mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-11-14 05:14:19 +00:00
ARIMA backtesting
This commit is contained in:
didier
@@ -1,4 +1,5 @@
|
||||
import argparse
|
||||
import datetime
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
from pandas.plotting import register_matplotlib_converters
|
||||
@@ -6,9 +7,15 @@ import pmdarima
|
||||
from statsmodels.tsa.arima.model import ARIMA
|
||||
from gamestonk_terminal.helper_funcs import (
|
||||
check_positive,
|
||||
get_next_stock_market_days,
|
||||
parse_known_args_and_warn,
|
||||
valid_date,
|
||||
patch_pandas_text_adjustment,
|
||||
get_next_stock_market_days,
|
||||
)
|
||||
from gamestonk_terminal.prediction_techniques.pred_helper import (
|
||||
print_pretty_prediction,
|
||||
price_prediction_backtesting_color,
|
||||
print_prediction_kpis,
|
||||
)
|
||||
|
||||
from gamestonk_terminal import feature_flags as gtff
|
||||
@@ -75,12 +82,54 @@ def arima(l_args, s_ticker, df_stock):
|
||||
default=False,
|
||||
help="results about ARIMA summary flag.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-e",
|
||||
"--end",
|
||||
action="store",
|
||||
type=valid_date,
|
||||
dest="s_end_date",
|
||||
default=None,
|
||||
help="The end date (format YYYY-MM-DD) to select - Backtesting",
|
||||
)
|
||||
|
||||
try:
|
||||
ns_parser = parse_known_args_and_warn(parser, l_args)
|
||||
if not ns_parser:
|
||||
return
|
||||
|
||||
# BACKTESTING
|
||||
if ns_parser.s_end_date:
|
||||
|
||||
if ns_parser.s_end_date < df_stock.index[0]:
|
||||
print(
|
||||
"Backtesting not allowed, since End Date is older than Start Date of historical data\n"
|
||||
)
|
||||
return
|
||||
|
||||
if (
|
||||
ns_parser.s_end_date
|
||||
< get_next_stock_market_days(
|
||||
last_stock_day=df_stock.index[0], n_next_days=5 + ns_parser.n_days
|
||||
)[-1]
|
||||
):
|
||||
print(
|
||||
"Backtesting not allowed, since End Date is too close to Start Date to train model\n"
|
||||
)
|
||||
return
|
||||
|
||||
future_index = get_next_stock_market_days(
|
||||
last_stock_day=ns_parser.s_end_date, n_next_days=ns_parser.n_days
|
||||
)
|
||||
|
||||
if future_index[-1] > datetime.datetime.now():
|
||||
print(
|
||||
"Backtesting not allowed, since End Date + Prediction days is in the future\n"
|
||||
)
|
||||
return
|
||||
|
||||
df_future = df_stock[future_index[0] : future_index[-1]]
|
||||
df_stock = df_stock[: ns_parser.s_end_date]
|
||||
|
||||
# Machine Learning model
|
||||
if ns_parser.s_order:
|
||||
t_order = tuple([int(ord) for ord in list(ns_parser.s_order)])
|
||||
@@ -122,13 +171,25 @@ def arima(l_args, s_ticker, df_stock):
|
||||
plt.figure()
|
||||
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
|
||||
if ns_parser.s_order:
|
||||
plt.title(
|
||||
f"ARIMA {str(t_order)} on {s_ticker} - {ns_parser.n_days} days prediction"
|
||||
)
|
||||
# BACKTESTING
|
||||
if ns_parser.s_end_date:
|
||||
plt.title(
|
||||
f"BACKTESTING: ARIMA {str(t_order)} on {s_ticker} - {ns_parser.n_days} days prediction"
|
||||
)
|
||||
else:
|
||||
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"
|
||||
)
|
||||
# BACKTESTING
|
||||
if ns_parser.s_end_date:
|
||||
plt.title(
|
||||
f"BACKTESTING: ARIMA {model.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]
|
||||
)
|
||||
@@ -153,13 +214,149 @@ def arima(l_args, s_ticker, df_stock):
|
||||
df_stock.index[-1], ymin, ymax, linewidth=1, linestyle="--", color="k"
|
||||
)
|
||||
|
||||
# BACKTESTING
|
||||
if ns_parser.s_end_date:
|
||||
plt.plot(
|
||||
df_future.index,
|
||||
df_future["5. adjusted close"],
|
||||
lw=2,
|
||||
c="tab:blue",
|
||||
ls="--",
|
||||
)
|
||||
plt.plot(
|
||||
[df_stock.index[-1], df_future.index[0]],
|
||||
[
|
||||
df_stock["5. adjusted close"].values[-1],
|
