基于SVM实现股票趋势预测，写出python代码

以下是一个简单的基于SVM的股票趋势预测的Python代码示例：

首先，我们需要导入必要的库：

import pandas as pd
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

然后，我们可以读取股票数据，例如：

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

接下来，我们需要对数据进行预处理，包括特征提取和标签编码等。例如，我们可以将每天的收盘价作为特征，将涨跌幅度编码为1或-1作为标签：

X = df['Close'].values.reshape(-1, 1)
y = np.where(df['Change'] > 0, 1, -1)

然后，我们可以将数据集分为训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

接下来，我们可以训练一个SVM分类器：

svm = SVC(kernel='linear', C=1.0, random_state=42)
svm.fit(X_train, y_train)

最后，我们可以使用测试集评估模型的性能：

y_pred = svm.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print('Accuracy:', acc)

完整代码如下：

import pandas as pd
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

df = pd.read_csv('stock_data.csv')
X = df['Close'].values.reshape(-1, 1)
y = np.where(df['Change'] > 0, 1, -1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
svm = SVC(kernel='linear', C=1.0, random_state=42)
svm.fit(X_train, y_train)
y_pred = svm.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print('Accuracy:', acc)