refactor: Git에서 데이터 제거, 설정·코드만 유지

파이프라인 산출물(data/, docs/)을 Git 추적에서 제외하고
히스토리를 단일 커밋으로 재구성해 저장소 용량을 경량화한다.

Co-authored-by: Cursor <cursoragent@cursor.com>

src/deepcoin/techniques/composite_divergence.py

@@ -0,0 +1,56 @@
+"""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,
+        )