Metadata-Version: 2.1
Name: okama
Version: 0.92
Summary: Modern Portfolio Theory (MPT) Python package
Home-page: https://okama.io/
Author: Sergey Kikevich
Author-email: sergey@rostsber.ru
License: MIT
Download-URL: https://github.com/mbk-dev/okama/archive/v0.81.tar.gz
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        # Okama
        
        _okama_ is a Python package developed for asset allocation and investment portfolio optimization tasks according to Modern Portfolio Theory (MPT).
        
        The package is supplied with **free** «end of day» historical stock markets data and macroeconomic indicators through API.
        >...entities should not be multiplied without necessity
        >
        > -- <cite>William of Ockham (c. 1287–1347)</cite>
        ## Okama main features
        
        - Investment portfolio constrained Markowitz Mean-Variance Analysis (MVA) and optimization
        - Rebalanced portfolio optimization
        - Monte Carlo Simulations for financial assets and investment portfolios
        - Popular risk metrics: VAR, CVaR, semi-deviation, variance and drawdowns
        - Forecasting models according to normal and lognormal distribution
        - Testing distribution on historical data
        - Dividend yield and other dividend indicators for stocks
        - Backtesting and comparing historical performance of broad range of assets and indexes in multiple currencies
        - Methods to track the performance of index funds (ETF) and compare them with benchmarks
        - Main macroeconomic indicators: inflation, central banks rates
        - Matplotlib visualization scripts for the Efficient Frontier, Transition map and assets risk / return performance
        
        ## Financial data and macroeconomic indicators
        
        ### End of day historical data
        
        - Stocks and ETF for main world markets
        - Mutual funds
        - Commodities
        - Currencies
        - Stock indexes
        
        ### Macroeconomic indicators
        
        - Inflation
        - Central bank rates
        
        ### Other historical data
        
        - Real estate prices
        - Top bank rates
        
        ## Installation
        
        `pip install okama`
        
        ## Getting started
        
        ### 1. Compare several assets from different stock markets. Get the USD-adjusted perfomance
        
        ```python
        import okama as ok
        x = ok.AssetList(['SPY.US', 'BND.US', 'DBXD.XETR'], ccy='USD')
        print(x)
        
        ```
        ![](../images/images/readmi01.jpg?raw=true) 
        
        Get the main parameters for the set:
        ```python
        x.describe(tickers=False)
        ```
        ![](../images/images/readmi02.jpg?raw=true) 
        
        Get the assets accumulated return, plot it and compare with the USD inflation:
        ```python
        x.wealth_indexes.plot()
        ```
        ![](../images/images/readmi03.jpg?raw=true) 
        
        ### 2. Create a dividend stocks portfolio with base currency EUR
        ```python
        weights = [0.3, 0.2, 0.2, 0.2, 0.1]
        assets = ['T.US', 'XOM.US', 'FRE.XETR', 'SNW.XETR', 'LKOH.MOEX']
        pf = ok.Portfolio(assets, weights=weights, ccy='EUR')
        print(pf)
        ```
        ![](../images/images/readmi04.jpg?raw=true) 
        
        Plot the dividend yield for each group of assets (based on stock currency).
        ```python
        pf.dividend_yield.plot()
        ```
        ![](../images/images/readmi05.jpg?raw=true) 
        
        ### 3. Draw an Efficient Frontier for 2 poular ETF: SPY and GLD
        ```python
        ls = ['SPY.US', 'GLD.US']
        curr = 'USD'
        frontier = ok.EfficientFrontierReb(ls, last_date='2020-10', ccy=curr, reb_period='year')  # Rebalancing periods is one year (dafault value)
        frontier.names
        ```
        ![](../images/images/readmi06.jpg?raw=true) 
        
        Get the Efficient Frontier points for rebalanced portfolios and plot the chart with the assets risk/CAGR points:
        ```python
        points = frontier.ef_points
        
        fig = plt.figure(figsize=(12,6))
        fig.subplots_adjust(bottom=0.2, top=1.5)
        ok.Plots(ls, ccy=curr).plot_assets(kind='cagr')  # plots the assets points on the chart
        ax = plt.gca()
        ax.plot(points.Risk, points.CAGR) 
        ```
        ![](../images/images/readmi07.jpg?raw=true)   
        <nowiki>*</nowiki> - *rebalancing period is one year*.
        
        ### 4. Get a Transition Map for allocations
        ```python
        ls = ['SPY.US', 'GLD.US', 'BND.US']
        map = ok.Plots(ls, ccy='USD').plot_transition_map(cagr=False)
        ```
        ![](../images/images/readmi08.jpg?v23-11-2020,raw=true "Transition map")  
        
        More examples are available in [Jupyter Notebooks](https://github.com/mbk-dev/okama/tree/master/notebooks).
        
        ## Communication
        
        For basic usage questions (e.g., "_Is XXX currency supported by okama?_") and for sharing ideas please use [GitHub Discussions](https://github.com/mbk-dev/okama/discussions/3).
        Russian language community is available at [okama.io forums](https://community.okama.io/c/python-okama).
        ## Issues
        
        We encourage you to report issues using the [Github tracker](https://github.com/mbk-dev/okama/issues). We welcome all kinds of issues, especially those related to correctness, documentation, performance, and feature requests.
        
        ## Contributing to okama
        
        All contributions, bug reports, bug fixes, documentation improvements, enhancements, frontend implementation and ideas are welcome.
        
        ## License
        
        MIT
        
Keywords: finance,investments,efficient frontier,python,optimization
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
