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e1bba247e2
* init commit for doc string fixes * quick fix * Forecast Guide Draft 1 * grammar * sentence edit * missing word * target column is a string, not a list. ooops * clarification on default target column * minor mods * more fixes * Fixed dashboard * val instead of tickerseries * Added fixes * $ --> <> * Fixed pylint * one quick change Co-authored-by: Danglewood <85772166+deeleeramone@users.noreply.github.com> Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com> Co-authored-by: colin99d <colin99delahunty@gmail.com>
113 lines
3.4 KiB
Python
113 lines
3.4 KiB
Python
"""Random Walk with Drift View"""
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__docformat__ = "numpy"
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import logging
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from typing import Union, Optional, List
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from datetime import datetime
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import pandas as pd
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import matplotlib.pyplot as plt
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from openbb_terminal.forecast import rwd_model
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.forecast import helpers
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logger = logging.getLogger(__name__)
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# pylint: disable=too-many-arguments
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@log_start_end(log=logger)
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def display_rwd_forecast(
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data: Union[pd.DataFrame, pd.Series],
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target_column: str = "close",
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dataset_name: str = "",
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n_predict: int = 5,
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start_window: float = 0.85,
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forecast_horizon: int = 5,
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export: str = "",
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residuals: bool = False,
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forecast_only: bool = False,
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start_date: Optional[datetime] = None,
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end_date: Optional[datetime] = None,
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naive: bool = False,
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export_pred_raw: bool = False,
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external_axes: Optional[List[plt.axes]] = None,
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):
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"""Display Random Walk with Drift Model
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Parameters
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----------
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data : Union[pd.Series, np.array]
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Data to forecast
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dataset_name: str
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The name of the ticker to be predicted
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target_column: Optional[str]:
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Target column to forecast. Defaults to "close".
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n_predict: int
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Number of days to forecast
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start_window: float
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Size of sliding window from start of timeseries and onwards
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forecast_horizon: int
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Number of days to forecast when backtesting and retraining historical
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export: str
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Format to export data
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residuals: bool
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Whether to show residuals for the model. Defaults to False.
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forecast_only: bool
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Whether to only show dates in the forecasting range. Defaults to False.
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start_date: Optional[datetime]
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The starting date to perform analysis, data before this is trimmed. Defaults to None.
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end_date: Optional[datetime]
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The ending date to perform analysis, data after this is trimmed. Defaults to None.
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naive: bool
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Whether to show the naive baseline. This just assumes the closing price will be the same
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as the previous day's closing price. Defaults to False.
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external_axes:Optional[List[plt.axes]]
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External axes to plot on
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"""
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data = helpers.clean_data(data, start_date, end_date, target_column, None)
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if not helpers.check_data(data, target_column, None):
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return
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(
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ticker_series,
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historical_fcast,
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predicted_values,
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precision,
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_model,
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) = rwd_model.get_rwd_data(
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data=data,
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target_column=target_column,
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n_predict=n_predict,
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start_window=start_window,
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forecast_horizon=forecast_horizon,
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)
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if ticker_series == []:
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return
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probabilistic = False
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helpers.plot_forecast(
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name="RWD",
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target_col=target_column,
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historical_fcast=historical_fcast,
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predicted_values=predicted_values,
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ticker_series=ticker_series,
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ticker_name=dataset_name,
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data=data,
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n_predict=n_predict,
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forecast_horizon=forecast_horizon,
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past_covariates=None,
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precision=precision,
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probabilistic=probabilistic,
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export=export,
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forecast_only=forecast_only,
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naive=naive,
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export_pred_raw=export_pred_raw,
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external_axes=external_axes,
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)
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if residuals:
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helpers.plot_residuals(
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_model, None, ticker_series, forecast_horizon=forecast_horizon
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)
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