Trading bot for Bybit exchange to trade BTCUSD perpetual contract. It use Redis database to cache the trades data.
That bot will only open shorts, because there are no liquidation price for 1x short on inverse contract and because in most cases funding rates for shorts are positive. If you want to make the bot trade longs you can just copy-paste the logic.
Bot logic: bot collects trades from Bybit websocket and calculates high, low and average values for the last 6 minutes. It opens the trade always at higher price and adds more if average price higher the entry.
Install libraries:
Run python3 -m venv .bot && source .bot/bin/activate to create virtual env and activate it.
pip install pybit==2.4.1
pip install redis
- Rename config-sample.py to config.py, open it and add your API key credentials. Save it.
- Run
docker-compose up -dto download and run Redis server for you. Don't forget to limit access to port 8001 on your server. If you don't have docker-compose just rundocker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latestto run Redis. - Run
python3 ws_trades_inverse_redis.pyto run the script that will collect the data using Bybit websockets and save it to the Redis database. - Run
python3 inverse_bot_v5.0.py
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