Designing Stock Market Trading Systems

Name: Designing Stock Market Trading Systems | With and without soft computing
Brand: Harriman House
Price: 67.49 EUR
Availability: OnlineOnly

With and without soft computing

Bruce Vanstone Tobias Hahn(Author)

Harriman House (Publisher)

1st Edition

Published on 20. May 2011

256 pages

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ePUB with Adobe-DRM

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978-0-85719-135-9 (ISBN)

€67.49incl. 7% vat

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Intro
Contents
Preface
Acknowledgements
Introduction
Chapter 1: Designing Stock Market Trading Systems
1.1 Introduction
1.2 Motivation
1.3 Scope and data
1.4 The efficient market hypothesis
1.5 The illusion of knowledge
1.6 Investing versus trading
1.6.1 Investing
1.6.2 Trading
1.7 Building a mechanical stock market trading system
1.8 The place of soft computing
1.9 How to use this book
Chapter 2: Introduction to Trading
2.1 Introduction
2.2 Different approaches to trading
2.2.1 Direction of trading
2.2.2 Time frame of trading
2.2.3 Type of behaviour exploited
2.2.3.1 Trend-based trading
2.2.3.2 Breakout trading
2.2.3.3 Momentum trading
2.2.3.4 Mean reversion trading
2.2.3.5 High frequency trading
2.3 Conclusion
2.4 The next step
Chapter 3: Fundamental Variables
3.1 Introduction
3.1.1 Benjamin Graham and value investing
3.2 Informational advantage and market efficiency
3.3 A note on adjustments
3.4 Core strategies
3.4.1 Intrinsic value estimates
3.4.2 Fundamental filters
3.4.3 Ranking filters
3.5 The elements of a fundamentals-based filter
3.5.1 Wealth of a firm and its shareholders
3.5.1.1 Book value
3.5.1.2 Current assets vs. current liabilities
3.5.1.3 Leverage metrics
3.5.2 Earnings capacity
3.5.3 Ability to generate cash
3.6 Fundamental ratios and industry comparisons
3.7 A final note on cross-country investing and research
3.8 The next step
3.9 Case Study: Analysing a variable
3.9.1 Introduction
3.9.2 Example - P/E Ratio
3.9.3 Wealth-Lab
3.9.4 SPSS
3.9.5 Outliers
Chapter 4: Technical Variables
4.1 Introduction
4.1.1 Charting
4.1.2 Technical indicators
4.1.3 Other approaches
4.2 Charting and pattern analysis
4.3 Technical indicators
4.3.1 Intermarket analysis
4.3.2 Moving averages
4.3.3 Volume
4.3.4 Momentum indicators
4.3.4.1 Moving Average Convergence/Divergence (MACD)
4.3.4.2 Relative Strength Indicator (RSI)
4.4 Alternative approaches
4.5 On use and misuse of technical analysis
Case Study: Does Technical Analysis Have Any Credibility?
Chapter 5: Soft Computing
5.1 Introduction
5.1.1 Types of soft computing
5.1.2 Expert systems
5.1.3 Case-based reasoning
5.1.4 Genetic algorithms
5.1.5 Swarm intelligence
5.1.6 Artificial neural networks
5.2 Review of research
5.2.1 Soft computing classifications
5.2.2 Research into time series prediction
5.2.3 Research into pattern recognition and classification
5.2.4 Research into optimisation
5.2.5 Research into ensemble approaches
5.3 Conclusion
5.4 The next step
Chapter 6: Creating Artificial Neural Networks
6.1 Introduction
6.2 Expressing your problem
6.3 Partitioning data
6.4 Finding variables of influence
6.5 ANN architecture choices
6.6 ANN training
6.6.1 Momentum
6.6.2 Training rate
6.7 ANN in-sample testing
6.8 Conclusion
6.9 The next step
Chapter 7: Trading Systems and Distributions
7.1 Introduction
7.2 Studying a group of trades
7.2.1 Average profitability metrics
7.2.1.1 The students t-test
7.2.1.2 The runs test
7.2.2 Winning metrics
7.2.3 Losing metrics
7.2.4 Summary metrics
7.2.5 Distributions
7.2.5.1 Short-term distribution
7.2.5.2 Medium-term distribution
7.2.5.3 Long-term distribution
7.2.6 Comparing two sets of raw trades
7.3 Conclusions
7.4 The next step
Chapter 8: Position Sizing
8.1 Introduction
8.1.1 Fixed position sizing
8.1.2 Kelly method
8.1.3 Optimal-f
8.1.4 Percentage of equity
8.1.5 Maximum risk percentage
8.1.6 Martingale
8.1.7 Anti-martingale
8.2 Pyramiding
8.3 Conclusions
8.4 The next step
Chapter 9: Risk
9.1 Introduction
9.2 Trade Risk
9.2.1 Stop-loss orders
9.2.2 Using maximum adverse excursion (MAE) to select the stoploss threshold
9.3 Risk of ruin
9.4 Portfolio Risk
9.5 Additional portfolio metrics
9.6 Monte Carlo Analysis
Case study: are stops useful in trend trading systems?
Chapter 10: Case Studies
10.1 Introduction
10.2 A note about data
10.3 A note about the case studies
10.4 Building a technical trading system with neural networks
10.4.1 Splitting data
10.4.2 Benchmark initial rules
10.4.3 Identify specific problems
10.4.4 Identify inputs and outputs for the ANN
10.4.5 Train the networks
10.4.6 Derive money management and risk settings
10.4.7 In-sample benchmarking
10.4.8 Out-of-sample benchmarking
10.4.9 Decide on final product
10.5 Building a fundamental trading system with neural networks
10.5.1 Splitting data
10.5.2 Benchmark initial rules
10.5.3 Identify specific problems
10.5.4 Identify inputs and outputs for ANN
10.5.5 Train the networks
10.5.6 Derive money management and risk settings
10.5.7 In-sample benchmarking
10.5.8 Out-of-sample benchmarking
10.5.9 Decide on final product
Final thoughts
Appendices
Script segments
Bibliography
Index

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Designing Stock Market Trading Systems

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