Estimate Analysis

# Estimate Analysis

Deep-dive into analyst estimates and revision trends for any stock using Yahoo Finance data.

## What it does

- Shows EPS and revenue estimate distributions across all periods (current/next quarter, current/next year)
- Tracks estimate revision trends over 7, 30, 60, and 90-day windows
- Counts upward vs downward revisions to measure revision breadth
- Compares growth estimates against industry, sector, and S&P 500 benchmarks
- Assesses historical estimate accuracy with beat/miss patterns

## Triggers

`estimate analysis for AAPL`, `analyst estimate trends for NVDA`, `EPS revisions for TSLA`, `how have estimates changed for MSFT`, `estimate revisions`, `EPS trend`, `revenue estimates`, `consensus changes`, `analyst estimates`, `growth estimates`, `are estimates going up or down`, `estimate momentum`, `revision trend`, `forward estimates`, `bull case vs bear case estimates`, `estimate spread`

## 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 all estimate-related methods


# Estimate Analysis — yfinance API Reference

Detailed reference for the yfinance estimate and analysis methods.

---

## Earnings Estimate

```python
ticker.earnings_estimate
```

Returns a DataFrame indexed by period with columns:
- `numberOfAnalysts` — analyst count
- `avg` — consensus average EPS
- `low` — lowest EPS estimate
- `high` — highest EPS estimate
- `yearAgoEps` — EPS from same period last year
- `growth` — expected growth rate (decimal: 0.127 = 12.7%)

Periods:
- `0q` — current quarter
- `+1q` — next quarter
- `0y` — current fiscal year
- `+1y` — next fiscal year

---

## Revenue Estimate

```python
ticker.revenue_estimate
```

Same period structure as earnings_estimate. Columns:
- `numberOfAnalysts`
- `avg` — consensus revenue
- `low`, `high` — range
- `yearAgoRevenue` — revenue from same period last year
- `growth` — expected growth rate (decimal)

**Note**: Revenue figures are in raw numbers. Format for display:
```python
def format_revenue(val):
    if val >= 1e12: return f"${val/1e12:.1f}T"
    if val >= 1e9:  return f"${val/1e9:.1f}B"
    if val >= 1e6:  return f"${val/1e6:.1f}M"
    return f"${val:,.0f}"
```

---

## EPS Trend

```python
ticker.eps_trend
```

Shows how the EPS consensus has changed over time. Returns a DataFrame with:

Index: same periods (0q, +1q, 0y, +1y)
Columns:
- `current` — current estimate
- `7daysAgo` — estimate 7 days ago
- `30daysAgo` — estimate 30 days ago
- `60daysAgo` — estimate 60 days ago
- `90daysAgo` — estimate 90 days ago

**Usage**: Calculate the change over each window to identify revision momentum:
```python
trend = ticker.eps_trend
for period in trend.index:
    row = trend.loc[period]
    change_90d = row['current'] - row['90daysAgo']
    change_30d = row['current'] - row['30daysAgo']
    pct_change_90d = change_90d / abs(row['90daysAgo']) * 100
    print(f"{period}: {change_90d:+.2f} ({pct_change_90d:+.1f}%) over 90 days")
```

---

## EPS Revisions

```python
ticker.eps_revisions
```

Shows the count of upward and downward estimate revisions. Returns a DataFrame with:

Index: periods (0q, +1q, 0y, +1y)
Columns:
- `upLast7days` — number of upward revisions in last 7 days
- `upLast30days` — number of upward revisions in last 30 days
- `downLast7days` — number of downward revisions in last 7 days
- `downLast30days` — number of downward revisions in last 30 days

**Revision ratio** (useful metric):
```python
revisions = ticker.eps_revisions
for period in revisions.index:
    row = revisions.loc[period]
    total_30d = row['upLast30days'] + row['downLast30days']
    if total_30d > 0:
        ratio = row['upLast30days'] / total_30d
        print(f"{period}: {ratio:.0%} bullish ({row['upLast30days']} up, {row['downLast30