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Financial Report Tracker

通过本地 Python 脚本调用财经数据服务,按股票代码跟踪财报发布日期、对比 EPS 与营收预期和实际值,并输出含关键发现、数据概览表与行动建议的 Markdown 分析报告。依赖 yfinance、requests、pandas,缺失字段标注为“数据不可用”。

openlark
openlark
热度 37.11
最近更新 2026-05-11 17:45:33
分类:数据依赖:代码执行 / 第三方 API应用场景:财务 / 报告撰写 / 市场分析 / 研究 ...

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add financial-report-tracker
Metadata
name
financial-report-tracker
description
Automatically track tech company financial reports and generate investment summaries. Supports retrieving earnings calendars, market expectation comparisons, key metric interpretation, and more.

Financial Report Tracker

Automatically track tech company financial reports and generate investment summaries. Suitable for investors tracking portfolio companies' earnings calendars and automatically summarizing earnings highlights and risks.

Use Cases

When users mention earnings reports, financial reports, EPS, revenue expectations, earnings interpretation, tracking a company's financials, and similar scenarios.

Prerequisites

Install Python dependencies before first use:

pip install yfinance requests pandas

Core Capabilities

  1. Earnings Calendar Tracking — Automatically retrieve target company earnings release dates
  2. Market Expectation Comparison — EPS/Revenue expectations vs. actual data
  3. Earnings Interpretation — Key metric changes and management guidance summary

Command List

Command Description Usage
track Track earnings release dates python scripts/earnings_tracker.py track <ticker>
preview Earnings preview analysis python scripts/earnings_tracker.py preview <ticker>
review Earnings interpretation python scripts/earnings_tracker.py review <ticker> --quarter <Q1/Q2/Q3/Q4>

Usage Workflow

Scenario 1: Track Earnings Date

Track Apple's next earnings release date and market expectations
python scripts/earnings_tracker.py track AAPL

Scenario 2: Earnings Preview Analysis

Pre-earnings expectation analysis
python scripts/earnings_tracker.py preview AAPL

Scenario 3: Earnings Review

Interpret key data from the latest earnings report
python scripts/earnings_tracker.py review AAPL --quarter Q1

Output Format

All commands output a standard Markdown format report:

# 📊 Financial Report Tracker Report

**Generated on**: YYYY-MM-DD HH:MM

## Key Findings
1. [Key finding 1]
2. [Key finding 2]
3. [Key finding 3]

## Data Overview
| Metric | Value | Trend | Rating |
|--------|-------|-------|--------|
| Metric A | XXX | ↑ | ⭐⭐⭐⭐ |
| Metric B | YYY | → | ⭐⭐⭐ |

## Detailed Analysis
[Multi-dimensional analysis based on actual data]

## Actionable Recommendations
| Priority | Recommendation | Expected Outcome |
|----------|----------------|------------------|
| 🔴 High | [Specific recommendation] | [Quantified expectation] |
| 🟡 Medium | [Specific recommendation] | [Quantified expectation] |
| 🟢 Low | [Specific recommendation] | [Quantified expectation] |

References

Notes

  • All analysis is based on data retrieved by the script; data is not fabricated
  • Missing data fields are marked "Data Unavailable" rather than guessed
  • It is recommended to combine with human judgment; AI analysis is for reference only