skills.rest
agiprolabs
agiprolabsCOMMUNITY
Explore all skills by this author →
GitHub

ohlcv-processing

Validate, clean, impute, resample, and normalize OHLCV market data.

266|54|Updated Mar 11, 2026
Search Tags:#time series#data cleaning#market data#anomaly detection#ohlcv#resampling
One-click install
npx skills add https://github.com/agiprolabs/claude-trading-skills --skill ohlcv-processing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ohlcv-processing
Source: https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/ohlcv-processing
Command: npx skills add https://github.com/agiprolabs/claude-trading-skills --skill ohlcv-processing

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, httpx, and includes scripts (resource) and references (resource) components.

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.

Frequently Asked Questions about ohlcv-processing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I clean OHLCV market data for backtesting?▼

To clean OHLCV market data for backtesting, you validate dataframe structure, detect anomalies like impossible candles, impute missing gaps, and resample timeframes to produce consistent, reliable price series.

What is the best way to handle missing data gaps in crypto time series?▼

Handling missing data gaps in crypto time series involves imputation techniques like forward fill or interpolation. This maintains temporal continuity and ensures accurate technical analysis across your dataset.

How do I resample 1-minute OHLCV bars to hourly timeframes using pandas?▼

Resampling 1-minute OHLCV bars to hourly timeframes aggregates open, high, low, close, and volume fields into coarser intervals. This standardizes market data for broader analytical perspectives.

Why does my market data have price spikes and zero volume anomalies?▼

Price spikes and zero volume anomalies in market data stem from exchange glitches or thin liquidity. Detecting and correcting these impossible candles ensures data integrity for reliable financial analysis.

Can I merge multi-source market data and prioritize higher volume?▼

Yes, you can merge multi-source market data by prioritizing higher volume providers. Combining datasets from different exchanges resolves discrepancies and creates a unified, robust historical record.

How do I normalize price series using percentage returns or z-scores?▼

Normalizing price series scales data using percentage returns or z-scores to achieve stationary statistical properties. This transformation allows direct comparison between different assets and timeframes.

Related Skills

Explore More Data & Analytics Skills→
charliedream1charliedream1
6.1k

smc

Generate trading signals from OHLCV data using ICT Smart Money Concepts.

Community
Advanced
20YN0420YN04

candlestick

Detects 15 classic candlestick patterns and generates composite trading signals from OHLCV data.

Community
Intermediate
Synter-Media-AISynter-Media-AI

anomaly-detector

Detect anomalies in advertising campaign metrics using Z-score and IQR methods.

Official
Advanced
teradata-labsteradata-labs
7

td-resample

Resample and interpolate Teradata time series data using TD_RESAMPLE.

Official
Advanced