
BMLL Technology
Research platform for combining alternative datasets with order book data.
Data Broker
main data source2014
Year Company Founded- 1
Discretionary Asset Manager Customers
employees
42
client focus
Hedge Fund
sectors
Agriculture, Airlines, Autos, Financial Services, Industrials
data delivery
Platform Based
website link
Extended Dataset Description
BMLL data is composed of time series data, representing market activity for multiple assets classes. The data (orders, trades, market trading states, etc.) is provided as reconstructed Level 2 (L2) and level 3 (L3) limit order book data and static securities metadata.
Pricing: Pay-as-you-go platform
Use Cases
Use case examples:
- Signal processing – analysing snapshots of a rebuilt level 3 limit order book at varying time intervals (10, 100, 500 ms) to identify trading signals around a number of derived factors, such as volume imbalance
- Academic research – a number of academic institutions (normally sponsored by exchanges or financial institutions) running research and analysis on market microstructure. An example can be seen here
- Market impact analysis – clients can query published market impact papers against historical market data, for example “Fluctuations and response in financial markets: the subtel nature of ‘random’ price changes”, J-P Bouchaud, Y. Gefen, M. Potters and M. Wyart, Quantitative Finance 4, 176-190 (2004).
- Sweep to fill – analysis of the cost of sweep to fill orders on given futures between given date ranges
- Smart order router performance analysis – look for instances of front running on orders sent through an SOR
- Broker and venue selection analysis – creating a Level 4 data set by uploading proprietary order data and analyse TCA, slippage or best execution
Product Differentiation
- Deepest granularity of financial trade data available, directly from the sources
- Unlimited compute power in a fully managed service
- Access to multiple proprietary and third party machine learning and data science software toolkits
- <1% of the financial community are currently extracting value from this data