CIG Stock Analysis
Summary
Backtest Summary - CIG
Generated: 2025-09-24 07:18:10
📊 Buy & Hold Benchmark
Total Return: +161.95%
Analysis Period: Medium-term
Date Range: {‘start’: Timestamp(‘2007-11-30 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 6507}
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 | CIG | 290.29% | 36.4% | 68.7% | 79.0% | 61.6% | 128.34% | 0.20 | -88.14% | 89 | 49.44% | $390,289 |
| dow_theory | CIG | 0.00% | 0.0% | 0.0% | 0.0% | 0.0% | 0.00% | 0.00 | 0.00% | 0 | 0.00% | $100,000 |
| volume_confirmation | CIG | 1216.65% | 36.4% | 68.7% | 75.1% | 47.4% | 1054.70% | 0.37 | -78.81% | 81 | 49.38% | $1,316,649 |
| bollinger_oscillators | CIG | -62.95% | 0.0% | 0.0% | 14.7% | -71.9% | -224.90% | -0.13 | -85.50% | 76 | 50.00% | $37,047 |
| macd_divergence | CIG | 108.45% | 36.4% | 68.7% | 95.3% | -52.3% | -53.50% | 0.00 | 0.00% | 1 | 0.00% | $208,451 |
| breakout_momentum | CIG | 232.29% | 36.4% | 68.7% | 74.8% | 9.6% | 70.34% | 0.15 | -85.64% | 75 | 49.33% | $332,289 |
| mean_reversion_multi_tf | CIG | 0.00% | 0.0% | 0.0% | 0.0% | 0.0% | 0.00% | 0.00 | 0.00% | 0 | 0.00% | $100,000 |
| relative_strength_rotation | CIG | 640.44% | 36.4% | 68.7% | 62.4% | 21.4% | 478.49% | 0.26 | -83.23% | 73 | 49.32% | $740,438 |
| gap_trading | CIG | 3.74% | 0.0% | 0.0% | 0.0% | 0.0% | -158.21% | 0.01 | -87.20% | 16 | 50.00% | $103,737 |
| volatility_expansion | CIG | 70.27% | 36.4% | 68.7% | 78.5% | -19.4% | -91.67% | 0.06 | -81.69% | 9 | 44.44% | $170,275 |
| momentum_kirkpatrick | CIG | 150.80% | 18.5% | 37.9% | 18.3% | -16.0% | -11.15% | 0.14 | -78.13% | 216 | 50.00% | $250,800 |
Best Strategy: volume_confirmation
- Symbol: CIG
- Total Return: 1216.65%
- Sharpe Ratio: 0.37
- Max Drawdown: -78.81%
- Final Portfolio Value: $1,316,649
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 2025-09-17
- 📊 Total Confidence: 18.1%
- 🟢 Composite: 20.2%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 17.0%
- 🟡 Institutional: 25.5%
- 🟣 Quantitative: 27.9%
🟢 Volume Confirmation: Last BUY on 2025-09-16
- 📊 Total Confidence: 25.4%
- 🟢 Composite: 28.4%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 25.3%
- 🟡 Institutional: 34.0%
- 🟣 Quantitative: 39.5%
🔴 Bollinger Oscillators: Last SELL on 2025-08-14
- 📊 Total Confidence: 0.0%
- 🟢 Composite: 0.0%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 0.0%
- 🟡 Institutional: 0.0%
- 🟣 Quantitative: 0.0%
🟢 Macd Divergence: Last BUY on 2020-08-20
- 📊 Total Confidence: 63.0%
- 🟢 Composite: 50.0%
- 🔵 Conservative: 30.0%
- 🔴 Aggressive: 85.1%
- 🟡 Institutional: 75.0%
- 🟣 Quantitative: 75.0%
🟢 Breakout Momentum: Last BUY on 2025-08-04
- 📊 Total Confidence: 16.7%
- 🟢 Composite: 19.0%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 16.0%
- 🟡 Institutional: 24.3%
- 🟣 Quantitative: 24.2%
🔴 Mean Reversion Multi Tf: Last SELL on 2021-08-12
- 📊 Total Confidence: 2.0%
- 🟢 Composite: 0.0%
- 🔵 Conservative: 10.0%
- 🔴 Aggressive: 0.0%
- 🟡 Institutional: 0.0%
- 🟣 Quantitative: 0.0%
🟢 Relative Strength Rotation: Last BUY on 2025-07-25
- 📊 Total Confidence: 20.6%
- 🟢 Composite: 23.2%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 20.1%
- 🟡 Institutional: 28.6%
- 🟣 Quantitative: 31.2%
🔴 Gap Trading: Last SELL on 2023-10-24
- 📊 Total Confidence: 12.9%
- 🟢 Composite: 17.0%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 14.8%
- 🟡 Institutional: 16.9%
- 🟣 Quantitative: 15.8%
🟢 Volatility Expansion: Last BUY on 2024-10-17
- 📊 Total Confidence: 46.2%
- 🟢 Composite: 49.7%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 74.2%
- 🟡 Institutional: 48.6%
- 🟣 Quantitative: 58.6%
🔴 Momentum Kirkpatrick: Last SELL on 2025-09-23
- 📊 Total Confidence: 71.1%
- 🟢 Composite: 67.7%
- 🔵 Conservative: 0.9%
- 🔴 Aggressive: 100.0%
- 🟡 Institutional: 87.0%
- 🟣 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
