When comes to trading, centralized exchange rule the crypto market. Based on some of the work we have done at IntoTheBlock, we believe around 40% of the daily volume in major cryptocurrencies is processed through centralized exchanges. Sadly, centralized exchanges are also one of the most difficult components to analyze in the crypto space and a regular source of poor/fake data.
However, if we are able to understand the behavior of crypto exchanges, they can become one of the most powerful and unique sources of intelligence for crypto assets.
At IntoTheBlock, we have been doing some creative machine learning work to understand the behavior of crypto exchanges. Our machine learning models are able to effectively identify the key elements of a crypto exchange and understanding their behavior.
That work has been fascinating from an intellectual standpoint but its rather a foundational element of a market intelligence strategy rather than a source of intelligence by itself.
By understanding the characteristics of centralized exchanges, we can develop unique signals and indicators that are optimized for crypto assets and have no equivalent in other asset classes.
Centralized crypto exchanges are both an element of obfuscation but also a unique source of intelligence for crypto assets. The centralized architecture of exchange hide a lot decouple their order book activities from the underlying blockchains.
From that perspective, order book metrics are a more accurate source of intelligence to detect real time patterns in daily trading activity.
However, we now know that we can use machine learning methods to understand the internal topology and patterns of centralized crypto exchanges.
Combining the order book and blockchain data records of centralized crypto exchanges provide a very unique source of intelligence.
Think about it, how much would investor pay to obtain relevant data from the fund flow activity related to exchanges such as NASDAQ or NYSE? Using a data-first approach to understand the behavior of centralized crypto exchanges leads us on two complementary directions:
i. Order Book Data Fees: Contain relevant patterns about the behavior of traders in a specific exchange.
ii. Blockchain Data Fees: Provide relevant intelligence of flow of funds in and out of exchanges as well as complementary metrics.
While the first element is common in other type of public market exchanges, the latter is very unique to crypto. If anything, monitoring the blockchain footprint of exchanges has some similarities with the analytic techniques that are often used in dark pools to track the activity of individual investors. Combining order book and blockchain analytics provide an incredible source of intelligence about the behavior of crypto exchanges. Before we get into specific metrics or indicators, let’s do a quick recap about the architecture of centralized crypto exchanges.
In previous posts, we’ve discussed some of the internals components of the architecture of centralized crypto exchanges. In essence, there are four key components that are relevant in the behavior of centralized crypto exchanges:
· Hot Wallets: Hot wallets are typically the main interaction point between external parties and an exchange. Exchanges use this type of wallets to make an asset available to trade.
· Cold Wallets: Exchanges use cold wallets as a secured storage of crypto-assets. This type of wallets typically hold larger amounts of assets that are not intended to be traded frequently.
· Deposit Addresses: Deposit addresses are, often temporary, on-chain addresses used to transfer funds into an exchange. The focus of this type of address is to facilitate user to exchange money flows.
· Withdrawal Addresses: Withdrawal addresses are, often temporary, on-chain addresses that are used to transfer funds out of the main exchange wallet. Sometimes withdrawal addresses can play a dual role as deposit addresses.
The interaction with public blockchains differentiate the behavior of centralized crypto exchanges from any other type of financial exchange in public markets. The combination of order book metrics and blockchain analysis yields very unique sources of intelligent that describe unique patterns of crypto assets. Here are some interesting ideas:
· Inflow-Outflow Volumes: Calculating the volume of inflows and outflows provides relevant metrics that could serve as predictors for large positions on a specific crypto assets. IntoTheBlock soon-to-be-available Inflows-Outflows signals tracks this trend very effectively.
· Exchange Liquidity: The difference between inflows and outflows provides a relevant sign of available liquidity for specific exchanges. Liquidity over time is an important measure of the ability of an exchange to fulfill orders.
· Cold Wallet Balances: Tracking the balances in cold wallets is also another measure of relevant liquidity as well as the proof of funds of specific exchanges.
· Exchange Liquidity Risk: Correlating the liquidity measure of an exchange with the recurrent spreads between bid and asks in order book is a relevant measure to track risks. Large liquidity levels indicate the ability of an exchange to fulfill orders versus potential levels of risks.
· Order-Book-Volume-Volatility Correlation: There is a well-documented relationship between the volume of a specific order book and volatility fluctuations. The main theoretical explanation for the relation is that the arrival of new information makes prices adjust to new equilibria over time.
· Token Listing Predictions: Large transfers about specific tokens to exchange deposit addresses could be a signal of a potential listing in the exchange. Listings typically result in short term spikes of a given token.
· Whale Activity Monitoring: Monitoring transfers from large token holder into exchange deposit addresses could.
· Smart Price Momentum: Smart price is a well established metrics of order book analytics that measures the spread between the bid and the asks over time correlated with the inverse volume of bid and ask orders.
· Deposit-Withdrawal Address Balance Momentum: An indicator that measures the ration on the balance of deposits and withdrawal addresses over time is a nice complement to the exchange inflow and outflow relationship and can measure the sentiment of investors in an exchange.
These are just some ideas of new indicators that can help to provide objective insights about the behavior of crypto investors in centralized crypto exchanges.
The ability to understand the blockchain architecture of an exchange provides a unique source of information to extract relevant patterns about the behavior of crypto investors as a group.
More importantly, centralized crypto exchange datasets serve as a natural complement to order book fees providing a cycle of information that has no equivalent in any other asset class.
(Disclaimer: The Author is the CTO at IntoTheBlock)