FinancePy
machine-learning-for-trading
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| FinancePy | machine-learning-for-trading | |
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FinancePy
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machine-learning-for-trading
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As someone who has zero knowledge in coding, and algo trading—but has more than 7 years of experience in manually trading, where should I start?
I would highly recommend the book Machine Learning for Algorithmic Trading: https://github.com/stefan-jansen/machine-learning-for-trading
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Papers for intro to Statistical Arbitrage
You have to be a little careful with ML if you’re putting it to use, a couple books that give some good practices are https://ml4trading.io/ and https://www.wiley.com/en-gb/Advances+in+Financial+Machine+Learning-p-9781119482086 .
What are some alternatives?
DCF - Basic Discounted Cash Flow library written in Python. Automatically fetches relevant financial documents for chosen company and calculates DCF based on specified parameters.
SDV - Synthetic Data Generation for tabular, relational and time series data.
Mad-Money-Backtesting - Backtesting recommendations from Mad Money and "The Cramer Effect/Bounce"
PyPortfolioOpt - Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
zero-to-mastery-ml - All course materials for the Zero to Mastery Machine Learning and Data Science course.
documentation - This repository contains the documentation for the current Quantiacs project. Check it out at: https://quantiacs.com/documentation/en/
cryptocurrency-price-prediction - Cryptocurrency Price Prediction Using LSTM neural network
gs-quant - Python toolkit for quantitative finance
TTM - # TTM Stock squeeze detection based on bollinger + keltner channels. Detects which stocks are out after bollinger is inside keltner channel.
fraud-detection-handbook - Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
dcf-basic - Basic DCF model to quickly value public companies.