THETRADETECHFX DAILY from the floor
THE TRADER OF THE FUTURE
A technologist as well as a market strategist
How important is it for trading desks to embrace TCA and collaborate with data scientists? For a trading desk, embracing TCA and deepening collaboration with data scientists is a must as I believe it’ s foundational to achieving high quality, consistent execution. TCA has evolved far beyond a post-trade compliance tool that it once was, to an intelligence engine that can help traders understand liquidity conditions, LP behaviour, venue quality in a real-time way. At iA, the objective is to integrate TCA at each stage of the trade process: pretrade, in-flight, and post-trade to ensure that insights continuously inform and refine execution decisions- using the information from TCA to feed into routing logic, algo selection and counterparty management. The goal is to work with data scientists to build this loop: cleaning and harmonising data, developing predictive signals, and therefore moving towards a data driven execution model with traders having oversight and exercising judgement. Ultimately, data scientists can help us to unlock intelligence embedded in the TCA to create actionable decisions.
When it comes to pre-trade analytics, what real difference can TCA make? There are three key areas where TCA can make a difference to pre-trade analytics: at the firm level, for portfolio managers, and traders. At a firm level, pre-trade TCA elevates the firm’ s execution framework by grounding decisions in data rather
The TRADE sits down with INDERJIT TAKK, VP, global head of trading at iA Global Asset Management, to unpack the empirical use of analytics across the foreign exchange space, including which innovations are front of mind, the importance of close collaboration between desks and data scientists, and the opportunities TCA poses for post-trade processes.
than intuition. By modelling expected transaction costs – including market impact, spreads and volatility across different execution styles, it embeds proactive cost control into the process. This strengthens governance, creates more consistent execution standards, and supports transparent conversations about trading efficiency and liquidity conditions. Elsewhere, pre-trade analytics give PMs a clearer understanding of expected performance once transaction costs account for the liquidity available to the firm. With realistic liquidity assumptions and cost estimates, PMs can better evaluate the trade-off between alpha and implementation drag, refine portfolio construction and improve projected Sharpe ratios. This leads to more informed allocation and timing decisions while reducing unexpected execution slippage. Finally, pre-trade TCA provides actionable insight before an order reaches the market. By estimating costs based on urgency, participation rates, venues and algorithms, traders can choose the most efficient execution path. Pre-trade TCA can highlight liquidity partners or ECNs that historically offer tighter spreads and lower slippage. Incorporating this information along with real-time volatility and spread-data, traders can adjust execution styles to fit prevailing market conditions.
On the post-trade side, how can the technology help to make better-informed trading decisions? Post-trade analytics enable traders to monitor implementation costs and therefore refine trading strategies and improve execution outcomes over time. Pre-trade analytics guide execution planning, while post-trade analysis measures actual results against defined benchmarks, providing the transparency and insights needed to refine future execution decisions. This enables traders to measure slippage, spread costs and market impact, leading to potentially identifying patterns of underperformance- whether it be an implementation strategy
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