Earnings Recap Skill

Step 1: Ensure yfinance Is Available

!`python3 -c "import yfinance; print('yfinance ' + yfinance.__version__ + ' installed')" 2>/dev/null || echo "YFINANCE_NOT_INSTALLED"`

YFINANCE_NOT_INSTALLED

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

Step 2: Identify the Ticker and Gather Data

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

earnings_history

quarterly_income_stmt

history()

info

news

Step 3: Determine the Most Recent Earnings

earnings_history

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

Step 4: Build the Earnings Recap

Section 1: Headline Result

• 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

Section 2: Earnings vs. Estimates Detail

earnings_history

Section 3: Quarterly Financial Trends

quarterly_income_stmt

• 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

• 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

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

Reference Files

• references/api_reference.md

  — Detailed yfinance API reference for earnings history and financial statement methods