[](http://okama.readthedocs.io/)
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[](https://pypi.org/project/okama/)
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[](https://opensource.org/licenses/MIT)
[](https://colab.research.google.com/github/mbk-dev/okama/blob/master/examples/01%20howto.ipynb)
[](https://github.com/psf/black)
# Okama
_okama_ is a library with investment portfolio analyzing & optimization tools. CFA recommendations are used in quantitative finance.
_okama_ goes 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>
## Table of contents
- [Okama main features](#okama-main-features)
- [Financial data and macroeconomic indicators](#financial-data-and-macroeconomic-indicators)
- [End of day historical data](#end-of-day-historical-data)
- [Macroeconomic indicators](#macroeconomic-indicators)
- [Other historical data](#other-historical-data)
- [Installation](#installation)
- [Getting started](#getting-started)
- [Documentation](#documentation)
- [Financial Widgets](#financial-widgets)
- [RoadMap](#roadmap)
- [Contributing to okama](#contributing-to-okama)
- [Communication](#communication)
## Okama main features
- Investment portfolio constrained Markowitz Mean-Variance Analysis (MVA) and optimization
- Rebalanced portfolio optimization with constraints (multi-period Efficient Frontier)
- Investment portfolios with contributions / withdrawals cash flows (DCF)
- Monte Carlo Simulations for financial assets and investment portfolios
- Popular risk metrics: VAR, CVaR, semi-deviation, variance and drawdowns
- Different financial ratios: CAPE10, Sharpe ratio, Sortino ratio, Diversification ratio
- Forecasting models according to normal, lognormal and other popular distributions
- 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
- Stock indexes
### Currencies
- FX currencies
- Crypto currencies
- Central bank exchange rates
### Macroeconomic indicators
For many countries (China, USA, United Kingdom, European Union, Russia, Israel etc.):
- Inflation
- Central bank rates
- CAPE10 (Shiller P/E) Cyclically adjusted price-to-earnings ratios
### Other historical data
- Real estate prices
- Top bank rates
## Installation
`pip install okama`
The latest development version can be installed directly from GitHub:
`git clone https://github.com/mbk-dev/okama@dev`
`poetry install`
## Getting started
### 1. Compare several assets from different stock markets. Get USD-adjusted performance
```python
import okama as ok
x = ok.AssetList(['SPY.US', 'BND.US', 'DBXD.XFRA'], ccy='USD')
x # all examples are for Jupyter Notebook/iPython. For raw Python interpreter use 'print(x)' instead.
```
Get the main parameters for the set:
```python
x.describe()
```
Get the assets accumulated return, plot it and compare with the USD inflation:
```python
x.wealth_indexes.plot()
```
### 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.XFRA', 'SNW.XFRA', 'LKOH.MOEX']
pf = ok.Portfolio(assets, weights=weights, ccy='EUR')
pf.table
```
Plot the dividend yield of the portfolio (adjusted to the base currency).
```python
pf.dividend_yield.plot()
```
### 3. Draw an Efficient Frontier for 2 popular ETF: SPY and GLD
```python
ls = ['SPY.US', 'GLD.US']
curr = 'USD'
last_date='2020-10'
# Rebalancing periods is one year (default value)
frontier = ok.EfficientFrontierReb(ls, last_date=last_date, ccy=curr, rebalancing_period='year')
frontier.names
```
Get the Efficient Frontier points for rebalanced portfolios and plot the chart with the assets risk/CAGR points:
```python
import matplotlib.pyplot as plt
points = frontier.ef_points
fig = plt.figure(figsize=(12,6))
fig.subplots_adjust(bottom=0.2, top=1.5)
frontier.plot_assets(kind='cagr') # plots the assets points on the chart
ax = plt.gca()
ax.plot(points.Risk, points.CAGR)
```
<nowiki>*</nowiki> - *rebalancing period is one year*.
### 4. Get a Transition Map for allocations
```python
ls = ['SPY.US', 'GLD.US', 'BND.US']
map = ok.EfficientFrontier(ls, ccy='USD').plot_transition_map(x_axe='risk')
```
More examples are available in form of [Jupyter Notebooks](https://github.com/mbk-dev/okama/tree/master/examples).
