earnings-recap

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Earnings Recap Skill

财报回顾 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.

通过 yfinance 调用雅虎财经数据生成财报后分析，覆盖实际值与预期值对比、超预期幅度、股价反应及财务背景，全面呈现财报发布后的整体情况。

重要提示：数据仅用于研究和教育目的，不构成投资建议。yfinance 与 Yahoo, Inc. 无附属关系。

Step 1: Ensure yfinance Is Available

步骤 1：确保 yfinance 可用

Current environment status:

!`python3 -c "import yfinance; print('yfinance ' + yfinance.__version__ + ' installed')" 2>/dev/null || echo "YFINANCE_NOT_INSTALLED"`

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.

当前环境状态：

!`python3 -c "import yfinance; print('yfinance ' + yfinance.__version__ + ' installed')" 2>/dev/null || echo "YFINANCE_NOT_INSTALLED"`

如果输出

YFINANCE_NOT_INSTALLED

，请安装：

python

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

如果已安装，直接跳到下一步。

Step 2: Identify the Ticker and Gather Data

步骤 2：识别股票代码并收集数据

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

从用户请求中提取股票代码，通过一个脚本获取所有相关的财报后数据：

python

import yfinance as yf
import pandas as pd
from datetime import datetime, timedelta

ticker = yf.Ticker("AAPL")  # 替换为实际股票代码

--- Earnings result ---

--- 财报结果 ---

earnings_hist = ticker.earnings_history

--- Financial statements ---

--- 财务报表 ---

quarterly_income = ticker.quarterly_income_stmt quarterly_cashflow = ticker.quarterly_cashflow quarterly_balance = ticker.quarterly_balance_sheet

--- Price reaction ---

--- 价格反应 ---

Get ~30 days of history to capture the reaction window

获取近30天的历史数据以覆盖反应窗口

hist = ticker.history(period="1mo")

--- Context ---

--- 背景信息 ---

info = ticker.info news = ticker.news recommendations = ticker.recommendations

undefined

info = ticker.info news = ticker.news recommendations = ticker.recommendations

undefined

What to extract

需提取的内容

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

数据源	关键字段	用途
`earnings_history`	epsEstimate, epsActual, epsDifference, surprisePercent	业绩超出/低于预期结果
`quarterly_income_stmt`	TotalRevenue, GrossProfit, OperatingIncome, NetIncome, BasicEPS	实际财务数据
`history()`	财报日期前后的收盘价	股价反应
`info`	currentPrice, marketCap, forwardPE	当前市场背景
`news`	近期头条	财报相关新闻

Step 3: Determine the Most Recent Earnings

步骤 3：确定最新一期财报

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

股价反应计算

python

import numpy as np

python

import numpy as np

Find the earnings date from earnings_history index

从 earnings_history 索引中获取财报日期

earnings_date = earnings_hist.index[0] # most recent

earnings_date = earnings_hist.index[0] # 最新日期

Get daily prices around the earnings date

获取财报日期前后的日度价格

hist_extended = ticker.history(start=earnings_date - timedelta(days=5), end=earnings_date + timedelta(days=5))

The reaction is typically measured as:

反应通常按以下方式计算：

- Close on the last trading day before earnings -> Close on the first trading day after

- 财报前最后一个交易日的收盘价 -> 财报后第一个交易日的收盘价

Be careful with before/after market reports

注意区分盘前/盘后发布的财报

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.

---

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


**注意**：具体的反应窗口取决于公司财报的发布时间（盘前 vs 盘后），价格数据会反映这一点——请在财报日期附近寻找连续收盘价之间的最大缺口。

---

Step 4: Build the Earnings Recap

步骤 4：生成财报回顾

Section 1: Headline Result

第一部分：核心结果

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."

首先展示核心数据：

EPS：实际值 vs 预期值，超出/低于预期的幅度，超预期百分比
营收：实际值 vs 去年同期（从 quarterly_income_stmt 的 TotalRevenue 获取）
股价反应：财报当日的涨跌幅

示例："AAPL 第三季度 EPS 超出预期 3.7%（实际 1.40 美元 vs 预期 1.35 美元），营收同比增长 5.4% 至 943 亿美元，股价受财报影响上涨 +2.1%。"

Section 2: Earnings vs. Estimates Detail

第二部分：财报与预期对比详情

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

指标	预期	实际	超预期幅度
EPS	$1.35	$1.40	+$0.05 (+3.7%)

如果用户查询的是特定季度（而非最新季度），请在

earnings_history

中回溯查找对应数据。

Section 3: Quarterly Financial Trends

第三部分：季度财务趋势

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

展示

quarterly_income_stmt

中最近 4 个季度的核心指标：

季度	营收	同比增速	毛利率	运营利润率	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

从原始财务数据计算利润率：

毛利率 = GrossProfit / TotalRevenue
运营利润率 = OperatingIncome / TotalRevenue

Section 4: Stock Price Reaction

第四部分：股价反应

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?)

财报当日/下一个交易日的涨跌幅
与该股平均财报日涨跌幅的对比（计算
```
earnings_history
```
中最近 4 次财报日的平均绝对涨跌幅）
当前股价相对财报日涨跌幅的表现（是否保持涨幅、回吐涨幅、还是进一步上涨？）

Section 5: Context & What Changed

第五部分：背景与变化

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

基于数据，标注以下内容：

利润率相对上一季度是扩张还是收缩
营收增长轨迹是否有明显变化
本次超预期/不及预期与该股历史表现的对比（从完整的
```
earnings_history
```
获取）
如果有可用的
```
recommendations
```
数据，展示当前分析师观点

Step 5: Respond to the User

步骤 5：响应用户

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

将财报回顾整理为清晰、结构化的摘要呈现：

核心结果先行："AAPL 于 [日期] 发布 2024 年第三季度财报：EPS 超出预期 3.7%，营收同比增长 5.4%。"
展示表格 呈现详细数据
突出重点：本次是明显超预期还是低基数下的达标？趋势是向好还是走弱？
保持客观——仅呈现数据，避免给出投资建议

Caveats to include

需包含的免责声明

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

雅虎财经数据可能未覆盖财报电话会议的全部细节（业绩指引、细分业务数据等）
营收预期难以精确对比——yfinance 提供财务报表中的同比对比数据
股价反应可能受当日大盘走势影响
本内容不构成投资建议

Reference Files

参考文件

```
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.

```
references/api_reference.md
```
—— yfinance API 的详细参考，包含财报历史和财务报表方法的说明

当你需要确切的方法签名或是处理财务数据的边缘情况时，请阅读参考文件。