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Add new ARIMA model to prediction techniques menu

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didier
2021-02-09 23:20:52 +00:00
parent c16f42ad0e
commit da8b1a862c
2 changed files with 87 additions and 3 deletions
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77
prediction_techniques/arima.py Normal file
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@@ -0,0 +1,77 @@
import argparse
from helper_funcs import *
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
import pmdarima
from statsmodels.tsa.arima.model import ARIMA
# -------------------------------------------------- ARIMA --------------------------------------------------
def arima(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(prog='arima',
description="""In statistics and econometrics, and in particular in time
series analysis, an autoregressive integrated moving average (ARIMA) model
is a generalization of an autoregressive moving average (ARMA) model. Both
of these models are fitted to time series data either to better understand
the data or to predict future points in the series (forecasting). """)
parser.add_argument('-d', "--days", action="store", dest="n_days", type=check_positive, default=5, help='prediction days')
parser.add_argument('-i', "--ic", action="store", dest="s_ic", type=str, default='aic', choices=['aic', 'aicc', 'bic', 'hqic', 'oob'], help='information criteria')
parser.add_argument('-s', "--seasonal", action="store_true", default=False, dest="b_seasonal", help='Use the fact that the market data is weekly')
parser.add_argument('-o', "--order", action="store", dest="s_order", type=str, help='arima model order')
parser.add_argument('-r', "--results", action="store_true", dest="b_results", default=False, help='results about ARIMA summary')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
if l_unknown_args:
print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
return
# Machine Learning model
if ns_parser.s_order:
t_order = tuple([int(ord) for ord in list(ns_parser.s_order)])
model = ARIMA(df_stock['4. close'].values, order=t_order).fit()
l_predictions = model.predict(start=len(df_stock['4. close'])+1, end=len(df_stock['4. close'])+ns_parser.n_days)
else:
if ns_parser.b_seasonal:
model = pmdarima.auto_arima(df_stock['4. close'].values, error_action='ignore', seasonal=True, m=5, information_criteria=ns_parser.s_ic)
else:
model = pmdarima.auto_arima(df_stock['4. close'].values, error_action='ignore', seasonal=False, information_criteria=ns_parser.s_ic)
l_predictions = model.predict(n_periods=ns_parser.n_days)
# Prediction data
l_pred_days = get_next_stock_market_days(last_stock_day=df_stock['4. close'].index[-1], n_next_days=ns_parser.n_days)
df_pred = pd.Series(l_predictions, index=l_pred_days, name='Price')
if ns_parser.b_results:
print(model.summary())
print("")
# Plotting
plt.plot(df_stock.index, df_stock['4. close'], lw=2)
if ns_parser.s_order:
plt.title(f"ARIMA {str(t_order)} on {s_ticker} - {ns_parser.n_days} days prediction")
else:
plt.title(f"ARIMA {model.order} on {s_ticker} - {ns_parser.n_days} days prediction")
plt.xlim(df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1] )
plt.xlabel('Time')
plt.ylabel('Share Price ($)')
plt.grid(b=True, which='major', color='#666666', linestyle='-')
plt.minorticks_on()
plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
plt.plot([df_stock.index[-1], df_pred.index[0]], [df_stock['4. close'].values[-1], df_pred.values[0]], lw=1, c='tab:green', linestyle='--')
plt.plot(df_pred.index, df_pred, lw=2, c='tab:green')
plt.axvspan(df_stock.index[-1], df_pred.index[-1], facecolor='tab:orange', alpha=0.2)
xmin, xmax, ymin, ymax = plt.axis()
plt.vlines(df_stock.index[-1], ymin, ymax, linewidth=1, linestyle='--', color='k')
plt.show()
# Print prediction data
df_pred = df_pred.apply(lambda x: f"{x:.2f} $")
print(df_pred.to_string())
print("")
except:
print("")

13
prediction_techniques/pred_menu.py
View File

@@ -12,6 +12,7 @@ register_matplotlib_converters()
from prediction_techniques import sma
from prediction_techniques import knn
from prediction_techniques import regression
from prediction_techniques import arima
# -----------------------------------------------------------------------------------------------------------------------
def print_prediction(s_ticker, s_start, s_interval):
@@ -35,6 +36,7 @@ def print_prediction(s_ticker, s_start, s_interval):
print(" quadratic quadratic regression (polynomial 2)")
print(" cubic cubic regression (polynomial 3)")
print(" regression regression (other polynomial)")
print(" arima autoregressive integrated moving average")
#print(" arima")
#print(" rnn")
#print(" lstm")
@@ -49,7 +51,8 @@ def pred_menu(df_stock, s_ticker, s_start, s_interval):
pred_parser = argparse.ArgumentParser(prog='technical_analysis', add_help=False)
pred_parser.add_argument('cmd', choices=['help', 'q', 'quit',
'sma', 'knn',
'linear', 'quadratic', 'cubic', 'regression'])
'linear', 'quadratic', 'cubic', 'regression',
'arima'])
print_prediction(s_ticker, s_start, s_interval)
@@ -81,11 +84,11 @@ def pred_menu(df_stock, s_ticker, s_start, s_interval):
elif ns_known_args.cmd == 'sma':
sma.simple_moving_average(l_args, s_ticker, s_interval, df_stock)
# ------------------------------------------ k NEAREST NEIGHBORS ------------------------------------------
# ------------------------------------------- k NEAREST NEIGHBORS ------------------------------------------
elif ns_known_args.cmd == 'knn':
knn.k_nearest_neighbors(l_args, s_ticker, s_interval, df_stock)
# ------------------------------------------------- REGRESSION -------------------------------------------------
# ----------------------------------------------- REGRESSION -------------------------------------------------
elif ns_known_args.cmd == 'linear':
regression.regression(l_args, s_ticker, s_interval, df_stock, regression.LINEAR)
@@ -98,6 +101,10 @@ def pred_menu(df_stock, s_ticker, s_start, s_interval):
elif ns_known_args.cmd == 'regression':
regression.regression(l_args, s_ticker, s_interval, df_stock, regression.USER_INPUT)
# ------------------------------------------------- ARIMA -------------------------------------------------
elif ns_known_args.cmd == 'arima':
arima.arima(l_args, s_ticker, s_interval, df_stock)
# ------------------------------------------------------------------------------------------------------------
else:
print("Command not recognized!")