Eikwang
91d32c6ffa
* 增加更多的历史记录，修正部份数据获取错误 * 增加更多的历史记录，修正部份API数据获取错误，增加备用API --------- Co-authored-by: bathfire <>
538 lines
19 KiB
Python
538 lines
19 KiB
Python
"""
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持仓管理器模块
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提供持仓股票管理和批量分析功能
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"""
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import time
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from typing import List, Dict, Optional, Tuple
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from datetime import datetime
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# 导入必要的模块
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from portfolio_db import portfolio_db
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class PortfolioManager:
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"""持仓管理器类"""
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def __init__(self, model="deepseek-chat"):
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"""
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初始化持仓管理器
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Args:
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model: AI模型（deepseek-chat 或 deepseek-reasoner）
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"""
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self.model = model
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self.db = portfolio_db
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# ==================== 持仓股票管理 ====================
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def add_stock(self, code: str, name: str, cost_price: Optional[float] = None,
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quantity: Optional[int] = None, note: str = "",
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auto_monitor: bool = True) -> Tuple[bool, str, Optional[int]]:
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"""
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添加持仓股票
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Args:
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code: 股票代码
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name: 股票名称
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cost_price: 持仓成本价
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quantity: 持仓数量
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note: 备注
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auto_monitor: 是否自动同步到监测
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Returns:
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(成功标志, 消息, 股票ID)
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"""
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try:
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# 验证股票代码格式
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code = code.strip().upper()
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if not code:
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return False, "股票代码不能为空", None
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# 检查股票代码是否已存在
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existing = self.db.get_stock_by_code(code)
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if existing:
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return False, f"股票代码 {code} 已存在", None
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# 添加到数据库
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stock_id = self.db.add_stock(code, name, cost_price, quantity, note, auto_monitor)
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return True, f"添加持仓股票成功: {code} {name}", stock_id
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except Exception as e:
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return False, f"添加失败: {str(e)}", None
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def update_stock(self, stock_id: int, **kwargs) -> Tuple[bool, str]:
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"""
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更新持仓股票信息
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Args:
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stock_id: 股票ID
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**kwargs: 要更新的字段
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Returns:
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(成功标志, 消息)
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"""
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try:
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success = self.db.update_stock(stock_id, **kwargs)
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if success:
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return True, "更新成功"
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else:
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return False, f"未找到股票ID: {stock_id}"
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except Exception as e:
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return False, f"更新失败: {str(e)}"
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def delete_stock(self, stock_id: int) -> Tuple[bool, str]:
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"""
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删除持仓股票（级联删除分析历史）
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Args:
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stock_id: 股票ID
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Returns:
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(成功标志, 消息)
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"""
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try:
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success = self.db.delete_stock(stock_id)
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if success:
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return True, "删除成功"
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else:
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return False, f"未找到股票ID: {stock_id}"
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except Exception as e:
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return False, f"删除失败: {str(e)}"
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def get_stock(self, stock_id: int) -> Optional[Dict]:
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"""获取单只持仓股票信息"""
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return self.db.get_stock(stock_id)
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def get_all_stocks(self, auto_monitor_only: bool = False) -> List[Dict]:
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"""获取所有持仓股票列表"""
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return self.db.get_all_stocks(auto_monitor_only)
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def search_stocks(self, keyword: str) -> List[Dict]:
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"""搜索持仓股票"""
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return self.db.search_stocks(keyword)
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def get_stock_count(self) -> int:
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"""获取持仓股票总数"""
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return self.db.get_stock_count()
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# ==================== 单只股票分析 ====================
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def analyze_single_stock(self, stock_code: str, period="1y",
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selected_agents: List[str] = None) -> Dict:
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"""
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分析单只股票（复用app.py中的分析逻辑）
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Args:
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stock_code: 股票代码
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period: 数据周期
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selected_agents: 选中的分析师列表
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Returns:
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分析结果字典
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"""
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print(f"\n{'='*60}")
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print(f"开始分析股票: {stock_code}")
