast Breakout Trading with Averaging Strategy (QFBTAS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 6.7.9 QT#3.1 Experienced Traders—Quantum Forecast Single-Product Hedging Strategy (QFSPHS) . . . . . . . 152 6.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 7 AI Powerful Tools in Quantum Finance . . . . . . . . . . . . . . . . . . . . . 159 7.1 An Overview of Artificial Intelligence . . . . . . . . . . . . . . . . . . . 160 7.1.1 From Greek Mythology to Turing Test . . . . . . . . . . . 160 7.1.2 Definition of AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 7.1.3 Strong Versus Weak AI . . . . . . . . . . . . . . . . . . . . . . 162 7.1.4 Strong (Hard) AI . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 7.1.5 Weak (Soft) AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 7.1.6 Classification of Artificial Intelligence . . . . . . . . . . . . 165 7.2 Neural Networks—The Brain of AI . . . . . . . . . . . . . . . . . . . . . 166 7.2.1 Biological Neural Network—Our Brain . . . . . . . . . . . 166 7.2.2 Integrate-and-Fire Operations in Biological Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 7.2.3 A Neuron Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 7.2.4 Single Hidden Layer Artificial Neural Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 7.2.5 Classification of ANNs by Machine Learning Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Contents xxi 7.2.6 Classification of ANNs by Areas of Application . . . . . 172 7.2.7 Auto-associative Network—Network Architecture . . . 173 7.2.8 Auto-associative Network—Network Training Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 7.2.9 Hopfield Network—Network Training Algorithm . . . . 175 7.2.10 Hopfield Network—Network Training Algorithm . . . . 175 7.2.11 Feedforward Backpropagation Network (FFBPN)—Network Architecture . . . . . . . . . . . . . . . . 177 7.2.12 Feedforward Backpropagation Network (FFBPN)—Training Algorithm . . . . . . . . . . . . . . . . . 178 7.2.13 Neural Network—Where to Go? . . . . . . . . . . . . . . . . 179 7.3 Genetic Algorithms—The Optimization Engine . . . . . . . . . . . . 180 7.3.1 Nature of Evolution—Genetic Algorithms . . . . . . . . . 180 7.3.2 Nature of Evolution—Genetic Algorithms . . . . . . . . . 181 7.3.3 GA—Population Initialization . . . . . . . . . . . . . . . . . . 182 7.3.4 GA—Fitness Evaluation Function . . . . . . . . . . . . . . . 182 7.3.5 GA—Parents Selection Scheme . . . . . . . . . . . . . . . . . 183 7.3.6 GA—Crossover Operations . . . . . . . . . . . . . . . . . . . . 183 7.3.7 GA—Mutation Operations . . . . . . . . . . . . . . . . . . . . 184 7.3.8 GA—Implementation . . . . . . . . . . . . . . . . . . . . . . . . 185 7.3.9 GA—Applications . . . . . . . . . . . . . . . . . . . . . . . . . . 186 7.4 Fuzzy Logic—The Fuzzification Engine . . . . . . . . . . . . . . . . . . 187 7.4.1 The World of Fuzziness, Chaos, and Uncertainty . . . . 187 7.4.2 The Birth of Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . 188 7.4.3 Fuzzy Theory and the Uncertainty Principle . . . . . . . . 189 7.4.4 ICMR Model—Fuzzy Identification Module . . . . . . . 190 7.4.5 ICMR Model—Fuzzy Categorization Module . . . . . . 191 7.4.6 ICMR Model—Fuzzy Modeling Module . . . . . . . . . . 192 7.4.7 ICMR Model—Fuzzification Versus Defuzzification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 7.4.8 ICMR Model—Fuzzy Reasoning Module . . . . . . . . . 193 7.4.9 Case Study—Fuzzy Air-Conditioning Control Systems, FACS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 7.4.10 Applications of Fuzzy Logic in Daily Life . . . . . . . . . 199 7.4.11 Fuzzy Expert Systems on Financial Trading . . . . . . . . 201 7.4.12 Fuzzy-neuro System in Quantum Finance . . . . . . . . . 201 7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 xxii Contents 8 Chaos and Fractals in Quantum Finance . . . . . . . . . . . . . . . . . . . . 209 8.1 Basic Concepts and Formulation on Chaos Theory . . . . . . . . . . 210 8.1.1 The World of Nonlinear Dynamics . . . . . . . . . . . . . . 210 8.1.2 Deterministic World Versus Probabilistic World . . . . . 