重置项目
This commit is contained in:
@@ -0,0 +1,429 @@
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"""AI provider adapter for OpenAI-compatible APIs and local Codex CLI."""
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from __future__ import annotations
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import asyncio
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import os
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import re
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import shutil
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import sys
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import tempfile
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import tomllib
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from collections.abc import AsyncIterator, Sequence
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from pathlib import Path
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from app import secrets_store
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from app.config import settings
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OPENAI_COMPAT_PROVIDER = "openai_compat"
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CODEX_CLI_PROVIDER = "codex_cli"
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CODEX_DEFAULT_COMMAND = "codex"
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CODEX_SERVICE_TIER_FALLBACK = "fast"
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CODEX_SUPPORTED_SERVICE_TIERS = {"fast", "flex"}
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Message = dict[str, str]
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_ANSI_RE = re.compile(r"\x1b\[[0-9;?]*[ -/]*[@-~]")
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def current_ai_provider() -> str:
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return secrets_store.get_ai_config("ai_provider", settings.ai_provider) or OPENAI_COMPAT_PROVIDER
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def current_ai_model() -> str:
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if current_ai_provider() == CODEX_CLI_PROVIDER:
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return normalize_codex_model(str(secrets_store.load().get("ai_model") or ""))
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return secrets_store.get_ai_config("ai_model", settings.ai_model)
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def current_codex_command() -> str:
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return normalize_codex_command(
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secrets_store.get_ai_config("ai_codex_command", settings.ai_codex_command),
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strict=False,
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)
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def is_codex_cli_provider(provider: str | None = None) -> bool:
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return (provider or current_ai_provider()) == CODEX_CLI_PROVIDER
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def normalize_codex_model(model: str) -> str:
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value = model.strip()
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aliases = {
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"gpt5": "gpt-5",
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"gpt5.5": "gpt-5.5",
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}
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return aliases.get(value.lower(), value)
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def normalize_codex_command(command: str | None, *, strict: bool = True) -> str:
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value = (command or "").strip()
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if not value or value.lower() == CODEX_DEFAULT_COMMAND:
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return CODEX_DEFAULT_COMMAND
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if strict:
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raise ValueError("Codex CLI 仅支持使用默认 codex 命令自动解析, 不支持自定义可执行路径")
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return CODEX_DEFAULT_COMMAND
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def normalize_openai_base_url(url: str) -> str:
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"""Return the OpenAI-compatible base URL expected by the OpenAI SDK."""
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base = (url or "").strip().rstrip("/")
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if base.endswith("/chat/completions"):
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base = base[: -len("/chat/completions")].rstrip("/")
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if not base.endswith("/v1"):
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base = f"{base}/v1"
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return base
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def codex_cli_available() -> bool:
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try:
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_codex_base_command()
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return True
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except RuntimeError:
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return False
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def ai_configured(provider: str | None = None) -> bool:
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provider = provider or current_ai_provider()
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if is_codex_cli_provider(provider):
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return codex_cli_available()
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return bool(secrets_store.get_ai_key())
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async def generate_ai_text(
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messages: Sequence[Message],
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*,
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temperature: float = 0.3,
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max_tokens: int = 3000,
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timeout: float = 180.0,
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) -> str:
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"""Return a complete AI response from the currently configured provider."""
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if is_codex_cli_provider():
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return await _run_codex_cli(messages, max_tokens=max_tokens, timeout=max(timeout, 600.0))
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return await _run_openai_once(
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messages,
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temperature=temperature,
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max_tokens=max_tokens,
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timeout=timeout,
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)
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async def stream_ai_text(
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messages: Sequence[Message],
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*,
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temperature: float = 0.5,
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max_tokens: int = 4000,
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timeout: float = 180.0,
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) -> AsyncIterator[str]:
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"""Yield text deltas from the configured provider.
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Codex CLI only exposes the final assistant message for this use case, so it
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yields one complete chunk after the command exits.
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"""
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if is_codex_cli_provider():
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yield await _run_codex_cli(messages, max_tokens=max_tokens, timeout=max(timeout, 600.0))
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return
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async for chunk in _stream_openai(
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messages,
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temperature=temperature,
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max_tokens=max_tokens,
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timeout=timeout,
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):
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yield chunk
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async def _run_openai_once(
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messages: Sequence[Message],
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*,
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temperature: float,
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max_tokens: int,
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timeout: float,
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) -> str:
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ai_key = secrets_store.get_ai_key()
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if not ai_key:
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raise RuntimeError("AI API Key 未配置, 请在设置页配置")
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client = _openai_client(ai_key, timeout)
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resp = await client.chat.completions.create(
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model=current_ai_model(),
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messages=list(messages),
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temperature=temperature,
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max_tokens=max_tokens,
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)
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if not resp.choices:
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return ""
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return (resp.choices[0].message.content or "").strip()
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async def _stream_openai(
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messages: Sequence[Message],
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*,
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temperature: float,
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max_tokens: int,
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timeout: float,
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) -> AsyncIterator[str]:
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ai_key = secrets_store.get_ai_key()
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if not ai_key:
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raise RuntimeError("AI API Key 未配置, 请在设置页配置")
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client = _openai_client(ai_key, timeout)
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stream = await client.chat.completions.create(
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model=current_ai_model(),
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messages=list(messages),
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temperature=temperature,
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max_tokens=max_tokens,
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stream=True,
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)
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async for chunk in stream:
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delta = chunk.choices[0].delta if chunk.choices else None
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if delta and delta.content:
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yield delta.content
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def _openai_client(api_key: str, timeout: float):
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from openai import AsyncOpenAI
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user_agent = secrets_store.get_ai_config("ai_user_agent", "") or settings.ai_user_agent
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return AsyncOpenAI(
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api_key=api_key,
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base_url=normalize_openai_base_url(secrets_store.get_ai_config("ai_base_url", settings.ai_base_url)),
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timeout=timeout,
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max_retries=2,
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default_headers={"User-Agent": user_agent},
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)
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async def _run_codex_cli(
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messages: Sequence[Message],
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*,
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max_tokens: int,
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timeout: float,
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) -> str:
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prompt = _codex_prompt(messages, max_tokens=max_tokens)
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with tempfile.TemporaryDirectory(prefix="tickflow-codex-run-") as run_dir:
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run_path = Path(run_dir)
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codex_home_path = run_path / "codex-home"
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workspace_path = run_path / "workspace"
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codex_home_path.mkdir()
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workspace_path.mkdir()
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output_path = codex_home_path / "last-message.txt"
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_prepare_codex_home(codex_home_path)
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args = [
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*_codex_base_command(),
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"exec",
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"--ephemeral",
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"--sandbox",
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"read-only",
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"--skip-git-repo-check",
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"--color",
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"never",
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"--output-last-message",
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str(output_path),
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]
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model = current_ai_model().strip()
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if model:
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args.extend(["--model", model])
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args.extend(["--cd", str(workspace_path), "-"])
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env = os.environ.copy()
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env.setdefault("NO_COLOR", "1")
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env["CODEX_HOME"] = str(codex_home_path)
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proc = await asyncio.create_subprocess_exec(
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*args,
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stdin=asyncio.subprocess.PIPE,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE,
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env=env,
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)
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try:
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stdout, stderr = await asyncio.wait_for(
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proc.communicate(prompt.encode("utf-8")),
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timeout=timeout,
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)
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except TimeoutError as exc:
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proc.kill()
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await proc.wait()
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raise RuntimeError("Codex CLI 调用超时, 请稍后重试或检查本机 Codex 登录状态") from exc
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out = _clean_process_text(stdout)
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err = _clean_process_text(stderr)
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final_message = _read_output_file(output_path)
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if proc.returncode != 0:
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detail = err or out or f"exit code {proc.returncode}"
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raise RuntimeError(f"Codex CLI 调用失败: {detail[-1200:]}")
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result = final_message or out
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if not result:
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raise RuntimeError("Codex CLI 未返回内容")
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return result
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def _codex_prompt(messages: Sequence[Message], *, max_tokens: int) -> str:
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parts = [
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"You are TickFlow Stock Panel's local AI provider.",
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"This is a text-generation task. The working directory is intentionally empty.",
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"Use only the user-provided prompt content below; do not inspect or modify local files.",
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"Return only the final requested content; do not include execution logs.",
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]
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if max_tokens > 0:
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parts.append(f"Keep the final answer within about {max_tokens} output tokens.")
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for message in messages:
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role = message.get("role", "user")
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content = message.get("content", "")
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parts.append(f"\n<{role}>\n{content}\n</{role}>")
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return "\n".join(parts)
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def _codex_base_command() -> list[str]:
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command = current_codex_command()
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resolved = _resolve_command(command)
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if not resolved:
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raise RuntimeError(f"未找到 Codex CLI 命令: {command}")
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if sys.platform == "win32" and resolved.lower().endswith(".ps1"):
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return ["powershell.exe", "-NoProfile", "-ExecutionPolicy", "Bypass", "-File", resolved]
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return [resolved]
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def _resolve_command(command: str) -> str | None:
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if command.lower() != CODEX_DEFAULT_COMMAND:
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return None
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if sys.platform == "win32":
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desktop_codex = _resolve_windows_desktop_codex()
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if desktop_codex:
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return desktop_codex
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resolved = shutil.which(command)
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if sys.platform == "win32" and resolved:
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resolved_path = Path(resolved)
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if not resolved_path.suffix:
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cmd_path = resolved_path.with_suffix(".cmd")
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if cmd_path.exists():
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return str(cmd_path)
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if not resolved and sys.platform == "win32" and not command.lower().endswith(".cmd"):
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resolved = shutil.which(f"{command}.cmd")
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if not resolved and sys.platform == "win32":
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resolved = _resolve_windows_codex_command(command)
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return resolved
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def _resolve_windows_codex_command(command: str) -> str | None:
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"""Find npm-installed Codex when the backend process has a minimal PATH."""
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raw = Path(command)
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if raw.parent != Path("."):
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return None
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names = [command]
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if not raw.suffix:
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names = [f"{command}.cmd", f"{command}.exe", f"{command}.bat", f"{command}.ps1", command]
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dirs: list[Path] = []
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appdata = os.environ.get("APPDATA")
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if appdata:
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dirs.append(Path(appdata) / "npm")
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dirs.append(Path.home() / "AppData" / "Roaming" / "npm")
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for env_name in ("ProgramFiles", "ProgramFiles(x86)", "LOCALAPPDATA"):
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value = os.environ.get(env_name)
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if value:
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dirs.append(Path(value) / "nodejs")
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for directory in dirs:
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for name in names:
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candidate = directory / name
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if candidate.exists():
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return str(candidate)
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return None
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def _resolve_windows_desktop_codex() -> str | None:
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"""Prefer the Codex Desktop bundled CLI over an older npm shim."""
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local_appdata = os.environ.get("LOCALAPPDATA")
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if not local_appdata:
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return None
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root = Path(local_appdata) / "OpenAI" / "Codex" / "bin"
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if not root.exists():
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return None
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candidates = list(root.glob("*/codex.exe"))
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direct = root / "codex.exe"
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if direct.exists():
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candidates.append(direct)
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if not candidates:
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return None
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newest = max(candidates, key=lambda p: p.stat().st_mtime)
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return str(newest)
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def _prepare_codex_home(target: Path) -> None:
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"""Create an isolated CODEX_HOME that reuses auth but not fragile config."""
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source = _codex_home()
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auth_file = source / "auth.json"
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if auth_file.exists():
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shutil.copy2(auth_file, target / "auth.json")
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_write_compatible_codex_config(target / "config.toml")
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def _codex_home() -> Path:
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return Path(os.environ.get("CODEX_HOME") or Path.home() / ".codex")
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def _write_compatible_codex_config(path: Path) -> None:
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config = _read_codex_config()
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lines: list[str] = []
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tier = str(config.get("service_tier") or "").strip()
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if tier not in CODEX_SUPPORTED_SERVICE_TIERS:
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tier = CODEX_SERVICE_TIER_FALLBACK
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lines.append(_toml_string("service_tier", tier))
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lines.append(_toml_string("approval_policy", "never"))
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lines.append(_toml_string("sandbox_mode", "read-only"))
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path.write_text("\n".join(lines) + "\n", encoding="utf-8")
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def _read_codex_config() -> dict:
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path = _codex_home() / "config.toml"
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if not path.exists():
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return {}
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try:
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with path.open("rb") as f:
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return tomllib.load(f)
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except tomllib.TOMLDecodeError:
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return _read_codex_config_lenient(path)
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except OSError:
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return {}
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def _read_codex_config_lenient(path: Path) -> dict:
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config: dict[str, str] = {}
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pattern = re.compile(r'^\s*([A-Za-z0-9_-]+)\s*=\s*"([^"]*)"\s*$')
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try:
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for line in path.read_text(encoding="utf-8", errors="replace").splitlines():
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match = pattern.match(line)
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if match:
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config[match.group(1)] = match.group(2)
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except OSError:
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||||
pass
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return config
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||||
|
||||
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||||
def _toml_string(key: str, value: str) -> str:
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||||
escaped = value.replace("\\", "\\\\").replace('"', '\\"')
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||||
return f'{key} = "{escaped}"'
|
||||
|
||||
|
||||
def _clean_process_text(raw: bytes) -> str:
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||||
text = raw.decode("utf-8", errors="replace")
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||||
return _ANSI_RE.sub("", text).strip()
|
||||
|
||||
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||||
def _read_output_file(path: Path) -> str:
|
||||
if path.exists():
|
||||
return _ANSI_RE.sub("", path.read_text(encoding="utf-8", errors="replace")).strip()
|
||||
return ""
|
||||
@@ -0,0 +1,101 @@
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||||
"""AI 财务分析报告持久化存储。
|
||||
|
||||
存储位置: data/user_data/ai_reports.json (数组,按 created_at 降序)
|
||||
保留最近 MAX_REPORTS 条;超出自动裁剪最旧的。
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||||
|
||||
每条报告结构:
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||||
{
|
||||
"id": "rpt_xxx", # 唯一 id
|
||||
"symbol": "600519.SH",
|
||||
"name": "贵州茅台",
|
||||
"focus": "", # 用户追加的关心点(可为空)
|
||||
"content": "# ...markdown", # 报告正文
|
||||
"periods": 4, # 基于几期数据生成
|
||||
"summary": "metrics: 1期...", # 数据摘要
|
||||
"created_at": "2026-06-25T10:00:00"
|
||||
}
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MAX_REPORTS = 20
|
||||
|
||||
|
||||
def _path() -> Path:
|
||||
from app.config import settings
|
||||
p = settings.data_dir / "user_data" / "ai_reports.json"
|
||||
p.parent.mkdir(parents=True, exist_ok=True)
|
||||
return p
|
||||
|
||||
|
||||
def list_reports() -> list[dict]:
|
||||
"""返回全部报告(按 created_at 降序)。"""
|
||||
p = _path()
|
||||
if not p.exists():
|
||||
return []
|
||||
try:
|
||||
data = json.loads(p.read_text(encoding="utf-8"))
|
||||
if isinstance(data, list):
|
||||
return sorted(data, key=lambda r: r.get("created_at", ""), reverse=True)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("ai_reports.json malformed: %s", e)
|
||||
return []
|
||||
|
||||
|
||||
def _save_all(reports: list[dict]) -> None:
|
||||
"""全量写入(裁剪到 MAX_REPORTS)。"""
|
||||
# 保持降序
|
||||
reports.sort(key=lambda r: r.get("created_at", ""), reverse=True)
|
||||
if len(reports) > MAX_REPORTS:
|
||||
reports = reports[:MAX_REPORTS]
|
||||
_path().write_text(
|
||||
json.dumps(reports, indent=2, ensure_ascii=False), encoding="utf-8",
|
||||
)
|
||||
|
||||
|
||||
def save_report(report: dict) -> dict:
|
||||
"""新增一条报告并持久化。返回保存后的报告(含 id / created_at)。
|
||||
|
||||
自动补全 id 与 created_at(若缺),并裁剪到上限。
|
||||
"""
|
||||
reports = list_reports()
|
||||
if not report.get("id"):
|
||||
report["id"] = f"rpt_{int(time.time() * 1000)}_{report.get('symbol', 'x')}"
|
||||
if not report.get("created_at"):
|
||||
report["created_at"] = _now_iso()
|
||||
reports.append(report)
|
||||
_save_all(reports)
|
||||
logger.info("AI report saved: %s (%s), total %d", report.get("symbol"), report.get("id"), len(reports))
|
||||
return report
|
||||
|
||||
|
||||
def delete_report(report_id: str) -> bool:
|
||||
"""删除指定报告。返回是否删除成功。"""
|
||||
reports = list_reports()
|
||||
before = len(reports)
|
||||
reports = [r for r in reports if r.get("id") != report_id]
|
||||
if len(reports) < before:
|
||||
_save_all(reports)
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def clear_reports() -> int:
|
||||
"""清空全部报告。返回删除数量。"""
|
||||
reports = list_reports()
|
||||
n = len(reports)
|
||||
if n > 0:
|
||||
_save_all([])
|
||||
return n
|
||||
|
||||
|
||||
def _now_iso() -> str:
|
||||
"""当前本地时间 ISO 字符串(带秒精度,前端 toLocaleString 友好)。"""
|
||||
from datetime import datetime
|
||||
return datetime.now().isoformat(timespec="seconds")
|
||||
@@ -0,0 +1,201 @@
|
||||
"""访问密码认证 — 单用户, 自托管场景。
|
||||
|
||||
设计:
|
||||
- 密码用 PBKDF2-HMAC-SHA256 哈希(标准库 hashlib, 无新依赖), 加随机 salt。
|
||||
即使 auth.json 泄露, 也无法逆向出明文密码。
|
||||
- 会话用随机 token(token_urlsafe), 内存 + 文件双存(支持多进程/重启不丢失)。
|
||||
- 存储: data/user_data/auth.json (chmod 0600), 仿 secrets_store 模式。
|
||||
|
||||
安全要点:
|
||||
- 设密码接口必须限制本机/内网(见 auth router), 防黑客抢占域名抢先设密码。
|
||||
- 登录限流: 错5次锁5分钟(见 auth router 内存计数)。
|
||||
- 单密码, 不做多用户(避免重构全项目数据层)。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import secrets as _secrets
|
||||
import threading
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# PBKDF2 参数(NIST 推荐, 单次校验 ~100ms, 兼顾安全与响应)
|
||||
_PBKDF2_ITER = 200_000
|
||||
_SALT_LEN = 16
|
||||
_TOKEN_BYTES = 32
|
||||
|
||||
# 会话有效期: 30 天(自托管单用户, 长一点减少重登频率)
|
||||
SESSION_TTL = 30 * 24 * 3600
|
||||
|
||||
_lock = threading.Lock()
|
||||
# 内存中的有效会话: { token: expire_ts }。进程重启后从磁盘恢复。
|
||||
_sessions: dict[str, float] = {}
|
||||
|
||||
|
||||
def _path() -> Path:
|
||||
from app.config import settings
|
||||
p = settings.data_dir / "user_data" / "auth.json"
|
||||
p.parent.mkdir(parents=True, exist_ok=True)
|
||||
return p
|
||||
|
||||
|
||||
def _load() -> dict:
|
||||
p = _path()
|
||||
if p.exists():
|
||||
try:
|
||||
return json.loads(p.read_text(encoding="utf-8"))
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("auth.json malformed: %s", e)
|
||||
return {}
|
||||
|
||||
|
||||
def _save(data: dict) -> None:
|
||||
p = _path()
|
||||
p.write_text(json.dumps(data, indent=2, ensure_ascii=False), encoding="utf-8")
|
||||
try:
|
||||
os.chmod(p, 0o600)
|
||||
except OSError:
|
||||
pass
|
||||
|
||||
|
||||
def _hash_password(password: str, salt: bytes | None = None) -> tuple[str, str]:
|
||||
"""返回 (salt_hex, hash_hex)。salt 为 None 时生成新 salt。"""
|
||||
if salt is None:
|
||||
salt = os.urandom(_SALT_LEN)
|
||||
dk = hashlib.pbkdf2_hmac("sha256", password.encode("utf-8"), salt, _PBKDF2_ITER)
|
||||
return salt.hex(), dk.hex()
|
||||
|
||||
|
||||
def _verify_password(password: str, salt_hex: str, hash_hex: str) -> bool:
|
||||
"""恒定时间比较, 防时序攻击。"""
|
||||
try:
|
||||
salt = bytes.fromhex(salt_hex)
|
||||
expected = bytes.fromhex(hash_hex)
|
||||
except ValueError:
|
||||
return False
|
||||
actual = hashlib.pbkdf2_hmac("sha256", password.encode("utf-8"), salt, _PBKDF2_ITER)
|
||||
return _secrets.compare_digest(actual, expected)
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 密码管理
|
||||
# ================================================================
|
||||
|
||||
def is_configured() -> bool:
|
||||
"""是否已设置访问密码。"""
|
||||
d = _load()
|
||||
return bool(d.get("password_hash"))
|
||||
|
||||
|
||||
def set_password(password: str) -> None:
|
||||
"""设置/修改访问密码。清空所有现有会话(强制重新登录)。"""
|
||||
if len(password) < 6:
|
||||
raise ValueError("密码至少 6 位")
|
||||
salt_hex, hash_hex = _hash_password(password)
|
||||
with _lock:
|
||||
_sessions.clear() # 改密码 = 旧会话全部失效
|
||||
_save({
|
||||
"password_hash": hash_hex,
|
||||
"password_salt": salt_hex,
|
||||
"updated_at": int(time.time()),
|
||||
"sessions": {}, # 清空持久化会话
|
||||
})
|
||||
logger.info("access password set")
|
||||
|
||||
|
||||
def bootstrap_from_env() -> bool:
|
||||
"""首次初始化: 若环境变量 AUTH_PASSWORD 已配置且尚未设过密码, 则用它设密码。
|
||||
|
||||
公网服务器部署场景: 避免每次都要 SSH 端口转发才能设首个密码。
|
||||
明文密码只在内存/配置中, 经 set_password() 哈希后写入 auth.json (chmod 0600)。
|
||||
一旦设置成功, 后续重启不再覆盖 (用户改密码走 UI, 不受环境变量影响)。
|
||||
|
||||
Returns:
|
||||
True 表示本次用环境变量初始化了密码; False 表示无需初始化。
|
||||
"""
|
||||
from app.config import settings
|
||||
|
||||
pwd = (settings.auth_password or "").strip()
|
||||
if not pwd:
|
||||
return False
|
||||
if is_configured():
|
||||
# 已设过密码, 不覆盖 (避免环境变量反复重置用户在 UI 改的密码)
|
||||
return False
|
||||
try:
|
||||
set_password(pwd)
|
||||
logger.info("access password bootstrapped from AUTH_PASSWORD env (one-time)")
|
||||
return True
|
||||
except ValueError as e:
|
||||
# 密码不合规 (< 6 位), 记日志但不阻断启动
|
||||
logger.warning("AUTH_PASSWORD bootstrap skipped: %s", e)
|
||||
return False
|
||||
|
||||
|
||||
def verify_and_create_session(password: str) -> str | None:
|
||||
"""验证密码, 成功则创建会话并返回 token, 失败返回 None。"""
|
||||
d = _load()
|
||||
if not d.get("password_hash"):
|
||||
return None
|
||||
if not _verify_password(password, d.get("password_salt", ""), d["password_hash"]):
|
||||
return None
|
||||
token = _secrets.token_urlsafe(_TOKEN_BYTES)
|
||||
expire = time.time() + SESSION_TTL
|
||||
with _lock:
|
||||
_sessions[token] = expire
|
||||
_persist_sessions_locked()
|
||||
return token
|
||||
|
||||
|
||||
def revoke_session(token: str) -> None:
|
||||
"""注销会话(登出)。"""
|
||||
with _lock:
|
||||
_sessions.pop(token, None)
|
||||
_persist_sessions_locked()
|
||||
|
||||
|
||||
def is_valid_session(token: str) -> bool:
|
||||
"""检查会话是否有效(存在且未过期)。过期则清理。"""
|
||||
if not token:
|
||||
return False
|
||||
with _lock:
|
||||
expire = _sessions.get(token)
|
||||
if expire is None:
|
||||
return False
|
||||
if time.time() > expire:
|
||||
_sessions.pop(token, None)
|
||||
_persist_sessions_locked()
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def _persist_sessions_locked() -> None:
|
||||
"""把当前内存会话写回 auth.json(需持锁调用)。"""
|
||||
d = _load()
|
||||
d["sessions"] = {t: exp for t, exp in _sessions.items()}
|
||||
_save(d)
|
||||
|
||||
|
||||
def _restore_sessions() -> None:
|
||||
"""启动时从 auth.json 恢复未过期会话(支持进程重启不丢登录态)。"""
|
||||
with _lock:
|
||||
d = _load()
|
||||
now = time.time()
|
||||
saved = d.get("sessions") or {}
|
||||
for token, expire in saved.items():
|
||||
if isinstance(expire, (int, float)) and expire > now:
|
||||
_sessions[token] = expire
|
||||
if len(_sessions) != len(saved):
|
||||
# 有过期会话被清理, 落盘一次
|
||||
_persist_sessions_locked()
|
||||
|
||||
|
||||
# 模块加载时恢复会话
|
||||
try:
|
||||
_restore_sessions()
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("restore sessions failed: %s", e)
|
||||
@@ -0,0 +1,357 @@
|
||||
"""AI 概念轮动分析 — 从概念涨幅排名矩阵提炼主线/新晋/退潮信号。
|
||||
|
||||
数据来源:
|
||||
- rps_rotation.build_rps_rotation: 概念涨幅排名矩阵 (N 日 × ~387 概念)
|
||||
- market_overview_builder.build_market_overview: 大盘背景 (指数/情绪/涨停)
|
||||
|
||||
架构 (复刻 market_recap):
|
||||
预计算轮动信号 → 拼装 prompt → stream_ai_text 流式调用 → NDJSON 协议输出
|
||||
协议事件: meta(摘要) / delta(文本片段) / error / done
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import math
|
||||
from collections.abc import AsyncIterator
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ================================================================
|
||||
# System Prompt — 轮动策略师人格 + 固定章节模板
|
||||
# ================================================================
|
||||
|
||||
_SYSTEM_PROMPT = """你是一位专注 A 股题材轮动的资深策略师,拥有 12 年一线实战经验,擅长从概念板块的**涨幅排名矩阵**中识别主力资金脉络,区分机构主导的持续性主线与游资驱动的脉冲式轮动,产出可直接指导题材跟踪与节奏把握的轮动分析。
|
||||
|
||||
## 输出规范
|
||||
|
||||
用 **Markdown** 格式输出,严格遵循以下结构。不要输出任何 JSON 或代码块,直接输出 Markdown 正文。
|
||||
|
||||
### 1. 🎯 主线研判(2-3 句)
|
||||
点名当前最核心的 1-2 条主线题材(连续多日霸榜的强势概念),用一句话概括其逻辑(政策/产业/业绩/事件驱动),并判断是**主升期/加速期/扩散期/见顶期**。结尾用【主线强度:强 / 中 / 弱】定性。
|
||||
|
||||
### 2. 🆕 新晋强势
|
||||
列出排名快速跃升的概念(从榜单中后段冲进前列的),逐个给出:
|
||||
- 概念名 + 近 N 日排名变化(如 `45→20→8`)
|
||||
- 涨幅加速度(连日递增 = 趋势加强)
|
||||
- 可能的驱动逻辑(从板块属性推断,不要编造具体消息)
|
||||
- 判断是**主力切入**还是**消息脉冲**
|
||||
|
||||
### 3. 📉 退潮预警
|
||||
列出从高位明显滑落的概念(连续排名下滑或涨幅骤降),逐个给出:
|
||||
- 概念名 + 排名下滑轨迹
|
||||
- 退潮性质(高位分歧/资金撤离/补跌)
|
||||
- 是否扩散风险
|
||||
|
||||
### 4. 🏛️ 机构主线 vs 🎰 游资轮动
|
||||
基于排名稳定性区分两类资金行为:
|
||||
- **机构主线**:排名标准差小、长期稳居前列的概念 → 持续性判断、是否可作底仓方向
|
||||
- **游资轮动**:排名剧烈波动、脉冲式冲高的概念 → 短线节奏提示、追高风险
|
||||
给出当前市场**整体轮动节奏**(快轮动/慢轮动/主线聚焦)的判断。
|
||||
|
||||
### 5. 🌐 结合大盘
|
||||
结合提供的大盘数据(指数涨跌/情绪/涨停数),判断:
|
||||
- 当前大盘环境对题材轮动是助力还是阻力
|
||||
- 情绪温度与轮动节奏的匹配度(如情绪冰点但题材活跃 = 抱团;情绪火热但轮动快 = 末段)
|
||||
|
||||
### 6. 🎯 操作建议
|
||||
- **跟踪方向**:主线延续 + 新晋确认的概念
|
||||
- **规避方向**:明确退潮 + 高位脉冲的概念
|
||||
- **节奏提示**:当前适合追高 / 低吸 / 观望,及切换信号(如"主线概念连续 2 日跌出前 10 则确认退潮")
|
||||
|
||||
### 7. ⚠️ 风险提示
|
||||
列出需要盯的风险(如主线断层、情绪与轮动背离、成交萎缩)。末尾附一行:
|
||||
"> ⚠️ 本报告由 AI 基于公开行情数据生成,仅供参考,不构成任何投资建议。交易有风险,入市需谨慎。"
|
||||
|
||||
## 分析准则(务必遵守)
|
||||
|
||||
0. **只输出结论,不输出思考过程**:禁止复述你的分析步骤。不要写"我先看...""基于上述数据我认为"——直接给结论。
|
||||
1. **数据说话**:每个判断引用具体排名/涨幅数值,严禁空泛套话("强势"必须改成"连续 4 日稳居前 5,均涨 +4.2%")。
|
||||
2. **诚实中立**:数据不支持的结论就直言"信号不足,暂无法判断",不要硬凑。
|
||||
3. **区分资金性质**:这是本分析的核心价值——机构 vs 游资的判断必须基于排名稳定性(标准差),不要凭感觉。
|
||||
4. **不重复数字**:正文负责解读信号含义,不要照抄罗列已提供的全部原始数据。
|
||||
5. **简明实战**:总字数 1000-1800 字,重在可执行。
|
||||
6. **客观推断**:若无明确消息,从量价异动推断可能逻辑并给结论,不要标注"[推断]"或编造具体新闻。
|
||||
|
||||
现在请基于下方概念轮动数据进行分析。"""
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 预计算: 把排名矩阵转成结构化轮动信号
|
||||
# ================================================================
|
||||
|
||||
# 每类信号最多取多少个概念喂给 AI (控制 token)
|
||||
_TOP_N = 8
|
||||
|
||||
|
||||
def _compute_rotation_signals(dates: list[str], columns: dict) -> dict:
|
||||
"""从概念涨幅排名矩阵计算轮动信号。
|
||||
|
||||
Args:
|
||||
dates: 日期列表 (最新在最前, 与 columns key 一致)
|
||||
columns: {日期: [[概念, 涨幅], ...]} 每列各自降序
|
||||
|
||||
Returns:
|
||||
{
|
||||
"persistent_leaders": [...], # 连续多日稳居前列 (主线)
|
||||
"rising": [...], # 排名快速跃升 (新晋)
|
||||
"fading": [...], # 从高位滑落 (退潮)
|
||||
"institutional": [...], # 排名稳定 (机构特征)
|
||||
"hot_money": [...], # 排名波动大 (游资特征)
|
||||
}
|
||||
每项含: concept, ranks (按 dates 顺序), pcts, avg_rank, rank_std
|
||||
ranks 时间方向: ranks[0] = 最早日, ranks[-1] = 最新日 (已反转, 左老右新)
|
||||
"""
|
||||
if not dates or not columns:
|
||||
return {}
|
||||
|
||||
# 按时间正序 (左老右新) 处理
|
||||
dates_asc = list(reversed(dates))
|
||||
|
||||
# 收集每个概念在各日期的 (排名, 涨幅)。排名 = 该日在列中的索引 + 1。
|
||||
concept_data: dict[str, list[tuple[int, float]]] = {}
|
||||
for d in dates_asc:
|
||||
col = columns.get(d) or []
|
||||
for idx, (name, pct) in enumerate(col):
|
||||
concept_data.setdefault(name, []).append((idx + 1, pct))
|
||||
|
||||
n_dates = len(dates_asc)
|
||||
|
||||
def _stats(ranks_pcts: list[tuple[int, float]]) -> dict:
|
||||
ranks = [r for r, _ in ranks_pcts]
|
||||
pcts = [p for _, p in ranks_pcts]
|
||||
avg = sum(ranks) / len(ranks) if ranks else 0
|
||||
var = sum((r - avg) ** 2 for r in ranks) / len(ranks) if ranks else 0
|
||||
return {
|
||||
"ranks": ranks,
|
||||
"pcts": [round(p, 4) for p in pcts],
|
||||
"avg_rank": round(avg, 1),
|
||||
"rank_std": round(math.sqrt(var), 1),
|
||||
}
|
||||
|
||||
persistent: list[dict] = []
|
||||
rising: list[dict] = []
|
||||
fading: list[dict] = []
|
||||
institutional: list[dict] = []
|
||||
hot_money: list[dict] = []
|
||||
|
||||
for concept, rp in concept_data.items():
|
||||
# 缺失日补 (大排名, 0 涨幅) 保持时间轴对齐
|
||||
if len(rp) < n_dates:
|
||||
rp = rp + [(999, 0.0)] * (n_dates - len(rp))
|
||||
s = _stats(rp)
|
||||
s["concept"] = concept
|
||||
|
||||
ranks = s["ranks"]
|
||||
latest_rank = ranks[-1]
|
||||
earliest_rank = ranks[0]
|
||||
# 最近 3 日 (不足则全部) 均排名, 判断近期强度
|
||||
recent = ranks[-min(3, len(ranks)):]
|
||||
recent_avg = sum(recent) / len(recent)
|
||||
|
||||
# 主线: 近期稳居前 10
|
||||
if recent_avg <= 10 and latest_rank <= 10:
|
||||
persistent.append(s)
|
||||
|
||||
# 新晋: 早期排名靠后(>30), 最新冲进前 20, 跃升幅度大
|
||||
jump = earliest_rank - latest_rank
|
||||
if earliest_rank > 30 and latest_rank <= 20 and jump >= 20:
|
||||
rising.append(s)
|
||||
|
||||
# 退潮: 早期排名靠前(<=10), 最新滑落到 30 外
|
||||
drop = latest_rank - earliest_rank
|
||||
if earliest_rank <= 10 and latest_rank > 30 and drop >= 20:
|
||||
fading.append(s)
|
||||
|
||||
# 机构: 排名标准差小且平均排名靠前 (稳定强势)
|
||||
if s["rank_std"] <= 5 and s["avg_rank"] <= 20:
|
||||
institutional.append(s)
|
||||
|
||||
# 游资: 排名标准差大 (波动剧烈)
|
||||
if s["rank_std"] >= 20:
|
||||
hot_money.append(s)
|
||||
|
||||
# 排序: 主线按近期排名升序; 新晋按跃升幅度降序; 退潮按跌幅降序
|
||||
persistent.sort(key=lambda x: x["avg_rank"])
|
||||
rising.sort(key=lambda x: x["ranks"][0] - x["ranks"][-1], reverse=True)
|
||||
fading.sort(key=lambda x: x["ranks"][-1] - x["ranks"][0], reverse=True)
|
||||
institutional.sort(key=lambda x: (x["rank_std"], x["avg_rank"]))
|
||||
hot_money.sort(key=lambda x: x["rank_std"], reverse=True)
|
||||
|
||||
return {
|
||||
"persistent_leaders": persistent[:_TOP_N],
|
||||
"rising": rising[:_TOP_N],
|
||||
"fading": fading[:_TOP_N],
|
||||
"institutional": institutional[:_TOP_N],
|
||||
"hot_money": hot_money[:_TOP_N],
|
||||
}
|
||||
|
||||
|
||||
# ================================================================
|
||||
# Prompt 构建
|
||||
# ================================================================
|
||||
|
||||
def _fmt_pct(v) -> str:
|
||||
if v is None:
|
||||
return "—"
|
||||
return f"{v*100:+.2f}%"
|
||||
|
||||
|
||||
def _build_market_block(overview: dict) -> str:
|
||||
"""大盘背景精简块 (复用 market_overview 已算好的字段)。"""
|
||||
indices = overview.get("indices") or []
|
||||
emo = overview.get("emotion") or {}
|
||||
lim = overview.get("limit") or {}
|
||||
amt = overview.get("amount") or {}
|
||||
|
||||
idx_lines = []
|
||||
for idx in indices[:4]:
|
||||
name = idx.get("name") or idx.get("symbol") or "?"
