RPC Stock Analysis
Summary
Backtest Summary - RPC
Generated: 2025-09-24 06:37:46
📊 Buy & Hold Benchmark
Total Return: +99.91%
Analysis Period: Short-term
Date Range: {‘start’: Timestamp(‘1997-12-16 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 10143}
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 | RPC | 347.00% | 4.1% | 17.8% | 17.8% | 2.4% | 247.09% | 0.20 | -52.59% | 132 | 50.00% | $447,003 |
| dow_theory | RPC | 0.00% | 0.0% | 0.0% | 0.0% | 0.0% | 0.00% | 0.00 | 0.00% | 0 | 0.00% | $100,000 |
| volume_confirmation | RPC | 272.44% | 4.1% | 17.8% | 17.8% | 0.3% | 172.53% | 0.17 | -58.53% | 120 | 50.00% | $372,441 |
| bollinger_oscillators | RPC | -3.11% | -6.8% | 14.9% | 1.4% | -31.9% | -103.02% | -0.00 | -92.25% | 123 | 49.59% | $96,893 |
| macd_divergence | RPC | 0.00% | 0.0% | 0.0% | 0.0% | 0.0% | 0.00% | 0.00 | 0.00% | 0 | 0.00% | $100,000 |
| breakout_momentum | RPC | 288.71% | 7.5% | 22.7% | 20.5% | 7.1% | 188.80% | 0.16 | -70.33% | 86 | 50.00% | $388,715 |
| mean_reversion_multi_tf | RPC | 38.99% | -3.4% | 10.3% | -2.6% | -37.2% | -60.92% | 0.00 | 0.00% | 1 | 0.00% | $138,994 |
| relative_strength_rotation | RPC | 255.36% | 1.2% | 5.3% | 5.3% | -13.9% | 155.45% | 0.16 | -57.07% | 110 | 50.00% | $355,361 |
| gap_trading | RPC | -79.93% | -3.4% | 10.3% | -2.6% | -37.2% | -179.84% | -0.25 | -87.18% | 11 | 45.45% | $20,071 |
| volatility_expansion | RPC | 337.19% | -3.4% | 10.3% | -2.6% | -37.2% | 237.28% | 0.21 | -63.49% | 11 | 45.45% | $437,189 |
| momentum_kirkpatrick | RPC | -0.33% | 11.6% | 27.8% | 27.8% | 8.0% | -100.24% | -0.00 | -74.49% | 328 | 50.00% | $99,671 |
Best Strategy: trend_momentum
- Symbol: RPC
- Total Return: 347.00%
- Sharpe Ratio: 0.20
- Max Drawdown: -52.59%
- Final Portfolio Value: $447,003
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: 20.7%
- 🟢 Composite: 20.8%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 17.6%
- 🟡 Institutional: 31.5%
- 🟣 Quantitative: 33.6%
🔴 Volume Confirmation: Last SELL on 2025-09-22
- 📊 Total Confidence: 19.3%
- 🟢 Composite: 19.8%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 16.7%
- 🟡 Institutional: 30.3%
- 🟣 Quantitative: 30.0%
🟢 Bollinger Oscillators: Last BUY on 2025-09-17
- 📊 Total Confidence: 12.8%
- 🟢 Composite: 13.2%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 10.5%
- 🟡 Institutional: 23.4%
- 🟣 Quantitative: 16.8%
🔴 Breakout Momentum: Last SELL on 2025-08-22
- 📊 Total Confidence: 17.5%
- 🟢 Composite: 19.7%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 16.6%
- 🟡 Institutional: 25.0%
- 🟣 Quantitative: 26.3%
🟢 Mean Reversion Multi Tf: Last BUY on 2019-03-26
- 📊 Total Confidence: 35.8%
- 🟢 Composite: 19.5%
- 🔵 Conservative: 25.6%
- 🔴 Aggressive: 23.4%
- 🟡 Institutional: 56.7%
- 🟣 Quantitative: 53.7%
🔴 Relative Strength Rotation: Last SELL on 2025-09-18
- 📊 Total Confidence: 18.9%
- 🟢 Composite: 19.5%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 16.5%
- 🟡 Institutional: 30.0%
- 🟣 Quantitative: 28.6%
🟢 Gap Trading: Last BUY on 2023-05-26
- 📊 Total Confidence: 0.0%
- 🟢 Composite: 0.0%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 0.0%
- 🟡 Institutional: 0.0%
- 🟣 Quantitative: 0.0%
🟢 Volatility Expansion: Last BUY on 2025-04-09
- 📊 Total Confidence: 14.8%
- 🟢 Composite: 19.5%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 16.6%
- 🟡 Institutional: 20.1%
- 🟣 Quantitative: 17.9%
🔴 Momentum Kirkpatrick: Last SELL on 2025-09-22
- 📊 Total Confidence: 14.9%
- 🟢 Composite: 14.8%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 12.3%
- 🟡 Institutional: 24.8%
- 🟣 Quantitative: 22.3%
📊 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
