app.py

# TODO: 
# - Add in financial metrics / income statement information to dataframe
# - Add in value investing logic warning flags
# - Add in calculation for current stock price value
# - Conditional formatting on stock price chart - green pos / red negative
# - Add in trendline to stock price chart
# - Build out requirements.txt and readme.md

from fileinput import close
from operator import index
import dash
from dash.dependencies import Input, Output
from dash import dcc, html
import numpy as np
from pandas_datareader import get_nasdaq_symbols
from alpha_vantage.timeseries import TimeSeries
from dateutil.relativedelta import relativedelta
from datetime import datetime as dt
import secret_keys

# create app
app = dash.Dash(__name__)
server = app.server

# create stock ticker dropdowns
nasdaqlist = get_nasdaq_symbols()

# default date options
end_stock_range = dt.now()
begin_stock_range = end_stock_range - relativedelta(years=5)

app.layout = html.Div([
    html.Div([

        html.H1('Value Investing'),
        # First let users choose stocks
        html.H2('Choose a stock ticker'),
        dcc.Dropdown(
            id='ticker-dropdown',
            # options=['AMZN','KO'],
            options=nasdaqlist['NASDAQ Symbol'].tolist()
            # value='AMZN'
        ),
        html.H2('Stock Price Graph'),
        dcc.Graph(id='stock-graph'),
        html.P('')

    ],style={'width': '50%', 'display': 'inline-block'}),
    html.H4('Date Range for Stock Chart'),
    dcc.DatePickerRange(
        id='stock-date-range-picker',
        min_date_allowed=dt(1910, 8, 5),
        max_date_allowed=dt.now(),
        initial_visible_month=begin_stock_range,
        start_date=begin_stock_range,
        end_date=end_stock_range
    ),
    html.Table(id='stock-date-range')
])

@app.callback(
    Output('stock-graph', 'figure'),
    Input('ticker-dropdown', 'value'),
    Input('stock-date-range-picker', 'start_date'),
    Input('stock-date-range-picker', 'end_date'),
    prevent_initial_call=True
)
def update_stock_graph(selected_dropdown_value, start_date, end_date):
    print("Selected ticker:", selected_dropdown_value)
    print("Start date:", start_date)
    print("End date:", end_date)

    # Initialize Alpha Vantage TimeSeries object with your API key in secret_keys.py file
    ts = TimeSeries(key=secret_keys.api_key, output_format='pandas')

    # Retrieve adjusted stock price data
    stockpricedf, _ = ts.get_daily_adjusted(
        symbol=selected_dropdown_value.strip(),
        outputsize='full'
    )

    # Filter the stock price data within the specified date range
    stockpricedf = stockpricedf.loc[start_date:end_date]

    return {
        'data': [{
            'x': stockpricedf.index,
            'y': stockpricedf['5. adjusted close']
        }]
    }



if __name__ == '__main__':
    app.run_server(debug=True, host="0.0.0.0", port="8050")