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https://github.com/Davelexic/fintech-awesome-libraries

compilation of libraries that have been helpful for data analysis
https://github.com/Davelexic/fintech-awesome-libraries

List: fintech-awesome-libraries

Last synced: 3 months ago
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compilation of libraries that have been helpful for data analysis

Awesome Lists containing this project

README 

        



## Awesome Fintech Libraries

  - [Books](#Books)

  - [Business](#Business)

  - [Cheat Sheets](#Cheat-sheets)

  - [Cryptography](#cryptography)

  - [Data Analysis](#data-analysis)

  - [Data Visualization](#data-visualization)

  - [Database Drivers](#database-drivers)

  - [Date and Time](#date-and-time)

  - [Decentralized Systems](#Decentralized-Systems)

  - [Deployment](#Deployment)

  - [Evaluation](#Evaluation)

  - [Machine Learning](#machine-learning)

  - [Automated Machine Learning](#Automated-Machine-Learning)

  - [Tensor Flow](#Tensor-Flow)

  - [Theory](#Theory)

  - [Statistics](#Statistics)

  - [Learn](#Learn)

  - [Work](#Work)

  - [Websites](#websites)

  - [Newsletters](#newsletters)



## Books



- [Free Programming Books](https://github.com/EbookFoundation/free-programming-books#readme)

- [Go Books](https://github.com/dariubs/GoBooks#readme)

- [Mind Expanding Books](https://github.com/hackerkid/Mind-Expanding-Books#readme)

- [Book Authoring](https://github.com/TalAter/awesome-book-authoring#readme)

- [Elixir Books](https://github.com/sger/ElixirBooks#readme)



## Business



- [Open Companies](https://github.com/opencompany/awesome-open-company#readme)

- [Places to Post Your Startup](https://github.com/mmccaff/PlacesToPostYourStartup#readme)

- [OKR Methodology](https://github.com/domenicosolazzo/awesome-okr#readme) - Goal setting & communication best practices.

- [Leading and Managing](https://github.com/LappleApple/awesome-leading-and-managing#readme) - Leading people and being a manager in a technology company/environment.

- [Indie](https://github.com/mezod/awesome-indie#readme) - Independent developer businesses.

- [Tools of the Trade](https://github.com/cjbarber/ToolsOfTheTrade#readme) - Tools used by companies on Hacker News.

- [Clean Tech](https://github.com/nglgzz/awesome-clean-tech#readme) - Fighting climate change with technology.

- [Wardley Maps](https://github.com/wardley-maps-community/awesome-wardley-maps#readme) - Provides high situational awareness to help improve strategic planning and decision making.

- [Social Enterprise](https://github.com/RayBB/awesome-social-enterprise#readme) - Building an organization primarily focused on social impact that is at least partially self-funded.

- [Engineering Team Management](https://github.com/kdeldycke/awesome-engineering-team-management#readme) - How to transition from software development to engineering management.

- [Developer-First Products](https://github.com/agamm/awesome-developer-first#readme) - Products that target developers as the user.



## Cheats sheets

* [Github Cheat sheet](https://github.com/tiimgreen/github-cheat-sheet#readme) - A collection of cool hidden and not so hidden features of Git and GitHub.

* [Git tips](https://github.com/git-tips/tips#readme) - Collection of tips for Git.

* [Data Manipulation](https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf)-Data mutation.

* [Data Science Cheatsheet](https://st11.ning.com/topology/rest/1.0/file/get/2808327959?profile=original)



## Cryptography



* [cryptography](https://cryptography.io/en/latest/) - A package designed to expose cryptographic primitives and recipes to Python developers.

* [paramiko](https://github.com/paramiko/paramiko) - The leading native Python SSHv2 protocol library.

* [passlib](https://passlib.readthedocs.io/en/stable/) - Secure password storage/hashing library, very high level.

* [pynacl](https://github.com/pyca/pynacl) - Python binding to the Networking and Cryptography (NaCl) library.



## Data Analysis



*Libraries for data analyzing.*



* [AWS Data Wrangler](https://github.com/awslabs/aws-data-wrangler) - Pandas on AWS.

* [Blaze](https://github.com/blaze/blaze) - NumPy and Pandas interface to Big Data.

* [Open Mining](https://github.com/mining/mining) - Business Intelligence (BI) in Pandas interface.

* [Optimus](https://github.com/ironmussa/Optimus) - Agile Data Science Workflows made easy with PySpark.

