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Cybernetic Trading Strategies: Developing a Profitable Trading System with State-of-the-Art Technologies

Book Description

"The computer can do more than show us pretty pictures. [It] can optimize, backtest, prove or disprove old theories, eliminate the bad ones and make the good ones better. Cybernetic Trading Strategies explores new ways to use the computer and finds ways to make a valuable machine even more valuable." --from the Foreword by John J. Murphy.

Until recently, the computer has been used almost exclusively as a charting and data-gathering tool. But as traders and analysts have quickly discovered, its capabilities are far more vast. Now, in this groundbreaking new book, Murray Ruggiero, a leading authority on cybernetic trading systems, unlocks their incredible potential and provides an in-depth look at the growing impact of advanced technologies on intermarket analysis. A unique resource, Cybernetic Trading Strategies provides specific instructions and applications on how to develop tradable market timing systems using neural networks, fuzzy logic, genetic algorithms, chaos theory, and machine induction methods.

Currently utilized by some of the most powerful financial institutions in the world--including John Deere and Fidelity Investments--today's advanced technologies go beyond subjective interpretations of market indicators to enhance traditional analysis. As a result, existing trading systems gain a competitive edge. Ruggiero reveals how "incorporating elements of statistical analysis, spectral analysis, neural networks, genetic algorithms, fuzzy logic, and other high-tech concepts into a traditional technical trading system can greatly improve the performance of standard trading systems." For example: spectral analysis can be used to detect when a market is trending earlier than classical indicators such as ADX.

Drawing on his extensive research on market analysis, Ruggiero provides an incisive overview of cyber-systems--systems that, when applied correctly, can increase trading returns by as much as 200% to 300%. The author covers a wide range of important topics, examining classical technical analysis methodologies and seasonal trading, as well as statistically based market prediction and the mechanization of subjective methods such as candlestick charts and the Elliott Wave. Precise explanations and dozens of real-world examples show you how to:

  • Incorporate advanced technologies into classical technical analysis methodologies.

  • Identify which of these technologies have the most market applicability.

  • Build trading systems to maximize reliability and profitability based on your own risk/reward criteria.

Most importantly, Cybernetic Trading Strategies takes you step by step through system testing and evaluation, a crucial step for controlling risk and managing money.

