Why Crypto Liquidity Aggregators are Crucial for Retail to Institutional Traders Alike
With only a couple of months left until the product launch, Ahmed Ismail, Founder, President & CEO of FLUID, explains why liquidity aggregators are crucial for retail to institutional traders alike in tackling one of the biggest challenges in the crypto industry – fragmented liquidity. FLUID is the low-latency digital asset liquidity aggregator delivering Best Execution by using predictive AI-based models to address fragmented liquidity in digital asset markets.
Ahmed is an ex-investment banker with 18 years of experience at some of the world’s largest financial institutions. He is an ex-Wall Street banker who worked at firms such as Bank of America and Credit Suisse, after which he became the youngest CEO for Jefferies in the Middle East. After his time in tradFi, he entered the crypto space. He co-founded HAYVN, the only fully regulated institutional OTC desk in the Middle East in Abu Dhabi, which is now a billion-dollar company.
The Biggest Problem in the Crypto Industry
Ahmed Ismail mentions liquidity fragmentation as one of the crypto industry’s biggest challenges. He says, “We all know equity markets, even the craziest ones, are pretty volatile, but nothing is as volatile as crypto, and that’s a big problem in our industry, and this is not normal. A major impact of the volatility is what’s called liquidity fragmentation. It means that liquidity on CEXes and DEXes is held in very few places. It is susceptible to bad players and price manipulation, causing flash crashes and subsequent losses in consumer investments.
Crypto liquidity is still many years behind liquidity in the traditional equities, FX, and commodities worlds and suffers from vast amounts of liquidity fragmentation, leading to high market inefficiencies such as poor price discovery, latency, volume across trading pairs, wide spreads, and siloed liquidity providers—making the industry illiquid.
Ahmed gives an example to explain why liquidity is essential. “Suppose you’re a trader and you bought cryptocurrency X. And suddenly, news comes out that the token has been compromised, and with it, the crypto exchange price plummets and sends the market into turmoil. Due to the crash, the exchange you bought it from has delisted the token, which brings us to the main question – how do you get out of that token? Where are your exits?”
“As a trader, whether you’re a retail or institutional trader, you need to find ways to scour the full breadth of the market to find buyers for that token you just bought from one place. This is where the importance of a liquidity aggregator comes in. This is why we are crucial to the ecosystem. As a liquidity aggregator across CEXes and DEXes, we provide access to all the top exchanges. Hence, you can have more time to react in a particular situation, such as cryptocurrency X.”
Best Execution
Best Execution is a legal mandate that requires brokers to seek the most favorable options to execute clients’ orders within the prevailing market environment. It is an important investor protection requirement that obligates a financial institution managing trades to exercise reasonable care when executing an order to obtain the most advantageous terms for the customer.
Unambiguously, cryptocurrency markets are complex. So big brokers, even crypto hedge funds, or banks use Best Execution, and they have to use low latency systems that require costly equipment co-located all across the globe to reduce latency.
FLUID did a study about this and derived that half a second of latency in trade creates a 2% gap risk on bitcoin. Simply put, if a user intends to purchase bitcoin, there is an inherent risk of acquiring that bitcoin and taking on a potential slippage of 2%. The most crucial goal for FLUID is providing Best Execution – making it possible for users to purchase any token in the digital asset market for the best price and the fastest time.
This is why FLUID Uses AI
While most liquidity aggregators use quant-based statistical methods, FLUID specifically uses subsets of AI, such as deep learning and machine learning, to predict behaviors of prices that are influenced by complex macro and microeconomic factors.
Machine learning and deep learning provide a multi-feature architecture, whereas quant methods are statistical methods that are only one feature. FLUID incorporates features such as sentiment analysis, Twitter, and CPI to create a hybrid AI model that predicts order books, cuts down on latency, and provides Best Execution to clients.
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