Pandas dot product with Multiindex

Question

My problem is quite common in finance.

Given an array w (1xN) of weights and a covariance matrix Q (NxN) of assets, one can calculate the covariance of the portfolio using the quadratic expression w' * Q * w, where * is the dot product.

I want to understand what is the best way to perform this operation when I have an history of weights W (T x N) and a 3D structure for covariance matrix (T, N, N).

import numpy as np
import pandas as pd

returns = pd.DataFrame(0.1 * np.random.randn(100, 4), columns=['A', 'B', 'C', 'D'])
covariance = returns.rolling(20).cov()

weights = pd.DataFrame(np.random.randn(100, 4), columns=['A', 'B', 'C', 'D'])

My solution so far was to converting pandas DataFrames to numpy, perform the calculation doing a loop and then converting back to pandas. Note that I need to explicitly check for the alignment of labels, since in reality covariance and weights could be calculated by different processes.

cov_dict = {key: covariance.xs(key, axis=0, level=0) for key in covariance.index.get_level_values(0)}

def naive_numpy(weights, cov_dict):

    expected_risk = {}

    # Extract columns, index before passing to numpy arrays
    # Columns
    cov_assets = cov_dict[next(iter(cov_dict))].columns
    avail_assets = [el for el in cov_assets if el in weights]

    # Indexes
    cov_dates = list(cov_dict.keys())
    avail_dates = weights.index.intersection(cov_dates)

    sel_weights = weights.loc[avail_dates, avail_assets]

    # Main loop and calculation
    for t, value in zip(sel_weights.index, sel_weights.values):
        expected_risk[t] = np.sqrt(np.dot(value, np.dot(cov_dict[t].values, value)))

    # Back to pandas DataFrame
    expected_risk = pd.Series(expected_risk).reindex(weights.index).sort_index()

    return expected_risk

Is there pure-pandas way to achieve the same result? Or is there any improvement on the code to make it more efficient? (despite using numpy, it is still quite slow).

Answer 1

I think numpy is definitely the best option. Though you loose that efficiency if you loop on values/dates.

My suggestion for calculating the rolling volatility of a portfolio (with no looping):

returns = pd.DataFrame(0.1 * np.random.randn(100, 4), columns=['A', 'B', 'C', 'D'])
covariance = returns.rolling(20).cov()
weights = pd.DataFrame(np.random.randn(100, 4), columns=['A', 'B', 'C', 'D'])

rows, columns = weights.shape

# Go to numpy:
w = weights.values
cov = covariance.values.reshape(rows, columns, columns)

A = np.matmul(w.reshape(rows, 1, columns), cov)
var = np.matmul(A, w.reshape(rows, columns, 1)).reshape(rows)
std_dev = np.sqrt(var)

# Back to pandas (in case you want that):
pd.Series(std_dev, index = weights.index)