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seasonal

Detect seasonal calendar effects in OHLCV data and generate trading signals.

1|Updated Jun 23, 2026
Search Tags:#market analysis#trading signals#calendar effects#OHLCV data#seasonal trading
One-click install
npx skills add https://github.com/644408071-design/Kokpop --skill seasonal-644408071-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seasonal
Source: https://github.com/644408071-design/Kokpop/tree/main/agent/src/skills/seasonal
Command: npx skills add https://github.com/644408071-design/Kokpop --skill seasonal-644408071-design

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of identifying and capitalizing on seasonal patterns in financial markets, providing insights that can inform trading strategies.

Core Features & Use Cases

  • Seasonal Pattern Detection: Detects and generates signals based on known seasonal patterns in financial markets.
  • Trading Signals: Generates trading signals from month-of-year and day-of-week effects suitable for OHLCV data.
  • Use Case: For example, it can identify the "sell in May" effect, which suggests a bearish trend from May to October.

Quick Start

Execute the seasonal skill with the command 'activate seasonal --start 2023-01-01 --end 2023-12-31' to analyze the market for seasonal trading opportunities within the specified date range.

Frequently Asked Questions about seasonal

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

FAQPage Schema
How do I identify seasonal patterns in OHLCV data for trading?▼

Seasonal trading signals are generated by detecting calendar effects like month-of-year and day-of-week patterns in OHLCV data. This approach identifies known market trends such as the "sell in May" effect to inform trading strategies.

Can I use pandas and numpy for seasonal market analysis?▼

Yes, you use pandas and numpy for the statistical analysis required in seasonal market analysis. These dependencies process OHLCV data to detect calendar effects and generate trading signals based on historical patterns.

What is the best way to detect calendar effects in financial markets?▼

Detecting calendar effects in financial markets involves analyzing historical OHLCV data for month-of-year and day-of-week patterns. This statistical approach identifies recurring seasonal trends to produce actionable trading signals.

How do I generate trading signals from month-of-year patterns?▼

Generating trading signals from month-of-year patterns involves running statistical analysis on OHLCV data within a specified date range. This detects seasonal trends like the "sell in May" effect and translates them into trading insights.

Do I need historical OHLCV data to analyze day-of-week effects?▼

Historical OHLCV data is required to analyze day-of-week effects. The price and volume history provides the statistical foundation needed to identify recurring seasonal patterns and generate accurate trading signals.

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