The rise of FX algos and TCA: Not just for large treasuries

Published  4 MIN READ

 


Algorithmic trading has been a widely used execution strategy across liquid markets, particularly in equities and futures, for some time. Although foreign exchange (FX) algos had a slow start, adoption is now rapidly on the rise, and the fastest growing segment of FX algo users is corporations.

Since 2015, algo penetration by corporates has increased from 10% of spot volume to 28%, according to a 2017 Greenwich Associates report1. Interestingly, the strongest increase in algorithmic trading came from smaller corporations trading less than USD1bn a year2. This meaningful increase in both the penetration and proportion of algorithmic trading flow executed by corporates shows that algorithmic trading tools have benefits for corporate traders all sizes of, not only the biggest, most sophisticated treasurers.

As corporations frequently have very large, chunky trades, with the order size often pre-known by the street, algorithms offer some significant benefits to corporates too. Firstly, many algos allow the corporate trader to be anonymous, so they can slowly work their order quietly in the marketplace, without the risk of someone trading against them.

Secondly, algorithms allow the corporate trader to slice their order into smaller clips in an automated fashion and trade across multiple liquidity pools, thereby narrowing the effective spread and lowering trading costs as a result. Slicing the order, or spreading a large order over some time, allows the trader to significantly reduce the market impact of transferring risk on a large trade. Lastly, the automated nature of algorithms enables a corporate trader to handle more orders and more order flow simultaneously. Algorithms can therefore increase a trader’s productivity.

Against this backdrop, it is no wonder that corporate usage of FX algos is rising rapidly.

A trading technique to suit corporates of all sizes

As the FX market becomes increasingly fragmented and more liquidity providers offer quality pricing through streaming electronic APIs, algorithmic trading has grown – and will likely continue to grow – in popularity. The speed of trading, coupled with the number of venues and variety of order types, make it impossible for a human trader to replicate algo behaviour on just one order, let alone multiple orders to be traded simultaneously. High-performing algorithms will assess the available liquidity sources, looking at prices, depth of liquidity and rates of fulfilment, and make the appropriate routing decisions in real time.

There are a number of different types of execution algorithms to meet the needs of corporate traders. Considerations such as: how fast a trader wishes to complete an order; whether the algorithm should adhere to a specific schedule like time-weighted average price (TWAP); or whether the liquidity pool should be agency only, principal only or a hybrid of the two, to allow the trader to control how the order gets executed, even though the micro-second decisions are made by the algo engine.

In another recent study by Greenwich Associates, 58% of traders (including corporates) found that algorithms materially reduced overall trading costs. Additionally, Greenwich also found that over a quarter of FX traders believed algorithms enabled them to have more time to spend on complex orders3.

Thus, in addition to improving execution quality on an order by order basis, algorithms also provide operational benefits of increasing a trader’s efficiency to devote time to harder to trade orders, such as NDFs, swaps and/or options.

Best execution is only truly achieved when it can be proved through reporting

With the introduction of the Bank for International Settlements’ (BIS) FX Global Code, there is an increased responsibility for the investor to prove their trades were executed using best practices.

To ensure they are getting quality execution from their broker, corporates are increasing their usage of Transaction Cost Analysis (TCA). TCA allows the treasurer to easily identify both performance and routing analytics. This allows them to see, on a post trade and historical basis, how their trades performed vs certain benchmarks, and if their trades had any market impact. TCA also enables the treasurer to identify exactly where the order is being executed.

Algorithmic trading lends itself well to TCA because the entire order chain from the first child order sliced to the market to the last child fill is logged in databases and can be easily extracted in order to analyse the orders performance against different price benchmarks observed in the market. Algorithms also provide full transparency as the TCA will log each destination the algorithms sourced liquidity from, giving the treasurer comfort that their orders were executed only in liquidity pools they had approved.

Looking forward

As treasurers grow more comfortable controlling their own order flow through the use of algorithmic trading, the market will see this trend continue.

With increased pressure from the Global Code of Conduct and other regulations such as MiFiD II mandating proactive policies around best execution for other asset classes, the FX market will certainly see a significant increase in trade done via algorithms.

The benefits are clear as algorithms can most effectively source liquidity from a fragmented market, reduce trading costs and market impact, and increase a trader’s efficiency. All of which will be beneficial for treasurers – and not just those with large, sophisticated operations.

 

Curtis Pfeiffer is Chief Business Officer at Pragma Securities. Pragma is an independent provider of algorithmic trading technology for banks and brokers. Its algorithms trade billions of dollars a day in foreign exchange, North American equities and futures markets.

 

References:

1 Greenwich Associates, Long-Term Investors embrace FX Algos Q2 2017
2 Greenwich Associates, Long-Term Investors embrace FX Algos Q2 2017
3 Greenwich Associates, “The Evolution of FX Algos: From “Nice to Have” to “Need to Have” Q1 2018