📄️ Introduction
With infrastructure in place for developing and deploying software, the next step is building financial platform components. This documentation guides you through market data ingestion systems and backend architecture.
📄️ Session Management
Trading sessions can more or less be distilled down to trading hours or times when traders will trade.
📄️ Symbol Management
Each exchange provides different methods for querying available symbols—some via WebSockets and others via REST protocols. To manage symbols across multiple exchanges, create a service that periodically queries available instruments and stores them in a database. This approach enables selective inclusion or exclusion of specific symbols.
📄️ Ticker
Introduction
📄️ Candlestick
Candlesticks are generated from quote and trade data. The typical approach involves reading from NATS pub/sub, maintaining forming candles in memory, and persisting completed candles to QuestDB.
📄️ 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.
📄️ Data API
Create an API that runs in Kubernetes to query QuestDB for candlesticks. Several API frameworks can be used for this purpose, such as Hapi, Express, or others based on project requirements.
📄️ Python
Python is widely used in finance and is well-suited for data analysis. Jupyter Notebook is recommended for interactive data analysis and exploration.