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2815f42a3c
* patternize validation of axes count with typo fix * remove unused console import * fix suffix_text arg name typo * Black * add docstring with typehint, fast return if true * Common : ta > fixing import * Portfolio : view fix * Black * Helper : simplify `is_valid_axes_count` Co-authored-by: didierlopes.eth <dro.lopes@campus.fct.unl.pt> Co-authored-by: Chavithra PARANA <chavithra@gmail.com> Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com>
96 lines
2.9 KiB
Python
96 lines
2.9 KiB
Python
"""Monte Carlo View"""
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__docformat__ = "numpy"
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import logging
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import os
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from typing import List, Optional, Union
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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from openbb_terminal.config_terminal import theme
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from openbb_terminal.common.prediction_techniques import mc_model
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from openbb_terminal.config_plot import PLOT_DPI
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.helper_funcs import (
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export_data,
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get_next_stock_market_days,
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plot_autoscale,
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is_valid_axes_count,
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)
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logger = logging.getLogger(__name__)
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@log_start_end(log=logger)
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def display_mc_forecast(
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data: Union[pd.DataFrame, pd.Series],
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n_future: int,
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n_sims: int,
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use_log=True,
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fig_title: str = "",
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export: str = "",
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time_res: str = "",
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external_axes: Optional[List[plt.Axes]] = None,
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):
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"""Display monte carlo forecasting
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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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n_future : int
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Number of days to forecast
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n_sims : int
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Number of simulations to run
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use_log : bool, optional
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Flag to use lognormal, by default True
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fig_title : str
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Figure title
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export: str
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Format to export data
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time_res : str
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Resolution for data, allowing for predicting outside of standard market days
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external_axes : Optional[List[plt.Axes]], optional
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External axes (2 axes are expected in the list), by default None
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"""
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predicted_values = mc_model.get_mc_brownian(data, n_future, n_sims, use_log)
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if not time_res or time_res == "1D":
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future_index = get_next_stock_market_days(data.index[-1], n_next_days=n_future) # type: ignore
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else:
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future_index = pd.date_range(data.index[-1], periods=n_future + 1, freq=time_res)[1:] # type: ignore
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# This plot has 2 axes
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if external_axes is None:
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_, axes = plt.subplots(1, 2, figsize=plot_autoscale(), dpi=PLOT_DPI)
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(ax1, ax2) = axes
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elif is_valid_axes_count(external_axes, 2):
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(ax1, ax2) = external_axes
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else:
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return
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ax1.plot(data)
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ax1.plot(future_index, predicted_values, alpha=0.3)
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start_timestamp = data.index[0]
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end_timestamp = future_index[-1]
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ax1.set_xlim(start_timestamp, end_timestamp)
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ax1.set_title(f"{fig_title} Data Predictions")
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sns.histplot(predicted_values[-1, :], ax=ax2, kde=True)
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ax2.set_xlabel("Final Value")
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ax2.axvline(
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x=data.values[-1], color=theme.down_color, label="Last Value", linestyle="-"
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)
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ax2.set_title(f"Distribution of final values after {n_future} steps.")
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ax2.set_xlim(np.min(predicted_values[-1, :]), np.max(predicted_values[-1, :]))
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ax2.legend()
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theme.style_primary_axis(ax1)
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theme.style_primary_axis(ax2)
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if external_axes is None:
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theme.visualize_output()
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export_data(export, os.path.dirname(os.path.abspath(__file__)), "mc")
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