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A Nintendo Switch homebrew app to automatically update your cheat files for your installed games.
Data Science Projects Repository
Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 150+ Indicators
Python Backtesting library for trading strategies
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.
A CNN fashion recommender to recommend clothing items based on similar style
Source code for the blog post on the evolution of the asset allocation methods
Machine Learning in Asset Management (by @firmai)
Analysis of SQL Leetcode and classic interview questions, common pitfalls, anti-patterns and handy tricks. Sample databases.
My solution to the book A Collection of Data Science Take-Home Challenges
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
A python tutorial on bayesian modeling techniques (PyMC3)
How to do Bayesian statistical modelling using numpy and PyMC3
A self-directed curriculum to learn Bayesian methods for hierarchical or multilevel modeling
Code for a dynamic multilevel Bayesian model to predict US presidential elections. Written in R and Stan.
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE
Scikit-learn style estimator for Minimum Spanning Tree Clustering in Python
A curated list of awesome mathematics resources
A statistical arbitrage strategy on treasury futures using mean-reversion property and meanwhile insensitive to the yield change
QuantStart Forex Backtesting and Live Trading
A middle-to-high level open source algorithm book designed with coding interview at heart!
Depricated repo. Please refer to mlfinlab
Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]