## Documentation
The official documentation is hosted on readthedocs.org: [https://okama.readthedocs.io/](https://okama.readthedocs.io/)
## Financial Widgets
[okama-dash](https://github.com/mbk-dev/okama-dash) repository has interactive financial widgets (multi-page web application)
build with _okama_ package and [Dash (plotly)](https://github.com/plotly/dash) framework. Working example is available at
[okama.io](https://okama.io/).
## RoadMap
The plan for _okama_ is to add more functions that will be useful to investors and asset managers.
- Add Omega ratio to EfficientFrontier, EfficientFrontierReb and Portfolio classes.
- Make complex withdrawals / contributions strategies in Portfolio class.
- Make complex portfolio rebalancing strategies.
- Add Black-Litterman asset allocation
- Accelerate optimization for multi-period Efficient Frontier: minimize_risk and maximize_risk methods of EfficientFrontierReb class.
- Make a single EfficientFrontier class for all optimizations: single-period or multu-period with rebalancing period as a parameter.
- Add different utility functions for optimizers: IRR, portfolio survival period, semi-deviation, VaR, CVaR, drawdowns etc.
- Add more functions based on suggestion of users.
## Contributing to okama
Contributions are *most welcome*. Have a look at the [Contribution Guide](https://github.com/mbk-dev/okama/blob/master/CONTRIBUTING.md) for more.
Feel free to ask questions on [Discussuions](https://github.com/mbk-dev/okama/discussions).
As contributors and maintainers to this project, you are expected to abide by okama' code of conduct. More information can be found at: [Contributor Code of Conduct](https://github.com/mbk-dev/okama/blob/master/CODE_OF_CONDUCT.md)
## 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).
## License
MIT
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"description": "\n[](http://okama.readthedocs.io/)\n[](https://www.python.org/)\n[](https://pypi.org/project/okama/)\n[](https://coveralls.io/github/mbk-dev/okama?branch=master)\n[](https://opensource.org/licenses/MIT)\n[](https://colab.research.google.com/github/mbk-dev/okama/blob/master/examples/01%20howto.ipynb)\n[](https://github.com/psf/black)\n\n# Okama\n\n_okama_ is a library with investment portfolio analyzing & optimization tools. CFA recommendations are used in quantitative finance.\n\n_okama_ goes with **free** \u00abend of day\u00bb historical stock markets data and macroeconomic indicators through API.\n>...entities should not be multiplied without necessity\n>\n> -- <cite>William of Ockham (c. 1287\u20131347)</cite>\n\n## Table of contents\n\n- [Okama main features](#okama-main-features)\n- [Financial data and macroeconomic indicators](#financial-data-and-macroeconomic-indicators)\n - [End of day historical data](#end-of-day-historical-data)\n - [Macroeconomic indicators](#macroeconomic-indicators)\n - [Other historical data](#other-historical-data)\n- [Installation](#installation)\n- [Getting started](#getting-started)\n- [Documentation](#documentation)\n- [Financial Widgets](#financial-widgets)\n- [RoadMap](#roadmap)\n- [Contributing to okama](#contributing-to-okama)\n- [Communication](#communication)\n\n## Okama main features\n\n- Investment portfolio constrained Markowitz Mean-Variance Analysis (MVA) and optimization\n- Rebalanced portfolio optimization with constraints (multi-period Efficient Frontier)\n- Investment portfolios with contributions / withdrawals cash flows (DCF)\n- Monte Carlo Simulations for financial assets and investment portfolios\n- Popular risk metrics: VAR, CVaR, semi-deviation, variance and drawdowns\n- Different financial ratios: CAPE10, Sharpe ratio, Sortino ratio, Diversification ratio \n- Forecasting models according to normal, lognormal and other popular distributions\n- Testing distribution on historical data\n- Dividend yield and other dividend indicators for stocks\n- Backtesting and comparing historical performance of broad range of assets and indexes in multiple currencies\n- Methods to track the performance of index funds (ETF) and compare them with benchmarks\n- Main macroeconomic indicators: inflation, central banks rates\n- Matplotlib visualization scripts