Cross-Sectional Crypto Factor Testing and Multi-Factor Portfolio Construction
Summary
This tutorial presents a beginner framework for testing factors across a universe of USDT perpetual futures. It starts with hourly price, volume, trade count, and aggressive-buy data, then describes factor evaluation through forward-return regressions, information coefficients, and cross-sectional grouping. In the grouped approach, symbols are ranked by a factor, divided into portfolios, and compared by return and risk measures; the example engine also accounts for trading fees and discusses position concentration and short-side exposure.
The article demonstrates several candidate factors and their correlations, then combines selected normalized inputs with hand-chosen weights. It also surveys equal weighting, historical factor returns, IC-based weighting, and principal components. The examples are exploratory rather than conclusive: backtest charts and reported correlations do not establish robustness, and results may depend on rebalancing frequency, start date, group size, survivorship, execution assumptions, and changing market regimes. The framework is useful for organizing research, but the chosen weights and factors require out-of-sample validation.
Key ideas
- A factor represents a hypothesis about information associated with future returns.
- Cross-sectional sorting and portfolio grouping can reveal whether returns vary monotonically with factor values.
- Regression coefficients, information coefficients, and factor return ratios provide complementary evaluation measures.
- Diversifying across more symbols can reduce the effect of a large move in any single position.
- Combining factors requires comparable scaling and attention to redundancy and factor weighting.
- Backtest results should be checked for sensitivity to dates, group sizes, rebalance intervals, and execution assumptions.
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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.