Why is the origin='start' parameter critical in this skill?

It ensures that resampled bars (like 4-hour or 1-day) align with the actual market open time (e.g., 9:30 AM) rather than standard 00:00 clock boundaries, preventing look-ahead bias and data misalignment.

Can I use this for crypto and equities?

While it is optimized for equity market alignment, the resampling logic can be adapted for any asset class by adjusting the base data and frequency parameters.

Is this skill compatible with modern pandas versions?

Yes, it specifically uses lowercase frequency strings (e.g., 'h' instead of 'H') to maintain compatibility with pandas 2.2 and above.

How does this skill handle GPU memory during training?

It implements a sequential training pattern and cleanup logic to prevent memory accumulation, which is a common cause of crashes when training multiple models for different timeframes in parallel.

Multi-Timeframe RL Training

Name: Multi-Timeframe RL Training
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Trains Reinforcement Learning models across multiple market timeframes with automated data resampling and professional market alignment.

About

This skill provides a standardized workflow for training Reinforcement Learning (RL) models for algorithmic trading across various time horizons, such as 1-hour, 4-hour, and daily charts. It solves critical challenges in financial data science by ensuring resampled data aligns perfectly with market open times (9:30 AM) rather than arbitrary clock boundaries. By managing sequential training and GPU memory cleanup, it allows developers to build robust, multi-perspective trading strategies that capture both short-term momentum and long-term trends while maintaining a consistent model naming convention for easy live deployment.

Key Features

Standardized model naming convention for automated discovery and loading
Automated OHLCV resampling with market-open alignment (origin='start')
Support for multi-symbol and multi-resolution training combinations
0 GitHub stars
Pandas 2.2+ compatible frequency mapping for precise time handling
Sequential training patterns to prevent GPU Out-of-Memory (OOM) errors

Use Cases

Training diversified trading agents that analyze both intraday patterns and daily trends
Automating the generation of model ensembles for complex signal aggregation
Building robust data pipelines for equity markets that require specific bar alignment

About

Key Features

Standardized model naming convention for automated discovery and loading
Automated OHLCV resampling with market-open alignment (origin='start')
Support for multi-symbol and multi-resolution training combinations
0 GitHub stars
Pandas 2.2+ compatible frequency mapping for precise time handling
Sequential training patterns to prevent GPU Out-of-Memory (OOM) errors

Use Cases

Training diversified trading agents that analyze both intraday patterns and daily trends
Automating the generation of model ensembles for complex signal aggregation
Building robust data pipelines for equity markets that require specific bar alignment