AssetAllocation/README.Rmd at main · rubetron/AssetAllocation · GitHub

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---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# AssetAllocation

<!-- badges: start -->

<!-- badges: end -->

The goal of AssetAllocation is to perform backtesting of customizable asset allocation strategies. The main function that the user interacts with is backtest_allocation().

## Installation

You can install the development version of AssetAllocation from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("rubetron/AssetAllocation")
```

## Example

Simple example using pre-loaded strategy (see the vignette for other examples):

```{r example1}
library(AssetAllocation)
## Example 1: backtesting one of the asset allocations in the package
us_60_40 <- asset_allocations$static$us_60_40

# test using the data set provided in the package
bt_us_60_40 <- backtest_allocation(us_60_40,
                                   ETFs$Prices,
                                   ETFs$Returns,
                                   ETFs$risk_free)

# plot returns
library(PerformanceAnalytics)
chart.CumReturns(bt_us_60_40$returns,
                 main = paste0("Backtest of the ",
                               bt_us_60_40$strat$name,
                               " portfolio"),
                 ylab = "Cumulative returns"
)

# show table with performance metrics
bt_us_60_40$table_performance


```

Another example creating a strategy from scratch, retrieving data from Yahoo Finance, and backtesting:

```{r example2}
library(AssetAllocation)
# create a strategy that invests equally in momentum (MTUM), value (VLUE), low volatility (USMV) and quality (QUAL) ETFs.

factors_EW  <- list(name = "EW Factors",
                      tickers = c("MTUM", "VLUE", "USMV", "QUAL"),
                      default_weights = c(0.25, 0.25, 0.25, 0.25),
                      rebalance_frequency = "month",
                      portfolio_rule_fn = "constant_weights")

# get data for tickers using getSymbols
factor_ETFs_data <- get_data_from_tickers(factors_EW$tickers,
                                      starting_date = "2013-08-01")
# backtest the strategy
bt_factors_EW <- backtest_allocation(factors_EW,factor_ETFs_data$P, factor_ETFs_data$R)

# plot returns
charts.PerformanceSummary(bt_factors_EW$returns,
                          main = bt_factors_EW$strat$name,
                               )

# table with performance metrics
bt_factors_EW$table_performance
```