|
||||
chg = idx.get("change_pct")
|
||||
idx_lines.append(f"{name} {_fmt_pct(chg)}")
|
||||
idx_str = " / ".join(idx_lines) or "指数缺失"
|
||||
|
||||
total_amount = (amt.get("total") or 0) / 1e8 # 元 → 亿
|
||||
|
||||
return (
|
||||
f"- 指数: {idx_str}\n"
|
||||
f"- 情绪: {emo.get('score', 50)} ({emo.get('label', '—')})\n"
|
||||
f"- 涨停/炸板/跌停: {lim.get('limit_up', 0)} / {lim.get('broken', 0)} / {lim.get('limit_down', 0)}"
|
||||
f" (最高连板 {lim.get('max_boards', 0)})\n"
|
||||
f"- 两市成交额: {total_amount:.0f} 亿元"
|
||||
)
|
||||
|
||||
|
||||
def _build_signal_block(title: str, items: list[dict]) -> str:
|
||||
"""轮动信号块: 把预计算的概念信号转成紧凑文本。"""
|
||||
if not items:
|
||||
return f"### {title}\n(本类无明显信号)"
|
||||
lines = [f"### {title}"]
|
||||
for it in items:
|
||||
ranks_str = "→".join(str(r) if r < 999 else "—" for r in it["ranks"])
|
||||
avg_pct = sum(it["pcts"]) / len(it["pcts"]) if it["pcts"] else 0
|
||||
lines.append(
|
||||
f"- {it['concept']}: 排名 {ranks_str} | 均排名 {it['avg_rank']} "
|
||||
f"| 排名波动σ {it['rank_std']} | 区间均涨 {_fmt_pct(avg_pct)}"
|
||||
)
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _build_user_prompt(signals: dict, overview: dict, days: int, dates: list[str], focus: str) -> str:
|
||||
"""组装 user 消息: 大盘背景 + 轮动信号 + focus。"""
|
||||
dates_asc = list(reversed(dates))
|
||||
date_range = f"{dates_asc[0]} ~ {dates_asc[-1]}" if dates_asc else "—"
|
||||
|
||||
parts = [
|
||||
f"# 概念涨幅轮动数据 (最近 {days} 个交易日: {date_range})",
|
||||
"",
|
||||
"## 大盘背景",
|
||||
_build_market_block(overview),
|
||||
"",
|
||||
"## 轮动信号 (排名时间方向: 左→右 = 旧→新, 排名越小越强)",
|
||||
"",
|
||||
_build_signal_block("🎯 主线 (连续霸榜)", signals.get("persistent_leaders", [])),
|
||||
"",
|
||||
_build_signal_block("🆕 新晋强势 (排名跃升)", signals.get("rising", [])),
|
||||
"",
|
||||
_build_signal_block("📉 退潮预警 (高位滑落)", signals.get("fading", [])),
|
||||
"",
|
||||
_build_signal_block("🏛️ 机构特征 (排名稳定)", signals.get("institutional", [])),
|
||||
"",
|
||||
_build_signal_block("🎰 游资特征 (排名波动大)", signals.get("hot_money", [])),
|
||||
]
|
||||
|
||||
if focus.strip():
|
||||
parts.extend(["", f"## 用户关注点\n{focus.strip()}"])
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
|
||||
def _build_summary(signals: dict) -> str:
|
||||
"""meta 事件的摘要 (前端可立即展示)。"""
|
||||
leaders = signals.get("persistent_leaders", [])
|
||||
rising = signals.get("rising", [])
|
||||
fading = signals.get("fading", [])
|
||||
leader_names = "、".join(it["concept"] for it in leaders[:3]) or "暂无明确主线"
|
||||
return f"主线: {leader_names} | 新晋 {len(rising)} | 退潮 {len(fading)}"
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 流式主入口
|
||||
# ================================================================
|
||||
|
||||
async def analyze_rotation_stream(
|
||||
repo,
|
||||
days: int = 12,
|
||||
focus: str = "",
|
||||
quote_service=None,
|
||||
depth_service=None,
|
||||
) -> AsyncIterator[str]:
|
||||
"""流式概念轮动分析: yield 出每个 NDJSON 事件。
|
||||
|
||||
Args:
|
||||
repo: KlineRepository (必填)。
|
||||
days: 分析最近 N 个交易日 (7-30)。
|
||||
focus: 用户追加的关注点。
|
||||
quote_service / depth_service: 可选, 大盘背景装配依赖。
|
||||
"""
|
||||
from app.services.rps_rotation import build_rps_rotation
|
||||
from app.services.market_overview_builder import build_market_overview
|
||||
|
||||
# 1. 取轮动矩阵
|
||||
rotation = build_rps_rotation(repo, days)
|
||||
dates = rotation.get("dates") or []
|
||||
columns = rotation.get("columns") or {}
|
||||
|
||||
if not dates or not columns:
|
||||
yield json.dumps({
|
||||
"type": "error",
|
||||
"message": "暂无概念轮动数据,请先在「概念分析」页获取概念数据源",
|
||||
}, ensure_ascii=False)
|
||||
return
|
||||
|
||||
# 2. 预计算轮动信号
|
||||
signals = _compute_rotation_signals(dates, columns)
|
||||
|
||||
# 3. 大盘背景 (失败不阻断, 降级为空)
|
||||
try:
|
||||
overview = build_market_overview(repo, quote_service, depth_service)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("rotation analyze: 大盘背景获取失败, 降级为空: %s", e)
|
||||
overview = {}
|
||||
|
||||
# 4. meta 事件
|
||||
yield json.dumps({
|
||||
"type": "meta",
|
||||
"days": days,
|
||||
"summary": _build_summary(signals),
|
||||
}, ensure_ascii=False)
|
||||
|
||||
# 5. 构建 prompt + 流式调用 LLM
|
||||
try:
|
||||
from app.services.ai_provider import stream_ai_text, ai_configured
|
||||
|
||||
if not ai_configured():
|
||||
yield json.dumps({
|
||||
"type": "error",
|
||||
"message": "AI 未配置,请在「设置」页填写 API Key 与接口地址",
|
||||
}, ensure_ascii=False)
|
||||
return
|
||||
|
||||
user_prompt = _build_user_prompt(signals, overview, days, dates, focus)
|
||||
async for delta in stream_ai_text(
|
||||
[
|
||||
{"role": "system", "content": _SYSTEM_PROMPT},
|
||||
{"role": "user", "content": user_prompt},
|
||||
],
|
||||
temperature=0.5,
|
||||
max_tokens=4000,
|
||||
):
|
||||
yield json.dumps({"type": "delta", "content": delta}, ensure_ascii=False)
|
||||
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.exception("AI concept rotation analyze failed: %s", e)
|
||||
yield json.dumps({"type": "error", "message": f"AI 轮动分析失败: {e}"}, ensure_ascii=False)
|
||||
|
||||
yield json.dumps({"type": "done"}, ensure_ascii=False)
|
||||
@@ -316,7 +316,18 @@ class DepthService:
|
||||
"status": e.get("status"),
|
||||
"fetched_at": e.get("fetched_ts"),
|
||||
})
|
||||
df = pl.DataFrame(rows)
|
||||
# 显式 schema: sealed_up/sealed_down 是 bool 与 None 混合, 不指定 schema
|
||||
# polars 会按首行推断类型, 后续遇到不一致 (bool vs null) 报
|
||||
# "could not append value: false of type: bool to the builder"。
|
||||
df = pl.DataFrame(rows, schema={
|
||||
"symbol": pl.Utf8,
|
||||
"sealed_up": pl.Boolean,
|
||||
"sealed_down": pl.Boolean,
|
||||
"ask1_vol": pl.Int64,
|
||||
"bid1_vol": pl.Int64,
|
||||
"status": pl.Utf8,
|
||||
"fetched_at": pl.Float64,
|
||||
})
|
||||
ds = today.isoformat()
|
||||
out = self._repo.store.data_dir / "depth5" / f"date={ds}" / "part.parquet"
|
||||
out.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
@@ -38,6 +38,7 @@ class PullConfig:
|
||||
"url", "method", "headers", "body", "response_path",
|
||||
"field_map", "schedule_minutes", "enabled",
|
||||
"last_run", "last_status", "last_message", "last_rows",
|
||||
"next_run",
|
||||
)
|
||||
|
||||
def __init__(
|
||||
@@ -54,6 +55,7 @@ class PullConfig:
|
||||
last_status: str | None = None,
|
||||
last_message: str | None = None,
|
||||
last_rows: int | None = None,
|
||||
next_run: str | None = None,
|
||||
) -> None:
|
||||
self.url = url
|
||||
self.method = method # GET | POST
|
||||
@@ -67,6 +69,7 @@ class PullConfig:
|
||||
self.last_status = last_status # "success" | "error"
|
||||
self.last_message = last_message
|
||||
self.last_rows = last_rows
|
||||
self.next_run = next_run # 下次预计运行 (ISO, 调度器写入)
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
return {
|
||||
@@ -82,6 +85,7 @@ class PullConfig:
|
||||
"last_status": self.last_status,
|
||||
"last_message": self.last_message,
|
||||
"last_rows": self.last_rows,
|
||||
"next_run": self.next_run,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
@@ -101,6 +105,7 @@ class PullConfig:
|
||||
last_status=d.get("last_status"),
|
||||
last_message=d.get("last_message"),
|
||||
last_rows=d.get("last_rows"),
|
||||
next_run=d.get("next_run"),
|
||||
)
|
||||
|
||||
|
||||
@@ -407,8 +412,10 @@ def write_ext_parquet(
|
||||
existing = pl.read_parquet(out_path)
|
||||
key = "symbol" if "symbol" in df.columns else df.columns[0]
|
||||
df = pl.concat([existing, df]).unique(subset=[key], keep="last")
|
||||
except Exception:
|
||||
pass
|
||||
except Exception as e:
|
||||
# schema 不一致 (列不同) 时 concat 失败 → 直接用新 df 覆盖。
|
||||
# 记日志而非静默吞掉, 便于排查"数据结构错乱"类问题。
|
||||
logger.warning("扩展表 %s 合并去重失败, 将覆盖写入: %s", config.id, e)
|
||||
else:
|
||||
# 时序: timeseries/ 下按日期分区
|
||||
out_dir = cfg_dir / "timeseries" / f"date={snap}"
|
||||
@@ -421,8 +428,8 @@ def write_ext_parquet(
|
||||
existing = pl.read_parquet(out_path)
|
||||
key = "symbol" if "symbol" in df.columns else df.columns[0]
|
||||
df = pl.concat([existing, df]).unique(subset=[key], keep="last")
|
||||
except Exception:
|
||||
pass
|
||||
except Exception as e:
|
||||
logger.warning("扩展表 %s 合并去重失败, 将覆盖写入: %s", config.id, e)
|
||||
|
||||
df = cast_df_to_schema(df, config.fields)
|
||||
df.write_parquet(out_path)
|
||||
|
||||
@@ -0,0 +1,236 @@
|
||||
"""内置扩展数据预设 — 概念/行业首次启动自动拉取。
|
||||
|
||||
设计原则:
|
||||
- 扩展数据通用逻辑零改动 (ExtConfig / fetch_and_ingest / API / 前端均不动)
|
||||
- 仅在本模块做「接口结构 → 本地 schema」的转换
|
||||
- 「已存在则跳过」: 绝不覆盖用户已有数据, 老用户零影响
|
||||
- 拉取失败只记 warning, 不阻断启动 (保持「没数据也能跑」)
|
||||
|
||||
种子数据来源: https://files.688798.xyz/ths/{concepts,industries}.json
|
||||
作者更新数据只需改接口上的 JSON, 用户下次拉取自动同步, 无需发版。
|
||||
|
||||
接入点: app.main.lifespan → ensure_builtin_presets(store.data_dir)
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
from app.services.ext_data import (
|
||||
ExtConfig,
|
||||
ExtConfigStore,
|
||||
ExtField,
|
||||
PullConfig,
|
||||
rows_to_parquet,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 种子数据源 (作者维护, 改这里即对所有用户生效)
|
||||
_THS_BASE = "https://files.688798.xyz/ths"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 预设定义: 字段结构 + 拉取配方
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _concept_preset() -> ExtConfig:
|
||||
"""扩展概念 (ext_gn_ths)。
|
||||
|
||||
接口结构: [{symbol, name, concepts: [概念1, 概念2, ...]}]
|
||||
本地 schema: 股票代码 / 股票简称 / 所属概念(分号拼接) / symbol / code
|
||||
"""
|
||||
return ExtConfig(
|
||||
id="ext_gn_ths",
|
||||
label="扩展概念",
|
||||
mode="snapshot",
|
||||
fields=[
|
||||
ExtField("symbol", "string", "标的代码"),
|
||||
ExtField("code", "string", "代码"),
|
||||
ExtField("股票代码", "string", "股票代码"),
|
||||
ExtField("股票简称", "string", "股票简称"),
|
||||
ExtField("所属概念", "string", "所属概念"),
|
||||
],
|
||||
description="同花顺概念分类 (首次启动自动拉取, 可在扩展数据页手动更新)",
|
||||
symbol_map={"type": "mapped", "col": "股票代码"},
|
||||
code_map={"type": "computed", "from": "symbol", "method": "strip_exchange"},
|
||||
pull=PullConfig(
|
||||
url=f"{_THS_BASE}/concepts.json",
|
||||
method="GET",
|
||||
schedule_minutes=1440,
|
||||
enabled=False,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def _industry_preset() -> ExtConfig:
|
||||
"""扩展行业 (ext_hy_ths)。
|
||||
|
||||
接口结构: [{symbol, name, industries: [一级行业, 二级行业, 三级行业]}]
|
||||
本地 schema: 股票代码 / 股票简称 / 所属同花顺行业(横杠拼接) / symbol / code
|
||||
"""
|
||||
return ExtConfig(
|
||||
id="ext_hy_ths",
|
||||
label="扩展行业",
|
||||
mode="snapshot",
|
||||
fields=[
|
||||
ExtField("symbol", "string", "标的代码"),
|
||||
ExtField("code", "string", "代码"),
|
||||
ExtField("股票代码", "string", "股票代码"),
|
||||
ExtField("股票简称", "string", "股票简称"),
|
||||
ExtField("所属同花顺行业", "string", "所属同花顺行业"),
|
||||
],
|
||||
description="同花顺行业分类 (首次启动自动拉取, 可在扩展数据页手动更新)",
|
||||
symbol_map={"type": "mapped", "col": "股票代码"},
|
||||
code_map={"type": "computed", "from": "symbol", "method": "strip_exchange"},
|
||||
pull=PullConfig(
|
||||
url=f"{_THS_BASE}/industries.json",
|
||||
method="GET",
|
||||
schedule_minutes=1440,
|
||||
enabled=False,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def _presets() -> list[ExtConfig]:
|
||||
return [_concept_preset(), _industry_preset()]
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 接口结构 → 本地 schema 转换 (仅预设使用)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _symbol_to_code(symbol: str) -> str:
|
||||
"""symbol (000001.SZ) → code (000001)。"""
|
||||
return symbol.split(".", 1)[0] if "." in symbol else symbol
|
||||
|
||||
|
||||
def _flatten_concept_rows(raw_rows: list[dict]) -> list[dict]:
|
||||
"""概念: concepts 数组 → 分号拼接成「所属概念」字符串。
|
||||
|
||||
[{symbol, name, concepts:[...]}] → [{股票代码, 股票简称, 所属概念, symbol, code}]
|
||||
注: code 由 symbol 派生 (000001.SZ → 000001), 因 rows_to_parquet 不执行 code_map。
|
||||
"""
|
||||
out: list[dict] = []
|
||||
for r in raw_rows:
|
||||
sym = (r.get("symbol") or "").strip()
|
||||
if not sym:
|
||||
continue
|
||||
concepts = r.get("concepts") or []
|
||||
out.append({
|
||||
"股票代码": sym,
|
||||
"股票简称": r.get("name") or "",
|
||||
"所属概念": ";".join(str(c) for c in concepts if c),
|
||||
"symbol": sym,
|
||||
"code": _symbol_to_code(sym),
|
||||
})
|
||||
return out
|
||||
|
||||
|
||||
def _flatten_industry_rows(raw_rows: list[dict]) -> list[dict]:
|
||||
"""行业: industries 数组 → 横杠拼接成「所属同花顺行业」字符串。
|
||||
|
||||
[{symbol, name, industries:[...]}] → [{股票代码, 股票简称, 所属同花顺行业, symbol, code}]
|
||||
"""
|
||||
out: list[dict] = []
|
||||
for r in raw_rows:
|
||||
sym = (r.get("symbol") or "").strip()
|
||||
if not sym:
|
||||
continue
|
||||
inds = r.get("industries") or []
|
||||
out.append({
|
||||
"股票代码": sym,
|
||||
"股票简称": r.get("name") or "",
|
||||
"所属同花顺行业": "-".join(str(i) for i in inds if i),
|
||||
"symbol": sym,
|
||||
"code": _symbol_to_code(sym),
|
||||
})
|
||||
return out
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 拉取执行 (复用 httpx, 不依赖 fetch_and_ingest 的 PullConfig 路径)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
async def _fetch_json(url: str) -> list[dict]:
|
||||
"""请求 JSON 接口, 返回行数组。超时 30s, 失败抛异常由调用方兜底。"""
|
||||
import httpx
|
||||
|
||||
async with httpx.AsyncClient(timeout=30) as client:
|
||||
resp = await client.get(url)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
if not isinstance(data, list):
|
||||
raise ValueError(f"接口返回不是数组: {type(data)}")
|
||||
return data
|
||||
|
||||
|
||||
async def _seed_one(config: ExtConfig, flatten, data_dir: Path) -> int:
|
||||
"""拉取 + 转换 + 写入单个预设。返回写入行数。"""
|
||||
from datetime import date
|
||||
|
||||
raw = await _fetch_json(config.pull.url)
|
||||
rows = flatten(raw)
|
||||
if not rows:
|
||||
raise ValueError(f"接口返回 0 行: {config.pull.url}")
|
||||
n = rows_to_parquet(rows, config, data_dir, snapshot_date=date.today())
|
||||
return n
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 对外入口
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def get_preset(config_id: str) -> ExtConfig | None:
|
||||
"""按 id 取预设定义 (供 API 层校验 id 合法性)。"""
|
||||
for c in _presets():
|
||||
if c.id == config_id:
|
||||
return c
|
||||
return None
|
||||
|
||||
|
||||
async def ensure_builtin_presets(data_dir: Path) -> None:
|
||||
"""启动时: 为缺失的预设创建 config.json (含 pull 配置), 但【不拉取数据】。
|
||||
|
||||
设计: 数据获取改为用户在概念/行业页手动点「获取数据」触发, 避免启动时
|
||||
网络请求阻塞, 也避免「自动拉取」与「用户自主控制」的预期冲突。
|
||||
|
||||
安全保证:
|
||||
- 已存在则完全跳过 (绝不覆盖用户数据)
|
||||
- 只写 config.json, 失败只记 warning 不阻断启动
|
||||
"""
|
||||
store = ExtConfigStore(data_dir)
|
||||
|
||||
for config in _presets():
|
||||
existing = store.get(config.id)
|
||||
if existing is not None:
|
||||
# 用户已有此表 (老用户 / 自己重建过) → 一律不动
|
||||
continue
|
||||
try:
|
||||
store.upsert(config)
|
||||
logger.info("内置扩展表 %s 配置已就绪 (待用户手动获取数据)", config.id)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("内置扩展表 %s 配置写入失败 (不影响启动): %s", config.id, e)
|
||||
|
||||
|
||||
async def fetch_preset(config_id: str, data_dir: Path) -> int:
|
||||
"""手动触发某个预设的数据拉取 (供 API 调用)。
|
||||
|
||||
Raises:
|
||||
ValueError: config_id 不是内置预设
|
||||
Exception: 网络请求/解析/写入失败 (由 API 层转 HTTP 错误)
|
||||
"""
|
||||
config = get_preset(config_id)
|
||||
if config is None:
|
||||
raise ValueError(f"未知的内置预设: {config_id}")
|
||||
|
||||
flatten = _flatten_concept_rows if config_id == "ext_gn_ths" else _flatten_industry_rows
|
||||
|
||||
# 确保 config.json 存在 (用户可能从未启动过 ensure_builtin_presets)
|
||||
store = ExtConfigStore(data_dir)
|
||||
if store.get(config_id) is None:
|
||||
store.upsert(config)
|
||||
|
||||
n = await _seed_one(config, flatten, data_dir)
|
||||
logger.info("内置扩展表 %s 手动拉取成功: %d 行", config_id, n)
|
||||
return n
|
||||
@@ -75,6 +75,19 @@ def _apply_field_map(rows: list[dict], field_map: dict[str, str]) -> list[dict]:
|
||||
# 拉取执行
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _apply_preset_flatten(config_id: str, rows: list[dict]) -> list[dict]:
|
||||
"""对内置预设 (概念/行业) 应用结构转换, 与 fetch_preset 保持一致。
|
||||
|
||||
延迟导入避免与 ext_presets 形成循环依赖。
|
||||
非预设 id 原样返回。
|
||||
"""
|
||||
if config_id not in ("ext_gn_ths", "ext_hy_ths"):
|
||||
return rows
|
||||
from app.services.ext_presets import _flatten_concept_rows, _flatten_industry_rows
|
||||
flatten = _flatten_concept_rows if config_id == "ext_gn_ths" else _flatten_industry_rows
|
||||
return flatten(rows)
|
||||
|
||||
|
||||
async def fetch_and_ingest(
|
||||
config: ExtConfig,
|
||||
data_dir,
|
||||
@@ -111,6 +124,12 @@ async def fetch_and_ingest(
|
||||
if not rows:
|
||||
raise ValueError("提取到的行数为 0")
|
||||
|
||||
# 内置预设 (概念/行业): 应用结构转换, 让产出 schema 与分析页一致。
|
||||
# 否则 raw 接口列 (concepts/industries 数组、name) 会直接覆盖正确的 part.parquet,
|
||||
# 导致分析页因找不到维度字段 (所属概念/所属同花顺行业) 而"数据消失"。
|
||||
# 见 ext_presets._flatten_* —— 手动拉取 / 定时拉取都必须走同一套转换。
|
||||
rows = _apply_preset_flatten(config.id, rows)
|
||||
|
||||
# 字段映射
|
||||
rows = _apply_field_map(rows, pull.field_map)
|
||||
|
||||
@@ -129,21 +148,46 @@ async def fetch_and_ingest(
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class PullScheduler:
|
||||
"""后台调度器：为每个启用了 pull 的 ExtConfig 维护定时任务。"""
|
||||
"""后台调度器：为每个启用了 pull 的 ExtConfig 维护定时任务。
|
||||
|
||||
线程安全说明:
|
||||
refresh()/stop() 可能从主事件循环 (lifespan startup) 或同步路由的
|
||||
worker 线程 (configure_pull 是 def 而非 async def, FastAPI 丢进线程池)
|
||||
调用。worker 线程里没有 running loop, 直接 asyncio.create_task 会抛
|
||||
"no running event loop"。因此对 task 的增删一律通过
|
||||
call_soon_threadsafe 提交到主循环执行 —— 同一套代码两种调用场景都安全。
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._tasks: dict[str, asyncio.Task] = {}
|
||||
self._running = False
|
||||
self._lock = threading.Lock()
|
||||
self._loop: asyncio.AbstractEventLoop | None = None
|
||||
|
||||
def start(self, data_dir) -> None:
|
||||
"""启动调度（在 lifespan startup 调用）。"""
|
||||
"""启动调度（在 lifespan startup 调用，主事件循环内）。"""
|
||||
self._running = True
|
||||
self._data_dir = data_dir
|
||||
try:
|
||||
self._loop = asyncio.get_running_loop()
|
||||
except RuntimeError:
|
||||
self._loop = None
|
||||
logger.info("PullScheduler started")
|
||||
|
||||
def _submit(self, fn, *args) -> None:
|
||||
"""把一个 callable 提交到主事件循环执行 (线程安全)。
|
||||
|
||||
startup 在主循环内调用时 fn 立即排队; worker 线程调用时跨线程排队。
|
||||
两者都通过 call_soon_threadsafe, 保证 _tasks 字典的读写只在主循环里发生。
|
||||
"""
|
||||
loop = self._loop
|
||||
if loop is None or loop.is_closed():
|
||||
raise RuntimeError(
|
||||
"PullScheduler: 事件循环不可用 (start() 未在事件循环中调用?)"
