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seasonal

Generate trading signals from seasonal calendar patterns in OHLCV data.

Updated May 15, 2026
Search Tags:#pandas#time-series#seasonal#trading-signals#ohclv#calendar-effects
One-click install
npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill seasonal-philipcoller-777
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seasonal
Source: https://github.com/philipcoller-777/Vibe-Trading-TV2/tree/main/agent/src/skills/seasonal
Command: npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill seasonal-philipcoller-777

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Markets exhibit seasonal patterns that are often missed by traders. This Skill automatically detects calendar-based effects to generate trading signals, saving time and enhancing pattern exploitation.

Core Features & Use Cases

  • Time-based signal generation: uses month-of-year and optional day-of-week effects to produce long/short/neutral signals.
  • Configurable calendar effects: customize bullish_months, bearish_months, and overlays to fit different markets.
  • Use Case: apply to OHLCV data across assets to identify January–March rallies or May–October weakness.

Quick Start

Run the seasonal strategy on your OHLCV dataset to produce calendar-based trading signals.

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 from seasonal calendar patterns in OHLCV data?▼

Calendar-based trading signals are generated by detecting month-of-year and optional weekday effects in OHLCV time-series data, producing long, short, or neutral positions when configured bullish and bearish months align with market conditions.

What are seasonal calendar effects and how do they impact time-series trading?▼

Seasonal calendar effects are recurring time-based patterns in OHLCV market data, such as January rallies or May weakness, that impact time-series trading by creating predictable bullish or bearish periods exploitable for automated signal generation.

Can I configure bullish and bearish months with a weekday overlay in pandas?▼

Yes, you can configure bullish_months, bearish_months, and optional weekday overlays in pandas, requiring combined confirmation if enabled, to refine time-series trading signals from your OHLCV dataset.

Do I need a specific Python framework to detect month-of-year effects in time-series data?▼

You do not need a specific Python framework beyond pandas to detect month-of-year effects, as the signal engine operates directly on OHLCV time-series data using configurable YAML parameters without external dependencies.

What is the best way to automate seasonal trading signal generation for OHLCV datasets?▼

The best way to automate seasonal trading signal generation is to use a Python signal engine that processes OHLCV datasets, applying configurable calendar effects like bullish and bearish months to output consistent time-based trading signals.

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