MVP Stock Analysis
Summary
Backtest Summary - MVP
Generated: 2025-09-24 06:51:04
📊 Buy & Hold Benchmark
Total Return: +117.90%
Analysis Period: Medium-term
Date Range: {‘start’: Timestamp(‘2008-06-19 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 6305}
This represents the return from buying at the start and holding until the end of the analysis period.
Performance Overview
| Strategy | Symbol | Total Return | 3M Return | 6M Return | 12M Return | 24M Return | Excess Return | Sharpe Ratio | Max Drawdown | Trades | Win Rate | Final Value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| trend_momentum | MVP | 134.73% | 25.8% | 42.1% | 51.1% | 32.0% | 16.84% | 0.18 | -70.04% | 83 | 49.40% | $234,735 |
| dow_theory | MVP | 0.00% | 0.0% | 0.0% | 0.0% | 0.0% | 0.00% | 0.00 | 0.00% | 0 | 0.00% | $100,000 |
| volume_confirmation | MVP | 65.22% | 25.8% | 42.1% | 51.5% | 27.3% | -52.68% | 0.12 | -71.69% | 67 | 49.25% | $165,219 |
| bollinger_oscillators | MVP | 28.76% | 0.0% | 0.0% | -2.3% | 13.1% | -89.14% | 0.05 | -71.67% | 66 | 50.00% | $128,758 |
| macd_divergence | MVP | 154.77% | 25.8% | 42.1% | 38.8% | 35.9% | 36.87% | 0.00 | 0.00% | 1 | 0.00% | $254,769 |
| breakout_momentum | MVP | 88.72% | 25.8% | 42.1% | 22.7% | 6.3% | -29.18% | 0.13 | -83.85% | 43 | 48.84% | $188,722 |
| mean_reversion_multi_tf | MVP | 289.95% | 25.8% | 42.1% | 38.8% | 35.9% | 172.05% | 0.00 | 0.00% | 1 | 0.00% | $389,951 |
| relative_strength_rotation | MVP | 177.59% | 25.8% | 32.0% | 31.2% | 19.9% | 59.69% | 0.22 | -71.04% | 61 | 49.18% | $277,587 |
| gap_trading | MVP | 14.91% | 25.8% | 42.1% | 38.8% | 35.9% | -102.99% | 0.03 | -76.86% | 5 | 40.00% | $114,911 |
| volatility_expansion | MVP | 95.13% | 25.8% | 42.1% | 38.8% | 35.9% | -22.77% | 0.13 | -72.56% | 5 | 40.00% | $195,125 |
| momentum_kirkpatrick | MVP | 162.18% | 18.7% | 27.2% | 22.0% | 29.2% | 44.28% | 0.23 | -70.33% | 171 | 49.71% | $262,179 |
Best Strategy: mean_reversion_multi_tf
- Symbol: MVP
- Total Return: 289.95%
- Sharpe Ratio: 0.00
- Max Drawdown: 0.00%
- Final Portfolio Value: $389,951
Key Metrics
- Initial Capital: $100,000
- Analysis Date: 2025-09-24
- Portfolio Manager: Active (Extreme returns fix applied)
Period Analysis
This report includes period-based return analysis for the following timeframes:
- 3M Return: Performance over the last 3 months
- 6M Return: Performance over the last 6 months
- 12M Return: Performance over the last 12 months
- 24M Return: Performance over the last 24 months
Period-based analysis helps identify strategy behavior across different market conditions and time horizons.
Recent Trading Signals
📊 Today’s Signals (2025-09-24)
⚪ No new trading signals detected in today’s analysis.
📈 Most Recent Signals by Strategy
🟢 Trend Momentum: Last BUY on 2025-09-22
- 📊 Total Confidence: 70.5%
- 🟢 Composite: 68.4%
- 🔵 Conservative: 1.8%
- 🔴 Aggressive: 100.0%
- 🟡 Institutional: 82.6%
- 🟣 Quantitative: 99.7%
🟢 Volume Confirmation: Last BUY on 2025-09-23
- 📊 Total Confidence: 48.9%
- 🟢 Composite: 49.7%
- 🔵 Conservative: 0.2%
- 🔴 Aggressive: 73.2%
- 🟡 Institutional: 55.6%
- 🟣 Quantitative: 66.0%
🔴 Bollinger Oscillators: Last SELL on 2025-09-23
- 📊 Total Confidence: 27.0%
- 🟢 Composite: 30.4%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 30.5%
- 🟡 Institutional: 35.8%
- 🟣 Quantitative: 38.0%
🟢 Macd Divergence: Last BUY on 2017-07-25
- 📊 Total Confidence: 66.0%
- 🟢 Composite: 50.0%
- 🔵 Conservative: 30.0%
- 🔴 Aggressive: 100.0%
- 🟡 Institutional: 75.0%
- 🟣 Quantitative: 75.0%
🟢 Breakout Momentum: Last BUY on 2025-08-25
- 📊 Total Confidence: 57.0%
- 🟢 Composite: 61.5%
- 🔵 Conservative: 0.2%
- 🔴 Aggressive: 87.3%
- 🟡 Institutional: 59.9%
- 🟣 Quantitative: 76.0%
🟢 Mean Reversion Multi Tf: Last BUY on 2023-08-25
- 📊 Total Confidence: 20.0%
- 🟢 Composite: 1.4%
- 🔵 Conservative: 11.2%
- 🔴 Aggressive: 1.7%
- 🟡 Institutional: 44.4%
- 🟣 Quantitative: 41.1%
🟢 Relative Strength Rotation: Last BUY on 2025-05-08
- 📊 Total Confidence: 72.7%
- 🟢 Composite: 69.2%
- 🔵 Conservative: 2.8%
- 🔴 Aggressive: 100.0%
- 🟡 Institutional: 92.2%
- 🟣 Quantitative: 99.1%
🟢 Gap Trading: Last BUY on 2023-05-19
- 📊 Total Confidence: 16.0%
- 🟢 Composite: 20.0%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 19.4%
- 🟡 Institutional: 20.0%
- 🟣 Quantitative: 20.6%
🟢 Volatility Expansion: Last BUY on 2023-07-20
- 📊 Total Confidence: 58.4%
- 🟢 Composite: 62.1%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 89.0%
- 🟡 Institutional: 63.0%
- 🟣 Quantitative: 77.7%
🟢 Momentum Kirkpatrick: Last BUY on 2025-09-23
- 📊 Total Confidence: 73.4%
- 🟢 Composite: 69.4%
- 🔵 Conservative: 3.0%
- 🔴 Aggressive: 100.0%
- 🟡 Institutional: 94.5%
- 🟣 Quantitative: 100.0%
📊 How Confidence Is Calculated
Confidence percentages tell you how much to trust a trading signal based on the strategy’s historical performance.
