// Chande Forecast Oscillator
//
// chande_forecast_oscillator - CFO
//
// (Actual - Forecast) / Forecast * 100, using linear regression forecast
//
// Inputs:    1
// Outputs:   1 (Oscillator %)
// Constants: 1 (period)

#include "ts.h"

typedef struct {
    uint_t k;
} ts_chande_forecast_oscillator_params_t;

ts_parameter_t const ts_chande_forecast_oscillator_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 1, .params_size = sizeof(ts_chande_forecast_oscillator_params_t) };

uint_t ts_chande_forecast_oscillator_offset( ts_constant_t const *c )
{
	( void )c;
	return ( c ? ((ts_chande_forecast_oscillator_params_t *)c)->k - 1 : 0 );
}

int ts_chande_forecast_oscillator(
	fp_t * const *y, uint_t y_n,
	fp_t const * const *x, uint_t x_n,
	uint_t n, ts_constant_t const *c
)
{
	TS_PROLOGUE( ts_chande_forecast_oscillator_p );
	if ( !c ) return -1;
	uint_t k = ((ts_chande_forecast_oscillator_params_t *)c)->k;
	if (k == 0 || n < k) return -1;

	for( uint_t i = k - 1; i < n; ++i )
	{
		fp_t sum_x = 0, sum_y = 0, sum_xy = 0, sum_x2 = 0;
		for( uint_t j = 0; j < k; ++j )
		{
			fp_t xv = (fp_t)j;
			fp_t yv = x[0][i - k + 1 + j];
			sum_x += xv;
			sum_y += yv;
			sum_xy += xv * yv;
			sum_x2 += xv * xv;
		}
		fp_t denom = (fp_t)k * sum_x2 - sum_x * sum_x;
		fp_t forecast;
		if( denom == 0 )
			forecast = sum_y / (fp_t)k;
		else
		{
			fp_t slope = ( (fp_t)k * sum_xy - sum_x * sum_y ) / denom;
			fp_t intercept = ( sum_y - slope * sum_x ) / (fp_t)k;
			forecast = intercept + slope * (fp_t)k;
		}
		if( forecast != 0 )
			y[0][i] = ( x[0][i] - forecast ) / forecast * 100.0;
		else
			y[0][i] = 0;
	}
	return 0;
}