feat(portfolio): PortfolioLedger (alloc, equity/DD, persistenza+resume)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/portfolio/ledger.py

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+"""Ledger aggregato del portafoglio: capitale, allocazioni, equity, PnL, peak/DD, persistenza."""
+from __future__ import annotations
+
+import json
+from datetime import datetime, timezone
+from pathlib import Path
+
+
+class PortfolioLedger:
+    def __init__(self, code: str, total_capital: float = 1000.0,
+                 data_dir: Path = Path("data/portfolios")):
+        self.code = code
+        self.initial_capital = total_capital
+        self.total_capital = total_capital
+        self.work_dir = Path(data_dir) / code
+        self.work_dir.mkdir(parents=True, exist_ok=True)
+        self.status_path = self.work_dir / "status.json"
+        self.equity_path = self.work_dir / "equity.jsonl"
+        self.events_path = self.work_dir / "events.jsonl"
+        self.equity = total_capital
+        self.peak = total_capital
+        self.max_dd = 0.0
+        self.weights: dict[str, float] = {}
+        self.alloc: dict[str, float] = {}
+        self.last_rebalance = ""
+        self._load()
+
+    def _load(self):
+        if not self.status_path.exists():
+            return
+        s = json.loads(self.status_path.read_text())
+        self.total_capital = s.get("total_capital", self.total_capital)
+        self.equity = s.get("equity", self.equity)
+        self.peak = s.get("peak", self.peak)
+        self.max_dd = s.get("max_dd", self.max_dd)
+        self.weights = s.get("weights", {})
+        self.alloc = s.get("alloc", {})
+        self.last_rebalance = s.get("last_rebalance", "")
+
+    def allocate(self, weights: dict[str, float]) -> dict[str, float]:
+        self.weights = dict(weights)
+        self.alloc = {sid: round(self.total_capital * w, 6) for sid, w in weights.items()}
+        self.last_rebalance = datetime.now(timezone.utc).isoformat()
+        self._append(self.events_path, {"event": "rebalance", "weights": self.weights,
+                                        "total_capital": self.total_capital})
+        return self.alloc
+
+    def update_equity(self, sleeve_equity: dict[str, float], pnl_day: float = 0.0):
+        self.equity = float(sum(sleeve_equity.values()))
+        if self.equity > self.peak:
+            self.peak = self.equity
+        dd = (self.peak - self.equity) / self.peak * 100 if self.peak > 0 else 0.0
+        self.max_dd = max(self.max_dd, dd)
+        self._append(self.equity_path, {
+            "ts": datetime.now(timezone.utc).isoformat(),
+            "equity": round(self.equity, 2), "dd": round(dd, 3),
+            "pnl_day": round(pnl_day, 2),
+            "pnl_total": round(self.equity - self.initial_capital, 2),
+        })
+
+    def save(self):
+        self.status_path.write_text(json.dumps({
+            "code": self.code, "total_capital": round(self.total_capital, 2),
+            "equity": round(self.equity, 2), "peak": round(self.peak, 2),
+            "max_dd": round(self.max_dd, 3), "weights": self.weights,
+            "alloc": self.alloc, "last_rebalance": self.last_rebalance,
+            "ts": datetime.now(timezone.utc).isoformat(),
+        }, indent=2))
+
+    @staticmethod
+    def _append(path: Path, row: dict):
+        with open(path, "a") as f:
+            f.write(json.dumps(row) + "\n")

tests/portfolio/test_ledger.py

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+from pathlib import Path
+from src.portfolio.ledger import PortfolioLedger
+
+
+def test_alloc_split_by_weights(tmp_path):
+    L = PortfolioLedger("PORTX", total_capital=1000.0, data_dir=tmp_path)
+    alloc = L.allocate({"a": 0.6, "b": 0.4})
+    assert alloc == {"a": 600.0, "b": 400.0}
+
+
+def test_update_tracks_equity_and_dd(tmp_path):
+    L = PortfolioLedger("PORTX", total_capital=1000.0, data_dir=tmp_path)
+    L.update_equity({"a": 700.0, "b": 500.0})
+    assert L.equity == 1200.0 and L.peak == 1200.0 and L.max_dd == 0.0
+    L.update_equity({"a": 500.0, "b": 400.0})
+    assert L.equity == 900.0
+    assert abs(L.max_dd - 25.0) < 1e-9
+
+
+def test_persist_and_resume(tmp_path):
+    L = PortfolioLedger("PORTX", total_capital=1000.0, data_dir=tmp_path)
+    L.update_equity({"a": 1100.0})
+    L.save()
+    L2 = PortfolioLedger("PORTX", total_capital=1000.0, data_dir=tmp_path)
+    assert L2.equity == 1100.0 and L2.peak == 1100.0
+    assert (tmp_path / "PORTX" / "equity.jsonl").exists()