비기계적 매도전략(가치보존) + 위성종목 추천 엔진 추가

매크로·실적·펀더멘털·공매도수급·호가미시구조·대내외 변수 5개 독립 팩터군의 confluence(최소 3/5 합의) 없이는 매도 트리거를 금지하는 정성적 매도판단 엔진과, 보유종목 제외 위성후보 추천 로직을 추가한다. - 단일 팩터 임계값 돌파만으로는 매도 신호를 생성하지 않음 (mechanical_sell_prohibited=true) - 데이터 결측 시 항상 DATA_MISSING/INSUFFICIENT_DATA_NO_ACTION — 추정값으로 채우지 않음 - KIS 호가10단계·공매도거래비중 + Naver 시세/수급 스크래핑 입력 연동 - SQLite 시계열 저장 + 사후 적중률 자체평가 (evaluate_qualitative_sell_strategy_accuracy_v1) - Gitea 일일 스케줄(장마감 후) + 파이프라인 계약 검증 게이트
2026-06-21 20:05:55 +09:00
parent 4cb206a269
commit da0e1b0f7e
15 changed files with 2321 additions and 0 deletions
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+"""qualitative_sell_strategy_v1 산출물의 SQLite 시계열 저장소.
+
+GAS/xlsx 구조와 완전히 분리된 추가(additive) 저장소다 — 이 모듈이 다루는 데이터는
+순수 Python 산출물(KIS API 수집 + confluence 판단 결과)이며, GAS가 쓰지도 읽지도
+않고 사람이 시트에서 직접 편집하지도 않는다. 기존 outputs/qualitative_sell_strategy/
+*.json 파일 출력을 대체하지 않고 병행 저장한다(JSON은 1회성 점검용, SQLite는 시계열
+추이 조회용). 표준 라이브러리 sqlite3만 사용 — 추가 의존성 없음.
+"""
+from __future__ import annotations
+
+import json
+import sqlite3
+from pathlib import Path
+from dataclasses import dataclass
+from typing import Any
+
+from src.quant_engine.storage_backend_v1 import StoreSpec, default_sqlite_store_path, normalize_store_spec
+
+SCHEMA = """
+CREATE TABLE IF NOT EXISTS sell_strategy_results (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    code TEXT NOT NULL,
+    generated_at TEXT NOT NULL,
+    action TEXT,
+    conviction TEXT,
+    market_regime TEXT,
+    composite_score REAL,
+    rationale TEXT,
+    raw_json TEXT NOT NULL,
+    inserted_at TEXT DEFAULT (datetime('now'))
+);
+CREATE INDEX IF NOT EXISTS idx_sell_strategy_code_time
+    ON sell_strategy_results(code, generated_at);
+
+CREATE TABLE IF NOT EXISTS satellite_recommendations (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    ticker TEXT NOT NULL,
+    generated_at TEXT NOT NULL,
+    satellite_action TEXT,
+    attractiveness_score REAL,
+    market_regime TEXT,
+    raw_json TEXT NOT NULL,
+    inserted_at TEXT DEFAULT (datetime('now'))
+);
+CREATE INDEX IF NOT EXISTS idx_satellite_ticker_time
+    ON satellite_recommendations(ticker, generated_at);
+"""
+
+
+@dataclass(frozen=True)
+class QualitativeSellStoreSpec(StoreSpec):
+    pass
+
+
+def default_qualitative_sell_store_path(root: Path) -> Path:
+    return default_sqlite_store_path(root, "qualitative_sell_strategy/qualitative_sell_strategy.db")
+
+
+def resolve_store_path(spec: QualitativeSellStoreSpec, root: Path) -> Path:
+    backend, location = normalize_store_spec(
+        spec,
+        root,
+        default_sqlite_name="qualitative_sell_strategy/qualitative_sell_strategy.db",
+    )
+    if backend != "sqlite":
+        raise ValueError(
+            "qualitative_sell_strategy_store_v1 currently executes on sqlite only; "
+            "the caller contract already allows future PostgreSQL swap-in."
+        )
+    return Path(location)
+
+
+def init_db(db_path: Path) -> None:
+    db_path.parent.mkdir(parents=True, exist_ok=True)
+    conn = sqlite3.connect(db_path)
+    try:
+        conn.executescript(SCHEMA)
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def insert_sell_strategy_result(db_path: Path, result: dict[str, Any]) -> None:
+    """build_qualitative_sell_inputs_v1.process_one()의 반환값(dict)을 그대로 받는다."""
+    init_db(db_path)
+    decision = result.get("decision") or {}
+    conn = sqlite3.connect(db_path)
+    try:
+        conn.execute(
+            "INSERT INTO sell_strategy_results "
+            "(code, generated_at, action, conviction, market_regime, composite_score, rationale, raw_json) "
+            "VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
+            (
+                result.get("code"),
+                result.get("generated_at"),
+                decision.get("action"),
+                decision.get("conviction"),
+                decision.get("market_regime"),
+                decision.get("composite_score"),
+                decision.get("rationale"),
+                json.dumps(result, ensure_ascii=False, default=str),
+            ),
+        )
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def insert_satellite_recommendation(db_path: Path, generated_at: str, candidate: dict[str, Any]) -> None:
+    """build_satellite_candidate_recommendations_v1.py results[i] 항목 하나를 받는다."""
+    init_db(db_path)
+    score = candidate.get("score") or {}
+    conn = sqlite3.connect(db_path)
+    try:
+        conn.execute(
+            "INSERT INTO satellite_recommendations "
+            "(ticker, generated_at, satellite_action, attractiveness_score, market_regime, raw_json) "
+            "VALUES (?, ?, ?, ?, ?, ?)",
+            (
+                candidate.get("ticker"),
+                generated_at,
+                score.get("satellite_action"),
+                score.get("attractiveness_score"),
+                score.get("market_regime"),
+                json.dumps(candidate, ensure_ascii=False, default=str),
+            ),
+        )
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def fetch_recent_sell_strategy_results(db_path: Path, code: str, limit: int = 20) -> list[dict[str, Any]]:
+    if not db_path.exists():
+        return []
+    conn = sqlite3.connect(db_path)
+    conn.row_factory = sqlite3.Row
+    try:
+        rows = conn.execute(
+            "SELECT code, generated_at, action, conviction, market_regime, composite_score, rationale "
+            "FROM sell_strategy_results WHERE code = ? ORDER BY generated_at DESC LIMIT ?",
+            (code, limit),
+        ).fetchall()
+        return [dict(row) for row in rows]
+    finally:
+        conn.close()