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Definitions ​

Please refer to the main definitions docs to understand the basic functions referenced below.

Targets ​

  • target_raw_return_20/60
    • timeline: 20D2L / 60D2L
    • bins=5, uniformity=10%, 40%, 50%
    • neutralizers: none
  • target_factor_neutral_20/60
    • timeline: 20D2L / 60D2L
    • bins=5, uniformity=10%, 40%, 50%
    • neutralizers: Standard Factors
  • target_factor_feat_neutral_20/60
    • timeline: 20D2L / 60D2L
    • bins=5, uniformity=10%, 40%, 50%
    • neutralizers: Standard Factors and other features not listed
  • target_chili_60
    • timeline: 60D2L
    • bins=5, uniformity=10%, 40%, 50%
    • neutralizers: everything and the proprietary Numerai risk model
    • other adjustments: ADV-weighted and per-level exposure constraints

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

Meta Portfolio ​

  • neutral weights
    • applying neutralizers matrix and sample weights vector to a submission
  • Stake-Weighted Portfolio (SWP)
    • Stake-weighted average of all neutral weights in Signals

Scores ​

  • submissions are cleaned before used in scoring:
    • drop invalid tickers
    • tie-kept rank the submission
    • fill nans with 0.5
  • ALPHA
    • dot product neutral weights with target_chili_60
    • timeline: 60 score days w/ 2 days returns lag (+ 2 days data lag)
  • MPC - Meta Portfolio Contribution
    • Gradient of the SWP multiplied by the target_chili_60 with respect to stakes
    • timeline: 60 score days w/ 2 days returns lag (+ 2 days data lag)
  • MMC - Meta Model Contribution
    • correlation contribution of a submission, SNWMMmin10, and target_factor_neutral_20
    • timeline: 20 score days w/ 2 days returns lag (+ 2 days data lag)
  • CORRV4 - Correlation v4
    • numerai corr of a submission and target_factor_feat_neutral_20
    • timeline: 20 score days w/ 2 days returns lag (+ 2 days data lag)
  • ICV2 - Information Coefficient V2
    • numerai corr between binned returns and submission
    • timeline: 20 score days w/ 2 days returns lag (+ 2 days data lag)
  • RIC - Residual Information Coefficient
    • numerai corr between target_factor_neutral_20 and submission
    • timeline: 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_factor_feat_neutral_20 and submission
    • neutralizers: Standard Factors and V4 Medium Safe Features
    • timeline: 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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