* [Orange](https://orange.biolab.si/) - Data mining, data visualization, analysis and machine learning through visual programming or scripts.

* [Pandas](http://pandas.pydata.org/) - A library providing high-performance, easy-to-use data structures and data analysis tools.

* [Pandas Profiling](https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/)- generates profile report off of pandas database.



## Data Visualization



*Libraries for visualizing data.*



### General Purposes

* [Matplotlib](https://github.com/matplotlib/matplotlib) - Plotting with Python.

* [seaborn](https://github.com/mwaskom/seaborn) - Statistical data visualization using matplotlib.

* [prettyplotlib](https://github.com/olgabot/prettyplotlib) - Painlessly create beautiful matplotlib plots.

* [python-ternary](https://github.com/marcharper/python-ternary) - Ternary plotting library for python with matplotlib.

* [missingno](https://github.com/ResidentMario/missingno) - Missing data visualization module for Python.

* [chartify](https://github.com/spotify/chartify/) - Python library that makes it easy for data scientists to create charts.

* [physt](https://github.com/janpipek/physt) - Improved histograms.

### Interactive plots

* [animatplot](https://github.com/t-makaro/animatplot) - A python package for animating plots build on matplotlib.

* [plotly](https://plot.ly/python/) - A Python library that makes interactive and publication-quality graphs.

* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.

* [Altair](https://altair-viz.github.io/) - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph

* [bqplot](https://github.com/bqplot/bqplot) - Plotting library for IPython/Jupyter notebooks

* [pyecharts](https://github.com/pyecharts/pyecharts) -  a charting and visualization library, to Python's interactive visual drawing library.

### Map

* [folium](https://python-visualization.github.io/folium/quickstart.html#Getting-Started) - Makes it easy to visualize data on an interactive open street map

* [geemap](https://github.com/giswqs/geemap) - Python package for interactive mapping with Google Earth Engine (GEE)

### Automatic Plotting

* [HoloViews](https://github.com/ioam/holoviews) - Stop plotting your data - annotate your data and let it visualize itself.

* [AutoViz](https://github.com/AutoViML/AutoViz): Visualize data automatically with 1 line of code (ideal for machine learning)

* [SweetViz](https://github.com/fbdesignpro/sweetviz): Visualize and compare datasets, target values and associations, with one line of code.



## Database Drivers



*Libraries for connecting and operating databases.*



* MySQL - [awesome-mysql](http://shlomi-noach.github.io/awesome-mysql/)

    * [mysqlclient](https://github.com/PyMySQL/mysqlclient-python) - MySQL connector with Python 3 support ([mysql-python](https://sourceforge.net/projects/mysql-python/) fork).

    * [PyMySQL](https://github.com/PyMySQL/PyMySQL) - A pure Python MySQL driver compatible to mysql-python.

* PostgreSQL - [awesome-postgres](https://github.com/dhamaniasad/awesome-postgres)

    * [psycopg2](http://initd.org/psycopg/) - The most popular PostgreSQL adapter for Python.

    * [queries](https://github.com/gmr/queries) - A wrapper of the psycopg2 library for interacting with PostgreSQL.

* SQlite - [awesome-sqlite](https://github.com/planetopendata/awesome-sqlite)

    * [sqlite3](https://docs.python.org/3/library/sqlite3.html) - (Python standard library) SQlite interface compliant with DB-API 2.0

    * [SuperSQLite](https://github.com/plasticityai/supersqlite) - A supercharged SQLite library built on top of [apsw](https://github.com/rogerbinns/apsw).



## Date and Time



*Libraries for working with dates and times.*



* [Arrow](https://arrow.readthedocs.io/en/latest/) - A Python library that offers a sensible and human-friendly approach to creating, manipulating, formatting and converting dates, times and timestamps.

* [Chronyk](https://github.com/KoffeinFlummi/Chronyk) - A Python 3 library for parsing human-written times and dates.

* [delorean](https://github.com/myusuf3/delorean/) - A library for clearing up the inconvenient truths that arise dealing with datetimes.

* [moment](https://github.com/zachwill/moment) - A Python library for dealing with dates/times.

* [Pendulum](https://github.com/sdispater/pendulum) - Python datetimes made easy.

* [PyTime](https://github.com/shinux/PyTime) - An easy-to-use Python module which aims to operate date/time/datetime by string.

