Messing around with pathfinding algos, built this visualizer
A*, Dijkstra, Greedy, BFS, DFS + 6 maze generators
lets you draw walls, generate mazes, and see the algorithm search step by step
Building a team of ai agents that do my algo trading research.
They research ideas, backtests them, runs risk audits, and moves the ones that survive into paper trading.
the goal isn't one perfect strategy. it's a pipeline of 10 to 30 small, uncorrelated edges.
launched my first product... tonight
claude code + me + the chrome web store kicking my ass 4 times... couple of days of work
AI prompts, portable across ChatGPT, Claude, Cursor, everything
PRODUCTHUNT = 50% off →
https://t.co/cQtpgv3FMm
Almost got caught in the hype and bought a Mac mini for my 🦞 OpenClaw
then remembered I have a whole gaming PC in my basement doing nothing
saved myself $1,400. lowkey better anyway
I realized most people showing profitable backtests are probably just fooling themselves.
The hard part in algo trading isn’t finding a chart that goes up.
It’s figuring out if the strategy survives reality.
Don’t fall into the “optimize your workflow” trap.
I keep organizing systems, improving automations, and tweaking my setup instead of shipping.
It feels productive, but it’s still avoidance.
I literally had to update my agent memory to call me out when I start side questing.
Did a little portfolio refresh today
https://t.co/DiUQO45GbZ
Here’s the current design. Still very much a WIP, but it’s finally starting to feel like a real place to show what I’m building.
@haasonline Exactly.. Paper trading tests the system. That’s where you find out if the edge is actually real
An 86% strategy means a lot less when fills are ugly.
Small breakthrough on my algo-trading side quest:
Found a strategy that backtested well across 1,431 trades from 2015–2026 — roughly 86% win rate after modeling costs.
Still early. Next step is paper trading and seeing if it survives outside the backtest.