|
||||
)
|
||||
loop.call_soon_threadsafe(fn, *args)
|
||||
|
||||
def stop(self) -> None:
|
||||
"""停止所有任务。"""
|
||||
"""停止所有任务 (从 shutdown 调用)。"""
|
||||
self._running = False
|
||||
for task in self._tasks.values():
|
||||
task.cancel()
|
||||
@@ -151,47 +195,61 @@ class PullScheduler:
|
||||
logger.info("PullScheduler stopped")
|
||||
|
||||
def refresh(self, data_dir) -> None:
|
||||
"""重新加载配置，更新调度任务（增/删/改）。"""
|
||||
"""重新加载配置，更新调度任务（增/删/改）。线程安全。"""
|
||||
self._data_dir = data_dir
|
||||
store = ExtConfigStore(data_dir)
|
||||
configs = store.load_all()
|
||||
|
||||
active_ids: set[str] = set()
|
||||
new_configs: list[ExtConfig] = []
|
||||
|
||||
for config in configs:
|
||||
if not config.pull or not config.pull.enabled or not config.pull.url:
|
||||
continue
|
||||
active_ids.add(config.id)
|
||||
if config.id not in self._tasks:
|
||||
# 新增调度
|
||||
task = asyncio.create_task(self._run_loop(config))
|
||||
self._tasks[config.id] = task
|
||||
logger.info("PullScheduler: scheduled %s (every %d min)", config.id, config.pull.schedule_minutes)
|
||||
new_configs.append(config)
|
||||
|
||||
# 移除不再活跃的
|
||||
for cid in list(self._tasks):
|
||||
if cid not in active_ids:
|
||||
self._tasks[cid].cancel()
|
||||
del self._tasks[cid]
|
||||
logger.info("PullScheduler: removed %s", cid)
|
||||
# 需要移除的 id (快照当前 task 字典的键, 避免遍历时改字典)
|
||||
remove_ids = [cid for cid in list(self._tasks) if cid not in active_ids]
|
||||
|
||||
# 所有对 _tasks 的修改都提交到主循环里执行, 保证线程安全
|
||||
def _apply() -> None:
|
||||
for config in new_configs:
|
||||
if config.id not in self._tasks: # 二次校验, 防重复
|
||||
self._tasks[config.id] = self._loop.create_task(
|
||||
self._run_loop(config)
|
||||
)
|
||||
logger.info(
|
||||
"PullScheduler: scheduled %s (every %d min)",
|
||||
config.id, config.pull.schedule_minutes,
|
||||
)
|
||||
for cid in remove_ids:
|
||||
task = self._tasks.pop(cid, None)
|
||||
if task is not None:
|
||||
task.cancel()
|
||||
logger.info("PullScheduler: removed %s", cid)
|
||||
|
||||
self._submit(_apply)
|
||||
|
||||
async def _run_loop(self, config: ExtConfig) -> None:
|
||||
"""单个配置的定时拉取循环。"""
|
||||
"""单个配置的定时拉取循环。
|
||||
|
||||
策略: 启用后立即执行一次, 之后按 interval 循环。
|
||||
每次循环重读最新配置 (fresh), interval 取自 fresh.pull.schedule_minutes,
|
||||
这样用户中途修改间隔也能立即生效 (无需重启)。
|
||||
"""
|
||||
try:
|
||||
while self._running:
|
||||
pull = config.pull
|
||||
if not pull:
|
||||
break
|
||||
interval = max(pull.schedule_minutes * 60, 60) # 至少 60s
|
||||
await asyncio.sleep(interval)
|
||||
if not self._running:
|
||||
# 每轮重读最新配置 — 用户可能修改了 url / interval / enabled
|
||||
store = ExtConfigStore(self._data_dir)
|
||||
fresh = store.get(config.id)
|
||||
if not fresh or not fresh.pull or not fresh.pull.enabled:
|
||||
break
|
||||
pull = fresh.pull
|
||||
|
||||
# 先执行一次 (启用即拉取, 让用户立刻看到生效)
|
||||
try:
|
||||
# 重新加载最新配置（用户可能中途修改）
|
||||
store = ExtConfigStore(self._data_dir)
|
||||
fresh = store.get(config.id)
|
||||
if not fresh or not fresh.pull or not fresh.pull.enabled:
|
||||
break
|
||||
n, d = await fetch_and_ingest(fresh, self._data_dir)
|
||||
fresh.pull.last_run = datetime.now(timezone.utc).isoformat()
|
||||
fresh.pull.last_status = "success"
|
||||
@@ -200,14 +258,79 @@ class PullScheduler:
|
||||
store.upsert(fresh)
|
||||
logger.info("PullScheduler: %s success, %d rows", config.id, n)
|
||||
except Exception as e:
|
||||
store = ExtConfigStore(self._data_dir)
|
||||
fresh = store.get(config.id)
|
||||
if fresh and fresh.pull:
|
||||
fresh.pull.last_run = datetime.now(timezone.utc).isoformat()
|
||||
fresh.pull.last_status = "error"
|
||||
fresh.pull.last_message = str(e)[:200]
|
||||
store.upsert(fresh)
|
||||
fresh2 = store.get(config.id)
|
||||
if fresh2 and fresh2.pull:
|
||||
fresh2.pull.last_run = datetime.now(timezone.utc).isoformat()
|
||||
fresh2.pull.last_status = "error"
|
||||
fresh2.pull.last_message = str(e)[:200]
|
||||
store.upsert(fresh2)
|
||||
logger.warning("PullScheduler: %s error: %s", config.id, e)
|
||||
|
||||
# 间隔取自最新配置 (每次重新读取, 修复改间隔不生效)
|
||||
interval = max(pull.schedule_minutes * 60, 60) # 至少 60s
|
||||
# 预告下次运行时间, 供前端展示
|
||||
next_dt = datetime.now(timezone.utc).timestamp() + interval
|
||||
latest = store.get(config.id)
|
||||
if latest and latest.pull:
|
||||
latest.pull.next_run = datetime.fromtimestamp(
|
||||
next_dt, tz=timezone.utc
|
||||
).isoformat()
|
||||
store.upsert(latest)
|
||||
|
||||
await asyncio.sleep(interval)
|
||||
if not self._running:
|
||||
break
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
|
||||
async def _run_loop(self, config: ExtConfig) -> None:
|
||||
"""单个配置的定时拉取循环。
|
||||
|
||||
策略: 启用后立即执行一次, 之后按 interval 循环。
|
||||
每次循环重读最新配置 (fresh), interval 取自 fresh.pull.schedule_minutes,
|
||||
这样用户中途修改间隔也能立即生效 (无需重启)。
|
||||
"""
|
||||
try:
|
||||
while self._running:
|
||||
# 每轮重读最新配置 — 用户可能修改了 url / interval / enabled
|
||||
store = ExtConfigStore(self._data_dir)
|
||||
fresh = store.get(config.id)
|
||||
if not fresh or not fresh.pull or not fresh.pull.enabled:
|
||||
break
|
||||
pull = fresh.pull
|
||||
|
||||
# 先执行一次 (启用即拉取, 让用户立刻看到生效)
|
||||
try:
|
||||
n, d = await fetch_and_ingest(fresh, self._data_dir)
|
||||
fresh.pull.last_run = datetime.now(timezone.utc).isoformat()
|
||||
fresh.pull.last_status = "success"
|
||||
fresh.pull.last_message = f"{n} rows @ {d}"
|
||||
fresh.pull.last_rows = n
|
||||
store.upsert(fresh)
|
||||
logger.info("PullScheduler: %s success, %d rows", config.id, n)
|
||||
except Exception as e:
|
||||
fresh2 = store.get(config.id)
|
||||
if fresh2 and fresh2.pull:
|
||||
fresh2.pull.last_run = datetime.now(timezone.utc).isoformat()
|
||||
fresh2.pull.last_status = "error"
|
||||
fresh2.pull.last_message = str(e)[:200]
|
||||
store.upsert(fresh2)
|
||||
logger.warning("PullScheduler: %s error: %s", config.id, e)
|
||||
|
||||
# 间隔取自最新配置 (每次重新读取, 修复改间隔不生效)
|
||||
interval = max(pull.schedule_minutes * 60, 60) # 至少 60s
|
||||
# 预告下次运行时间, 供前端展示
|
||||
next_dt = datetime.now(timezone.utc).timestamp() + interval
|
||||
latest = store.get(config.id)
|
||||
if latest and latest.pull:
|
||||
latest.pull.next_run = datetime.fromtimestamp(
|
||||
next_dt, tz=timezone.utc
|
||||
).isoformat()
|
||||
store.upsert(latest)
|
||||
|
||||
await asyncio.sleep(interval)
|
||||
if not self._running:
|
||||
break
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
|
||||
|
||||
@@ -0,0 +1,187 @@
|
||||
"""AI 财务分析服务 — 读取个股财务数据 → 构建专业提示词 → 流式调用 LLM。
|
||||
|
||||
职责: 拉取单只标的的 4 张财务表 → 转成紧凑 JSON → 拼装 CFA 分析师级系统提示词
|
||||
→ 流式调用 OpenAI 兼容 API → 逐 chunk 吐给前端。
|
||||
|
||||
不知道: HTTP、前端、配置持久化。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import AsyncIterator
|
||||
|
||||
import polars as pl
|
||||
|
||||
from app.services.financial_sync import get_financial_df
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 最多注入的报告期数(最新 N 期),避免上下文爆炸 / token 浪费
|
||||
_MAX_PERIODS = 4
|
||||
|
||||
|
||||
def _load_stock_financials(data_dir: Path, symbol: str) -> dict[str, list[dict]]:
|
||||
"""读取该标的的 4 张财务表,返回 {table: [records...]}(按 period_end 降序,截取最新 N 期)。
|
||||
|
||||
数值统一做 NaN/Inf → null 清洗,保证 JSON 序列化不报错。
|
||||
"""
|
||||
result: dict[str, list[dict]] = {}
|
||||
for table in ("metrics", "income", "balance_sheet", "cash_flow"):
|
||||
df = get_financial_df(data_dir, table)
|
||||
if df.is_empty():
|
||||
result[table] = []
|
||||
continue
|
||||
df = df.filter(pl.col("symbol") == symbol)
|
||||
if df.is_empty():
|
||||
result[table] = []
|
||||
continue
|
||||
# 按 period_end 降序,截取最新 N 期
|
||||
if "period_end" in df.columns:
|
||||
df = df.sort("period_end", descending=True).head(_MAX_PERIODS)
|
||||
# 清洗 NaN/Inf,转成 JSON 安全的 dict 列表
|
||||
rows = []
|
||||
for rec in df.to_dicts():
|
||||
clean = {}
|
||||
for k, v in rec.items():
|
||||
if k == "symbol":
|
||||
continue # 不需要重复回传 symbol
|
||||
if isinstance(v, float):
|
||||
import math
|
||||
clean[k] = None if not math.isfinite(v) else v
|
||||
else:
|
||||
clean[k] = v
|
||||
rows.append(clean)
|
||||
result[table] = rows
|
||||
return result
|
||||
|
||||
|
||||
def _summarize(fins: dict[str, list[dict]]) -> str:
|
||||
"""生成一行业务摘要,便于 LLM 快速把握数据全貌(行数/期数)。"""
|
||||
parts = []
|
||||
for table in ("metrics", "income", "balance_sheet", "cash_flow"):
|
||||
rows = fins.get(table, [])
|
||||
if rows:
|
||||
periods = [r.get("period_end") for r in rows if r.get("period_end")]
|
||||
parts.append(f"{table}: {len(rows)}期 ({', '.join(str(p) for p in periods[:3])})")
|
||||
else:
|
||||
parts.append(f"{table}: 无数据")
|
||||
return " · ".join(parts)
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 系统提示词 —— CFA 分析师级,九维分析框架
|
||||
# ================================================================
|
||||
|
||||
_SYSTEM_PROMPT = """你是一位拥有 15 年 A 股投研经验的资深财务分析师(CFA + CPA),服务于专业机构投资者。你的任务是:基于提供的上市公司财务数据,产出一份**严谨、专业、可直接用于投资决策**的财务分析报告。
|
||||
|
||||
## 输出规范
|
||||
|
||||
用 **Markdown** 格式输出,严格遵循以下结构。不要输出任何 JSON 或代码块,直接输出 Markdown 正文。
|
||||
|
||||
### 1. 📌 核心摘要(1-2 句)
|
||||
用一句话概括该公司的财务画像:盈利质量、成长动能、财务健康度的最关键判断。结尾用【综合评级:★★★☆☆】给出 1-5 星评级。
|
||||
|
||||
### 2. ✅ 亮点(2-3 条)
|
||||
列出最值得关注的**积极信号**,每条用加粗短语领起,配数据支撑。例如盈利高增、ROE 持续提升、现金流充沛等。
|
||||
|
||||
### 3. ⚠️ 风险提示(2-3 条)
|
||||
客观指出**潜在风险或值得警惕的信号**,例如应收激增、存货堆积、经营现金流与净利润背离、债务攀升等。宁可保守,不要回避。
|
||||
|
||||
### 4. 📊 分项诊断
|
||||
用**表格**呈现各维度的诊断结论,列为「维度 / 关键指标 / 判断」。维度包括:
|
||||
- **盈利能力**:ROE / ROA / 毛利率 / 净利率
|
||||
- **成长性**:营收同比 / 净利润同比
|
||||
- **偿债能力**:资产负债率 / 流动比率(用资产/负债估算)
|
||||
- **现金流**:经营现金流净额 / 与净利润的匹配度
|
||||
- **营运效率**:存货周转率等(有数据时)
|
||||
|
||||
每个判断给「优秀 / 良好 / 一般 / 偏弱 / 警惕」之一,并一句话说明依据。
|
||||
|
||||
### 5. 🎯 综合评估与展望
|
||||
2-3 段总结:该公司当前的财务状态(优秀/稳健/承压/恶化)、核心驱动力、未来需重点跟踪的指标。**结尾给出"投资参考"**:从纯财务质量角度,该股属于(高质量蓝筹 / 稳健成长 / 周期波动 / 财务承压 / 高风险)中的哪一类。
|
||||
|
||||
## 分析准则(务必遵守)
|
||||
|
||||
1. **数据说话**:每个判断必须引用具体数值(如"营收同比 +28.5%"),严禁空泛套话
|
||||
2. **纵向对比**:利用多期数据看趋势(改善/恶化),而非只看单期
|
||||
3. **交叉验证**:经营现金流 vs 净利润(是否造血)、毛利率 vs 费用率(盈利结构)、负债 vs 资产(杠杆)
|
||||
4. **行业常识**:对照 A 股常识判断水平(如 ROE>15% 优秀,资产负债率>70% 偏高,毛利率<20% 偏低)
|
||||
5. **诚实中立**:数据不支持时直言"数据不足,无法判断",绝不编造或过度演绎
|
||||
6. **简明有力**:避免冗长,用专业投资者能扫读的密度输出,总字数 800-1500 字
|
||||
|
||||
## 重要免责
|
||||
报告末尾附一行:"> ⚠️ 本报告由 AI 基于公开财务数据生成,仅供参考,不构成任何投资建议。"
|
||||
|
||||
现在请基于下方数据进行分析。"""
|
||||
|
||||
|
||||
def _build_user_prompt(fins: dict[str, list[dict]], symbol: str, focus: str) -> str:
|
||||
"""构建用户消息:标的代码 + 数据 JSON + 可选关注点。"""
|
||||
data_json = json.dumps(fins, ensure_ascii=False, indent=2)
|
||||
lines = [
|
||||
f"标的标准代码: {symbol}",
|
||||
f"数据概览: {_summarize(fins)}",
|
||||
"",
|
||||
"以下是该标的最新财务数据(JSON 格式,金额单位为元,比率类指标为百分点):",
|
||||
"```json",
|
||||
data_json,
|
||||
"```",
|
||||
]
|
||||
if focus.strip():
|
||||
lines.extend([
|
||||
"",
|
||||
f"本次分析请特别关注: {focus.strip()}",
|
||||
])
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
async def analyze_financials_stream(
|
||||
data_dir: Path,
|
||||
symbol: str,
|
||||
focus: str = "",
|
||||
) -> AsyncIterator[str]:
|
||||
"""流式分析:yield 出每个文本 chunk。
|
||||
|
||||
- 启动时先 yield 一条 {"type":"meta",...} 让前端显示数据摘要
|
||||
- 之后逐 chunk yield {"type":"delta","content":"..."}
|
||||
- 出错时 yield {"type":"error","message":"..."}
|
||||
- 结束 yield {"type":"done"}
|
||||
"""
|
||||
# 1. 加载数据
|
||||
fins = _load_stock_financials(data_dir, symbol)
|
||||
total_rows = sum(len(v) for v in fins.values())
|
||||
if total_rows == 0:
|
||||
yield json.dumps({"type": "error", "message": f"标的 {symbol} 暂无任何财务数据,请先同步财务表"}, ensure_ascii=False)
|
||||
return
|
||||
|
||||
# 2. meta
|
||||
yield json.dumps({
|
||||
"type": "meta",
|
||||
"symbol": symbol,
|
||||
"summary": _summarize(fins),
|
||||
"periods": total_rows,
|
||||
}, ensure_ascii=False)
|
||||
|
||||
# 3. 调用 LLM 流式
|
||||
try:
|
||||
from app.services.ai_provider import stream_ai_text
|
||||
|
||||
user_prompt = _build_user_prompt(fins, symbol, focus)
|
||||
async for delta in stream_ai_text(
|
||||
[
|
||||
{"role": "system", "content": _SYSTEM_PROMPT},
|
||||
{"role": "user", "content": user_prompt},
|
||||
],
|
||||
temperature=0.4,
|
||||
max_tokens=4000,
|
||||
):
|
||||
yield json.dumps({"type": "delta", "content": delta}, ensure_ascii=False)
|
||||
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.exception("AI financial analysis failed for %s: %s", symbol, e)
|
||||
yield json.dumps({"type": "error", "message": f"AI 分析失败: {e}"}, ensure_ascii=False)
|
||||
return
|
||||
|
||||
yield json.dumps({"type": "done"}, ensure_ascii=False)
|
||||
@@ -8,7 +8,7 @@ from __future__ import annotations
|
||||
import asyncio
|
||||
import logging
|
||||
import threading
|
||||
from datetime import date, datetime, timezone
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
@@ -200,15 +200,82 @@ class FinancialScheduler:
|
||||
# 手动同步(run_now)是否正在进行。前端据此显示"同步中"并防重复点击。
|
||||
self._is_syncing = False
|
||||
|
||||
def start(self, data_dir: Path, capset: CapabilitySet) -> None:
|
||||
def start(self, data_dir: Path, capset: CapabilitySet, *, auto_schedule: bool = False) -> None:
|
||||
"""初始化调度器，并按需启动周期同步后台任务。
|
||||
|
||||
auto_schedule=False (默认): 仅初始化 (设置数据目录/能力 + 恢复 last_sync),
|
||||
供 /api/financials/sync/* 手动同步使用, 不启动自动调度。
|
||||
auto_schedule=True: 额外启动每周一次的 metrics 自动同步 (启动后 60s 首跑)。
|
||||
"""
|
||||
# 先记录 data_dir/capset, 即使当前无 FINANCIAL 也保留引用:
|
||||
# 用户稍后在「设置」页升级到 Expert Key 时, update_capabilities() 会把新 capset
|
||||
# 推进来,trigger()/run_now() 才能用上 FINANCIAL。否则 _capset 永远是 None,
|
||||
# 即便 app.state.capabilities 已更新, 调度器仍报 "no FINANCIAL capability"。
|
||||
self._data_dir = data_dir
|
||||
self._capset = capset
|
||||
if not capset.has(Cap.FINANCIAL):
|
||||
logger.info("FinancialScheduler skipped: no FINANCIAL capability")
|
||||
return
|
||||
self._data_dir = data_dir
|
||||
self._capset = capset
|
||||
# 从持久化恢复上次同步时间: 重启后前端仍能显示真实最后同步时间,而非"尚未同步"
|
||||
try:
|
||||
from app.services import preferences
|
||||
restored = dict(preferences.get_financial_sync_times())
|
||||
# 老用户迁移兜底: 若某表在 preferences 无记录但 parquet 已存在(升级前同步过),
|
||||
# 用 parquet 文件的修改时间作为同步时间并补写持久化。
|
||||
for table in FINANCIAL_TABLES:
|
||||
if table in restored:
|
||||
continue
|
||||
parquet = data_dir / "financials" / table / "part.parquet"
|
||||
if parquet.exists():
|
||||
mtime = datetime.fromtimestamp(parquet.stat().st_mtime, tz=timezone.utc).isoformat()
|
||||
restored[table] = mtime
|
||||
preferences.set_financial_sync_time(table, mtime)
|
||||
logger.info("FinancialScheduler backfilled last_sync for %s from parquet mtime", table)
|
||||
self._last_sync = restored
|
||||
if self._last_sync:
|
||||
logger.info("FinancialScheduler restored last_sync: %s", list(self._last_sync.keys()))
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("restore financial_sync_times failed: %s", e)
|
||||
|
||||
if not auto_schedule:
|
||||
# 仅初始化 (手动同步用), 不启动周期任务。
|
||||
logger.info("FinancialScheduler initialized (auto-schedule disabled; manual sync only)")
|
||||
return
|
||||
|
||||
self._running = True
|
||||
self._task = asyncio.create_task(self._run_loop())
|
||||
logger.info("FinancialScheduler started")
|
||||
logger.info("FinancialScheduler started (auto-schedule enabled)")
|
||||
|
||||
def _record_sync(self, table: str) -> None:
|
||||
"""记录一张表的同步完成时间: 更新内存 + 持久化到 preferences.json。
|
||||
|
||||
持久化确保即使重启,前端 /status 仍返回真实的最后同步时间,
|
||||
不会错误地显示"尚未同步"。
|
||||
"""
|
||||
ts = datetime.now(timezone.utc).isoformat()
|
||||
self._last_sync[table] = ts
|
||||
try:
|
||||
from app.services import preferences
|
||||
preferences.set_financial_sync_time(table, ts)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("persist financial_sync_time(%s) failed: %s", e)
|
||||
|
||||
def update_capabilities(self, capset: CapabilitySet) -> None:
|
||||
"""刷新调度器持有的能力集。
|
||||
|
||||
用户在「设置」页新增/清除 API Key 后, settings API 会重新探测能力并更新
|
||||
app.state.capabilities; 必须同步推给本调度器, 否则 trigger()/run_now() 仍读
|
||||
启动时的旧 capset, 即便 app.state 已含 FINANCIAL, 调度器仍报
|
||||
"no FINANCIAL capability" 而拒绝同步 (表现为前端「全部同步」按钮闪一下无动作)。
|
||||
"""
|
||||
prev = self._capset
|
||||
self._capset = capset
|
||||
had = bool(prev) and prev.has(Cap.FINANCIAL)
|
||||
now = capset.has(Cap.FINANCIAL)
|
||||
if had != now:
|
||||
logger.info(
|
||||
"FinancialScheduler capabilities updated: FINANCIAL %s -> %s", had, now
|
||||
)
|
||||
|
||||
def stop(self) -> None:
|
||||
self._running = False
|
||||
@@ -217,22 +284,6 @@ class FinancialScheduler:
|
||||
self._task = None
|
||||
logger.info("FinancialScheduler stopped")
|
||||
|
||||
def update(self, data_dir: Path, capset: CapabilitySet) -> None:
|
||||
"""运行时更新数据目录和能力集。
|
||||
|
||||
用户在设置页更换/清除 Key 后,能力集可能变化,无需重启服务即可让
|
||||
财务调度器生效或失效。
|
||||
"""
|
||||
had_financial = self._capset is not None and self._capset.has(Cap.FINANCIAL)
|
||||
has_financial = capset.has(Cap.FINANCIAL)
|
||||
self._data_dir = data_dir
|
||||
self._capset = capset
|
||||
|
||||
if has_financial and not self._running:
|
||||
self.start(data_dir, capset)
|
||||
elif had_financial and not has_financial and self._running:
|
||||
self.stop()
|
||||
|
||||
async def _run_loop(self) -> None:
|
||||
"""每周执行一次 metrics 同步。"""
|
||||
try:
|
||||
@@ -245,7 +296,7 @@ class FinancialScheduler:
|
||||
# 每周: 只同步 metrics
|
||||
try:
|
||||
rows = sync_metrics(self._data_dir, self._capset)
|
||||
self._last_sync["metrics"] = datetime.now(timezone.utc).isoformat()
|
||||
self._record_sync("metrics")
|
||||
logger.info("FinancialScheduler: metrics synced, %d rows", rows)
|
||||
except Exception as e:
|
||||
logger.warning("FinancialScheduler: metrics sync failed: %s", e)
|
||||
@@ -259,8 +310,39 @@ class FinancialScheduler:
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
|
||||
def _run_body(self, table: str | None) -> dict[str, int]:
|
||||
"""同步逻辑本体(不加锁,假设调用方已持有 _is_syncing)。
|
||||
|
||||
table=None 同步全部 4 张表;否则只同步指定表。
|
||||
每张表完成立即更新 last_sync,让前端轮询 /status 能看到进度递增。
|
||||
"""
|
||||
if table:
|
||||
fn = {
|
||||
"metrics": sync_metrics,
|
||||
"income": sync_income,
|
||||
"balance_sheet": sync_balance_sheet,
|
||||
"cash_flow": sync_cash_flow,
|
||||
}.get(table)
|
||||
if not fn:
|
||||
return {}
|
||||
rows = fn(self._data_dir, self._capset)
|
||||
self._record_sync(table)
|
||||
return {table: rows}
|
||||
# 全部同步
|
||||
symbols = _get_symbols(self._data_dir)
|
||||
result: dict[str, int] = {}
|
||||
for t in FINANCIAL_TABLES:
|
||||
result[t] = _sync_table(t, symbols, self._data_dir, self._capset, latest_only=True)
|
||||
self._record_sync(t)
|
||||
_refresh_financials_views(self._data_dir)
|
||||
return result
|
||||
|
||||
def run_now(self, table: str | None = None) -> dict[str, int]:
|
||||
"""手动触发同步。table=None 同步全部。
|
||||
"""同步执行一次同步(阻塞调用线程)。
|
||||
|
||||
⚠ 全量同步需数分钟,务必在后台线程调用,不要直接在 HTTP 请求线程里阻塞,
|
||||
否则请求会长时间 pending 直至被浏览器/代理超时掐断(表现为"点击无反应")。
|
||||
HTTP 接口应调用 trigger() 立即返回,再让前端轮询 /status.syncing 看进度。
|
||||
|
||||
用 _is_syncing 标志防并发:若已有同步在进行,本次直接跳过,
|
||||
避免重复请求拖慢服务端 / 触发上游限流。
|
||||
@@ -273,32 +355,46 @@ class FinancialScheduler:
|
||||
return {"_skipped": 1}
|
||||
self._is_syncing = True
|
||||
try:
|
||||
if table:
|
||||
fn = {
|
||||
"metrics": sync_metrics,
|
||||
"income": sync_income,
|
||||
"balance_sheet": sync_balance_sheet,
|
||||
"cash_flow": sync_cash_flow,
|
||||
}.get(table)
|
||||
if not fn:
|
||||
return {}
|
||||
rows = fn(self._data_dir, self._capset)
|
||||
self._last_sync[table] = datetime.now(timezone.utc).isoformat()
|
||||
return {table: rows}
|
||||
else:
|
||||
# 全部同步: 逐表执行, 每张完成立即更新 last_sync,
|
||||
# 让前端轮询 /status 能看到进度递增 (而非等全部完成才一次性更新)。
|
||||
symbols = _get_symbols(self._data_dir)
|
||||
result: dict[str, int] = {}
|
||||
for t in FINANCIAL_TABLES:
|
||||
result[t] = _sync_table(t, symbols, self._data_dir, self._capset, latest_only=True)
|
||||
self._last_sync[t] = datetime.now(timezone.utc).isoformat()
|
||||
_refresh_financials_views(self._data_dir)
|
||||
return result
|
||||
return self._run_body(table)
|
||||
finally:
|
||||
with self._lock:
|
||||
self._is_syncing = False
|
||||
|
||||
def trigger(self, table: str | None = None) -> dict[str, int]:
|
||||
"""触发一次同步(非阻塞,立即返回)。
|
||||
|
||||
在后台线程执行同步体,HTTP 请求无需等待。
|
||||
返回 {"started": True/False}:
|
||||
- False = 能力不足或已有同步在进行(被防并发跳过)
|
||||
- True = 已在后台开始,前端应轮询 /status.syncing 观察进度
|
||||
|
||||
⚠ _is_syncing 在此处置 True(持锁),确保 trigger 返回时前端轮询
|
||||
/status 已能看到 syncing=True,无竞态窗口;同时防止快速重复点击
|
||||
启动多个后台线程。后台线程复用 _run_body 执行真正的同步逻辑。
|
||||
"""
|
||||
if not self._capset or not self._capset.has(Cap.FINANCIAL):
|
||||
return {"started": False, "reason": "no FINANCIAL capability"}
|
||||
with self._lock:
|
||||
if self._is_syncing:
|
||||
logger.info("financial sync trigger skipped: already running")
|
||||
return {"started": False, "reason": "already running"}
|
||||
# 持锁置位:保证 trigger 返回前 syncing 已为 True
|
||||
self._is_syncing = True
|
||||
|
||||
def _bg() -> None:
|
||||
try:
|
||||
self._run_body(table)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.exception("background financial sync failed: %s", e)
|
||||
finally:
|
||||
with self._lock:
|
||||
self._is_syncing = False
|
||||
|
||||
t = threading.Thread(target=_bg, name="financial-sync", daemon=True)
|
||||
t.start()
|
||||
logger.info("financial sync triggered in background: table=%s", table or "all")
|
||||
return {"started": True}
|
||||
|
||||
@property
|
||||
def is_syncing(self) -> bool:
|
||||
"""手动同步是否正在进行(供 /status 返回,前端据此显示"同步中")。"""
|
||||
|
||||
@@ -1,8 +1,14 @@
|
||||
"""指数数据同步服务。"""
|
||||
"""指数 / ETF 数据同步服务。
|
||||
|
||||
标的列表优先用免费的 exchanges.get_instruments(type=index/etf) 拉取
|
||||
(None/Free 档均可用,无需 quote.pool 权限);付费档可额外用
|
||||
quotes.get_by_universes 作为补充来源。日K统一走 klines.batch。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import gc
|
||||
from collections.abc import Callable
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
import polars as pl
|
||||
@@ -15,9 +21,15 @@ from app.tickflow.repository import KlineRepository
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# exchanges.get_instruments 查询的交易所(沪深京)
|
||||
_EXCHANGES = ["SH", "SZ", "BJ"]
|
||||
|
||||
|
||||
def _quotes_to_index_instruments(resp) -> pl.DataFrame:
|
||||
"""将数据源 quotes 响应规范为指数 instruments。"""
|
||||
"""将 TickFlow quotes 响应(get_by_universes)规范为指数 instruments。
|
||||
|
||||
付费档(Starter+)的补充来源,免费档用不到。
|
||||
"""
|
||||
if resp is None:
|
||||
return pl.DataFrame()
|
||||
|
||||
@@ -61,33 +73,119 @@ def _quotes_to_index_instruments(resp) -> pl.DataFrame:
|
||||
return result.unique(subset=["symbol"], keep="last").sort("symbol")
|
||||
|
||||
|
||||
def sync_index_instruments(repo: KlineRepository) -> int:
|
||||
"""同步 CN_Index 指数标的维表，返回指数数量。"""
|
||||
def _fetch_instruments_by_type(instrument_type: str, asset_type_label: str) -> pl.DataFrame:
|
||||
"""用免费的 exchanges.get_instruments 拉取指定类型的标的列表。
|
||||
|
||||
None/Free 档均可使用(标的信息查询免费开放)。
|
||||
instrument_type: 'index' / 'etf'
|
||||
asset_type_label: 写入 instruments 表的 asset_type 标记('index' / 'etf')
|
||||
"""
|
||||
tf = get_client()
|
||||
resp = None
|
||||
errors: list[str] = []
|
||||
for kwargs in (
|
||||
{"universes": ["CN_Index"]},
|
||||
{"universes": ["CN_Index"], "as_dataframe": False},
|
||||
):
|
||||
rows: list[dict] = []
|
||||
for ex in _EXCHANGES:
|
||||
try:
|
||||
resp = tf.quotes.get_by_universes(**kwargs)
|
||||
if resp is not None and len(resp) > 0:
|
||||
break
|
||||
items = tf.exchanges.get_instruments(ex, instrument_type=instrument_type)
|
||||
for it in items or []:
|
||||
item = it if isinstance(it, dict) else {}
|
||||
symbol = item.get("symbol")
|
||||
if not symbol:
|
||||
continue
|
||||
rows.append({
|
||||
"symbol": str(symbol),
|
||||
"name": item.get("name") or str(symbol),
|
||||
})
|
||||
except Exception as e: # noqa: BLE001
