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technical-basic

Analyze OHLCV data to generate composite trading signals via three-dimensional voting.

Updated Jul 8, 2026
Search Tags:#finance#pandas#technical analysis#trading#indicators#signal engine
One-click install
npx skills add https://github.com/hxhyyy/Vibe-Trading --skill technical-basic-hxhyyy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: technical-basic
Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/technical-basic
Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill technical-basic-hxhyyy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, requests.

What problem does it solve?

This Skill addresses the challenge of interpreting complex market data by synthesizing multiple technical indicators into a single, actionable trading signal, reducing the cognitive load of manual chart analysis.

Core Features & Use Cases

  • Three-Dimensional Voting: Combines trend (EMA/ADX), mean-reversion (Bollinger Bands/RSI), and volume-price (OBV) analysis to filter out market noise.
  • Automated Signal Generation: Provides clear long, short, or stand-aside signals based on configurable thresholds.
  • Use Case: Use this to scan a portfolio of assets like BTC-USDT or ETH-USDT to identify potential entry or exit points based on historical OHLCV data.

Quick Start

Use the technical-basic skill to generate trading signals for the BTC-USDT pair using the default indicator parameters.

Frequently Asked Questions about technical-basic

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

FAQPage Schema
How do I generate trading signals from OHLCV market data using pandas?▼

To generate composite trading signals from OHLCV market data using pandas, this Skill applies a three-dimensional voting mechanism across trend, mean-reversion, and volume-price indicators to output clear long, short, or stand-aside actions.

What is a three-dimensional voting mechanism for technical analysis?▼

A three-dimensional voting mechanism for technical analysis combines trend indicators like EMA and ADX, mean-reversion tools like Bollinger Bands and RSI, and volume-price metrics like OBV to filter market noise and synthesize a single actionable trading signal.

Can I scan a cryptocurrency portfolio for entry and exit points with numpy?▼

Yes, you can scan a cryptocurrency portfolio for entry and exit points with numpy by feeding historical OHLCV data for assets like BTC-USDT into this Skill to compute vectorized technical indicator calculations and output directional trading signals.

Does this technical analysis approach require configuring indicator thresholds?▼

Yes, this technical analysis approach requires configurable thresholds to automate signal generation, allowing you to adjust the sensitivity of the trend, mean-reversion, and volume-price voting layers for your specific asset evaluation strategy.

What are the limitations of using composite technical signals for trading?▼

The limitation of using composite technical signals for trading is that the output relies entirely on historical OHLCV data, meaning the synthesized signals cannot predict sudden market shifts or fundamental changes outside the configured indicator parameters.

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