Live Deployment

Quant deployments are the operational step where a trained and validated release becomes a live signal source.

Deployment inputs

A healthy quant deployment should be based on:

• a quant release
• the artifact behind that release
• the runtime compatibility of that artifact
• the delivery channels attached to the deployment

Live inference flow

At a high level:

1. the market runtime detects a signal moment
2. the quant runtime loads the artifact
3. the runtime calls predict(model, market_data, config)
4. the result is normalized into a deployment-safe signal
5. the signal is routed to delivery channels

Delivery targets

Quant live signals can be routed into:

• Telegram
• Discord
• web dashboard feeds
• MT4 / MT5 terminal workflows

The important distinction is that PyP owns the runtime and routing layer even when the strategy logic came from user-authored Python.

Runtime compatibility

Not every artifact target should run in the same runtime environment.

That is why deployment must stay runtime-aware.

Examples:

• lightweight custom Python may fit the edge-safe path
• preserved heavy exact-model families may require a container-backed runtime path

This is not marketing language. It is capacity planning. Deployment should only be allowed when the runtime contract is honest.

Deployment best practice

• deploy releases, not raw jobs
• keep runtime package declarations explicit
• choose the cheapest runtime that honestly supports the artifact
• route to observation channels first if you want confidence before terminal execution