Hello Victor, there is no 'right' answer here, this is just an easy and somewhat robust way to do it. I use a moving average of the spread because the spread is the signal we are interested in, but like any signal it is subject to spurious jumps. We don't want our strategy trading when the spread momentarily jumps for a weird reason, we want it trading when the spread is definitely higher than normal. A moving average will smooth out the signal a little bit and make us less twitchy. Now that we have a moving average as the proxy for the 'current' spread, we can compare the current spread to the distribution of spreads using a z-score.
To see why this might help, consider the following case: One stock suddenly falls by a huge amount due to a strange market movement, your algorithm places an order to trade on the next minute bar. By this point in time, the price has recovered as it was only a momentary dip. Your order fills at the new price, and your algorithm makes no money but pays trading fees. A computer located right next to the trading floor operating on nanoseconds might be able to take advantage of these fluctuations, but you can't really otherwise.
Another option is to use Kalman filters instead of moving averages, as described here.
Let me know if this makes sense, I'd be happy to discuss this further.
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