Cybernetic Trading Strategies
Автор(ы): | Ruggiero Murray A.
06.10.2007
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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: |
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] |
Формат: | djvu |
Размер: | 3226678 байт |
Язык: | ENG |
Рейтинг: | 99 |
Открыть: | Ссылка (RU) |