Skip to content
All library documents

Using Sharpe Ratio as a Trading Strategy Objective

Article Quant Q&A · Author: MK23
Read the original

Summary

The document raises a methodological question about directly optimizing a trading rule or neural network with negative Sharpe ratio as its loss. The author sees a clear use in portfolio optimization but questions its behavior for a single asset with bounded trade weights. In that setup, returns are capped on the upside, while a near-zero return standard deviation could make the ratio unbounded.

This is a problem statement rather than a resolved analysis: it does not provide an answer, experiments, or a proposed safeguard. It highlights that maximizing a ratio with a potentially vanishing denominator may fail to reward useful returns and could favor variance reduction instead. Any practical objective would need to account for the return scale and control the denominator, but the document itself does not assess specific remedies or establish that this failure necessarily occurs in all settings.

Key ideas

  • Direct Sharpe optimization is raised as a possible loss function for trading rules and neural networks.
  • The author questions how the objective behaves for a single asset with bounded trade weights.
  • A near-zero return standard deviation can make the Sharpe ratio unbounded in the stated setup.
  • The document presents an unresolved concern rather than evidence or a tested solution.

Tags

StatisticsMachine learningBacktesting
Full text
# Can I use the Sharpe Ratio as an objective function in algorithmic trading?


# Can I use the Sharpe Ratio as an objective function in algorithmic trading?












I’m experimenting with custom loss functions for different trading rules and have come across a few articles citing success in directly using the (negative) Sharpe Ratio as a loss function, particularly within neural networks.

Intuitively, I can see where it would help with portfolio optimization, but I don’t see how it could work for trading a single asset. Given capped trade weights [-1,1], returns are bounded on the upside but stdev can be 0 resulting in infinite Sharpe. Isn’t any algorithm claiming to maximize Sharpe just suboptimally minimizing variance?

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.