---
title: "Efficient Estimation of Bid-Ask Spreads from Open, High, Low, and Close Prices"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{bidask}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  warning = FALSE,
  message = FALSE
)
```

This vignette illustrates how to estimate bid-ask spreads from open, high, low, and close prices. Let's start by loading the package:

```{r setup}
library(bidask)
```

The package offers two ways to estimate bid-ask spreads:

1. `edge()`: designed for tidy data.
2. `spread()`: designed for `xts` objects.

The function `edge()` implements the efficient estimator described in [Ardia, Guidotti, & Kroencke (2024)](https://doi.org/10.1016/j.jfineco.2024.103916). Open, high, low, and close prices are to be passed as separate vectors. 

The function `spread()` requires an `xts` object containing columns named `Open`, `High`, `Low`, `Close` and it provides additional functionalities, such as additional estimators and rolling estimates.

An output value of 0.01 corresponds to a spread estimate of 1%.

Examples are provided below.

## Tidy data

The function `edge()` can be easily used with tidy data and the `dplyr` grammar. In the following example, we estimate bid-ask spreads for cryptocurrencies.

Download daily prices for Bitcoin and Ethereum using the [crypto2](https://cran.r-project.org/package=crypto2) package:

```{r}
library(dplyr)
library(crypto2)
df <- crypto_list(only_active=TRUE) %>%
  filter(symbol %in% c("BTC", "ETH")) %>%
  crypto_history(start_date = "20200101", end_date = "20221231")
```

```{r}
head(df)
```

Estimate the spread for each coin in each year:

```{r}
df %>%
  mutate(yyyy = format(timestamp, "%Y")) %>%
  group_by(symbol, yyyy) %>%
  arrange(timestamp) %>%
  summarise(EDGE = edge(open, high, low, close))
```

## `xts` objects

The function `spread()` provides additional functionalities for [xts](https://cran.r-project.org/package=xts) objects. In the following example, we estimate bid-ask spreads for equities.

Download daily data for Microsoft (MSFT) using the [quantmod](https://cran.r-project.org/package=quantmod) package:

```{r}
library(quantmod)
x <- getSymbols("MSFT", auto.assign = FALSE, start = "2019-01-01", end = "2022-12-31")
head(x)
```

This is an `xts` object:

```{r}
class(x)
```
So we can estimate the spread with:

```{r}
spread(x)
```

By default, the call above is equivalent to:

```{r}
edge(open = x[,1], high = x[,2], low = x[,3], close = x[,4])
```

But `spread()` also provides additional functionalities. For instance, estimate the spread for each month and plot the estimates:

```{r}
sp <- spread(x, width = endpoints(x, on = "months"))
plot(sp)
```

Or estimate the spread using a rolling window of 21 obervations:

```{r}
sp <- spread(x, width = 21)
plot(sp)
```

To illustrate higher-frequency estimates, we are going to download intraday data from Alpha Vantage. You must register with Alpha Vantage in order to download their data, but the one-time registration is fast and free. Register at https://www.alphavantage.co/ to receive your key. You can set the API key globally as follows:

```{r}
setDefaults(getSymbols.av, api.key = "<API-KEY>")
```

Download minute data for Microsoft:

```r
x <- getSymbols(
  Symbols = "MSFT", 
  auto.assign = FALSE, 
  src = "av", 
  periodicity = "intraday", 
  interval = "1min", 
  output.size = "full")
```

```{r, include=FALSE}
x <- read.csv(system.file("extdata", "msft.csv", package = "bidask"))
x <- xts(x[,-1], order.by = as.POSIXct(x[,1]))
```

```{r}
head(x)
```

Estimate the spread for each day and plot the estimates:

```{r}
sp <- spread(x, width = endpoints(x, on = "day"))
plot(sp)
```

## GitHub 

If you find this package useful, please [star the repo](https://github.com/eguidotti/bidask)!

The repository also contains implementations for Python, C++, MATLAB, and more.

## Cite as

> Ardia, D., Guidotti, E., Kroencke, T.A. (2024). Efficient Estimation of Bid-Ask Spreads from Open, High, Low, and Close Prices. *Journal of Financial Economics*, 161, 103916. [doi: 10.1016/j.jfineco.2024.103916](https://doi.org/10.1016/j.jfineco.2024.103916)

A BibTex  entry for LaTeX users is:

```bibtex
@article{edge,
  title = {Efficient estimation of bid–ask spreads from open, high, low, and close prices},
  journal = {Journal of Financial Economics},
  volume = {161},
  pages = {103916},
  year = {2024},
  doi = {https://doi.org/10.1016/j.jfineco.2024.103916},
  author = {David Ardia and Emanuele Guidotti and Tim A. Kroencke},
}
```