obAnalytics is an R package intended for visualisation and analysis of limit order data.

This guide is structured as an end-to-end walk-through and is intended to demonstrate the main features and functionality of the package.

Loading data

The main focus of this package is reconstruction of a limit order book. The processData function will perform data processing based on a supplied CSV file, the format of which is defined in the Expected csv schema section.

The data processing consists of a number of stages:

  1. Cleaning of duplicate and erroneous data.

  2. Identification of sequential event relationships.

  3. Inference of trade events via order-matching.

  4. Inference of order types (limit vs market).

  5. Construction of volume by price level series.

  6. Construction of order book summary statistics.

Limit order events are related to one another by volume deltas (the change in volume for a limit order). To simulate a matching-engine, and thus determine directional trade data, volume deltas from both sides of the limit order book are ordered by time, yielding a sequence alignment problem, to which the the Needleman-Wunsch algorithm has been applied.

# load and process example csv data from the package inst/extdata directory.
csv.file <- system.file("extdata", "orders.csv.xz", package="obAnalytics")
lob.data <- processData(csv.file)

Expected csv schema

The CSV file is expected to contain 7 columns:

Expected CSV schema.
Column name Description
id Numeric limit order unique identifier.
timestamp Time in milliseconds when event received locally.
exchange.timestamp Time in milliseconds when order first created on the exchange.
price Price level of order event.
volume Remaining order volume.
action Event action describes the limit order lifecycle. One of: created, modified, deleted.
direction Side of order book. On of: bid or ask.

Preprocessed example data

For illustrative purposes, the package contains a sample of preprocessed data. The data, taken from the Bitstamp (bitcoin) exchange on 2015-05-01, consists of 50,393 limit order events and 482 trades occuring from midnight up until ~5am.

The sample data, which has been previously processed by the processData function, may be attached to the environment with the data() function:


The lob.data object is a list containing four data.frames.

lob.data summary.
data.frame Summary
events Limit order events.
trades Inferred trades (executions).
depth Order book price level depth through time.
depth.summary Limit order book summary statistics.

The contents of which are briefly discussed in the following sections.


The events data.frame contains the lifecycle of limit orders and makes up the core data of the obAnalytics package. Each row corresponds to a single limit order action, of which three types are possible.

Possible limit order actions.
Event action Meaning
created The order is created with a specified amount of volume and a limit price.
changed The order has been partially filled. On each modification, the remaining volume will decrease.
deleted The order may be deleted at the request of the trader or, in the event that the order has been completely filled, deleted by the exchange. An order deleted by the exchange as a result of being filled will have 0 remaining volume at time of deletion.

In addition to the event action type, a row consists of a number of attributes relating to the lifecycle of a limit order.

Limit order event attributes.
Attribute Meaning
event.id Event Id.
id Limit Order Id.
timestamp Local event timestamp (local time the event was observed).
exchange.timestamp Exchange order creation time.
price Limit order price level.
volume Remaining limit order volume.
action Event action: created, changed, deleted. (as described above).
direction Order book side: bid, ask.
fill For changed or deleted events, indicates the change in volume between this event and the last.
matching.event Matching event.id if this event is part of a trade. NA otherwise.
type Limit order type (see Event types below.)
aggressiveness.bps The distance of the order from the edge of the book in Basis Points (BPS). If an order is placed exactly at the best bid/ask queue, this value will be 0. If placed behind the best bid/ask, the value will be negative. A positive value is indicative of a innovative order: The order was placed inside the bid/ask spread, which would result in the change to the market midprice.

An individual limit order (referenced by the id attribute) may be of six different types, all of which have been classified by onAnalytics.

Order types.
Limit order type Meaning
unknown It was not possible to infer the order type given the available data.
flashed-limit Order was created then subsequently deleted. 96% of example data. These types of orders are also referred to as fleeting orders in the literature.
resting-limit Order was created and left in order book indefinitely until filled.
market-limit Order was partially filled before landing in the order book at it’s limit price. This may happen when the limit order crosses the book because, in the case of a bid order, it’s price is >= the current best ask. However there is not enough volume between the current best ask and the order limit price to fill the order’s volume completely.
market Order was completely filled and did not come to rest in the order book. Similarly to a market-limit, the market order crosses the order book. However, it’s volume is filled before reaching it’s limit price. Both market-limit and market orders are referred to as marketable limit orders in the literature.
pacman A limit-price modified in situ (exchange algorithmic order). The example data contains a number of these order types. They occur when a limit order’s price attribute is updated. In the example data, this occurs from a special order type offered by the exchange which, in the case of a bid, will peg the limit price to the best ask once per second until the order has been filled.

