feat(b1): growth-discipline paper-forward blend (composes multistrat, faithful)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

src/fxhnt/application/paper_strategies.py

@@ -4,9 +4,10 @@ full series each run (the tracker books only the new tail); live-booking strateg
 positions in `extra`."""
 from __future__ import annotations
 
+import os
 from typing import Any
 
-from fxhnt.domain.strategies import multistrat, sixtyforty
+from fxhnt.domain.strategies import growth_discipline, multistrat, sixtyforty
 from fxhnt.ports.market_data import DailyBarClient
 
 _SIXTYFORTY_WEIGHTS = [("SPY", 0.6), ("IEF", 0.4)]
@@ -28,3 +29,14 @@ class MultiStratStrategy:
     def advance(self, last_date: str | None, extra: dict[str, Any]) -> tuple[list[tuple[str, float]], dict[str, Any]]:
         closes = {s: self._bars.adj_closes(s) for s in multistrat.INSTRUMENTS}
         return multistrat.book_series(closes), extra
+
+
+class GrowthDisciplineStrategy:
+    def __init__(self, bars: DailyBarClient) -> None:
+        self._bars = bars
+
+    def advance(self, last_date: str | None, extra: dict[str, Any]) -> tuple[list[tuple[str, float]], dict[str, Any]]:
+        w = float(os.environ.get("GD_EQUITY_WEIGHT", "0.70"))
+        qqq = self._bars.adj_closes("QQQ")
+        closes = {s: self._bars.adj_closes(s) for s in multistrat.INSTRUMENTS}
+        return growth_discipline.daily_returns(qqq, closes, w), extra

src/fxhnt/domain/strategies/growth_discipline.py

@@ -0,0 +1,13 @@
+"""Growth-discipline blend: w·QQQ + (1−w)·multistrat book, on the intersection of their dates. Pure."""
+from __future__ import annotations
+
+from fxhnt.domain.strategies import multistrat
+
+
+def daily_returns(qqq: dict[str, float], multistrat_closes: dict[str, dict[str, float]],
+                  w_equity: float = 0.70) -> list[tuple[str, float]]:
+    ms = dict(multistrat.book_series(multistrat_closes))
+    qdates = sorted(qqq)
+    qret = {qdates[i]: qqq[qdates[i]] / qqq[qdates[i - 1]] - 1.0 for i in range(1, len(qdates))}
+    common = sorted(set(ms) & set(qret))
+    return [(d, w_equity * qret[d] + (1.0 - w_equity) * ms[d]) for d in common]

tests/integration/test_paper_strategies.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 from fxhnt.adapters.persistence.state_reader import ForwardStateReader
 from fxhnt.application.forward_tracker import ForwardTracker
-from fxhnt.application.paper_strategies import SixtyFortyStrategy
+from fxhnt.application.paper_strategies import GrowthDisciplineStrategy, SixtyFortyStrategy
 
 
 class FakeDailyBars:
@@ -67,3 +67,68 @@ def test_multistrat_vol_targets_and_caps_leverage() -> None:
     # tested is that the book is vol-CONTROLLED (well under the cap, never blowing past target), not that
     # it hits 0.10 exactly. Verified bit-for-bit identical to the foxhunt original on this data.
     assert 0.02 <= vol <= 0.15            # vol-controlled (unlevered book; widened per faithful port)
+
+
+def test_growth_discipline_blend_matches_w_qqq_plus_multistrat() -> None:
+    from fxhnt.domain.strategies.growth_discipline import daily_returns
+    from fxhnt.domain.strategies.multistrat import book_series, INSTRUMENTS
+    import datetime
+    import numpy as np
+    rng = np.random.default_rng(1)
+    # Enough dates that multistrat's 63-day warmup still leaves booked days (mirror multistrat test).
+    start = datetime.date(2025, 1, 1)
+    dates = [(start + datetime.timedelta(days=i)).isoformat() for i in range(300)]
+    closes = {}
+    for k, s in enumerate(INSTRUMENTS):
+        steps = rng.normal(0.0003 * (1 if k % 2 else -1), 0.01, len(dates))
+        px = 100.0 * np.cumprod(1 + steps)
+        closes[s] = {d: float(px[i]) for i, d in enumerate(dates)}
+    # QQQ shares the same date grid so its consecutive-day return aligns with the book's dates exactly.
+    qsteps = rng.normal(0.0004, 0.011, len(dates))
+    qpx = 100.0 * np.cumprod(1 + qsteps)
+    qqq = {d: float(qpx[i]) for i, d in enumerate(dates)}
+
+    w = 0.70
+    rows = daily_returns(qqq, closes, w)
+    gd = dict(rows)
+
+    # Reconstruct the expectation: r_multistrat from book_series, r_QQQ as consecutive-day return.
+    ms = dict(book_series(closes))
+    qdates = sorted(qqq)
+    qret = {qdates[i]: qqq[qdates[i]] / qqq[qdates[i - 1]] - 1.0 for i in range(1, len(qdates))}
+    expected = {d: w * qret[d] + (1.0 - w) * ms[d] for d in (set(ms) & set(qret))}
+
+    common = set(gd) & set(expected)
+    assert len(common) >= 1
+    for d in common:
+        assert abs(gd[d] - expected[d]) < 1e-9
+
+
+def test_growth_discipline_service_round_trips_through_reader(tmp_path) -> None:
+    import datetime
+    import numpy as np
+    from fxhnt.domain.strategies.multistrat import INSTRUMENTS
+    rng = np.random.default_rng(2)
+    start = datetime.date(2025, 1, 1)
+    dates = [(start + datetime.timedelta(days=i)).isoformat() for i in range(300)]
+    data = {}
+    for k, s in enumerate(INSTRUMENTS):
+        steps = rng.normal(0.0003 * (1 if k % 2 else -1), 0.01, len(dates))
+        px = 100.0 * np.cumprod(1 + steps)
+        data[s] = {d: float(px[i]) for i, d in enumerate(dates)}
+    qsteps = rng.normal(0.0004, 0.011, len(dates))
+    qpx = 100.0 * np.cumprod(1 + qsteps)
+    data["QQQ"] = {d: float(qpx[i]) for i, d in enumerate(dates)}
+    bars = FakeDailyBars(data)
+
+    p = str(tmp_path / "gd_state.json")
+    ForwardTracker(GrowthDisciplineStrategy(bars), p).step()       # freeze at inception
+    # Append one new trading day across every series so the second step books >= 1 day.
+    nd = (start + datetime.timedelta(days=len(dates))).isoformat()
+    for s in list(INSTRUMENTS) + ["QQQ"]:
+        last = data[s][dates[-1]]
+        data[s][nd] = last * 1.001
+    ForwardTracker(GrowthDisciplineStrategy(bars), p).step()
+    summary, rows = ForwardStateReader().read(p, "gd")
+    assert summary.days == len(rows)
+    assert summary.days >= 1