Sometimes, the graphs of stocks are useful. Sometimes these are not. In general, do your own research, none of this is financial advice.
And while doing that, if you want to analyze stocks with just a few lines of python, this article might help? This simple yet powerful script helps you spot potential buy and sell opportunities for Apple (AAPL) using two classic technical indicators: moving averages and RSI.
Understanding the Strategy
1. SMA Crossover: The Trend Following Signal
The script first calculates two Simple Moving Averages (SMA):
The crossover strategy is simple:
This works because moving averages smooth out price noise, helping identify the overall trend direction.
2. RSI: The Overbought/Oversold Indicator
The Relative Strength Index (RSI) measures whether a stock is overbought or oversold:
By combining SMA crossovers (trend confirmation) and RSI extremes (timing), we get stronger signals.
This plot is generated with less than 40 lines of python code
The code looks like that:
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import yfinance as yf import matplotlib.pyplot as plt import seaborn as sns
# Get data ticker = “AAPL” df = yf.download(ticker, start=“2024-01-01”, end=“2025-01-01”) sns.set_style(“whitegrid”)
# Moving Averages df[‘SMA_20’] = df[‘Close’].rolling(20).mean() df[‘SMA_50’] = df[‘Close’].rolling(50).mean()
# RSI Calculation delta = df[‘Close’].diff(1) gain = delta.clip(lower=0) loss = –delta.clip(upper=0) rs = gain.rolling(14).mean() / loss.rolling(14).mean() df[‘RSI’] = 100 – (100 / (1 + rs))
# Signals df[‘Buy’] = (df[‘SMA_20’] > df[‘SMA_50’]) & (df[‘RSI’] < 30) df[‘Sell’] = (df[‘SMA_20’] < df[‘SMA_50’]) & (df[‘RSI’] > 70)
# Plot plt.figure(figsize=(12,6)) plt.plot(df[‘Close’], label=‘Price’) plt.plot(df[‘SMA_20’], label=’20-day SMA’) plt.plot(df[‘SMA_50’], label=’50-day SMA’) plt.scatter(df.index[df[‘Buy’]], df[‘Close’][df[‘Buy’]], color=‘green’, marker=‘^’, label=‘Buy’) plt.scatter(df.index[df[‘Sell’]], df[‘Close’][df[‘Sell’]], color=‘red’, marker=‘v’, label=‘Sell’) plt.title(f‘{ticker} Combined Trading Signals (SMA + RSI)’) plt.legend() plt.show() |
The code above, but in way more details is explained in the YT video below:
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