In Design, Testing and Optimisation of Trading Systems by Pardo he suggests using standard error as a measure of precision in the value of a random variable (meaning the value of some function of your trading system like mean profit per trade).
Pardo makes some assumptions in the use of standard error to do with the distribution (spread of values) of the random variable - but let's not go into that for the sake of brevity.
Pardo uses 1/sqrt
where n is the number of trades. The standard error gets smaller the larger the value of n, when n = 10 the standard error is 0.316, when n = 20 it is 0.223, and when n = 100 it is 0.1. In other words, the more trades you have the more certain you can be of the result of some function like mean profit per trade. If you multiply the standard error by 100 you get a measure of the percentage of precision and you should use this when stating results and as a guide as to how many trades to backtest.
Let's take an example, you manually backtest 10 trades and you calculate the mean profit to be 30 per trade, you would state mean profit per trade is 30 +/- 31.6% - (the 31.6% is the standard error for n=10 multiplied by 100).
As statisticians like to be conservative you would use 30 - 31.6% and not 30 + 31.6% when calculating how many trades you need to make before you can buy that new Porsche ;-)
In short, for manual backtesting anything less than 10 trades would generally be considered meaningless.
But (you just knew there was one!) having said that, unfortunately there are a whole bunch of other considerations in backtesting and it's hard to give any golden rule without also considering the wider backtesting methodology .
Andy.