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From personal experience, you need a large enough sample to guarantee not overfitting, and that sample cant be old because regimes and political influences change over time. What you want to know: How many trades were loosing trades, and how many losses in a row ir produces, that becomes the baseline to make sure everything is still working as intended.
Use the same strategy you created on 12months of out of sample data; it can be before or after the data you used to fit it into. Of the results are somewhat similar you have a real edge.
On my case, I ran BTC, ETH, SOL, XRP and LINK, each with its own algo based on a similar strategy with diferent parameters due to inherenr diferences from each pair. At first everything was going smooth, had a ML behind the algo and aditional constraints. But then only BTC and ETH were doing ok while the rest was broken.. I couldnt fix it or understand what was going wrong at first, but then I realized.. there are some times that the best strategy is not trading at all.. these pairs have greater volatility and ranges when the market gets shaky and my algo coulndt mitigate that.
On end, I stuck with ETH and BTC while still making a few tweeks here and there because they survived where the others broke. Win rate dropped but the system could still make it even within a month.. move one year forward and then I tested the other algos again.. they rock on good periods but wreck everything on bad ones.
Currently I use a personal martingale system with risk adjusted positions and volatility variable TPs and SLs that produced ~550 trades in a year with a ~52% win rate, a max loss streak of 4 in a row and a profit factor of 1.6. This held for a year after the data used for training and also performed similar for the year before that data.. Its running on ETH and BTC only, each pair trades amost twice a day on average and yet, I still put my fingers on it to manually close or adjust positions when hitting a 2-loss streak because someday it could become unstable and stop being an edge..
When I said 169 is not enough its because it probably didnt produce enough loss streaks for you to evaluate against hard probability, and that in my view, is how you assess how much of an edge you really have

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