Post
by **stancramer** » Tue Mar 23, 2004 12:56 pm

The way people often describe it, is "measure the correlation of the equity curve **returns**."

If you're testing without positionsizing (such as one-lot tests), returns* = ( equity** - equity[(i-1)] )*

If you're testing with positionsizing (such as fixed fractional), returns* = ( (equity** / equity[(i-1)]) - 1 )*

The idea is to produce a scatter diagram with returns(instrument_1) on the horizontal axis, and returns(instrument_2) on the vertical axis. The correlation function measures the grouping-ness [or non-grouping-ness] of the returns in the scatter diagram.

If the points are perfectly smeared out on the scatter diagram (math talk: uniformly distributed over the domain), then the correlation is zero. But if the points are grouped together in something approximating a straight line, then the correlation is large. If the straight line slopes upward, the correlation is a large positive number (such as +0.8733), and if the straight line slopes downward, the correlation is a large negative number (such as -0.93698).

BTW: this suggests partitioning the equity curve return data into four pieces, corresponding to the four quadrants of the scatterplot.

1. (return_A > 0), (return_B > 0)

2. (return_A < 0), (return_B > 0)

3. (return_A < 0), (return_B < 0)

4. (return_A > 0), (return_B < 0)

The ideal holy grail would be to find A and B such that there are no points at all in the 3rd quadrant. The third quadrant is the Double Whammy zone, the two-stage rocket to bankruptcy.