Parrondo Strategies for Artificial Traders

TL;DR

This paper explores Parrondo strategies applied to artificial trading, demonstrating that certain variations can outperform traditional buy-and-hold and investor strategies using real Swedish stock market data.

Contribution

It introduces Parrondo-based trading strategies for artificial traders and evaluates their performance against standard strategies under different information scenarios.

Findings

Buy-low-sell-random Parrondo variation outperforms buy-and-hold.

Parrondo strategies can outperform value and trend strategies under certain assumptions.

Performance improves with increased information accuracy.

Abstract

On markets with receding prices, artificial noise traders may consider alternatives to buy-and-hold. By simulating variations of the Parrondo strategy, using real data from the Swedish stock market, we produce first indications of a buy-low-sell-random Parrondo variation outperforming buy-and-hold. Subject to our assumptions, buy-low-sell-random also outperforms the traditional value and trend investor strategies. We measure the success of the Parrondo variations not only through their performance compared to other kinds of strategies, but also relative to varying levels of perfect information, received through messages within a multi-agent system of artificial traders.