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67 lines
1.8 KiB
67 lines
1.8 KiB
import pandas as pd
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from sklearn.preprocessing import StandardScaler
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from sklearn.decomposition import PCA
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import numpy as np
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def Fr():
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# 读取数据
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data = pd.read_excel('Data.xlsx', index_col=0) # 确保索引是股票代码
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# 只选择数值列
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numeric_cols = data.select_dtypes(include=[np.number]).columns
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data = data[numeric_cols]
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# 处理非正数
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data = data[data > 0]
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data = data.dropna()
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# 读取股票代码
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co = pd.read_excel('stkcode.xlsx', index_col=0) # 确保索引是股票代码
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Co = pd.Series(co['name'].values, index=co.index)
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# 打印调试信息
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print("data.index:", data.index)
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print("co.index:", co.index)
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# 确保索引匹配
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common_indices = data.index.intersection(Co.index)
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if common_indices.empty:
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raise ValueError("No common indices found between data.index and co.index")
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# 仅使用共同的索引
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data_common = data.loc[common_indices]
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# 打印调试信息
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print("common_indices length:", len(common_indices))
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print("data_common length:", len(data_common))
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# 标准化
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scaler = StandardScaler()
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X = scaler.fit_transform(data_common)
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# PCA
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pca = PCA(n_components=0.95) # 累计贡献率为95%
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Y = pca.fit_transform(X)
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gxl = pca.explained_variance_ratio_
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# 计算综合得分
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F = np.dot(Y, gxl)
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# 打印调试信息
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print("F length:", len(F))
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# 创建得分序列
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fs1 = pd.Series(F, index=data_common.index)
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Fscore1 = fs1.sort_values(ascending=False)
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Co1 = Co[common_indices]
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# 打印调试信息
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print("Co1 length:", len(Co1))
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fs2 = pd.Series(F, index=Co1.index)
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Fscore2 = fs2.sort_values(ascending=False)
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return Fscore1, Fscore2
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