MACD & RSI Oscillator Engine MCP for AI. Get mathematically precise trading signals from price arrays.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

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MACD & RSI Oscillator Engine calculates precise MACD and Relative Strength Index (RSI) technical indicators directly from price arrays. This engine handles the complex, multi-pass exponential averaging required for accurate quantitative analysis—a task most LLMs fail at due to mathematical complexity.

What your AI can do

Calculate macd rsi

Pass a data array and configuration parameters; this function calculates MACD or RSI technical oscillators from the price data.

Calculate Technical Indicators

Pass a data array and configuration parameters to calculate the exact values for MACD or RSI.

Ask an AI about this

Included with Plan

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AI Agent

MACD & RSI Oscillator Engine: 1 Tool for Quantitative Analysis

Use the calculate_macd_rsi tool to generate mathematically accurate MACD and RSI indicators from any price array.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using MACD & RSI Oscillator Engine on Vinkius

Calculate Macd Rsi

Pass a data array and configuration parameters; this function calculates MACD or RSI technical oscillators from the price data.

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Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The MACD & RSI Oscillator Engine integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "macd-rsi-oscillator-engine": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the MACD & RSI Oscillator Engine tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"macd-rsi-oscillator-engine": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

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Start with MACD & RSI Oscillator Engine, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by technicalindicators. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Stop guessing at technical levels with generalized AI prompts.

Right now, when you need to know if an asset is oversold based on RSI, the process is a pain. You either have to write up complex Python code using specific math libraries every single time, or—more often—you just ask your AI assistant and it gives you a vague, unverified answer.

With MACD & RSI Oscillator Engine MCP Server, you bypass all that boilerplate. Your agent just calls `calculate_macd_rsi`. You feed the data, tell it what to check, and immediately get precise numbers. No code, no guesswork—just math.

MACD & RSI Oscillator Engine MCP Server: Get accurate signals with `calculate_macd_rsi`.

The manual steps that vanish are the setup of the indicator calculation chain and the constant fear of mathematical error. You never have to worry about correctly implementing multiple exponential averages again.

Now, your agent operates at a higher level. It doesn't calculate; it analyzes the output from `calculate_macd_rsi`. This difference is huge: you move from data crunching to pure strategy.

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What your AI can actually do with this

MACD & RSI Engine - Calculate Trading Signals MCP Server

This server exposes the calculate_macd_rsi tool, which calculates precise MACD or Relative Strength Index (RSI) technical oscillators directly from price arrays. You feed this engine a data array and specific configuration parameters, letting it handle the complex, multi-pass exponential averaging required for accurate quantitative analysis. It's designed specifically because standard LLMs fail at these calculations; they can't manage the deep mathematical passes needed to generate reliable trading signals.

The calculate_macd_rsi tool requires two primary inputs: a data array and a set of configuration parameters. The price data array must contain historical prices that you want analyzed. The accompanying parameters tell the engine exactly which indicators you need and what their specific settings should be. When you execute this function, it processes the raw price data through institutional-grade technical indicator mathematics.

When calculating MACD, for instance, the configuration parameters let you define the fast period, slow period, and signal smoothing period. The tool takes your price array, performs multiple exponential averaging passes using those specified periods, and returns a precise set of MACD values ready for immediate use in an analysis agent.

Similarly, if you need RSI, the parameters specify the lookback period—like the common 14-period setting—and the engine calculates the exact relative strength index based on that definition.

The tool doesn't just give you numbers; it gives you actionable signals by providing the calculated oscillator values at every point in your data array. You define the indicator type (MACD or RSI) and its specific parameters, and the function returns a structured output containing those precise numerical results. For MACD, this means getting the main MACD line value, the signal line value, and often the histogram value itself.

For RSI, you get the calculated index for every point in the data set.

This engine handles the mathematical heavy lifting that usually breaks an agent's internal math capabilities. You don't worry about writing complex financial formulas or debugging multi-pass exponential smoothing; you just define your desired parameters—say, MACD with a 12/26/9 setup, or RSI over a 14-period lookback—and the server spits out accurate results every time.

It ensures that whether you're running autonomous trading simulations or simply analyzing historical price movements, the signals are mathematically sound and ready for your agent to process.

You pass the data array along with the required parameters defining either MACD or RSI calculation requirements. The tool then executes the necessary exponential averaging passes across the entire dataset. It calculates the specific technical oscillator values based on those inputs—the resulting output is a structured set of accurate, ready-to-use numerical results that you can immediately feed into subsequent decision-making logic in your AI client.

You're getting clean, precise financial data points derived from complex mathematical processes, all managed through one reliable function call.

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Server ID 019e38bb-2a33-7371-a400-e6d19757e004

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Here's how it actually works

The bottom line is: you get mathematically perfect indicator values without writing any complex math code yourself.

Provide the engine with your price data array (e.g., 100 days of closing prices) and specify the indicator type and parameters (e.g., 'RSI, period 14').

The agent invokes calculate_macd_rsi, which runs the complex, multi-pass mathematical calculation on the provided dataset.

You receive an exact numerical output for the requested indicators, ready to feed into a decision script or report.

Who is this actually for?

This server is for quants and developers who need trading signals, not generalized text. If your workflow relies on accurate technical analysis—like backtesting or real-time signal generation—you're here. Forget vague dashboards; you need reliable math.

Quantitative Analyst

Uses the tool to validate indicator parameters (e.g., checking if RSI is overbought/oversold) against historical data for model testing.

Algorithmic Developer

Integrates calculate_macd_rsi into Python agents or workflows to generate actionable trade signals based on indicator crossovers.

