Analyst Ratings Dataset

Access Wall Street analyst ratings, price targets, and recommendation changes. Track consensus ratings and target price movements for systematic trading strategies.

What’s Included

The Analyst Ratings dataset provides:

Current Rating: Buy, hold, or sell recommendation
Price Target: Analyst’s target price (with historical changes)
Analyst Info: Firm name and rating action
Rating Changes: Upgrades, downgrades, and reiterations
Historical Data: Track rating changes over time

Coverage

Coverage	Details
Markets	S&P 500, NASDAQ, NYSE
Sources	Major investment banks and research firms
Update Frequency	Daily
Historical Data	3+ years

Rating Categories

Rating	Meaning	Signal
Strong Buy	Highest conviction recommendation	Bullish
Buy / Outperform	Expect stock to beat market	Bullish
Hold / Neutral	Expect market performance	Neutral
Underperform	Expect stock to lag market	Bearish
Sell	Recommend selling	Bearish

Note: The targetPrice field is a string (e.g., "$275").

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

df = fb.analyst_ratings.ticker("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/analyst-ratings/AAPL", headers=headers)
result = response.json()

# Access ratings from the response envelope
ratings = result["data"]["ratings"]
for r in ratings[:5]:
    print(f"{r['date']}: {r['institution']} - {r['rating']} (Target: {r['targetPrice']})")

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

Use Cases

Upgrade/Downgrade Scanner

Scan for recent rating changes:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def scan_rating_changes(tickers):
    """Find stocks with recent upgrades or downgrades"""
    upgrades = []
    downgrades = []

    for symbol in tickers:
        try:
            df = fb.analyst_ratings.ticker(symbol, as_dataframe=True)

            for _, row in df.head(5).iterrows():  # Last 5 ratings
                if row["action"] == "Upgrade":
                    upgrades.append({
                        "symbol": symbol,
                        "institution": row["institution"],
                        "rating": row["rating"],
                        "targetPrice": row["targetPrice"],
                        "date": row.name
                    })
                elif row["action"] == "Downgrade":
                    downgrades.append({
                        "symbol": symbol,
                        "institution": row["institution"],
                        "rating": row["rating"],
                        "targetPrice": row["targetPrice"],
                        "date": row.name
                    })
        except Exception:
            continue

    return {"upgrades": upgrades, "downgrades": downgrades}

tickers = ["AAPL", "MSFT", "GOOGL", "AMZN", "NVDA", "META", "TSLA"]
changes = scan_rating_changes(tickers)

print("Recent Upgrades:")
for u in changes["upgrades"]:
    print(f"  {u['symbol']}: {u['institution']} -> {u['rating']} ({u['targetPrice']})")

print("\nRecent Downgrades:")
for d in changes["downgrades"]:
    print(f"  {d['symbol']}: {d['institution']} -> {d['rating']} ({d['targetPrice']})")

Price Target Upside

Calculate upside potential to consensus target:

import re
from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def parse_target_price(target_str):
    """Parse target price string like '$275' and return numeric value"""
    if not target_str:
        return None
    # Match price patterns like $190, $205.50, etc.
    prices = re.findall(r'\$?([\d,]+\.?\d*)', str(target_str).replace(',', ''))
    if prices:
        # Return the last price (new target) or only price
        return float(prices[-1])
    return None

def calculate_upside(symbol, current_price):
    """Calculate upside to analyst target"""
    df = fb.analyst_ratings.ticker(symbol, as_dataframe=True)

    if df.empty:
        return None

    # Parse target prices from string format
    targets = []
    for target_str in df["targetPrice"]:
        target = parse_target_price(target_str)
        if target:
            targets.append(target)

    if not targets:
        return None

    avg_target = sum(targets) / len(targets)
    upside = ((avg_target - current_price) / current_price) * 100

    return {
        "symbol": symbol,
        "current_price": current_price,
        "avg_target_price": round(avg_target, 2),
        "upside_percent": round(upside, 2),
        "num_analysts": len(targets)
    }

# Example usage (you'd get current price from market data)
result = calculate_upside("AAPL", 185.00)
if result:
    print(f"{result['symbol']}: {result['upside_percent']}% upside to ${result['avg_target_price']}")

Consensus Trend Analysis

Track how analyst sentiment is changing over time:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def analyze_rating_trend(symbol):
    """Analyze if analyst sentiment is improving or deteriorating"""
    df = fb.analyst_ratings.ticker(symbol, as_dataframe=True)

    rating_scores = {
        "Sell": 1, "Underperform": 2, "Hold": 3, "Neutral": 3,
        "Buy": 4, "Outperform": 4, "Overweight": 4, "Strong Buy": 5
    }

    df["score"] = df["rating"].map(rating_scores).fillna(3)

    if len(df) < 2:
        return "insufficient_data"

    recent_avg = df["score"].head(5).mean()
    older_avg = df["score"].iloc[5:10].mean() if len(df) > 5 else recent_avg

    if recent_avg > older_avg + 0.3:
        return "improving"
    elif recent_avg < older_avg - 0.3:
        return "deteriorating"
    else:
        return "stable"

trend = analyze_rating_trend("AAPL")
print(f"Analyst sentiment: {trend}")

Analyst Ratings API Reference - Endpoint details, parameters, and response schema
AI Price Forecasts - Combine with predictions
News Sentiment - Market sentiment data