Hello, I could really use some help with my capstone project. The problem I have is way over my head.

For your reference this is the link to the project:

https://www.codecademy.com/paths/finance-python/tracks/analyze-financial-data-with-python-capstone/modules/analyze-financial-data-with-python-capstone/informationals/analyze-financial-data-with-python-capstone

My problem lies with a function I am using. (optimal_portfolio)

I got the function from a previous lesson on codeacademy:

Initially the function wouldn’t work because as_matrix() is no longer used or something?

So, I replaced it with to_numpy().

However, now I get an error that is way over my head:

opt/miniconda3/lib/python3.7/site-packages/cvxopt/coneprog.py", line 2067, in coneqp

raise ValueError(“Rank(A) < p or Rank([P; A; G]) < n”)

ValueError: Rank(A) < p or Rank([P; A; G]) < n

If someone could help me I would really appreciate it

Here is the optimal_portfolio function:

```
def optimal_portfolio(returns):
n = returns.shape[1]
returns = np.transpose(returns.to_numpy()) # originally as_matrix changed to values
N = 100
mus = [10**(5.0 * t/N - 1.0) for t in range(N)]
# Convert to cvxopt matrices
S = opt.matrix(np.cov(returns))
pbar = opt.matrix(np.mean(returns, axis=1))
# Create constraint matrices
G = -opt.matrix(np.eye(n)) # negative n x n identity matrix
h = opt.matrix(0.0, (n ,1))
A = opt.matrix(1.0, (1, n))
b = opt.matrix(1.0)
# Calculate efficient frontier weights using quadratic programming
portfolios = [solvers.qp(mu*S, -pbar, G, h, A, b)['x']
for mu in mus]
## CALCULATE RISKS AND RETURNS FOR FRONTIER
returns = [blas.dot(pbar, x) for x in portfolios]
risks = [np.sqrt(blas.dot(x, S*x)) for x in portfolios]
## CALCULATE THE 2ND DEGREE POLYNOMIAL OF THE FRONTIER CURVE
m1 = np.polyfit(returns, risks, 2)
x1 = np.sqrt(m1[2] / m1[0])
# CALCULATE THE OPTIMAL PORTFOLIO
wt = solvers.qp(opt.matrix(x1 * S), -pbar, G, h, A, b)['x']
return np.asarray(wt), returns, risks
```