* [pytz](https://launchpad.net/pytz) - World timezone definitions, modern and historical.

* [when.py](https://github.com/dirn/When.py) - Providing user-friendly functions to help perform common date and time actions.

* [tslearn](https://github.com/rtavenar/tslearn) - Machine learning toolkit dedicated to time-series data.

* [tick](https://github.com/X-DataInitiative/tick) - Module for statistical learning, with a particular emphasis on time-dependent modelling.

* [Prophet](https://github.com/facebook/prophet) - Automatic Forecasting Procedure.

* [PyFlux](https://github.com/RJT1990/pyflux) - Open source time series library for Python.

* [bayesloop](https://github.com/christophmark/bayesloop) - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

* [luminol](https://github.com/linkedin/luminol) - Anomaly Detection and Correlation library.

* [dateutil](https://dateutil.readthedocs.io/en/stable/) - Powerful extensions to the standard datetime module

* [maya](https://github.com/timofurrer/maya) - makes it very easy to parse a string and for changing timezones.



## Decentralized Systems



* [Bitcoin](https://github.com/igorbarinov/awesome-bitcoin#readme) - Bitcoin services and tools for software developers.

* [Ripple](https://github.com/vhpoet/awesome-ripple#readme) - Open source distributed settlement network.

* [Non-Financial Blockchain](https://github.com/machinomy/awesome-non-financial-blockchain#readme) - Non-financial blockchain applications.

* [Mastodon](https://github.com/tleb/awesome-mastodon#readme) - Open source decentralized microblogging network.

* [Ethereum](https://github.com/ttumiel/Awesome-Ethereum#readme) - Distributed computing platform for smart contract development.

* [Blockchain AI](https://github.com/steven2358/awesome-blockchain-ai#readme) - Blockchain projects for artificial intelligence and machine learning.

* [EOSIO](https://github.com/DanailMinchev/awesome-eosio#readme) - A decentralized operating system supporting industrial-scale apps.

* [Corda](https://github.com/chainstack/awesome-corda#readme) - Open source blockchain platform designed for business.

* [Waves](https://github.com/msmolyakov/awesome-waves#readme) - Open source blockchain platform and development toolset for Web 3.0 apps and decentralized solutions.

* [Substrate](https://github.com/substrate-developer-hub/awesome-substrate#readme) - Framework for writing scalable, upgradeable blockchains in Rust.



## Deployment

* [datapane](https://datapane.com/) - A collection of APIs to turn scripts and notebooks into interactive reports.

* [binder](https://mybinder.org/) - Enable sharing and execute Jupyter Notebooks

* [fastapi](https://fastapi.tiangolo.com/) - Modern, fast (high-performance), web framework for building APIs with Python

* [streamlit](https://www.streamlit.io/) - Make it easy to deploy machine learning model



## Evaluation

* [recmetrics](https://github.com/statisticianinstilettos/recmetrics) - Library of useful metrics and plots for evaluating recommender systems.

* [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metric.

* [sklearn-evaluation](https://github.com/edublancas/sklearn-evaluation) - Model evaluation made easy: plots, tables and markdown reports. 

* [AI Fairness 360](https://github.com/IBM/AIF360) - Fairness metrics for datasets and ML models, explanations and algorithms to mitigate bias in datasets and models.



## Machine Learning



*Libraries for Machine Learning. Also see [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python).*



* [gym](https://github.com/openai/gym) - A toolkit for developing and comparing reinforcement learning algorithms.

* [H2O](https://github.com/h2oai/h2o-3) - Open Source Fast Scalable Machine Learning Platform.

* [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metrics.

* [NuPIC](https://github.com/numenta/nupic) - Numenta Platform for Intelligent Computing.

* [scikit-learn](http://scikit-learn.org/) - The most popular Python library for Machine Learning.

* [Spark ML](http://spark.apache.org/docs/latest/ml-guide.html) -  scalable Machine Learning library.

* [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library.

* [MindsDB](https://github.com/mindsdb/mindsdb) - MindsDB is an open source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using standard queries.

* [Shogun](http://www.shogun-toolbox.org/) - Machine learning toolbox.

* [xLearn](https://github.com/aksnzhy/xlearn) - High Performance, Easy-to-use, and Scalable Machine Learning Package.

* [cuML](https://github.com/rapidsai/cuml) - RAPIDS Machine Learning Library.

