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seasonal

Generate trading signals from month and weekday calendar effects in OHLCV data.

2|Updated May 13, 2026
Search Tags:#market analysis#trading signals#seasonal#quantitative trading#calendar effect
One-click install
npx skills add https://github.com/thanhtai040805/AI_Invest --skill seasonal-thanhtai040805
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seasonal
Source: https://github.com/thanhtai040805/AI_Invest/tree/main/ai-engine/app/domain/services/quant/skills_data/seasonal
Command: npx skills add https://github.com/thanhtai040805/AI_Invest --skill seasonal-thanhtai040805

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, and includes scripts (resource) components.

What problem does it solve?

This Skill helps identify and capitalize on seasonal patterns and calendar effects in financial markets to generate trading signals.

Core Features & Use Cases

  • Month Effect: Generate signals based on bullish or bearish months (e.g., China A-share "spring rally").
  • Day-of-Week Effect: Add optional weekday overlays (e.g., "sell in May" effect).
  • Combined Mode: Require both month and weekday signals for confirmation.
  • Use Case: Enhance a trading strategy by automatically adjusting positions based on calendar-driven market behavior.

Quick Start

Use the seasonal skill with bullish_months=[1,2,3,11,12] and bearish_months=[5,6,7,8,9] to generate monthly trading signals for the stock 'AAPL'.

Frequently Asked Questions about seasonal

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

FAQPage Schema
How do I generate trading signals based on seasonal and calendar effects?▼

To generate trading signals based on seasonal and calendar effects, you process OHLCV financial market data through time-based pattern recognition. This skill applies month and day-of-week overlays to identify bullish or bearish periods.

What is the month effect in quantitative trading and how is it applied?▼

The month effect in quantitative trading identifies historically bullish or bearish months to generate market signals. You apply it by specifying bullish and bearish month arrays, such as targeting a spring rally, to adjust asset positions accordingly.

Can I combine month and day-of-week effects for trading signal confirmation?▼

Yes, you can combine month and day-of-week effects for trading signal confirmation using the combined mode. This requires both the monthly and weekday overlays to align before executing a position adjustment.

Do I need pandas and numpy to analyze calendar effects in market data?▼

Yes, you need pandas and numpy to analyze calendar effects in market data. Pandas handles time-series data manipulation for OHLCV records, while numpy performs the numerical operations required for signal calculation.

Does this calendar effect strategy work for any asset class?▼

The calendar effect strategy works across various assets that have OHLCV financial market data. You can apply these time-based patterns to analyze stocks, indices, or other instruments for quantitative trading.

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