dekun
bfbd6879d6
Proactive monitoring for manual/hub closes and new opens prevents overtrading via in-app alerts, configurable WeChat links, and supervisor chat. Co-authored-by: Cursor <cursoragent@cursor.com>
112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
"""交易监管：AI 评语与用户回聊。"""
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from __future__ import annotations
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from typing import Any, Optional
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from hub_ai.client import generate_text, model_label
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from hub_ai.config import (
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CHAT_MAX_OUTPUT_TOKENS,
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CHAT_TEMPERATURE,
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trading_day_reset_hour,
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)
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from hub_ai.context import build_chat_context, format_chat_context_for_chat
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from hub_ai.prompts import SUPERVISOR_SYSTEM, build_supervisor_ai_prompt, build_supervisor_chat_prompt
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from hub_ai.supervisor_store import (
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append_supervisor_ai_message,
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ensure_supervisor_session,
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get_supervisor_session_state,
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)
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from hub_ai.store import append_chat_message
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from hub_trades_lib import current_trading_day
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def generate_supervisor_ai_reply(
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*,
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event: dict,
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warnings: list[dict],
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trading_day: str,
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session_id: str,
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exchanges: list[dict],
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) -> str:
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ctx = build_chat_context(exchanges, trading_day=trading_day)
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brief = format_chat_context_for_chat(ctx, max_chars=6000)
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user_prompt = build_supervisor_ai_prompt(
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context_text=brief,
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trading_day=trading_day,
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event=event,
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warnings=warnings,
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)
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return generate_text(
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system=SUPERVISOR_SYSTEM,
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user=user_prompt,
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temperature=min(0.35, CHAT_TEMPERATURE),
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max_tokens=min(512, CHAT_MAX_OUTPUT_TOKENS),
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max_continuations=1,
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)
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def make_supervisor_ai_reply_fn(exchanges: list[dict]):
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def _fn(*, event: dict, warnings: list[dict], trading_day: str, session_id: str) -> str:
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return generate_supervisor_ai_reply(
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event=event,
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warnings=warnings or [],
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trading_day=trading_day,
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session_id=session_id,
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exchanges=exchanges,
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)
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return _fn
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def send_supervisor_chat(
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exchanges: list[dict],
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message: str,
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*,
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trading_day: str | None = None,
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) -> dict[str, Any]:
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text = (message or "").strip()
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if not text:
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return {"ok": False, "msg": "消息不能为空"}
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day = (trading_day or "").strip()[:10] or current_trading_day(
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reset_hour=trading_day_reset_hour()
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)
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session = ensure_supervisor_session(day)
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sid = str(session.get("id") or "")
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prior = session.get("messages") or []
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ctx = build_chat_context(exchanges, trading_day=day)
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brief = format_chat_context_for_chat(ctx, max_chars=6000)
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recent = []
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for m in prior[-8:]:
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role = m.get("role")
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if role not in ("user", "assistant", "system"):
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continue
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label = {"user": "用户", "assistant": "监管", "system": "系统"}.get(role, role)
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recent.append(f"{label}：{str(m.get('content') or '').strip()}")
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user_prompt = build_supervisor_chat_prompt(
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context_text=brief,
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trading_day=day,
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history_lines="\n".join(recent),
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user_message=text,
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)
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reply = generate_text(
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system=SUPERVISOR_SYSTEM,
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user=user_prompt,
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temperature=min(0.4, CHAT_TEMPERATURE),
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max_tokens=min(768, CHAT_MAX_OUTPUT_TOKENS),
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max_continuations=1,
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)
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if not reply or reply.strip().startswith("AI "):
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return {"ok": False, "msg": reply or "AI 生成失败", "session_id": sid}
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append_chat_message(sid, "user", text)
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session = append_supervisor_ai_message(sid, reply.strip())
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state = get_supervisor_session_state(day)
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return {
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"ok": True,
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"trading_day": day,
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"session": session,
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"reply": reply.strip(),
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"model": model_label(),
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"message_count": state.get("message_count"),
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"unread_system": state.get("unread_system"),
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}
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