diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py
index 24d16460..a5e549d4 100644
--- a/freqtrade/templates/freqaiprimer.py
+++ b/freqtrade/templates/freqaiprimer.py
@@ -544,6 +544,16 @@ class FreqaiPrimer(IStrategy):
         df_1h['bb_lower_1h'] = bb_ma_1h - (bb_std_value * bb_std_1h)
         df_1h['bb_upper_1h'] = bb_ma_1h + (bb_std_value * bb_std_1h)
         
+        # 添加 EMA5 和 EMA20 用于入场过滤
+        df_1h['ema_5_1h'] = df_1h['close'].ewm(span=5, adjust=False).mean()
+        df_1h['ema_20_1h'] = df_1h['close'].ewm(span=20, adjust=False).mean()
+        
+        # 检测 EMA5 向上穿越 EMA20
+        df_1h['ema5_cross_above_ema20'] = (
+            (df_1h['ema_5_1h'] > df_1h['ema_20_1h']) & 
+            (df_1h['ema_5_1h'].shift(1) <= df_1h['ema_20_1h'].shift(1))
+        )
+        
         # 使用 rolling 计算 RSI（减少看前偏差）
         delta_1h = df_1h['close'].diff()
         gain_1h = delta_1h.where(delta_1h > 0, 0).rolling(window=rsi_length_value).mean()
@@ -575,12 +585,12 @@ class FreqaiPrimer(IStrategy):
         df_1h = df_1h.set_index('date').reindex(dataframe['date']).ffill().bfill().reset_index()
         df_1h = df_1h.rename(columns={'index': 'date'})
 # Include macd_1h and macd_signal_1h in the column selection
-        df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 'macd_1h', 'macd_signal_1h']].ffill()
+        df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 'macd_1h', 'macd_signal_1h', 'ema_5_1h', 'ema_20_1h', 'ema5_cross_above_ema20']].ffill()
 
 # Validate that all required columns are present
         required_columns = ['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 
                             'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
-                            'macd_1h', 'macd_signal_1h']
+                            'macd_1h', 'macd_signal_1h', 'ema_5_1h', 'ema_20_1h', 'ema5_cross_above_ema20']
         missing_columns = [col for col in required_columns if col not in df_1h.columns]
         if missing_columns:
             logger.error(f"[{metadata['pair']}] 缺少以下列: {missing_columns}")
@@ -597,7 +607,7 @@ class FreqaiPrimer(IStrategy):
             logger.error(f"[{metadata['pair']}] 缺少以下列: {missing_columns}")
             raise KeyError(f"缺少以下列: {missing_columns}")
         
-        df_1h = df_1h[required_columns]  # 确保包含 macd_1h 和 macd_signal_1h
+        df_1h = df_1h[required_columns]  # 确保包含所有必需的列
         
         # 合并 1h 数据
         dataframe = dataframe.merge(df_1h, how='left', on='date').ffill()
@@ -744,8 +754,11 @@ class FreqaiPrimer(IStrategy):
         # 辅助条件: 3m 和 15m 趋势确认（允许部分时间框架不一致）
         trend_confirmation = (dataframe['trend_3m'] == 1) | (dataframe['trend_15m'] == 1)
         
+        # 新增：EMA5向上穿越EMA20过滤条件（核心入场限制）
+        ema5_cross_filter = dataframe['ema5_cross_above_ema20'] == 1
+        
         # 合并所有条件（减少强制性条件）
-        # 至少满足多个条件中的一定数量
+        # 至少满足多个条件中的一定数量，并且必须满足EMA5向上穿越EMA20
         condition_count = (
             close_to_bb_lower_1h.astype(int) +
             rsi_condition_1h.astype(int) +
@@ -754,7 +767,9 @@ class FreqaiPrimer(IStrategy):
             (volume_spike | bb_width_condition).astype(int) +  # 成交量或布林带宽度满足其一即可
             trend_confirmation.astype(int)
         )
-        final_condition = condition_count >= self.min_condition_count.value
+        # 修改最终条件：必须同时满足最小条件数量和EMA5向上穿越EMA20
+        basic_condition = condition_count >= self.min_condition_count.value
+        final_condition = basic_condition & ema5_cross_filter
         
