2016-06-09 Meeting notes
Date
Attendees
- Bruce Skingle
- David Schachter
- Frank Tarsillo
- Johan Forsell
- Johan Sandersson
Agenda
| Time | Item | Who | Notes |
|---|---|---|---|
| 5min | Welcome | All |
|
| 15min | Implementation Update | Bruce Skingle | Update on progress of implementation of MessageML including EntiytyML within the Symphony platform Availability of the MessageML parser |
| 15min | Demo / PoC Opportunities | All | Discussion of what cross enterprise interworking might be demonstrable |
| 15min | Object Standardization | All | Try to reach agreement on final definitions of some financial objects |
Minutes
Only a few people joined the call, at least one other person had difficulty joining so there may have been a logistics problem.
Bruce announced that a version of the MessageML parser capable of supporting the new EntityML markup will be released as part of Sprint39, which has just finished development and will hopefully be released in about 2 week's time (the release date has yet to be formally announced). This will allow the API Agent to ingest messages including complex financial objects into existing non-production environments. A full implementation of MessageML throughout the system will take more time (at least 2 sprints) but this will enable us to execute a proof of concept in the immediate future.
It is intended that the source code for the MessageML parser and example usage code will be shipped as part of the S39 agent-sdk package and a pre-release version of this will be made available asap.
The Symphony team will endeavor to produce a PoC showing a message being sent via the Agent API containing a financial security object which will be rendered by an App to display a ticking price from a public market data source.
The Working Group diuscussed the possibiliy of a proof of concept involving a bot developed by FactSet and an App developed by Markit (based on the source code of the App described above). The demo would show a user typing a more or less free text query into a chat room, the FactSet bot will read the query and perform a lookup to identify a financial security and then post a message containing an Entity describing that security into ythe room. The Markit App will then render that object showing price and other data sourced from Markit.
Action items
- Former user (Deleted) to see if access to (possibly delayed or restricted) market data could be obtained for demonstration purposes.
- Former user (Deleted) to check into the handling of whitespace on a message with two hashtags
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