新增合约全历史拉取与 K 线打分叠加功能

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

tushare/src/api.py

@@ -22,6 +22,10 @@ class RunRangeRequest(BaseModel):
     end_date: str
 
 
+class RunFullRequest(BaseModel):
+    ts_code: str
+
+
 class RunResponse(BaseModel):
     ts_code: str
     trade_date: str
@@ -131,6 +135,37 @@ def run_range(req: RunRangeRequest):
     }
 
 
+@app.post("/api/v1/run/full")
+def run_full(req: RunFullRequest):
+    """拉取指定合约全部历史数据，保存 candles，对所有可打分日期逐日打分并保存。"""
+    df = fetcher.fetch_contract(req.ts_code)
+    storage.save_candles(df)
+
+    results, warnings, total_days, scored_count = scorer.score_all(df)
+
+    for r in results:
+        storage.save_score(r)
+
+    skipped_count = total_days - scored_count
+
+    return {
+        "ts_code": req.ts_code,
+        "total_days": total_days,
+        "scored_count": scored_count,
+        "skipped_count": skipped_count,
+        "warnings": warnings,
+        "results": [
+            {
+                "trade_date": r.trade_date,
+                "close": r.close,
+                "composite": r.composite,
+                "signal": r.signal,
+            }
+            for r in results
+        ],
+    }
+
+
 @app.get("/api/v1/scores")
 def list_scores(
     ts_code: Optional[str] = Query(None),

tushare/src/scorer.py

@@ -254,3 +254,29 @@ def score_range(
             warnings.append(str(e))
 
     return results, warnings
+
+
+def score_all(df: pd.DataFrame) -> tuple[list[ScoreResult], list[str], int, int]:
+    """对 DataFrame 中所有有足够前置数据的交易日逐日打分。
+
+    Returns:
+        (results, warnings, total_days, scored_count)
+    """
+    if len(df) < 31:
+        raise ValueError(f"数据量不足(仅 {len(df)} 行),需要至少 31 行")
+
+    results: list[ScoreResult] = []
+    warnings: list[str] = []
+
+    for idx in range(30, len(df)):
+        subset = df.iloc[: idx + 1].copy()
+        trade_date = str(df.iloc[idx]["trade_date"])
+        try:
+            result = score_daily(subset)
+            results.append(result)
+        except ValueError as e:
+            warnings.append(f"{trade_date}: {e}")
+
+    total_days = len(df) - 30
+    scored_count = len(results)
+    return results, warnings, total_days, scored_count