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macrofin_dashboard_app.py
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macrofin_dashboard_app.py
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import datetime as dt
import pandas as pd
from pandas_datareader.fred import FredReader
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import streamlit as st
import requests
import yfinance as yf
### Get the Data
start_date = '2005-01-01'
end_date = dt.datetime.today().strftime("%Y-%m-%d")
recession_periods = [
("2008-01-01", "2009-06-01"), # Submortgage Crisis
("2020-04-01", "2020-06-01") # Covid Recession
]
## Macroeconomics Data
def chart_recession_periods(fig, recession_periods):
for start_date, end_date in recession_periods:
fig.add_vrect(x0=start_date, x1=end_date, fillcolor='grey', opacity=0.5, line_width=0)
def get_sp500_data():
"""
This function will return the S&P 500 data.
"""
sp500 = yf.download('^GSPC', start=start_date, end=end_date)['Adj Close']
sp500 = pd.DataFrame(sp500)
sp500.columns = ['S&P 500']
sp500['Diff (%)'] = round(sp500.pct_change() * 100, 2)
return sp500
def get_commodities_data():
# Define the assets and the ticker you'd like to get HERE
assets = {
'Gold': 'GC=F',
'Crude Oil': 'CL=F',
'Brent Crude Oil': 'BZ=F',
'Natural Gas': 'NG=F'
}
commodities_data = {}
for asset_name, asset_ticker in assets.items():
commodities_data[asset_name] = yf.download(asset_ticker, start=start_date, end=end_date)['Adj Close']
commodities_data = pd.concat(commodities_data, axis=1)
commodities_data.columns = assets.keys()
return commodities_data
def get_treasury_yield_data():
treasury_yield_10y = FredReader('DGS10', start_date).read()
treasury_yield_2y = FredReader('DGS2', start_date).read()
treasury_yield = pd.concat([treasury_yield_10y, treasury_yield_2y], axis=1)
treasury_yield.columns = ['10Y', '2Y']
treasury_yield['Spread'] = treasury_yield['10Y'] - treasury_yield['2Y']
return treasury_yield
def get_ccc_data():
return FredReader('BAMLH0A3HYC', start_date).read()
def get_vix_data():
return yf.download('^VIX', start=start_date)['Adj Close']
## Financial Market Data
def get_eurtwd_data():
finmind_url = "https://api.finmindtrade.com/api/v4/data"
params = {
"dataset": "TaiwanExchangeRate",
"data_id": "EUR",
"start_date": start_date,
}
try:
response = requests.get(finmind_url, params=params)
response.raise_for_status()
data = response.json()['data']
eurtwd_data = pd.DataFrame(data).set_index('date')
eurtwd_data.index = pd.to_datetime(eurtwd_data.index)
# Impute missing data (-1) with the previous value
eurtwd_data.replace(-1, method="ffill", inplace=True)
# Calculate the average of 'cash_sell' and 'cash_buy'
eurtwd_data['TWDEUR'] = (eurtwd_data['cash_sell'] + eurtwd_data['cash_buy']) / 2
# Calculate percentage change
eurtwd_data['Diff (%)'] = round(eurtwd_data['TWDEUR'].pct_change() * 100, 2)
# Drop rows with NaN values
eurtwd_data.dropna(inplace=True)
# Select relevant columns
eurtwd = eurtwd_data[['TWDEUR', 'Diff (%)']]
return eurtwd
except requests.exceptions.RequestException as e:
print(f"An error occurred: {e}")
return None
def get_eurusd_data():
usd_eur = pd.DataFrame(yf.download('EUR=X', start=start_date, end=end_date)['Adj Close'])
usd_eur.columns = ['USDEUR']
usd_eur['USDEUR'] = round(usd_eur['USDEUR'], 2)
usd_eur['Diff (%)'] = round(usd_eur['USDEUR'].pct_change() * 100, 3)
return usd_eur
def get_crypto_data():
# Define the assets and the ticker you'd like to get HERE
crypto = {
'Bitcoin': 'BTC-USD',
'Ethereum': 'ETH-USD'
}
crypto_data = {}
for crypto_name, crypto_ticker in crypto.items():
crypto_data[crypto_name] = yf.download(crypto_ticker, start=start_date, end=end_date)['Adj Close']
crypto_data = pd.concat(crypto_data, axis=1)
crypto_data.columns = crypto.keys()
return crypto_data
def get_stock_data():
# Define the assets and the ticker you'd like to get HERE
stocks = {
'ASML': 'ASML',
'Maersk': 'MAERSK-B.CO',
'Airbnb': 'ABNB'
}
stocks_data = {}
for stock_name, stock_ticker in stocks.items():
