Clean and standardize OHLCV time-series data for technical analysis.
npx skills add https://github.com/dnkrow/skill --skill ohlcv-processing-dnkrow
Please help me install this Agent Skill. Skill: ohlcv-processing Source: https://github.com/dnkrow/skill/tree/main/claude-global/ohlcv-processing Command: npx skills add https://github.com/dnkrow/skill --skill ohlcv-processing-dnkrow
This Skill eliminates broken or inconsistent OHLCV candle data that causes misleading indicators, noisy anomaly flags, and invalid resampling results.
Run the end-to-end pipeline in demo mode to generate synthetic OHLCV with injected anomalies and produce cleaned, resampled, and normalized output.
High-intent search queries and answers about installing and using this skill.
To clean and standardize OHLCV time-series data, you must enforce a UTC DatetimeIndex, coerce OHLCV column types, deduplicate timestamps, and detect anomalies like spikes or impossible candles. This produces consistent columns for reliable downstream indicator computation.
Merging OHLCV data from two sources requires timestamp alignment and conflict resolution, typically by preferring higher-volume bars. This resolves provider discrepancies and returns provenance via a source field for reliable dataset tracking.
Resampling fine-grained OHLCV bars into coarser timeframes requires OHLCV-aware aggregation rules to maintain candle integrity. This ensures accurate high, low, open, and close values while consolidating volume data for higher-timeframe analysis.
Handling missing bars in OHLCV time-series data involves detecting gaps and filling them using forward-fill or time interpolation with max-gap limits. This generates a continuous dataset and produces a quality summary for review.
Yes, this OHLCV data processing approach works with pandas and numpy. It leverages these dependencies to validate schemas, detect anomalies, normalize prices using returns or z-scores, and prepare datasets for technical analysis workflows.
OHLCV data needs anomaly detection before calculating technical indicators because broken or inconsistent candle data causes misleading indicators, noisy anomaly flags, and invalid resampling results. Detecting zero-volume bars, negative prices, and NaNs ensures reliable analysis.
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