Here we list the various Jupyter notebooks we have written to illustrate how to use tcapy, in particular for understanding how to call the library programatically.
Sometimes GitHub might not render the Jupyter notebooks, in which case you can use the nbviewer link. You can also run all the Jupyter notebooks interactively in Binder. Note, for those examples which use databases/multithreading, they will not function fully in Binder. However, all other examples, which don't rely on databases/multithreading do work. First we recommend the quick guide below:
- A 10 minute view of tcapy - quick guide to doing simple TCA calculcations in Python with tcapy (nbviewer or run on Binder)
Then we'd recommend going through the more detailed examples:
- Introducing tcapy and explaining TCA - how to use tcapy in more detail and gives many examples of how to call it programmatically (nbviewer or run on Binder)
- Compliance and other more involved TCA calculations - how to do TCA calculations for compliance and other more involved use cases (nbviewer or run on Binder)
- Populating databases for tcapy - how to populate your trade/order (MySQL/SQLite/Microsoft SQL Server) and market data databases (Arctic/MongoDB/PyStore) (nbviewer or run on Binder)
- Excel/xlwings with tcapy - how to run tcapy from Excel with xlwings, with a demo spreadsheet (nbviewer or run on Binder)
- Additional benchmark and metrics for tcapy - we go through some of the benchmarks and metrics available in tcapy in more detail (nbviewer or run on Binder)
- Real life tcapy case study on an asset manager's trade data - we do a real life TCA case study on the FX trade data of a Swedish asset manager (nbviewer or run on Binder)
- Market microstructure with tcapy - generating results for spreads and volatility using tick data (nbviewer or run on Binder)