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COMP226 Assignment 2: Strategy Development
创建日期：2024-06-17 14:57:56
标签： COMP226
Strategy Development

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COMP226 Assignment 2: Strategy Development

Continuous
Assessment
Number
2 (of 2)
Weighting 10%
Assignment
Circulated
Monday 15 April 2024 (week 9)
Deadline Monday 13 May 2024 (week 13)
Submission
Mode
Submit up to two files to the CodeGrade assignment on Canvas: strategy.R
(required to get marks) and results.yaml (optional).
Learning
Outcomes
Assessed
This assignment addresses the following learning outcomes:
• Understand the spectrum of computer-based trading applications and
techniques, from profit-seeking trading strategies to execution algorithms.
• Be able to design trading strategies and evaluate critically their historical
performance and robustness.
• Understand the common pitfalls in developing trading strategies with historical
data.
Summary of
Assessment • The goal is to implement and optimize a well-defined trading strategy within the
backtester_2024 framework.
• Marks are available for the correct implementation of 10 functions in
strategy.R (70%).
• Further marks (that require a correct implementation in strategy.R) are
available for the results of a cross-validated optimisation that you can include in
results.yaml (30%).
• CodeGrade pre-deadline tests and offline example outputs are available to help
you check the correctness of your work.
Submission
necessary to
pass module
No
Late Submission
Penalty
Standard UoL policy; resubmissions after the deadline may not be considered.
Expected time
taken
Roughly 8-12 hours
Before you move on and read more about the assignment and start working on it, please make sure you
have worked through "backtester.pdf" (and the corresponding lectures if you want), which is an intro to the
backtester_2024 framework. Only return to this document when you already have the framework up and
running.
First, let's recall the contents of the backtester_2024.zip:
backtester_2024
nnn DATA
n nnn A2
n n nnn 01.csv
n n nnn 02.csv
n n nnn 03.csv
n n nnn 04.csv
n nnn EXAMPLE
n nnn 01.csv
n nnn 02.csv
n nnn 03.csv
n nnn 04.csv
n nnn 05.csv
nnn a2_main_template.R
nnn a2_periods.R
nnn a2_test_checks_and_getTMA.R
nnn a2_yamls
n nnn rsjs
n n nnn results.yaml
n nnn x1xxx
n n nnn results.yaml
n nnn x1yyy
n n nnn results.yaml
n nnn x1zzz
n nnn results.yaml
nnn example_strategies.R
nnn framework
n nnn backtester.R
n nnn data.R
n nnn processResults.R
nnn main.R
nnn strategies
nnn a2_strategy_template.R
nnn bbands_contrarian.R
nnn bbands_holding_period.R
nnn bbands_trend_following.R
nnn copycat.R
nnn fixed.R
nnn rsi_contrarian.R
10 directories, 28 files
In the above listing, the following files/directories are specifically there for assignment 2:
• a2_main_template.R
• a2_periods.R
• a2_test_checks_and_getTMA.R
• strategies/a2_strategy_template.R
• a2_example_yamls
• DATA/A2
The relevance of these files and directories will be explained below. The rest of the document is split into
three parts:
• Part 1 describes the 10 functions that should be implemented to fully complete strategy.R; you
should start from a2_strategy_template.R;
• Part 2 describes how to create (the optional) results.yaml;
• Part 3 describes submission via CodeGrade and the available pre-deadline tests.
In addition to pre-deadline tests on CodeGrade, example outputs are provided (in this document and as
files) so that you can test whether you have implemented things correctly.
As for assignment 1, the pre-deadline tests will determine your mark for the first part, corresponding to 70%
of the overall marks that are available. Assuming that you have achieved full marks on the first part, the
pre-deadline tests will check that the form of results.yaml is correct, and that it uses the expected student
username (i.e., your one) and corresponding time periods; the pre-deadline tests do not check the
correctness of the other fields in results.yaml, which will be checked post deadline only if you pass the
pre-deadline test for results.yaml. For those other fields, you should use the examples provided (which
are in the subdirectory a2_example_yamls).
