Update app.py

app.py

@@ -1,106 +1,263 @@
 import streamlit as st
 import pandas as pd
-import yfinance as yf
 import numpy as np
-import datetime
-
-def fetch_stock_data(symbol, start_date, end_date, interval):
-    try:
-        valid_intervals = {
-            "1m": 8,     # 1-minute data is available for up to 8 days
-            "2m": 60,    # 2-minute data is available for up to 60 days
-            "5m": 60,    # 5-minute data is available for up to 60 days
-            "15m": 60,   # 15-minute data is available for up to 60 days
-            "30m": 60,   # 30-minute data is available for up to 60 days
-            "60m": 730,  # 60-minute data is available for up to 730 days (2 years)
-            "90m": 730,  # 90-minute data is available for up to 730 days (2 years)
-            "1h": 730,   # 1-hour data is available for up to 730 days (2 years)
-            "1d": 10000, # Daily data is available for up to maximum available
-            "5d": 10000, # 5-day data is available for up to maximum available
-            "1wk": 10000,# Weekly data is available for up to maximum available
-            "1mo": 10000,# Monthly data is available for up to maximum available
-            "3mo": 10000 # Quarterly data is available for up to maximum available
-        }
-
-        if interval not in valid_intervals:
-            st.error(f"Invalid interval: {interval}. Please choose from {list(valid_intervals.keys())}")
-            return
+import yfinance as yf
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import ta
+from datetime import datetime, timedelta
+import pytz
 
-        max_days = valid_intervals[interval]
-        start_date_pd = pd.to_datetime(start_date)
-        end_date_pd = pd.to_datetime(end_date)
-        max_start_date = end_date_pd - pd.to_timedelta(max_days, unit='d')
+def calculate_technical_indicators(data):
+    """Calculate comprehensive technical indicators"""
+    # Trend Indicators
+    data['SMA_20'] = ta.trend.sma_indicator(data['Close'], window=20)
+    data['SMA_50'] = ta.trend.sma_indicator(data['Close'], window=50)
+    data['SMA_200'] = ta.trend.sma_indicator(data['Close'], window=200)
+    data['EMA_20'] = ta.trend.ema_indicator(data['Close'], window=20)
+    
+    # Momentum Indicators
+    data['RSI'] = ta.momentum.rsi(data['Close'], window=14)
+    data['MACD'] = ta.trend.macd_diff(data['Close'])
+    data['ROC'] = ta.momentum.roc(data['Close'], window=12)
+    
+    # Volume Indicators
+    data['OBV'] = ta.volume.on_balance_volume(data['Close'], data['Volume'])
+    data['MFI'] = ta.volume.money_flow_index(data['High'], data['Low'], data['Close'], data['Volume'])
+    
+    # Volatility Indicators
+    bb = ta.volatility.BollingerBands(close=data['Close'])
+    data['BB_upper'] = bb.bollinger_hband()
+    data['BB_middle'] = bb.bollinger_mavg()
+    data['BB_lower'] = bb.bollinger_lband()
+    data['ATR'] = ta.volatility.average_true_range(data['High'], data['Low'], data['Close'])
+    
+    # Additional Indian Market Specific Calculations
+    data['Daily_Return'] = data['Close'].pct_change() * 100
+    data['52W_High'] = data['Close'].rolling(window=252).max()
+    data['52W_Low'] = data['Close'].rolling(window=252).min()
+    data['Distance_From_52W_High'] = ((data['Close'] - data['52W_High'])/data['52W_High']) * 100
+    
+    return data
 
