Okama

New features:
- rolling_window parameter in AssetList functions: `index_corr()`, `index_beta()`, `tracking_error()`
- `index_corr()` and `index_rolling_corr()` are combined into a single function `index_corr()` in the AssetList
- AssetList, Prtfolio, EfficentFrontier and EfficentFrontierReb are now **sequences** and has `__getitem__`, `__iter__` methods.


Fix:
- Avoid running `get_namspaces()` and other aliases in __init__.py (this resulted in the database requests during library import)
- `EfficientFrontier.plot_pair_ef()` faled if inflation=False
- Tickers with dot "." like BRK.B

The release uses runtime Python 3.8. This version is recomended for development. Previous versions of okama were using legacy Python 3.7.

New features:

- `EfficentFrontier().get_monte_carlo()` method return risk, return and weights data for random portfolios.

Fix:

- Columns order is lost in `Portfolio().weights_ts`
- minor bugs

Version 1.2.2 will be the last Python 3.7 release. In further development we will use Python 3.8.

Updated:
- Update classes for new FOREX data format (AssetList, Portfolio and all ListMaker inherited classes are affected)

Fixed:
- compatible issues with `importlib-metadata` package

New features:

- `get_tangency_portfolio()` can calculate tangency portfolio weights for CAGR (rate of return with geometric mean).

Fix:

- base currency `first_date` and `last_date` were calculated wrong in AssetList, Portfolio and EfficientFrontier

New Macroeconomic class `Indicator`
3 macroeconomic classes are available (and [Documented](https://okama.readthedocs.io/en/master/stubs/okama.Inflation.html)):

- `Indicator` : Macroeconomic indicators and ratios. (`.RATIO` **NEW namespace**)
- `Inflation` : Inflation related data and methods (`.INFL` namespace)
- `Rates` : Rates of central banks and banks (`.RATE` namesapce)

Cyclically adjusted price-to-earnings ratios (CAPE10) for 20+ countries are in the DataBase: USA_CAPE10.RATIO, CHN_CAPE10.RATIO, CHN_CAPE10.RATIO etc.

Daily value time series for Macro classes
`Rate` class has `.values_daily` property which can be used with bak raters and some other symbols:
`ok.Rate("RUONIA.RATE").values_daily`
all Macro classes have `.values_monthly` property.

`.describe()` methods in all macroeconomic classes
`.describe()` methods show descriptive statistics for YTD and given periods:

- arithmetic mean
- median
- max and min values

`Inflation` class `.describe()` method is different. It generates inflation-specific statistics:

- YTD compound inflation
- Annual inflation (geometric mean) for a given list of periods
- max 12 months inflation for the periods
- Annual inflation (geometric mean) for the whole history

Rolling tracking difference for stock indexes and ETFs

`.tracking_difference()` and `tracking_difference_annualized()` in `AssetList` class are now methods (where properties). Methods have `rolling_window` attribute to set wolling window size (in months).
To calclulate 24 months movig tracking difference:
`x.tracking_difference(rolling_window=24)`

**Full Changelog**: https://github.com/mbk-dev/okama/compare/v1.1.6...v1.2.0

Introduce parallel computing for heavy calculations in multi-period portfolio optimization (Efficient Frontier).

Minor changes:

- okama uses `black` for code formatting
- development dependencies use Python 3.7 to stay compatible with Google Colab