With up-to-date information from one of the field's leading authorities, Cybernetic Trading Strategies is the definitive guide to developing, implementing, and testing today's cutting-edge computer trading technologies.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Foreword
  5. Preface
    1. HOW TO GET THE MOST OUT OF THIS BOOK
  6. Acknowledgments
  7. Contents
  8. Introduction
  9. Part One: CLASSICAL MARKET PREDICTION
    1. 1: Classical Intermarket Analysis as a Predictive Tool
      1. WHAT IS INTERMARKET ANALYSIS?
      2. USING INTERMARKET ANALYSIS TO DEVELOP FILTERS AND SYSTEMS
      3. USING INTERMARKET DIVERGENCE TO TRADE THE S&P500
      4. PREDICTING T-BONDS WITH INTERMARKET DIVERGENCE
      5. PREDICTING GOLD USING INTERMARKET ANALYSIS
      6. USING INTERMARKET DIVERGENCE TO PREDICT CRUDE
      7. PREDICTING THE YEN WITH T-BONDS
      8. USING INTERMARKET ANALYSIS ON STOCKS
    2. 2: Seasonal Trading
      1. TYPES OF FUNDAMENTAL FORCES
      2. CALCULATING SEASONAL EFFECTS
      3. MEASURING SEASONAL FORCES
      4. THE RUGGIERO/BARNA SEASONAL INDEX
      5. STATIC AND DYNAMIC SEASONAL TRADING
      6. JUDGING THE RELIABILITY OF A SEASONAL PATTERN
      7. COUNTERSEASONAL TRADING
      8. CONDITIONAL SEASONAL TRADING
      9. OTHER MEASUREMENTS FOR SEASONALITY
      10. BEST LONG AND SHORT DAYS OF WEEK IN MONTH
      11. TRADING DAY-OF-MONTH ANALYSIS
      12. DAY-OF-YEAR SEASONALITY
      13. USING SEASONALITY IN MECHANICAL TRADING SYSTEMS
      14. COUNTERSEASONAL TRADING
    3. 3: Long-Term Patterns and Market Timing for Interest Rates and Stocks
      1. INFLATION AND INTEREST RATES
      2. PREDICTING INTEREST RATES USING INFLATION
      3. FUNDAMENTAL ECONOMIC DATA FOR PREDICTING INTEREST RATES
      4. A FUNDAMENTAL STOCK MARKET TIMING MODEL
    4. 4: Trading Using Technical Analysis
      1. WHY IS TECHNICAL ANALYSIS UNJUSTLY CRITICIZED?
      2. PROFITABLE METHODS BASED ON TECHNICAL ANALYSIS
    5. 5: The Commitment of Traders Report
      1. WHAT IS THE COMMITMENT OF TRADERS REPORT?
      2. HOW DO COMMERCIAL TRADERS WORK?
      3. USING THE COT DATA TO DEVELOP TRADING SYSTEMS
  10. Part Two: STATISTICALLY BASED MARKET PREDICTION
    1. 6: A Trader's Guide to Statistical Analysis
      1. MEAN, MEDIAN, AND MODE
      2. TYPES OF DISTRIBUTIONS AND THEIR PROPERTIES
      3. THE CONCEPT OF VARIANCE AND STANDARD DEVIATION
      4. HOW GAUSSIAN DISTRIBUTION, MEAN, AND STANDARD DEVIATION INTERRELATE
      5. STATISTICAL TESTS' VALUE TO TRADING SYSTEM DEVELOPERS
      6. CORRELATION ANALYSIS
    2. 7: Cycle-Based Trading
      1. THE NATURE OF CYCLES
      2. CYCLE-BASED TRADING IN THE REAL WORLD
      3. USING CYCLES TO DETECT WHEN A MARKET IS TRENDING
      4. ADAPTIVE CHANNEL BREAKOUT
      5. USING PREDICTIONS FROM MEM FOR TRADING
    3. 8: Combining Statistics and Intermarket Analysis
      1. USING CORRELATION TO FILTER INTERMARKET PATTERNS
      2. PREDICTIVE CORRELATION
      3. USING THE CRB AND PREDICTIVE CORRELATION TO PREDICT GOLD
      4. INTERMARKET ANALYSIS AND PREDICTING THE EXISTENCE OF A TREND
    4. 9: Using Statistical Analysis to Develop Intelligent Exits
      1. THE DIFFERENCE BETWEEN DEVELOPING ENTRIES AND EXITS
      2. DEVELOPING DOLLAR-BASED STOPS
      3. USING SCATTER CHARTS OF ADVERSE MOVEMENT TO DEVELOP STOPS
      4. ADAPTIVE STOPS
    5. 10: Using System Feedback to Improve Trading System Performance
      1. HOW FEEDBACK CAN HELP MECHANICAL TRADING SYSTEMS
      2. HOW TO MEASURE SYSTEM PERFORMANCE FOR USE AS FEEDBACK
