Hey everyone, After watching Nate's trading video, I was inspired to build my own solution. I’ve spent the last few months developing a custom trading bot for Hyperliquid, and the results have been incredible: I’ve achieved a 45% profit over the last two months, with backtests showing a reliable 100% annual return. The strategy is built around Momentum Breakouts. Here are the three pillars that made this bot significantly more effective and efficient: 1. Smarter Architecture (Saving Credits) Instead of relying on expensive automation platforms for every execution, I used Cursor and Claude to program the logic once. The bot runs as a monitor/scanner via a Cronjob. This setup executes the trading code independently, saving a massive amount of API credits while maintaining high performance. 2. Momentum & Market Selection The core of the strategy is a Momentum Breakout approach. The most important lesson I’ve learned: Don’t trade in sideways markets. Everything depends on the "Universe" you select and the filters you apply. By using specific filters to identify the current trend direction, the bot only enters when there is real movement, avoiding the "chop" of horizontal ranges. 3. Walk-Forward Testing (Validating the Edge) To ensure the strategy is actually profitable and not just "curve-fitted" to past data, I use Walk-Forward/Out-of-Sample testing. - The algorithm is trained on a specific time interval. - It is then applied to a completely unknown set of data where the future is "hidden." This rigorous testing gives me the confidence that the strategy is robust and ready for real market conditions. I'm curious—is anyone else here automating their Hyperliquid strategies? If you have questions about setting up Cronjobs, using Cursor for trading code, or my filtering process for trend direction, let’s discuss i n the comments! 👇

I’m Elias . Building an AI automation agency from scratch. Today wasn’t anything crazy… but it was one of those “things finally clicked” days. Spent most of the time fixing a client workflow that was giving inconsistent outputs. At first I thought the issue was the prompt (as usual), so I kept tweaking it for like 2–3 hours… Didn’t really fix it. Then I looked at what the client was actually sending as input —and yeah… it was a mess. Random context, no structure, missing details. So instead of touching the AI again, I did this: → added a simple input form (goal, context, tone, constraints) → cleaned the data before it even hits the model Same exact prompt after that. Output instantly became usable. Lowkey annoying how much time I wasted on prompts when the real issue was upstream 😅 Anyway, Day 1 lesson: If your AI output sucks, check the input first. Let’s see what breaks tomorrow.