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

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

earnings_estimate

revenue_estimate

eps_trend

eps_revisions

growth_estimates

earnings_history

Step 3: Route Based on User Intent

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

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

revenue_estimate

EPS Estimates:

Revenue Estimates:

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

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

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

Highlight whether the company is expected to grow faster or slower than its peers.

Section 5: Historical Estimate Accuracy

From

earnings_history

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:

1. Lead with the key insight: "AAPL estimates are trending higher across all periods, with positive revision breadth (80% of recent revisions are upward)."

2. Show the tables for each section the user cares about

3. 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?

4. 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.