Details

<p>Preface ix</p> <p>About the Author xiii</p> <p><b>Chapter 1 Unified Filter Theory 1</b></p> <p>Transfer Response 1</p> <p>Nonrecursive Filters 3</p> <p>Recursive Filters 8</p> <p>Generalized Filters 10</p> <p>Programming the Filters 11</p> <p>Wave Amplitude, Power, and Decibels (dB) 13</p> <p>Key Points to Remember 13</p> <p><b>Chapter 2 SMAs, EMAs, or Other? 15</b></p> <p>Simple Moving Averages (SMAs) 15</p> <p>Exponential Moving Averages (EMAs) 18</p> <p>Weighted Moving Averages (WMAs) 21</p> <p>Median Filter 22</p> <p>Key Points to Remember 23</p> <p><b>Chapter 3 Smoothing Filters on Steroids 25</b></p> <p>Nonrecursive Filters 25</p> <p>Modified Simple Moving Averages 29</p> <p>Modified Least-Squares Quadratics 30</p> <p>SuperSmoother 31</p> <p>SuperSmoother Filter Applications 34</p> <p>Key Points to Remember 36</p> <p><b>Chapter 4 Decyclers 39</b></p> <p>Decycler Construction 39</p> <p>Decycler Application 41</p> <p>Decycler Oscillator 43</p> <p>Key Points to Remember 45</p> <p><b>Chapter 5 Band-Pass Filters 47</b></p> <p>Band-Pass Filter 47</p> <p>Band-Pass Filter Q 51</p> <p>Automatic Gain Control (AGC) 54</p> <p>Spectral Dilation Removal 56</p> <p>Band-Pass Filter 56</p> <p>Measuring the Cycle Period 58</p> <p>Key Points to Remember 61</p> <p><b>Chapter 6 Market Structure and the Hurst Coefficient 63</b></p> <p>Fractal Dimension 65</p> <p>Computing the Hurst Coefficient 67</p> <p>The Hurst Coefficient in Action 68</p> <p>Drunkard’s Walk Hypothesis for Market Structure 70</p> <p>Key Points to Remember 74</p> <p><b>Chapter 7 Spectral Dilation 77</b></p> <p>Frequency Content of Indicator Outputs 77</p> <p>Roofing Filter as an Indicator 80</p> <p>Impact of Spectral Dilation on</p> <p>Conventional Indicators 83</p> <p>Key Points to Remember 88</p> <p><b>Chapter 8 Autocorrelation 91</b></p> <p>Background 91</p> <p>Autocorrelation 93</p> <p>Autocorrelation Periodogram 102</p> <p>Autocorrelation Reversals 110</p> <p>Key Points to Remember 113</p> <p><b>Chapter 9 Fourier Transforms 115</b></p> <p>Spectral Dilation 116</p> <p>Discrete Fourier Transform (DFT) 117</p> <p>Key Points to Remember 124</p> <p><b>Chapter 10 Comb Filter Spectral Estimates 125</b></p> <p>Spectral Dilation 125</p> <p>Computing a Comb Filter Spectral Estimate 126</p> <p>Key Points to Remember 133</p> <p><b>Chapter 11 Adaptive Filters 135</b></p> <p>Adaptive Relative Strength Index (RSI) 135</p> <p>Adaptive Stochastic Indicator 142</p> <p>Adaptive CCI (Commodity Channel Index) 147</p> <p>Adaptive Band-Pass Filter 152</p> <p>Adaptive Indicator Comparison 157</p> <p>Key Points to Remember 158</p> <p><b>Chapter 12 The Even Better Sinewave Indicator 159</b></p> <p>Even Better Sinewave Approach 160</p> <p>Even Better Sinewave Description 160</p> <p>Using the Even Better Sinewave Indicator 162</p> <p>Key Points to Remember 164</p> <p><b>Chapter 13 Convolution 165</b></p> <p>Theoretical Foundation 165</p> <p>Heat Map Display 168</p> <p>Computing Convolution 169</p> <p>Key Points to Remember 174</p> <p><b>Chapter 14 The Hilbert Transformer 175</b></p> <p>Analytic Signals 176</p> <p>Hilbert Transformer Mathematics 177</p> <p>Computing the Hilbert Transformer 181</p> <p>The Hilbert Transformer Indicator 183</p> <p>Using the Hilbert Transformer to</p> <p>Compute the Dominant Cycle 186</p> <p>Dual Differentiator 187</p> <p>Phase Accumulation 189</p> <p>Homodyne 192</p> <p>Key Points to Remember 194</p> <p><b>Chapter 15 Indicator Transforms 195</b></p> <p>Fisher Transform 195</p> <p>Inverse Fisher Transform 198</p> <p>Cube Transform 200</p> <p>Key Points to Remember 202</p> <p><b>Chapter 16 SwamiCharts 203</b></p> <p>SwamiCharts Overview 204</p> <p>SwamiCharts RSI 205</p> <p>SwamiCharts Stochastic 210</p> <p>Roll Your Own SwamiCharts 216</p> <p>Key Points to Remember 216</p> <p><b>Chapter 17 Swing-Trading Strategies 217</b></p> <p>Conventional Wisdom 219</p> <p>Anticipating the Turning Point 220</p> <p>Sine Wave Uniqueness 221</p> <p>Safety Valve 224</p> <p>Exiting a Trade 225</p> <p>Stop Loss 225</p> <p>Evaluating a Trading Strategy 226</p> <p>Monte Carlo Evaluation 227</p> <p>Stockspotter.com 228</p> <p>Key Points to Remember 229</p> <p>About the Website 231</p> <p>Index 233</p>

<p><b>John F. Ehlers</b> worked as an electrical engineer at one of the largest aerospace companies in the industry before retiring as a senior engineering fellow. A graduate of the University of Missouri, he has been a private trader since 1976, specializing in technical analysis. The discoverer of Maximum Entropy Spectrum Analysis, he writes extensively on technical trading and speaks internationally on the subject.

<p><b>Cycles are a unique kind of trading analytics</b>, being one of the few types of market data that can be accurately measured. But understanding what the cycles mean and which trades to make based on them is an extremely complex process. <i>Cycle Analytics for Traders</i> is a technical resource for self-directed traders that explains the scientific underpinnings of the filters and indicators used to make effective and profitable trading decisions. Rather than simply using cycle analytics on blind faith, this book explores and explains the how and why of cycles. <p>Though technical in nature, <i>Cycle Analytics for Traders</i> emphasizes simplicity rather than mathematical purity, taking a pragmatic real-world approach to attaining effective trading results. It allows traders to think of indicators and trading strategies in the frequency domain as well as their motions in the time domain, letting them select the most efficient filter lengths for the job at hand. Traders with little mathematical background will learn how to assess general market conditions to their advantage while technically astute traders will be able to create indicators and strategies that automatically adapt to measured market conditions using the computer code described here. <p>Additionally, author John Ehlers explains several vital concepts all traders should understand: how to eliminate or use Spectral Dilation to their advantage; how to use Automatic Gain Control to normalize indicator amplitude swings; the fact that all indicators are statistical rather than absolute; how to use advanced cookbook filters; several different methods for estimating market spectra and sifting out the Dominant Cycle; and how to use transforms to improve the display and interpretation of indicators. <p><i>Cycle Analytics for Traders</i> shows traders how to approach trading as a statistical process that should be judged from the long-term view, rather than a small sample set of just a few trades—no matter how profitable those few are. With this practical and informative book as a guide, any trader can master cycle analytics, letting statistics and science light the way to long-term trading success.

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