import argparse
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
import datetime
from datetime import datetime
from alpha_vantage.timeseries import TimeSeries
from helper_funcs import *
from fbprophet import Prophet
import warnings
warnings.simplefilter("ignore")

# ----------------------------------------------------- FBPROPHET -----------------------------------------------------
def fbprophet(l_args, s_ticker, s_interval, df_stock):
    parser = argparse.ArgumentParser(prog='fbprophet',
                                     description="""Facebook Prophet is a forecasting procedure that is fast and provides 
                                     completely automated forecasts that can be tuned by hand by data scientists and analysts.
                                     It was developed by Facebook's data science team and is open source. """)

    parser.add_argument('-d', "--days", action="store", dest="n_days", type=check_positive, default=5, help='prediction days')

    try:
        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)

        if l_unknown_args:
            print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
            return

        df_stock = df_stock.sort_index(ascending=True)
        df_stock.reset_index(level=0, inplace=True)
        df_stock = df_stock[['date', '5. adjusted close']]
        df_stock = df_stock.rename(columns={"date": "ds", "5. adjusted close": "y"})
        df_stock['ds'] = pd.to_datetime(df_stock['ds'])

        model = Prophet(yearly_seasonality=False, daily_seasonality=False)
        model.fit(df_stock)

        l_pred_days = get_next_stock_market_days(last_stock_day=pd.to_datetime(df_stock['ds'].values[-1]), n_next_days=ns_parser.n_days)
        close_prices = model.make_future_dataframe(periods=ns_parser.n_days)
        forecast = model.predict(close_prices)

        fig, ax = plt.subplots()
        model.plot(forecast, ax=ax, xlabel='Time', ylabel='Share Price ($)')
        xmin, xmax, ymin, ymax = ax.axis()
        ax.vlines(df_stock['ds'].values[-1], ymin, ymax, linewidth=2, linestyle='--', color='k')
        plt.axvspan(df_stock['ds'].values[-1], l_pred_days[-1], facecolor='tab:orange', alpha=0.2)
        plt.ylim(ymin, ymax)
        plt.xlim(df_stock['ds'].values[0], get_next_stock_market_days(l_pred_days[-1], 1)[-1])
        plt.title(f"Fb Prophet on {s_ticker} - {ns_parser.n_days} days prediction")
        plt.show()

        print("")
        print("Predicted share price:")
        df_pred = forecast['yhat'][-ns_parser.n_days:].apply(lambda x: f"{x:.2f} $")
        df_pred.index = l_pred_days
        print(df_pred.to_string())
        print("")

    except:
        print("")