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print(f"{'='*60}\n")
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try:
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# 导入app.py中的分析函数
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from app import analyze_single_stock_for_batch
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# 构建分析师配置
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if selected_agents is None:
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enabled_analysts_config = {
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'technical': True,
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'fundamental': True,
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'fund_flow': True,
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'risk': True,
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'sentiment': False,
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'news': False
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}
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else:
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enabled_analysts_config = {
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'technical': 'technical' in selected_agents,
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'fundamental': 'fundamental' in selected_agents,
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'fund_flow': 'fund_flow' in selected_agents,
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'risk': 'risk' in selected_agents,
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'sentiment': 'sentiment' in selected_agents,
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'news': 'news' in selected_agents
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}
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# 调用首页的分析函数
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result = analyze_single_stock_for_batch(
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symbol=stock_code,
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period=period,
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enabled_analysts_config=enabled_analysts_config,
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selected_model=self.model
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)
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# 检查结果
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if not result.get("success", False):
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error_msg = result.get("error", "未知错误")
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print(f"\n[ERROR] 分析失败: {error_msg}")
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return {"success": False, "error": error_msg}
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print(f"\n{'='*60}")
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print(f"分析完成！")
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print(f"{'='*60}\n")
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return result
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except Exception as e:
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print(f"\n[ERROR] 分析失败: {str(e)}")
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import traceback
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traceback.print_exc()
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return {"success": False, "error": str(e)}
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# ==================== 批量分析 ====================
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def batch_analyze_sequential(self, stock_codes: List[str], period="1y",
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selected_agents: List[str] = None,
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progress_callback=None) -> Dict:
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"""
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顺序批量分析（逐只分析）
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Args:
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stock_codes: 股票代码列表
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period: 数据周期
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selected_agents: 选中的分析师列表
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progress_callback: 进度回调函数 callback(current, total, code, status)
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Returns:
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批量分析结果字典
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"""
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print(f"\n{'='*60}")
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print(f"开始批量分析 (顺序模式): {len(stock_codes)}只股票")
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print(f"{'='*60}\n")
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start_time = time.time()
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results = []
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failed = []
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for i, code in enumerate(stock_codes, 1):
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print(f"\n--- 分析进度: {i}/{len(stock_codes)} ---")
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if progress_callback:
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progress_callback(i, len(stock_codes), code, "analyzing")
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try:
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result = self.analyze_single_stock(code, period, selected_agents)
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if result.get("success"):
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results.append({
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"code": code,
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"result": result
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})
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if progress_callback:
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progress_callback(i, len(stock_codes), code, "success")
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else:
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failed.append({
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"code": code,
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"error": result.get("error", "未知错误")
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})
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if progress_callback:
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progress_callback(i, len(stock_codes), code, "failed")
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except Exception as e:
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print(f"[ERROR] 股票 {code} 分析失败: {str(e)}")
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failed.append({
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"code": code,
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"error": str(e)
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})
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if progress_callback:
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progress_callback(i, len(stock_codes), code, "error")
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elapsed_time = time.time() - start_time
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print(f"\n{'='*60}")
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print(f"批量分析完成！")
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print(f"成功: {len(results)}只, 失败: {len(failed)}只, 耗时: {elapsed_time:.1f}秒")
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print(f"{'='*60}\n")
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return {
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"success": True,
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"mode": "sequential",
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"total": len(stock_codes),
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"succeeded": len(results),
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"failed": len(failed),
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"results": results,
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"failed_stocks": failed,
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"elapsed_time": elapsed_time
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}
