Here is a list of stock symbols: SMH, SOXX, IYW, XLK, QQQ, IWY, SPY, SPYV, IWM, DIA, based on the past 3 year price history, would you be able to calculate a portfolio allocation model with optimum Sharpe ratio？
To calculate a portfolio allocation model with the optimum Sharpe ratio using the provided stock symbols and their past 3-year price history, you'll need to perform several steps. Below is a guide on how you can do this using Python and relevant libraries.
Step-by-Step Guide
Install Required Libraries: Ensure you have the necessary libraries installed:
sh pip install yfinance numpy pandas scipy
Import Libraries and Download Data: Import the required modules and download historical data for the given stock symbols using the yfinance library.
python import numpy as np import pandas as pd import yfinance as yf from scipy.optimize import minimize # Define the list of stock symbols tickers = ['SMH', 'SOXX', 'IYW', 'XLK', 'QQQ', 'IWY', 'SPY', 'SPYV', 'IWM', 'DIA'] # Download historical price data from Yahoo Finance data = yf.download(tickers, start="2021-06-01", end="2024-06-01")['Adj Close'] # Calculate daily returns returns = data.pct_change().dropna() # Calculate the mean returns and the covariance matrix mean_returns = returns.mean() cov_matrix = returns.cov() # Assuming a risk-free rate of 2% (0.02) risk_free_rate = 0.02 Open in:Code Editor Define Portfolio Performance and Optimization Functions: python # Function to calculate portfolio performance metrics def portfolio_performance(weights, mean_returns, cov_matrix, risk_free_rate): returns = np.sum(mean_returns * weights) * 252 # Annualized return std = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights))) * np.sqrt(252) # Annualized risk sharpe_ratio = (returns - risk_free_rate) / std return std, returns, sharpe_ratio # Function to minimize the negative Sharpe ratio def negative_sharpe_ratio(weights, mean_returns, cov_matrix, risk_free_rate): return -portfolio_performance(weights, mean_returns, cov_matrix, risk_free_rate)[2] # Optimization function to find the optimal portfolio allocation def optimize_portfolio(mean_returns, cov_matrix, risk_free_rate): num_assets = len(mean_returns) args = (mean_returns, cov_matrix, risk_free_rate) constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1}) # Sum of weights must be 1 bounds = tuple((0.0, 1.0) for _ in range(num_assets)) # No short-selling, weights between 0 and 1 result = minimize(negative_sharpe_ratio, num_assets * [1. / num_assets,], args=args, method='SLSQP', bounds=bounds, constraints=constraints) return result Open in:Code Editor Run Optimization and Display Results: python # Optimize the portfolio opt_results = optimize_portfolio(mean_returns, cov_matrix, risk_free_rate) opt_weights = opt_results.x # Calculate portfolio performance metrics for the optimized weights opt_std, opt_return, opt_sharpe = portfolio_performance(opt_weights, mean_returns, cov_matrix, risk_free_rate) # Display the results print("Optimized Portfolio Weights:") for ticker, weight in zip(tickers, opt_weights): print(f"{ticker}: {weight:.4f}") print(f"\nAnnualized Return: {opt_return:.2f}") print(f"Annualized Volatility: {opt_std:.2f}") print(f"Sharpe Ratio: {opt_sharpe:.2f}") Open in:Code Editor Interpretation of Results
The output will show the optimal weights for each stock symbol, the annualized return, annualized volatility, and the Sharpe ratio for the optimized portfolio. Here is an example of what the output might look like:
text Optimized Portfolio Weights: SMH: 0.1000 SOXX: 0.2000 IYW: 0.1500 XLK: 0.0500 QQQ: 0.1000 IWY: 0.1000 SPY: 0.2000 SPYV: 0.0500 IWM: 0.0500 DIA: 0.0000 Annualized Return: 0.18 Annualized Volatility: 0.12 Sharpe Ratio: 1.33 Notes Risk-Free Rate: The risk-free rate in this example is assumed to be 2%. Adjust it according to current market conditions or use a historical average. Data Frequency: This example uses daily returns. You can modify it to use weekly or monthly returns as needed.
刚才与AI对话，感觉AI已经不是什么忽悠我们的玩具了，是真正的人工智能！
以下是我与AI的对话：
Here is a list of stock symbols: SMH, SOXX, IYW, XLK, QQQ, IWY, SPY, SPYV, IWM, DIA, based on the past 3 year price history, would you be able to calculate a portfolio allocation model with optimum Sharpe ratio？
To calculate a portfolio allocation model with the optimum Sharpe ratio using the provided stock symbols and their past 3-year price history, you'll need to perform several steps. Below is a guide on how you can do this using Python and relevant libraries.
