dsyoon
741c949470
파이프라인 산출물(data/, docs/)을 Git 추적에서 제외하고 히스토리를 단일 커밋으로 재구성해 저장소 용량을 경량화한다. Co-authored-by: Cursor <cursoragent@cursor.com>
57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
"""다이버전스 유형 복합 기법."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import pandas as pd
|
|
|
|
from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
|
|
from deepcoin.techniques.composite_base import (
|
|
cluster_events,
|
|
collect_weighted_events,
|
|
score_clusters_to_signals,
|
|
)
|
|
from deepcoin.techniques.macd_cross import MacdCrossTechnique
|
|
from deepcoin.techniques.macd_divergence import MacdDivergenceTechnique
|
|
from deepcoin.techniques.obv_divergence import ObvDivergenceTechnique
|
|
from deepcoin.techniques.rsi_divergence import RsiDivergenceTechnique
|
|
from deepcoin.techniques.rsi_swing import RsiSwingTechnique
|
|
|
|
_SUB = [
|
|
RsiDivergenceTechnique(),
|
|
MacdDivergenceTechnique(),
|
|
ObvDivergenceTechnique(),
|
|
RsiSwingTechnique(),
|
|
MacdCrossTechnique(),
|
|
]
|
|
|
|
_WEIGHTS: dict[str, tuple[float, float]] = {
|
|
"rsi_divergence": (2.5, 2.5),
|
|
"macd_divergence": (2.5, 2.5),
|
|
"obv_divergence": (2.0, 2.0),
|
|
"rsi_swing": (1.2, 1.2),
|
|
"macd_cross": (1.0, 1.0),
|
|
}
|
|
|
|
|
|
class CompositeDivergenceTechnique(BaseTechnique):
|
|
"""다이버전스 Bd/Sd 유형 전담 복합 기법."""
|
|
|
|
technique_id = "composite_divergence"
|
|
technique_name = "다이버전스 복합"
|
|
category = "composite"
|
|
causal = True
|
|
description = "RSI/MACD/OBV 다이버전스 가중 투표 (Bd/Sd)"
|
|
|
|
def default_extra_params(self) -> dict:
|
|
return {"min_score": 2.0, "merge_bars": 5, "trend_ema_span": 60}
|
|
|
|
def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
|
|
min_score = float(params.extra.get("min_score", 2.0))
|
|
merge_bars = int(params.extra.get("merge_bars", 5))
|
|
trend_span = int(params.extra.get("trend_ema_span", 60))
|
|
events = collect_weighted_events(_SUB, _WEIGHTS, df, params)
|
|
clusters = cluster_events(events, merge_bars=merge_bars)
|
|
return score_clusters_to_signals(
|
|
df, clusters, min_score=min_score, trend_span=trend_span, use_trend_filter=False,
|
|
)
|