Algorithmic trading - Examples and tutorials | Databento Docs

Algorithmic trading

A high-frequency liquidity-taking strategy

Implement simulated trading (paper trading) using live data.

Build prediction models with machine learning

Construct alpha signals using Databento and regression models from sklearn.

Execution slippage and markouts

Plot markout curves for transaction cost analysis and measuring execution quality.

Matching engine latencies

Measure matching engine and feed latency with nanosecond, PTP timestamps.

Using messaging rates as a proxy for implied volatility

Determine and compare implied volatility (IV) and messaging rates.

Mean reversion and portfolio optimization

Fetch cross-sectional returns. Build a mean reversion strategy and optimal portfolios.

Pairs trading based on cointegration

A cross-venue pairs trading strategy between WTI (CME) and Brent (ICE) crude.

Build a real-time stock screener

Build a live stock screener for all U.S. equity tickers.