/estimate-analysis

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

If your host has not already executed the shell probes below (for example, in Codex), run the commands inside the `!` wrappers with its shell tool before choosing a setup path. Use the actual output; if shell access is unavailable, report that limitation.

Deep-dives into analyst estimates and revision trends using Yahoo Finance data via [yfinance](https://github.com/ranaroussi/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:

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

| 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? |

---

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.

---

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 directi