211 8.1.3 Chaotic Flow Versus Chaotic Maps . . . . . . . . . . . . . . 212 8.1.4 Origin of Chaos Theory—Lorenz Attractor . . . . . . . . 213 8.2 Characteristics of Chaotic Systems . . . . . . . . . . . . . . . . . . . . . . 215 8.2.1 Sensitive Dependence on Initial Conditions . . . . . . . . 216 8.2.2 Highly Nonlinear but Deterministic in Nature . . . . . . 217 8.2.3 Bifurcation Phenomenon . . . . . . . . . . . . . . . . . . . . . . 218 8.2.4 Applications of Chaos Theory . . . . . . . . . . . . . . . . . . 218 8.2.5 Chaos Theory Versus Uncertainty Principle . . . . . . . . 219 8.3 Fractals in Quantum Finance . . . . . . . . . . . . . . . . . . . . . . . . . . 221 8.3.1 Fractal Versus Chaos—Two Sides of a Coin . . . . . . . 221 8.3.2 A Brief History of Fractals . . . . . . . . . . . . . . . . . . . . 222 8.3.3 Self-similarity in Mandelbrot Set Fractals . . . . . . . . . . 223 8.3.4 Fractal Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . 223 8.3.5 Natural Phenomena with Fractal Features . . . . . . . . . . 225 8.4 Applications of Chaos Theory and Fractals in Quantum Finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 8.4.1 Chaos Theory in Quantum Finance . . . . . . . . . . . . . . 227 8.4.2 Fractal Finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 9 Chaotic Neural Networks in Quantum Finance . . . . . . . . . . . . . . . . 235 9.1 Quantum World of Chaotic Oscillation . . . . . . . . . . . . . . . . . . 236 9.1.1 Multiverse and Quantum Theory . . . . . . . . . . . . . . . . 236 9.1.2 Chaos in Brain—Brainwave . . . . . . . . . . . . . . . . . . . 237 9.2 Chaotic Neural Oscillators . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 9.2.1 An Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 9.2.2 Wang-Oscillator—The Structure . . . . . . . . . . . . . . . . 240 9.2.3 Wang-Oscillator—Bifurcation Diagram . . . . . . . . . . . 241 9.2.4 Wang-Oscillator—Major Contributions and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 9.3 Lee-Oscillator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 9.3.1 The Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 9.3.2 Lee-Oscillator—Bifurcation Diagram . . . . . . . . . . . . . 244 9.3.3 Lee-Oscillator—Potential Applications . . . . . . . . . . . . 245 9.4 Quantum Finance Oscillators (QFO) . . . . . . . . . . . . . . . . . . . . 246 9.4.1 The Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 9.4.2 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 Contents xxiii 9.5 Applications of QFO in Quantum Finance . . . . . . . . . . . . . . . . 248 9.5.1 The Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 9.5.2 QFO for Real-Time Financial Prediction Using Chaotic Neural Networks . . . . . . . . . . . . . . . . . . . . . 250 9.5.3 Chaotic Fuzzy-neuro Oscillators—Quantum Finance Signals (QFS) . . . . . . . . . . . . . . . . . . . . . . . 251 9.5.4 Chaotic Deep Neural Networks in Quantum Finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 9.5.5 Chaotic Multiagent Trading System . . . . . . . . . . . . . . 252 9.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Part II Quantum Finance Applications 10 Quantum Price Levels for Worldwide Financial Products . . . . . . . 263 10.1 Financial Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 10.1.1 An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 10.1.2 Primary Versus Secondary Market . . . . . . . . . . . . . . . 265 10.1.3 Types of Financial Markets . . . . . . . . . . . . . . . . . . . . 266 10.1.4 OTC Market and Market Makers . . . . . . . . . . . . . . . . 266 10.1.5 Forex, OTC, and Market Maker . . . . . . . . . . . . . . . . 267 10.1.6 Forex Trading Characteristics . . . . . . . . . . . . . . . . . . 268 10.1.7 Retail Forex Trading Platforms . . . . . . . . . . . . . . . . . 271 10.2 Supports and Resistances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 10.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 10.2.2 Rising and Declining Supports and Resistances . . . . . 273 10.2.3 Role Reversal on Support and Resistance . . . . . . . . . 273 10.2.4 Trend Reversal on Support and Resistance . . . . . . . . . 274 10.2.5 Support and Resistance Versus QPLs . . . . . . . . . . . . 