我们如何强制对股票进行最小分配和最大分配？

如何解决我们如何强制对股票进行最小分配和最大分配？

我正在运行下面的代码，它给了我你在代码示例末尾看到的结果。

import pandas as pd  
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.optimize as sco
import datetime as dt
import math
from datetime import datetime,timedelta
from pandas_datareader import data as wb
from sklearn.cluster import KMeans
np.random.seed(777)


start = '2020-06-21'
end = '2021-06-21'
# N = 90
# start = datetime.Now() - timedelta(days=N)
# end = dt.datetime.today()



tickers = ['AXP','AAPL','BA','CAT','UNH','RTX','VZ','V','WBA','WMT','dis','RLLCF','GME','AMC','DOW']

thelen = len(tickers)

price_data = []
for ticker in tickers:
    prices = wb.DataReader(ticker,start = start,end = end,data_source='yahoo')[['Adj Close']]
    price_data.append(prices.assign(ticker=ticker)[['ticker','Adj Close']])

df = pd.concat(price_data)
df.dtypes
df.head()
df.shape

pd.set_option('display.max_columns',500)

df = df.reset_index()
df = df.set_index('Date')
table = df.pivot(columns='ticker')
# By specifying col[1] in below list comprehension
# You can select the stock names under multi-level column
table.columns = [col[1] for col in table.columns]
table.head()

def portfolio_annualised_performance(weights,mean_returns,cov_matrix):
    returns = np.sum(mean_returns*weights ) *252
    std = np.sqrt(np.dot(weights.T,np.dot(cov_matrix,weights))) * np.sqrt(252)
    return std,returns
  
def random_portfolios(num_portfolios,cov_matrix,risk_free_rate):
    results = np.zeros((3,num_portfolios))
    weights_record = []
    for i in range(num_portfolios):
        weights = np.random.random(thelen)
        weights /= np.sum(weights)
        weights_record.append(weights)
        portfolio_std_dev,portfolio_return = portfolio_annualised_performance(weights,cov_matrix)
        results[0,i] = portfolio_std_dev
        results[1,i] = portfolio_return
        results[2,i] = (portfolio_return - risk_free_rate) / portfolio_std_dev
    return results,weights_record


returns = table.pct_change()
mean_returns = returns.mean()
cov_matrix = returns.cov()
num_portfolios = 10000
risk_free_rate = 0.0178



def display_simulated_ef_with_random(mean_returns,num_portfolios,risk_free_rate):
    results,weights = random_portfolios(num_portfolios,risk_free_rate)
    
    max_sharpe_idx = np.argmax(results[2])
    sdp,rp = results[0,max_sharpe_idx],results[1,max_sharpe_idx]
    max_sharpe_allocation = pd.DataFrame(weights[max_sharpe_idx],index=table.columns,columns=['allocation'])
    max_sharpe_allocation.allocation = [round(i*100,2)for i in max_sharpe_allocation.allocation]
    max_sharpe_allocation = max_sharpe_allocation.T
    
    min_vol_idx = np.argmin(results[0])
    sdp_min,rp_min = results[0,min_vol_idx],min_vol_idx]
    min_vol_allocation = pd.DataFrame(weights[min_vol_idx],columns=['allocation'])
    min_vol_allocation.allocation = [round(i*100,2)for i in min_vol_allocation.allocation]
    min_vol_allocation = min_vol_allocation.T
    
    print("-")
    print("Maximum Sharpe Ratio Portfolio Allocation\n")
    print("Annualised Return:",round(rp,2))
    print("Annualised Volatility:",round(sdp,2))
    print("\n")
    print(max_sharpe_allocation)
    print("-")
    print("Minimum Volatility Portfolio Allocation\n")
    print("Annualised Return:",round(rp_min,round(sdp_min,2))
    print("\n")
    print(min_vol_allocation)
    
    plt.figure(figsize=(10,7))
    plt.scatter(results[0,:],c=results[2,cmap='YlGnBu',marker='o',s=10,alpha=0.3)
    plt.colorbar()
    plt.scatter(sdp,rp,marker='*',color='r',s=500,label='Maximum Sharpe ratio')
    plt.scatter(sdp_min,rp_min,color='g',label='Minimum volatility')
    plt.title('Simulated Portfolio Optimization based on Efficient Frontier')
    plt.xlabel('annualised volatility')
    plt.ylabel('annualised returns')
    plt.legend(labelspacing=0.8)
    

display_simulated_ef_with_random(mean_returns,risk_free_rate)

如何设置约束以强制最小分配为 2%，从而强制 RLLCF 分配 2%，并根据现在应用的相同相对权重分配其余 98%？我完全理解如果我们开始应用约束，它将是非优化的，但我正在尝试测试一个特定的用例。另外，我想将最大约束设置为 12% 之类的值，并根据现在应用的相同相对权重分配其余约束。我该怎么做？