{"id":26874,"url":"https://github.com/Davelexic/fintech-awesome-libraries","name":"fintech-awesome-libraries","description":"compilation of libraries that have been helpful for data analysis","projects_count":196,"last_synced_at":"2026-09-05T02:00:17.098Z","repository":{"id":154088633,"uuid":"376351714","full_name":"Davelexic/fintech-awesome-libraries","owner":"Davelexic","description":"compilation of libraries that have been helpful for data analysis","archived":false,"fork":false,"pushed_at":"2021-06-12T21:02:43.000Z","size":265,"stargazers_count":6,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2026-08-16T08:07:07.680Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Davelexic.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2021-06-12T17:47:25.000Z","updated_at":"2025-06-26T10:30:28.000Z","dependencies_parsed_at":"2024-01-16T00:17:34.592Z","dependency_job_id":"9620f39f-dc4d-45d9-ba84-7b58aa4471b7","html_url":"https://github.com/Davelexic/fintech-awesome-libraries","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Davelexic/fintech-awesome-libraries","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Davelexic%2Ffintech-awesome-libraries","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Davelexic%2Ffintech-awesome-libraries/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Davelexic%2Ffintech-awesome-libraries/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Davelexic%2Ffintech-awesome-libraries/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Davelexic","download_url":"https://codeload.github.com/Davelexic/fintech-awesome-libraries/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Davelexic%2Ffintech-awesome-libraries/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":336071963,"owners_count":37022356,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-08-22T15:14:58.755Z","status":"online","status_checked_at":"2026-09-05T02:00:06.064Z","response_time":108,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-13T12:57:20.974Z","updated_at":"2026-09-05T02:00:17.100Z","primary_language":"Python","list_of_lists":false,"displayable":true,"categories":["Machine Learning","Theory","Books","Cheats sheets","Database Drivers","Learn","Business","Work","Decentralized Systems","Cryptography","Data Analysis","Date and Time","Websites","Newsletters","Data Visualization","Deployment","Tensor Flow","Statistics","Evaluation"],"sub_categories":["Automatic Plotting","Automated Machine Learning","Interactive plots","Map","General Purposes"],"readme":"\u003cimg src=\"https://thumbor.forbes.com/thumbor/fit-in/1200x0/filters%3Aformat%28jpg%29/https%3A%2F%2Fspecials-images.forbesimg.com%2Fdam%2Fimageserve%2F1128466997%2F0x0.jpg%3Ffit%3Dscale\" width=\"600\" height=\"400\"\u003e\n\n\n## Awesome Fintech Libraries\n  - [Books](#Books)\n  - [Business](#Business)\n  - [Cheat Sheets](#Cheat-sheets)\n  - [Cryptography](#cryptography)\n  - [Data Analysis](#data-analysis)\n  - [Data Visualization](#data-visualization)\n  - [Database Drivers](#database-drivers)\n  - [Date and Time](#date-and-time)\n  - [Decentralized Systems](#Decentralized-Systems)\n  - [Deployment](#Deployment)\n  - [Evaluation](#Evaluation)\n  - [Machine Learning](#machine-learning)\n  - [Automated Machine Learning](#Automated-Machine-Learning)\n  - [Tensor Flow](#Tensor-Flow)\n  - [Theory](#Theory)\n  - [Statistics](#Statistics)\n  - [Learn](#Learn)\n  - [Work](#Work)\n  - [Websites](#websites)\n  - [Newsletters](#newsletters)\n\n## Books\n\n- [Free Programming Books](https://github.com/EbookFoundation/free-programming-books#readme)\n- [Go Books](https://github.com/dariubs/GoBooks#readme)\n- [Mind Expanding Books](https://github.com/hackerkid/Mind-Expanding-Books#readme)\n- [Book Authoring](https://github.com/TalAter/awesome-book-authoring#readme)\n- [Elixir Books](https://github.com/sger/ElixirBooks#readme)\n\n## Business\n\n- [Open Companies](https://github.com/opencompany/awesome-open-company#readme)\n- [Places to Post Your Startup](https://github.com/mmccaff/PlacesToPostYourStartup#readme)\n- [OKR Methodology](https://github.com/domenicosolazzo/awesome-okr#readme) - Goal setting \u0026 communication best practices.