274 10.3 The MetaTrader (MT) Platform and MQL . . . . . . . . . . . . . . . . 276 10.3.1 An Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 10.3.2 MQL—MetaQuotes Language . . . . . . . . . . . . . . . . . . 277 10.3.3 MQL—A Brief Overview . . . . . . . . . . . . . . . . . . . . . 277 10.3.4 MQL—Time Series and Indicators Access Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 10.3.5 MQL—Trade Functions . . . . . . . . . . . . . . . . . . . . . . 279 10.3.6 MQL—Technical Indicators . . . . . . . . . . . . . . . . . . . 280 10.3.7 MQL Programming Tips . . . . . . . . . . . . . . . . . . . . . . 281 10.4 System Implementation—QPL Evaluation for Worldwide Financial Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 xxiv Contents 10.5 MQL Program for QPLs Evaluation of Worldwide Financial Products (QPL2019.mq4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 10.5.1 System Flowchart and Main Program Logic . . . . . . . . 283 10.5.2 Program Declaration . . . . . . . . . . . . . . . . . . . . . . . . . 286 10.5.3 Program Initialization: init() . . . . . . . . . . . . . . . . . . . 286 10.5.4 Process 1: Calculate All K Values . . . . . . . . . . . . . . . 286 10.5.5 Process 2: READ ALL Daily Time Series . . . . . . . . . 289 10.5.6 Process 3: Calculate Mean (mu) and Standard Deviation (sigma) of Return Array . . . . . . . . . . . . . . 290 10.5.7 Process 4: Generate the Returns Wavefunction Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 10.5.8 Process 5: Evaluate Lambda Value for the Returns Wavefunction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 10.5.9 Process 6: Using QP Schrodinger Equation to FIND the First 21 Energy Levels . . . . . . . . . . . . . 295 10.5.10 Process 7: Program End . . . . . . . . . . . . . . . . . . . . . . 295 10.5.11 Implementation Results . . . . . . . . . . . . . . . . . . . . . . . 295 10.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 11 Time Series Chaotic Neural Oscillatory Networks for Financial Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 11.2 TSCNON—System Architecture . . . . . . . . . . . . . . . . . . . . . . . 302 11.3 TSCNON—System Implementation Framework . . . . . . . . . . . . 304 11.4 Workshop #2—Quantum Finance Forecast System (TSCNON.Mq4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 11.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 11.4.2 Main Program Logic . . . . . . . . . . . . . . . . . . . . . . . . . 307 11.4.3 Process #1—Program Initialization . . . . . . . . . . . . . . 309 11.4.4 Process #2—Check for Forecast Day . . . . . . . . . . . . . 315 11.4.5 Process #3—Loop over ALL Trading Products . . . . . 315 11.4.6 Process #4—Read FC_DT/HL/PF Data Files . . . . . . . 315 11.4.7 Process #5—Create TSCNON and Initialize ALL LEE-Oscillators . . . . . . . . . . . . . . . . . . . . . . . . 321 11.4.8 Process #6—Register Time Series Data . . . . . . . . . . . 321 11.4.9 Process #7—TSCNON Training . . . . . . . . . . . . . . . . 321 11.4.10 Process #8—TSCNON Forecast . . . . . . . . . . . . . . . . 322 11.4.11 Process #9—Check Forecast Within Range or NOT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 11.4.12 Process #10.1—Forecast Completed, Store Forecast Results and Total Time Taken . . . . . . . . . . . 325 Contents xxv 11.4.13 Process #10.2—Write FC_HL Data File and Close All Files . . . . . . . . . . . . . . . . . . . . . . . . . . 325 11.5 TSCNON—Implementation Results . . . . . . . . . . . . . . . . . . . . . 326 11.6 TSCNON—System Performance . . . . . . . . . . . . . . . . . . . . . . . 329 11.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 12 Chaotic Type-2 Transient-Fuzzy Deep Neuro-Oscillatory Network (CT2TFDNN) for Worldwide Financial Prediction . . . . . . 339 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 12.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 12.2.1 An Overview on Type-1 and Interval Type-2 Fuzzy Logic Systems on Financial Prediction . . . . . . . 342 12.2.2 Lee-Oscillators with Retrograde Signaling (LORS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 12.3 CT2TFDNN—The System . . . . . . . . . . . . . . . . . . . . . . . . . . . 