wer-inc
Official@wer-inc
Provides quantitative financial modeling, GPU-accelerated data processing, and Graph Attention Network training for high-frequency trading infrastructure.
Agent Skills by wer-inc
Showing 5 vetted skills indexed across 1 GitHub repositories.
wer-incatft-research
Quantify Sharpe, RankIC, and hit ratio metrics across horizons and cohorts.
wer-incatft-pipeline
Provision fresh or historical Parquet datasets for ATFT-GAT-FAN using GPU-accelerated ETL.
wer-incatft-code-quality
Automate code-quality checks across the ATFT-GAT-FAN codebase.
wer-incatft-training
Orchestrates Graph Attention Network forecaster training runs with hyper-parameter sweeps and GPU monitoring on A100s.
wer-incatft-autonomy
Coordinate Claude and Codex agents for ATFT-GAT-FAN stack maintenance.
Frequently Asked Questions About wer-inc
FAQPage SchemaWhat specific financial metrics can be calculated using these capabilities?▼
These capabilities enable the calculation of Sharpe ratios, RankIC, and hit ratio metrics across various time horizons and financial cohorts. The system is designed to quantify performance benchmarks specifically for the ATFT-GAT-FAN stack, ensuring rigorous evaluation of predictive financial models.
Which hardware is required to run the training processes?▼
Training processes are optimized for A100 hardware to support intensive Graph Attention Network computations. The infrastructure includes monitoring for GPU utilization during hyper-parameter sweeps to ensure efficient resource allocation and model convergence during the training lifecycle.
How is data prepared for the forecasting models?▼
Data preparation utilizes GPU-accelerated processing to provision both fresh and historical Parquet datasets. This ensures high-throughput ingestion for the ATFT-GAT-FAN stack, allowing for rapid iteration on large-scale financial datasets without bottlenecks in the data delivery layer.