Replay is ok now

web/app.py

@@ -353,6 +353,112 @@ def analyze_chan(df):
     for bi in bi_list:
         bi.cal_macd_div()
         #print(bi.start_time, bi.macd_hist, bi.macd_div)
+
+def generate_replay_data(df, client_tz):
+    """生成逐步计算的回放数据"""
+    print(f"开始生成回放数据，K线总数: {len(df)}")
+    
+    replay_data = {}
+    
+    # 为每个K线索引计算分析结果
+    for i in range(1, len(df) + 1):  # 从1开始，至少需要1根K线
+        try:
+            # 截取到当前索引的数据
+            current_df = df.iloc[:i].copy()
+            
+            # 添加技术指标
+            current_df = add_indicators(current_df)
+            
+            # 进行缠论分析
+            analysis_result = analyze_chan(current_df)
+            
+            # 计算MACD
+            macd_data = calculate_macd(current_df)
+            
+            # 构建该索引对应的分析结果
+            step_data = {
+                'kline_data': clean_dataframe_for_json(current_df).to_dict('records'),
+                'bi_list': [{
+                    'start_time': bi.start_klc.end_time if isinstance(bi.start_klc.end_time, str) else bi.start_klc.end_time.astimezone(client_tz).isoformat(),
+                    'end_time': (bi.end_klc.end_time if isinstance(bi.end_klc.end_time, str) else bi.end_klc.end_time.astimezone(client_tz).isoformat()) if bi.end_klc else None,
+                    'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
+                    'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
+                    'end_price': bi.end_klc.high if convert_direction(bi.dir) == 1 else bi.end_klc.low if bi.end_klc else None,
+                    'direction': convert_direction(bi.dir),
+                    'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
+                } for bi in analysis_result['bi_list'] if bi.end_klc],
+                'seg_list': [{
+                    'start_time': seg.start_bi.start_klc.end_time if isinstance(seg.start_bi.start_klc.end_time, str) else seg.start_bi.start_klc.end_time.astimezone(client_tz).isoformat(),
+                    'end_time': (seg.end_bi.end_klc.end_time if isinstance(seg.end_bi.end_klc.end_time, str) else seg.end_bi.end_klc.end_time.astimezone(client_tz).isoformat()) if seg.end_bi else None,
+                    'sure_time': format_time_safely(seg.sure_time, client_tz) if seg.sure_time else None,
+                    'start_price': seg.start_bi.start_klc.low if convert_direction(seg.dir) == 1 else seg.start_bi.start_klc.high,
+                    'end_price': seg.end_bi.end_klc.high if convert_direction(seg.dir) == 1 else seg.end_bi.end_klc.low if seg.end_bi else None,
+                    'direction': convert_direction(seg.dir)
+                } for seg in analysis_result['seg_list'] if seg.end_bi],
+                'zs_list': [{
+                    'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
+                    'end_time': (zs.end_klc.end_time if isinstance(zs.end_klc.end_time, str) else zs.end_klc.end_time.astimezone(client_tz).isoformat()) if zs.end_klc else None,
+                    'zg': zs.zg,
+                    'zd': zs.zd,
+                    'is_sure': zs.is_sure
+                } for zs in analysis_result['zs_list'] if zs.end_klc],
+                'uncompleted_zs_list': [{
+                    'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
+                    'end_time': None,
+                    'zg': zs.zg,
+                    'zd': zs.zd,
+                    'is_sure': zs.is_sure
+                } for zs in analysis_result['zs_list'] if not zs.is_sure],
+                'trade_points': [{
+                    'type': point['type'],
+                    'time': format_time_safely(point['time'], client_tz),
+                    'price': point['price'],
+                    'desc': point['desc']
+                } for point in analysis_result['trade_points']],
+                'macd': macd_data,
+                'bollinger': {
+                    'upper': current_df['bb_upper'].tolist(),
+                    'middle': current_df['bb_middle'].tolist(),
+                    'lower': current_df['bb_lower'].tolist()
+                },
+                'element_bollinger': {
+                    'upper': current_df['element_bb_upper'].tolist(),
+                    'middle': current_df['element_bb_middle'].tolist(),
+                    'lower': current_df['element_bb_lower'].tolist()
+                },
+                'klc_fx_info': [{
+                    'time': format_time_safely(point['time'], client_tz),
+                    'price': float(point['price']),
+                    'fx_type': point['fx_type'],
+                    'is_bottom': bool(point['is_bottom']),
+                    'fx_strength': float(point['fx_strength']),
+                    'fx_strength_level': str(point['fx_strength_level']),
+                    'is_strong_fx': bool(point['is_strong_fx'])
+                } for point in analysis_result['klc_fx_info']],
+                'klu_fx_info': [{
+                    'time': format_time_safely(point['time'], client_tz),
+                    'price': float(point['price']),
+                    'fx_type': point['fx_type'],
+                    'is_bottom': bool(point['is_bottom']),
+                    'fx_strength': float(point['fx_strength']),
+                    'fx_strength_level': str(point['fx_strength_level']),
+                    'is_strong_fx': bool(point['is_strong_fx']),
+                    'fx_confirmed': bool(point['fx_confirmed'])
+                } for point in analysis_result['klu_fx_info']]
+            }
+            
+            replay_data[i-1] = step_data  # 使用0-based索引
+            
+            # 每处理100个点输出一次进度
+            if i % 100 == 0 or i == len(df):
+                print(f"生成回放数据进度: {i}/{len(df)}")
+                
+        except Exception as e:
+            print(f"生成第{i}步回放数据时出错: {e}")
+            continue
+    
+    print(f"回放数据生成完成，总步数: {len(replay_data)}")
+    return replay_data
     
     # 获取原始K线数据用于KLU分型分析
     klu_list = []
@@ -822,9 +928,13 @@ 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'
+    
     print(f"API请求参数: symbol={symbol}, timeframe={timeframe}, element_timeframe={element_timeframe}")
     print(f"时间范围: start_time={start_time}, end_time={end_time}")
     print(f"elements_only参数: 原始值={elements_only_param}, 处理后={elements_only}")
+    print(f"need_replay_data参数: {need_replay_data}")
     
     # 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):
@@ -862,6 +972,14 @@ 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:
+            print("开始生成回放数据...")
+            replay_data = generate_replay_data(df, client_tz)
+            print(f"回放数据生成完成，包含 {len(replay_data)} 个步骤")
+        else:
+            replay_data = None
+        
         # 添加主周期分析结果到返回数据
         result.update({
             'kline_data': clean_dataframe_for_json(df).to_dict('records'),
@@ -936,6 +1054,10 @@ def analyze():
                 'fx_confirmed': bool(point['fx_confirmed'])          # 分型是否确认
             } for point in analysis_result['klu_fx_info']]
         })
+        
+        # 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
     else:
         print(f"只请求元素数据，跳过主周期数据处理 (elements_only={elements_only})")