COMP226
Assignment 1
Rahul Savani
[email protected]
Reconstruct a Limit Order Book
Continuous
Assessment
Number
1 (of 2)
Weighting 10%
Assignment
Circulated
09:00 Tuesday 18 February 2020 (updated
2020-02-20)
Deadline 17:00 Friday 6 March 2020
Submission
Mode
Electronic only
http://www.csc.liv.ac.uk/cgi-bin/submit.pl
Submit a single file "MWS-username.R",
where MWS-username should be replaced
with your MWS username.
Learning
Outcomes
Assessed
Have an understanding of market
microstructure and its impact on trading.
Goal of
Assignment
Reconstruct a limit order book from order
messages
Marking Criteria Correctness (85%); Code Readability (15%)
Submission
necessary in
order to satisfy
module
requirements
No
Late
Submission
Penalty
Standard UoL policy; resubmissions after
the deadline will NOT be considered.
Expected time
taken
Roughly 8-12 hours
Learning Outcome
Have an understanding of market microstructure and its
impact on trading
Marking
• 85% correctness (determined primarily by automatic tests)
If your code passes all the tests you get the full 85%
• 15% readability (5+5+5 for naming, comments, formatting)
Code readability
• Use good variable names that describe what is being stored
• best_bid, executed_so_far, rather than x and y
& good function names that describe what the function does
• Write informative comments, e.g.,
# Check if the order id is in the book already
• Use consistent spacing
Code/data zip handout
• Download comp226_a1.zip
• Unzip comp226_a1.zip
• This will yield a directory called comp226_a1
Code/data zip handout
comp226_a1
├── input
│   ├── book_1.csv
│   ├── book_2.csv
│   ├── book_3.csv
│   ├── empty.txt
│   ├── message_a.txt
│   ├── message_ar.txt
│   ├── message_arc.txt
│   ├── message_ex_add.txt
│   ├── message_ex_cross.txt
│   ├── message_ex_reduce.txt
│   └── message_ex_same_price.txt
├── output
│   ├── book_1-message_a.out
│   ├── book_1-message_ar.out
│   ├── book_1-message_arc.out
│   ├── book_2-message_a.out
│   ├── book_2-message_ar.out
│   ├── book_2-message_arc.out
│   ├── book_3-message_a.out
│   ├── book_3-message_ar.out
│   └── book_3-message_arc.out
└── skeleton.R
2 directories, 21 files
Code/data zip handout
• skeleton.R: code template that contains 6 empty functions
that you need to complete
• input: subdirectory that contains two types of input files,
initial book files and message files
• output: subdirectory that contains sample output that
allows you to check your code implementations
skeleton.R
$ Rscript skeleton.R input/book_1.csv input/empty.txt
$ask
oid price size
1 a 105 100
$bid
oid price size
1 b 95 100
Total volume:
Best prices:
Mid-price:
Spread:
input/book_1.csv is the initial book, input/empty.txt is the
message file (empty in this case)
"Main" part of code template
if (!interactive()) {
options(warn=-1)
args <- commandArgs(trailingOnly = TRUE)
if (length(args) != 2) {
stop("Must provide two arguments: ")
}
book_path <- args[1]; data_path <- args[2]
if (!file.exists(data_path) || !file.exists(book_path)) {
stop("File does not exist at path provided.")
}
book <- book.load(book_path)
book <- book.reconstruct(data.load(data_path), init=book)
book.summarise(book)
}
"Main" part of code template
• checks that there are two command line arguments
• assigns them to the appropriate variables (first to initial book
file path, second to message file path)
• loads the initial book
• reconstructs the book according to the messages
• prints out the book
• prints out the book stats
Rscript from Rstudio
Rscript skeleton.R input/book_1.csv input/empty.txt
• In R studio, you can call Rscript from the "terminal" tab (as
opposed to the "console")
• On Windows, use Rscript.exe not Rscript:
Rscript.exe skeleton.R input/book_1.csv input/empty.txt
4 + 2 functions to implement
Limit order book stats:
1. book.total_volume <- function(book) [10%]
2. book.best_prices <- function(book) [10%]
3. book.midprice <- function(book) [10%]
4. book.spread <- function(book) [10%]
Updating the limit order book:
5. book.reduce <- function(book, message) [15%]
6. book.add <- function(book, message) [30%]
input/book_1.csv
oid,side,price,size
a,S,105,100
b,B,95,100
oid side price size
a S 105 100
b B 95 100
• oid (order id) is used to process (partial) cancellations of
orders that arise in "reduce" messages
• side: 'B' for a buy/bid; 'S' for a sell/ask order
• price and size are self-explanatory
Limit order book stats
• book.total_volumes should return a list with named
elements, bid and ask where bid (ask) should be the total
volume in the bid (ask) book
• book.best_prices should return a list with two named
elements, bid and ask where bid (ask) should be the best
bid (ask) price
• book.midprice should return the midprice
• book.spread should return the spread
Expected output
$ Rscript solution.R input/book_1.csv input/empty.txt
$ask
oid price size
1 a 105 100
$bid
oid price size
1 b 95 100
Total volume: 100 100
Best prices: 95 105
Mid-price: 100
Spread: 10
Message format
• message files contain one message per line (terminated by a
single linefeed character, '\n')
• each message is a series of fields separated by spaces
• two types of messages: "Add" and "Reduce" messages.