🎯 Current Method: Composite (Balanced)
- Sharpe Ratio: Up to 20 points (risk-adjusted returns)
- Win Rate: Up to 30 points (percentage of profitable trades)
- Total Return: Up to 50 points (overall profitability)
📈 Available Confidence Methods:
- 🟢 Composite (Balanced): Current method - balanced approach for most traders
- 🔵 Conservative (Risk-Averse): Emphasizes safety and downside protection
- 🔴 Aggressive (Growth-Focused): Prioritizes high returns over risk
- 🟡 Institutional (Modern Portfolio Theory): Professional fund management approach
- 🟣 Quantitative (Statistical): Mathematical and statistical measures
🎯 Confidence Levels:
- 70%+: Strong performer - trust this signal more
- 50-70%: Decent performer - moderate trust
- 30-50%: Weak performer - be cautious
- <30%: Poor performer - low trust
💡 Signal Interpretation
- 🟢 BUY signals: Suggest potential upward price movement
- 🔴 SELL signals: Suggest potential downward price movement
- ⚪ HOLD signals: Suggest maintaining current position
- 📊 Confidence: Higher percentages indicate stronger signal conviction
- 🎯 CONSENSUS: Overall recommendation based on multiple strategy agreement
📚 Detailed Confidence Method Explanations
🟢 Composite (Balanced) - Current Method
Formula: (Sharpe×20) + (WinRate×30) + (Return×50)
Used by: Individual traders, retail investors
Why: Balanced approach that considers risk, consistency, and returns equally. Good for most trading styles.
Example: Strategy with 0.4 Sharpe, 60% win rate, 80% return = (0.4×20) + (0.6×30) + (0.8×50) = 66% confidence
🔵 Conservative (Risk-Averse)
Formula: (Sharpe×25) + (WinRate×35) + (Return×40) - DrawdownPenalty + SafetyBonus
Used by: Pension funds, insurance companies, risk-averse investors
Why: Prioritizes capital preservation over growth. Heavily penalizes strategies with large drawdowns.
Key Features:
- Higher weight on consistency (win rate)
- Penalty for drawdowns >5%
- Bonus for low-risk strategies
- Caps returns at 50% to avoid overvaluing risky strategies
🔴 Aggressive (Growth-Focused)
Formula: (Return×60) + (Sharpe×15) + (WinRate×25) + HighReturnBonus
Used by: Hedge funds, growth investors, aggressive traders
Why: Maximizes returns regardless of risk. Suitable for investors who can tolerate volatility.
Key Features:
- 60% weight on raw returns
- Lower weight on risk adjustment
- Bonus for strategies with >50% returns
- Allows returns up to 200% contribution
🟡 Institutional (Modern Portfolio Theory)
Formula: InfoRatio + Consistency + RiskAdjustedReturn + ReturnComponent + SignificanceBonus
Used by: Mutual funds, pension funds, institutional investors
Why: Based on academic finance theory and institutional requirements. Emphasizes statistical significance.
Key Features:
- Information ratio (like Sharpe but more robust)
- Return-to-drawdown ratio
- Bonus for statistically significant results (>100 trades)
- Follows modern portfolio theory principles
🟣 Quantitative (Statistical)
Formula: CalmarRatio + SterlingRatio + WinRate + Return + SampleSize + StatisticalSignificance
Used by: Quantitative funds, algorithmic trading systems, research institutions
Why: Uses advanced statistical measures and mathematical optimization. Most rigorous approach.
Key Features:
- Calmar ratio (return/max drawdown)
- Sterling ratio (similar to Calmar)
- Sample size adjustment for statistical validity
- T-statistic proxy for significance testing
- Mathematical optimization of weights
🏛️ Financial Industry Context
Goldman Sachs: Uses similar multi-factor scoring for strategy selection
Renaissance Technologies: Employs statistical significance testing like our Quantitative method
Bridgewater: Emphasizes risk parity similar to our Conservative approach
AQR: Uses academic factors like our Institutional method
Two Sigma: Applies quantitative methods similar to our Statistical approach