\n- [Leading and Managing](https://github.com/LappleApple/awesome-leading-and-managing#readme) - Leading people and being a manager in a technology company/environment.\n- [Indie](https://github.com/mezod/awesome-indie#readme) - Independent developer businesses.\n- [Tools of the Trade](https://github.com/cjbarber/ToolsOfTheTrade#readme) - Tools used by companies on Hacker News.\n- [Clean Tech](https://github.com/nglgzz/awesome-clean-tech#readme) - Fighting climate change with technology.\n- [Wardley Maps](https://github.com/wardley-maps-community/awesome-wardley-maps#readme) - Provides high situational awareness to help improve strategic planning and decision making.\n- [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.\n- [Engineering Team Management](https://github.com/kdeldycke/awesome-engineering-team-management#readme) - How to transition from software development to engineering management.\n- [Developer-First Products](https://github.com/agamm/awesome-developer-first#readme) - Products that target developers as the user.\n\n## Cheats sheets\n* [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.\n* [Git tips](https://github.com/git-tips/tips#readme) - Collection of tips for Git.\n* [Data Manipulation](https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf)-Data mutation.\n* [Data Science Cheatsheet](https://st11.ning.com/topology/rest/1.0/file/get/2808327959?profile=original)\n\n## Cryptography\n\n* [cryptography](https://cryptography.io/en/latest/) - A package designed to expose cryptographic primitives and recipes to Python developers.\n* [paramiko](https://github.com/paramiko/paramiko) - The leading native Python SSHv2 protocol library.\n* [passlib](https://passlib.readthedocs.io/en/stable/) - Secure password storage/hashing library, very high level.\n* [pynacl](https://github.com/pyca/pynacl) - Python binding to the Networking and Cryptography (NaCl) library.\n\n\n## Data Analysis\n\n*Libraries for data analyzing.*\n\n* [AWS Data Wrangler](https://github.com/awslabs/aws-data-wrangler) - Pandas on AWS.\n* [Blaze](https://github.com/blaze/blaze) - NumPy and Pandas interface to Big Data.\n* [Open Mining](https://github.com/mining/mining) - Business Intelligence (BI) in Pandas interface.\n* [Optimus](https://github.com/ironmussa/Optimus) - Agile Data Science Workflows made easy with PySpark.\n* [Orange](https://orange.biolab.si/) - Data mining, data visualization, analysis and machine learning through visual programming or scripts.\n* [Pandas](http://pandas.pydata.org/) - A library providing high-performance, easy-to-use data structures and data analysis tools.\n* [Pandas Profiling](https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/)- generates profile report off of pandas database.\n\n## Data Visualization\n\n*Libraries for visualizing data.*\n\n### General Purposes\n* [Matplotlib](https://github.com/matplotlib/matplotlib) - Plotting with Python.\n* [seaborn](https://github.com/mwaskom/seaborn) - Statistical data visualization using matplotlib.\n* [prettyplotlib](https://github.com/olgabot/prettyplotlib) - Painlessly create beautiful matplotlib plots.\n* [python-ternary](https://github.com/marcharper/python-ternary) - Ternary plotting library for python with matplotlib.\n* [missingno](https://github.com/ResidentMario/missingno) - Missing data visualization module for Python.\n* [chartify](https://github.com/spotify/chartify/) - Python library that makes it easy for data scientists to create charts.\n* [physt](https://github.com/janpipek/physt) - Improved histograms.\n### Interactive plots\n* [animatplot](https://github.com/t-makaro/animatplot) - A python package for animating plots build on matplotlib.\n* [plotly](https://plot.ly/python/) - A Python library that makes interactive and publication-quality graphs.\n* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.