Why use Alpaca over yfinance for crypto data?

Alpaca provides cleaner data for crypto, whereas yfinance often contains zero-volume bars and gaps that can break training models.

Does this work with the Alpaca free tier?

While the code structure is similar, the free tier limits you to approximately 2 years of history. This skill is specifically optimized for the Algo Trader Plus tier features.

How much historical data can I access with this skill?

With the Alpaca Algo Trader Plus subscription, you can access over 5 years of historical data, though 4 years (1460 days) is the recommended 'sweet spot' for most symbols.

What timeframe is best for 4-year historical training?

The skill recommends a 1-hour timeframe (1Hour) as a base for balancing data granularity with model stability over long durations.

Is caching required for this skill?

Yes, aggressive caching is highly recommended because fetching 4+ years of intraday data is time-consuming and can hit API rate limits or cause timeouts.

Alpaca Algo Trader Plus Data Connector

Name: Alpaca Algo Trader Plus Data Connector
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Leverages the Alpaca Algo Trader Plus subscription to fetch extended historical market data for training robust trading models.

About

This skill optimizes Claude's ability to handle financial data extraction and model training by integrating with the Alpaca Algo Trader Plus API. It enables the acquisition of over four years of historical OHLCV data, bypassing the standard two-year limit found in free tiers. The skill provides verified workflows for data fetching, aggressive caching strategies for large datasets, and specific configurations to ensure data integrity for both equities and crypto. It is an essential utility for developers building production-grade algorithmic trading systems that require high-quality, long-term historical bars.

Key Features

Comparative data source validation between Alpaca and yfinance
Optimized DataFetcher configuration for 1460-day lookback periods
0 GitHub stars
Pre-configured error handling for symbol delistings and API timeouts
Automated caching strategies to handle large financial datasets efficiently
Extended historical data access for 5+ years of equity and crypto history

Use Cases

Backtesting trading algorithms across multiple years of intraday bars
Training machine learning models on long-term historical market trends
Migrating from free-tier data sources to professional-grade Alpaca subscriptions

About

Key Features

Comparative data source validation between Alpaca and yfinance
Optimized DataFetcher configuration for 1460-day lookback periods
0 GitHub stars
Pre-configured error handling for symbol delistings and API timeouts
Automated caching strategies to handle large financial datasets efficiently
Extended historical data access for 5+ years of equity and crypto history

Use Cases

Backtesting trading algorithms across multiple years of intraday bars
Training machine learning models on long-term historical market trends
Migrating from free-tier data sources to professional-grade Alpaca subscriptions