Software is at the centre of what Cuemacro does. We use our data analytics frameworks for our quantitative research of markets. We have already developed a large number of open source libraries and proprietary libraries in Python which are available to clients. We can be commissioned by clients to write data analytics software customised to their use cases, whether that involves backtesting trading strategies, transaction cost analysis, natural language processing etc.

Open Source Libraries

Saeed Amen has written several open source libraries in Python designed for finance. Nearly 5 years ago, Saeed released the pythalesians library. In summer 2016, Saeed split up the library into several more specialised modules, with a rewritten and easier to use API. finmarketpy has nearly 2000 stars on GitHub and has been forked over 300 times. It is now the second most popular Python trading library on GitHub, measured by number of stars. Cuemacro’s proprietary analytics and trading strategies are built on top of these open source libraries. Cuemacro can also be contracted to add new features to these libraries on request.


  • finmarketpy – backtest trading strategies and analyse financial markets
  • findatapy – download market data from multiple sources using a simple unified interface
  • chartpy – create beautiful visualisations using matplotlib, bokeh and plotly using an easy to use API

Proprietary Libraries

Cuemacro has also written a number of proprietary market analytics libraries in Python, which are available to clients for an annual licence. Clients get 100% of the source code for true transparency and are free to modify the code as they want. For more details on these proprietary libraries please contact


  • tcapy – effortlessly perform transaction cost analysis for FX spot in Python, with web GUI and advanced features like parallelisation and caching
  • gammatime – advanced Python modelling library for FX vol traders with a proprietary model to help forecast FX implied volatility combined with an Excel add-in
  • finaddpy – efficiently cache market data
  • fincurvepy – easily construct continuous futures time series from individual contracts