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feat(Core): ensemble models, correct forward returns calculation, scaling, only train from when asset returns are available, major bug fixed in walk_forward_train_test (#35)
* fix(Core): correct forward returns calculation, classifiers are now working again, only train from when asset returns are available * feat(Utils): added get_first_valid_return_index() * feat(Ensemble): return models from `run_whole_pipeline` * feat(Ensemble): added ensemble step, fixed walk_forward_train_test predictions index confusion, * chore(Pipeline): remove unnecessary extra ensemble results dataframe * refactor(Core): removed unnecessary ensemble_train_predict, moved run_single_asset_trainig_pipeline to a separate file * feat(Training): added scaling on expanding window (the past) to walk_forward_train_test(), now printing out mean sharpe ratio * feat(CI): added environment.yml file * chore(Environment): update env.yml * feat(CI): added testing workflow * fix(CI): renamed enviroment.yml * fix(Tests): added missing new parameter to walk_forward_train_test()
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@@ -49,13 +49,3 @@ def get_stock_price_av(symbol: str, start_date: str = None) -> pd.DataFrame:
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df = df.rename_axis('time')
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return df
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# %%
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# btc = get_crypto_price_av(symbol = 'BTC', exchange = 'USD', start_date = '2018-01-01')
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# btc
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# %%
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# spy = get_stock_price_av(symbol = 'SPY', start_date = '2018-01-01')
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# spy
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