# How to “uncluster” a set of financial data?

I am attempting to evaluate and compare the profit factor of different "test runs" of a FOREX trading strategy.

My problem is that, despite an average time between orders of 2hr+, some of these runs can have 20+ orders in a row, every 5 minutes, in the same direction. I need some way to normalize these clusters that occur without just throwing the data out.

I want to treat the cluster as 1 data point by averaging the gain/loss of each trade within the cluster.

I was thinking of doing it with a moving time window in the following manner:

For each order:
Weight = 1/(N+1)
N =  count of consecutive orders within 30 minutes of the current order.


But I am not sure if that is correct.

• Why would you average them? If you need to consolidate them into a single point, why not add them together? – Henry Sep 17 '11 at 8:13
• @Henry Because if I added them together it would give each point within the cluster as much weight as a point outside of the cluster. I want the cluster to be weighed against other results as if it was 1 data point. – Mike Furlender Sep 17 '11 at 8:26
• Why? If your strategy says "buy $\$100$" every five minutes for an hour, then you have bought$\$1200$ not $\$100\$ – Henry Sep 17 '11 at 8:31
• No, because my "strategy" involves a metatest based on the performance of which I want to manage my bet sizes. – Mike Furlender Sep 17 '11 at 9:57