Mono Trade

Mono Trade is market-data infrastructure for research, scanners, and repeatable finance experiments.

The interesting part is not a magic trading signal. The interesting part is making messy market data boring enough to trust: collect it, normalize it, compare it, and keep the system observable when it runs again tomorrow. That is the work most finance experiments skip, and it is the work that determines whether later research means anything.

What it explores

• Structured market snapshots that can be reused instead of eyeballed once.
• Scanners that watch larger instrument sets without turning into dashboard chaos.
• Python connectors and data pipelines that fail loudly instead of silently drifting.
• Intraday snapshot bars and repeatable analysis surfaces.
• A clear split between research infrastructure and any execution logic.
• Operational checks that make scheduled data work easier to trust.

Why this project exists

Finance experiments often die in notebook soup. One script pulls data, another ranks symbols, a third saves a file with an unclear timestamp, and a week later nobody knows which result was real. Mono Trade is the opposite direction: build the dull infrastructure first, then let research sit on something solid.

That means the project is more about data discipline than prediction. If a scanner changes, it should be clear what changed. If a market snapshot fails, the failure should be visible. If a research question comes back later, the earlier data should be inspectable instead of trapped in a one-off notebook run.

Current system direction

The public story around Mono Trade is scanner and snapshot infrastructure. It is designed to support larger watchlists, repeatable market-data pulls, and comparisons across instruments without turning every question into manual browser work.

The recent public update summarized scanners and intraday snapshots because those are the pieces that make the system feel less like a script collection and more like a research stack. A good market-data system should answer “what did we know at the time?” and “can we re-run this?” before it tries to answer “what should we buy?”

How it fits into Seb Builds

Mono Trade is part of the broader theme of making workflows observable. Elson makes voice work inspectable. Hermes makes agent work repeatable. Mono Trade applies the same mindset to financial research: collect the inputs, structure the outputs, and leave a trail that can be audited later.

Current direction

The next useful direction is more boring reliability: cleaner data contracts, better snapshots, clearer scanner outputs, and stronger separation between data collection, research, and any future execution layer.

Public boundary

This is software and data-engineering research. It does not publish financial advice, trading recommendations, private account data, broker credentials, or execution rules.