Add AI trading supervisor with WeChat push and daily session

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>

manual_trading_hub/hub_ai/supervisor.py

@@ -0,0 +1,111 @@
+"""交易监管：AI 评语与用户回聊。"""
+from __future__ import annotations
+
+from typing import Any, Optional
+
+from hub_ai.client import generate_text, model_label
+from hub_ai.config import (
+    CHAT_MAX_OUTPUT_TOKENS,
+    CHAT_TEMPERATURE,
+    trading_day_reset_hour,
+)
+from hub_ai.context import build_chat_context, format_chat_context_for_chat
+from hub_ai.prompts import SUPERVISOR_SYSTEM, build_supervisor_ai_prompt, build_supervisor_chat_prompt
+from hub_ai.supervisor_store import (
+    append_supervisor_ai_message,
+    ensure_supervisor_session,
+    get_supervisor_session_state,
+)
+from hub_ai.store import append_chat_message
+from hub_trades_lib import current_trading_day
+
+
+def generate_supervisor_ai_reply(
+    *,
+    event: dict,
+    warnings: list[dict],
+    trading_day: str,
+    session_id: str,
+    exchanges: list[dict],
+) -> str:
+    ctx = build_chat_context(exchanges, trading_day=trading_day)
+    brief = format_chat_context_for_chat(ctx, max_chars=6000)
+    user_prompt = build_supervisor_ai_prompt(
+        context_text=brief,
+        trading_day=trading_day,
+        event=event,
+        warnings=warnings,
+    )
+    return generate_text(
+        system=SUPERVISOR_SYSTEM,
+        user=user_prompt,
+        temperature=min(0.35, CHAT_TEMPERATURE),
+        max_tokens=min(512, CHAT_MAX_OUTPUT_TOKENS),
+        max_continuations=1,
+    )
+
+
+def make_supervisor_ai_reply_fn(exchanges: list[dict]):
+    def _fn(*, event: dict, warnings: list[dict], trading_day: str, session_id: str) -> str:
+        return generate_supervisor_ai_reply(
+            event=event,
+            warnings=warnings or [],
+            trading_day=trading_day,
+            session_id=session_id,
+            exchanges=exchanges,
+        )
+
+    return _fn
+
+
+def send_supervisor_chat(
+    exchanges: list[dict],
+    message: str,
+    *,
+    trading_day: str | None = None,
+) -> dict[str, Any]:
+    text = (message or "").strip()
+    if not text:
+        return {"ok": False, "msg": "消息不能为空"}
+    day = (trading_day or "").strip()[:10] or current_trading_day(
+        reset_hour=trading_day_reset_hour()
+    )
+    session = ensure_supervisor_session(day)
+    sid = str(session.get("id") or "")
+    prior = session.get("messages") or []
+    ctx = build_chat_context(exchanges, trading_day=day)
+    brief = format_chat_context_for_chat(ctx, max_chars=6000)
+    recent = []
+    for m in prior[-8:]:
+        role = m.get("role")
+        if role not in ("user", "assistant", "system"):
+            continue
+        label = {"user": "用户", "assistant": "监管", "system": "系统"}.get(role, role)
+        recent.append(f"{label}：{str(m.get('content') or '').strip()}")
+    user_prompt = build_supervisor_chat_prompt(
+        context_text=brief,
+        trading_day=day,
+        history_lines="\n".join(recent),
+        user_message=text,
+    )
+    reply = generate_text(
+        system=SUPERVISOR_SYSTEM,
+        user=user_prompt,
+        temperature=min(0.4, CHAT_TEMPERATURE),
+        max_tokens=min(768, CHAT_MAX_OUTPUT_TOKENS),
+        max_continuations=1,
+    )
+    if not reply or reply.strip().startswith("AI "):
+        return {"ok": False, "msg": reply or "AI 生成失败", "session_id": sid}
+    append_chat_message(sid, "user", text)
+    session = append_supervisor_ai_message(sid, reply.strip())
+    state = get_supervisor_session_state(day)
+    return {
+        "ok": True,
+        "trading_day": day,
+        "session": session,
+        "reply": reply.strip(),
+        "model": model_label(),
+        "message_count": state.get("message_count"),
+        "unread_system": state.get("unread_system"),
+    }