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technical-basic

Combine trend, mean-reversion, and volume-price indicators into voting-based trading signals.

Updated May 15, 2026
Search Tags:#ema#technical-indicators#rsi#trading-signals#adx#obv#bollinger-bands
One-click install
npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill technical-basic-philipcoller-777
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: technical-basic
Source: https://github.com/philipcoller-777/Vibe-Trading-TV2/tree/main/agent/src/skills/technical-basic
Command: npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill technical-basic-philipcoller-777

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Consolidates trend (EMA cross + ADX strength), mean-reversion (BB + RSI), and volume-price (OBV) indicators into a single, voting-based signal engine, reducing manual indicator stitching and speeding up strategy prototyping.

Core Features & Use Cases

  • Three-dimensional voting combines multiple indicator dimensions to produce long, short, or neutral signals.
  • Pure pandas implementation works on any OHLCV data without external dependencies.
  • Configurable parameters for EMA, ADX, Bollinger Bands, RSI, and OBV enable quick backtesting and live trading adaptations.

Quick Start

Instantiate the SignalEngine with default parameters and feed it a dict of OHLCV DataFrames to generate signals.

Frequently Asked Questions about technical-basic

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

FAQPage Schema
How do I combine multiple technical indicators into a single trading signal?▼

You can combine technical indicators into a single trading signal by using a voting-based signal engine that consolidates EMA cross, ADX, Bollinger Bands, RSI, and OBV into long, short, or neutral outcomes for OHLCV time-series data.

Can I backtest trading strategies using pandas without external dependencies?▼

Yes, you can backtest trading strategies using a pure pandas implementation that processes OHLCV DataFrames without external dependencies, enabling quick strategy prototyping and live trading adaptations.

How does a voting-based signal engine work with mean-reversion and trend indicators?▼

A voting-based signal engine works by aggregating trend strength, mean-reversion conditions, and volume-price momentum across configured indicators to produce a deterministic long, short, or neutral signal.

Are the parameters for RSI, ADX, and Bollinger Bands configurable for different assets?▼

Yes, parameters for EMA, ADX, Bollinger Bands, RSI, and OBV are fully configurable, allowing you to adapt the signal engine for backtesting and live deployment across different OHLCV assets.

What is the best way to generate clean trading signals from OHLCV data?▼

The best way to generate clean trading signals from OHLCV data is to consolidate trend, mean-reversion, and volume-price indicators into a unified voting system, eliminating manual indicator stitching.

What are the limitations of using a composite voting engine for trading signals?▼

A limitation of using a composite voting engine is that it produces deterministic outcomes based solely on configured indicator parameters, meaning signal quality depends on the OHLCV data inputs and parameter tuning rather than adaptive market conditions.

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