Articles
December 6, 2023
Originally published in FX Algo News, December 2023
In foreign exchange (FX) markets, participants are continuously seeking ways to enhance efficiency, reduce costs, and optimize their trading strategies. In particular, participants are looking to shift their execution to more sophisticated algorithmic (algo) trading strategies and away from traditional Request for Quote (RFQ) or Risk Transfer. This evolution in trading practices reflects a broader industry move towards automation and data-driven decision-making. This article aims to delve into the reasons behind the growing preference for executing using algos rather than traditional methods like RFQ or Risk Transfer.
We will explore five key considerations for market participants contemplating a switch to using FX algos:
- Reducing total execution cost with algos: Understanding how algo trading can lead to more efficient and cost-effective execution.
- Effective FX algo strategies: Evaluating the three main algo strategies that stand out as viable alternatives to traditional methods.
- Choosing algo providers: The importance of selecting the right algo providers and why limiting the choice to a few is beneficial.
- Systematic benchmarking and analytics: How a structured approach to benchmarking and analytics is vital in assessing algo trading performance.
- Considering the end-to-end impact: Understanding the broader implications of changing your execution model on overall trading operations.
Through the insights of Hilltop Walk Consulting and a detailed analysis of these points above, we can start to provide FX market participants with a guide on transitioning to algo trading, ensuring more informed decisions and successful outcomes in their trading strategies.
Reducing total execution cost with algos
Embracing larger trades for cost efficiency
A key criterion for realizing the reduced execution costs promised by algo trading is the consistent execution of FX trades significantly larger than the minimum interdealer trade size of around $1 million; trades should consistently be in excess of $10 million to $20 million. Smaller trades cannot be broken down any further by an algo and have less market impact to consider, so it is less likely that they are suited to this execution style.
Understanding the cost dynamics
The primary motivation for shifting from RFQ/Risk Transfer execution to FX algos lies in the potential for reduced execution costs. However, this doesn’t necessarily mean each trade will cost less in isolation. Transitioning to algo trading involves transferring market risk from the market-maker to the trader. This shift means that the final price of each trade executed using an FX algo depends on the fluctuations in the underlying currency price during the execution period.
The shift in risk and its implications
In traditional market-making, the provider does not know the price at which they will be able to hedge the trade. As a result, they make a profit on some client risk-transfer trades and a loss on others, incorporating a risk premium into their pricing to mitigate this uncertainty. In contrast, algo execution usually incurs a smaller, risk-free algo usage fee, as market-makers no longer need to charge a risk premium to guarantee an upfront price.
Variance in execution costs
While switching to algos is likely to reduce the expected total execution cost, it also increases the variance in execution costs between different trades. This increased variance requires a level of comfort from traders, as they must be prepared for fluctuations in costs from one trade to another. Furthermore, due to this variance, it may take a substantial number of trades and a significant time period to gather firm evidence of a reduction in execution costs.
Effective FX algo strategies
Choosing the right algo strategies is crucial for effective execution. It can be difficult to navigate the myriad of different algo providers and differently named algo implementations. However, there are really just three primary algo strategies that participants should consider as alternatives to traditional RFQ or Risk Transfer execution. Understanding these strategies and their implications is key for market participants looking to optimize their FX execution.
1. Execution Scheduling algo
This strategy is characterized by its systematic approach to executing trades over a specified period. The most common form in FX trading is the Time Weighted Average Price (TWAP), where the trader specifies the amount to be executed and the time period, aiming to match the average price throughout the execution. Variations include the Volume Weighted Average Price (VWAP), which weights the execution by the volume traded during the period, and the Percentage of Volume (PoV), where the trader specifies a percentage of the total market volume they wish to transact in the execution of the trade.
The rationale behind spreading out execution is twofold: benchmarking a trade against an average price over time instead of a single arrival price, and reducing the market impact of the trade. The duration of execution is a critical decision for the user, as a longer period may result in lower market impact but introduces more price variance due to market movements during execution.
2. Arrival Price algos
When executing via RFQ with a market-maker, a market participant locks in a cost against the Arrival Price, the mid-market price at the start of execution. Arrival Price algos aim to minimize execution cost relative to this Arrival Price, typically using an implementation shortfall model to balance the potential market impact of fast execution against the risk of price changes during slower execution.
Many Arrival Price algos adapt their strategy in real-time based on market conditions, such as accelerating execution if the market moves against the algo User or slowing down if the market moves favourably. This approach is similar to how market-makers hedge risk in risk transfer trades, making Arrival Price algos potentially a good starting point for participants transitioning from RFQ to algo trading. However, understanding the complexity of these models often leads new algo users to begin with Execution Scheduling algos.
3. Market Impact Minimisation algos
These algos focus on minimizing the market impact of a trade, striving to ensure that the market price follows its natural path as if the trade had not occurred. While this objective is appealing, these algos typically do not perform well against Arrival Price benchmarks. As these algos look to leverage supply and demand imbalances, they tend to slow down execution when the market moves against the buying currency and accelerate when it moves favourably, leading to highly variable execution speeds.
