Building a Quantitative Trading Business: Strategy Research, Backtesting, and Risk
Summary
These notes summarize a practical framework for developing an independent quantitative trading operation. They cover choosing strategies that fit a trader’s capital, skills, time, and income goals; screening ideas with benchmark comparisons, Sharpe ratio, drawdown, and robustness questions; and then testing them carefully. The backtesting discussion highlights survivorship and look-ahead bias, data quality, transaction costs, overfitting, and the value of out-of-sample checks and paper trading.
The later sections address brokerage and execution systems, operational risks, position leverage, and portfolio allocation. The notes describe the Kelly criterion as a way to scale leverage and allocate capital across strategies, while emphasizing model and software risk and the need to start small. They also review statistical arbitrage methods such as mean reversion, momentum, cointegration, factor models, and seasonality, and argue that strategy capacity and market competition affect returns. These are broad book notes rather than a tested prescription; several claims depend on assumptions, and historical performance cannot guarantee future results.
Key ideas
- Strategy choice should reflect capital, skills, available time, and whether the goal is income or long-term growth.
- Backtests should account for data quality, survivorship bias, look-ahead bias, overfitting, and trading costs.
- Out-of-sample evaluation and paper trading can reveal weaknesses that historical simulation misses.
- Model, software, execution, leverage, and operational failures are distinct sources of trading risk.
- Statistical arbitrage methods include mean reversion, momentum, cointegration, factor models, and seasonality.
- Strategy capacity and competition influence whether an edge can persist at different scales.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.