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.
52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
"""이평선 정배열 점수 — 5개 조건 충족 개수 / 5 × 100."""
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import pandas as pd
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from .base import ScoreNode
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class MaAlignment(ScoreNode):
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name = "ma_alignment"
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label = "이평선 정배열"
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default_params = {"ma_periods": [50, 150, 200]}
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param_schema = {
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"type": "object",
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"properties": {
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"ma_periods": {"type": "array", "items": {"type": "integer"}}
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},
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}
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def compute(self, ctx, params: dict) -> pd.Series:
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ma_periods = params.get("ma_periods", self.default_params["ma_periods"])
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if len(ma_periods) != 3:
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raise ValueError("ma_periods must have 3 entries (short, medium, long)")
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ma_s, ma_m, ma_l = (int(x) for x in ma_periods)
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prices = ctx.prices
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if prices.empty:
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return pd.Series(dtype=float)
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ordered = prices.sort_values("date")
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min_history = max(252, ma_l)
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def _score(s: pd.Series) -> float:
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closes = s.astype(float).reset_index(drop=True)
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if len(closes) < min_history:
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return float("nan")
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close = closes.iloc[-1]
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ma_short = closes.rolling(ma_s).mean().iloc[-1]
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ma_medium = closes.rolling(ma_m).mean().iloc[-1]
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ma_long = closes.rolling(ma_l).mean().iloc[-1]
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low52 = closes.iloc[-252:].min()
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conds = [
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close > ma_short,
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ma_short > ma_medium,
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ma_medium > ma_long,
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close > ma_long,
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close >= low52 * 1.25,
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]
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return sum(conds) / 5 * 100.0
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raw = ordered.groupby("ticker", group_keys=False)["close"].apply(_score)
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return raw.fillna(0.0)
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