Cybernetic Trading Strategies

Автор(ы):Ruggiero Murray A.
06.10.2007
Год изд.:1997
Описание: Так и видим черный ящик, в котором крутятся зубчатые колеса. Даешь на вход барана - получаешь на выходе две палки колбасы. Кто знает продолжение этого анекдота, тот помнит, как называется агрегат обратного принципа действия - когда на выходе получаются те, кто верит в хитроумные формулы и кибернетические MTC. Трейдеры с уклоном в программирование, определенно, найдут её для себя весьма полезной. И да поможет и бог.
Оглавление:
Cybernetic Trading Strategies — обложка книги. Обложка книги.
Introduction [1]
PART ONE. CLASSICAL MARKET PREDICTION
  1. Classical Intermarket Analysis as a Predictive Tool [9]
    What Is Intermarket Analysis? [9]
    Using Intermarket Analysis to Develop Filters and Systems [27]
    Using Intermarket Divergence to Trade the S&P500 [29]
    Predicting T-Bonds with Intermarket Divergence [32]
    Predicting Gold Using Intermarket Analysis [35]
    Using Intermarket Divergence to Predict Crude [36]
    Predicting the Yen with T-Bonds [38]
    Using Intermarket Analysis on Stocks [39]
  2. Seasonal Trading [42]
    Types of Fundamental Forces [42]
    Calculating Seasonal Effects [43]
    Measuring Seasonal Forces [43]
    The Ruggiero/Barna Seasonal Index [45]
    Static and Dynamic Seasonal Trading [45]
    Judging the Reliability of a Seasonal Pattern [46]
    CouiUerseasonal Trading [47]
    Conditional Seasonal Trading [47]
    Other Measurements for Seasonally [48]
    Best Long and Short Days of Week in Month [49]
    Trading Day-of-Month Analysis [51]
    Day-of-Year Seasonality [52]
    Using Seasonality in Mechanical Trading Systems [53]
    Counterseasonal Trading [55]
  3. Long-Term Patterns and Market Timing for Interest Rates and Stocks [60]
    Inflation and Interest Rates [60]
    Predicting Interest Rates Using Inflation [62]
    Fundamental Economic Data for Predicting Interest Rates [63]
    A Fundamental Stock Market Timing Model [68]
  4. Trading Using Technical Analysis [70]
    Why Is Technical Analysis Unjustly Criticized? [70]
    Profitable Methods Based on Technical Analysis [73]
  5. The Commitment of Traders Report [86]
    What Is the Commitment of Traders Report? [86]
    How Do Commercial Traders Work? [87]
    Using the COT Data to Develop Trading Systems [87]
PART TWO. STATISTICALLY BASED MARKET PREDICTION
  6. A Trader's Guide to Statistical Analysis [95]
    Mean. Median, and Mode [96]
    Types of Distributions and Their Properties [96]
    The Concept of Variance and Standard Deviation [98]
    How Gaussian Distribution, Mean, and Standard Deviation Interrelate [98]
    Statistical Tests' Value to Trading System Developers [99]
    Correlation Analysis [101]
  7. Cycle-Based Trading [103]
    The Nature of Cycles [105]
    Cycle-Based Trading in the Real World [108]
    Using Cycles to Detect When a Market Is Trending [109]
    Adaptive Channel Breakout [114]
    Using Predictions from MEM for Trading [115]
  8. Combining Statistics and Intermarket Analysis [119]
    Using Correlation to Filter Intermarket Patterns [119]
    Predictive Correlation [123]
    Using the CRB and Predictive Correlation to Predict Gold [124]
    Intermarket Analysis and Predicting the Existence of a Trend [126]
  9. Using Statistical Analysis to Develop Intelligent Exits [130]
    The Difference between Developing Entries and Exits [130]
    Developing Dollar-Based Stops [131]
    Using Scatter Charts of Adverse Movement to Develop Stops [132]
    Adaptive Stops [137]
  10. Using System Feedback to Improve Trading System Performance [140]
    How Feedback Can Help Mechanical Trading Systems [140]
