Original Summary

While building my trading automation platform, I realized I was optimizing for the wrong thing. I used to think mostly in terms of: Did this strategy make more money? Now I think much more in terms of: Did this whole system survive bear markets? A trading bot is not just an entry signal. It’s the combination of: entry logic + DCA + TP + SL + position sizing + pair selection + max active trades + timing. So instead of trying to prove one indicator works, I care about whether the full configuration keeps performing as the sample grows. My current process is basically: build a complete configuration forward-test it without touching the rules compare it against BTC / ETH / altcoin holding benchmarks keep promising versions frozen clone them change only one meaningful variable per clone let all versions run side-by-side For example: Original bot Clone A: different TP Clone B: different SL Clone C: different DCA spacing Clone D: different entry condition Clone E: different position sizing Then I just let the data accumulate. The interesting part for me is that in crypto, beating buy-and-hold during a green month is not necessarily the main objective. Crypto can be extremely volatile, with deep drawdowns and relatively short periods where a lot of the upside happens. So what I care about most is how a configuration behaves when the market turns ugly. Can it preserve capital? avoid catastrophic drawdowns? stay alive through bad regimes? still participate when conditions improve? ... That matters more to me than simply having the highest short-term ROI. A “winner” would need things like positive expectancy, acceptable drawdown, decent profit factor, enough trades, performance across different market conditions, and no dependence on one lucky coin or one short period. And the biggest rule: once the test starts, the configuration stays frozen. This testing philosophy is basically what I’m building into LabNarrative . From a SaaS/product perspective, I’m increasingly convinced that the product should make it easier to run experiments, compare variants, and preserve their history, rather than just help users launch more bots. Curious how other SaaS founders think about this: Do you build your product around helping users get the highest immediate result, or around helping them build a process that keeps working over time?   submitted by   /u/GreatBreadfruit8139 [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SaaS
  • 发布时间：2026/9/22 16:11:24