ohlcv-processing

What problem does it solve?

This Skill addresses the critical need for clean, consistent OHLCV (Open, High, Low, Close, Volume) data, which is often messy and unreliable in financial markets, especially crypto.

Core Features & Use Cases

• Data Validation: Checks for structural integrity, correct data types, and valid price ranges.
• Anomaly Detection & Handling: Identifies and corrects issues like price spikes, zero volume, impossible candles, and negative prices.
• Gap Filling: Imputes missing data points using forward fill or interpolation.
• Resampling: Aggregates data into coarser timeframes (e.g., 1-minute to 1-hour).
• Normalization: Scales price data using methods like percentage returns or z-scores.
• Multi-Source Merging: Combines data from different providers, prioritizing higher volume.
• Use Case: Prepare raw tick data from a crypto exchange for backtesting a trading strategy by cleaning anomalies, filling gaps, and resampling to hourly bars.

Quick Start

Run the full OHLCV processing pipeline on your DataFrame my_ohlcv_data and resample it to 1-hour bars.