Put/Call Ratio Dataset

Access options market data including put/call ratios. Track options positioning and market sentiment derived from derivatives activity for systematic trading strategies.

What’s Included

The Put/Call Ratio dataset provides:

Put/Call Ratio: Ratio of put volume to call volume
Volume Data: Total puts and calls traded
Open Interest: Outstanding options contracts
Historical Data: Track changes in options sentiment over time
Daily Updates: Fresh data every trading day

Coverage

Coverage	Details
Markets	S&P 500, NASDAQ, NYSE
Data Points	Volume, open interest, ratios
Update Frequency	Daily
Historical Data	2+ years

Quick Start

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

# Get put/call data as DataFrame
df = fb.options.put_call("S&P 500", "AAPL", as_dataframe=True)
print(df.head())

For complete code examples in Python, JavaScript, C++, Rust, and cURL, see the API Reference.

Interpreting Put/Call Ratios

Volume Put/Call Ratio

Ratio	Interpretation	Signal
> 1.2	Excessive put buying	Contrarian bullish (fear may be overdone)
1.0 - 1.2	Elevated put activity	Bearish sentiment
0.7 - 1.0	Normal range	Neutral
0.5 - 0.7	Elevated call activity	Bullish sentiment
< 0.5	Excessive call buying	Contrarian bearish (euphoria may be overdone)

Open Interest Put/Call Ratio

Open interest ratios show longer-term positioning:

Rising OI Ratio: Increasing hedging/bearish positioning
Falling OI Ratio: Decreasing hedges/bullish positioning
Stable OI Ratio: Consistent market positioning

Use Cases

Contrarian Sentiment Signal

Use extreme put/call readings as contrarian signals:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def get_contrarian_signal(market, ticker):
    """Generate contrarian signal from put/call ratio"""
    data = fb.options.put_call(market, ticker)

    if not data.get("putCallData"):
        return None

    latest = data["putCallData"][0]
    ratio = latest["ratio"]

    # Calculate 20-day average for context
    ratios = [d["ratio"] for d in data["putCallData"][:20]]
    avg_ratio = sum(ratios) / len(ratios)
    std_dev = (sum((r - avg_ratio) ** 2 for r in ratios) / len(ratios)) ** 0.5

    # Z-score
    z_score = (ratio - avg_ratio) / std_dev if std_dev > 0 else 0

    if z_score > 2:
        return {"signal": "contrarian_buy", "z_score": z_score, "ratio": ratio}
    elif z_score < -2:
        return {"signal": "contrarian_sell", "z_score": z_score, "ratio": ratio}
    else:
        return {"signal": "neutral", "z_score": z_score, "ratio": ratio}

signal = get_contrarian_signal("S&P 500", "AAPL")
print(f"Signal: {signal['signal']} (Z-score: {signal['z_score']:.2f})")

Options Flow Scanner

Screen for unusual options activity:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def scan_unusual_options(tickers):
    """Find stocks with unusual put/call ratios"""
    unusual = []

    for ticker in tickers:
        try:
            data = fb.options.put_call("S&P 500", ticker)

            if not data.get("putCallData"):
                continue

            latest = data["putCallData"][0]
            ratio = latest["ratio"]

            # Flag extremes
            if ratio > 1.5 or ratio < 0.4:
                unusual.append({
                    "ticker": ticker,
                    "ratio": ratio,
                    "signal": "high_puts" if ratio > 1.5 else "high_calls",
                    "date": latest["date"]
                })
        except Exception:
            continue

    return sorted(unusual, key=lambda x: abs(x["ratio"] - 1), reverse=True)

tickers = ["AAPL", "MSFT", "GOOGL", "AMZN", "NVDA", "META", "TSLA"]
unusual = scan_unusual_options(tickers)

for u in unusual:
    print(f"{u['ticker']}: P/C Ratio {u['ratio']:.2f} - {u['signal']}")

Sentiment Trend Analysis

Track how options sentiment is changing:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def analyze_sentiment_trend(market, ticker, days=20):
    """Analyze if options sentiment is getting more bullish or bearish"""
    data = fb.options.put_call(market, ticker)

    if not data.get("putCallData") or len(data["putCallData"]) < days:
        return None

    recent = data["putCallData"][:days//2]
    older = data["putCallData"][days//2:days]

    recent_avg = sum(d["ratio"] for d in recent) / len(recent)
    older_avg = sum(d["ratio"] for d in older) / len(older)

    change = ((recent_avg - older_avg) / older_avg) * 100

    if change > 10:
        trend = "increasingly_bearish"
    elif change < -10:
        trend = "increasingly_bullish"
    else:
        trend = "stable"

    return {
        "ticker": ticker,
        "recent_avg": recent_avg,
        "older_avg": older_avg,
        "change_percent": change,
        "trend": trend
    }

trend = analyze_sentiment_trend("S&P 500", "AAPL")
print(f"Trend: {trend['trend']} (Change: {trend['change_percent']:.1f}%)")

Put/Call Data API Reference - Endpoint details, parameters, and response schema
News Sentiment - Combine with sentiment data
AI Price Forecasts - Combine with predictions
Python SDK Documentation