         # 设置入场信号
         dataframe.loc[final_condition, 'enter_long'] = 1
@@ -828,14 +843,24 @@ class FreqaiPrimer(IStrategy):
         dataframe.loc[final_condition_updated, 'enter_price'] = dataframe.loc[final_condition_updated, 'close'] * 0.9833
         
         # 增强调试信息
-        #self.strategy_log(f"[{metadata['pair']}] 入场条件检查:")
-        #self.strategy_log(f"  - 价格接近布林带下轨: {close_to_bb_lower_1h.sum()} 次")
-        #self.strategy_log(f"  - RSI 超卖: {rsi_condition_1h.sum()} 次")
-        #self.strategy_log(f"  - StochRSI 超卖: {stochrsi_condition_1h.sum()} 次")
-        #self.strategy_log(f"  - MACD 上升趋势: {macd_condition_1h.sum()} 次")
-        #self.strategy_log(f"  - 成交量或布林带宽度: {(volume_spike | bb_width_condition).sum()} 次")
-        #self.strategy_log(f"  - 趋势确认: {trend_confirmation.sum()} 次")
-        #self.strategy_log(f"  - 最终条件: {final_condition.sum()} 次")
+        ema5_cross_count = ema5_cross_filter.sum()
+        basic_condition_count = basic_condition.sum()
+        final_condition_count = final_condition.sum()
+        
+        self.strategy_log(f"[{metadata['pair']}] 入场条件检查:")
+        self.strategy_log(f"  - 价格接近布林带下轨: {close_to_bb_lower_1h.sum()} 次")
+        self.strategy_log(f"  - RSI 超卖: {rsi_condition_1h.sum()} 次")
+        self.strategy_log(f"  - StochRSI 超卖: {stochrsi_condition_1h.sum()} 次")
+        self.strategy_log(f"  - MACD 上升趋势: {macd_condition_1h.sum()} 次")
+        self.strategy_log(f"  - 成交量或布林带宽度: {(volume_spike | bb_width_condition).sum()} 次")
+        self.strategy_log(f"  - 趋势确认: {trend_confirmation.sum()} 次")
+        self.strategy_log(f"  - EMA5向上穿越EMA20: {ema5_cross_count} 次")
+        self.strategy_log(f"  - 基本条件满足: {basic_condition_count} 次")
+        self.strategy_log(f"  - 最终条件(基本+EMA穿越): {final_condition_count} 次")
+        
+        # 如果有EMA穿越但最终条件未满足，输出详细信息
+        if ema5_cross_count > 0 and final_condition_count == 0:
+            self.strategy_log(f"[{metadata['pair']}] 注意：检测到 {ema5_cross_count} 次EMA5向上穿越，但由于其他条件不足未能生成入场信号")
         # 在populate_entry_trend方法末尾添加
         # 计算条件间的相关性
         conditions = DataFrame({
diff --git a/test_ema_filter.py b/test_ema_filter.py
new file mode 100644
index 00000000..82ae0b99
--- /dev/null
+++ b/test_ema_filter.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+"""
+测试EMA5向上穿越EMA20入场过滤条件的脚本
+"""
+
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from freqtrade.templates.freqaiprimer import FreqaiPrimer
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+
+def create_test_dataframe():
+    """创建测试用的模拟数据"""
+    # 创建时间序列
+    dates = pd.date_range(start='2024-01-01', periods=100, freq='1H')
+    
+    # 创建价格数据（模拟趋势）
+    np.random.seed(42)
+    prices = []
+    current_price = 100.0
+    
+    for i in range(100):
+        # 模拟EMA5向上穿越EMA20的情况
+        if i < 30:
+            # 前30个周期：下跌趋势
+            change = np.random.normal(-0.005, 0.01)
+        elif i < 50:
+            # 30-50周期：横盘整理
