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b71abcfbf4
* quick econ fix * black * keys and feature flags * terminal name 👀 * some more replacements * some more replacements * edit pyproject * gst -> openbb * add example portfolios back to git * Update api from gst * sorry. skipping some tests * another round of names * another round of test edits * Missed some .gst refs and update timezone * water mark stuff * Fixing Names in terminal.spec and name of GTFF_DEFAULTS to OBBFF_DEFAULTS * fix more GST to OpenBB Terminal * Logging : merge conflicts with main * Revert wrong files Co-authored-by: Andrew <andrew.kenreich@gmail.com> Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt> Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
60 lines
1.5 KiB
Python
60 lines
1.5 KiB
Python
"""Monte Carlo Model"""
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__docformat__ = "numpy"
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import logging
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from typing import Union
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import numpy as np
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import pandas as pd
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from scipy.stats import norm
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from openbb_terminal.decorators import log_start_end
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logger = logging.getLogger(__name__)
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DISTRIBUTIONS = ["normal", "lognormal"]
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@log_start_end(log=logger)
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def get_mc_brownian(
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data: Union[pd.Series, np.ndarray], n_future: int, n_sims: int, use_log=True
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) -> np.ndarray:
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"""Performs monte carlo forecasting for brownian motion with drift
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Parameters
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----------
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data : Union[pd.Series, np.ndarray]
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Input data.
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n_future : int
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Number of future steps
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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 log returns, by default True
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Returns
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-------
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np.ndarray
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Array of predictions. Has shape (n_future, n_sims)
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"""
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changes = data.pct_change().dropna() # type: ignore
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if use_log:
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changes = np.log(1 + changes)
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dist_mean = changes.mean()
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dist_var = changes.var()
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dist_drift = dist_mean - 0.5 * dist_var
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dist_std = np.sqrt(dist_var)
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random_steps = norm.ppf(np.random.rand(n_future, n_sims))
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predicted_change = np.exp(dist_drift + dist_std * random_steps)
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possible_paths = np.zeros_like(predicted_change)
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possible_paths[0] = data.values[-1] # type: ignore
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for t in range(1, n_future):
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possible_paths[t] = possible_paths[t - 1] * predicted_change[t]
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return possible_paths
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