Before writing any code for the system we began with a diagram detailing how each piece would work together. We had multiple diagrams to clarify how the network would behave, how the protocol needed to be written, and how the dynamic algorithm scripts needed to be integrated.
From manual to autonomous trading, our client chose Protogenos to take his futures trading to the next level. We designed a system that he could securely login from and had support to upload custom trading algorithms; technology currently unavailable anywhere else in the market.
This project was large, but nothing our team couldn’t handle. However our client depended on us for our software expertise and they had plans to scale the system later. They requested online documentation that could be followed with step-by-step instructions to build the project from source. It includes database table schemas, high-level overview, how to launch the server correctly, and finally the Python API that allows our client to upload scripts to be used as algorithms on-the-fly.
The Python API pages of the documentation provides function names and their use. It includes example scripts for future reference. By allowing the client to upload algorithms on-the-fly, the system becomes programmable, changing the way the system learns which algorithms are best.
The Python API features retrieving orders by fill type, by instrument, PnL, available balance, trades, etc. The API allows algorithms buy, sell, and determine how many open orders exist to name a few.
This system is intricate and feature-rich. If you would like to know more about building a similar product, contact us right now.