\n* [Altair](https://altair-viz.github.io/) - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph\n* [bqplot](https://github.com/bqplot/bqplot) - Plotting library for IPython/Jupyter notebooks\n* [pyecharts](https://github.com/pyecharts/pyecharts) -  a charting and visualization library, to Python's interactive visual drawing library.\n### Map\n* [folium](https://python-visualization.github.io/folium/quickstart.html#Getting-Started) - Makes it easy to visualize data on an interactive open street map\n* [geemap](https://github.com/giswqs/geemap) - Python package for interactive mapping with Google Earth Engine (GEE)\n### Automatic Plotting\n* [HoloViews](https://github.com/ioam/holoviews) - Stop plotting your data - annotate your data and let it visualize itself.\n* [AutoViz](https://github.com/AutoViML/AutoViz): Visualize data automatically with 1 line of code (ideal for machine learning)\n* [SweetViz](https://github.com/fbdesignpro/sweetviz): Visualize and compare datasets, target values and associations, with one line of code.\n\n\n## Database Drivers\n\n*Libraries for connecting and operating databases.*\n\n* MySQL - [awesome-mysql](http://shlomi-noach.github.io/awesome-mysql/)\n    * [mysqlclient](https://github.com/PyMySQL/mysqlclient-python) - MySQL connector with Python 3 support ([mysql-python](https://sourceforge.net/projects/mysql-python/) fork).\n    * [PyMySQL](https://github.com/PyMySQL/PyMySQL) - A pure Python MySQL driver compatible to mysql-python.\n* PostgreSQL - [awesome-postgres](https://github.com/dhamaniasad/awesome-postgres)\n    * [psycopg2](http://initd.org/psycopg/) - The most popular PostgreSQL adapter for Python.\n    * [queries](https://github.com/gmr/queries) - A wrapper of the psycopg2 library for interacting with PostgreSQL.\n* SQlite - [awesome-sqlite](https://github.com/planetopendata/awesome-sqlite)\n    * [sqlite3](https://docs.python.org/3/library/sqlite3.html) - (Python standard library) SQlite interface compliant with DB-API 2.0\n    * [SuperSQLite](https://github.com/plasticityai/supersqlite) - A supercharged SQLite library built on top of [apsw](https://github.com/rogerbinns/apsw).\n\n## Date and Time\n\n*Libraries for working with dates and times.*\n\n* [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.\n* [Chronyk](https://github.com/KoffeinFlummi/Chronyk) - A Python 3 library for parsing human-written times and dates.\n* [delorean](https://github.com/myusuf3/delorean/) - A library for clearing up the inconvenient truths that arise dealing with datetimes.\n* [moment](https://github.com/zachwill/moment) - A Python library for dealing with dates/times.\n* [Pendulum](https://github.com/sdispater/pendulum) - Python datetimes made easy.\n* [PyTime](https://github.com/shinux/PyTime) - An easy-to-use Python module which aims to operate date/time/datetime by string.\n* [pytz](https://launchpad.net/pytz) - World timezone definitions, modern and historical.\n* [when.py](https://github.com/dirn/When.py) - Providing user-friendly functions to help perform common date and time actions.\n* [tslearn](https://github.com/rtavenar/tslearn) - Machine learning toolkit dedicated to time-series data.\n* [tick](https://github.com/X-DataInitiative/tick) - Module for statistical learning, with a particular emphasis on time-dependent modelling.\n* [Prophet](https://github.com/facebook/prophet) - Automatic Forecasting Procedure.\n* [PyFlux](https://github.com/RJT1990/pyflux) - Open source time series library for Python.\n* [bayesloop](https://github.com/christophmark/bayesloop) - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.\n* [luminol](https://github.com/linkedin/luminol) - Anomaly Detection and Correlation library.\n* [dateutil](https://dateutil.readthedocs.io/en/stable/) - Powerful extensions to the standard datetime module\n* [maya](https://github.com/timofurrer/maya) - makes it very easy to parse a string and for changing timezones.\n\n\n## Decentralized Systems\n\n* [Bitcoin](https://github.com/igorbarinov/awesome-bitcoin#readme) - Bitcoin services and tools for software developers.