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.