h-v3-mcp-engine

Name: h-v3-mcp-engine
Availability: InStock
Author: okx-wallet-h

Calculate technical indicators and generate trading signals for cryptocurrencies from OKX market data.

Updated Aug 23, 2026

Search Tags:#technical analysis #market data #algorithmic trading #crypto #trading signals

Ready to run

npx skills add https://github.com/okx-wallet-H/h-wallet-skills --skill h-v3-mcp-engine

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: h-v3-mcp-engine
Source: https://github.com/okx-wallet-H/h-wallet-skills/tree/main/skills/h-v3-mcp-engine
Command: npx skills add https://github.com/okx-wallet-H/h-wallet-skills --skill h-v3-mcp-engine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, and includes scripts (resource) components.

What problem does it solve?

This Skill provides an automated engine to perform technical analysis and generate trading signals for cryptocurrencies based on market data.

Core Features & Use Cases

Market Data Analysis: Fetches historical K-line data from OKX and computes indicators like RSI, MACD, Bollinger Bands, and Hurst exponent.
Signal Generation: Produces buy, sell, or hold signals based on multi-factor scoring integrating trend strength, momentum, and volatility.
Use Case: A trader wants to automatically analyze BTC price movements on a 4-hour chart and receive actionable trading signals.
Real-time Monitoring: Can be integrated into trading bots for automated decision-making based on live data.

Quick Start

Use the engine to scan symbol 'BTC' on a 4-hour timeframe and get a detailed trading signal.

Frequently Asked Questions about h-v3-mcp-engine

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate crypto trading signals using technical analysis?▼

To generate crypto trading signals, this tool fetches historical K-line data from OKX and computes indicators like RSI, MACD, and Bollinger Bands. It then applies a multi-factor scoring system integrating trend strength, momentum, and volatility to output actionable buy, sell, or hold signals.

What is the best way to calculate RSI and MACD for algorithmic trading?▼

Calculating RSI and MACD for algorithmic trading is handled by Python scripts utilizing numpy and pandas. The engine fetches market data, performs strict data validation, and computes these indicators alongside the Hurst exponent to produce multi-factor trading signals.

Can I use Python scripts to analyze BTC price movements on a 4-hour chart?▼

Yes, you can analyze BTC price movements on a 4-hour chart by running the provided Python scripts. The engine specifically supports scanning specified symbols and timeframes to compute technical market indicators and generate detailed trading signals.

Does this technical analysis tool require numpy and pandas?▼

Yes, this technical analysis tool requires numpy and pandas as dependencies. These Python libraries are essential for fetching market data, calculating indicators like Bollinger Bands, and executing the multi-factor scoring logic for trading signals.

How do real-time technical indicators work with automated trading bots?▼

Real-time technical indicators work with automated trading bots by continuously fetching live market K-line data to compute metrics like RSI and MACD. The engine then outputs multi-factor trading signals that bots can use for automated decision-making.

What are the limitations of using multi-factor scoring for crypto market data?▼

A limitation of using multi-factor scoring for crypto market data is the strict adherence to data validation and safety standards, which may restrict signal generation if historical K-line data is incomplete or fails validation checks.