Built by an engineer who codes live trading algorithms
Our CTO runs an autonomous orderflow trading system on real futures markets. The same engineering discipline ships every chatbot, automation, and workflow we build for clients.
Most marketing agencies are run by marketers. Nova is run by a CTO who spends his evenings writing C# and Python that places real orders on real futures markets — specifically, an orderflow scalping system targeting MES and ES contracts on the Chicago Mercantile Exchange. That system is not a side hobby or a paper-trading toy. It is a production codebase with hundreds of automated tests, a dedicated Windows VPS co-located near CME for low-latency execution, and an autonomous loop that detects setups, sizes positions, places orders, and manages risk without a human at the keyboard.
The reason this matters to you, as a Nova client, is that the engineering bar required to write software that places live financial orders is dramatically higher than the bar required to write a marketing chatbot. When real money is at risk every second, you cannot ship code that silently fails, leaks state, or catches exceptions and pretends nothing happened. You build systems where every callback is wrapped, every error is logged with structured journal events, every lifecycle hook is hardened, and every assumption is verified by a test that runs before the code ships.
That mindset transfers directly to client work. When we build you a WhatsApp automation, we treat it the same way we treat the trading system: every flow is testable, every failure mode is named, and every silent break is caught early. When we ship a chatbot, the knowledge base is versioned, the handoff rules are deterministic, and the integration points are monitored. When we build a workflow on n8n or Make, error handling and retry logic are built in from day one — not added the third time it breaks at 2am.
The trading system uses a three-layer confluence model: liquidity heatmap, absorption and footprint reading, and volume profile levels. Nine independent detectors run in parallel, score the setup against a configurable threshold, and only fire when the layers agree. The same pattern shows up in the AI systems we build for clients — multiple signals, scored together, with a clear threshold for action. It is not a coincidence. Discipline in one domain produces discipline in the next.
None of this is theatre. The algo runs on a VPS in Chicago today, executes on live data feeds, and is currently being prepared for a funded futures evaluation. The codebase is open to scrutiny — Nova's CTO has shipped through real production incidents, including a live trading bug that cost real money, and rebuilt the safety layer the same week so it cannot recur. That is the same person writing your marketing automation. The standard does not drop because the domain changes.
If you are choosing between Nova and a content agency that learned Canva, this is the difference. We build systems. We test them. We monitor them. And when something breaks, we already know how to find it — because we have done it on a system where the cost of being wrong is measured in dollars per second, not in lost engagement.