Python 基于现有数据帧中的值创建浮动范围

上下文

我目前正在尝试构建一个函数，根据一组参数选择投资组合。dataframe有一组基于以下功能的投资组合：

for x in range (10000):
    weights = np.random.random(num_assets)
    weights /= np.sum(weights)
    Portfolio_returns_earnings_1.append(np.sum(weights * Portfolio_rtns_earnings_1.mean() * 250)) # expected returns
    Portfolio_Volatilities_earnings_1.append(np.sqrt(np.dot(weights.T,np.dot(Portfolio_rtns_earnings_1.cov() * 250, weights)))) # standard deviation 
    Portfolio_sharpe_ratio_1.append(np.sum(weights * ed.sharpe_ratio_annual(Portfolio_rtns_earnings_1, 0.0085, 250)))
    Portfolio_sortino_ratio_1.append(np.sum(ed.sortino_ratio_annual(Portfolio_rtns_earnings_1, 0.0085, 250) * weights))
    Portfolio_kurtosis_1.append(np.sum(ed.kurtosis(Portfolio_rtns_earnings_1) * weights))                               
    Portfolio_skewness_1.append(np.sum(ed.skewness(Portfolio_rtns_earnings_1) * weights))
    Portfolio_var_gaussian_1.append(np.sum(ed.var_gaussian(Portfolio_rtns_earnings_1, level=5, modified=False) * weights))
    Portfolio_cvar_historic_1.append(np.sum(ed.cvar_historic(Portfolio_rtns_earnings_1, level=5) * weights))
    Portfolio_drawdown_1.append(np.sum(ed.drawdown_Portfolio(Portfolio_rtns_earnings_1)['Drawdown'] * weights))
    Northgate_Returns_weight_earnings_1.append(weights[0])
    Quartix_Returns_weight_earnings_1.append(weights[1])  
    Rockwell_Returns_weight_earnings_1.append(weights[2])

 ...

#Creating a table of the results
Portfolios = pd.DataFrame({'Return': Portfolio_returns_earnings_1, 
                                   'Volatility': Portfolio_Volatilities_earnings_1,
                                   'Sharpe Ratio': Portfolio_sharpe_ratio_1,
                                   'Sortino Ratio': Portfolio_sortino_ratio_1,
                                   'Portfolio Kurtosis': Portfolio_kurtosis_1,
                                   'Portfolio Skewness':Portfolio_skewness_1,
                                   'Portfolio Var Gaussian': Portfolio_var_gaussian_1,
                                   'Portfolio CVAR historic': Portfolio_cvar_historic_1,
                                   'Portfolio Drawdown':Portfolio_drawdown_1,
                                   'Northgate Weight': Northgate_Returns_weight_earnings_1,
                                   'Quartix Weight': Quartix_Returns_weight_earnings_1, 
                                   'Rockwell Weight': Rockwell_Returns_weight_earnings_1,
                                    })


Return  Volatility  Sharpe Ratio    Sortino Ratio   Portfolio Kurtosis  Portfolio Skewness  Portfolio Var Gaussian  Portfolio CVAR historic Portfolio Drawdown  Northgate Weight    Quartix Weight  Rockwell Weight
0   0.157144    0.008917    10.743102   14.443058   2.367912    0.005490    0.000951    0.001024    -0.000201   0.240870    0.422511    0.336619
1   0.129853    0.009276    7.988633    5.868966    2.418320    0.312885    0.001163    0.001118    -0.000256   0.366644    0.599671    0.033684
2   0.135209    0.014865    8.240307    17.646808   2.002489    -0.155758   0.001275    0.001254    -0.000405   0.680641    0.056276    0.263083
3   0.152637    0.011010    10.135540   18.359734   2.161798    -0.159434   0.001067    0.001121    -0.000295   0.440554    0.183086    0.376360
4   0.180362    0.010065    13.122746   20.467785   2.376077    -0.204672   0.000751    0.000925    -0.000135   0.091280    0.335758    0.572963
... ... ... ... ... ... ... ... ... ... ... ... ...
9995    0.188837    0.012406    13.828527   28.357812   2.150223    -0.511562   0.000765    0.000975    -0.000200   0.227595    0.017368    0.755038
9996    0.162845    0.011783    11.153250   22.005659   2.125295    -0.292163   0.000995    0.001092    -0.000281   0.408235    0.094708    0.497057
9997    0.146355    0.009434    9.569746    14.002218   2.269262    0.007745    0.001080    0.001106    -0.000270   0.389526    0.343976    0.266498
9998    0.153782    0.011364    10.239199   19.134432   2.142994    -0.189115   0.001065    0.001123    -0.000299   0.449203    0.154743    0.396054
9999    0.149577    0.008857    9.955576    12.897889   2.348428    0.057063    0.001022    0.001063    -0.000230   0.303659    0.429706    0.266634

由此，我可以根据列参数选择投资组合选项（行）。一个例子包括：

#Find the min and max range of the portfolio volatility to use to extract custom portfolio from dataframe
min(Portfolios['Volatility']), max(Portfolios['Volatility'])

# Custom Portfolios
# Within this range of volatility maximized, what is is the Portfolio Return? 
Max_Volatility_Return = Portfolios[(Portfolios['Volatility']>=0.008813526) & (Portfolios['Volatility']<=0.019963162)].max()['Return']
# Find the row in the dataframe where the return equals the above return - to derive weights
Portfolio_1 = Portfolios.iloc[np.where(Portfolios['Return']==Max_Volatility_Return)]

此方法的问题是范围超出了dataframe中列因子的最大值。当我使用max时，它只给了我一个值。当附加到空白列表时，只生成两个值

另一种方法是从这个。这里的问题是，如果不在数据帧中，我似乎无法控制生成的值的数量。将for循环与range一起使用似乎无助于解决问题

我想创建的逻辑如下：

从“因子”列的“最小值”和“最大值”开始和结束

迭代x次，但生成的数字在系数列的数据帧范围内（

min和max

）

选择生成这些范围的步数/小数点

创建一个for循环，根据该循环，对于这些范围中的每一个，最优投资组合都是从数据帧中导出的，如上图所示

创建所有这些值的平均值，以构建最佳投资组合

任何帮助都将不胜感激

def range_float(start, stop, step):
    x = start 
    while x <= stop:
        yield x
        x = x + step

rf = range_float(min(Portfolios['Volatility']), 1000, .001)

for i in rf:
    print(i)