Tim Cartledge, Chief Data Officer at Tradefeedr:
Tim Cartledge
Following extensive consultation with the industry, our unique algo forecasting suite is now live and in use by both algo clients and providers alike. We have developed two products which use our forecasting tool, one for pre-trade forecasting and the other for post-trade. Both differ considerably from existing pre- and post-trade analysis tools in their ability to compare for the first time individual algo performance against the market average.
Our product suite is available as an API and compares actual trades and shows a forecast of what the performance would have been for the currency pair at that time and in those same market conditions. This enables algo users to compare their execution performance to see if it was in line with the rest of the industry, or if their performance was better or worse, faster or slower, more risk or less.
On the pre-trade side, algo users are also able to forecast what the expected performance would be for their execution. It can look at a particular currency pair in a particular size and compare the expected performance for different levels of aggression for the algo. That’s available now as an API and we will also be releasing an interactive front end in the coming weeks.
The tool which has already been live for some time – and which Deutsche Bank has been working with – is the post-trade tool. This allows you to look at your actual trades and compare execution performance with the average expectation for those same conditions. We produce three headline numbers: Tradefeedr Global Forecast, which is based on all of the algo trades in our database; Tradefeedr Fast, which is based on the fastest third of algos in the market, and Tradefeedr slow, which is based on the slowest third of algos in the market.
What is proving valuable to algo providers, such as Deutsche Bank, is that the tool can also forecast the performance of individual bank algos as well. Using the same methodology, the forecast can look at the expected performance of individual algos and compare those to the market. What we are doing is complimentary to the work of the banks, who can now combine the insights from our forecasting tools and provide additional background around the algos, flows, market conditions etc to help clients achieve their execution goals.
The really novel element to our service is the standardisation of the algo executions. Not all algo executions are the same depending on currency pair, time of execution, market volatility etc. This is really the first time clients are able to compare algos using a level playing field to show what the algo execution would look like indifferent conditions. It takes the noise out of the process and allows for really fair comparisons.
The modelling we do is based on the liquidity seeking algos in the market, which are allowed to run at the natural speed of the market. Even if you’re manually trading, you now have this database which shows what the market response was when the liquidity seeking algos tried to do a certain amount in a currency pair and you now also know how long it took them. That’s really useful information about market impact and fundamental market behaviour, which feeds into the FX sales teams and the advisory work they do with their clients. We can also see that most clients who use a certain algo are having good experience with that algo, but they will still need to be in communication with the algo provider to get the best out of their performance.
Vittorio Nuti, Global Head FX Algos at Deutsche Bank:
Vittorio Nuti
We are very excited by the Tradefeedr algo forecasting tools as they offer a more pragmatic and quantitative approach to analysing algo performance. The tools provide clients with a holistic view of the algo market, they allow clients to view different algos and compare their strengths and weaknesses. Here on the FX team, we very much appreciate what the tools have to offer as they have made it much easier to talk about the algo performance data with our clients. We are able to use the data to help our clients to try and improve their execution performance based on real results. Having it based on independent data allows the client to compare the performance of an algo, instead of having to judge based only on their experience of what worked well or not. At Deutsche Bank, we are able to demonstrate to clients using this service that our algos perform well above the global average, both in terms of speed and lower cost of execution. The tools are based on independent analysis, so this provides us with an objective way to discuss the performance of an algo with clients as they are able to compare the execution to the rest of the market. The algo data in Tradefeedr represents our standardised settings. Once clients can see that performance, we can also discuss how these can be customised to their execution targets, such as more passive or slower execution versus our standard set.
Tradefeedr have collected the data and aggregated it into two matrices, which simplifies this very complex data in a way that the user can readily understand. This quantitative approach has resulted in a very robust, powerful tool for algo users which will add to their understanding of algos and further enhance their execution performance.