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def batch_analyze_parallel(self, stock_codes: List[str], period="1y",
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selected_agents: List[str] = None,
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max_workers: int = 3,
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progress_callback=None) -> Dict:
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"""
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并行批量分析（多线程）
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Args:
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stock_codes: 股票代码列表
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period: 数据周期
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selected_agents: 选中的分析师列表
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max_workers: 最大并发数（默认3）
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progress_callback: 进度回调函数
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Returns:
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批量分析结果字典
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"""
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print(f"\n{'='*60}")
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print(f"开始批量分析 (并行模式): {len(stock_codes)}只股票, 并发数: {max_workers}")
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print(f"{'='*60}\n")
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start_time = time.time()
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results = []
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failed = []
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completed = 0
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with ThreadPoolExecutor(max_workers=max_workers) as executor:
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# 提交所有任务
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future_to_code = {
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executor.submit(self.analyze_single_stock, code, period, selected_agents): code
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for code in stock_codes
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}
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# 处理完成的任务
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for future in as_completed(future_to_code):
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code = future_to_code[future]
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completed += 1
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try:
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result = future.result()
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if result.get("success"):
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results.append({
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"code": code,
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"result": result
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})
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print(f"\n[{completed}/{len(stock_codes)}] {code} 分析完成")
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if progress_callback:
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progress_callback(completed, len(stock_codes), code, "success")
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else:
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failed.append({
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"code": code,
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"error": result.get("error", "未知错误")
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})
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print(f"\n[{completed}/{len(stock_codes)}] {code} 分析失败: {result.get('error')}")
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if progress_callback:
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progress_callback(completed, len(stock_codes), code, "failed")
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except Exception as e:
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failed.append({
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"code": code,
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"error": str(e)
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})
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print(f"\n[{completed}/{len(stock_codes)}] {code} 分析异常: {str(e)}")
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if progress_callback:
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progress_callback(completed, len(stock_codes), code, "error")
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elapsed_time = time.time() - start_time
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print(f"\n{'='*60}")
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print(f"批量分析完成！")
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print(f"成功: {len(results)}只, 失败: {len(failed)}只, 耗时: {elapsed_time:.1f}秒")
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print(f"{'='*60}\n")
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return {
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"success": True,
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"mode": "parallel",
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"total": len(stock_codes),
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"succeeded": len(results),
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"failed": len(failed),
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"results": results,
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"failed_stocks": failed,
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"elapsed_time": elapsed_time
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}
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def batch_analyze_portfolio(self, mode="sequential", period="1y",
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selected_agents: List[str] = None,
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max_workers: int = 3,
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progress_callback=None) -> Dict:
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"""
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批量分析所有持仓股票
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Args:
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mode: 分析模式 ("sequential" 或 "parallel")
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period: 数据周期
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selected_agents: 选中的分析师列表
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max_workers: 并行模式下的最大并发数（默认3）
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progress_callback: 进度回调函数
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Returns:
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批量分析结果字典
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"""
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# 获取所有持仓股票
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stocks = self.get_all_stocks()
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if not stocks:
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return {
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"success": False,
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"error": "没有持仓股票"
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}
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stock_codes = [stock['code'] for stock in stocks]
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# 根据模式选择分析方法
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if mode == "parallel":
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return self.batch_analyze_parallel(stock_codes, period, selected_agents, max_workers, progress_callback)
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else:
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return self.batch_analyze_sequential(stock_codes, period, selected_agents, progress_callback)
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# ==================== 分析结果保存 ====================
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def save_analysis_results(self, analysis_results: Dict) -> List[int]:
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"""
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保存批量分析结果到数据库
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Args:
