mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-28 21:12:16 +00:00
Refactor prediction menu to controller, and fbprophet optional
This commit is contained in:
didier
@@ -16,6 +16,9 @@ USE_PROMPT_TOOLKIT = strtobool(os.getenv("GTFF_USE_PROMPT_TOOLKIT", "False"))
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# Enable Prediction features
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ENABLE_PREDICT = strtobool(os.getenv("GTFF_ENABLE_PREDICT", "False"))
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# Enable FB Prophet
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ENABLE_FBPROPHET = strtobool(os.getenv("GTFF_ENABLE_FBPROPHET", "False"))
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# Enable plot autoscaling
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USE_PLOT_AUTOSCALING = strtobool(os.getenv("GTFF_USE_PLOT_AUTOSCALING", "False"))
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207
gamestonk_terminal/prediction_techniques/pred_controller.py
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207
gamestonk_terminal/prediction_techniques/pred_controller.py
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@@ -0,0 +1,207 @@
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import argparse
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from typing import List
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import pandas as pd
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from datetime import datetime
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import matplotlib.pyplot as plt
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from gamestonk_terminal import feature_flags as gtff
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from gamestonk_terminal.helper_funcs import get_flair
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from gamestonk_terminal.menu import session
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from gamestonk_terminal.prediction_techniques import (
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arima,
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ets,
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knn,
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neural_networks,
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regression,
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sma,
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)
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from prompt_toolkit.completion import NestedCompleter
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class PredictionTechniquesController:
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"""Prediction Techniques Controller class"""
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# Command choices
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CHOICES = [
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"help",
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"q",
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"quit",
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"sma",
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"ets",
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"knn",
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"linear",
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"quadratic",
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"cubic",
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"regression",
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"arima",
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"mlp",
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"rnn",
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"lstm",
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]
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if gtff.ENABLE_FBPROPHET:
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CHOICES.append("prophet")
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def __init__(
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self,
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stock: pd.DataFrame,
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ticker: str,
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start: datetime,
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interval: str,
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):
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"""Constructor"""
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self.stock = stock
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self.ticker = ticker
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self.start = start
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self.interval = interval
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self.pred_parser = argparse.ArgumentParser(add_help=False, prog="pred")
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self.pred_parser.add_argument(
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"cmd",
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choices=self.CHOICES,
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)
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@staticmethod
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def print_help(self):
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""" Print help """
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s_intraday = (f"Intraday {self.interval}", "Daily")[self.interval == "1440min"]
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if self.start:
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print(
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f"\n{s_intraday} Stock: {self.ticker} (from {self.start.strftime('%Y-%m-%d')})"
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)
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else:
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print(f"\n{s_intraday} Stock: {self.ticker}")
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print("\nPrediction Techniques:")
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print(" help show this prediction techniques menu again")
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print(" q quit this menu, and shows back to main menu")
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print(" quit quit to abandon program")
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print("")
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print(" sma simple moving average")
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print(" ets exponential smoothing (e.g. Holt-Winters)")
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print(" knn k-Nearest Neighbors")
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print(" linear linear regression (polynomial 1)")
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print(" quadratic quadratic regression (polynomial 2)")
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print(" cubic cubic regression (polynomial 3)")
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print(" regression regression (other polynomial)")
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print(" arima autoregressive integrated moving average")
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print(" mlp MultiLayer Perceptron")
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print(" rnn Recurrent Neural Network")
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print(" lstm Long-Short Term Memory")
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if gtff.ENABLE_FBPROPHET:
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print(" prophet Facebook's prophet prediction")
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print("")
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def switch(self, an_input: str):
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"""Process and dispatch input
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Returns
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-------
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True, False or None
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False - quit the menu
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True - quit the program
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None - continue in the menu
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"""
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(known_args, other_args) = self.pred_parser.parse_known_args(an_input.split())
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return getattr(
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self, "call_" + known_args.cmd, lambda: "Command not recognized!"
