---
id: gh-fs-estimate-analysis
name: "estimate-analysis"
url: https://skills.yangsir.net/skill/gh-fs-estimate-analysis
author: himself65
domain: finance
tags: ["estimate-analysis", "finance", "analysis"]
install_count: 568
rating: 4.20 (20 reviews)
github: https://github.com/himself65/finance-skills/tree/main/skills/estimate-analysis
---

# estimate-analysis

> 分析师预期深度分析，追踪修正趋势、增长预测及历史准确性表现。

**Stats**: 568 installs · 4.2/5 (20 reviews)

## Before / After 对比

### 金融分析

**Before**:

手动收集数据、整理报表，效率低且容易遗漏关键指标

**After**:

一键获取专业分析，数据实时更新，覆盖多维度指标

## Readme

# Estimate Analysis Skill

Deep-dives into analyst estimates and revision trends using Yahoo Finance data via [yfinance](https://github.com/ranaroussi/yfinance). Covers EPS and revenue estimate distributions, revision momentum, growth projections, and multi-period comparisons — the full picture of where the street thinks a company is heading.

**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:

```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 Estimate Data

Extract the ticker from the user's request. Fetch all estimate-related data in one script.

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

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

# --- Estimate data ---
earnings_est = ticker.earnings_estimate      # EPS estimates by period
revenue_est = ticker.revenue_estimate        # Revenue estimates by period
eps_trend = ticker.eps_trend                 # EPS estimate changes over time
eps_revisions = ticker.eps_revisions         # Up/down revision counts
growth_est = ticker.growth_estimates         # Growth rate estimates

# --- Historical context ---
earnings_hist = ticker.earnings_history      # Track record
info = ticker.info                           # Company basics
quarterly_income = ticker.quarterly_income_stmt  # Recent actuals
```

### What each data source provides

| Data Source | What It Shows | Why It Matters |
|---|---|---|
| `earnings_estimate` | Current EPS consensus by period (0q, +1q, 0y, +1y) | The estimate levels — what analysts expect |
| `revenue_estimate` | Current revenue consensus by period | Top-line expectations |
| `eps_trend` | How the EPS estimate has changed (7d, 30d, 60d, 90d ago) | Revision direction — rising or falling expectations |
| `eps_revisions` | Count of upward vs downward revisions (7d, 30d) | Revision breadth — are most analysts raising or cutting? |
| `growth_estimates` | Growth rate estimates vs peers and sector | Relative positioning |
| `earnings_history` | Actual vs estimated for last 4 quarters | Calibration — how good are these estimates historically? |

---

## Step 3: Route Based on User Intent

The user might want different levels of analysis. Route accordingly:

| User Request | Focus Area | Key Sections |
|---|---|---|
| General estimate analysis | Full analysis | All sections |
| "How have estimates changed" | Revision trends | EPS Trend + Revisions |
| "What are analysts expecting" | Current consensus | Estimate overview |
| "Growth estimates" | Growth projections | Growth Estimates |
| "Bull vs bear case" | Estimate range | High/low spread analysis |
| Compare estimates across periods | Multi-period | Period comparison table |

When in doubt, provide the full analysis — more context is better.

---

## Step 4: Build the Estimate Analysis

### Section 1: Estimate Overview

Present the current consensus for all available periods from `earnings_estimate` and `revenue_estimate`:

**EPS Estimates:**

| Period | Consensus | Low | High | Range Width | # Analysts | YoY Growth |
|---|---|---|---|---|---|---|
| Current Qtr (0q) | $1.42 | $1.35 | $1.50 | $0.15 (10.6%) | 28 | +12.7% |
| Next Qtr (+1q) | $1.58 | $1.48 | $1.68 | $0.20 (12.7%) | 25 | +8.3% |
| Current Year (0y) | $6.70 | $6.50 | $6.95 | $0.45 (6.7%) | 30 | +10.2% |
| Next Year (+1y) | $7.45 | $7.10 | $7.85 | $0.75 (10.1%) | 28 | +11.2% |

**Revenue Estimates:**

| Period | Consensus | Low | High | # Analysts | YoY Growth |
|---|---|---|---|---|---|
| Current Qtr | $94.

---
*Source: https://skills.yangsir.net/skill/gh-fs-estimate-analysis*
*Markdown mirror: https://skills.yangsir.net/api/skill/gh-fs-estimate-analysis/markdown*