Contains Shell Commands

This skill contains shell command directives (

!command

) that may execute system commands. Review carefully before installing.

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.