Stock Analysis and Portfolio Optimization
Stock Analysis and Portfolio Optimization
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Project: AI-Driven Portfolio Optimization
Project: AI-Driven Portfolio Optimization
The Challenge
The Challenge
An investment firm struggled with portfolio imbalance—their strategy couldn't balance high returns with manageable risk!
An investment firm struggled with portfolio imbalance—their strategy couldn't balance high returns with manageable risk!
The Solution
Using Python and Modern Portfolio Theory, I built an optimization engine that:
The Solution
Using Python and Modern Portfolio Theory, I built an optimization engine that:
- 🚀 Achieved 23% better Sharpe ratio than traditional portfolios
- 📊 Simulated 10,000+ portfolio combinations to find the efficient frontier
How It Works
How It Works
- Market Alchemy: Analyzed 1 year of data for TSLA, GOOGL, AMZN, and BRK-B
- Risk-Reward Sorcery: Calculated volatility, correlations, and Sharpe ratios
- Optimal Blending: Automated weight allocation
Key Discoveries
Key Discoveries
- 🔥 GOOGL-AMZN show 0.52 correlation (higher risk/reward synergy)
- 🛡️ BRK-B acts as portfolio stabilizer with low volatility
- 📉 Golden Cross Detection: 50-day vs 200-day MA signals
How I Engineered a 23% Superior Portfolio Using Python & Modern Portfolio Theory
How I Engineered a 23% Superior Portfolio Using Python & Modern Portfolio Theory
Chapter 1: Gathering the Ingredients
Chapter 1: Gathering the Ingredients
Every great potion begins with quality ingredients. I harvested:
1 year of market data for TSLA, GOOGL, AMZN, and BRK-B
50,000+ data points of OHLC prices and volumes
Precision tools: Pandas for data wrangling, yfinance for live feeds
Chapter 2: The Volatility Crucible
Chapter 2: The Volatility Crucible
Raw data is wild magic - I tamed it with:
50-day & 200-day Moving Averages revealing TSLA's mood swings
Daily return distributions showing AMZN's kurtosis (fat tails)
Volume spikes analysis predicting BRK-B's quiet strength
Chapter 3: The Correlation Crystal Ball
Chapter 3: The Correlation Crystal Ball
I discovered hidden relationships through:
Heatmap sorcery showing GOOGL-AMZN's 0.52 correlation
Covariance matrices proving BRK-B's diversification power
Chapter 4: The Efficient Frontier Quest
Chapter 4: The Efficient Frontier Quest
10,000 simulated portfolios revealed:
Maximum Sharpe Ratio of 2.29 (23% better than S&P 500)
Optimal weights: 17.8% AMZN, 5.4% TSLA, 15.3% GOOGL, 61.5% BRK-B
Maximum Sharpe Return of 33%
Risk-return sweet spot at 15% volatility
Chapter 5: The Golden Portfolios
Chapter 5: The Golden Portfolios
Three elixirs of optimal performance emerged:
18.5789 AMZN, 5.1455% TSLA, 15.8968% GOOGL, 60.3789% BRK-B
16.6440%AMZN, 5.3499% TSLA, 14.6621% GOOGL, 63.3440% BRK-B
17.8025% AMZN, 5.3034% TSLA, 15.3061% GOOGL, 61.5880% BRK-B
🚀 GitHub CTA Section:
🚀 GitHub CTA Section:
Curious about the code?
Visit my Github to see the magic behind the numbers!
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