feat(stock-lab): Screener 엔진 + combine + ScreenerResult + 노드 레지스트리

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

stock-lab/app/screener/__init__.py

@@ -3,8 +3,10 @@
 See docs/superpowers/specs/2026-05-12-stock-screener-board-design.md
 """
 
-# Phase 2 완료 후 활성화:
-# from .engine import Screener, ScreenContext, ScreenerResult
-# from .registry import NODE_REGISTRY, GATE_REGISTRY
+from .engine import Screener, ScreenContext, ScreenerResult
+from .registry import NODE_REGISTRY, GATE_REGISTRY
 
-__all__ = []
+__all__ = [
+    "Screener", "ScreenContext", "ScreenerResult",
+    "NODE_REGISTRY", "GATE_REGISTRY",
+]

stock-lab/app/screener/engine.py

@@ -67,3 +67,95 @@ class ScreenContext:
         if self.prices.empty:
             return pd.Series(dtype=float)
         return self.prices.sort_values("date").groupby("ticker")["high"].last()
+
+
+# ---- combine + Screener (Phase 2) ----
+
+from . import position_sizer as _ps
+
+
+def combine(scores: dict, weights: dict) -> pd.Series:
+    """Weighted average across score nodes. ValueError if all weights = 0."""
+    active = {k: w for k, w in weights.items() if w > 0 and k in scores}
+    if not active:
+        raise ValueError("no active score nodes (all weights = 0)")
+
+    df = pd.DataFrame({k: scores[k] for k in active})
+    w = pd.Series(active)
+    weighted = (df.fillna(0).multiply(w, axis=1)).sum(axis=1) / w.sum()
+    return weighted
+
+
+@dataclass
+class ScreenerResult:
+    asof: dt.date
+    survivors_count: int
+    scores: dict           # node name → pd.Series
+    weights: dict
+    ranked: pd.Series      # ticker → total_score (sorted desc, head=top_n)
+    rows: list             # list of dicts (for serialization)
+    warnings: list
+
+
+class Screener:
+    def __init__(self, gate, score_nodes, weights: dict, node_params: dict,
+                 gate_params: dict, top_n: int = 20, sizer_params: dict = None):
+        self.gate = gate
+        self.score_nodes = score_nodes
+        self.weights = weights
+        self.node_params = node_params
+        self.gate_params = gate_params
+        self.top_n = top_n
+        self.sizer_params = sizer_params or {"atr_window": 14, "atr_stop_mult": 2.0, "rr_ratio": 2.0}
+
+    def run(self, ctx: ScreenContext) -> ScreenerResult:
+        warnings: list = []
+
+        survivors = self.gate.filter(ctx, self.gate_params)
+        if len(survivors) == 0:
+            raise ValueError("no survivors after hygiene gate")
+        if len(survivors) < 100:
+            warnings.append(f"survivors_count={len(survivors)} < 100 — 백분위 정규화 신뢰도 낮음")
+
+        scoped = ctx.restrict(survivors)
+        scores: dict = {}
+        for n in self.score_nodes:
+            w = self.weights.get(n.name, 0)
+            if w <= 0:
+                continue
+            try:
+                scores[n.name] = n.compute(scoped, self.node_params.get(n.name, {}))
+            except Exception as e:
+                warnings.append(f"node '{n.name}' failed: {e}")
+                scores[n.name] = pd.Series(0.0, index=scoped.master.index)
+
+        total = combine(scores, self.weights)
+        ranked = total.sort_values(ascending=False).head(self.top_n)
+
+        sizing = _ps.plan_positions(scoped, list(ranked.index), self.sizer_params)
+        latest_close = scoped.latest_close()
+
+        rows = []
+        for rank_idx, ticker in enumerate(ranked.index, start=1):
+            s = sizing.get(ticker, {})
+            row = {
+                "rank":          rank_idx,
+                "ticker":        ticker,
+                "name":          str(scoped.master.loc[ticker, "name"]),
+                "total_score":   float(ranked.loc[ticker]),
+                "scores":        {k: float(v.get(ticker, 0.0)) for k, v in scores.items()},
+                "close":         int(latest_close.get(ticker, 0)),
+                "market_cap":    int(scoped.master.loc[ticker, "market_cap"] or 0),
+                "entry_price":   s.get("entry_price"),
+                "stop_price":    s.get("stop_price"),
+                "target_price":  s.get("target_price"),
+                "atr14":         s.get("atr14"),
+                "r_pct":         s.get("r_pct"),
+            }
+            rows.append(row)
+
+        return ScreenerResult(
+            asof=ctx.asof, survivors_count=len(survivors),
+            scores=scores, weights=self.weights,
+            ranked=ranked, rows=rows, warnings=warnings,
+        )

