#!/usr/bin/env python3
"""batch_reassess.py — 批量补全九维分析（逐只处理，间隔防限流）

用法: python3 batch_reassess.py [--all] [--code XXXXXX]

流程：收集最新数据 → 调LLM(gateway)写九维分析+策略 → 保存到DB
"""
import sys, json, subprocess, sqlite3, re, time
from datetime import datetime

DB = "/home/hmo/MoFin/data/mofin.db"
GATEWAY = "http://127.0.0.1:8643/v1/chat/completions"
COOLDOWN_HOURS = 1

def has_llm_analysis(code):
    """检查是否为LLM生成的九维分析（>500字）"""
    conn = sqlite3.connect(DB)
    r = conn.execute("SELECT LENGTH(full_analysis) FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
    conn.close()
    return r and r[0] and r[0] > 500

def in_cooldown(code):
    """冷却期检查"""
    conn = sqlite3.connect(DB)
    r = conn.execute("SELECT reassessed_at FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
    conn.close()
    if not r or not r[0]:
        return False
    try:
        last = datetime.fromisoformat(r[0])
        diff = (datetime.now() - last).total_seconds() / 3600
        return diff < COOLDOWN_HOURS
    except:
        return False

def collect_data(code):
    """收集最新数据"""
    data = {"code": code}
    
    # 从DB读策略
    conn = sqlite3.connect(DB)
    r = conn.execute("SELECT name, entry_low, entry_high, stop_loss, take_profit, timing_signal, action, rr_ratio, tech_snapshot, sector_context, stock_category FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
    if r:
        data["name"] = r[0]
        data["entry_low"] = r[1] or 0
        data["entry_high"] = r[2] or 0
        data["stop_loss"] = r[3] or 0
        data["take_profit"] = r[4] or 0
        data["timing_signal"] = r[5] or ""
        data["action"] = r[6] or ""
        data["rr_ratio"] = r[7] or 0
        data["tech_snapshot"] = r[8] or ""
        data["sector_context"] = r[9] or ""
        data["stock_category"] = r[10] or ""
    conn.close()
    
    # 从腾讯API拉最新价和基本面
    prefix = "sh" if str(code).startswith(("6","9")) else "sz"
    try:
        r = subprocess.run(["curl", "-s", f"http://qt.gtimg.cn/q={prefix}{code}"], capture_output=True, timeout=10)
        parts = r.stdout.decode("gbk", errors="ignore").split("~")
        data["price"] = float(parts[3]) if len(parts) > 3 and parts[3] else 0
        data["pe"] = parts[39] if len(parts) > 39 and parts[39] else ""
        data["mcap"] = parts[44] if len(parts) > 44 and parts[44] else ""
        data["change_pct"] = parts[32] if len(parts) > 32 and parts[32] else "0"
    except:
        data["price"] = 0
    
    # 大盘
    try:
        conn = sqlite3.connect(DB)
        mr = conn.execute("SELECT structure FROM macro_context_log ORDER BY id DESC LIMIT 1").fetchone()
        if mr and mr[0]:
            s = json.loads(mr[0])
            data["macro"] = s.get("description", "大盘震荡")
        conn.close()
    except:
        data["macro"] = "大盘震荡"
    
    return data

def build_prompt(data):
    """构建LLM prompt，要求输出完整策略"""
    return f"""你是一个资深A股分析师。请对{data['code']} {data.get('name','')}做一个完整的九维矩阵分析，并输出策略参数。

当前数据：
大盘：{data.get('macro','震荡')}
最新价：{data.get('price',0)}  涨跌：{data.get('change_pct','0')}%
PE={data.get('pe','?')}  市值={data.get('mcap','?')}亿
行业：{data.get('sector_context','?')}
技术面：{data.get('tech_snapshot','')[:200]}
当前信号：{data.get('timing_signal','?')}  分类：{data.get('stock_category','?')}
原策略：{(data.get('action','') or '')[:200]}

请严格按以下格式输出：

① 大盘×基本面 [一句话]
② 大盘×消息面 [一句话]
③ 大盘×技术面 [一句话]
④ 大盘×资金流 [一句话]
⑤ 行业×基本面 [一句话]
⑥ 行业×消息面 [一句话]
⑦ 行业×技术面 [一句话]
⑧ 个股×基本面 [一句话]
⑨ 个股×消息面 [一句话]

【综合结论】（买入/关注/观望/卖出）
【操作建议】具体操作建议
【买入区间】最低价~最高价
【建议止损】数字
【建议止盈】数字
【建议仓位】总资产的百分之几（如8%），只写数字+%号，不要写文字描述"""

def parse_response(text):
    """从LLM回复中提取策略参数"""
    result = {"signal": "", "entry_low": 0, "entry_high": 0, "stop_loss": 0, "take_profit": 0, "position": ""}
    
    # 信号
    sl = [l for l in text.split("\n") if "综合结论" in l]
    if sl:
        for kw in ["买入","关注","观望","卖出"]:
            if kw in sl[0]:
                result["signal"] = kw
                break
    
    # 买入区间
    zl = [l for l in text.split("\n") if "买入区间" in l]
    if zl:
        nums = re.findall(r'[\d.]+', zl[0])
        if len(nums) >= 2:
            result["entry_low"] = float(nums[0])
            result["entry_high"] = float(nums[1])
    
    # 止损
    for l in text.split("\n"):
        if "建议止损" in l:
            nums = re.findall(r'[\d.]+', l)
            if nums: result["stop_loss"] = float(nums[0])
    