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)(other_args)
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def call_help(self, _):
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"""Process Help command"""
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self.print_help(self)
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def call_q(self, _):
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"""Process Q command - quit the menu"""
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return False
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def call_quit(self, _):
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"""Process Quit command - quit the program"""
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return True
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def call_sma(self, other_args: List[str]):
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"""Process sma command"""
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sma.simple_moving_average(other_args, self.ticker, self.stock)
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def call_ets(self, other_args: List[str]):
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"""Process ets command"""
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ets.exponential_smoothing(other_args, self.ticker, self.stock)
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def call_knn(self, other_args: List[str]):
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"""Process knn command"""
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knn.k_nearest_neighbors(other_args, self.ticker, self.stock)
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def call_linear(self, other_args: List[str]):
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"""Process linear command"""
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regression.regression(other_args, self.ticker, self.stock, regression.LINEAR)
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def call_quadratic(self, other_args: List[str]):
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"""Process quadratic command"""
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regression.regression(other_args, self.ticker, self.stock, regression.QUADRATIC)
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def call_cubic(self, other_args: List[str]):
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"""Process cubic command"""
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regression.regression(other_args, self.ticker, self.stock, regression.CUBIC)
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def call_regression(self, other_args: List[str]):
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"""Process regression command"""
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regression.regression(
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other_args, self.ticker, self.stock, regression.USER_INPUT
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)
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def call_arima(self, other_args: List[str]):
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"""Process arima command"""
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arima.arima(other_args, self.ticker, self.stock)
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def call_mlp(self, other_args: List[str]):
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"""Process mlp command"""
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neural_networks.mlp(other_args, self.ticker, self.stock)
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def call_rnn(self, other_args: List[str]):
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"""Process rnn command"""
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neural_networks.rnn(other_args, self.ticker, self.stock)
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def call_lstm(self, other_args: List[str]):
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"""Process lstm command"""
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neural_networks.lstm(other_args, self.ticker, self.stock)
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if gtff.ENABLE_FBPROPHET:
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def call_prophet(self, other_args: List[str]):
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"""Process prophet command"""
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from gamestonk_terminal.prediction_techniques import fbprophet
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fbprophet.fbprophet(other_args, self.ticker, self.stock)
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def menu(stock: pd.DataFrame, ticker: str, start: datetime, interval: str):
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"""Comparison Analysis Menu"""
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pred_controller = PredictionTechniquesController(stock, ticker, start, interval)
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pred_controller.call_help(None)
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while True:
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# Get input command from user
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if session and gtff.USE_PROMPT_TOOLKIT:
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completer = NestedCompleter.from_nested_dict(
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{c: None for c in pred_controller.CHOICES}
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)
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an_input = session.prompt(
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f"{get_flair()} (pred)> ",
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completer=completer,
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)
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else:
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an_input = input(f"{get_flair()} (pred)> ")
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try:
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plt.close("all")
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process_input = pred_controller.switch(an_input)
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if process_input is not None:
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return process_input
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except SystemExit:
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print("The command selected doesn't exist\n")
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continue
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@@ -1,145 +0,0 @@
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import argparse
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import matplotlib.pyplot as plt
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from gamestonk_terminal import feature_flags as gtff
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from gamestonk_terminal.helper_funcs import get_flair
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from gamestonk_terminal.menu import session
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from gamestonk_terminal.prediction_techniques import (
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arima,
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ets,
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fbprophet,
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knn,
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neural_networks,
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regression,
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sma,
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)
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from prompt_toolkit.completion import NestedCompleter
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def print_prediction(s_ticker, s_start, s_interval):
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""" Print help """
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s_intraday = (f"Intraday {s_interval}", "Daily")[s_interval == "1440min"]
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if s_start:
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print(f"\n{s_intraday} Stock: {s_ticker} (from {s_start.strftime('%Y-%m-%d')})")
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else:
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print(f"\n{s_intraday} Stock: {s_ticker}")
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print("\nPrediction Techniques:")
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print(" help show this prediction techniques menu again")
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print(" q quit this menu, and shows back to main menu")
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print(" quit quit to abandon program")
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print("")
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print(" sma simple moving average")
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print(" ets exponential smoothing (e.g. Holt-Winters)")
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print(" knn k-Nearest Neighbors")
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print(" linear linear regression (polynomial 1)")
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print(" quadratic quadratic regression (polynomial 2)")
