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dash_rss.py
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dash_rss.py
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import streamlit as st
import yfinance as yf
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import feedparser
@st.cache_data
def load_data(ticker):
data = yf.download(ticker)
return data
def add_ema(data, periods):
for period in periods:
data[f'EMA_{period}'] = data['Close'].ewm(span=period, adjust=False).mean()
return data
def add_rsi(data, window=14):
delta = data['Close'].diff(1)
gain = delta.where(delta > 0, 0)
loss = -delta.where(delta < 0, 0)
avg_gain = gain.rolling(window=window).mean()
avg_loss = loss.rolling(window=window).mean()
rs = avg_gain / avg_loss
data['RSI'] = 100 - (100 / (1 + rs))
return data
def add_macd(data):
short_ema = data['Close'].ewm(span=12, adjust=False).mean()
long_ema = data['Close'].ewm(span=26, adjust=False).mean()
data['MACD'] = short_ema - long_ema
data['Signal Line'] = data['MACD'].ewm(span=9, adjust=False).mean()
return data
@st.cache_data
def get_fundamental_metrics(ticker):
stock = yf.Ticker(ticker)
info = stock.info
metrics = {
'P/E Ratio': info.get('trailingPE', 'N/A'),
'ROE': info.get('returnOnEquity', 'N/A'),
'ROA': info.get('returnOnAssets', 'N/A'),
'Gross Margin': info.get('grossMargins', 'N/A'),
'Profit Margin': info.get('profitMargins', 'N/A'),
'Debt to Equity': info.get('debtToEquity', 'N/A'),
'Current Ratio': info.get('currentRatio', 'N/A'),
'Price to Book': info.get('priceToBook', 'N/A'),
'Earnings Per Share': info.get('trailingEps', 'N/A'),
'Dividend Yield': info.get('dividendYield', 'N/A')
}
for key, value in metrics.items():
if isinstance(value, (int, float)):
metrics[key] = round(value, 2)
elif value == 'N/A':
metrics[key] = 'N/A'
else:
try:
metrics[key] = round(float(value), 2)
except ValueError:
metrics[key] = 'N/A'
return metrics
@st.cache_data
def fetch_rss_feed(ticker):
feed_url = f"https://finance.yahoo.com/rss/headline?s={ticker}"
feed = feedparser.parse(feed_url)
return feed
st.title('Interactive Stock Chart with Technical Indicators and Fundamental Metrics')
# Sidebar for user inputs and news feed
st.sidebar.title('Stock Ticker and News')
ticker = st.sidebar.text_input('Enter Stock Ticker', 'GOOGL').upper()
# Main content
data = load_data(ticker)
periods = st.slider('Select Time Period (in days)', 30, 365, 180)
selected_emas = st.multiselect('Select EMA periods', [200, 50, 20], default=[200, 50, 20])
add_rsi_plot = st.checkbox('Add RSI Subplot')
add_macd_plot = st.checkbox('Add MACD Subplot')
st.subheader('Select Fundamental Metrics to Display')
metrics = get_fundamental_metrics(ticker)
selected_metrics = st.multiselect('Choose metrics', list(metrics.keys()), default=['P/E Ratio', 'ROE', 'Profit Margin'])
if selected_metrics:
st.subheader('Fundamental Metrics')
for i in range(0, len(selected_metrics), 3):
cols = st.columns(3)
for j in range(3):
if i + j < len(selected_metrics):
metric = selected_metrics[i + j]
cols[j].metric(label=metric, value=metrics[metric])
data = add_ema(data, selected_emas)
data_period = data[-periods:]
if add_rsi_plot:
data_period = add_rsi(data_period)
if add_macd_plot:
data_period = add_macd(data_period)
rows = 1 + add_rsi_plot + add_macd_plot
price_range = [data_period['Close'].min() * 0.95, data_period['Close'].max() * 1.05]
rsi_range = [0, 100]
macd_range = [0, 0]
if add_macd_plot:
macd_range = [
min(data_period['MACD'].min(), data_period['Signal Line'].min()) * 1.05,
max(data_period['MACD'].max(), data_period['Signal Line'].max()) * 1.05
]
fig = make_subplots(rows=rows, cols=1, shared_xaxes=True,
vertical_spacing=0.15,
row_heights=[0.5] + [0.25] * (rows - 1),
subplot_titles=('Price', 'RSI', 'MACD')[:rows])
fig.add_trace(go.Candlestick(x=data_period.index,
open=data_period['Open'],
high=data_period['High'],
low=data_period['Low'],
close=data_period['Close'],
name='Candlesticks'), row=1, col=1)
for period in selected_emas:
fig.add_trace(go.Scatter(x=data_period.index, y=data_period[f'EMA_{period}'], mode='lines', name=f'EMA_{period}'), row=1, col=1)
current_row = 2
if add_rsi_plot:
fig.add_trace(go.Scatter(x=data_period.index, y=data_period['RSI'], mode='lines', name='RSI'), row=current_row, col=1)
fig.update_yaxes(range=rsi_range, row=current_row, col=1, title='RSI')
current_row += 1
if add_macd_plot:
fig.add_trace(go.Scatter(x=data_period.index, y=data_period['MACD'], mode='lines', name='MACD'), row=current_row, col=1)
fig.add_trace(go.Scatter(x=data_period.index, y=data_period['Signal Line'], mode='lines', name='Signal Line'), row=current_row, col=1)
fig.update_yaxes(range=macd_range, row=current_row, col=1, title='MACD')
fig.update_layout(
title=f'{ticker} Stock Price and Indicators',
xaxis_title='Date',
yaxis_title='Price',
height=400 + 200 * (rows - 1),
margin=dict(l=50, r=50, t=50, b=50),
legend=dict(x=0, y=1, traceorder='normal'),
xaxis_rangeslider_visible=False,
hovermode='x unified'
)
st.plotly_chart(fig)
# RSS feed in the sidebar
st.sidebar.subheader(f"Recent News for {ticker}")
st.sidebar.text("Loading news...") # Simple loading message
feed = fetch_rss_feed(ticker)
if feed.entries:
for entry in feed.entries[:5]: # Display the 5 most recent news items
st.sidebar.markdown(f"**{entry.title}**")
st.sidebar.markdown(f"[Read more]({entry.link})")
st.sidebar.markdown(f"*Published: {entry.published}*")
st.sidebar.markdown("---")
else:
st.sidebar.write("No news found for the given ticker symbol.")
# Remove the loading message after fetching is complete
st.sidebar.empty()