Estimate Analysis

by himself6501fc7b4b34aeNo license3.3K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 3 days ago

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.

AI-generated overview

Analyzes sell-side analyst EPS and revenue estimates and revision trends using Yahoo Finance data via yfinance.

What it does
Pulls consensus EPS and revenue estimates, estimate ranges, revision trends over 7/30/60/90 days, revision breadth, growth estimates versus industry, sector and the S&P 500, and historical estimate accuracy from Yahoo Finance through yfinance. It organizes these into sections covering estimate overview, revision trends, revision breadth, growth comparisons and past surprise history. The output is an interpreted written analysis with supporting tables, including caveats that the data is for research and educational purposes only and is not financial advice.
When to use it
Use it when a user wants more than a single estimate lookup, such as estimate revisions or momentum, EPS trend, consensus changes, forward or next-quarter and annual estimates, the bull versus bear estimate spread, or growth projections across periods. It suits questions about how analyst expectations for a company are changing.
Requirements
Requires Python with yfinance and pandas; the skill instructs installing yfinance via pip if it is not present. It needs network access to Yahoo Finance data and a ticker symbol. It ships no scripts, only instructions and a reference file.

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 "exec('try:\n import yfinance\n print(\'yfinance \' + yfinance.__version__ + \' installed\')\nexcept Exception:\n print(\'YFINANCE_NOT_INSTALLED\')')"`

If YFINANCE_NOT_INSTALLED, install it:

python
import subprocess, syssubprocess.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.

python
import yfinance as yfimport pandas as pd
ticker = yf.Ticker("AAPL")  # replace with actual ticker
# --- Estimate data ---earnings_est = ticker.earnings_estimate      # EPS estimates by periodrevenue_est = ticker.revenue_estimate        # Revenue estimates by periodeps_trend = ticker.eps_trend                 # EPS estimate changes over timeeps_revisions = ticker.eps_revisions         # Up/down revision countsgrowth_est = ticker.growth_estimates         # Growth rate estimates
# --- Historical context ---earnings_hist = ticker.earnings_history      # Track recordinfo = ticker.info                           # Company basicsquarterly_income = ticker.quarterly_income_stmt  # Recent actuals

What each data source provides

Data SourceWhat It ShowsWhy It Matters
earnings_estimateCurrent EPS consensus by period (0q, +1q, 0y, +1y)The estimate levels — what analysts expect
revenue_estimateCurrent revenue consensus by periodTop-line expectations
eps_trendHow the EPS estimate has changed (7d, 30d, 60d, 90d ago)Revision direction — rising or falling expectations
eps_revisionsCount of upward vs downward revisions (7d, 30d)Revision breadth — are most analysts raising or cutting?
growth_estimatesGrowth rate estimates vs peers and sectorRelative positioning
earnings_historyActual vs estimated for last 4 quartersCalibration — how good are these estimates historically?

Step 3: Route Based on User Intent

Match the depth of the analysis to the question:

User RequestFocus AreaKey Sections
General estimate analysisFull analysisAll sections
"How have estimates changed"Revision trendsEPS Trend + Revisions
"What are analysts expecting"Current consensusEstimate overview
"Growth estimates"Growth projectionsGrowth Estimates
"Bull vs bear case"Estimate rangeHigh/low spread analysis
Compare estimates across periodsMulti-periodPeriod comparison table

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


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.

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.


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.

Source and attribution

Source:himself65/finance-skillsinplugins/market-analysis/skills/estimate-analysisat commit01fc7b4

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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