app.py

import dash
from dash import dcc
from dash import 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.svm import SVR


def get_stock_price_fig(df):

    fig = px.line(df,
                  x="Date",
                  y=["Close", "Open"],
                  title="Closing and Openning Price vs Date")

    return fig


def get_more(df):
    df['EWA_20'] = df['Close'].ewm(span=20, adjust=False).mean()
    fig = px.scatter(df,
                     x="Date",
                     y="EWA_20",
                     title="Exponential Moving Average vs Date")
    fig.update_traces(mode='lines+markers')
    return fig


app = dash.Dash(
    __name__,
    external_stylesheets=[
        "https://fonts.googleapis.com/css2?family=Roboto&display=swap"
    ])
server = app.server
# html layout of site
app.layout = html.Div(
    [
        html.Div(
            [
                # Navigation
                html.P("Welcome to the Stock Dash App!", className="start"),
                html.Div([
                    html.P("Input stock code: "),
                    html.Div([
                        dcc.Input(id="dropdown_tickers", type="text"),
                        html.Button("Submit", id='submit'),
                    ],
                             className="form")
                ],
                         className="input-place"),
                html.Div([
                    dcc.DatePickerRange(id='my-date-picker-range',
                                        min_date_allowed=dt(1995, 8, 5),
                                        max_date_allowed=dt.now(),
                                        initial_visible_month=dt.now(),
                                        end_date=dt.now().date()),
                ],
                         className="date"),
                html.Div([
                    html.Button(
                        "Stock Price", className="stock-btn", id="stock"),
                    html.Button("Indicators",
                                className="indicators-btn",
                                id="indicators"),
                    dcc.Input(id="n_days",
                              type="text",
                              placeholder="number of days"),
                    html.Button(
                        "Forecast", className="forecast-btn", id="forecast")
                ],
                         className="buttons"),
                # here
            ],
            className="nav"),

        # content
        html.Div(
            [
                html.Div(
                    [  # header
                        html.Img(id="logo"),
                        html.P(id="ticker")
                    ],
                    className="header"),
                html.Div(id="description", className="decription_ticker"),
                html.Div([], id="graphs-content"),
                html.Div([], id="main-content"),
                html.Div([], id="forecast-content")
            ],
            className="content"),
    ],
    className="container")


# callback for company info
@app.callback([
    Output("description", "children"),
    Output("logo", "src"),
    Output("ticker", "children"),
    Output("stock", "n_clicks"),
    Output("indicators", "n_clicks"),
    Output("forecast", "n_clicks")
], [Input("submit", "n_clicks")], [State("dropdown_tickers", "value")])
def update_data(n, val):  # inpur parameter(s)
    if n == None:
        return "Hey there! Please enter a legitimate stock code to get details.", "https://melmagazine.com/wp-content/uploads/2019/07/Screen-Shot-2019-07-31-at-5.47.12-PM.png", "Stonks", None, None, None
        # raise PreventUpdate
    else:
        if val == None:
            raise PreventUpdate
        else:
            ticker = yf.Ticker(val)
            inf = ticker.info
            df = pd.DataFrame().from_dict(inf, orient="index").T
            df[['logo_url', 'shortName', 'longBusinessSummary']]
            return df['longBusinessSummary'].values[0], df['logo_url'].values[
                0], df['shortName'].values[0], None, None, None


# callback for stocks graphs
@app.callback([
    Output("graphs-content", "children"),
], [
    Input("stock", "n_clicks"),
    Input('my-date-picker-range', 'start_date'),
    Input('my-date-picker-range', 'end_date')
], [State("dropdown_tickers", "value")])
def stock_price(n, start_date, end_date, val):
    if n == None:
        return [""]
        #raise PreventUpdate
    if val == None:
        raise PreventUpdate
    else:
        if start_date != None:
            df = yf.download(val, str(start_date), str(end_date))
        else:
            df = yf.download(val)

    df.reset_index(inplace=True)
    fig = get_stock_price_fig(df)
    return [dcc.Graph(figure=fig)]


# callback for indicators
@app.callback([Output("main-content", "children")], [
    Input("indicators", "n_clicks"),
    Input('my-date-picker-range', 'start_date'),
    Input('my-date-picker-range', 'end_date')
], [State("dropdown_tickers", "value")])
def indicators(n, start_date, end_date, val):
    if n == None:
        return [""]
    if val == None:
        return [""]

    if start_date == None:
        df_more = yf.download(val)
    else:
        df_more = yf.download(val, str(start_date), str(end_date))

    df_more.reset_index(inplace=True)
    fig = get_more(df_more)
    return [dcc.Graph(figure=fig)]


# callback for forecast
@app.callback([Output("forecast-content", "children")],
              [Input("forecast", "n_clicks")],
              [State("n_days", "value"),
               State("dropdown_tickers", "value")])
def forecast(n, n_days, val):
    if n == None:
        return [""]
    if val == None:
        raise PreventUpdate
    fig = prediction(val, int(n_days) + 1)
    return [dcc.Graph(figure=fig)]


if __name__ == '__main__':
    app.run_server(debug=True)