FUNDI is a data-driven project that analyzes the relationship between cryptocurrency futures funding rates and market price movements.
The goal of this project is to explore whether funding rate signals can provide meaningful insights into market sentiment and future price trends.
- Python
- Pandas / NumPy
- Binance Futures API
- Matplotlib / Plotly (for visualization)
- Collects funding rate history from Binance Futures
- Collects candlestick (kline) price data
- Handles pagination for large time ranges
- Stores data in CSV format
- Converts timestamps to UTC datetime format
- Cleans and normalizes numeric fields
- Removes duplicate records
- Aligns funding rate data with price data
project-root/
├── data/
│ └── raw/
│ ├── btcusdt_funding_rate.csv
│ └── btcusdt_1h_klines.csv
├── src/
│ └── data_collector.py
└── README.md
Funding rates reflect the imbalance between long and short positions in the market.
- Positive funding rate → More long positions
- Negative funding rate → More short positions
This project investigates:
Can extreme funding rates signal potential price reversals?
- Merge funding rate and price datasets
- Feature engineering (lagged returns, volatility, etc.)
- Backtesting trading strategies
- Machine learning models for prediction
- Detect over-leveraged market conditions
- Identify potential trend reversals
- Build quantitative trading signals
This project is for research and educational purposes only. It does not constitute financial advice.
Bibi Backend Developer & Quant Enthusiast