mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-28 21:12:16 +00:00
ETS backtesting
This commit is contained in:
didier
@@ -1,4 +1,5 @@
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import argparse
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import datetime
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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@@ -6,9 +7,15 @@ from pandas.plotting import register_matplotlib_converters
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from statsmodels.tsa.holtwinters import ExponentialSmoothing
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from gamestonk_terminal.helper_funcs import (
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check_positive,
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get_next_stock_market_days,
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parse_known_args_and_warn,
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valid_date,
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patch_pandas_text_adjustment,
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get_next_stock_market_days,
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)
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from gamestonk_terminal.prediction_techniques.pred_helper import (
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print_pretty_prediction,
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price_prediction_backtesting_color,
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print_prediction_kpis,
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)
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from gamestonk_terminal import feature_flags as gtff
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@@ -94,12 +101,55 @@ def exponential_smoothing(l_args, s_ticker, df_stock):
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default=5,
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help="Seasonal periods.",
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)
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parser.add_argument(
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"-e",
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"--end",
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action="store",
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type=valid_date,
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dest="s_end_date",
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default=None,
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help="The end date (format YYYY-MM-DD) to select - Backtesting",
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)
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try:
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ns_parser = parse_known_args_and_warn(parser, l_args)
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if not ns_parser:
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return
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# BACKTESTING
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if ns_parser.s_end_date:
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if ns_parser.s_end_date < df_stock.index[0]:
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print(
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"Backtesting not allowed, since End Date is older than Start Date of historical data\n"
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)
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return
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if (
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ns_parser.s_end_date
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< get_next_stock_market_days(
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last_stock_day=df_stock.index[0], n_next_days=5 + ns_parser.n_days
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)[-1]
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):
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print(
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"Backtesting not allowed, since End Date is too close to Start Date to train model\n"
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)
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return
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future_index = get_next_stock_market_days(
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last_stock_day=ns_parser.s_end_date, n_next_days=ns_parser.n_days
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)
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if future_index[-1] > datetime.datetime.now():
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print(
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"Backtesting not allowed, since End Date + Prediction days is in the future\n"
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)
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return
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df_future = df_stock[future_index[0] : future_index[-1]]
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df_stock = df_stock[: ns_parser.s_end_date]
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# Get ETS model
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model, title = get_exponential_smoothing_model(
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df_stock["5. adjusted close"].values,
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ns_parser.trend,
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@@ -131,7 +181,11 @@ def exponential_smoothing(l_args, s_ticker, df_stock):
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# Plotting
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plt.figure()
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plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
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plt.title(f"{title} on {s_ticker}")
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# BACKTESTING
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if ns_parser.s_end_date:
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plt.title(f"BACKTESTING: {title} on {s_ticker}")
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else:
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plt.title(f"{title} on {s_ticker}")
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plt.xlim(
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df_stock.index[0],
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@@ -168,15 +222,161 @@ def exponential_smoothing(l_args, s_ticker, df_stock):
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color="k",
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)
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# BACKTESTING
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if ns_parser.s_end_date:
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plt.plot(
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df_future.index,
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df_future["5. adjusted close"],
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lw=2,
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c="tab:blue",
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ls="--",
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)
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plt.plot(
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[df_stock.index[-1], df_future.index[0]],
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[
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df_stock["5. adjusted close"].values[-1],
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df_future["5. adjusted close"].values[0],
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],
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lw=1,
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c="tab:blue",
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linestyle="--",
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)
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if gtff.USE_ION:
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plt.ion()
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plt.show()
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# Print prediction data
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print_pretty_prediction(
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df_pred, df_stock["5. adjusted close"].values[-1]
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)
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# BACKTESTING
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if ns_parser.s_end_date:
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plt.figure()
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plt.subplot(211)
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plt.plot(
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df_future.index,
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df_future["5. adjusted close"],
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lw=2,
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c="tab:blue",
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ls="--",
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)
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plt.plot(df_pred.index, df_pred, lw=2, c="green")
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plt.scatter(
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df_future.index,
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df_future["5. adjusted close"],
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c="tab:blue",
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lw=3,
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)
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plt.plot(
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[df_stock.index[-1], df_future.index[0]],
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[
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df_stock["5. adjusted close"].values[-1],
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df_future["5. adjusted close"].values[0],
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],
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lw=2,
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c="tab:blue",
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ls="--",
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)
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plt.scatter(df_pred.index, df_pred, c="green", lw=3)
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plt.plot(
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[df_stock.index[-1], df_pred.index[0]],
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[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
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lw=2,
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c="green",
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ls="--",
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)
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plt.title("BACKTESTING: Real data price versus Prediction")
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plt.xlim(
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df_stock.index[-1],
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df_pred.index[-1] + datetime.timedelta(days=1),
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)
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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(
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b=True, which="minor", color="#999999", linestyle="-", alpha=0.2
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)
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plt.legend(["Real data", "Prediction data"])
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plt.xticks([])
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plt.subplot(212)
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plt.axhline(y=0, color="k", linestyle="--", linewidth=2)
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plt.plot(
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df_future.index,
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100
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* (df_pred.values - df_future["5. adjusted close"].values)
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/ df_future["5. adjusted close"].values,
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lw=2,
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c="red",
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)
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plt.scatter(
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df_future.index,
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100
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* (df_pred.values - df_future["5. adjusted close"].values)
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/ df_future["5. adjusted close"].values,
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c="red",
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lw=5,
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)
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plt.title("BACKTESTING: Error between Real data and Prediction [%]")
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plt.plot(
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[df_stock.index[-1], df_future.index[0]],
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[
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0,
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100
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* (
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df_pred.values[0]
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- df_future["5. adjusted close"].values[0]
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)
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/ df_future["5. adjusted close"].values[0],
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],
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lw=2,
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ls="--",
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c="red",
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)
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plt.xlim(
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df_stock.index[-1],
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df_pred.index[-1] + datetime.timedelta(days=1),
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)
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plt.xlabel("Time")
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plt.ylabel("Prediction Error (%)")
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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(
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b=True, which="minor", color="#999999", linestyle="-", alpha=0.2
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)
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plt.legend(["Real data", "Prediction data"])
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if gtff.USE_ION:
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plt.ion()
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plt.show()
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# Refactor prediction dataframe for backtesting print
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df_pred.name = "Prediction"
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df_pred = df_pred.to_frame()
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df_pred["Real"] = df_future["5. adjusted close"]
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if gtff.USE_COLOR:
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patch_pandas_text_adjustment()
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print("Time Real [$] x Prediction [$]")
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print(
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df_pred.apply(
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price_prediction_backtesting_color, axis=1
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).to_string()
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)
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else:
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print(df_pred[["Real", "Prediction"]].round(2).to_string())
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print("")
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print_prediction_kpis(
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df_pred["Real"].values, df_pred["Prediction"].values
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)
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else:
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# Print prediction data
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print_pretty_prediction(
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df_pred, df_stock["5. adjusted close"].values[-1]
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)
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print("")
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else:
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