Definitions
Please refer to the main definitions docs to understand the basic functions referenced below.
Targets
target_20d
timeline: 20D2L
bins=5, uniformity=10%, 40%, 50%
neutralizers: Standard Factors and other factors not listed
target_20d_factor_neutral
timeline: 20D2L
bins=5, uniformity=10%, 40%, 50%
neutralizers: Standard Factors and other factors not listed
target_20d_factor_feat_neutral
timeline: 20D2L
bins=5, uniformity=10%, 40%, 50%
neutralizers: Standard Factors and other features not listed
Meta Models
Signals submissions are cleaned first by:
tie-kept-rank each submission
fill nans in each submission with 0.5
tie-kept-rank each submission
gaussianize each submission
Signals Stake-Weighted Meta Model w/ minimum stake (SSWMM)
Stake-weighted average of cleaned Signals submissions
Signals Naive-Weighted Meta Model w/ minimum stake (SNWMM)
Average of cleaned Signals submissions
Signals Naive-Weighted Meta Model w/ minimum 10 NMR stake (SNWMMmin10)
Average of cleaned Signals submissions with at least 10 NMR stake
Scores
submissions are cleaned before used in scoring:
drop invalid tickers
tie-kept rank the submission
fill nans with 0.5
MMC - Meta Model Contribution
correlation contribution of a submission, SNWMMmin10, and target_20d_factor_neutral
timeline: 20D2L (+ 2 days data lag)
CORRV4 - Correlation v4
numerai corr of a submission and target_20d_factor_feat_neutral
20 score days w/ 2 days returns lag (+ 2 days data lag)
ICV2 - Information Coefficient V2
spearman correlation between binned returns and submission
20 score days w/ 2 days returns lag (+ 2 days data lag)
RIC - Residual Information Coefficient
spearman correlation between target_20d_factor_neutral and submission
20 score days w/ 2 days returns lag (+ 2 days data lag)
FNCV4 - Feature Neutral Correlation v4
submission is tie-kept-ranked then gaussianized then neutralized
tie-broken-rank correlation between target_20d_factor_feat_neutral and submission
neutralizers: Standard Factors and V4 Medium Safe Features
20 score days w/ 2 days returns lag (+ 2 days data lag)
CWSNMM - Corr w/ Signals Naive Meta Model
s` = tie-kept rank, then gaussianize, then pow 1.5 a submission s
calculate pearson correlation between s` and SNWMM
timeline: 4 days data lag / not dependant on returns
MCWSM - Max Corr w/ Signals Models
Maximum pearson correlation of a submission with any other Signals submission
only compared to other submissions made in the same round
timeline: 4 days data lag / not dependant on returns
APCWSM - Average Pairwise Corr w/ Signals Models
Average pearson correlation of a submission with each other Signals submission
only compared to other submissions made in the same round
timeline: 4 days data lag / not dependant on returns
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