technical-basic

Compute composite trading signals from OHLCV data using pandas.

Updated Apr 12, 2026
One-click install
npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill technical-basic-daddyelonmusk69
Or copy as Structured Prompt for Agent
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.