Step-by-Step GuideInstall Required Libraries: Ensure you have the necessary libraries installed:
sh pip install yfinance numpy pandas scipyImport Libraries and Download Data: Import the required modules and download historical data for the given stock symbols using the yfinance library.
python import numpy as np import pandas as pd import yfinance as yf from scipy.optimize import minimize # Define the list of stock symbols tickers = ['SMH', 'SOXX', 'IYW', 'XLK', 'QQQ', 'IWY', 'SPY', 'SPYV', 'IWM', 'DIA'] # Download historical price data from Yahoo Finance data = yf.download(tickers, start="2021-06-01", end="2024-06-01")['Adj Close'] # Calculate daily returns returns = data.pct_change().dropna() # Calculate the mean returns and the covariance matrix mean_returns = returns.mean() cov_matrix = returns.cov() # Assuming a risk-free rate of 2% (0.02) risk_free_rate = 0.02 Open in:Code Editor Define Portfolio Performance and Optimization Functions: python # Function to calculate portfolio performance metrics def portfolio_performance(weights, mean_returns, cov_matrix, risk_free_rate): returns = np.sum(mean_returns * weights) * 252 # Annualized return std = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights))) * np.sqrt(252) # Annualized risk sharpe_ratio = (returns - risk_free_rate) / std return std, returns, sharpe_ratio # Function to minimize the negative Sharpe ratio def negative_sharpe_ratio(weights, mean_returns, cov_matrix, risk_free_rate): return -portfolio_performance(weights, mean_returns, cov_matrix, risk_free_rate)[2] # Optimization function to find the optimal portfolio allocation def optimize_portfolio(mean_returns, cov_matrix, risk_free_rate): num_assets = len(mean_returns) args = (mean_returns, cov_matrix, risk_free_rate) constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1}) # Sum of weights must be 1 bounds = tuple((0.0, 1.0) for _ in range(num_assets)) # No short-selling, weights between 0 and 1 result = minimize(negative_sharpe_ratio, num_assets * [1. / num_assets,], args=args, method='SLSQP', bounds=bounds, constraints=constraints) return result Open in:Code Editor Run Optimization and Display Results: python # Optimize the portfolio opt_results = optimize_portfolio(mean_returns, cov_matrix, risk_free_rate) opt_weights = opt_results.x # Calculate portfolio performance metrics for the optimized weights opt_std, opt_return, opt_sharpe = portfolio_performance(opt_weights, mean_returns, cov_matrix, risk_free_rate) # Display the results print("Optimized Portfolio Weights:") for ticker, weight in zip(tickers, opt_weights): print(f"{ticker}: {weight:.4f}") print(f"\nAnnualized Return: {opt_return:.2f}") print(f"Annualized Volatility: {opt_std:.2f}") print(f"Sharpe Ratio: {opt_sharpe:.2f}") Open in:Code Editor Interpretation of ResultsThe output will show the optimal weights for each stock symbol, the annualized return, annualized volatility, and the Sharpe ratio for the optimized portfolio. Here is an example of what the output might look like:
text Optimized Portfolio Weights: SMH: 0.1000 SOXX: 0.2000 IYW: 0.1500 XLK: 0.0500 QQQ: 0.1000 IWY: 0.1000 SPY: 0.2000 SPYV: 0.0500 IWM: 0.0500 DIA: 0.0000 Annualized Return: 0.18 Annualized Volatility: 0.12 Sharpe Ratio: 1.33 Notes Risk-Free Rate: The risk-free rate in this example is assumed to be 2%. Adjust it according to current market conditions or use a historical average. Data Frequency: This example uses daily returns. You can modify it to use weekly or monthly returns as needed.太厉害了！
去年ChatGPT刚出来时，我也问了这些问题，没有得到满意的回答。才几个月，AI的回答近乎完美！
AI给出了答案：计算出投资分配的最佳组合百分比，Python的程序，一年回报百分比，风险百分比，Sharpe值！
我们还需要理财顾问吗？
我们还需要程序员吗？
看来Jasen Huang不是在瞎吹，真的，很多工作不久就会被AI取代了。
要论诊断的精确缜密严谨细致，人脑比不上机器。动手的外科医生还好，可以继续高薪
学医要尽量向动手的方向靠拢！
该干嘛干嘛，过好每一天，enjoy the moment
非移生孩子多
人口减少