DEMA measures the smoothed trend of price action with significantly reduced lag compared to traditional moving averages. It matters because lag is the enemy of timely signals—traditional EMAs lag by roughly N/2 bars, making them slow to react to trend changes. DEMA's extrapolation formula (2×EMA₁ - EMA₂) mathematically projects the EMA forward by one lag unit, creating a "lead indicator" that anticipates rather than follows.
Introduced by Patrick Mulloy in the January 1994 issue of *Technical Analysis of Stocks & Commodities*, DEMA was designed to reduce the lag of trend-following indicators. Mulloy realized that smoothing always introduces lag, but by combining single and double smoothing, you could mathematically negate some of that delay.
The insight was elegant: if EMA1 lags price by $L$ bars, and EMA2 lags EMA1 by another $L$ bars, then the expression $2 \times \text{EMA1} - \text{EMA2}$ extrapolates forward by $L$, canceling the lag for linear trends. This principle later inspired TEMA (triple) and the broader family of lag-compensating filters.
## Architecture & Physics
DEMA is a composite indicator built from two EMAs in a cascade arrangement.
The "physics" relies on the fact that EMA2 lags EMA1 roughly as much as EMA1 lags the price. The coefficient 2 on EMA1 and -1 on EMA2 creates a unity-gain filter ($2 - 1 = 1$) that projects forward by one lag unit.
A: DEMA(N) responds roughly like EMA(N×0.7) but with more overshoot. The lag reduction makes it faster but noisier.
**Q: When should I use DEMA vs TEMA?**
A: DEMA for most cases—it's 80% of TEMA's lag reduction with 50% less computation. Use TEMA only if DEMA still lags too much.
**Q: Does DEMA work well in sideways markets?**
A: Poorly. The extrapolation amplifies noise, creating false signals. Combine with trend strength filters.
**Q: Can DEMA be used for any period?**
A: Yes, but very short periods (<5) amplify noise excessively. Very long periods (>50) lose the lag-reduction benefit.
**Q: How does bar correction work?**
A: When `isNew=false`, QuanTAlib rolls back both EMA states to pre-update values, then reapplies the correction. This ensures identical results regardless of update order.
1.**Overshoot on Reversals**: Because DEMA extrapolates using the EMA "velocity," it overshoots when price reverses direction. This is the fundamental tradeoff for reduced lag—the filter commits to trends and resists reversals.
2.**"Double" Misconception**: DEMA is *not* a double-smoothed average (EMA of EMA). That would increase lag. DEMA uses the double-smooth as a correction term to reduce lag.
3.**Warmup Period**: DEMA needs approximately $2N$ bars to converge fully, as EMA2 requires EMA1 to stabilize first. Use `IsHot` to detect convergence.
4.**Comparing Periods with EMA**: DEMA(20) is not equivalent to EMA(20) in responsiveness. Due to lag reduction, DEMA(20) behaves more like EMA(14-16) in terms of crossover timing.
5.**Signal Noise Amplification**: The extrapolation amplifies high-frequency components. In choppy markets, DEMA produces more whipsaws than EMA.
6.**Bar Correction**: Use `isNew=false` when correcting the current bar (same timestamp, revised price). State rollback ensures consistent results.