stock-lab/app/screener/registry.py

@@ -0,0 +1,24 @@
+"""Registry of node classes (single source of truth for /nodes endpoint)."""
+
+from .nodes.hygiene      import HygieneGate
+from .nodes.foreign_buy  import ForeignBuy
+from .nodes.volume_surge import VolumeSurge
+from .nodes.momentum     import Momentum20
+from .nodes.high52w      import High52WProximity
+from .nodes.rs_rating    import RsRating
+from .nodes.ma_alignment import MaAlignment
+from .nodes.vcp_lite     import VcpLite
+
+NODE_REGISTRY: dict = {
+    "foreign_buy":  ForeignBuy,
+    "volume_surge": VolumeSurge,
+    "momentum":     Momentum20,
+    "high52w":      High52WProximity,
+    "rs_rating":    RsRating,
+    "ma_alignment": MaAlignment,
+    "vcp_lite":     VcpLite,
+}
+
+GATE_REGISTRY: dict = {
+    "hygiene": HygieneGate,
+}

stock-lab/app/test_screener_engine.py

@@ -0,0 +1,55 @@
+import datetime as dt
+import pandas as pd
+import pytest
+
+from app.screener.engine import ScreenContext, Screener, combine
+from app.screener.nodes.hygiene import HygieneGate
+from app.screener.nodes.foreign_buy import ForeignBuy
+from app.screener.nodes.momentum import Momentum20
+from app.screener._test_fixtures import make_master, make_prices, make_flow, make_kospi
+
+
+def _ctx(master, prices, flow):
+    return ScreenContext(master=master, prices=prices, flow=flow,
+                         kospi=make_kospi(days=260),
+                         asof=dt.date(2026, 5, 12))
+
+
+def test_combine_weighted_average():
+    scores = {
+        "foreign_buy": pd.Series({"A": 80, "B": 20}),
+        "momentum":    pd.Series({"A": 60, "B": 40}),
+    }
+    weights = {"foreign_buy": 2.0, "momentum": 1.0}
+    out = combine(scores, weights)
+    # A: (80*2 + 60*1)/3 = 73.33
+    assert abs(out["A"] - 73.333) < 0.1
+    assert abs(out["B"] - 26.666) < 0.1
+
+
+def test_combine_all_zero_weight_raises():
+    scores = {"foreign_buy": pd.Series({"A": 80})}
+    with pytest.raises(ValueError, match="no active"):
+        combine(scores, {"foreign_buy": 0})
+
+
+def test_screener_run_end_to_end():
+    asof = dt.date(2026, 5, 12)
+    master = make_master(["GOOD", "SMALL"],
+                         market_caps={"GOOD": 200_000_000_000, "SMALL": 1_000_000_000})
+    prices = make_prices(["GOOD", "SMALL"], days=260, asof=asof, trend_pct=0.1)
+    flow = make_flow(["GOOD", "SMALL"], days=260, asof=asof,
+                     foreign_per_day={"GOOD": 100_000_000, "SMALL": 0})
+    ctx = _ctx(master, prices, flow)
+
+    screener = Screener(
+        gate=HygieneGate(),
+        score_nodes=[ForeignBuy(), Momentum20()],
+        weights={"foreign_buy": 1.0, "momentum": 1.0},
+        node_params={"foreign_buy": {"window_days": 5}, "momentum": {"window_days": 20}},
+        gate_params={**HygieneGate.default_params, "min_listed_days": 0},
+        top_n=10,
+    )
+    result = screener.run(ctx)
+    assert result.survivors_count == 1                  # SMALL은 게이트 탈락
+    assert result.ranked.index[0] == "GOOD"