Generate a post-earnings analysis for any stock using Yahoo

Earnings Recap Skill

Generates a post-earnings analysis using Yahoo Finance data via yfinance. Covers the actual vs estimated numbers, surprise magnitude, stock price reaction, and financial context — a complete picture of what happened.

Important: Data is for research and educational purposes only. Not financial advice. yfinance is not affiliated with Yahoo, Inc.

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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.

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Step 2: Identify the Ticker and Gather Data

Extract the ticker from the user's request. Fetch all relevant post-earnings data in one script.

import yfinance as yf
import pandas as pd
from datetime import datetime, timedelta

ticker = yf.Ticker("AAPL")  # replace with actual ticker

# --- Earnings result ---
earnings_hist = ticker.earnings_history

# --- Financial statements ---
quarterly_income = ticker.quarterly_income_stmt
quarterly_cashflow = ticker.quarterly_cashflow
quarterly_balance = ticker.quarterly_balance_sheet

# --- Price reaction ---
# Get ~30 days of history to capture the reaction window
hist = ticker.history(period="1mo")

# --- Context ---
info = ticker.info
news = ticker.news
recommendations = ticker.recommendations

What to extract

Data Source	Key Fields	Purpose
earnings_history	epsEstimate, epsActual, epsDifference, surprisePercent	Beat/miss result
quarterly_income_stmt	TotalRevenue, GrossProfit, OperatingIncome, NetIncome, BasicEPS	Actual financials
history()	Close prices around earnings date	Stock price reaction
info	currentPrice, marketCap, forwardPE	Current context
news	Recent headlines	Earnings-related news

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Step 3: Determine the Most Recent Earnings

The most recent earnings result is the first row (most recent date) in earnings_history. Use its date to:

1. Identify the earnings date for the price reaction analysis
2. Match to the corresponding quarter in the financial statements
3. Calculate stock price reaction — compare the close before earnings to the next trading day's close (or open, depending on whether earnings were before/after market)

Price reaction calculation

import numpy as np

# Find the earnings date from earnings_history index
earnings_date = earnings_hist.index[0]  # most recent

# Get daily prices around the earnings date
hist_extended = ticker.history(start=earnings_date - timedelta(days=5),
                                end=earnings_date + timedelta(days=5))

# The reaction is typically measured as:
# - Close on the last trading day before earnings -> Close on the first trading day after
# Be careful with before/after market reports
if len(hist_extended) >= 2:
    pre_price = hist_extended['Close'].iloc[0]
    post_price = hist_extended['Close'].iloc[-1]
    reaction_pct = ((post_price - pre_price) / pre_price) * 100

Note: The exact reaction window depends on when the company reported (before market open vs after close). The price data will reflect this — look for the biggest gap between consecutive closes near the earnings date.

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Step 4: Build the Earnings Recap

Section 1: Headline Result

Lead with the key numbers:

• EPS: Actual vs. Estimate, beat/miss by how much, surprise %
• Revenue: Actual vs. prior year (from quarterly_income_stmt TotalRevenue)
• Stock reaction: % move on earnings day

Example: "AAPL beat Q3 EPS estimates by 3.7% ($1.40 actual vs $1.35 expected). Revenue grew 5.4% YoY to $94.3B. The stock rose +2.1% on the report."

Section 2: Earnings vs. Estimates Detail

Metric	Estimate	Actual	Surprise
EPS	$1.35	$1.40	+$0.05 (+3.7%)

If the user asked about a specific quarter (not the most recent), look further back in earnings_history.

Section 3: Quarterly Financial Trends

Show the last 4 quarters of key metrics from quarterly_income_stmt:

Quarter	Revenue	YoY Growth	Gross Margin	Operating Margin	EPS
Q3 2024	$94.3B	+5.4%	46.2%	30.1%	$1.40
Q2 2024	$85.8B	+4.9%	46.0%	29.8%	$1.33
Q1 2024	$119.6B	+2.1%	45.9%	33.5%	$2.18
Q4 2023	$89.5B	-0.3%	45.2%	29.2%	$1.26

Calculate margins from the raw financials:

• Gross Margin = GrossProfit / TotalRevenue
• Operating Margin = OperatingIncome / TotalRevenue

Section 4: Stock Price Reaction

• The % move on the earnings day/next session
• How it compares to the stock's average earnings-day move (calculate the average absolute move from the last 4 earnings dates in earnings_history)
• Where the stock is now relative to the earnings-day move (has it held, given back gains, extended further?)

Section 5: Context & What Changed

Based on the data, note:

• Whether margins expanded or compressed vs prior quarter
• Any notable changes in revenue growth trajectory
• How the beat/miss compares to the stock's historical pattern (from the full earnings_history)
• Current analyst sentiment from recommendations if available

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Step 5: Respond to the User

Present the recap as a clean, structured summary:

1. Lead with the headline: "AAPL reported Q3 2024 earnings on [date]: Beat EPS by 3.7%, revenue +5.4% YoY."
2. Show the tables for detail
3. Highlight what matters: Was this a meaningful beat or a low-bar situation? Is the trend improving or deteriorating?
4. Keep it factual — present the data, avoid making investment recommendations

Caveats to include

• Yahoo Finance data may not include all details from the earnings call (guidance, segment breakdowns)
• Revenue estimates are harder to compare precisely — yfinance provides YoY comparison from financial statements
• Price reaction may be influenced by broader market moves on the same day
• This is not financial advice

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Reference Files

• references/api_reference.md — Detailed yfinance API reference for earnings history and financial statement methods

Read the reference file when you need exact method signatures or to handle edge cases in the financial data.