AI时代Python金融大数据分析实战：ChatGPT让金融大数据分析插上翅膀

# 代码示例：获取股票数据并可视化
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
# 下载股票数据
data = yf.download('AAPL', start='2020-01-01', end='2021-01-01')
# 绘制股票走势图
plt.figure(figsize=(10, 6))
plt.plot(data['Close'], label='AAPL')
plt.title('AAPL Stock Price')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.show()

# 代码示例：投资组合优化
import numpy as np
from scipy.optimize import minimize
# 假设我们有一些资产的收益率数据
returns = np.random.rand(4)
weights = np.random.rand(4)
def portfolio_return(weights, returns):
    return np.sum(returns * weights)
def portfolio_volatility(weights, cov_matrix):
    return np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
# 最小化波动率的投资组合优化
def min_volatility(weights):
    return portfolio_volatility(weights, cov_matrix)
# 定义约束条件
constraints = ({'type': 'eq', 'fun': lambda weights: np.sum(weights) - 1})
bounds = tuple((0, 1) for asset in range(4))
# 运行优化
optimized = minimize(min_volatility, weights, method='SLSQP', bounds=bounds, constraints=constraints)

# 导入必要的库
import pandas as pd
import matplotlib.pyplot as plt
# 读取并展示时间序列数据
data = pd.read_csv('financial_data.csv')
data['Date'] = pd.to_datetime(data['Date'])
data.set_index('Date', inplace=True)
plt.figure(figsize=(10, 6))
plt.plot(data)
plt.title('Financial Time Series Data')
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()

# 导入必要的库
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
# 准备数据集
X = financial_data.drop('Label', axis=1)
y = financial_data['Label']
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 构建随机森林分类器模型
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
# 预测并评估模型
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print(f'Model Accuracy: {accuracy}')

# 在这里插入您的大数据分析代码示例，例如数据清洗、特征工程、建模等。