Summary

Backtest Summary - DNP

Generated: 2025-09-24 07:13:59

📊 Buy & Hold Benchmark

Total Return: +1127.47%
Analysis Period: Long-term (Multi-year)
Date Range: {‘start’: Timestamp(‘2017-04-19 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 3079}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Best Strategy: bollinger_oscillators

• Symbol: DNP
• Total Return: 521.13%
• Sharpe Ratio: 0.95
• Max Drawdown: -21.48%
• Final Portfolio Value: $621,127

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: 76.1%
  • 🟢 Composite: 73.4%
  • 🔵 Conservative: 8.0%
  • 🔴 Aggressive: 100.0%
  • 🟡 Institutional: 99.0%
  • 🟣 Quantitative: 100.0%

• 🔴 Volume Confirmation: Last SELL on 2025-09-19

  • 📊 Total Confidence: 22.6%
  • 🟢 Composite: 28.4%
  • 🔵 Conservative: 0.0%
  • 🔴 Aggressive: 23.1%
  • 🟡 Institutional: 30.0%
  • 🟣 Quantitative: 31.6%

• 🟢 Bollinger Oscillators: Last BUY on 2025-08-14

  • 📊 Total Confidence: 33.2%
  • 🟢 Composite: 36.3%
  • 🔵 Conservative: 21.6%
  • 🔴 Aggressive: 29.7%
  • 🟡 Institutional: 40.9%
  • 🟣 Quantitative: 37.4%

• 🔴 Breakout Momentum: Last SELL on 2025-08-22

  • 📊 Total Confidence: 76.9%
  • 🟢 Composite: 74.8%
  • 🔵 Conservative: 9.8%
  • 🔴 Aggressive: 100.0%
  • 🟡 Institutional: 100.0%
  • 🟣 Quantitative: 100.0%

• 🔴 Mean Reversion Multi Tf: Last SELL on 2021-12-14

  • 📊 Total Confidence: 79.3%
  • 🟢 Composite: 75.5%
  • 🔵 Conservative: 29.8%
  • 🔴 Aggressive: 100.0%
  • 🟡 Institutional: 100.0%
  • 🟣 Quantitative: 91.3%

• 🔴 Relative Strength Rotation: Last SELL on 2025-08-13

  • 📊 Total Confidence: 15.3%
  • 🟢 Composite: 18.8%
  • 🔵 Conservative: 0.0%
  • 🔴 Aggressive: 16.8%
  • 🟡 Institutional: 19.7%
  • 🟣 Quantitative: 21.4%

• 🔴 Gap Trading: Last SELL on 2022-05-10

  • 📊 Total Confidence: 1.7%
  • 🟢 Composite: 5.2%
  • 🔵 Conservative: 0.0%
  • 🔴 Aggressive: 2.2%
  • 🟡 Institutional: 0.9%
  • 🟣 Quantitative: 0.0%

• 🔴 Volatility Expansion: Last SELL on 2020-03-16

  • 📊 Total Confidence: 78.7%
  • 🟢 Composite: 78.2%
  • 🔵 Conservative: 23.1%
  • 🔴 Aggressive: 100.0%
  • 🟡 Institutional: 100.0%
  • 🟣 Quantitative: 92.3%

• 🔴 Momentum Kirkpatrick: Last SELL on 2025-09-03

  • 📊 Total Confidence: 26.0%
  • 🟢 Composite: 27.8%
  • 🔵 Conservative: 4.6%
  • 🔴 Aggressive: 22.6%
  • 🟡 Institutional: 39.7%
  • 🟣 Quantitative: 35.3%

📊 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