ibd-rs-rating 0.1.0

Unofficial IBD-style Relative Strength Rating for 4,600+ US stocks

IBD-Style Relative Strength Rating

Unofficial IBD-style RS Rating for 4,600+ US stocks, updated daily.

The only open-source project that provides true percentile-ranked RS Ratings (1-99) — not just weighted returns.

Installation · Quick Start · API Reference · How It Works

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Why This Project?

IBD's Relative Strength (RS) Rating is one of the most powerful tools for momentum investing — used by William O'Neil, Mark Minervini, and thousands of growth investors. But IBD doesn't provide it for free, and existing open-source alternatives only calculate weighted returns without the crucial percentile ranking step.

This project solves that. We calculate true RS Ratings (1-99) for 4,600+ US stocks daily:

• RS 99 = outperforming 99% of all stocks over the past year
• RS 50 = median performer
• RS 1 = bottom 1%

from rs_rating import RS

rs = RS()
rs.get("NVDA")
# {'ticker': 'NVDA', 'date': '2026-03-19', 'rs_raw': 0.1666, 'rs_rating': 70}

No API key needed. No rate limits. Just pip install and go.

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Installation

pip install ibd-rs-rating

Zero dependencies — uses only Python standard library (urllib, json).

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Quick Start

from rs_rating import RS

rs = RS()

# Get latest RS Rating for a stock
rs.get("AAPL")
# {'ticker': 'AAPL', 'date': '2026-03-19', 'rs_raw': 0.2841, 'rs_rating': 72}

# Get RS Rating for a specific date
rs.get("AAPL", date="2026-03-01")

# Top 10 stocks by RS Rating
rs.top(10)
# [{'ticker': 'MU', 'rs_rating': 99, 'rs_raw': 1.99}, ...]

# RS Rating history (last 30 days)
rs.history("NVDA")
# [{'date': '2026-03-19', 'rs_raw': 0.17, 'rs_rating': 70}, ...]

# Compare multiple stocks
rs.compare(["NVDA", "AMD", "AVGO", "INTC"])
# [{'ticker': 'AVGO', 'rs_rating': 85}, {'ticker': 'NVDA', 'rs_rating': 70}, ...]

# Filter: stocks with RS ≥ 90
rs.filter(min_rating=90)
# Returns all stocks in the top 10%

# SPY & QQQ benchmark RS (raw score, not ranked)
rs.reference()
# [{'ticker': 'SPY', 'rs_raw': 0.049}, {'ticker': 'QQQ', 'rs_raw': 0.063}]

# Stocks with biggest RS Rating improvement (last 5 trading days)
rs.movers(days=5, n=10)
# [{'ticker': 'XYZ', 'rs_rating': 85, 'prev_rating': 60, 'change': 25}, ...]

# Biggest RS losers
rs.movers(days=5, n=10, direction="down")

# Available date range
rs.dates()
# {'first': '2025-03-21', 'last': '2026-03-19'}

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Available Stocks

4,600+ US-listed stocks are tracked and updated daily. See the full list with current RS ratings:

data/tickers.csv

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API Reference

RS(url=None, key=None)

Create a client instance. No arguments needed — connects to the public API by default.

.get(ticker, date=None) → dict | None

Get RS rating for a single ticker. Returns latest if no date specified.

Parameter	Type	Description
ticker	str	Stock symbol (case-insensitive)
date	str	Optional. "YYYY-MM-DD" format

.history(ticker, start=None, end=None, days=30) → list

Get RS rating history for a ticker.

Parameter	Type	Description
ticker	str	Stock symbol
start	str	Start date "YYYY-MM-DD"
end	str	End date "YYYY-MM-DD"
days	int	Recent days (default: 30, ignored if start is set)

.top(n=20, date=None) → list

Get top N stocks ranked by RS Rating.

.bottom(n=20, date=None) → list

Get bottom N stocks ranked by RS Rating.

.filter(min_rating=None, max_rating=None, date=None) → list

Filter stocks by RS Rating range.

