earnings-preview

---
name: earnings-preview
description: >
license: MIT
---

# Earnings Preview Skill

Generates a pre-earnings briefing using Yahoo Finance data via [yfinance](https://github.com/ranaroussi/yfinance). Pulls together upcoming earnings date, consensus estimates, historical accuracy, analyst sentiment, and key financial context — everything you need before an earnings call.

**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 "exec('try:\n import yfinance\n print(\'yfinance \' + yfinance.__version__ + \' installed\')\nexcept Exception:\n print(\'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 All Data

Extract the ticker symbol from the user's request. If they mention a company name without a ticker, look it up. Then fetch all relevant data in one script to minimize API calls.

```python
import yfinance as yf
import pandas as pd
from datetime import datetime

ticker = yf.Ticker("AAPL")  # replace with actual ticker

# --- Core data ---
info = ticker.info
calendar = ticker.calendar

# --- Estimates ---
earnings_est = ticker.earnings_estimate
revenue_est = ticker.revenue_estimate

# --- Historical track record ---
earnings_hist = ticker.earnings_history

# --- Analyst sentiment ---
price_targets = ticker.analyst_price_targets
recommendations = ticker.recommendations

# --- Recent financials for context ---
quarterly_income = ticker.quarterly_income_stmt
quarterly_cashflow = ticker.quarterly_cashflow
```

### What to extract from each source

| Data Source | Key Fields | Purpose |
|---|---|---|
| `calendar` | Earnings Date, Ex-Dividend Date | When earnings are and key dates |
| `earnings_estimate` | avg, low, high, numberOfAnalysts, yearAgoEps, growth (for 0q, +1q, 0y, +1y) | Consensus EPS expectations |
| `revenue_estimate` | avg, low, high, numberOfAnalysts, yearAgoRevenue, growth | Revenue expectations |
| `earnings_history` | epsEstimate, epsActual, epsDifference, surprisePercent | Beat/miss track record |
| `analyst_price_targets` | current, low, high, mean, median | Street price targets |
| `recommendations` | Buy/Hold/Sell counts | Sentiment distribution |
| `quarterly_income_stmt` | TotalRevenue, NetIncome, BasicEPS | Recent trajectory |

---

## Step 3: Build the Earnings Preview

Assemble the data into a structured briefing. The goal is to give the user everything they need in one glance.

### Section 1: Earnings Date & Key Info

Report the upcoming earnings date from `calendar`. Include:
- Company name, ticker, sector, industry
- Upcoming earnings date (and whether it's before/after market)
- Current stock price and recent performance (1-week, 1-month)
- Market cap

### Section 2: Consensus Estimates

Present the current quarter estimates from `earnings_estimate` and `revenue_estimate`:

| Metric | Consensus | Low | High | # Analysts | Year Ago | Growth |
|---|---|---|---|---|---|---|
| EPS | $1.42 | $1.35 | $1.50 | 28 | $1.26 | +12.7% |
| Revenue | $94.3B | $92.1B | $96.8B | 25 | $89.5B | +5.4% |

If the estimate range is unusually wide (high/low spread > 20% of consensus), note that as a sign of high uncertainty.

### Section 3: Historical Beat/Miss Track Record

From `earnings_history`, show the last 4 quarters:

| Quarter | EPS Est | EPS Actual | Surprise | Beat/Miss |
|---|---|---|---|---|
| Q3 2024 | $1.35 | $1.40 | +3.7% | Beat |
| Q2 2024 | $1.30 | $1.33 | +2.3% | Beat |
| Q1 2024 | $1.52 | $1.53 | +0.7% | Beat |
| Q4 2023 | $2.10 | $2.18 | +3.8% | Beat |

Summarize: "AAPL has beaten EPS estimates in 4 of the last 4 quarters by an average of 2.6%."

### Section 4: Analyst Sentiment

From `recommendations` and `analyst_price_targets`:

- Current recommendation distribution (Strong Buy / Buy / Hold / Sell / Strong Sell)
- Price target range: low, mean, median, high vs. current price
- Implied upside/downside from mean target

### Section 5: Key Metrics to Watch

Based on the quarterly financials, highlight 3-5 things the market will focus on:
- Revenue growth trend (accelerating or decelerating?)
- Margin trajectory (expanding or compressing?)
- Any notable line items that changed significantly quarter-over-quarter
- Segment breakdowns if available in the data

This section requires judgment — think about what matters for this specific company/sector.

---

## Step 4: Respond to the User

Present the preview as a clean, structured briefing:

1. **Lead with the headline**: "AAPL reports earnings on [date]. Here's what to expect."
2. **Show all 5 sections** with clear headers and tables
3. **End with a brief summary**: 2-3 sentences capturing the overall setup (bullish/bearish lean based on estimates, track record, and sentiment — frame as "the street expects" not personal recommendation)

### Caveats to include
- Estimates can change up until the report date
- Historical beats don't guarantee future beats
- Yahoo Finance data may lag real-time consensus by a few hours
- This is not financial advice

---

## Reference Files

- `references/api_reference.md` — Detailed yfinance API reference for earnings and estimate methods

Read the reference file when you need exact method signatures or edge case handling.