MA_Close_Price.py

# 1) IMPORT LIBRARIES
import yfinance as yf               # yfinance to fetch historical market data
import pandas as pd                # pandas for data manipulation (DataFrame)
import matplotlib.pyplot as plt    # matplotlib for plotting

# 2) DOWNLOAD TESLA (TSLA) DATA
#    - 'start' is the first date (inclusive)
#    - 'end' is the last date (exclusive)
tesla = yf.download(
    tickers='TSLA', 
    start='2023-01-01', 
    end='2025-04-24', 
    progress=False                # disable download progress bar
)

# 3) EXTRACT CLOSE PRICE
#    We only need the 'Close' column for moving averages
close = tesla['Close']

# 4) CALCULATE MOVING AVERAGES
#    rolling(window=N).mean() computes the N-day simple moving average
ma10 = close.rolling(window=10).mean()   # 10-day MA
ma20 = close.rolling(window=20).mean()   # 20-day MA
ma50 = close.rolling(window=50).mean()   # 50-day MA

# 5) PLOT CLOSE PRICE AND MOVING AVERAGES
plt.figure(figsize=(12, 6))              # set figure size

# Plot the raw close price
plt.plot(close.index, close, label='Close Price', linewidth=1)

# Plot each moving average
plt.plot(ma10.index, ma10, label='10-Day MA', linewidth=1)
plt.plot(ma20.index, ma20, label='20-Day MA', linewidth=1)
plt.plot(ma50.index, ma50, label='50-Day MA', linewidth=1)

# 6) FORMAT THE PLOT
plt.title('Tesla Close Price & 10/20/50-Day Moving Averages\n2023–2025')  # title
plt.xlabel('Date')                        # x-axis label
plt.ylabel('Price (USD) and Moving Averages')                  # y-axis label
plt.legend()                              # show legend
plt.grid(True)                            # add grid for readability

# 7) DISPLAY THE PLOT
plt.tight_layout()                        # adjust padding
plt.show()                                # render the plot