We are often asked which are the first use cases for a decentralized data stack like the one we envisage at Streamr. What we know is that the list of likely use cases is long, ranging from environmental monitoring, secure chat channels, pay-by-use metering, peer-to-peer package delivery, to physical asset tracking and other location-based services.
But whilst we like to think big, we also like to focus on things of the tangible sort. One use case which is close to our hearts is quantitative asset management and algorithmic trading in particular. In that spirit, we outline below the process for building and deploying a decentralized trading system using the Streamr network and Streamr Engine, and discuss ideas for building a profitable crypto algo.
This is a topic where we can only scratch the surface at one go, and this post is the first in a series. In subsequent write-ups we’ll get our hands dirty with historical crypto data and other relevant inputs, proceeding to explore, visualize, and quantify the data and the basic statistical properties and relationships, while showing how to go about building live algos for trading and investing in cryptocurrencies.
A nose for data
In the words of a certain English detective, “It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.” (Sir Arthur Conan Doyle: A Scandal in Bohemia, pg. 163).
Data is the necessary raw material which is needed in the development and testing of investment ideas, and clearly it is something you’ll need in the eventual live deployment of your systematic trading strategies. But unless you work for a large bank or a well-funded trading operation, it is a tedious and time-consuming task to find, download, clean, synchronize, and process financial data and any other kinds of relevant inputs.
This is what we want t...