technical-basic

Compute composite trading signals from OHLCV data using pandas indicators.

Updated Apr 19, 2026

Search Tags:#pandas #technical-analysis #ohlcv #trading-signals #quant #adx

Ready to run

npx skills add https://github.com/ajithkumar31082004-bit/Vibe-Trading --skill technical-basic-ajithkumar31082004-bit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: technical-basic
Source: https://github.com/ajithkumar31082004-bit/Vibe-Trading/tree/main/Vibe-Trading-main/agent/src/skills/technical-basic
Command: npx skills add https://github.com/ajithkumar31082004-bit/Vibe-Trading --skill technical-basic-ajithkumar31082004-bit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Core technical indicator collection that combines trend EMA with ADX, mean reversion with Bollinger Bands and RSI, and volume-price signals OBV/volume ratio to generate a composite signal via three-dimensional voting for OHLCV data, simplifying signal generation for traders.

Core Features & Use Cases

EMA cross + ADX trend strength for directional momentum
Bollinger Bands + RSI mean reversion for overbought/oversold conditions
OBV + volume ratio confirmation for momentum
Three-dimensional voting to produce long/short/neutral signals across assets Real-world use: feed any OHLCV dataset to generate trading signals suitable for backtesting or live deployment with a pandas-based engine.

Quick Start

Create a SignalEngine instance and call generate with a dict mapping symbol to OHLCV DataFrame to obtain signals.

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 using pandas?▼

You can generate trading signals from OHLCV data using pandas by passing a mapping of symbols to OHLCV DataFrames into a SignalEngine, which computes EMA, ADX, Bollinger Bands, RSI, and OBV indicators to return 1, -1, or 0 signal values per asset.

What is a composite technical signal and how does three-dimensional voting work?▼

A composite technical signal combines trend, mean-reversion, and volume-price indicators into a single directional output. Three-dimensional voting aggregates EMA cross, Bollinger Bands plus RSI, and OBV signals to decide a final long, short, or neutral position.

Can I apply technical analysis indicators to multiple assets at the same time?▼

Yes, you can apply technical analysis indicators to multiple assets simultaneously by passing a dictionary mapping each symbol to its respective OHLCV DataFrame, which returns a corresponding signal series for each individual asset.

Do I need to install any external libraries besides pandas to compute ADX and RSI?▼

Besides pandas, you need to install numpy to compute ADX and RSI. The calculation engine relies purely on pandas and numpy to implement Wilder-smoothed ADX, EMA cross, and other volume-price indicator logic.

What's the best way to combine trend strength and mean reversion for backtesting?▼

The best way to combine trend strength and mean reversion for backtesting is using a voting rule that integrates Wilder-smoothed ADX for directional momentum with Bollinger Bands and RSI for overbought or oversold conditions.

Why does my volume-price momentum signal return 0 instead of 1 or -1?▼

Your volume-price momentum signal returns 0 when the three-dimensional voting rule evaluates trend, mean reversion, and volume indicators without a clear majority, resulting in a neutral consensus rather than a definitive long or short signal.