Databento

Define the asset universe, schema, and date range in the backtesting environment.

Fetch historical Market By Order (MBO) data via the Python SDK.

Parse the returned data into pandas DataFrames or proprietary backtesting engines.

Run the simulation to generate performance metrics and risk profiles.

Initialize a WebSocket connection securely with the Databento API key.

Subscribe to specific ticker symbols and desired schemas (e.g., trades, quotes).

Implement an event loop to process incoming tick data continuously.

Trigger execution logic and send orders to a broker based on live market signals.

Create a Databento account using a verified academic email address.

Use the web portal interface to select the required datasets and timeframes.

Download the data in CSV or Parquet format for local processing.

Import the data into analytical software like R or Python for statistical analysis.

Schedule a daily cron job to securely pull historical market snapshots.

Request aggregate OHLCV data for all portfolio constituents.

Feed the normalized data into the firm's proprietary risk models.

Generate automated risk exposure reports for compliance and management.

Set up a backend service to interface directly with the Databento API.

Fetch delayed or real-time data based on the application's licensing tier.

Cache the data via Redis to minimize redundant API calls and lower costs.

Serve the formatted data to the frontend via a RESTful API or GraphQL.

Query years of historical limit order book data from Databento.

Stream the data in DBN format for ultra-fast disk ingestion.

Extract key features (e.g., spread, order imbalance) using PyTorch or TensorFlow data loaders.

Train, validate, and tune the predictive models iteratively.