wy-z

wy-z / arctic-incr-cache

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ArcticDB-backed time series cache with incremental updates — fetch once, upsert the gap.

19
0
100% credibility
Found Mar 14, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A Python library for efficiently caching and incrementally updating time series data like stock prices in a persistent storage system.

How It Works

1
🔍 Discover smart data saver

You learn about a helpful tool that remembers historical stock prices and smartly updates only the new parts without re-fetching everything.

2
🛠️ Connect your pieces

You link it to your data provider for fresh prices and your personal storage spot to keep everything organized.

3
🌟 Grab first history

You ask for recent prices of a stock like AAPL, and it fetches the full set, saves it safely, and hands it right back to you.

4
🔄 Ask for updates

Later, you request more recent data, and it instantly serves what it has while quietly adding just the missing pieces.

5
⚡ Get speedy results

Every time you check, you get complete, up-to-date history lightning-fast without waiting on slow downloads.

🎉 Data always ready

Now your stock tracking feels effortless, with fresh info at your fingertips whenever you need it.

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AI-Generated Review

What is arctic-incr-cache?

This Python library builds an ArcticDB-backed cache for time series data like OHLCV bars. It fetches a full window from your API on the first call, stores it, then handles incremental updates by fetching only the gap to the new end date and upserting. You avoid redundant API hits while getting fresh data up to your requested end, with automatic exclusion of incomplete bars.

Why is it gaining traction?

It shines by fetching once for the bulk, then just the gap for incr updates, slashing costs on rate-limited APIs. Timezone-aware handling for symbols, intraday support via bar_minutes, and concurrency options like gevent make it drop-in ready for async apps. Developers notice fewer API calls and reliable caching without manual merge logic.

Who should use this?

Quant devs and backtesters pulling daily or minute bars from APIs like Polygon or Alpha Vantage into ArcticDB. Python data engineers in trading pipelines needing incremental cache refreshes without full refetches. Anyone managing symbol-specific timezones in multi-market series data.

Verdict

Grab it if you're already on ArcticDB and hate API thrashing—solid docs, pip install, and pre-commit tests show care despite beta status. With 19 stars and 1.0% credibility score, it's early but functional; test in a side project before prod.

(178 words)

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