添加新的判断依据

ChanKLC.py

@@ -77,6 +77,7 @@ class ChanKLC():
 				self.continue_div = klu.continue_div
 			if klu.macd_state != Chan_MACD_STATE.UNKNOWN:
 				self.state = klu.macd_state
+			klu.set_klc(self)
 		self.klc_dir = Chan_KLINE_DIR.UP if self.close > self.open else Chan_KLINE_DIR.DOWN
 		self.cal_indicators()
 	def cal_fx(self):
@@ -175,7 +176,7 @@ class ChanKLC():
 					return False
 		else:
 			# high小于，low大于，右包含
-			if self.low >= klu.low:
+			if self.low >= klu.low-10:
 				self.add_klu(klu=klu)
 				# gn>gn-1
 				if self.dir == Chan_KLINE_DIR.UP:

ChanKLU.py

@@ -1,4 +1,4 @@
-from ChanEnum import Chan_FX_TYPE, Chan_KLU_TYPE, Chan_K_DIR, Chan_MACD_STATE, Chan_MACDHIST_STATE, Chan_PRICE_TREND, Chan_KLU_PATTERN
+from ChanEnum import Chan_FX_TYPE, Chan_KLU_TYPE, Chan_K_DIR, Chan_MACD_STATE, Chan_MACDHIST_STATE, Chan_PRICE_TREND, Chan_KLU_PATTERN, Chan_KLC_FX
 class ChanKLU:
 	def __init__(self, time, open, high, low, close, volume):
 		# _time, _close, _open, _high, _low, _extra_info={}
@@ -79,6 +79,13 @@ class ChanKLU:
 		self.exception = False
 	def set_trend(self, trend):
 		self.trend = trend
+	def set_separate_div(self, separate_div):
+		self.separate_div = separate_div
+		if self.klc:
+			if self.klc.klc_fx_type != Chan_KLC_FX.UNKNOWN:
+				self.separate_div = 99
+			else:
+				self.separate_div = 0
 	def set_next(self, next):
 		self.next = next
 		#if self.fx_type != Chan_FX_TYPE.UNKNOWN and self.fx_strength > 1:

ChanLun.py

@@ -115,7 +115,7 @@ class ChanLun():
 		return self.tf_df.cal_bi_zs(seg_list)
 	def get_decimal(self, value):
 		return Decimal("{:.2f}".format(value))
-	def get_klc_list(self, dataframe):
+	def get_klc_list(self, klu_list):
-		return self.tf_df.get_klc_list(dataframe)
+		return self.tf_df.get_klc_list(klu_list)
 	def get_klu_list(self, dataframe):
 		return self.tf_df.cal_klu_pattern(self.get_kl_data(dataframe))

ChanMACD.py

@@ -38,7 +38,7 @@ class ChanMACD():
 					else:
 						signal = klu.pre.signal < klu.signal and klu.next.signal > klu.signal
 				sd = klu.separate_div
-				if sd > 1 and ((hist > 0 and hist < 20) or (hist < 0 and hist > -20)):
+				if sd > 1 and ((hist > 0 and hist < 200) or (hist < 0 and hist > -200)):
 					sd_list.append(klu.time)
 					print(klu.time, sd)
 		return sd_list

ChanMACDUnitTF.py

@@ -61,9 +61,9 @@ class ChanMACDUnitTF():
                                 self.div_peak_list.append(histset.peak_klu)
                                 #print("Unittf: ", self.start_klu.time)
                                 if histset.peak_klu.macd > 0 and histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE:
-                                    histset.peak_klu.separate_div = self.div_count
+                                    histset.peak_klu.set_separate_div(self.div_count)
                                 elif histset.peak_klu.macd < 0 and histset.histset_dir == Chan_MACDHISTSET_DIR.UNDER:
-                                    histset.peak_klu.separate_div = self.div_count
+                                    histset.peak_klu.set_separate_div(self.div_count)
                     else:
                         if histset.peak_klu:
                             self.peak_klu = histset.peak_klu

TF_DF.py

@@ -492,42 +492,8 @@ class TF_DF():
 		bi_list = self.cal_bi_list(self.get_klc_list(dataframe))
 		return bi_list
 	def get_kl_data(self, dataframe:DataFrame):
-		fields = "time,open,high,low,close,volume"
+		return self.cal_kl_data(dataframe)
-		klu_list = []
+	def get_klc_list(self, klu_list):
-		last_klu = None
-		for i in range(0, len(dataframe)):
-			item = dataframe.iloc[i]
-			date = item['date']
-			o = item['open']
-			h = item['high']
-			l = item['low']
-			c = item['close']
-			v = item['volume']
-			#time_obj = date.fromtimestamp(date)
-			#date = date + timedelta(hours=8)
-			time_str = date.strftime('%Y-%m-%d %H:%M:%S')
-			item_data = [
-				time_str,
-				o,
-				h,
-				l,
-				c,
-				v
-			]
-			#klu =  KLU(self.create_item_dict(item_data, GetColumnNameFromFieldList(fields)))
-			klu = ChanKLU(time_str, o, h, l, c, v)
-			#print(klu.time, klu.open, klu.high, klu.low, klu.close, klu.volume)	
-			klu.set_idx(i)
-			klu_list.append(klu)
-			if last_klu:
-				last_klu.set_next(klu)
-				klu.set_pre(last_klu)
-			last_klu = klu
-			if 'macd' in item:
-				klu.set_indicators(item)
-		return klu_list
-	def get_klc_list(self, dataframe):
-		klu_list = self.get_klu_list(dataframe)
 		klc_list = []
 		last_klu = None
 		macd = ChanMACD(klu_list)
@@ -1664,6 +1630,6 @@ class TF_DF():
 			if is_near_support or volume_confirmation:
 				third_klu.set_pattern(Chan_KLU_PATTERN.TWEEZER_BOTTOM)
 				return
-
+	
 	def get_decimal(self, value):
 		return Decimal("{:.2f}".format(value))

