pages/6_Learn.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px

# Import header
from src.utils import render_header

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

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

def main():
    render_header()
    
    st.title("📚 Interactive Learning Center")
    st.markdown("Master the art of portfolio optimization through interactive simulations and real-world examples.")
    
    tab1, tab2, tab3, tab4 = st.tabs([
        "🎓 Core Concepts", 
        "🧮 Interactive Simulators", 
        "🧠 Quiz Mode", 
        "🌍 Famous Portfolios"
    ])
    
    # --- TAB 1: CORE CONCEPTS (Text + Visuals) ---
    with tab1:
        col1, col2 = st.columns([2, 1])
        with col1:
            st.markdown("### 1. Modern Portfolio Theory (MPT)")
            st.info("""
            **The Big Idea:** You don't have to pick the "best" stock. You need to pick the right *combination* of stocks.
            
            MPT proves that by combining assets that don't move together (low correlation), you can **reduce risk** without reducing expected returns.
            """)
            
            st.markdown("### 2. The Sharpe Ratio")
            st.warning("""
            **Formula:** $(Return - Risk Free Rate) / Volatility$
            
            Think of it as **"Return per unit of Stress"**.
            * If Portfolio A gives 15% return with huge daily swings (high stress)...
            * And Portfolio B gives 14% return with steady growth (low stress)...
            * **Portfolio B has a higher Sharpe Ratio** and is mathematically "better."
            """)
            
        with col2:
            st.markdown("### 🔑 Key Glossary")
            st.markdown("""
            | Term | Meaning |
            |------|---------|
            | **Volatility** | How wildly prices swing. High vol = High risk. |
            | **Drawdown** | The % drop from the peak. "How much can I lose?" |
            | **Beta** | Sensitivity to the market. Beta 1.5 moves 1.5x the market. |
            | **Alpha** | Returns earned *above* the market benchmark. |
            """)

    # --- TAB 2: SIMULATORS (Interactive) ---
    with tab2:
        st.subheader("🛠️ Financial Lab")
        
        sim_type = st.selectbox("Select Simulator", ["📉 The Cost of Volatility", "💰 Power of Compounding"])
        
        if sim_type == "📉 The Cost of Volatility":
            st.markdown("#### Why does consistency matter?")
            st.write("See how two portfolios with the **same average return** end up with different money due to volatility.")
            
            col1, col2 = st.columns(2)
            with col1:
                st.markdown("**🐢 Steady Portfolio**")
                st.write("Returns: +10%, +10%, +10%, +10%")
                steady = [100, 110, 121, 133.1, 146.41]
                st.line_chart(steady)
                st.metric("Final Value", "₹146.41")
            
            with col2:
                st.markdown("**🐇 Volatile Portfolio**")
                st.write("Returns: +50%, -30%, +50%, -30%")
                # 100 -> 150 -> 105 -> 157.5 -> 110.25
                volatile = [100, 150, 105, 157.5, 110.25]
                st.line_chart(volatile)
                st.metric("Final Value", "₹110.25", "-24% vs Steady")
            
            st.error("💡 **Lesson:** Large losses destroy wealth faster than large gains build it. Avoiding big losses is key.")

        elif sim_type == "💰 Power of Compounding":
            col1, col2 = st.columns(2)
            with col1:
                initial = st.number_input("Initial Investment (₹)", value=100000)
                monthly = st.number_input("Monthly SIP (₹)", value=10000)
            with col2:
                rate = st.slider("Expected Annual Return (%)", 5, 25, 12)
                years = st.slider("Time Period (Years)", 5, 40, 20)
            
            # Calculate
            months = years * 12
            monthly_rate = rate / 100 / 12
            future_value = initial * (1 + monthly_rate)**months
            future_value += monthly * (((1 + monthly_rate)**months - 1) / monthly_rate) * (1 + monthly_rate)
            
            total_invested = initial + (monthly * months)
            wealth_gained = future_value - total_invested
            
            st.metric("Projected Wealth", f"₹{future_value:,.0f}", f"Invested: ₹{total_invested:,.0f}")
            
            # Chart
            years_list = list(range(years + 1))
            values = []
            for y in years_list:
                m = y * 12
                val = initial * (1 + monthly_rate)**m
                if m > 0:
                    val += monthly * (((1 + monthly_rate)**m - 1) / monthly_rate) * (1 + monthly_rate)
                values.append(val)
                
            df_chart = pd.DataFrame({"Year": years_list, "Wealth": values})
            st.area_chart(df_chart, x="Year", y="Wealth")

    # --- TAB 3: QUIZ ---
    with tab3:
        st.subheader("🧠 Test Your Knowledge")
        
        q1 = st.radio("1. What is the main goal of diversification?", 
                      ["To maximize returns at all costs", 
                       "To reduce risk without sacrificing expected returns", 
                       "To buy every stock in the market"])
        
        if q1 == "To reduce risk without sacrificing expected returns":
            st.success("Correct! ✅")
        elif q1:
            st.error("Incorrect. Diversification is primarily about risk management.")
            
        st.markdown("---")
            
        q2 = st.radio("2. If a stock has a correlation of -1.0 with your portfolio, adding it will:", 
                      ["Increase portfolio risk", 
                       "Have no effect", 
                       "Significantly reduce portfolio risk"])
        
        if q2 == "Significantly reduce portfolio risk":
            st.success("Correct! ✅ Negative correlation provides the best hedge.")
        elif q2:
            st.error("Incorrect. -1.0 means it moves in the exact opposite direction, cancelling out risk.")

    # --- TAB 4: FAMOUS PORTFOLIOS ---
    with tab4:
        st.subheader("🌍 Famous Portfolio Strategies")
        
        strategies = {
            "60/40 Classic": {"Equity": 60, "Bonds": 40, "Gold": 0},
            "All Weather (Ray Dalio)": {"Equity": 30, "Bonds": 55, "Gold": 15},
            "Aggressive Growth": {"Equity": 90, "Bonds": 10, "Gold": 0},
            "Permanent Portfolio": {"Equity": 25, "Bonds": 25, "Gold": 25, "Cash": 25}
        }
        
        selected_strat = st.selectbox("Choose a Strategy to Visualize", list(strategies.keys()))
        data = strategies[selected_strat]
        
        fig = px.pie(
            values=list(data.values()), 
            names=list(data.keys()),
            title=f"{selected_strat} Allocation",
            hole=0.4
        )
        st.plotly_chart(fig, use_container_width=True)
        
        if selected_strat == "All Weather (Ray Dalio)":
            st.info("ℹ️ **Why this works:** Designed to perform well in all economic environments (inflation, deflation, growth, recession) by heavily weighting bonds and commodities.")
        elif selected_strat == "60/40 Classic":
            st.info("ℹ️ **Why this works:** The standard benchmark. Equities provide growth, Bonds provide stability and income.")

if __name__ == "__main__":
    main()