Time Series Quant Finance Server
БесплатноНе проверенAn MCP server that calculates technical analysis indicators for stock tickers using yfinance and pandas-ta, providing real-time financial data and quantitative
Описание
An MCP server that calculates technical analysis indicators for stock tickers using yfinance and pandas-ta, providing real-time financial data and quantitative analysis tools for LLMs.
README
An MCP (Model Context Protocol) server built using FastMCP that calculates technical analysis indicators for financial stock tickers using yfinance and pandas-ta.
This server provides LLMs with access to real-time financial market data and quantitative analysis tools, enabling them to analyze stock trends, momentum, volatility, and key moving averages.
Features
- Automatic Baseline Trends: Always includes 50-day Simple Moving Average (SMA) and 200-day SMA to establish baseline trends.
- Multiple Technical Indicators: Support for:
- RSI (Relative Strength Index)
- MACD (Moving Average Convergence Divergence)
- BBANDS (Bollinger Bands)
- ATR (Average True Range)
- SMA (Simple Moving Average with custom lengths)
- EMA (Exponential Moving Average with custom lengths)
- Robust Multi-index Handling: Programmatically flattens
yfinancemulti-level indices to prevent Pandas crashes. - JSON Output: Returns the last 5 trading days of historical and calculated data, cleaned and formatted for easy consumption by an LLM.
Installation & Setup
Prerequisites
- Python >= 3.12
- uv (recommended package manager) or standard
pip
1. Clone & Install Dependencies
Using uv (recommended):
# Install dependencies using uv
uv sync
Or using standard pip and virtual environments:
# Create and activate a virtual environment
python3 -m venv .venv
source .venv/bin/activate
# Install dependencies
pip install -r requirements.txt
Running and Debugging
Running via FastMCP Dev Inspector
FastMCP includes a built-in development inspector that lets you interact with and test the server in a web UI.
# Using uv
uv run fastmcp dev server.py
# Or using standard python
python server.py dev
Alternatively, you can use the official MCP Inspector:
npx -y @modelcontextprotocol/inspector uv run server.py
MCP Configuration
To integrate this server with client applications like Claude Desktop, add the configuration to your MCP settings file (typically ~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows).
Configuration using uv (Recommended)
{
"mcpServers": {
"finance-server": {
"command": "uv",
"args": [
"run",
"--directory",
"/absolute/path/to/mcp-finance-server",
"server.py"
]
}
}
}
Configuration using Virtual Environment Python
{
"mcpServers": {
"finance-server": {
"command": "/absolute/path/to/mcp-finance-server/.venv/bin/python",
"args": [
"/absolute/path/to/mcp-finance-server/server.py"
]
}
}
}
Make sure to replace /absolute/path/to/mcp-finance-server with the actual path to the repository on your system.
Tools Reference
calculate_technical_indicators
Fetches daily historical stock data for the last year and calculates requested technical indicators.
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
ticker |
string |
Yes | - | Stock ticker symbol (e.g. "AAPL", "MSFT", "TSLA"). |
requested_indicators |
array[string] |
Yes | - | List of indicators to calculate. Supported values: "RSI", "MACD", "BBANDS", "ATR", "SMA", "EMA". |
rsi_length |
integer |
No | 14 |
Period length for RSI calculation. |
macd_fast |
integer |
No | 12 |
Fast period for MACD. |
macd_slow |
integer |
No | 26 |
Slow period for MACD. |
macd_signal |
integer |
No | 9 |
Signal period for MACD. |
bbands_length |
integer |
No | 20 |
Period length for Bollinger Bands. |
bbands_std |
float |
No | 2.0 |
Standard deviation multiplier for Bollinger Bands. |
atr_length |
integer |
No | 14 |
Period length for ATR. |
custom_sma_lengths |
array[integer] |
No | [] |
List of custom SMA lengths to calculate (excluding baseline 50 and 200). |
custom_ema_lengths |
array[integer] |
No | [] |
List of custom EMA lengths to calculate. |
Return Value
Returns a JSON-formatted string mapping date strings to indicator values for the last 5 trading days.
Example return structure (shortened for readability):
{
"2026-07-07": {
"Open": 182.5,
"High": 184.2,
"Low": 181.8,
"Close": 183.9,
"Volume": 54200000,
"Baseline_SMA_50": 178.4,
"Primary_Trend_SMA_200": 170.2,
"RSI_14": 62.4
},
"2026-07-08": { ... },
"2026-07-09": { ... },
"2026-07-10": { ... },
"2026-07-11": { ... }
}
Установка Time Series Quant Finance Server
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/AHNEMER/Stock-analysis-MCP-serverFAQ
Time Series Quant Finance Server MCP бесплатный?
Да, Time Series Quant Finance Server MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для Time Series Quant Finance Server?
Нет, Time Series Quant Finance Server работает без API-ключей и переменных окружения.
Time Series Quant Finance Server — hosted или self-hosted?
Доступен hosted-вариант: Unyly запускает сервер в облаке, локальная установка не обязательна.
Как установить Time Series Quant Finance Server в Claude Desktop, Claude Code или Cursor?
Открой Time Series Quant Finance Server на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
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