Most traders using AI right now are still stuck copy-pasting CSV files or chart screenshots into a chat window.
Over the past few days, I wired up a completely different architecture on my Windows machine—and recorded an unedited 30-minute walkthrough above so you can see how it behaves during live market hours.
I connected a desktop AI agent (Grokbot) directly to a headless version of my custom 400-command quant terminal, AlphaMind, running on live Rithmic futures data. Instead of acting like a chatbot, it reads my active local Chrome ports, monitors simulated bot leaderboards across Micro NQ, ES, Gold, and Treasuries, and evaluates market regimes on the fly.
The standout moment happens around the 21-minute mark in the video above.
During a heavy volatility spike, I pointed the agent at a local port running one of my historically profitable NQ Machine Learning bots. It inspected the live probability dashboards across all three underlying models—Logistic Regression, Random Forest, and Gradient Boosting—and compared their walk-forward backtest expectations against the exact momentum regime unfolding on the tape.
Its conclusion? Despite strong historical performance, current intraday conditions meant all three models would slowly bleed out after commissions and slippage. Having an automated second set of eyes catch regime mismatch in real time—before deploying live capital—changes everything.
If you watch the full walkthrough above, here is what else you will see:
0:46 – How the headless AlphaMine backend processes 400+ commands alongside live Rithmic feeds.
5:05 – Prompting 10,000-word institutional quant breakdowns on NVDA and BTC (complete with Monte Carlo paths, seasonality cycles, options chain math, and Kelly sizing).
12:18 – Querying live browser tables with plain English to isolate the fastest futures movers in seconds.
16:18 – Generating Python microstructure toolkits alongside ultra-low latency C++ execution layers (including Black-76 pricing and custom risk engines).
23:14 – Why I officially dropped Cursor from my workflow (got tired of hitting third-party model credit walls) and moved completely to VS Code + Cline alongside Grokbot.
(Note: I also published the full NVDA and BTC quantitative research outputs directly on the main Orderbook Edge archive here on Substack if you want to inspect the raw math after watching, and you can always find my deeper C++ execution builds over at hftcode dot com).
Click the video player at the top of this email to watch the full live test.
Once you’ve watched it, hit reply directly to this email and let me know: what coding stack or local agent setup are you currently running for your own market research? I read every response.
Talk soon,
Brian
Disclaimer: Everything shown in this video and post is strictly for educational and software demonstration purposes and is not financial advice. Futures and options trading carry substantial risk.
