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, calls, and combined volume traded
Price: Underlying asset price at time of observation
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, ratios, price
Update Frequency	Daily
Historical Data	2+ years

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

df = fb.options.put_call("AAPL", as_dataframe=True)
print(df)

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.finbrain.tech"

headers = {"Authorization": f"Bearer {API_KEY}"}

response = requests.get(f"{BASE_URL}/v2/put-call-ratio/AAPL", headers=headers)
result = response.json()

# Access put/call data from the response envelope
for entry in result["data"][:5]:
    print(f"{entry['date']}: Ratio {entry['ratio']} | Calls {entry['callVolume']} | Puts {entry['putVolume']}")

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

Visualization

Plot put/call ratio trends with the built-in SDK chart:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

# One-line interactive options chart (stacked volume bars + ratio line)
fb.plot.options("NVDA")

Options Put/Call Chart — NVDA put/call ratio over time

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(symbol):
    """Generate contrarian signal from put/call ratio"""
    df = fb.options.put_call(symbol, as_dataframe=True)

    if df.empty:
        return None

    ratio = df["ratio"].iloc[0]

    # Calculate 20-day average for context
    ratios = df["ratio"].head(20)
    avg_ratio = ratios.mean()
    std_dev = ratios.std()

    # 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("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 symbol in tickers:
        try:
            df = fb.options.put_call(symbol, as_dataframe=True)

            if df.empty:
                continue

            ratio = df["ratio"].iloc[0]
            date = str(df.index[0])

            # Flag extremes
            if ratio > 1.5 or ratio < 0.4:
                unusual.append({
                    "symbol": symbol,
                    "ratio": ratio,
                    "signal": "high_puts" if ratio > 1.5 else "high_calls",
                    "date": 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['symbol']}: 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(symbol, days=20):
    """Analyze if options sentiment is getting more bullish or bearish"""
    df = fb.options.put_call(symbol, as_dataframe=True)

    if df.empty or len(df) < days:
        return None

    recent_avg = df["ratio"].head(days // 2).mean()
    older_avg = df["ratio"].iloc[days // 2:days].mean()

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

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

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

trend = analyze_sentiment_trend("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