joelowj/mtl-tsmom
Multi Task Learning Time Series Momentum
This project helps quantitative researchers and traders construct diversified, risk-adjusted time-series momentum portfolios. By inputting historical futures contract price data, it leverages deep multi-task learning to jointly optimize momentum signals and volatility estimation. The output is a strategy that aims to deliver stronger abnormal returns and better tail risk protection compared to traditional methods.
No commits in the last 6 months.
Use this if you are a quantitative researcher or strategist looking to develop or enhance time-series momentum trading strategies for futures contracts.
Not ideal if you are looking for a complete, out-of-the-box backtesting framework or live trading system, as this project provides only core research components.
Stars
25
Forks
8
Language
Python
License
Apache-2.0
Category
Last pushed
May 18, 2024
Commits (30d)
0
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