GIT_FEED

janushendersonassetallocation/loman

Control complex calculation flow for quantitative research and trading

View on GitHub

What it does

Loman is a Python tool that automatically keeps track of complex calculation chains, so that when one piece of data changes, only the affected downstream calculations get re-run rather than everything starting over from scratch. Think of it like a smart spreadsheet that knows exactly which cells need updating when you change a number, but built for sophisticated financial models and data pipelines used in trading and quantitative research.

Why it matters

For teams building financial products, trading systems, or any data-heavy platform, this kind of tool can dramatically cut down on wasted computation time and the human errors that come from manually managing which calculations are stale or current. It signals that there is real developer demand for better tooling around quantitative finance workflows — a space where reliability and speed directly translate to money.

17Active

On the radar — signal detected

Stars
117
Forks
26
Contributors
16
Language
Python
Downloads (7d)
1.2k

pypi/loman

Score updated Feb 23, 2026

Related projects

AFNI is a comprehensive software toolkit used by neuroscientists to process, analyze, and visualize brain scan images, including the functional MRI scans (brain imaging that shows activity over time) used in research studies. It handles every step of the brain imaging workflow, from initial data collection through final statistical analysis and visual reporting.

// why it matters Brain imaging research underpins a massive and growing market spanning clinical neurology, mental health diagnostics, and neurotechnology, and AFNI is a foundational open-source tool trusted by academic and medical research institutions worldwide. For founders or investors in brain health, medical imaging, or research software, understanding that AFNI represents the established standard workflow gives important context for where new AI-driven or cloud-based neuroimaging products can integrate or compete.

C185 stars117 forks81 contrib

Apache Spark is a powerful open-source platform that lets companies process and analyze massive amounts of data very quickly — think analyzing billions of records in seconds rather than hours. It works with multiple programming languages and includes built-in tools for everything from running database-style queries to training AI models and processing live data streams.

// why it matters With over 42,000 stars and nearly 30,000 forks, Spark is effectively the industry standard for large-scale data processing, meaning any data-heavy product — from recommendation engines to fraud detection — likely depends on it or competes with tools built on it. Builders and investors should recognize that Spark represents the backbone of modern data infrastructure, making it a critical dependency to understand when evaluating data pipelines, AI products, or analytics platforms.

Scala43.0k stars29.1k forks3403 contrib

Apache Iceberg is an open standard for storing and managing massive data tables in a way that multiple analytics tools can reliably read and write to at the same time. Think of it as a universal filing system for huge datasets that keeps everything organized and consistent, no matter which analytics software your team is using.

// why it matters For companies building data-heavy products, Iceberg eliminates the costly problem of being locked into a single analytics vendor — your data stays portable and accessible across tools like Spark, Flink, and Presto simultaneously. With nearly 9,000 stars and 784 contributors, it has become an industry standard that signals where enterprise data infrastructure is heading, making it a critical consideration for any product strategy involving large-scale data.

Java8.7k stars3.1k forks784 contrib

Foxglove SDK is a toolkit that lets robotics and engineering teams record, stream, and visually explore complex sensor data — think camera feeds, GPS tracks, and sensor readings — all in one place. It connects to the popular Foxglove visualization platform, allowing teams to replay and analyze what their robots or autonomous systems are doing in real time or from saved recordings.

// why it matters As robotics, autonomous vehicles, and industrial automation become major investment areas, teams need better tools to understand and debug what their machines are actually doing — and Foxglove is positioning itself as the standard observability platform for that space. With 43 contributors, support for multiple programming languages, and integration with the widely-used ROS robotics framework, this SDK signals a maturing ecosystem that could become a critical dependency for any company building physical AI products.

Rust208 stars77 forks44 contrib
// SUBSCRIBE

The repos that moved this week, why they matter, and what to watch next. One email. No noise.