QuantStart’s 2020 Survey of Quantitative Trading Topics
Summary
The document reports a reader survey about which quantitative trading subjects the QuantStart community wanted to study in 2020. Machine learning and deep learning led the responses, followed by mathematical finance and coding and data science. Tactical asset allocation and trading infrastructure tied, while cryptocurrency trading and careers advice received fewer votes. The article gives approximate vote rankings and notes that respondents could select multiple topics or suggest others, so the results reflect this mailing list rather than a representative survey of traders as a whole.
The survey results shaped the publication’s planned coverage: applying machine learning to noisy, autocorrelated financial data; foundational mathematics; programming and data science tools; and tactical allocation strategies. The discussion describes tactical allocation as a way to explore portfolio construction, risk management and backtesting without daily execution. These are editorial plans and audience preferences, not evidence that any strategy is profitable or that the proposed tutorials were completed.
Key ideas
- Survey responses ranked machine learning and deep learning as the most requested topic.
- Mathematical finance and coding and data science also drew substantial interest.
- Tactical asset allocation and trading infrastructure received equal support.
- The article stresses that financial time series differ from many standard machine learning datasets.
- The survey informed planned educational content but does not evaluate trading performance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.