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// 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 const *)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 const *)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;
}
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