Visualisation
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)
The above plot shows all price levels between $227 and $245 for ~5 hours of price level data with the market midprice indicated in white. The volume has been scaled down from Satoshi to Bitcoin for legibility (1 Bitcoin = 10^8 Satoshi). Note the large sell/ask orders at $238 and $239 respectively.
Zooming into the same price level data, between 1am and 2am and providing trades data to the plot will show trade executions. In the below plot which has been centred around the bid/ask spread, shows the points at which market sell (circular red) and buy (circular green) orders have been executed with respect to the order book price levels.
# plot 1 hour of trades centred around the bid/ask spread.
plotPriceLevels(lob.data$depth, trades=lob.data$trades,
price.from=236, price.to=237.75, volume.scale=10^-8, col.bias=0.2,
start.time=as.POSIXct("2015-05-01 01:00:00.000", tz="UTC"),
end.time=as.POSIXct("2015-05-01 02:00:00.000", tz="UTC"))
Zooming in further to a 30 minute window, it is possible to display the bid ask spread clearly. In the below plot, show.mp has been set to FALSE. This has the effect of displaying the actual spread (bid = green, ask = red) instead of the (bid+ask)/2 midprice.
# zoom in to 30 minutes of bid/ask quotes.
plotPriceLevels(lob.data$depth, spread, price.from=235.25, price.to=237,
start.time=as.POSIXct("2015-05-01 00:45:00.000", tz="UTC"),
end.time=as.POSIXct("2015-05-01 01:15:00.000", tz="UTC"),
volume.scale=10^-8, col.bias=0.5, show.mp=F)
Zooming in, still further, to ~4 minutes of data focussed around the spread, shows (in this example) the bid rising and then dropping, while, by comparison, the ask price remains static.
This is a common pattern: 2 or more algorithms are competing to be ahead of each other. They both wish to be at the best bid+1, resulting in a cyclic game of leapfrog until one of the competing algorithm withdraws, in which case the remaining algorithm “snaps” back to the next best bid (+1). This behavior results in a sawtooth pattern which has been characterised by some as market- manipulation. It is simply an emergent result of event driven limit order markets.
# zoom in to 4 minutes of bid/ask quotes.
plotPriceLevels(lob.data$depth, spread, price.from=235.90, price.to=236.25,
start.time=as.POSIXct("2015-05-01 00:55:00.000", tz="UTC"),
end.time=as.POSIXct("2015-05-01 00:59:00.000", tz="UTC"),
volume.scale=10^-8, col.bias=0.5, show.mp=F)
By filtering the price or volume range it is possible to observe the behavior of individual market participants. This is perhaps one of the most useful and interesting features of this tool.
In the below plot, the displayed price level volume has been restricted between 8.59 an 8.72 bitcoin, resulting in obvious display of an individual algo. Here, the algo. is most likely seeking value by placing limit orders below the bid price waiting for a market impact in the hope of reversion by means of market resilience (the rate at which market makers fill a void after a market impact).
plotPriceLevels(lob.data$depth, spread, price.from=232.5, price.to=237.5,
volume.scale=10^-8, col.bias=1, show.mp=T,
end.time=as.POSIXct("2015-05-01 01:30:00.000", tz="UTC"),
volume.from=8.59, volume.to=8.72)
Using the same volume filtering approach, the next plot shows the behaviour of an individual market maker operating with a bid/ask spread limited between 3.63 and 3.83 bitcoin respectively. The rising bid prices at times are due to the event driven “leapfrog” phenomenon discussed previously.
plotPriceLevels(lob.data$depth, price.from=235.65, price.to=237.65,
volume.scale=10^-8, col.bias=1,
start.time=as.POSIXct("2015-05-01 01:00:00.000", tz="UTC"),
end.time=as.POSIXct("2015-05-01 03:00:00.000", tz="UTC"),
volume.from=3.63, volume.to=3.83)
Liquidity
The plotVolumePercentiles function plots the available volume in 25bps increments on each side of the order book in the form of a stacked area graph.
The resulting graph is intended to display the market “quality” either side of the limit order book: The amount of volume available at increasing depths in the order book which would effect the VWAP (Volume Weighted Average Price) of a market order.
The example below favours buyers, since there is more volume available within 25 BPS of the best ask price in comparison to the thinner market -25 BPS below the best bid.
The top of the graph depicts the ask side of the book, whilst the bottom depicts the bid side. Percentiles and order book sides can be separated by an optional subtle line (perc.line) for improved legibility.
plotVolumePercentiles(lob.data$depth.summary, volume.scale=10^-8, perc.line=F,
start.time=as.POSIXct("2015-05-01 01:00:00.000", tz="UTC"),
end.time=as.POSIXct("2015-05-01 04:00:00.000", tz="UTC"))
Zooming in to a 5 minute window and enabling the percentile borders with (perc.line=FALSE), the amount of volume available at each 25 bps price level is exemplified.
# visualise 5 minutes of order book liquidity.
# data will be aggregated to second-by-second resolution.
plotVolumePercentiles(lob.data$depth.summary,
start.time=as.POSIXct("2015-05-01 04:30:00.000", tz="UTC"),
end.time=as.POSIXct("2015-05-01 04:35:00.000", tz="UTC"),
volume.scale=10^-8)
Order cancellations
Visualising limit order cancellations can provide insights into order placement processes. The plotVolumeMap() function generates a visualisation of limit order cancellation events (excluding market and market limit orders).
plotVolumeMap(lob.data$events, volume.scale=10^-8, log.scale = T)
Interestingly, the order cancellation visualisation shows the systematic activity of individual market participants. By filtering the display of cancellation events within a volume range, it is possible to isolate what are most likely individual order placement strategies. The following graph shows an individual strategy cancelling orders within the [3.5, 4] volume range.
plotVolumeMap(lob.data$events, volume.scale=10^-8, volume.from=3.5, volume.to=4)
Restricting the volume between [8.59, 8.72] shows a strategy placing orders at a fixed price at a fixed distance below the market price.
plotVolumeMap(lob.data$events, volume.scale=10^-8, volume.from=8.59,
volume.to=8.72)