The following table demonstrates a small snapshot (1 second) of event data. Some of the attributes have been omitted or renamed for readability.

one.sec <- with(lob.data, {
  events[events$timestamp >= as.POSIXct("2015-05-01 04:55:10", tz="UTC") & 
         events$timestamp <= as.POSIXct("2015-05-01 04:55:11", tz="UTC"),  ]
one.sec$volume <- one.sec$volume*10^-8
one.sec$fill <- one.sec$fill*10^-8
one.sec$aggressiveness.bps <- round(one.sec$aggressiveness.bps, 2)
one.sec <- one.sec[, c("event.id", "id", "price", "volume", "action", 
    "direction", "fill", "matching.event", "type", "aggressiveness.bps")]
colnames(one.sec) <- c(c("event.id", "id", "price", "vol", "action", "dir", 
    "fill", "match", "type", "agg"))
print(one.sec, row.names=F)
event.id id price vol action dir fill match type agg
48258 65619043 235.84 0.000000 deleted ask 1.6379919 49021 market-limit NA
48443 65619136 235.98 0.000000 deleted ask 0.2118824 49022 resting-limit -6.36
48617 65619223 237.12 20.762160 deleted ask 0.0000000 NA flashed-limit -47.03
48879 65619359 236.18 15.988592 deleted ask 0.0000000 NA flashed-limit -7.20
48997 65619419 235.83 0.000000 deleted ask 1.8498742 NA unknown NA
49001 65619421 236.01 15.435748 changed ask 6.6832516 49023 resting-limit NA
49020 65619430 236.05 8.533126 changed bid 1.8498742 NA market NA
49021 65619430 236.05 6.895134 changed bid 1.6379919 48258 market NA
49022 65619430 236.05 6.683252 changed bid 0.2118824 48443 market NA
49023 65619430 236.05 0.000000 deleted bid 6.6832516 49001 market NA
49024 65619431 235.06 13.200000 created bid 0.0000000 NA resting-limit -28.85
49027 65619432 236.03 13.200000 created ask 0.0000000 NA flashed-limit -0.85
49029 65619433 233.71 8.665815 created bid 0.0000000 NA flashed-limit -86.11
49030 65619433 233.71 8.665815 deleted bid 0.0000000 NA flashed-limit -86.11
49031 65619434 236.94 22.749000 created ask 0.0000000 NA flashed-limit -39.41


The package automatically infers execution/trade events from the provided limit order data.

The trades data.frame contains a log of all executions ordered by local timestamp.

In addition to the usual timestamp, price and volume information, each row also contains the trade direction (buyer or seller initiated) and maker/taker limit order ids.

The maker/taker event and limit order ids can be used to group trades into market impacts - An example of which will be demonstrated later in this guide.

trades.ex <- tail(lob.data$trades, 10)
trades.ex$volume <- round(trades.ex$volume*10^-8, 2)
print(trades.ex, row.names=F)
timestamp price volume direction maker.event.id taker.event.id maker taker
2015-05-01 04:59:27.503 235.73 0.01 buy 49630 49777 65619731 65619806
2015-05-01 04:59:27.532 235.79 0.02 buy 49672 49778 65619752 65619806
2015-05-01 04:59:41.568 235.77 0.02 buy 49802 49821 65619818 65619826
2015-05-01 04:59:55.877 235.77 0.02 buy 49803 49871 65619818 65619851
2015-05-01 04:59:59.217 235.77 0.38 buy 49804 49877 65619818 65619854
2015-05-01 05:00:08.361 235.77 0.12 sell 49878 49894 65619854 65619862
2015-05-01 05:00:08.395 235.58 0.21 sell 49406 49895 65619615 65619862
2015-05-01 05:00:08.424 235.01 0.07 sell 46221 49896 65618028 65619862
2015-05-01 05:00:10.108 235.79 0.02 buy 49816 49900 65619824 65619864
2015-05-01 05:03:13.566 235.45 0.05 sell 49992 50255 65619912 65620048

