Top Performer Scanner

Discover which stocks were the real market leaders each year, based on actual returns within the most liquid universe — not the mega-cap names everyone already knows.

When to use

• "Which stocks performed best in 2024?"
• "Find the top returning stocks by year"
• "Show me the true market leaders without survivorship bias"
• "Would my strategy's filters have caught the explosive movers?"
• When building or validating a stock selection filter

What it does

Step 1: Get True Top 500 (get_true_top_500.py)

python3 get_true_top_500.py

1. Downloads the complete list of US-traded symbols from the NASDAQ FTP directory
2. Filters out ETFs and test issues
3. Downloads 2019-2026 historical price data via Yahoo Finance (batch download)
4. For each year, calculates average daily dollar volume to find the Top 500 most liquid stocks
5. Computes annual returns for these 500 stocks
6. Outputs the Top 15 performers per year with returns and volume data

Why this matters: Most "top performer" lists suffer from survivorship bias (they only look at stocks that still exist today) or selection bias (they only check well-known names). This script starts from the full NASDAQ directory and filters dynamically per year.

Output: nasdaq_top500_performers.csv

year, rank, ticker, return, avg_daily_vol_m
2024, 1, APP, 7.45, 892
2024, 2, MSTR, 5.12, 2340
...

Step 2: Feasibility Analysis (analyze_feasibility.py)

python3 analyze_feasibility.py

Tests multiple stock scanning configurations against the discovered top performers:

• Would your filter have caught APP before its 745% run?
• How many trading days would each filter trigger on these explosive names?
• Compare tight (Top 500 volume) vs broad (Top 1000 volume) universe thresholds

This prevents building an "air-tight fortress" filter that accidentally excludes every future multibagger.

Example Output

================ TOP 15 PERFORMERS OF 2024 (Among NASDAQ Top 500 Liquidity) ================
 1. APP   : 745.1% (Avg Daily Vol: $892M)
 2. MSTR  : 512.3% (Avg Daily Vol: $2340M)
 3. PLTR  : 340.8% (Avg Daily Vol: $1567M)
 4. CVNA  : 284.2% (Avg Daily Vol: $445M)
...

Use Cases

1. Strategy Validation: Check if your selection filter would have caught the big movers
2. Universe Design: Determine the right liquidity threshold (Top 500 vs 1000 vs 2000)
3. Backtesting Reality Check: Ensure your backtest universe includes the explosive names
4. Research: Study what top performers have in common (sector, cap size, volume patterns)

Filters Applied

• Only common stocks (no ETFs, no test issues)
• Ticker length <= 4 characters, alphabetic only (excludes warrants, units, etc.)
• Start price >= $5 (excludes penny stocks)
• Liquidity ranked by average daily dollar volume per year

Dependencies

pip3 install pandas yfinance

Internet access required for NASDAQ FTP and Yahoo Finance data.

Rules

• Internet access is required. This skill downloads data from NASDAQ FTP (ftp://ftp.nasdaqtrader.com) and Yahoo Finance. It will not work in offline or air-gapped environments.
• Do not use results to make real trading decisions without independent verification. This tool identifies historical top performers — past performance does not predict future returns.
• The liquidity filter (Top N by dollar volume) is applied per-year, not across the entire period. A stock in the Top 500 in 2024 may not have been in the Top 500 in 2023. This is intentional to avoid survivorship bias.
• Penny stocks (start price < $5) are excluded by default. This prevents extreme percentage returns from low-priced stocks from dominating the rankings. Adjust --min-price only if you understand the implications.
• Yahoo Finance data may have gaps or adjusted prices. Cross-reference critical findings with a second data source before building strategy logic around specific stocks.