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Types of Data * Data Time Frames * Data Quality 2
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Simulators 13
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Types of Simulators * Programming the Simulator * Simulator Output @erformance summnry reports; trade-by-trade reports) * Simulator Perfomxmce (speed: capacity: power) l Reliability of Simulators - Choosing the Right Simulator * Simulators Used in This Book
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Optimizers and Optimization 29
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What Optimizers Do * How Optimizers Are Used * Lpes of Optimization (implicit optimizers; brute force optimizers; user-guided optimization; genetic optimizers; optimization by simulated annealing; analytic optimizers; linearpmgrwnming) l How to Fail with Optimization (small samples: large fxmztneter sets; no veri~cation) . How to Succeed with O&mization (h-ge, representative samples; few rules andparameters; veriicatim @results) * Alternatives to Traditional Optimization * Optimizer Tools and Information * Which Optimizer Is forYou
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Statistics 51
Why Use Statistics to Evaluate Trading Systems l Sampling * Optimization and . Evaluating a System Statistically Curve-Fitting l Sample Size and Representativeness * Example 1: Evaluating the Out-of-Sample Test (what ifthe distribution is not normal what if there is serial dependence what if the markets change ) l Example 2: Evaluating the In-Sample Tests * Interpreting the Example Statistics (optimization i-esults; verification results) l Other Statistical Techniques and Their Use (genetically
evoJved systems; multiple regression; monte car10 simulations; out-of-sample testing; walk-forward testing) * Conclusion
PART II
The Study of Entries Introduction 71
What Constitutes a Good Entry * Orders Used in Entries (stop orders; limit orders; market orders; selecting appropriate orders) * Entry Techniques Covered in This Book
(breakouts and moving averages; oscillators; seasonality: lunar and solar phenomena: cycles and rhythms; neural networks; geneticaNy evolved entry rules) * Standardized
Exits * Equalization of Dollar Volatility * Basic Test Portfolio and Platfcnm
5 Breakout Models 83
Kinds of Breakouts l Characteristics of Breakouts . Testing Breakout Models l Channel Breakout Entries (close only channel breakouts; highest higMowest low bnxzkouts) l Volatility Breakout Entries l Volatility Breakout Variations (long positions only; currencies only; adx tremififilter) . Summary Analyses (breakout types: entry orders; interactions; restrictions andjilters; analysis by market) * Conclusion l What Have We Lamed 6 Moving Average Models 109 What is a Moving Average - Purpose of a Moving Average * The Issue of Lag l Types of Moving Averages l Types of Moving Average Entry Models l Characteristics of Moving Average Entries l Orders Used to Effect Entries * Test Methodology Tests of Trend-Following Models * Tests of Counter-Trend Models * Conclusion l What Have We Learned
7
Oscillator-Based
Entries
What Is an Oscillator l Kinds of Oscillators * Generating Entries with Oscillators * Characteristics of Oscillator Entries . Test Methodology l Test Results (teas of overbought/oversold models; tests of signal line models; tests of divergence models; summary analyses) - Conclusion * What Have We Learned S
Seasonality 153
What Is Seasonality l Generating Seasonal Entries l Characteristics of Seasonal Entries . Orders Used to Effect Seasonal Entries . Test Methodology . Test Results (test of the basic crossover model; tests of the basic momentum model: tests of the crossover model with con$mtion; tests of the C~SSOV~~ model with confirmation and inversions: summary analyses) * Conclusion * What Have We Learned Chmter 9
Lunar and Solar Rhythms
Legitimacy or Lunacy l Lunar Cycles and Trading (generating lunar entries: lunar test methodology; lunar test results; tests of the basic cmmo~er model; tests of the basic momentum model: tests of the cnx~mer model with confirmation; test.s of the crmmver model with confirmation and inversions; summary analyses; conclusion) * Solar Activity and Trading (generazing solar entries: solar test results: conclusion) * What Have We Learned 10
Cycle-Based Entries 2Q3
Cycle Detection Using MESA l Detecting Cycles Using Filter Banks (butterworth jilters; wavelet-basedjilters) * Generating Cycle Entries Using Filter Banks * Characteristics of Cycle-Based Entries . Test Methodology . Test Results . Conclusion l What Have We Learned 11
Neural Networks 227
What Are Neural Networks (feed-forward neural networks) . Neural Networks in Trading l Forecasting with Neural Networks l Generating Entries with Neural Predictions . Reverse Slow %K Model (code for the reverse slow % k model: test methodology for the reverse slow % k model; training results for the reverse slow %k model) l Turning Point Models (code for the turning point models; test methodology
for the turning point models; training resulrs for the turning point models) * Trading Results for All Models (@ading results for the reverse slow %k model: frading results for the bottom ruming point model; trading results for the top turning poinf model) * Summary Analyses l Conclusion * What Have We Learned
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