What is the recommended architecture for standard production trading?

A configuration of (1024, 512, 256) is generally recommended as it provides a robust balance between predictive capacity and inference speed.

Why does the first hidden layer dominate the model size?

In trading models with high-dimensional input features, such as large lookback windows, the first layer multiplication (input_dim × first_hidden_dim) typically accounts for 85-90% of the total parameter count.

Can I swap models with different hidden_dims during inference?

No, architecture mismatches will cause system failures. Models must be loaded with the exact hidden dimensions used during their training phase.

How do I estimate the file size of my training checkpoint?

The file size is roughly the total parameter count multiplied by 12 bytes. This accounts for float32 weights plus the two moment vectors stored by the Adam optimizer.

Network Architecture Sizing for Trading RL

Name: Network Architecture Sizing for Trading RL
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Optimizes PPO neural network dimensions to balance trading model capacity, inference speed, and hardware memory usage.

About

This skill provides specialized guidance for sizing Proximal Policy Optimization (PPO) network architectures specifically for algorithmic trading models. It helps developers determine the ideal hidden layer dimensions based on hardware constraints—such as NVIDIA A100 or H100 VRAM—as well as market data complexity and inference latency requirements. By detailing the relationship between layer width and parameter count, it enables precise control over model capacity, ensuring that traders can maximize predictive power without compromising the execution speed necessary for live market environments.

Key Features

Provides GPU-tiered configuration templates for high-performance and low-memory hardware
Diagnostic tools for comparing model architectures and troubleshooting OOM errors
Calculates expected model parameter counts and file sizes based on hidden dimensions
Analyzes trade-offs between layer depth and first-layer width on total model size
Automates the verification of model architectures from PyTorch checkpoints
0 GitHub stars

Use Cases

Scaling model capacity for volatile assets like cryptocurrency using 4-layer architectures
Sizing a reinforcement learning model for high-frequency trading where low-latency inference is critical
Debugging unexpected model file sizes or architecture mismatches between training and inference

About

Key Features

Provides GPU-tiered configuration templates for high-performance and low-memory hardware
Diagnostic tools for comparing model architectures and troubleshooting OOM errors
Calculates expected model parameter counts and file sizes based on hidden dimensions
Analyzes trade-offs between layer depth and first-layer width on total model size
Automates the verification of model architectures from PyTorch checkpoints
0 GitHub stars

Use Cases

Scaling model capacity for volatile assets like cryptocurrency using 4-layer architectures
Sizing a reinforcement learning model for high-frequency trading where low-latency inference is critical
Debugging unexpected model file sizes or architecture mismatches between training and inference