Upload market_overview.py

market_overview.py

@@ -0,0 +1,139 @@
+import streamlit as st
+import plotly.graph_objects as go
+import pandas as pd # Added import statement for pandas
+from utils.data_loader import load_nifty50_symbols, fetch_stock_data, get_stock_info
+
+st.title("Market Overview")
+
+# Create sector categories
+sectors = {
+    "IT": ["TCS.NS", "INFY.NS", "HCLTECH.NS", "TECHM.NS", "WIPRO.NS"],
+    "Banking & Finance": ["HDFCBANK.NS", "ICICIBANK.NS", "SBIN.NS", "AXISBANK.NS", "KOTAKBANK.NS",
+                         "BAJFINANCE.NS", "BAJAJFINSV.NS", "HDFC.NS", "INDUSINDBK.NS"],
+    "Energy & Oil": ["RELIANCE.NS", "ONGC.NS", "POWERGRID.NS", "NTPC.NS", "BPCL.NS"],
+    "Automobile": ["TATAMOTORS.NS", "M&M.NS", "MARUTI.NS", "HEROMOTOCO.NS", "EICHERMOT.NS"],
+    "Consumer Goods": ["HINDUNILVR.NS", "ITC.NS", "NESTLEIND.NS", "BRITANNIA.NS", "TITAN.NS"],
+    "Metals & Mining": ["TATASTEEL.NS", "HINDALCO.NS", "JSWSTEEL.NS", "COALINDIA.NS"],
+    "Pharmaceuticals": ["SUNPHARMA.NS", "DRREDDY.NS", "CIPLA.NS", "DIVISLAB.NS"],
+    "Infrastructure": ["LT.NS", "ADANIPORTS.NS", "ULTRACEMCO.NS", "SHREECEM.NS", "GRASIM.NS"],
+    "Others": ["BHARTIARTL.NS", "ASIANPAINT.NS", "HDFCLIFE.NS", "SBILIFE.NS", "UPL.NS", 
+              "ADANIENT.NS", "BAJAJ-AUTO.NS", "APOLLOHOSP.NS", "DMART.NS", "PIDILITIND.NS"]
+}
+
+# Sector selection
+selected_sector = st.selectbox("Select Sector", list(sectors.keys()))
+
+# Load data for selected sector
+cols = st.columns(3)
+
+# Market Movers for selected sector
+with cols[0]:
+    st.subheader("Top Gainers")
+    gainers_data = []
+    for symbol in sectors[selected_sector]:
+        data = fetch_stock_data(symbol, period='1d')
+        if data is not None:
+            change = ((data['Close'][-1] - data['Open'][0]) / data['Open'][0]) * 100
+            info = get_stock_info(symbol)
+            company_name = info['name'] if info else symbol
+            gainers_data.append({
+                'Company': company_name,
+                'Symbol': symbol,
+                'Change%': change
+            })
+
+    # Sort gainers by change percentage
+    gainers_data = sorted(gainers_data, key=lambda x: x['Change%'], reverse=True)[:3]
+    st.dataframe(pd.DataFrame(gainers_data))
+
+with cols[1]:
+    st.subheader("Top Losers")
+    losers_data = []
+    for symbol in sectors[selected_sector]:
+        data = fetch_stock_data(symbol, period='1d')
+        if data is not None:
+            change = ((data['Close'][-1] - data['Open'][0]) / data['Open'][0]) * 100
+            info = get_stock_info(symbol)
+            company_name = info['name'] if info else symbol
+            losers_data.append({
+                'Company': company_name,
+                'Symbol': symbol,
+                'Change%': change
+            })
+
+    # Sort losers by change percentage
+    losers_data = sorted(losers_data, key=lambda x: x['Change%'])[:3]
+    st.dataframe(pd.DataFrame(losers_data))
+
+with cols[2]:
+    st.subheader("Most Active")
+    active_data = []
+    for symbol in sectors[selected_sector]:
+        info = get_stock_info(symbol)
+        if info:
+            active_data.append({
+                'Company': info['name'],
+                'Symbol': symbol,
+                'Volume': info['volume']
+            })
+
+    # Sort by volume
+    active_data = sorted(active_data, key=lambda x: x['Volume'], reverse=True)[:3]
+    st.dataframe(pd.DataFrame(active_data))
+
+# Sector Performance Heat Map
+st.subheader(f"{selected_sector} Performance Heat Map")
+heat_map_data = []
+for symbol in sectors[selected_sector]:
+    data = fetch_stock_data(symbol, period='1d')
+    info = get_stock_info(symbol)
+    if data is not None and info:
+        change = ((data['Close'][-1] - data['Open'][0]) / data['Open'][0]) * 100
+        heat_map_data.append({
+            'Company': info['name'],
+            'Symbol': symbol,
+            'Change%': change,
+            'Market Cap': info['market_cap'],
+            'Volume': info['volume']
+        })
+
+if heat_map_data:
+    fig = go.Figure(data=[go.Treemap(
+        labels=[f"{d['Company']}<br>{d['Symbol']}" for d in heat_map_data],
+        parents=[''] * len(heat_map_data),
+        values=[d['Market Cap'] for d in heat_map_data],
+        textinfo="label+value+percent",
+        marker=dict(
+            colors=[d['Change%'] for d in heat_map_data],
+            colorscale='RdYlGn',
+            showscale=True
+        ),
+        hovertemplate="""
+        Company: %{label}<br>
+        Change: %{color:.2f}%<br>
+        Market Cap: ₹%{value:,.0f}<br>
+        <extra></extra>
+        """
+    )])
+
+    fig.update_layout(title=f"{selected_sector} Companies Heat Map")
+    st.plotly_chart(fig, use_container_width=True)
+
+# Detailed Sector Analysis
+st.subheader(f"{selected_sector} Companies")
+detailed_data = []
+for symbol in sectors[selected_sector]:
+    info = get_stock_info(symbol)
+    if info:
+        detailed_data.append({
+            'Company': info['name'],
+            'Symbol': symbol,
+            'Market Cap (Cr)': info['market_cap'] / 10000000,  # Convert to Crores
+            'P/E Ratio': info['pe_ratio'],
+            'Volume': info['volume'],
+            'Recommendation': info['recommendation'],
+            'Target Price': info['target_price']
+        })
+
+if detailed_data:
+    st.dataframe(pd.DataFrame(detailed_data))