FinTech Researcher

Feeds the engine diverse price series (crypto, equities) to test divergence patterns and assess signal reliability across different markets.

What Changes When You Connect

Signal Accuracy: You get exact MACD and RSI values every time. This isn't estimation; it's institutional-grade math, which is critical when detecting subtle crossover patterns.

Direct Signal Detection: Use the results to check specific conditions—like if a price high creates a lower RSI value (bearish divergence). The engine handles this complex comparison for you.

Handles Complexity: Don't risk using an LLM that fails on multi-pass exponential averages. calculate_macd_rsi manages the heavy math so your agent just needs to interpret the signal.

Efficient Workflow: Instead of writing boilerplate code to calculate indicators, you pass the data and let the engine run it. Your agent stays focused on strategy, not calculus.

Multi-Market Ready: The tool accepts any price array. Use it for daily stock closes, 1-hour crypto ticks, or even commodity futures.

See it in action

01 01

Checking for Oversold Conditions

A day trader sees a dip and asks their agent: 'Calculate the RSI using this week's prices. Is the latest reading below 30?' The agent runs calculate_macd_rsi, gets the precise value, and confirms if the asset is in an oversold zone.

02 02

Verifying Bullish Crosses

A quant needs to confirm a potential buy signal. They provide 100 days of data and ask their agent to run MACD analysis, specifically checking if the MACD line crossed above the Signal line in the last three periods. The engine confirms the crossover point.

03 03

Detecting Bearish Divergence

A researcher spots a new price peak but suspects weakness. They input the historical data and ask the agent to calculate RSI while checking for divergence patterns, confirming if the current high is accompanied by an RSI lower than previous highs.

04 04

Backtesting Signal Performance

A developer needs to test a strategy based on MACD crossovers. They feed calculate_macd_rsi thousands of historical price points and ask the agent to flag every instance where the indicator crossed above zero, building a clean list for backtesting.

The honest tradeoffs

Asking an LLM directly

Anti-pattern

Prompting your AI client: 'Calculate RSI over 100 days of data and tell me if it's oversold.' The result is often a generalized answer or, worse, mathematically incorrect.

The Fix

Always use the calculate_macd_rsi tool. Pass the price array and parameters directly to the function call. This forces the calculation through reliable, dedicated math libraries.

Using basic scripting

Anti-pattern

Writing complex Python code just to calculate a simple indicator like RSI every time you test a new theory. The setup is bulky and prone to indexing errors.

The Fix

Let the engine handle it. Call calculate_macd_rsi with your data payload. It abstracts away the math library complexity, letting you focus on the signal logic.

Relying on visualization

Anti-pattern

Looking at a charting platform to manually eyeball if two lines crossed or if RSI dipped below 30.

The Fix

Use calculate_macd_rsi and ask your agent for the exact crossover point or value. Getting precise, numerical output is always better than an estimate.

When It Fits, When It Doesn't

Use this server if your workflow requires absolute mathematical certainty when analyzing financial indicators. If you need to detect crossovers (MACD), check divergence patterns (RSI), or perform multi-pass exponential averages on price data, the calculate_macd_rsi tool is mandatory.

Don't use it if you just need a general summary of market sentiment—a simple database lookup might suffice. If your goal is merely to compare two unrelated assets without indicator math, don't bother. This engine is hyper-focused on precise quantitative signals; its strength is in complexity and reliability for indicators.

Questions you might have

What does RSI indicate? +

RSI ranges from 0 to 100. Traditionally, a value above 70 indicates an asset is Overbought, and below 30 indicates it is Oversold.

What are the MACD parameters? +

It uses the standard 12-period Fast EMA, 26-period Slow EMA, and 9-period Signal line to generate exact histograms.

Is this for crypto or stocks? +

Both. Technical oscillators are mathematically agnostic and work on any sequential numeric data array, regardless of the asset class.

How do I format the input data array when calling `calculate_macd_rsi`? +

You must provide a simple, sequential array of closing prices. The function expects pure numerical values for every point in time. Don't worry about including high, low, or opening prices.

What happens if I pass non-numeric or incomplete data to `calculate_macd_rsi`? +

The tool will return a specific validation error message immediately. It doesn't just fail; it tells you exactly which index in the array needs fixing so you can correct your input.

Is there a performance limit on how many data points I can pass to `calculate_macd_rsi`? +

The engine handles large historical datasets efficiently. While extreme inputs might hit runtime limits, it is optimized for deep time series analysis and runs much faster than an LLM calculation.

Does `calculate_macd_rsi` require the data to be sorted by date? +

No, you just need a chronological sequence of prices. The tool calculates indicators based on array position (order) rather than requiring explicit date stamps in your input.

If I want to check multiple indicators at once, can I use `calculate_macd_rsi`? +

Yes, the tool is designed to calculate both MACD and RSI simultaneously. You just need to specify all necessary configuration parameters (like period lengths) in your single request.

Connect to your AI in seconds.

Calculate macd rsi

MACD & RSI Oscillator Engine: 1 Tool for Quantitative Analysis

Make your AI actually useful.

Calculate Macd Rsi

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

Stop guessing at technical levels with generalized AI prompts.

MACD & RSI Oscillator Engine MCP Server: Get accurate signals with `calculate_macd_rsi`.

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Checking for Oversold Conditions

Verifying Bullish Crosses

Detecting Bearish Divergence

Backtesting Signal Performance

The honest tradeoffs

Asking an LLM directly

Using basic scripting

Relying on visualization

When It Fits, When It Doesn't

Questions you might have