* [modAL](https://github.com/cosmic-cortex/modAL) - Modular active learning framework for Python3. 

* [Sparkit-learn](https://github.com/lensacom/sparkit-learn) - PySpark + scikit-learn = Sparkit-learn. 

* [mlpack](https://github.com/mlpack/mlpack) - A scalable C++ machine learning library (Python bindings).

* [MLxtend](https://github.com/rasbt/mlxtend) - Extension and helper modules for Python's data analysis and machine learning libraries.

* [hyperlearn](https://github.com/danielhanchen/hyperlearn) - 50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels.

* [Reproducible Experiment Platform (REP)](https://github.com/yandex/rep) - Machine Learning toolbox for Humans.

* [seqlearn](https://github.com/larsmans/seqlearn) - Sequence classification toolkit for Python. 

* [pystruct](https://github.com/pystruct/pystruct) - Simple structured learning framework for Python.

* [sklearn-expertsys](https://github.com/tmadl/sklearn-expertsys) - Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models.

* [RuleFit](https://github.com/christophM/rulefit) - Implementation of the rulefit.

* [metric-learn](https://github.com/all-umass/metric-learn) - Metric learning algorithms in Python.

* [pyGAM](https://github.com/dswah/pyGAM) - Generalized Additive Models in Python.

* [Karate Club](https://github.com/benedekrozemberczki/karateclub) - An unsupervised machine learning library for graph structured data.

* [Little Ball of Fur](https://github.com/benedekrozemberczki/littleballoffur) - A library for sampling graph structured data.

* [causalml](https://github.com/uber/causalml) - Uplift modeling and causal inference with machine learning algorithms.



### Automated Machine Learning

* [TPOT](https://github.com/rhiever/tpot) - Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. 

* [auto-sklearn](https://github.com/automl/auto-sklearn) - An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

* [MLBox](https://github.com/AxeldeRomblay/MLBox) - A powerful Automated Machine Learning python library.



## Tensor Flow

* [TensorFlow](https://github.com/tensorflow/tensorflow) - Computation using data flow graphs for scalable machine learning by Google.

* [TensorLayer](https://github.com/zsdonghao/tensorlayer) - Deep Learning and Reinforcement Learning Library for Researcher and Engineer. 

* [TFLearn](https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API for TensorFlow. 

* [Sonnet](https://github.com/deepmind/sonnet) - TensorFlow-based neural network library.

* [tensorpack](https://github.com/ppwwyyxx/tensorpack) - A Neural Net Training Interface on TensorFlow.

* [Polyaxon](https://github.com/polyaxon/polyaxon) - A platform that helps you build, manage and monitor deep learning models. 

* [NeuPy](https://github.com/itdxer/neupy) - NeuPy is a Python library for Artificial Neural Networks and Deep Learning

* [tfdeploy](https://github.com/riga/tfdeploy) - Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy.

* [tensorflow-upstream](https://github.com/ROCmSoftwarePlatform/tensorflow-upstream) - TensorFlow ROCm port. 

* [TensorFlow Fold](https://github.com/tensorflow/fold) - Deep learning with dynamic computation graphs in TensorFlow. 

* [tensorlm](https://github.com/batzner/tensorlm) - Wrapper library for text generation / language models at char and word level with RNN. 

* [TensorLight](https://github.com/bsautermeister/tensorlight) - A high-level framework for TensorFlow. 

* [Mesh TensorFlow](https://github.com/tensorflow/mesh) - Model Parallelism Made Easier. 

* [Ludwig](https://github.com/uber/ludwig) - A toolbox, that allows to train and test deep learning models without the need to write code. 

* [Keras](https://keras.io) - A high-level neural networks API running on top of TensorFlow.

* [keras-contrib](https://github.com/keras-team/keras-contrib) - Keras community contributions.

* [Hyperas](https://github.com/maxpumperla/hyperas) - Keras + Hyperopt: A very simple wrapper for convenient hyperparameter.

* [Elephas](https://github.com/maxpumperla/elephas) - Distributed Deep learning with Keras & Spark.

* [Hera](https://github.com/keplr-io/hera) - Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.

* [Spektral](https://github.com/danielegrattarola/spektral) - Deep learning on graphs.

* [qkeras](https://github.com/google/qkeras) - A quantization deep learning library.



