Couguar
Belgium
5 months ago
🔍 Optimized Investing: How AI Helps Build Smarter Portfolios In my last post, I shared how Google Gemini enhances my investment analysis. Now, let's dive into portfolio optimization—how we balance risk and return to maximize gains. 📊 Step 1: Cleaning & Visualizing Data Each dot on the graph represents a stock or crypto, plotted by its risk (volatility) and return (historical growth). Before optimizing, I filter out: ✅ Extreme outliers (e.g., assets with one-time 10,000% surges) ✅ High-risk, low-return assets ✅ Negative-return assets (not ideal for growth-focused portfolios) 🎲 Step 2: Random Portfolios vs. Optimization Randomly selected portfolios (🟢 green dots) already lower risk while maintaining decent returns—showcasing diversification. But can we do better? 🚀 Step 3: The Efficient Frontier By optimizing portfolio allocation, we build smarter portfolios (🔵 blue curve) that maximize returns for a given risk level. Key insights: 📈 A single asset may have 25% return but 30% risk. A random portfolio lowers risk to 20%, while an optimized portfolio could reduce it to just 10% for the same return! 📉 If aiming for 15% risk, a random portfolio may yield 20% returns, while an optimized one can achieve 30%+. 💡 Key Takeaways ✅ Diversification works – lowers risk while keeping returns steady. ✅ Optimization is even better – nearly doubles return for the same risk vs. random portfolios. ⚠ The Catch? This model is based on historical data—not a future guarantee! However, it provides a structured approach to reduce risks and enhance gains. 📎 Full details & visuals in my blog post: www.investmenttales.com/post/efficient-investing-turning-complex-data-into-simple-strategies What’s your investment strategy—random or optimized? Let’s discuss in the comments! 💬⬇
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