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https://github.com/OpenBB-finance/OpenBB.git
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7ba964d61e
* Basic integration of crypto pred * Add loading into crypto pred * Change all pred _views to allow for other datetime than daily * small mypy change * Add pred menu description and catch if no coin is loaded. Co-authored-by: James Maslek <james@jamess-mbp.myfiosgateway.com>
255 lines
7.6 KiB
Python
255 lines
7.6 KiB
Python
""" Regression View"""
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__docformat__ = "numpy"
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from typing import Union
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import os
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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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patch_pandas_text_adjustment,
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get_next_stock_market_days,
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plot_autoscale,
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export_data,
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)
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from gamestonk_terminal.common.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.common.prediction_techniques import regression_model
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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=too-many-arguments
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def display_regression(
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dataset: str,
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values: Union[pd.Series, pd.DataFrame],
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poly_order: int,
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n_input: int,
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n_predict: int,
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n_jumps: int,
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s_end_date: str = "",
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export: str = "",
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time_res: str = "",
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):
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"""Display predications for regression models
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Parameters
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----------
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dataset : str
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Title for data
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values : Union[pd.Series, pd.DataFrame]
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Data to fit
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poly_order : int
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Order of polynomial to fit
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n_input : int
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Length of input sequence
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n_predict : int
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Length of prediction sequence
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n_jumps : int
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Number of jumps in data
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s_end_date : str, optional
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Start date for backtesting
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export : str, optional
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Format for exporting figures
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time_res : str
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Resolution for data, allowing for predicting outside of standard market days
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"""
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# BACKTESTING
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if s_end_date:
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if not time_res:
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future_index = get_next_stock_market_days(
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last_stock_day=s_end_date, n_next_days=n_predict
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)
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else:
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future_index = pd.date_range(
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s_end_date, periods=n_predict + 1, freq=time_res
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)[1:]
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df_future = values[future_index[0] : future_index[-1]]
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values = values[:s_end_date] # type: ignore
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l_predictions, _ = regression_model.get_regression_model(
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values, poly_order, n_input, n_predict, n_jumps
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)
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# Prediction data
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if not time_res:
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l_pred_days = get_next_stock_market_days(
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last_stock_day=values.index[-1],
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n_next_days=n_predict,
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)
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else:
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l_pred_days = pd.date_range(
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values.index[-1], periods=n_predict + 1, freq=time_res
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)[1:]
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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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fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
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ax.plot(values.index, values, lw=2)
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# BACKTESTING
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if s_end_date:
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ax.set_title(
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f"BACKTESTING: Regression (polynomial {poly_order}) on {dataset} - {n_predict} step prediction"
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)
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else:
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ax.set_title(
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f"Regression (polynomial {poly_order}) on {dataset} - {n_predict} step prediction"
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)
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ax.set_xlim(values.index[0], l_pred_days[-1])
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ax.set_xlabel("Time")
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ax.set_ylabel("Value")
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ax.grid(b=True, which="major", color="#666666", linestyle="-")
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ax.minorticks_on()
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ax.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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ax.plot(
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[values.index[-1], df_pred.index[0]],
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[values.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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ax.plot(df_pred.index, df_pred, lw=2, c="tab:green")
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ax.axvspan(values.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2)
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_, _, ymin, ymax = plt.axis()
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ax.vlines(values.index[-1], ymin, ymax, linewidth=1, linestyle="--", color="k")
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# BACKTESTING
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if s_end_date:
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ax.plot(
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df_future.index,
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df_future,
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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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ax.plot(
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[values.index[-1], df_future.index[0]],
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[
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values.values[-1],
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df_future.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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fig.tight_layout()
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if gtff.USE_ION:
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plt.ion()
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plt.show()
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export_data(export, os.path.dirname(os.path.abspath(__file__)), "regression")
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print("")
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# BACKTESTING
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if s_end_date:
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fig, ax = plt.subplots(1, 2, figsize=plot_autoscale(), dpi=PLOT_DPI)
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ax0 = ax[0]
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ax0.plot(
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df_future.index,
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df_future,
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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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ax0.plot(df_pred.index, df_pred, lw=2, c="green")
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ax0.scatter(df_future.index, df_future, c="tab:blue", lw=3)
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ax0.plot(
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[values.index[-1], df_future.index[0]],
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[
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values.values[-1],
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df_future.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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ax0.scatter(df_pred.index, df_pred, c="green", lw=3)
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ax0.plot(
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[values.index[-1], df_pred.index[0]],
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[values.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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ax0.set_title("BACKTESTING: Real data vs Prediction")
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ax0.set_xlim(values.index[-1], df_pred.index[-1])
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ax0.set_xticks([values.index[-1], df_pred.index[-1]])
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ax0.set_ylabel("Value")
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ax0.grid(b=True, which="major", color="#666666", linestyle="-")
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ax0.minorticks_on()
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ax0.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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ax0.legend(["Real data", "Prediction data"])
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ax0.set_xticks([])
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ax1 = ax[1]
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ax1.axhline(y=0, color="k", linestyle="--", linewidth=2)
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ax1.plot(
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df_future.index,
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100 * (df_pred.values - df_future.values) / df_future.values,
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lw=2,
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c="red",
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)
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ax1.scatter(
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df_future.index,
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100 * (df_pred.values - df_future.values) / df_future.values,
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c="red",
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lw=5,
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)
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ax1.set_title("BACKTESTING: Error between Real data and Prediction [%]")
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ax1.plot(
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[values.index[-1], df_future.index[0]],
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[
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0,
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100 * (df_pred.values[0] - df_future.values[0]) / df_future.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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ax1.set_xlim(values.index[-1], df_pred.index[-1])
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ax1.set_xticks([values.index[-1], df_pred.index[-1]])
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ax1.set_xlabel("Time")
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ax1.set_ylabel("Prediction Error (%)")
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ax1.grid(b=True, which="major", color="#666666", linestyle="-")
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ax1.minorticks_on()
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ax1.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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ax1.legend(["Real data", "Prediction data"])
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fig.tight_layout()
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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
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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(df_pred.apply(price_prediction_backtesting_color, axis=1).to_string())
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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, values.values[-1])
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print("")
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