crypto-mft-researcher

To prevent overfitting in crypto quantitative research, apply rigorous backtesting and overfitting detection techniques designed for non-stationary crypto markets. Evaluating signal Information Coefficient (IC) and decay characteristics also ensures robust model quality.

For short-term crypto price movement predictions, engineer features derived from funding rates, open interest, liquidations, order book dynamics, and options implied volatility. These derivatives metrics capture market positioning and momentum effectively.

Train a LightGBM model for 1-minute crypto prediction horizons by selecting appropriate labels and using engineered features like funding rate momentum and OI-price divergence. Rigorously evaluate the signal's Information Coefficient to assess predictive performance.

Yes, this approach works for non-stationary crypto markets by employing advanced backtesting and overfitting detection techniques tailored to crypto volatility. It specifically evaluates signal decay to adapt to rapidly changing market conditions.

The best way to evaluate MFT alpha signal quality is by measuring Information Coefficient (IC) and analyzing signal decay characteristics. Rigorous backtesting methodologies specifically designed for crypto market non-stationarity ensure reliable evaluation.