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technical-basic

Combine EMA, ADX, Bollinger Bands, RSI, and OBV into trading signals.

6.1k|1.2k|Updated Jun 9, 2022
Search Tags:#pandas#technical indicators#trend analysis#backtest#ohlcv#volume-price#signal engine
One-click install
npx skills add https://github.com/charliedream1/ai_quant_trade --skill technical-basic-charliedream1
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: technical-basic
Source: https://github.com/charliedream1/ai_quant_trade/tree/main/a_%E5%85%A8%E7%BD%91%E4%BC%98%E7%A7%80%E8%B5%84%E6%BA%90/10_%E5%A4%A7%E6%A8%A1%E5%9E%8B/07_skill%E5%8C%85/vibe_trading_skills/technical-basic
Command: npx skills add https://github.com/charliedream1/ai_quant_trade --skill technical-basic-charliedream1

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Consolidates a set of classic technical indicators into a single, executable signal engine for any OHLCV dataset, reducing manual setup and decision latency.

Core Features & Use Cases

  • Trend dimension: EMA(12/26) and ADX to gauge direction and strength.
  • Mean reversion: Bollinger Bands and RSI to identify overbought/oversold conditions.
  • Volume-price confirmation: OBV combined with volume dynamics for participation signals.
  • Three-dimensional voting: long/short/neutral decisions based on cross-dimension consensus.
  • Works on any pandas DataFrame with open/high/low/close/volume for backtesting and live trading.

Quick Start

Instantiate a SignalEngine and pass a DataFrame with the required OHLCV columns to receive per-asset trading 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 using pandas?▼

You can combine multiple technical indicators into a single trading signal by instantiating a SignalEngine with pure pandas code, which uses a three-dimensional voting mechanism across EMA, ADX, Bollinger Bands, RSI, and OBV to output long, short, or neutral decisions.

What is the best way to generate OHLCV backtest signals for trend and mean reversion strategies?▼

Generating OHLCV backtest signals is best handled by a unified signal engine that evaluates trend dimensions via EMA and ADX, mean reversion via Bollinger Bands and RSI, and volume-price confirmation via OBV to produce a consensus trading decision.

Can I use this technical indicator solution with any asset dataset that has open, high, low, close, and volume columns?▼

Yes, you can use this with any asset dataset. The solution processes any pandas DataFrame containing open, high, low, close, and volume columns, making it suitable for backtesting and live trading decisions across various financial assets.

How does the three-dimensional voting mechanism work for volume-price trend analysis?▼

The three-dimensional voting mechanism works by combining trend, mean reversion, and volume-price scenarios to achieve cross-dimension consensus, using OBV and volume dynamics alongside other indicators to output a definitive long, short, or neutral signal.

Do I need additional libraries or dependencies to run the signal engine for OHLCV data?▼

No additional libraries are needed to run the signal engine. The solution is implemented using pure pandas code with no external dependencies required, provided your DataFrame contains the necessary OHLCV columns.

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