Tradefeedr Launches FX Algo Forecasting Suite

Tradefeedr, the independent FX data analytics platform, has recently launched its Algo Forecasting Suite. This new suite is designed to help clients analyze and choose the most appropriate FX execution algorithms. The Algo Forecasting Suite has been developed and rigorously back-tested against Tradefeedr’s global database, resulting in industry-leading accuracy, with a mean global forecast differing from the actual result by only 0.06 basis points.

“FX Algo” is a term used to describe computer programs that use algorithms to automatically execute trades in the foreign exchange market, based on specific criteria and conditions.

The suite supports client decision-making in terms of whether to use an FX algo, expected algo behavior, and the most suitable algo given market conditions, risk appetite, time, or audit constraints. It also provides post-trade analytics compared to Tradefeedr forecasts and the opportunity cost of not using alternative execution algos.

FX Algo Forecasting is available via API and Excel, which allows clients to create their own pre- and post-trade automation. Over the coming weeks, it will be added to the Tradefeedr dashboard of services. The new service is expected to allow clients to access accurate and independent data to better inform their algo execution strategies and analyze performance after the trade. The Tradefeedr Cost Of Liquidity Score is also a key feature, allowing the analysis of algo performance across different markets and conditions on a like-for-like basis.

“The Algo Forecasting Suite allows clients to access accurate and independent data to better inform their algo execution strategies, and to analyse performance after the trade,” Tim Cartledge, Chief Data Officer at Tradefeedr has commented “At the heart of the new service, we have developed the Tradefeedr Cost Of Liquidity Score, where we have pioneered a method of collapsing volatility, liquidity provider pricing, currency pair and time of day down to a single number. This allows us to analyse algo performance across significantly different markets and conditions to ensure that comparisons are made on a like for like basis.”