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| #------------------- | |
| # Imports | |
| #------------------- | |
| import streamlit as st | |
| import yfinance as yf | |
| import pandas as pd | |
| import numpy as np | |
| import plotly.graph_objs as go | |
| import plotly.io as pio | |
| from bs4 import BeautifulSoup | |
| import requests | |
| from datetime import datetime | |
| # today's date | |
| today = datetime.today().strftime('%d %B %Y') | |
| st.set_page_config(layout="wide") | |
| #------------------- | |
| # Web scraping Yahoo Finance | |
| #------------------- | |
| dic = {} | |
| url = 'https://finance.yahoo.com/cryptocurrencies?offset=0&count=100' | |
| soup = BeautifulSoup(requests.get(url).text) | |
| # store values in separate lists and then in a dictionary | |
| for listing in soup.find_all('div', attrs={'id':'fin-scr-res-table'}): | |
| symbol_list = [] | |
| name_list = [] | |
| price_list = [] | |
| change_list = [] | |
| mcap_list = [] | |
| for symbol in listing.find_all('td', attrs={'aria-label':'Symbol'}): | |
| symbol_list.append(symbol.text) | |
| dic['Symbol'] = symbol_list | |
| for name in listing.find_all('td', attrs={'aria-label':'Name'}): | |
| name_list.append(name.text) | |
| dic['Name'] = name_list | |
| for price in listing.find_all('td', attrs={'aria-label':'Price (Intraday)'}): | |
| price_list.append(price.text) | |
| dic['Price'] = price_list | |
| for change in listing.find_all('td', attrs={'aria-label':'% Change'}): | |
| change_list.append(change.text) | |
| dic['% Change'] = change_list | |
| for mcap in listing.find_all('td', attrs={'aria-label':'Market Cap'}): | |
| mcap_list.append(mcap.text) | |
| dic['Market Cap'] = mcap_list | |
| # create a dataframe from dictionary | |
| df_scrape = pd.DataFrame(dic) | |
| df_scrape.Symbol = df_scrape.Symbol.str.replace('-USD','') | |
| df_scrape.Name = df_scrape.Name.str.replace(' USD','') | |
| dic1 = dict(zip(df_scrape.Symbol,df_scrape.Name)) | |
| #------------------- | |
| # Streamlit Sidebar | |
| #------------------- | |
| fiat = ['USD','EUR','GBP'] | |
| tokens = df_scrape.Symbol.values | |
| # filters selectbox | |
| st.sidebar.title("Filters") | |
| select_token = st.sidebar.selectbox('Tokens', tokens) | |
| select_fiat = st.sidebar.selectbox('Fiat', fiat) | |
| # special expander objects | |
| st.sidebar.markdown('***') | |
| with st.sidebar.expander('Help'): | |
| st.markdown(''' | |
| - Select token and fiat of your choice. | |
| - Interactive plots can be zoomed or hovered to retrieve more info. | |
| - Plots can be downloaded using Plotly tools.''') | |
| with st.sidebar.expander('Sources'): | |
| st.markdown(''' | |
| - Python Libraries: yfinance, BeautifulSoup, Plotly, Pandas, Streamlit | |
| - Prices: https://finance.yahoo.com | |
| - Logos: https://cryptologos.cc/ | |
| ''') | |
| st.write("Dashboard adapted from https://github.com/rohithteja/the-crypto-dashboard") | |
| #------------------- | |
| # Add crypto logo and name | |
| #------------------- | |
| col1, col2 = st.columns([1,10]) | |
| with col1: | |
| try: | |
| st.image(f'logos/{select_token}.png',width=70) | |
| except: | |
| pass | |
| with col2: | |
| st.markdown(f'''## {dic1[select_token]}''') | |
| #------------------- | |
| # Candlestick chart with moving averages | |
| #------------------- | |
| st.markdown(''' | |
| - The following is an interactive Candlestick chart for the price fluctuations over the past 5 years. | |
| - Simple moving averages were computed for 20, 50 and 100 day frequencies. | |
| - Aids in trading strategy and to better interpret the price fluctuations.''') | |
| # download 5 year crypto prices from Yahoo Finance | |
| df = yf.download(tickers=f'{select_token}-{select_fiat}', period = '5y', interval = '1d') | |
| # compute moving averages | |
| df['MA100'] = df.Close.rolling(100).mean() | |
