yf-data

Collect, normalize, and validate OHLCV market data from yfinance.

1|Updated Jan 14, 2026
One-click install
npx skills add https://github.com/phanijapps/zbot --skill yf-data
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: yf-data
Source: https://github.com/phanijapps/zbot/tree/main/gateway/templates/skills/yf-data
Command: npx skills add https://github.com/phanijapps/zbot --skill yf-data

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Collects, normalizes, and validates market data directly from yfinance to ensure reliable datasets for downstream analysis.

Core Features & Use Cases

  • Fetch OHLCV data for stocks, ETFs, indices, forex, crypto, and futures using yfinance.
  • Batch multi-symbol downloads with timezone-safe alignment to UTC.
  • Normalize columns to a consistent schema (open/high/low/close/adj_close/volume) and flag data quality issues.
  • Use cases include feeding historical price data into analytics pipelines, backtesting, and risk modeling.

Quick Start

Fetch OHLCV data for your target symbols using the yf-data skill.

Frequently Asked Questions about yf-data

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

FAQPage Schema
How do I fetch OHLCV data from yfinance for multiple symbols?▼

Batch multi-symbol OHLCV downloads from yfinance normalize columns to open, high, low, close, adj_close, and volume. Timestamps are aligned to UTC, and data quality issues are flagged for downstream analytics pipelines.

What asset classes does yfinance OHLCV data collection support?▼

OHLCV data collection supports stocks, ETFs, indices, forex, crypto, and futures. The process validates symbols, uses yf.download with a history fallback, and normalizes results into a consistent schema for analytics pipelines.

How does timezone normalization work for yfinance market data?▼

Timezone normalization aligns yfinance market data timestamps to UTC to prevent misaligned datasets across multiple symbols. This timezone-safe alignment ensures consistent chronological ordering for downstream analytics, backtesting, and risk modeling.

Can I use normalized yfinance data directly for backtesting and risk modeling?▼

Normalized yfinance data is designed for backtesting and risk modeling. The skill outputs a consistent OHLCV schema with adj_close and reports data-quality issues, ensuring reliable datasets for downstream analytics pipelines.

What are the limitations of using yf.download for historical price data?▼

yf.download is the primary fetch method for historical price data with a history fallback for reliability. Limitations include potential data-quality issues, which the skill validates and reports rather than silently ignoring during multi-symbol batching.