stocks_data[stock_name] = yf.download(stock_ticker, start=start_date, end=end_date)['Adj Close']
stocks_data = pd.concat(stocks_data, axis=1)
stocks_data.columns = stocks_data.keys()
return stocks_data
### Macro Page
## Charts
def make_treasury_chart():
treasury_yield_data = get_treasury_yield_data()
fig_treasury = px.line(treasury_yield_data, x=treasury_yield_data.index, y=['10Y', '2Y'], title='10Y 2Y Treasury Yield Spread')
fig_treasury.add_bar(x=treasury_yield_data.index, y=treasury_yield_data['Spread'], name='Spread')
chart_recession_periods(fig_treasury, recession_periods)
return fig_treasury
def make_ccc_sp500_chart():
sp500 = yf.download('^GSPC', start=start_date)['Adj Close']
ccc = get_ccc_data()
ccc_sp500 = pd.concat([ccc, sp500], axis=1)
ccc_sp500.columns = ['CCC-Rated Bond Yield Spread', 'S&P 500']
fig_ccc_sp500 = make_subplots(specs=[[{"secondary_y": True}]])
# Add CCC-Rated Bond Yield Spread line plot (primary y-axis)
fig_ccc_sp500.add_trace(
go.Scatter(x=ccc_sp500.index, y=ccc_sp500['CCC-Rated Bond Yield Spread'], name='CCC'),
secondary_y=False
)
# Add S&P 500 line plot (secondary y-axis)
fig_ccc_sp500.add_trace(
go.Scatter(x=ccc_sp500.index, y=ccc_sp500['S&P 500'], name='S&P 500'),
secondary_y=True
)
# Update layout
fig_ccc_sp500.update_layout(
title='CCC-Rated Bond Yield Spread and S&P 500 Over Time',
xaxis_title='Date',
yaxis_title='CCC',
yaxis2_title='S&P 500',
template='plotly_dark'
)
chart_recession_periods(fig_ccc_sp500, recession_periods)
return fig_ccc_sp500
def make_vix_chart():
vix = get_vix_data()
sp500 = yf.download('^GSPC', start=start_date)['Adj Close']
vix_sp500 = pd.concat([vix, sp500], axis=1)
vix_sp500.columns = ['VIX', 'S&P 500']
fig_vix_sp500 = make_subplots(specs=[[{"secondary_y": True}]])
# Add VIX line plot (primary y-axis)
fig_vix_sp500.add_trace(
go.Scatter(x=vix_sp500.index, y=vix_sp500['VIX'], name='VIX'),
secondary_y=False
)
# Add S&P 500 line plot (secondary y-axis)
fig_vix_sp500.add_trace(
go.Scatter(x=vix_sp500.index, y=vix_sp500['S&P 500'], name='S&P 500'),
secondary_y=True
)
# Update layout
fig_vix_sp500.update_layout(
title='VIX and S&P 500 Over Time',
xaxis_title='Date',
yaxis_title='VIX',
yaxis2_title='S&P 500',
yaxis=dict(
title='VIX'
),
yaxis2=dict(
title='S&P 500',
overlaying='y',
side='right'
)
)
chart_recession_periods(fig_vix_sp500, recession_periods)
return fig_vix_sp500
def display_chart_mac():
tab1_mac, tab2_mac, tab3_mac = st.tabs([
"10Y 2Y Treasury Yield Spread",
"CCC-rated Bond Yield Spread and S&P 500",
"Chicago Board Options Exchange Volatility Index (VIX)"
])
# 1. Treasury yeild spread
with tab1_mac:
tab1_fig = make_treasury_chart()
st.plotly_chart(tab1_fig, theme="streamlit", use_container_width=True)
# 2. CCC Bond Yield Spread and S&P 500
with tab2_mac:
tab2_fig = make_ccc_sp500_chart()
st.plotly_chart(tab2_fig, theme="streamlit", use_container_width=True)
# 3. VIX and S&P 500
with tab3_mac:
tab3_fig = make_vix_chart()
st.plotly_chart(tab3_fig, theme="streamlit", use_container_width=True)
def display_commodities_chart_mac():
commodities_data = get_commodities_data()
col1_com, col2_com = st.columns([3, 1])
with col2_com:
selected_commodities = st.selectbox(
'Select the commodities to display:',
commodities_data.columns
)
recent_commodities_data = commodities_data[[selected_commodities]].sort_values('Date', ascending=False).head(8)
st.dataframe(recent_commodities_data)
with col1_com:
com_fig = px.line(commodities_data, x=commodities_data.index, y=selected_commodities, title=f'{selected_commodities} Prices Over Time')
com_fig.update_traces(line=dict(color='green'))
st.plotly_chart(com_fig)
### Financial Market Page
## Investment Portfolio
# Current prices of portfolio invested
def get_current_prices(investment):
current_prices = {}
for asset in investment['asset'].unique():
ticker = yf.Ticker(asset)
current_prices[asset] = ticker.history(period='1d')['Close'][0]
return current_prices