\n* [Ripple](https://github.com/vhpoet/awesome-ripple#readme) - Open source distributed settlement network.\n* [Non-Financial Blockchain](https://github.com/machinomy/awesome-non-financial-blockchain#readme) - Non-financial blockchain applications.\n* [Mastodon](https://github.com/tleb/awesome-mastodon#readme) - Open source decentralized microblogging network.\n* [Ethereum](https://github.com/ttumiel/Awesome-Ethereum#readme) - Distributed computing platform for smart contract development.\n* [Blockchain AI](https://github.com/steven2358/awesome-blockchain-ai#readme) - Blockchain projects for artificial intelligence and machine learning.\n* [EOSIO](https://github.com/DanailMinchev/awesome-eosio#readme) - A decentralized operating system supporting industrial-scale apps.\n* [Corda](https://github.com/chainstack/awesome-corda#readme) - Open source blockchain platform designed for business.\n* [Waves](https://github.com/msmolyakov/awesome-waves#readme) - Open source blockchain platform and development toolset for Web 3.0 apps and decentralized solutions.\n* [Substrate](https://github.com/substrate-developer-hub/awesome-substrate#readme) - Framework for writing scalable, upgradeable blockchains in Rust.\n\n## Deployment\n* [datapane](https://datapane.com/) - A collection of APIs to turn scripts and notebooks into interactive reports.\n* [binder](https://mybinder.org/) - Enable sharing and execute Jupyter Notebooks\n* [fastapi](https://fastapi.tiangolo.com/) - Modern, fast (high-performance), web framework for building APIs with Python\n* [streamlit](https://www.streamlit.io/) - Make it easy to deploy machine learning model\n\n## Evaluation\n* [recmetrics](https://github.com/statisticianinstilettos/recmetrics) - Library of useful metrics and plots for evaluating recommender systems.\n* [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metric.\n* [sklearn-evaluation](https://github.com/edublancas/sklearn-evaluation) - Model evaluation made easy: plots, tables and markdown reports. \n* [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.\n\n\n\n## Machine Learning\n\n*Libraries for Machine Learning. Also see [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python).*\n\n* [gym](https://github.com/openai/gym) - A toolkit for developing and comparing reinforcement learning algorithms.\n* [H2O](https://github.com/h2oai/h2o-3) - Open Source Fast Scalable Machine Learning Platform.\n* [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metrics.\n* [NuPIC](https://github.com/numenta/nupic) - Numenta Platform for Intelligent Computing.\n* [scikit-learn](http://scikit-learn.org/) - The most popular Python library for Machine Learning.\n* [Spark ML](http://spark.apache.org/docs/latest/ml-guide.html) -  scalable Machine Learning library.\n* [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library.\n* [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.\n* [Shogun](http://www.shogun-toolbox.org/) - Machine learning toolbox.\n* [xLearn](https://github.com/aksnzhy/xlearn) - High Performance, Easy-to-use, and Scalable Machine Learning Package.\n* [cuML](https://github.com/rapidsai/cuml) - RAPIDS Machine Learning Library.\n* [modAL](https://github.com/cosmic-cortex/modAL) - Modular active learning framework for Python3. \n* [Sparkit-learn](https://github.com/lensacom/sparkit-learn) - PySpark + scikit-learn = Sparkit-learn. \n* [mlpack](https://github.com/mlpack/mlpack) - A scalable C++ machine learning library (Python bindings).\n* [MLxtend](https://github.com/rasbt/mlxtend) - Extension and helper modules for Python's data analysis and machine learning libraries.\n* [hyperlearn](https://github.com/danielhanchen/hyperlearn) - 50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels.\n* [Reproducible Experiment Platform (REP)](https://github.com/yandex/rep) - Machine Learning toolbox for Humans.\n* [seqlearn](https://github.com/larsmans/seqlearn) - Sequence classification toolkit for Python. \n* [pystruct](https://github.com/pystruct/pystruct) - Simple structured learning framework for Python.