skills

Search and analyze arXiv papers for quantitative trading research. Use when researching academic papers, finding relevant studies, or literature review.

Look up documentation using Context7 for code and API references. Use when finding documentation, API references, or technical guides.

Complete machine learning pipeline for trading: feature engineering, AutoML, deep learning, and financial RL. Use for automated parameter sweeps, feature creation, model training, and anti-leakage validation.

Comprehensive strategy development workflow from ideation to validation. Use when creating trading strategies, running backtests, parameter optimization, or walk-forward validation.

Extract and learn patterns from trading data continuously. Use when identifying recurring patterns, building adaptive models, or continuous improvement workflows.

NautilusTrader algorithmic trading platform for strategy development and live trading. Use when building trading strategies, backtesting, or deploying to Hyperliquid.

Order flow MAE optimization workflow - extract features, create strategies, run backtests with exhaustion-timed entries to minimize drawdown

Unified documentation lookup and academic research skill for quant trading workflows. Covers API/library documentation retrieval, Context7 integration for versioned docs, and arXiv paper search for market microstructure, forecasting, and strategy research.

Translate trading strategies between different frameworks and languages. Use when converting Pine Script to Python, porting strategies to NautilusTrader, or cross-platform migration.

Generate professional tearsheets with custom SVG visualizations using the QuantStats library. Creates performance reports with MAE analysis, leverage recommendations, and trade lists. Use when analyzing strategy performance or generating visual reports.