Conference Dailys TRADETech Daily 2018 - Page 31

A DV E RTOR IAL Data is in the DNA of our EMS TRADETech Daily talks to Chris Hollands, head of European sales and account management at TradingScreen (TS), about the increasingly pivotal role of the EMS in the capture, aggregation and accessibility of data across the order life cycle. How does the EMS help the buy-side trader to manage the proliferation of data? Chris Hollands: As electronic trading has proliferated across the asset classes, the data requirement to inform each step of the order life cycle has increased. The role of the EMS in aggregating and consolidating data is essential across multiple categories, starting with referential data, which often can be a massive challenge especially as it relates to integrating to downstream systems supporting different symbologies, such as PMSs or OMSs and execution counterpar- ties and venues. A key advantage that our SaaS (Software- as-a-Service) infrastructure provides us with is an embedded multi-asset class product master, which means potential symbolo- gy issues, and the sourcing of the product data itself, disappear. The next category is real-time market data ranging from centrally provisioned, specialist vendor feeds, to local pricing APIs leveraging existing data sources down to IOIs, RFQs and streaming prices from liquidity providers and venues in the OTC world. Managing this complexity, and all of the associated connectivity in an efficient and cost-effective manner is a con- stantly evolving challenge but one where the right EMS can clearly provide the solution. Best execution requirements under MiFID II have extended the focus from equities into the other asset classes, such as listed derivatives, fixed income and even FX, out- side of spot. So inherently data has become a broader theme. There’s also the down- stream implications, i.e. communicating all of this order and execution data into the OMS, where under MiFID II, the buy-side’s record-keeping and reporting obligations have multiplied. relevant information regarding the place- ment, the handling and the execution of orders. This encompasses the feedback loop between the centralised dealing desk and the portfolio managers explaining exactly how and why the order is being worked. All of this data needs to be captured. The EMS is uniquely positioned to fulfil this function and to feed this information to other systems such as the OMS. What other significant changes have there been to the way data workflows are managed under MiFID II? CH: The use of broker and specialist vendor pre-, in- and post-trade analytics and their close interaction with the buy-side trader’s book of orders is driving the active pursuit of best execution. MiFID II requires the buy- side to maintain a seven-year audit trail of all Given these developments, how is TS ap- proaching the development of its offering? CH: That brings us to the whole open and broker neutral nature of our platform, our propensity to integrate to third parties and not try ourselves, to be all things to all people. We don’t profess to be specialists in some of the quantitative analytics, which are of interest to the buy-side, hence we partner. In that in-trade and post-trade sphere, how important a part does data visualisation play now? CH: With the proliferation of data, the ability to visualise it and to derive meaning from it in a timely manner becomes more critical. For in-trade analytics, configurable exception-based alerting mechanisms are becoming the norm. For post-trade, we have embedded Tableau, a market leading report- ing and data visualisation package, which syncs up with both our transaction database and our tick-by-tick database. Clients can then use these out-of-the-box tools to create their own customised TCA reports to fit their precise needs, rather than us providing standard, box-ticking type reports. Are you seeing new applications of data to help drive decision-making? CH: A current and growing trend in equities is the Algo Wheel, a best execution tool to make sure that order flow is allocated ‘appropriately’ across the selected coun- terparties and to provide a way to review that. Here the post trade execution data from Algo Wheel-generated order flow can be used to determine the selection of the future counterparties and the algo tactic(s) FV6VfW2B$Br42"RR( vFFR&ƖfW&FbFFFR&ƗGFf7VƗ6RBBFFW&fPVrg&BFVǒW"&V6W0&R7&F6( ХvR&R'FW&rFR26FRvFvVF7VVFVBƖ6RvF66'B&R&VfW'&VBT2'FW"f"fFR6&R&fFR&涖r77FVFPv&BbǗF72vRfRgVǒFVw&BЦVBvFD2FV6vW2BfbFP&W72vR&RV6VBF&RFRf'7BT0FFVw&FRF&D6ƕ6VG&&W@77FVf"U26'&FR&BG&F~( 27BЧG&FRFFFW6RFVw&F2WVW"'W6FR6ƖVG2vFFR&ƗGFWG&7B@W6RFF&V66Vǒ2FWvBFvFW@&W7G&7FrFVFRV6bW"VFW'&6RƖ6F&w&֖rFW&f6RvV'6FRvfW0'W6FW2WrvFWG&7BFFvPfR&V6VFǒV6VB$U5Bv62fW'V6BfW&Rvb'&vp&FW"FFFW"Ff&BWG&7FpWV7WFFFFW&R&Rח&Bbv2vR6V'WB7B&֖VFǒ'&VrvVVVǐ'&W"WWG&B'&VrVvRffW FR'&FW7B&vRbF2BFRЦVfW&ƗGग77VRFUG&FTWw26У3