app.py

import starfishX as sx
import pandas as pd
import matplotlib.pyplot as plt
import streamlit as st
from PIL import Image
import base64
from bs4 import BeautifulSoup
import requests
import json

#---------------------------------#
# Page layout
## Page expands to full width
st.set_page_config(layout="wide")

my_page = st.sidebar.radio('Page Navigation', ['Thai Stock Exchange App', 'Cryptocurrency Price App'])

if my_page == 'Thai Stock Exchange App':
  
  image = Image.open('logo.png')

  st.image(image, width = 250)
  
  st.write("""
  # Thai Stock Exchange App
  """)
  # Create get_stock function to pull stock data
  def get_stock(stock, startdate='2016-01-01'):

    df = sx.loadHistData(stock, start=startdate,
                        OHLC=True, Volume=True)
    
    return df

  stock_symbol = st.text_input("Stock Symbol (ex. AOT, PTT, etc.)", 'AOT')
  date = st.selectbox('select startdate', [2010, 2011, 2012, 2013, 2014, 
  2015, 2016, 2017, 2018, 2019, 2020])
  start_date = f'{date}-01-01'

  try:
    df = get_stock(stock=stock_symbol, startdate=start_date)

    st.dataframe(df)

    st.dataframe(df.describe())
    
    # Stock symbol
    st.write(stock_symbol)

    # Closing Price
    st.write("""
    ## Closing Price
    """)
    st.line_chart(df.CLOSE)

    # Volume Price
    st.write("""
    ## Volume 
    """)
    st.line_chart(df.VOLUME)

    # display income statement
    st.write("""
    ## Income Statement
    """)
    st.dataframe(sx.getIncomeStatement(stock_symbol))

    # display financial ratio
    st.write("""
    ## Financial ratio
    """)
    st.dataframe(sx.getFinanceRatio(stock_symbol))

  except:
    st.error('The data of this stock is currently not available')
    st.error('ไม่พบข้อมูล')

elif my_page == 'Cryptocurrency Price App':

  image = Image.open('logo2.jpeg')

  st.image(image, width = 500)

  st.title('Crypto Price App')
  st.markdown("""
  This app retrieves cryptocurrency prices for the top 100 cryptocurrency from the **CoinMarketCap**!
  """)
  #---------------------------------#
  # About
  expander_bar = st.beta_expander("About")
  expander_bar.markdown("""
  * **Python libraries:** base64, pandas, streamlit, numpy, matplotlib, seaborn, BeautifulSoup, requests, json, time
  * **Data source:** [CoinMarketCap](http://coinmarketcap.com).
  """)


  #---------------------------------#
  # Page layout (continued)
  ## Divide page to 3 columns (col1 = sidebar, col2 and col3 = page contents)
  col1 = st.sidebar
  col2, col3 = st.beta_columns((2,1))

  #---------------------------------#
  # Sidebar + Main panel
  col1.header('Input Options')

  ## Sidebar - Currency price unit
  currency_price_unit = col1.selectbox('Select currency for price', ('USD','BTC','ETH'))

  # Web scraping of CoinMarketCap data
  @st.cache
  def load_data():
      cmc = requests.get('https://coinmarketcap.com')
      soup = BeautifulSoup(cmc.content, 'html.parser')

      data = soup.find('script', id='__NEXT_DATA__', type='application/json')
      coins = {}
      coin_data = json.loads(data.contents[0])
      listings = coin_data['props']['initialState']['cryptocurrency']['listingLatest']['data']
      for i in listings:
        coins[str(i['id'])] = i['slug']

      coin_name = []
      coin_symbol = []
      marketCap = []
      percentChange24h = []
      percentChange7d = []
      price = []
      volume24h = []

      for i in listings:
        coin_name.append(i['slug'])
        coin_symbol.append(i['symbol'])
        price.append(i['quote'][currency_price_unit]['price'])
        percentChange24h.append(i['quote'][currency_price_unit]['percentChange24h'])
        percentChange7d.append(i['quote'][currency_price_unit]['percentChange7d'])
        marketCap.append(i['quote'][currency_price_unit]['marketCap'])
        volume24h.append(i['quote'][currency_price_unit]['volume24h'])

      df = pd.DataFrame(columns=['coin_name', 'coin_symbol', 'marketCap', 'percentChange24h', 'percentChange7d', 'price', 'volume24h'])
      df['coin_name'] = coin_name
      df['coin_symbol'] = coin_symbol
      df['price'] = price
      df['percentChange24h'] = percentChange24h
      df['percentChange7d'] = percentChange7d
      df['marketCap'] = marketCap
      df['volume24h'] = volume24h
      return df

  df = load_data()

  ## Sidebar - Cryptocurrency selections
  sorted_coin = sorted(df['coin_symbol'])
  selected_coin = col1.multiselect('Cryptocurrency', sorted_coin, sorted_coin)

  df_selected_coin = df[(df['coin_symbol'].isin(selected_coin))] # Filtering data

  ## Sidebar - Number of coins to display
  num_coin = col1.slider('Display Top N Coins', 1, 100, 100)
  df_coins = df_selected_coin[:num_coin]

  ## Sidebar - Percent change timeframe
  percent_timeframe = col1.selectbox('Percent change time frame',
                                      ['7d','24h', '1h'])
  percent_dict = {"7d":'percentChange7d',"24h":'percentChange24h'}
  selected_percent_timeframe = percent_dict[percent_timeframe]

  sort_values = col1.selectbox('Sort values?', ['Yes', 'No'])

  col2.subheader('Price Data of Selected Cryptocurrency')
  col2.write('Data Dimension: ' + str(df_selected_coin.shape[0]) + ' rows and ' + str(df_selected_coin.shape[1]) + ' columns.')

  col2.dataframe(df_coins)


  def filedownload(df):
      csv = df.to_csv(index=False)
      b64 = base64.b64encode(csv.encode()).decode()  # strings <-> bytes conversions
      href = f'<a href="data:file/csv;base64,{b64}" download="crypto.csv">Download CSV File</a>'
      return href

  col2.markdown(filedownload(df_selected_coin), unsafe_allow_html=True)


  col2.subheader('Table of % Price Change')
  df_change = pd.concat([df_coins.coin_symbol, df_coins.percentChange24h, df_coins.percentChange7d], axis=1)
  df_change = df_change.set_index('coin_symbol')
  df_change['positive_percentChange24h'] = df_change['percentChange24h'] > 0
  df_change['positive_percentChange7d'] = df_change['percentChange7d'] > 0
  col2.dataframe(df_change)


  # Conditional creation of Bar plot (time frame)
  col3.subheader('Bar plot of % Price Change')

  if percent_timeframe == '7d':
      if sort_values == 'Yes':
          df_change = df_change.sort_values(by=['percentChange7d'])
      col3.write('*7 days period*')
      plt.figure(figsize=(5,25))
      plt.subplots_adjust(top = 1, bottom = 0)
      df_change['percentChange7d'].plot(kind='barh', color=df_change.positive_percentChange7d.map({True: 'g', False: 'r'}))
      col3.pyplot(plt)
  elif percent_timeframe == '24h':
      if sort_values == 'Yes':
          df_change = df_change.sort_values(by=['percentChange24h'])
      col3.write('*24 hour period*')
      plt.figure(figsize=(5,25))
      plt.subplots_adjust(top = 1, bottom = 0)
      df_change['percentChange24h'].plot(kind='barh', color=df_change.positive_percentChange24h.map({True: 'g', False: 'r'}))
      col3.pyplot(plt)