Quant Research Lab

Reproducible strategy experiments — explicit hypotheses, sound methods, and honestly reported results.

Self-contained research experiments on market data. The purpose is not to publish profitable strategies — it is to ask well-posed questions and answer them rigorously. A negative result, analysed carefully, is as valuable here as a positive one.

Every experiment follows the same structure:

  1. Research question
  2. Hypothesis
  3. Dataset
  4. Methodology
  5. Code
  6. Results
  7. Limitations
  8. What I learned
  9. Next experiment

The tone is deliberately that of a researcher, not a signal seller. Not “this model predicts Tesla with 80% accuracy,” but: this experiment tests whether lagged return, volatility, and volume features contain statistically useful information for short-horizon directional classification, evaluated with walk-forward validation to reduce look-ahead bias.

Experiments

Published experiments appear below. More are in progress.

Planned experiments

  • Can short-horizon Tesla returns be predicted from lagged return, volatility, and volume features?
  • Does the volatility regime materially change a strategy’s performance?
  • Can a Hidden Markov Model identify distinct market states out-of-sample?
  • Does volume confirm momentum, or add nothing once price is known?
  • Does regime-filtering improve a simple RSI rule, or just overfit it?
  • Does news sentiment improve entry timing beyond price-based features?
  • Can reinforcement learning improve position sizing under a fixed signal?