Embracing a new risk paradigm
A defining feature of risk-transfer execution (e.g. RFQ) is that the moment you agree on a rate, the market risk transfers to the liquidity provider. In contrast, algorithmic execution places that risk on your own book while the order is being filled, which could span minutes or even hours. This shift entails more than just tolerating additional market exposure – it requires rethinking how risk is measured, monitored, and mitigated.
Longer execution windows
Algos split large orders into smaller “child” orders, which get executed over a time period dependent on the selected strategy. During this extended execution window, anything from unexpected geopolitical headlines to economic data releases can move prices substantially. The key is understanding the interplay between the algo’s objectives (e.g., minimising market impact) and the desk’s broader trading objectives. Risk tolerance needs to be considered as part of evaluating the benefits of an algorithmic execution schedule.
Monitoring instead of executing
When execution is nearly instantaneous in a risk-transfer model, a key part of a trader’s job is the timing of each trade. Under an algo regime, the day-to-day role expands to overseeing partial fills, interpreting real-time metrics, and assessing liquidity conditions. Traders assume a supervisory function, adjusting parameters like urgency or limit prices if the market changes unexpectedly. Robust data and processes are required to enable swift, informed decision-making at times of market stress.
The psychological adjustment
For many desks, one of the biggest hurdles in algo adoption is the psychological transition from the certainty of a single quote, to managing evolving risk in real time. Traders accustomed to finalising a trade in seconds may find it unsettling to see partial fills accumulate gradually, with the final cost not fully known until the parent order is complete. This longer horizon requires cultivating patience, trust in the algorithm’s logic, and a willingness to tolerate fluctuations in P&L.
Traders may feel heightened accountability for day-to-day volatility, so real-time analytics become essential tools to temper uncertainty. Ultimately, the emotional shift is just as significant as the operational one – recognising that today’s slippage can be offset by tomorrow’s cost savings, provided the parameters and analytics are well-managed.
Understanding the data
One of the most radical changes that comes with algo adoption is the scale and sophistication of data you will need to process. In a risk-transfer world, relevant data points might be focused on the quoted spread and a measure of slippage. Algo execution, however, produces a flood of information across every stage of the parent and child trade lifecycle, requiring more robust data governance and analysis.
Pre-trade analytics
Before launching an algo order, many desks conduct analysis to select optimal execution strategies. Vendor platforms can aggregate street-wide data to provide a broad view of liquidity. Meanwhile, proprietary analytics might leverage internal trade history and real-time market indicators to fine-tune key algo parameters, such as aggressiveness or time-slicing intervals.
In-flight monitoring
Unlike a one-off RFQ, an algo order needs continuous oversight. Real-time dashboards track partial fills, benchmark slippage, and other performance indicators as the execution unfolds. This helps traders detect anomalies and adjust parameters accordingly. For instance, escalating slippage in a volatile market might prompt the trader to reduce the algo’s participation rate or implement tighter constraints.
Post-trade TCA
Once the order completes, transaction cost analysis (TCA) becomes more granular than under a risk-transfer model. There are now more dimensions to investigate, comparing the average fill price against established benchmarks, and further analysing the performance of child trades to understand how you got there.
Granular breakdowns: Detailed TCA can reveal precisely where each fill occurred, the average price relative to market mid, and which portion of any slippage might be attributed to volatility or specific routing logic.
Ongoing improvements: Over time, these metrics inform adjustments to algo parameters, highlight opportunities for improved routing decisions, and establish a feedback loop for continuous optimisation.
Third-party benchmarking
Independent TCA and benchmarking providers offer a broader market perspective by pooling and anonymising data from numerous participants. Engaging with these services can help you understand how your execution metrics compare to a wider dataset, and using an independent vendor rather than analytics from your liquidity providers protects against conflicts of interest.
Peer comparisons: If your average slippage for a given pair is consistently higher than the aggregated benchmark, it may indicate that your chosen algo parameters (or even your provider’s routing logic) are suboptimal.
Performance validation: Third-party benchmarks also help validate provider claims and reinforce internal governance. Demonstrating how your performance aligns with industry norms can bolster confidence in the approach.
Data infrastructure and governance
Handling the sheer volume of pre-trade, in-flight, and post-trade data reliably demands well-coordinated efforts among trading, IT, and data-science teams. Depending on how much is built in-house versus relying on vendor services, this may include databases, automated pipelines, and data-quality checks. These are often supported by specialised roles like data engineers or quants. Effective data governance ensures each data point can be captured, reconciled, and analysed effectively, meeting both operational and regulatory requirements.
By laying this data foundation, buy-side firms can transform raw execution information into actionable insights. Successful desks typically blend market understanding with the output of quantitative analysis, using both to refine ongoing execution strategies in a continuous-improvement cycle.
