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name: estimate-analysis description: > Analyze sell-side analyst estimates and how they are changing, using Yahoo Finance data (yfinance): EPS and revenue consensus by period, estimate ranges and dispersion, revision trends over 7/30/60/90 days and up/down revision breadth, growth estimates vs industry, sector, and the S&P 500, and historical estimate accuracy. Use this skill when the user wants more than a single estimate lookup: estimate revisions or momentum, EPS trend, consensus changes, forward or next-quarter and annual estimates, the bull vs bear estimate spread, or growth projections across periods.

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.

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Step 1: Ensure yfinance Is Available

Current environment status:

!`python3 -c "exec('try:\n import yfinance\n print(\'yfinance \' + yfinance.__version__ + \' installed\')\nexcept Exception:\n print(\'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.

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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

Data Source	What It Shows	Why It Matters
earnings_estimate	Current EPS consensus by period (0q, +1q, 0y, +1y)	The estimate levels — what analysts expect
revenue_estimate	Current revenue consensus by period	Top-line expectations
eps_trend	How the EPS estimate has changed (7d, 30d, 60d, 90d ago)	Revision direction — rising or falling expectations
eps_revisions	Count of upward vs downward revisions (7d, 30d)	Revision breadth — are most analysts raising or cutting?
growth_estimates	Growth rate estimates vs peers and sector	Relative positioning
earnings_history	Actual vs estimated for last 4 quarters	Calibration — how good are these estimates historically?

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Step 3: Route Based on User Intent

Match the depth of the analysis to the question:

User Request	Focus Area	Key Sections
General estimate analysis	Full analysis	All sections
"How have estimates changed"	Revision trends	EPS Trend + Revisions
"What are analysts expecting"	Current consensus	Estimate overview
"Growth estimates"	Growth projections	Growth Estimates
"Bull vs bear case"	Estimate range	High/low spread analysis
Compare estimates across periods	Multi-period	Period comparison table

A general request gets the full analysis; a narrow question gets the matching sections.

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Step 4: Build the Estimate Analysis

Section 1: Estimate Overview

Present the current consensus for every available period (0q, +1q, 0y, +1y) from earnings_estimate and revenue_estimate: consensus, low, high, range width (as a % of consensus), analyst count, and YoY growth. Flag:

• Range width — ranges wider than 15% of consensus signal high uncertainty
• Analyst coverage — fewer than 5 analysts means thin coverage
• Growth trajectory — whether growth accelerates or decelerates across periods

Section 2: Revision Trends (EPS Trend)

Often the most actionable section. From eps_trend, show each period's current estimate against its value 7, 30, 60, and 90 days ago, and summarize the direction and whether the recent moves are accelerating.

How to read it:

• 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 or deceleration matters more than the total move

Section 3: Revision Breadth (EPS Revisions)

From eps_revisions, show up vs down revision counts over the last 7 and 30 days for each period, and the 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 for each period (and its past 5-year annual growth) with its industry, sector, and the S&P 500, and say whether it is expected to grow faster or slower than its peers.

Section 5: Historical Estimate Accuracy

From earnings_history, show estimate vs actual EPS and the surprise % for the last four quarters, then assess:

• Beat rate — how many of the four quarters beat
• Average surprise — magnitude and direction
• Trend in surprise — are beats getting bigger or smaller? A shrinking surprise with rising estimates can mean the bar is catching up to reality.

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Step 5: Synthesize and Respond

Lead with the key insight — the direction and breadth of revisions across periods — then show the tables for the sections the user cares about. Interpret rather than just tabulate: does the revision trend confirm or contradict the stock's recent price action, how does the growth outlook compare with what the current P/E prices in, and what does the estimate-accuracy history say about today's consensus?

Flag the nuances that apply: estimates cluster around consensus, so the real distribution of outcomes is wider than low/high suggests; revision momentum can reverse on a single guidance change or macro event; Yahoo Finance estimates can lag real-time consensus providers by hours or days; out-year (+1y) estimates are inherently less reliable. Close with the standing caveats: analyst estimates reflect a consensus view, not certainty; revisions are a signal, not a guarantee; this is not financial advice.

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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.