A package for backtesting and data
snooping.
Installation
You can install the development version of snoop from GitHub with:
# install.packages("devtools")
devtools::install_github("Reckziegel/snoop")Workflow
snoop aims to automate routines for portfolio construction purposes inside the tiyverse.
The current workflow is the following:
- Build a rolling
tibblewithconstruct_rolling_infrastructure() - Chose a rebalance periodicity with
construct_rebalance_infrastructure() - Rebalance with
rebalance_portfolio() - Compute the main statistics with
extract_statistics()
Toy Example
library(snoop)
# Step 0: Get the data
stocks <- tibble::tibble(
time = as.Date('2009-01-01') + 0:99,
X = stats::rnorm(100, 0, 1),
Y = stats::rnorm(100, 0, 2),
Z = stats::rnorm(100, 0, 4)
)
# Step 1: Rolling Infraestructure
roll <- construct_rolling_infrastructure(stocks, .initial = 50)
roll
#> # A tibble: 50 x 3
#> .date .analysis .assessment
#> <date> <list> <list>
#> 1 2009-02-20 <tibble [50 x 3]> <tibble [1 x 3]>
#> 2 2009-02-21 <tibble [50 x 3]> <tibble [1 x 3]>
#> 3 2009-02-22 <tibble [50 x 3]> <tibble [1 x 3]>
#> 4 2009-02-23 <tibble [50 x 3]> <tibble [1 x 3]>
#> 5 2009-02-24 <tibble [50 x 3]> <tibble [1 x 3]>
#> 6 2009-02-25 <tibble [50 x 3]> <tibble [1 x 3]>
#> 7 2009-02-26 <tibble [50 x 3]> <tibble [1 x 3]>
#> 8 2009-02-27 <tibble [50 x 3]> <tibble [1 x 3]>
#> 9 2009-02-28 <tibble [50 x 3]> <tibble [1 x 3]>
#> 10 2009-03-01 <tibble [50 x 3]> <tibble [1 x 3]>
#> # ... with 40 more rows
# Step 2: Rebalance Infraestructure
rebal <- construct_rebalance_infrastructure(roll, .by = "week")
rebal # information is under the hood
#> # A tibble: 50 x 3
#> .date .analysis .assessment
#> <date> <list> <list>
#> 1 2009-02-20 <tibble [50 x 3]> <tibble [1 x 3]>
#> 2 2009-02-21 <tibble [50 x 3]> <tibble [1 x 3]>
#> 3 2009-02-22 <tibble [50 x 3]> <tibble [1 x 3]>
#> 4 2009-02-23 <tibble [50 x 3]> <tibble [1 x 3]>
#> 5 2009-02-24 <tibble [50 x 3]> <tibble [1 x 3]>
#> 6 2009-02-25 <tibble [50 x 3]> <tibble [1 x 3]>
#> 7 2009-02-26 <tibble [50 x 3]> <tibble [1 x 3]>
#> 8 2009-02-27 <tibble [50 x 3]> <tibble [1 x 3]>
#> 9 2009-02-28 <tibble [50 x 3]> <tibble [1 x 3]>
#> 10 2009-03-01 <tibble [50 x 3]> <tibble [1 x 3]>
#> # ... with 40 more rows
# Step 3: Rebalance Portfolio
mu_sigma <- function(.data) {
# Mean Variance Strategy
list(mu = colMeans(.data), sigma = stats::cov(.data))
}
# Step 4: Compute the main statistics
optimal <- rebalance_portfolio(rebal, mu_sigma, .strategy = "mean_variance")
optimal
#> # A tibble: 50 x 4
#> .date .analysis .assessment .optimization
#> <date> <list> <list> <list>
#> 1 2009-02-20 <tibble [50 x 3]> <tibble [1 x 3]> <named list [6]>
#> 2 2009-02-21 <tibble [50 x 3]> <tibble [1 x 3]> <named list [6]>
#> 3 2009-02-22 <tibble [50 x 3]> <tibble [1 x 3]> <named list [6]>
#> 4 2009-02-23 <tibble [50 x 3]> <tibble [1 x 3]> <named list [6]>
#> 5 2009-02-24 <tibble [50 x 3]> <tibble [1 x 3]> <named list [6]>
#> 6 2009-02-25 <tibble [50 x 3]> <tibble [1 x 3]> <named list [6]>
#> 7 2009-02-26 <tibble [50 x 3]> <tibble [1 x 3]> <named list [6]>
#> 8 2009-02-27 <tibble [50 x 3]> <tibble [1 x 3]> <named list [6]>
#> 9 2009-02-28 <tibble [50 x 3]> <tibble [1 x 3]> <named list [6]>
#> 10 2009-03-01 <tibble [50 x 3]> <tibble [1 x 3]> <named list [6]>
#> # ... with 40 more rows
# Step 4: Compute Statistics
metrics <- extract_statistics(optimal)
metrics
#> # A tibble: 50 x 11
#> .date .analysis .assessment .optimization .weights .return .volatility
#> <date> <list> <list> <list> <list> <dbl> <dbl>
#> 1 2009-02-20 <tibble> <tibble> <named list> <dbl [3]> -0.969 0.861
#> 2 2009-02-21 <tibble> <tibble> <named list> <dbl [3]> -0.843 0.852
#> 3 2009-02-22 <tibble> <tibble> <named list> <dbl [3]> -0.146 0.858
#> 4 2009-02-23 <tibble> <tibble> <named list> <dbl [3]> 0.284 0.857
#> 5 2009-02-24 <tibble> <tibble> <named list> <dbl [3]> -0.713 0.852
#> 6 2009-02-25 <tibble> <tibble> <named list> <dbl [3]> 0.617 0.837
#> 7 2009-02-26 <tibble> <tibble> <named list> <dbl [3]> -0.387 0.813
#> 8 2009-02-27 <tibble> <tibble> <named list> <dbl [3]> 0.393 0.798
#> 9 2009-02-28 <tibble> <tibble> <named list> <dbl [3]> 0.0109 0.786
#> 10 2009-03-01 <tibble> <tibble> <named list> <dbl [3]> 1.96 0.785
#> # ... with 40 more rows, and 4 more variables: .skewness <dbl>,
#> # .kurtosis <dbl>, .value_at_risk <dbl>, .expected_shortfall <dbl>