+            change = np.random.normal(0, 0.005)
+        else:
+            # 50-100周期：上涨趋势（EMA5应该向上穿越EMA20）
+            change = np.random.normal(0.01, 0.015)
+        
+        current_price = current_price * (1 + change)
+        prices.append(max(current_price, 1))  # 确保价格为正数
+    
+    # 创建DataFrame
+    df = pd.DataFrame({
+        'date': dates,
+        'open': prices,
+        'high': [p * (1 + abs(np.random.normal(0, 0.01))) for p in prices],
+        'low': [p * (1 - abs(np.random.normal(0, 0.01))) for p in prices],
+        'close': prices,
+        'volume': [np.random.uniform(1000, 5000) for _ in range(100)]
+    })
+    
+    return df
+
+def test_ema_calculation():
+    """测试EMA计算和交叉检测"""
+    print("=== 测试EMA5和EMA20计算 ===")
+    
+    # 创建测试数据
+    df = create_test_dataframe()
+    
+    # 计算EMA
+    df['ema_5'] = df['close'].ewm(span=5, adjust=False).mean()
+    df['ema_20'] = df['close'].ewm(span=20, adjust=False).mean()
+    
+    # 检测交叉
+    df['ema5_cross_above_ema20'] = (
+        (df['ema_5'] > df['ema_20']) & 
+        (df['ema_5'].shift(1) <= df['ema_20'].shift(1))
+    )
+    
+    # 显示关键点的数据
+    cross_points = df[df['ema5_cross_above_ema20'] == True]
+    print(f"检测到 {len(cross_points)} 次EMA5向上穿越EMA20")
+    
+    if len(cross_points) > 0:
+        print("\n交叉点详情:")
+        for idx, row in cross_points.iterrows():
+            print(f"  时间: {row['date']}, 价格: {row['close']:.2f}, "
+                  f"EMA5: {row['ema_5']:.2f}, EMA20: {row['ema_20']:.2f}")
+    
+    # 验证交叉逻辑
+    print("\n=== 验证交叉检测逻辑 ===")
+    for i in range(1, min(10, len(df))):
+        current_ema5 = df['ema_5'].iloc[i]
+        current_ema20 = df['ema_20'].iloc[i]
+        prev_ema5 = df['ema_5'].iloc[i-1]
+        prev_ema20 = df['ema_20'].iloc[i-1]
+        cross_flag = df['ema5_cross_above_ema20'].iloc[i]
+        
+        # 手动验证交叉条件
+        manual_cross = (current_ema5 > current_ema20) and (prev_ema5 <= prev_ema20)
+        
+        if cross_flag or manual_cross:
+            status = "✓" if cross_flag == manual_cross else "✗"
+            print(f"{status} 索引{i}: 当前({current_ema5:.3f}>{current_ema20:.3f}), "
+                  f"前一个({prev_ema5:.3f}<={prev_ema20:.3f}), 检测结果: {cross_flag}")
+
+def test_strategy_integration():
+    """测试策略集成"""
+    print("\n=== 测试策略集成 ===")
+    
+    try:
+        # 创建策略实例
+        strategy = FreqaiPrimer(config={})
+        print("✓ 策略实例创建成功")
+        
+        # 检查是否有必要的属性
+        required_attrs = ['ema5_cross_above_ema20']
+        print("策略属性检查:")
+        for attr in required_attrs:
+            if hasattr(strategy, attr):
+                print(f"  ✓ {attr}")
+            else:
+                print(f"  ✗ {attr}")
+                
+    except Exception as e:
+        print(f"✗ 策略集成测试失败: {e}")
+
+if __name__ == "__main__":
+    print("开始测试EMA5向上穿越EMA20过滤条件...")
+    test_ema_calculation()
+    test_strategy_integration()
+    print("\n测试完成！")
\ No newline at end of file