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.

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 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

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:

• Identify the earnings date for the price reaction analysis

• Match to the corresponding quarter in the financial statements

• 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.

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

Step 5: Respond to the User

Present the recap as a clean, structured summary:

• Lead with the headline: "AAPL reported Q3 2024 earnings on [date]: Beat EPS by 3.7%, revenue +5.4% YoY."

• Show the tables for detail

• Highlight what matters: Was this a meaningful beat or a low-bar situation? Is the trend improving or deteriorating?

• 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

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.