galafis/ml-volatility-forecasting
Ml Volatility Forecasting - Professional Python project
What's novel
Ml Volatility Forecasting - Professional Python project
Code Analysis
3 files read · 2 roundsA Python class-based pipeline that ingests OHLCV data, engineers financial features, trains XGBoost/RF/GB models to forecast future volatility, and serializes the model for production use.
Strengths
Strong feature engineering logic covering standard financial metrics (volatility windows, momentum, volume proxies). Excellent handling of model serialization (saving scaler alongside model) which is a common pitfall. Clean test suite with proper fixtures and coverage of core functionality including model persistence.
Weaknesses
Poor modularity due to all logic residing in a single file (~300 LOC), mixing data generation, feature engineering, and ML training. Minimal error handling (e.g., no logging, basic exception raising). Synthetic data generator lacks real-world complexity (holidays, weekends).
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
8
Contributors
1
Files
9
Active weeks
3
Repository
Language
Python
Stars
1
Forks
0
License
MIT