import config_terminal as cfg
import argparse
import datetime
from datetime import datetime
from helper_funcs import *
import config_terminal as cfg
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

from prediction_techniques import sma
from prediction_techniques import knn
from prediction_techniques import regression
from prediction_techniques import arima
from prediction_techniques import fbprophet
from prediction_techniques import neural_networks

# -----------------------------------------------------------------------------------------------------------------------
def print_prediction(s_ticker, s_start, s_interval):
    """ Print help """

    s_intraday = (f'Intraday {s_interval}', 'Daily')[s_interval == "1440min"]

    if s_start:
        print(f"\n{s_intraday} Stock: {s_ticker} (from {s_start.strftime('%Y-%m-%d')})")
    else:
        print(f"\n{s_intraday} Stock: {s_ticker}")

    print("\nPrediction Techniques:")
    print("   help        show this prediction techniques menu again")
    print("   q           quit this menu, and shows back to main menu")
    print("   quit        quit to abandon program")
    print("")
    print("   sma         simple moving average")
    print("   knn         k-Nearest Neighbors")
    print("   linear      linear regression (polynomial 1)")
    print("   quadratic   quadratic regression (polynomial 2)")
    print("   cubic       cubic regression (polynomial 3)")
    print("   regression  regression (other polynomial)")
    print("   arima       autoregressive integrated moving average")
    print("   prophet     Facebook's prophet prediction")
    print("   mlp         MultiLayer Perceptron")
    print("   rnn         Recurrent Neural Network")
    print("   lstm        Long-Short Term Memory")
    print("")


# ---------------------------------------------------- MENU ----------------------------------------------------
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)
    pred_parser.add_argument('cmd', choices=['help', 'q', 'quit',
                                             'sma', 'knn',
                                             'linear', 'quadratic', 'cubic', 'regression',
                                             'arima', 'prophet', 'mlp', 'rnn', 'lstm'])

    print_prediction(s_ticker, s_start, s_interval)

    # Loop forever and ever
    while True:
        # Get input command from user
        as_input = input('> ')

        # 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

        # ------------------------------------------ SIMPLE MOVING AVERAGE ------------------------------------------
        elif ns_known_args.cmd == 'sma':
            sma.simple_moving_average(l_args, s_ticker, s_interval, df_stock)

        # ------------------------------------------- k NEAREST NEIGHBORS ------------------------------------------
        elif ns_known_args.cmd == 'knn':
            knn.k_nearest_neighbors(l_args, s_ticker, s_interval, df_stock)

        # ----------------------------------------------- REGRESSION -------------------------------------------------
        elif ns_known_args.cmd == 'linear':
            regression.regression(l_args, s_ticker, s_interval, df_stock, regression.LINEAR)

        elif ns_known_args.cmd == 'quadratic':
            regression.regression(l_args, s_ticker, s_interval, df_stock, regression.QUADRATIC)

        elif ns_known_args.cmd == 'cubic':
            regression.regression(l_args, s_ticker, s_interval, df_stock, regression.CUBIC)

        elif ns_known_args.cmd == 'regression':
            regression.regression(l_args, s_ticker, s_interval, df_stock, regression.USER_INPUT)

        # ------------------------------------------------- ARIMA -------------------------------------------------
        elif ns_known_args.cmd == 'arima':
            arima.arima(l_args, s_ticker, s_interval, df_stock)

        # ------------------------------------------------ FBPROPHET ------------------------------------------------
        elif ns_known_args.cmd == 'prophet':
            fbprophet.fbprophet(l_args, s_ticker, s_interval, df_stock)

        # ---------------------------------------------- NEURAL NETWORK ----------------------------------------------
        elif ns_known_args.cmd == 'mlp':
            neural_networks.mlp(l_args, s_ticker, s_interval, df_stock)

        elif ns_known_args.cmd == 'rnn':
            neural_networks.rnn(l_args, s_ticker, s_interval, df_stock)

        elif ns_known_args.cmd == 'lstm':
            neural_networks.lstm(l_args, s_ticker, s_interval, df_stock)

        # ------------------------------------------------------------------------------------------------------------
        else:
            print("Command not recognized!")