Original Summary

Everyone thinks building a trading bot is just writing a script to buy when the moving average crosses over. This is the easy part. The hard part is the guardrails. Figuring out when a signal is actually garbage and blocking the trade. I'm building an AI trading platform right now (still in pre launch ) and honestly almost none of my time has gone into the AI. It has all gone into the risk and audit layers a good prediction doesn't mean much if the system falls apart when things go wrong Here is what I have actually built so far. I wrote a risk function that checks daily losses, draw down, confidence, exposure limits, etc. It returns an approve or reject. The problem is l'm still trying to make sure those sex actually apply to every single execution path in production. I also added SHA-256 hash chains for the audit records. The problem is I am still trying to make sure those checks actually apply to every single execution path in production. I wrote some tests for rejecting bad trades too. But testing it in a sandbox isn't the same as proving every production order path is a fail. The production wiring isn't done. The demo data on the site is simulated and I have zero verified live trading performance to show right now. Because I am currently at pre launch, the hardest thing is trust. Nobody is going to give an algorithm real money just because the UI looks nice. So for the SaaS and fintech guys, what would actually make you trust and all go with your own money? Do you need months of paper trading? Time stamp locks that show the losses just as much as the wins? Or just hard coded loss limits?   submitted by   /u/Friendly_Winter3543 [link]   [comments]

  • 情报分类：开源项目与落地
  • 分类依据：内容涉及项目实践、创业、副业或变现
  • 信息来源：Reddit · SaaS
  • 发布时间：2026/10/21 02:07:33