AIZEN-I

Artificial Intelligence Trading Model Version: v1.0 - Prediction horizon: 72h

Dataset Overview

This dataset encompasses predictions generated by the AIZEN_crypto model in production from November 15, 2023, to May 29, 2024. These predictions were utilized to validate the model's performance.

Technical Data Report on AIZEN AI Technology

Performance Achievement

The AIZEN AI model boasts a 69% win rate in predicting over 68 selected tokens in the cryptocurrency market, a significant accomplishment. This report outlines the technical elements contributing to this success:

Wide Scope

• Diverse Asset Management: The AI model's ability to manage predictions across 68 different tokens implies extensive training and optimization for a diverse range of assets, each with unique behaviors and volatility. This complexity enhances the significance of the high win rate.

• Robust Returns: Exceptional returns were observed for both Long trading only and Long & Short trading strategies.

  • Cumulative Returns (Annualized in brackets):

    • Long Trades: 107.15% (298.73%)

    • Short Trades: 38.42% (147.09%)

    • Both Long and Short Trades: 145.56% (404.32%)

Automation and Efficiency

• Autonomous Operation: Full automation allows the AI to operate without human intervention, making real-time decisions based on data analytics. The 69% win rate in these conditions highlights the model's robustness and reliability in dynamic market scenarios.

Risk Management

• Excellent Risk-Adjusted Returns: High Sharpe Ratios indicate favorable risk-adjusted returns for all trading strategies. Ratios above 1 are considered good, while our strategies score higher than 3, indicating exceptional performance.

  • Sharpe Ratios:

    • Long Trades: 3.41

    • Short Trades: 6.73

    • Both Long and Short Trades: 3.57

• Advanced Techniques: The model includes mechanisms like stop-loss orders to mitigate potential losses and allows strategy adjustments to fit individual risk appetites.

Market Volatility

• Adaptive Performance: The model maintains a high win rate across various tokens, demonstrating its effectiveness in navigating different market conditions.

• Modest Drawdowns: Backtesting data, covering two significant “pump n dump” events, showed modest drawdowns even without risk management tools like stop-losses or portfolio constraints.

  • Maximum Drawdowns (Portfolio):

    • Long Trades: -51.80%

    • Short Trades: -35.07%

    • Both Long and Short Trades: -44.58%

Scalability

• Efficient Data Handling: The model's success in predicting price movements of 68 tokens demonstrates its scalability and efficiency in processing large datasets and performing complex computations.

Profit Potential

• Optimized Risk-Reward Ratios: A 69% win rate significantly enhances profitability potential, assuming the model maintains favorable risk-reward ratios.

Benchmarking

• High Performance Standards: A win rate above 60% is excellent in traditional financial markets. Thus, a 69% win rate in the volatile crypto market sets a high benchmark for performance.

• Exceptional Alpha: Consistent overperformance over the BTCUSDT benchmark is demonstrated by Alpha:

  • Long Trades: 41.63%

  • Short Trades: -110.01%

  • Both Long and Short Trades: 147.22%

sMAPE: Evaluating Predictive Accuracy

• Overall sMAPE: 9.5%

• Accuracy Metric: The Symmetric Mean Absolute Percentage Error (sMAPE) measures the accuracy of the model's predictions, with lower values indicating higher accuracy. An sMAPE of 9.5% signifies highly accurate predictions across the dataset.

• Performance Interpretation: On average, the model predicts price movements with 90.5% accuracy over a 72-hour period.

Differentiating Win Rate and sMAPE

• Win Rate:

  • Definition: Measures the proportion of successful trades (those yielding a profit) out of the total trades executed.

  • Significance: A high win rate indicates the model's effectiveness in executing profitable trades and showcases its decision-making capabilities and risk management.

• sMAPE:

  • Definition: Quantifies the accuracy of the model's price predictions, with lower values indicating more precise forecasts.

  • Significance: A low sMAPE value demonstrates the model's proficiency in predicting price movements accurately, which is critical for making informed trading decisions.

Complementary Insights

• Holistic Performance Evaluation: A high win rate combined with a low sMAPE provides a comprehensive view of the model's performance. The win rate indicates trading success, while the sMAPE measures predictive accuracy.

• Balanced Metrics: While a high win rate shows frequent execution of profitable trades, the sMAPE highlights the precision of price forecasts, ensuring decisions are based on accurate predictions.

Conclusion

The AIZEN AI model's 69% win rate across 68 selected tokens underscores its sophisticated capabilities in managing complex and volatile market conditions. Coupled with effective risk management and scalability, this performance signifies strong predictive accuracy and substantial profitability potential.

The low sMAPE value of 9.5% further emphasizes the model's precision in predicting price movements, reinforcing its robustness and reliability as a fully automated trading system.

The complementary nature of the win rate and sMAPE metrics provides a holistic evaluation of the model's effectiveness and accuracy in the cryptocurrency market.

Note: The model was also trained on PERL, but this token has since been delisted and behaved abnormally during the testing period, leading to poor statistical performance.