Using volume in an EOD trading system
I sometimes wonder if there’s anything useful in the volume data accompanying daily OHLC values from sites like Yahoo, et all. I haven’t had much success in the past. I’ve mostly ignored it.
Well, I got inspired to give it a go again and think there’s potential to use volume to enhance a trading system. Enough data mining can solve any problem, right?
I’ll describe a system I tested on various index ETFs and then compare the results with and without a volume filter. My goal isn’t to prove the system is great, just that using volume can potentially enhance a system’s profitability.
Ok, here are some rules for a base mean reversion system:
Buy at the close when 1) the close value is less than yesterday’s close AND 2) the close value is at least .5% below the 10 day SMA. Sell position 4 days later at the close.
Now we’ll add a volume filter:
In addition to the previous criteria, only buy at the close if current day’s volume is less than the volume of EITHER of the previous 2 days. (Note: we could use 1 day or 3 days, the farther out you go the less restrictive this filter becomes).
I think my initial thought was that buying on a bigger volume day would be better. Perhaps signaling capitulation. But no, a lower volume day is better in this case. And I guess that makes sense too as a type of negative divergence or perhaps exhaustion of sellers.
Here are some summary results for the system with and without the volume filter. Each ETF is tested from inception to July 9, 2013 using Yahoo Finance data.
The volume filter cut down significantly the number of trades. While total profits of the backtests were lower, the average trade profit and winning percentages were generally improved.
I’ve attached an excel book containing the backtest, summary stats, and profit curves for each ETF. Enjoy!
Excel Download – Trade System with Volume Filter