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fix sharpe calc in feature neutralization, add line about importance of BMC in target ensemble
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‎feature_neutralization.ipynb

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"id": "meowEBs-PwtB",
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"for col in group_neutral_cols:\n",
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" corr_mean = group_neutral_corr[col].mean()\n",
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" corr_std = group_neutral_corr[col].std()\n",
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" corr_sharpe = mean / std\n",
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" corr_sharpe = corr_mean / corr_std\n",
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" corr_rolling_max = group_neutral_cumsum[col].expanding(min_periods=1).max()\n",
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" corr_max_drawdown = (corr_rolling_max - group_neutral_cumsum[col]).max()\n",
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" mmc_mean = per_era_mmc[col].mean()\n",
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" mmc_std = per_era_mmc[col].std()\n",
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" mmc_sharpe = mean / std\n",
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" mmc_sharpe = mmc_mean / mmc_std\n",
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" mmc_rolling_max = cumsum_mmc[col].expanding(min_periods=1).max()\n",
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" mmc_max_drawdown = (rolling_max - cumsum_mmc[col]).max()\n",
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" group_neutral_summary_metrics[col] = {\n",
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"metadata": {
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"id": "ds3HPim6M-s5"
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‎target_ensemble.ipynb

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"\n",
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"It's also helpful to if we measured the contribution of your models to all of Numerai's benchmark models. We call this Benchmark Model Contribution or `BMC`. On the website, `BMC` measures your model's contribution to a weighted ensemble of all of our Benchmark Models.\n",
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"\n",
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"This is an important metric to track because it tells you how additive your model is to Numerai's known models and, by extension, how additive you might be to the Meta Model in the future.\n",
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"\n",
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"To keep things simple, we will use an unweighted ensemble of Numerai's Benchmarks to measure your models' BMC, let's take a look:"
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]
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