\n* [sklearn-expertsys](https://github.com/tmadl/sklearn-expertsys) - Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models.\n* [RuleFit](https://github.com/christophM/rulefit) - Implementation of the rulefit.\n* [metric-learn](https://github.com/all-umass/metric-learn) - Metric learning algorithms in Python.\n* [pyGAM](https://github.com/dswah/pyGAM) - Generalized Additive Models in Python.\n* [Karate Club](https://github.com/benedekrozemberczki/karateclub) - An unsupervised machine learning library for graph structured data.\n* [Little Ball of Fur](https://github.com/benedekrozemberczki/littleballoffur) - A library for sampling graph structured data.\n* [causalml](https://github.com/uber/causalml) - Uplift modeling and causal inference with machine learning algorithms.\n\n### Automated Machine Learning\n* [TPOT](https://github.com/rhiever/tpot) - Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. \n* [auto-sklearn](https://github.com/automl/auto-sklearn) - An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.\n* [MLBox](https://github.com/AxeldeRomblay/MLBox) - A powerful Automated Machine Learning python library.\n\n## Tensor Flow\n* [TensorFlow](https://github.com/tensorflow/tensorflow) - Computation using data flow graphs for scalable machine learning by Google.\n* [TensorLayer](https://github.com/zsdonghao/tensorlayer) - Deep Learning and Reinforcement Learning Library for Researcher and Engineer. \n* [TFLearn](https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API for TensorFlow. \n* [Sonnet](https://github.com/deepmind/sonnet) - TensorFlow-based neural network library.\n* [tensorpack](https://github.com/ppwwyyxx/tensorpack) - A Neural Net Training Interface on TensorFlow.\n* [Polyaxon](https://github.com/polyaxon/polyaxon) - A platform that helps you build, manage and monitor deep learning models. \n* [NeuPy](https://github.com/itdxer/neupy) - NeuPy is a Python library for Artificial Neural Networks and Deep Learning\n* [tfdeploy](https://github.com/riga/tfdeploy) - Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy.\n* [tensorflow-upstream](https://github.com/ROCmSoftwarePlatform/tensorflow-upstream) - TensorFlow ROCm port. \n* [TensorFlow Fold](https://github.com/tensorflow/fold) - Deep learning with dynamic computation graphs in TensorFlow. \n* [tensorlm](https://github.com/batzner/tensorlm) - Wrapper library for text generation / language models at char and word level with RNN. \n* [TensorLight](https://github.com/bsautermeister/tensorlight) - A high-level framework for TensorFlow. \n* [Mesh TensorFlow](https://github.com/tensorflow/mesh) - Model Parallelism Made Easier. \n* [Ludwig](https://github.com/uber/ludwig) - A toolbox, that allows to train and test deep learning models without the need to write code. \n* [Keras](https://keras.io) - A high-level neural networks API running on top of TensorFlow.\n* [keras-contrib](https://github.com/keras-team/keras-contrib) - Keras community contributions.\n* [Hyperas](https://github.com/maxpumperla/hyperas) - Keras + Hyperopt: A very simple wrapper for convenient hyperparameter.\n* [Elephas](https://github.com/maxpumperla/elephas) - Distributed Deep learning with Keras \u0026 Spark.\n* [Hera](https://github.com/keplr-io/hera) - Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.\n* [Spektral](https://github.com/danielegrattarola/spektral) - Deep learning on graphs.\n* [qkeras](https://github.com/google/qkeras) - A quantization deep learning library.\n\n\n## Theory\n\n- [Papers We Love](https://github.com/papers-we-love/papers-we-love#readme)\n- [Talks](https://github.com/JanVanRyswyck/awesome-talks#readme)\n- [Algorithms](https://github.com/tayllan/awesome-algorithms#readme)\n\t- [Education](https://github.com/gaerae/awesome-algorithms-education#readme) - Learning and practicing.