Collect
Binance and Yahoo provider adapters supply OHLCV candles.
Market data, close to the source
Your candles. Your database.
Fetch OHLCV market data, keep it in SQLite, and compute indicators with a Python library built on the standard library.
tickstorePROJECT OVERVIEW
01Fetch candles
02Inspect coverage
03Calculate indicators
Illustrated workflow · diagram, not market data
01 / Inside the project
Built around the work.
Start with the implemented workflow, then follow the source to understand how it works.
Store
Local SQLite storage keeps the series available to your own research tools.
Calculate
Resampling and SMA, EMA, RSI, ATR, Bollinger band and VWAP helpers.
02 / Explore the workflow
Three steps to understand it.
03 / Run it yourself
Start from the source.
Python 3.9 or later. Network access is required for provider downloads.
Clone the repository and run these commands from its root. Review the README before connecting external services.
Open repository ↗SOURCE CHECKOUT
git clone https://github.com/honeyamn10-source/tickstore.git
cd tickstore
python -m pip install .
tickstore fetch BTCUSDT --exchange binance --interval 1h --limit 100 --save
tickstore status BTCUSDT --interval 1h
tickstore indicators BTCUSDT --interval 1h --sma 20 --rsi 1404 / Know the scope
Clear expectations.
This stores candle data, not an exchange tick feed. Provider availability, rate limits and data quality vary; it does not execute trades.
Check the latest results in GitHub Actions. A passing automated check covers its tested paths, not every real-world integration.