Evolving team structures and skills
When using algos, traders must understand quantitative metrics, interpret dashboards, and know how an algo’s parameters influence market impact. Many desks now look for trader-analyst hybrids – individuals comfortable evaluating market fundamentals, but also able to work with data-science tools. These professionals must understand the broader macro picture, market microstructure, and the data-driven logic of advanced algorithms.
Operational and cultural alignment
Introducing algo execution typically prompts a cultural shift. Traders must collaborate more directly with IT, risk management, compliance, and data teams to ensure the algorithms operate effectively and within institutional constraints. Breaking down any historical silos between these teams is essential.
Many organisations organise training programs or workshops to grow an understanding of new trading methods and associated data analysis. This might happen alongside other technical topics such as the adoption of AI in other workflows.
These changes can create an environment where execution decisions are more data-driven, requiring an organised multidisciplinary approach while still benefiting from market expertise and discretion when appropriate.
Algo provider client coverage
As you move away from pure risk-transfer, liquidity and algo providers must now differentiate on execution logic, liquidity access, and analytics.
Validating performance claims
Providers will tout their adaptive order-routing, private liquidity sources and internalisation rates. Comparing them directly is complex. You may need to run controlled pilot programs and independent TCA/benchmarking before deciding which suite of algos performs best for your specific flow.
Level of transparency
While some providers disclose the internal details of their algorithms, others operate black-box models. Balancing your need for transparency with the provider’s proprietary interests can be tricky.
Ultimately, the conversation moves from “Who has the best price?” to “Which partner’s algorithms align with our objectives and can demonstrate superior results through robust performance metrics?”.
Reconciling variance with benchmarks
Under a risk-transfer model, you know your spread at the outset. With algos, performance against a chosen benchmark (often the arrival price mid) can vary based on intraday volatility, market liquidity, and the aggressiveness of your parameters. In some instances, you will outperform that mid, especially if the market moves favourably during execution. In others, you may see slippage.
Managing this variance involves:
Refined parameters
Adjusting time-slicing intervals, strategies, or limit prices if the market becomes too volatile.
Risk tolerance
Determining whether the goal is to match a benchmark, minimise market impact, or complete the order as swiftly as possible.
Post-trade evaluation
Analysing whether the variability you encountered could have been mitigated by a more adaptive algo or a different style (e.g., liquidity-seeking vs. scheduled intervals).
Firms often discover that while variance can be uncomfortable in the short run, the net effect over many trades tends to yield more favourable execution costs than a pure risk-transfer model.
Realising the benefits of algo execution
Despite the additional complexity, many buy-side desks find the advantages compelling:
Lower and verifiable execution costs
By carefully slicing orders, controlling aggression, and leveraging multiple liquidity sources, algos often reduce total transaction costs relative to an all-in spread. Advanced TCA can quantify these cost savings, building a strong business case for continued algo use.
Increased market insight
Actively monitoring partial fills and observing where and when an algo sources liquidity can enhance the desk’s understanding of FX market structure. This knowledge can influence broader trading or hedging strategies.
More strategic use of trader time
Because the mechanics of order slicing are automated, traders can focus on higher-level tasks: risk management, strategy selection, or deeper market analysis.
Adaptive control
Algo parameters are customisable in real time, enabling traders to swiftly pivot if volatility spikes or if liquidity dries up.
Conclusions
Moving to algo-based FX execution is more than a technology swap; it is a strategic evolution that impacts people, processes, and infrastructure across the organisation. Traders must adapt to an environment in which intraday risk, extended execution horizons, and granular performance metrics become the norm and decisions are increasingly driven by data rather than instinct alone.
Key to success is recognising that algo adoption is an iterative process. Pilot programs can help teams test how various strategies perform before rolling them out at scale. At the same time, sophisticated data governance that encompasses in-flight monitoring, post-trade TCA, and third-party benchmarking should facilitate continuous improvement. This structured feedback loop allows firms to refine their execution parameters and systematically reduce costs over time.
None of this can take root, however, without strong organisational alignment. By breaking down silos between trading, IT, compliance, and data teams, buy-side desks foster a collaborative environment. Successful firms also pay close attention to liquidity provider, algo provider and vendor relationships.
In the end, the transition to FX algo trading challenges long-held practices but offers tangible rewards: more transparent pricing, finer control over execution styles, and a path to verified cost savings. For buy-side organisations willing to invest in the right talent, data capabilities, and collaborative culture, algorithmic execution can bring substantial benefits and lay foundations for future market evolution.
Hilltop Walk Consulting provides expert advisory services in financial markets. We use our deep industry experience to provide practical and effective solutions. Our team works collaboratively with clients, turning complex challenges into opportunities for enhanced performance and informed decision-making.
