Overlap studies
Triple Exponential Moving Average
Low-lag moving average combining three exponential-smoothing stages.
- Engine
- TA-Lib
- Function
TEMA- Input series
- close
- Outputs
- 1
How it works
TEMA combines the first, second, and third sequential EMAs as 3*EMA1 - 3*EMA2 + EMA3 to reduce lag while preserving a single moving-average output.
Use case
Use TEMA as a responsive trend feature for smoothing, crossovers, and earlier directional-change detection than conventional moving averages.
Parameters
| Parameter | Type | Default | Bounds or options | Description |
|---|---|---|---|---|
| Time Period | integer | 30 | 1…100000 | Period used by each sequential EMA stage in the TEMA calculation. |
Outputs
| Output | Type | Description |
|---|---|---|
| TEMA | number | Triple Exponential Moving Average value for the bar. |
Reading the result
TEMA combines multiple exponential smoothing stages to reduce lag relative to a conventional EMA while preserving a smooth price-level output. It is not simply an EMA run three times; the combined expression compensates for part of the smoothing delay.
The additional state and longer initialization history can make early comparisons sensitive to dataset boundaries.
Common mistakes
- Assuming the output is always smoother than an EMA of the same period.
- Expecting reduced lag without increased sensitivity to recent movement.
- Comparing short datasets before the recursive history stabilizes.
- Confusing TEMA with the separately named T3 moving-average type.
DataCat workflow notes
When evaluating TEMA against EMA or DEMA, compare reaction, noise, and downstream value on the same final provider rows. Do not select it only because it appears more advanced.