      3. METHODS OF VIEWING TRADING PERFORMANCE FOR USE AS FEEDBACK
      4. WALK FORWARD EQUITY FEEDBACK
      5. HOW TO USE FEEDBACK TO DEVELOP ADAPTIVE SYSTEMS OR SWITCH BETWEEN SYSTEMS
      6. WHY DO THESE METHODS WORK?
    6. 11: An Overview of Advanced Technologies
      1. THE BASICS OF NEURAL NETWORKS
      2. MACHINE INDUCTION METHODS
      3. GENETIC ALGORITHMS—AN OVERVIEW
      4. DEVELOPING THE CHROMOSOMES
      5. EVALUATING FITNESS
      6. INITIALIZING THE POPULATION
      7. THE EVOLUTION
      8. UPDATING A POPULATION
      9. CHAOS THEORY
      10. STATISTICAL PATTERN RECOGNITION
      11. FUZZY LOGIC
  11. Part Three: MAKING SUBJECTIVE METHODS MECHANICAL
    1. 12: How to Make Subjective Methods Mechanical
      1. TOTALLY VISUAL PATTERNS RECOGNITION
      2. SUBJECTIVE METHODS DEFINITION USING FUZZY LOGIC
      3. HUMAN-AIDED SEMIMECHANICAL METHODS
      4. MECHANICALLY DEFINABLE METHODS
      5. MECHANIZING SUBJECTIVE METHODS
    2. 13: Building the Wave
      1. AN OVERVIEW OF ELLIOTT WAVE ANALYSIS
      2. TYPES OF FIVE-WAVE PATTERNS
      3. USING THE ELLIOTT WAVE OSCILLATOR TO IDENTIFY THE WAVE COUNT
      4. TRADESTATION TOOLS FOR COUNTING ELLIOTT WAVES
      5. EXAMPLES OF ELLIOTT WAVE SEQUENCES USING ADVANCED GET
    3. 14: Mechanically Identifying and Testing Candlestick Patterns
      1. HOW FUZZY LOGIC JUMPS OVER THE CANDLESTICK
      2. FUZZY PRIMITIVES FOR CANDLESTICKS
      3. DEVELOPING A CANDLESTICK RECOGNITION UTILITY STEP-BY-STEP
  12. Part Four: TRADING SYSTEM DEVELOPMENT AND TESTING
    1. 15: Developing a Trading System
      1. STEPS FOR DEVELOPING A TRADING SYSTEM
      2. SELECTING A MARKET FOR TRADING
      3. DEVELOPING A PREMISE
      4. DEVELOPING DATA SETS
      5. SELECTING METHODS FOR DEVELOPING A TRADING SYSTEM
      6. DESIGNING ENTRIES
      7. DEVELOPING FILTERS FOR ENTRY RULES
      8. DESIGNING EXITS
      9. PARAMETER SELECTION AND OPTIMIZATION
      10. UNDERSTANDING THE SYSTEM TESTING AND DEVELOPMENT CYCLE
      11. DESIGNING AN ACTUAL SYSTEM
    2. 16: Testing, Evaluating, and Trading a Mechanical Trading System
      1. THE STEPS FOR TESTING AND EVALUATING A TRADING SYSTEM
      2. TESTING A REAL TRADING SYSTEM
  13. Part Five: USING ADVANCED TECHNOLOGIES TO DEVELOP TRADING STRATEGIES
    1. 17: Data Preprocessing and Postprocessing
      1. DEVELOPING GOOD PREPROCESSING—AN OVERVIEW
      2. SELECTING A MODELING METHOD
      3. THE LIFE SPAN OF A MODEL
      4. DEVELOPING TARGET OUTPUT(S) FOR A NEURAL NETWORK
      5. SELECTING RAW INPUTS
      6. DEVELOPING DATA TRANSFORMS
      7. EVALUATING DATA TRANSFORMS
      8. DATA SAMPLING
      9. DEVELOPING DEVELOPMENT, TESTING, AND OUT-OF-SAMPLE SETS
      10. DATA POSTPROCESSING
    2. 18: Developing a Neural Network Based on Standard Rule-Based Systems
      1. A NEURAL NETWORK BASED ON AN EXISTING TRADING SYSTEM
      2. DEVELOPING A WORKING EXAMPLE STEP-BY-STEP
    3. 19: Machine Learning Methods for Developing Trading Strategies
      1. USING MACHINE INDUCTION FOR DEVELOPING TRADING RULES
      2. EXTRACTING RULES FROM A NEURAL NETWORK
      3. COMBINING TRADING STRATEGIES
      4. POSTPROCESSING A NEURAL NETWORK
      5. VARIABLE ELIMINATION USING MACHINE INDUCTION
      6. EVALUATING THE RELIABILITY OF MACHINE-GENERATED RULES
    4. 20: Using Genetic Algorithms for Trading Applications
      1. USES OF GENETIC ALGORITHMS IN TRADING
      2. DEVELOPING TRADING RULES USING A GENETIC ALGORITHM—AN EXAMPLE
  14. References and Readings
  15. Index