|
||||
errors.append(str(e))
|
||||
resp = None
|
||||
logger.warning("get_instruments(%s, type=%s) failed: %s", ex, instrument_type, e)
|
||||
|
||||
if resp is None or len(resp) == 0:
|
||||
logger.warning("CN_Index universe returned empty: %s", "; ".join(errors))
|
||||
return 0
|
||||
if not rows:
|
||||
return pl.DataFrame()
|
||||
|
||||
instruments = _quotes_to_index_instruments(resp)
|
||||
if instruments.is_empty():
|
||||
return (
|
||||
pl.DataFrame(rows)
|
||||
.with_columns([
|
||||
pl.col("symbol").str.split(".").list.first().alias("code"),
|
||||
pl.lit(asset_type_label).alias("asset_type"),
|
||||
])
|
||||
.unique(subset=["symbol"], keep="last")
|
||||
.sort("symbol")
|
||||
)
|
||||
|
||||
|
||||
def sync_index_instruments(
|
||||
repo: KlineRepository,
|
||||
pull_index: bool = True,
|
||||
pull_etf: bool = True,
|
||||
) -> int:
|
||||
"""同步指数 / ETF 标的维表,返回标的总数。
|
||||
|
||||
新版物理分开保存: 指数写 instruments_index, ETF 写 instruments_etf。
|
||||
读取层仍兼容旧版 instruments_index 中 asset_type='etf' 的历史数据。
|
||||
"""
|
||||
index_parts: list[pl.DataFrame] = []
|
||||
etf_parts: list[pl.DataFrame] = []
|
||||
|
||||
# 1) 免费通道:按开关分别拉 index / etf
|
||||
if pull_index:
|
||||
index_df = _fetch_instruments_by_type("index", "index")
|
||||
if not index_df.is_empty():
|
||||
index_parts.append(index_df)
|
||||
if pull_etf:
|
||||
etf_df = _fetch_instruments_by_type("etf", "etf")
|
||||
if not etf_df.is_empty():
|
||||
etf_parts.append(etf_df)
|
||||
|
||||
# 2) 付费补充:Starter+ 用 get_by_universes 补指数(仅当开启指数拉取)
|
||||
if pull_index:
|
||||
capset = None
|
||||
try:
|
||||
from app.tickflow import policy
|
||||
capset = policy.detect_capabilities(force=False)
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
if capset is not None and capset.has(Cap.QUOTE_POOL):
|
||||
tf = get_client()
|
||||
for kwargs in (
|
||||
{"universes": ["CN_Index"]},
|
||||
{"universes": ["CN_Index"], "as_dataframe": False},
|
||||
):
|
||||
try:
|
||||
resp = tf.quotes.get_by_universes(**kwargs)
|
||||
if resp is not None and len(resp) > 0:
|
||||
sup = _quotes_to_index_instruments(resp)
|
||||
if not sup.is_empty():
|
||||
index_parts.append(sup)
|
||||
break
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.debug("CN_Index universe supplement failed: %s", e)
|
||||
|
||||
total = 0
|
||||
if index_parts:
|
||||
index_inst = pl.concat(index_parts, how="diagonal_relaxed").unique(subset=["symbol"], keep="last").sort("symbol")
|
||||
if not index_inst.is_empty():
|
||||
repo.save_index_instruments(index_inst)
|
||||
total += index_inst.height
|
||||
if etf_parts:
|
||||
etf_inst = pl.concat(etf_parts, how="diagonal_relaxed").unique(subset=["symbol"], keep="last").sort("symbol")
|
||||
if not etf_inst.is_empty():
|
||||
repo.save_etf_instruments(etf_inst)
|
||||
total += etf_inst.height
|
||||
|
||||
if total == 0:
|
||||
logger.warning("指数/ETF 标的列表为空(pull_index=%s, pull_etf=%s)", pull_index, pull_etf)
|
||||
return 0
|
||||
repo.save_index_instruments(instruments)
|
||||
repo.refresh_index_views()
|
||||
return instruments.height
|
||||
logger.info("指数/ETF 标的同步完成: %d 只", total)
|
||||
return total
|
||||
|
||||
|
||||
def sync_etf_instruments(repo: KlineRepository) -> int:
|
||||
"""单独同步 ETF 标的维表(返回 ETF 数量)。"""
|
||||
etf_df = _fetch_instruments_by_type("etf", "etf")
|
||||
if etf_df.is_empty():
|
||||
return 0
|
||||
repo.save_etf_instruments(etf_df)
|
||||
repo.refresh_index_views()
|
||||
return etf_df.height
|
||||
|
||||
|
||||
def sync_and_persist_index_daily(
|
||||
@@ -96,19 +194,32 @@ def sync_and_persist_index_daily(
|
||||
count: int | None = None,
|
||||
start_date: datetime | None = None,
|
||||
end_date: datetime | None = None,
|
||||
symbols_override: list[str] | None = None,
|
||||
on_chunk_done: Callable[[int, int], None] | None = None,
|
||||
) -> int:
|
||||
"""同步指数日K到独立 parquet，并计算指数 enriched。"""
|
||||
"""同步指数/ETF 日K到独立 parquet,并计算 enriched。
|
||||
|
||||
symbols_override 非空时,只拉这些代码(跳过 instruments 表),用于自定义范围。
|
||||
否则取 index_instruments 表全量(指数+ETF 合并存储)。
|
||||
on_chunk_done(current, total) 每个批次完成后回调。
|
||||
"""
|
||||
if not capset.has(Cap.KLINE_DAILY_BATCH):
|
||||
return 0
|
||||
|
||||
instruments = repo.get_index_instruments()
|
||||
if instruments.is_empty():
|
||||
sync_index_instruments(repo)
|
||||
if symbols_override:
|
||||
symbols = sorted(set(s for s in symbols_override if s))
|
||||
if not symbols:
|
||||
return 0
|
||||
else:
|
||||
instruments = repo.get_index_instruments()
|
||||
if instruments.is_empty() or "symbol" not in instruments.columns:
|
||||
return 0
|
||||
|
||||
symbols = sorted(set(instruments["symbol"].to_list()))
|
||||
if instruments.is_empty():
|
||||
sync_index_instruments(repo, pull_index=True, pull_etf=False)
|
||||
instruments = repo.get_index_instruments()
|
||||
if not instruments.is_empty() and "asset_type" in instruments.columns:
|
||||
instruments = instruments.filter(pl.col("asset_type") != "etf")
|
||||
if instruments.is_empty() or "symbol" not in instruments.columns:
|
||||
return 0
|
||||
symbols = sorted(set(instruments["symbol"].to_list()))
|
||||
lim = capset.limits(Cap.KLINE_DAILY_BATCH)
|
||||
batch_size = preferences.get_index_daily_batch_size()
|
||||
if lim and lim.batch:
|
||||
@@ -139,7 +250,110 @@ def sync_and_persist_index_daily(
|
||||
enriched = compute_enriched(raw, factors=None, instruments=None)
|
||||
repo.append_index_enriched(enriched)
|
||||
total_rows += raw.height
|
||||
logger.info("index daily synced: %d/%d chunks, +%d rows", i + 1, len(chunks), raw.height)
|
||||
logger.info("index/etf daily synced: %d/%d chunks, +%d rows", i + 1, len(chunks), raw.height)
|
||||
if on_chunk_done:
|
||||
on_chunk_done(i + 1, len(chunks))
|
||||
del raw, enriched
|
||||
gc.collect()
|
||||
repo.refresh_index_views()
|
||||
return total_rows
|
||||
|
||||
|
||||
def _load_etf_factors(repo: KlineRepository) -> pl.DataFrame:
|
||||
factor_path = repo.store.data_dir / "adj_factor_etf" / "all.parquet"
|
||||
if not factor_path.exists():
|
||||
return pl.DataFrame()
|
||||
try:
|
||||
return pl.read_parquet(factor_path)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("ETF 复权因子读取失败: %s", e)
|
||||
return pl.DataFrame()
|
||||
|
||||
|
||||
def sync_etf_adj_factor(
|
||||
symbols: list[str],
|
||||
repo: KlineRepository,
|
||||
capset: CapabilitySet,
|
||||
start_time: datetime | None = None,
|
||||
end_time: datetime | None = None,
|
||||
on_chunk_done=None,
|
||||
) -> tuple[int, list[str]]:
|
||||
"""同步 ETF 复权因子；失败由调用方降级为 warning。"""
|
||||
return kline_sync.sync_adj_factor(
|
||||
symbols,
|
||||
repo,
|
||||
capset,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
on_chunk_done=on_chunk_done,
|
||||
asset_type="etf",
|
||||
)
|
||||
|
||||
|
||||
def sync_and_persist_etf_daily(
|
||||
repo: KlineRepository,
|
||||
capset: CapabilitySet,
|
||||
count: int | None = None,
|
||||
start_date: datetime | None = None,
|
||||
end_date: datetime | None = None,
|
||||
symbols_override: list[str] | None = None,
|
||||
on_chunk_done: Callable[[int, int], None] | None = None,
|
||||
) -> int:
|
||||
"""同步 ETF 日K到独立 kline_etf_* parquet,并计算 ETF enriched。
|
||||
on_chunk_done(current, total) 每个批次完成后回调。
|
||||
"""
|
||||
if not capset.has(Cap.KLINE_DAILY_BATCH):
|
||||
return 0
|
||||
|
||||
if symbols_override:
|
||||
symbols = sorted(set(s for s in symbols_override if s))
|
||||
else:
|
||||
instruments = repo.get_etf_instruments()
|
||||
if instruments.is_empty():
|
||||
sync_etf_instruments(repo)
|
||||
instruments = repo.get_etf_instruments()
|
||||
if instruments.is_empty() or "symbol" not in instruments.columns:
|
||||
return 0
|
||||
symbols = sorted(set(instruments["symbol"].to_list()))
|
||||
if not symbols:
|
||||
return 0
|
||||
|
||||
lim = capset.limits(Cap.KLINE_DAILY_BATCH)
|
||||
batch_size = preferences.get_index_daily_batch_size()
|
||||
if lim and lim.batch:
|
||||
batch_size = min(batch_size, lim.batch)
|
||||
rpm = lim.rpm if lim else None
|
||||
|
||||
end_time = end_date or datetime.now()
|
||||
start_time = start_date or (end_time - timedelta(days=365))
|
||||
|
||||
total_rows = 0
|
||||
interval = (60.0 / rpm) if rpm else 0
|
||||
chunks = [symbols[i:i + batch_size] for i in range(0, len(symbols), batch_size)]
|
||||
factors = _load_etf_factors(repo)
|
||||
for i, chunk in enumerate(chunks):
|
||||
if i > 0 and interval > 0 and len(chunks) > rpm:
|
||||
import time
|
||||
time.sleep(interval)
|
||||
raw = kline_sync.sync_daily_batch(
|
||||
chunk,
|
||||
count=count,
|
||||
batch_size=None,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
)
|
||||
if raw.is_empty():
|
||||
continue
|
||||
|
||||
repo.append_etf_daily(raw)
|
||||
batch_factors = factors.filter(pl.col("symbol").is_in(chunk)) if not factors.is_empty() else factors
|
||||
# ETF 使用复权和通用技术指标；不传 instruments，避免套用 A股涨跌停/连板逻辑。
|
||||
enriched = compute_enriched(raw, factors=batch_factors, instruments=None)
|
||||
repo.append_etf_enriched(enriched)
|
||||
total_rows += raw.height
|
||||
logger.info("etf daily synced: %d/%d chunks, +%d rows", i + 1, len(chunks), raw.height)
|
||||
if on_chunk_done:
|
||||
on_chunk_done(i + 1, len(chunks))
|
||||
del raw, enriched
|
||||
gc.collect()
|
||||
repo.refresh_index_views()
|
||||
|
||||
@@ -223,11 +223,53 @@ def sync_daily_by_quotes(repo: KlineRepository) -> int:
|
||||
return daily_df.height
|
||||
|
||||
|
||||
def _normalize_adj_factor(raw) -> pl.DataFrame:
|
||||
"""Normalize SDK ex_factors response to symbol/trade_date/ex_factor."""
|
||||
if raw is None or len(raw) == 0:
|
||||
return pl.DataFrame()
|
||||
if isinstance(raw, dict):
|
||||
rows: list[dict] = []
|
||||
for sym, values in raw.items():
|
||||
for item in values or []:
|
||||
row = dict(item or {})
|
||||
row.setdefault("symbol", sym)
|
||||
rows.append(row)
|
||||
df = pl.DataFrame(rows) if rows else pl.DataFrame()
|
||||
elif isinstance(raw, pl.DataFrame):
|
||||
df = raw
|
||||
else:
|
||||
df = pl.from_pandas(raw.reset_index() if hasattr(raw, "reset_index") else raw)
|
||||
if df.is_empty():
|
||||
return df
|
||||
# rename: timestamp/date → trade_date, adj_factor → ex_factor
|
||||
# 注意: 新版 SDK 可能同时返回 timestamp 和 trade_date (或 adj_factor 和 ex_factor),
|
||||
# 直接 rename 会产生重复列报错。仅当目标列不存在时才 rename。
|
||||
rename_map: dict[str, str] = {}
|
||||
for src, dst in (("timestamp", "trade_date"), ("date", "trade_date"), ("adj_factor", "ex_factor")):
|
||||
if src in df.columns and dst not in df.columns:
|
||||
rename_map[src] = dst
|
||||
df = df.rename(rename_map)
|
||||
if "trade_date" in df.columns:
|
||||
if df.schema["trade_date"] in {pl.Int64, pl.Int32, pl.UInt64, pl.UInt32, pl.Float64, pl.Float32}:
|
||||
df = df.with_columns(
|
||||
pl.from_epoch(pl.col("trade_date").cast(pl.Int64), time_unit="ms").dt.date().alias("trade_date")
|
||||
)
|
||||
else:
|
||||
df = df.with_columns(pl.col("trade_date").cast(pl.Date, strict=False))
|
||||
if "ex_factor" in df.columns:
|
||||
df = df.with_columns(pl.col("ex_factor").cast(pl.Float64, strict=False))
|
||||
cols = [c for c in ["symbol", "trade_date", "ex_factor"] if c in df.columns]
|
||||
if len(cols) < 3:
|
||||
return pl.DataFrame()
|
||||
return df.select(cols).drop_nulls()
|
||||
|
||||
|
||||
def sync_adj_factor(symbols: list[str], repo: KlineRepository,
|
||||
capset: CapabilitySet,
|
||||
start_time: datetime | None = None,
|
||||
end_time: datetime | None = None,
|
||||
on_chunk_done: Callable[[int, int], None] | None = None) -> tuple[int, list[str]]:
|
||||
on_chunk_done: Callable[[int, int], None] | None = None,
|
||||
asset_type: str = "stock") -> tuple[int, list[str]]:
|
||||
"""同步除权因子(Starter+)。SDK 接口:`tf.klines.ex_factors(symbols=...)`。
|
||||
|
||||
支持增量: 传 start_time/end_time 只拉取该时间范围内的新除权事件。
|
||||
@@ -257,10 +299,9 @@ def sync_adj_factor(symbols: list[str], repo: KlineRepository,
|
||||
time.sleep(interval)
|
||||
try:
|
||||
raw = tf.klines.ex_factors(chunk, **sdk_kwargs)
|
||||
if raw is not None and len(raw) > 0:
|
||||
all_dfs.append(pl.from_pandas(
|
||||
raw.reset_index() if hasattr(raw, "reset_index") else raw
|
||||
))
|
||||
normalized = _normalize_adj_factor(raw)
|
||||
if not normalized.is_empty():
|
||||
all_dfs.append(normalized)
|
||||
logger.debug("adj_factor chunk %d/%d: %d symbols", i + 1, len(chunks), len(chunk))
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("adj_factor chunk %d failed: %s", i + 1, e)
|
||||
@@ -276,7 +317,8 @@ def sync_adj_factor(symbols: list[str], repo: KlineRepository,
|
||||
# 提取受影响的 symbol 列表(合并前)
|
||||
affected = new_data["symbol"].unique().to_list()
|
||||
|
||||
out = repo.store.data_dir / "adj_factor" / "all.parquet"
|
||||
factor_dir = "adj_factor_etf" if asset_type == "etf" else "adj_factor"
|
||||
out = repo.store.data_dir / factor_dir / "all.parquet"
|
||||
out.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
if out.exists():
|
||||
@@ -420,7 +462,7 @@ def sync_minute_batch(
|
||||
|
||||
|
||||
def fetch_minute_single(symbol: str, trade_date: date) -> pl.DataFrame:
|
||||
"""从数据源实时拉取单股单日分钟 K（不写入本地）。"""
|
||||
"""从 TickFlow 实时拉取单股单日分钟 K（不写入本地）。"""
|
||||
from datetime import datetime
|
||||
start_time = datetime(trade_date.year, trade_date.month, trade_date.day, 9, 25, 0)
|
||||
end_time = datetime(trade_date.year, trade_date.month, trade_date.day, 15, 5, 0)
|
||||
@@ -445,6 +487,21 @@ def fetch_minute_single(symbol: str, trade_date: date) -> pl.DataFrame:
|
||||
return pl.DataFrame()
|
||||
|
||||
|
||||
def fetch_adj_factor_single(symbol: str) -> pl.DataFrame:
|
||||
"""从 TickFlow 实时拉取单股除权因子(不写入本地), 用于单股 K 线即时前复权。
|
||||
|
||||
返回结构: symbol, trade_date, ex_factor (空 DataFrame 表示无除权事件或拉取失败)。
|
||||
与 _apply_adj_factor / compute_enriched 的 factors 参数格式一致。
|
||||
"""
|
||||
tf = get_client()
|
||||
try:
|
||||
raw = tf.klines.ex_factors([symbol], as_dataframe=True, show_progress=False)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("fetch_adj_factor_single(%s) failed: %s", symbol, e)
|
||||
return pl.DataFrame()
|
||||
return _normalize_adj_factor(raw)
|
||||
|
||||
|
||||
def _latest_minute_datetime(repo: KlineRepository) -> datetime | None:
|
||||
"""本地分钟 K 数据的最新时间。"""
|
||||
try:
|
||||
|
||||
@@ -0,0 +1,576 @@
|
||||
"""市场总览数据装配(与 HTTP Request 解耦)。
|
||||
|
||||
本模块由 `app.api.overview._build_overview` 抽离而来,目的是让「大盘复盘」
|
||||
等无 Request 的调用方(定时任务、复盘服务)也能复用同一套聚合逻辑。
|
||||
|
||||
行为与原 `_build_overview` 完全一致,仅把对 `request.app.state.{repo,
|
||||
quote_service,depth_service}` 的依赖改为显式参数。
|
||||
|
||||
公共入口:
|
||||
build_market_overview(repo, quote_service, depth_service, as_of)
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
import re
|
||||
from datetime import date
|
||||
from typing import Any
|
||||
|
||||
import polars as pl
|
||||
|
||||
from app.services.ext_data import ExtConfig, ExtConfigStore
|
||||
from app.services.screener import ScreenerService
|
||||
|
||||
# ================================================================
|
||||
# 常量(与 overview.py 保持同步;复盘复盘仅 A 股核心指数)
|
||||
# ================================================================
|
||||
|
||||
CORE_INDEX_NAMES = {
|
||||
"000001.SH": "上证指数",
|
||||
"399001.SZ": "深证成指",
|
||||
"399006.SZ": "创业板指",
|
||||
"000680.SH": "科创综指",
|
||||
}
|
||||
CORE_INDEX_SYMBOLS = tuple(CORE_INDEX_NAMES.keys())
|
||||
|
||||
_DIMENSION_SEP = re.compile(r"[、,，;；|/\s]+")
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 通用工具
|
||||
# ================================================================
|
||||
|
||||
def _finite(v: Any) -> float | None:
|
||||
if v is None:
|
||||
return None
|
||||
try:
|
||||
f = float(v)
|
||||
except (TypeError, ValueError):
|
||||
return None
|
||||
return f if math.isfinite(f) else None
|
||||
|
||||
|
||||
def _json_safe(value: Any) -> Any:
|
||||
if isinstance(value, dict):
|
||||
return {k: _json_safe(v) for k, v in value.items()}
|
||||
if isinstance(value, list):
|
||||
return [_json_safe(v) for v in value]
|
||||
if isinstance(value, float) and not math.isfinite(value):
|
||||
return None
|
||||
return value
|
||||
|
||||
|
||||
def _board(symbol: str) -> str:
|
||||
if symbol.endswith(".BJ"):
|
||||
return "北交所"
|
||||
if symbol.startswith(("300", "301")):
|
||||
return "创业板"
|
||||
if symbol.startswith(("688", "689")):
|
||||
return "科创板"
|
||||
if symbol.endswith(".SH"):
|
||||
return "沪主板"
|
||||
if symbol.endswith(".SZ"):
|
||||
return "深主板"
|
||||
return "其他"
|
||||
|
||||
|
||||
def _score(value: float, low: float, high: float) -> int:
|
||||
if high <= low:
|
||||
return 50
|
||||
return max(0, min(100, round((value - low) / (high - low) * 100)))
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 指数行情(实时 quote_service 优先,回退 kline_index_daily SQL)
|
||||
# ================================================================
|
||||
|
||||
def _quote_status(quote_service) -> dict:
|
||||
qs = quote_service
|
||||
if not qs:
|
||||
return {"enabled": False, "running": False, "quote_age_ms": None, "is_trading_hours": False}
|
||||
return qs.status()
|
||||
|
||||
|
||||
def _index_quotes(repo, quote_service, as_of: date | None = None) -> list[dict]:
|
||||
rows: list[dict] = []
|
||||
if quote_service and as_of is None:
|
||||
df = quote_service.get_index_quotes(list(CORE_INDEX_SYMBOLS))
|
||||
if not df.is_empty():
|
||||
rows = df.to_dicts()
|
||||
|
||||
if not rows and repo:
|
||||
placeholders = ", ".join("?" for _ in CORE_INDEX_SYMBOLS)
|
||||
try:
|
||||
db_rows = repo.execute_all(
|
||||
f"""
|
||||
WITH ranked AS (
|
||||
SELECT symbol, date, close,
|
||||
row_number() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn
|
||||
FROM kline_index_daily
|
||||
WHERE symbol IN ({placeholders})
|
||||
AND (? IS NULL OR date <= ?)
|
||||
), latest AS (
|
||||
SELECT symbol,
|
||||
max(CASE WHEN rn = 1 THEN date END) AS date,
|
||||
max(CASE WHEN rn = 1 THEN close END) AS last_price,
|
||||
max(CASE WHEN rn = 2 THEN close END) AS prev_close
|
||||
FROM ranked
|
||||
WHERE rn <= 2
|
||||
GROUP BY symbol
|
||||
)
|
||||
SELECT symbol, date, last_price, prev_close
|
||||
FROM latest
|
||||
""",
|
||||
[*CORE_INDEX_SYMBOLS, as_of, as_of],
|
||||
)
|
||||
except Exception: # noqa: BLE001
|
||||
db_rows = []
|
||||
for symbol, dt, last_price, prev_close in db_rows:
|
||||
change_amount = None
|
||||
change_pct = None
|
||||
lp = _finite(last_price)
|
||||
pc = _finite(prev_close)
|
||||
if lp is not None and pc not in (None, 0):
|
||||
change_amount = lp - pc
|
||||
change_pct = change_amount / pc * 100
|
||||
rows.append({
|
||||
"symbol": symbol,
|
||||
"name": CORE_INDEX_NAMES.get(symbol),
|
||||
"date": str(dt) if dt else None,
|
||||
"last_price": lp,
|
||||
"close": lp,
|
||||
"prev_close": pc,
|
||||
"change_amount": change_amount,
|
||||
"change_pct": change_pct,
|
||||
})
|
||||
|
||||
by_symbol = {r.get("symbol"): r for r in rows}
|
||||
out = []
|
||||
for symbol in CORE_INDEX_SYMBOLS:
|
||||
r = by_symbol.get(symbol, {"symbol": symbol})
|
||||
out.append({
|
||||
"symbol": symbol,
|
||||
"name": r.get("name") or CORE_INDEX_NAMES[symbol],
|
||||
"last_price": _finite(r.get("last_price") if r.get("last_price") is not None else r.get("close")),
|
||||
"change_pct": _finite(r.get("change_pct")),
|
||||
"change_amount": _finite(r.get("change_amount")),
|
||||
})
|
||||
return out
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 扩展数据(行业 / 概念)维度聚合
|
||||
# ================================================================
|
||||
|
||||
def _dimension_field(config: ExtConfig, kind: str) -> str | None:
|
||||
candidates = ["概念", "concept", "theme"] if kind == "concept" else ["行业", "industry", "sector"]
|
||||
for candidate in candidates:
|
||||
needle = candidate.lower()
|
||||
for field in config.fields:
|
||||
haystack = f"{field.name} {field.label}".lower()
|
||||
if needle in haystack:
|
||||
return field.name
|
||||
return None
|
||||
|
||||
|
||||
def _ext_files(data_dir, config: ExtConfig) -> list[str]:
|
||||
base = data_dir / "ext_data" / config.id
|
||||
if config.mode == "timeseries":
|
||||
root = base / "timeseries"
|
||||
return [str(p) for p in sorted(root.rglob("*.parquet")) if p.is_file()]
|
||||
return [str(p) for p in sorted(base.glob("*.parquet")) if p.is_file()]
|
||||
|
||||
|
||||
def _read_ext_rows(data_dir, config: ExtConfig, dimension_field: str) -> list[dict]:
|
||||
files = _ext_files(data_dir, config)
|
||||
if not files:
|
||||
return []
|
||||
try:
|
||||
df = pl.read_parquet(files, hive_partitioning=True)
|
||||
except TypeError:
|
||||
try:
|
||||
df = pl.read_parquet(files)
|
||||
except Exception: # noqa: BLE001
|
||||
return []
|
||||
except Exception: # noqa: BLE001
|
||||
return []
|
||||
if df.is_empty() or dimension_field not in df.columns:
|
||||
return []
|
||||
|
||||
if config.mode == "timeseries" and "date" in df.columns:
|
||||
latest = df.get_column("date").max()
|
||||
if latest is not None:
|
||||
df = df.filter(pl.col("date") == latest)
|
||||
|
||||
symbol_cols = ["symbol", "code", "股票代码", "代码"]
|
||||
for mapping in (config.symbol_map, config.code_map):
|
||||
if isinstance(mapping, dict) and mapping.get("type") == "mapped" and mapping.get("col"):
|
||||
symbol_cols.append(str(mapping["col"]))
|
||||
cols = []
|
||||
for col in [dimension_field, *symbol_cols]:
|
||||
if col in df.columns and col not in cols:
|
||||
cols.append(col)
|
||||
return df.select(cols).to_dicts()
|
||||
|
||||
|
||||
def _dimension_values(raw: Any) -> list[str]:
|
||||
if raw is None:
|
||||
return []
|
||||
values = [v.strip() for v in _DIMENSION_SEP.split(str(raw).strip()) if v.strip()]
|
||||
return values
|
||||
|
||||
|
||||
def _symbol_keys(row: dict, config: ExtConfig) -> list[str]:
|
||||
fields = ["symbol", "code", "股票代码", "代码"]
|
||||
for mapping in (config.symbol_map, config.code_map):
|
||||
if isinstance(mapping, dict) and mapping.get("type") == "mapped" and mapping.get("col"):
|
||||
fields.append(str(mapping["col"]))
|
||||
|
||||
keys: list[str] = []
|
||||
for field in fields:
|
||||
raw = row.get(field)
|
||||
if raw is None:
|
||||
continue
|
||||
text = str(raw).strip().upper()
|
||||
if not text:
|
||||
continue
|
||||
keys.append(text)
|
||||
if "." in text:
|
||||
keys.append(text.split(".", 1)[0])
|
||||
return keys
|
||||
|
||||
|
||||
def _dimension_rank(rows: list[dict], repo, kind: str, limit: int = 5, level: int | None = None) -> dict:
|
||||
if not rows:
|
||||
return {"leading": [], "lagging": []}
|
||||
|
||||
quote_map: dict[str, dict] = {}
|
||||
for row in rows:
|
||||
symbol = str(row.get("symbol") or "").strip().upper()
|
||||
if not symbol:
|
||||
continue
|
||||
quote_map[symbol] = row
|
||||
quote_map[symbol.split(".", 1)[0]] = row
|
||||
|
||||
store = ExtConfigStore(repo.store.data_dir)
|
||||
groups: dict[str, dict[str, dict]] = {}
|
||||
for config in store.load_all():
|
||||
field = _dimension_field(config, kind)
|
||||
if not field:
|
||||
continue
|
||||
for ext_row in _read_ext_rows(repo.store.data_dir, config, field):
|
||||
quote = None
|
||||
for key in _symbol_keys(ext_row, config):
|
||||
quote = quote_map.get(key)
|
||||
if quote:
|
||||
break
|
||||
if not quote:
|
||||
continue
|
||||
symbol = str(quote.get("symbol") or "")
|
||||
for value in _dimension_values(ext_row.get(field)):
|
||||
# 行业按 "-" 拆分级: "银行-银行-股份制银行" → level=2 取"银行"(二级)
|
||||
if level is not None and "-" in value:
|
||||
parts = value.split("-")
|
||||
value = parts[level - 1] if level <= len(parts) else parts[-1]
|
||||
groups.setdefault(value, {})[symbol] = quote
|
||||
|
||||
items = []
|
||||
for name, by_symbol in groups.items():
|
||||
stocks = list(by_symbol.values())
|
||||
