From 9f8d0247944e687690809023d317f2af17ce1b5a Mon Sep 17 00:00:00 2001 From: Zhe Wang Date: Sun, 12 Dec 2021 10:14:56 +0000 Subject: [PATCH] add pandas alternertives --- Readme.md | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/Readme.md b/Readme.md index 3e41efd..053e1d2 100644 --- a/Readme.md +++ b/Readme.md @@ -22,13 +22,17 @@ How do we pick the projects? - (Optional) Reasonable test coverage Overall, I tend to pick decent or promising libraries that closely related to systematic trading instead of including as many libraries as possible. -**Please raise a PR if you found some good fit projects for this repo or remove some outdated projects. Thanks!** + + **Please raise a PR if you found some good fit projects for this repo or remove some outdated projects. Thanks!** - [Awesome Systematic Trading](#awesome-systematic-trading) - [Backtest + live trading](#backtest--live-trading) - [General purpose](#general-purpose) - [Crypto currency focus](#crypto-currency-focus) - [Basic libraries](#basic-libraries) + - [Fundamental libraries](#fundamental-libraries) + - [Alternative libraries](#alternative-libraries) + - [Pandas Alternatives](#pandas-alternatives) - [Analytic tools](#analytic-tools) - [Metrics compution](#metrics-compution) - [Indicators](#indicators) @@ -72,17 +76,27 @@ Overall, I tend to pick decent or promising libraries that closely related to sy ## Basic libraries +### Fundamental libraries + - [Cvxpy](https://github.com/cvxpy/cvxpy) | `Python`, `C++` | - A Python-embedded modeling language for convex optimization problems. - [Numpy](https://github.com/numpy/numpy) | `Python`, `C` | - The fundamental package for scientific computing with Python - [Scipy](https://github.com/scipy/scipy) | `Python`, `C` | - Fundamental algorithms for scientific computing in Python - [Pandas](https://github.com/pandas-dev/pandas) | `Python`, `Cython` | - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more -- [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. - [Sikit-learn](https://github.com/scikit-learn/scikit-learn) | `Python`, `Cython` | - Machine learning in Python - [Keras](https://github.com/keras-team/keras) | `Python` | - The most user friendly Deep Learning for humans in Python - [TensorFlow](https://github.com/tensorflow/tensorflow) | `Python`, `C++` | - More low level Deep Learning framework - [Pytorch](https://github.com/pytorch/pytorch) | `Python` | - Tensors and Dynamic neural networks in Python with strong GPU acceleration - [PyMC](https://github.com/pymc-devs/pymc) | `Python` | - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara +### Alternative libraries + +#### 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. +- [Dask](https://github.com/dask/dask) | `Python` | - Parallel computing with task scheduling in Python with a Pandas like API +- [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 + ## Analytic tools ### Metrics compution