The Hitchhiker's Guide To Dark Pools In DeFi: Part One

The Hitchhiker's Guide To Dark Pools In DeFi: Part One

After reshaping TradFi, dark pools are making inroads into DeFi. We explore the fundamentals of dark pools and and the impact on DeFi markets in this article.

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Dark pools are rapidly emerging as the next frontier of Ethereum’s decentralized finance (DeFi) sector. Dark pool designs mitigate issues like price uncertainty and poor trade privacy in onchain exchanges–issues that have made outside investors wary of DeFi, despite obvious benefits like access to 24/7 liquidity and novel yield generation mechanisms. 

In this article we provide an overview of dark pools and explore their role in traditional finance and DeFi. We further explain the mechanics of crypto-native dark pools and discuss potential roadblocks to broader adoption of onchain dark pools. 

Introduction: Dark pools in traditional finance 

Despite sounding ominous and illegal, dark pools are actually a longstanding component of the (highly regulated) traditional finance system. Below is a definition of a dark pool from Investopedia:

“A dark pool is a privately organized financial forum or exchange for trading securities. Dark pools allow institutional investors to trade without exposure until after the trade has been executed and reported. Dark pools are a type of alternative trading system (ATS) that gives certain investors the opportunity to place large orders and make trades without publicly revealing their intentions during the search for a buyer or seller.”

Dark pools are popular among institutional investors, high net worth individuals, hedge funds, mutual fund companies, and other entities that wish to execute large-scale trades anonymously. The desire to conduct trades anonymously derives from the sensitivity of market prices to perceived demand and supply (further increased by electronic trading platforms that enable near-instant responses to even weak signals). This is especially true of traditional exchanges where the orderbook is public and people can place or cancel orders at will. 

The order book in a central limit orderbook (CLOB) exchange is public. (source)

Suppose Alice places a market-sell order to sell 500 Tesla shares on an exchange. This is a small order that will have barely any impact on the price of Tesla shares offered on the exchange. However, Alice placing an order to sell 10 million shares of Tesla stock is a different thing altogether. 

In this scenario, a large sell order visible in the orderbook signals a potential drop in demand for Tesla stock. Sophisticated trading firms, particularly those utilizing high-frequency trading (HFT) algorithms, are likely to pick up on this signal. They may act quickly by selling their holdings before Alice’s order can be executed, anticipating a decline in Tesla's stock price. Consequently, the market value of Tesla shares could decrease, leading to a worse execution price for Alice. If Alice isn't leveraging advanced trading techniques, her trade might end up at a loss because the price drop occurs before her order is filled.

The problem is further complicated by the presence of HFT firms who employ proprietary algorithms capable of responding in real-time to activity in a central limit orderbook (CLOB) exchange. Here are some hypothetical scenarios:

Frontrunning 

Imagine Alice, an investor, decides to sell a large number of Tesla shares on a traditional stock exchange. If she places her sell order in the market, the details of this order, including the size and intent, become publicly visible to other participants before the trade is finalized. A sophisticated trading firm, equipped with high-speed trading algorithms, might notice this large order and quickly act on this information.

For instance, the trading firm could decide to sell its own Tesla shares before Alice’s order is executed, anticipating that her large sell order will drive down the stock’s price. By doing so, the firm locks in a higher price for its shares before the market reacts to Alice's sell-off. Once Alice’s large order is executed, the flood of shares hitting the market pushes the price lower, and the trading firm can then buy back the same stock at a discounted rate, profiting from the difference.

This practice, called frontrunning, exploits the visibility of Alice’s order to gain a financial advantage at her expense. The result for Alice is a worse execution price for her trade because the market reacts negatively before her order is completed. Frontrunning is a significant issue in traditional financial systems where orderbooks are public, allowing certain participants to act on information before others have the chance.

 

Quote fading 

Let’s continue with Alice’s example, but this time focusing on the behavior of market makers—entities that provide buy and sell quotes on an exchange. Suppose Alice’s large sell order becomes visible on the exchange’s public orderbook. A market maker initially had a standing offer to buy Tesla shares at $200 each. Upon seeing Alice’s sizable sell order, the market maker might suspect that the increased supply will cause the price of Tesla shares to drop.

