# Block 1: Install required packages (if not already installed)
!pip install yfinance matplotlib --quiet

# Block 2: Import necessary libraries
import yfinance as yf
import matplotlib.pyplot as plt
import pandas as pd

# Block 3: Set display options for better visualization
pd.set_option("display.max_rows", 10)
pd.set_option("display.float_format", "{:.2f}".format)

# Block 4: Download historical stock data for Apple (AAPL)
stock = yf.Ticker("AAPL")
df = stock.history(period="1y")
df.head()

# Block 5: Basic Information
print("Company Info:")
stock.info['longBusinessSummary'][:300] + "..."

Company Info:





'Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising Ai...'

# Block 6: Describe the dataset
df.describe()

# Block 7: Plot closing price
plt.figure(figsize=(12, 5))
plt.plot(df['Close'], label="Close Price", color='blue')
plt.title("Apple (AAPL) - Closing Price")
plt.xlabel("Date")
plt.ylabel("Price (USD)")
plt.legend()
plt.grid(True)
plt.show()

# Block 8: Calculate Moving Averages
df['MA20'] = df['Close'].rolling(window=20).mean()
df['MA50'] = df['Close'].rolling(window=50).mean()
df[['Close', 'MA20', 'MA50']].tail()

# Block 9: Plot Moving Averages
plt.figure(figsize=(12, 6))
plt.plot(df['Close'], label='Close Price', alpha=0.5)
plt.plot(df['MA20'], label='20-day MA', color='orange')
plt.plot(df['MA50'], label='50-day MA', color='green')
plt.title("Moving Averages - AAPL")
plt.xlabel("Date")
plt.ylabel("Price (USD)")
plt.legend()
plt.grid(True)
plt.show()

# Block 10: Volume analysis
df['Volume'].plot(kind='hist', bins=30, color='purple', figsize=(10, 4), title='Trading Volume Distribution')

<Axes: title={'center': 'Trading Volume Distribution'}, ylabel='Frequency'>

# Block 11: Daily Returns
df['Daily Return'] = df['Close'].pct_change()
df['Daily Return'].dropna().plot(figsize=(10, 5), linestyle='--', marker='o', title="Daily Return (%)")

<Axes: title={'center': 'Daily Return (%)'}, xlabel='Date'>

# Block 12: Correlation Matrix (simulate multiple stocks)
tickers = ['AAPL', 'MSFT', 'GOOG', 'AMZN']
df_multi = yf.download(tickers, period="6mo")['Close']
df_multi = df_multi.pct_change().dropna()
correlation = df_multi.corr()
correlation

C:\Users\HP\AppData\Local\Temp\ipykernel_6508\2024470801.py:3: FutureWarning: YF.download() has changed argument auto_adjust default to True
  df_multi = yf.download(tickers, period="6mo")['Close']
[*********************100%***********************]  4 of 4 completed

# Block 13: Heatmap of correlation
import seaborn as sns
plt.figure(figsize=(8, 6))
sns.heatmap(correlation, annot=True, cmap="coolwarm")
plt.title("Correlation Between Stocks")

Text(0.5, 1.0, 'Correlation Between Stocks')

# Block 14: Candlestick Chart (optional for advanced visuals)
!pip install plotly

Requirement already satisfied: plotly in c:\users\hp\miniconda3\envs\py312\lib\site-packages (6.2.0)
Requirement already satisfied: narwhals>=1.15.1 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from plotly) (1.44.0)
Requirement already satisfied: packaging in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from plotly) (24.2)

import plotly.graph_objects as go
fig = go.Figure(data=[go.Candlestick(x=df.index,
                open=df['Open'],
                high=df['High'],
                low=df['Low'],
                close=df['Close'])])
fig.update_layout(title='Candlestick Chart - AAPL', xaxis_rangeslider_visible=False)
fig.show()

# Block 15: Highest closing price
highest = df['Close'].max()
print(f"Highest Closing Price in the Last 1 Year: ${highest:.2f}")

Highest Closing Price in the Last 1 Year: $258.40

# Block 16: Lowest closing price
lowest = df['Close'].min()
print(f"Lowest Closing Price in the Last 1 Year: ${lowest:.2f}")

Lowest Closing Price in the Last 1 Year: $172.19

# Block 17: Save processed data to CSV
df.to_csv("AAPL_Processed.csv")
print("Saved to AAPL_Processed.csv")

Saved to AAPL_Processed.csv

# Block 18: Display final data sample
df.tail()