The University of Bristol
Faculty of Science and Engineering
School of Engineering Mathematics and Technology
COMSM0093: Introduction to Financial Technology (INFT)
Academic Year 2023-24.
Main Coursework: the final summative coursework, worth 70% weighting of the unit.
Coursework specification release date: Thursday 23rd November 2023.
Coursework submission deadline: Wednesday 10th January, 1pm (Blackboard)
Jupyter Notebook exploring automated trading in the BSE financial-market simulator.
Maximum length 2000 words.
Rather than write a static printed conference-paper, for this assessment you are required to create
a fully functioning Jupyter notebook, with text, graphics, and executable code segments, all in one
zip-compressed folder. It should be written as if the final intended use for the notebook is in future
teaching on INFT: you don’t need to explain the background history or write a literature review, but
your notebook should have a definite narrative, explaining what is going on – as in the Jupyter
notebook of the Vernon Smith demo experiments that is available on the BSE GitHub repository
(Cliff, 2023). The maximum length of your notebook is 2000 words of Markdown. You are provided
with a template notebook which contains a word count function. Do not edit this word count
function.
There are two submission points for this coursework.
1. INFT Coursework Code Submission:
You should submit a single zip-compressed folder containing:
• Your Jupyter notebook
• A printed PDF version of your notebook (to create a PDF, use either the “export” function
of VSCode or the “Print” option in the “File” menu if using web browser or JupyterLab).
• (If you have attempted Part D and made edits to your local version of BSE.py then you
should also include a copy of your edited version of BSE.py).
2. INFT Coursework Turnitin:
You should submit:
• A copy of your printed PDF version of your notebook here. This file will be checked by our
plagiarism software.
For this coursework submission you are required to work alone: you should not share code or data
or text or images with any other student.
The coursework is in four parts:
Part A: This requires you to use BSE to perform a straightforward A/B comparison between two
types of trader-agent. Assign appropriate values to parameters passed to BSE’s
market_session function to set up a market as follows:
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• Set the supply/demand to be based on that used by Vernon Smith in Chart 4 of his
landmark 1962 paper in the Journal of Political Economy (Smith, 1962): all sellers should
have the same limit price of 310, giving a flat supply curve; buyer limit prices should range
from a maximum of 490 at the high end of the demand curve, down to 250 at the low end,
with equal-sized steps along the curve.
• Set the population of buyers, and also the population of sellers, to be R% traders of type
SHVR and (100-R)% traders of type ZIC. Smith’s Chart 4 shows 13 buyers and 13 sellers, but
in your version of the experiment you should use 20 buyers and 20 sellers.
Then run n=50 independent and identically distributed (IID) repetitions of this market session with
R=50 to gather a data-set of profitability figures for the two trader types. Select and apply an
appropriate visualization of this data-set. Select and apply an appropriate statistical test, or
sequence of tests, to determine whether, in the n=50 data-set you generated, traders of type SHVR
are statistically more profitable than ZIC or statistically less profitable than ZIC, or whether the
profitability of the two is, in your experiments, statistically indistinguishable. Then repeat this
experiment again, but now using n=500. Write narrative text in the Jupyter notebook to explain
your experiment set-up, the results, your visualization and analysis, and to state what conclusions
you draw. [10 marks]
Part B: Generate more data sets as for Part A, but now for R=10, 20, 30, 40, 60, 70, 80, and 90, for
n=50 and n=500. Select and apply appropriate visualization methods for this data set, and select
and apply an appropriate test or sequence of tests to determine at each R-value whether SHVR is
statistically more profitable than ZIC, less profitable than ZIC, or indistinguishable from ZIC. Write
narrative text in the notebook explaining what you did and what your findings are. [15 marks]
Part C: Add code to your notebook to run a new set of experiments, in which there are four
different types of trader, SHVR/GVWY/ZIC/ZIP, and generate data from market sessions in which
the ratios of these traders are (25:25:25:25), all four permutations of (40,20,20,20)1
, all four
permutations of (10,30,30,30), and all four permutations of (70,10,10,10). Again, select and apply
appropriate visualizations, and select and apply appropriate statistical test(s) for each ratio’s data,
to determine whether there are any statistically significant differences in profitability between
SHVR, GVWY, ZIC, and ZIP at any of the specified ratios. [20 marks]
Part D
NOTE: To do well in Part D you will need to edit BSE.py and execute relatively large numbers
of market-session simulations using your edited code, which collectively can use up quite a
lot of CPU time. Once you understand what to do, making the necessary edits to the code
might not take you very long but to generate enough data you may then need to set
simulation experiments to run on your laptop/desktop PC for several hours or more of
continuous processing (e.g. overnight), during which you can be working on other things, or
sleeping. Because of this, it is important to plan ahead, and to not leave until the last
minute before starting work on this part of the coursework. What is required to do well here
is readily achievable on a current mid-range personal computer: you do not require access
to high-performance supercomputers like the University’s BlueCrystal/BluePebble facilities,
but you should expect to have to wait quite a while for enough data to be generated.