# Get the portfolio value and return
def calculate_portfolio_value_and_return(investment, current_prices):
investment['current_value'] = investment.apply(
lambda row: row['amount_invested'] * (current_prices[row['asset']] / row['price_at_investment']), axis=1
)
portfolio_value = investment.groupby('date')['current_value'].sum().reset_index()
portfolio_current_value = round(portfolio_value['current_value'].sum(), 2)
initial_value = investment['amount_invested'].sum()
return_rate = round(((portfolio_current_value - initial_value) / initial_value * 100), 2)
return [portfolio_current_value, return_rate]
## Metrics
sp500 = get_sp500_data()
twdeur = get_eurtwd_data()
usdeur = get_eurusd_data()
def display_main_figures_fin():
# Get investment portfolio value & return
investment = pd.read_excel('Investment.xlsx', sheet_name='Investment', parse_dates=['date'])
current_prices = get_current_prices(investment=investment)
portfolio_value_and_return = calculate_portfolio_value_and_return(investment=investment, current_prices=current_prices)
fin1, fin2, fin3, fin4, fin5, fin6 = st.columns(6)
fin1.metric(label='Date: ', value=end_date)
fin2.metric(label='Portfolio Value (USD)', value=portfolio_value_and_return[0])
fin3.metric(label='Portfolio Return (%)', value=portfolio_value_and_return[1])
fin4.metric(label='S&P 500', value=round(sp500['S&P 500'][-1], 2), delta=f"{sp500['Diff (%)'][-1]}"+"%")
fin5.metric(label='USD / EUR', value=usdeur['USDEUR'][-1], delta=f"{usdeur['Diff (%)'][-1]}"+"%")
fin6.metric(label='TWD / EUR', value=twdeur['TWDEUR'][-1], delta=f"{twdeur['Diff (%)'][-1]}"+"%")
## Charts
def display_stock_chart_fin():
stock_data = get_stock_data()
col1_stock, col2_stock = st.columns([3, 1])
with col2_stock:
# Select which stock prices to show
selected_stock = st.selectbox(
'Select the stock to display:',
stock_data.columns
)
recent_stock_data = stock_data[[selected_stock]].sort_values('Date', ascending=False).head(8)
st.dataframe(recent_stock_data)
with col1_stock:
stock_fig = px.line(stock_data, x=stock_data.index, y=selected_stock, title=f'{selected_stock} Stock Prices Over Time')
st.plotly_chart(stock_fig)
def display_crypto_chart_fin():
crypto_data = get_crypto_data()
col_1_crypto, col_2_crypto = st.columns([3, 1])
with col_2_crypto:
selected_crypto = st.selectbox(
'Select the crypto to display:',
crypto_data.columns
)
recent_crypto_data = crypto_data[[selected_crypto]].sort_values('Date', ascending=False).head(8)
st.dataframe(recent_crypto_data)
with col_1_crypto:
crypto_fig = px.line(crypto_data, x=crypto_data.index, y=selected_crypto, title=f'{selected_crypto} Prices Over Time')
crypto_fig.update_traces(line=dict(color='yellow'))
st.plotly_chart(crypto_fig)
### Page Configuration
def macrofin_page_config():
st.set_page_config(
page_title="Macroeconomics & Financial Market Dashboard",
page_icon="📈",
layout="wide",
initial_sidebar_state="expanded",
menu_items={
'About': 'Type the introduction of the page.'
}
)
## Page Sidebar and Main Page
def macrofin_page_layout():
with st.sidebar:
st.title("Macroeconomics & Financial Market Dashboard")
page_option = st.selectbox(
'Sections:',
('Financial Market', 'Macroeconomics')
)
container = st.container(border=True)
container.write(
"An interactive dashboard that allows you to track the asset prices, your investment portfolio, and some key indicators of financial markets. 📈🌎"
)
col1_contact, col2_contact = st.columns(2)
with col1_contact:
st.link_button("Source Code", "https://github.com/yrwang0913")
with col2_contact:
st.link_button("Contact Me", "https://www.linkedin.com/in/yrwang0913/")
if page_option == 'Financial Market':
st.subheader('Financial Market Indicators')
display_main_figures_fin()
display_stock_chart_fin()
display_crypto_chart_fin()
else:
st.subheader('Macroeconomics Indicators')
display_commodities_chart_mac()
display_chart_mac()
### Page Layout
def main():
## Page Config
macrofin_page_config()
## Sidebar
macrofin_page_layout()
### Run the code
if __name__ == '__main__':
main()