pages/5_Settings.py

import streamlit as st
import pandas as pd
from datetime import datetime
import json
from src.utils import render_header

# ============ PAGE CONFIGURATION ============
st.set_page_config(
    page_title="Settings | NSE Optimizer",
    page_icon="⚙️",
    layout="wide"
)

# ============ MAIN APP ============

def main():
    render_header()
    st.title("⚙️ Settings & Preferences")
    st.markdown("Configure your portfolio optimizer preferences and manage your data")
    
    st.markdown("---")
    
    # Initialize session state with ALL required keys (fallback if Main_Page didn't run)
    if 'user_preferences' not in st.session_state:
        st.session_state.user_preferences = {
            'risk_free_rate': 0.0654,
            'default_years': 2,
            'default_simulations': 1000,
            'brokerage_rate': 0.0003,
            'confidence_level': 0.95,
            'rebalance_threshold': 0.05
        }
    
    # Ensure all keys exist (safe update for existing sessions)
    defaults = {
        'risk_free_rate': 0.0654,
        'default_years': 2,
        'default_simulations': 1000,
        'brokerage_rate': 0.0003,
        'confidence_level': 0.95,
        'rebalance_threshold': 0.05
    }
    
    for key, value in defaults.items():
        if key not in st.session_state.user_preferences:
            st.session_state.user_preferences[key] = value
    
    if 'portfolio_history' not in st.session_state:
        st.session_state.portfolio_history = []
    
    prefs = st.session_state.user_preferences
    
    # ============ RISK PARAMETERS ============
    st.markdown("## 📊 Risk Parameters")
    
    col1, col2 = st.columns(2)
    
    with col1:
        risk_free_rate = st.slider(
            "Risk-free Rate (%)",
            0.0, 15.0,
            prefs['risk_free_rate'] * 100,
            0.1,
            help="Used for Sharpe ratio calculation. Current India 10Y G-Sec: 6.54%"
        ) / 100
        
        default_years = st.slider(
            "Default Historical Data (Years)",
            1, 10,
            prefs['default_years'],
            1,
            help="Default period for historical data analysis"
        )
    
    with col2:
        default_simulations = st.slider(
            "Default Monte Carlo Simulations",
            500, 5000,
            prefs['default_simulations'],
            500,
            help="Number of scenarios to simulate (more = more accurate but slower)"
        )
        
        confidence_level = st.slider(
            "VaR Confidence Level (%)",
            90, 99,
            int(prefs['confidence_level'] * 100),
            1,
            help="Confidence level for Value at Risk calculations"
        ) / 100
    
    st.markdown("---")
    
    # ============ TRADING PARAMETERS ============
    st.markdown("## 💰 Trading Parameters")
    
    col1, col2 = st.columns(2)
    
    with col1:
        brokerage_rate = st.slider(
            "Brokerage Rate (%)",
            0.0, 0.5,
            prefs['brokerage_rate'] * 100,
            0.01,
            help="Transaction cost percentage. Typical range: 0.03% - 0.10%"
        ) / 100
        
        st.info(f"""
        **Estimated Cost per ₹1,00,000 trade:**
        - Brokerage: ₹{(100000 * brokerage_rate):,.2f}
        - Plus: STT, GST, Stamp Duty (approx 0.1%)
        """)
    
    with col2:
        rebalance_threshold = st.slider(
            "Rebalancing Threshold (%)",
            1.0, 20.0,
            prefs.get('rebalance_threshold', 0.05) * 100,
            1.0,
            help="Trigger rebalancing when allocation drifts by this percentage"
        ) / 100
    
    st.markdown("---")
    
    # ============ SAVE/RESET BUTTONS ============
    col1, col2, col3 = st.columns([2, 1, 2])
    
    with col1:
        if st.button("💾 Save Preferences", type="primary", use_container_width=True):
            st.session_state.user_preferences = {
                'risk_free_rate': risk_free_rate,
                'default_years': default_years,
                'default_simulations': default_simulations,
                'brokerage_rate': brokerage_rate,
                'confidence_level': confidence_level,
                'rebalance_threshold': rebalance_threshold
            }
            st.success("✅ Preferences saved successfully!")
            st.balloons()
    
    with col3:
        if st.button("🔄 Reset to Defaults", use_container_width=True):
            st.session_state.user_preferences = defaults.copy()
            st.success("✅ Reset to default settings!")
            st.rerun()
    
    st.markdown("---")
    
    # ============ CACHE MANAGEMENT ============
    st.markdown("## ⚡ Cache Management")
    
    col1, col2 = st.columns(2)
    
    with col1:
        if st.button("🔄 Clear Data Cache", use_container_width=True):
            st.cache_data.clear()
            st.success("✅ Market data cache cleared!")
    
    with col2:
        if st.button("🗑️ Clear All Caches", use_container_width=True):
            st.cache_data.clear()
            st.cache_resource.clear()
            st.success("✅ All application caches cleared!")
    
    st.markdown("---")
    
    # ============ DATA EXPORT ============
    st.markdown("## 📤 Data Export")
    
    col1, col2 = st.columns(2)
    
    with col1:
        if st.button("📥 Export Settings (JSON)", use_container_width=True):
            export_data = {
                'preferences': st.session_state.user_preferences,
                'export_date': datetime.now().isoformat(),
                'version': '2.0'
            }
            json_str = json.dumps(export_data, indent=2)
            st.download_button(
                "Download Settings",
                json_str,
                f"nse_optimizer_settings_{datetime.now().strftime('%Y%m%d')}.json",
                "application/json",
                use_container_width=True
            )
    
    with col2:
        if st.session_state.portfolio_history:
            if st.button("📥 Export History (CSV)", use_container_width=True):
                history_data = []
                for portfolio in st.session_state.portfolio_history:
                    history_data.append({
                        'Date': portfolio['date'].strftime('%Y-%m-%d %H:%M:%S'),
                        'Amount': portfolio['amount'],
                        'Stocks': portfolio['stocks'],
                        'Sharpe Ratio': portfolio['sharpe']
                    })
                df = pd.DataFrame(history_data)
                csv = df.to_csv(index=False)
                st.download_button(
                    "Download History",
                    csv,
                    f"portfolio_history_{datetime.now().strftime('%Y%m%d')}.csv",
                    "text/csv",
                    use_container_width=True
                )
        else:
            st.info("No portfolio history to export")

if __name__ == "__main__":
    main()