1 When we say “…all four permutations of (N, M, M, M)…” we mean the following set of permutations: (N, M, M, M);
(M, N, M, M); (M, M, N, M); and (M, M, M, N).
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BSE edits: If you make edits to your local version of BSE.py you will need to include a copy of
your edited BSE.py in the zip file that you submit. You must also clearly comment your local
BSE.py to indicate where and how you have made edits (so that we can see the changes you
have made) and you should also refer to these edits in your Jupyter Notebook (include a
brief description of the changes you have made and why, and state the lines of code and
function name where edits have been made so that we can easily find your changes).
Part D: A recent paper by Cliff (2023) describes the long-term co-evolutionary dynamics of markets
populated by ZIP traders that are continually trying to improve their hyperparameter values, to
trade more profitably, by using a simple stochastic hillclimber (SH) method to search for better
hyperparameter values, thereby giving a variant of the ZIP adaptive trading strategy known as
ZIPSH. As described in more depth in (Cliff, 2023) the SH mechanism in a single ZIPSH trader
maintains a private local population of k different candidate strategies, tests the profitability of
each of them in sequence, and then identifies the most profitable of the k as the ‘elite” strategy; it
then creates k-1 new “child” strategies, each child being a mutation of the elite strategy, and
replaces the non-elite members of the old population with these new children; and then it
evaluates the profitability of each member of the new population to identify its elite individual,
which is then mutated to create k-1 children; and so on, repeating this process forever.
This simple SH process is referred to as a hillclimber because it is often explained via the metaphor
of a walker in the outdoors trying to find the highest part of a landscape, while blindfolded: the
walker has a walking stick, and she finds the highest nearby piece of ground by using the stick to
prod k different patches of ground around her, to determine which is the highest (here “nearby”
means within prodding distance of her walking stick held from an outstretched arm); and then she
takes a step to the highest point prodded; and then she starts prodding again. The SH process is
stochastic because the mutation process introduces random changes rather than
systematic/deterministic ones: returning to our walker, if her k=4 then on each iteration she could
systematically always prod nearby patches of ground to the compass north, south, east, and west –
that would be a fixed, deterministic pattern; or she could instead pick four directions at random on
each iteration – that would be a stochastic choice of local sample-points.
In ZIP there are five key parameters (the trader’s initial margin, its learning rate beta, the
momentum parameter used to smooth over noise in the learning process, and the trader’s two
constants ca and cr which respectively determine absolute and relative perturbations when forming
the trader’s target price used in the ZIP learning process), so the candidate strategies in ZIPSH can
each be thought of as five-dimensional (5D) vectors of real numbers, and the stochastic mutation
process involves adding random values to each of the five: in ZIPSH, each random mutation value is
drawn from a zero-mean Gaussian distribution with a small variance. It’s not essential to use a
Gaussian distribution, but it is important that the distribution for mutations is zero-mean, because
any nonzero value of the mean would introduce a systematic bias; and it’s important that the
variance is small to preserve the notion of “nearby”, i.e. to keep the k-point search local at each
step.
A process as simple as SH can often work pretty well, but it has some definite drawbacks. It works
well in situations where the fitness landscape (in our ZIPSH example, this is the profitability at each
point in the 5D space of possible strategy vectors) is smooth or locally correlated – that is, where
the difference in fitness/profitability of any two points in the strategy space typically grows smaller
as the distance between the two points under consideration reduces. Unfortunately, many real-
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world fitness landscapes are not smooth, but may instead have sudden major step-changes up or
down, or may have fractal-like “ruggedness”, and in practice very often the only way to know for
sure what the fitness landscape is actually like is to sample it at many points, which can be a very
time-consuming process.