for the Efficient Frontier, Transition map and assets risk / return performance\n\n## Financial data and macroeconomic indicators\n\n### End of day historical data\n\n- Stocks and ETF for main world markets\n- Mutual funds\n- Commodities\n- Stock indexes\n\n### Currencies\n\n- FX currencies\n- Crypto currencies\n- Central bank exchange rates\n\n### Macroeconomic indicators\nFor many countries (China, USA, United Kingdom, European Union, Russia, Israel etc.): \n\n- Inflation\n- Central bank rates\n- CAPE10 (Shiller P/E) Cyclically adjusted price-to-earnings ratios\n\n### Other historical data\n\n- Real estate prices\n- Top bank rates\n\n## Installation\n\n`pip install okama`\n\nThe latest development version can be installed directly from GitHub:\n`git clone https://github.com/mbk-dev/okama@dev`\n\n`poetry install`\n\n\n## Getting started\n\n### 1. Compare several assets from different stock markets. Get USD-adjusted performance\n\n```python\nimport okama as ok\n\nx = ok.AssetList(['SPY.US', 'BND.US', 'DBXD.XFRA'], ccy='USD')\nx # all examples are for Jupyter Notebook/iPython. For raw Python interpreter use 'print(x)' instead.\n\n```\n \n\nGet the main parameters for the set:\n```python\nx.describe()\n```\n \n\nGet the assets accumulated return, plot it and compare with the USD inflation:\n```python\nx.wealth_indexes.plot()\n```\n \n\n### 2. Create a dividend stocks portfolio with base currency EUR\n\n```python\nweights = [0.3, 0.2, 0.2, 0.2, 0.1]\nassets = ['T.US', 'XOM.US', 'FRE.XFRA', 'SNW.XFRA', 'LKOH.MOEX']\npf = ok.Portfolio(assets, weights=weights, ccy='EUR')\npf.table\n```\n \n\nPlot the dividend yield of the portfolio (adjusted to the base currency).\n\n```python\npf.dividend_yield.plot()\n```\n \n\n### 3. Draw an Efficient Frontier for 2 popular ETF: SPY and GLD\n```python\nls = ['SPY.US', 'GLD.US']\ncurr = 'USD'\nlast_date='2020-10'\n# Rebalancing periods is one year (default value)\nfrontier = ok.EfficientFrontierReb(ls, last_date=last_date, ccy=curr, rebalancing_period='year')\nfrontier.names\n```\n \n\nGet the Efficient Frontier points for rebalanced portfolios and plot the chart with the assets risk/CAGR points:\n```python\nimport matplotlib.pyplot as plt\n\npoints = frontier.ef_points\n\nfig = plt.figure(figsize=(12,6))\nfig.subplots_adjust(bottom=0.2, top=1.5)\nfrontier.plot_assets(kind='cagr') # plots the assets points on the chart\nax = plt.gca()\nax.plot(points.Risk, points.CAGR) \n```\n \n<nowiki>*</nowiki> - *rebalancing period is one year*.\n\n### 4. 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Working example is available at \n[okama.io](https://okama.io/).\n\n \n\n## RoadMap\n\nThe plan for _okama_ is to add more functions that will be useful to investors and asset managers.\n\n- Add Omega ratio to EfficientFrontier, EfficientFrontierReb and Portfolio classes.\n- Make complex withdrawals / contributions strategies in Portfolio class.\n- Make complex portfolio rebalancing strategies.\n- Add Black-Litterman asset allocation \n- Accelerate optimization for multi-period Efficient Frontier: minimize_risk and maximize_risk methods of EfficientFrontierReb class.\n- Make a single EfficientFrontier class for all optimizations: single-period or multu-period with rebalancing period as a parameter.\n- Add different utility functions for optimizers: IRR, portfolio survival period, semi-deviation, VaR, CVaR, drawdowns etc.\n- Add more functions based on suggestion of users.\n\n## Contributing to okama\n\nContributions are *most welcome*. Have a look at the [Contribution Guide](https://github.com/mbk-dev/okama/blob/master/CONTRIBUTING.md) for more. \nFeel free to ask questions on [Discussuions](https://github.com/mbk-dev/okama/discussions). \nAs contributors and maintainers to this project, you are expected to abide by okama' code of conduct. More information can be found at: [Contributor Code of Conduct](https://github.com/mbk-dev/okama/blob/master/CODE_OF_CONDUCT.md)\n\n## Communication\n\nFor 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).\nRussian language community is available at [okama.io forums](https://community.okama.io).\n\n## License\n\nMIT\n",
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