Ehlers Instantaneous Trend (iTrend)
alpha = 0.07 (0.01–0.2) Overview
The Ehlers Instantaneous Trend (iTrend) is a zero-lag trend indicator developed by John Ehlers, an electrical engineer who pioneered the application of digital signal processing (DSP) to financial markets. Unlike traditional moving averages, iTrend is designed to recognize that markets exist in two modes - trending and cycling - and attempts to extract the trend component by removing the dominant cycle. This innovative approach creates a smooth trendline that adapts quickly to changing market conditions while minimizing the lag that plagues conventional moving averages.
The indicator employs a sophisticated calculation using a five-bar FIR (Finite Impulse Response) filter with specific coefficients derived from DSP theory. The core formula is: iTrend = (α - α²/4) × Price + 0.5 × α² × Price[1] - (α - 0.75 × α²) × Price[2] + 2 × (1 - α) × iTrend[1] - (1 - α)² × iTrend[2], where α (alpha) is the smoothing factor, typically set to 0.07. Ehlers recommends using a 15-period setting for the dominant cycle when trading daily E-mini futures, though this can be adjusted based on the market and timeframe being analyzed.
Interpretation & Trading Signals
Primary Trading Signals:
- Price Above iTrend: Uptrend confirmed, look for buying opportunities
- Price Below iTrend: Downtrend confirmed, consider selling opportunities
- iTrend Slope: Steeper slopes indicate stronger trends, flatter slopes suggest weakening
- Crossover System: Long when iTrend crosses above lagged version, short on cross below
Alternative Strategies:
- Mean Reversion: Backtests show better results using opposite signals for mean reversion
- Multiple Timeframes: Compare iTrend across different periods for confirmation
- Cycle Mode Filter: Avoid signals when market is in strong cycle mode
- Momentum Confirmation: Combine with other Ehlers indicators like MAMA or Fisher Transform
Practical Considerations:
- Mixed Results: Original trend-following approach shows poor backtesting performance
- False Signals: Prone to whipsaws in choppy or low-volatility markets
- Parameter Sensitivity: Alpha value requires optimization for different markets
- DSP Knowledge: Understanding signal processing concepts helps proper application
Example Usage
Code examples will be available once the Rust implementation is complete.
Performance Analysis
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