seasonal-calendar-effects
CommunityTurn calendar patterns into trading signals.
Finance & Accounting#time-series#backtesting#trading strategy#signal generation#seasonality#calendar effects
Authorloanntc
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Markets often exhibit recurring time-based behaviors, but manually translating month/week seasonal tendencies into consistent trade signals is slow and error-prone.
Core Features & Use Cases
- Month-effect signal generation: Produces long (+1), short (-1), or flat (0) decisions based on whether each bar’s month is configured as bullish or bearish.
- Optional weekday overlay: Adds Monday/Friday (or any weekday list) effects and applies a combined-confirmation rule so trades only occur when month and weekday agree.
- Use case: Backtest an equity or crypto strategy that targets the “spring rally” (Jan–Mar) and “sell in May” (May–Oct) effects using any OHLCV timeframe.
Quick Start
Run the seasonal-calendar-effects SignalEngine on your OHLCV DataFrame(s) to generate a per-bar signal series indicating long, short, or neutral positions.
Dependency Matrix
Required Modules
pandasnumpy
Components
scripts
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: seasonal-calendar-effects Download link: https://github.com/loanntc/Paave/archive/main.zip#seasonal-calendar-effects Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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