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refactor(Training): use date indexes instead of integers, need this to prepare for Events (#185)

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This commit is contained in:
Mark Aron Szulyovszky
2022-01-24 12:22:30 +01:00
committed by GitHub
parent e80fffdb65
commit e6e2317fe0
15 changed files with 50 additions and 52 deletions
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run_portfolio_reporting.py
+3 -3
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@@ -98,17 +98,17 @@ def create_quantile_weights(predictions: pd.DataFrame, availability: pd.DataFram
row = row.apply(only_select_bottom_top)
no_of_nonzero_predictions = row[row != 0].count()
units = min(1 / no_of_nonzero_predictions, 0.25)
weights.iloc[index] = row * units
weights.loc[index] = row * units
return weights
predictions = pd.read_csv('output/predictions.csv', index_col=0)
predictions.index = pd.DatetimeIndex(predictions.index)
predictions.columns = ['_'.join(col.replace("model_", "").split("_")[:2]) for col in predictions.columns]
first_index = get_first_valid_return_index(predictions[predictions.columns[0]])
predictions = predictions.iloc[first_index:]
predictions.reset_index(drop=True, inplace=True)
close = load_only_returns(data_collections['daily_crypto'], 'date', 'price')
close = load_only_returns(data_collections['daily_crypto'], 'price')
close = close.iloc[first_index:-1]
close.columns = [col.replace("_returns", "") for col in close.columns]
close = close[predictions.columns]