Strategy Research
Strategy research encompasses the full lifecycle of developing, testing, and validating systematic trading hypotheses. This includes hypothesis generation, backtesting, performance evaluation, and robustness analysis.
Why It Matters
The systematic evaluation of trading hypotheses requires rigorous methodology to avoid common pitfalls such as overfitting, data mining bias, and unrealistic assumptions about execution. Proper strategy research separates genuine signals from noise.
Research Questions
- How can we systematically generate and evaluate trading hypotheses?
- What validation procedures best identify overfit strategies?
- How should transaction costs and slippage be modeled?
- What is the minimum data requirement for reliable strategy evaluation?
Methods
Experiments
Research in progress. Current focus on developing frameworks for automated hypothesis generation and evaluation, with emphasis on preventing overfitting and ensuring out-of-sample validity.
Validation
Strategies are evaluated through multiple independent validation procedures including walk-forward testing, out-of-sample holdout, and Monte Carlo simulation of returns.
Related Projects
Autonomous Strategy Research Lab
An experimental research environment designed to automate parts of quantitative strategy discovery, testing, and validation.
View projectGold AI Strategy Lab
An experimental research environment for studying machine-learning-assisted strategy research on historical gold-market data.
View project