skills.rest
OpenSucker
OpenSuckerOFFICIAL
Explore all skills by this author →
GitHub

technical-basic

Compute and blend EMA, ADX, RSI, Bollinger Bands, and OBV into trading signals.

15|2|Updated May 1, 2026
Search Tags:#market analysis#technical indicators#RSI#EMA#trading signals#ADX
One-click install
npx skills add https://github.com/OpenSucker/OpenSucker --skill technical-basic-opensucker
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: technical-basic
Source: https://github.com/OpenSucker/OpenSucker/tree/main/skills/vibe_skills/technical-basic
Command: npx skills add https://github.com/OpenSucker/OpenSucker --skill technical-basic-opensucker

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive collection of core technical indicators to assist traders in analyzing market trends and reversals, streamlining technical analysis workflows.

Core Features & Use Cases

  • Indicator Aggregation: Combines trend, mean reversion, and volume-price indicators into a unified analysis engine.
  • Signal Generation: Produces buy, sell, or hold signals based on multi-dimensional voting logic.
  • Use Case: A trader analyzes daily OHLCV data for Bitcoin to determine optimal entry or exit points by combining EMA, RSI, ADX, Bollinger Bands, and OBV signals.

Quick Start

Input your OHLCV data into the engine and run the generate method to receive trading signals for multiple assets.

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 multiple technical indicators?▼

Generate trading signals by computing and blending EMA, RSI, ADX, Bollinger Bands, and OBV indicators into a multi-dimensional voting logic that outputs buy, sell, or hold decisions for financial markets.

Can I use pandas and numpy to calculate market trend indicators for OHLCV data?▼

Yes, you can use pandas and numpy to process daily OHLCV data and calculate market trend indicators, as these libraries handle the data processing and analysis required to compute EMA and ADX values.

What is the best way to combine RSI and Bollinger Bands for market analysis?▼

The best way to combine RSI and Bollinger Bands for market analysis is using an indicator aggregation engine that merges mean reversion and trend signals to produce precise entry and exit decisions.

Do I need volume-price indicators like OBV alongside EMA for accurate trading signals?▼

Yes, combining volume-price indicators like OBV with trend indicators like EMA enhances signal accuracy by validating price movements with trading volume across multiple analytical dimensions.

How does multi-dimensional voting logic work for technical indicator signals?▼

Multi-dimensional voting logic works by aggregating computed values from EMA, ADX, RSI, Bollinger Bands, and OBV, then weighing their combined outputs to generate a unified buy, sell, or hold signal.

What are the limitations of using blended technical indicators for market analysis?▼

Blended technical indicators rely entirely on historical OHLCV data and mathematical formulas, meaning they cannot predict unprecedented market events and require pandas and numpy environments to function.

Related Skills

Explore More Finance & Accounting Skills→
hxhyyyhxhyyy

technical-basic

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

Community
Intermediate
tarotmansatarotmansa

trade-signal

Translate market data into Buy/Sell/Hold signals with targets and stop levels.

Community
Advanced
DaddyElonMusk69DaddyElonMusk69

technical-basic

Compute composite trading signals from OHLCV data using pandas.

Community
Advanced
kayzaakayzaa
5

Quant Engine

Run quantitative trading algorithms and backtest strategies on historical market data using Python.

Community
Advanced