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Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity

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This PR - enables support for plotly 6 - removes EOL Python 3.8 - upgrades and fixes the base docker image

Hello, I notice the following error when trying to use a customised objective: `CVXPY note: This failure was encountered while trying to certify that a matrix is positive semi-definite (see...

## Description This addresses a Future Warning in `pypfopt/hierarchical_portfolio.py` caused by multiplying integer-type weights with float values. Due to stricter type enforcement, this would cause errors in future pandas versions....

Bumps [jinja2](https://github.com/pallets/jinja) from 3.1.4 to 3.1.6. Release notes Sourced from jinja2's releases. 3.1.6 This is the Jinja 3.1.6 security release, which fixes security issues but does not otherwise change behavior...

dependencies

> As of v1.2.3, it's possible to change the solvers by assigning `ef.solver = "..."` before optimising. It might be the case that one of the other solvers (see a...

**Describe the bug** Below error is prompted when class discrete_allocation / lp_portfolio was run, as package cvxpy has removed the ECOS dependency in ver. 1.6.0 (https://github.com/cvxpy/cvxpy/releases/tag/v1.6.0) "SolverError: The solver ECOS_BB...

bug

**Describe the bug** In the example provided in the github page of the package. The market caps tickers do not allign with the dataframe (stock.csv) tickers **Expected behavior** The example...

bug