今天與AI對話，非常神奇的經驗！僅僅幾個月，AI已經有跨越式的進步！

剛才與AI對話，感覺AI已經不是什麽忽悠我們的玩具了，是真正的人工智能！

以下是我與AI的對話：

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

1. Install Required Libraries: Ensure you have the necessary libraries installed:

   sh

   pip install yfinance numpy pandas scipy
   

2. 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

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3. 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

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4. 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}")

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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

1. 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.
2. Data Frequency: This example uses daily returns. You can modify it to use weekly or monthly returns as needed.

太厲害了！

去年ChatGPT剛出來時，我也問了這些問題，沒有得到滿意的回答。才幾個月，AI的回答近乎完美！

AI給出了最完美的答案：計算出投資分配的最佳組合百分比，Python的程序，一年回報百分比，風險百分比，Sharpe值！

我們還需要理財顧問嗎？

我們還需要程序員嗎？

看來Jasen Huang不是在瞎吹，真的，很多工作不久就會被AI取代了。