Galaxy Report: 1.27 Billion Trades Reveal the Truth About Polymarket Retail Traders' Gains and Losses

Author: Will Owens, Analyst at Galaxy Research

Compiled by: Jiahua, ChainCatcher

Introduction

Since its launch in 2020, the international version of Polymarket has matched 1.27 billion orders for 3.07 million wallets, with a nominal trading volume of $82.8 billion. As all transactions are settled on-chain, we can view complete records, including each position, purchase price, holding time, and settlement income, to understand users' actual behavior in the prediction market. This report narrows the scope to 2.9 million accounts whose trading patterns align with manual operation characteristics.

This report poses five questions regarding these accounts: After making a profit, do traders cash out or continue to ride the wave? Does profit make them bolder, while losses make them more cautious? How are gains and losses distributed? Do traders typically focus on specific areas, and does this focus yield better returns? What are the differences between profitable and unprofitable traders?

Studying retail trading behavior usually relies on information disclosed by brokers or survey data. However, since Polymarket settles on-chain, there is no need to debate the reasonableness of the sampling scope.

The core conclusion is that 69% of retail accounts ultimately incur losses, with this group collectively losing $339 million.

In 2024, Polymarket is primarily known for the U.S. election night. Subsequently, the company returned to the U.S. market through a subsidiary licensed by the Commodity Futures Trading Commission (CFTC) and introduced a taker fee for the first time in early 2026. The platform discussed below is the international version, which operates independently from the U.S. app, each having its own order book.

Earlier this year, Polymarket adjusted its fee structure. Since the data in this report covers the platform's entire history, some accounts had all their transactions occur before the platform began charging fees.

This is also the first complete NFL season since both Polymarket and its competitor Kalshi entered the U.S. market. Both platforms have increased their investments as a result. At the start of the season, Polymarket invited sports stars like LeBron James, Eli Manning, and Derek Jeter to serve as "promotional partners," which has also sparked some opposition.

Alongside this promotional push, a social feature called Squads was launched within the U.S. app. Squads are private groups where users can discuss the prediction market and trade based on other members' choices, all without leaving Polymarket. This feature aims to bring discussions from group chats onto the platform. Another platform has begun to incorporate social trading features, a trend also seen in pump.fun's recent announcement.

Faced with such a large potential trading volume, both platforms are spending money to expand their user base, attracting precisely the type of traders who perform the worst in this report. Whether the composition of traders will change significantly in the coming year or simply see a substantial increase in numbers remains to be observed.

Core Summary

Among the 2.9 million "retail" accounts, 69.2% ultimately incur losses.

This group collectively lost $338.9 million.

Losses seem to increase user churn rates.

After incurring losses, 15.2% of accounts did not trade again within 30 days.

After making a profit, this rate is 6.1%.

If traders indeed have an advantage in a specific area, focusing on that area can yield better returns.

44.1% of traders concentrated over 60% of their trading activity in one area.

Traders focused on sports performed the worst, possibly because many of them are just casual enthusiasts or occasional participants rather than market makers or arbitrageurs.

Traders focused on technology and science performed the best, with some possibly possessing insider information, while others may be experts in their respective fields.

Profitable traders tend to invest larger amounts per trade.

The median single position amount for profitable traders is $13.96, while for losing traders, it is $10.

Research Methodology

Dataset: Polymarket settles on-chain, meaning that complete records of every order, position, purchase price, and settlement income are publicly available. All analyses in this report are based on these records.

Research Subjects: We aim to study human behavior, so we need to exclude automated accounts that trade via scripts. While it is impossible to accurately determine which accounts are using automated trading, we can estimate this through the "number of orders per active day." This metric equals the total number of orders for an account divided by the number of days the account has actually traded.

The number of orders per active day follows a continuous distribution without a natural dividing line. Therefore, the criteria for division require the researcher’s judgment. We set the threshold at 50 orders per active day, excluding 125,429 accounts, which account for 4.1% of the total. Although these accounts "only" make up 4.1%, they contributed to 80.8% of all orders and 41% of the nominal trading volume. This filtering method is designed to identify the situation where a small number of accounts account for a large proportion of trading activity.

Retail Traders: Our filtering criterion is trading frequency. Even if a trader has ample funds, as long as they make their own judgments and manually click to place orders, they will be classified as a retail trader here. Therefore, the research conclusions apply to accounts that trade at a manual pace, rather than the strictly defined "retail traders" in economic terms.

Profit Measurement: If the value of an account's position at settlement is higher than its purchase cost, we consider it profitable, regardless of whether the holder actually redeems it. Positions that expire at zero are usually not redeemed; if we only count redemption records, most losses would be excluded, making this group’s performance appear better than it actually is.

An important limitation of this report is that we identify traders through wallet addresses but cannot reliably determine whether two addresses belong to the same person. Therefore, a person trading with multiple wallets will be counted as "multiple accounts" in this analysis.

It is especially important to note this when understanding the conclusion that "losers are more likely to stop trading." An account that appears to exit may simply have switched to a new wallet.

How Are Gains and Losses Distributed?

