This version improve the performance of portfolio optimization
algorithms, ability to parallelize the Monte Carlo simulations, and
simulation caching. It brings also new useful functions as
render_scenario_snapshot(),
plot_future_saving_rates(), and
get_default_gompertz_parameters(). With
render_scenario_snapshot() function, it is possible to get
a table with cash flow and net worth statements for a given year in a
simulated scenario. The result of new features in this version can be
seen in the blog post: Can
Juliet manage without Romeo? How much she should spend, save, and invest
to achieve that?.
NLOPT_LD_SLSQP (Sequential Least-Squares Quadratic
Programming). The optimization is no more robust and faster.opts list to
simulate_scenario() function with optimization parameters
nloptr optimization function and thus changing algorithm or
its options. The list of possible options and their default values can
be obtain with nloptr::nloptr.print.options().simulate_scenario() function to reproduce the same results
for the same scenario, also for Monte Carlo samples.auto_parallel argument to
simulate_scenario() function to automatically parallelize
the Monte Carlo simulations.use_cache argument in
simulate_scenarios() function to caching also all data of
all Monte Carlo samples for a scenario (not only each sample
separately).plot_scenarios()
which does not reorder scenarios anymore.render_scenario_snapshot() function to render a
table with cash flow and net worth statements for a given year (first by
default) in a simulated scenario.plot_future_saving_rates() for plotting the
future saving rates.Household and
HouseholdMember objects.plot_expected_allocation().age() function as convenient wrapper for getting
age of a household member when defining triggers for expected income or
spending streams.get_default_gompertz_parameters() function to get
default parameters for the Gompertz model for a given country, sex, and
age.This major release introduces probably first in the world (open-sourced) implementation of a new multilevel life-cycle modeling of a household finances. It connects life-cycle models with single-period net-worth Markowitz optimization models and allows for high degree of personalization. The implementation is based on a novel theoretical framework, described by Thomas M. Idzorek and Paul D. Kaplan in “Lifetime Financial Advice: A Personalized Optimal Multilevel Approach” (2024).
Household R6 class that aggregates information
about a household and its members and HouseholdMember class
that aggregates information about a single member of a household.set_cache(), reset_cache() and
get_cache().calc_life_expectancy() function to calculate the
life expectancy of a person based on the Gompertz model.plot_life_expectancy() function to visualize the
probability of dying and life expectancy of every member of a
Household object and similar plot_survival()
function for visualizing the probability of survival of each of the
members of a Household and the probability of at least one
member of a Household to survive.create_portfolio_template() function to create an
exemplary Portoflio object - a template for default
portfolio with two asset classes: GlobalStocksIndexFund and
InflationProtectedBonds and with some reasonable default
values that should be updated and customised further.calc_portfolio_parameters() function to calculate
the parameters of a Portfolio object like current value,
weights of assets, expected return and standard deviation of the whole
portfolio.calc_optimal_asset_allocation() function to
calculate optimal allocation for a particular Household and
Portfolio objects.plot_optimal_portfolio() function to visualize
the current and optimal allocation to assets for a particular
Portfolio object.simulate_scenario() function to simulate a single
scenario of cash flows for a particular Household and
Portfolio objects using expected returns of the assets in
portfolio or random returns in multiple Monte Carlo samples.simulate_scenarios() function to simulate
multiple scenarios of cash flows for a particular Household
and Portfolio objects. The scenarios parameters can
triggered by different events like different years of retirement.plot_scenarios() function to visualize the
scenarios metrics that help with choosing the optimal parameters for the
best scenario, e.g. the optimal year for retirement.plot_expected_allocation() function to visualize
the expected allocation of assets for a particular scenario over the
household life cycle.plot_expected_capital() function to visualize the
expected financial, human, and total capital, as well as liabilities for
a particular scenario over the household life cycle.plot_future_income() and
plot_future_spending() functions to visualize the future
income and spending for a particular scenario over the household life
cycle. The plot_future_spending() function allows to plot
the relation of discretionary and non-discretionary spending, structure
of non-discretionary spending and possible levels of discretionary
spending over time based on Monte Carlo simulations.This release provides functions for calculating probability of retirement ruin and functions for calibrating the underlying Gompertz model with the use of the mortality rates.
calc_retirement_ruin() and
plot_retirement_ruin() to calculate and visualize the
probability of retirement ruin.retirement-ruin option to run_app()
function for running package new interactive app.calc_gompertz_survival_probability() to calculate
the probability of survival using the Gompertz model.calc_gompertz_parameters() to calculate the
parameters of the Gompertz model based on mortality rates and
calc_gompertz_joint_parameters() to calculate the
parameters of the joint Gompertz model.plot_gompertz_calibration() for visually checking
the calibration of the Gompertz model and
plot_joint_survival() for plotting the survival
probabilities for two individuals and their joint survival
probability.read_hmd_life_tables() for reading the ‘Human
Mortality Database’ (HMD) life tables files.Initial release of the package includes functions designed to support the optimal allocation of your investment portfolio with the use of the ‘Merton Share’ formula.
calc_purchasing_power() and
plot_purchasing_power() to calculate and visualize the
effect of real interest rates—whether positive (compounding) or negative
(inflation)—on the purchasing power of savings.calc_optimal_risky_asset_allocation() for
calculating optimal allocation using the ‘Merton Share’ formula.calc_risk_adjusted_return() and
plot_risk_adjusted_returns() to calculate and visualize the
effect of allocation to risky assets on the risk-adjusted return.run_app() function that runs package interactive
apps: risk-adjusted-returns and
purchasing-power.