Linear Regression Slope

Technical Analysis Indicator: linregslope

Fork me on GitHub

Function Prototype

/* Linear Regression Slope */
/* Type: indicator */
/* Input arrays: 1    Options: 1    Output arrays: 1 */
/* Inputs: real */
/* Options: period */
/* Outputs: linregslope */
int ti_linregslope_start(TI_REAL const *options);
int ti_linregslope(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 Linear Regression Slope is a momentum indicator. It fits a line over a moving window using simple linear regression, and returns the slope of that line.

It takes one parameter, the period n. Larger values for n will have a greater smoothing effect on the input data but will also create more lag.

It is calculated as:

$$\overline{x} = \frac{1}{n} \sum_{i=1}^{n} i$$
$$\overline{y}_{t} = \frac{1}{n} \sum_{i=0}^{n-1} in_{t-i}$$
$$linregslope_{t} = \frac {\sum_{i=1}^{n}(i-\overline{x})(in_{(t-n+i)}-\overline{y}_{t})} {\sum_{i=1}^{n}(i-\overline{x})^{2}}$$

See Also

References

Example Usage

Calling From C

/* Example usage of Linear Regression Slope */
/* Assuming that 'input' is a pre-loaded array of size 'in_size'. */
TI_REAL *inputs[] = {input};
TI_REAL options[] = {5}; /* period */
TI_REAL *outputs[1]; /* linregslope */

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

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

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

Calling From Lua (with Tulip Chart bindings)

-- Example usage of Linear Regression Slope
linregslope = ti.linregslope(input, 5)

Example Calculation

period = 5

dateinputlinregslope
2005-11-0181.59
2005-11-0281.06
2005-11-0382.87
2005-11-0483.00
2005-11-0783.610.60
2005-11-0883.150.49
2005-11-0982.840.01
2005-11-1083.990.12
2005-11-1184.550.27
2005-11-1484.360.41
2005-11-1585.530.58
2005-11-1686.540.61
2005-11-1786.890.69
2005-11-1887.770.82
2005-11-2187.290.48

Chart

 

Other Indicators

Previous indicator: Linear Regression Intercept

Next indicator: Vector Natural Log

Random indicator: Qstick