Finace_Dash_App

app.py

import dash
import dash_auth
import dash_core_components as dcc
import dash_html_components as html
#import dash_enterprise_auth as auth
from dash.dependencies import Input, Output
import plotly.express as px
import pandas as pd

# Keep this out of source code repository - save in a file or a database
VALID_USERNAME_PASSWORD_PAIRS = {
    'hello': 'world'
}



df = pd.read_csv("data.csv")

app = dash.Dash(__name__)

auth = dash_auth.BasicAuth(
    app,
    VALID_USERNAME_PASSWORD_PAIRS
)

server = app.server



app.title = "Visualizing Finance Data"

app.layout = html.Div([
        dcc.Dropdown(
        id="ticker",
        options=[{"label": x, "value": x} 
                 for x in df.columns[1:]],
        value=df.columns[1],
        clearable=False,
    ),
    dcc.Graph(id="time-series-chart"),
    html.Div(
    dcc.Markdown("*Hello! You are looking at INPX Stock Prices from January 1, 2020 to January 15 2021. This is data has been collected from Yahoo Finace's Website. Feel free to watch the different versions of prices by using the DropDown Option above. Thanks!*")
)
])

# # For downloading the data in CSV format
#     html.Div([
#     dash_table.DataTable(
#     id='table',
#     columns=[{"name": i, "id": i} for i in df.columns],
#     data=df.to_dict('records'),
#     export_format='xlsx',
#     export_headers='display',
#     merge_duplicate_headers=True,
#     page_current=0,
#     page_size=10
#     )]),

@app.callback(
    Output("time-series-chart", "figure"), 
    [Input("ticker", "value")])
def display_time_series(ticker):
    fig = px.line(df, x='Date', y=ticker)
    return fig





app.run_server(debug=True)

data.csv

data_collection.py

import pandas_datareader as data
import pandas as pd

# Define the instruments to download. We would like to see Apple, Microsoft and the S&P500 index.
#tickers = ['AAPL', 'MSFT', '^GSPC']

# We would like all available data from 01/01/2000 until 12/31/2016.
start_date = '2020-01-01'
end_date = '2021-01-16'

# User pandas_reader.data.DataReader to load the desired data. As simple as that.
panel_data = data.DataReader('INPX', 'yahoo', start_date, end_date)

panel_data.to_csv("data.csv")