To avoid purchasing the shares at $200 only to watch their value decline, the market maker quickly cancels or modifies its buy order. This action, known as quote fading, effectively removes liquidity from the market. When Alice’s sell order finally executes, there are fewer buyers left, and she has to settle for a lower price—perhaps $195 instead of $200.

Quote fading unfairly disadvantages traders like Alice by allowing liquidity providers to adjust their quotes based on insider-like knowledge of other participants' trades. Because the orderbook is public in centralized limit orderbook (CLOB) exchanges, market makers can see incoming orders in real time and react accordingly. Unfortunately, Alice has no way to prevent her trade from being affected by this practice, as it stems from the transparency of the orderbook itself.

Why dark pools?

Dark pools appeared in traditional finance as a response to the aforementioned problems. Unlike a “lit” exchange, dark pools execute trades outside of public exchanges like the NYSE (New York Stock Exchange) and Nasdaq. Orders submitted by buyers and sellers are matched directly and no one but the central operator has information about the orderbook. 

More importantly, each person trading through a dark pool is only aware of their own order(s) and the clearing price. Unless the central operator leaks information, it is impossible to know anything about other users–such as their identities and size/value of orders–even when trading assets with counterparties. 

This has several implications for people who wish to trade with minimal exposure to market fluctuations. Specifically, traders can conduct large-scale trades without telegraphing intent to buy or sell a particular stock to the public and reduce the impact of a trade on the stock market. This increases the certainty that a significant trade won’t suffer frontrunning or quote fading and the seller (or buyer) will have the best price available.

Suppose Alice decides to sell 10 billion Tesla shares in a dark pool, setting an ask price of $1 per share. The dark pool identifies and matches Alice's order with Bob's corresponding order to buy 10 billion Tesla shares at the same valuation. When the trade executes, the public remains unaware of the transaction details until after settlement. Only then does the market learn that 10 billion shares changed hands, but without knowing the identities of the buyer or seller, thereby protecting both parties' trading intentions and strategies.

We can see how trading via a dark pool protects Alice’s interests and increases quality of execution and certainty of clearing price:

  • Bob doesn’t know anything about Alice and only knows he received 10 billion Tesla shares for $10 billion, and someone received $10 billion for those shares. Bob cannot fade the quote because the orderbook is concealed—Bob must plan to actually buy shares to know someone has 10 billion shares to sell (this information is known once the order is matched).

  • Frontrunning Alice’s trade is difficult since the central operator obfuscates details of pending buy and sell orders and market liquidity. The only way Alice’s trade becomes public information is if the dark pool administrator shares the information with external parties (this is illegal, however). 

 

Today, there are dozens of pools in operation and estimates suggest that 40 percent of electronic trades are conducted via dark pools. The growing popularity of dark pools has coincided with growing regulation, especially given pool operators’ privileged access to information about pending orders (Credit Suisse and Barclays were fined a combined $150m in 2016 for leaking information about dark pool trades to external parties).

Dark pools in DeFi 

(source)

If dark pools are necessary in TradFi, they are arguably even more critical in DeFi due to the inherent transparency of blockchain systems and the challenges this creates for maintaining trade privacy and execution quality. This is especially true for decentralized exchanges (DEXes) that facilitate electronic trades and provide functionality similar to traditional exchanges. 

  • Archival nodes can query the blockchain for information about historical transactions interacting with an AMM pool and cross-reference it to onchain activity associated with a particular address. This makes it trivial for anyone to copy trading strategies employed by alpha traders.

  • The mempool which stores information about pending transactions is public and available to anyone connected to a full node. This makes DEX users susceptible to the quote fading problem where people cancel buy/sell orders in response to a large trade capable of moving the market and leads to worst-price execution for traders.

  • The post-state of a DEX can be computed trivially by anyone observing the mempool, which opens the door to malicious MEV (maximal extractable value) extraction by validators and MEV bots. These actors can observe the impact of a trade on the DEX pool and decide to frontrun or sandwich the transaction if simulating state changes reveals potential profits. (That users send transactions “in the clear” for inclusion in a block compounds the problem.)

  • The DEX trade might fail to go through if a block producer deliberately censors a user’s transaction. Since account information is publicly available, validators can build up profiles for specific addresses and choose to discriminate against those counterparties when processing transactions.

  • Validators might see information about a transaction and decide to exclude it from the next block. Users cannot obfuscate transaction details from validators or avoid disclosing intent to buy/sell tokens.