One well-known problem is that simple search processes like SH can easily become trapped on
local maxima – when our blindfolded walker reaches the top of a small hill, every point she prods
with her stick will be lower than her current position, and so from then onwards she is stuck, even
if the slopes of a much higher hill lies only a few steps away. Similarly, an individual ZIPSH trader
may evolve its private population of k strategies to the point where every mutant it generates
always has lower profitability than the current elite strategy, and so the improvement in
profitability stops, even if the current elite strategy is not particularly good. Another problem is
that the outcome of a SH process can be very sensitive to initial conditions: whatever values are in
the population of candidate strategies at the start of the process can have a big effect on how the
ZIPSH process unfolds over time and where it ends up, what the final elite strategy is. To revisit the
metaphor: for our hillwalker, which point of high ground she finally ends up on could depend a lot
on where on the map she started walking from. And, finally, if there is significant noise in the
fitness function (in ZIPSH, this is the method of evaluation of the profitability of any one candidate
strategy), this can have a major destabilising effect on the efficiency and final outcome of the SH
search, because as noise levels increase, so more and more of the SH “adaptive steps” will be missteps, taking the search in the wrong (or, at least, a suboptimal) direction.
Using the latest BSE version 1.8 (released November 2023), we can illustrate and explore ZIPSH’s
adaptive process in action by setting up a population of traders in which there is only one ZIPSH
trader – here we will make that ZIPSH trader a buyer, with all other traders in the market being the
non-adaptive ZIC strategy.
Because transaction-price time series in financial markets are almost always noisy (and especially
because the ZIC traders we’re using here so highly stochastic), the process of evaluating the
profitability of our sole ZIPSH trader’s candidate strategies will involve monitoring the profitability
of each individual strategy over an appropriately long period of time. In BSEv1.8 the function
init_stratwaittime() within ZIP’s __init__() function chooses a random evaluation period
of between two and three hours (i.e. 7200 to 10800 seconds). This evaluation period determines
how many ‘adaptive steps’ (i.e., cycles through the loop of evaluating the current population of
strategies, identifying the elite, and mutating the elite to make children that will form the next
population) can be taken in any one 24-hour period. For example, if a ZIPSH trader evaluates each
candidate strategy for two hours, and uses a population size of k=4, then evaluating the whole
population takes eight hours and so in any one day of continuous round-the-clock trading that
trader can take no more than three adaptive steps. Furthermore, because we don’t know in
advance how many adaptive steps the trader will need to demonstrate the adaptation is working
well, we should in the first instance run our experiment for a fairly large number of days, some
number of weeks of continuous 24x7 trading: the graphs below show the results from BSEv1.8
market sessions lasting 30 days of continuous trading.
Figure 1 shows the profitability (measured as profit-per-second, or PPS) of the sole ZIPSH buyer in
the otherwise all-ZIC market, with 10 buyers and 10 sellers, from a single 30-day market session.
Here the demand curve ran from 150 down to 125, and the supply curve ran from 50 up to 75,
which means that every trader should be expected to find a counterparty for a transaction on
every order assigned to it. To highlight the effect of the progressive adaptation of hyperparameter
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values, in all the illustrative experiments discussed here the initial values of the ZIP
hyperparameters margin_buy (the buyer’s initial margin), beta (its learning rate), and momntm
(its momentum) were all deliberately set to very low values,2 values so low that they result in
reduced profitability: that is, these illustrative experiments have been deliberately set up to make
it easy for the ZIPSH to improve over time: by increasing these initially low hyperparameter values,
profitability is likely to increase.
It's important to remember that the situation our single ZIPSH buyer faces remains highly
challenging, and is unlike any real-world market scenario: for our ZIPSH, every other trader in the
market is a ZIC, and ZIC quote-prices are just (appropriately bounded) random noise values which
change very frequently.
Figure 1: profitability of a sole ZIPSH buyer in a market populated by ZIC traders. Horizontal axis is
time, labelled in days; vertical axis is the profit-per-second (PPS) generated by the ZIPSH trader.
Nevertheless, as can be seen in Figure 1, there is indeed a clear improvement in profitability, which
rises from a PPS of roughly 0.25 at Day 0 to a PPS near 1.0 at Day 30, an increase of roughly 300%
in this experiment. So, the SH mechanism has to a decent extent tailored the single ZIPSH trader’s
strategy hyperparameters to make it better suited to trading in the market full of ZIC traders, with
these specific supply and demand curves. This shows that, in principle, SH can perform fairly well
for such a primitively simple adaptation mechanism.
2 Specifically, in all these experiments init_beta() used U(0.01,0.01); init_momntm() used U(0.0,0.1); and
init_margin() used U(0.05,0.05), where in each case U(min, max) denotes a real-valued random draw from a
uniform distribution over the interval [min, max] – when min=max, the outcome of the random draw is just a constant
with probability one.
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Figure 2: Time-series showing evolution of the five key ZIP hyperparameters in the ZIPSH
experiment illustrated in Figure 1: in each graph the horizontal axis is time, labelled in days, and
the vertical axis is the named parameter-value.
