mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
b5ddee8dce
* feat(HPO): added `run_hpo` script * fix(Linter): ran * feat(HPO): removed any reference to sweep (superseeded by optuna) * fix(HPO): optimize for sharpe * fix(Config): removed glassnode data, save trials from hpo * feat(Labelling): added three-balanced method works again * fix(BetSizing): set the correct class labels * fix(HPO): powerset should return what's expected, added two new normalization methods * fix(Linter): ran * fix(DataLoader): sort the dataframe when fetching data * fix(Config): only take z-score of other assets
30 lines
972 B
Python
30 lines
972 B
Python
from run_pipeline import run_pipeline
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from config import get_default_ensemble_config, get_dev_config
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import pandas as pd
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all_results = []
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all_predictions = []
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for index in range(6):
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_, _, _, _, results_1, predictions_1, _ = run_pipeline(
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project_name="price-prediction",
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with_wandb=False,
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config=get_default_ensemble_config(),
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)
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all_results.append(results_1)
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all_predictions.append(predictions_1)
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correlations = pd.Series()
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for asset_name in [c for c in all_predictions[0].columns if "ensemble" in c]:
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predictions_for_asset = pd.concat(
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[preds[asset_name] for preds in all_predictions], axis=1
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)
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correlations[asset_name] = predictions_for_asset.corr().mean()[0]
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print("Correlation for asset ", asset_name, ": ", correlations[asset_name])
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correlations["Overall"] = correlations.mean()
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print("Average correlation across all assests: ", correlations.mean())
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correlations.to_csv("output/correlations.csv")
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