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.
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
"""VCP-lite — 단기/장기 일중 변동성 비율 기반 수축률."""
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import pandas as pd
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from .base import ScoreNode, percentile_rank
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class VcpLite(ScoreNode):
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name = "vcp_lite"
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label = "VCP-lite (변동성 수축)"
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default_params = {"short_window": 40, "long_window": 252}
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param_schema = {
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"type": "object",
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"properties": {
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"short_window": {"type": "integer", "minimum": 10, "maximum": 120, "default": 40},
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"long_window": {"type": "integer", "minimum": 60, "maximum": 504, "default": 252},
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},
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}
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def compute(self, ctx, params: dict) -> pd.Series:
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short_w = int(params.get("short_window", 40))
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long_w = int(params.get("long_window", 252))
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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").copy()
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ordered["range_pct"] = (ordered["high"] - ordered["low"]) / ordered["close"]
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def _ratio(s: pd.Series) -> float:
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if len(s) < long_w:
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return float("nan")
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short_vol = s.tail(short_w).mean()
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long_vol = s.tail(long_w).mean()
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if long_vol == 0 or pd.isna(long_vol):
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return float("nan")
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return 1 - (short_vol / long_vol)
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raw = ordered.groupby("ticker", group_keys=False)["range_pct"].apply(_ratio)
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return percentile_rank(raw).fillna(50.0)
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