data_provider/main.py

@@ -29,10 +29,10 @@ TIMEFRAME_TO_MS: Dict[str, int] = {
     "1w": 604_800_000,
 }
 DERIVED_TIMEFRAME_PLAN: Dict[str, List[str]] = {
-    "1m": ["2m", "3m", "4m", "5m", "10m", "15m", "20m", "25m", "30m"],
+    "1m": ["2m", "3m", "4m", "5m", "10m", "15m", "20m", "25m", "30m", "45m"],
-    "1h": ["2h", "3h", "4h", "6h", "8h", "12h", "16h"],
+    "1h": ["2h", "3h", "4h", "6h", "8h", "12h", "16h", "20h"],
     "1d": ["2d", "3d", "4d", "5d", "6d"],
-    "1w": ["2w"],
+    "1w": ["2w", "3w"],
 }
 CSV_FIELDNAMES = ["timestamp", "datetime", "open", "high", "low", "close", "volume"]
 DEFAULT_LIMIT = 500
@@ -583,14 +583,14 @@ class DataProvider:
             resampled_df = resampled.copy()
             if "date" in resampled_df.columns:
                 dates = pd.to_datetime(resampled_df["date"], utc=True, errors="coerce")
-                resampled_df["timestamp"] = (dates.view("int64") // 1_000_000).astype("int64")
+                resampled_df["timestamp"] = (dates.astype("int64", copy=False) // 1_000_000).astype("int64")
             elif isinstance(resampled_df.index, pd.DatetimeIndex):
                 idx = resampled_df.index
                 if idx.tz is None:
                     idx = idx.tz_localize("UTC")
                 else:
                     idx = idx.tz_convert("UTC")
-                resampled_df["timestamp"] = (idx.view("int64") // 1_000_000).astype("int64")
+                resampled_df["timestamp"] = (idx.astype("int64", copy=False) // 1_000_000).astype("int64")
             else:
                 raise HTTPException(status_code=500, detail=f"聚合结果缺少 timestamp 列 ({timeframe})")
         resampled_df = resampled_df.dropna(subset=["timestamp"]).sort_values("timestamp")

web/app.py

@@ -521,7 +521,7 @@ def analyze_chan(df, symbol=None, timeframe=None):
     ema52_dict = None
     # 获取分析结果
     klu_list = chan.get_kl_data(df)
-    klc_list = chan.get_klc_list(df)
+    klc_list = chan.get_klc_list(klu_list)
     bi_list = chan.cal_bi_list(klc_list)
     #for index in range(0, 10):
         #print(bi_list[index].start_time, bi_list[index].start_klc.end_time, bi_list[index].dir)
@@ -1209,9 +1209,6 @@ def analyze():
     elements_only_param = request.args.get('elements_only')
     elements_only = elements_only_param == 'true'
     
-    # 获取是否需要回放数据的参数
-    need_replay_data = request.args.get('need_replay_data', 'false').lower() == 'true'
-    
     # 验证小周期是否小于主周期
     if element_timeframe and not is_smaller_or_equal_timeframe(element_timeframe, timeframe):
         return jsonify({'error': '分形元素时间周期必须小于或等于主图表时间周期'})
@@ -1243,12 +1240,6 @@ def analyze():
         # 计算MACD
         macd_data = calculate_macd(df)
         
-        # 如果需要回放数据，生成逐步计算的回放数据
-        if need_replay_data:
-            replay_data = generate_replay_data(df, client_tz, symbol, element_timeframe, start_time, end_time)
-        else:
-            replay_data = None
-        
         # 基于已有 KLC 列表生成趋势标记（不做额外计算）
         klc_trend = []
         try:
@@ -1391,9 +1382,6 @@ def analyze():
             'klc_trend': klc_trend
         })
         
-        # 如果生成了回放数据，添加到返回结果中
-        if replay_data is not None:
-            result['replay_data'] = replay_data
     
     # 如果有指定分形元素时间周期，获取小周期数据
     if element_timeframe:
@@ -1670,19 +1658,5 @@ def get_uncompleted_seg_list(seg_list, client_tz):
     
     return result
 
-def format_fx_type(fx_type):
-    """格式化分型类型显示"""
-    fx_type_map = {
-        'TOP1': '顶分型1',
-        'TOP2': '顶分型2', 
-        'TOP3': '顶分型3',
-        'BOTTOM1': '底分型1',
-        'BOTTOM2': '底分型2',
-        'BOTTOM3': '底分型3',
-        'TOP': '顶分型',
-        'BOTTOM': '底分型'
-    }
-    return fx_type_map.get(fx_type, fx_type)
-
 if __name__ == '__main__':
     app.run(debug=True, host='0.0.0.0', port=8128)