earnings-recap

# Earnings Recap Skill

Generates a post-earnings analysis using Yahoo Finance data via [yfinance](https://github.com/ranaroussi/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:

```python
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.

```python
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:

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

```python
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:

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