Initial commit

2025-06-10 01:16:09 +08:00
commit 05bab03b8c
155 changed files with 19979 additions and 0 deletions
@@ -0,0 +1,100 @@
+from datetime import datetime
+import pandas as pd
+from Common.CEnum import AUTYPE, DATA_FIELD, KL_TYPE
+from Common.CTime import CTime
+from Common.func_util import kltype_lt_day, str2float
+from KLine.KLine_Unit import CKLine_Unit
+from .CommonStockAPI import CCommonStockApi
+
+
+class WebDataAPI(CCommonStockApi):
+    """Web应用的数据API适配器，用于将web应用的数据转换为chan.py格式"""
+    
+    # 类变量用于存储数据
+    _data_cache = {}
+    
+    def __init__(self, code, k_type=KL_TYPE.K_DAY, begin_date=None, end_date=None, autype=AUTYPE.QFQ):
+        super().__init__(code, k_type, begin_date, end_date, autype)
+    
+    @classmethod
+    def set_data(cls, code, df_data):
+        """设置指定代码的数据"""
+        cls._data_cache[code] = df_data
+    
+    def get_kl_data(self):
+        """将DataFrame数据转换为CKLine_Unit迭代器"""
+        df_data = self._data_cache.get(self.code)
+        if df_data is None or len(df_data) == 0:
+            return
+        
+        # 确保数据按时间排序并重置索引
+        df_data = df_data.sort_values('date').reset_index(drop=True)
+        print(f"WebDataAPI: 处理 {len(df_data)} 条K线数据，K线级别: {self.k_type}")
+        
+        # 检查并处理重复时间
+        if 'timestamp' in df_data.columns:
+            df_data = df_data.drop_duplicates(subset=['timestamp'], keep='last')
+            df_data = df_data.sort_values('timestamp').reset_index(drop=True)
+        
+        prev_timestamp = None
+        for idx, row in df_data.iterrows():
+            # 转换时间格式
+            if isinstance(row['date'], str):
+                time_obj = datetime.fromisoformat(row['date'].replace('Z', '+00:00'))
+            else:
+                time_obj = row['date']
+            
+            # 检查时间戳确保单调递增
+            if 'timestamp' in row:
+                current_timestamp = row['timestamp']
+                if prev_timestamp is not None and current_timestamp <= prev_timestamp:
+                    print(f"跳过重复或倒序时间戳: {current_timestamp}, 上个时间戳: {prev_timestamp}")
+                    continue
+                prev_timestamp = current_timestamp
+            
+            # 创建CTime对象 - 根据K线级别智能决定auto参数
+            # 对于日线及以上级别，且时分秒为0的情况，使用auto=True
+            # 对于分钟级别或有具体时分的数据，使用auto=False确保精确时间
+            use_auto = False  # 默认不使用auto，确保时间精确
+            
+            # 只有在日线级别且时分秒都为0时才考虑使用auto
+            if self.k_type in [KL_TYPE.K_DAY, KL_TYPE.K_WEEK, KL_TYPE.K_MON]:
+                if time_obj.hour == 0 and time_obj.minute == 0 and time_obj.second == 0:
+                    use_auto = True
+            
+            ctime = CTime(
+                time_obj.year, 
+                time_obj.month, 
+                time_obj.day, 
+                time_obj.hour, 
+                time_obj.minute,
+                time_obj.second,
+                auto=use_auto
+            )
+            
+            # 输出详细调试信息（只输出前几条）
+            if idx < 3:
+                print(f"第{idx+1}条数据: 原始时间={time_obj}, CTime={ctime}, auto={use_auto}, timestamp={ctime.ts}")
+            
+            # 创建数据字典
+            data_dict = {
+                DATA_FIELD.FIELD_TIME: ctime,
+                DATA_FIELD.FIELD_OPEN: float(row['open']),
+                DATA_FIELD.FIELD_HIGH: float(row['high']),
+                DATA_FIELD.FIELD_LOW: float(row['low']),
+                DATA_FIELD.FIELD_CLOSE: float(row['close']),
+                DATA_FIELD.FIELD_VOLUME: float(row['volume']) if 'volume' in row and pd.notna(row['volume']) else 0.0
+            }
+            
+            yield CKLine_Unit(data_dict, autofix=True)
+    
+    def SetBasciInfo(self):
+        pass
+    
+    @classmethod
+    def do_init(cls):
+        pass
+    
+    @classmethod
+    def do_close(cls):
+        pass