Python script - Bybit Trades Data Saver using ClickHouse (ultra-fast solution for generating analytics)
Script will take the Trades data from Bybit REST API, save it to ClickHouse and then take it back from ClickHouse in JSON format.
First install ClickHoouse:
sudo apt-get install apt-transport-https ca-certificates dirmngr
sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv E0C56BD4
echo "deb https://repo.clickhouse.tech/deb/stable/ main/" | sudo tee
/etc/apt/sources.list.d/clickhouse.list
sudo apt-get update
sudo apt-get install -y clickhouse-server clickhouse-client
And run it:
sudo service clickhouse-server start
clickhouse-client
Next create python virtual environment and activate it:
python3 -m venv .pb-ch && source .pb-ch/bin/activate
And then install necessary libraries:
pip install clickhouse-driver
pip install pandas
pip install pybit==2.4.1
MIT License - Feel free to modify and distribute.
Contributions, issues, and feature requests are welcome! Feel free to check issues page.
This project is for informational and educational purposes only. You should not use this information or any other material as legal, tax, investment, financial, or other advice. Nothing contained here is a recommendation, endorsement, or offer by me to buy or sell any securities or other financial instruments.
If you intend to use real money, use it at your own risk.
Under no circumstances will I be responsible or liable for any claims, damages, losses, expenses, costs, or liabilities of any kind, including but not limited to direct or indirect damages for loss of profits.
Quantitative researcher and trading systems engineer with end-to-end ownership of systematic strategies — from research and statistical validation to low-latency execution and production deployment.
Core focus areas:
- Systematic strategy design and validation
- Market microstructure analysis (order book dynamics, liquidations, volume, delta, liquidity, spread behavior, funding)
- Backtesting framework development (tick-level and historical data)
- Execution engine architecture and order lifecycle management
- Real-time market data processing
- Risk-aware system design
- Production-grade trading infrastructure (24/7 environments)
Experience across crypto (CEX, DEX), FX, and exchange-traded markets.
- Languages: Python, C++, MQL5
- Execution & Connectivity: REST, WebSocket, FIX
- Infrastructure: Linux, Docker, Redis, PostgreSQL, ClickHouse
- Analytics: NumPy, Pandas, custom backtesting frameworks
Email: ryu8777@gmail.com