From f6a11c36cd2c481da8476168095e08f6c5596fba Mon Sep 17 00:00:00 2001
From: yanweidonog <david.yan@qq.com>
Date: Mon, 16 Feb 2026 18:27:49 +0800
Subject: [PATCH] dev

---
 holdings.py | 106 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 106 insertions(+)
 create mode 100644 holdings.py

diff --git a/holdings.py b/holdings.py
new file mode 100644
index 0000000..06a737b
--- /dev/null
+++ b/holdings.py
@@ -0,0 +1,106 @@
+# coding:gbk
+import datetime
+
+account = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
+# 全局变量用于跟踪已触发9%盈利的持仓
+triggered_positions = {}
+def init(ContextInfo):
+    # 设置对应的资金账号
+    ContextInfo.set_account(account)
+    holdings = ContextInfo.get_holdings()
+
+def account_callback(ContextInfo, accountInfo):
+    print(show_data(accountInfo)) 
+
+def show_data(data):
+    tdata = {}
+    for ar in dir(data):
+        if ar[:2] != 'm_':continue
+        try:
+            tdata[ar] = data.__getattribute__(ar)
+        except:
+            tdata[ar] = '<CanNotConvert>'
+    return tdata
+
+def quote_callback(ContextInfo, quote_info):
+    """行情数据更新回调"""
+    current_time = datetime.datetime.now()
+    
+    # 只在交易时间内执行
+    if current_time.hour >= 9 and current_time.hour < 15:
+        # 从行情信息中提取symbol和当前价格
+        symbol = quote_info.m_symbol  # 假设行情数据包含股票代码
+        current_price = quote_info.m_last_price  # 假设行情数据包含最新价格
+        
+        # 调用检查函数，传入当前行情价格
+        check_and_sell_positions_with_price(ContextInfo, symbol, current_price)
+
+def check_and_sell_positions_with_price(ContextInfo, symbol, current_price):
+    """带行情价格的持仓检查函数"""
+    holdings = ContextInfo.get_holdings()
+    current_time = datetime.datetime.now()
+    today = current_time.date()
+    
+    # 只处理指定股票
+    if symbol in holdings:
+        position = holdings[symbol]
+        
+        # 获取持仓的开仓日期
+        open_date = getattr(position, 'm_open_date', None)
+        
+        # 跳过今日开仓的股票
+        if open_date and open_date.date() == today:
+            print(f"股票 {symbol} 为今日开仓，跳过监控")
+            return
+
+        cost_basis = position.m_cost_price
+        quantity = position.m_quantity
+        
+        # 计算当前盈利率
+        profit_pct = calculate_profit_percentage(current_price, cost_basis)
+        
+        # 如果盈利达到9%，开始监控
+        if profit_pct >= 9:
+            if symbol not in triggered_positions:
+                # 首次触发9%盈利，记录最高价和时间
+                triggered_positions[symbol] = {
+                    'new_price': current_price,
+                    'trigger_time': current_time,
+                    'cost_basis': cost_basis,
+                    'quantity': quantity
+                }
+                print(f"股票 {symbol} 盈利达到9%，开始监控，当前价格: {current_price}")
+            else:
+                # 更新最高价
+                if current_price > triggered_positions[symbol]['new_price']:
+                    triggered_positions[symbol]['new_price'] = current_price
+                    triggered_positions[symbol]['trigger_time'] = current_time
+                    print(f"股票 {symbol} 刷新最高价: {current_price}")
+                
+                # 检查是否需要平仓（从最高点回撤）
+                last_price = triggered_positions[symbol]['new_price']
+                drawdown_pct = ((last_price - current_price) / last_price) * 100
+                
+                # 当回撤达到一定比例时平仓（例如0.5%回撤）
+                if drawdown_pct >= 0.5:
+                    print(f"股票 {symbol} 从最高点回撤 {drawdown_pct:.2f}%，执行平仓")
+                    ContextInfo.place_order(symbol, 'SELL', quantity, current_price)
+                    # 移除已平仓的持仓
+                    del triggered_positions[symbol]
+        else:
+            # 如果盈利低于9%，且之前被监控过，则移除监控
+            if symbol in triggered_positions:
+                print(f"股票 {symbol} 盈利回落至 {profit_pct:.2f}%，取消监控")
+                del triggered_positions[symbol]
+
+def calculate_profit_percentage(current_price, cost_basis):
+    return ((current_price - cost_basis) / cost_basis) * 100
+
+def get_current_price(symbol):
+    # 这里需要实现获取实时价格的逻辑
+    # 可能需要调用QMT的行情接口
+    pass
+
+# 计算持仓盈利率
+def calculate_profit_percentage(current_price, cost_basis):
+    return ((current_price - cost_basis) / cost_basis) * 100
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