SPL Stock Analysis
Summary
Backtest Summary - SPL
Generated: 2025-09-24 06:55:45
📊 Buy & Hold Benchmark
Total Return: +11167.36%
Analysis Period: Long-term (Multi-year)
Date Range: {‘start’: Timestamp(‘1993-06-22 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 11781}
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 | SPL | 900.81% | -4.7% | -10.0% | 12.9% | 16.1% | -10266.55% | 0.25 | -65.21% | 148 | 50.00% | $1,000,807 |
| dow_theory | SPL | 0.00% | 0.0% | 0.0% | 0.0% | 0.0% | 0.00% | 0.00 | 0.00% | 0 | 0.00% | $100,000 |
| volume_confirmation | SPL | 591.07% | -4.7% | -8.1% | 15.3% | 20.6% | -10576.29% | 0.21 | -77.06% | 132 | 50.00% | $691,072 |
| bollinger_oscillators | SPL | 300.93% | 5.9% | -0.1% | 2.8% | 44.9% | -10866.43% | 0.15 | -74.74% | 139 | 49.64% | $400,933 |
| macd_divergence | SPL | 0.00% | 0.0% | 0.0% | 0.0% | 0.0% | 0.00% | 0.00 | 0.00% | 0 | 0.00% | $100,000 |
| breakout_momentum | SPL | 2813.46% | -1.8% | -29.5% | -14.7% | 24.4% | -8353.90% | 0.35 | -79.02% | 82 | 50.00% | $2,913,461 |
| mean_reversion_multi_tf | SPL | 218.28% | 3.9% | -10.3% | 12.1% | 69.9% | -10949.08% | 0.14 | -79.16% | 3 | 33.33% | $318,280 |
| relative_strength_rotation | SPL | 1182.61% | -8.4% | -17.7% | 3.7% | 22.7% | -9984.75% | 0.28 | -65.56% | 119 | 49.58% | $1,282,611 |
| gap_trading | SPL | 2597.92% | 0.0% | 0.0% | 0.0% | 54.4% | -8569.44% | 0.40 | -58.00% | 14 | 50.00% | $2,697,923 |
| volatility_expansion | SPL | 578.90% | 0.0% | 0.0% | 0.0% | 0.0% | -10588.46% | 0.20 | -79.16% | 8 | 50.00% | $678,901 |
| momentum_kirkpatrick | SPL | 1450.24% | 3.3% | -8.0% | -19.3% | 11.2% | -9717.12% | 0.32 | -55.41% | 390 | 50.00% | $1,550,240 |
Best Strategy: breakout_momentum
- Symbol: SPL
- Total Return: 2813.46%
- Sharpe Ratio: 0.35
- Max Drawdown: -79.02%
- Final Portfolio Value: $2,913,461
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 SELL on 2025-09-23
- 📊 Total Confidence: 24.6%
- 🟢 Composite: 24.5%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 21.7%
- 🟡 Institutional: 35.3%
- 🟣 Quantitative: 41.5%
🔴 Volume Confirmation: Last SELL on 2025-09-23
- 📊 Total Confidence: 21.8%
- 🟢 Composite: 22.2%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 19.2%
- 🟡 Institutional: 32.6%
- 🟣 Quantitative: 35.2%
🟢 Bollinger Oscillators: Last BUY on 2025-09-03
- 📊 Total Confidence: 19.2%
- 🟢 Composite: 19.4%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 16.5%
- 🟡 Institutional: 29.7%
- 🟣 Quantitative: 30.2%
🔴 Breakout Momentum: Last SELL on 2025-08-22
- 📊 Total Confidence: 32.5%
- 🟢 Composite: 36.1%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 34.7%
- 🟡 Institutional: 41.5%
- 🟣 Quantitative: 50.3%
🟢 Mean Reversion Multi Tf: Last BUY on 2024-07-25
- 📊 Total Confidence: 10.1%
- 🟢 Composite: 13.8%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 11.7%
- 🟡 Institutional: 14.1%
- 🟣 Quantitative: 11.1%
🟢 Relative Strength Rotation: Last BUY on 2025-07-03
- 📊 Total Confidence: 25.9%
- 🟢 Composite: 26.4%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 23.7%
- 🟡 Institutional: 37.3%
- 🟣 Quantitative: 42.0%
🔴 Gap Trading: Last SELL on 2025-04-08
- 📊 Total Confidence: 29.8%
- 🟢 Composite: 35.9%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 34.1%
- 🟡 Institutional: 38.3%
- 🟣 Quantitative: 40.9%
🔴 Volatility Expansion: Last SELL on 2022-03-09
- 📊 Total Confidence: 16.6%
- 🟢 Composite: 21.9%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 19.0%
- 🟡 Institutional: 22.3%
- 🟣 Quantitative: 19.6%
🔴 Momentum Kirkpatrick: Last SELL on 2025-09-23
- 📊 Total Confidence: 28.8%
- 🟢 Composite: 28.6%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 26.0%
- 🟡 Institutional: 40.3%
- 🟣 Quantitative: 49.1%
📊 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
