One-minute OHLCV data gives analysts a detailed view of price and volume behaviour inside each trading session. This guide presents a practical Python workflow.
1. Inspect the Schema
Confirm DateTime, Open, High, Low, Close, and Volume. Parse timestamps, sort chronologically, and remove duplicate timestamps before calculating indicators.
2. Validate Sessions
Check timezone, trading calendars, early closes, market holidays, and gaps caused by trading halts. Investigate missing minutes instead of filling them automatically.
3. Create Causal Features
Useful starting features include lagged returns, rolling volatility, candle range, and relative volume. Every feature at time t must use only information available at or before time t.
4. Avoid Leakage
Split data chronologically and include fees, spread, latency, and slippage. Visualize returns and missing observations before modeling.
Read our price data guide or browse US stock datasets.
For research and education only; not investment advice.