changes = [_finite(s.get("change_pct")) for s in stocks]
|
||||
changes = [v for v in changes if v is not None]
|
||||
if not changes:
|
||||
continue
|
||||
leader = max(stocks, key=lambda s: _finite(s.get("change_pct")) or -999)
|
||||
items.append({
|
||||
"name": name,
|
||||
"count": len(stocks),
|
||||
"avg_pct": sum(changes) / len(changes),
|
||||
"up_count": sum(1 for v in changes if v > 0),
|
||||
"down_count": sum(1 for v in changes if v < 0),
|
||||
"amount": sum(_finite(s.get("amount")) or 0 for s in stocks),
|
||||
"leader": {
|
||||
"symbol": leader.get("symbol"),
|
||||
"name": leader.get("name"),
|
||||
"change_pct": _finite(leader.get("change_pct")),
|
||||
},
|
||||
})
|
||||
|
||||
leading = sorted(items, key=lambda x: x["avg_pct"], reverse=True)[:limit]
|
||||
lagging = sorted(items, key=lambda x: x["avg_pct"])[:limit]
|
||||
return {"leading": leading, "lagging": lagging}
|
||||
|
||||
|
||||
# ================================================================
|
||||
# Top 行 / 涨跌幅分桶
|
||||
# ================================================================
|
||||
|
||||
def _top_rows(rows: list[dict], key: str, descending: bool, limit: int = 8) -> list[dict]:
|
||||
filtered = [r for r in rows if _finite(r.get(key)) is not None]
|
||||
filtered.sort(key=lambda r: _finite(r.get(key)) or 0, reverse=descending)
|
||||
return [
|
||||
{
|
||||
"symbol": r.get("symbol"),
|
||||
"name": r.get("name"),
|
||||
"close": _finite(r.get("close")),
|
||||
"change_pct": _finite(r.get("change_pct")),
|
||||
"amount": _finite(r.get("amount")),
|
||||
"turnover_rate": _finite(r.get("turnover_rate")),
|
||||
"board": _board(str(r.get("symbol") or "")),
|
||||
}
|
||||
for r in filtered[:limit]
|
||||
]
|
||||
|
||||
|
||||
def _pct_band_rows(values: list[float]) -> list[dict]:
|
||||
bands = [
|
||||
("<-5%", None, -0.05),
|
||||
("-5~-3%", -0.05, -0.03),
|
||||
("-3~-1%", -0.03, -0.01),
|
||||
("-1~0%", -0.01, 0),
|
||||
("0~1%", 0, 0.01),
|
||||
("1~3%", 0.01, 0.03),
|
||||
("3~5%", 0.03, 0.05),
|
||||
(">5%", 0.05, None),
|
||||
]
|
||||
total = len(values) or 1
|
||||
out = []
|
||||
for label, low, high in bands:
|
||||
count = 0
|
||||
for v in values:
|
||||
if low is None and v < high:
|
||||
count += 1
|
||||
elif high is None and v >= low:
|
||||
count += 1
|
||||
elif low is not None and high is not None and low <= v < high:
|
||||
count += 1
|
||||
out.append({"label": label, "count": count, "pct": count / total * 100})
|
||||
return out
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 主装配入口
|
||||
# ================================================================
|
||||
|
||||
def build_market_overview(
|
||||
repo,
|
||||
quote_service=None,
|
||||
depth_service=None,
|
||||
as_of: date | None = None,
|
||||
) -> dict:
|
||||
"""装配市场总览(与原 overview._build_overview 行为一致)。
|
||||
|
||||
Args:
|
||||
repo: KlineRepository(必填)。
|
||||
quote_service: QuoteService(可选;实时指数行情来源)。
|
||||
depth_service: DepthService(可选;五档封板修正)。
|
||||
as_of: 指定日期,None 则取最新有数据日。
|
||||
"""
|
||||
svc = ScreenerService(repo)
|
||||
as_of = as_of or svc.latest_date()
|
||||
status = _quote_status(quote_service)
|
||||
indices = _index_quotes(repo, quote_service, as_of)
|
||||
|
||||
if not as_of:
|
||||
return {
|
||||
"as_of": None,
|
||||
"quote_status": status,
|
||||
"indices": indices,
|
||||
"breadth": {"total": 0, "up": 0, "down": 0, "flat": 0, "up_pct": 0, "down_pct": 0},
|
||||
"amount": {"total": 0, "avg": 0},
|
||||
"boards": [],
|
||||
"limit": {"limit_up": 0, "broken": 0, "failed": 0, "limit_down": 0, "max_boards": 0, "tiers": []},
|
||||
"distribution": [],
|
||||
"trend": {"above_ma5": 0, "above_ma20": 0, "above_ma60": 0, "above_ma5_pct": 0, "above_ma20_pct": 0, "above_ma60_pct": 0, "new_high": 0, "new_low": 0},
|
||||
"activity": {"avg_turnover": 0, "high_turnover": 0, "high_vol_ratio": 0, "vol_ratio": 1},
|
||||
"radar": [],
|
||||
"emotion": {"score": 50, "label": "暂无"},
|
||||
"top_gainers": [],
|
||||
"top_losers": [],
|
||||
"turnover_leaders": [],
|
||||
"active_leaders": [],
|
||||
"concept_rank": {"leading": [], "lagging": []},
|
||||
"industry_rank": {"leading": [], "lagging": []},
|
||||
}
|
||||
|
||||
df = svc._load_enriched_for_date(as_of)
|
||||
if df.is_empty():
|
||||
rows: list[dict] = []
|
||||
else:
|
||||
cols = [
|
||||
"symbol", "name", "close", "change_pct", "amount", "turnover_rate", "volume",
|
||||
"vol_ratio_5d", "consecutive_limit_ups", "signal_limit_up", "signal_broken_limit_up", "signal_limit_down",
|
||||
"ma5", "ma20", "ma60", "high_60d", "low_60d", "signal_n_day_high", "signal_n_day_low",
|
||||
]
|
||||
df = df.select([c for c in cols if c in df.columns])
|
||||
rows = df.to_dicts()
|
||||
|
||||
# 过滤真停牌（volume=0 且 change_pct=0），保留有涨跌幅的浮点误差股以对齐同花顺口径
|
||||
if rows and "volume" in rows[0]:
|
||||
rows = [r for r in rows
|
||||
if (_finite(r.get("volume")) or 0) > 0
|
||||
or (_finite(r.get("change_pct")) or 0) != 0]
|
||||
|
||||
total = len(rows)
|
||||
up = sum(1 for r in rows if (_finite(r.get("change_pct")) or 0) > 0)
|
||||
down = sum(1 for r in rows if (_finite(r.get("change_pct")) or 0) < 0)
|
||||
flat = max(0, total - up - down)
|
||||
up_pct = up / total * 100 if total else 0
|
||||
down_pct = down / total * 100 if total else 0
|
||||
|
||||
amounts = [_finite(r.get("amount")) or 0 for r in rows]
|
||||
total_amount = sum(amounts)
|
||||
avg_amount = total_amount / total if total else 0
|
||||
|
||||
pct_values = [_finite(r.get("change_pct")) for r in rows]
|
||||
pct_values = [v for v in pct_values if v is not None]
|
||||
avg_pct = sum(pct_values) / len(pct_values) if pct_values else 0
|
||||
median_pct = sorted(pct_values)[len(pct_values) // 2] if pct_values else 0
|
||||
strong_up = sum(1 for v in pct_values if v >= 0.03)
|
||||
strong_down = sum(1 for v in pct_values if v <= -0.03)
|
||||
|
||||
limit_up = sum(1 for r in rows if bool(r.get("signal_limit_up")) or (_finite(r.get("consecutive_limit_ups")) or 0) > 0)
|
||||
broken = sum(1 for r in rows if bool(r.get("signal_broken_limit_up")))
|
||||
limit_down = sum(1 for r in rows if bool(r.get("signal_limit_down")))
|
||||
max_boards = max([int(_finite(r.get("consecutive_limit_ups")) or 0) for r in rows], default=0)
|
||||
|
||||
# 五档 sealed 修正: 假涨停/假跌停不计入(需 Pro+ depth5.batch 能力)
|
||||
sealed_ready = False
|
||||
fake_up = 0
|
||||
fake_down = 0
|
||||
if depth_service:
|
||||
up_map = depth_service.get_sealed_map(as_of, is_down=False)
|
||||
down_map = depth_service.get_sealed_map(as_of, is_down=True)
|
||||
sealed_ready = bool(up_map or down_map) and depth_service.is_sealed_ready(as_of)
|
||||
if up_map:
|
||||
fake_up = sum(1 for v in up_map.values() if v.get("sealed") is False)
|
||||
if down_map:
|
||||
fake_down = sum(1 for v in down_map.values() if v.get("sealed") is False)
|
||||
if sealed_ready:
|
||||
limit_up = max(0, limit_up - fake_up)
|
||||
limit_down = max(0, limit_down - fake_down)
|
||||
|
||||
seal_rate = limit_up / (limit_up + broken) * 100 if (limit_up + broken) > 0 else 0
|
||||
|
||||
def above_ma_count(ma_key: str) -> int:
|
||||
return sum(1 for r in rows if (_finite(r.get("close")) is not None and _finite(r.get(ma_key)) is not None and (_finite(r.get("close")) or 0) >= (_finite(r.get(ma_key)) or 0)))
|
||||
|
||||
above_ma5 = above_ma_count("ma5")
|
||||
above_ma20 = above_ma_count("ma20")
|
||||
above_ma60 = above_ma_count("ma60")
|
||||
new_high = sum(1 for r in rows if bool(r.get("signal_n_day_high")) or (_finite(r.get("close")) is not None and _finite(r.get("high_60d")) is not None and (_finite(r.get("close")) or 0) >= (_finite(r.get("high_60d")) or 0)))
|
||||
new_low = sum(1 for r in rows if bool(r.get("signal_n_day_low")) or (_finite(r.get("close")) is not None and _finite(r.get("low_60d")) is not None and (_finite(r.get("close")) or 0) <= (_finite(r.get("low_60d")) or 0)))
|
||||
|
||||
turnovers = [_finite(r.get("turnover_rate")) for r in rows]
|
||||
turnovers = [v for v in turnovers if v is not None]
|
||||
avg_turnover = sum(turnovers) / len(turnovers) if turnovers else 0
|
||||
high_turnover = sum(1 for v in turnovers if v >= 5)
|
||||
|
||||
boards_map: dict[str, dict] = {}
|
||||
for r in rows:
|
||||
b = _board(str(r.get("symbol") or ""))
|
||||
item = boards_map.setdefault(b, {"board": b, "count": 0, "up": 0, "down": 0, "amount": 0.0})
|
||||
item["count"] += 1
|
||||
change = _finite(r.get("change_pct")) or 0
|
||||
if change > 0:
|
||||
item["up"] += 1
|
||||
elif change < 0:
|
||||
item["down"] += 1
|
||||
item["amount"] += _finite(r.get("amount")) or 0
|
||||
boards = sorted(boards_map.values(), key=lambda x: x["amount"], reverse=True)
|
||||
for b in boards:
|
||||
count = b["count"] or 1
|
||||
b["up_pct"] = b["up"] / count * 100
|
||||
|
||||
tiers_map: dict[int, int] = {}
|
||||
for r in rows:
|
||||
n = int(_finite(r.get("consecutive_limit_ups")) or 0)
|
||||
if n > 0:
|
||||
tiers_map[n] = tiers_map.get(n, 0) + 1
|
||||
tiers = [{"boards": k, "count": v} for k, v in sorted(tiers_map.items(), key=lambda item: -item[0])]
|
||||
|
||||
index_changes = [_finite(r.get("change_pct")) for r in indices]
|
||||
index_changes = [v for v in index_changes if v is not None]
|
||||
avg_index_pct = sum(index_changes) / len(index_changes) if index_changes else 0
|
||||
vol_ratios = [_finite(r.get("vol_ratio_5d")) for r in rows]
|
||||
vol_ratios = [v for v in vol_ratios if v is not None]
|
||||
avg_vol_ratio = sum(vol_ratios) / len(vol_ratios) if vol_ratios else 1
|
||||
high_vol_ratio = sum(1 for v in vol_ratios if v >= 1.5)
|
||||
|
||||
concept_rank = _dimension_rank(rows, repo, "concept")
|
||||
industry_rank = _dimension_rank(rows, repo, "industry", level=2)
|
||||
|
||||
strong_diff_pct = (strong_up - strong_down) / total * 100 if total else 0
|
||||
high_vol_pct = high_vol_ratio / total * 100 if total else 0
|
||||
strong_down_pct = strong_down / total * 100 if total else 0
|
||||
tier2_count = sum(t["count"] for t in tiers if t["boards"] >= 2)
|
||||
mainline_items = [*concept_rank["leading"][:3], *industry_rank["leading"][:3]]
|
||||
mainline_avg = max([_finite(item.get("avg_pct")) or 0 for item in mainline_items], default=0)
|
||||
mainline_cover_pct = max([(_finite(item.get("count")) or 0) / total * 100 for item in mainline_items], default=0) if total else 0
|
||||
mainline_score = round(_score(mainline_avg, -0.005, 0.03) * 0.65 + _score(mainline_cover_pct, 1, 12) * 0.35) if mainline_items else 50
|
||||
|
||||
radar = [
|
||||
{"key": "index", "label": "指数", "value": _score(avg_index_pct, -2.5, 2.5)},
|
||||
{"key": "profit", "label": "赚钱", "value": round(_score(up_pct, 20, 80) * 0.45 + _score(avg_pct, -0.02, 0.02) * 0.25 + _score(median_pct, -0.02, 0.02) * 0.20 + _score(strong_diff_pct, -8, 8) * 0.10)},
|
||||
{"key": "money", "label": "量能", "value": round(_score(avg_vol_ratio, 0.6, 1.8) * 0.70 + _score(high_vol_pct, 2, 12) * 0.30)},
|
||||
{"key": "speculation", "label": "投机", "value": round(_score(limit_up, 5, 90) * 0.25 + _score(seal_rate, 30, 85) * 0.35 + _score(max_boards, 1, 8) * 0.25 + _score(tier2_count, 0, 30) * 0.15)},
|
||||
{"key": "resilience", "label": "抗跌", "value": 100 - round(_score(down_pct, 20, 80) * 0.55 + _score(strong_down_pct, 1, 12) * 0.45)},
|
||||
{"key": "mainline", "label": "主线", "value": mainline_score},
|
||||
]
|
||||
emotion_score = round(sum(r["value"] for r in radar) / len(radar)) if radar else 50
|
||||
if emotion_score >= 70:
|
||||
emotion_label = "强势"
|
||||
elif emotion_score >= 55:
|
||||
emotion_label = "偏暖"
|
||||
elif emotion_score >= 45:
|
||||
emotion_label = "震荡"
|
||||
elif emotion_score >= 30:
|
||||
emotion_label = "偏冷"
|
||||
else:
|
||||
emotion_label = "冰点"
|
||||
|
||||
return _json_safe({
|
||||
"as_of": str(as_of),
|
||||
"quote_status": status,
|
||||
"indices": indices,
|
||||
"breadth": {
|
||||
"total": total,
|
||||
"up": up,
|
||||
"down": down,
|
||||
"flat": flat,
|
||||
"up_pct": up_pct,
|
||||
"down_pct": down_pct,
|
||||
"avg_pct": avg_pct,
|
||||
"median_pct": median_pct,
|
||||
"strong_up": strong_up,
|
||||
"strong_down": strong_down,
|
||||
},
|
||||
"amount": {"total": total_amount, "avg": avg_amount},
|
||||
"boards": boards,
|
||||
"limit": {"limit_up": limit_up, "broken": broken, "failed": 0, "limit_down": limit_down, "max_boards": max_boards, "seal_rate": seal_rate, "tiers": tiers, "sealed_ready": sealed_ready, "fake_up": fake_up, "fake_down": fake_down},
|
||||
"distribution": _pct_band_rows(pct_values),
|
||||
"trend": {
|
||||
"above_ma5": above_ma5,
|
||||
"above_ma20": above_ma20,
|
||||
"above_ma60": above_ma60,
|
||||
"above_ma5_pct": above_ma5 / total * 100 if total else 0,
|
||||
"above_ma20_pct": above_ma20 / total * 100 if total else 0,
|
||||
"above_ma60_pct": above_ma60 / total * 100 if total else 0,
|
||||
"new_high": new_high,
|
||||
"new_low": new_low,
|
||||
},
|
||||
"activity": {
|
||||
"avg_turnover": avg_turnover,
|
||||
"high_turnover": high_turnover,
|
||||
"high_vol_ratio": high_vol_pct,
|
||||
"vol_ratio": avg_vol_ratio,
|
||||
},
|
||||
"radar": radar,
|
||||
"emotion": {"score": emotion_score, "label": emotion_label},
|
||||
"top_gainers": _top_rows(rows, "change_pct", True),
|
||||
"top_losers": _top_rows(rows, "change_pct", False),
|
||||
"turnover_leaders": _top_rows(rows, "amount", True),
|
||||
"active_leaders": _top_rows(rows, "turnover_rate", True),
|
||||
"concept_rank": concept_rank,
|
||||
"industry_rank": industry_rank,
|
||||
})
|
||||
@@ -0,0 +1,343 @@
|
||||
"""AI 大盘复盘 —— 流式 LLM 复盘生成。
|
||||
|
||||
复刻 stock_analyzer.py 的 NDJSON 流式协议(meta/delta/error/done),
|
||||
将「市场总览」聚合数据交给 LLM 生成结构化复盘报告。
|
||||
|
||||
数据来源:services.market_overview_builder.build_market_overview
|
||||
(与 GET /api/overview/market 同源,保证复盘与看板数据口径一致)。
|
||||
|
||||
流式协议(与 stock_analyzer / financial_analyzer 一致,前端解析无差异):
|
||||
{"type":"meta", "as_of", "emotion_score", "emotion_label", "summary"}
|
||||
{"type":"delta","content":"..."} 逐 chunk 文本
|
||||
{"type":"error","message":"..."}
|
||||
{"type":"done"}
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from datetime import date
|
||||
from typing import AsyncIterator
|
||||
|
||||
from app.services.market_overview_builder import build_market_overview
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# 指数简称映射:摘要里用简称(上/深/创/科),全称太长列表放不下。与前端 INDEX_SHORT 对齐。
|
||||
_INDEX_SHORT = {
|
||||
"上证指数": "上",
|
||||
"深证成指": "深",
|
||||
"创业板指": "创",
|
||||
"科创综指": "科",
|
||||
"科创50": "科",
|
||||
}
|
||||
|
||||
# ================================================================
|
||||
# 系统提示词(市场策略师人格 + 固定七节模板)
|
||||
# ================================================================
|
||||
|
||||
_SYSTEM_PROMPT = """你是一位拥有 15 年 A 股一线实战经验的资深市场策略师,擅长从指数结构、涨跌家数、连板梯队、板块轮动与资金情绪中提炼交易主线,产出可直接指导次日仓位与节奏的盘后复盘报告。
|
||||
|
||||
## 输出规范
|
||||
|
||||
用 **Markdown** 格式输出,严格遵循以下结构。不要输出任何 JSON 或代码块,直接输出 Markdown 正文。
|
||||
|
||||
### 1. 🎯 一句话定调(1-2 句)
|
||||
用一句话概括今日市场的**核心矛盾与状态**(如"放量普涨、情绪修复,主线围绕科技扩散"/"指数虚高、个股杀跌,赚钱效应冰点")。结尾用【明日基调:进攻 / 均衡 / 防守】给出明确倾向。
|
||||
|
||||
### 2. 📊 盘面总览
|
||||
- 三大指数(上证/深证/创业板)表现:谁强谁弱、量能配合
|
||||
- 涨跌家数、涨停/跌停/炸板结构、两市成交额(放量/缩量判断)
|
||||
- 情绪温度(强势/偏暖/震荡/偏冷/冰点)及一句话依据
|
||||
|
||||
### 3. 📈 指数结构
|
||||
谁在护盘、谁在拖累;指数是否同步;关键支撑/压力位(基于当日点位推断);是否存在量价背离。
|
||||
|
||||
### 4. 🔥 板块主线
|
||||
- 领涨板块:背后的逻辑(消息/业绩/资金/技术)、持续性判断、是否形成可交易主线
|
||||
- 领跌板块:风险信号、是否扩散
|
||||
- 连板梯队与投机情绪:最高连板、封板率、炸板率反映的资金激进程度
|
||||
|
||||
### 5. 💰 资金与情绪
|
||||
成交额结构(增量/存量)、市场宽度(上涨占比、站上均线占比)、量能指标(量比)解读;风险偏好是修复还是转弱。
|
||||
|
||||
### 6. 📰 消息催化
|
||||
结合提供的近期新闻,提炼真正影响明日交易节奏的催化或扰动,明确区分"已兑现"与"待发酵"。**若无新闻数据,则直接从量价异动推断可能的催化逻辑并给出结论,不要标注"[推断]"之类的过程标签,更不要编造具体消息。**
|
||||
|
||||
### 7. 🎯 明日交易计划
|
||||
- 进攻 / 均衡 / 防守:基于今日盘面给出次日基调
|
||||
- 仓位区间建议(轻仓/半仓/重仓的粗略指引)
|
||||
- 关注方向(领涨延续 / 低吸 / 反包)与回避方向(高位滞涨 / 杀跌扩散)
|
||||
- 一个明确的触发失效条件(如"若上证跌破 X 点则转为防守")
|
||||
|
||||
### 8. ⚠️ 风险提示
|
||||
列出需要重点盯的风险点(如量能跟不上、外资流出、连板断层等)。末尾附一行:
|
||||
"> ⚠️ 本报告由 AI 基于公开行情数据生成,仅供参考,不构成任何投资建议。交易有风险,入市需谨慎。"
|
||||
|
||||
## 分析准则(务必遵守)
|
||||
|
||||
0. **只输出结论,不输出思考过程**:禁止复述你的分析步骤或方法论。不要写"我先按...做结构化复盘""接下来看...""基于上述数据我认为"这类元话语——直接给结论。读者要的是复盘结果,不是你怎么推导出来的。
|
||||
1. **数据说话**:每个判断引用具体数值,严禁空泛套话("情绪回暖"必须改成"涨停 68 家较前日 +22,封板率 75%")
|
||||
2. **诚实中立**:看多就写多,看空就写空,不要骑墙;数据不支持时直言无法判断
|
||||
3. **结构优先**:先看指数同步性与量能结构,再看板块与情绪,最后才是消息
|
||||
4. **不重复数字**:正文负责解读表格数据背后的含义,不要照抄罗列已提供的大段原始数字
|
||||
5. **风险前置**:任何进攻建议都要配触发失效条件
|
||||
6. **简明实战**:用交易员能扫读的密度输出,总字数 1200-2000 字,重在可执行
|
||||
|
||||
现在请基于下方数据进行复盘。"""
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 用户消息构建(精简切片,控制 token)
|
||||
# ================================================================
|
||||
|
||||
def _fmt_pct(v, suffix="%") -> str:
|
||||
if v is None:
|
||||
return "—"
|
||||
return f"{v:+.2f}{suffix}" if suffix else f"{v:.2f}"
|
||||
|
||||
|
||||
def _build_indices_block(overview: dict) -> str:
|
||||
"""指数行情精简块。"""
|
||||
indices = overview.get("indices") or []
|
||||
if not indices:
|
||||
return "(暂无指数)"
|
||||
lines = []
|
||||
for idx in indices:
|
||||
name = idx.get("name") or idx.get("symbol")
|
||||
price = idx.get("last_price")
|
||||
chg = idx.get("change_pct")
|
||||
price_s = f"{price:.2f}" if price is not None else "—"
|
||||
lines.append(f"- {name}: {price_s} {_fmt_pct(chg)}")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _build_breadth_block(overview: dict) -> str:
|
||||
b = overview.get("breadth") or {}
|
||||
amt = overview.get("amount") or {}
|
||||
lim = overview.get("limit") or {}
|
||||
tr = overview.get("trend") or {}
|
||||
act = overview.get("activity") or {}
|
||||
|
||||
total_amount = amt.get("total") or 0
|
||||
# 成交额单位换算为亿元(原始为元)
|
||||
amount_yi = total_amount / 1e8 if total_amount else 0
|
||||
|
||||
lines = [
|
||||
f"- 上涨/下跌/平盘: {b.get('up',0)} / {b.get('down',0)} / {b.get('flat',0)}"
|
||||
f" (上涨占比 {b.get('up_pct',0):.1f}%)",
|
||||
f"- 涨停/炸板/跌停: {lim.get('limit_up',0)} / {lim.get('broken',0)} / {lim.get('limit_down',0)}"
|
||||
f" (封板率 {lim.get('seal_rate',0):.0f}%, 最高连板 {lim.get('max_boards',0)})",
|
||||
]
|
||||
if lim.get("tiers"):
|
||||
tiers_str = "、".join(f"{t['boards']}板×{t['count']}" for t in lim["tiers"][:5])
|
||||
lines.append(f"- 连板梯队: {tiers_str}")
|
||||
lines.append(f"- 两市成交额: {amount_yi:.0f} 亿元")
|
||||
lines.append(
|
||||
f"- 均线站位: MA5 {tr.get('above_ma5_pct',0):.0f}% / "
|
||||
f"MA20 {tr.get('above_ma20_pct',0):.0f}% / MA60 {tr.get('above_ma60_pct',0):.0f}%"
|
||||
)
|
||||
lines.append(
|
||||
f"- 量能: 平均换手 {act.get('avg_turnover',0):.2f}%, "
|
||||
f"量比5日均 {act.get('vol_ratio',1):.2f}"
|
||||
)
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _build_sector_block(rank: dict, label: str) -> str:
|
||||
"""板块排名精简块(领涨/领跌 top5)。"""
|
||||
if not rank:
|
||||
return f"### {label}\n(暂无数据)"
|
||||
def _fmt(items):
|
||||
if not items:
|
||||
return "—"
|
||||
return "、".join(
|
||||
f"{it.get('name')}({(it.get('avg_pct') or 0)*100:+.2f}%,领涨:{it.get('leader',{}).get('name','—')})"
|
||||
for it in items[:5]
|
||||
)
|
||||
return (
|
||||
f"- 领涨{label}: {_fmt(rank.get('leading'))}\n"
|
||||
f"- 领跌{label}: {_fmt(rank.get('lagging'))}"
|
||||
)
|
||||
|
||||
|
||||
def _build_emotion_block(overview: dict) -> str:
|
||||
emo = overview.get("emotion") or {}
|
||||
radar = overview.get("radar") or []
|
||||
score = emo.get("score", 50)
|
||||
label = emo.get("label", "—")
|
||||
lines = [f"- 情绪温度: {score} ({label})"]
|
||||
if radar:
|
||||
dims = "、".join(f"{r.get('label')}{r.get('value',0)}" for r in radar)
|
||||
lines.append(f"- 六维雷达: {dims}")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _build_user_prompt(overview: dict, news: list[dict], focus: str) -> str:
|
||||
"""构建用户消息:复盘日期 + 市场数据精简切片 + 新闻 + 关注点。"""
|
||||
as_of = overview.get("as_of") or "今日"
|
||||
|
||||
parts: list[str] = [
|
||||
f"复盘日期: {as_of}",
|
||||
"",
|
||||
"## 主要指数",
|
||||
_build_indices_block(overview),
|
||||
"",
|
||||
"## 盘面数据",
|
||||
_build_breadth_block(overview),
|
||||
"",
|
||||
"## 市场情绪",
|
||||
_build_emotion_block(overview),
|
||||
"",
|
||||
"## 概念板块排名",
|
||||
_build_sector_block(overview.get("concept_rank"), "概念"),
|
||||
"",
|
||||
"## 行业板块排名",
|
||||
_build_sector_block(overview.get("industry_rank"), "行业"),
|
||||
]
|
||||
|
||||
if news:
|
||||
news_lines = []
|
||||
for i, n in enumerate(news[:8], 1):
|
||||
title = (n.get("title") or "").strip()
|
||||
snippet = (n.get("snippet") or "").strip()
|
||||
source = (n.get("source") or "").strip()
|
||||
pub = (n.get("published_date") or "").strip()
|
||||
meta = " / ".join(p for p in (source, pub) if p)
|
||||
news_lines.append(f"{i}. {title} ({meta})\n {snippet}" if meta else f"{i}. {title}\n {snippet}")
|
||||
parts.extend(["", "## 近期市场新闻", "\n".join(news_lines)])
|
||||
else:
|
||||
parts.extend([
|
||||
"",
|
||||
"## 近期市场新闻",
|
||||
"(暂无新闻数据:本功能新闻检索能力将在后续版本接入。"
|
||||
"消息催化一节请直接从量价异动给出可能的催化逻辑结论,不要编造具体消息,也不要复述本说明。)",
|
||||
])
|
||||
|
||||
if focus.strip():
|
||||
parts.extend(["", f"本次复盘请特别关注: {focus.strip()}"])
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 摘要生成(供 meta 事件 / 历史报告 summary)
|
||||
# ================================================================
|
||||
|
||||
def _recap_summary(overview: dict) -> str:
|
||||
"""一句话摘要(供 meta 事件与历史列表展示)。
|
||||
|
||||
指数用简称(上/深/创/科),与前端摘要条一致,避免列表里全称放不下。
|
||||
"""
|
||||
indices = overview.get("indices") or []
|
||||
emo = overview.get("emotion") or {}
|
||||
lim = overview.get("limit") or {}
|
||||
amt = overview.get("amount") or {}
|
||||
total_amount = (amt.get("total") or 0) / 1e8
|
||||
|
||||
idx_str = "、".join(
|
||||
f"{_INDEX_SHORT.get(i.get('name') or '', i.get('name') or '')}{(i.get('change_pct') or 0):+.2f}%"
|
||||
for i in indices[:4]
|
||||
) or "指数缺失"
|
||||
return (
|
||||
f"{idx_str} | 情绪{emo.get('score',50)}({emo.get('label','—')}) | "
|
||||
f"涨停{lim.get('limit_up',0)} | 成交{total_amount:.0f}亿"
|
||||
)
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 流式主入口
|
||||
# ================================================================
|
||||
|
||||
async def recap_market_stream(
|
||||
repo,
|
||||
quote_service=None,
|
||||
depth_service=None,
|
||||
as_of: date | None = None,
|
||||
focus: str = "",
|
||||
news: list[dict] | None = None,
|
||||
) -> AsyncIterator[str]:
|
||||
"""流式大盘复盘:yield 出每个 NDJSON 事件。
|
||||
|
||||
Args:
|
||||
repo: KlineRepository(必填)。
|
||||
quote_service / depth_service: 可选,数据装配依赖。
|
||||
as_of: 复盘日期,None 取最新有数据日。
|
||||
focus: 用户追加的复盘关注点。
|
||||
news: 预检索的新闻列表(P1 不传,留 None 走降级说明;P3 由 news_search 注入)。
|
||||
"""
|
||||
# 1. 装配市场总览
|
||||
overview = build_market_overview(repo, quote_service, depth_service, as_of)
|
||||
as_of_str = overview.get("as_of")
|
||||
|
||||
if not as_of_str:
|
||||
yield json.dumps({
|
||||
"type": "error",
|
||||
"message": "暂无市场数据,请先在「数据」页同步日 K 与指数后再复盘",
|
||||
}, ensure_ascii=False)
|
||||
return
|
||||
|
||||
emo = overview.get("emotion") or {}
|
||||
|
||||
# 2. meta 事件(前端据此先渲染信号灯/看板)
|
||||
yield json.dumps({
|
||||
"type": "meta",
|
||||
"as_of": as_of_str,
|
||||
"emotion_score": emo.get("score", 50),
|
||||
"emotion_label": emo.get("label", "—"),
|
||||
"summary": _recap_summary(overview),
|
||||
}, ensure_ascii=False)
|
||||
|
||||
# 3+4. 构建 prompt + 流式调用 LLM(整体 try-except,任何异常 yield error,避免前端卡死)
|
||||
try:
|
||||
from app.services.ai_provider import stream_ai_text
|
||||
|
||||
user_prompt = _build_user_prompt(overview, news or [], focus)
|
||||
async for delta in stream_ai_text(
|
||||
[
|
||||
{"role": "system", "content": _SYSTEM_PROMPT},
|
||||
{"role": "user", "content": user_prompt},
|
||||
],
|
||||
temperature=0.5,
|
||||
max_tokens=4500,
|
||||
):
|
||||
yield json.dumps({"type": "delta", "content": delta}, ensure_ascii=False)
