Nikita Aull Nikita Aull

Crypto Data Analysis

Python pandas scikit-learn mplfinance ClickHouse
Released

A quantitative analysis system for cryptocurrency markets focused on detecting trading anomalies from Binance data.

Analysis Modules

  • Walls detector - identifies large orders and imbalances in the order book
  • Strike detector - detects rapid price spikes (>8% in milliseconds)
  • Feature extraction - linear regression analysis, price noise and fluctuation metrics
  • Imbalance analysis - order book bid/ask imbalance detection
  • Support levels - automatic identification of significant price levels

Data Pipeline

Downloads OHLC and aggregated trade data from Binance REST API. Multi-threaded processing across 1m, 5m, 1h timeframes. ClickHouse integration for large-scale historical analysis.

Visualization

Candlestick charts via mplfinance. Level and strike markers overlay. Jupyter notebooks for interactive exploration.