@_zheergen: Man Group, one of the world's largest hedge funds, has open-sourced its database engine. Guys! This DataFrame database is specifically designed for quant trading. Man Group's open-source ArcticDB is a high-performance, serverless DataFrame database designed for P...
Summary
Man Group has open-sourced ArcticDB, a high-performance, serverless DataFrame database designed for Python data science and quantitative finance scenarios, supporting billions of rows of time-series data, S3/LMDB/Azure storage, time travel and snapshots, and C++ engine acceleration.
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Cached at: 08/14/26, 03:36 PM
One of the world’s largest hedge funds, Man Group, has open-sourced its database engine
Folks! This DataFrame database is purpose-built for quantitative trading
ArcticDB, open-sourced by Man Group, is a high-performance, serverless DataFrame database designed for Python data science and quantitative scenarios. It currently has 2.5K stars.
Core features:
Directly read and write Pandas DataFrames, feels very natural to use Optimized specifically for time series, capable of handling billions of rows of data Serverless architecture, data can be stored on S3, LMDB, or Azure Supports version rollback (Time Travel) and snapshots Flexible schema, supports appending and modifying at any time C++ engine acceleration for fast query and processing
For quant researchers who need to manage large-scale market data, factors, or backtest data, this tool solves the pain point of “slow data access and troublesome management.”
Previously, storing market data: CSV fills up the disk → switch to a database → queries are slow as hell → give up. Now with ArcticDB, you can run billions of rows of market data locally. Quants finally have a proper solution for their “data warehouse.”
爱吃折耳根的Ace (@_zheergen): Must-watch for quant: using gradient descent to automatically generate optimal support and resistance lines
Highly recommended for friends who want to truly automate support and resistance lines.
This hardcore 6-minute video from neurotrader specifically addresses the pain point that traditional technical analysis requires manual line drawing, which varies from person to person and cannot be automated.
The video demonstrates a very elegant mathematical algorithm: how to use Python to automatically compute and draw the optimal trend line (Support
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