# Double Exponential Moving Average

## Technical Analysis Indicator: dema ## Function Prototype

/* Double Exponential Moving Average */
/* Type: overlay */
/* Input arrays: 1    Options: 1    Output arrays: 1 */
/* Inputs: real */
/* Options: period */
/* Outputs: dema */
int ti_dema_start(TI_REAL const *options);
int ti_dema(int size,
TI_REAL const *const *inputs,
TI_REAL const *options,
TI_REAL *const *outputs);

## Description

This documentation is still a work in progress. It has omissions, and it probably has errors too. If you see any issues, or have any general feedback, please get in touch.

The Double Exponential Moving Average is similar to the Exponential Moving Average, but provides less lag.

It can be expressed in terms of the Exponential Moving Average as follows:

$$dema = 2 \cdot ema(in) - ema(ema(in))$$

TI implements a clever algorithm which allows dema to be calculated in one pass through the input data.

## Example Usage

### Calling From C

/* Example usage of Double Exponential Moving Average */
/* Assuming that 'input' is a pre-loaded array of size 'in_size'. */
TI_REAL *inputs[] = {input};
TI_REAL options[] = {5}; /* period */
TI_REAL *outputs; /* dema */

/* Determine how large the output size is for our options. */
const int out_size = in_size - ti_dema_start(options);

/* Allocate memory for output. */
outputs = malloc(sizeof(TI_REAL) * out_size); assert(outputs != 0); /* dema */

/* Run the actual calculation. */
const int ret = ti_dema(in_size, inputs, options, outputs);
assert(ret == TI_OKAY);


### Calling From Lua (with Tulip Chart bindings)

-- Example usage of Double Exponential Moving Average
dema = ti.dema(input, 5)


## Example Calculation

period = 5

dateinputdema
2005-11-0181.59
2005-11-0281.06
2005-11-0382.87
2005-11-0483.00
2005-11-0783.61
2005-11-0883.15
2005-11-0982.84
2005-11-1083.99
2005-11-1184.5584.16
2005-11-1484.3684.38
2005-11-1585.5385.13
2005-11-1686.5486.06
2005-11-1786.8986.73
2005-11-1887.7787.53
2005-11-2187.2987.65

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