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Okama 1.4.0 introduces investment strategies with contributions and withdrawals in Portfolio class. New methods support discounted cash flows (DCF) and Monte Carlo simulation for portfolio longevity.

New features

DCF methods for contributions and withdrawals in Portfolio class

New Portfolio class attributes (all are optional):

• initial_amount - Portfolio initial investment FV (at last_date)
• cashflow - portfolio monthly cash flow FV (at last_date). Negative value corresponds to withdrawals. Positive value corresponds to contributions. Cash flow value is indexed each month by inflation.
• discount_rate - cash flow discount rate required to calculate PV values.

New dcf. methods in Portfolio class:

• dcf.plot_forecast_monte_carlo() method to plot Monte Carlo simulation for portfolio future wealth indexes optionally together with historical wealth index.
• dcf.monte_carlo_survival_period() method to generate a survival period distribution for a portfolio with cash flows by Monte Carlo simulation.
• dcf.wealth_index property to calculate wealth index time series for the portfolio with contributions and withdrawals.
• dcf.survival_period property to calculate the period when the portfolio has positive balance considering withdrawals on the historical data.
• dcf.survival_date property to get the date when the portfolio balance become negative considering withdrawals on the historical data.
• dcf.cashflow_pv property to calculate the discounted value (PV) of the cash flow amount at the historical first_date.
• dcf.initial_amount_pv property to calculate the discounted value (PV) of the initial investments at the historical first_date.

New properties in Portfolio class

• assets_dividend_yield property to calculate last twelve months (LTM) dividend yield time series (monthly) for each asset
• dividends_annual property to get calendar year dividends sum time series for each asset.

New methods and properties in AssetList class

• get_rolling_risk_annual() method to calculate annualized risk rolling time series for each asset.
• get_dividend_mean_yield() method to calculate the arithmetic mean for annual dividend yield over a specified period.
• dividend_yield_annual property to calculate last twelve months (LTM) dividend yield annual time series.

Changes in existing methods & properties

• Asset_List.risk_annual returns expanding risk time series (not float)
• Portfolio.recovery_period returns time series of recovery periods over historical data (not a single period)
• describe() method shows the rate of return arithmetic mean (expected return) in Portfolio, AssetList classes
• new xy_text argument in plot_assets() method to position better point labels in Portofolio, AssetList classes

New Jupyter Notebooks with examples

• 04 investment portfolios with DCF.ipynb for investment strategies with cash flow - portfolio withdrawals / contributions. Backtest the portfolio and forecast portfolio longevity with Monte Carlo simulation.
• 05 macroeconomics - inflation rates.ipynb to analyze inflation, key rates and other macro indicators historical data.

Bugs fixed

• Duplicate tickers in the assets are no longer allowed and are automatically corrected (AssetList, Portfolio, EfficientFrontier, EfficientFrontierReb)

Okama now supports Panadas 2.0.0 and further versions. There is no backward compatibility with previous Pandas versions.

New features:

• New rebalancing periods for portfolios: "half-year" and "quarter"
• new set_values_monthly() method in Inflation to forecast data and change previos values
• new dividend_yield_annual property in AssetList calculates dividend yield time series for calendar years
• new get_dividend_mean_yield() method in AssetList shows mean dividend yield for a given period
• plot_cml() has new y_axe parameter to switch from CAGR to mean rate of returns in the plots
• asset_dividend_yield property renamed to dividend_yield in AssetList

okama works with Python 3.11 now.

FIX:

• aliases symbols_in_namespace and no_dividends_namespaces were not imported in __init__.py

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):

• 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

Use new financial API (api.okama.io) for historical data requests.