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314 lines
11 KiB
Python
314 lines
11 KiB
Python
import argparse
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import datetime
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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from pandas.plotting import register_matplotlib_converters
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from gamestonk_terminal.helper_funcs import (
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check_positive,
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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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plot_autoscale,
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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.config_plot import PLOT_DPI
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from gamestonk_terminal import feature_flags as gtff
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register_matplotlib_converters()
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# pylint: disable=unused-argument
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def simple_moving_average(l_args, s_ticker, df_stock):
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parser = argparse.ArgumentParser(
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add_help=False,
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prog="sma",
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description="""
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Moving Averages are used to smooth the data in an array to
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help eliminate noise and identify trends. The Simple Moving Average is literally
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the simplest form of a moving average. Each output value is the average of the
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previous n values. In a Simple Moving Average, each value in the time period carries
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equal weight, and values outside of the time period are not included in the average.
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This makes it less responsive to recent changes in the data, which can be useful for
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filtering out those changes.
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""",
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)
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parser.add_argument(
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"-l",
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"--length",
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action="store",
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dest="n_length",
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type=check_positive,
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default=20,
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help="length of SMA window.",
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)
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parser.add_argument(
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"-d",
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"--days",
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action="store",
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dest="n_days",
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type=check_positive,
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default=5,
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help="prediction days.",
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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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# Prediction data
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l_predictions = list()
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for pred_day in range(ns_parser.n_days):
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if pred_day < ns_parser.n_length:
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l_ma_stock = df_stock["5. adjusted close"].values[
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-ns_parser.n_length + pred_day :
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]
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else:
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l_ma_stock = list()
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l_predictions.append(np.mean(np.append(l_ma_stock, l_predictions)))
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l_pred_days = get_next_stock_market_days(
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last_stock_day=df_stock["5. adjusted close"].index[-1],
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n_next_days=ns_parser.n_days,
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)
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df_pred = pd.Series(l_predictions, index=l_pred_days, name="Price")
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# Plotting
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plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
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plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
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# BACKTESTING
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if ns_parser.s_end_date:
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plt.title(
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f"BACKTESTING: {ns_parser.n_length} Moving Average on {s_ticker} - {ns_parser.n_days} days prediction"
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)
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else:
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plt.title(
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f"{ns_parser.n_length} Moving Average on {s_ticker} - {ns_parser.n_days} days prediction"
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)
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plt.xlim(
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df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1]
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)
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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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df_ma = df_stock["5. adjusted close"].rolling(window=ns_parser.n_length).mean()
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plt.plot(df_ma.index, df_ma, lw=2, linestyle="--", c="tab:orange")
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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=1,
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c="tab:green",
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linestyle="--",
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)
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plt.plot(df_pred.index, df_pred, lw=2, c="tab:green")
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plt.axvspan(
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df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
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)
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_, _, ymin, ymax = plt.axis()
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plt.vlines(
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df_stock.index[-1], ymin, ymax, linewidth=1, linestyle="--", 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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# BACKTESTING
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if ns_parser.s_end_date:
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plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
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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, df_future["5. adjusted close"], c="tab:blue", 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(df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1))
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plt.xticks(
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[df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1)],
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visible=True,
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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(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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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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* (df_pred.values[0] - df_future["5. adjusted close"].values[0])
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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(df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1))
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plt.xticks(
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[df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1)],
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visible=True,
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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(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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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(df_pred["Real"].values, df_pred["Prediction"].values)
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else:
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# Print prediction data
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print_pretty_prediction(df_pred, df_stock["5. adjusted close"].values[-1])
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print("")
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except Exception as e:
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print(e)
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print("")
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