写一篇基于随机森林模型的沪深300指数价格预测研究的python代码

在Python中，我们可以使用scikit-learn库中的RandomForestRegressor来进行沪深300指数价格的预测。这里是一个基础示例，假设我们已经有了历史数据作为特征集(X)和收盘价作为目标变量(y)：

# 导入所需库
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import datetime

# 加载数据（假设数据存储在CSV文件中）
data = pd.read_csv('stock_data.csv') # 请替换为实际数据文件路径

# 数据预处理
data['Date'] = pd.to_datetime(data['Date'])  # 将日期列转换为datetime类型
data.set_index('Date', inplace=True)  # 设置日期为索引
data.sort_index(inplace=True)

# 特征选择和数据切分
features = ['feature1', 'feature2', ...]  # 根据实际情况填写特征列名
X = data[features]
y = data['Close']

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

# 特征缩放
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# 创建并训练随机森林模型
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train_scaled, y_train)

# 预测
predictions = rf_model.predict(X_test_scaled)

# 打印预测结果
print("Predicted closing prices for the test set:")
print(predictions)

# 评估性能
from sklearn.metrics import mean_squared_error, r2_score
mse = mean_squared_error(y_test, predictions)
r2 = r2_score(y_test, predictions)
print(f"Mean Squared Error: {mse}, R-squared: {r2}")

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