stock-historical-index

Retrieve full historical end-of-day price data for market indices using Octagon MCP. Use when analyzing index performance over time, tracking market trends, calculating returns, and understanding market context for individual stock analysis.

27 stars

Best use case

stock-historical-index is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Retrieve full historical end-of-day price data for market indices using Octagon MCP. Use when analyzing index performance over time, tracking market trends, calculating returns, and understanding market context for individual stock analysis.

Teams using stock-historical-index should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/stock-historical-index/SKILL.md --create-dirs "https://raw.githubusercontent.com/OctagonAI/skills/main/skills/stock-historical-index/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/stock-historical-index/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How stock-historical-index Compares

Feature / Agentstock-historical-indexStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Retrieve full historical end-of-day price data for market indices using Octagon MCP. Use when analyzing index performance over time, tracking market trends, calculating returns, and understanding market context for individual stock analysis.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Stock Historical Index

Retrieve full historical end-of-day price data for market indices using the Octagon MCP server.

## Prerequisites

Ensure Octagon MCP is configured in your AI agent (Cursor, Claude Desktop, Windsurf, etc.). See [references/mcp-setup.md](references/mcp-setup.md) for installation instructions.

## Workflow

### 1. Identify Parameters

Determine your query parameters:
- **Index Symbol**: ^GSPC (S&P 500), ^DJI (Dow), ^IXIC (NASDAQ), etc.
- **Start Date**: Beginning of date range
- **End Date**: End of date range

### 2. Execute Query via Octagon MCP

Use the `octagon-agent` tool with a natural language prompt:

```
Retrieve full historical end-of-day price data for the <INDEX> index from <START_DATE> to <END_DATE>.
```

**MCP Call Format:**

```json
{
  "server": "octagon-mcp",
  "toolName": "octagon-agent",
  "arguments": {
    "prompt": "Retrieve full historical end-of-day price data for the ^GSPC index from 2025-01-01 to 2025-04-30."
  }
}
```

### 3. Expected Output

The agent returns comprehensive daily index data:

| Date | Open | High | Low | Close | Volume | Change | Change % | VWAP |
|------|------|------|-----|-------|--------|--------|----------|------|
| 2025-04-30 | 5,499.44 | 5,581.84 | 5,433.24 | 5,569.07 | 5.45B | +69.63 | +1.27% | 5,520.90 |
| 2025-04-29 | 5,508.87 | 5,571.95 | 5,505.70 | 5,560.82 | 4.75B | +51.95 | +0.94% | 5,536.84 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |

**Key Statistics**:
- Highest single-day volume: 9.49B on 2025-04-09
- Largest daily gain: +9.90% on 2025-04-09
- Largest daily loss: -4.12% on 2025-04-04
- Trading days covered: 79

**Data Sources**: octagon-stock-data-agent

### 4. Interpret Results

See [references/interpreting-results.md](references/interpreting-results.md) for guidance on:
- Analyzing index price trends
- Calculating period returns
- Understanding volume patterns
- Identifying significant market moves

## Example Queries

**S&P 500 History:**
```
Retrieve full historical end-of-day price data for the ^GSPC index from 2025-01-01 to 2025-04-30.
```

**NASDAQ Composite:**
```
Get historical data for ^IXIC from 2024-01-01 to 2024-12-31.
```

**Dow Jones:**
```
Show ^DJI historical prices for Q1 2025.
```

**Russell 2000:**
```
Retrieve historical data for ^RUT from 2024-06-01 to 2025-06-01.
```

**Multiple Indices:**
```
Compare ^GSPC and ^IXIC performance from 2025-01-01 to 2025-03-31.
```

## Common Index Symbols

### US Major Indices

| Symbol | Index | Description |
|--------|-------|-------------|
| ^GSPC | S&P 500 | 500 large-cap US stocks |
| ^DJI | Dow Jones | 30 blue-chip stocks |
| ^IXIC | NASDAQ Composite | All NASDAQ stocks |
| ^NDX | NASDAQ 100 | 100 largest NASDAQ |
| ^RUT | Russell 2000 | 2000 small-cap stocks |

### Sector Indices

| Symbol | Index | Description |
|--------|-------|-------------|
| ^XLK | Technology | Tech sector |
| ^XLF | Financials | Financial sector |
| ^XLV | Healthcare | Healthcare sector |
| ^XLE | Energy | Energy sector |
| ^XLI | Industrials | Industrial sector |

### Volatility Indices

| Symbol | Index | Description |
|--------|-------|-------------|
| ^VIX | VIX | Market volatility |
| ^VXN | VXN | NASDAQ volatility |

