74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
import pandas as pd
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import numpy as np
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import sys
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import os
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from core.biz.metrics_calculation import MetricsCalculation
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def test_k_shape():
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# 创建测试数据
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test_data = pd.DataFrame({
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'open': [9.3030000000],
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'high': [9.3030000000],
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'low': [9.3020000000],
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'close': [9.3020000000]
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})
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print("测试数据:")
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print(test_data)
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print()
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# 计算基本特征
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test_data['high_low_diff'] = test_data['high'] - test_data['low']
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test_data['open_close_diff'] = abs(test_data['close'] - test_data['open'])
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test_data['open_close_fill'] = test_data['open_close_diff'] / test_data['high_low_diff']
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test_data['price_range_ratio'] = test_data['high_low_diff'] / test_data['close'] * 100
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print("计算的特征:")
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print(f"high_low_diff: {test_data['high_low_diff'].iloc[0]}")
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print(f"open_close_diff: {test_data['open_close_diff'].iloc[0]}")
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print(f"open_close_fill: {test_data['open_close_fill'].iloc[0]}")
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print(f"price_range_ratio: {test_data['price_range_ratio'].iloc[0]}%")
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print()
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# 检查"一字"条件
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price_range_ratio = test_data['price_range_ratio'].iloc[0]
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open_close_fill = test_data['open_close_fill'].iloc[0]
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print("条件检查:")
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print(f"price_range_ratio < 0.01: {price_range_ratio < 0.01}")
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print(f"open_close_fill > 0.9: {open_close_fill > 0.9}")
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print()
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# 使用MetricsCalculation类
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mc = MetricsCalculation()
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# 为了测试，我们需要创建一个有足够数据的DataFrame
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# 复制测试数据多次以创建滚动窗口
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extended_data = pd.concat([test_data] * 25, ignore_index=True)
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# 运行set_k_shape函数
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result = mc.set_k_shape(extended_data.copy())
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print("分类结果:")
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print(f"k_shape: {result['k_shape'].iloc[0]}")
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print()
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# 详细分析为什么没有被分类为"一字"
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print("详细分析:")
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print(f"价格范围比例: {price_range_ratio:.6f}%")
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print(f"实体占比: {open_close_fill:.6f}")
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print()
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if price_range_ratio < 0.01:
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print("✓ 满足价格范围比例 < 0.01% 的条件")
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else:
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print(f"✗ 不满足价格范围比例 < 0.01% 的条件 (实际: {price_range_ratio:.6f}%)")
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if open_close_fill > 0.9:
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print("✓ 满足实体占比 > 0.9 的条件")
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else:
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print(f"✗ 不满足实体占比 > 0.9 的条件 (实际: {open_close_fill:.6f})")
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if __name__ == "__main__":
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test_k_shape() |