Multi-Indicator Crypto Trading with Simulated Confidence Filtering

Summary

This crypto strategy combines a linear-regression approximation to KAMA with TEMA, MACD, RSI, and volume conditions. Long entries require the regression line to cross above TEMA, positive MACD alignment, RSI above its oversold threshold, elevated volume, and a confidence score above a threshold; short entries use corresponding crossover and momentum conditions. The score averages normalized MACD, RSI distance from 50, and volume relative to its average. ATR sets the stop distance, while a risk-reward multiple determines the take-profit level. The published example specifies BTC/USDT futures settings, but the document supplies no performance results.

Despite the AI label, the confidence filter is a hand-built arithmetic score, not a trained or adaptive machine-learning model. The strategy has many conditions and parameters, which may reduce trade frequency and create overfitting risk. Its indicators can lag, and actual slippage and commissions may differ from assumptions. The text recommends robustness checks across market conditions and discusses regime filters, trailing stops, and position sizing as possible refinements.

Key ideas

• The strategy combines TEMA and a linear-regression approximation to KAMA with MACD, RSI, volume, and ATR.
• Entries require a crossover plus momentum, volume, and confidence-score conditions.
• The confidence score is a simplified formula, not a trained machine-learning model.
• ATR sets stop distance, and a specified risk-reward multiple determines the target.
• Multiple parameters and lagging indicators create overfitting and missed-opportunity risks; no performance results are reported.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.