What statistical test can I use to detect clumping? I have time series data that represent dates/times of trades taken in a financial market. 
I would like to assign a score to this data that represents whether the trades are mostly clustered around particular time values or if they are  mostly spread out evenly. I am going to have about 1000+ results per dataset.
Example situation one (High degree of "clustering" ): 
1. 01/01/01 : 13:00 
2. 01/01/01 : 13:10
3. 01/01/01 : 13:15
4. 01/01/01 : 13:25
5. 03/05/01 : 17:20
6. 03/05/01 : 17:35
7. 03/05/01 : 17:40
8. 03/05/01 : 17:45

Example situation two( Low degree of "clustering)"
1. 01/01/01 : 13:00
2. 01/05/01 : 02:30
4. 02/12/01 : 06:40
5. 02/25/01 : 02:30
6. 03/30/01 : 21:10
7. 04/12/01 : 02:20
8. 05/02/01 : 03:25

I can of course convert all the timestamps to posix time or whatnot so doing calculation with the time values won't be a problem.   
I was thinking possibly standard error?
(For those who want more background info: I am using backtest results to modulate the size of my entry position in a complex manner. If the results contain trades that are clustered together, then they don't really count as 1 trade each (more like one big trade). This means that such results are untrustworthy and I should not act on them.)
Thanks!
 A: I would simply calculate a rolling window of the number of trades (or dollar volume) per hour, day, week, or whatever time frame that makes sense.   For example, you might use 1 day as the rolling window.  If 1 trade per day is a low degree of clustering then 10 trades per day might be a high degree of clustering.   If so, then a linear "y" scale for a "clustering plot" is probably reasonable.
Here's an example:
Edit 2 ===========================================
Below is the updated version of the plot.   Just like the previous plots, the gray line is from a "window" of 1 day where the "cluster number" is the number of trades for the previous day.   The new blue line is from a "window" of 5 days where the "cluster number" is the sum of the trades for the previous 5 days divided by 5 (the divide by 5 is to scale the result so it can be directly compared to a 1 day "window").    The new purple line is from a "window" that sums the trades for 10 days and then divides by 10, and the new green line is from a "window" for 20 days, divided by 20.
The last day in the plot (far right hand side) is for the day 2010-07-02 where the values are:
1 day window = 0
5 day window = 2
10 day window = 1.5
20 day window = 1.25
If you had chosen a "window" of 5 days, then before you trade on 2010-07-03 (assuming that's the next trading day), your "cluster number" would be 2 (averaging 2 trades per day for the previous 5 days).
Just like any moving average, the longer the "window", the smoother the plot.  However, this smoothing delays the peaks and valleys.  Compare the gray peak in early April with the blue peak, then the purple peak, and then the green peak.   This may not be a big issue for the current use, but  I thought it was a good idea to point it out.
The bottom line is, you'll have to play around with different "windows" to zero-in on your desired smoothness and timeliness.

A: Maybe use an adaptation of J-Charts and/or Market Profile charts, but instead of plotting price (y-axis) vs volume (x-axis) you could plot time of trade (y-axis) vs no. of trades (x-axis) and use colours to delineate different trading days or averages of no. of trades at these times for different look back periods.