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analysis_results: 批量分析结果字典
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Returns:
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保存的分析记录ID列表
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"""
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saved_ids = []
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if not analysis_results.get("success"):
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print("[WARN] 分析未成功，跳过保存")
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return saved_ids
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for item in analysis_results.get("results", []):
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code = item.get("code")
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result = item.get("result", {})
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# 获取持仓股票ID
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stock = self.db.get_stock_by_code(code)
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if not stock:
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print(f"[WARN] 未找到持仓股票: {code}，跳过保存")
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continue
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stock_id = stock['id']
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# 提取分析结果关键信息
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final_decision = result.get("final_decision", {})
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stock_info = result.get("stock_info", {})
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# 使用正确的字段名
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rating = final_decision.get("rating", "持有")
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# 确保信心度为float类型，避免Arrow序列化错误
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confidence_raw = final_decision.get("confidence_level", 5.0)
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try:
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confidence = float(confidence_raw)
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# 确保信心度在合理范围内
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if confidence < 0:
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confidence = 0.0
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elif confidence > 10:
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confidence = 10.0
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except (ValueError, TypeError):
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# 如果转换失败，使用默认值
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confidence = 5.0
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current_price = stock_info.get("current_price", 0.0)
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target_price_str = final_decision.get("target_price", "")
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entry_range = final_decision.get("entry_range", "")
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take_profit_str = final_decision.get("take_profit", "")
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stop_loss_str = final_decision.get("stop_loss", "")
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# 解析目标价格
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import re
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target_price = None
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if target_price_str:
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try:
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numbers = re.findall(r'\d+\.?\d*', str(target_price_str))
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if numbers:
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target_price = float(numbers[0])
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except:
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pass
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# 解析进场区间
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entry_min, entry_max = None, None
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if entry_range and isinstance(entry_range, str) and "-" in entry_range:
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try:
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parts = entry_range.split("-")
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entry_min = float(parts[0].strip())
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entry_max = float(parts[1].strip())
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except:
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pass
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# 解析止盈止损
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take_profit, stop_loss = None, None
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if take_profit_str:
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try:
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numbers = re.findall(r'\d+\.?\d*', str(take_profit_str))
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if numbers:
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take_profit = float(numbers[0])
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except:
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pass
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if stop_loss_str:
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try:
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numbers = re.findall(r'\d+\.?\d*', str(stop_loss_str))
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if numbers:
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stop_loss = float(numbers[0])
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except:
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pass
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# 生成摘要（使用advice或summary字段）
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summary = final_decision.get("advice", final_decision.get("summary", ""))[:500] # 限制长度
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try:
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# 保存到数据库
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analysis_id = self.db.save_analysis(
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stock_id, rating, confidence, current_price, target_price,
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entry_min, entry_max, take_profit, stop_loss, summary
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)
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saved_ids.append(analysis_id)
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except Exception as e:
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print(f"[ERROR] 保存分析结果失败 ({code}): {str(e)}")
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print(f"\n[OK] 保存分析结果: {len(saved_ids)}条记录")
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return saved_ids
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# ==================== 分析历史查询 ====================
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def get_analysis_history(self, stock_id: int, limit: int = 10) -> List[Dict]:
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"""获取股票分析历史"""
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return self.db.get_analysis_history(stock_id, limit)
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|
||
def get_latest_analysis(self, stock_id: int) -> Optional[Dict]:
|
||
"""获取最新一次分析"""
|
||
return self.db.get_latest_analysis(stock_id)
|
||
|
||
def get_all_latest_analysis(self) -> List[Dict]:
|
||
"""获取所有持仓股票的最新分析"""
|
||
return self.db.get_all_latest_analysis()
|
||
|
||
def get_rating_changes(self, stock_id: int, days: int = 30) -> List[Tuple]:
|
||
"""获取评级变化"""
|
||
return self.db.get_rating_changes(stock_id, days)
|
||
|
||
|
||
# 创建全局实例
|
||
portfolio_manager = PortfolioManager()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# 测试代码
|
||
print("="*60)
|
||
print("持仓管理器测试")
|
||
print("="*60)
|
||
|
||
manager = PortfolioManager()
|
||
|
||
# 测试添加持仓
|
||
success, msg, stock_id = manager.add_stock("000001", "平安银行", 12.5, 1000, "测试持仓")
|
||
print(f"\n添加持仓: {msg}")
|
||
|
||
# 测试获取所有持仓
|
||
stocks = manager.get_all_stocks()
|
||
print(f"\n持仓数量: {len(stocks)}")
|
||
for stock in stocks:
|
||
print(f" {stock['code']} {stock['name']} - 成本:{stock['cost_price']}, 数量:{stock['quantity']}")
|
||
|
||
print("\n[OK] 持仓管理器测试完成")
|
||
|