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Quantify the market , contextualise the trade Berenberg ’ s Data Science and Algorithmic Engineering teams draw on a multitude of sources , including partnering with BMLL , leveraging their Level 3 data to layer market event datasets with client trade data by linking unique order IDs via matching engine timestamps . Level 3 data draws insight into cancellations , amends , quote submissions , order queue details and unfilled orders . This allows Berenberg ’ s TIA to deliver a robust A | B testing environment across 5 years of market and trade data to efficiently optimise algorithmic performance .
Elias Hatchuel , lead architect of TIA , states , “ The grouping of these extensive datasets into buckets based on provenance ( negative or positive alpha , trader behaviour , etc ) or numerical computation ( slippage , % ADV ), relative to trading outcomes , is where trade outperformance and algorithmic differentiation is truly realised .”
Separating trader contribution from algo performance Traditional TCA has often struggled with complex orders , iceberg detection and multi-day trades resulting in an under appreciation of the value the buy-side trader can add on discretionary order flow through tactical parent order management . Very few platforms can successfully map the multi-day parent-to-child order relationship . This vacuum produces inconclusive outcomes leading to statistically significant datasets being excluded from the execution analysis process .
TIA intuitively ties together information at the parent and amend level to encapsulate a multitude of workflows . When
Jason Rand , global head of electronic trading and distribution , Berenberg
a trader makes an amendment , irrespective of its nature ( limits , urgency levels , part rates , strategy changes ), a synthetic order is generated , benchmarked , and linked as a separate order all whilst being tied back to the overall parent .
“ Being able to systematically quantify and separate the trader ’ s contribution from the algo ’ s performance at each amend allows the buy-side trader to measure the value of their decisions and the effectiveness of the strategy both independently and collectively . It also provides visibility and true attribution to slippage ,” states Rand .
The relationship between alpha profiling & short-term price dynamics In addition to order clustering , one of TIA ’ s most notable practical applications is its ability to identify alpha profiles . Alpha profiling provides traders with the ability to break down trading outcomes in their simplest forms ; positive alpha ( momentum against ), negative alpha ( value for ), or no alpha ( neutral ).
Whether it ’ s by winning versus losing trades , buckets of ADV , or short-term price signals , applying alpha curves enable a continuous data feedback loop between TIA ’ s predictive analytics , Berenberg ’ s algorithmic engine ( Genesis ), and the SOR ’ s decision-making process .
Alpha curves identify price action in concise time horizons using set intervals around the order lifecycle . They succinctly highlight essential market dynamics and reinforce the iterative learning and balancing process between timing risk and liquidity capture .
TIA can generate alpha curves by a seemingly infinite number of factors , including algo wheel buckets , fund managers , traders , seasonality , or volatility regimes . Accounting for pre-trade momentum , delay cost , interval movement / impact , and price action from last fill to close are all essential in establishing the optimal rate of trading .
Conclusion The partnership between Berenberg and BMLL is about democratising access to Europe ’ s most comprehensive analytics engines and largest data lakes . Overlaying millions of child orders on top of billions of market data points no longer requires an army of quantitative researchers or data engineers ; it ’ s encapsulated in TIA . “ By sitting side-by-side with our proprietary algorithmic offering , TIA enables Berenberg and its clients to compete at the highest levels of electronic trading . This isn ’ t ‘ AI / ML as a service ’ or jargonistic terminology , it ’ s far simpler than that . It ’ s about mapping complex relationships , standardising benchmarks , and giving all market participants , large and small , the chance to produce better outcomes for their clients ,” says Rand .
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