Computerized Trading Strategies

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Computerized Trading Strategies

Автор(ы): Jurik Mark

06.10.2007
Год изд.: 1997
Описание: Как использовать компьютер для торговли на форексе? Для начала нужно научиться делать двойной клик левой кнопкой мыши. Для этого возьмем в правую руку мышку. Нет, мышеловку покупать не надо! Мышь — это жаргон. Полное название — «манипулятор оптический типа мышь». Вот она. Возмит её в правую руку и положите на коврик. Придрок, ты куда системник поволок?! При чем тут прихожая?! Собака на коврике спит?!?! {sencored}
Оглавление:
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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]
Counterseasonal 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 Technical Analysis Is 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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