We are suddenly running out of background workers and were told on Slack to increase timescaledb.max_background_workers from 8 to 16.

The documentation makes it seem like the timescaledb-tune tool will automatically set the key to a correct value. However the tool [always sets the value to 8](https://github.com/timescale/timesca

As a 3rd party developer there is a reasonable expectation of accuracy when reading the published api documentation and additional tools from same org keep valid URL’s and service references.

Expected:

• this repo and readme linked via Alpaca docs would be valid

Actual:

• GDAX still referenced working and dead links to this service (the coinbase pro rebrand > 1year ago)

Increase code coverage by:

• Adding tests for quandl API and alphavantager API. Include encrypted API key to pass Travis.
• Review code cov and add tests as appropriate.

Hi there, this repo looks great.

One question: can ndarray be used directly or conveniently with plotly?

line 33
portfolio.risks <- rbind(portfolio.parity$risk_contribution/sum(portfolio.parity$risk_contribution), getCovRiskBudgets(portfolio.tangency))

2 issues:

1. parity portfolio is "parity" - why take weights?
2. the column name is relative_risk_contribution

so the line should really be just
portfolio.risks <- rbind(portfolio.parity$relative_risk_contribution, getCovRiskBudgets(portfolio

How to reproduce:

In [1]: import pyEX
In [2]: client = pyEX.client.Client()

Adding "MC" or "Monte-Carlo" to labels of Monte Carlo optimisation plots. Easier to distinguish it from the Efficient Frontier output.