Codex e49b959b2e malkhut(docs): update with parallel training benchmarks + optimizations
- 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
2026-07-13 09:27:21 +02:00
Description
Sentiment analysis engine with ONNX FinBERT + LoRA adapters
7.8 MiB
Languages
Python 99.9%
Dockerfile 0.1%