The derivatives market having a significant number of investors trading in futures contracts, is vulnerable to manipulation by some perpetrators. Protecting market participants from a prevalent manipulation called circular trading and providing a fair market has always been a challenging task for regulators. This kind of malpractice is represented by the trading behaviors of a group of investors who trade among themselves frequently to increase the price of the commodity and consequently make forged prosperity. This paper presents a network-based approach for detecting investors involved in circular trading in the futures market. This is done initially by constructing the daily networks of investors’ trades, then, extracting all trade cycles of various lengths from these daily networks to arrive at the group of initial suspicious cycle traders. Finally, in order to exclude investors who are randomly involved in suspicious cycles, price fluctuations over time were analyzed. The proposed approach has been conducted on real data from Iran Mercantile Exchange (IME) and as a warning system, has succeeded in detecting anomalous traders effectively.