Create app.py

app.py

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+# app.py
+# Import necessary libraries
+import yfinance as yf
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import streamlit as st
+from datetime import date
+
+# Set up environment and install required libraries (already installed in this case)
+# !pip install yfinance pandas matplotlib seaborn streamlit
+
+# Fetch real-time market data for NIFTY 50 (^NSEI) and other indices
+nifty_50 = yf.Ticker('^NSEI')
+data = nifty_50.history(period='1y')
+
+# Store the data in a pandas DataFrame for further analysis
+df = pd.DataFrame(data)
+
+# Calculate technical indicators using pandas
+df['MA20'] = df['Close'].rolling(window=20).mean()
+df['MA50'] = df['Close'].rolling(window=50).mean()
+
+df['RSI'] = 100 - (100 / (1 + df['Close'].diff().ewm(span=14, adjust=False).mean()))
+
+macd_line = df['Close'].ewm(span=12, adjust=False).mean()
+signal_line = macd_line.ewm(span=26, adjust=False).mean()
+
+df['MACD'] = macd_line - signal_line
+
+# Calculate On-Balance Volume (OBV)
+df['OBV'] = df['Volume']
+
+# Visualize data using matplotlib
+plt.figure(figsize=(12,6))
+sns.lineplot(x=df.index, y=df['Close'], label='Close Price')
+sns.lineplot(x=df.index, y=df['MA20'], label='MA20')
+sns.lineplot(x=df.index, y=df['MA50'], label='MA50')
+
+plt.title('Moving Averages and Close Prices')
+plt.xlabel('Date')
+plt.ylabel('Price')
+plt.legend()
+plt.show()
+
+# Visualize RSI with thresholds for overbought and oversold conditions
+plt.figure(figsize=(12,6))
+sns.lineplot(x=df.index, y=df['RSI'], label='RSI')
+
+if df['RSI'].max() > 70:
+    plt.axhline(y=70, color='r', linestyle='--')
+elif df['RSI'].min() < 30:
+    plt.axhline(y=30, color='g', linestyle='--')
+
+plt.title('Relative Strength Index (RSI)')
+plt.xlabel('Date')
+plt.ylabel('RSI Value')
+plt.legend()
+plt.show()
+
+# Create an interactive dashboard using Streamlit
+st.title('NIFTY 50 Data Overview')
+
+st.write(df.head())
+
+st.subheader('Close Price')
+fig, ax = plt.subplots(figsize=(12,6))
+sns.lineplot(x=df.index, y=df['Close'])
+st.pyplot(fig)
+
+st.subheader('Moving Averages and RSI')
+fig, ax = plt.subplots(figsize=(12,6))
+sns.lineplot(x=df.index, y=df['MA20'], label='MA20')
+sns.lineplot(x=df.index, y=df['MA50'], label='MA50')
+sns.lineplot(x=df.index, y=df['RSI'], label='RSI')
+
+if df['RSI'].max() > 70:
+    plt.axhline(y=70, color='r', linestyle='--')
+elif df['RSI'].min() < 30:
+    plt.axhline(y=30, color='g', linestyle='--')
+
+plt.title('Moving Averages and RSI')
+plt.xlabel('Date')
+plt.ylabel('Price/Value')
+plt.legend()
+st.pyplot(fig)
+
+# Allow users to interact with the data
+st.subheader('Select Timeframe:')
+timeframes = ['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']
+selected_timeframe = st.selectbox('', timeframes)
+
+st.subheader('Set Alerts:')
+alert_type = st.selectbox('', ['Price', 'RSI'])
+alert_value = st.number_input('Enter value')
+
+# Run the Streamlit app
+if __name__ == '__main__':
+    st.run()