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To offer greater diversification to investors, US-based Plimsoll Capital introduced Armada, a portfolio of automated strategies. Tom Parry, Director of Algorithmic Trading, tells AT how the FX-focused asset manager built the platform on which Armada now sails.
Plimsoll Takes the Plunge
Why did Plimsoll adopt to automated trading?
Plimsoll has been in involved in the FX market since 2002, but we only really become involved in automated trading this year with the introduction of Armada, which is our portfolio of automated high-frequency, statistical arbitrage and market-making strategies. We designed Armada to be non-correlated with Headwind, our existing discretionary strategy, so we could offer the two strategies individually or collectively to investors. By choosing to allocate to both strategies they are able to achieve additional diversification between automated and discretionary investment styles. Over 80% are institutional managed accounts and they are generally looking for pure alpha rather than a currency overlay programme.
We’ve started to make some of the FX-specific execution algorithms developed for Armada available to Headwind, as well as automating some of the trade signal generation and pattern identification processes that suggest potential trading opportunities for the Headwind programme. This new automated functionality for Headwind doesn’t actually generate a trade, but causes an order entry window to ‘pop-up’ on the portfolio manager’s screen with all the necessary order entry fields populated to make the trade and offers a choice of execution algorithms, but the final decision to take the trade is still ultimately based on the trader’s view of the market.
What has been the impact on the Headwind strategy?
This increased level of automation has greatly simplified Headwind’s workflow for Randall Durie, the portfolio manager, and enabled him to effectively monitor more potential trading signals and price patterns. For example, a number of our clients have accounts with different FX trading platforms, so previously each time he needed to execute a trade, he had to do so on a on a point-and-click basis across four or five venues’ GUIs. Obviously, this type of workflow greatly increases the probability of adverse price impact by the time you get the trade off on the last platform. Through automation we have been able to eliminate this risk and provide our clients with a more consistent level of execution regardless of where they decide to fund the account.
Randall DuRie (Principal and Portfolio Manager, Headwind), Thomas Parry (Director of Algorithmic Trading and Portfolio Manager, Armada), Daniel Bremmer (Director of Operations).
How different is developing automated trading models for FX compared with equities?
One of the biggest challenges in developing FX models is the lack of any single consolidated, data source. Ideally, the market data that you’re using to build your trading model should come from the same source as where it will ultimately be traded because of the small differences between data sources at the tick level. Although the vast majority of these differences are eliminated when data is aggregated at higher time resolutions (i.e., tick vs. 10-min OHLC bars), these differences at the tick level are both extremely significant and relevant for those developing high-frequency or statistical arbitrage types of trading models. Additionally, you should always try to use market data that comes from firm (i.e. executable) prices, as opposed to indicative prices whenever possible. Accordingly, we only use data from Currenex and HotSpotFXi, and have developed a number of proprietary algorithms that enable us to fully reconstruct the order book at a given point in time to be able to accurately estimate where we would have got filled and where we would have had to go up a couple of price levels and estimate the potential price impact of the trade. ...