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