📄️ Introduction
Now that we have a platform where we can develop software and deploy it, we can start looking it how to build a financial platform. I can't teach you how these are built but I think we can try our best.
📄️ Session Management
Trading sessions can more or less be distilled down to trading hours or times when traders will trade.
📄️ Ticker Plant
The idea of a ticker plant is being able to subscribe to ticker feeds and be able to aggregate that data into databases that can be useful. Now there are key items that you must consider when building a ticker plant.
📄️ Data API
For simplicity, we can create an api that runs in Kubernetes that queries QuestDB for candlesticks. For this, I'm using Hapi. You are free to use Express or whichever API framework you like.
📄️ Back Filling Data
Backfilling is the process of querying API's or other methods to retrieve historical candlesticks. Every liquidity firm has it's own API and they are largely different. Some will send you json messages that are easily readible and others will send you lists of strings and numbers. It's not consistent for sure but can be done.
📄️ Python
Python is everywhere in finance and it would suit you to use it in your data analysis. I recommend installing the Jupyter Notebook so you can do you data science.