49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
"""RS Rating — IBD 가중 (3m=2,6m=1,9m=1,12m=1)."""
|
|
|
|
import pandas as pd
|
|
|
|
from .base import ScoreNode, percentile_rank
|
|
|
|
|
|
_PERIOD_TO_DAYS = {"3m": 63, "6m": 126, "9m": 189, "12m": 252}
|
|
|
|
|
|
class RsRating(ScoreNode):
|
|
name = "rs_rating"
|
|
label = "RS Rating (시장 대비 상대강도)"
|
|
default_params = {"weights": {"3m": 2, "6m": 1, "9m": 1, "12m": 1}}
|
|
param_schema = {
|
|
"type": "object",
|
|
"properties": {
|
|
"weights": {"type": "object"}
|
|
},
|
|
}
|
|
|
|
def compute(self, ctx, params: dict) -> pd.Series:
|
|
weights: dict = params.get("weights", self.default_params["weights"])
|
|
prices = ctx.prices
|
|
kospi = ctx.kospi
|
|
if prices.empty or kospi.empty:
|
|
return pd.Series(dtype=float)
|
|
|
|
ordered = prices.sort_values("date")
|
|
|
|
def _excess_for_ticker(g: pd.DataFrame) -> float:
|
|
closes = g.set_index("date")["close"]
|
|
total = 0.0
|
|
wsum = 0.0
|
|
for period, w in weights.items():
|
|
k = _PERIOD_TO_DAYS.get(period, 0)
|
|
if len(closes) <= k or len(kospi) <= k:
|
|
continue
|
|
r_stock = closes.iloc[-1] / closes.iloc[-(k + 1)] - 1
|
|
r_market = kospi.iloc[-1] / kospi.iloc[-(k + 1)] - 1
|
|
total += w * (r_stock - r_market)
|
|
wsum += w
|
|
return total / wsum if wsum else float("nan")
|
|
|
|
raw = ordered.groupby("ticker", group_keys=False).apply(
|
|
_excess_for_ticker, include_groups=False
|
|
)
|
|
return percentile_rank(raw).fillna(50.0)
|