post-1 Seed Labs co-founders Yameen Malik, Ahmad Jawaid, Rabeel Jawaid, and Zabi Mohebzada (Seed Labs)

Seed Labs is building a high performance, low fee decentralised exchange called dTrade. The trading platform will launch with deep liquidity bolstered by some of the largest market-makers (MMs). The newly announced $22.8mm raise for the market-making fund comes from Hypersphere, Polychain, DeFiance, Alameda, CMS, Divergence, MGNR, Altonomy, and more. The capital has been committed to MMs who will provide liquidity to the orderbooks on the trading platform for various assest classes.

An Overview of How Market-Making Works

Market-makers are market participants that place both buy and sell orders for some tradable asset. MMs provide an essential service of efficient markets, allowing traders to open and exit positions smoothly at close to fair prices. In return, MMs capture the market-maker spread or the bid-ask spread from traders' activity. To illustrate how this works, let's imagine an XYZ/USD market where the fair price for the XYZ asset is 100 USD. For simplicity, let's say that the MM places the closest bid (buy order) at 99 USD per XYZ and the nearest offer (sell order) at 101

USD per XYZ. If both orders get filled by other market participants, then neglecting fees, the MM takes home a profit of 2 USD for each unit of XYZ.

This article aims to provide the intuition behind how MM can be done using two oversimplified approaches. However, it's important to note that these are generalizations, and are not mutually exclusive. In reality, all MMs have different ways of quoting markets and profiting from the bid-ask spread.

The first approach involves quoting bids and offers around a fair price on a single exchange using a statistical assumption that the demand from sellers and buyers is balanced in the long run. For that reason, we will call it a statistical approach. This approach requires modeling the markets to continuously determine the fair price and update the quotes around the fair price of an asset, such as XYZ, for example.

The second way to make markets is to replicate prices from another exchange with extra spread. For example, when the bid price for XYZ on Binance is 99 USD, and the offer price is 101 USD, the MMs would quote a bid at 98.5 USD and offer XYZ for 101.5 USD on dTrade. Whenever someone hits the 98.5 USD bid on dTrade, the MMs would immediately sell on Binance at 99 USD. The reverse is also true; whenever someone lifts the 101.5 USD offer on dTrade, the MMs buy XYZ for 101 USD on Binance. Assuming the 0.5 USD difference covers the transaction fees on the two exchanges, the MMs pocket risk-free profit and stay neutral to market movements thanks to balanced exposure on the long and short sides. For that reason, we will call this approach a hedging or delta-neutral approach. The example above is for two exchanges, but the hedging approach to making markets can be extended to many exchanges simultaneously.

Lower Spreads with Statistical Approach to Market-Making

Statistical MM relies on continuously analyzing price feeds and information from many sources to update the quoting strategy. Fed with past data, a pricing model continuously computes a fair price for an asset to layer bids and offers around it and profits from the spread. The quotes move up and down accordingly when the fair price changes. This way of market making facilitates markets without much depth in the orderbook, by reducing the bid-ask spread.

Higher Depth with Delta-Neutral Approach to Market-Making

The hedging or delta-neutral MM refers to simultaneously quoting markets on different exchanges and balancing the overall risk exposure to stay neutral to market movements. Whenever their sell order gets filled on one exchange, a MM looks to buy the same asset a little cheaper elsewhere, intending to capture the bid-ask spread. Such MMs aim to minimize exposure to market volatility and always stay protected from volatility. The significant advantage here is that hedging MMs are exposed to less risk and can quote much more depth. However, the bid-ask spreads are typically higher since MMs need to pay two maker or taker fees on the two exchanges they use for hedging. Hedging MMs are also able to quote markets with leverage more easily than statistical MMs.

Advantages

Compared to hedging MM, the advantages of statistical MM are fewer trading fees, absence of overhead costs due to moving spot inventory, lower bid-ask spread, higher uptime, resilience to volatility, lower capital requirement. In turn, compared to statistical MM, the advantages of hedging MM are less market risk, higher market depth, and that it is easier to use leverage to make markets. The hedging MM style is also conceptually easy to understand and proven effective.

Disadvantages

One downside of the hedging MM is that staying delta-neutral isn't always easy in practice. Other disadvantages are managerial costs associated with the periodic rebalancing of spot inventory, and volatility. For instance, when a lot of selling or buying occurs on one side of the market, the MM may run out of capital and have to rebalance funds between exchanges or add funds. This process takes time and limits uptime. Additionally, when prices move fast, hedging becomes hard. Hedging MM becomes more challenging during market volatility as balancing exposure becomes difficult in a rapidly falling or rising market. In such times, hedging MMs pull their quotes out entirely and leave the orderbook empty. That's why the hedging MM style has negative skew - it makes small profits most of the time but can result in infrequent, significant losses during market volatility.

On the other hand, statistical MMs models typically never stop quoting markets. Instead, when momentum picks up, the statistical approach leaves the MM with price exposure, often one-sided, a significant disadvantage. Another disadvantage is the markets are difficult to model, and profitable models don't stay profitable long.

comparing-statistical-and-hedging-market-making
Conclusion

Whereas statistical MMs provide low bid-ask spreads, hedging MMs offer high market depth. Given their complementary nature, combining both MM styles yields the most efficient markets. We believe that both approaches are valuable, and thus, we signed up both types of MMs to provide the traders on dTrade some of the most liquid markets in the world!

If you’re interested in joining Seed Labs, please head over to the careers page!

Disclaimer:

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