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Research and backtest quantitative strategies in notebooks and code

A reactive Julia notebook that reruns dependent cells and keeps package-environment details with the notebook.

AI-native business intelligence for ad hoc analysis, dashboards, and data workflows.

A beginner-focused Python IDE that makes variables and program execution visible while you learn.

Run Python programs and isolate project packages with the official CPython interpreter.

Load, reshape, and analyze labeled tabular and time-series data with Python.

Run analytical SQL in an embedded database from a local process or Python program.

Manage Python project dependencies, virtual environments, and package metadata from `pyproject.toml`.

Focused Python challenges with detailed solution study

Collaborative data workspace for notebooks, apps and dashboards

A Python property-based testing library that generates inputs to challenge the properties your code should preserve.

Explore Python interactively with completion, object introspection and persistent command history.

Lint and format Python projects with one configurable command-line tool.

Install Python command-line applications into isolated environments or run them once without a permanent install.

Run Python tests with readable assertions, fixtures, parametrization, and failure reports.

Transform Python array functions with just-in-time compilation, automatic differentiation, and vectorization.

A Windows Python IDE pairing a script editor with an interpreter, debugger, project tools and unit-test interface.

Build Python HTTP APIs with typed parameters and automatically generated OpenAPI documentation.

Trace Python services and AI workflows with correlated OpenTelemetry data.

Render data-driven text and HTML from reusable templates with Python-inspired syntax.

Generate reference documentation from a Python project's modules and docstrings.

Calculate returns, portfolio statistics and allocation weights in Python

Run a Python project's configured test and lint commands in isolated environment matrices.

Compile suitable Python numerical functions to machine code when they are first called.