I built Sagan Trade after seeing every quant fund struggle with the same problem:
their models work until they catastrophically fail, and no one can explain why.
We just published research showing symbolic regression statistically significantly
outperforms deep learning for trading:
The key insight: instead of black-box neural networks, we fit price/volume data to
transparent polynomial+Fourier basis functions. Every signal is a mathematical
equation you can inspect and audit.
Technical highlights:
- R² > 0.94 fitting accuracy on price series
- FunctionGemma LLM for post-prediction explainability
- Sub-50ms signal generation with Numba JIT
- Three execution modes: Coordinated, Market Neutral, Long-Only
Try it:
pip install sagan-trade
I'm here to answer questions about the methodology, results, or implementation!
Do you have a link to more of your symbolic engine research? I'd be curious to read about it. Particularly which polynomials and basis functions resulted in the +11.84% return. That seems hard to replicate, otherwise traders will take advantage of it right away.
Hi HN,
I built Sagan Trade after seeing every quant fund struggle with the same problem: their models work until they catastrophically fail, and no one can explain why.
We just published research showing symbolic regression statistically significantly outperforms deep learning for trading:
- Symbolic engine: +11.84% return, 2.46 Sharpe, -3.09% max drawdown - TFT-PINN model: -37.52% return, -2.47 Sharpe, -35.10% max drawdown - Statistical significance: p=0.0206
The key insight: instead of black-box neural networks, we fit price/volume data to transparent polynomial+Fourier basis functions. Every signal is a mathematical equation you can inspect and audit.
Technical highlights: - R² > 0.94 fitting accuracy on price series - FunctionGemma LLM for post-prediction explainability - Sub-50ms signal generation with Numba JIT - Three execution modes: Coordinated, Market Neutral, Long-Only
Try it: pip install sagan-trade
I'm here to answer questions about the methodology, results, or implementation!
Do you have a link to more of your symbolic engine research? I'd be curious to read about it. Particularly which polynomials and basis functions resulted in the +11.84% return. That seems hard to replicate, otherwise traders will take advantage of it right away.
Why did you remove the source code?