Manage every USD stock position except an explicit unmanaged list

rebalance treated only today's S&P 500 members as the strategy's, so a held
name that left the index (or was renamed on Wikipedia) was never sold and
its value shrank the investable amount, unlike quant-trading's engine, which
sells non-members at the next rebalance. Now every USD stock/ETF position
is managed except execution.unmanaged (AAOI); a holding without bars is
priced from the account for its exit. On the paper account the managed set
is unchanged (the 10 current members).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

config.toml

@@ -28,3 +28,7 @@ cash_buffer = 0.02      # fraction of NAV kept in cash
 resize_band = 0.10      # leave a held name alone while within 10% of its target value
 min_trade_value = 50.0  # skip trades below max(min_trade_value, min_trade_fraction * NAV);
 min_trade_fraction = 0.001  # full exits always trade
+# IBKR symbols rebalance never trades; their value is set aside before sizing. Every
+# other USD stock/ETF position is the strategy's and is sold once it is not selected,
+# including a name that has left the index.
+unmanaged = ["AAOI"]

src/ibkr_quant/cli.py

@@ -148,15 +148,16 @@ async def rebalance(cfg: Config, submit: bool) -> None:
 
     async with Broker(cfg.ib) as broker:
         state = await broker.account_state()
-        managed = [h for h in state.holdings if h.sec_type == "STK" and h.currency == "USD" and h.symbol in universe]
+        managed = [h for h in state.holdings if h.sec_type == "STK" and h.currency == "USD" and h.symbol not in ex.unmanaged]
         unmanaged_value = sum(h.market_value_usd for h in state.holdings if h not in managed)
         investable = state.nav_usd * (1 - ex.cash_buffer) - unmanaged_value
         weights = select_with_buffer(score, {h.symbol for h in managed}, st.top_n, st.buffer)
-        # Sized at today's open, the latest price in the bars; filled at the close.
+        # Sized at today's open, the latest price in the bars; filled at the close. A holding
+        # that left the index has no bars and takes the account's price; its exit size does not use it.
         orders = plan_orders(
             weights,
             {h.symbol: h.quantity for h in managed},
-            opens.iloc[-1].dropna().to_dict(),
+            {h.symbol: h.market_price for h in managed} | opens.iloc[-1].dropna().to_dict(),
             investable,
             max(ex.min_trade_value, ex.min_trade_fraction * state.nav_usd),
             ex.resize_band,

src/ibkr_quant/config.py

@@ -38,6 +38,7 @@ class ExecutionConfig:
     resize_band: float
     min_trade_value: float
     min_trade_fraction: float
+    unmanaged: list[str]
 
 
 @dataclass(frozen=True)

tests/test_rebalance.py

@@ -1,5 +1,15 @@
+import asyncio
+import dataclasses
+import datetime as dt
+from pathlib import Path
+from unittest.mock import AsyncMock
+
+import pandas as pd
 import pytest
 
+from ibkr_quant import cli
+from ibkr_quant.broker import AccountState, Broker, Holding
+from ibkr_quant.config import load_config
 from ibkr_quant.rebalance import plan_orders
 
 
@@ -41,3 +51,30 @@ def test_resize_band_keeps_near_target_holdings():
     )
     # AAPL is 8% under target and stays; MSFT is 40% under and tops up.
     assert [(o.symbol, o.delta) for o in orders] == [("MSFT", 6)]
+
+
+def test_rebalance_sells_a_holding_that_left_the_index_and_skips_unmanaged(monkeypatch, tmp_path):
+    cfg = load_config(Path(__file__).resolve().parents[1] / "config.toml")
+    cfg = dataclasses.replace(cfg, root=tmp_path, strategy=dataclasses.replace(cfg.strategy, top_n=2),
+                              execution=dataclasses.replace(cfg.execution, unmanaged=["XYZ"]))
+    idx = pd.bdate_range(end=dt.date(2026, 10, 13), periods=300)
+    panel = pd.DataFrame(100.0, index=idx, columns=["AAA", "BBB", "CCC"])
+    monkeypatch.setattr(cli, "load_universe", lambda _: list(panel.columns))
+    monkeypatch.setattr(cli, "load_bars", lambda *_: (panel, panel))
+    monkeypatch.setattr(cli, "sessions_until_rebalance", lambda *_: 0)
+    monkeypatch.setattr(cli, "signal_overnight_recovery",
+                        lambda o, c: pd.DataFrame({"AAA": 0.9, "BBB": 0.8, "CCC": 0.1}, index=o.index))
+    holdings = [Holding("AAA", "STK", "USD", 44, 100.0, 4_400.0),
+                Holding("OLD", "STK", "USD", 10, 30.0, 300.0),     # left the index: no bars
+                Holding("XYZ", "STK", "USD", 10, 100.0, 1_000.0)]  # unmanaged
+    broker = Broker(cfg.ib)
+    monkeypatch.setattr(Broker, "__aenter__", AsyncMock(return_value=broker))
+    monkeypatch.setattr(Broker, "__aexit__", AsyncMock(return_value=None))
+    monkeypatch.setattr(cli, "Broker", lambda _: broker)
+    monkeypatch.setattr(broker, "account_state", AsyncMock(return_value=AccountState("DU123", 10_000, 4_300, holdings)))
+
+    asyncio.run(cli.rebalance(cfg, submit=False))
+
+    plan = pd.read_csv(next((tmp_path / "runs").glob("*/plan.csv")))
+    # investable = 10,000 * 0.98 - 1,000 (XYZ) = 8,800: 4,400 per name, so AAA is on target.
+    assert list(zip(plan.symbol, plan.delta, plan.price)) == [("OLD", -10, 30.0), ("BBB", 44, 100.0)]