Original Summary

I'm a Fullstack developer and recently started building a side project around a problem I had myself. I follow trading signals through Telegram, and manually executing them gets repetitive: Telegram → read signal → calculate risk → position size → SL → multiple TPs → exchange. So I started building a system that automates that flow. At first I thought the difficult part would be connecting Telegram to an exchange API. It wasn't. The difficult part turned out to be trust and safety . For example: What happens if the Telegram message is edited after it was posted? What if the same signal arrives twice? What if the signal is already 20 minutes old? What if AI incorrectly parses a number? What if the market price has already moved outside the entry range? How do you make sure a parsing error can never override a user's risk limits? The architecture I'm moving toward now is basically: Telegram → AI Parser → Validation → Risk Engine → Execution The important part is that the parser doesn't control the money. It can say: BTC LONG Entry: X SL: Y TP1-TP5: ... But a separate deterministic risk engine decides whether the trade is allowed to execute based on things like maximum risk, position size, signal age and price deviation. If anything looks uncertain, the system should do nothing rather than guess. Another thing I underestimated was logging. I'm now thinking every trade should have a complete timeline: signal received → parsed → validated → order submitted → filled That way the user can actually measure latency, slippage and execution quality instead of only looking at the final PnL. Building this has made me realize that for financial SaaS, the product isn't really the automation. The product is giving the user enough confidence to let the automation run. For people who have built SaaS products that perform actions on behalf of users: how did you approach the trust problem? Do users care more about seeing exactly what the system did and why, or about having more automation features?   submitted by   /u/Glittering_Pass8656 [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：2026/10/1 03:00:51