A liquidity provider has many responsibilities; one of them is to help their customers validate that they are compliant with best execution. Another one is to analyze order books to advise their customers, for example, about their order book spread or which trading opportunities they may miss.
Best execution ensures you conform to the auction rules of the trading you are participating in (for example, sell to the highest bidder). The order book is the list of buys and sells organized by price levels.
The challenge to adequately cater to the needs of your customer for a liquidity provider are multiple.
As order data comes in, rebuilding the order book takes longer and longer since there are more orders to go through. This creates pressure on the data system, especially during peak activity. When doing end-of-day analysis, building the complete order book means going through all the day’s orders.
However, building the order book is half the battle since the liquidity provider needs to estimate the order book spread to see if a participant could improve their trading with a tighter book.
Connecting the order book with the relationship matrix (who may trade with whom) helps to advise users regarding what opportunity they would have if they expanded the breadth of actors they are trading width.
In addition to the order book, participants need to ensure they are complying with best execution. For example, ensuring they are selling to the highest bidder. This is done by matching a trade with orders available at that time.
These specialized processes are complicated to do with a regular database management system and often require specialized tools or programs that lack flexibility and generally result in an expensive, complex toolchain.
Quasar is connected directly to the market data provider and captures the data without a third-party tool. Capture is done with virtually no latency.
The data is stored, compressed, and managed by Quasar. Information is organized in timeseries and is tagged in a way that makes queries easy and convenient. This approach is future-proof and allows you to change your mind “whenever you want” without touching the data itself.
Relationship matrixes are also ingested in the database as they get updated.
Queries use a SQL interface, dedicated API, or one of the numerous connectors available. For example, traders can load data directly into Tableau using the Quasar ODBC driver. Quants can load timeseries directly into Python Pandas using the Quasar Pandas API.
Quasar has a built-in order book computation module that enables sub-second order book rebuilding at any time of the day, even for deep order books. These order books are then accessible from a web browser using a dedicated dashboard built using the Quasar framework.
Quasar can also compute the top and the spread of any order book (for the whole pool or a given actor). From there, evaluating the spread of a specific actor relative market activities is a straightforward task and allows to provide high-value advice to a customer.
Using the dashboard, the liquidity provider can also see if a given actor order book would be matched by another actor they are currently not trading with and thus
Lastly, best execution can be done using advanced joins available in Quasar (ASOF JOINS) that instantly match a trade with an order at a given time.