Stocks & Commodities V. 31:12 (32-35): Repeated Median Velocity Strategy, Part 2 by Dennis Meyers, PhD

Stocks & Commodities V. 31:12 (32-35): Repeated Median Velocity Strategy, Part 2 by Dennis Meyers, PhD
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Repeated Median Velocity Strategy, Part 2 by Dennis Meyers, PhD

All Aboard The Trend!

Here in the second part of this article, you’ll identify optimal parameters and find out how the system performed.

Last month in part 1, I described the repeated median velocity (RMV) strategy and how it can be used to place buy & sell orders as well as how to test this strategy. Here in part 2, I will discuss how to find the optimized system parameters so you can increase your overall trading profits.

Finding the system parameters

In the RMV strategy, there are three strategy parameters to find: N, vup, and vdn. For the test data, I ran the TradeStation optimization engine on the Russell 2000 index emini futures (TF) one-minute price bars from March 30, 2011 to May 3, 2013 with the following optimization ranges for the repeated median strategy inputs:

* N from 20 to 70 in steps of 10

* vup from 0.5 to 10 steps of 0.5

* vdn from 0.5 to 10 in steps of 0.5.

I created 105 30-day in-sample periods each followed by a seven-day out-of-sample period for the in-sample/out-of-sample periods (see sidebar “Walk-Forward Out-Of-Sample Performance Summary” at This will produce 2,400 different input combinations or cases of the strategy input parameters for each of the 105 in-sample/out-of-sample files for the two years of one-minute bar TF data.

What I am trying to do is statistically identify the best performance metric (which I call a filter) or combination of best performance metrics that I can apply to the in-sample section that will give me strategy inputs that will produce, on average, valid profits in the out-of-sample section, or future data. When I run an optimization over many combinations of inputs in TradeStation, it creates an output page with each strategy input combination as its rows, and, as its columns, various trading performance measures such as profit factor, total net profits, and so on.

A simple filter would be to choose the strategy input optimization row in the in-sample section that had the highest net profit, or choose a row that had the best profit factor with its associated strategy inputs. Unfortunately, this type of simple metric performance filter has been found to rarely produce good out-of-sample results. Some of the more complicated metric filters have been found to produce good out-of-sample results, as they minimize spurious price movement biases.

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