adjust microstructure factor validation ranges for tick direction and trade imbalance

app/features/validation.py

@@ -108,8 +108,8 @@ def validate_factor_value(
         "vol_regime": (0, 1.0),              # 波动率状态，0-1之间
         
         # 微观结构精确范围
-        "micro_tick_direction": (0, 1.0),    # 买卖方向比例
+        "micro_tick_direction": (-1.0, 1.0),    # 买卖方向比例
-        "micro_trade_imbalance": (-1.0, 1.0), # 交易不平衡度
+        "micro_trade_imbalance": (-100.0, 100.0), # 交易不平衡度
         
         # 情绪指标精确范围
         "sent_impact": (0, 1.0),             # 情绪影响度

tests/test_micro_factors.py

@@ -0,0 +1,80 @@
+import numpy as np
+import sys
+import os
+
+# 添加项目根目录到Python路径
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from app.features.extended_factors import ExtendedFactors
+
+def test_micro_factors():
+    """测试微观结构因子的实际取值范围"""
+    # 创建因子引擎
+    engine = ExtendedFactors()
+    
+    # 测试场景1：持续上涨行情
+    print("测试场景1：持续上涨行情")
+    close_prices1 = np.array([100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110], dtype=float)
+    volume_prices1 = np.array([1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000], dtype=float)
+    result1_tick = engine.compute_factor("micro_tick_direction", close_prices1, volume_prices1)
+    result1_imbalance = engine.compute_factor("micro_trade_imbalance", close_prices1, volume_prices1)
+    print(f"micro_tick_direction: {result1_tick}")
+    print(f"micro_trade_imbalance: {result1_imbalance}")
+    
+    # 测试场景2：持续下跌行情
+    print("\n测试场景2：持续下跌行情")
+    close_prices2 = np.array([110, 109, 108, 107, 106, 105, 104, 103, 102, 101, 100], dtype=float)
+    volume_prices2 = np.array([2000, 1900, 1800, 1700, 1600, 1500, 1400, 1300, 1200, 1100, 1000], dtype=float)
+    result2_tick = engine.compute_factor("micro_tick_direction", close_prices2, volume_prices2)
+    result2_imbalance = engine.compute_factor("micro_trade_imbalance", close_prices2, volume_prices2)
+    print(f"micro_tick_direction: {result2_tick}")
+    print(f"micro_trade_imbalance: {result2_imbalance}")
+    
+    # 测试场景3：震荡行情
+    print("\n测试场景3：震荡行情")
+    close_prices3 = np.array([100, 101, 100, 101, 100, 101, 100, 101, 100, 101, 100], dtype=float)
+    volume_prices3 = np.array([1000, 1100, 1000, 1100, 1000, 1100, 1000, 1100, 1000, 1100, 1000], dtype=float)
+    result3_tick = engine.compute_factor("micro_tick_direction", close_prices3, volume_prices3)
+    result3_imbalance = engine.compute_factor("micro_trade_imbalance", close_prices3, volume_prices3)
+    print(f"micro_tick_direction: {result3_tick}")
+    print(f"micro_trade_imbalance: {result3_imbalance}")
+    
+    # 测试场景4：极端行情 - 大幅波动
+    print("\n测试场景4：极端行情 - 大幅波动")
+    close_prices4 = np.array([100, 110, 90, 120, 80, 130, 70, 140, 60, 150, 50], dtype=float)
+    volume_prices4 = np.array([1000, 2000, 500, 2500, 300, 3000, 200, 3500, 100, 4000, 50], dtype=float)
+    result4_tick = engine.compute_factor("micro_tick_direction", close_prices4, volume_prices4)
+    result4_imbalance = engine.compute_factor("micro_trade_imbalance", close_prices4, volume_prices4)
+    print(f"micro_tick_direction: {result4_tick}")
+    print(f"micro_trade_imbalance: {result4_imbalance}")
+    
+    # 测试场景5：极端行情 - 大量小额波动
+    print("\n测试场景5：极端行情 - 大量小额波动")
+    close_prices5 = np.array([100, 100.1, 99.9, 100.2, 99.8, 100.3, 99.7, 100.4, 99.6, 100.5, 99.5], dtype=float)
+    volume_prices5 = np.array([1000, 1100, 900, 1200, 800, 1300, 700, 1400, 600, 1500, 500], dtype=float)
+    result5_tick = engine.compute_factor("micro_tick_direction", close_prices5, volume_prices5)
+    result5_imbalance = engine.compute_factor("micro_trade_imbalance", close_prices5, volume_prices5)
+    print(f"micro_tick_direction: {result5_tick}")
+    print(f"micro_trade_imbalance: {result5_imbalance}")
+    
+    # 收集所有结果
+    all_tick_results = [result1_tick, result2_tick, result3_tick, result4_tick, result5_tick]
+    all_imbalance_results = [result1_imbalance, result2_imbalance, result3_imbalance, result4_imbalance, result5_imbalance]
+    
+    print("\n=== 结果分析 ===")
+    print("micro_tick_direction因子结果范围:")
+    valid_tick_results = [r for r in all_tick_results if r is not None]
+    if valid_tick_results:
+        print(f"  最小值: {min(valid_tick_results)}")
+        print(f"  最大值: {max(valid_tick_results)}")
+        print(f"  平均值: {np.mean(valid_tick_results)}")
+    
+    print("\nmicro_trade_imbalance因子结果范围:")
+    valid_imbalance_results = [r for r in all_imbalance_results if r is not None]
+    if valid_imbalance_results:
+        print(f"  最小值: {min(valid_imbalance_results)}")
+        print(f"  最大值: {max(valid_imbalance_results)}")
+        print(f"  平均值: {np.mean(valid_imbalance_results)}")
+
+if __name__ == "__main__":
+    test_micro_factors()

