first commit

app1.py

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+import streamlit as st
+import pandas as pd
+import yfinance as yf
+import plotly.graph_objs as go
+from datetime import datetime, timedelta
+import requests
+
+# Define the layout using Streamlit components
+st.title('Stonks20.com')
+stock_symbol = st.text_input('Enter the stock symbol:', 'AAPL')
+date_range = st.date_input('Select the dates:', [(datetime.today() - timedelta(days=365)).date(), datetime.today().date()])
+submit_button = st.button('Submit')
+
+# Fetch stock data and update the graphs
+if submit_button:
+    try:
+        start_date, end_date = date_range
+        stock_data = yf.download(stock_symbol, start=start_date, end=end_date)
+
+        # Create a Candlestick chart
+        candlestick_fig = go.Figure(data=[go.Candlestick(x=stock_data.index,
+                                                         open=stock_data['Open'],
+                                                         high=stock_data['High'],
+                                                         low=stock_data['Low'],
+                                                         close=stock_data['Close'])])
+        candlestick_fig.update_layout(
+            title=f'{stock_symbol} Stock Price',
+            xaxis_title='Date',
+            yaxis_title='Price',
+            autosize=True
+        )
+        st.plotly_chart(candlestick_fig)
+
+        # Create a Volume chart
+        volume_fig = go.Figure(data=[go.Bar(x=stock_data.index,
+                                            y=stock_data['Volume'])])
+        volume_fig.update_layout(
+            title='Volume',
+            xaxis_title='Date',
+            yaxis_title='Volume',
+            autosize=True
+        )
+        st.plotly_chart(volume_fig)
+
+        # Create a Moving Average chart
+        moving_average_fig = go.Figure(data=[
+            go.Scatter(x=stock_data.index, y=stock_data['Close'], name='Price'),
+            go.Scatter(x=stock_data.index, y=stock_data['Close'].rolling(window=50).mean(), name='50-day MA'),
+            go.Scatter(x=stock_data.index, y=stock_data['Close'].rolling(window=200).mean(), name='200-day MA')
+        ])
+        moving_average_fig.update_layout(
+            title='Moving Averages',
+            xaxis_title='Date',
+            yaxis_title='Price',
+            autosize=True
+        )
+        st.plotly_chart(moving_average_fig)
+
+        # Create an RSI chart
+        delta = stock_data['Close'].diff()
+        gain = delta.mask(delta < 0, 0)
+        loss = -delta.mask(delta > 0, 0)
+        avg_gain = gain.rolling(window=14).mean()
+        avg_loss = loss.rolling(window=14).mean()
+        rs = avg_gain / avg_loss
+        rsi = 100 - (100 / (1 + rs))
+
+        rsi_fig = go.Figure(data=[go.Scatter(x=stock_data.index, y=rsi, name='RSI', line=dict(color='blue'))])
+        rsi_fig.update_layout(
+            title='Relative Strength Index (RSI)',
+            xaxis_title='Date',
+            yaxis_title='RSI',
+            autosize=True
+        )
+        st.plotly_chart(rsi_fig)
+
+    except Exception as e:
+        st.error(f'Error: {str(e)}')
+
+# Fetch news articles from GNews API
+response = requests.get(f"https://gnews.io/api/v4/search?q={stock_symbol}&token=09fdb169f86cad27b874f8a4872bd913")
+if response.status_code == 200:
+    news_articles = response.json()['articles']
+    if news_articles:
+        st.subheader('News')
+        for article in news_articles:
+            st.markdown(f"## {article['title']}")
+            st.markdown(article['description'])
+            st.markdown(f"[Read More]({article['url']})")
+    else:
+        st.warning('No news articles found for the given stock symbol.')
+else:
+    st.error('Failed to fetch news articles. Please check your API key and try again.')

requirements.txt

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+streamlit 
+pandas
+yfinance
+plotly
+datetime
+requests