Minara strategy's returns skyrocketed from 53% to 3,668%!

In the field of quantitative trading, a strategy's initial performance is often just the starting point, and true alpha usually comes from systematic parameter optimization and multi-dimensional backtesting. Recently, I used the @minara platform's AI scoring adaptive super trend BTC (AI-Scored Adaptive SuperTrend BTC) strategy to conduct a complete practical test on BTCUSDT 4-hour chart, from basic backtesting to AI-driven optimization, achieving an exponential increase in returns!
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Strategy construction process
1. Initial strategy generation
I input the prompt to Minara AI:
"Create a quantitative strategy for BTCUSDT 4h based on 'AI-Scored Adaptive SuperTrend BTC' strategy."
The platform quickly generated the complete code and automatically loaded the core logic:
• SuperTrend indicator (ATR Length=14, Base Multiplier=2.5, more sensitive version)
• Multi-factor AI scoring engine (default minimum signal score threshold of 65 points)
• Stop-loss mode: SuperTrend reversal stop-loss
• Take-profit mode: Risk-reward ratio RR=2.5
• Leverage: 10x (default template value)
2. First backtest results (baseline)
Time range: 2022-04-29 to 2026-04-28 (4 years of data)
• Total return: +53.93%
• Maximum drawdown: 26.85%
• Win rate: 43.4%
• Profit/loss ratio: 1.23
• Number of trades: 143
• Sharpe ratio: 0.56
The performance is average, but still far from institutional-level results.
3. AI optimization phase
After completing the backtest, I directly launched the platform's built-in optimization engine (6 iterations). Minara AI automatically searches through thousands of combinations of SuperTrend parameters, minimum signal scores, position management, take-profit and stop-loss logic, etc., and conducts comprehensive evaluations based on historical data.
The best version emerged!
Other excellent variants also reached +3,617.41% (baseline optimized version), fully validating the efficiency of AI parameter search.
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Why can optimization achieve exponential improvements?
Traditional quantitative methods require traders to manually tune parameters, repeatedly backtest, which often takes weeks. Minara Strategy Studio's AI optimization engine is essentially automated hyperparameter search + multi-objective evaluation (returns, Sharpe ratio, drawdown, win rate, etc.), completing an exhaustive search that is difficult for humans to match within minutes. This is the key for beginners to quickly access professional-level strategies—no programming skills needed, no finance PhD required, just a clear strategy logic, and AI can handle the industrial-grade refinement from 0 to 1.
As a beginner myself: just keep optimizing in mind.
Experience the link 👇