问题描述
我正在处理一些股票并试图找到一个马科维茨边界，但是，当我尝试绘图时，我只收到一个重复 400 次的唯一值。我已经在 youtube 和这里看了，但我找不到我做错的地方。我会把完整的代码放在下面更清楚，但我的错误发生在最后一部分，所以没有必要阅读所有这些，至少不是最初。我将不胜感激任何帮助。这段代码的基础可以在 Fábio Neves 在linkedIn 上的一篇文章中找到。
#normalization of returns that i catched before(group of 10 stocks)
log_ret = np.log(carteira/carteira.shift(1))
np.random.seed(42)
num_ports = 6000
all_weights = np.zeros((num_ports,len(carteira.columns)))
ret_arr = np.zeros(num_ports)
vol_arr = np.zeros(num_ports)
sharpe_arr = np.zeros(num_ports)
for x in range(num_ports):
weights = np.array(np.random.random(10))
weights /= np.sum(weights)
all_weights[x,:] = weights
ret_arr [x] = np.sum( (log_ret.mean() *weights * 264))
vol_arr[x] = np.sqrt(np.dot(weights.T,np.dot(log_ret.cov()*264,weights )))
sharpe_arr[x] = ret_arr[x]/vol_arr[x]
#my sharpe ratio is the porfolio 1506
print(sum(all_weights[1506,:]))
#i can see the best composition ret an vol from my sharpe_arr defined above.
max_sr_ret = ret_arr[sharpe_arr.argmax()]
max_sr_vol = vol_arr[sharpe_arr.argmax()]
def get_ret_vol_sr(weights):
weights = np.array(weights)
ret = np.sum(log_ret.mean()*264)
vol = np.sqrt(np.dot(weights.T,weights)))
sr = ret/vol
return np.array ([ret,vol,sr])
def neg_sharpe(weights):
return get_ret_vol_sr(weights)[2]* -1
def check_sum(weights):
return np.sum(weights) -1
cons = ({'type': 'eq','fun':check_sum})
bounds = ((0,1),(0,1))
init_guess = [0.1,0.1,0.1]
opt_results = minimize(neg_sharpe,init_guess,method='SLSQP',bounds = bounds,constraints=cons)
norm_opt_results = get_ret_vol_sr(opt_results.x)
norm_opt_results /= np.sum(norm_opt_results)
def minimize_volatility(weights):
weights = np.array(weights)
vol = np.sqrt(np.dot(weights.T,weights)))
return vol
def getReturn(weights):
weights = np.array(weights)
ret = np.sum(log_ret.mean()*weights*264)
return ret
#the biggest return is something like 0.85
frontier_y = np.linspace(0,0.85,400)
frontier_x = []
for i in frontier_y:
cons = ({'type': 'eq','fun':check_sum},{'type': 'eq','fun': lambda w:getReturn(weights)- i})
result = minimize(minimize_volatility,method= 'SLSQP',constraints = cons)
frontier_x.append(result['fun'])
我不知道我的问题出在哪里，因为我的frontier_y没问题，我收到了：
#这只是减少扩展的一部分
print(frontier_y)
[0. 0.00213033 0.00426065 0.00639098 0.0085213 0.01065163
0.01278195 0.01491228 0.01704261 0.01917293 0.02130326 0.02343358
0.02556391 0.02769424 0.02982456 0.03195489 0.03408521 0.03621554
0.03834586 0.04047619 0.04260652 0.04473684 0.04686717 0.04899749
0.05112782 0.05325815 0.05538847 0.0575188 0.05964912 0.06177945
0.06390977 0.0660401 0.06817043 0.07030075 0.07243108 0.0745614
0.07669173 0.07882206 0.08095238 0.08308271 0.08521303 0.08734336
0.08947368 0.09160401 0.09373434 0.09586466 0.09799499 0.10012531
0.10225564 0.10438596 0.10651629 0.10864662 0.11077694 0.11290727]
但是，当我尝试绘制 frontier_x 时，我收到了四百次 0.2725108192006289...
print(frontier_x)
[0.2725108192006289,0.2725108192006289,再次感谢您的帮助
解决方法
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