feat(stock-lab): ScreenContext.load/restrict + 합성 픽스쳐

stock-lab/app/screener/_test_fixtures.py

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+"""Synthetic fixtures for screener tests — no DB / no FDR / no naver."""
+
+import datetime as dt
+import pandas as pd
+
+
+def make_master(tickers: list[str], market_caps: dict | None = None,
+                preferred: set | None = None, managed: set | None = None) -> pd.DataFrame:
+    market_caps = market_caps or {t: 100_000_000_000 for t in tickers}
+    preferred = preferred or set()
+    managed = managed or set()
+    return pd.DataFrame([
+        {
+            "ticker": t,
+            "name": f"테스트{t}",
+            "market": "KOSPI",
+            "market_cap": market_caps.get(t),
+            "is_managed": int(t in managed),
+            "is_preferred": int(t in preferred),
+            "is_spac": 0,
+            "listed_date": None,
+        }
+        for t in tickers
+    ]).set_index("ticker")
+
+
+def make_prices(tickers: list[str], days: int = 260, start_close: int = 50000,
+                trend_pct: float = 0.0,
+                asof: dt.date = dt.date(2026, 5, 12)) -> pd.DataFrame:
+    """trend_pct: 일별 종가 등락률(%). 양수면 상승 추세."""
+    rows = []
+    for t in tickers:
+        close = start_close
+        for i in range(days):
+            day_idx = days - 1 - i              # asof가 마지막
+            date = asof - dt.timedelta(days=day_idx)
+            high = int(close * 1.012)
+            low  = int(close * 0.988)
+            rows.append({
+                "ticker": t, "date": date.isoformat(),
+                "open": close, "high": high, "low": low, "close": close,
+                "volume": 1_000_000, "value": close * 1_000_000,
+            })
+            close = int(close * (1 + trend_pct / 100))
+    return pd.DataFrame(rows)
+
+
+def make_flow(tickers: list[str], days: int = 260,
+              foreign_per_day: dict | None = None,
+              asof: dt.date = dt.date(2026, 5, 12)) -> pd.DataFrame:
+    foreign_per_day = foreign_per_day or {t: 0 for t in tickers}
+    rows = []
+    for t in tickers:
+        for i in range(days):
+            day_idx = days - 1 - i
+            date = asof - dt.timedelta(days=day_idx)
+            rows.append({
+                "ticker": t, "date": date.isoformat(),
+                "foreign_net": foreign_per_day.get(t, 0),
+                "institution_net": 0,
+            })
+    return pd.DataFrame(rows)
+
+
+def make_kospi(days: int = 260, start: int = 2500, trend_pct: float = 0.0,
+               asof: dt.date = dt.date(2026, 5, 12)) -> pd.Series:
+    values = []
+    dates = []
+    v = start
+    for i in range(days):
+        day_idx = days - 1 - i
+        d = asof - dt.timedelta(days=day_idx)
+        dates.append(d.isoformat())
+        values.append(v)
+        v = v * (1 + trend_pct / 100)
+    return pd.Series(values, index=dates, name="kospi")

stock-lab/app/screener/engine.py

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+"""Screener engine — ScreenContext (Phase 0) + Screener/combine (Phase 2)."""
+
+from __future__ import annotations
+
+import datetime as dt
+import sqlite3
+from dataclasses import dataclass, replace
+
+import pandas as pd
+
+
+@dataclass(frozen=True)
+class ScreenContext:
+    """1회 실행 동안 공유되는 읽기 전용 데이터 컨테이너."""
+    master: pd.DataFrame                       # index=ticker
+    prices: pd.DataFrame                       # cols: ticker,date,open,high,low,close,volume,value
+    flow:   pd.DataFrame                       # cols: ticker,date,foreign_net,institution_net
+    kospi:  pd.Series                          # index=date(str), name="kospi"
+    asof:   dt.date
+
+    @classmethod
+    def load(cls, conn: sqlite3.Connection, asof: dt.date,
+             lookback_days: int = 252 * 2) -> "ScreenContext":
+        cutoff = (asof - dt.timedelta(days=int(lookback_days * 1.5))).isoformat()
+        asof_iso = asof.isoformat()
+
+        master = pd.read_sql_query(
+            "SELECT * FROM krx_master",
+            conn, index_col="ticker",
+        )
+        prices = pd.read_sql_query(
+            "SELECT ticker,date,open,high,low,close,volume,value "
+            "FROM krx_daily_prices WHERE date BETWEEN ? AND ? ORDER BY date",
+            conn, params=(cutoff, asof_iso),
+        )
+        flow = pd.read_sql_query(
+            "SELECT ticker,date,foreign_net,institution_net "
+            "FROM krx_flow WHERE date BETWEEN ? AND ? ORDER BY date",
+            conn, params=(cutoff, asof_iso),
+        )
+
+        # KOSPI 지수: MVP에서는 005930(삼성전자) 종가를 시장 대용으로 사용.
+        # 후속 슬라이스에서 ^KS11 별도 캐시.
+        kospi = pd.Series(dtype=float, name="kospi")
+        if "005930" in master.index and not prices.empty:
+            sub = prices[prices["ticker"] == "005930"].set_index("date")["close"]
+            kospi = sub.copy()
+            kospi.name = "kospi"
+
+        return cls(master=master, prices=prices, flow=flow, kospi=kospi, asof=asof)
+
+    def restrict(self, tickers) -> "ScreenContext":
+        tickers = pd.Index(tickers)
+        return replace(
+            self,
+            master=self.master.loc[self.master.index.intersection(tickers)],
+            prices=self.prices[self.prices["ticker"].isin(tickers)],
+            flow=self.flow[self.flow["ticker"].isin(tickers)],
+        )
+
+    def latest_close(self) -> pd.Series:
+        if self.prices.empty:
+            return pd.Series(dtype=float)
+        return self.prices.sort_values("date").groupby("ticker")["close"].last()
+
+    def latest_high(self) -> pd.Series:
+        if self.prices.empty:
+            return pd.Series(dtype=float)
+        return self.prices.sort_values("date").groupby("ticker")["high"].last()