estimate-analysis
Analyst estimate deep-dive with revision trends, growth projections, and historical accuracy tracking
npx skills add himself65/finance-skillsBefore / After Comparison
1 组Manually collecting data and reports, slow and error-prone
One-click professional analysis with real-time data across multiple dimensions
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 "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. |
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