Earnings Preview

Earnings Preview Skill

Generates a pre-earnings briefing using Yahoo Finance data via 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 "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 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.

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:

• Lead with the headline: "AAPL reports earnings on [date]. Here's what to expect."
• Show all 5 sections with clear headers and tables
• 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.