I’m using this function to compute CWMM:

```
cwmm <- function(mm, preds, era) {
pred_dt <- data.table('era' = era, 'pred' = preds, 'mm' = mm)
pred_dt[, preds_ranked_gauss := qnorm((rank(pred, na.last = 'keep') - 0.5) / .N), by = .(era)]
pred_dt[, preds_ranked_gauss_pot := sign(preds_ranked_gauss) * abs(preds_ranked_gauss)^1.5]
pred_dt[, mm_ranked_gauss := qnorm((rank(mm, na.last = 'keep') - 0.5) / .N), by = .(era)]
pred_dt[, mm_ranked_gauss_pot := sign(mm_ranked_gauss) * abs(mm_ranked_gauss)^1.5]
corr_dt <- pred_dt[, .(CWMM = cor(mm_ranked_gauss, preds_ranked_gauss, method = 'pearson'),
CWMM_pot = cor(mm_ranked_gauss_pot, preds_ranked_gauss_pot, method = 'pearson')), by = .(era)]
return(corr_dt)
}
> cwmm(mm, preds, era)
era CWMM CWMM_pot
<int> <num> <num>
1: 1100 0.8589433 0.8400368
2: 1101 0.8624765 0.8466103
3: 1102 0.8651279 0.8510147
4: 1103 0.8685777 0.8562474
5: 1104 0.8814365 0.8703542
```

The CWMM of the model in numerai CWMM column is always greater than 0.93

Do you know where is the problem? Can you reproduce CWMM?

In numerai-tools in github there isn’t the script for computing CWMM.