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technical-indicators

Generate composite long, short, and neutral signals from OHLCV data via indicator voting.

Updated Apr 14, 2026
Search Tags:#pandas#technical analysis#backtesting#indicators#ohlcv#trading signals
One-click install
npx skills add https://github.com/loanntc/Paave --skill technical-indicators-loanntc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: technical-indicators
Source: https://github.com/loanntc/Paave/tree/main/skills/technical-basic
Command: npx skills add https://github.com/loanntc/Paave --skill technical-indicators-loanntc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, requests.

What problem does it solve?

It solves the problem of turning raw OHLCV market data into actionable trading signals by combining trend, mean-reversion, and volume-price confirmation into a single decision output.

Core Features & Use Cases

  • Composite indicator voting: Produces a unified signal (1 long, -1 short, 0 neutral) by voting across three indicator dimensions: EMA/ADX trend, Bollinger Bands/RSI mean reversion, and OBV/volume-ratio participation.
  • Pure pandas implementation: Computes RSI using Wilder-style EWM smoothing, calculates the full ADX chain (+DM/-DM → TR → DI → DX → ADX), and derives OBV via cumulative signed volume—all operating directly on pandas Series/DataFrames.
  • Practical scenarios: Useful for backtesting systematic strategies, screening assets for potential long/short setups, and generating per-timestamp directional labels from any OHLCV dataset (crypto, stocks, or other markets).

Quick Start

Use the technical-indicators skill to generate trading signals for your OHLCV DataFrame(s) by calling its SignalEngine.generate with a mapping of symbol to a DataFrame containing open/high/low/close/volume columns.

Frequently Asked Questions about technical-indicators

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

FAQPage Schema
How do I generate trading signals from an OHLCV DataFrame in pandas?▼

To generate trading signals from an OHLCV DataFrame in pandas, you pass a mapping of symbols to DataFrames with open/high/low/close/volume columns to the SignalEngine, which outputs composite long, short, and neutral directional labels based on indicator voting.

Can I use this Skill to label backtest data with EMA, RSI, and ADX indicators?▼

Yes, you can use this Skill to label backtest data by computing Wilder-smoothed RSI, the full ADX trend chain, and EMA values directly on pandas Series, generating deterministic integer directional labels for systematic strategy prototyping.

What is composite indicator voting for OHLCV time series?▼

Composite indicator voting for OHLCV time series combines EMA/ADX trend, Bollinger Bands/RSI mean-reversion, and OBV/volume-ratio participation into a single unified signal output of 1 for long, -1 for short, or 0 for neutral.

Does this technical analysis tool work with crypto and stock market data?▼

Yes, this technical analysis tool works with crypto, stocks, and other markets by processing any OHLCV dataset through a unified pandas interface, making it suitable for screening assets and generating per-timestamp directional signals across multiple symbols.

How does OBV calculation work with pandas for volume confirmation?▼

OBV calculation for volume confirmation works by computing cumulative signed volume from close price differences directly on pandas Series, combining the result with a volume ratio to validate trend and mean-reversion signals.

Do I need numpy and pandas to compute Bollinger Bands and RSI signals?▼

Yes, you need numpy and pandas installed as dependencies to compute Bollinger Bands and Wilder-smoothed RSI signals, as the implementation operates entirely on pandas Series and DataFrames with numpy support.

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