THETRADETECHFX DAILY
When it comes to adapting execution strategies in real time, what are the most important factors? How relevant are counterparty considerations? Counterparty considerations are certainly relevant- especially in fragmented FX markets- but the most important factor in adapting execution strategies is market conditions, as they dictate liquidity, volatility, and the risk of signalling. Our focus is on minimising footprint while capturing spread opportunities and reducing slippage versus arrival. This requires continuously balancing urgency with discretion. The first decision when approaching a transaction is whether to trade risk via RFQ or execute through algorithms. Real-time, TCA-driven insights into spreads, depth, and volatility are essential to make informed choices. We combine this information with historical post-trade analysis to generate the optimal execution strategy. When executing via algorithms, we consistently leverage bank algos. Before selecting a provider, we evaluate fill ratios, internalisation rates, and historical slippage, while also considering which algo style best suits prevailing market conditions. This ensures alignment between strategy and liquidity environment. Once an algo is running, we rely on it to adapt within defined parameters. While we generally avoid micro-managing individual trades, we monitor performance in real time and remain flexible to intervene if needed- for example, in response to unexpected slippage, fill rates, POV issues, or underperformance against benchmarks. Finally, we have incorporated the FX Global Code algo due diligence templates into our framework for onboarding and evaluating providers. This, combined with external TCA, enables us to conduct meaningful post-trade analysis and benchmark performance across counterparties. Our overall approach is adaptive, data-driven, and disciplined, ensuring consistent best execution.
Is navigating unique datasets becoming more or less of a challenge? It’ s a mixed picture. On the one hand, technology has made collecting and analysing data easier. With the help of TCA tools from banks and other external TCA providers we can now process huge volumes of trade data and counterparty fills in real time and / or post trade. On the other hand, some concepts such as order internalisation still do not always mean the same thing with every provider, which makes it hard to compare them. Each liquidity provider has slightly different data and therefore the challenge and next step will be for us from collecting data to making it actionable for execution decisions in real time. Given the nature of our business we will need to work together
‘ The ultimate goal is a self-optimising execution ecosystem’
The TRADE sits down with DARIO PONS, trader at Cardano, to unpack the most important factors when it comes to FX execution strategies, including how unique datasets are being approached, auto-execution thresholds, and the future outlook for optimisation-focused innovations.
with banks or other liquidity providers in order to achieve this. I also believe that further enhancements to the FX Global Code could help align data definitions and data transparency.
When it comes to analytics and autoexecution thresholds, what’ s the current state of play? Most people agree that some portion of the flow could be automated. This number is rising and especially under normal market circumstances I expect this number to grow further. The real challenge here is to find the optimal balance between automation and the involvement of traders. I think this summarises the current state of play today and I believe that with the help of pre- and post-trade TCA tools this decision-making model will be more and more sophisticated taking real-time market conditions and counterparty performance into account and creating a self-improving feedback loop. This should result in a framework where execution thresholds are dynamic and adjust for relevant market circumstances.
Traders will need to shift focus on picking up high-touch transactions( in broken markets) which cannot be executed automatically within the thresholds or to intervene during execution in case of changing market circumstances. The role of traders will change a bit. So, the state of play will be thresholds are becoming more adaptive, but human oversight remains essential.
What would you most like to see in future execution optimisation-focused innovations? I’ d like to see two things: first, greater transparency in data / liquidity quality- tools that give real-time insight into basic and more advanced liquidity conditions like traded volumes, volatility, spreads. But also in LP behaviour, internalisation volumes, rejections, and market impact. As mentioned before, I think the FX Global Code could have a role to play here in order to make sure those definitions mean the same thing across all venues and liquidity providers. Second, more seamless integration of analytics into execution, where feedback from TCA directly informs the next trading decision in real time taking current market circumstances into account. Ultimately, the goal is a self-optimising execution ecosystem, where strategies continuously learn and adapt across venues, counterparties, and market conditions. For sure in some degree AI could play a role here but traders need to have oversight to intervene if necessary. In conclusion, the future lies in real-time, closed-loop optimisation- TCA that doesn’ t just report post-trade but feeds directly into execution engines, creating self-learning strategies with traders challenging the system and choose from the different system driven recommendations.
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