115 lines
4.0 KiB
Python
115 lines
4.0 KiB
Python
import yfinance as yf
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import pandas as pd
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import numpy as np
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from scipy.optimize import minimize
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import matplotlib.pyplot as plt
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import warnings
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warnings.filterwarnings("ignore")
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####
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class Portfolio:
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def __init__(self, data_symbol, sample_num = 10000):
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self.data_symbol = data_symbol
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self.data = self.create_stock_data(self.data_symbol)
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self.length = len(self.data.columns)
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self.sample_num = sample_num
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# 用雅虎财经API创建Data
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def create_stock_data(self, data_symbol):
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data = pd.DataFrame()
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for symbol in data_symbol:
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try:
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symbol_data = yf.download(symbol, start=start_date, end=end_date)
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data = pd.concat([data, symbol_data['Close']], axis=1)
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except:
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print("请检查股票代码拼写是否有误！")
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data.columns = data_symbol
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return data
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def calculate(self, line=True):
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# 初始化参数
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self.all_weights = np.zeros((self.sample_num, len(self.data.columns)))
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self.ret_arr = np.zeros(self.sample_num)
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self.vol_arr = np.zeros(self.sample_num)
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self.sharpe_arr = np.zeros(self.sample_num)
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self.log_ret = np.log(self.data/self.data.shift(1))
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for x in range(self.sample_num):
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# 随机给不同资产赋予权重
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self.weights = np.array(np.random.random(self.length))
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self.weights = self.weights/np.sum(self.weights)
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self.all_weights[x,:] = self.weights
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# 计算期望收益
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# 252是美股每年的交易天数
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self.ret_arr[x] = np.sum( (self.log_ret.mean() * self.weights * 252))
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# 计算期望风险
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self.vol_arr[x] = np.sqrt(np.dot(self.weights.T, np.dot(self.log_ret.cov()*252, self.weights)))
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# 计算夏普比率
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self.sharpe_arr[x] = self.ret_arr[x]/ self.vol_arr[x]
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self.frontier_x = []
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self.frontier_y = np.linspace(0, 0.3, 200)
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for possible_return in self.frontier_y:
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cons = ({'type':'eq','fun': self.check_sum}, {'type':'eq','fun':lambda w : self.get_ret_vol_sr(w)[0]-possible_return})
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init_guess = np.ones(self.length) / (self.length)
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bounds = ((0,1),)*(self.length)
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self.result = minimize(self.minimize_volatility, init_guess, method='SLSQP', bounds=bounds, constraints=cons)
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self.frontier_x.append(self.result['fun'])
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def draw(self, line=True):
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self.calculate(line)
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plt.figure(figsize=(20,8))
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plt.scatter(self.vol_arr, self.ret_arr, c=self.sharpe_arr, cmap='viridis', s=25)
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plt.colorbar(label='Sharpe Ratio')
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plt.xlabel('Volatility')
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plt.ylabel('Return')
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plt.xticks(np.arange(0, 0.5, step=0.02))
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if(line):
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plt.plot(self.frontier_x, self.frontier_y, 'r--', linewidth=3)
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plt.show()
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def report(self):
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self.optim_number = self.sharpe_arr.argmax()
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self.optim_weights = self.all_weights[self.optim_number]
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print("模拟最佳投资组合: ")
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for i in range(self.length):
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print("股票代码：{}, 比例：{:.2f}%".format(self.data.columns[i], self.optim_weights[i]*100))
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self.best_results_arr = self.get_ret_vol_sr( self.optim_weights)
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print("期望年化收益: {:.2f}%, 风险: {:.2f}%".format(self.best_results_arr[0]*100, self.best_results_arr[1]*100, self.best_results_arr[2]))
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def get_ret_vol_sr(self, weights):
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weights = np.array(weights)
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ret = np.sum(self.log_ret.mean() * weights) * 252
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vol = np.sqrt(np.dot(weights.T, np.dot(self.log_ret.cov()*252, weights)))
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sr = ret/vol
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return np.array([ret, vol, sr])
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def neg_sharpe(self, weights):
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return self.get_ret_vol_sr(weights)[2] * -1
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def check_sum(self, weights):
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#return 0 if sum of the weights is 1
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return np.sum(weights)-1
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def minimize_volatility(self, weights):
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return self.get_ret_vol_sr(weights)[1]
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if __name__ == "__main__":
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####根据需要调整下面的参数####
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# 数据起始日期
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start_date = "2015-01-01"
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end_date = "2021-01-01"
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# 选择需要哪些资产的数据
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data_array = ["600519.SS", "0700.HK", "AAPL", "MSFT","^GSPC","GOLD", "TSLA"]
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portfolio = Portfolio(data_array, sample_num=10000)
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portfolio.draw()
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portfolio.report()
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