Among retail accounts, 69.2% ultimately incur losses. This group collectively lost $338.9 million.

The median gain/loss for retail accounts is approximately a loss of $3, with half of the accounts' gains and losses ranging between a loss of $36.64 and a gain of $0.40. These amounts are insufficient to change anyone's life. As expected, larger gains and losses are concentrated at the extremes: the 1st percentile accounts lost $4,804, while the 99th percentile accounts gained $3,381.

In terms of invested amounts, accounts at the median lost about 0.5% of their invested funds, while the 10th percentile accounts lost 90%. Overall, the vast majority of accounts only lost a small amount of money, with only a few accounts losing thousands of dollars.

We classified 125,429 accounts as "automated accounts," which ultimately collectively gained $246.8 million. Their gain/loss distribution also meets expectations: a large number of accounts obtain trading rewards through repeated trading, while accounts truly engaged in market making and arbitrage are much fewer.

The gains and losses of these two groups do not completely offset each other. About $92 million of the difference comes from factors outside the trader group, primarily from unsettled positions.

After Making Money, Do Traders Cash Out or Continue Trading?

Most people continue. Profits often keep users on the platform: after making a profit, only 6.1% of accounts did not establish a position again within 30 days; after incurring losses, this rate is 15.2%. The likelihood of losing accounts exiting is about 2.5 times that of profitable accounts.

Intuitively, making money should lead traders to invest more in the next trade. However, the unadjusted data shows the opposite result: after making a profit, 46.6% of the next position amount is larger; after incurring losses, this rate is 50.2%. However, this comparison is influenced by other factors.

The purchase price of losing positions is much lower than that of profitable positions, with medians of $0.43 and $0.86, respectively. Therefore, when the purchase price is lower, it is easier to increase the amount invested in the next trade. (If the prediction comes true, the settlement income for each contract is $1; otherwise, it is zero. Thus, a price of $0.43 means the market believes the probability of the event occurring is 43%; those buying at this price believe the true probability is higher.)

After controlling for purchase price, the conclusion reverses. Within the same price range, traders who made profits are more likely to increase their investment than those who incurred losses. This phenomenon is mainly concentrated in the price range above $0.50. Below this price, the responses to profits and losses are almost the same, possibly because traders view these trades as low-probability attempts and do not overinterpret any single outcome.

Does Profit Make Traders More Willing to Take Risks? Do Losers Increase Their Investment?

Losers typically contract. Here, "risk" refers to expected loss, not the total principal that could be lost. Assuming an average price p for buying t tokens, the buyer's expected loss is t × p × (1 − p). This means that a position with a predicted event occurrence probability of 99% and an amount of $100,000, while large in scale, does not qualify as a high-risk position by this standard.

Regardless of whether the previous trade was profitable or not, traders' risk usually decreases. When trading again, the risk they take is often slightly lower than that of the recently settled position. However, the contraction after making a profit is significantly smaller: after making a profit, 48.4% of the next position's risk is higher than the previous one; after incurring losses, this rate is 44.7%. The median changes in risk for both groups are zero, indicating that most traders simply return to their previous risk levels.

We categorize traders into five equal groups based on the typical risk they take on their positions. Q1 takes the least risk, while Q5 takes the most. The comparison of behavior after profits and losses is made within each group, thus comparing traders' own performance after different outcomes.

Do Traders Focus on One Domain or Engage in Multiple Domains? How Does Profitability Differ?

We define "single-domain traders" as those who have participated in at least five categorized prediction markets, with over 60% belonging to the same domain. According to this definition, 44.1% of traders are single-domain traders, while 55.9% are "cross-domain traders."

We can answer these questions because Polymarket adds labels to each prediction market. To avoid categorizing all traders as "single-domain traders," we consolidate these labels into ten domains: cryptocurrency, sports, politics, finance, economics, weather, culture, international affairs, technology and science, and business. Subcategories typically also carry the label of their parent category; for example, a football prediction market is also labeled as sports.

Single-domain traders perform slightly worse, with a final profit ratio of 28.1%, while cross-domain traders have a ratio of 30.4%. This is because 61% of single-domain traders are concentrated in the three worst-performing domains: sports, politics, and culture.

A trader who only bets on NFL games on Sundays probably isn't doing actuarial analysis.

Clearly, if traders have an advantage in a certain domain, focusing on that domain can yield better returns. A trader who only engages in prediction markets related to OpenAI model releases likely has some advantage, which may not necessarily be insider information but could simply be a knack for analyzing public information. A trader who only bets on NFL games on Sundays probably isn't doing actuarial analysis.

Sports alone account for 47% of all single-domain traders, with a profit account ratio of 25.1%, the lowest among all domains. In contrast, single-domain traders in all other domains, excluding sports, politics, and culture, have profit ratios higher than the 30.4% of cross-domain traders. Specifically, the finance domain has a ratio of 36.8%, and the technology and science domain has a ratio of 41.2%. It should be noted that technology and science is a smaller category with fewer samples.