| df['MA50'] = df.Close.rolling(50).mean() | |
| df['MA20'] = df.Close.rolling(20).mean() | |
| # Plotly candlestick chart | |
| fig = go.Figure(data= | |
| [go.Candlestick(x=df.index, | |
| open=df.Open, | |
| high=df.High, | |
| low=df.Low, | |
| close=df.Close, | |
| name=f'{select_token}'), | |
| go.Scatter(x=df.index, y=df.MA20, | |
| line=dict(color='yellow',width=1),name='MA20'), | |
| go.Scatter(x=df.index, y=df.MA50, | |
| line=dict(color='green',width=1),name='MA50'), | |
| go.Scatter(x=df.index, y=df.MA100, | |
| line=dict(color='red',width=1),name='MA100')]) | |
| fig.update_layout(go.Layout(xaxis = {'showgrid': False}, | |
| yaxis = {'showgrid': False}), | |
| title=f'{dic1[select_token]} Price Fluctuation with Moving Averages', | |
| yaxis_title=f'Price ({select_fiat})', | |
| xaxis_rangeslider_visible=False) | |
| st.plotly_chart(fig, use_container_width=True) | |
| #------------------- | |
| # Line Chart with daily trends | |
| #------------------- | |
| st.markdown('## Daily Trends') | |
| st.markdown(f''' | |
| - Line graph below shows the price fluctuation of {dic1[select_token]} every minute for today's date ({today}). | |
| - The data is automatically updated for the current day. | |
| - The horizontal line shows the current day's open price. | |
| - Green portion indicates the price greater than open price and red for lower. | |
| ''') | |
| # download daily crypto prices from Yahoo Finance | |
| df = yf.download(tickers=f'{select_token}-{select_fiat}', period = '1d', interval = '1m') | |
| # Plotly line chart | |
| fig = go.Figure() | |
| fig.add_scattergl(x=df.index, y=df.Close, | |
| line={'color': 'green'},name='Up trend') | |
| fig.add_scattergl(x=df.index, y=df.Close.where(df.Close <= df.Open[0]), | |
| line={'color': 'red'},name='Down trend') | |
| fig.add_hline(y=df.Open[0]) | |
| fig.update_layout(go.Layout(xaxis = {'showgrid': False}, | |
| yaxis = {'showgrid': False}), | |
| title=f'{dic1[select_token]} Daily Trends in Comparison to Open Price', | |
| yaxis_title=f'Price ({select_fiat})',template='plotly_dark', | |
| xaxis_rangeslider_visible=False) | |
| st.plotly_chart(fig, use_container_width=True) | |
| #------------------- | |
| # Table showing top 25 cryptos | |
| #------------------- | |
| st.markdown('## Top 25 Cryptocurrency Prices and Stats') | |
| st.markdown(''' | |
| - Realtime price changes (in USD). | |
| - Values updated every few minutes. | |
| - Colour coded column indicates the increase or decrease in price. | |
| ''') | |
| # create table from webscraped data | |
| df_scrape = df_scrape.rename(columns={'Symbol':'Token'}) | |
| df_scrape['% Change'] = df_scrape['% Change'].str.replace('%','').str.replace(',','').astype(float) | |
| df_scrape["color"] = df_scrape["% Change"].map(lambda x:'red' if x<0 else 'green') | |
| cols_to_show = ['Name','Token', 'Price', '% Change', 'Market Cap'] | |
| # to change color of "% change" column | |
| fill_color = [] | |
| n = len(df_scrape) | |
| for col in cols_to_show: | |
| if col!='% Change': | |
| fill_color.append(['black']*n) | |
| else: | |
| fill_color.append(df_scrape["color"].to_list()) | |
| # Plotly Table | |
| data=[go.Table(columnwidth = [20,15,15,15,15], | |
| header=dict(values=[f"<b>{col}</b>" for col in cols_to_show], | |
| font=dict(color='white', size=20), | |
| height=30, | |
| line_color='black', | |
| fill_color='dimgrey', | |
| align=['left','left', 'right','right','right']), | |
| cells=dict(values=df_scrape[cols_to_show].values.T, | |
| fill_color=fill_color, | |
| font=dict(color='white', size=20), | |
| height=30, | |
| line_color='black', | |
| align=['left','left', 'right','right','right']))] | |
| fig = go.Figure(data=data) | |
| fig.update_layout(go.Layout(xaxis = {'showgrid': False}, | |
| yaxis = {'showgrid': False})) | |
| st.plotly_chart(fig, use_container_width=True) | |