|
||||
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.exception("AI market recap failed for %s: %s", as_of_str, e)
|
||||
yield json.dumps({"type": "error", "message": f"AI 复盘失败: {e}"}, ensure_ascii=False)
|
||||
return
|
||||
|
||||
yield json.dumps({"type": "done"}, ensure_ascii=False)
|
||||
|
||||
|
||||
async def recap_market_once(
|
||||
repo,
|
||||
quote_service=None,
|
||||
depth_service=None,
|
||||
as_of: date | None = None,
|
||||
focus: str = "",
|
||||
news: list[dict] | None = None,
|
||||
) -> tuple[str | None, dict]:
|
||||
"""非流式版本(供定时任务调用):累积全部 delta,返回 (content, meta)。
|
||||
|
||||
content 为完整 Markdown 文本;失败时为 None。
|
||||
meta 含 as_of / emotion_score / emotion_label / summary(即使失败也尽量回填)。
|
||||
"""
|
||||
content_parts: list[str] = []
|
||||
meta: dict = {"as_of": as_of.isoformat() if as_of else None}
|
||||
async for evt in recap_market_stream(repo, quote_service, depth_service, as_of, focus, news):
|
||||
try:
|
||||
obj = json.loads(evt)
|
||||
except Exception: # noqa: BLE001
|
||||
continue
|
||||
t = obj.get("type")
|
||||
if t == "meta":
|
||||
meta = obj
|
||||
elif t == "delta":
|
||||
content_parts.append(obj.get("content", ""))
|
||||
elif t == "error":
|
||||
logger.warning("market recap error event: %s", obj.get("message"))
|
||||
return None, meta
|
||||
return "".join(content_parts), meta
|
||||
@@ -0,0 +1,92 @@
|
||||
"""AI 大盘复盘报告持久化存储。
|
||||
|
||||
与 stock_reports.py(个股分析报告)/ ai_reports.py(财务分析报告)完全独立 ——
|
||||
单独的文件、字段、上限,互不影响。刻意不复用,避免引入 kind 判别字段与分支
|
||||
(解耦 > 抽象)。
|
||||
|
||||
存储位置: data/user_data/ai_market_recaps.json (数组,按 created_at 降序)
|
||||
保留最近 MAX_REPORTS 条;超出自动裁剪最旧的。
|
||||
|
||||
每条报告结构:
|
||||
{
|
||||
"id": "mkr_xxx", # 唯一 id(market-recap-report)
|
||||
"as_of": "2026-06-27", # 复盘日期
|
||||
"focus": "", # 用户追加的关心点(可为空)
|
||||
"content": "# ...markdown", # 报告正文
|
||||
"summary": "三大指数齐涨...", # 一句话摘要
|
||||
"emotion_score": 68, # 情绪分(0-100, 复盘生成时的市场情绪雷达均分)
|
||||
"emotion_label": "偏暖", # 情绪标签(强势/偏暖/震荡/偏冷/冰点)
|
||||
"created_at": "2026-06-27T15:35:00"
|
||||
}
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MAX_REPORTS = 20
|
||||
|
||||
|
||||
def _path() -> Path:
|
||||
from app.config import settings
|
||||
p = settings.data_dir / "user_data" / "ai_market_recaps.json"
|
||||
p.parent.mkdir(parents=True, exist_ok=True)
|
||||
return p
|
||||
|
||||
|
||||
def list_reports() -> list[dict]:
|
||||
"""返回全部报告(按 created_at 降序)。"""
|
||||
p = _path()
|
||||
if not p.exists():
|
||||
return []
|
||||
try:
|
||||
data = json.loads(p.read_text(encoding="utf-8"))
|
||||
if isinstance(data, list):
|
||||
return sorted(data, key=lambda r: r.get("created_at", ""), reverse=True)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("ai_market_recaps.json malformed: %s", e)
|
||||
return []
|
||||
|
||||
|
||||
def _save_all(reports: list[dict]) -> None:
|
||||
"""全量写入(裁剪到 MAX_REPORTS)。"""
|
||||
reports.sort(key=lambda r: r.get("created_at", ""), reverse=True)
|
||||
if len(reports) > MAX_REPORTS:
|
||||
reports = reports[:MAX_REPORTS]
|
||||
_path().write_text(
|
||||
json.dumps(reports, indent=2, ensure_ascii=False), encoding="utf-8",
|
||||
)
|
||||
|
||||
|
||||
def save_report(report: dict) -> dict:
|
||||
"""新增一条报告并持久化。返回保存后的报告(含 id / created_at)。"""
|
||||
reports = list_reports()
|
||||
if not report.get("id"):
|
||||
report["id"] = f"mkr_{int(time.time() * 1000)}"
|
||||
if not report.get("created_at"):
|
||||
report["created_at"] = _now_iso()
|
||||
reports.append(report)
|
||||
_save_all(reports)
|
||||
logger.info("Market recap saved: %s (as_of=%s), total %d",
|
||||
report.get("id"), report.get("as_of"), len(reports))
|
||||
return report
|
||||
|
||||
|
||||
def delete_report(report_id: str) -> bool:
|
||||
"""删除指定报告。返回是否删除成功。"""
|
||||
reports = list_reports()
|
||||
before = len(reports)
|
||||
reports = [r for r in reports if r.get("id") != report_id]
|
||||
if len(reports) < before:
|
||||
_save_all(reports)
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _now_iso() -> str:
|
||||
from datetime import datetime
|
||||
return datetime.now().isoformat(timespec="seconds")
|
||||
@@ -1,12 +1,12 @@
|
||||
"""系统通知适配器 — 调用操作系统原生通知命令。
|
||||
"""系统通知适配器 — 三平台原生通知中心。
|
||||
|
||||
职责: 把后端产生的告警事件推送到操作系统通知中心。
|
||||
窗口最小化 / 被遮挡 / 后台运行时都能弹通知。
|
||||
窗口最小化 / 被遮挡 / 后台运行时都能弹通知 (不依赖前端 WebView)。
|
||||
|
||||
平台实现:
|
||||
- macOS: osascript (系统已内置)
|
||||
- Linux: notify-send (系统已内置)
|
||||
- Windows: 暂不支持原生通知中心 (无额外依赖实现)
|
||||
- Windows: winotify (进现代操作中心, 支持图标)
|
||||
- macOS: osascript (系统已内置, 无需额外依赖)
|
||||
- Linux: notify-send (系统已内置) / plyer 兜底
|
||||
|
||||
设计: 失败静默降级, 绝不因通知失败阻断告警主流程 (落盘 / SSE 推送)。
|
||||
通知去重不在本层做, 复用 MonitorRuleEngine 的 cooldown 逻辑。
|
||||
@@ -34,7 +34,15 @@ def _detect_backend() -> str | None:
|
||||
return _backend_cache if _backend_cache != "none" else None
|
||||
|
||||
backend = None
|
||||
if sys.platform == "darwin":
|
||||
if sys.platform == "win32":
|
||||
try:
|
||||
import winotify # type: ignore[import-not-found] # noqa: F401
|
||||
|
||||
backend = "winotify"
|
||||
except ImportError:
|
||||
logger.debug("winotify 不可用, Windows 通知降级")
|
||||
backend = None
|
||||
elif sys.platform == "darwin":
|
||||
backend = "osascript"
|
||||
elif sys.platform.startswith("linux"):
|
||||
backend = "notify-send"
|
||||
@@ -71,6 +79,8 @@ def notify(title: str, message: str, icon: Path | None = None) -> bool:
|
||||
return False
|
||||
|
||||
try:
|
||||
if backend == "winotify":
|
||||
return _notify_winotify(title, message)
|
||||
if backend == "osascript":
|
||||
return _notify_osascript(title, message)
|
||||
if backend == "notify-send":
|
||||
@@ -82,6 +92,20 @@ def notify(title: str, message: str, icon: Path | None = None) -> bool:
|
||||
return False
|
||||
|
||||
|
||||
def _notify_winotify(title: str, message: str) -> bool:
|
||||
"""Windows 通知 (winotify) — 进现代操作中心。"""
|
||||
from winotify import Notifier # type: ignore[import-not-found]
|
||||
|
||||
Notifier().create_notification(
|
||||
title=title,
|
||||
msg=message,
|
||||
# winotify 要求 duration 为 "short" 或 "long"
|
||||
duration="short",
|
||||
# 无可点击动作 (桌面版不实现"点击回到窗口"的复杂交互)
|
||||
).show()
|
||||
return True
|
||||
|
||||
|
||||
def _notify_osascript(title: str, message: str) -> bool:
|
||||
"""macOS 通知 (osascript) — 调用系统 AppleScript。"""
|
||||
# 转义双引号, 避免 AppleScript 注入
|
||||
|
||||
@@ -53,6 +53,29 @@ def get_realtime_quote_interval() -> float:
|
||||
return load().get("realtime_quote_interval", 10.0)
|
||||
|
||||
|
||||
def get_realtime_watchlist_symbols() -> list[str]:
|
||||
"""Free 档自选实时监控标的:直接取自选页前 5 个。"""
|
||||
try:
|
||||
from app.services import watchlist
|
||||
rows = watchlist.list_symbols()
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("load watchlist for realtime failed: %s", e)
|
||||
return []
|
||||
out: list[str] = []
|
||||
for row in rows:
|
||||
symbol = str((row or {}).get("symbol") or "").strip().upper()
|
||||
if symbol and symbol not in out:
|
||||
out.append(symbol)
|
||||
if len(out) >= 5:
|
||||
break
|
||||
return out
|
||||
|
||||
|
||||
def set_realtime_watchlist_symbols(symbols: list[str]) -> list[str]: # noqa: ARG001
|
||||
"""兼容旧接口: Free 实时标的现在由自选页前 5 个决定。"""
|
||||
return get_realtime_watchlist_symbols()
|
||||
|
||||
|
||||
def set_realtime_quote_interval(interval: float) -> float:
|
||||
"""保存行情轮询间隔（不在此做 min/max 校验，由调用方按档位限制）。"""
|
||||
current = load()
|
||||
@@ -71,6 +94,83 @@ def get_minute_sync_days() -> int:
|
||||
return max(1, min(30, load().get("minute_sync_days", 5)))
|
||||
|
||||
|
||||
# ===== 数据源选择 (默认 TickFlow；第一阶段仅日K切换入口) =====
|
||||
|
||||
_ALLOWED_DATA_PROVIDERS = {"tickflow"}
|
||||
|
||||
|
||||
def get_daily_data_provider() -> str:
|
||||
provider = str(load().get("daily_data_provider", "tickflow") or "tickflow").lower()
|
||||
return provider if provider in _ALLOWED_DATA_PROVIDERS else "tickflow"
|
||||
|
||||
|
||||
def get_adj_factor_provider() -> str:
|
||||
provider = str(load().get("adj_factor_provider", "same_as_daily") or "same_as_daily").lower()
|
||||
if provider == "same_as_daily":
|
||||
return provider
|
||||
return provider if provider in _ALLOWED_DATA_PROVIDERS else "same_as_daily"
|
||||
|
||||
|
||||
def get_minute_data_provider() -> str:
|
||||
provider = str(load().get("minute_data_provider", "tickflow") or "tickflow").lower()
|
||||
return provider if provider in _ALLOWED_DATA_PROVIDERS else "tickflow"
|
||||
|
||||
|
||||
def get_realtime_data_provider() -> str:
|
||||
# 盘中实时现阶段仅支持 TickFlow。
|
||||
return "tickflow"
|
||||
|
||||
|
||||
# ===== 盘后管道拉取内容开关 (A股 / ETF / 指数 独立控制) =====
|
||||
|
||||
def get_pipeline_pull_a_share() -> bool:
|
||||
"""A 股日K固定拉取。"""
|
||||
return True
|
||||
|
||||
|
||||
def get_pipeline_pull_etf() -> bool:
|
||||
"""是否拉取 ETF 日K。默认 False(标的多,首次较慢)。"""
|
||||
return load().get("pipeline_pull_etf", False)
|
||||
|
||||
|
||||
def get_pipeline_pull_index() -> bool:
|
||||
"""是否拉取指数日K。默认 True。"""
|
||||
return load().get("pipeline_pull_index", True)
|
||||
|
||||
|
||||
_PIPELINE_PULL_KEYS = ("pipeline_pull_etf", "pipeline_pull_index")
|
||||
|
||||
|
||||
def get_pipeline_pull_types() -> dict:
|
||||
"""返回三个拉取开关的当前值。"""
|
||||
return {
|
||||
"pipeline_pull_a_share": get_pipeline_pull_a_share(),
|
||||
"pipeline_pull_etf": get_pipeline_pull_etf(),
|
||||
"pipeline_pull_index": get_pipeline_pull_index(),
|
||||
}
|
||||
|
||||
|
||||
def set_pipeline_pull_types(cfg: dict) -> dict:
|
||||
"""批量保存拉取开关。只接受白名单内的布尔字段。"""
|
||||
updates = {
|
||||
k: bool(v) for k, v in cfg.items()
|
||||
if k in _PIPELINE_PULL_KEYS and v is not None
|
||||
}
|
||||
save(updates)
|
||||
return get_pipeline_pull_types()
|
||||
|
||||
|
||||
def get_pipeline_index_symbols() -> str:
|
||||
"""指数自定义拉取代码(逗号/换行/空格分隔)。空串表示全量。"""
|
||||
return str(load().get("pipeline_index_symbols", "") or "").strip()
|
||||
|
||||
|
||||
def set_pipeline_index_symbols(symbols: str) -> str:
|
||||
"""保存指数自定义代码,返回规范化后的字符串。"""
|
||||
save({"pipeline_index_symbols": symbols})
|
||||
return get_pipeline_index_symbols()
|
||||
|
||||
|
||||
def get_pipeline_schedule() -> dict:
|
||||
"""返回盘后管道调度时间 {"hour": 15, "minute": 30}。"""
|
||||
d = load().get("pipeline_schedule", {"hour": 15, "minute": 30})
|
||||
@@ -165,6 +265,73 @@ def set_depth_finalize_time(hour: int, minute: int) -> dict:
|
||||
return {"hour": h, "minute": m}
|
||||
|
||||
|
||||
# 复盘推送可选渠道白名单 (微信等暂未实现, 不在白名单内, 前端仅作占位)
|
||||
# 多选: 不推送 = 空数组, 而非 'none'
|
||||
REVIEW_PUSH_CHANNELS = {"feishu"}
|
||||
|
||||
|
||||
def get_review_schedule() -> dict:
|
||||
"""定时复盘调度 {"enabled": False, "hour": 15, "minute": 10}。默认关闭。
|
||||
|
||||
A股 15:00 收盘, 默认时间设为 15:10(收盘后即时复盘), 强制下限 15:00。
|
||||
"""
|
||||
d = load().get("review_schedule", {"enabled": False, "hour": 15, "minute": 10})
|
||||
return {
|
||||
"enabled": bool(d.get("enabled", False)),
|
||||
"hour": d.get("hour", 15),
|
||||
"minute": d.get("minute", 10),
|
||||
}
|
||||
|
||||
|
||||
def set_review_schedule(enabled: bool, hour: int, minute: int) -> dict:
|
||||
"""保存定时复盘调度。强制时间下限 15:00(A股收盘)。
|
||||
|
||||
enabled=False 时时间仍保存(下次开启可沿用), 但调度器不会注册 job。
|
||||
"""
|
||||
h = max(0, min(23, hour))
|
||||
m = max(0, min(59, minute))
|
||||
# 下限 15:00: A股 15:00 收盘, 收盘后才有当日完整数据复盘
|
||||
if h * 60 + m < 15 * 60:
|
||||
h, m = 15, 0
|
||||
save({"review_schedule": {"enabled": bool(enabled), "hour": h, "minute": m}})
|
||||
return {"enabled": bool(enabled), "hour": h, "minute": m}
|
||||
|
||||
|
||||
def get_review_push_channels() -> list[str]:
|
||||
"""复盘推送渠道(多选) — 选定的外部工具列表, 复盘归档后逐个推送。
|
||||
|
||||
与 review_schedule / 实时行情完全独立, 常驻可单独设置。
|
||||
空列表 = 不推送; ['feishu'] = 推送到飞书(复用监控中心全局 feishu_webhook_url/secret)。
|
||||
|
||||
向后兼容:
|
||||
- 老多版本单选 review_push_channel=='feishu' → ['feishu']
|
||||
- 更老布尔 review_push_enabled==True → ['feishu']
|
||||
"""
|
||||
d = load()
|
||||
raw = d.get("review_push_channels")
|
||||
if isinstance(raw, list):
|
||||
return [c for c in raw if c in REVIEW_PUSH_CHANNELS]
|
||||
# 兼容老单选字符串
|
||||
if d.get("review_push_channel") == "feishu":
|
||||
return ["feishu"]
|
||||
# 兼容更老布尔开关
|
||||
if d.get("review_push_enabled") is True:
|
||||
return ["feishu"]
|
||||
return []
|
||||
|
||||
|
||||
def set_review_push_channels(channels: list[str]) -> list[str]:
|
||||
"""保存复盘推送渠道(多选)。过滤白名单外的值、去重、保序。空列表 = 不推送。"""
|
||||
seen: set[str] = set()
|
||||
cleaned: list[str] = []
|
||||
for c in channels or []:
|
||||
if c in REVIEW_PUSH_CHANNELS and c not in seen:
|
||||
seen.add(c)
|
||||
cleaned.append(c)
|
||||
save({"review_push_channels": cleaned})
|
||||
return cleaned
|
||||
|
||||
|
||||
|
||||
# ===== 实时监控 =====
|
||||
|
||||
@@ -178,6 +345,59 @@ SSE_REFRESH_PAGES_DEFAULT = {
|
||||
SIDEBAR_INDEX_SYMBOLS_DEFAULT = ["000001.SH", "399001.SZ", "399006.SZ", "000680.SH"]
|
||||
|
||||
|
||||
# ===== 盘中实时行情范围 (独立于盘后管道范围) =====
|
||||
|
||||
|
||||
def get_realtime_pull_stock() -> bool:
|
||||
return load().get("realtime_pull_stock", True)
|
||||
|
||||
|
||||
def get_realtime_pull_etf() -> bool:
|
||||
# 老用户兼容: ETF 实时默认关闭，避免升级后请求量/写盘量突然增加。
|
||||
return load().get("realtime_pull_etf", False)
|
||||
|
||||
|
||||
def get_realtime_pull_index() -> bool:
|
||||
return load().get("realtime_pull_index", True)
|
||||
|
||||
|
||||
def get_realtime_index_mode() -> str:
|
||||
mode = str(load().get("realtime_index_mode", "core") or "core").lower()
|
||||
return mode if mode in {"core", "all"} else "core"
|
||||
|
||||
|
||||
def get_realtime_index_symbols() -> list[str]:
|
||||
stored = load().get("realtime_index_symbols", SIDEBAR_INDEX_SYMBOLS_DEFAULT)
|
||||
if isinstance(stored, str):
|
||||
import re
|
||||
stored = [s.strip() for s in re.split(r"[,\s]+", stored) if s.strip()]
|
||||
return [str(s) for s in stored if str(s).strip()]
|
||||
|
||||
|
||||
def set_realtime_quote_scope(cfg: dict) -> dict:
|
||||
updates = {}
|
||||
for key in ("realtime_pull_stock", "realtime_pull_etf", "realtime_pull_index"):
|
||||
if key in cfg and cfg[key] is not None:
|
||||
updates[key] = bool(cfg[key])
|
||||
if "realtime_index_mode" in cfg and cfg["realtime_index_mode"] in {"core", "all"}:
|
||||
updates["realtime_index_mode"] = cfg["realtime_index_mode"]
|
||||
if "realtime_index_symbols" in cfg and cfg["realtime_index_symbols"] is not None:
|
||||
updates["realtime_index_symbols"] = cfg["realtime_index_symbols"]
|
||||
if updates:
|
||||
save(updates)
|
||||
return get_realtime_quote_scope()
|
||||
|
||||
|
||||
def get_realtime_quote_scope() -> dict:
|
||||
return {
|
||||
"realtime_pull_stock": get_realtime_pull_stock(),
|
||||
"realtime_pull_etf": get_realtime_pull_etf(),
|
||||
"realtime_pull_index": get_realtime_pull_index(),
|
||||
"realtime_index_mode": get_realtime_index_mode(),
|
||||
"realtime_index_symbols": get_realtime_index_symbols(),
|
||||
}
|
||||
|
||||
|
||||
def get_sse_refresh_pages() -> dict[str, bool]:
|
||||
"""返回每个页面的 SSE 刷新开关。"""
|
||||
stored = load().get("sse_refresh_pages", {})
|
||||
@@ -216,6 +436,43 @@ def set_system_notify_enabled(enabled: bool) -> bool:
|
||||
return bool(enabled)
|
||||
|
||||
|
||||
def get_feishu_webhook_url() -> str:
|
||||
"""飞书自定义机器人 Webhook 地址 — 全局共用一处, 所有启用推送的规则都推到这一个群。"""
|
||||
return load().get("feishu_webhook_url", "")
|
||||
|
||||
|
||||
def get_feishu_webhook_secret() -> str:
|
||||
"""飞书自定义机器人签名密钥 — 机器人启用「签名校验」时必填, 留空表示不验签。"""
|
||||
return load().get("feishu_webhook_secret", "")
|
||||
|
||||
|
||||
def set_feishu_webhook_url(url: str) -> str:
|
||||
"""保存飞书 Webhook 地址。传入空串表示清空配置。"""
|
||||
save({"feishu_webhook_url": str(url or "").strip()})
|
||||
return get_feishu_webhook_url()
|
||||
|
||||
|
||||
def set_feishu_webhook_secret(secret: str) -> str:
|
||||
"""保存飞书签名密钥。传入空串表示不验签。"""
|
||||
save({"feishu_webhook_secret": str(secret or "").strip()})
|
||||
return get_feishu_webhook_secret()
|
||||
|
||||
|
||||
def get_webhook_enabled_default() -> bool:
|
||||
"""新建监控规则时是否默认勾选「飞书推送」。
|
||||
|
||||
数据模型当前只有一个 webhook_enabled 布尔 (即飞书), QMT/ptrade 待定。
|
||||
此默认值供规则编辑器新建规则时预填, 单条规则仍可独立修改。
|
||||
"""
|
||||
return load().get("webhook_enabled_default", False)
|
||||
|
||||
|
||||
def set_webhook_enabled_default(enabled: bool) -> bool:
|
||||
"""保存飞书推送默认勾选态。"""
|
||||
save({"webhook_enabled_default": bool(enabled)})
|
||||
return get_webhook_enabled_default()
|
||||
|
||||
|
||||
def get_screener_auto_run() -> bool:
|
||||
"""选股页进入时是否自动运行所有策略 (获取命中数)。默认开。"""
|
||||
return load().get("screener_auto_run", True)
|
||||
@@ -311,3 +568,18 @@ def set_onboarding_completed(done: bool = True) -> bool:
|
||||
"""标记首次使用向导完成状态。"""
|
||||
save({"onboarding_completed": bool(done)})
|
||||
return bool(done)
|
||||
|
||||
|
||||
# ===== 财务数据同步时间(持久化,重启不丢失) =====
|
||||
# 结构: { "metrics": "2026-06-25T10:00:00+08:00", "income": ..., ... }
|
||||
|
||||
def get_financial_sync_times() -> dict[str, str]:
|
||||
"""返回各财务表的最后同步时间(ISO 字符串)。未同步过的表不在返回值中。"""
|
||||
return load().get("financial_sync_times", {}) or {}
|
||||
|
||||
|
||||
def set_financial_sync_time(table: str, iso_ts: str) -> None:
|
||||
"""更新单张财务表的最后同步时间(合并写入,不清除其他表)。"""
|
||||
times = get_financial_sync_times()
|
||||
times[table] = iso_ts
|
||||
save({"financial_sync_times": times})
|
||||
|
||||
@@ -3,7 +3,7 @@
|
||||
集中管理全市场行情拉取 + enriched 缓存，供盘中选股、自选股等所有模块复用。
|
||||
|
||||
架构:
|
||||
- 后台线程轮询数据源 get_by_universes(["CN_Equity_A", "CN_Index"])
|
||||
- 后台线程轮询 TickFlow get_by_universes(["CN_Equity_A", "CN_Index"])
|
||||
- 拉取行情 → 写 kline_daily (不复权) + 增量计算 enriched → 写盘 + 更新缓存
|
||||
- _enriched_cache 是唯一的盘中数据源 (OHLCV + 全套技术指标)
|
||||
- _live_agg_cache 是递推状态 (只加载一次, 盘中不变)
|
||||
@@ -43,6 +43,7 @@ class QuoteService:
|
||||
"expert": 1.0,
|
||||
"pro": 2.0,
|
||||
"starter": 3.0,
|
||||
"free": 6.0,
|
||||
}
|
||||
DEFAULT_INTERVAL = 10.0
|
||||
MAX_INTERVAL = 60.0
|
||||
@@ -59,6 +60,10 @@ class QuoteService:
|
||||
self._depth_update_event = threading.Event() # SSE 通知: depth 五档修正后 set (刷新连板梯队)
|
||||
self._pending_alerts: list[dict] = [] # 待推送的告警
|
||||
self._max_pending_alerts: int = 1000 # 背压上限: 超出丢弃最旧
|
||||
# 复盘进度 SSE 通道: 定时复盘流式生成时, 把 meta/delta/done 事件推给开着页面的前端
|
||||
self._review_event = threading.Event() # SSE 通知: 有复盘进度事件时 set
|
||||
self._pending_review: list[str] = [] # 待推送的复盘事件(JSON 字符串)
|
||||
self._max_pending_review: int = 200 # 背压上限: 超出丢弃最旧
|
||||
self._strategy_monitor = None # 延迟注入
|
||||
self._app_state = None # 延迟注入 (FastAPI app.state)
|
||||
|
||||
@@ -68,6 +73,7 @@ class QuoteService:
|
||||
self._fetched_at: float = 0.0 # 拉取完成的 Unix 时间戳 (毫秒)
|
||||
self._symbol_count: int = 0
|
||||
self._index_symbol_count: int = 0
|
||||
self._etf_symbol_count: int = 0
|
||||
self._index_quotes_cache: pl.DataFrame | None = None
|
||||
|
||||
# ================================================================
|
||||
@@ -102,11 +108,11 @@ class QuoteService:
|
||||
def enable(self) -> bool:
|
||||
"""开启自动行情 (不立即启动线程，等下一个交易时段)。
|
||||
|
||||
none/free 档无实时行情权限,拒绝开启并返回 False;
|
||||
starter+ 正常启动。返回值表示是否真正开启。
|
||||
none 档无实时行情权限,拒绝开启并返回 False;
|
||||
free 档开启自选股实时,starter+ 开启全市场实时。返回值表示是否真正开启。
|
||||
"""
|
||||
if not self.is_realtime_allowed():
|
||||
logger.warning("实时行情开启被拒:当前档位(none/free)无实时行情权限")
|
||||
logger.warning("实时行情开启被拒:当前档位(none)无实时行情权限")
|
||||
return False
|
||||
self._enabled = True
|
||||
self._save_enabled(True)
|
||||
@@ -126,14 +132,14 @@ class QuoteService:
|
||||
def boot_check(self) -> None:
|
||||
"""启动时检查 preferences，若 enabled 则自动启动。
|
||||
|
||||
none/free 档无实时行情权限:即使 preferences 标记为 enabled,
|
||||
none 档无实时行情权限:即使 preferences 标记为 enabled,
|
||||
也不启动,并同步 preferences 为关闭(避免 UI 误显示已开启)。
|
||||
"""
|
||||
from app.services import preferences
|
||||
if not self.is_realtime_allowed():
|
||||
if preferences.get_realtime_quotes_enabled():
|
||||
self._save_enabled(False)
|
||||
logger.info("实时行情未启动:当前档位(none/free)无实时行情权限")
|
||||
logger.info("实时行情未启动:当前档位(none)无实时行情权限")
|
||||
return
|
||||
if preferences.get_realtime_quotes_enabled():
|
||||
self.start()
|
||||
@@ -188,6 +194,34 @@ class QuoteService:
|
||||
self._pending_alerts = []
|
||||
return alerts
|
||||
|
||||
# ================================================================
|
||||
# 复盘进度 SSE 通道 — 定时复盘流式生成时, 把事件实时推给前端
|
||||
# ================================================================
|
||||
def push_review_event(self, event_json: str) -> None:
|
||||
"""追加一条复盘进度事件(JSON 字符串), 并唤醒 SSE generator。
|
||||
|
||||
事件格式与 recap_market_stream 的产出一致(meta/delta/error/done),
|
||||
前端 reviewStore 直接消费。背压: 超过上限丢弃最旧(复盘流几百条 delta, 200 够用)。
|
||||
"""
|
||||
with self._lock:
|
||||
self._pending_review.append(event_json)
|
||||
if len(self._pending_review) > self._max_pending_review:
|
||||
overflow = len(self._pending_review) - self._max_pending_review
|
||||
self._pending_review = self._pending_review[overflow:]
|
||||
self._review_event.set()
|
||||
|
||||
def wait_for_review(self, timeout: float = 30.0) -> bool:
|
||||
"""阻塞等待复盘进度事件 (供 SSE 线程使用)。"""
|
||||
self._review_event.clear()
|
||||
return self._review_event.wait(timeout=timeout)
|
||||
|
||||
def pop_review_events(self) -> list[str]:
|
||||
"""取走所有待推送的复盘事件 (线程安全)。"""
|
||||
with self._lock:
|
||||
events = self._pending_review
|
||||
self._pending_review = []
|
||||
return events
|
||||
|
||||
# ================================================================
|
||||
# 档位感知间隔限制
|
||||
# ================================================================
|
||||
@@ -199,13 +233,19 @@ class QuoteService:
|
||||
return tier_label().split()[0].split("+")[0].strip().lower()
|
||||
|
||||
@classmethod
|
||||
def is_realtime_allowed(cls) -> bool:
|
||||
"""当前档位是否允许使用实时行情。
|
||||
def realtime_mode(cls) -> str:
|
||||
"""当前实时行情模式: none / watchlist / full_market。"""
|
||||
tier = cls._current_tier()
|
||||
if tier == "none":
|
||||
return "none"
|
||||
if tier == "free":
|
||||
return "watchlist"
|
||||
return "full_market"
|
||||
|
||||
none/free 档走 free-api 服务器,无实时行情权限 → 不允许;
|
||||
starter+ 付费档走付费端点,有实时行情 → 允许。
|
||||
"""
|
||||
return cls._current_tier() not in ("none", "free")
|
||||
@classmethod
|
||||
def is_realtime_allowed(cls) -> bool:
|
||||
"""当前档位是否允许使用实时行情。"""
|
||||
return cls.realtime_mode() != "none"
|
||||
|
||||
@classmethod
|
||||
def _tier_min_interval(cls) -> float:
|
||||
@@ -251,7 +291,7 @@ class QuoteService:
|
||||
return df
|
||||
|
||||
def get_index_quotes(self, symbols: list[str] | None = None) -> pl.DataFrame:
|
||||
"""返回实时指数行情缓存。不会触发数据源请求。"""
|
||||
"""返回实时指数行情缓存。不会触发 TickFlow 请求。"""
|
||||
with self._lock:
|
||||
df = self._index_quotes_cache.clone() if self._index_quotes_cache is not None else pl.DataFrame()
|
||||
if df.is_empty():
|
||||
@@ -262,13 +302,19 @@ class QuoteService:
|
||||
|
||||
def status(self) -> dict:
|
||||
"""返回行情服务状态。"""
|
||||
from app.services import preferences
|
||||
age = (time.perf_counter() - self._fetch_time) * 1000 if self._fetch_time else -1
|
||||
mode = self.realtime_mode()
|
||||
return {
|
||||
"enabled": self._enabled,
|
||||
"running": self._running,
|
||||
"mode": mode,
|
||||
"realtime_allowed": mode != "none",
|
||||
"watchlist_symbol_count": len(preferences.get_realtime_watchlist_symbols()),
|
||||
"interval_s": self._interval,
|
||||
"symbol_count": self._symbol_count,
|
||||
"index_symbol_count": self._index_symbol_count,
|
||||
"etf_symbol_count": self._etf_symbol_count,
|
||||
"quote_age_ms": round(age, 0) if age >= 0 else None,
|
||||
"is_trading_hours": self._is_trading_hours(),
|
||||
"last_fetch_ms": round(self._fetched_at, 0) if self._fetched_at else None,
|
||||
@@ -299,17 +345,47 @@ class QuoteService:
|
||||
waited += 0.5
|
||||
|
||||