Figure 2 shows the changes in the ZIPSH trader’s hyperparameter values over the duration of the
same experiment as was illustrated in Figure 1. As you can see, each of the hyperparameters is
gradually altered to add to the improvement and maintenance of higher profitability.
But this is an inherently stochastic system: run it again and the plots of the time-evolution of the
trader’s profitability and its hyperparameter values will differ from the results seen above, and so
then a key question is: how can we characterise the expected or average behaviour of the system?
Put another way, how representative of the usual behaviour of the system is the single set of
results chosen to be illustrated in Figures 1 and 2?
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Figure 3: Profitability results from a further nine IID repetitions of the ZIPSH experiment illustrated
in Figure 1: graph axes and labels are as for Figure 1; see text for further discussion.
To illustrate the variability, Figure 3 shows the profitability time-series from a further nine
independent and identically distributed (IID) runs of the market session that generated Figures 1
and 2: as you can see, there is a fair amount of variation between different IID repetitions of the
same single-ZIPSH session. In each of these experiments, because key hyperparameters were
initialised to very low values, initial profitability is often in the range 0.25 to 1.5, and in half the
runs shown here there is then a slow, steady, low-variance increase in profitability scores by 50%-
100% over the course of the experiment (e.g., from PPS=1.0 at Day 0 to PPS=1.5 at Day 30; or from
PPS=0.5 at Day 0 to PPS=1.0 at Day 30). In those runs that do not show a consistent low-variance
improvement in profitability, markedly higher profit scores of 1.5 to almost 3.0 can be achieved for
sustained periods of time, but with manifestly higher variance and often without enduring stability.
These periods of higher scores often result from ZIPSH finding ‘extreme’ solutions, such as
switching off learning (setting beta=0) and instead relying on a very high margin_buy value,
which can give an increased probability of more profitable trades but which leaves the trader no
way of adjusting to a lower margin if the initial margin_buy value turns out to be too high -- in
which case the trader will struggle to make any profit at all and then the adaptive SH process is
likely to switch to strategies with nonzero beta and reduced margin_buy, thereby reverting to
the lower-variance (but lower-profit) adaptive class of strategy.
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These illustrative results from ZIC markets with a single ZIPSH buyer do show that the stochastic
hillclimber can indeed adapt the ZIP hyperparameters to increase profitability; but remember this
is a contrived example with the initial values of the five ZIP hyperparameter set deliberately low,
and we’ve only shown the results from 10 runs here, which is a small sample-size: for statistically
rigorous analysis, we would need to generate and analyse considerably more than 10 runs.
D.1. For this part of the coursework assessment, edit your local copy of the BSEv1.8 code to give
your own replication of the single-ZIPSH experiments described above, and then execute
sufficiently many IID runs to be able to rigorously characterise outcomes of this system via
appropriate visualizations and analyses of results, using an appropriate statistical significance test
to determine whether, in your results, there is conclusive evidence that in these all-ZIC-plus-singleZIPSH sessions, ZIPSH does show a reliable improvement in profitability. In doing this, you may
choose to run your market sessions for less than 30 days, so long as you justify that choice –
shorter-duration sessions will take less time to simulate. Similarly, you may choose to alter the
init_stratwaittime() function to give each strategy less evaluation time, which will increase
the number of adaptive steps per day but will increase the noise level in the profitability scores,
and again if you do that then you should explain and justify your choice. You are free to make
further edits to BSEv1.8 if you wish, so long as they also are explained and justified. [20 marks]
D.2. Next, introduce your own variations or extensions to explore the extent to which the singleZIP-vs-multiple-ZIC results are affected by changes in the design of the experiment, and/or changes
in the ZIP hyperparameter adaptation technique. For example, you might want to explore the
effects of changes to the shapes of the supply and demand curves, and/or changes in the number
of traders in the markets, and/or changes in the type of evolutionary hyperparameter optimization
that the ZIP traders use (i.e., something other than SH). For any such change, you will need to
select and apply appropriate visualizations, and you will need to select and apply appropriate
statistical tests to see whether the change you introduced does lead to a statistically significant
alteration/improvement in profitability. [35 marks]
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Appendix A: On plagiarism, use of AI, etc.
For this coursework submission you are required to work alone: you should not share code or data
or text or images with any other student. You should also make sure that your notebook, cde, etc
are not viewable or copyable by other students. Working collaboratively with other students, or
not preventing other students from copying your work, would be viewed as unauthorised groupwork which is an offence under the University’s regulations on plagiarism and cheating,
Please be reminded that use of ChatGPT or any similar generative-AI system to create text which
you submit for this coursework as if it was your own original work is a contravention of the
University’s regulations on cheating, and will be dealt with as plagiarism. If you are found to have
committed plagiarism on this coursework, the most likely result is that your grade for INFT would
be set to 0% for this assessment, resulting in a fail. You would then have to re-sit this unit over the
summer of 2024, meaning you cannot start work on your individual project until September 2024,
thereby delaying your completion of the MSc until December 2024, and the award of your final
degree transcript etc would not take place until February or March 2025.