# Stocks with RS between 80 and 95
rs.filter(min_rating=80, max_rating=95)

.compare(tickers, date=None) → list

Compare RS ratings for a list of tickers, sorted by rating descending.

rs.compare(["AAPL", "MSFT", "GOOG", "AMZN", "META"])

.reference(date=None) → list

Get RS raw scores for benchmark indices (SPY, QQQ). These are not percentile-ranked — they provide a baseline to compare individual stocks against the market.

.movers(days=5, n=20, direction="up") → list

Get stocks with the biggest RS Rating change over recent trading days. Perfect for finding emerging momentum leaders.

Parameter	Type	Description
days	int	Lookback period in trading days (default: 5)
n	int	Number of results (default: 20)
direction	str	"up" for gainers, "down" for losers

rs.movers(days=5, n=10, direction="up")
# [{'ticker': 'XYZ', 'rs_rating': 85, 'prev_rating': 60, 'change': 25}, ...]

.dates() → dict

Get the available date range for RS data.

rs.dates()
# {'first': '2025-03-21', 'last': '2026-03-19'}

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How It Works

The Formula

RS Rating follows IBD's reverse-engineered methodology:

RS Raw = 0.4 × ROC(63) + 0.2 × ROC(126) + 0.2 × ROC(189) + 0.2 × ROC(252)

Where ROC(n) = cumulative price return over the last n trading days.

This gives 5x more weight to the most recent quarter compared to the oldest quarter — designed to catch stocks with accelerating momentum.

Quarter	Effective Weight
Most recent (0-3 months)	100%
2nd quarter (3-6 months)	60%
3rd quarter (6-9 months)	40%
Oldest (9-12 months)	20%

The raw score is then percentile-ranked across all ~4,600 stocks to produce a rating from 1 to 99.

Data Pipeline

Finviz Screener → Ticker list (~4,600 stocks)
       ↓
yfinance → Daily close prices (2 years history)
       ↓
RS calculation → Vectorized pandas computation
       ↓
Supabase PostgreSQL → Stored & served via REST API
       ↓
GitHub Actions → Automated daily update (weekdays, after market close)

Universe

• ~4,600 US-listed stocks (NYSE, NASDAQ, AMEX)
• Market cap > $50M (micro-cap and above)
• Excludes ETFs and shell companies (SPACs)
• Includes ADRs (BABA, TSM, etc.)
• SPY & QQQ tracked as reference benchmarks

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Self-Hosting

Want to run your own instance? The calculation engine is included.

git clone https://github.com/your-username/IBD-RS-Rating.git
cd IBD-RS-Rating
pip install -e ".[pg]"

# Local mode (SQLite)
python -m ibd_rs init      # Download 2yr data + calculate RS (~30 min)
python -m ibd_rs update    # Daily update (~3 min)
python -m ibd_rs top 20    # View top stocks

# Cloud mode (Supabase)
export DATABASE_URL="postgresql://..."
python -m ibd_rs init      # Loads data into Supabase

CLI Commands

Command	Description
python -m ibd_rs init	Initial setup: download data + compute RS
python -m ibd_rs update	Daily update: new prices + RS recalc
python -m ibd_rs top [N]	Top N stocks by RS Rating
python -m ibd_rs lookup TICKER	RS history for a ticker
python -m ibd_rs status	Database statistics
python -m ibd_rs export	Export to CSV

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Accuracy

Compared against actual IBD MarketSmith RS Ratings:

Range	Accuracy	Notes
RS 90+	±1-3 points	Near-exact match for top performers
RS 60-90	±5-10 points	Systematic offset due to universe size difference
RS < 30	±3-6 points	Both agree stock is weak

Ranking order is consistent — the same stocks appear at the top. The absolute values may differ slightly because IBD's exact formula and universe are proprietary.

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Disclaimer

This project is not affiliated with Investor's Business Daily (IBD) or William O'Neil + Co. RS Ratings are calculated using a reverse-engineered approximation of IBD's methodology. For official ratings, subscribe to IBD MarketSmith.

This tool is for educational and research purposes. It is not financial advice.

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License

MIT