BisonFi: How an Aggregator Captured Orders with Over $2 Billion in Weekly Trading Volume?

BisonFi executes trades on-chain, but the pricing strategy, capital management, and program upgrades are still controlled by the operators.

Written by: angelilu, Foresight News

A name unfamiliar to many users has topped the seven-day trading volume chart on Solana.

On October 26, the DefiLlama leaderboard showed that BisonFi had a trading volume of approximately $2.03 billion in the past seven days, surpassing Pump's $1.78 billion, Orca's $1.49 billion, and Raydium's $1.09 billion. During this statistical window, BisonFi ranked first on the list.

Image source: Lince

However, users seeking to learn more about it may find it difficult to confirm even its official website and Twitter. Unlike many DeFi projects, BisonFi has not attracted retail liquidity providers through public deposit entrances.

There has also been confusion regarding the team behind BisonFi. Early reports linked it to Solana treasury company Forward Industries, but Forward Industries has issued a statement denying that BisonFi is one of its projects. Therefore, the actual operating team of BisonFi still lacks clear public information.

BisonFi belongs to Prop AMM, which stands for proprietary automated market maker. Here, "proprietary" means that the market-making team uses its own funds and assets to transact with users. The team is responsible for pricing, adjusting inventory, earning the spread between buy and sell prices, and also bears the losses caused by price fluctuations.

Users mainly interact with BisonFi through aggregators. According to 30-day data displayed on the Dune panel, 46.8% of transactions in BisonFi's trading wallet were conducted through DFlow, and 43.9% through Jupiter v6, totaling about 90.7%. Users choose the aggregator, and the actual execution of the exchange may be BisonFi.

Users do not need to know BisonFi; they may have already transacted with it. Why has this market maker, relying on a professional team to provide capital and executing orders through aggregators, achieved such a large trading volume? How does its pricing method differ from the liquidity pools familiar to users, such as Raydium and Orca?

How Do Professional Market Makers Compete for Better Quotes?

Traditional AMMs allow ordinary users to participate in market making. Users deposit assets into liquidity pools, becoming LPs, while traders exchange with the pool, and LPs share the transaction fees.

This mechanism lowers the threshold for providing liquidity, but LPs also face a problem: the prices in other markets may have changed, and the quotes in the pool may still deviate. Arbitrageurs eliminate price discrepancies through trading, which alters the asset composition in the pool. While LPs receive transaction fees, they may also find themselves at a disadvantage in these transactions.

Prop AMMs delegate pricing and risk management to professional operators. A study by Helius on Prop AMMs previously pointed out that market makers can proactively update their pricing curves based on external prices and their own strategies, concentrating funds around the current price. The lower transaction fees on Solana allow market makers to frequently adjust on-chain quotes at a lower cost, keeping up with market price changes.

Assuming the market price of SOL suddenly rises, if the market maker continues to sell at the original low price, faster traders will buy the assets. If the team adjusts the quotes in time, they have the opportunity to reduce such losses; if the team holds too much SOL, they can also adjust the buy and sell prices to lower the willingness to continue buying.

This explains the logic behind professional market makers competing for orders: the team needs to manage transaction prices, capital allocation, and position risks simultaneously. How good a price they are willing to offer traders depends on their judgment of the next transaction and the risks after execution.

However, Prop AMMs are not inherently superior to all traditional pools. Concentrated liquidity AMMs also allow LPs to adjust price ranges, and different trading pairs, order sizes, and market conditions can all affect pricing results. The trading volume of BisonFi indicates that it has secured orders, but it does not alone prove that it is cheaper in every exchange.

What Kind of Orders Does BisonFi's Business Depend On?

Aggregators allow market makers to avoid building a user entry from scratch.

Traders input amounts in Jupiter or other exchange products, and the routing system compares available paths. Market makers only need to connect and provide competitive quotes to have the opportunity to take orders. Users may never know which protocol was called in the background.

BisonFi's data reflects this relationship quite directly. According to Dune panel data, BisonFi's 30-day snapshot shows approximately $9.09 billion in trading volume, 36.54 million exchange events, and 687,500 trading addresses.

In addition to the number of addresses, the concentration of orders is also noteworthy. The top 20 trading addresses on the Dune panel contributed about 48.86% of the trading volume, with the largest address accounting for about 10.85%. The identities and strategies of these addresses have not been confirmed, but this distribution indicates that a few high-volume addresses have a significant impact on BisonFi's business.

The orders taken by BisonFi also include arbitrage trades. Arbitrageurs look for price differences between different trading pools, executing the same funds in consecutive exchanges to ultimately obtain more assets.

For example, in an on-chain transaction on October 26, a trader used Jupiter to complete exchanges in BisonFi, Scorch, and Meteora. They initially invested about 1.52405 wrapped SOL (WSOL) and ultimately received about 1.52509 SOL. After deducting the network fee for this transaction, they gained approximately 0.001 SOL. This profit comes from the price difference along the entire path and belongs to the trader, making it impossible to determine how much BisonFi earned from this.

More Choices for Traders, New Competition for Ordinary LPs

The expansion of Prop AMMs means different changes for different participants.

Traders first gain more sources of quotes. If professional teams can reduce market-making losses and reflect some advantages in their quotes, users may be able to exchange more assets for the same input amount. However, the final outcome for users still depends on the execution price, fees, and slippage at the time of execution; rankings cannot replace these comparisons.

Ordinary LPs face order competition. In the past, users could participate in market making for a trading pair by depositing coins; now, professional teams are also competing for the same batch of trades. When orders shift to Prop AMMs, the fee opportunities for existing pools may decrease. Currently, BisonFi does not have a publicly verifiable retail deposit entrance, making it impossible for ordinary users to simply replicate the "deposit assets, share market-making profits" participation method.

Aggregators play an increasingly important role in this. BisonFi obtains distribution through aggregators, eliminating the need to directly attract users, and linking its business with routing rules, access conditions, and execution performance. For market makers that heavily rely on external entries, trading volume rankings can rise, and orders may flow away with changes in competitors' quotes.

Competition among Prop AMMs is also fierce. Its competing protocol HumidiFi has almost continuously occupied the top spot in Solana DEX weekly trading volume from mid-August 2025 to mid-January 2026. BisonFi first surpassed it in January this year and now holds a leading position in the Solana Prop AMM space.

In the Prop AMM field, specialization has created an information gap. Users can view transaction records but may not fully understand the underlying pricing strategies, risk exposures, and management arrangements. Moreover, the identity of BisonFi's operating team remains undisclosed, and these information gaps increase the difficulty for users to assess risks.