Statistics II - Extra Activity 1

Activity Overview

This activity analyzes the daily stock market performance of Apple Inc. (NASDAQ:AAPL) for the first three quarters of 2025, from January 1st to September 30th. The goal was to collect live financial data, describe its structure, and identify interesting trends and observations using statistical thinking.

Embedded Google Spreadsheet Data

Data Source & Collection Method

The data for this analysis was sourced directly from Google Finance, which provides real-time and historical financial market data.

The data was collected dynamically and automatically within Google Sheets itself by using the built-in GOOGLEFINANCE function. This powerful function allows for pulling current or historical securities data directly into a spreadsheet. The specific formula used was: =GOOGLEFINANCE("NASDAQ:AAPL", "all", DATE(2025,1,1), DATE(2025,9,30), "DAILY")

This method ensured the data was accurate and formatted cleanly for immediate analysis.

Understanding the Data

The dataset consists of over 180 rows, where each row represents a single trading day. The data has 6 columns, which are described as follows:

  • Date: The specific date of the trading day.
  • Open: The price of one share of AAPL stock when the NASDAQ market opened on that day.
  • High: The highest price that one share of AAPL reached during that trading day.
  • Low: The lowest price that one share of AAPL reached during that trading day.
  • Close: The final price of one share of AAPL when the NASDAQ market closed for the day. This is often used as the primary measure of a stock’s value for a given day.
  • Volume: The total number of AAPL shares that were traded (bought and sold) during that day.

Detailed Observations

From analyzing the daily stock data for Apple from January to September 2025, I made several interesting observations:

  • Observation 1: A Day of Extreme Volatility: The data revealed that April 09, 2025, was a day of significant market activity. The stock price showed its highest intraday volatility on this date, fluctuating by $28.72. This suggests a major news event or announcement likely occurred, causing significant uncertainty and trading throughout the day.

  • Observation 2: Volume and Volatility Correlation: Confirming the significance of that date, April 09, 2025, was also the day with the highest trading volume by far, with over 184 million shares traded. This massive volume was coupled with a substantial price increase of $26.90 from the opening bell to the closing bell. This strong correlation shows the market reacted decisively and positively to whatever news drove the activity on that day.

  • Observation 3: A Story of Two Halves: The stock’s performance told a story of two distinct halves. The first two quarters were challenging, with the stock declining by -8.91% in Q1 and another -8.07% in Q2. However, there was a dramatic turnaround in the third quarter, which saw powerful growth of 22.52%. This strong recovery in Q3 completely erased the earlier losses, resulting in a net gain of 4.42% for the entire nine-month period.

  • Observation 4: Balanced Intraday Trading: Over the entire period, the number of days the stock closed higher than it opened was 49.73%. This is almost a perfect 50/50 split, which indicates that there was no consistent positive or negative bias during the trading hours themselves; the market was remarkably balanced day-to-day.

Methodology

I used python to do basic analysis, the code used was is as follows:

import pandas as pd

file_path = '/content/STATS-2 Activity-1 - APPLE Stock.csv'
df = pd.read_csv(file_path)

# --- Data Cleaning and Preparation ---

# Clean up the 'Date' column to keep only the date part
df['Date'] = df['Date'].str.split(' ').str[0]

# Convert the 'Date' column to a proper datetime format
df['Date'] = pd.to_datetime(df['Date'], format='%d/%m/%Y')

# Set the 'Date' as the index for easier time-based analysis
df.set_index('Date', inplace=True)

# Drop the original 'ROW NUMBER' column
df.drop('ROW NUMBER', axis=1, inplace=True)

print("Data loaded and cleaned successfully.")

# 1. Volatility Analysis
df['Intraday_Volatility'] = df['High'] - df['Low']
max_volatility_day = df.loc[df['Intraday_Volatility'].idxmax()]
max_volatility_date = df['Intraday_Volatility'].idxmax().strftime('%B %d, %Y')
max_volatility_value = max_volatility_day['Intraday_Volatility']

# 2. Volume Spikes
max_volume_day = df.loc[df['Volume'].idxmax()]
max_volume_date = df['Volume'].idxmax().strftime('%B %d, %Y')
max_volume_value = max_volume_day['Volume']
price_change_on_max_volume_day = max_volume_day['Close'] - max_volume_day['Open']

# 3. Quarterly Growth Trend
q1_end = '2025-03-31'
q2_end = '2025-06-30'
q3_end = '2025-09-30'

# Get closing prices for each quarter
q1_close_start = df.loc[:q1_end].iloc[0]['Close']
q1_close_end = df.loc[:q1_end].iloc[-1]['Close']
q2_close_start = df.loc[q1_end:q2_end].iloc[1]['Close'] # Start from the next day
q2_close_end = df.loc[:q2_end].iloc[-1]['Close']
q3_close_start = df.loc[q2_end:q3_end].iloc[1]['Close'] # Start from the next day
q3_close_end = df.loc[:q3_end].iloc[-1]['Close']

# Calculate percentage growth
q1_growth = ((q1_close_end - q1_close_start) / q1_close_start) * 100
q2_growth = ((q2_close_end - q2_close_start) / q2_close_start) * 100
q3_growth = ((q3_close_end - q3_close_start) / q3_close_start) * 100
overall_growth = ((df.iloc[-1]['Close'] - df.iloc[0]['Close']) / df.iloc[0]['Close']) * 100

# 4. "Close" vs "Open" Relationship
days_positive_close = (df['Close'] > df['Open']).sum()
total_days = len(df)
percentage_positive_days = (days_positive_close / total_days) * 100

print("--- Analysis Results ---")
print("\n1. Volatility Analysis:")
print(f"Date with highest volatility: {max_volatility_date}")
print(f"Volatility value (High - Low): ${max_volatility_value:.2f}")

print("\n2. Volume Spikes:")
print(f"Date with highest volume: {max_volume_date}")
print(f"Volume on that day: {max_volume_value:,}")
print(f"Price change on that day (Close - Open): ${price_change_on_max_volume_day:.2f}")

print("\n3. Quarterly Growth Trend:")
print(f"Q1 (Jan-Mar) Growth: {q1_growth:.2f}%")
print(f"Q2 (Apr-Jun) Growth: {q2_growth:.2f}%")
print(f"Q3 (Jul-Sep) Growth: {q3_growth:.2f}%")
print(f"Overall Trend (Jan-Sep): {overall_growth:.2f}%")

print("\n4. 'Close' vs 'Open' Relationship:")
print(f"Percentage of days with Close > Open: {percentage_positive_days:.2f}%")

Output for the code:

--- Analysis Results ---

1. Volatility Analysis:
Date with highest volatility: April 09, 2025
Volatility value (High - Low): $28.72

2. Volume Spikes:
Date with highest volume: April 09, 2025
Volume on that day: 184,395,885.0
Price change on that day (Close - Open): $26.90

3. Quarterly Growth Trend:
Q1 (Jan-Mar) Growth: -8.91%
Q2 (Apr-Jun) Growth: -8.07%
Q3 (Jul-Sep) Growth: 22.52%
Overall Trend (Jan-Sep): 4.42%

4. 'Close' vs 'Open' Relationship:
Percentage of days with Close > Open: 49.73%