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# Awesome Systematic Trading
> or Quantitative Trading
> or Quantitative Trading + a bit data science infra
[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
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### 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
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#### 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