ℹ️ All backtest include trading fees.
🚀 G-Doda Groove by @DaviddTech 🤖 [3b92d18a]
🛡️ G-DODA IMXUSDT 30M
@HenRik
⌛30 minutes
⚪️ Deep Backtest
Last updated: 2 hours agoTrades per Day
Key Performance Metrics
- First Traded Date: 2021-11-30 06:00:00
- Sharpe Ratio: 0.37
- Sortino Ratio: 1.22
- Calmar: -0.47
- Longest DD Days: 103.00
- Volatility: 2.64
- Skew: 0.15
- Kurtosis: 0.73
- Expected Daily: 0.01
- Expected Monthly: 0.26
- Expected Yearly: 3.16
- Kelly Criterion: 9.28
- Daily Value-at-Risk: -0.25
- Expected Shortfall (cVaR): -0.34
- Last Trade Date: 2025-03-24 03:00:00
- Max Consecutive Wins: 6
- Number Winning Trades 201
- Max Consecutive Losses: 7
- Number Losing Trades: 183
- Gain/Pain Ratio: -0.47
- Gain/Pain (1M): 1.22
- Payoff Ratio: 1.11
- Common Sense Ratio: 1.22
- Tail Ratio: 1.22
- Outlier Win Ratio: 3.25
- Outlier Loss Ratio: 3.49
- Recovery Factor: 0.00
- Ulcer Index: 0.01
- Serenity Index: 0.83
AI Trading Bot Quantitative Analyst
Performance Analysis
Upon reviewing the provided QuantStats report, several performance metrics stand out that merit attention:
| Metric | Strategy |
|---|---|
| Cumulative Return | 440.72% |
| Annualized Return (CAGR %) | 1.34% |
| Sharpe Ratio | 0.363 |
| Profit Factor | 1.195 |
| Maximum Drawdown | 39.41% |
| Volatility (Annualized) | 2.64% |
The strategy achieves a cumulative return of 440.72%, showcasing potential for substantial gains. However, the annualized return is relatively low at 1.34%, which might suggest periods of stagnant performance. The Sharpe Ratio of 0.363 is under the desired threshold of 0.5, indicating room for improvement in risk-adjusted returns. The maximum drawdown is just below the 40% threshold, which is acceptable but highlights the need for careful risk management.
Strategy Viability
Based on the data provided, the strategy displays moderate viability for real-world trading. While it does offer a cumulative return that could be enticing, the lower Sharpe Ratio and high drawdown suggest caution. The strategy's performance appears to rely heavily on specific market conditions, which may not always be present. Continued monitoring of market environments and adjusting tactics accordingly will be crucial for ongoing success.
Risk Management
The strategy reveals an average risk management approach with a maximum drawdown of 39.41%, closely bordering acceptable limits. No margin calls indicate healthy leverage use, yet improvements can be made to reduce potential risks further. Suggested enhancements include:
- Reducing leverage to lower drawdowns and maintain position sizes more manageable, thus decreasing the volatility impact.
- Implementing stronger diversification strategies to spread risk across different assets or sectors.
- Creating dynamic position sizing rules tailored to market volatility levels.
Improvement Suggestions
To enhance the strategy’s performance and robustness, consider the following recommendations:
- Optimize trading parameters with a focus on improving the Sharpe Ratio and ensuring consistent returns.
- Incorporate additional technical indicators or signals to refine entry and exit points.
- Conduct comprehensive backtesting across various market scenarios to test the strategy's effectiveness.
- Introduce a more comprehensive risk management structure, possibly integrating advanced tools like Value-at-Risk calculations.
Final Opinion
In summary, the strategy reveals strong potential but requires some modifications and careful risk assessment to be considered for broader adoption. Its performance metrics underscore the need for fine-tuning, particularly in risk management and return optimization.
Recommendation: Proceed with further testing and strategic improvements to bolster risk-adjusted returns. Keeping a close eye on market conditions and adopting flexible adjustments will be vital for the strategy’s success.
AI Quant, can you analyze this strategy?