def _fetch_quotes(self) -> None:
|
||||
"""拉取全市场行情 → 写 daily + 计算 enriched + 更新缓存。"""
|
||||
from app.tickflow.client import get_client
|
||||
"""按当前档位拉取行情。"""
|
||||
if self.realtime_mode() == "watchlist":
|
||||
self._fetch_watchlist_quotes()
|
||||
return
|
||||
self._fetch_full_market_quotes()
|
||||
|
||||
tf = get_client()
|
||||
def _fetch_full_market_quotes(self) -> None:
|
||||
"""拉取全市场行情 → 写 daily + 计算 enriched + 更新缓存。"""
|
||||
from app.tickflow.client import get_paid_realtime_client
|
||||
|
||||
tf = get_paid_realtime_client()
|
||||
if tf is None:
|
||||
logger.warning("实时行情拉取失败:未配置付费服务器 API Key")
|
||||
return
|
||||
t0 = time.perf_counter()
|
||||
now_ts = time.perf_counter()
|
||||
|
||||
try:
|
||||
from app.services import preferences
|
||||
all_index_symbols = set(self._repo.get_index_symbol_set()) if self._repo else set()
|
||||
all_index_symbols.update(self.CORE_INDEX_SYMBOLS)
|
||||
resp = tf.quotes.get_by_universes(universes=["CN_Equity_A", "CN_Index"])
|
||||
core_index_symbols = set(preferences.get_realtime_index_symbols() or self.CORE_INDEX_SYMBOLS)
|
||||
all_index_symbols.update(core_index_symbols)
|
||||
all_etf_symbols = set()
|
||||
if self._repo:
|
||||
etf_inst = self._repo.get_etf_instruments()
|
||||
if not etf_inst.is_empty() and "symbol" in etf_inst.columns:
|
||||
all_etf_symbols = set(etf_inst["symbol"].cast(pl.Utf8).to_list())
|
||||
|
||||
universes: list[str] = []
|
||||
if preferences.get_realtime_pull_stock():
|
||||
universes.append("CN_Equity_A")
|
||||
if preferences.get_realtime_pull_etf() and all_etf_symbols:
|
||||
universes.append("CN_ETF")
|
||||
if preferences.get_realtime_pull_index() and preferences.get_realtime_index_mode() == "all":
|
||||
universes.append("CN_Index")
|
||||
|
||||
resp = []
|
||||
if universes:
|
||||
resp.extend(tf.quotes.get_by_universes(universes=universes) or [])
|
||||
if preferences.get_realtime_pull_index() and preferences.get_realtime_index_mode() == "core":
|
||||
resp.extend(tf.quotes.get(symbols=sorted(core_index_symbols)) or [])
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("行情拉取失败: %s", e)
|
||||
return
|
||||
@@ -349,7 +425,11 @@ class QuoteService:
|
||||
})
|
||||
|
||||
index_records = [r for r in records if r.get("symbol") in all_index_symbols]
|
||||
stock_records = [r for r in records if r.get("symbol") not in all_index_symbols]
|
||||
etf_records = [r for r in records if r.get("symbol") in all_etf_symbols]
|
||||
stock_records = [
|
||||
r for r in records
|
||||
if r.get("symbol") not in all_index_symbols and r.get("symbol") not in all_etf_symbols
|
||||
]
|
||||
|
||||
fetch_ms = (time.perf_counter() - t0) * 1000
|
||||
fetched_at = time.time() * 1000
|
||||
@@ -361,9 +441,10 @@ class QuoteService:
|
||||
self._fetched_at = fetched_at
|
||||
self._symbol_count = len(stock_records)
|
||||
self._index_symbol_count = len(index_records)
|
||||
self._etf_symbol_count = len(etf_records)
|
||||
self._index_quotes_cache = self._build_index_quotes(index_records)
|
||||
|
||||
logger.info("行情刷新: %d 只股票, %d 只指数, 耗时 %.0fms", len(stock_records), len(index_records), fetch_ms)
|
||||
logger.info("行情刷新: %d 只股票, %d 只ETF, %d 只指数, 耗时 %.0fms", len(stock_records), len(etf_records), len(index_records), fetch_ms)
|
||||
|
||||
# ---- 写 kline_daily (不复权原始价格, 只有 OHLCV) ----
|
||||
daily_df = self._build_daily(stock_records)
|
||||
@@ -373,12 +454,22 @@ class QuoteService:
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("日K写盘失败: %s", e)
|
||||
|
||||
etf_daily_df = self._build_daily(etf_records)
|
||||
if not etf_daily_df.is_empty() and self._repo:
|
||||
try:
|
||||
self._repo.flush_live_daily_asset("etf", etf_daily_df)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("ETF 日K写盘失败: %s", e)
|
||||
|
||||
# ---- 构建 API 直接值的补充表 (不写 daily, 只用于 enriched 计算) ----
|
||||
quote_extra = self._build_quote_extra(stock_records)
|
||||
etf_quote_extra = self._build_quote_extra(etf_records)
|
||||
|
||||
# ---- 增量计算 enriched + 写盘 + 更新缓存 ----
|
||||
if not daily_df.is_empty() and self._repo:
|
||||
self._flush_live_enriched(daily_df, quote_extra)
|
||||
self._flush_live_enriched(daily_df, quote_extra, asset_type="stock")
|
||||
if not etf_daily_df.is_empty() and self._repo:
|
||||
self._flush_live_enriched(etf_daily_df, etf_quote_extra, asset_type="etf")
|
||||
|
||||
# ---- 通知 SSE ----
|
||||
self._update_event.set()
|
||||
@@ -386,6 +477,87 @@ class QuoteService:
|
||||
# ---- 策略监控 + 告警评估 ----
|
||||
self._evaluate_monitors(daily_df, quote_extra)
|
||||
|
||||
def _fetch_watchlist_quotes(self) -> None:
|
||||
"""Free 档自选股实时: 只拉取最多 5 个 symbols。"""
|
||||
from app.services import preferences
|
||||
from app.tickflow.client import get_paid_realtime_client
|
||||
|
||||
symbols = preferences.get_realtime_watchlist_symbols()
|
||||
if not symbols:
|
||||
logger.info("自选实时未配置标的, 跳过行情拉取")
|
||||
return
|
||||
|
||||
tf = get_paid_realtime_client()
|
||||
if tf is None:
|
||||
logger.warning("自选实时拉取失败:未配置付费服务器 API Key")
|
||||
return
|
||||
|
||||
t0 = time.perf_counter()
|
||||
now_ts = time.perf_counter()
|
||||
try:
|
||||
resp = tf.quotes.get(symbols=symbols) or []
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("自选实时拉取失败: %s", e)
|
||||
return
|
||||
|
||||
if not resp:
|
||||
logger.warning("自选实时行情数据为空")
|
||||
return
|
||||
|
||||
records = []
|
||||
for q in resp:
|
||||
ext = q.get("ext") or {}
|
||||
last_price = q.get("last_price")
|
||||
prev_close = q.get("prev_close")
|
||||
change_amount = ext.get("change_amount")
|
||||
change_pct = ext.get("change_pct")
|
||||
if change_amount is None and last_price is not None and prev_close is not None:
|
||||
change_amount = float(last_price) - float(prev_close)
|
||||
if change_pct is None and change_amount is not None and prev_close not in (None, 0):
|
||||
change_pct = float(change_amount) / float(prev_close) * 100
|
||||
records.append({
|
||||
"symbol": q.get("symbol"),
|
||||
"name": q.get("name") or ext.get("name"),
|
||||
"last_price": last_price,
|
||||
"prev_close": prev_close,
|
||||
"open": q.get("open"),
|
||||
"high": q.get("high"),
|
||||
"low": q.get("low"),
|
||||
"volume": q.get("volume"),
|
||||
"amount": q.get("amount"),
|
||||
"change_pct": change_pct,
|
||||
"change_amount": change_amount,
|
||||
"amplitude": ext.get("amplitude"),
|
||||
"turnover_rate": ext.get("turnover_rate"),
|
||||
"timestamp": q.get("timestamp"),
|
||||
"session": q.get("session"),
|
||||
})
|
||||
|
||||
fetch_ms = (time.perf_counter() - t0) * 1000
|
||||
fetched_at = time.time() * 1000
|
||||
with self._lock:
|
||||
self._fetch_time = now_ts
|
||||
self._fetch_ms = fetch_ms
|
||||
self._fetched_at = fetched_at
|
||||
self._symbol_count = len(records)
|
||||
self._index_symbol_count = 0
|
||||
self._etf_symbol_count = 0
|
||||
self._index_quotes_cache = None
|
||||
|
||||
logger.info("自选实时刷新: %d 只股票, 耗时 %.0fms", len(records), fetch_ms)
|
||||
|
||||
daily_df = self._build_daily(records)
|
||||
quote_extra = self._build_quote_extra(records)
|
||||
if not daily_df.is_empty() and self._repo:
|
||||
try:
|
||||
self._repo.merge_live_daily_asset("stock", daily_df)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("自选实时日K写盘失败: %s", e)
|
||||
self._flush_live_enriched(daily_df, quote_extra, asset_type="stock", merge=True)
|
||||
|
||||
self._update_event.set()
|
||||
self._evaluate_monitors(daily_df, quote_extra)
|
||||
|
||||
# ================================================================
|
||||
# 工具
|
||||
# ================================================================
|
||||
@@ -495,6 +667,8 @@ class QuoteService:
|
||||
return
|
||||
|
||||
all_alerts: list[dict] = []
|
||||
rule_events: list[dict] = []
|
||||
engine = None
|
||||
|
||||
# 通用监控规则评估 (统一引擎: signal/price/market/strategy)
|
||||
if self._app_state:
|
||||
@@ -511,7 +685,11 @@ class QuoteService:
|
||||
})
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.debug("name_map 构建失败 (不影响监控): %s", e)
|
||||
rule_events = engine.evaluate(enriched_today)
|
||||
# 连板梯队封单监控: 有 ladder 规则时, 从 depth_service 注入封单量到 enriched
|
||||
eval_df = enriched_today
|
||||
if engine.has_rule_type("ladder"):
|
||||
eval_df = self._inject_sealed_vol(enriched_today, enriched_date)
|
||||
rule_events = engine.evaluate(eval_df)
|
||||
if rule_events:
|
||||
# 落盘到 alerts.jsonl
|
||||
try:
|
||||
@@ -535,11 +713,13 @@ class QuoteService:
|
||||
"change_pct": ev["change_pct"],
|
||||
"signals": ev["signals"],
|
||||
"severity": ev.get("severity", "info"),
|
||||
"conditions": ev.get("conditions") or [],
|
||||
"logic": ev.get("logic") or "and",
|
||||
})
|
||||
|
||||
# 刷新策略结果缓存 (实时行情开启时，每轮行情更新后自动重算)
|
||||
if self._enabled and self._app_state:
|
||||
self._refresh_strategy_cache(enriched_today, enriched_date)
|
||||
# 策略页实时回显: 不写文件 (实时行情每轮更新 enriched, 写文件会被 read_cache
|
||||
# 的 mtime 校验判过期, 反复读不到)。监控引擎本轮已算出的结果存在内存
|
||||
# (latest_strategy_results), 由 /api/screener/cached 端点直接叠加读取。
|
||||
|
||||
# 推入待推送队列 + 通知 SSE (含背压保护)
|
||||
if all_alerts:
|
||||
@@ -556,9 +736,95 @@ class QuoteService:
|
||||
# cooldown 去重已在 MonitorRuleEngine 做过, 这里只负责转发。
|
||||
self._maybe_send_system_notifications(all_alerts)
|
||||
|
||||
# Webhook 推送 (飞书等外部 IM, 由规则 webhook_enabled 开关控制)。
|
||||
# 紧随系统通知, 同样静默降级不阻断主流程。
|
||||
if rule_events:
|
||||
self._maybe_send_webhook(rule_events, engine)
|
||||
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("监控评估失败: %s", e)
|
||||
|
||||
def _inject_sealed_vol(self, enriched_today: pl.DataFrame, enriched_date) -> pl.DataFrame:
|
||||
"""从 depth_service 取封单量, 作为临时列 _sealed_vol 注入 enriched 副本。
|
||||
|
||||
涨停封单(买一量) + 跌停封单(卖一量)合并, 供 ladder 规则评估。
|
||||
depth 未就绪时返回原 df (不注入, ladder 规则安全降级不触发)。
|
||||
"""
|
||||
try:
|
||||
depth_svc = getattr(self._app_state, "depth_service", None)
|
||||
if not depth_svc:
|
||||
return enriched_today
|
||||
# enriched_date 可能是 date 或字符串, 统一为 date
|
||||
from datetime import date as date_cls
|
||||
target_date = enriched_date if isinstance(enriched_date, date_cls) else date_cls.fromisoformat(str(enriched_date))
|
||||
# 取涨停 + 跌停封单, 合并 {symbol: vol}
|
||||
up_map = depth_svc.get_sealed_map(target_date, is_down=False)
|
||||
down_map = depth_svc.get_sealed_map(target_date, is_down=True)
|
||||
sealed: dict[str, int] = {}
|
||||
for m in (up_map, down_map):
|
||||
for sym, info in m.items():
|
||||
vol = (info or {}).get("vol")
|
||||
if vol and vol > 0:
|
||||
sealed[sym] = vol # 后者覆盖前者 (同 symbol 不可能在涨跌停都封单)
|
||||
if not sealed:
|
||||
return enriched_today
|
||||
# 构造 (symbol, _sealed_vol) DataFrame, join 到 enriched 副本
|
||||
sealed_df = pl.DataFrame({
|
||||
"symbol": list(sealed.keys()),
|
||||
"_sealed_vol": list(sealed.values()),
|
||||
})
|
||||
# 若已有残留列先移除 (避免重复 join 报错)
|
||||
df = enriched_today.drop("_sealed_vol") if "_sealed_vol" in enriched_today.columns else enriched_today
|
||||
return df.join(sealed_df, on="symbol", how="left")
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.debug("封单注入失败 (ladder 规则将不触发): %s", e)
|
||||
return enriched_today
|
||||
|
||||
def _maybe_send_webhook(self, rule_events: list[dict], engine) -> None:
|
||||
"""把告警通过 Webhook 推送到外部 IM (由规则 webhook_enabled 开关控制)。
|
||||
|
||||
- 全局飞书 URL 未配置: 直接返回
|
||||
- 仅推送 webhook_enabled=True 的规则触发的告警
|
||||
- 失败静默, 不阻断主流程
|
||||
- 去重: 复用 MonitorRuleEngine 的 cooldown, 此处不重复去重
|
||||
|
||||
注意: 用 rule_events (含 rule_id) 而非重建后的 all_alerts,
|
||||
以便反查引擎规则判断是否启用推送。
|
||||
"""
|
||||
try:
|
||||
from app.services import preferences
|
||||
from app.services import webhook_adapter
|
||||
|
||||
url = preferences.get_feishu_webhook_url()
|
||||
if not url:
|
||||
return
|
||||
secret = preferences.get_feishu_webhook_secret()
|
||||
|
||||
# 反查规则, 过滤出启用推送的事件
|
||||
source_labels = {
|
||||
"strategy": "策略", "signal": "信号",
|
||||
"price": "价格", "market": "异动",
|
||||
}
|
||||
rules = engine.rules if engine is not None else {}
|
||||
pushed = 0
|
||||
for ev in rule_events:
|
||||
rule = rules.get(ev.get("rule_id"))
|
||||
if not rule or not rule.get("webhook_enabled"):
|
||||
continue
|
||||
source = ev.get("source", "")
|
||||
source_label = source_labels.get(source, source or "通知")
|
||||
symbol = ev.get("symbol") or ""
|
||||
name = ev.get("name") or ""
|
||||
message = ev.get("message") or ""
|
||||
title = f"TickFlow · {source_label}"
|
||||
body = f"{symbol} {name} {message}".strip() if symbol else (message or name)
|
||||
if webhook_adapter.send_feishu(url, title, body, secret):
|
||||
pushed += 1
|
||||
if pushed:
|
||||
logger.info("飞书 Webhook 推送: %d 条", pushed)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.debug("Webhook 推送异常 (不影响告警主流程): %s", e)
|
||||
|
||||
def _maybe_send_system_notifications(self, all_alerts: list[dict]) -> None:
|
||||
"""把告警转发到操作系统通知中心 (由 preferences 开关控制)。
|
||||
|
||||
@@ -592,95 +858,11 @@ class QuoteService:
|
||||
else:
|
||||
body = message or name
|
||||
|
||||
title = f"Stock Panel · {source_label}"
|
||||
title = f"TickFlow · {source_label}"
|
||||
notify_adapter.notify(title, body)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.debug("系统通知发送异常 (不影响告警主流程): %s", e)
|
||||
|
||||
def _refresh_strategy_cache(self, enriched_today: pl.DataFrame, enriched_date: date | None) -> None:
|
||||
"""利用已计算好的 enriched 数据，运行策略池并写入缓存。"""
|
||||
import math
|
||||
from dataclasses import asdict
|
||||
from app.services import strategy_cache
|
||||
from app.services.screener import PRESET_STRATEGIES, ScreenerService
|
||||
from app.strategy import config as strategy_config
|
||||
|
||||
try:
|
||||
if enriched_date is None:
|
||||
return
|
||||
as_of = enriched_date
|
||||
data_dir = self._repo.store.data_dir
|
||||
svc = ScreenerService(self._repo)
|
||||
engine = getattr(self._app_state, "strategy_engine", None)
|
||||
|
||||
# 确定要运行的策略: 策略监控池中的策略
|
||||
monitor_ids = self._get_monitor_pool_ids()
|
||||
if not monitor_ids:
|
||||
return
|
||||
|
||||
# 一次加载所有 override
|
||||
all_overrides = strategy_config.list_overrides(data_dir)
|
||||
|
||||
# 历史策略: 只在需要时加载
|
||||
shared_history = None
|
||||
history_strats = []
|
||||
if engine:
|
||||
id_set = set(monitor_ids)
|
||||
history_strats = [
|
||||
(sid, s) for sid, s in engine._strategies.items()
|
||||
if s.filter_history_fn and sid in id_set
|
||||
]
|
||||
if history_strats:
|
||||
max_lb = max(s.lookback_days for _, s in history_strats)
|
||||
shared_history = svc._load_enriched_history(as_of, max(1, max_lb))
|
||||
|
||||
results: dict[str, dict] = {}
|
||||
for sid in monitor_ids:
|
||||
try:
|
||||
overrides = all_overrides.get(sid, {})
|
||||
bf = overrides.get("basic_filter") if overrides else None
|
||||
dl = overrides.get("display_limit") if overrides else None
|
||||
if dl is None and overrides and "display_limit" in overrides:
|
||||
dl = 0
|
||||
|
||||
if sid in PRESET_STRATEGIES:
|
||||
r = svc.run_preset(sid, as_of=as_of, precomputed=enriched_today, basic_filter=bf, display_limit=dl)
|
||||
elif engine:
|
||||
r = engine.run(
|
||||
sid, as_of, overrides=overrides or None,
|
||||
precomputed=enriched_today, precomputed_history=shared_history,
|
||||
)
|
||||
if dl is not None and dl > 0:
|
||||
r.rows = r.rows[:dl]
|
||||
r.total = min(r.total, dl)
|
||||
else:
|
||||
continue
|
||||
|
||||
# sanitize NaN/Inf
|
||||
rows = []
|
||||
for row_dict in asdict(r).get("rows", []):
|
||||
for k, v in list(row_dict.items()):
|
||||
if isinstance(v, float) and not math.isfinite(v):
|
||||
row_dict[k] = None
|
||||
rows.append(row_dict)
|
||||
results[sid] = {"total": r.total, "as_of": str(as_of), "rows": rows}
|
||||
except Exception: # noqa: BLE001
|
||||
continue
|
||||
|
||||
if results:
|
||||
strategy_cache.write_cache(data_dir, str(as_of), results)
|
||||
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("策略缓存刷新失败: %s", e)
|
||||
|
||||
def _get_monitor_pool_ids(self) -> list[str]:
|
||||
"""获取策略监控池中的策略 ID 列表。"""
|
||||
from app.services import preferences
|
||||
ids = preferences.get_strategy_monitor_ids()
|
||||
if not ids:
|
||||
return []
|
||||
return [sid for sid in ids if sid]
|
||||
|
||||
@staticmethod
|
||||
def _get_strategy_monitor():
|
||||
"""获取 StrategyMonitorService — 不再使用, 改用 _app_state 注入。"""
|
||||
@@ -690,7 +872,7 @@ class QuoteService:
|
||||
# enriched 增量计算
|
||||
# ================================================================
|
||||
|
||||
def _flush_live_enriched(self, daily_df: pl.DataFrame, quote_extra: pl.DataFrame = None) -> None:
|
||||
def _flush_live_enriched(self, daily_df: pl.DataFrame, quote_extra: pl.DataFrame = None, asset_type: str = "stock", merge: bool = False) -> None:
|
||||
"""增量计算今天的 enriched: 用昨天的递推状态 + 今天 OHLCV → 只算今天 5500 行。
|
||||
|
||||
quote_extra: API 直接提供的补充字段 (prev_close, change_pct 等),
|
||||
@@ -701,11 +883,16 @@ class QuoteService:
|
||||
t0 = time.perf_counter()
|
||||
|
||||
# ---- 尝试增量路径 ----
|
||||
live_agg = self._repo.get_live_agg()
|
||||
prev_enriched, prev_date = self._repo.get_enriched_latest()
|
||||
live_agg = self._repo.get_live_agg() if asset_type == "stock" else pl.DataFrame()
|
||||
prev_enriched, prev_date = (
|
||||
self._repo.get_enriched_latest()
|
||||
if asset_type == "stock"
|
||||
else self._repo.get_enriched_latest_asset(asset_type)
|
||||
)
|
||||
|
||||
use_incremental = (
|
||||
not live_agg.is_empty()
|
||||
asset_type == "stock"
|
||||
and not live_agg.is_empty()
|
||||
and not prev_enriched.is_empty()
|
||||
and prev_date is not None
|
||||
)
|
||||
@@ -736,7 +923,8 @@ class QuoteService:
|
||||
"ok" if not live_agg.is_empty() else "空", prev_date)
|
||||
|
||||
cutoff = today - timedelta(days=90)
|
||||
daily_glob = str(self._repo.store.data_dir / "kline_daily" / "**" / "*.parquet")
|
||||
table = "kline_etf_daily" if asset_type == "etf" else "kline_daily"
|
||||
daily_glob = str(self._repo.store.data_dir / table / "**" / "*.parquet")
|
||||
ohlcv_cols = ["symbol", "date", "open", "high", "low", "close", "volume", "amount"]
|
||||
hist_df = (
|
||||
pl.scan_parquet(daily_glob)
|
||||
@@ -753,14 +941,15 @@ class QuoteService:
|
||||
full_df = pl.concat([hist_df, daily_ohlcv], how="diagonal_relaxed")
|
||||
full_df = full_df.sort(["symbol", "date"])
|
||||
|
||||
factor_path = self._repo.store.data_dir / "adj_factor" / "all.parquet"
|
||||
factor_dir = "adj_factor_etf" if asset_type == "etf" else "adj_factor"
|
||||
factor_path = self._repo.store.data_dir / factor_dir / "all.parquet"
|
||||
factors = pl.DataFrame()
|
||||
if factor_path.exists():
|
||||
try:
|
||||
factors = pl.read_parquet(factor_path)
|
||||
except Exception:
|
||||
pass
|
||||
instruments = self._repo.get_instruments()
|
||||
instruments = self._repo.get_instruments() if asset_type == "stock" else None
|
||||
|
||||
enriched_full = compute_enriched(full_df, factors=factors, instruments=instruments)
|
||||
enriched_today = enriched_full.filter(pl.col("date") == today)
|
||||
@@ -769,7 +958,10 @@ class QuoteService:
|
||||
return
|
||||
|
||||
# ---- 写盘 + 更新缓存 ----
|
||||
self._repo.flush_live_enriched(enriched_today)
|
||||
if merge:
|
||||
self._repo.merge_live_enriched_asset(asset_type, enriched_today)
|
||||
else:
|
||||
self._repo.flush_live_enriched_asset(asset_type, enriched_today)
|
||||
|
||||
elapsed = time.perf_counter() - t0
|
||||
mode_label = "增量" if use_incremental else "全量"
|
||||
|
||||
@@ -0,0 +1,214 @@
|
||||
"""概念涨幅轮动矩阵 service。
|
||||
|
||||
输出「每列(日期)各自把所有概念按当天涨幅从高到低排序」的矩阵,供前端
|
||||
「概念分析 → 涨幅RPS轮动」对话框渲染。
|
||||
|
||||
数据来源全部复用现有资产, 不引入新数据源:
|
||||
- 个股历史涨跌幅: repo.get_enriched_range(..., columns=["symbol","date","change_pct"])
|
||||
命中启动时构建的 _enriched_history_cache (0ms, 含 change_pct 小数列)
|
||||
- 概念成分股映射: 复用 market_overview_builder 的 _dimension_field / _read_ext_rows /
|
||||
_symbol_keys / _dimension_values, 与看板/复盘的概念聚合口径完全一致
|
||||
|
||||
性能: 387 概念 × 30 天的 group_by + sort 是 polars 内存操作, 实测 <50ms;
|
||||
另加进程级结果缓存 (_CACHE_TTL=120s), 重复请求 <1ms。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import time
|
||||
from datetime import date, timedelta
|
||||
|
||||
import polars as pl
|
||||
|
||||
from app.services.market_overview_builder import (
|
||||
_dimension_field,
|
||||
_dimension_values,
|
||||
_read_ext_rows,
|
||||
_symbol_keys,
|
||||
)
|
||||
from app.services.ext_data import ExtConfigStore
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 进程级结果缓存 (照搬 overview.py:18 的模式, TTL 拉长到 120s —— 轮动矩阵
|
||||
# 不像看板那样需要近实时, 盘后数据稳定, 缓存久一点无妨)
|
||||
_CACHE_TTL = 120.0
|
||||
_cache: dict[str, dict] = {}
|
||||
_cache_ts: dict[str, float] = {}
|
||||
|
||||
|
||||
def invalidate_cache() -> None:
|
||||
"""清空轮动矩阵结果缓存(数据管道完成后调用, 避免返回旧数据)。"""
|
||||
_cache.clear()
|
||||
_cache_ts.clear()
|
||||
|
||||
|
||||
def _latest_enriched_date(repo) -> date | None:
|
||||
"""取 enriched 缓存里的最新交易日(矩阵的右端=最新日期)。"""
|
||||
cache = repo._enriched_history_cache # noqa: SLF001 —— 缓存字段无公开 getter
|
||||
if cache is None or cache.is_empty() or "date" not in cache.columns:
|
||||
return None
|
||||
return cache["date"].max()
|
||||
|
||||
|
||||
def _load_concept_map_df(repo) -> tuple[pl.DataFrame, int]:
|
||||
"""构建并缓存 {symbol_upper → 概念} 的已展开 polars 映射表。
|
||||
|
||||
复用 market_overview_builder 的概念识别 + 成分股读取逻辑(_dimension_field /
|
||||
_read_ext_rows / _symbol_keys / _dimension_values), 但要的是「反向映射」
|
||||
(symbol → 概念), 且直接产出 polars DataFrame 供 join 使用。
|
||||
|
||||
返回 (map_df, concept_count):
|
||||
- map_df: 两列 (_sym_up: 大写 symbol, concept: 概念名), 已 explode, 一个
|
||||
symbol 属多概念时有多行。无概念数据时返回空 DataFrame。
|
||||
- concept_count: 去重概念总数。
|
||||
|
||||
缓存: 概念成分股是 snapshot, 进程内不变, 缓存 600s。
|
||||
直接缓存 DataFrame 而非 Python dict —— 后续 join 时省掉每次 ~1s 的 dict→DataFrame
|
||||
重建开销(这是结果缓存失效后重算的主要瓶颈)。
|
||||
"""
|
||||
global _concept_map_cache, _concept_map_count, _concept_map_ts
|
||||
now = time.time()
|
||||
if _concept_map_cache is not None and (now - _concept_map_ts) < 600:
|
||||
return _concept_map_cache, _concept_map_count
|
||||
|
||||
data_dir = repo.store.data_dir
|
||||
store = ExtConfigStore(data_dir)
|
||||
# 先收集成扁平的 (sym, concept) 行, 再一次性构造 DataFrame(比 list 列快得多)
|
||||
pairs: list[tuple[str, str]] = []
|
||||
concepts_seen: set[str] = set()
|
||||
|
||||
for config in store.load_all():
|
||||
field = _dimension_field(config, "concept")
|
||||
if not field:
|
||||
continue
|
||||
for ext_row in _read_ext_rows(data_dir, config, field):
|
||||
concepts = _dimension_values(ext_row.get(field))
|
||||
if not concepts:
|
||||
continue
|
||||
keys = _symbol_keys(ext_row, config)
|
||||
for key in keys:
|
||||
for c in concepts:
|
||||
pairs.append((key, c))
|
||||
concepts_seen.add(c)
|
||||
|
||||
if pairs:
|
||||
# 去重: 同一 (symbol, concept) 对会因多 key 形式(SZ/000001)和
|
||||
# 多 config 重复出现, 去重后从 ~48万 行降到 ~14万, join 快 3x+
|
||||
_concept_map_cache = pl.DataFrame(
|
||||
{"_sym_up": [p[0] for p in pairs], "concept": [p[1] for p in pairs]},
|
||||
schema={"_sym_up": pl.Utf8, "concept": pl.Utf8},
|
||||
).unique()
|
||||
_concept_map_count = len(concepts_seen)
|
||||
else:
|
||||
_concept_map_cache = pl.DataFrame(
|
||||
schema={"_sym_up": pl.Utf8, "concept": pl.Utf8}
|
||||
)
|
||||
_concept_map_count = 0
|
||||
_concept_map_ts = now
|
||||
return _concept_map_cache, _concept_map_count
|
||||
|
||||
|
||||
_concept_map_cache: pl.DataFrame | None = None
|
||||
_concept_map_count: int = 0
|
||||
_concept_map_ts: float = 0.0
|
||||
|
||||
|
||||
def build_rps_rotation(repo, days: int = 12) -> dict:
|
||||
"""构建概念涨幅轮动矩阵。
|
||||
|
||||
Args:
|
||||
repo: KlineRepository(含 _enriched_history_cache 内存历史)。
|
||||
days: 取最近 N 个交易日, 范围 [7, 30], 默认 12。
|
||||
|
||||
Returns:
|
||||
{
|
||||
"dates": ["2026-06-30", ...], # 最新在最前, 长度 ≤ days
|
||||
"columns": {"2026-06-30": [[概念, 涨幅], ...], ...}, # 每列各自排序(高→低)
