jmrichardson/tuneta
Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models
This project helps quantitative traders and financial analysts automatically optimize and select technical indicators for their trading models. You provide historical price data (OHLCV) and a target, like next day's return. It then outputs a refined set of technical indicators with optimized parameters that are highly predictive of your target and minimally correlated with each other, ready to be used in machine learning models.
457 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to build robust machine learning models for financial markets and want to systematically identify the most effective and non-redundant technical indicators.
Not ideal if you're looking for a simple charting tool or a system to manually backtest predefined trading strategies without a machine learning component.
Stars
457
Forks
81
Language
Python
License
MIT
Category
Last pushed
Oct 13, 2023
Commits (30d)
0
Dependencies
14
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