
Estimate Analysis
FreeAnalyze analyst estimates and trends for stocks.
Free · Opens the source repo
What Estimate Analysis does
The Estimate Analysis skill provides a comprehensive look into analyst estimates and revision trends for any stock, leveraging data from Yahoo Finance through the yfinance library. This skill is particularly useful for users who want to dig deeper into the expectations surrounding a company’s earnings per share (EPS) and revenue forecasts, as well as how these estimates have evolved over time. By providing insights into consensus estimates, revision trends, and growth projections, this skill helps users make informed decisions based on the latest analyst data.
To use the skill effectively, users need to ensure that the yfinance library is installed in their environment. Once set up, the skill can process requests to fetch and analyze estimate-related data for various stocks. Users can request detailed analyses, such as how EPS estimates have changed over time, the direction of revisions, and comparisons of estimates across different periods. The skill allows for a nuanced understanding of the market sentiment surrounding a stock, making it a valuable tool for investors and analysts alike.
The skill breaks down the data into several key sections, including estimate overviews, revision trends, and growth estimates, each providing critical insights into the stock's expected performance. For example, it can show how current estimates compare to historical data, identify trends in analyst revisions, and highlight growth expectations relative to industry benchmarks. This structured approach ensures that users can quickly find the information they need to assess a company's outlook effectively.
Overall, the Estimate Analysis skill is designed for anyone involved in stock analysis, whether they are individual investors, financial analysts, or data scientists looking to incorporate financial metrics into their research. Its emphasis on historical context and trend analysis makes it a powerful resource for understanding market expectations and making strategic investment decisions.
When to use it
Use this skill when you need detailed insights into analyst estimates, including trends and revisions, for specific stocks.
When not to use it
This skill is not suitable for simple stock price lookups or when you need real-time trading data.
What you can build with it
Analyzing EPS Revisions
Use the skill to track how EPS estimates for a company like AAPL have changed over the last 90 days.
Comparing Analyst Estimates
Request a comparison of current and historical revenue estimates for a stock to understand growth expectations.
Understanding Market Sentiment
Analyze the revision trends for a stock to gauge whether analysts are becoming more bullish or bearish.
How to install Estimate Analysis
View source1. Install with the skills CLI
npx skills add himself65/finance-skills/estimate-analysis --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by himself65Estimate 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
| 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 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:
| Period | Up (last 7d) | Down (last 7d) | Up (last 30d) | Down (last 30d) |
|---|---|---|---|---|
| Current Qtr | 5 | 1 | 12 | 3 |
| Next Qtr | 3 | 2 | 8 | 5 |
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:
| Entity | Current Qtr | Next Qtr | Current Year | Next Year | Past 5Y Annual |
|---|---|---|---|---|---|
| AAPL | +12.7% | +8.3% | +10.2% | +11.2% | +14.5% |
| Industry | +9.1% | +7.0% | +8.5% | +9.0% | — |
| Sector | +11.3% | +8.8% | +10.0% | +10.5% | — |
| S&P 500 | +7.5% | +6.2% | +8.0% | +8.5% | — |
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:
| Quarter | Estimate | Actual | Surprise % | Direction |
|---|---|---|---|---|
| Q3 2024 | $1.35 | $1.40 | +3.7% | Beat |
| Q2 2024 | $1.30 | $1.33 | +2.3% | Beat |
| Q1 2024 | $1.52 | $1.53 | +0.7% | Beat |
| Q4 2023 | $2.10 | $2.18 | +3.8% | Beat |
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:
-
Lead with the key insight: "AAPL estimates are trending higher across all periods, with positive revision breadth (80% of recent revisions are upward)."
-
Show the tables for each section the user cares about
-
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?
-
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.
Frequently asked questions about Estimate Analysis
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