mirror of
https://github.com/OpenBB-finance/OpenBB.git
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88 lines
4.9 KiB
Python
88 lines
4.9 KiB
Python
import argparse
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from helper_funcs import *
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import pandas as pd
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import matplotlib.pyplot as plt
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from pandas.plotting import register_matplotlib_converters
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register_matplotlib_converters()
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import pmdarima
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from statsmodels.tsa.arima.model import ARIMA
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# -------------------------------------------------- ARIMA --------------------------------------------------
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def arima(l_args, s_ticker, s_interval, df_stock):
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parser = argparse.ArgumentParser(prog='arima',
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description="""In statistics and econometrics, and in particular in time
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series analysis, an autoregressive integrated moving average (ARIMA) model
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is a generalization of an autoregressive moving average (ARMA) model. Both
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of these models are fitted to time series data either to better understand
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the data or to predict future points in the series (forecasting).
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ARIMA(p,d,q) where parameters p, d, and q are non-negative integers, p is
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the order (number of time lags) of the autoregressive model, d is the degree
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of differencing (the number of times the data have had past values subtracted),
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and q is the order of the moving-average model.""")
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parser.add_argument('-d', "--days", action="store", dest="n_days", type=check_positive, default=5,
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help='prediction days.')
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parser.add_argument('-i', "--ic", action="store", dest="s_ic", type=str, default='aic',
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choices=['aic', 'aicc', 'bic', 'hqic', 'oob'], help='information criteria.')
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parser.add_argument('-s', "--seasonal", action="store_true", default=False, dest="b_seasonal",
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help='Use weekly seasonal data.')
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parser.add_argument('-o', "--order", action="store", dest="s_order", type=str,
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help='arima model order (p,d,q) in format: pdq.')
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parser.add_argument('-r', "--results", action="store_true", dest="b_results", default=False,
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help='results about ARIMA summary flag.')
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try:
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(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
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if l_unknown_args:
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print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
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return
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# Machine Learning model
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if ns_parser.s_order:
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t_order = tuple([int(ord) for ord in list(ns_parser.s_order)])
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model = ARIMA(df_stock['5. adjusted close'].values, order=t_order).fit()
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l_predictions = model.predict(start=len(df_stock['5. adjusted close'])+1, end=len(df_stock['5. adjusted close'])+ns_parser.n_days)
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else:
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if ns_parser.b_seasonal:
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model = pmdarima.auto_arima(df_stock['5. adjusted close'].values, error_action='ignore', seasonal=True, m=5, information_criteria=ns_parser.s_ic)
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else:
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model = pmdarima.auto_arima(df_stock['5. adjusted close'].values, error_action='ignore', seasonal=False, information_criteria=ns_parser.s_ic)
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l_predictions = model.predict(n_periods=ns_parser.n_days)
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# Prediction data
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l_pred_days = get_next_stock_market_days(last_stock_day=df_stock['5. adjusted close'].index[-1], n_next_days=ns_parser.n_days)
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df_pred = pd.Series(l_predictions, index=l_pred_days, name='Price')
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if ns_parser.b_results:
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print(model.summary())
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print("")
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# Plotting
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plt.plot(df_stock.index, df_stock['5. adjusted close'], lw=2)
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if ns_parser.s_order:
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plt.title(f"ARIMA {str(t_order)} on {s_ticker} - {ns_parser.n_days} days prediction")
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else:
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plt.title(f"ARIMA {model.order} on {s_ticker} - {ns_parser.n_days} days prediction")
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plt.xlim(df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1] )
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plt.xlabel('Time')
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plt.ylabel('Share Price ($)')
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plt.grid(b=True, which='major', color='#666666', linestyle='-')
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plt.minorticks_on()
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plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
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plt.plot([df_stock.index[-1], df_pred.index[0]], [df_stock['5. adjusted close'].values[-1], df_pred.values[0]], lw=1, c='tab:green', linestyle='--')
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plt.plot(df_pred.index, df_pred, lw=2, c='tab:green')
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plt.axvspan(df_stock.index[-1], df_pred.index[-1], facecolor='tab:orange', alpha=0.2)
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xmin, xmax, ymin, ymax = plt.axis()
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plt.vlines(df_stock.index[-1], ymin, ymax, linewidth=1, linestyle='--', color='k')
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plt.show()
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# Print prediction data
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print("Predicted share price:")
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df_pred = df_pred.apply(lambda x: f"{x:.2f} $")
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print(df_pred.to_string())
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print("")
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except:
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print("")
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