The median number of prediction markets participated in by single-domain traders is 18, while for cross-domain traders, it is 4. Since determining whether to focus on one domain requires traders to have participated in at least five categorized prediction markets, low-activity accounts are defaulted to being categorized as cross-domain traders.

What Are the Differences Between Profitable and Losing Traders?

By comparing the median holding time and median single position amount of profitable and losing traders, several conclusions can be drawn.

In terms of position amount, the median single position amount for profitable traders is $13.96, while for losing traders, it is $10.00. However, profitable traders also trade more frequently, so this difference may simply be due to differing activity levels rather than differences in the amount invested. When grouping traders by the cumulative number of positions established, in each group, the single investment of profitable traders is not lower than that of losing traders, and in most groups, it is significantly higher.

Among traders who have established between 5 to 9 positions, the median single position amounts for profitable and losing traders are $12.53 and $7.05, respectively; among those who have established between 50 to 99 positions, the amounts are $13.14 and $8.90, respectively.

Holding time does not clarify the issue much. Looking at all traders together, profitable traders have shorter holding times, with a median of about 20 hours, while losing traders have about 25 hours.

However, when grouped by activity level, the relationship of holding time changes. In some groups, profitable traders hold longer; in others, losing traders hold longer. Therefore, we cannot conclude that there is a clear relationship between "being more patient" and "being more likely to profit" from this data.

This differs from trading in meme coins. In the meme coin market, engaging in "scalping" or quickly trading new pairs tends to yield better profits. They are usually sniper traders or experienced traders who can quickly realize profits within seconds.

For more on these types of traders, refer to our meme coin report; for insights on the nature of KOLs and the trading behaviors they induce, refer to our social trading report. It should be noted that this type of "trading" is quite different from trading in perpetual contracts or prediction markets.

Outlook

The original goal of prediction markets was to aggregate information and leverage collective intelligence. However, in recent years, they have increasingly been criticized as mere gambling platforms. This report presents the profit and loss distribution of Polymarket's user base: 69% ultimately lose money, with the largest user group focusing on single-domain trading primarily in sports-related prediction markets.

Polymarket began charging a fee for taking orders on cryptocurrency price prediction markets starting January 2026, and by the end of March, the fee structure had covered almost all categories. For example, at a price of $0.50 per contract, buying 100 contracts in the cryptocurrency market with an investment of $50 requires the taker to pay a fee of $1.75, equivalent to 3.5% of the single investment amount.

The lowest rates are for political, financial, and technology categories at 2.0%; sports category is at 2.5%. The median proportion of funds lost by retail accounts throughout the trading history is approximately 0.5% of total investments. Nowadays, in cases where the probability of an event is 50%, the cost of a single taker transaction is already several times this proportion.

This report does not negate the value of prediction markets as tools for discovering the truth.

It can be said that, without other forms of subsidy, the majority of participants losing money is precisely the premise for information aggregation to occur. Funds with information advantages need to transact with traders who do not possess such advantages. If all participants in Polymarket were equally savvy, no one would trade. This report does not negate the value of prediction markets as tools for discovering the truth. Polymarket can allow the majority of participants to lose money while providing predictive references for those who do not participate in trading. The cost of prediction is borne by these noise traders.

This report is strictly limited to the international version of the platform. Polymarket's U.S. exchange operates independently and is a major focus of the company's recent investments. For example, the funds invested to invite LeBron James for collaboration reflect this. According to Front Office Sports, Polymarket pays James $15 million annually, about four times his NBA playing income this year. To comply with NBA regulations, his promotional content is limited to American football-related prediction markets. Jet and Manning have also signed cooperation agreements with the platform.

These marketing investments aim to attract the group with the poorest performance in this dataset. From a business perspective, this is not difficult to understand. Polymarket probably does not care whether users are profitable. At least in the short term, the platform is more motivated to attract taker users who participate in trading without screening rather than more professional limit order traders.

On Polymarket, the counterparty for each transaction is a public address. Anyone who disagrees with the analysis in this report can verify it themselves.

Recently, a controversy involving Kalshi is also worth noting. On September 20, a quantitative trader on X named beniduboss accused the exchange of exaggerating the trading volume of cryptocurrency perpetual contracts. This is a serious accusation.

Beni cited data showing that the 24-hour trading volume of ETH-PERP was about $538.6 million, while the open contract amount was only about $3.1 million, which is highly unusual in the perpetual contract market. In contrast, Hyperliquid's ETH-PERP typically has about $1.3 billion in 24-hour trading volume and about $3.1 billion in open contract amounts.

Kalshi officially denied the accusation, stating that the other party confused the number of contracts in prediction markets with the nominal trading volume of perpetual contracts. As of the writing of this article, regulators have not taken action.

It is worth noting that outsiders cannot actually clarify this matter. Kalshi's public data stream does not indicate the identities of the parties in each transaction, so it is impossible to determine whether the trading parties are controlled by the same entity based solely on this data. However, on Polymarket, the counterparty for each transaction is a public address. This means that anyone who disagrees with the analysis in this report can verify it themselves.

As prediction markets continue to evolve and enter regulated trading platforms using proprietary order books, it is no longer a given that outsiders can verify trading volume data.

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