• Here's an example, which contains an "Add" message
followed by a "Reduce" message:
A c S 97 36
R a 50
Add messages
'A' oid side price size
• 'A': fixed string that identifies this as an "Add" message
• oid: "order id" used by subsequent "Reduce" messages
• side: 'B' for a bid, 'S' for an ask
• price: limit price of this order
• size: size of this order
Reduce messages
'R' oid size
• 'R': fixed string identifying this as a "Reduce" message
• oid: "order id" identifies the order to be reduced
• size: amount by which to reduce the size of the order (not
the new size of the order); if size is equal to or greater than
the existing size of the order, the order is removed from the
book
Processing messages
• "Reduce" messages affect at most one existing limit order
• "Add" messages either:
• add a single row to the book (orders at the same
price are stored separately to preserve "oid"s)
• cross the spread and then (partially) remove any
number of orders on the other side of the book (and
may result in a new limit order of unmatched
volume)
• Example message files are split into cases that include
crosses and those that don't to help you develop your code
incrementally and test it on inputs of differing difficulty
Ex: initial book
$ Rscript solution.R input/book_1.csv input/empty.txt
$ask
oid price size
1 a 105 100
$bid
oid price size
1 b 95 100
Total volume: 100 100
Best prices: 95 105
Mid-price: 100
Spread: 10
Ex: processing a reduce message
$ cat input/message_ex_reduce.txt
R a 50
$ Rscript solution.R input/book_1.csv input/message_ex_reduce.txt
$ask
oid price size
1 a 105 50
$bid
oid price size
1 b 95 100
Total volume: 100 50
Best prices: 95 105
Mid-price: 100
Spread: 10
Ex: Non-crossing add message
$ cat input/message_ex_add.txt
A c S 97 36
$ Rscript solution.R input/book_1.csv input/message_ex_add.txt
$ask
oid price size
2 a 105 100
1 c 97 36
$bid
oid price size
1 b 95 100
Total volume: 100 136
Best prices: 95 97
Mid-price: 96
Spread: 2
Ex: crossing add message
$ cat input/message_ex_cross.txt
A c B 106 101
$ Rscript solution.R input/book_1.csv input/message_ex_cross.txt
$ask
[1] oid price size
<0 rows> (or 0-length row.names)
$bid
oid price size
1 c 106 1
2 b 95 100
Total volume: 101 0
Best prices: 106 NA
Mid-price: NA
Spread: NA
9 longer sample output files
messages_a messages_ar messages_arc
book_1
book_2
book_3
output
├── book_1-message_a.out
├── book_1-message_ar.out
├── book_1-message_arc.out
├── book_2-message_a.out
├── book_2-message_ar.out
├── book_2-message_arc.out
├── book_3-message_a.out
├── book_3-message_ar.out
└── book_3-message_arc.out
0 directories, 9 files
Hints for order book stats
For book.spread and book.midprice a nice implementation would
use book.best_prices, which you should then implement first.
Hints for book.add and book.reduce
A possible way to implement book.add and book.reduce that
makes use of the different example message files is the following:
• First, do a partial implementation of book.add, namely
implement add messages that do not cross. Check your
implementation with message_a.txt.
• Next, implement book.reduce fully. Check your combined
(partial) implementation of book.add and book.reduce with
message_ar.txt and book_3.csv (only this combination with
message_ar.txt has no crosses).
• Finally, complete the implementation of book.add to deal with
crosses. Check your implementation with message_arc.txt
and any initial book or with message_ar.txt and book_1.csv
or book_2.csv
Submission
Remember to submit a single "MWS-username.R" file, where
MWS-username should be replaced with your MWS username.
Warning
The department uses automatic plagiarism & collusion
detection
Do not discuss or show your work to other students
Plagiarizers and colluders will likely receive a mark of zero
Several students were found to have plagiarised in previous
years and this had serious consequences for their studies
(last year, two students had their studies terminated
and left without a degree).
Measure of Software Similarity
http://theory.stanford.edu/~aiken/moss/
Moss is automatically run on all pairs of submitted files
It reports a percentage similarity score
It can not be fooled by:
• Reodering
• Change of variable/function names
• Different Comments
Moss sample output
File 1 File 2 Lines Matched
x1x1.R (99%) x1x2.R (99%) 110
x1x4.R (99%) x1x7.R (99%) 110
x1x3.R (99%) x1z3.R (97%) 24
x1x3.R (51%) x1x9.R (52%) 46
x1x3.R (48%) x1x9.R (50%) 44
x1x6.R (53%) x1z1.R (26%) 26
x1x9.R (28%) x1x8.R (16%) 26
x1z2.R (29%) x1z4.R (7%) 15
x1z3.R (15%) x1x3.R (25%) 23