Each row, representing a single trade, consists of the following attributes:

Trade data attributes.
Attribute Meaning
timestamp Local event timestamp.
price Price at which the trade occurred.
volume Amount of traded volume.
direction The trade direction: buy or sell.
maker.event.id Corresponding market making event id in events data.frame.
taker.event.id Corresponding market taking event id in events data.frame.
maker Id of the market making limit order in events data.frame.
taker Id of the market taking limit order in events data.frame.


The depth data.frame describes the amount of available volume for all price levels in the limit order book through time. Each row corresponds to a limit order event, in which volume has been added or removed.

The data.frame represents a run-length-encoding of the cumulative sum of depth for all price levels and consists of the following attributes:

Depth attributes.
Attribute Meaning
timestamp Time at which volume was added or removed.
price Order book price level.
volume Amount of remaining volume at this price level.
side The side of the price level: bid or ask.

Depth summary

The depth.summary data.frame contains various summary statistics describing the state of the order book after every limit order event. The metrics are intended to quantify the shape of the order book through time.

Order book summary metrics.
Attribute Meaning
timestamp Local timestamp corresponding to events.
best.bid.price Best bid price.
best.bid.vol Amount of volume available at the best bid.
bid.vol25:500bps The amount of volume available for 20 25bps percentiles below the best bid.
best.ask.price The best ask price.
best.ask.vol Amount of volume available at the best ask.
ask.vol25:500bps The amount of volume available for 20 25bps percentiles above the best ask.


The package provides a number of functions for the visualisation of limit order events and order book liquidity. The visualisations all make use of the ggplot2 plotting system.

Order book shape

The purpose of the cumulative volume graph is to quickly identify the shape of the limit order book for the given point in time. The “shape” is defined as the cumulative volume available at each price level, starting at the best bid/ask.

Using this shape, it is possible to visually summarise order book imbalance and market depth.

# get a limit order book for a specific point in time, limited to +- 150bps
# above/below best bid/ask price.
lob <- orderBook(lob.data$events, 
    tp=as.POSIXct("2015-05-01 04:38:17.429", tz="UTC"), bps.range=150)

# visualise the order book liquidity.
plotCurrentDepth(lob, volume.scale=10^-8)

In the figure above, an order book has been reconstructed with the orderBook function for a specific point in time. The visualisation produced with the plotCurrentDepth function depicts a number of order book features. Firstly, the embedded bar chart at the bottom of the plot shows the amount of volume available at specific price levels ranging from the bid side on the left (blue) through to the ask side (red) on the right. Secondly, the blue and red lines show the cumulative volume of the bar chart for the bid and ask sides of the order book respectively. Finally, the two subtle vertical lines at price points $234 and $238 show the position of the top 1% largest limit orders.

Price level volume

The available volume at each price level is colour coded according to the range of volume at all price levels. The colour coding follows the visible spectrum, such that larger amounts of volume appear “hotter” than smaller amounts, where cold = blue, hot = red.

Since the distribution of limit order size exponentially decays, it can be difficult to visually differentiate: most values will appear to be blue. The function provides price, volume and a colour bias range to overcome this.

Setting col.bias to 0 will colour code volume on the logarithmic scale, while setting col.bias < 1 will “squash” the spectrum. For example, a uniform col.bias of 1 will result in 1/3 blue, 1/3 green, and 1/3 red applied across all volume - most values will be blue. Setting the col.bias to 0.5 will result in 1/7 blue, 2/7 green, 4/7 red being applied such that there is greater differentiation amongst volume at smaller scales.

# plot all lob.data price level volume between $233 and $245 and overlay the 
# market midprice.
spread <- getSpread(lob.data$depth.summary)
plotPriceLevels(lob.data$depth, spread, price.from=233, price.to=245, 
    volume.scale=10^-8, col.bias=0.25, show.mp=T)