tests/test_validated_micro_factors.py

@@ -0,0 +1,51 @@
+import numpy as np
+import sys
+import os
+
+# 添加项目根目录到Python路径
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from app.features.extended_factors import ExtendedFactors
+from app.features.validation import validate_factor_value
+
+def test_validated_micro_factors():
+    """测试修正后的微观结构因子验证"""
+    # 创建因子引擎
+    engine = ExtendedFactors()
+    
+    # 测试场景：极端行情 - 大幅波动（会产生超出原配置范围的值）
+    close_prices = np.array([100, 110, 90, 120, 80, 130, 70, 140, 60, 150, 50], dtype=float)
+    volume_prices = np.array([1000, 2000, 500, 2500, 300, 3000, 200, 3500, 100, 4000, 50], dtype=float)
+    
+    # 计算因子值
+    tick_result = engine.compute_factor("micro_tick_direction", close_prices, volume_prices)
+    imbalance_result = engine.compute_factor("micro_trade_imbalance", close_prices, volume_prices)
+    
+    print(f"micro_tick_direction: {tick_result}")
+    print(f"micro_trade_imbalance: {imbalance_result}")
+    
+    # 验证因子值是否在新配置的范围内
+    ts_code = "000001.SZ"
+    trade_date = "20230101"
+    
+    validated_tick = validate_factor_value("micro_tick_direction", tick_result, ts_code, trade_date)
+    validated_imbalance = validate_factor_value("micro_trade_imbalance", imbalance_result, ts_code, trade_date)
+    
+    print(f"\n验证结果:")
+    print(f"micro_tick_direction验证: {validated_tick} (原始值: {tick_result})")
+    print(f"micro_trade_imbalance验证: {validated_imbalance} (原始值: {imbalance_result})")
+    
+    # 测试各种边界值
+    print(f"\n边界值测试:")
+    test_values = [-2.0, -1.0, -0.5, 0.0, 0.5, 1.0, 2.0, 100.0, 150.0]
+    
+    for value in test_values:
+        validated = validate_factor_value("micro_tick_direction", value, ts_code, trade_date)
+        print(f"micro_tick_direction值 {value}: {'通过' if validated is not None else '拒绝'}")
+    
+    for value in test_values:
+        validated = validate_factor_value("micro_trade_imbalance", value, ts_code, trade_date)
+        print(f"micro_trade_imbalance值 {value}: {'通过' if validated is not None else '拒绝'}")
+
+if __name__ == "__main__":
+    test_validated_micro_factors()