def pred_menu(df_stock, s_ticker, s_start, s_interval):
# Add list of arguments that the prediction techniques parser accepts
pred_parser = argparse.ArgumentParser(prog="pred", add_help=False)
choices = [
"help",
"q",
"quit",
"sma",
"ets",
"knn",
"linear",
"quadratic",
"cubic",
"regression",
"arima",
"prophet",
"mlp",
"rnn",
"lstm",
]
pred_parser.add_argument("cmd", choices=choices)
completer = NestedCompleter.from_nested_dict({c: None for c in choices})
print_prediction(s_ticker, s_start, s_interval)
# Loop forever and ever
while True:
# Get input command from user
if session and gtff.USE_PROMPT_TOOLKIT:
as_input = session.prompt(
f"{get_flair()} (pred)> ",
completer=completer,
)
else:
as_input = input(f"{get_flair()} (pred)> ")
# Images are non blocking - allows to close them if we type other command
plt.close("all")
# Parse prediction techniques command of the list of possible commands
try:
(ns_known_args, l_args) = pred_parser.parse_known_args(as_input.split())
except SystemExit:
print("The command selected doesn't exist\n")
continue
if ns_known_args.cmd == "help":
print_prediction(s_ticker, s_start, s_interval)
elif ns_known_args.cmd == "q":
# Just leave the FA menu
return False
elif ns_known_args.cmd == "quit":
# Abandon the program
return True
elif ns_known_args.cmd == "sma":
sma.simple_moving_average(l_args, s_ticker, df_stock)
elif ns_known_args.cmd == "ets":
ets.exponential_smoothing(l_args, s_ticker, df_stock)
elif ns_known_args.cmd == "knn":
knn.k_nearest_neighbors(l_args, s_ticker, df_stock)
elif ns_known_args.cmd == "linear":
regression.regression(l_args, s_ticker, df_stock, regression.LINEAR)
elif ns_known_args.cmd == "quadratic":
regression.regression(l_args, s_ticker, df_stock, regression.QUADRATIC)
elif ns_known_args.cmd == "cubic":
regression.regression(l_args, s_ticker, df_stock, regression.CUBIC)
elif ns_known_args.cmd == "regression":
regression.regression(l_args, s_ticker, df_stock, regression.USER_INPUT)
elif ns_known_args.cmd == "arima":
arima.arima(l_args, s_ticker, df_stock)
elif ns_known_args.cmd == "prophet":
fbprophet.fbprophet(l_args, s_ticker, df_stock)
elif ns_known_args.cmd == "mlp":
neural_networks.mlp(l_args, s_ticker, df_stock)
elif ns_known_args.cmd == "rnn":
neural_networks.rnn(l_args, s_ticker, df_stock)
elif ns_known_args.cmd == "lstm":
neural_networks.lstm(l_args, s_ticker, df_stock)
else:
print("Command not recognized!")
def call_regression(self, other_args: List[str]):
"""Process regression command"""
regression.regression(other_args, self.ticker, self.stock,
regression.USER_INPUT)
def call_quadratic(self, other_args: List[str]):
"""Process quadratic command"""
regression.regression(other_args, self.ticker, self.stock,
regression.QUADRATIC)
def call_cubic(self, other_args: List[str]):
"""Process cubic command"""
regression.regression(other_args, self.ticker, self.stock,
regression.CUBIC)
def call_linear(self, other_args: List[str]):
"""Process linear command"""
regression.regression(other_args, self.ticker, self.stock,
regression.LINEAR)