\n- [Algorithm Visualizations](https://github.com/enjalot/algovis#readme)\n- [Artificial Intelligence](https://github.com/owainlewis/awesome-artificial-intelligence#readme)\n- [Search Engine Optimization](https://github.com/marcobiedermann/search-engine-optimization#readme)\n- [Competitive Programming](https://github.com/lnishan/awesome-competitive-programming#readme)\n- [Math](https://github.com/rossant/awesome-math#readme)\n- [Recursion Schemes](https://github.com/passy/awesome-recursion-schemes#readme) - Traversing nested data structures.\n\n## Statistics\n* [pandas_summary](https://github.com/mouradmourafiq/pandas-summary) - Extension to pandas dataframes describe function. \n* [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects.\n* [statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in Python.\n* [stockstats](https://github.com/jealous/stockstats) - Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.\n* [weightedcalcs](https://github.com/jsvine/weightedcalcs) - A pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.\n* [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs) - Pairwise Multiple Comparisons Post-hoc Tests.\n* [Alphalens](https://github.com/quantopian/alphalens) - Performance analysis of predictive (alpha) stock factors.\n\n## Learn\n\n- [CLI Workshoppers](https://github.com/therebelrobot/awesome-workshopper#readme) - Interactive tutorials.\n- [Learn to Program](https://github.com/karlhorky/learn-to-program#readme)\n- [Speaking](https://github.com/matteofigus/awesome-speaking#readme)\n- [Tech Videos](https://github.com/lucasviola/awesome-tech-videos#readme)\n- [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning#readme)\n- [Computer History](https://github.com/watson/awesome-computer-history#readme)\n- [Educational Games](https://github.com/yrgo/awesome-educational-games#readme) - Learn while playing.\n- [Product Management](https://github.com/dend/awesome-product-management#readme) - Learn how to be a better product manager.\n- [Roadmaps](https://github.com/liuchong/awesome-roadmaps#readme) - Gives you a clear route to improve your knowledge and skills.\n- [YouTubers](https://github.com/JoseDeFreitas/awesome-youtubers#readme) - Watch video tutorials from YouTubers that teach you about technology.\n\n## Work\n\n- [Slack](https://github.com/matiassingers/awesome-slack#readme) - Team collaboration.\n\t- [Communities](https://github.com/filipelinhares/awesome-slack#readme)\n- [Remote Jobs](https://github.com/lukasz-madon/awesome-remote-job#readme)\n- [Productivity](https://github.com/jyguyomarch/awesome-productivity#readme)\n- [Niche Job Boards](https://github.com/tramcar/awesome-job-boards#readme)\n- [Programming Interviews](https://github.com/DopplerHQ/awesome-interview-questions#readme)\n- [Code Review](https://github.com/joho/awesome-code-review#readme) - Reviewing code.\n- [Creative Technology](https://github.com/j0hnm4r5/awesome-creative-technology#readme) - Businesses \u0026 groups that specialize in combining computing, design, art, and user experience.\n\n## Websites\n\n* Tutorials\n    * [Full Stack Python](https://www.fullstackpython.com/)\n    * [Python Cheatsheet](https://www.pythoncheatsheet.org/)\n    * [Real Python](https://realpython.com)\n    * [The Hitchhiker’s Guide to Python](https://docs.python-guide.org/)\n    * [Ultimate Python study guide](https://github.com/huangsam/ultimate-python)\n* Libraries\n    * [Awesome Python @LibHunt](https://python.libhunt.com/)\n* Others\n    * [Python ZEEF](https://python.zeef.com/alan.richmond)\n    * [Pythonic News](https://news.python.sc/)\n    * [What the f*ck Python!](https://github.com/satwikkansal/wtfpython)\n    * [kdnuggets](https://www.kdnuggets.com)\n    * [O'reilly](https://www.oreilly.com/)\n    * [Towards Data Science](https://towardsdatascience.com/)\n    * [Reddit-Data Science](https://www.reddit.com/r/datascience)\n## Newsletters\n\n* [Awesome Python Newsletter](http://python.libhunt.com/newsletter)\n* [Pycoder's Weekly](http://pycoders.com/)\n* [Python Tricks](https://realpython.com/python-tricks/)\n* [Python Weekly](http://www.pythonweekly.com/)\n* [Data Elixir](https://dataelixir.com)\n\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/davelexic%2Ffintech-awesome-libraries/projects"}