FIN312 Mathematics And Programming For FinTech is a course offered by the SUSS that explores the use of mathematical and programming techniques in financial technology. The course covers a wide range of topics, including risk management, asset pricing, and derivative instruments.

Students will learn how to use mathematical models to solve financial problems and develop programming skills to implement these models. The course is designed for students with a strong interest in financial technology and a background in mathematics and computer science. It will prepare students for careers in fields such as risk management, investment banking, and financial engineering.

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Here are some assignment briefs to help you get started. They are as follows:

FIN312 Assignment brief 1: Distinguish between structured and unstructured data used commonly in financial applications.

There are two main types of data used in financial contexts: structured and unstructured data. Structured data is highly organized and can be easily analyzed, while unstructured data is more free-form and relatively unorganized.

Structured data is typically numerical in nature, and can be found in sources like financial reports, sales records, or inventory counts. This type of data is easy to input into computers for further analysis and can be used to answer specific questions or track trends over time.

Unstructured data, on the other hand, cannot be as easily categorized or interpreted by machines. This type of data includes things like customer feedback forms, social media posts, or online reviews. While this information can be valuable, it is often more difficult to draw conclusions from unstructured data due to its more free-form nature.

Both types of data have their advantages and disadvantages, and both are used extensively in financial contexts. The type of data that is most useful for a given situation will depend on the specific question being asked or the problem being addressed.

Assignment brief 2: Formulate statistical models to represent financial data.

There are a number of different statistical models that can be used to represent financial data, depending on the type of data and the questions being asked.

One common model is the linear regression model, which is used to estimate the relationship between two or more variables. This type of model can be used to predict future values for one or more variables based on past values for those variables.

Another common model is the logistic regression model, which is used to predict whether or not a particular outcome will occur (e.g., whether or not a customer will make a purchase). This type of model can be used to identify factors that are associated with a particular outcome.

There are many other statistical models that can be used to represent financial data, and the appropriate model for a given situation will depend on the specific question being asked or the problem being addressed.

Assignment brief 3: Appraise cryptographic primitives underlying security in cryptocurrency networks and calculate network statistics regarding the systems.

Cryptocurrencies are built on cryptographic primitives, which are mathematical algorithms used to secure data and protect against counterfeiting. Cryptography is used in many aspects of cryptocurrency security, including blockchain integrity, wallets, and transactions.

Blockchain integrity is ensured through the use of cryptographic hash functions, which map data of arbitrary size to a fixed-size output. Cryptographic hash functions are one-way functions, meaning that it is computationally infeasible to determine the input data from the output data. This property is essential for blockchains, as it ensures that transaction data cannot be tampered with without changing the corresponding hash value.

Wallets also use cryptography for security. A cryptocurrency wallet typically contains a private key and a public key. The private key is used to sign transactions, and the public key is used to verify those transactions. This ensures that only the owner of the wallet can create transactions and that all transactions are valid.

Transactions between two parties are also secured through cryptography. Cryptocurrencies use digital signatures to ensure that only the intended recipient can access the transaction data. Digital signatures are generated using the private key and can be verified using the public key. This ensures that transactions cannot be altered or forged and that only the intended recipient can view the transaction data.

Cryptography is a critical component of cryptocurrency security, and understanding how it works is essential for anyone looking to invest in or use cryptocurrencies.

FIN312 Assignment brief 4: Implement Python programs to acquire financial data using APIs.

There are many great Python libraries for interacting with financial APIs, including Urllib2, Requests, and Pandas. In this answer, I’ll walk you through an example of using the Pandas library to download historical stock data from the Yahoo! Finance API.

First, you’ll need to install the Pandas library. You can do this by running the following command in your terminal:

pip install pandas

Next, you’ll need to create a file named “finance.py” and insert the following code into it:

import pandas as pd

import urllib2

def get_historical_stock_data(symbol):

 url = “http://ichart.finance.yahoo.com/table.csv?s=” + symbol

 data = urllib2.urlopen(url).read()

 return data

 data = get_historical_stock_data(“AAPL”)

df = pd.read_csv(StringIO(data), parse_dates=[‘Date’])

print df

This code will download the historical stock data for Apple Inc. (AAPL) and store it in a Pandas data frame. The data frame can then be used for various analyses, such as calculating moving averages or plotting the data.

There are many other ways to acquire financial data using Python, and the appropriate method will depend on the specific data you’re looking for. For example, if you’re interested in real-time stock data, you may want to use the Yahoo! Finance WebSocket API.

Assignment brief 5: Use Python to automate large-scale financial calculations.

Python is a great language for automating large-scale financial calculations. Here are a few libraries that can be used to do this:

1. The Statsmodels library provides a wide range of statistical tools for finance, including time series analysis, regression modeling, and forecasting.
2. The OpenMDAO project is an open-source framework that enables different types of optimization to be performed on financial models.
3. PyFolio is a Python library for managing portfolios of stocks and other securities. It includes tools for performing backtesting, securing capital gains, and creating optimization strategies.
4. Brian exercises complete control over runtime conditions and memory usage in compiled code form, making it ideal for large-scale simulations.

With the help of these libraries, you can automate just about any financial calculation you can think of. For example, you could use Brian to create a model of a stock market and then use PyFolio to backtest different trading strategies against that model. Or you could use Statsmodels to perform time series analysis on historical data in order to develop a forecasting model. The possibilities are endless!

FIN312 Assignment brief 6: Operate a financial information system for obtaining market data and information.

There are many different financial information systems out there, and the one you choose will depend on your specific needs. Some popular options include:

2. Quandl: Quandl offers a wide range of financial data, including stock prices, currency exchange rates, interest rates, and economic indicators. All data is available in both raw and processed form and can be downloaded in a variety of formats.
3. Bloomberg: Bloomberg is a leading provider of financial news and data. Its terminal product offers real-time access to financial markets, and its historical data goes back decades.
4. Thomson Reuters Eikon: Eikon is a comprehensive financial information system that offers real-time data, news, analytics, and trading capabilities. It also has a very powerful programming interface that can be used to automate financial calculations.
5. FactSet: FactSet provides access to a wide range of financial data, including stock prices, economic indicators, and company financials. It also offers tools for performing analysis, building models, and creating visualizations.

Each of these systems has its own strengths and weaknesses, so it’s important to choose the one that best meets your needs. For example, if you need access to real-time data, then Bloomberg or Eikon would be a better choice than Quandl or FactSet. On the other hand, if you’re primarily interested in historical data, then Quandl or FactSet would be a better choice.

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