gahusb
ace0339d33
- git mv stock-lab/ → stock/ - docker-compose.yml: 서비스 키 + container_name + build.context + frontend.depends_on + agent-office STOCK_LAB_URL → STOCK_URL - agent-office/app: config.py, service_proxy.py, agents/stock.py, tests/ STOCK_LAB_URL → STOCK_URL - nginx/default.conf: proxy_pass http://stock-lab → http://stock (3 lines) - CLAUDE.md / README.md / STATUS.md / scripts/ 문구 갱신 - stock/ 내부 자기 참조 갱신 lab 네이밍 정책 (feedback_lab_naming.md) graduation. API URL / Python import / DB 파일명 변경 없음.
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
"""RS Rating — IBD 가중 (3m=2,6m=1,9m=1,12m=1)."""
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import pandas as pd
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from .base import ScoreNode, percentile_rank
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_PERIOD_TO_DAYS = {"3m": 63, "6m": 126, "9m": 189, "12m": 252}
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class RsRating(ScoreNode):
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name = "rs_rating"
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label = "RS Rating (시장 대비 상대강도)"
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default_params = {"weights": {"3m": 2, "6m": 1, "9m": 1, "12m": 1}}
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param_schema = {
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"type": "object",
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"properties": {
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"weights": {"type": "object"}
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},
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}
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def compute(self, ctx, params: dict) -> pd.Series:
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weights: dict = params.get("weights", self.default_params["weights"])
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prices = ctx.prices
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kospi = ctx.kospi
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if prices.empty or kospi.empty:
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return pd.Series(dtype=float)
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ordered = prices.sort_values("date")
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def _excess_for_ticker(g: pd.DataFrame) -> float:
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closes = g.set_index("date")["close"]
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total = 0.0
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wsum = 0.0
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for period, w in weights.items():
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k = _PERIOD_TO_DAYS.get(period, 0)
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if len(closes) <= k or len(kospi) <= k:
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continue
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r_stock = closes.iloc[-1] / closes.iloc[-(k + 1)] - 1
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r_market = kospi.iloc[-1] / kospi.iloc[-(k + 1)] - 1
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total += w * (r_stock - r_market)
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wsum += w
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return total / wsum if wsum else float("nan")
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raw = ordered.groupby("ticker", group_keys=False).apply(
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_excess_for_ticker, include_groups=False
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)
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return percentile_rank(raw).fillna(50.0)
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