将项目文件整理到 refer 目录

2026-07-03 22:26:00 +08:00
parent 49880e785f
commit 9f57af4d37
1223 changed files with 98 additions and 0 deletions
@@ -0,0 +1,154 @@
+"""AI 策略生成器 — 读取策略开发文档 + 调用 LLM 生成策略代码。
+
+职责: 接收用户自然语言描述 → 读取 docs/strategy-guide.md → 调用 LLM → 返回策略代码。
+不知道: 引擎内部、API、前端、配置持久化、回测。
+"""
+from __future__ import annotations
+
+import ast
+import logging
+import re
+import tempfile
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+# 策略开发文档路径
+GUIDE_PATH = Path(__file__).resolve().parent.parent.parent.parent / "docs" / "strategy-guide.md"
+
+_SYSTEM_PREFIX = """你是A股量化策略设计专家。根据用户描述的需求，参考下方的《策略开发指南》生成一个完整的策略Python文件。
+
+核心约束:
+- 只创建这一个 .py 文件，不要修改任何现有文件，不要跨文件引用
+- 只 import polars as pl，不 import 其他模块
+
+要求:
+1. 用户可能调整的策略阈值通过 META["params"] 暴露；公式常数、固定窗口边界、布尔开关不必强行参数化
+2. 遵循指南中的文件结构，但优先贴合用户规则，不要为了套模板歪曲策略含义
+3. ENTRY_SIGNALS/EXIT_SIGNALS 根据策略逻辑自行选择匹配的信号列，不要照搬示例
+4. scoring 权重根据策略核心逻辑定制，总和 = 1.0
+5. 优先使用 Polars 表达式、窗口函数、聚合和 with_columns/filter 实现，避免逐行/逐股 Python 循环；只有表达式难以描述的复杂状态机才使用 partition_by/to_dicts
+6. 直接输出Python代码，不要输出其他内容
+
+--- 策略开发指南 ---
+
+"""
+
+
+class AIStrategyGenerator:
+    """AI 策略生成器"""
+
+    def __init__(self) -> None:
+        self._guide_cache: str | None = None
+
+    def _get_guide(self) -> str:
+        if self._guide_cache is None:
+            if GUIDE_PATH.exists():
+                self._guide_cache = GUIDE_PATH.read_text(encoding="utf-8")
+            else:
+                logger.warning("strategy-guide.md not found at %s", GUIDE_PATH)
+                self._guide_cache = ""
+        return self._guide_cache
+
+    async def generate(self, user_prompt: str) -> dict:
+        """根据用户描述生成策略代码
+
+        Returns: {"code": str, "meta": dict, "valid": bool, "error": str | None}
+        """
+        guide = self._get_guide()
+
+        # 调用 LLM
+        code = await self._call_llm(user_prompt, guide)
+
+        # 验证
+        try:
+            self._validate_safety(code)
+        except ValueError as e:
+            return {"code": code, "meta": {}, "valid": False, "error": str(e)}
+
+        # 试加载获取 META
+        try:
+            meta = self._extract_meta(code)
+        except Exception as e:
+            return {"code": code, "meta": {}, "valid": False, "error": f"解析META失败: {e}"}
+
+        return {"code": code, "meta": meta, "valid": True, "error": None}
+
+    async def _call_llm(self, user_prompt: str, guide: str) -> str:
+        """调用 OpenAI 兼容 API（流式，避免 CDN 长连接超时）"""
+        from openai import AsyncOpenAI
+        from app import secrets_store
+
+        ai_key = secrets_store.get_ai_key()
+        if not ai_key:
+            raise RuntimeError("AI API Key 未配置，请在设置页面配置")
+
+        client = AsyncOpenAI(
+            api_key=ai_key,
+            base_url=secrets_store.get_ai_config("ai_base_url", "https://api.alysc.top"),
+            timeout=180.0,
+            max_retries=2,
+        )
+        # 使用流式请求：CDN 收到首个 token 后会持续转发，不会因等待超时
+        stream = await client.chat.completions.create(
+            model=secrets_store.get_ai_config("ai_model", "gpt-5.5"),
+            messages=[
+                {"role": "system", "content": _SYSTEM_PREFIX + guide},
+                {"role": "user", "content": user_prompt},
+            ],
+            temperature=0.3,
+            max_tokens=3000,
+            stream=True,
+        )
+        chunks: list[str] = []
+        async for chunk in stream:
+            delta = chunk.choices[0].delta if chunk.choices else None
+            if delta and delta.content:
+                chunks.append(delta.content)
+        content = "".join(chunks).strip()
+        # 提取代码块
+        if "```python" in content:
+            content = content.split("```python", 1)[1].split("```", 1)[0].strip()
+        elif "```" in content:
+            content = content.split("```", 1)[1].split("```", 1)[0].strip()
+        return content
+
+    @staticmethod
+    def _validate_safety(code: str) -> None:
+        """AST 级安全检查"""
+        tree = ast.parse(code)
+
+        forbidden_modules = {"os", "sys", "subprocess", "socket", "shutil",
+                             "pathlib", "http", "urllib", "requests", "httpx"}
+        forbidden_calls = {"open", "exec", "eval", "compile", "__import__",
+                           "globals", "locals", "vars", "dir", "getattr",
+                           "setattr", "delattr", "type", "input"}
+
+        for node in ast.walk(tree):
+            if isinstance(node, ast.Import):
+                for alias in node.names:
+                    if alias.name.split(".")[0] not in ("polars",):
+                        if alias.name.split(".")[0] in forbidden_modules:
+                            raise ValueError(f"禁止 import {alias.name}")
+            if isinstance(node, ast.ImportFrom):
+                if node.module and node.module.split(".")[0] not in ("polars",):
+                    if node.module.split(".")[0] in forbidden_modules:
+                        raise ValueError(f"禁止 from {node.module} import")
+            if isinstance(node, ast.Call):
+                if isinstance(node.func, ast.Name) and node.func.id in forbidden_calls:
+                    raise ValueError(f"禁止调用 {node.func.id}()")
+
+    @staticmethod
+    def _extract_meta(code: str) -> dict:
+        """从代码字符串中提取 META 字典（不执行代码）"""
+        tree = ast.parse(code)
+        for node in ast.walk(tree):
+            if isinstance(node, ast.Assign):
+                for target in node.targets:
+                    if isinstance(target, ast.Name) and target.id == "META":
+                        # 找到 META 赋值，用 compile+eval 安全提取
+                        # 只允许字面量
+                        meta_node = node.value
+                        code_obj = compile(ast.Expression(meta_node), "<meta>", "eval")
+                        return eval(code_obj, {"__builtins__": {}})  # noqa: S307
+        return {}