efficient frontier/stock analyze

Question

Consider the following task. Using a 10-year period I should calculate the portfolio weights in January and then use these weights in February to calculate the portfolio return and standard deviation. The program should then continue to calculate the weights In February and then use these weights in February to calculate the portfolio returns and standard deviation in marts. This should be done through all the 131 months in the data meaning I should only calculate the weights in the first month of the dataset.

import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import yfinance as yf

p_ret = []  # Define an empty array for portfolio returns
p_vol = []  # Define an empty array for portfolio volatility

tickers = ['AAPL', 'AMZN', 'XOM']
start_date = datetime.date(2010, 1, 2)
end_date = datetime.date(2020, 12, 31)
daily_data = yf.download(tickers, start=start_date, end=end_date)  # definere datasættet
daily_data = daily_data['Adj Close'].dropna()
Vector_of_ones = np.array([1,1,1])
frames = [v for _, v in daily_data.groupby(pd.Grouper(freq='M'))]

rf = 0.01 # risk free asset
weights = []
df = pd.DataFrame(columns=tickers)
for w in frames:
    #corr_matrix = w.pct_change().apply(lambda x: np.log(1 + x)).corr()
    mu = (w.resample('D').last().pct_change().sum())
    individual_asset_return = np.subtract(np.transpose(mu), np.dot(Vector_of_ones,rf))
    # individual_asset_return = daily_data.pct_change().mean()  # finder gennemsnittet
    df.loc[+1] = [individual_asset_return[tickers[0]], individual_asset_return[tickers[1]],
                  individual_asset_return[tickers[2]]]
    df.index = df.index - 1
    df = df.sort_index()
    for d in range(len(df)):
        cov_matrix = w.pct_change().apply(lambda x: np.log(1 + x)).cov()
        liste = df.iloc[d].tolist()
        a = np.dot(np.linalg.inv(cov_matrix), np.transpose(np.array(liste)))
        omega_weights = a / (np.dot(np.transpose(Vector_of_ones), a)) # expression to find weights
    weights.append(omega_weights)
    for afkast in frames[1:]:  #loop to find the portfolio returns and standard deviation
        cov_matrix1 = afkast.pct_change().apply(lambda x: np.log(1 + x)).cov()
        #corr_matrix1 = afkast.pct_change().apply(lambda x: np.log(1 + x)).corr()
        df1 = df.iloc[1:, :]
        for d1 in range(len(df)):
            liste1 = df.iloc[d1].tolist()
    portfolio_return = np.dot(np.transpose(omega_weights),
                              mu)
    p_ret.append(portfolio_return)
    volatility_portfolio = np.sqrt(np.dot(np.transpose(omega_weights), np.dot(cov_matrix1, omega_weights)))
    p_vol.append(volatility_portfolio)

    data = {'Returns': p_ret, 'Volatility': p_vol}
    for counter, symbol in enumerate(afkast.columns.tolist()):
        # print(counter, symbol)
        data[symbol + ' weight'] = [w[counter] for w in weights]

portfolios = pd.DataFrame(data)  # laver dataframe som sortere sådan at den med mindst volatility er øverst
portfolios['Date'] = pd.date_range(start=start_date, periods=len(portfolios), freq='M')
portfolios.plot(x='Date', y='Returns', kind='line')
# portfolios.plot(x = 'Date', y = 'Volatility', kind = 'line')
plt.show()
print(portfolios.head())

As you probably can see I’m not an advanced coder but I hope I could some help where my code is wrong if there is anything wrong.

I really appreciate any help you can provide.