As a result, Market Impact Minimisation algos are generally suitable for sophisticated market participants who have a continuous need to buy or sell a particular currency in significant volumes.
The choice of FX algo strategy depends on the specific needs and circumstances of the market participant. While Execution Scheduling algos offer a more straightforward approach suitable for many, Arrival Price and Market Impact Minimisation algos cater to more specific needs and require a deeper understanding of market dynamics. Regardless of the choice, each strategy offers distinct advantages and plays a crucial role in the effective execution of FX trades in today’s algorithm-driven market.
Choosing algo providers: Striking the right balance
Selecting the appropriate algo providers can significantly influence the effectiveness of shifting trading to algos. Typically, algo users will benefit from having more than one algo provider, but probably not more than three.
The challenge of benchmarking and comparison
Benchmarking and comparing outcomes from different algo providers is considerably more challenging than it is for RFQ providers. This difficulty arises due to the expected variance in outcomes between different trades executed using the same FX algo. Each provider’s algorithms might perform differently under various market conditions, making direct comparisons less straightforward.
Quality of major market-maker algos
Most major FX market-makers now offer high-quality FX algo solutions, partly because they utilize these algorithms for their internal risk management. These providers typically have a deep understanding of market dynamics and access to extensive data, which enables them to develop sophisticated and effective algo solutions.
Specialized algo providers
Besides major market-makers, there are other algo providers who may have access to specific pools of liquidity or unique execution methods. These providers can offer distinct advantages, especially in niche areas or for certain types of trades. Their specialized approaches can sometimes provide superior outcomes compared to more general offerings from larger players.
Balancing fees with fit and support
While minimizing fees is a natural goal for any algo user, the fee difference between providers is often marginal compared to the benefits of choosing an algo that fits a specific use-case well. Equally important is the provider’s user support function, which can be crucial in responding to queries and issues. A provider with strong and responsive support can add significant value, potentially outweighing any fee differential.
Avoiding over-reliance on a single provider
Although most market participants will find they need fewer algo providers than market-makers for RFQ business, it is advisable not to rely solely on a single provider. Having more than one provider ensures diversification, reduces dependency risks, and can provide different perspectives and solutions for executing trades.
Systematic benchmarking and analytics in FX algo trading
A strategic approach to benchmarking and analytics is crucial for evaluating the effectiveness of algo trade executions. algo trading lends itself to a systematic methodology not just in trade execution but also in performance analysis. One of the advantages of algo trading is the vast amount of data generated, which covers both details of the trade execution and the underlying market conditions. This data, while rich, can often be overwhelming, making the extraction of actionable insights a challenge, especially given the variance in outcomes inherent to algo trading.
To navigate this complexity, it’s essential to adopt a systematic and appropriate approach for each algo execution. Using the right benchmarks tailored to the specific algorithm is a crucial step. For example, it would be ineffective to measure the performance of a Market Impact Minimisation algorithm against an Arrival Price benchmark. It’s imperative to match the benchmark to the intended function of the algo.
While most algo providers offer their own analytics, relying solely on these may not be prudent. There are inherent biases to be aware of, such as a provider effectively ‘grading their own homework’, which can skew objectivity. Moreover, when multiple providers are used, it becomes challenging to compare performance across different algos due to the lack of a standardized framework.
To mitigate these concerns, engaging a third-party benchmarking and analytics provider is often beneficial. Numerous providers offer a variety of features, with some permitting the pooling of activity with other algo users on an anonymized basis. This can significantly enhance the analytical process by enabling a comparative performance analysis across a broader data set, thus providing a more comprehensive view of an algo’s efficacy in different market scenarios.
Considering the end-to-end impact of changing your execution model
When transitioning to FX algo trading, it’s crucial to understand that the change involves much more than merely altering the execution protocol. It’s critical to consider the broader implications, and to implement a comprehensive end-to-end plan for successful adoption. This plan must define how algos will be used, including the selection of providers, the choice of algo strategies, and the determination of specific parameters such as the duration for a TWAP.
The migration requires a series of interconnected changes that go beyond the execution method itself, however. Key areas of change are:
- Counterparties and credit risk: Transitioning to algo trading might involve changing execution counterparties, which in turn affects the credit risk profile. It’s important to reassess and manage these risks appropriately in the new trading environment.
- Execution platforms: A change in trading methodology often necessitates a shift to different trading platforms that are optimized for algo trading. This could mean new software, interfaces, and possibly infrastructure.
- Role of executing trader: algo trading transforms the role of the trader. Instead of making individual trading decisions, the focus shifts towards overseeing algo strategies and ensuring their effective implementation.
- Trade affirmation and settlement: The processes for affirming and settling trades may also change. This could involve adapting to new systems or protocols that align with the systematic nature of algo trading.
Conclusion
Navigating the complexities of the transition we have been talking about can be challenging. With their deep expertise and experience in the FX market, the team at Hilltop Walk Consulting bring unique insights into the process. Allan Guild, having led FX Client algo Trading at HSBC, and James Chapman, with his background in FX Market-Making at Lucid Markets, have seen first-hand the evolving landscape of FX trading. Their boutique consultancy is dedicated to assisting clients in navigating these changes.
