Quantitative Delivery

Whilst clients demand high standards of accuracy, consistency, and timeliness, data in itself is not the ultimate goal. Our historic data has a proven track record of giving signals which help clients improve their investment decisions and lead to improved returns.

We scrutinise and enhance source announcements as we process them ensuring that the data which reaches clients is accurate and consistent with our proprietary method. This enables back-testing of multiple strategies to reach reliable conclusions.


Combining our investment grade insider data with more traditional data sets such as fundamentals, news flow, or broker estimates can lend weight to stock sentiment or identify new stock opportunities. Our clients use a variety of features within the data itself to test different hypotheses and can build their own algorithms based on conclusions from historical testing. Our operations staff are on hand to help with this by providing pointers to help with historical analysis, or delivering data derived on bespoke algorithms as required by each client.


Some data is parsed (machine read) for efficiency but production also requires source announcements to be read by analysts. This ensures that reported transactions are consistently classified and accurately processed often correcting errors within the source data itself.

Our data production process encompasses numerous checks and balances to minimise the risk of error. Some of these checks require third party data feeds (such as pricing or identifiers) with others referencing our own database of historic trades, people or companies.

We have a “black box” approach to data errors meaning we investigate every instance and make changes to avoid a repetition where possible. As such our suite of in-house production tools undergo regular updates to reflect constant improvement.

Data Use

Clients can consider numerous factors within insider data – testing strategies against our historic point-in-time dataset. Our own algorithms add data components to help identify the trades likely to indicate share price performance.

Indicative factors within the data include:

  • Insider’s position,
  • Trade size relative to insiders holding across multiple stocks
  • Analysis of historic trades to establish track record

Factors created by our own algorithms identify trades:

  • “Smart Insiders” based on track record
  • Paying up trades

If you are from a regulated institution, you can request a trial