How to Identify Trending Stocks Using Python?

If you’re a trader or investor, identifying trending stocks can give you an edge in the market. Stocks in a strong uptrend often continue their momentum, while stocks in a downtrend may keep falling. In this guide, we’ll show you how to find the top trending stocks using Python by calculating their trend slopes.

Understanding Stock Trends

A stock trend is determined by the direction in which its price is moving over a period of time. A positive slope indicates an uptrend (bullish momentum), while a negative slope signals a downtrend (bearish momentum). By using Linear Regression, we can calculate the trend slope for each stock and identify the top 10 uptrending and downtrending stocks.

Steps to Find Trending Stocks

1. Fetch stock data using the yfinance library.
2. Use linear regression to calculate the slope of closing prices over the last 30 days.
3. Sort stocks based on their slope values.
4. Filter out non-trending stocks (only consider positive slopes for uptrends and negative slopes for downtrends).
5. Extract the top 10 uptrending and downtrending stocks.

Python Code to Find Top Trending Stocks

Here’s the Python script to accomplish this task:

import yfinance as yf
import pandas as pd
from sklearn.linear_model import LinearRegression
import numpy as np

# Define the list of stocks
stocks = ["AAPL", "MSFT", "GOOGL", "TSLA", "AMZN", "NVDA", "META", "NFLX", "AMD", "INTC",
          "BABA", "UBER", "DIS", "V", "PYPL", "SQ", "JPM", "BA", "SPOT", "SHOP"]

# Define the time window for trend calculation
window = 30  

# Function to calculate the slope of the closing price trend
def calculate_slope(stock):
    try:
        # Fetch historical stock data
        df = yf.download(stock, period="6mo", interval="1d", progress=False)

        # Ensure there are enough data points
        if len(df) < window:
            return None  # Skip stocks with insufficient data

        # Prepare data for linear regression (last `window` days)
        X = np.arange(window).reshape(-1, 1)  # Days as independent variable
        y = df['Close'].iloc[-window:].values.reshape(-1, 1)  # Closing prices

        # Fit linear regression model
        model = LinearRegression().fit(X, y)
        slope = model.coef_[0][0]  # Extract slope

        return slope
    
    except Exception as e:
        print(f"Error processing {stock}: {e}")
        return None

# Store results in a DataFrame
stock_slopes = [(stock, calculate_slope(stock)) for stock in stocks]
df = pd.DataFrame(stock_slopes, columns=["Stock", "Slope"]).dropna()  # Remove None values

# Filter and sort stocks
df_up = df[df["Slope"] &gt; 0].sort_values(by="Slope", ascending=False)  # Only positive slopes
df_down = df[df["Slope"] &lt; 0].sort_values(by="Slope", ascending=True)  # Only negative slopes

# Get top 10 uptrend stocks
top_10_stocks = df_up.head(10)["Stock"].tolist()

# Get bottom 10 downtrend stocks
bottom_10_stocks = df_down.head(10)["Stock"].tolist()

# Display results
print("Top 10 Trending Stocks (Uptrend):", top_10_stocks)
print("Bottom 10 Trending Stocks (Downtrend):", bottom_10_stocks)

How This Works

• The script fetches 6 months of historical stock data.
• It calculates the trend slope for the past 30 days using Linear Regression.
• Stocks with a positive slope are considered uptrending, and those with a negative slope are considered downtrending.
• Finally, it sorts and extracts the top 10 stocks in each category.

Example Output

Top 10 Trending Stocks (Uptrend): ['NVDA', 'AAPL', 'MSFT', 'GOOGL', 'TSLA', 'META', 'AMZN', 'NFLX', 'V', 'DIS']
Bottom 10 Trending Stocks (Downtrend): ['BABA', 'UBER', 'PYPL', 'SQ', 'JPM', 'BA', 'SPOT', 'SHOP', 'INTC', 'AMD']

Why Use This Method?

✅ Easy to implement using Python and free data sources.
✅ Objective trend detection using Linear Regression.
✅ Helps in stock selection by identifying momentum.

This method can be integrated into an algorithmic trading system or used as a stock filtering tool for further analysis. Let us know if you find this useful! 🚀

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Disclaimer: The information provided in this article is for educational and informational purposes only and should not be construed as financial, investment, or legal advice. The content is based on publicly available information and personal opinions and may not be suitable for all investors. Investing involves risks, including the loss of principal. Always conduct your own research and consult a qualified financial advisor before making any investment decisions. The author and website assume no liability for any financial losses or decisions made based on the information presented.