346 12.3.1 CT2TFDNN—System Framework . . . . . . . . . . . . . . . 346 12.3.2 Financial Signal Generator Module (FSGM) . . . . . . . 350 12.3.3 Chaotic Type-2 Transient-Fuzzification Module (CT2TFM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 12.3.4 GA-Based TOP-10 Financial Signals Selection Module (GAT10FSSM) . . . . . . . . . . . . . . . . . . . . . . 353 12.3.5 Chaotic T2 Transient-Fuzzy Deep Neural Network Module (CT2TFDNNM) . . . . . . . . . . . . . . . 356 12.4 CT2TFDNN—System Implementation . . . . . . . . . . . . . . . . . . . 359 12.4.1 Quantum Finance Forecast Center . . . . . . . . . . . . . . . 359 12.4.2 129 Worldwide Financial Products . . . . . . . . . . . . . . 359 12.4.3 CT2TFDNN Network Parameter Settings . . . . . . . . . . 360 12.4.4 System Implementation and Pilot Run . . . . . . . . . . . . 361 12.5 CT2TFDNN—Performance Analysis . . . . . . . . . . . . . . . . . . . . 361 12.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 12.5.2 System Training Performance Analysis . . . . . . . . . . . 362 12.5.3 System Forecast Simulation Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 12.5.4 Actual Daily Forecast Performance Analysis . . . . . . . 366 12.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 13 Quantum Trader—A Multiagent-Based Quantum Financial Forecast and Trading System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 13.2 Quantum Forecaster—Chaotic FFBP-Based Time Series Supervised-Learning Neural Networks for Financial Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 xxvi Contents 13.2.1 Time Series Prediction Using Supervised-Learning- Based Artificial Neural Networks . . . . . . . . . . . . . . . 377 13.2.2 Quantum Finance CSL Network—Chaotic FFBP-Based SL Network for Time Series Financial Prediction with Quantum Field Signals (QFF) . . . . . . 377 13.3 Quantum Trader—Chaotic RBF-Based Actor-Critic Reinforcement-Learning Networks—Optimization of Trading Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 13.3.1 An Overview of Reinforcement Learning (RL) . . . . . . 378 13.3.2 Discrete-Time Actor-Critic RL Model . . . . . . . . . . . . 380 13.3.3 Radial Basis Function Neural Network (RBFN) for RL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 13.3.4 Quantum Finance CRL Network—Chaotic RBF-Based Actor-Critic RL Network for the Optimization of Trading Strategy . . . . . . . . . . 382 13.3.5 Quantum Finance CRL Network System Architecture for the Optimization of Trading Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 13.3.6 Quantum Finance CRL Network Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 13.4 System Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 13.4.1 Quantum Finance—TEQNA 5-Tier Architecture . . . . 385 13.4.2 Quantum Trader System Implementation Using 129 Worldwide Financial Products . . . . . . . . . . . . . . 386 13.4.3 Quantum Trader—System Implementation . . . . . . . . . 387 13.4.4 Quantum Finance Daily Forecast at QFFC . . . . . . . . . 389 13.4.5 Quantum Forecaster Performance Test . . . . . . . . . . . . 390 13.4.6 Quantum Trader Trading Strategy Performance Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 13.4.7 Quantum Trader Worldwide Trading Strategy Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394 13.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 14 Future Trends in Quantum Finance . . . . . . . . . . . . . . . . . . . . . . . . 399 14.1 Introduction—The Dawn of Quantum Computing . . . . . . . . . . . 400 14.2 Two Sides of the Same Coin—Hard Quantum Computing Versus Soft Quantum Computing . . . . . . . . . . . . . . . . . . . . . . . 400 14.3 From Quantum Finance to AI Fintech . . . . . . . . . . . . . . . . . . . 401 14.4 Quantum Finance System Development Platform . . . . . . . . . . . 402 Contents xxvii 14.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 Appendix A: List of 139 Financial Products . . . . . . . . . . . . . . . . . . . . . . . 407 Appendix B: List of 39 Financial Indicators . . . . . . . . . . . . . . . . . . . . . . . 411 xxviii Contents

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