Part 1: strategy implementation (70%)
The trading strategy that you should implement is a triple moving average (TMA) momentum strategy, which
is described in slides 4.7. The specification of the strategy and the functions that it should comprise are given
in full detail, so the correctness of your code can and will be checked automatically.
Two template files are provided to get you started:
• strategies/a2_strategy_template.R, which should become the file strategy.R that you
eventually submit;
• a2_main_template.R, which uses DATA/A2 and strategies/a2_strategy_template.R.
If you source a2_main_template.R with no edits to these two files you will get an error:
Error in if (store$iter > params$lookbacks$long) { :
argument is of length zero
This is because the strategy requires a parameter called lookbacks that you will need to pass in from
a2_main_template.R. Read on to see what form this parameter should take, and, more generally, how
you should be editing these two files.
a2_strategy_template.R contains 10 incomplete functions that you need to complete. The first 6
functions (checkE01,..., checkE06) are error checks for the inputs to getTMA. These error checks are all
one-liners, worth 3% each. They are intentionally meant to be straightforward to implement. The next three
functions compute the moving averages (getTMA), use them to compute the position sign
(getPosSignFromTMA), and compute the position size (getPosSize). The final, tenth function,
getOrders combines the last three to implement that actual trading strategy. Recall that every strategy in
the backtester framework has a getOrders function.
The TMA momentum strategy that you should implement uses three moving averages with different
lookbacks (window lengths). The short lookback should be smaller than the medium one, which in turn
should be smaller than the long lookback. In every trading period, the strategy will compute the value of these
three moving averages (for the series that it trades on, which will be determined by params$series). You
will achieve this by completing the implementation of the function getTMA.
The following table indicates the position that the strategy will take depending on the relative values of the
three moving averages (MAs). You will compute this position (sign, but not size) by completing the function
getPosSignFromTMA. The system is out of the market (i.e., flat) when the relationship between the short
MA and the medium MA does not match the relationship between the medium MA and the long MA.
MA MA MA Position
short MA < medium MA < long MA short
short MA > medium MA > long MA long
The function getPosSignFromTMA takes the output of getTMA as input. The position size, i.e., the number
of units to be long or short, is determined by getPosSize. As for all strategies in the backtester framework,
the positions are given to the backtester by getOrders. Here are the detailed specification and marks
available for these 10 functions.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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MATH 239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 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CIVL2010 ECMT2150 CS 333 CP1406 KIT308 Economics 701 ECON3740 ECE 5759 BFC3340 FINC2012 COMP4500 MATH3871 FIT3080 18-819C Economic ECO202Y5Y PHY100 FINC-780 Econ 100B EC602 IE582 EE450 COMP9517 WRDS 150B ECON 570 LECTURE 2 ECA5101 ECE 4420 ADAD9311 FIT3031 CS 544 MF 703 CS 6140 KIT501 ENGSCI 344 CSC171 IE 332 FINM8007 21S2 COSC 1114 FE545 E471 SIT102 CSC108 MAT301 IAE 101 COP3503 STAT2004 EC 303 CS1026 STAT1203 MATH3171 ISOM5160 IFN657 FIT2014 ACCT2102 DSCI550 STATS 330 ELEC421 COMP3670 QBUS6840 MEES:698B MAST30027 MIET1077 Economics IFT 6758 FIT5122 BUSN5101 CptS 111 HRMT5502 Math2018 PP 312 INFS3200 CZ3005 CSSE1001 SENG 2011 MKTG 553Q BUFN 640 COMP507 ETF5952 DATA 311 ECE4043 BANK3011 finance M3H623544 HW1 INFS 7410 AI6103 STAT3888 INFS3208 ACCT7103 ECOS3013 PSYCH 20B F79MA STAT3006 ENGG952 2X MGSC 7000 ECE4132 MSF 526 MATH 3033 CDS 511 EEEE4120 EEET2465 ECA5103 MATH2070 ELEC3104 BU51019 EEE8147 ENGR20003 COSC 1107 ETF3200 FNCE 435 MSFI 435 ISyE6420 GENG4405 HW ETC5523 IE 7315 MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A 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