| Description |
xi, 200 pages : illustrations ; 23 cm |
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regular print |
| Contents |
Contents note continued: 3.Security exchanges at a glance -- 3.1.Financial markets -- 3.2.Security exchanges -- 3.2.1.Entities participating in markets -- 3.2.2.Order-driven and quote-driven markets -- 3.2.3.World's largest and most long-standing security exchanges -- 3.2.4.Types of orders -- 3.2.5.Pit trading versus electronic trading -- 3.3.Exchange clearing systems -- 4.Basic tenets of automated trading -- 4.1.Indicators and oscillators -- 4.1.1.Moving averages -- 4.1.2.Average directional index -- 4.1.3.Average true range -- 4.1.4.Relative strength index -- 4.1.5.Bollinger bands -- 4.2.Money management -- 4.3.Statistics -- 4.4.The sensitivity to changes of parameters -- 5.Simulation and backtesting of trading strategies -- 5.1.The value of simulation in trading -- 5.2.Human factor in the trading chain -- 5.3.Modeling of intra-bar price movements -- 5.4.Modeling of order execution -- 5.5.Modeling of time and price skews -- 5.6.Discrete Event System Specification -- |
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Contents note continued: 5.6.1.DEVS formalism -- 5.6.2.Simulators and coordinators for DEVS -- 5.7.Simulation of the trading environment -- 5.7.1.The data provider component -- 5.7.2.The delay component -- 5.7.3.The order execution component -- 5.7.4.The ATS component -- 5.7.5.The parallel run of multiple trades -- 5.8.Embedding trading strategies into the simulation -- 5.8.1.Simulation case study -- 6.Optimization of trading strategies -- 6.1.Parametric trading strategies -- 6.1.1.Choosing an appropriate fitness function -- 6.1.2.Parametric surface -- 6.2.Exhaustive search -- 6.3.Genetic algorithms -- 6.3.1.Inspiration from nature -- 6.3.2.Computational model of genetic evolution -- 6.3.3.Optimization case study -- 7.Fuzzy approach to trading strategies -- 7.1.Concept of uncertainty and the basics of fuzzy logic theory -- 7.1.1.Linguistic variables and fuzzy sets -- 7.2.Fuzzy logic and fuzzy inference -- 7.2.1.Fuzzification -- 7.2.2.Inference engine and evaluation of rules -- |
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Contents note continued: 7.2.3.Defuzzication -- 7.3.Fuzzy-based trading strategies -- 7.3.1.Triple Screen Trading System -- 7.3.2.Fuzzy approach to the Triple Screen Trading System -- 7.4.Analysis of sensitivity and robustness -- 7.4.1.Sensitivity analysis of the whole market system -- 7.4.2.Sensitivity analysis of the signaling system -- 7.5.Case study |
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Machine generated contents note: 1.Reality, the intersection of multiple theories -- 1.1.Efficient market hypothesis -- 1.2.The theory of chaos -- 1.3.Behavioral market theory -- 2.The dynamics of crowd behavior -- 2.1.Methodologies for the study of markets -- 2.2.The system theory point of view -- 2.2.1.The exchange of energy and information -- 2.2.2.The crowd's life cycle -- 2.2.3.Unexpected events and shocks -- 2.2.4.Generalized turnover patterns -- 2.2.5.Generalized pro-trend patterns -- 2.3.The wave principle -- 2.3.1.The hierarchical organization of Elliott waves -- 2.3.2.The direction of waves -- 2.3.3.The mode of waves -- 2.3.4.The hierachy of complete cycles -- 2.3.5.Variations of motive waves -- 2.3.6.Variations of corrective waves -- 2.3.7.The principle of alternation -- 2.4.Fibonacci mathematics in financial markets -- 2.4.1.The golden ratio -- 2.4.2.The golden rectangle and golden spiral -- 2.4.3.The application of Fibonacci numbers in financial markets -- |
| Summary |
This book deals with the issue of problematic market price prediction in the context of crowd behaviour affected by the psychology of the masses. It highlights the contrast between a phenomenon of mass psychology and the efficient market hypothesis, which is essentially based on a common economic theory. The basic assumption is that if there is a model of interaction between masses and agents participating in markets, then there also exist means for prediction of the whole market's behaviour, though nevertheless the behaviour of every single agent is not predictable. From a practical point of view, this book describes technical analysis methods used to predict price movements, and discusses a soft computing approach used in a composition of automated trading systems. This book brings alternative, soft computing computational models to trading strategies and innovatively combines two different areas of science - artificial intelligence and technical analysis. One of the main benefits of this book is a demonstration that the soft computing approach in a combination with the 'soft' social sciences accounts more reliable results than the conventional mathematical models |
| Bibliography |
Includes bibliographical references (pages 195-197) |
| Subject |
Artificial intelligence -- Industrial applications.
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Business -- Data processing.
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Expert systems (Computer science) -- Industrial applications.
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Finance -- Computer programs.
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Stock exchanges -- Data processing.
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Stocks -- Charts, diagrams, etc.
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| Genre/Form |
Graphs.
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| ISBN |
1906659532 (paperback) |
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9781906659530 (paperback) |
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