initial commit: ts time series analysis library v0.1.0

- 354 financial and mathematical time-series indicators
- Uniform C11 API with consistent function signatures
- 349 passing tests with -Werror
- Static and shared library builds
- pkg-config integration
- Python bindings
- Fuzzing and sanitizer support
- Security hardening flags

1 files changed, 67 insertions, 0 deletions

diff --git a/lib/chande_forecast_oscillator.c b/lib/chande_forecast_oscillator.c
new file mode 100644
index 0000000..6e65bbe
--- /dev/null
+++ b/lib/chande_forecast_oscillator.c
@@ -0,0 +1,67 @@
+// 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;
+}
+
+
+