-        if start_date_pd < max_start_date:
-            st.warning(f"The selected start date exceeds the maximum allowed range for {interval} interval. Adjusting start date to {max_start_date.strftime('%Y-%m-%d')}")
-            start_date_pd = max_start_date
+def plot_charts(data, symbol, interval):
+    """Create interactive charts using plotly"""
+    # Determine the number of rows based on the interval
+    minute_intervals = ["1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h"]
+    if interval in minute_intervals:
+        rows = 3
+        subplot_titles = ('Price & Volume', 'RSI', 'MACD')
+        row_heights = [0.5, 0.25, 0.25]
+    else:
+        rows = 4
+        subplot_titles = ('Price & Volume', 'RSI', 'MACD', 'Technical Indicators')
+        row_heights = [0.4, 0.2, 0.2, 0.2]
+    
+    fig = make_subplots(rows=rows, cols=1, 
+                       shared_xaxes=True,
+                       vertical_spacing=0.05,
+                       subplot_titles=subplot_titles,
+                       row_heights=row_heights)
+    
+    # Main candlestick chart
+    fig.add_trace(go.Candlestick(x=data.index,
+                                open=data['Open'],
+                                high=data['High'],
+                                low=data['Low'],
+                                close=data['Close'],
+                                name='OHLC'),
+                  row=1, col=1)
+    
+    # Add Moving Averages
+    if 'SMA_20' in data.columns:
+        fig.add_trace(go.Scatter(x=data.index, y=data['SMA_20'], name='SMA 20', line=dict(color='orange')), row=1, col=1)
+    if 'SMA_50' in data.columns:
+        fig.add_trace(go.Scatter(x=data.index, y=data['SMA_50'], name='SMA 50', line=dict(color='blue')), row=1, col=1)
+    if 'SMA_200' in data.columns:
+        fig.add_trace(go.Scatter(x=data.index, y=data['SMA_200'], name='SMA 200', line=dict(color='red')), row=1, col=1)
+    
+    if interval not in minute_intervals:
+        # Volume bars for daily and higher intervals
+        colors = ['red' if row['Open'] - row['Close'] >= 0 else 'green' for index, row in data.iterrows()]
+        fig.add_trace(go.Bar(x=data.index, y=data['Volume'], name='Volume', marker_color=colors), row=1, col=1)
+    
+    # RSI
+    if 'RSI' in data.columns:
+        fig.add_trace(go.Scatter(x=data.index, y=data['RSI'], name='RSI', line=dict(color='purple')), row=2, col=1)
+        fig.add_hline(y=70, line_dash="dash", line_color="red", row=2, col=1)
+        fig.add_hline(y=30, line_dash="dash", line_color="green", row=2, col=1)
+    
+    # MACD
+    if 'MACD' in data.columns:
+        fig.add_trace(go.Scatter(x=data.index, y=data['MACD'], name='MACD', line=dict(color='blue')), row=3, col=1)
+    
+    if rows == 4:
+        # Bollinger Bands
+        if 'BB_upper' in data.columns and 'BB_middle' in data.columns and 'BB_lower' in data.columns:
+            fig.add_trace(go.Scatter(x=data.index, y=data['BB_upper'], name='BB Upper',
+                                    line=dict(color='gray', dash='dash')), row=4, col=1)
+            fig.add_trace(go.Scatter(x=data.index, y=data['BB_middle'], name='BB Middle',
+                                    line=dict(color='gray')), row=4, col=1)
+            fig.add_trace(go.Scatter(x=data.index, y=data['BB_lower'], name='BB Lower',
+                                    line=dict(color='gray', dash='dash')), row=4, col=1)
+    
+    # Update layout
+    fig.update_layout(
+        title=f'{symbol} Technical Analysis Dashboard',
+        yaxis_title='Price',
+        height=800 if rows == 4 else 600,
+        showlegend=True,
+        xaxis_rangeslider_visible=False
+    )
+    
+    return fig
 
