股票数据相关性分析

导入相关包

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import akshare as ak
from sklearn import cluster, covariance, manifold
%matplotlib inline #Jupyter Notebook显示图形专用
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False

获取指数成分

dd=ak.index_stock_cons_sina('000300')
dd

获取相关股票价格数据

def get_50_code():#获取上证50成分股代码#dd=pro.index_weight(index_code='000016.SH')dd=ak.index_stock_cons_sina('000300')#dd=dd[dd.trade_date=='20211217']codes=dd.symbol.values#获取全市场股票基本信息#df = pro.stock_basic(exchange='', list_status='L')#df = df[df.ts_code.isin(codes50)]names=dd.name.valuesstocks=dict(zip(codes,names))#print(stocks)return stocksdef get_data(code,start='20191210',end='20220207'):df=ak.stock_zh_a_daily(symbol=code)df.index=pd.to_datetime(df.date)df=df.sort_index()#print(df)return df#将股票数据横向拼接 
codes, names = np.array(sorted(get_50_code().items())).T
data=pd.DataFrame({name:(get_data(code).close-get_data(code).open) for code,name in zip(codes,names)})
variation=data.dropna().values
#data.head()

codes, names = np.array(sorted(get_50_code().items())).T
data=pd.DataFrame({name:(get_data(code).close-get_data(code).open)
for code,name in zip(codes,names)})
variation=data.dropna().values

根据相关系数进行分组

# 相关系数
edge_model = covariance.GraphicalLassoCV()
X = variation.copy()
X /= X.std(axis=0)
edge_model.fit(X)
_, labels = cluster.affinity_propagation(edge_model.covariance_)
n_labels = labels.max()for i in range(n_labels + 1):print('Cluster %i: %s' % ((i + 1), ', '.join(names[labels == i])))

可视化

# 数据可视化
# 为了将上述聚类分析进行可视化，需要在一个2D画布上布置不同的股票。为此，需要使用“流形”技术来检索二维嵌入。模型的输出组合成一个二维图，其中节点代表股票名称，边表示:
# 集群标签用于定义节点的颜色使用稀疏协方差模型来显示边缘的强度二维嵌入用于在平面中定位节点
node_position_model = manifold.LocallyLinearEmbedding(n_components=2, eigen_solver='dense', n_neighbors=6)embedding = node_position_model.fit_transform(X.T).T# 可视化
plt.figure(1, facecolor='w', figsize=(10, 8))
plt.clf()
ax = plt.axes([0., 0., 1., 1.])
plt.axis('off')# 计算偏相关系数
partial_correlations = edge_model.precision_.copy()
d = 1 / np.sqrt(np.diag(partial_correlations))
partial_correlations *= d
partial_correlations *= d[:, np.newaxis]
non_zero = (np.abs(np.triu(partial_correlations, k=1)) > 0.02)# 使用嵌入的坐标绘制节点
plt.scatter(embedding[0], embedding[1], s=100 * d ** 2, c=labels,cmap=plt.cm.nipy_spectral)# 画相互关联的边
start_idx, end_idx = np.where(non_zero)
segments = [[embedding[:, start], embedding[:, stop]]for start, stop in zip(start_idx, end_idx)]
values = np.abs(partial_correlations[non_zero])
lc = LineCollection(segments,zorder=0, cmap=plt.cm.hot_r,norm=plt.Normalize(0, .7 * values.max()))
lc.set_array(values)
lc.set_linewidths(15 * values)
ax.add_collection(lc)#向每个节点添加一个标签，难点在于定位标签，以避免与其他标签重叠
for index, (name, label, (x, y)) in enumerate(zip(names, labels, embedding.T)):dx = x - embedding[0]dx[index] = 1dy = y - embedding[1]dy[index] = 1this_dx = dx[np.argmin(np.abs(dy))]this_dy = dy[np.argmin(np.abs(dx))]if this_dx > 0:horizontalalignment = 'left'x = x + .002else:horizontalalignment = 'right'x = x - .002if this_dy > 0:verticalalignment = 'bottom'y = y + .002else:verticalalignment = 'top'y = y - .002plt.text(x, y, name, size=10,horizontalalignment=horizontalalignment,verticalalignment=verticalalignment,bbox=dict(facecolor='w',edgecolor=plt.cm.nipy_spectral(label / float(n_labels)),alpha=.6))plt.xlim(embedding[0].min() - .15 * embedding[0].ptp(),embedding[0].max() + .10 * embedding[0].ptp(),)
plt.ylim(embedding[1].min() - .03 * embedding[1].ptp(),embedding[1].max() + .03 * embedding[1].ptp())plt.show()

效果图