SMA_EMA_LWMA.py

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

# 2) DOWNLOAD TESLA (TSLA) DATA
#    - 'start' is inclusive, 'end' is exclusive
tesla = yf.download(
    tickers='TSLA', 
    start='2023-01-01', 
    end='2025-04-25', 
    progress=False                # disable the progress bar
)

# 3) EXTRACT CLOSE PRICE SERIES
close = tesla['Close']             # get only the 'Close' column

# 4) CALCULATE 30-DAY SIMPLE MOVING AVERAGE (SMA)
sma15 = close.rolling(window=30).mean()  

# 5) CALCULATE 30-DAY EXPONENTIAL MOVING AVERAGE (EMA)
#    - span=30 sets the decay so that the effective window ≈30 days
ema30 = close.ewm(span=30, adjust=False).mean()  

# 6) CALCULATE 30-DAY LINEARLY WEIGHTED MOVING AVERAGE (LWMA)
#    - weights 1,2,…,30 so the most recent day gets weight 30
weights = np.arange(1, 31)                                          
lwma30 = close.rolling(window=30).apply(
    lambda prices: np.dot(prices, weights) / weights.sum(), raw=True
)

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

plt.plot(close.index, close, label='Close Price', linewidth=1)  
plt.plot(sma15.index, sma15, label='30-Day SMA', linewidth=1)   
plt.plot(ema30.index, ema30, label='30-Day EMA', linewidth=1)   
plt.plot(lwma30.index, lwma30, label='30-Day LWMA', linewidth=1) 

# 8) FORMAT THE PLOT
plt.title('Tesla Close Price with 30-Day SMA, EMA & LWMA\n2023–2025')  
plt.xlabel('Date')                   # x-axis label
plt.ylabel('Price (USD) and Moving Average')             # y-axis label
plt.legend()                         # show legend
plt.grid(True)                       # add grid lines for readability
plt.tight_layout()                   # adjust layout margins

# 9) DISPLAY THE PLOT
plt.show()                           # render the plot window (or inline in notebook)