||||
df_future["5. adjusted close"].values[0],
|
||||
],
|
||||
lw=1,
|
||||
c="tab:blue",
|
||||
linestyle="--",
|
||||
)
|
||||
|
||||
if gtff.USE_ION:
|
||||
plt.ion()
|
||||
|
||||
plt.show()
|
||||
|
||||
# Print prediction data
|
||||
print_pretty_prediction(df_pred, df_stock["5. adjusted close"].values[-1])
|
||||
# BACKTESTING
|
||||
if ns_parser.s_end_date:
|
||||
plt.figure()
|
||||
plt.subplot(211)
|
||||
plt.plot(
|
||||
df_future.index,
|
||||
df_future["5. adjusted close"],
|
||||
lw=2,
|
||||
c="tab:blue",
|
||||
ls="--",
|
||||
)
|
||||
plt.plot(df_pred.index, df_pred, lw=2, c="green")
|
||||
plt.scatter(
|
||||
df_future.index, df_future["5. adjusted close"], c="tab:blue", lw=3
|
||||
)
|
||||
plt.plot(
|
||||
[df_stock.index[-1], df_future.index[0]],
|
||||
[
|
||||
df_stock["5. adjusted close"].values[-1],
|
||||
df_future["5. adjusted close"].values[0],
|
||||
],
|
||||
lw=2,
|
||||
c="tab:blue",
|
||||
ls="--",
|
||||
)
|
||||
plt.scatter(df_pred.index, df_pred, c="green", lw=3)
|
||||
plt.plot(
|
||||
[df_stock.index[-1], df_pred.index[0]],
|
||||
[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
|
||||
lw=2,
|
||||
c="green",
|
||||
ls="--",
|
||||
)
|
||||
plt.title("BACKTESTING: Real data price versus Prediction")
|
||||
plt.xlim(df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1))
|
||||
plt.xticks(
|
||||
[df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1)],
|
||||
visible=True,
|
||||
)
|
||||
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.legend(["Real data", "Prediction data"])
|
||||
plt.xticks([])
|
||||
|
||||
plt.subplot(212)
|
||||
plt.axhline(y=0, color="k", linestyle="--", linewidth=2)
|
||||
plt.plot(
|
||||
df_future.index,
|
||||
100
|
||||
* (df_pred.values - df_future["5. adjusted close"].values)
|
||||
/ df_future["5. adjusted close"].values,
|
||||
lw=2,
|
||||
c="red",
|
||||
)
|
||||
plt.scatter(
|
||||
df_future.index,
|
||||
100
|
||||
* (df_pred.values - df_future["5. adjusted close"].values)
|
||||
/ df_future["5. adjusted close"].values,
|
||||
c="red",
|
||||
lw=5,
|
||||
)
|
||||
plt.title("BACKTESTING: Error between Real data and Prediction [%]")
|
||||
plt.plot(
|
||||
[df_stock.index[-1], df_future.index[0]],
|
||||
[
|
||||
0,
|
||||
100
|
||||
* (df_pred.values[0] - df_future["5. adjusted close"].values[0])
|
||||
/ df_future["5. adjusted close"].values[0],
|
||||
],
|
||||
lw=2,
|
||||
ls="--",
|
||||
c="red",
|
||||
)
|
||||
plt.xlim(df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1))
|
||||
plt.xticks(
|
||||
[df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1)],
|
||||
visible=True,
|
||||
)
|
||||
plt.xlabel("Time")
|
||||
plt.ylabel("Prediction Error (%)")
|
||||
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.legend(["Real data", "Prediction data"])
|
||||
|
||||
if gtff.USE_ION:
|
||||
plt.ion()
|
||||
|
||||
plt.show()
|
||||
|
||||
# Refactor prediction dataframe for backtesting print
|
||||
df_pred.name = "Prediction"
|
||||
df_pred = df_pred.to_frame()
|
||||
df_pred["Real"] = df_future["5. adjusted close"]
|
||||
|
||||
if gtff.USE_COLOR:
|
||||
|
||||
patch_pandas_text_adjustment()
|
||||
|
||||
print("Time Real [$] x Prediction [$]")
|
||||
print(
|
||||
df_pred.apply(
|
||||
price_prediction_backtesting_color, axis=1
|
||||
).to_string()
|
||||
)
|
||||
else:
|
||||
print(df_pred[["Real", "Prediction"]].round(2).to_string())
|
||||
|
||||
print("")
|
||||
print_prediction_kpis(df_pred["Real"].values, df_pred["Prediction"].values)
|
||||
|
||||
else:
|
||||
# Print prediction data
|
||||
print_pretty_prediction(df_pred, df_stock["5. adjusted close"].values[-1])
|
||||
print("")
|
||||
|
||||
except Exception as e:
|
||||
|
||||
Reference in New Issue
Block a user