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("🛠ī¸ 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("**đŸĸ 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("ℹī¸ **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("ℹī¸ **Why this works:** The standard benchmark. Equities provide growth, Bonds provide stability and income.") if __name__ == "__main__": main()