feat(portfolio): build_worker_for (worker esecutori con capitale da alloc pool)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-05-29 16:02:35 +02:00
parent 169819fe31
commit 2b3d3e3ff8
2 changed files with 63 additions and 0 deletions
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+"""PortfolioRunner: faccia live del portafoglio (capitale pool, sizing, ribilancio, ledger).
+Riusa i worker esistenti come esecutori e il data layer Cerbero v2."""
+from __future__ import annotations
+
+from pathlib import Path
+
+from src.portfolio.base import SleeveSpec, Portfolio
+from src.portfolio.ledger import PortfolioLedger
+from src.live.strategy_worker import StrategyWorker
+from src.live.pairs_worker import PairsWorker
+from src.live.multi_runner import MLWorkerWrapper
+from src.live.strategy_loader import load_strategy
+
+# Codice-breve sleeve -> nome modulo Strategy in scripts/strategies/
+_STRAT_MODULE = {
+    "MR01": "MR01_bollinger_fade", "MR02": "MR02_donchian_fade",
+    "MR07": "MR07_return_reversal", "SH01": "SH01_shape_ml",
+    # DIP01/TR01/ROT02 sono honest a sé: vedi nota nel design (worker dedicati in fase 2)
+}
+DATA_DIR = Path("data/paper_trades")
+
+
+def build_worker_for(spec: SleeveSpec, alloc_capital: float, leverage: float,
+                     data_dir: Path = DATA_DIR, position_size: float = 0.15):
+    """Costruisce il worker esecutore per uno sleeve con capitale = quota allocata."""
+    if spec.kind == "pairs":
+        return PairsWorker(
+            asset_a=spec.a, asset_b=spec.b, tf=spec.tf, params=spec.params,
+            capital=alloc_capital, position_size=position_size, leverage=leverage,
+            fee_rt=0.001, name="PR01_pairs_reversion", data_dir=data_dir,
+        )
+    module = _STRAT_MODULE.get(spec.name)
+    if module is None:
+        raise ValueError(f"sleeve live non ancora supportato: {spec.name} "
+                         f"(honest DIP01/TR01/ROT02 richiedono worker dedicati, fase 2)")
+    strategy = load_strategy(module)
+    worker = StrategyWorker(
+        strategy=strategy, asset=spec.asset, tf=spec.tf, capital=alloc_capital,
+        position_size=position_size, leverage=leverage, params=spec.params, data_dir=data_dir,
+    )
+    if spec.kind == "ml":                       # SH01: retraining periodico
+        return MLWorkerWrapper(worker, {"retrain_hours": 24})
+    return worker