## Understanding Index Data

### Price Components

| Field | Description |
|-------|-------------|
| Open | First trade price of day |
| High | Highest price of day |
| Low | Lowest price of day |
| Close | Last trade price of day |
| Volume | Total shares traded |
| Change | Point change from prior close |
| Change % | Percentage change |
| VWAP | Volume-weighted average price |

### Daily Range Analysis

| Metric | Calculation |
|--------|-------------|
| Daily Range | High - Low |
| Range % | (High - Low) / Open |
| Position in Range | (Close - Low) / (High - Low) |

## Return Calculations

### Period Returns

| Period | Formula |
|--------|---------|
| Daily | (Close - Prior Close) / Prior Close |
| Weekly | (Friday Close - Monday Open) / Monday Open |
| Monthly | (Month End - Month Start) / Month Start |
| YTD | (Current - Year Start) / Year Start |

### Example

From the data:
- Start (Jan 2): 5,868.56
- End (Apr 30): 5,569.07
- Return: (5,569.07 - 5,868.56) / 5,868.56 = -5.10%

### Cumulative Returns

```
Cumulative = (1 + r1) × (1 + r2) × ... × (1 + rn) - 1
```

## Volume Analysis

### Volume Patterns

| Pattern | Interpretation |
|---------|----------------|
| High volume + up | Strong buying |
| High volume + down | Strong selling |
| Low volume + up | Weak rally |
| Low volume + down | Lack of sellers |

### Volume Metrics

| Metric | Purpose |
|--------|---------|
| Average daily volume | Baseline |
| Volume spike | Unusual activity |
| Volume trend | Participation changes |

### Example

From the data:
- Highest volume: 9.49B on 2025-04-09
- This coincided with +9.90% gain (major rally)

## Trend Analysis

### Trend Identification

| Pattern | Characteristics |
|---------|-----------------|
| Uptrend | Higher highs, higher lows |
| Downtrend | Lower highs, lower lows |
| Consolidation | Range-bound |
| Reversal | Trend change |

### Moving Averages

| MA | Use |
|----|-----|
| 50-day | Short-term trend |
| 200-day | Long-term trend |
| Golden Cross | 50 > 200 (bullish) |
| Death Cross | 50 < 200 (bearish) |

## Volatility Analysis

### Measuring Volatility

| Metric | Calculation |
|--------|-------------|
| Daily Range % | (High - Low) / Close |
| Daily Change | Absolute daily change |
| Std Deviation | Dispersion of returns |

### Volatility Context

| Daily Change % | Market Condition |
|----------------|------------------|
| <0.5% | Low volatility |
| 0.5-1% | Normal |
| 1-2% | Elevated |
| >2% | High volatility |
| >4% | Extreme |

### Example

From the data:
- Largest gain: +9.90%
- Largest loss: -4.12%
- Range: 14.02%
- **Interpretation**: Period of elevated volatility

## Key Market Events

### Identifying Significant Days

| Criteria | Threshold |
|----------|-----------|
| Big up day | >2% gain |
| Big down day | >2% loss |
| Volume spike | >2x average |
| Range expansion | >2x normal range |

### Event Analysis

| From Data | Event |
|-----------|-------|
| +9.90% on Apr 9 | Major rally |
| -4.12% on Apr 4 | Significant selloff |
| 9.49B volume | Highest participation |

## Benchmarking Use

### Stock vs. Index

| Comparison | Formula |
|------------|---------|
| Alpha | Stock Return - Index Return |
| Beta | Stock Vol / Index Vol × Correlation |
| Relative Strength | Stock / Index |

### Example Use

- Your stock returned +15%
- S&P 500 returned -5.10%
- Alpha: +20.10% outperformance

## Common Use Cases

### Market Context
```
What was the overall market doing when my stock fell?
```

### Return Comparison
```
How did the S&P 500 perform in Q1 2025?
```

### Volatility Assessment
```
What were the biggest up and down days for the market in 2024?
```

### Trend Analysis
```
Is the market in an uptrend or downtrend?
```

### Volume Analysis
```
What were the highest volume days for the S&P 500?
```

## Analysis Tips

1. **Use for context**: Index performance explains stock moves.

2. **Calculate alpha**: Your returns vs. market.

3. **Watch volume**: High volume days are significant.

4. **Track extremes**: Big up/down days signal sentiment.

5. **Compare indices**: Different indices, different signals.

6. **Consider VIX**: Volatility index for fear gauge.

## Integration with Other Skills

| Skill | Combined Use |
|-------|--------------|
| stock-performance | Stock vs. index comparison |
| sector-performance-snapshot | Sector vs. index |
| stock-quote | Current vs. historical |
| historical-market-cap | Market cap vs. index |

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