    # 止盈
    for l in text.split("\n"):
        if "建议止盈" in l:
            nums = re.findall(r'[\d.]+', l)
            if nums: result["take_profit"] = float(nums[0])
    
    # 仓位（提取百分比）
    for l in text.split("\n"):
        if "建议仓位" in l:
            nums = re.findall(r'[\d.]+', l)
            pct = ""
            if nums:
                # 取第一个合理的百分比（1-100之间）
                for n in nums:
                    f = float(n)
                    if 1 <= f <= 100:
                        pct = f"{f:.0f}%"
                        break
            # 如果没有百分比，用文字描述映射到百分比
            raw = l.replace("建议仓位","").strip()
            if not pct:
                if "轻仓" in raw: pct = "3%"
                elif "中" in raw and "仓" in raw: pct = "5%"
                elif "重仓" in raw: pct = "10%"
                elif "清仓" in raw or "零仓" in raw: pct = "0%"
                else: pct = "5%"
            result["position"] = pct
            break
    
    return result

def save_result(code, full_text, parsed):
    """保存LLM结果到DB"""
    conn = sqlite3.connect(DB)
    now = datetime.now().isoformat()
    
    updates = ["full_analysis=?", "reassessed_at=?"]
    params = [full_text, now]
    
    if parsed["signal"]:
        updates.append("timing_signal=?")
        params.append(parsed["signal"])
    if parsed["entry_low"] > 0:
        updates.append("entry_low=?")
        params.append(parsed["entry_low"])
    if parsed["entry_high"] > 0:
        updates.append("entry_high=?")
        params.append(parsed["entry_high"])
    if parsed["stop_loss"] > 0:
        updates.append("stop_loss=?")
        params.append(parsed["stop_loss"])
    if parsed["take_profit"] > 0:
        updates.append("take_profit=?")
        params.append(parsed["take_profit"])
    if parsed["position"]:
        updates.append("position_advice=?")
        params.append(parsed["position"])
    
    params.append(code)
    sql = f"UPDATE holding_strategies SET {', '.join(updates)} WHERE code=? AND status='active'"
    conn.execute(sql, params)
    conn.commit()
    conn.close()

def process_stock(code):
    """处理单只股票"""
    print(f"\n{'='*50}")
    print(f"处理: {code}")
    print(f"{'='*50}")
    
    if has_llm_analysis(code):
        print(f"  ⏭ 已有LLM九维分析，跳过")
        return False
    
    if in_cooldown(code):
        print(f"  ⏭ 冷却期内，跳过")
        return False
    
    print(f"  收集数据...", flush=True)
    data = collect_data(code)
    if not data.get("price"):
        print(f"  ⚠️ 无价格数据，跳过")
        return False
    
    print(f"  调LLM生成九维分析...", flush=True)
    prompt = build_prompt(data)
    
    try:
        r = subprocess.run(["curl", "-s", "--max-time", "300",
            "-H", "Content-Type: application/json",
            "-H", "Authorization: Bearer hermes123",
            "-d", json.dumps({"model":"deepseek-v4-flash","messages":[{"role":"user","content":prompt}],"max_tokens":2048}),
            GATEWAY], capture_output=True, timeout=310)
        
        if r.returncode != 0:
            print(f"  ❌ curl失败: {r.stderr.decode()[:100]}")
            return False
        
        resp = json.loads(r.stdout)
        if "choices" not in resp:
            print(f"  ❌ API异常: {str(resp)[:200]}")
            return False
        
        full_text = resp["choices"][0]["message"]["content"]
        print(f"  ✅ LLM返回({len(full_text)}字)", flush=True)
        
        parsed = parse_response(full_text)
        print(f"  信号={parsed['signal']} 区间={parsed['entry_low']}~{parsed['entry_high']} 损={parsed['stop_loss']} 盈={parsed['take_profit']} 仓位={parsed['position']}")
        
        save_result(code, full_text, parsed)
        print(f"  ✅ 已保存到DB")
        return True
        
    except subprocess.TimeoutExpired:
        print(f"  ❌ 超时")
        return False
    except Exception as e:
        print(f"  ❌ 错误: {e}")
        return False

def main():
    codes = []
    if "--code" in sys.argv:
        idx = sys.argv.index("--code")
        codes = [sys.argv[idx+1]]
    else:
        # 所有自选策略
        conn = sqlite3.connect(DB)
        rows = conn.execute("SELECT code FROM holding_strategies WHERE status='active' AND decision_type='自选策略' ORDER BY code").fetchall()
        conn.close()
        codes = [r[0] for r in rows]
    
    print(f"待处理: {len(codes)}只")
    
    ok = 0
    fail = 0
    skip = 0
    for i, code in enumerate(codes):
        if has_llm_analysis(code):
            print(f"  [{i+1}/{len(codes)}] ⏭ {code} 已有LLM分析")
            skip += 1
            continue
        
        print(f"  [{i+1}/{len(codes)}] ", end="", flush=True)
        if process_stock(code):
            ok += 1
        else:
            fail += 1
        
        # 间隔15秒（防gateway过载）
        if i < len(codes) - 1:
            print(f"  等待15秒...", flush=True)
            time.sleep(15)
    
    print(f"\n{'='*50}")
    print(f"完成: {ok}成功, {fail}失败, {skip}跳过")
    print(f"{'='*50}")

if __name__ == "__main__":
    main()