weight_by_risk_parity: Risk Parity Weighting Suite

In PortfolioTesteR: Test Investment Strategies with English-Like Code

Risk Parity Weighting Suite

Description

Collection of risk-based weighting methods for portfolio construction. Each method allocates capital based on risk characteristics rather than market capitalization or equal weights.

Usage

weight_by_risk_parity(
  selected_df,
  prices_df,
  method = c("inverse_vol", "equal_risk", "max_div"),
  lookback_periods = 252,
  min_periods = 60
)

Arguments

selected_df	Binary selection matrix (data.frame with Date column).
prices_df	Price data for risk calculations (typically daily). Returns are calculated internally from prices.
method	Optimization method for risk parity.
lookback_periods	Number of periods for risk estimation (default: 252).
min_periods	Minimum periods required (default: 60).

Details

Methods

inverse_vol

Weights proportional to 1 / volatility (e.g., 1 / sd of returns over lookback_periods). Lower volatility stocks receive higher weights.

equal_risk

Equal Risk Contribution (ERC): finds weights so each position contributes equally to total portfolio risk (iterative optimization).

max_div

Maximum Diversification: maximizes the ratio of weighted average volatility to portfolio volatility.

The function accepts price data and calculates returns internally, ensuring consistency with other library functions. Daily prices are recommended for accurate volatility estimation.

Value

Weight matrix with the same dates as selected_df; each row sums to 1.

Examples

data("sample_prices_daily")
data("sample_prices_weekly")
momentum <- calc_momentum(sample_prices_weekly, lookback = 12)
selected <- filter_top_n(momentum, n = 10)
weight_by_risk_parity(selected, sample_prices_daily, method = "inverse_vol")
weight_by_risk_parity(selected, sample_prices_daily, method = "equal_risk")
weight_by_risk_parity(selected, sample_prices_daily, method = "max_div")

PortfolioTesteR documentation built on Nov. 5, 2025, 5:23 p.m.