    How to Measure System Performance for Use as Feedback [141]
    Methods of Viewing Trading Performance for Use as Feedback [141]
    Walk Forward Equity Feedback [142]
    How to Use Feedback to Develop Adaptive Systems or Switch between Systems [147]
    Why Do These Methods Work? [147]
  11. An Overview of Advanced Technologies [149]
    The Basics of Neural Networks [149]
    Machine Induction Methods [153]
    Genetic Algorithms-An Overview [160]
    Developing the Chromosomes [161]
    Evaluating Fitness [162]
    Initializing the Population [163]
    The Evolution [163]
    Updating a Population [168]
    Chaos Theory [168]
    Statistical Pattern Recognition [171]
    Fuzzy Logic [172]
PART THREE. MAKING SUBJECTIVE METHODS MECHANICAL
  12. How to Make Subjective Methods Mechanical [179]
    Totally Visual Patterns Recognition [180]
    Subjective Methods Definition Using Fuzzy Logic [180]
    Human-Aided Semimechanical Methods [180]
    Mechanically Definable Methods [183]
    Mechanizing Subjective Methods [183]
  13. Building the Wave [184]
    An Overview of Elliott Wave Analysis [184]
    Types of Five-Wave Patterns [186]
    Using the Elliott Wave Oscillator to Identify the Wave Count [187]
    Trade Station Tools for Counting Elliott Waves [188]
    Examples of Elliott Wave Sequences Using Advanced GET [194]
  14. Mechanically Identifying and Testing Candlestick Patterns [197]
    How Fuzzy Logic Jumps Over the Candlestick [197]
    Fuzzy Primitives for Candlesticks [199]
    Developing a Candlestick Recognition Utility Step-by-Step [200]
PART FOUR. TRADING SYSTEM DEVELOPMENT AND TESTING
  15. Developing a Trading System [209]
    Steps for Developing a Trading System [209]
    Selecting a Market for Trading [209]
    Developing a Premise [211]
    Developing Data Sets [211]
    Selecting Methods for Developing a Trading System [212]
    Designing Entries [214]
    Developing Filters for Entry Rules [215]
    Designing Exits [216]
    Parameter Selection and Optimization [217]
    Understanding the System Testing and Development Cycle [217]
    Designing an Actual System [218]
  16. Testing, Evaluating, and Trading a Mechanical Trading System [225]
    The Steps for Testing and Evaluating a Trading System [226]
    Testing a Real Trading System [231]
PART FIVE. USING ADVANCED TECHNOLOGIES TO DEVELOP TRADING STRATEGIES
  17. Data Preprocessing and Postprocessing [241]
    Developing Good Preprocessing-An Overview [241]
    Selecting a Modeling Method [243]
    The Life Span of a Model [243]
    Developing Target Output(s) for a Neural Network [244]
    Selecting Raw Inputs [248]
    Developing Data Transforms [249]
    Evaluating Data Transforms [254]
    Data Sampling [257]
    Developing Development, Testing, and Out-of-Sample Sets [257]
    Data Postprocessing [258]
  18. Developing a Neural Network Based on Standard Rule-Based Systems [259]
    A Neural Network Based on an Existing Trading System [259]
    Developing a Working Example Step-by-Step [264]
  19. Machine Learning Methods for Developing Trading Strategies [280]
    Using Machine Induction for Developing Trading Rules [281]
    Extracting Rules from a Neural Network [283]
    Combining Trading Strategies [284]
    Postprocessing a Neural Network [285]
    Variable Elimination Using Machine Induction [286]
    Evaluating the Reliability of Machine-Generated Rules [287]
  20. Using Genetic Algorithms for Trading Applications [290]
    Uses of Genetic Algorithms in Trading [290]
    Developing Trading Rules Using a Genetic Algorithm— An Example [293]
  References and Readings [307]
  Index [310]
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