Wall Street isn't a monolith. Stop using a single "Mean Estimate." Access the raw, disaggregated forecasts of 5,000+ individual analysts. Track the "Smart Money" revisions, measure Analyst Conviction, and filter out stale data to build a consensus that actually predicts price action.
The Signal is in the Revision. Static targets are noise. The alpha lies in the rate of change and the divergence of opinion.
Standard feeds average everyone. We don't. Our "Smart Consensus" algorithm automatically excludes estimates older than 30 days and weighs 5-star rated analysts higher than underperformers. You get the number the market is actually pricing in, not a stale average.
Track the "Revision Ratio" in real-time. Our feed explicitly calculates the net number of Upgrades vs. Downgrades over a rolling 7, 30, and 90-day window. A surging Revision Ratio is often a leading indicator of a breakout before the earnings print.
Volatility traders love uncertainty. We provide the "Standard Deviation" of estimates, allowing you to measure the divergence of opinion. High dispersion often precedes massive volatility events, while low dispersion signals a consensus trade.
Show me all price target upgrades for TSLA this week.
Download the full history of revisions to backtest "Analyst Momentum" factors.
Query the analyst notes: "Why did Morgan Stanley downgrade Apple despite the earnings beat?"
Sync our Analyst_Master table to your warehouse for enterprise-grade modeling.
{
"ticker": "AMD",
"date": "2025-10-15",
"consensus": {
"eps_mean": 3.45,
"eps_high": 3.80,
"eps_low": 3.10,
"revision_trend_30d": "Positive",
"analyst_count": 34
},
"recent_actions": [
{
"analyst_name": "Stacy Rasgon",
"firm": "Bernstein",
"action": "Maintain",
"rating_from": "Outperform",
"rating_to": "Outperform",
"price_target_from": 140,
"price_target_to": 150,
"date": "2025-10-12"
}
]
} revision_trend_30d : A quick directional flag. If the consensus EPS has moved up in the last 30 days, this signals positive momentum. eps_high vs. eps_low The spread between the most bullish and bearish analyst. Use this range to define your "Best Case" and "Worst Case" scenario modeling. price_target_to The new target. By comparing this to price_target_from, you can instantly calculate the "Magnitude" of the upgrade (e.g., +$10). firm : Filter by influence. You might care more about a Goldman Sachs upgrade than a boutique regional firm.
Our team of experienced financial advisors is here to provide personalized guidance and support.
Daily. Every night, we re-calculate the mean, median, high, and low estimates based on any new notes published that day.
Yes. Our backend tracks the historical accuracy of individual analysts. While the standard API returns all data, our Enterprise tier allows you to filter specifically for "Top 10% Accurate" analysts.
"Rating" is the recommendation (Buy/Hold/Sell). "Target Price" is the forecasted stock price in 12 months. We provide both. Often, an analyst will keep a "Buy" rating but lower the "Target Price"—a subtle bearish signal our data captures.
Yes. Through our Vector Feed, you can access the summary narratives explaining why a rating was changed (e.g., "lowering target due to softening PC demand").
We map over 100+ distinct broker taxonomies into a standardized 5-Point Scale (1=Strong Sell, 5=Strong Buy) for easy quantitative comparison. However, we also retain the Original Raw Text field, allowing you to build NLP models that detect subtle shifts in language (e.g., a broker moving from "Conviction Buy" to just "Buy").
Yes. For accurate backtesting, it is critical to include estimates for companies that have since been delisted or acquired. Our history includes the full analyst coverage for "dead" tickers exactly as it appeared at the time, ensuring your "Consensus Revision" models aren't artificially inflated by only looking at today's winners.
