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adjust microstructure factor validation ranges for tick direction and trade imbalance

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sam 2025-10-09 08:43:10 +08:00
parent f1ded59dce
commit 028ae56d06
3 changed files with 133 additions and 2 deletions
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4
app/features/validation.py
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@ -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_trade_imbalance": (-1.0, 1.0), # 交易不平衡度
"micro_tick_direction": (-1.0, 1.0), # 买卖方向比例
"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

80
tests/test_micro_factors.py Normal file
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@ -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()

51
tests/test_validated_micro_factors.py Normal file
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@ -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()