Adriano Dal Pastro a29748e3d8 perf(backtest): vectorize engine + parallel LLM propose + multi-fold validation tool ...

- backtest/engine.py: state machine numpy invece di pd.iterrows()
  - 16.8x speedup su 2y (470ms -> 28ms), 11.3x su 7y (1744ms -> 154ms)
  - 7 parity test parametrici vs reference iterrows assicurano equivalenza
- orchestrator/run.py + run_phase1.py: --llm-concurrency N
  - ThreadPoolExecutor parallelizza hypothesis_agent.propose() per generazione
  - 5-8x speedup wall time GA loop (OpenRouter qwen-2.5 regge 6-10 concorrenti)
  - default 1 = backward-compat sequenziale
- scripts/validate_run.py: validation multi-fold standalone
  - prende run_id + top-K + N-folds expanding-window su dataset esteso (7y)
  - rivela overfitter mascherati da fitness IS alta (vedi
    phase1-extended-001: elite IS Sharpe 1.93 collassa OOS a -1.00)
  - ranking per robust_score = min(fitness_oos) su tutti i fold

Test 250/250 pass.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-16 10:53:48 +00:00

..

multi_swarm_core

perf(backtest): vectorize engine + parallel LLM propose + multi-fold validation tool

2026-05-16 10:53:48 +00:00

strategy_crypto

refactor(prompts): strategy_crypto v3.2 — consolidamento + 3 invariant tests

2026-05-15 21:54:13 +00:00