Ruggiero, Murray A. Cybernetic trading strategies

Ruggiero, Murray A., - Cybeetic trading strategies: developing a profitable trading
sysfem with state-of-the-art technologies -1 9 9 7, -163p.
introduction
Part one classical market prediction
Classical Intermarket Analysis as a Predictive Tool
What Is Intermarket Analysis?
Using Intermarket Analysis to Develop Filters and Systems
Using Intermarket Divergence to Trade the S&P
Predicting T-Bonds with Intermarket Divergence
Predicting Gold Using Intermarket Analysis
Using Intermarket Divergence to Predict Crude
Predicting the Yen with T-Bonds
Using Intermarket Analysis on Stocks
Seasonal Trading
Types of Fundamental Forces
Calculating Seasonal Effects
Measuring Seasonal Forces
The RuggierolBaa Seasonal Index
Static and Dynamic Seasonal Trading
Judging the Reliability of a Seasonal Patte
Counterseasonal Trading

Conditional Seasonal Trading
Other Measurements for Seasonality
Best Long and Short Days of Week in Month
Trading Day-of-Month Analysis
Day-of-Year Seasonality
Using Seasonality in Mechanical Trading Systems
Counterseasonal Trading
Long-Term Pattes and Market Timing for Interest
Rates and Stocks
Inflation and Interest Rates
Predicting Interest Rates Using Inflation
Fundamental Economic Data for Predicting Interest Rates
A Fundamental Stock Market Timing Model
Trading Using Technical Analysis
Why Is Technical Analysis Unjustly Criticized?
Profitable Methods Based on Technical Analysis
The Commitment of Traders Report
What Is the Commitment of Traders Report?
How Do Commercial Traders Work?
Using the COT Data to Develop Trading Systems
Part two statistically based market prediction
A Trader’s Guide to Statistical Analysis
Mean. Median, and Mode
Types of Distributions and Their Properties
The Concept of Variance and Standard Deviation
How Gaussian Distribution, Mean, and Standard
Deviation Interrelate
Statistical Tests’ Value to Trading System Developers
Correlation Analysis
Cycle-Based Trading
The Nature of Cycles
Cycle-Based Trading~in the Real World
Using Cycles to Detect When a Market Is Trending
Adaptive Channel Breakout
Using Predictions from MEM for Trading
Combining Statistics and Intermarket Analysis
Using Correlation to Filter Intermarket Pattes
Predictive Correlation
Using the CRB and Predictive Correlation to Predict Gold
Intermarket Analysis and Predicting the Existence of a Trend
Using Statistical Analysis to Develop Intelligent Exits
The Difference between Developing Entries and Exits
Developing Dollar-Based Stops
Using Scatter Charts of Adverse Movement to Develop Stops
Adaptive Stops
Using System Feedback to Improve Trading
System Performance
How Feedback Can Help Mechanical Trading Systems
How to Measure System Performance for Use as Feedback
Methods of Viewing Trading Performance for Use as Feedback
Walk Forward Equity Feedback
How to Use Feedback to Develop Adaptive Systems or Switch
between Systems
Why Do These Methods Work?
An Overview of Advanced Technologies
The Basics of Neural Networks
Machine Induction Methods
Genetic Algorithms-An Overview
Developing the Chromosomes
Evaluating Fitness
Initializing the Population
The Evolution
Updating a Population
Chaos Theory
Statistical Patte Recognition
Fuzzy Logic
How to Make Subjective Methods Mechanical
Totally Visual Pattes Recognition
Subjective Methods Definition Using Fuzzy Logic
Human-Aided Semimechanical Methods
Mechanically Definable Methods
Mechanizing Subjective Methods
Building the Wave
An Overview of Elliott Wave Analysis
Types of Five-Wave Pattes
Using the Elliott Wave Oscillator to Identify the Wave Count
TradeStation Tools for Counting Elliott Waves
Examples of Elliott Wave Sequences Using Advanced GET
Mechanically Identifying and Testing Candlestick Pattes
How Fuzzy Logic Jumps Over the Candlestick
Fuzzy Primitives for Candlesticks
Developing a Candlestick Recognition Utility Step-by-Step
Part four trading system development and testing
Developing a Trading System
Steps for Developing a Trading System
Selecting a Market for Trading
Developing a Premise
Developing Data Sets
Selecting Methods for Developing a Trading System
Designing Entries
Developing Filters for Entry Rules
Designing Exits
Parameter Selection and~optimization
Understanding the System Testing and Development Cycle
Designing an Actual System
Testing, Evaluating, and Trading a Mechanical
Trading System
The Steps for Testing and Ev&ating a Trading System
Testing a Real Trading System
Data Preprocessing and Postprocessing
Developing Good Preprocessing-An Overview
Selecting a Modeling Method
The Life Span of a Model
Developing Target Output(s) for a Neural Network
Selecting Raw Inputs
Developing Data Transforms
Evaluating Data Transforms
Data Sampling
Developing Development, Testing, and Out-of-Sample Sets
Data Postprocessing
Developing a Neural Network Based on Standard
Rule-Based Systems
A Neural Network Based on an Existing Trading System
Developing a Working Example Step-by-Step
Machine Leaing Methods for Developing
Trading Strategies
Using Machine Induction for Developing Trading Rules
Extracting Rules from a Neural Network
Combining Trading Strategies
Postprocessing a Neural Network
Variable Elimination Using Machine Induction
Evaluating the Reliability of Machine-Generated Rules
Using Genetic Algorithms for Trading Applications
Uses of Genetic Algorithms in Trading
Developing Trading Rules Using a Genetic Algorithm-
An Example
References and Readings
Index