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日期:2021-01-16 11:45

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.

7


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