/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](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

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

| 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 |

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:**

| Period | Consensus | Low | High | Range Width | # Analysts | YoY Growth | |---|---|---|---|---|---|---| | Current Qtr (0q) | $1.42 | $1.35 | $1.50 | $0.15 (10.6%) | 28 | +12.7% | | Next Qtr (+1q) | $1.58 | $1.48 | $1.68 | $0.20 (12.7%) | 25 | +8.3% | | Current Year (0y) | $6.70 | $6.50 | $6.95 | $0.45 (6.7%) | 30 | +10.2% | | Next Year (+1y) | $7.45 | $7.10 | $7.85 | $0.75 (10.1%) | 28 | +11.2% |

**Revenue Estimates:**

| Period | Consensus | Low | High | # Analysts | YoY Growth | |---|---|---|---|---|---| | Current Qtr | $94.3B | $92.1B | $96.8B | 25 | +5.4% | | Next Qtr | $102.1B | $99.5B | $105.0B | 22 | +6.1% |

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:

| Period | Current | 7 Days Ago | 30 Days Ago | 60 Days Ago | 90 Days Ago | |---|---|---|---|---|---| | Current Qtr | $1.42 | $1.41 | $1.40 | $1.38 | $1.35 | | Next Qtr | $1.58 | $1.57 | $1.56 | $1.55 | $1.54 | | Current Year | $6.70 | $6.68 | $6.65 | $6.58 | $6.50 | | Next Year | $7.45 | $7.43 | $7.40 | $7.35 | $7.28 |

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 upwar