KILLED · cost > edge
Hypothesis
Decompose the alt return covariance into principal components; the top eigenportfolios are systematic risk, the residual (idiosyncratic) returns mean-revert. Trade the residual, hedged against the top factors.
Math — residual after factor projection
Eigendecompose the correlation matrix $C = V\Lambda V^\top$, keep top-$k$ factors $F$, regress each asset on them:
$$ r_{i,t} = \sum_{j=1}^{k}\beta_{ij}F_{j,t} + \tilde r_{i,t} $$
Trade the cumulative idiosyncratic residual as an OU process (cf. Avellaneda & Lee 2010):
$$ s_{i,t}=\sum_{\tau\le t}\tilde r_{i,\tau},\qquad \text{score}_i = -\frac{s_{i,t}-\bar s_i}{\sigma_{s_i}} $$
Method
Top-5 eigenportfolios as factors on 40 alts, daily refit, dollar-neutral residual book, 1h replay with full two-sided fees + funding + borrow.
Results
| Gross Sharpe (residual book) | 1.9 |
| Turnover | ~340%/week |
| Net Sharpe after fees | 0.2 |
| Eigenvector stability (top-5) | low — rotates weekly |
Gross signal is real (Sharpe ~1.9) but turnover kills it: ~340%/week at crypto taker fees leaves Sharpe ~0.2 net. Eigenvectors also rotate, so the factor hedge is stale. Works for an equities desk at 5 bps, not at crypto cost structure.
A high gross Sharpe with high turnover is a fee-rebate strategy, not an alpha strategy. The cost structure decides which textbook strategies are even reachable.