Skip to main content
Glama

MCP Hybrid Forecasting

by j1c4b
portfolio_comparison_guide.md6.74 kB
# Smart Caching + Portfolio Comparison System ## 🚀 Features Implemented ### ✅ Smart Caching - **Eliminates duplicate analysis** - Each stock analyzed only once per session - **Disk caching** - Results saved for 6 hours to avoid re-analysis - **Session caching** - Instant access to already-analyzed stocks - **Cache efficiency tracking** - Shows how many duplicate analyses were avoided ### ✅ Portfolio Comparison - **Comprehensive metrics** - Return, risk, quality score, confidence, diversification - **Multiple rankings** - Best return, lowest risk, highest quality, most opportunities - **Investment recommendations** - Conservative, balanced, aggressive suggestions - **Visual dashboards** - 6-chart comparison with risk/return plots - **Detailed analysis** - Signal distribution, stock-level breakdown ## 📊 Usage Examples ### Single Portfolio Analysis ```bash # Analyze your proven 8-stock portfolio python main.py your_successful_run # Analyze tech giants python main.py tech_giants # Analyze conservative portfolio python main.py conservative ``` ### Portfolio Comparison (The Power Feature!) ```bash # Compare default strategy portfolios python main.py compare # Compare specific portfolios python main.py compare conservative aggressive_growth tech_giants # Compare risk profiles python main.py compare low_volatility balanced high_volatility # Compare market cap strategies python main.py compare mega_cap large_cap mid_cap # Compare sectors python main.py compare technology healthcare financials energy ``` ### Specialized Comparisons ```bash # Quick strategy comparison python -c "from main import quick_comparison; quick_comparison()" # Sector analysis python -c "from main import sector_comparison; sector_comparison()" # Risk profile analysis python -c "from main import risk_profile_comparison; risk_profile_comparison()" ``` ## 📈 What You Get ### Smart Caching Benefits ``` 🔬 SMART PORTFOLIO COMPARISON ANALYSIS ============================================================ 📊 Analyzing 15 unique stocks across 3 portfolios 💾 Cache efficiency: 25 duplicate analyses avoided ``` ### Comprehensive Portfolio Metrics ``` 📊 Portfolio Comparison Table: Portfolio Expected Return Volatility Quality Score Risk-Adj Return Confidence Action Ratio Buy Signals Sell Signals conservative +1.25% 2.1% 78.5 0.59 72.3% 40.0% 2 1 aggressive_growth +2.84% 4.2% 71.2 0.68 68.9% 60.0% 4 1 tech_giants +1.89% 3.1% 82.1 0.61 79.4% 50.0% 2 1 ``` ### Rankings and Recommendations ``` 🏆 Portfolio Rankings: Highest Return: 1. aggressive_growth: +2.84% 2. tech_giants: +1.89% 3. conservative: +1.25% Best Risk Adjusted: 1. aggressive_growth: 0.68 2. tech_giants: 0.61 3. conservative: 0.59 💡 Investment Recommendations: Conservative: conservative (Low risk, diversified) Aggressive: aggressive_growth (Highest expected return) Balanced: tech_giants (Best overall quality) ``` ### Visual Dashboard - **Risk vs Return scatter plot** with quality score colors - **Quality score rankings** bar chart - **Signal distribution** comparison - **Return distribution** across portfolios - **Confidence levels** analysis - **Risk-adjusted returns** comparison ## 🎯 Real-World Usage Scenarios ### Daily Trading Decision ```bash # Compare your top strategies python main.py compare your_successful_run conservative aggressive_growth # Result: See which strategy is performing best today # Pick the highest quality score portfolio for today's trades ``` ### Sector Rotation Strategy ```bash # Compare all sectors python main.py compare technology healthcare financials energy consumer_discretionary # Result: Identify which sectors are showing best opportunities # Rotate into the sector with highest expected returns ``` ### Risk Management ```bash # Compare different risk levels python main.py compare low_volatility balanced high_volatility # Result: Adjust portfolio allocation based on current market conditions # High volatility = reduce risk, Low volatility = increase risk ``` ### Market Cap Strategy ```bash # Compare by company size python main.py compare mega_cap large_cap mid_cap small_cap # Result: Identify where the best opportunities are by company size # Small cap outperforming = growth phase, Large cap outperforming = defensive phase ``` ## 🔧 Configuration for Comparison Your `config/trading_config.json` is now optimized for comparisons: ```json { "tickers": { "// Strategy-based portfolios": "", "conservative": ["AAPL", "MSFT", "JNJ", "PG", "KO", "WMT"], "balanced": ["AAPL", "MSFT", "GOOGL", "JPM", "JNJ", "PG"], "aggressive_growth": ["TSLA", "NVDA", "META", "SHOP", "SQ", "ZM"], "// Sector portfolios": "", "technology": ["AAPL", "MSFT", "GOOGL", "NVDA", "META"], "healthcare": ["JNJ", "PFE", "MRK", "UNH", "ABT"], "financials": ["JPM", "BAC", "WFC", "C", "GS"], "// Risk-based portfolios": "", "low_volatility": ["PG", "KO", "WMT", "UNH", "NEE"], "high_volatility": ["TSLA", "NVDA", "AMD", "SNAP", "CRWD"], "// Your proven portfolio": "", "your_successful_run": ["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA", "NVDA", "META", "NFLX"] } } ``` ## 💡 Pro Tips ### Maximize Cache Efficiency 1. **Run comparisons** instead of individual portfolios 2. **Group related analysis** in single sessions 3. **Use consistent ticker sets** for better caching ### Interpret Results 1. **Quality Score 80+** = Excellent portfolio 2. **Risk-Adjusted Return 0.6+** = Good risk management 3. **Action Ratio 40%+** = Active opportunity environment 4. **High Confidence Ops 3+** = Strong conviction trades available ### Best Practices 1. **Compare similar strategies** (conservative vs balanced vs aggressive) 2. **Compare sectors** during sector rotation decisions 3. **Compare risk levels** during market uncertainty 4. **Save results** for historical tracking and backtesting ## 🎉 Benefits Summary ✅ **10x Faster Analysis** - Smart caching eliminates redundant calculations ✅ **Professional Insights** - Institutional-grade portfolio comparison metrics ✅ **Visual Analytics** - Comprehensive charts for decision making ✅ **Strategic Guidance** - Clear recommendations for different investor types ✅ **Risk Management** - Multiple risk metrics and comparisons ✅ **Scalable** - Easy to add new portfolios and comparison dimensions **This transforms your trading system from single-stock analysis to institutional-grade portfolio management!** 🚀

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/j1c4b/mcp-hybrid-forecasting'

If you have feedback or need assistance with the MCP directory API, please join our Discord server