COMP226 Assignment 1: Reconstruct aLimit Order BookContinuousAssessment Number1 (of 2)Weighting 10%Assignment Circulated 09:00 Tuesday 18 February 2020 (updated 2020-02-20)Deadline 17:00 Friday 6 March 2020Submission Mode Electronic onlySubmit a single file MWS-username.R, where MWS-usernameshould be replaced with your MWS username.Learning OutcomesAssessedHave an understanding of market microstructure and its impacton trading.Goal of Assignment Reconstruct a limit order book from order messagesMarking Criteria Code correctness (85%); Code readability (15%)Submission necessaryin order to satisfymodule requirementsNoLate SubmissionPenaltyStandard UoL policy; resubmissions after the deadline willNOT be considered.Expected time taken Roughly 8-12 hoursWarningYour code will be put through the departments automatic plagiarism and collusiondetection system. Students found to have plagiarized or colluded will likely receive amark of zero. Do not discuss or show your work to others. In previous years, twostudents had their studies terminated and left without a degree because of plagiarism.Rscript from RstudioIn this assigment, we use Rscript (which is provided by R) to run our code, e.g.,Rscript skeleton.R input/book_1.csv input/empty.txtIn 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.txtDistributed code and sample input and output dataAs a first step, please download comp226_a1.zip comp226_a1_v3.zip from:https://student.csc.liv.ac.uk/internal/modules/comp226/_downloads/comp226_a1_v3.zipThen unzip comp226_a1.zip, which will yield the following contents in the directorycomp226_a1: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.R2 directories, 21 filesBrief summaryThe starting point for the assignment is a code skeleton, provided in a file called skeleton.R.This file runs without error, but does not produce the desired output because it contains 6empty functions. To complete the assignment you will need to correctly complete these 6functions.You should submit a single R file that contains your implementation of some or ideally all ofthese 6 functions. Your submission will be marked via a combination of:• automated tests (for code correctness, 85%, breakdown by function given below);and• human visual inspection (for code readability, 15%, in particular, for appropriatenaming of variables and functions (5%), good use of comments (5%), and sensible,consistent code formatting (5%)).Correct sample output is provided so that you can check whether your code implemetationsproduces the correct output.skeleton.R versus solution.RYou are given skeleton.R, which you should extend by implementing 6 functions.Throughout this handout, we also generate example output using a file solution.R thatcontains a correct implementation of all 6 of these functions. Obviously, you are notgiven the file solution.R, however the example output will be helpful for checking thatyour function implementations work correctly.Two sets of functions to implementAs described in detail in the rest of this document, you are required to implement thefollowing 6 functions. The percentage in square brackets correspond to the breakdown of thecorrectness marks by function.Limit order book stats:1. book.total_volume 2. book.best_prices 3. book.midprice 4. book.spread Updating the limit order book:5. book.reduce 6. book.add WarningDo not make changes to the rest of the code in skeleton.R, only implement these 6functions. Penalties may be applied if other changes are present in your submission.Running skeleton.RAn example of calling skeleton.R follows.Rscript skeleton.R input/book_1.csv input/empty.txtAs seen in this example, skeleton.R takes as arguments the path to two input files:1. initial order book (input/book_1.csv in the example)2. order messages to be processed (input/empty.txt in the example)Note: the order of the arguments matters.Lets see part of the source code and the output that it produces.if (!interactive()) { options(warn=-1) args if (length(args) != 2) { stop(Must provide two arguments: ) } book_path if (!file.exists(data_path) || !file.exists(book_path)) { stop(File does not exist at path provided.) } book book book.summarise(book)}So in short, this part of the code:• checks that there are two command line arguments• assigns them to the appropriate variables (the first to the initial book file path, thesecond to the message file path)• loads the initial book• reconstructs the book according to the messages• prints out the book• prints out the book statsLets see the output for the example above:$ Rscript skeleton.R input/book_1.csv input/empty.txt$ask oid price size1 a 105 100$bid oid price size1 b 95 100Total volume:Best prices:Mid-price:Spread:Now lets see what the output would look like for a correct implementation:$ Rscript solution.R input/book_1.csv input/empty.txt$ask oid price size1 a 105 100$bid oid price size1 b 95 100Total volume: 100 100Best prices: 95 105Mid-price: 100Spread: 10You will see that now the order book stats have been included in the output, because thefour related functions that are empty in skeleton.R have been implemented in solution.R.The initial order bookHere is the contents of input/book_1.csv, which is one of the 3 provided examples of aninitial book:oid,side,price,sizea,S,105,100b,B,95,100Lets justify the columns to help parse this input:oid side price sizea S 105 100b B 95 100The first row is a header row. Every subsequent row contains a limit order, which isdescribed by the following fields:• oid (order