Build a Free Stock Momentum Scanner with Python (No API Key Needed)

Python Investor’s Toolkit: Part 1: Momentum · Part 2: Monte Carlo · Part 3: Mean Reversion · Part 4: Earnings Quality · Part 5: Macro Regime

Part 1 of 5 — The Python Investor’s Toolkit: Practical quant tools built with free data.

Momentum is one of the most well-documented factors in finance: stocks that have outperformed over the past 3–12 months tend to continue outperforming over the next 1–3 months. Academic research going back to Jegadeesh & Titman (1993) consistently confirms it. Yet most retail investors never act on it because the screening tools that surface momentum signals cost hundreds of dollars a month.

This post builds a working momentum scanner from scratch using only free tools. You’ll be able to scan the entire S&P 500, rank stocks by composite momentum, and export results to a CSV — all in under 60 lines of Python.

What You’ll Need

pip install yfinance pandas numpy

The Strategy: 3/6/12-Month Composite Momentum

Rather than relying on a single lookback period, this scanner uses a composite momentum score — the equal-weighted average of 3-month, 6-month, and 12-month price returns. This reduces sensitivity to any single period’s noise and is closer to how institutional quant desks implement momentum factors.

The Code

import yfinance as yf
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

def get_sp500_tickers():
    """Scrape S&P 500 tickers from Wikipedia — no API key needed."""
    table = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')[0]
    return table['Symbol'].str.replace('.', '-').tolist()

def calculate_momentum(ticker, periods=[63, 126, 252]):
    """
    Calculate momentum returns for a single ticker.
    63 ≈ 3 months, 126 ≈ 6 months, 252 ≈ 12 months of trading days.
    """
    try:
        end = datetime.today()
        start = end - timedelta(days=400)  # buffer for 252 trading days
        data = yf.download(ticker, start=start, end=end, progress=False)

        if data.empty or len(data) < 252:
            return None

        close = data['Adj Close']
        results = {'ticker': ticker}

        for period in periods:
            ret = (close.iloc[-1] / close.iloc[-period] - 1) * 100
            results[f'mom_{period}d'] = round(ret, 2)

        # Composite momentum score (equal-weighted average)
        results['composite'] = round(
            np.mean([results[f'mom_{p}d'] for p in periods]), 2
        )
        return results
    except Exception:
        return None

def run_momentum_scan(tickers, top_n=20):
    """Run the full momentum scan and print top N results."""
    results = []
    total = len(tickers)

    for i, ticker in enumerate(tickers):
        if i % 50 == 0:
            print(f"  Scanning {i}/{total}...")
        result = calculate_momentum(ticker)
        if result:
            results.append(result)

    df = pd.DataFrame(results)
    df = df.sort_values('composite', ascending=False).reset_index(drop=True)
    df.index += 1  # rank starts at 1

    print(f"n--- Top {top_n} Momentum Stocks ---")
    print(df.head(top_n).to_string())

    # Also show bottom 10 (contrarian short candidates)
    print(f"n--- Bottom 10 Momentum (Weakest) ---")
    print(df.tail(10).to_string())

    df.to_csv('momentum_scan.csv', index=True)
    print("nFull results saved to momentum_scan.csv")
    return df

# --- Run it ---
tickers = get_sp500_tickers()
results = run_momentum_scan(tickers, top_n=20)

How to Read the Output

The scanner prints a ranked table. The key column is composite — the average momentum across all three periods. A stock ranked #1 with a composite of +85 has averaged +85% across its 3/6/12-month returns, placing it at the top of the momentum universe.

Practical Customization

You don’t have to scan the S&P 500. Replace the ticker list with any universe you track:

# Scan your personal watchlist instead
tickers = ['AAPL', 'MSFT', 'NVDA', 'GOOGL', 'META', 'AMZN', 'TSLA', 'AVGO']
results = run_momentum_scan(tickers, top_n=len(tickers))

# Or scan a specific sector ETF's holdings
# (pull holdings list from ETF provider's website and paste tickers)

What’s Next

Now that you can identify the strongest-trending stocks, Part 2 of this series shows you how to stress-test your portfolio with Monte Carlo simulation — so you understand your actual downside before you size any position.

Series: The Python Investor’s Toolkit
Part 1: Momentum Scanner (this post)
Part 2: Monte Carlo Portfolio Stress-Test
Part 3: Mean Reversion Alert System
Part 4: Earnings Quality Analyzer (SEC EDGAR)
Part 5: Macro Regime Detector (FRED API)