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technical-basic

Aggregate trend, mean-reversion, and volume-price indicators into composite trading signals.

Updated Jul 29, 2026
Search Tags:#signals#pandas#technical analysis#trading#indicators#ohlcv
One-click install
npx skills add https://github.com/santoosaraujo/vibe-trading-claude --skill technical-basic-santoosaraujo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: technical-basic
Source: https://github.com/santoosaraujo/vibe-trading-claude/tree/main/.claude/skills/technical-basic
Command: npx skills add https://github.com/santoosaraujo/vibe-trading-claude --skill technical-basic-santoosaraujo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, requests.

What problem does it solve?

This Skill addresses the challenge of synthesizing multiple, often conflicting, technical indicators into a single, actionable trading signal for any OHLCV dataset.

Core Features & Use Cases

  • Multi-Dimensional Analysis: Combines trend (EMA/ADX), mean-reversion (Bollinger/RSI), and volume-price (OBV) indicators.
  • Voting Mechanism: Uses a three-dimensional voting system to filter noise and confirm market direction.
  • Use Case: Quickly evaluate the technical health of a list of assets by generating long, short, or neutral signals based on historical price and volume data.

Quick Start

Use the technical-basic skill to generate trading signals for the provided OHLCV data map.

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 data in pandas?▼

To generate trading signals from OHLCV data in pandas, you can aggregate trend, mean-reversion, and volume-price indicators into a composite voting system that outputs long, short, or neutral market directions.

What is the best way to combine multiple technical indicators without conflicting signals?▼

Combining multiple technical indicators without conflicting signals is achieved through a three-dimensional voting mechanism that synthesizes trend, mean-reversion, and volume-price data to filter market noise and confirm direction.

Can I use numpy and pandas to calculate technical analysis indicators for a list of assets?▼

Yes, you can use numpy and pandas to calculate technical analysis indicators by performing vectorized calculations on historical price and volume series to quickly evaluate the technical health of multiple assets.

Does the composite signal voting system work with standard EMA, ADX, and RSI indicators?▼

The composite signal voting system works with standard EMA, ADX, and RSI indicators by mapping them into trend and mean-reversion dimensions alongside Bollinger Bands and OBV to confirm market direction.

What technical indicators are required to identify entry and exit points in financial time-series data?▼

Identifying entry and exit points in financial time-series data requires combining EMA and ADX for trend, Bollinger Bands and RSI for mean-reversion, and OBV for volume-price confirmation.

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