In academic writing, plagiarism is the inclusion of any idea or any language from someone else
without giving due credit by citing and referencing that source in your work. This applies if the
source is print or electronic, published or unpublished, another student’s work, or any other person,
or a machine like ChatGPT.
The University's Examination Regulations state that “Any thesis, dissertation, essay, or other course
work must be the student’s own work and must not contain plagiarised material.  Any instance of
plagiarism in such coursework will be treated as an offence under these regulations.” (Section 3.1).
Section 4 of the University’s Examination Regulations give information on the University's
procedures for dealing with cases of plagiarism. More information about plagiarism, and how to
avoid it is available from the Library website.
The University’s guidance on use of generative AI tools such as ChatGPT is available here:
https://www.ole.bris.ac.uk/bbcswebdav/courses/Study_Skills/using-ai-at-university/index.html
Appendix B: Referencing
When you reference papers in your notebook text, you should reference them using a consistent
referencing system, such as the APA referencing style, and you should normally cite sources in the
text. As a general rule, you should avoid using footnotes to reference. If you include a quote, it
should be in quotation marks, and a page number included in the in-text reference. Whilst you
should normally avoid larger quotes, if you include them, you should also indent the text by
increasing the left-hand margin width for the quoted passage of text. If you cite a paper, book,
article, or any other source in your essay, you should also include a full reference to the paper in the
reference list at the end of the notebook. Please do not list sources in your reference list that you
have not cited in the notebook.
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References
D. Cliff, (2023), Recurrence-Plot Visualization and Quantitative Analysis of Long-Term CoEvolutionary Dynamics in a Simulated Financial Market with ZIP Traders. Available at
SSRN: https://ssrn.com/abstract=4495631
D. Cliff, (2023), Bristol Stock Exchange. Open-source Python code repository available at
https://github.com/davecliff/BristolStockExchange
V. Smith (1962), An Experimental Study of Competitive Market Behavior. The Journal of Political
Economy, 70(2): 111-137. https://www.jstor.org/stable/1861810
University marking criteria (postgraduate)
Fail (<50%)  Pass (50%-59%)   (60%-69%)  (70%+) 
Attainment of
Learning Outcomes 
An inadequate level of
knowledge and
understanding of the
basic concepts
addressed by the
question. 
A reasonable knowledge
and understanding of
the topic, although more
basic than at merit level,
and at least of essential
reading and material
covered in lectures. 
A good knowledge and
understanding of the topic
with clear indication of some
reading beyond essential
recommendations or material
covered in lectures.  A ‘good’
knowledge implies largely an
absence of outright mistakes
with clear evidence that you
have understood the material
and are not merely repeating
it. 
An authoritative
knowledge and
understanding of the topic
gained from wide-ranging
reading that goes beyond
essential
recommendations or
material covered in
lectures. 
Application of
Methods 
Able to use a proportion
of the basic methods
and techniques taught. 
Able to use most of the
methods and techniques
taught. 
Able to use well a range of
methods and techniques to
come to conclusions. 
Mastery of a wide range
of methods and
techniques. 
Analysis,
Comprehension and
Synthesis 
Inappropriate or
irrelevant selection of
content. 
An ability to use relevant
material which is
directed to the
question. 
An ability to use relevant
material which is directed to
the question. 
An ability to manipulate
material intelligently and
to direct it to the question
set. 
Technical Mastery  Limited technical
competence with major
shortcomings in
significant areas of the
subject. 
Some technical
competence but with
shortcomings in
significant areas of the
subject. 
A good general level of
technical competence perhaps
with some shortcomings. 
A high level of technical
competence with very few
mistakes of any kind. 
Evaluation/Critical
Analysis 
Little evidence of a
critical perspective or of
analysis so that the
whole remains largely
descriptive. 
Some evidence of the
ability to engage
critically with content,
although not fully
developed. 
A good ability to evaluate
critically relevant theory and
research. 
A well-developed ability to
engage critically with that
material. 
Quality of
presentation 
Poor presentation  Adequate presentation  Very good presentation  Excellent presentation 
Note: Because the marking criteria consider a number of dimensions, it is unlikely that a single piece of work fits nicely
into all of the descriptions above. For example, a piece of work may have excellent presentation, but due to
significant errors, and major deficiencies, the piece of work may still be awarded a fail mark.
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