Stock Performance(AFRM, APP and RIOT) : Exploring the Descriptives

Introduction

This project is focused on analyzing the stock performance of three(3) high-performing stocks, using a 1(one) year-long daily stock market performance. The data set includes date, variables for opening stock price, highest stock sales price per day, lowest stock sales price per day, and closing sales price for the day. The data set includes AFRM, APP and RIOT data set. AFRM is a tech stock. It is a high-performing business globally known as Affirm Holdings, Inc. Riot Platforms, Inc. is a Blockchain Tech company, Riot Blockchain incorporated, with proven years of record success. At the same time, AppLovin Corporation (APP) is a mobile technology company. It specializes in mobile app development, marketing, and monetization.

Objective: Analysis Objective

The project focuses on providing an analytical exploration of 1-year daily stock market data set with the aim of providing key summaries that highlight performance insight, especially the peak and trough points.

Methods

Data was collected through Yahoo Finance using dedicated Python libraries for fetching finance data(yfinance ). Pandas libraries were also imported and used to set up the data frame and data analysis.

• Python Codes:

  [Uploadingimport yfinance as yf import pandas as pd

Define the stock symbols for the companies of interest

symbols = ['AFRM', 'APP', 'RIOT']

Specify the date range for the past month

end_date = pd.to_datetime('today') start_date = end_date - pd.DateOffset(months=1)

Create dictionaries to store data for each stock

stock_data = {} key_stats = {}

Fetch historical stock data and key financial ratios

for symbol in symbols: # Fetch historical stock data stock_data[symbol] = yf.download(symbol, start=start_date, end=end_date)

# Fetch key financial ratios
key_stats[symbol] = yf.Ticker(symbol).info

Print or analyze the data as needed

for symbol in symbols: print(f"\nStock: {symbol}") print("\nHistorical Stock Data:") print(stock_data[symbol].head())

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Data Cleaning:

The beauty of using the Yahoo Finance library in Pythion is that it offers the advantage of fetching pre-cleaned or pre-arranged data sets, according to the preset specification.

Data Analysis and Data Visualization :

### Descriptive Statistics Descriptive statistics offers key summary statistics for the businesses' stock market performance under the period of review, 1-year of daily stock market data.

The objective of the analysis is to provide summaries and visualization snapshots that could highlight performance insights among the firms

### Stock Return Analysis This step analyzes performance return per firm and produces visualization to emphasize peaks and trough periods. Peaks are the steep or high points in the visualization which represent points of high returns. While troughs are the low points in the visualization which emphasizes the points of stock sales performance or returns in the review period.

Further analysis and visual representation of data using box plots

Further analysis and visual representation of data using distribution plots

Reference

1. Python
2. Python libraries: Yahoo Finance (finance)
3. Python libraries: Pandas
4. Bloomberg