What happens if I don't have Alpaca keys configured?

The skill triggers a 'fail-fast' error that halts the training process immediately, providing clear instructions on how to set up your APCA_API_KEY_ID and APCA_API_SECRET_KEY.

Why is yfinance data discouraged for crypto training?

yfinance often contains ~50% zero-volume bars and missing data points for cryptocurrencies, which causes selection filters to fail and results in poor model accuracy.

How do I provide my API keys to the skill?

You can use Google Colab Secrets, local environment variables, or point the DataFetcher to a secure local keys file.

Does this skill affect stock/equity data?

While it prioritizes Alpaca for all data, the fail-fast requirement is specifically enforced for crypto where yfinance data quality is most problematic.

Trading Data Source Priority

Name: Trading Data Source Priority
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Enforces high-quality Alpaca API data usage for crypto trading to prevent model failures caused by unreliable data fallbacks.

About

This skill ensures data integrity in algorithmic trading workflows by prioritizing the Alpaca API over unreliable fallbacks like yfinance, specifically for cryptocurrency data. It implements a 'fail-fast' mechanism that immediately alerts users if crypto trading is enabled without proper Alpaca API configuration, preventing wasted compute time and poor model performance caused by the zero-volume bars and missing data points common in free data sources. By ensuring 'garbage in' is stopped at the source, it guarantees that training pipelines only proceed with production-grade market data.

Key Features

Prevention of 'garbage in, garbage out' model training
Fail-fast logic for unconfigured Alpaca API keys
0 GitHub stars
Automated data quality validation for crypto symbols
Clear diagnostic error messaging with fix instructions
Support for Colab Secrets and environment variables

Use Cases

Debugging failed symbol selection filters in trading bots
Preventing model training on corrupted or gapped crypto data
Ensuring consistent data source usage across development environments

About

Key Features

Prevention of 'garbage in, garbage out' model training
Fail-fast logic for unconfigured Alpaca API keys
0 GitHub stars
Automated data quality validation for crypto symbols
Clear diagnostic error messaging with fix instructions
Support for Colab Secrets and environment variables

Use Cases

Debugging failed symbol selection filters in trading bots
Preventing model training on corrupted or gapped crypto data
Ensuring consistent data source usage across development environments