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日期:2021-01-17 09:45

BEEM061 Main Assignment Part B Brief
December 21, 2020
Abstract
Your main assignment (80%) must be handed in by Friday 15th
January 2021. It consists of two equally weighted parts: part A) A
1,500 word essay based on Topic 2; and part B) A technical task-based
assignment. This document outlines your tasks for Part B, which on
its own contributes 40% to your overall module grade. Throughout
the following tasks you MUST solve them using Jupyter Notebooks
where appropriate, with each line of code stored. You will submit
your assignment as a set of documents with your notebooks stored
separately (with the .ipynb extension so that they can be easily verified).
You are welcome to store your own code on your own github
repository or elsewhere, but the .ipynb files must be submitted.
1
1 Explore the Bitcoin Blockchain and Basic
Web Coding
(25 marks)
1.1 Extract Information From Your Own Transaction
(15 marks)
? Download a Bitcoin SV Wallet (we recommend Centbee) and share
your address with your module lead, who will then send you a tiny
amount (0.001 units of Bitcoin SV, roughly 10 pence).
? Use this to send an even tinier amount (0.001 units of Bitcoin SV,
roughly 1 pence) back (or to another address).
? Once you have done this, go to your transaction history and find a way
to locate the transaction on the blockchain. Centbee has a feature for
viewing the transaction on the blockchain. Take a note of which block
your transaction is in by taking its block height.
? From a Jupyter notebook, extract the following information from the
same block by fetching data from the whatsonchain API.
https://api.whatsonchain.com/v1/bsv/main/block/height/ place block
height here
Your notebook should fetch, then print your data in JSON format, and
you should obtain the following for the block with your transaction in
it:
– txcount
– time
– totalFees
– confirmations
– miner
Include some code that converts the unix timestamp into human readable
format to the nearest second.
Explain what each of these parts of the block are in words.
2
1.2 Extract Information from Famous Blocks (5 marks)
For the famous transactions below, go through the same process to obtain the
time they occurred, including some code that converts the unix timestamp
into human readable format.
The First ever transaction from Satoshi to Hal Finney in 2010
f4184fc596403b9d638783cf57adfe4c75c605f6356fbc91338530e9831e9e16
The Pizza purchase for 10,000BTC in 2010
a1075db55d416d3ca199f55b6084e2115b9345e16c5cf302fc80e9d5fbf5d48d
1.3 Basic Web Coding (5 marks)
Construct your own simple web page in a simple text editor and save at as a
.html file that can be read by a web browser like Chrome. This page should
include a javascript function that allows the viewer to change an image back
and forth when they click on it.
3
2 Time Series Investigation of Bitcoin Price
(50 marks)
You are working for a FinTech firm that provides customers with real time
financial data and analysis. Part of the marketing strategy for this firm is
providing a regular newsletter via a blog discussing current issues for personal
portfolio management. Your boss has asked you to investigate the idea that
Bitcoin is mostly viewed as a store of value. To provide the background to
this report, you are required to carry out the following:
2.1 Obtain Time Series Data (5 marks)
Obtain the following data by calling the FRED api from a Jupyter notebook,
and provide simple time series plots of the raw data:
1. BTC Bitcoin Price in US Dollars CBBTCUSD
2. Gold Price (including gold plated with platinum), unwrought ID7108
3. S&P500 (index measure of the overall US stock market) SP500
4. 3-Month Treasury Bill Secondary Market Rate (measure of the risk free
rate) TB3MS
You may find it helpful to label them in your Python code as the following:
1. bitcoinprice
2. goldprice
3. market
4. riskfreerate
4
2.2 Data Transformations (15 marks)
? Conduct your analysis from January 2016 to as recent as possible
? Transform series 1 and 3 from daily to monthly data
? Then, for series 1-3, you need to transform monthly price observations
into monthly returns by obtaining new series:
ln
xt
xt?1
!
where xt
is the value of a variable for a particular observation and xt?1
is its value 1 month before.
To convert the series 4 annualised percentages to monthly simply divide
this series by 12. You now have 4 transformed series which we will label as
follows:
1. rbt
2. rgt
3. rmt
4. rf t
2.3 Data Analysis(30 marks)
What is the correlation between:
1. Bitcoin Returns rbt and Gold Returns rgt
2. Bitcoin Returns rbt and Market Returns rmt
Interpret these results by comparing the view that Bitcoin is an alternative
to gold to the view that Bitcoin is a new form of high-risk high-return
asset.
According to the assumptions behind the strict form of CAPM theory,
equations of the following form should fully explain returns to holding any
particular asset, here for bitcoin and gold:
5
(rbt ? rf t) = αb + βb(rmt ? rf t) + ubt
(rgt ? rf t) = αg + βg(rmt ? rf t) + ugt
where ubt and ugt are idiosyncratic unpredictable error terms associated
with Bitcoin and Gold respectively. According to the strict form of CAPM,
α should be zero, and β provides a systematic measure of how high up the
risk/return trade-off the asset is. Estimate α and β for Bitcoin and Gold
using OLS regression, and interpret the results.
6
3 Machine Learning in Practice (25 marks)
The background to this section is found at this repository: https://github.com/SarunasGirdenas/fintech
presentation. A recording of this session with full subtitles
can also be found on ele under the ’TOPIC 4 AI and Machine Learning for
FinTech’. Sarunas is also happy to answer any questions you may have via
his email address here: sarunas.girdenas@prudential.co.uk.
3.1 High Level Description of FinTech Firm (10 marks)
Provide a high level description of Sarunas’ FinTech firm in words. You are
not expected to explain technical parts in depth, but provide a mechanical
description of what each of the four structural parts do, how they interact,
and what they achieve overall.
3.2 Written Description of Python Code (10 marks)
Reproduce the model (saved under model building.ipynb) within your own
Jupyter notebook. To do this you will have to download the large dataset
from Kaggle following Sarunas’ instructions. This data will need to be saved
in your active Jupyter notebook directory. Once you have reproduced it with
the same results, using cell markdown, choose 5 lines of the code and include
brief verbal descriptions of what those lines perform. Finally, save this as
your own Jupyter notebook and include this in your submission.
3.3 Improve the Model(5 marks)
How could the model in the previous section be improved? You are not
expected to actually improve it, but you must include descriptions of how
the accuracy of the model could be improved, including alternative modelling
strategies.
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