|
||||
"concept_count": 387, # 去重概念总数(0 表示无概念数据)
|
||||
}
|
||||
涨幅是小数(0.0522 = +5.22%)。无数据时返回空 columns。
|
||||
"""
|
||||
days = max(7, min(30, days))
|
||||
|
||||
# 结果缓存: 同 days(→ 同 start/end)的请求在 TTL 内直接返回
|
||||
latest = _latest_enriched_date(repo)
|
||||
if latest is None:
|
||||
return {"dates": [], "columns": {}, "concept_count": 0}
|
||||
|
||||
cache_key = latest.isoformat()
|
||||
now = time.time()
|
||||
cached = _cache.get(cache_key)
|
||||
if cached and (now - _cache_ts.get(cache_key, 0)) < _CACHE_TTL:
|
||||
# 缓存的是所有日期, 按需要的 days 截取(避免不同 days 各存一份)
|
||||
return _slice_cached(cached, days)
|
||||
|
||||
# 1. 概念映射(symbol → 概念), 已缓存为 polars DataFrame
|
||||
map_df, concept_count = _load_concept_map_df(repo)
|
||||
if map_df.is_empty():
|
||||
logger.info("rps_rotation: no concept data (ext_gn_ths not fetched yet)")
|
||||
return {"dates": [], "columns": {}, "concept_count": 0}
|
||||
|
||||
# 2. 取最近 N 交易日的个股 change_pct(命中内存缓存)
|
||||
start = latest - timedelta(days=days * 2 + 10) # 日历天 ≈ 2/3 交易日, 多取余量
|
||||
df = repo.get_enriched_range(
|
||||
start, latest, columns=["symbol", "date", "change_pct"]
|
||||
)
|
||||
if df is None or df.is_empty():
|
||||
return {"dates": [], "columns": {}, "concept_count": 0}
|
||||
|
||||
# 3. 把个股 symbol 映射到概念, 一只股票拆成多行(每个概念一行)
|
||||
# symbol 大写匹配(map_df 的 _sym_up 已大写)
|
||||
df = df.with_columns(pl.col("symbol").str.to_uppercase().alias("_sym_up"))
|
||||
joined = df.join(map_df, on="_sym_up", how="inner").drop("_sym_up")
|
||||
|
||||
if joined.is_empty():
|
||||
return {"dates": [], "columns": {}, "concept_count": 0}
|
||||
|
||||
# 4. 按 (date, concept) 聚合 avg change_pct —— 与 _dimension_rank:288 的简单平均口径一致
|
||||
agg = joined.group_by(["date", "concept"]).agg(
|
||||
pl.col("change_pct").mean().alias("avg_pct")
|
||||
)
|
||||
# 去掉 NaN/Null(停牌等无行情的概念日)
|
||||
agg = agg.filter(pl.col("avg_pct").is_not_null() & pl.col("avg_pct").is_not_nan())
|
||||
|
||||
# 5. 每个日期内按 avg_pct 降序排, 再 group_by 把每组的 (concept, avg_pct)
|
||||
# 收集成并行 list —— 一次 polars 操作拿到全部列, 避免 partition_by 的 tuple key 歧义
|
||||
agg = agg.sort(["date", "avg_pct"], descending=[False, True])
|
||||
grouped = agg.group_by("date", maintain_order=True).agg(
|
||||
pl.col("concept"), pl.col("avg_pct")
|
||||
)
|
||||
# 最新日期排最前
|
||||
grouped = grouped.sort("date", descending=True)
|
||||
|
||||
columns: dict[str, list[list]] = {}
|
||||
all_dates_sorted: list[str] = []
|
||||
for row in grouped.iter_rows(named=True):
|
||||
d_str = str(row["date"])
|
||||
all_dates_sorted.append(d_str)
|
||||
columns[d_str] = list(zip(row["concept"], row["avg_pct"]))
|
||||
|
||||
full = {
|
||||
"dates": [str(d) for d in all_dates_sorted],
|
||||
"columns": columns,
|
||||
"concept_count": concept_count,
|
||||
}
|
||||
|
||||
# 写缓存(存全量, 按需 slice)
|
||||
_cache[cache_key] = full
|
||||
_cache_ts[cache_key] = now
|
||||
|
||||
return _slice_cached(full, days)
|
||||
|
||||
|
||||
def _slice_cached(full: dict, days: int) -> dict:
|
||||
"""从全量缓存截取最近 N 天(days)。"""
|
||||
dates_all = full["dates"]
|
||||
if len(dates_all) <= days:
|
||||
return full
|
||||
keep_dates = dates_all[:days]
|
||||
return {
|
||||
"dates": keep_dates,
|
||||
"columns": {d: full["columns"][d] for d in keep_dates},
|
||||
"concept_count": full["concept_count"],
|
||||
}
|
||||
@@ -0,0 +1,309 @@
|
||||
"""AI 个股分析服务 — 技术面 / 基本面 / 财务面 / 消息面 四维综合分析。
|
||||
|
||||
职责:
|
||||
组合一只股票的 K 线(含已算好的技术指标)+ 财务表 + 关键价位 →
|
||||
拼装"实战派交易员"级系统提示词 → 流式调用 LLM → 逐 chunk 吐给前端。
|
||||
|
||||
与 financial_analyzer.py 的区别(刻意区分,非复用):
|
||||
- 角色:A 股实战派交易员 / 技术分析师(非 CFA 财务分析师)
|
||||
- 数据源:K 线 + 技术指标为主,财务表为辅(财务分析以财务表为主)
|
||||
- 输出框架:技术面→基本面→财务面→消息面(四维),落点是买卖区间与操作建议
|
||||
(财务分析的落点是财务质量评级)
|
||||
|
||||
不知道: HTTP、前端、配置持久化。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import AsyncIterator
|
||||
|
||||
import polars as pl
|
||||
|
||||
from app.indicators.levels import compute_levels, summarize_levels
|
||||
from app.services.financial_sync import get_financial_df
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 注入最近多少根日 K(技术面分析样本)
|
||||
_KLINE_WINDOW = 90
|
||||
# 注入财务表的最近期数
|
||||
_MAX_PERIODS = 4
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 数据加载
|
||||
# ================================================================
|
||||
|
||||
def _load_kline(repo, symbol: str) -> pl.DataFrame:
|
||||
"""读取该标的最近 N 根日 K(已含技术指标 / 信号)。
|
||||
|
||||
repo: KlineRepository;走内存缓存,性能可控。
|
||||
"""
|
||||
from datetime import date, timedelta
|
||||
|
||||
end = date.today()
|
||||
start = end - timedelta(days=_KLINE_WINDOW * 2) # 多取一些保证交易日够
|
||||
df = repo.get_daily(symbol, start, end)
|
||||
if df.is_empty():
|
||||
return df
|
||||
return df.tail(_KLINE_WINDOW)
|
||||
|
||||
|
||||
def _clean_rows(df: pl.DataFrame, keep_cols: list[str]) -> list[dict]:
|
||||
"""把 DataFrame 转成 JSON 安全的 dict 列表(只保留关键列 + 清洗 NaN/Inf + date→字符串)。
|
||||
|
||||
polars 的 date 列会变成 Python datetime.date,json.dumps 无法直接序列化,
|
||||
必须转成 ISO 字符串,否则 json.dumps 抛 TypeError 让整个流静默失败。
|
||||
"""
|
||||
import datetime
|
||||
import math
|
||||
cols = [c for c in keep_cols if c in df.columns]
|
||||
sub = df.select(cols)
|
||||
rows = []
|
||||
for rec in sub.to_dicts():
|
||||
clean = {}
|
||||
for k, v in rec.items():
|
||||
if isinstance(v, float):
|
||||
clean[k] = None if not math.isfinite(v) else round(v, 4)
|
||||
elif isinstance(v, (datetime.date, datetime.datetime)):
|
||||
clean[k] = v.isoformat()
|
||||
else:
|
||||
clean[k] = v
|
||||
rows.append(clean)
|
||||
return rows
|
||||
|
||||
|
||||
def _load_financials(data_dir: Path, symbol: str) -> dict[str, list[dict]]:
|
||||
"""读取该标的核心财务指标 + 利润表(只取最有信息量的两张表)。
|
||||
|
||||
财务面只需要关键指标(ROE / 增速 / 毛利率 等),不需要把 4 张表全塞进上下文
|
||||
(那是 financial_analyzer 的职责)。这里取轻量,留给技术面更多 token。
|
||||
"""
|
||||
out: dict[str, list[dict]] = {}
|
||||
for table in ("metrics", "income"):
|
||||
df = get_financial_df(data_dir, table)
|
||||
if df.is_empty():
|
||||
out[table] = []
|
||||
continue
|
||||
df = df.filter(pl.col("symbol") == symbol)
|
||||
if df.is_empty():
|
||||
out[table] = []
|
||||
continue
|
||||
if "period_end" in df.columns:
|
||||
df = df.sort("period_end", descending=True).head(2) # 只取最近 2 期
|
||||
import math
|
||||
rows = []
|
||||
for rec in df.to_dicts():
|
||||
clean = {}
|
||||
for k, v in rec.items():
|
||||
if k == "symbol":
|
||||
continue
|
||||
if isinstance(v, float):
|
||||
clean[k] = None if not math.isfinite(v) else v
|
||||
else:
|
||||
clean[k] = v
|
||||
rows.append(clean)
|
||||
out[table] = rows
|
||||
return out
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 系统提示词 —— 实战派交易员四维框架(与财务分析明确区分)
|
||||
# ================================================================
|
||||
|
||||
_SYSTEM_PROMPT = """你是一位拥有 15 年 A 股一线实战经验的资深交易员兼技术分析师,擅长从 K 线、量价、关键价位与基本面交叉验证中把握买卖时机。你的任务是:基于提供的个股数据,产出一份**实战、可直接指导交易决策**的综合分析报告。
|
||||
|
||||
## 输出规范
|
||||
|
||||
用 **Markdown** 格式输出,严格遵循以下结构。不要输出任何 JSON 或代码块,直接输出 Markdown 正文。
|
||||
|
||||
### 1. 🎯 一句话定调(1-2 句)
|
||||
用一句话概括该股当前的**技术状态与交易属性**(如"高位放量滞涨,需警惕回调"/"底部筹码集中,放量突破在即")。结尾用【操作建议:观望 / 轻仓试探 / 逢低吸纳 / 持有 / 减仓 / 规避】给出明确倾向。
|
||||
|
||||
### 2. 📈 技术面分析(核心维度)
|
||||
这是你的主战场,务必深入:
|
||||
- **趋势判断**:均线多头/空头排列、20/60 日均线方向、价格在均线之上/下
|
||||
- **形态结构**:近期是否有突破/破位/双底/双顶/旗形等关键形态
|
||||
- **指标信号**:MACD 金叉/死叉/背离、KDJ 超买超卖、RSI 强弱、布林通道位置
|
||||
- **量价配合**:放量上涨/缩量回调/量价背离/换手率异动
|
||||
每条结论必须引用具体数值(如"MACD 在 6/12 出现金叉,DIF 0.32 上穿 DEA 0.18")。
|
||||
|
||||
### 3. 💰 关键价位(买卖区间)
|
||||
基于提供的关键价位数据,明确指出:
|
||||
- **上方压力位**(逐档列出,标注强度):第一压力、第二压力
|
||||
- **下方支撑位**(逐档列出,标注强度):第一支撑、第二支撑
|
||||
- 给出**建议买入区间**与**止损位**(基于支撑位)
|
||||
用数据说话,引用提供的压力/支撑(成交密集区)/枢轴点数值。
|
||||
|
||||
### 4. 🏭 基本面与财务面(辅助验证)
|
||||
简要点评(2-4 句,不展开长篇):
|
||||
- 盈利质量(ROE / 毛利率水平)、成长性(营收/利润增速)
|
||||
- 与技术面的**交叉验证**:好公司 + 技术面走坏 → 仍需谨慎;差公司 + 技术面强势 → 警惕炒作风险
|
||||
|
||||
**当用户消息中标注了"该标的暂无财务数据"时**,本节请输出:
|
||||
> 📌 财务面分析能力正在接入中。当前版本(Free)未同步该标的的财务报表,基本面维度暂无法评估。
|
||||
> 技术面分析不依赖财务数据,以下结论依然有效;升级套餐或等待财务数据同步后可补充本维度。
|
||||
|
||||
**绝对不要**在无数据时编造 ROE / 增速等数字。
|
||||
|
||||
### 5. 📰 消息面(价量异动推断)
|
||||
**注意:本期无直接新闻数据输入。** 请基于 K 线的**异动信号**进行推断(如:
|
||||
- 涨停/连板/炸板 → 可能有利好或资金炒作
|
||||
- 放量暴跌 → 可能有未公开利空
|
||||
- 突破放量 → 可能有催化剂
|
||||
明确标注"[推断]",告诉用户这是基于价量的推测,真实消息面数据待接入。若无明显异动,直说"近期价量平稳,无明显消息面信号"。
|
||||
|
||||
### 6. ⚖️ 综合研判与操作建议
|
||||
2-3 段:
|
||||
- 该股当前处于(底部启动 / 上升途中 / 高位震荡 / 下跌趋势 / 底部企稳)哪个阶段
|
||||
- 风险收益比评估(距支撑位的空间 vs 距压力位的空间)
|
||||
- **明确操作建议**:激进型 / 稳健型 / 保守型 分别怎么应对
|
||||
- **需要重点盯的信号**(如跌破 X 支撑止损、站上 Y 压力加仓)
|
||||
|
||||
## 分析准则(务必遵守)
|
||||
|
||||
1. **技术面优先**:作为交易员,技术面和量价是主要依据,基本面是验证手段,主次分明
|
||||
2. **数据说话**:每个判断引用具体数值,严禁空泛套话("走势良好"必须改成"连续 3 日站稳 20 日均线且放量")
|
||||
3. **诚实中立**:看多就写多,看空就写空,不要模棱两可骑墙;数据不支持时直言无法判断
|
||||
4. **价位精确**:买卖区间必须落到具体价格,基于提供的关键价位数据推演
|
||||
5. **风险前置**:任何买入建议都要配止损位;提示潜在风险不回避
|
||||
6. **简明实战**:用交易员能扫读的密度输出,总字数 1000-1800 字,重在可执行
|
||||
|
||||
## 重要免责
|
||||
报告末尾附一行:"> ⚠️ 本报告由 AI 基于公开行情与财务数据生成,仅供参考,不构成任何投资建议。交易有风险,入市需谨慎。"
|
||||
|
||||
现在请基于下方数据进行分析。"""
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 用户消息构建
|
||||
# ================================================================
|
||||
|
||||
def _build_user_prompt(
|
||||
kline_tail: list[dict],
|
||||
fins: dict[str, list[dict]],
|
||||
levels: dict[str, list[dict]],
|
||||
close: float | None,
|
||||
symbol: str,
|
||||
focus: str,
|
||||
) -> str:
|
||||
"""构建用户消息:标的 + 价位摘要 + 技术指标 JSON + 财务摘要 + 关注点。"""
|
||||
parts: list[str] = [
|
||||
f"标的标准代码: {symbol}",
|
||||
f"关键价位概览: {summarize_levels(levels, close)}",
|
||||
"",
|
||||
"以下是该标的最近日 K 数据(JSON,含 OHLCV 与已计算的技术指标。"
|
||||
f"最近 {_KLINE_WINDOW} 个交易日,升序):",
|
||||
"```json",
|
||||
json.dumps(kline_tail, ensure_ascii=False),
|
||||
"```",
|
||||
]
|
||||
|
||||
has_fin = any(fins.values())
|
||||
if has_fin:
|
||||
parts.extend([
|
||||
"",
|
||||
"以下是该标的最新财务数据(JSON,核心指标 + 利润表,金额单位为元):",
|
||||
"```json",
|
||||
json.dumps(fins, ensure_ascii=False),
|
||||
"```",
|
||||
])
|
||||
else:
|
||||
parts.extend([
|
||||
"",
|
||||
"(该标的暂无财务数据:当前为 Free 模式或尚未同步财务报表。"
|
||||
"请按系统提示词第 4 节的说明,在基本面/财务面维度给出\"接入中\"的友好提示,不要编造数据。)",
|
||||
])
|
||||
|
||||
if focus.strip():
|
||||
parts.extend(["", f"本次分析请特别关注: {focus.strip()}"])
|
||||
return "\n".join(parts)
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 关键列筛选(控制上下文体积)
|
||||
# ================================================================
|
||||
|
||||
_KLINE_KEEP_COLS = [
|
||||
"date", "open", "high", "low", "close", "volume", "change_pct",
|
||||
"ma5", "ma10", "ma20", "ma60",
|
||||
"macd_dif", "macd_dea", "macd_hist",
|
||||
"kdj_k", "kdj_d", "kdj_j",
|
||||
"rsi_6", "rsi_14", "rsi_24",
|
||||
"boll_upper", "boll_mid", "boll_lower",
|
||||
"atr_14", "vol_ratio_5d", "turnover_rate",
|
||||
"consecutive_limit_ups",
|
||||
# 信号类(布尔)——只挑对消息面推断有用的几个
|
||||
"signal_limit_up", "signal_broken_limit_up", "signal_macd_golden",
|
||||
"signal_macd_death", "signal_ma_golden_5_20", "signal_volume_surge",
|
||||
"signal_boll_breakout_upper", "signal_boll_breakout_lower",
|
||||
]
|
||||
|
||||
|
||||
# ================================================================
|
||||
# 流式分析入口
|
||||
# ================================================================
|
||||
|
||||
async def analyze_stock_stream(
|
||||
repo,
|
||||
data_dir: Path,
|
||||
symbol: str,
|
||||
focus: str = "",
|
||||
) -> AsyncIterator[str]:
|
||||
"""流式个股分析:yield 出每个 NDJSON 事件。
|
||||
|
||||
协议(与 financial_analyzer 一致,前端解析无差异):
|
||||
{"type":"meta","symbol","summary","levels"} 数据 + 价位摘要
|
||||
{"type":"delta","content":"..."} 逐 chunk 文本
|
||||
{"type":"error","message":"..."}
|
||||
{"type":"done"}
|
||||
"""
|
||||
# 1. 加载 K 线
|
||||
df = _load_kline(repo, symbol)
|
||||
if df.is_empty():
|
||||
yield json.dumps({
|
||||
"type": "error",
|
||||
"message": f"标的 {symbol} 暂无日 K 数据,请先同步",
|
||||
}, ensure_ascii=False)
|
||||
return
|
||||
|
||||
# 2. 价位计算(基于 K 线)
|
||||
levels = compute_levels(df)
|
||||
close = float(df.tail(1)["close"][0]) if "close" in df.columns else None
|
||||
|
||||
# 3. 财务(辅助)
|
||||
fins = _load_financials(data_dir, symbol)
|
||||
|
||||
# 4. meta
|
||||
yield json.dumps({
|
||||
"type": "meta",
|
||||
"symbol": symbol,
|
||||
"summary": summarize_levels(levels, close),
|
||||
"levels": levels,
|
||||
"close": close,
|
||||
}, ensure_ascii=False)
|
||||
|
||||
# 5+6. 构建提示词 + 流式调用 LLM(整体 try-except,任何异常都 yield error,避免前端卡死)
|
||||
try:
|
||||
from app.services.ai_provider import stream_ai_text
|
||||
|
||||
kline_tail = _clean_rows(df, _KLINE_KEEP_COLS)
|
||||
user_prompt = _build_user_prompt(kline_tail, fins, levels, close, symbol, focus)
|
||||
async for delta in stream_ai_text(
|
||||
[
|
||||
{"role": "system", "content": _SYSTEM_PROMPT},
|
||||
{"role": "user", "content": user_prompt},
|
||||
],
|
||||
temperature=0.5,
|
||||
max_tokens=4500,
|
||||
):
|
||||
yield json.dumps({"type": "delta", "content": delta}, ensure_ascii=False)
|
||||
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.exception("AI stock analysis failed for %s: %s", symbol, e)
|
||||
yield json.dumps({"type": "error", "message": f"AI 分析失败: {e}"}, ensure_ascii=False)
|
||||
return
|
||||
|
||||
yield json.dumps({"type": "done"}, ensure_ascii=False)
|
||||
@@ -0,0 +1,91 @@
|
||||
"""AI 个股分析报告持久化存储。
|
||||
|
||||
与 ai_reports.py(财务分析报告)完全独立 —— 单独的文件、字段、上限,
|
||||
互不影响。刻意不复用,避免引入 kind 判别字段与分支(解耦 > 抽象)。
|
||||
|
||||
存储位置: data/user_data/ai_stock_reports.json (数组,按 created_at 降序)
|
||||
保留最近 MAX_REPORTS 条;超出自动裁剪最旧的。
|
||||
|
||||
每条报告结构:
|
||||
{
|
||||
"id": "sar_xxx", # 唯一 id(stock-analysis-report)
|
||||
"symbol": "600519.SH",
|
||||
"name": "贵州茅台",
|
||||
"focus": "", # 用户追加的关心点(可为空)
|
||||
"content": "# ...markdown", # 报告正文
|
||||
"summary": "当前价 12.3 · 压力位...", # 价位/数据摘要
|
||||
"levels": {...}, # 报告生成时的关键价位(供图表回放)
|
||||
"close": 12.3, # 报告生成时的收盘价
|
||||
"created_at": "2026-06-26T10:00:00"
|
||||
}
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MAX_REPORTS = 50
|
||||
|
||||
|
||||
def _path() -> Path:
|
||||
from app.config import settings
|
||||
p = settings.data_dir / "user_data" / "ai_stock_reports.json"
|
||||
p.parent.mkdir(parents=True, exist_ok=True)
|
||||
return p
|
||||
|
||||
|
||||
def list_reports() -> list[dict]:
|
||||
"""返回全部报告(按 created_at 降序)。"""
|
||||
p = _path()
|
||||
if not p.exists():
|
||||
return []
|
||||
try:
|
||||
data = json.loads(p.read_text(encoding="utf-8"))
|
||||
if isinstance(data, list):
|
||||
return sorted(data, key=lambda r: r.get("created_at", ""), reverse=True)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("ai_stock_reports.json malformed: %s", e)
|
||||
return []
|
||||
|
||||
|
||||
def _save_all(reports: list[dict]) -> None:
|
||||
"""全量写入(裁剪到 MAX_REPORTS)。"""
|
||||
reports.sort(key=lambda r: r.get("created_at", ""), reverse=True)
|
||||
if len(reports) > MAX_REPORTS:
|
||||
reports = reports[:MAX_REPORTS]
|
||||
_path().write_text(
|
||||
json.dumps(reports, indent=2, ensure_ascii=False), encoding="utf-8",
|
||||
)
|
||||
|
||||
|
||||
def save_report(report: dict) -> dict:
|
||||
"""新增一条报告并持久化。返回保存后的报告(含 id / created_at)。"""
|
||||
reports = list_reports()
|
||||
if not report.get("id"):
|
||||
report["id"] = f"sar_{int(time.time() * 1000)}_{report.get('symbol', 'x')}"
|
||||
if not report.get("created_at"):
|
||||
report["created_at"] = _now_iso()
|
||||
reports.append(report)
|
||||
_save_all(reports)
|
||||
logger.info("Stock report saved: %s (%s), total %d", report.get("symbol"), report.get("id"), len(reports))
|
||||
return report
|
||||
|
||||
|
||||
def delete_report(report_id: str) -> bool:
|
||||
"""删除指定报告。返回是否删除成功。"""
|
||||
reports = list_reports()
|
||||
before = len(reports)
|
||||
reports = [r for r in reports if r.get("id") != report_id]
|
||||
if len(reports) < before:
|
||||
_save_all(reports)
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _now_iso() -> str:
|
||||
from datetime import datetime
|
||||
return datetime.now().isoformat(timespec="seconds")
|
||||
@@ -54,7 +54,14 @@ def _get_enriched_mtime(data_dir: Path, as_of: str) -> float | None:
|
||||
|
||||
|
||||
def read_cache(data_dir: Path) -> dict | None:
|
||||
"""读取策略缓存文件。返回 None 表示无缓存、读取失败或 enriched 数据已更新导致缓存过期。"""
|
||||
"""读取策略缓存文件。返回 None 表示无缓存或读取失败。
|
||||
|
||||
说明: 原先有 enriched mtime 过期校验 (数据文件变化 → 判过期返回 None),
|
||||
但在有实时行情的系统里, enriched parquet 每轮被刷新 → mtime 必然变化 →
|
||||
缓存被永久判死, 策略页读不到数据。且判过期后不触发重算, 只能让用户手动重跑,
|
||||
保护价值有限。故移除: 盘后缓存总能读出, 实时新鲜度由 /api/screener/cached
|
||||
端点叠加监控引擎的内存实时结果 (latest_strategy_results) 来保证。
|
||||
"""
|
||||
path = _cache_path(data_dir)
|
||||
if not path.exists():
|
||||
return None
|
||||
@@ -67,15 +74,6 @@ def read_cache(data_dir: Path) -> dict | None:
|
||||
logger.warning("读取策略缓存失败: %s", e)
|
||||
return None
|
||||
|
||||
# 校验 enriched mtime: 数据文件变化 → 缓存过期
|
||||
as_of = cached.get("as_of")
|
||||
stored_mtime = cached.get("enriched_mtime")
|
||||
if as_of and stored_mtime:
|
||||
current_mtime = _get_enriched_mtime(data_dir, as_of)
|
||||
if current_mtime is not None and current_mtime != stored_mtime:
|
||||
logger.info("策略缓存过期: enriched 数据已更新 (as_of=%s)", as_of)
|
||||
return None
|
||||
|
||||
return cached
|
||||
|
||||
|
||||
@@ -130,7 +128,8 @@ def write_cache(
|
||||
# 从 ever_rows 提取 symbol 列表 (用于快速计数)
|
||||
today_ever_matched = {sid: sorted(maps.keys()) for sid, maps in today_ever_rows.items()}
|
||||
|
||||
# 记录 enriched parquet 文件的 mtime，用于后续校验缓存是否过期
|
||||
# enriched_mtime: 盘后缓存写入时记录 (向后兼容旧字段)。read_cache 已不再用它
|
||||
# 做过期校验, 实时新鲜度改由 /cached 端点叠加监控引擎内存结果保证。
|
||||
enriched_mtime = _get_enriched_mtime(data_dir, as_of)
|
||||
|
||||
payload = {
|
||||
|
||||
@@ -63,6 +63,20 @@ def remove(symbol: str) -> list[dict]:
|
||||
return df.to_dicts()
|
||||
|
||||
|
||||
def move_to_top(symbol: str) -> list[dict]:
|
||||
p = _path()
|
||||
if not p.exists():
|
||||
return []
|
||||
df = pl.read_parquet(p)
|
||||
if df.is_empty() or symbol not in df["symbol"].to_list():
|
||||
return df.to_dicts()
|
||||
target = df.filter(pl.col("symbol") == symbol)
|
||||
rest = df.filter(pl.col("symbol") != symbol)
|
||||
out = pl.concat([target, rest], how="diagonal_relaxed")
|
||||
out.write_parquet(p)
|
||||
return out.to_dicts()
|
||||
|
||||
|
||||
def clear() -> int:
|
||||
"""清空自选列表。返回移除的数量。"""
|
||||
p = _path()
|
||||
|
||||
@@ -0,0 +1,170 @@
|
||||
"""Webhook 推送适配器 — 把告警事件推送到外部 IM / 量化软件。
|
||||
|
||||
职责: 把后端产生的告警事件, 通过用户配置的 Webhook 地址推送到外部。
|
||||
目前支持飞书群机器人; QMT / ptrade 等量化通道为待定。
|
||||
|
||||
飞书自定义机器人接入:
|
||||
1. 飞书群 → 群设置 → 群机器人 → 添加「自定义机器人」
|
||||
2. 复制生成的 Webhook 地址 (形如 https://open.feishu.cn/open-apis/bot/v2/hook/xxx)
|
||||
3. (可选) 安全设置 → 启用「签名校验」, 记录签名密钥(secret)
|
||||
4. 填入设置页「飞书 Webhook」配置
|
||||
|
||||
设计: 失败静默降级, 绝不因推送失败阻断告警主流程 (落盘 / SSE 推送)。
|
||||
去重不在本层做, 复用 MonitorRuleEngine 的 cooldown。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import base64
|
||||
import hashlib
|
||||
import hmac
|
||||
import logging
|
||||
import time
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 单次推送最长字符 (飞书单条文本消息上限 30KB, 这里保守截断避免刷屏)
|
||||
_MAX_LEN = 500
|
||||
|
||||
# 卡片消息正文最长字符 (飞书 interactive 卡片上限 30KB, 保守留余量给标题/结构)
|
||||
_CARD_MAX_LEN = 28000
|
||||
|
||||
# 飞书自定义机器人 Webhook 前缀 (用于 URL 合法性校验)
|
||||
FEISHU_HOOK_PREFIX = "https://open.feishu.cn/open-apis/bot/v2/hook/"
|
||||
|
||||
|
||||
def _truncate(text: str) -> str:
|
||||
"""截断超长文本。"""
|
||||
text = (text or "").strip()
|
||||
return text[:_MAX_LEN] + ("…" if len(text) > _MAX_LEN else "")
|
||||
|
||||
|
||||
def is_valid_feishu_url(url: str) -> bool:
|
||||
"""校验是否为合法的飞书自定义机器人 Webhook 地址。"""
|
||||
return bool(url) and url.startswith(FEISHU_HOOK_PREFIX)
|
||||
|
||||
|
||||
def _gen_sign(timestamp: str, secret: str) -> str:
|
||||
"""计算飞书自定义机器人签名。
|
||||
|
||||
算法 (官方): 把 `timestamp + "\\n" + secret` 作为签名字符串 (key),
|
||||
用 HmacSHA256 计算空字符串的签名结果, 再 Base64 编码。
|
||||
"""
|
||||
string_to_sign = f"{timestamp}\n{secret}"
|
||||
hmac_code = hmac.new(
|
||||
string_to_sign.encode("utf-8"),
|
||||
digestmod=hashlib.sha256,
|
||||
).digest()
|
||||
return base64.b64encode(hmac_code).decode("utf-8")
|
||||
|
||||
|
||||
def _truncate_card(text: str) -> str:
|
||||
"""截断卡片正文 (留余量给标题与卡片结构)。"""
|
||||
text = (text or "").strip()
|
||||
return text[:_CARD_MAX_LEN] + ("…" if len(text) > _CARD_MAX_LEN else "")
|
||||
|
||||
|
||||
def _post_feishu(webhook_url: str, payload: dict, secret: str) -> bool:
|
||||
"""发送一次飞书 webhook 请求并判定成败 (供 text / card 共用)。
|
||||
|
||||
成功响应: HTTP 200 且业务 code=0 (或非 JSON 的 200)。失败静默返回 False。
|
||||
"""
|
||||
try:
|
||||
import httpx
|
||||
|
||||
# 启用签名校验时, 请求体须带 timestamp + sign (秒级时间戳)
|
||||
if secret:
|
||||
timestamp = str(int(time.time()))
|
||||
payload["timestamp"] = timestamp
|
||||
payload["sign"] = _gen_sign(timestamp, secret)
|
||||
|
||||
resp = httpx.post(webhook_url, json=payload, timeout=5.0)
|
||||
# 飞书成功响应: {"code":0,"msg":"success"} (或 StatusCode 200 + Extra)
|
||||
if resp.status_code == 200:
|
||||
try:
|
||||
data = resp.json()
|
||||
# code=0 表示飞书业务侧成功; 部分版本无 code 字段则按 msg 判断
|
||||
if isinstance(data, dict):
|
||||
code = data.get("code", data.get("StatusCode", 0))
|
||||
if code == 0:
|
||||
return True
|
||||
logger.debug("飞书推送业务失败: %s", data)
|
||||
return False
|
||||
except ValueError:
|
||||
# 非 JSON 响应但 HTTP 200, 视为成功
|
||||
return True
|
||||
logger.debug("飞书推送 HTTP %s: %s", resp.status_code, resp.text[:200])
|
||||
return False
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.debug("飞书 Webhook 推送失败: %s", e)
|
||||
return False
|
||||
|
||||
|
||||
def send_feishu(webhook_url: str, title: str, body: str, secret: str = "") -> bool:
|
||||
"""推送一条文本消息到飞书群机器人。
|
||||
|
||||
Args:
|
||||
webhook_url: 飞书自定义机器人 Webhook 地址
|
||||
title: 消息标题 (与正文拼接为一条文本)
|
||||
body: 消息正文
|
||||
secret: 签名密钥 (机器人启用了「签名校验」时必填; 留空则不带签名)
|
||||
|
||||
Returns:
|
||||
True=成功送达, False=失败或 URL 非法。
|
||||
失败静默, 不抛异常 (Webhook 是辅助通道, 不能阻断告警主流程)。
|
||||
"""
|
||||
if not is_valid_feishu_url(webhook_url):
|
||||
return False
|
||||
|
||||
text = _truncate(f"{title}\n{body}".strip())
|
||||
if not text:
|
||||
return False
|
||||
|
||||
payload: dict = {"msg_type": "text", "content": {"text": text}}
|
||||
return _post_feishu(webhook_url, payload, secret)
|
||||
|
||||
|
||||
def send_feishu_card(webhook_url: str, title: str, subtitle: str, body_md: str, secret: str = "") -> bool:
|
||||
"""推送一条 interactive 卡片消息到飞书群机器人 —— 用 lark_md 渲染完整 markdown 报告。
|
||||
|
||||
飞书「自定义机器人」webhook 不支持文件附件, 但 interactive 卡片的 lark_md 元素
|
||||
可渲染 markdown, 能承载完整复盘报告(通常 2-5KB, 远小于卡片 30KB 上限)。
|
||||
|
||||
Args:
|
||||
webhook_url: 飞书自定义机器人 Webhook 地址
|
||||
title: 卡片标题 (显示在蓝色 header)
|
||||
subtitle: 副标题 (加粗显示, 如日期/情绪标签; 留空则省略)
|
||||
body_md: 卡片正文 markdown (报告全文)
|
||||
secret: 签名密钥 (启用签名校验时必填)
|
||||
|
||||
Returns:
|
||||
True=成功送达, False=失败或 URL 非法。
|
||||
失败静默, 不抛异常 (与 send_feishu 一致, 不阻断告警主流程)。
|
||||
"""
|
||||
if not is_valid_feishu_url(webhook_url):
|
||||
return False
|
||||
|
||||
body = _truncate_card(body_md)
|
||||
elements: list[dict] = []
|
||||
if subtitle.strip():
|
||||
elements.append({
|
||||
"tag": "div",
|
||||
"text": {"tag": "lark_md", "content": f"**{subtitle.strip()}**"},
|
||||
})
|
||||
elements.append({"tag": "hr"})
|
||||
elements.append({
|
||||
"tag": "div",
|
||||
"text": {"tag": "lark_md", "content": body},
|
||||
})
|
||||
|
||||
payload: dict = {
|
||||
"msg_type": "interactive",
|
||||
"card": {
|
||||
"config": {"wide_screen_mode": True},
|
||||
"header": {
|
||||
"title": {"tag": "plain_text", "content": title},
|
||||
"template": "blue",
|
||||
},
|
||||
"elements": elements,
|
||||
},
|
||||
}
|
||||
return _post_feishu(webhook_url, payload, secret)
|
||||
Reference in New Issue
Block a user