import pandas as pd
from datetime import datetime
from datetime import timedelta
import numpy as np
import statsmodels.api as sm

import plotly.express as px
import plotly.graph_objects as go

import warnings
warnings.filterwarnings("ignore")

# from sklearn.metrics import mean_squared_error

df = pd.read_csv('us-shareprices-daily.csv', sep=';')

def get_model_accuracy(data, ticker_symbol):
    
    stock_data = data[data['Ticker'] == ticker_symbol]


    # get MSE for testing data using 85/15 split for chosen stock symbol

    train_data, test_data = stock_data[0:int(len(stock_data)*0.85)], stock_data[int(len(stock_data)*0.85):]
    training_data = train_data['Close'].values
    test_data = test_data['Close'].values
    history = [x for x in training_data]
    model_predictions = []
    N_test_observations = len(test_data)
    for time_point in range(N_test_observations):
        model = sm.tsa.statespace.SARIMAX(history, order=(1,1,1))
        model_fit = model.fit(disp=0)
        output = model_fit.forecast()
        yhat = output[0]
        model_predictions.append(yhat)
        true_test_value = test_data[time_point]
        history.append(true_test_value)

    MSE_error = mean_squared_error(test_data, model_predictions)
    return 'Testing Mean Squared Error is {}'.format(MSE_error)


def arima_chart(tickers):
    df = pd.read_csv('data_and_sp500.csv')
    df = df[['Date']+tickers]
    fig = px.line(df, x='Date', y=df.columns)

    for ticker in tickers:
        x = np.array(df['Date'])
        y = np.array(df[ticker])
        ticker_df = pd.concat([df['Date'], df[ticker]], axis=1)

        model = sm.tsa.statespace.SARIMAX(ticker_df[ticker], order=(21,1,7))
        model_fit = model.fit(disp=-1)
        # print(model_fit.summary())
        forecast = model_fit.forecast(7, alpha=0.05)#.predict(start=1259, end=1289)
        begin_date = datetime.strptime('2021-10-22', '%Y-%m-%d')
        forecast_dates = [begin_date+timedelta(days=i-1258) for i in forecast.index]
        fig.add_trace(go.Scatter(x=forecast_dates, y=forecast.to_list(),
                                    mode='lines',
                                    name='{} forecast'.format(ticker)))

    fig.update_xaxes(range=[begin_date-timedelta(days=120), begin_date+timedelta(days=10)])
    st.plotly_chart(fig, use_container_width=True)