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Unsupervised ML for PnL Optimization by samoutheboss is a document available to read on EtoBox.
What is Unsupervised ML for PnL Optimization about?
This document presents an unsupervised machine learning framework designed to optimize Profit and Loss (PnL) in quantitative finance by maximizing the Sharpe Ratio through a linear signal derived from exogenous variables. The approach utilizes a linear regression-like model to create trading signals, which were empirically tested on an ETF representing U.S. Treasury bonds, yielding a Sharpe ratio of 1.2 over a backtest period from 2000 to 2023. The study also discusses regularization techniques and potentia
- Author
- samoutheboss
- Language
- EN