Stars
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
Framework for developing, backtesting, and deploying automated trading algorithms and trading bots.
Feature Engineering and Feature Importance in Machine Learning for Financial Markets
Python library for portfolio optimization built on top of scikit-learn
Examples and guides for using the Gemini API
Mastering Python for Finance – Second Edition, published by Packt
A Free Newsletter for Quantitative and Algorithmic Trading, Portfolio Analysis, and Investing
Data and scripts for learning along with the Socratica SQL series
Turns Data and AI algorithms into production-ready web applications in no time.
A complete set of volatility estimators based on Euan Sinclair's Volatility Trading
Python training for business analysts and traders
Roadmap for becoming Python developer.
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Python for Algorithmic Trading Cookbook, published by Packt
Machine Learning with R, Fourth Edition- published by Packt
Portfolio analytics for quants, written in Python
Quantitative analysis, strategies and backtests
List of awesome resources for machine learning-based algorithmic trading
Hands-On Machine Learning for Algorithmic Trading, published by Packt
The aim of this project is to showcase a simple trading strategy within options market implementations.
Useful functions, tutorials, and other Python-related things
Quant/Algorithm trading resources with an emphasis on Machine Learning
Introduction to SQL and Database Design
Building a foundation for deep learning with mathematics and neuroscience
This is the code notebook for the blog post on using Python and Auto ARIMA
This is the repo for the Udemy Course Python Dashboards with Plotly's Dash
Course Files for Complete Python 3 Bootcamp Course on Udemy
Time series easier, faster, more fun. Pytimetk.
A collection of scripts and notebooks to help you get started quickly.