41 lines
2.6 KiB
Plaintext
41 lines
2.6 KiB
Plaintext
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|dk r4t j |
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|k dt j |
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| k dd¦ « ¦ « } nt j |
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¦ « } |j |j df } t | ¦ « t | ¦ « k r6t j | dgt | ¦ « t | ¦ « z
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z ¦ « } t j
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| | j ¬¦ « }
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dd¬¦ « \ } }
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t j
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dk |
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dk ||¬ ¦ « }|S )
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a`
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Runs a vectorbt backtest for a given pre-trained model.
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Parameters
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----------
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model : fitted scikit-learn model
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Already fitted model (e.g. RandomForestRegressor).
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X : pd.DataFrame
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The full feature DataFrame (or the portion you want to backtest).
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selected_features : list
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List of feature names used by the model.
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data : pd.DataFrame
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Original DataFrame containing at least a 'close' column.
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scaler : fitted scaler
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The StandardScaler (or other) used to scale features.
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init_cash : float
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Starting capital for the backtest.
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freq : str
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Frequency for vectorbt (e.g. '4H', '1D').
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threshold : float
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Minimum absolute predicted return to place a trade (optional).
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Returns
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-------
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pf : vbt.Portfolio
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The resulting vectorbt portfolio object.
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r é éÿÿÿÿr Úclose)ÚindexÚinner)ÚjoinÚaxis)ÚentriesÚexitsÚ init_cashÚfreq)Ú transformÚpredictÚnpÚwhereÚsignÚlocr
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