Update app.py

app.py

@@ -1,99 +1,80 @@
-# 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'])
+# Step 1: Define a function to fetch real-time market data
+def fetch_data(ticker_symbol):
+    ticker = yf.Ticker(ticker_symbol)
+    data = ticker.history(period="1y")  # Fetches 1 year of historical data
+    return data
+
+# Step 2: Define a function to calculate technical indicators
+def calculate_indicators(df):
+    # Calculate Moving Averages
+    df['MA20'] = df['Close'].rolling(window=20).mean()
+    df['MA50'] = df['Close'].rolling(window=50).mean()
+    
+    # Calculate RSI
+    delta = df['Close'].diff(1)
+    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+    rs = gain / loss
+    df['RSI'] = 100 - (100 / (1 + rs))
+    
+    # Calculate MACD
+    df['EMA12'] = df['Close'].ewm(span=12, adjust=False).mean()
+    df['EMA26'] = df['Close'].ewm(span=26, adjust=False).mean()
+    df['MACD'] = df['EMA12'] - df['EMA26']
+    df['Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
+    
+    # Calculate OBV
+    df['OBV'] = (df['Volume'] * ((df['Close'] - df['Close'].shift(1)) > 0).astype(int) - 
+                 df['Volume'] * ((df['Close'] - df['Close'].shift(1)) < 0).astype(int)).cumsum()
+    
+    return df
+
+# Step 3: Streamlit UI Setup for Stock Selection
+st.title("Indian Share Market Analysis")
+
+# Add a stock selector input box
+st.sidebar.header("Select Stock Ticker")
+ticker_symbol = st.sidebar.text_input("Enter Stock Ticker (e.g., RELIANCE.NS, ^NSEI)", "^NSEI")
+
+# Step 4: Fetch Data and Calculate Indicators
+nifty_data = fetch_data(ticker_symbol)
+nifty_data = calculate_indicators(nifty_data)
+
+# Step 5: Display Stock Data
+st.subheader(f"Data Overview for {ticker_symbol}")
+st.write(nifty_data.head())
+
+# Step 6: Visualizations
+st.subheader(f"Close Price for {ticker_symbol}")
+fig, ax = plt.subplots(figsize=(12, 6))
+sns.lineplot(x=nifty_data.index, y=nifty_data['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')
+st.subheader(f"Moving Averages and RSI for {ticker_symbol}")
+fig, ax = plt.subplots(figsize=(12, 6))
+sns.lineplot(x=nifty_data.index, y=nifty_data['MA20'], label='MA20')
+sns.lineplot(x=nifty_data.index, y=nifty_data['MA50'], label='MA50')
+sns.lineplot(x=nifty_data.index, y=nifty_data['RSI'], label='RSI')
+plt.axhline(y=70, color='red', linestyle='--', label='Overbought (70)')
+plt.axhline(y=30, color='green', linestyle='--', label='Oversold (30)')
 plt.legend()
+plt.title("Moving Averages and RSI")
 st.pyplot(fig)
 
-# Allow users to interact with the data
-st.subheader('Select Timeframe:')
+# Step 7: Interactive Timeframe Selection and Alerts
+st.sidebar.subheader("Select Timeframe:")
 timeframes = ['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']
-selected_timeframe = st.selectbox('', timeframes)
+selected_timeframe = st.sidebar.selectbox('Timeframe', timeframes)
 
-st.subheader('Set Alerts:')
-alert_type = st.selectbox('', ['Price', 'RSI'])
-alert_value = st.number_input('Enter value')
+st.sidebar.subheader("Set Alerts:")
+alert_type = st.sidebar.selectbox('Alert Type', ['Price', 'RSI'])
+alert_value = st.sidebar.number_input('Enter Alert Value')
 
-# Run the Streamlit app
-if __name__ == '__main__':
-    st.run()
+# Step 8: Run the Streamlit App (Note: Do not use st.run())
+# Save as `app.py` and run it using `streamlit run app.py`