Last night’s stream ran 2.5 hours for a reason.
I finally did a full teardown of the internal system I teased last week — the complete C++ low latency trading system with a JavaScript front end that now converts our profitable Python bots directly into C++ executables.
If you missed it, the full replay is on YouTube [Live tab], and I turned the transcript into a 4000-word definitive guide.
This video is the summary version. No fluff.
Inside this video on building a C++ low latency trading system:
I break down why 68% of you now want C++ over Python, and why Python is still king for research but loses on execution.
[00:00] Why I had to build a C++ low latency trading system
From mid-April to mid-July, my AI research engine generated ~3,000 complete strategies. About 10% were somewhat profitable. The problem wasn’t finding alpha - it was executing it without losing it to stale data and dependency bloat.
[08:00] The #1 Architecture Secret for any C++ low latency trading system
Most builds fail at the prompt. My non-negotiable prompt for a true C++ low latency trading system: “No third-party dependencies, no frameworks, no libraries. Standard IO only and native C++ calls.”
That one line forces determinism. GLM 5.2 took that prompt and auto-built multi-threaded execution - detecting threads on load without me asking. That’s the difference between a toy bot and an institutional-grade C++ low latency trading system.
[32:00] Execution Layer: Rithmic API vs Interactive Brokers
IB TWS allows one connection. You can’t run 10 bots on it. I built a Rithmic Trading Gateway where multiple strategies inside my C++ low latency trading system connect to one gateway.
For $140/mo ($40 data + $100 API) you get .NET, C++, and Protobuf for Python/JS. And the hidden 4th option - R | Diamond API - is the direct bridge to the CME Aurora Data Center. This is what fixes the stale data issue that kills retail bots on IB from New Jersey.
[52:00] How I built the entire C++ low latency trading system for <$200
In June I spent $600+ on AI. This build was $200 coding + $100 debugging using GLM 5.2.
Z.ai’s new Zcode tool with GLM-5.2 did 75% of the work - scaffold, code, debug. I only switched to Haiku 4.5 / Sonnet when the C++ low latency trading system project got too large for it to handle. Is it better than Claude Opus 4.8 on SWE-bench? No. Is it 95% as good for 3% of the cost for a C++ low latency trading system? Yes.
My stack: GLM 5.2 + Cline VS Code extension + OpenRouter so you’re never locked to one provider.
[1:25:00] What actually feeds the C++ low latency trading system now
We’re not in a trend regime. We’re in a range-bound, volatile, mean-reverting market. That’s why my NASDAQ mean reversion strategy is beating the market since June 1st inside the C++ low latency trading system.
I also show how I use Redis Pub/Sub instead of WebSockets (WebSockets drop ticks under load) and how telling the AI your exact NVIDIA board gets you hardware-optimized C++.
Full 4000-Word Guide:
I wrote up everything from last night here: The Ultimate Guide to Building a C++ Low Latency Trading System - including the exact prompts, the Rithmic setup, and the Python to C++ conversion pipeline.
Resources mentioned:
Full courses library: https://www.quantlabsnet.com/challenges
Advanced Futures Options with Source Code (26 Days) - institutional hedging / arbitrage course
My pre-market reports + pseudocode drops:
Starter Python packs for IB paper trading: hftcode.com
Question for you: Are you still trying to execute in Python, or have you started converting to a C++ low latency trading system? What is breaking for you - the architecture, the gateway, or the AI conversion?
Drop it in comments and I’ll cover it in Tuesday’s 7pm EST live.
