In a recent analysis of trading activity on Kalshi, a regulated exchange that offers contracts for difference on a variety of assets, researchers have uncovered a striking pattern in the way volume is generated for two of the platform’s most popular products: Bitcoin perpetual contracts and Ether perpetual contracts. The data indicates that a small number of unusually large, repetitive trades are responsible for the majority of the observed trading volume, raising questions about the true nature of the market activity and the reliability of volume metrics as a proxy for genuine investor interest. The investigation, conducted by the editorial team at CoinDesk, focused on a sample of transaction data collected over a defined period.
For Ether perpetual contracts, the analysts identified a single trade size of $5,499 that alone contributed to 57 percent of the total sampled volume. In other words, more than half of the Ether perpetual activity in the sample can be traced back to one specific trade amount that appeared repeatedly.
This pattern suggests that a single participant—or a coordinated group of participants—may be repeatedly executing orders of the same size, thereby inflating the apparent liquidity of the contract. A similar phenomenon was observed for Bitcoin perpetual contracts. Here, the researchers found that two recurring trade sizes—$2,500 and $5,000—together accounted for 54 percent of the sampled Bitcoin perpetual volume.
While the individual trade sizes are smaller than the $5,499 figure seen in the Ether market, the combined effect of these two amounts still represents more than half of the total activity captured in the sample. The consistency of these trade sizes points to a systematic approach to order placement, rather than a random distribution of trade values that one would expect in a healthy, diversified market. To understand why this matters, it is helpful to consider how volume is typically interpreted by market participants. High trading volume is often taken as a sign of robust market depth, active participation, and reliable price discovery.
Traders, analysts, and algorithmic systems use volume data to gauge the strength of price movements and to assess the risk of slippage when entering or exiting positions. When volume is artificially inflated by repetitive, identical trades, however, the signal becomes distorted. The market may appear more liquid than it actually is, potentially leading participants to underestimate execution risk.
There are several plausible explanations for why such repetitive trade patterns might emerge on a platform like Kalshi. One possibility is that a market maker or liquidity provider is using a strategy that involves placing a series of similarly sized orders to meet regulatory requirements for minimum volume or to qualify for certain fee structures. Another scenario could involve a single large trader who is systematically breaking down a sizable position into multiple identical blocks to manage exposure or to avoid triggering large price impacts.
Yet another, more concerning explanation would be the presence of wash trading—where the same entity simultaneously buys and sells the same contract to create the illusion of activity without any genuine change in ownership. Kalshi operates under the oversight of the Commodity Futures Trading Commission (CFTC), which imposes strict rules on market manipulation and requires transparent reporting of trading activity.
While the platform’s compliance framework is designed to detect and deter illicit behavior, the detection of repetitive trade sizes in publicly available data highlights the challenges regulators face in distinguishing legitimate market‑making activity from manipulative practices. The fact that these patterns were observable in a sampled dataset suggests that more granular, real‑time monitoring may be necessary to ensure market integrity. The implications for investors are also noteworthy. Retail traders who rely on volume‑weighted average price (VWAP) strategies or who use volume as a filter for entering trades might be misled by the inflated numbers.
Institutional participants, who often allocate capital based on liquidity metrics, could allocate resources inefficiently if they assume the market depth is greater than it truly is. Moreover, the presence of large, repetitive trades can increase the risk of price spikes or gaps when the underlying order flow changes, as the market may lack the diverse participation needed to absorb shocks smoothly. From a broader perspective, this case study underscores a recurring theme in the rapidly evolving world of digital asset derivatives: the need for robust data analytics and transparent reporting standards.
As more platforms emerge offering novel contract types—ranging from traditional commodities to speculative crypto‑based instruments—the ability to parse genuine market activity from statistical artifacts becomes increasingly critical. Researchers, regulators, and platform operators must collaborate to develop tools that can flag anomalous patterns, such as disproportionate trade size repetition, and investigate their root causes. In conclusion, the CoinDesk analysis of Kalshi’s Bitcoin and Ether perpetual contracts reveals that a small set of repetitive, large‑size trades dominate the sampled volume for both assets. A single $5,499 trade accounts for 57 percent of Ether perpetual volume, while $2,500 and $5,000 trades together make up 54 percent of Bitcoin perpetual volume.
This concentration of activity raises important questions about the authenticity of the reported liquidity, the potential for market manipulation, and the adequacy of existing oversight mechanisms. Market participants should approach volume figures on such platforms with a degree of caution, and regulators may need to consider more sophisticated monitoring techniques to ensure that the apparent depth of these emerging markets reflects true investor participation rather than engineered statistics.