e49b959b2e95b0866627fe97f7874d7745627815
- Training Scaling: 7x speedup at 8 workers, 87% efficiency, 1718 score/min - Vectorized reward: numba JIT bypasses FeatureVector dict allocation - DuckDB: sub-µs reads via in-memory materialization - VBT analysis: post-sim trade metrics (Sharpe, Sortino, VaR) - CMA-ES now wires workers into training loop via CMAESTrainer(workers=N) - Updated all subsystem tables, test counts, performance benchmarks
Description
Sentiment analysis engine with ONNX FinBERT + LoRA adapters
Languages
Python
99.9%
Dockerfile
0.1%