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Update portfolio.py

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This commit is contained in:
ForrestLinSen 2021-01-30 13:43:27 +08:00
parent 4e5a954174
commit eadc4042a6
1 changed files with 18 additions and 18 deletions
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portfolio.py
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@ -8,11 +8,12 @@ warnings.filterwarnings("ignore")
class Portfolio:
def __init__(self, data_symbol, sample_num = 10000):
def __init__(self, data_symbol, line=True,sample_num = 10000):
self.data_symbol = data_symbol
self.data = self.create_stock_data(self.data_symbol)
self.length = len(self.data.columns)
self.sample_num = sample_num
self.calculate(line=line)
# 用雅虎财经API创建Data
def create_stock_data(self, data_symbol):
@ -26,7 +27,7 @@ class Portfolio:
data.columns = data_symbol
return data
def calculate(self, line=True):
def calculate(self, line):
# 初始化参数
self.all_weights = np.zeros((self.sample_num, len(self.data.columns)))
self.ret_arr = np.zeros(self.sample_num)
@ -39,18 +40,18 @@ class Portfolio:
self.weights = np.array(np.random.random(self.length))
self.weights = self.weights/np.sum(self.weights)
self.all_weights[x,:] = self.weights
# 计算期望收益
# 252是美股每年的交易天数
self.ret_arr[x] = np.sum( (self.log_ret.mean() * self.weights * 252))
# 计算期望风险
self.vol_arr[x] = np.sqrt(np.dot(self.weights.T, np.dot(self.log_ret.cov()*252, self.weights)))
# 计算夏普比率
self.sharpe_arr[x] = self.ret_arr[x]/ self.vol_arr[x]
self.frontier_x = []
self.frontier_y = np.linspace(0, 0.3, 200)
self.frontier_y = np.linspace(0.15, 0.5, 200)
for possible_return in self.frontier_y:
cons = ({'type':'eq','fun': self.check_sum}, {'type':'eq','fun':lambda w : self.get_ret_vol_sr(w)[0]-possible_return})
@ -60,9 +61,8 @@ class Portfolio:
self.frontier_x.append(self.result['fun'])
def draw(self, line=True):
self.calculate(line)
plt.figure(figsize=(20,8))
plt.scatter(self.vol_arr, self.ret_arr, c=self.sharpe_arr, cmap='viridis', s=25)
plt.scatter(self.vol_arr, self.ret_arr, c=self.sharpe_arr, cmap='viridis', s=10)
plt.colorbar(label='Sharpe Ratio')
plt.xlabel('Volatility')
plt.ylabel('Return')
@ -74,12 +74,12 @@ class Portfolio:
def report(self):
self.optim_number = self.sharpe_arr.argmax()
self.optim_weights = self.all_weights[self.optim_number]
print("模拟最佳投资组合: ")
print("基于所选风险资产的模拟最佳组合: ")
for i in range(self.length):
print("股票代码：{}, 比例：{:.2f}%".format(self.data.columns[i], self.optim_weights[i]*100))
self.best_results_arr = self.get_ret_vol_sr( self.optim_weights)
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]))
def get_ret_vol_sr(self, weights):
weights = np.array(weights)
@ -101,12 +101,12 @@ class Portfolio:
####根据需要调整下面的参数####
if __name__ == "__main__":
# 数据起始日期
start_date = "2015-01-01"
end_date = "2021-01-01"
# 选择需要哪些资产的数据
data_array = ["600519.SS", "0700.HK", "AAPL", "MSFT","^GSPC","GOLD", "TSLA"]
# 数据起始日期
start_date = "2016-01-01"
end_date = "2021-01-01"
# 选择需要哪些资产的数据
data_array = ["600519.SS", "0700.HK","BABA", "AAPL", "MSFT","^GSPC","GOLD", "TSLA"]
portfolio = Portfolio(data_array, sample_num=10000)
portfolio.draw()
portfolio.report()
portfolio = Portfolio(data_array, sample_num=100000)
portfolio.report()
portfolio.draw()