technical-basic

Name: technical-basic
Availability: InStock
Author: wudye

Generates composite trading signals from OHLCV data using pandas-based indicators and voting logic.

Updated May 5, 2026

Search Tags:#pandas #technical analysis #backtesting #indicators #ohlcv #trading signals #adx

Ready to run

npx skills add https://github.com/wudye/traderAssistHK --skill technical-basic-wudye

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: technical-basic
Source: https://github.com/wudye/traderAssistHK/tree/main/backend/src/skills/technical-basic
Command: npx skills add https://github.com/wudye/traderAssistHK --skill technical-basic-wudye

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, requests.

What problem does it solve?

It reduces the effort of manually combining multiple technical indicators by automatically producing a single actionable trading signal from OHLCV data.

Core Features & Use Cases

Composite signal generation: Fuses trend (EMA + ADX), mean reversion (Bollinger Bands + RSI), and volume-price confirmation (OBV + volume ratio/OBV MA) into one long/short/neutral output.
Deterministic pandas implementation: Uses pure pandas computations (e.g., Wilder-style EWM for RSI/ADX) suitable for backtesting and repeatable analysis.
Voting logic for trade direction: Applies a three-dimensional voting rule to decide when conditions align for a long or short, otherwise staying neutral.

Quick Start

Instantiate the engine with default parameters, pass a mapping of symbol to an OHLCV DataFrame containing open/high/low/close/volume columns, and read the returned signal Series where 1 means long, -1 means short, and 0 means stand aside.

Frequently Asked Questions about technical-basic

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

FAQPage Schema

How do I generate composite trading signals from OHLCV data in pandas?▼

To generate composite trading signals from OHLCV data, use a pandas-based engine that fuses EMA/ADX trend, RSI/Bollinger Bands mean reversion, and OBV volume indicators into a single long/short/neutral output via a three-dimensional voting rule.

How do I combine RSI, ADX, and Bollinger Bands into a single buy or sell signal?▼

Combine RSI, ADX, and Bollinger Bands into a single buy or sell signal by applying a voting logic that evaluates trend strength, mean-reversion extremes, and volume-price confirmation together, returning deterministic values of 1, -1, or 0.

Can I use pandas to calculate Wilder EWM smoothing for technical indicators?▼

Yes, you can use pandas to calculate Wilder EWM smoothing for technical indicators. This approach uses pure pandas computations for RSI and ADX calculations, making it deterministic and suitable for repeatable backtesting without external libraries.

What is the best way to backtest trading strategies across multiple symbols using pandas?▼

The best way to backtest trading strategies across multiple symbols is to pass a mapping of symbols to OHLCV DataFrames, letting a composite signal engine process trend, mean-reversion, and volume indicators to return deterministic signal Series for each dataset.

Does this composite signal engine work with numpy and requests for backtesting?▼

Yes, the composite signal engine works with numpy and requests for backtesting. It relies on pandas and numpy for computing Wilder-style EWM indicators and can use requests to fetch OHLCV time series data for processing.

Why does my technical indicator backtest produce inconsistent buy and sell signals?▼

Inconsistent buy and sell signals often occur when indicators are calculated independently. Using a deterministic pandas implementation with Wilder EWM smoothing and a unified voting rule ensures repeatable 1, -1, or 0 outputs across backtests.