## Theory



- [Papers We Love](https://github.com/papers-we-love/papers-we-love#readme)

- [Talks](https://github.com/JanVanRyswyck/awesome-talks#readme)

- [Algorithms](https://github.com/tayllan/awesome-algorithms#readme)

	- [Education](https://github.com/gaerae/awesome-algorithms-education#readme) - Learning and practicing.

- [Algorithm Visualizations](https://github.com/enjalot/algovis#readme)

- [Artificial Intelligence](https://github.com/owainlewis/awesome-artificial-intelligence#readme)

- [Search Engine Optimization](https://github.com/marcobiedermann/search-engine-optimization#readme)

- [Competitive Programming](https://github.com/lnishan/awesome-competitive-programming#readme)

- [Math](https://github.com/rossant/awesome-math#readme)

- [Recursion Schemes](https://github.com/passy/awesome-recursion-schemes#readme) - Traversing nested data structures.



## Statistics

* [pandas_summary](https://github.com/mouradmourafiq/pandas-summary) - Extension to pandas dataframes describe function. 

* [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects.

* [statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in Python.

* [stockstats](https://github.com/jealous/stockstats) - Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.

* [weightedcalcs](https://github.com/jsvine/weightedcalcs) - A pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.

* [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs) - Pairwise Multiple Comparisons Post-hoc Tests.

* [Alphalens](https://github.com/quantopian/alphalens) - Performance analysis of predictive (alpha) stock factors.



## Learn



- [CLI Workshoppers](https://github.com/therebelrobot/awesome-workshopper#readme) - Interactive tutorials.

- [Learn to Program](https://github.com/karlhorky/learn-to-program#readme)

- [Speaking](https://github.com/matteofigus/awesome-speaking#readme)

- [Tech Videos](https://github.com/lucasviola/awesome-tech-videos#readme)

- [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning#readme)

- [Computer History](https://github.com/watson/awesome-computer-history#readme)

- [Educational Games](https://github.com/yrgo/awesome-educational-games#readme) - Learn while playing.

- [Product Management](https://github.com/dend/awesome-product-management#readme) - Learn how to be a better product manager.

- [Roadmaps](https://github.com/liuchong/awesome-roadmaps#readme) - Gives you a clear route to improve your knowledge and skills.

- [YouTubers](https://github.com/JoseDeFreitas/awesome-youtubers#readme) - Watch video tutorials from YouTubers that teach you about technology.



## Work



- [Slack](https://github.com/matiassingers/awesome-slack#readme) - Team collaboration.

	- [Communities](https://github.com/filipelinhares/awesome-slack#readme)

- [Remote Jobs](https://github.com/lukasz-madon/awesome-remote-job#readme)

- [Productivity](https://github.com/jyguyomarch/awesome-productivity#readme)

- [Niche Job Boards](https://github.com/tramcar/awesome-job-boards#readme)

- [Programming Interviews](https://github.com/DopplerHQ/awesome-interview-questions#readme)

- [Code Review](https://github.com/joho/awesome-code-review#readme) - Reviewing code.

- [Creative Technology](https://github.com/j0hnm4r5/awesome-creative-technology#readme) - Businesses & groups that specialize in combining computing, design, art, and user experience.



## Websites



* Tutorials

    * [Full Stack Python](https://www.fullstackpython.com/)

    * [Python Cheatsheet](https://www.pythoncheatsheet.org/)

    * [Real Python](https://realpython.com)

    * [The Hitchhiker’s Guide to Python](https://docs.python-guide.org/)

    * [Ultimate Python study guide](https://github.com/huangsam/ultimate-python)

* Libraries

    * [Awesome Python @LibHunt](https://python.libhunt.com/)

* Others

    * [Python ZEEF](https://python.zeef.com/alan.richmond)

    * [Pythonic News](https://news.python.sc/)

    * [What the f*ck Python!](https://github.com/satwikkansal/wtfpython)

    * [kdnuggets](https://www.kdnuggets.com)

    * [O'reilly](https://www.oreilly.com/)

    * [Towards Data Science](https://towardsdatascience.com/)

    * [Reddit-Data Science](https://www.reddit.com/r/datascience)

## Newsletters



* [Awesome Python Newsletter](http://python.libhunt.com/newsletter)

* [Pycoder's Weekly](http://pycoders.com/)

* [Python Tricks](https://realpython.com/python-tricks/)

* [Python Weekly](http://www.pythonweekly.com/)

* [Data Elixir](https://dataelixir.com)