<aside> 💡 Background: This project explores a FTSE100 dataset to analyse financial trends**.**

For this project, I used Python, Pandas, and Matplotlib on Jupyter.

This page includes my code snippets for exploring, cleaning and analysing the dataset.

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The Problem Statement

An investment firm is looking to optimise its portfolio by identifying potential stocks listed on the FTSE100 for investment. They want to leverage historical stock data and broker recommendations to make informed decisions. Analysis is needed to identify companies with favourable recommendations, analyse sector-wise trends, and provide insights that will assist the investment firm in making strategic investment decisions.

Key Objectives:

1. Evaluate the current recommendation distribution across different companies and sectors.
2. Identify companies with a strong positive sentiment (e.g., Strong Buy or Buy) from brokers.
3. Explore sector-wise trends to understand which sectors are currently favoured by brokers.

The Dataset

The dataset includes the following information:

1. Company: The name of the company.
2. Ticker: The stock market ticker symbol for the company.
3. Sector: Industry in which the company operates in
4. Mid-price (p): Average between the 'buying' and the 'selling' price of a particular stock in pence.
5. Change: The percentage change in the current vs previous day’s stock price. A positive change is what allows investors to make a profit.
6. Our view: The current recommendation or view on the stock (e.g., Hold, Buy).
7. Brokers: The number of brokers providing tracking and analysing the stock.