+def fetch_stock_data(symbol, start_date, end_date, interval):
+    """Fetch stock data with error handling and data validation"""
+    try:
+        # Convert dates to IST timezone for accuracy
+        ist = pytz.timezone('Asia/Kolkata')
+        end_date_ist = datetime.now(ist).date()
+        
         ticker = yf.Ticker(symbol)
-        data = ticker.history(start=start_date_pd.strftime('%Y-%m-%d'), end=end_date_pd.strftime('%Y-%m-%d'), interval=interval)
-        if not data.empty:
-            data.reset_index(inplace=True)
-            data.rename(columns={'index': 'Datetime'}, inplace=True)
-            # Calculate On-Balance Volume (OBV)
-            data['OBV'] = (np.sign(data['Close'].diff()) * data['Volume']).fillna(0).cumsum()
-            # Calculate Relative Strength Index (RSI)
-            delta = data['Close'].diff()
-            gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
-            loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
-            rs = gain / loss
-            data['RSI'] = 100 - (100 / (1 + rs))
-            # Calculate Exponential Moving Average (EMA)
-            data['EMA'] = data['Close'].ewm(span=20, adjust=False).mean()
-            # Calculate Simple Moving Average (SMA)
-            data['SMA'] = data['Close'].rolling(window=20).mean()
-
-            st.write(f"Data for {symbol} fetched successfully with OBV, RSI, EMA, and SMA included.")
-            st.write(data)
-        else:
-            st.error(f"No data available for {symbol}. Please check the symbol or try a different date range.")
+        data = ticker.history(start=start_date.strftime('%Y-%m-%d'), end=end_date.strftime('%Y-%m-%d'), interval=interval)
+        
+        if data.empty:
+            st.error(f"No data available for {symbol} with interval '{interval}'. Please verify the symbol, interval, or try a different date range.")
+            return None
+            
+        # Calculate all technical indicators
+        data = calculate_technical_indicators(data)
+        
+        # Save data to CSV
+        filename = f'{symbol}_{interval}.csv'
+        data.to_csv(filename)
+        
+        return data
+        
     except Exception as e:
         st.error(f"Error fetching data for {symbol}: {e}")
+        return None
 
