Python计算股票收益与风险

# 创建空的DataFrame变量，用于存储股票数据
StockPrices = pd.DataFrame()
market_value_list=[]  #存储每支股票的平均市值
# 创建股票代码的列表
ticker_list = ['abc001', 'abc002', 'abc003', 'abc004', 'abc005','abc006','abc007','abc008','abc009','abc010']
# 使用循环，挨个获取每只股票的数据，并存储每日收盘价
for ticker in ticker_list:
    stock_data = read_excel('test.xlsx',sheet_name=ticker,parse_dates=['date'], index_col='date')
    StockPrices[ticker] = stock_data['close'] #获取每支股票的收盘价
    #将每支股票的市值均值存入列表中
    market_value_list.append((stock_data['high']+stock_data['high']).mean()) 
StockPrices.index.name = 'date'  # 日期为索引列
# 输出数据的前5行
print(StockPrices.head())

# 计算每日收益率，并丢弃缺失值
StockReturns = StockPrices.pct_change().dropna()
# 打印前5行数据
print(StockReturns.head())

plt.figure(figsize = (10,8))
area = np.pi*20
plt.scatter(StockReturns.mean(), StockReturns.std(),alpha = 0.5,s =area)
plt.xlabel('Expected returns')
plt.ylabel('Risk')

# Label the scatter plots
for label, x, y in zip(StockReturns.columns, StockReturns.mean(), StockReturns.std()):
    plt.annotate(
        label, 

        xy = (x, y), xytext = (50, 50),
        textcoords = 'offset points', ha = 'right', va = 'bottom',
        arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3,rad=-0.3'))

box_df = pd.concat([StockReturns],axis=1)
box_df.columns = StockReturns.columns
box_df.plot(kind='box',figsize=(8,6),colormap='jet')

import math
def plot_distribution(StockReturns, cols=5, width=20, height=15, hspace=0.2, wspace=0.5):
    plt.style.use('seaborn-whitegrid')
    fig = plt.figure(figsize=(width,height))
    fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=wspace, hspace=hspace)
    rows = math.ceil(float(StockReturns.shape[1]) / cols)
    for i, column in enumerate(StockReturns.columns):
        ax = fig.add_subplot(rows, cols, i + 1)
        ax.set_title(column)
        if StockReturns.dtypes[column] == np.object:
            g = plt.plot(y=column, data=StockReturns)
            substrings = [s.get_text()[:18] for s in g.get_yticklabels()]
            g.set(yticklabels=substrings)
        else:
            #直方图，频数
            g = plt.plot(StockReturns[column])
    
plot_distribution(StockReturns, cols=2, width=20, height=50, hspace=0.45, wspace=0.5)

Stockmean = StockReturns.mean().dropna()
print("日平均收益：")
print(Stockmean)