From 1111a932f5699b03fd607679b7b6c96c3a8e549b Mon Sep 17 00:00:00 2001 From: Zhe Wang Date: Wed, 16 Mar 2022 22:04:31 +0000 Subject: [PATCH] update --- Readme.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/Readme.md b/Readme.md index d59a5ca..afd7bf5 100644 --- a/Readme.md +++ b/Readme.md @@ -1,6 +1,6 @@ # Awesome Systematic Trading -> or Quantitative Trading +> or Quantitative Trading + a bit data science infra [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) @@ -149,10 +149,11 @@ Note: the one marked as `Live Trading` has reasonable live trading support for a ### Computation Graph - [Dask](https://github.com/dask/dask) | `Python` | - Parallel computing with task scheduling in Python with a Pandas like API +- [Ray](https://github.com/ray-project/ray) | `Python`, `C++` | - An open source framework that provides a simple, universal API for building distributed applications. - [Incremental (JaneStreet)](https://github.com/janestreet/incremental) | `Ocaml` | - Incremental is a library that gives you a way of building complex computations that can update efficiently in response to their inputs changing, inspired by the work of Umut Acar et. al. on self-adjusting computations. Incremental can be useful in a number of applications - [GraphKit](https://github.com/yahoo/graphkit) | `Python` | - A lightweight Python module for creating and running ordered graphs of computations. - [Man MDF](https://github.com/man-group/mdf) | `Python` | - Data-flow programming toolkit for Python -- [Tributary](https://github.com/timkpaine/tributary) | `Python` | - Streaming reactive and dataflow graphs in Python +- [Tributary](https://github.com/timkpaine/tributary) | `Python` | - Streaming reactive and dataflow graphs in Python ### Alternative libraries @@ -163,6 +164,7 @@ Note: the one marked as `Live Trading` has reasonable live trading support for a #### Pandas Alternatives - [Polars](https://github.com/pola-rs/polars) | `Rust`, `Python` | - Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model. +- [Vaex](https://github.com/vaexio/vaex) | `Python`, `C++` | - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second - [Modin](https://github.com/modin-project/modin) | `Python` | - Modin: Speed up your Pandas workflows by changing a single line of code - [Koalas](https://github.com/databricks/koalas) | `Python` | - Koalas: pandas API on Apache Spark