technical-basic

Name: technical-basic
Availability: InStock
Author: daddyelonmusk69

Compute composite trading signals from OHLCV data using pandas.

Updated Apr 12, 2026

Search Tags:#signals #pandas #backtesting #trading #technical-analysis #quant

Ready to run

npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill technical-basic-daddyelonmusk69

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: technical-basic
Source: https://github.com/DaddyElonMusk69/motis-agent/tree/main/skills/finance/technical-basic
Command: npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill technical-basic-daddyelonmusk69

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a unified engine that computes trend, mean-reversion, and volume-price signals from OHLCV data, consolidating core indicators into a single actionable signal.

Core Features & Use Cases

Three-dimensional signal: combines EMA/ADX trend, Bollinger Bands with RSI mean reversion, and OBV/volume cues for a robust trading signal.
Pandas-native implementation: operates on standard OHLCV DataFrames without external dependencies.
Use Case: backtest a strategy by generating signals from historical data and feeding them into a risk-management module.

Quick Start

Create a SignalEngine and call generate on your OHLCV data.

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?▼

A composite trading signal combines multiple indicators—such as EMA/ADX trend, Bollinger Bands with RSI mean reversion, and OBV volume cues—into one actionable value, helping you avoid conflicting signals from isolated metrics.

Can I use this technical analysis pipeline for backtesting strategies?▼

Yes, you can use this technical analysis pipeline for backtesting by applying it to historical OHLCV data and feeding the resulting composite signals into your risk-management or strategy execution module.

What is the best way to combine EMA, RSI, and OBV indicators without external dependencies?▼

The best way to combine EMA, RSI, and OBV indicators without external dependencies is using a pandas-native pipeline that computes trend, mean-reversion, and volume-price signals directly on standard OHLCV DataFrames.

Does this technical analysis engine require external libraries beyond pandas?▼

No, this technical analysis engine does not require external libraries beyond pandas. It operates on standard OHLCV DataFrames using a dependency-light pipeline to compute composite trading signals.

When should I use a composite trading signal instead of individual technical indicators?▼

You should use a composite trading signal instead of individual technical indicators when you need a unified, robust output that consolidates trend, mean-reversion, and volume-price data for real-time signaling or backtesting.