📈 Day 79 of #100DaysOfCode in Python: Financial Analysis Deep Dive

Photo by Desola Lanre-Ologun on Unsplash

Welcome to Day 79! Today, we’re venturing into the world of financial analysis using Python. We’ll explore how Python can be a powerful tool in analyzing stock market data, assessing risk, and formulating trading strategies.

1. Getting Started with Financial Analysis in Python

• Pandas: Essential for data manipulation and analysis. Use it to handle financial data sets.
• NumPy: Useful for numerical calculations, especially for financial computations.
• matplotlib and seaborn: For visualizing financial trends and data.

2. Fetching Financial Data

• pandas_datareader: A library that allows you to read in data from various financial sources, including Yahoo Finance, Google Finance, and others.

import pandas_datareader as pdr
df = pdr.get_data_yahoo('AAPL', start='2020-01-01', end='2021-01-01')

3. Analyzing Stock Performance

• Price Trends: Analyze historical price data, calculate moving averages, and identify trends.
• Volume Analysis: Examine trading volumes in conjunction with price movements to gauge investor sentiment.