Top Performer Scanner

Find the true top-performing US stocks per year by downloading all NASDAQ-listed symbols, filtering by liquidity (Top 500 daily dollar volume), and ranking b...

Security Scan

The skill's code, runtime instructions, and declared requirements are consistent with its stated purpose (downloading NASDAQ symbols, pulling Yahoo Finance data, and ranking liquid top performers); nothing in the package appears to attempt unrelated access or data exfiltration.

Purpose & Capability

Name/description (finding top-performing US stocks by liquidity-ranked universe) match the included scripts and declared dependencies (pandas, yfinance). The required binaries (python3, pip3) are appropriate. Small documentation mismatches exist (SKILL.md mentions output file 'nasdaq_top500_performers.csv' while the script default is 'top_performers.csv'), but this is a correctness/documentation issue, not a security mismatch.

Instruction Scope

SKILL.md instructs the agent to run the two included Python scripts and explicitly states network access to NASDAQ FTP and Yahoo Finance is required. The scripts only access the declared endpoints (ftp://ftp.nasdaqtrader.com and Yahoo Finance via yfinance) and perform local data processing; they do not read unrelated system files, environment variables, or send data to unexpected external endpoints.

Install Mechanism

No install spec is provided (instruction-only install), which is low risk. A requirements.txt lists pandas and yfinance, matching the SKILL.md pip install suggestion. No remote arbitrary binary downloads, no extract-from-URL behavior, and no unusual install locations.

Credentials

The skill requests no environment variables or credentials. The external network access (NASDAQ FTP and Yahoo Finance) is necessary for the task and is declared in SKILL.md. No unrelated secrets or service tokens are requested.

Persistence & Privilege

always:false and the skill does not request or attempt to modify other skills or system-wide agent settings. It does not request persistent privileges or background execution.

Assessment

This package appears to do what it says: download NASDAQ symbol data and Yahoo Finance prices, compute liquidity-ranked universes per year, and report top performers. Before installing/running: (1) run in a controlled environment (virtualenv) and install requirements from requirements.txt; (2) be aware it needs internet access and may download large amounts of historical data (yfinance batch downloads can be heavy and rate-limited); (3) verify outputs before using for trading — the author also warns about data gaps/adjustments; (4) note small doc mismatches (expected output filename differs from SKILL.md) and the ticker filter (alphabetic tickers with length <=4) which may exclude some valid modern tickers — review/adjust filters if you need broader universes; (5) review and, if necessary, adjust date ranges and --min-price to match your analysis needs.

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.