mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-28 21:12:16 +00:00
7ba964d61e
* Basic integration of crypto pred * Add loading into crypto pred * Change all pred _views to allow for other datetime than daily * small mypy change * Add pred menu description and catch if no coin is loaded. Co-authored-by: James Maslek <james@jamess-mbp.myfiosgateway.com>
80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
"""Monte Carlo View"""
|
|
__docformat__ = "numpy"
|
|
|
|
from typing import Union
|
|
import os
|
|
|
|
import pandas as pd
|
|
import numpy as np
|
|
import seaborn as sns
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.dates as mdates
|
|
|
|
from gamestonk_terminal.common.prediction_techniques import mc_model
|
|
from gamestonk_terminal.helper_funcs import (
|
|
plot_autoscale,
|
|
get_next_stock_market_days,
|
|
export_data,
|
|
)
|
|
from gamestonk_terminal.config_plot import PLOT_DPI
|
|
from gamestonk_terminal import feature_flags as gtff
|
|
|
|
|
|
def display_mc_forecast(
|
|
data: Union[pd.Series, np.ndarray],
|
|
n_future: int,
|
|
n_sims: int,
|
|
use_log=True,
|
|
export: str = "",
|
|
time_res: str = "",
|
|
):
|
|
"""Display monte carlo forecasting
|
|
|
|
Parameters
|
|
----------
|
|
data : Union[pd.Series, np.array]
|
|
Data to forecast
|
|
n_future : int
|
|
Number of days to forecast
|
|
n_sims : int
|
|
Number of simulations to run
|
|
use_log : bool, optional
|
|
Flag to use lognormal, by default True
|
|
export: str
|
|
Format to export data
|
|
time_res : str
|
|
Resolution for data, allowing for predicting outside of standard market days
|
|
"""
|
|
predicted_values = mc_model.get_mc_brownian(data, n_future, n_sims, use_log)
|
|
if not time_res or time_res == "1D":
|
|
future_index = get_next_stock_market_days(data.index[-1], n_next_days=n_future) # type: ignore
|
|
else:
|
|
future_index = pd.date_range(data.index[-1], periods=n_future + 1, freq=time_res)[1:] # type: ignore
|
|
|
|
dateFmt = mdates.DateFormatter("%m/%d/%Y")
|
|
|
|
fig, ax = plt.subplots(1, 2, figsize=plot_autoscale(), dpi=PLOT_DPI)
|
|
|
|
ax[0].plot(data)
|
|
ax[0].plot(future_index, predicted_values, alpha=0.3)
|
|
ax[0].set_title("Data Predictions")
|
|
ax[0].xaxis.set_major_formatter(dateFmt)
|
|
ax[0].tick_params(axis="x", labelrotation=45)
|
|
ax[0].grid("on")
|
|
|
|
sns.histplot(predicted_values[-1, :], ax=ax[1], kde=True)
|
|
ax[1].set_xlabel("Price")
|
|
ax[1].axvline(x=data.values[-1], c="k", label="Last Value", lw=3, ls="-") # type: ignore
|
|
ax[1].set_title(f"Distribution of final values after {n_future} steps.")
|
|
ax[1].set_xlim(np.min(predicted_values[-1, :]), np.max(predicted_values[-1, :]))
|
|
ax[1].grid("on")
|
|
ax[1].legend()
|
|
|
|
fig.tight_layout(pad=2)
|
|
|
|
if gtff.USE_ION:
|
|
plt.ion()
|
|
plt.show()
|
|
export_data(export, os.path.dirname(os.path.abspath(__file__)), "mc")
|
|
print("")
|