# Load financial data financial_data = pd.read_csv('OPTProject_Financial.csv') # Calculate expected return and risk for each stock financial_data['ExpectedReturn'] = financial_data['price'] * financial_data['ret_mean'] * 20 financial_data['ExpectedRisk'] = financial_data['price'] * financial_data['ret_std'] * 20 # Define the optimization function for partial investments def optimize_partial_stocks(stocks, budget, risk_limit): c = -stocks['ExpectedReturn'].values # Negative return for maximization A = [stocks['price'].values, stocks['ExpectedRisk'].values] # Constraints b = [budget, risk_limit] bounds = [(0, price) for price in stocks['price'].values] # Fractional investment allowed # Linear programming optimization result = linprog(c, A_ub=A, b_ub=b, bounds=bounds, method='highs') if result.success: stocks['Investment'] = result.x total_money_spent = np.dot(stocks['price'].values, result.x) expected_return = np.dot(stocks['ExpectedReturn'].values, result.x) expected_risk = np.dot(stocks['ExpectedRisk'].values, result.x) ------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[6], line 32 30 budget = 20000 31 risk_limit = 5000 ---> 32 result = optimize_partial_stocks(financial_data, budget, risk_limit) 33 if result: 34 num_stocks_bought, total_money_spent, expected_risk, expected_return, stocks