Portfolio Analysis with Python | Data Collection, Cleaning & Statistical Analysis | Part 1

Portfolio Analysis with Python | Part 1: Data Preparation, Cleaning & Statistical Analysis Welcome to the first stage of my Portfolio Analysis with Python series. Before building and optimising an investment portfolio, it is essential to understand the data behind the assets being analysed. In this first stage, I focus on establishing a reliable analytical foundation by importing financial market data, cleaning and preparing it for analysis, and conducting important statistical and exploratory checks. In this video, we explore the early stages of a quantitative portfolio analysis workflow, including: 📊 Importing financial market data 🧹 Data cleaning and preprocessing 📅 Checking and preparing time-series data 📈 Exploring descriptive statistics 🔍 Statistical validation and data quality checks 📉 Analysing asset returns 🔗 Correlation analysis between assets ⚖️ Understanding relationships and dependencies within the portfolio 📊 Exploring risk and return characteristics 🧠 Preparing the dataset for the next stages of portfolio construction and optimisation This stage is important because portfolio optimisation is only as reliable as the data and statistical assumptions behind it. Before applying quantitative models, we need to understand the behaviour, distribution, relationships, risks, and characteristics of the assets in the portfolio. This project is part of my broader exploration of Financial Engineering, Quantitative Finance, Financial Data Science, Machine Learning, and Data-Driven Investment Analysis. The next stages of this project will build upon this foundation as I continue exploring portfolio construction, risk analysis, diversification, optimisation, and quantitative investment decision-making. If you're interested in learning how Python, statistics, and quantitative methods can be applied to financial markets and investment analysis, subscribe and follow the journey. 🔔 Subscribe for more content on: Financial Data Science Quantitative Finance Financial Engineering Portfolio Analysis Python for Finance Machine Learning in Finance Risk Management Financial Modelling Time-Series Analysis Data Analytics Subscribe and follow along as we build and analyse real financial and quantitative projects from the ground up. Interested in turning your business data into meaningful insights and smarter decisions? 📊🚀 Visit Augmatics and discover how we can help your organisation through data analytics, financial analytics, business intelligence, predictive analytics, dashboards, and data-driven decision support. 🌐 augustmarathonanalytics.com, augmatics.net Let’s turn data into insight—and insight into action. #Python #PortfolioAnalysis #QuantitativeFinance #FinancialEngineering #PortfolioAnalysis #Python #QuantitativeFinance #FinancialEngineering #FinancialDataScience #PythonForFinance #DataScience #InvestmentAnalysis #PortfolioManagement #QuantitativeAnalysis