We are in the learning phase. This page will eventually contain our own research and observations. For now, it reflects the topics we are studying as we prepare to build.
How markets actually work at the order book level. Liquidity, price formation, and the mechanics of execution.
Time series analysis, hypothesis testing, and the challenge of separating real patterns from noise in financial data.
How to think about, measure, and control risk in systematic trading. Position sizing, drawdown management, and tail risk.
How to combine signals into portfolios. Optimization, constraints, diversification, and the gap between theory and practice.
Mean reversion, momentum, factor models, and the lifecycle of trading strategies from idea to implementation to decay.
The engineering side: data pipelines, order management, latency, and building systems that work reliably under pressure.
Backtesting is a minefield. Out-of-sample testing helps, but how much data is enough? When does optimization become curve-fitting?
Simple models are transparent and robust. Complex models can capture more structure. Where is the right trade-off for our scale and goals?
Markets change. Data feeds drop. Models stop working. How do you design infrastructure that fails safely instead of catastrophically?
Financial data is full of patterns that look meaningful but are not. How do you set a bar for statistical significance that is honest?
We publish what we learn as we go. Our blog has 125+ articles covering topics from algorithmic trading to risk management.
Read the Blog
This page will be updated as we produce original research.
Nothing here constitutes investment advice or recommendations.