Group Repository for the Data Science Mini-Project (EMATM0050)

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Group Number: OL2

Problem Assigned: B

Group Members: Jack Inwood, Felix Mayo, Samantha Starling, Adam Rapley

Project Title: Level-2 Financial Market Data Analytics

Overview:

This repository contains the implementation of data-analytics techniques for identifying trading signals ("buy" or "sell") in Level-2 financial market data. The goal of this project is to explore advanced analytics methods to predict near-term movements in price and evaluate the profitability of automated trading strategies based on these signals.

Problem Description:

The global financial markets generate vast amounts of data, especially in the form of Level-2 data, which summarizes all bids and asks currently resting at the exchange for a tradable asset. This data includes price and quantity information for buy and sell orders, resulting in large and complex datasets with significant potential for advanced data analysis. The challenge is to implement and evaluate data-analytics techniques to extract meaningful trading signals from this data, ultimately aiming to develop profitable automated trading strategies.

Key Features:

• Predictive Modeling Techniques: The repository includes implementations of various predictive modeling techniques, focusing on methods beyond conventional forcasting approaches like ARIMA.
• Trading Simulator: A simple trading simulator is provided to evaluate the performance of automated trading systems based on the signals generated by the predictive modeling techniques.
• Modular Design: The codebase is designed to be modular, allowing easy experimentation with different analytics techniques, reinforcement learning algorithms, and trading strategies.