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.

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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.

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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

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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:

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:

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.

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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

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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.

Earnings Preview

Generate a pre-earnings briefing for any stock using Yahoo Finance data.

What it does

• Shows upcoming earnings date and key dates
• Presents consensus EPS and revenue estimates with analyst count and range
• Reviews the company's historical beat/miss track record (last 4 quarters)
• Summarizes analyst sentiment (buy/hold/sell distribution, price targets)
• Highlights key metrics to watch based on recent quarterly trends

Triggers

earnings preview for AAPL, what to expect from TSLA earnings, MSFT reports next week, pre-earnings analysis, what are analysts expecting, will GOOGL beat earnings, earnings beat/miss history, upcoming earnings, consensus estimates, EPS expectations, what's the street expecting, earnings season preview

Prerequisites

• Python 3.8+
• yfinance (auto-installed if missing)

Platform

All platforms (Claude Code, Claude.ai, other agents)

Setup

No setup required — yfinance pulls data from Yahoo Finance without authentication.

Reference Files

• references/api_reference.md — yfinance API reference for earnings and estimate methods

Earnings Preview — yfinance API Reference

Detailed reference for the yfinance methods used by the earnings-preview skill.

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Calendar

ticker.calendar

Returns a dictionary with upcoming events:

• Earnings Date — list of datetime objects (usually a range like [start, end])
• Ex-Dividend Date — next ex-dividend date
• Dividend Date — next dividend payment date

Edge cases:

• Some tickers return an empty dict if no upcoming events are scheduled
• Earnings dates may show as a 2-day range (the company hasn't specified exact date/time)

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Earnings Estimate

ticker.earnings_estimate

Returns a DataFrame indexed by period:

• 0q — current quarter
• +1q — next quarter
• 0y — current year
• +1y — next year

Columns:

• numberOfAnalysts — number of analysts covering
• avg — consensus average EPS
• low — lowest estimate
• high — highest estimate
• yearAgoEps — EPS from the same period last year
• growth — expected growth rate (decimal, e.g., 0.127 = 12.7%)

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Revenue Estimate

ticker.revenue_estimate

Same structure as earnings_estimate but for revenue:

• numberOfAnalysts, avg, low, high, yearAgoRevenue, growth

Note: Revenue figures are in raw numbers (not millions/billions). Format appropriately for display.

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Earnings History

ticker.earnings_history

Returns a DataFrame with the last 4 quarters of actual vs estimated earnings:

Columns:

• epsEstimate — consensus EPS estimate at the time
• epsActual — reported EPS
• epsDifference — actual minus estimate
• surprisePercent — surprise as a percentage (decimal)

Index is datetime of each earnings report.

Note: surprisePercent is already in decimal form (0.037 = 3.7%). Multiply by 100 for display.

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Analyst Price Targets

ticker.analyst_price_targets

Returns a dictionary:

• current — current price
• low — lowest analyst target
• high — highest analyst target
• mean — average target
• median — median target

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Recommendations

ticker.recommendations

Returns a DataFrame with recommendation counts by period. Columns typically:

• strongBuy, buy, hold, sell, strongSell
• Index represents the period

Use the most recent row for current analyst sentiment distribution.

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Quarterly Financial Statements

ticker.quarterly_income_stmt   # Income statement
ticker.quarterly_balance_sheet  # Balance sheet
ticker.quarterly_cashflow       # Cash flow

Each returns a DataFrame with financial line items as rows and quarter dates as columns (most recent first).

Key income statement rows for earnings preview:

• Total Revenue
• Gross Profit
• Operating Income
• Net Income
• Basic EPS / Diluted EPS
• EBITDA

Tip: Compare the last 2-4 quarters to identify trends in revenue growth, margin expansion/compression, and EPS trajectory.

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Company Info

ticker.info

Key fields for context:

• shortName — company name
• sector, industry — classification
• marketCap — market capitalization
• currentPrice — current stock price
• previousClose — last closing price
• trailingPE, forwardPE — P/E ratios
• fiftyTwoWeekHigh, fiftyTwoWeekLow — 52-week range

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Historical Prices (for recent performance)

# 1-month performance
hist = ticker.history(period="1mo")
# 1-week performance
hist = ticker.history(period="5d")

Use to calculate % change for recent performance context.

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Error Handling

Always wrap data fetches in try/except:

try:
    data = ticker.earnings_estimate
    if data is None or (hasattr(data, 'empty') and data.empty):
        print("No earnings estimate data available")
except Exception as e:
    print(f"Error: {e}")

Common issues:

• No calendar data: Company hasn't announced next earnings date
• Empty estimates: Ticker may not have analyst coverage (small caps, foreign stocks)
• Stale data: Yahoo Finance estimates may not update in real-time; note this to the user