id) is stored in the book and used to process (partial) cancellations of ordersthat arise in reduce messages, described below;• side identifies whether this is a bid (B for buy) or an ask (S for sell);• price and size are self-explanatory.Existing code in skeleton.R will read in a file like input/book_1.csv and create thecorresponding two (possibly empty) orders book as two data frames that will be stored in thelist book, a version of which will be passed to all of the six functions that you are required toimplement.Note that if we now change the message file to a non-empty one, skeleton.R will producethe same output (since it doesnt parse the messages; you need to write the code, functions5 and 6, to do that):$ Rscript skeleton.R input/book_1.csv input/message_a.txt$ask oid price size1 a 105 100$bid oid price size1 b 95 100Total volume:Best prices:Mid-price:Spread:If correct message parsing and book updating is implemented, book would be updatedaccording to input/adds_only.txt to give the following output:$ Rscript solution.R input/book_1.csv input/message_a.txt$askBefore we go into details on the message format and reconstructing the order book, letsdiscuss the first four functions that compute the book stats, which we also see correctlycomputed in this example.Computing limit order book statsThe first four of the functions that you need to implement compute limit order book stats,and can be developed and tested without parsing the order messages at all. In particular,you can develop and test the first four functions using an empty message file,input/empty.txt, as in the first example above.The return values of the four functions should be as follows (where as usual in R singlenumbers are actually numeric vectors of length 1):• book.total_volumes should return a list with two named elements, bid, which shouldcontain the total volume in the bid book, and ask, which should contain the total volumein the ask book;• book.best_prices bid, which should contain the best bid price, and ask, which should contain the best askprice;• book.midprice should the midprice of the book;• book.spread should the spread of the book;You should check that the output of these functions in the example above that usessolution.R are what you expect them to be.We now move on to the reconstructing the order book from the messages in the inputmessage file.Reconstructing the order book from messagesYou do not need to look into the details of the (fully implemented) functionsbook.reconstruct or book.handle that manage the reconstruction the book from thestarting initial book according to the messages.In the next section, we describe that there are two types of message, Add messages andReduce messages.代写COMP226课程作业、代做MWS留学生作业、R编程设计作业调试、R实验作业代做 代写留学生 Statistics统 All you need to know to complete the assignment is that messages inthe input file are processed in order, i.e., line by line, with Add messages passed tobook.add and Reduce messages passed to book.reduce, along with the current book inboth cases.Message FormatThe market data log contains one message per line (terminated by a single linefeedcharacter, \n), and each message is a series of fields separated by spaces.There are two types of messages: Add and Reduce messages. Heres an example,which contains an Add message followed by a Reduce message:A c S 97 36R a 50An Add message looks like this:A oid side price size• A: fixed string identifying this as an Add message;• oid: order id used by subsequent Reduce messages;• side: B for a buy order (a bid), and an S for a sell order (an ask);• price: limit price of this order;• size: size of this order.A Reduce message looks like this: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); ifsize is equal to or greater than the existing size of the order, the order is removed fromthe book.Processing messagesReduce messages will affect at most one existing limit order in the book.Add messages will either:• not cross the spread and then add a single row to the book (orders at the same priceare stored separately to preserve their distinct oids);• cross the spread and in that case can affect any number of orders on the other side ofthe book (and may or may not result in a remaining limit order for residual volume).The provided example message files are split into cases that include crosses and those thatdont to help you develop your code incrementally and test it on inputs of differing difficulty.We do an example of each case, one by one. In each example we start frominput/book_1.csv; we only show this initial book in the first case.Example of processing a reduce message$ Rscript solution.R input/book_1.csv input/empty.txt$ask oid price size1 a 105 100$bid oid price size1 b 95 100Total volume: 100 100Best prices: 95 105Mid-price: 100Spread: 10$ cat input/message_ex_reduce.txtR a 50$ Rscript solution.R input/book_1.csv input/message_ex_reduce.txt$ask oid price size1 a 105 50$bid oid price size1 b 95 100Total volume: 100 50Best prices: 95 105Mid-price: 100Spread: 10Example of processing an add (non-crossing) message$ cat input/message_ex_add.txtA c S 97 36$ Rscript solution.R input/book_1.csv input/message_ex_add.txt$ask oid price size2 a 105 1001 c 97 36$bid oid price size1 b 95 100Total volume: 100 136Best prices: 95 97Mid-price: 96Spread: 2Example of processing a crossing add message$ cat input/message_ex_cross.txtA c B 106 101$ Rscript solution.R input/book_1.csv input/message_ex_cross.txt$ask[1] oid price size (or 0-length row.names)$bid oid price size1 