Just some random thoughts on the matter.
You never "know" for sure, because, in theory at least, the conditions that existed in the market during your test period may have been unique, and have changed in the current market. With one trade per contract every three years or so, I guess there's a real possibility of some kind of outlier in that data. Statistics involves assumptions. Not comforting, I know. So much of this is having confidence or a reason to believe we have an edge, without ever knowing for sure. Well, sometimes you almost maybe know for sure.
Short statistical answer: Maybe. More would be better.
Long statistical answer: Statistics involves assumptions, and with only 100 data points, you're not on super ground, but it depends on what kind of results those 100 data points have. Assume that a trade is a trade, regardless of contract or source. BIG assumption. Then with 100 trades you have a payout distribution that can be tested.
You could take the 100 trades, convert them into +/- percent of equity, and categorize them into a distribution that had a mean, standard deviation, skewness, kurtosis, etc., and perform some tests on that, too. With only 100 in the sample, unless it looks really promising from a profitability standpoint, you might want to proceed with some caution. Any big bad losses? Look at the losses and what the contracts were doing, and think if that's a common situation or what the cause might be, how to eliminate those big losses.
Winning percentage could be tested with the binomial approximation to the normal, which assumes some things, like a normal distribution. The population mean winning percentage is estimated by your sample of 100 trades, and the standard deviation is the square root of the product of win %, loss %, and number in the sample (in this case, 100). For example, if your sample was 35% winners, then statistically you can have about 98% confidence that your system has a future winning percentage between 25% and 45%. Assuming your assumptions are good.
Now if you take your average win and average loss amounts as percentages of equity, and *assume* that these distributions are solid, you could calculate your expectancy based on the lower bound (25% wins in the example), the sample amount (35% wins in the example), and the upper bound (45% wins in the example) of the winning percentage. Are all of those numbers "good" to you? Could you take that system at the lower bound of win % and turn over enough trades to make a good return? Is the system even profitable at that lower bound? What's the maximum number of losses in a row you could expect at that lower bound, and with ten trades a year, what are the odds that you go winless in a year? Would that make you stop trading?
Bottom line, we all have to make decisions with very imperfect data. You have to be comfortable with what your analysis of those 100 trades gives you. If you're not, could you backtest longer? More contracts? I don't know.