Can I calculate multiple indicators simultaneously?

Yes, you can use the 'calculate_all_indicators' tool to perform a batch calculation of all supported metrics at once.

How are the results provided?

Calculated values are returned as an updated DataFrame with new columns appended for each specific indicator value.

Does this skill require external financial APIs?

This skill focuses on calculation logic. It is typically used in conjunction with a data fetching skill to process the retrieved market data.

What data format is required for these calculations?

The skill requires a pandas DataFrame containing a 'Close' price column, and ideally Open, High, Low, and Volume for full indicator support.

Are the indicator parameters customizable?

Absolutely. You can specify custom periods for Moving Averages, fast/slow periods for MACD, and standard deviations for Bollinger Bands.

Technical Stock Indicators

Name: Technical Stock Indicators
Author: YFOOOO

byYFOOOO

Ciencia de Datos y ML

Calculates core financial technical indicators including MA, MACD, RSI, and Bollinger Bands for stock market analysis and quantitative trading.

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The Technical Indicators skill provides a comprehensive suite of financial analysis tools for Claude. It enables automated calculation of essential market metrics like Moving Averages, MACD crossovers, RSI momentum, and Bollinger Band volatility. Designed for quantitative researchers and developers, this skill processes OHLCV data using high-performance vectorized operations, facilitating rapid trend identification, strategy backtesting, and market signal generation directly within your development workflow.

Características Principales

Customizable periods and parameters for tailored analysis
Support for major indicators: MA, MACD, RSI, and BOLL
Batch processing mode to calculate all indicators in one call
High-performance vectorized calculations using pandas
Seamless integration with financial DataFrames and OHLCV data
0 GitHub stars

Casos de Uso

Automated technical analysis and market trend identification
Generating buy/sell signals based on technical crossovers and momentum
Quantitative backtesting of algorithmic trading strategies

Acerca de

Características Principales

Customizable periods and parameters for tailored analysis
Support for major indicators: MA, MACD, RSI, and BOLL
Batch processing mode to calculate all indicators in one call
High-performance vectorized calculations using pandas
Seamless integration with financial DataFrames and OHLCV data
0 GitHub stars

Casos de Uso

Automated technical analysis and market trend identification
Generating buy/sell signals based on technical crossovers and momentum
Quantitative backtesting of algorithmic trading strategies