model.py

def prediction(stock, n_days):
    import dash
    import dash_core_components as dcc
    import dash_html_components as html
    from datetime import datetime as dt
    import yfinance as yf
    from dash.dependencies import Input, Output, State
    from dash.exceptions import PreventUpdate
    import pandas as pd
    import plotly.graph_objs as go
    import plotly.express as px
    # model
    from model import prediction
    from sklearn.model_selection import train_test_split
    from sklearn.model_selection import GridSearchCV
    import numpy as np
    from sklearn.svm import SVR
    from datetime import date, timedelta
    # load the data

    df = yf.download(stock, period='60d')
    df.reset_index(inplace=True)
    df['Day'] = df.index

     #days is a list of list from 0th to 59th day 
    days = list()
    for i in range(len(df.Day)):
        days.append([i])
    #print(days)
    # Splitting the dataset

    X = days
    Y = df[['Close']]

    x_train, x_test, y_train, y_test = train_test_split(X,
                                                        Y,
                                                        test_size=0.1,
                                                        shuffle=False)

    gsc = GridSearchCV(
        estimator=SVR(kernel='rbf'),
        param_grid={
            'C': [0.001, 0.01, 0.1, 1, 100, 1000],
            'epsilon': [
                0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10,
                50, 100, 150, 1000
            ],
            'gamma': [0.0001, 0.001, 0.005, 0.1, 1, 3, 5, 8, 40, 100, 1000]
        },
        cv=5,
        scoring='neg_mean_absolute_error',
        verbose=0,
        n_jobs=-1)

    y_train = y_train.values.ravel()
    y_train
    grid_result = gsc.fit(x_train, y_train)
    best_params = grid_result.best_params_
    best_svr = SVR(kernel='rbf',
                   C=best_params["C"],
                   epsilon=best_params["epsilon"],
                   gamma=best_params["gamma"],
                   max_iter=-1)

    # Support Vector Regression Models

    # RBF model
    #rbf_svr = SVR(kernel='rbf', C=1000.0, gamma=4.0)
    rbf_svr = best_svr

    rbf_svr.fit(x_train, y_train)

    output_days = list()
    for i in range(1, n_days):
        output_days.append([i + x_test[-1][0]])
    #print(output_days)


    dates = []
    current = date.today()
    for i in range(n_days):
        current += timedelta(days=1)
        dates.append(current)
    print(dates)

    # plot Results
    # fig = go.Figure()
    # fig.add_trace(
    #     go.Scatter(x=np.array(x_test).flatten(),
    #                y=y_test.values.flatten(),
    #                mode='markers',
    #                name='data'))
    # fig.add_trace(
    #     go.Scatter(x=np.array(x_test).flatten(),
    #                y=rbf_svr.predict(x_test),
    #                mode='lines+markers',
    #                name='test'))
    fig = go.Figure()
    fig.add_trace(
        go.Scatter(
            x=dates,  # np.array(ten_days).flatten(), 
            y=rbf_svr.predict(output_days),
            mode='lines+markers',
            name='data'))
    fig.update_layout(
        title="Predicted Close Price of next " + str(n_days - 1) + " days",
        xaxis_title="Date",
        yaxis_title="Closed Price",
        # legend_title="Legend Title",
    )

    return fig