diff --git a/portfolio.py b/portfolio.py
new file mode 100644
index 0000000..1f0d6c9
--- /dev/null
+++ b/portfolio.py
@@ -0,0 +1,114 @@
+import yfinance as yf
+import pandas as pd
+import numpy as np
+from scipy.optimize import minimize
+import matplotlib.pyplot as plt
+import warnings
+warnings.filterwarnings("ignore")
+
+
+
+####
+class Portfolio:
+  def __init__(self, data_symbol, 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
+
+  # 用雅虎财经API创建Data
+  def create_stock_data(self, data_symbol):
+    data = pd.DataFrame()
+    for symbol in data_symbol:
+      try:
+        symbol_data = yf.download(symbol, start=start_date, end=end_date)
+        data = pd.concat([data, symbol_data['Close']], axis=1)
+      except:
+        print("请检查股票代码拼写是否有误！")
+    data.columns = data_symbol
+    return data
+
+  def calculate(self, line=True):
+    # 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)
+    self.vol_arr = np.zeros(self.sample_num)
+    self.sharpe_arr = np.zeros(self.sample_num)
+    self.log_ret = np.log(self.data/self.data.shift(1))
+
+    for x in range(self.sample_num):
+      # 随机给不同资产赋予权重
+      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)
+
+    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})
+      init_guess = np.ones(self.length) / (self.length)
+      bounds = ((0,1),)*(self.length)
+      self.result = minimize(self.minimize_volatility, init_guess, method='SLSQP', bounds=bounds, constraints=cons)
+      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.colorbar(label='Sharpe Ratio')
+    plt.xlabel('Volatility')
+    plt.ylabel('Return')
+    plt.xticks(np.arange(0, 0.5, step=0.02))
+    if(line):
+      plt.plot(self.frontier_x, self.frontier_y, 'r--', linewidth=3)
+    plt.show()
+
+  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: ")
+    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)
+      ret = np.sum(self.log_ret.mean() * weights) * 252
+      vol = np.sqrt(np.dot(weights.T, np.dot(self.log_ret.cov()*252, weights)))
+      sr = ret/vol
+      return np.array([ret, vol, sr])
+
+  def neg_sharpe(self, weights):
+    return self.get_ret_vol_sr(weights)[2] * -1
+
+  def check_sum(self, weights):
+      #return 0 if sum of the weights is 1
+      return np.sum(weights)-1
+
+  def minimize_volatility(self, weights):
+      return self.get_ret_vol_sr(weights)[1]
+
+if __name__ == "__main__":
+
+####根据需要调整下面的参数####
+	# 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"]
+
+	portfolio = Portfolio(data_array, sample_num=10000)
+	portfolio.draw()
+	portfolio.report()