Stock Portfolio Analysis & Data Manipulation — Python

MSc Financial Technology — Programming for Financial Data Science National College of Ireland, Dublin | Student ID: 24198170

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Overview

This repository contains a collection of Jupyter notebooks covering foundational Python programming skills applied to real-world financial and stock market data. The work spans portfolio construction, risk-based valuation logic, control flow for trading decisions, stock data manipulation and portfolio performance visualisation using NumPy and Matplotlib.

Stocks covered across the notebooks include Apple (AAPL), Microsoft (MSFT), Netflix (NFLX), Google (GOOG), Tesla (TSLA), Amazon (AMZN), NVIDIA (NVDA), Intel (INTC), Meta (META), Adobe (ADBE) and Salesforce (CRM).

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Objectives

• Construct and manipulate client portfolio dictionaries in Python
• Apply risk-adjusted valuation logic (high-risk vs low-risk asset discounts)
• Use conditional logic (if/elif/else) and loops (for/while) for trading decisions
• Load, clean and transform real stock market CSV data using Pandas
• Standardise date formats and engineer new financial features
• Calculate daily price change (Close − Open) across all tickers
• Compute portfolio performance metrics including weighted returns and volatility
• Visualise portfolio data using bar charts, scatter plots and pie charts with Matplotlib

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Dataset

Column	Description
Ticker	Stock symbol (AAPL, MSFT, NFLX, GOOG)
Date	Trading date (reformatted to YYYY-MM-DD)
Open	Opening price (USD)
High	Intraday high price (USD)
Low	Intraday low price (USD)
Close	Closing price (USD)
Adj Close	Dividend/split-adjusted closing price
Volume	Number of shares traded
Daily_Change	Engineered feature: Close − Open

Period: 07 Feb 2023 – 05 May 2023 | Records: 248 rows × 9 columns

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Key Tasks Completed

Assessment 1 — Portfolio Construction & Stock Data Manipulation

• Built a Python list of client portfolio dictionaries with fields: Client_id, Asset_type, Value_usd, is_high_risk
• Portfolios: AAPL ($254.43, high risk), MSFT ($509.23, high risk), ETH ($3,331.31, low risk)
• Applied conditional logic:
  • High-risk assets → 10% discount applied (×0.90)
  • Low-risk assets → 5% discount applied (×0.95)
• Loaded multi-stock CSV dataset using pandas.read_csv()
• Standardised date column from DD/MM/YYYY to YYYY-MM-DD using pd.to_datetime()
• Engineered Daily_Change column: Close − Open for each trading day
• Performed per-ticker filtering and grouped summary statistics

Assessment 2 — Python Basics for Stock Trading Logic

• Created a stock price dictionary
• Applied if/elif/else logic to classify stock as overvalued, stable or a bargain based on price threshold
• Used a for loop to iterate through all tickers and print their current prices
• Implemented a while loop to simulate NVDA price incrementing by $5 until reaching a target of $250
• Demonstrated core Python control flow concepts in a financial context

Lab 7 — Portfolio Performance Visualisation

• Defined a 10-stock portfolio: MSFT, GOOG, TSLA, AMZN, NVDA, NFLX, DIS, PYPL, ADBE, CRM
• Used NumPy arrays for annual returns (ranging from −10% to +50%) and volatility values
• Defined investment allocation weights across all 10 tickers totalling 100%
• Computed weighted portfolio return and weighted portfolio volatility
• Created a bar chart of annual returns per stock
• Created a scatter plot of volatility vs return for risk-return analysis
• Created a pie chart of portfolio allocation weights with MSFT slice exploded for emphasis

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How to Run

1. Clone this repository or download the files
2. Open any .ipynb file in Jupyter Notebook or Google Colab
3. For Class-Assessment.ipynb, ensure dataset.csv is in the same folder
4. Run all cells in order (Cell → Run All)

Requirements: Python 3.x, pandas, numpy, matplotlib — install via pip install pandas numpy matplotlib

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Author

Mohd Nizam Nasir Shaikh