estimate-analysis

name: estimate-analysis description: > Deep-dive into analyst estimates and revision trends for any stock using Yahoo Finance data. Use when the user wants to understand analyst estimate direction, how EPS or revenue forecasts changed over time, compare estimate distributions, or analyze growth projections across periods. Triggers: "estimate analysis for AAPL", "analyst estimate trends for NVDA", "EPS revisions for TSLA", "how have estimates changed for MSFT", "estimate revisions", "EPS trend", "revenue estimates", "consensus changes", "analyst estimates", "estimate distribution", "growth estimates for", "estimate momentum", "revision trend", "forward estimates", "next quarter estimates", "annual estimates", "estimate spread", "bull vs bear estimates", "estimate range", or any request about tracking or comparing analyst estimates/revisions. Use this skill when the user asks about estimates beyond a simple lookup — if they want context, trends, or analysis, this is the right skill.

Estimate Analysis Skill

Deep-dives into analyst estimates and revision trends using Yahoo Finance data via yfinance. Covers EPS and revenue estimate distributions, revision momentum, growth projections, and multi-period comparisons — the full picture of where the street thinks a company is heading.

Important: Data is for research and educational purposes only. Not financial advice. yfinance is not affiliated with Yahoo, Inc.

Step 1: Ensure yfinance Is Available

Current environment status:

!`python3 -c "import yfinance; print('yfinance ' + yfinance.__version__ + ' installed')" 2>/dev/null || echo "YFINANCE_NOT_INSTALLED"`

If YFINANCE_NOT_INSTALLED, install it:

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

If already installed, skip to the next step.

Step 2: Identify the Ticker and Gather Estimate Data

Extract the ticker from the user's request. Fetch all estimate-related data in one script.

import yfinance as yf
import pandas as pd

ticker = yf.Ticker("AAPL")  # replace with actual ticker

# --- Estimate data ---
earnings_est = ticker.earnings_estimate      # EPS estimates by period
revenue_est = ticker.revenue_estimate        # Revenue estimates by period
eps_trend = ticker.eps_trend                 # EPS estimate changes over time
eps_revisions = ticker.eps_revisions         # Up/down revision counts
growth_est = ticker.growth_estimates         # Growth rate estimates

# --- Historical context ---
earnings_hist = ticker.earnings_history      # Track record
info = ticker.info                           # Company basics
quarterly_income = ticker.quarterly_income_stmt  # Recent actuals

What each data source provides

Step 3: Route Based on User Intent

The user might want different levels of analysis. Route accordingly:

When in doubt, provide the full analysis — more context is better.

Step 4: Build the Estimate Analysis

Section 1: Estimate Overview

Present the current consensus for all available periods from earnings_estimate and revenue_estimate:

EPS Estimates:

Revenue Estimates:

Calculate and flag:

• Range width as % of consensus — wide ranges (>15%) signal high uncertainty
• Analyst coverage — fewer than 5 analysts means thin coverage, note this
• Growth trajectory — is growth accelerating or decelerating across periods?

Section 2: Revision Trends (EPS Trend)

This is often the most actionable section. From eps_trend, show how estimates have moved:

Summarize the trend: "Current quarter EPS estimates have risen 5.2% over the last 90 days, with most of the increase in the last 30 days — accelerating upward revision momentum."

Key interpretation:

• Rising estimates ahead of earnings = positive setup (the bar is rising)
• Falling estimates = analysts cutting numbers, often a negative signal
• Flat estimates = no new information being priced in
• Recent acceleration/deceleration matters more than the total move

Section 3: Revision Breadth (EPS Revisions)

From eps_revisions, show the up vs. down count:

Calculate a revision ratio: Up / (Up + Down). Ratios above 0.7 are strongly bullish; below 0.3 are bearish.

Section 4: Growth Estimates

From growth_estimates, compare the company's expected growth to benchmarks:

Highlight whether the company is expected to grow faster or slower than its peers.

Section 5: Historical Estimate Accuracy

From earnings_history, assess how reliable estimates have been:

Calculate:

• Beat rate: X of 4 quarters
• Average surprise: magnitude and direction
• Trend in surprise: Are beats getting bigger or smaller? A shrinking surprise with rising estimates could mean the bar is catching up to reality.

Step 5: Synthesize and Respond

Present the analysis with clear structure:

1. Lead with the key insight: "AAPL estimates are trending higher across all periods, with positive revision breadth (80% of recent revisions are upward)."

2. Show the tables for each section the user cares about

3. Provide interpretive context:

   • Is the revision trend confirming or contradicting the stock's recent price action?
   • How does the growth outlook compare to what's priced into the current P/E?
   • What's the relationship between estimate accuracy history and current estimate levels?

4. Flag risks and nuances:

   • Estimates cluster around consensus — the "real" distribution of outcomes is wider than low/high suggests
   • Revision momentum can reverse quickly on a single data point (guidance change, macro event)
   • Yahoo Finance estimates may lag behind real-time consensus providers by hours or days
   • Growth estimates for out-years (+1y) are inherently less reliable

Caveats to always include

• Analyst estimates reflect a consensus view, not certainty
• Estimate revisions are a signal but not a guarantee of future performance
• This is not financial advice

Reference Files

• references/api_reference.md — Detailed yfinance API reference for all estimate-related methods

Read the reference file when you need exact return formats or edge case handling.