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print(" cubic cubic regression (polynomial 3)")
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print(" regression regression (other polynomial)")
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print(" arima autoregressive integrated moving average")
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print(" prophet Facebook's prophet prediction")
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print(" mlp MultiLayer Perceptron")
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print(" rnn Recurrent Neural Network")
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print(" lstm Long-Short Term Memory")
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print("")
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def pred_menu(df_stock, s_ticker, s_start, s_interval):
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# Add list of arguments that the prediction techniques parser accepts
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pred_parser = argparse.ArgumentParser(prog="pred", add_help=False)
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choices = [
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"help",
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"q",
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"quit",
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"sma",
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"ets",
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"knn",
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"linear",
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"quadratic",
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"cubic",
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"regression",
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"arima",
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"prophet",
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"mlp",
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"rnn",
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"lstm",
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]
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pred_parser.add_argument("cmd", choices=choices)
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completer = NestedCompleter.from_nested_dict({c: None for c in choices})
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print_prediction(s_ticker, s_start, s_interval)
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# Loop forever and ever
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while True:
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# Get input command from user
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if session and gtff.USE_PROMPT_TOOLKIT:
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as_input = session.prompt(
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f"{get_flair()} (pred)> ",
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completer=completer,
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)
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else:
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as_input = input(f"{get_flair()} (pred)> ")
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# Images are non blocking - allows to close them if we type other command
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plt.close("all")
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# Parse prediction techniques command of the list of possible commands
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try:
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(ns_known_args, l_args) = pred_parser.parse_known_args(as_input.split())
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except SystemExit:
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print("The command selected doesn't exist\n")
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continue
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if ns_known_args.cmd == "help":
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print_prediction(s_ticker, s_start, s_interval)
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elif ns_known_args.cmd == "q":
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# Just leave the FA menu
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return False
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elif ns_known_args.cmd == "quit":
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# Abandon the program
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return True
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elif ns_known_args.cmd == "sma":
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sma.simple_moving_average(l_args, s_ticker, df_stock)
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elif ns_known_args.cmd == "ets":
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ets.exponential_smoothing(l_args, s_ticker, df_stock)
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elif ns_known_args.cmd == "knn":
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knn.k_nearest_neighbors(l_args, s_ticker, df_stock)
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elif ns_known_args.cmd == "linear":
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regression.regression(l_args, s_ticker, df_stock, regression.LINEAR)
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elif ns_known_args.cmd == "quadratic":
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regression.regression(l_args, s_ticker, df_stock, regression.QUADRATIC)
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elif ns_known_args.cmd == "cubic":
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regression.regression(l_args, s_ticker, df_stock, regression.CUBIC)
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elif ns_known_args.cmd == "regression":
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regression.regression(l_args, s_ticker, df_stock, regression.USER_INPUT)
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elif ns_known_args.cmd == "arima":
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arima.arima(l_args, s_ticker, df_stock)
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elif ns_known_args.cmd == "prophet":
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fbprophet.fbprophet(l_args, s_ticker, df_stock)
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elif ns_known_args.cmd == "mlp":
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neural_networks.mlp(l_args, s_ticker, df_stock)
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elif ns_known_args.cmd == "rnn":
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neural_networks.rnn(l_args, s_ticker, df_stock)
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elif ns_known_args.cmd == "lstm":
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neural_networks.lstm(l_args, s_ticker, df_stock)
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else:
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print("Command not recognized!")
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@@ -252,7 +252,7 @@ def main():
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try:
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# pylint: disable=import-outside-toplevel
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from gamestonk_terminal.prediction_techniques import pred_menu as pm
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from gamestonk_terminal.prediction_techniques import pred_controller
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except ModuleNotFoundError as e:
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print("One of the optional packages seems to be missing")
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print("Optional packages need to be installed")
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@@ -265,7 +265,7 @@ def main():
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continue
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if s_interval == "1440min":
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b_quit = pm.pred_menu(df_stock, s_ticker, s_start, s_interval)
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b_quit = pred_controller.menu(df_stock, s_ticker, s_start, s_interval)
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# If stock data is intradaily, we need to get data again as prediction
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# techniques work on daily adjusted data. By default we load data from
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# Alpha Vantage because the historical data loaded gives a larger
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@@ -282,7 +282,7 @@ def main():
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# pylint: disable=no-member
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df_stock_pred = df_stock_pred.sort_index(ascending=True)
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df_stock_pred = df_stock_pred[s_start:]
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b_quit = pm.pred_menu(
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b_quit = pred_controller.menu(
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df_stock_pred, s_ticker, s_start, s_interval="1440min"
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)
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except Exception as e:
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Block a user