基于贝叶斯的股票预测python代码实现

基于贝叶斯的股票预测可以使用贝叶斯线性回归模型来实现。以下是一个简单的Python代码实现：

首先，需要导入所需的库和数据集：

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import BayesianRidge
from sklearn.metrics import mean_squared_error

data = pd.read_csv('stock_data.csv')

接着，对数据集进行分割，将其划分为训练集和测试集：

X = data[['Feature 1', 'Feature 2', ... , 'Feature n']]
y = data['Target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

然后，对特征进行多项式转换：

poly = PolynomialFeatures(degree=2)
X_train_poly = poly.fit_transform(X_train)
X_test_poly = poly.transform(X_test)

接着，使用贝叶斯线性回归模型进行拟合：

model = BayesianRidge()
model.fit(X_train_poly, y_train)

最后，使用模型对测试集进行预测，并计算均方误差：

y_pred = model.predict(X_test_poly)
mse = mean_squared_error(y_test, y_pred)
print('Mean Squared Error:', mse)

完整代码如下：

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import BayesianRidge
from sklearn.metrics import mean_squared_error

data = pd.read_csv('stock_data.csv')

X = data[['Feature 1', 'Feature 2', ... , 'Feature n']]
y = data['Target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

poly = PolynomialFeatures(degree=2)
X_train_poly = poly.fit_transform(X_train)
X_test_poly = poly.transform(X_test)

model = BayesianRidge()
model.fit(X_train_poly, y_train)

y_pred = model.predict(X_test_poly)
mse = mean_squared_error(y_test, y_pred)
print('Mean Squared Error:', mse)