Tradingview MCP
atilaahmettaner/tradingview-mcp
AI Trading Intelligence — live prices, 30+ technical indicators, backtesting (6 strategies), walk-forward overfitting detection, trade logs, equity curves, licensed news sentiment (Marketaux), and…
Retrieve full historical end-of-day price data for market indices using Octagon MCP.
Install Stock Historical Index
$ npx skills add OctagonAI/skills --skill stock-historical-index -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install OctagonAI/skills stock-historical-index --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/stock-historical-index .claude/skills/stock-historical-index && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "stock-historical-index" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/stock-historical-index into .claude/skills/stock-historical-index/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-historical-index", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/OctagonAI/skills/tree/main/skills/stock-historical-indexType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add OctagonAI/skills --skill stock-historical-index -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install OctagonAI/skills stock-historical-index --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/stock-historical-index .agents/skills/stock-historical-index && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "stock-historical-index" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/stock-historical-index into .agents/skills/stock-historical-index/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-historical-index", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add OctagonAI/skills --skill stock-historical-index -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install OctagonAI/skills stock-historical-index --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/stock-historical-index .cursor/skills/stock-historical-index && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "stock-historical-index" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/stock-historical-index into .cursor/skills/stock-historical-index/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-historical-index", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/OctagonAI/skills.git --path skills/stock-historical-index--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add OctagonAI/skills --skill stock-historical-index -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install OctagonAI/skills stock-historical-index --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/stock-historical-index .gemini/skills/stock-historical-index && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "stock-historical-index" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/stock-historical-index into .gemini/skills/stock-historical-index/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-historical-index", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install OctagonAI/skills stock-historical-indexInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add OctagonAI/skills --skill stock-historical-index -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/stock-historical-index .github/skills/stock-historical-index && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "stock-historical-index" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/stock-historical-index into .github/skills/stock-historical-index/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-historical-index", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add OctagonAI/skills --skill stock-historical-index -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install OctagonAI/skills stock-historical-index --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/stock-historical-index .opencode/skills/stock-historical-index && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "stock-historical-index" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/stock-historical-index into .opencode/skills/stock-historical-index/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-historical-index", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
stock-historical-index- GitHub stars
- 127
- Token cost
- ~2k tokens
- SKILL.md length
- 705 words
- Files
- 5 (incl. references)
- Skills in repo
- 53
- Repo updated
- First seen
- Licence
- MIT
At a glance
Retrieve full historical end-of-day price data for market indices using Octagon MCP.
- Works in 4 steps: Identify Parameters → Execute Query via Octagon MCP → Expected Output → …
- Analyzing index performance over time
- SKILL.md covers Prerequisites, Workflow, Example Queries and Common Index Symbols, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Stock Historical Index is an agent skill from OctagonAI/skills. 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.
Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `README.md`, `marketplace.json` and `references/interpreting-results.md`).
It sits in Business, Finance & HR, covering Stock and market analysis, Time tracking and reporting and MCP servers. It works with Model Context Protocol. The repository describes itself as: A collection of Claude skills for agentic financial research by Octagon. The licence is MIT.
When your agent uses it
- Analyzing index performance over time
- Tracking market trends
- Calculating returns
- Understanding market context for individual stock analysis
Example prompts
- “/stock-historical-index”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 51e938c. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Stock Historical Index loads about 2k tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 66 tokens; SKILL.md has 705 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from OctagonAI/skills at commit 51e938c, republished under its MIT licence (© OctagonAI). 705 words, ~1,986 tokens.
Download SKILL.mdSave it as
.claude/skills/stock-historical-index/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- stock-historical-index
- description
- 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.
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 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:
{
"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 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) - 1Volume 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 |
Show full SKILL.md (283 more words)Show less
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
Use for context: Index performance explains stock moves.
Calculate alpha: Your returns vs. market.
Watch volume: High volume days are significant.
Track extremes: Big up/down days signal sentiment.
Compare indices: Different indices, different signals.
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 |
© OctagonAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (references) in skills/stock-historical-index of OctagonAI/skills.
- SKILL.md
- README.md
- marketplace.json
- references/interpreting-results.md
- references/mcp-setup.md
Open the folder on GitHubat commit 51e938c
Compare with similar skills
Stock Historical Index next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Stock Historical Index this skillOctagonAI/skills | 127 | — | ~2k | Automated safety check: Pass | MIT | |
| Tradingview MCPatilaahmettaner/tradingview-mcp | 5k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Polymarket Tennislivetennisapi/livetennisapi-mcp | 152 | — | ~3k | Automated safety check: Pass | MIT | |
| Okx Sentiment Trackerdex-original/okx-agent-trade-kit | 110 | 1 repos | ~3.8k | Automated safety check: Pass | MIT | |
| Wind MCP SkillWind-Alice/AliceMarket | 134 | 1 repos | ~1.3k | Automated safety check: Pass | None | |
| Helium MCPcomposio-community/awesome-codex-skills | 17k | — | ~599 | Automated safety check: Pass | None |
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Questions about Stock Historical Index
What does Stock Historical Index do?
Retrieve full historical end-of-day price data for market indices using Octagon MCP. Stock Historical Index is an agent skill from OctagonAI/skills. Retrieve full historical end-of-day price data for market indices using Octagon MCP.
When should I use Stock Historical Index?
Stock Historical Index fits situations like: analyzing index performance over time; tracking market trends; calculating returns; understanding market context for individual stock analysis.
How do I install Stock Historical Index in Claude Code?
Run `npx skills add OctagonAI/skills --skill stock-historical-index -a claude-code`. Or copy the skill folder (skills/stock-historical-index in OctagonAI/skills) into .claude/skills/stock-historical-index in your project. Claude Code loads it when a task matches its description.
How do I install Stock Historical Index in Codex?
Run `npx skills add OctagonAI/skills --skill stock-historical-index -a codex`. Or copy the skill folder (skills/stock-historical-index in OctagonAI/skills) into .agents/skills/stock-historical-index in your project. Codex loads it when a task matches its description.
Can I use Stock Historical Index in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add OctagonAI/skills --skill stock-historical-index -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/stock-historical-index, .gemini/skills/stock-historical-index, .github/skills/stock-historical-index and .opencode/skills/stock-historical-index in your project.
What does Stock Historical Index need to run?
SKILL.md names no scripts, command-line tools or credentials: Stock Historical Index is instructions for the agent only.
Does Stock Historical Index access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Stock Historical Index safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Stock Historical Index use?
Stock Historical Index is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Stock Historical Index use?
About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.5k tokens, read only when the agent opens those files.
What are the alternatives to Stock Historical Index?
Skills that share tags, products or a category with Stock Historical Index: Tradingview MCP (atilaahmettaner/tradingview-mcp, 5k stars), Polymarket Tennis (livetennisapi/livetennisapi-mcp, 152 stars), Okx Sentiment Tracker (dex-original/okx-agent-trade-kit, 110 stars) and Wind MCP Skill (Wind-Alice/AliceMarket, 134 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Stock Historical Index?
OctagonAI (a GitHub organization) maintains it in OctagonAI/skills, which has 127 GitHub stars. The repository holds 53 skills in this directory. The repository was last updated on June 5, 2026.
Source: OctagonAI/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.