mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
b1c04afb13
* refactor(Naming): use `primary_models` & `meta_labeling_models` * refactor(Naming): using primary * meta_labeling across config and in pipeline * feat(Pipeline): added back Ensemble models * fix(Pipeline): compiler error * fix(Config): typo * chore(Pipeline): removed unused averaging step * revert the changes in discretizing * chore(Pipeline): remove sharpe improvement logging * fix(Pipeline): ensemble predictions should be a pd.Series instead of a DataFrame * fix(Pipeline): discard unnecessary ensemble_probabilities * fix(Pipeline): fixes regarding various meta-labeling ensemble bugs * fix(Reporting): use the new naming convention * fix(Reporting): use the right variable * feat(Sweep): new sweep for ensemble models * fix(Sweep): config reference * fix(Config): simplified dev config * fix(Models): use the faster LR model * fix(Models): use LGBM in the meta-labeling model for speed * fix(Selection): always use the first model for feature selection, commented out caching from select_features() as it's close to redundant in terms of speed
39 lines
797 B
Python
39 lines
797 B
Python
# #%%
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# import pandas as pd
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# import pandas_ta as ta
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# from config.config import get_default_ensemble_config
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# from config.preprocess import preprocess_config
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# from data_loader.load_data import load_data
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# # %%
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# model_config, training_config, data_config = get_default_ensemble_config()
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# model_config, training_config, data_config = preprocess_config(model_config, training_config, data_config)
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# data_config['target_asset'] = data_config['assets'][0]
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# X, y, target_returns = load_data(**data_config)
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# # %%
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# X.ta.donchian()
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# # %%
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# X.ta.ema()
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# # %%
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# X.ta.adjusted = "ADA_USD_returns"
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# # %%
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# X.ta.sma(length=10)
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# # %%
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# X
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# # %%
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# X.ta.categories
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# # %%
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# ind_list = X.ta.indicators(as_list=True)
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# # %%
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# ind_list
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# # %%
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# X.ta.ao('ADA_USD_returns', length=10)
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# # %%
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# ta.ao() |