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Study an exploratory Python engine for historical equities tests

Maintain event-driven Python backtests with the Zipline research model

Run event handlers, HTTP workloads, or isolated compute without managing servers.

Deploy automatically scaling functions with HTTP, scheduled, and event-driven triggers on Scaleway.

Develop Python strategies with graphical backtesting and market gateways

Research and backtest quantitative strategies in notebooks and code

Compare options history with the risks of current positions

Choose Python research and backtesting engines for the task

Build event-driven market simulations with a shared strategy engine

Deploy event-driven code or container-backed functions on Oracle Cloud Infrastructure.

Build event-driven applications with a cross-platform asynchronous I/O library.

Workforce schedules that distinguish event-linked shifts from independent weekly shifts

Build event-driven serverless applications with triggers, bindings, and several hosting plans.

Test entry and exit rules against historical price bars

Replay historical markets across charts and depth-of-market views

Backtest portfolio allocations with cash flows, benchmarks and simulations

Build historical market simulations with configurable orders and costs

Develop and compare trading strategies in a Windows backtesting workspace

Run event-triggered or HTTP-triggered functions on Tencent Cloud without maintaining servers.

Manages hotel event inquiries, proposals and venue bookings.

Run containerized apps, source builds, functions and batch jobs on IBM's managed serverless runtime.

Build interactive terminal applications in Go with an event-driven model, update, and view loop.

Create a sign-up sheet for volunteer shifts or event slots and share it with participants.

Application error reporting and event monitoring with searchable exception context.