August 2014
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John Ehlers’ Early Onset Trend Indicator Tells You When to Get In the Market and When to Get Out Before the Trend Is Over
by Marge Sherald, CEO
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In the August 2014 issue of Technical Analysis of STOCKS & COMMODITIES magazine John Ehlers introduced his Quotient Transform indicator as a means of “detecting a trend early and knowing how long to stick with it.” Most analysts use a variation of moving averages to determine a trend, but at the risk of the indicator introducing so much lag that you miss some of the opportunity to profit. When we created the chart for the Traders’ Tip that appears in the magazine (thanks to some indicator programming by Denham Ward), we only displayed two different variations of the indicator on the chart, as per the editor’s guidance for the tip. However, if you read the entire article, you discover that John Ehlers included details for creating trading systems using his quotient transform indicator. He says the quotient transform indicator may be applied to oscillator indicators as long as they are transformed into an indicator that produces output values between -1 and 1. The article includes his suggestions for making this transformation. For example, if you want to use the RSI indicator which normally produces values between zero and 100, he suggests subtracting 50 from the value of the RSI indicator and dividing the result by 50. We did this and created the Scaled RSI indicator that is displayed on the chart below. The chart displays the Scaled RSI indicator and four different versions of the Quotient Transform indicator that substitute the Scaled RSI in order to create the trading rules.
The Trading Rules Long Entry: Quotient Transform RSI 0.8 > 0 Long Exit: Quotient Transform RSI 0.4 < 0 Short Entry: Quotient Transform RSI – 0.8 < 0 Short Exit: Quotient Transform RSI -0.4 > 0 The values 0.8 and 0.4 are constant values used to compute the Quotient Transform indicators. Positive values are used on the long side, and negative values are used for the short rules. (See the complete article for an understanding of his methods.) The smaller constant of 0.4 is used to exit the trade before the trend has run its course. We optimized only the constant values and the results looked promising. SPY and energy stocks Duke Energy, Consolidated Edison, and Southern all produced positive equity curves. Click here to download the chart we created and the required Quotient Transform.DLL. Once you extract the files, copy the .DLL to your c:\NeuroShell Trader 6\template directory. When you open the chart you will have access to the Quotient Transform indicator for use in other charts. |
ChaosHunter Templates Save Time OPTIMIZING Models
by Marge Sherald, CEO
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After we ran the article in last month’s newsletter on how to create templates for ChaosHunter, we had a few tech support calls that reminded me of something we forgot to mention. The callers generally asked what we had done to make ChaosHunter optimization so much faster in Release 4. While we would like to claim some credit, the truth is that nothing was done to increase optimization speed in Release 4. However, the users asking the question were building trading models and had discovered the Steve Ward Trading Models templates (both daily and intraday) that come with ChaosHunter. Since he used the program for 10 years before it came on the market, we figured he probably knew what he was doing when he created the templates. When I build trading models I often start with his template and adjust according to the instrument and the type of model. When I find something I like, I save a copy as a template for later use. ChaosHunter Release 4 Since we didn’t speed up optimization, you might be wondering what we did add in Release 4. First of all we added the ability to edit the formula found by ChaosHunter and immediately apply the modified formula to your data file and view the results. This is another time saver because you don’t have to export the formula to another program before you can tweak it and see the results. We also added a graph to view the results on the out-of-sample data set WHILE THE MODEL IS BEING OPTIMIZED. Prior to release 4 you could view the statistics for the out-of-sample data, but the graph makes it much easier to recognize a good model. We also added a complete new example that addressed the problem of predicting processing load by the hour when you only have a limited number of inputs.
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