rebuild(screener): add controlled formulas and rolling backtests

2026-07-30 10:45:40 +08:00
parent 08f69b0641
commit b79b4ba280
21 changed files with 616 additions and 37 deletions
@@ -0,0 +1,113 @@
+from __future__ import annotations
+
+import hashlib
+import json
+from typing import Any
+
+from backend.features.screener.catalog import CatalogError, factor_catalog, validate_formula
+
+PROMPT_VERSION = "screener:formula-compiler:v1"
+
+
+def compile_messages(description: str) -> list[dict[str, str]]:
+    factors = factor_catalog()["factors"]
+    schema = {
+        "universe": {"exclude_st": True, "listed_days_min": 120},
+        "filters": [{"field": "amount_billion", "op": ">=", "value": 1}],
+        "score": [{"field": "return_20d", "weight": 1.0, "direction": "desc"}],
+        "limit": 30,
+        "min_score": 0.5,
+    }
+    return [
+        {
+            "role": "system",
+            "content": (
+                "你是受控选股公式编译器。只输出一个JSON对象，不得输出Markdown、解释、股票或代码。"
+                "只能使用给定因子；filters运算符仅限 >、>=、<、<=、==、!=、between、in；"
+                "score权重必须为0至1小数且总和等于1；direction仅限asc或desc；"
+                "limit为1至50，min_score为0至1。无法完全表达时选择最接近的已知因子，不得创造字段。"
+                f"\nJSON结构：{json.dumps(schema, ensure_ascii=False, separators=(',', ':'))}"
+                f"\n可用因子：{json.dumps(factors, ensure_ascii=False, separators=(',', ':'))}"
+            ),
+        },
+        {"role": "user", "content": description},
+    ]
+
+
+def description_key(description: str) -> str:
+    digest = hashlib.sha256(description.encode("utf-8")).hexdigest()[:16]
+    return f"formula:{digest}"
+
+
+def parse_compiled_formula(content: str) -> dict[str, Any]:
+    text = content.strip()
+    if text.startswith("```"):
+        lines = text.splitlines()
+        if len(lines) < 3 or lines[-1].strip() != "```":
+            raise CatalogError("模型返回的公式格式无效")
+        text = "\n".join(lines[1:-1]).strip()
+    try:
+        raw = json.loads(text)
+    except json.JSONDecodeError as exc:
+        raise CatalogError("模型未返回有效JSON公式") from exc
+    if not isinstance(raw, dict):
+        raise CatalogError("模型返回的公式必须是对象")
+    return normalize_custom_formula(raw)
+
+
+def normalize_custom_formula(raw: dict[str, Any]) -> dict[str, Any]:
+    universe = raw.get("universe")
+    filters = raw.get("filters")
+    scores = raw.get("score")
+    if not isinstance(universe, dict) or not isinstance(filters, list) or not isinstance(
+        scores, list
+    ):
+        raise CatalogError("模型返回的公式结构不完整")
+    exclude_st = universe.get("exclude_st", True)
+    listed_days = universe.get("listed_days_min", 120)
+    if not isinstance(exclude_st, bool) or not isinstance(listed_days, int):
+        raise CatalogError("股票范围设置无效")
+    normalized_scores = []
+    for score in scores:
+        if not isinstance(score, dict):
+            raise CatalogError("评分因子结构无效")
+        normalized_scores.append(
+            {
+                "field": score.get("field"),
+                "weight": score.get("weight"),
+                "direction": score.get("direction", "desc"),
+            }
+        )
+    if any(not isinstance(item, dict) for item in filters):
+        raise CatalogError("筛选条件结构无效")
+    numeric_weights = [item["weight"] for item in normalized_scores]
+    if all(
+        isinstance(value, (int, float)) and not isinstance(value, bool)
+        for value in numeric_weights
+    ):
+        total = sum(float(value) for value in numeric_weights)
+        if abs(total - 100) <= 0.0001:
+            for item in normalized_scores:
+                item["weight"] = float(item["weight"]) / 100
+    minimum = raw.get("min_score")
+    if isinstance(minimum, (int, float)) and not isinstance(minimum, bool) and 1 < minimum <= 100:
+        minimum = float(minimum) / 100
+    formula = {
+        "universe": {
+            "exclude_st": exclude_st,
+            "listed_days_min": listed_days,
+        },
+        "filters": [
+            {
+                "field": item.get("field"),
+                "op": item.get("op"),
+                "value": item.get("value"),
+            }
+            for item in filters
+        ],
+        "score": normalized_scores,
+        "limit": raw.get("limit"),
+        "min_score": minimum,
+    }
+    validate_formula(formula)
+    return formula