c 106 12 b 95 100Total volume: 101 0Best prices: 106 NAMid-price: NASpread: NASample outputWe provide sample output for 9 cases, namely all combinations of the following 3 initialbooks and 3 message files.The 3 initial books are found in the input subdirectory and are called:• book_1.csv• book_2.csv• book_3.csvThe 3 message files are also found in the input subdirectory and are called:filemessages_a.txt add messages only, i.e., requires book.add but not book.reduce; forall three initial books, none of the messages cross the spreedmessages_ar.txt add and reduce messages, but for the initial book book_3.csv, noadd message crosses the spreadmessages_arc.txt add and reduce messages, with some adds that cross the spread forall three initial booksThe 9 output files can be found in the output subdirectory of the comp226_a1 directory.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.out0 directories, 9 filesHints for order book statsFor 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.reduceA possible way to implement book.add and book.reduce that makes use of the differentexample message files is the following:• First, do a partial implementation of book.add, namely implement add messages that donot cross. Check your implementation with message_a.txt.• Next, implement book.reduce fully. Check your combined (partial) implementation ofbook.add and book.reduce with message_ar.txt and book_3.csv (only thiscombination with message_ar.txt has no crosses).• Finally, complete the implementation of book.add to deal with crosses. Check yourimplementation with message_arc.txt and any initial book or with message_ar.txt andbook_1.csv or book_2.csv.Hint on book.sortIn comp226_a1_v3 there is a book.sort method, with sort code as follows:book.sort if (sort_ask && nrow(book$ask) >= 1) { book$ask nchar(book$ask$oid), book$ask$oid, decreasing=F),] row.names(book$ask) } if (sort_bid && nrow(book$bid) >= 1) { book$bid nchar(book$bid$oid), book$bid$oid, decreasing=F),] row.names(book$bid) } book}This method will ensure that limit orders are sorted first by price and second by time ofarrival (so that for two orders at the same price, the older one is nearer the top of thebook).You are welcome (and encouraged) to use book.sort in your own implementations. Inparticualar, by using it you can avoid having to find exactly where to place an order in thebook.Hint on using logging in book.reconstructIn comp226_a1_v3 a logging option has been added to book.reconstruct:book.reconstruct if (nrow(data) == 0) return(book) if (is.null(init)) init book function(b, i) { new_book if (log) { cat(Step, i, \n\n) book.summarise(new_book, with_stats=F) cat(====================\n\n) } new_book }, 1:nrow(data), init, ) book.sort(book)}You can turn on logging by changing log=F to log=T. Then book.summarise will be used togive output after each message is processed by book.reconstruct.Hint on stringsAsFactors=FALSENotice the use of` stringsAsFactors=FALSE in the book.load function (similarly indata.load) from skeleton.R.book.load df path, fill=NA, stringsAsFactors=FALSE, header=TRUE, sep=, ) book.sort(list( ask=df[df$side == S, c(oid, price, size)], bid=df[df$side == B, c(oid, price, size)] ))}Its use here is not optional, it is necessary and what ensures that the oid column ofbook$bid and book$ask have type character.It is also crucial that you make sure that you ensure that the type of your oid columns inyour books remain character rather than factors. The following examples will explain theuse of stringsAsFactors and help you to achieve this.First we introduce a function that will check the type of this column on different data framesthat we will construct:check checks is.factor(df$oid)) for (check in checks) cat(sprintf(%20s: %5s, check, eval(parse(text=check))), \n)}Now lets use this function to explore different cases. First we look at the case of reading acsv.> check(read.csv(input/book_1.csv))is.character(df$oid): FALSE is.factor(df$oid): TRUE> check(read.csv(input/book_1.csv, stringsAsFactors=FALSE))is.character(df$oid): TRUE is.factor(df$oid): FALSEWhat about creating a data.frame?> check(data.frame(oid=a, price=1))is.character(df$oid): FALSE is.factor(df$oid): TRUE> check(data.frame(oid=a, price=1, stringsAsFactors=FALSE))is.character(df$oid): TRUE is.factor(df$oid): FALSEWhat about using rbind?> empty_df > non_empty_df > check(rbind(empty_df, data.frame(oid=a, price=1)))is.character(df$oid): FALSE is.factor(df$oid): TRUE> check(rbind(empty_df, non_empty_df))is.character(df$oid): TRUE is.factor(df$oid): FALSE> check(rbind(non_empty_df, data.frame(oid=a, price=1)))is.character(df$oid): TRUE is.factor(df$oid): FALSENote that with a non-empty data frame, the existing type persists! However, when thedata.frame is empty the type of the oid column is malleable and it is crucial to usestringsAsFactors=FALSE. We see the same behaviour when we rbind a list with adata.frame.> check(rbind(empty_df, list(oid=a, price=1)))is.character(df$oid): FALSE is.factor(df$oid): TRUE> check(rbind(empty_df, list(oid=a, price=1), stringsAsFactors=FALSE))is.character(df$oid): TRUE is.factor(df$oid): FALSE> check(rbind(non_empty_df, list(oid=a, price=1)))is.character(df$oid): TRUE is.factor(df$oid): FALSEAgain, it is crucial to use stringsAsFactors=FALSE when the data.frame is empty. Isuggest to use it in every case.SubmissionRemember to submit a single MWS-username.R file, where MWS-username should bereplaced with your MWS username.转自：http://www.6daixie.com/contents/18/4954.html