How does this skill improve performance?

It stores fetched market data locally in a persistent cache, allowing subsequent runs to load data instantly (seconds) rather than waiting minutes for external API responses.

What is the CachingDataFetcher class?

It is a wrapper class that wraps your base data fetcher to automatically handle the logic of checking the cache, fetching missing data, and saving updates without modifying your existing code.

Is this the most current version of the caching skill?

This skill documents the v2.5.1 time-based approach. It has been superseded by the persistent-cache-gap-filling skill (v2.8.0) which uses a more advanced gap-filling logic for historical data.

Can I customize the cache expiry settings?

Yes, the skill allows you to set specific expiry windows (e.g., 3 days for universe selection and 7 days for training) to balance data freshness with execution speed.

Selection Data Caching for Trading

Name: Selection Data Caching for Trading
Author: smith6jt-cop

bysmith6jt-cop

데이터 과학 및 ML

Optimizes financial data retrieval by caching market symbol data to reduce API latency and avoid rate limits during trading bot development.

소개

This skill implements a unified caching system for trading bots, specifically designed to bridge the gap between symbol selection and model training. By utilizing a wrapper pattern called CachingDataFetcher, it intercepts data requests and stores results in a shared persistent cache, such as Google Drive. This significantly reduces runtimes from several minutes to just seconds for repeated tasks. While this version focuses on a time-based expiry system (v2.5.1), it provides a robust architectural pattern for managing financial time-series data efficiently within AI-driven trading environments.

주요 기능

Persistent storage support for Google Drive and local environments
0 GitHub stars
Built-in cache statistics tracking for performance monitoring
Configurable time-based cache expiry for different workflow phases
Automatic fallback to API when cache misses or data is insufficient
Unified CachingDataFetcher wrapper for seamless API integration

사용 사례

Sharing market data between discovery and model training phases
Speeding up ticker universe selection in algorithmic trading pipelines
Managing API rate limits when processing hundreds of historical symbols

소개

주요 기능

Persistent storage support for Google Drive and local environments
0 GitHub stars
Built-in cache statistics tracking for performance monitoring
Configurable time-based cache expiry for different workflow phases
Automatic fallback to API when cache misses or data is insufficient
Unified CachingDataFetcher wrapper for seamless API integration

사용 사례

Sharing market data between discovery and model training phases
Speeding up ticker universe selection in algorithmic trading pipelines
Managing API rate limits when processing hundreds of historical symbols