feat: named live books; start selected_2026_09 (26 strategies)

A live book is a cohort of strategies, each with its own account, started
on one date with one capital and a flat fee per order (trader.LIVE_BOOKS).
The original book keeps its state in data/; a named book in
data/books/<book>/. The monitor runs every book; auto/status/log/compare/
replay take --book. A fresh account opens on the book's first session.

selected_2026_09: US $10,000 and $1 per order from 2026-09-25, CN ¥50,000
and ¥5 per order from 2026-09-28 (the first CN session after the choice).
Strategies beat their benchmark in the Apr-Sep 2026 point-in-time replay
and passed a 10-year PIT backtest (US Sharpe >= 0.6, MaxDD > -50%; CN
Sharpe >= 0.2, CAGR > 0), no daily variants, at most two frequencies per
factor signal, plus the three 2026-09 research strategies.

bridge.py live --book and bridge.py books feed a book selector on the
live page (/api/strategies/live?book=, /api/strategies/live/books).

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

tests/test_live_pipeline.py

@@ -3,6 +3,8 @@ order sizing, and how results are reported."""
 
 import unittest
 from datetime import datetime, timezone
+from types import SimpleNamespace
+from unittest import mock
 
 import numpy as np
 import pandas as pd
@@ -61,6 +63,47 @@ class PanelRefreshTests(unittest.TestCase):
         self.assertEqual(len(cleaned), 4)
 
 
+class LiveBookTests(unittest.TestCase):
+    BOOK = {"label": "test", "start": {"us": "2026-09-16"}, "capital": {"us": 5_000.0},
+            "fee_per_order": {"us": 1.0}, "strategies": {"us": ["momentum"]}}
+
+    def _run(self, state: dict):
+        close = _panel({"AAA": [10, 11, 12, 13, 14], "BBB": [20, 20, 21, 21, 22],
+                        "SPY": [1, 1, 1, 1, 1]})
+        args = SimpleNamespace(market="us", strategy="momentum", book="test", capital=None,
+                               tx_cost=0.001, fixed_fee=2.0, integer_shares=True)
+        equal = pd.DataFrame(0.5, index=close.index, columns=["AAA", "BBB"])
+        with mock.patch.dict(trader.LIVE_BOOKS, {"test": self.BOOK}), \
+                mock.patch.object(trader, "strategy_universe", lambda m, n: (["AAA", "BBB"], "SPY")), \
+                mock.patch.object(trader, "pit_candidates", lambda m, n, s: []), \
+                mock.patch.object(trader, "load_strategy_prices", lambda *a, **k: (close, close, close)), \
+                mock.patch.object(trader, "session_signals", lambda m, n, t, days, c, o: equal.loc[days]):
+            return trader._process_sessions(args, state, refresh=False)
+
+    def test_a_new_account_starts_on_the_book_date_with_its_capital_and_flat_fee(self):
+        state = self._run({})
+        self.assertEqual(min(state["daily_equity"]), "2026-09-16")
+        self.assertEqual(state["initial_capital"], 5_000.0)
+        self.assertTrue(state["trade_log"])
+        self.assertTrue(all(t["commission"] == 1.0 for t in state["trade_log"]))
+
+    def test_no_account_is_opened_before_the_book_starts(self):
+        with mock.patch.dict(self.BOOK, {"start": {"us": "2026-10-01"}}):
+            self.assertIsNone(self._run({}))
+
+    def test_book_strategies_are_live_strategies_of_their_market(self):
+        for book, cfg in trader.LIVE_BOOKS.items():
+            for market, names in cfg["strategies"].items():
+                for name in names:
+                    self.assertIn(name, trader.STRATEGY_REGISTRY, book)
+                    self.assertNotIn(name, trader.LIVE_EXCLUDED_STRATEGIES, book)
+                    self.assertTrue(trader.strategy_supports_market(name, market), (book, name))
+                self.assertEqual(len(names), len(set(names)), book)
+                pd.Timestamp(cfg["start"][market])
+                self.assertGreater(cfg["capital"][market], 0)
+                self.assertGreaterEqual(cfg["fee_per_order"][market], 0)
+
+
 class SessionAndOrderTests(unittest.TestCase):
     def test_todays_bar_is_not_a_close_until_the_session_settles(self):
         raw = _panel({"SPY": [1, 1, 1]}, start="2026-09-22")