-# Stock List
-stock_list = [
-    "RELIANCE.NS", "TCS.NS", "INFY.NS", "HDFCBANK.NS", "ICICIBANK.NS",
-    "KOTAKBANK.NS", "SBIN.NS", "LT.NS", "HINDUNILVR.NS", "BHARTIARTL.NS"
-]
-
-# Streamlit app layout
-st.title("Share Market Data")
-symbol = st.selectbox("Select Stock Symbol", options=stock_list)
-interval = st.selectbox("Select Data Interval", options=["1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h", "1d", "5d", "1wk", "1mo", "3mo"])
-
-# Allow user to select a start date
-start_date = st.date_input("Select Start Date", pd.to_datetime('2023-01-01'))
-end_date = pd.to_datetime('today').strftime('%Y-%m-%d')
-
-valid_intervals = {
-    "1m": 8,     # 1-minute data is available for up to 8 days
-    "2m": 60,    # 2-minute data is available for up to 60 days
-    "5m": 60,    # 5-minute data is available for up to 60 days
-    "15m": 60,   # 15-minute data is available for up to 60 days
-    "30m": 60,   # 30-minute data is available for up to 60 days
-    "60m": 730,  # 60-minute data is available for up to 730 days (2 years)
-    "90m": 730,  # 90-minute data is available for up to 730 days (2 years)
-    "1h": 730,   # 1-hour data is available for up to 730 days (2 years)
-    "1d": 10000, # Daily data is available for up to maximum available
-    "5d": 10000, # 5-day data is available for up to maximum available
-    "1wk": 10000,# Weekly data is available for up to maximum available
-    "1mo": 10000,# Monthly data is available for up to maximum available
-    "3mo": 10000 # Quarterly data is available for up to maximum available
-}
-
-max_days = valid_intervals[interval]
-end_date_pd = pd.to_datetime('today')
-max_start_date = end_date_pd - pd.to_timedelta(max_days, unit='d')
-
-# Adjust the start date if it exceeds the limit
-if pd.to_datetime(start_date) < max_start_date:
-    st.warning(f"The selected start date exceeds the maximum allowed range for {interval} interval. Adjusting start date to {max_start_date.strftime('%Y-%m-%d')}")
-    start_date = max_start_date
-
-fetch_data = st.button("Fetch and Display Data")
+def main():
+    st.set_page_config(layout="wide")
+    st.title("Indian Stock Market Technical Analysis Dashboard")
+    
+    # Define minute-level and daily/higher intervals
+    minute_intervals = ["1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h"]
+    daily_or_higher_intervals = ["1d", "5d", "1wk", "1mo", "3mo"]
+    all_intervals = minute_intervals + daily_or_higher_intervals
+    
+    # Sidebar for inputs
+    st.sidebar.header("Configuration")
+    symbol = st.sidebar.text_input("Enter Stock Symbol (e.g., RELIANCE.NS, TCS.NS):", "RELIANCE.NS")
+    
+    interval = st.sidebar.selectbox(
+        "Select Data Interval",
+        all_intervals,
+        index=8  # Default to '1d'
+    )
+    
+    # Timezone and current date in IST
+    ist = pytz.timezone('Asia/Kolkata')
+    end_date_ist = datetime.now(ist).date()
+    
+    # Handle start date based on interval
+    if interval in minute_intervals:
+        # Automatically set start_date to last 7 days
+        start_date = end_date_ist - timedelta(days=7)
+        st.sidebar.write(f"**Note:** For interval '{interval}', only the last 7 days of data are available.")
+        st.sidebar.write(f"**Start Date:** {start_date.strftime('%Y-%m-%d')}")
+    else:
+        # Allow user to select start_date for daily and higher intervals
+        # Define a reasonable maximum history, e.g., 5 years
+        max_history = 5 * 365  # 5 years
+        default_start = end_date_ist - timedelta(days=365)  # Default to 1 year ago
+        start_date = st.sidebar.date_input(
+            "Start Date",
+            value=default_start,
+            min_value=end_date_ist - timedelta(days=max_history),
+            max_value=end_date_ist - timedelta(days=1)
+        )
+    
+    # Define end_date as today
+    end_date = end_date_ist
+    
+    # Add fetch button
+    if st.sidebar.button("Fetch Data and Update Charts"):
+        with st.spinner('Fetching data...'):
+            data = fetch_stock_data(symbol, start_date, end_date, interval)
+            
+            if data is not None:
+                # Display basic info
+                st.subheader(f"Analysis for {symbol}")
+                current_price = data['Close'][-1]
+                if len(data) >= 2:
+                    daily_change = ((data['Close'][-1] - data['Close'][-2])/data['Close'][-2]) * 100
+                else:
+                    daily_change = 0.0  # Not enough data to calculate change
+                
+                col1, col2, col3, col4 = st.columns(4)
+                col1.metric("Current Price", f"₹{current_price:.2f}", f"{daily_change:.2f}%")
+                
+                if '52W_High' in data.columns and '52W_Low' in data.columns:
+                    col2.metric("52 Week High", f"₹{data['52W_High'][-1]:.2f}")
+                    col3.metric("52 Week Low", f"₹{data['52W_Low'][-1]:.2f}")
+                
+                if 'RSI' in data.columns:
+                    col4.metric("RSI", f"{data['RSI'][-1]:.2f}")
+                
+                # Plot interactive charts
+                fig = plot_charts(data, symbol, interval)
+                st.plotly_chart(fig, use_container_width=True)
+                
+                # Technical Analysis Summary
+                st.subheader("Technical Analysis Summary")
+                
+                # Simple trading signals
+                signals = []
+                if 'RSI' in data.columns:
+                    rsi = data['RSI'][-1]
+                    if rsi < 30:
+                        signals.append("RSI indicates oversold conditions")
+                    elif rsi > 70:
+                        signals.append("RSI indicates overbought conditions")
+                
+                if 'MACD' in data.columns:
+                    macd = data['MACD'][-1]
+                    if macd > 0:
+                        signals.append("MACD is positive - Bullish momentum")
+                    else:
+                        signals.append("MACD is negative - Bearish momentum")
+                
+                if 'SMA_20' in data.columns and 'SMA_50' in data.columns:
+                    current_price = data['Close'][-1]
+                    sma_20 = data['SMA_20'][-1]
+                    sma_50 = data['SMA_50'][-1]
+                    
+                    if current_price > sma_20 and current_price > sma_50:
+                        signals.append("Price is above major moving averages - Bullish")
+                    elif current_price < sma_20 and current_price < sma_50:
+                        signals.append("Price is below major moving averages - Bearish")
+                
+                for signal in signals:
+                    st.write(f"• {signal}")
+                
+                if not signals:
+                    st.write("No clear technical signals detected.")
+                
+                # Display raw data in expandable section
+                with st.expander("View Raw Data"):
+                    st.dataframe(data)
+                    
+                # Provide download link for CSV
+                csv = data.to_csv(index=True)
+                st.download_button(
+                    label="Download data as CSV",
+                    data=csv,
+                    file_name=f'{symbol}_{interval}.csv',
+                    mime='text/csv',
+                )
 
-if fetch_data and symbol:
-    fetch_stock_data(symbol, start_date.strftime('%Y-%m-%d'), end_date, interval)
+if __name__ == "__main__":
+    main()