Practical Applications of Evolutionary Computation to Financial Engineering

Transcription

1 Hitoshi Iba and Claus C. Aranha Practical Applications of Evolutionary Computation to Financial Engineering Robust Techniques for Forecasting, Trading and Hedging 4Q Springer

2 Contents 1 Introduction to Genetic Algorithms What Is Evolutionary Computation? Basic Principles of Evolutionary Computation Introduction to GA Introduction to GP WhyGAandGP? 15 2 Advanced Topics in Evolutionary Computation Multi-objective Optimization : Risk or Return?.\ Memes and Memetic Algorithms Memes - The Cultural Genes Can the Meme Survive in the World of Finance? The Memetic Algorithm Baldwinian Evolution Baldwin Effects on FX Trading Rule Optimization 34' 2.3 Real-Valued GAs Differential Evolution Particle Swarm Optimization Randomness Issues in Evolutionary Computation Tree Generation for Genetic Programming Experiments with Predicting Time-Series Data 55 3 Financial Engineering Basic Concepts in Financial Engineering The Technical and Fundamental Approaches Market Elements, Technical Trading Concepts Price Prediction Option Pricing and the Black-Scholes Formula Trend Analysis 69

3 X Contents Strategies for Trend Analysis Overview of Technical Indicators Automated Stock Trading Portfolio Optimization Problem Definition Evolutionary Approaches to Portfolio Optimization 82 4 Predicting Financial Data Methods for Time Series Prediction STROGANOFF GMDH Process in STROGANOFF Crossover in STROGANOFF....7.' Mutation in STROGANOFF Fitness Evaluation in STROGANOFF Recombination Guidance in STROGANOFF STROGANOFF Algorithm Application to Financial Prediction STROGANOFF Parameters and Experimental Conditions GP Parameters and Experimental Conditions Validation Method Experimental Results Comparative Experiment with Neural Networks Inductive Genetic Programming Polynomial Neural Networks PNN Approaches Basic IGP Framework Ill PNN vs. Linear ARMA Models PNN vs. Neural Network Models PNN for Forecasting Cross-Currency Exchange Rates Challenging Black-Scholes Formula Is the Precise Prediction Really Important? Trend Analysis The Data Classification Problem The MVGPC * Classification by Genetic Programming Majority Voting System Applying MVGPC to Trend Analysis MVGPC Extension Trading Rule Generation for Foreign Exchange (FX) Automated Trading Methods Using Evolutionary Computation Applications of GA and GP Application of PSO and DE 144

4 Contents XI 6.2 Price Prediction Based Trading System Trend Prediction Generating Trading Rules Experimental Results The GA-GP Trading System Why Optimize Indicators' Parameters? Fitness Function Implementation of the GA-GP System Practical Test of the GA-GP System Using DE and PSO for FX Trading Moving Average FeaturevBased Trading System Dealing Simulation Portfolio Optimization A Simple GA for Portfolio Optimization Genome Representation Evolutionary Operators Selection Method Fitness Function Testing the Array-Based GA MTGA - GA Representation for Portfolio Optimization Main Strategies of the MTGA Implementation of the MTGA, Hybridization Policy Test-Driving the MTGA Implementation Issues for Portfolio Optimization Dynamic Data and Portfolio Rebalancing Asset Lots and Portfolio Weighting Trader Policies Alternative Risk and Return Measures 201 A Software Packages 203 A.I Introduction." 203 A.2 Multi-objective Optimization by GA '' A.3 Time Series Prediction by GP 207 A.4 Majority Voting GP Classification System 208 A.5 STROGANOFF Time Series Prediction and System Identification. 209 A.6 Portfolio Optimization Testing Suite 214 B GAGPTrader 219 B.I System Requirements 219 B.2 Preparing the GAGPTrader for Installation 220 B.2.1 Meta Trader 4 Demo Account 223 B.3 GAGPTrader Trial Version Installation 224

5 XII Contents B.4 Setting Up the GAGPTrader Trial Version 227 B.5 Parameters 230 B.6 Output Log 232 B.7 Description of the Symbols Used in the Charts 233 References 235 Index 243