Mostly experiments based on "Advances in financial machine learning" book
Source code for Algorithmic Trading with Python (2020) by Chris Conlan
Deep Learning and Machine Learning stocks represent a promising long-term or short-term opportunity for investors and traders.
Advances in Financial Machine Learning
A curated list of practical financial machine learning tools and applications.
As described in Advances of Machine Learning by Marcos Prado.
Machine Learning in Asset Management
Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.
Repository containing notebooks of my posts on Medium
Useful resources for Mongolian NLP
Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD
All my quant analysis from financial market.
Quantitative analysis, strategies and backtests
Just learning a Vulkan API with SDL2.
Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations
A starter code to review distribution of a strategy returns and assess Monte Carlo distribution of returns
Feature Engineering and Feature Importance of Machine Learning in Financial Market.
This is my financial trading system using ML (Random forest & LSTM). Most of the methods are based on 'Advances in Financial Machine Learning' by Lopez de Prado.
just some volatility related experiments
Applying Hidden Markov Models to model Gold Intraday Volatility by detecting regime switches from low-vol regimes to high-vol
A complete set of volatility estimators based on Euan Sinclair's Volatility Trading