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, 68 insertions, 0 deletions

diff --git a/lib/choppiness_index.c b/lib/choppiness_index.c
new file mode 100644
index 0000000..55ecb46
--- /dev/null
+++ b/lib/choppiness_index.c
@@ -0,0 +1,68 @@
+// Choppiness Index
+//
+// choppiness_index - CHOP
+//
+// 100 * log(SUM(H-L) / Range) / log(k), measures trend vs chop
+//
+// Inputs:    3 (HLC)
+// Outputs:   1 (CHOP)
+// Constants: 0
+
+#include <math.h>
+#include "ts.h"
+
+typedef struct {
+    uint_t k;
+} ts_choppiness_index_params_t;
+
+ts_parameter_t const ts_choppiness_index_p = { .n_inputs = 3, .n_outputs = 1, .n_constants = 1, .params_size = sizeof(ts_choppiness_index_params_t) };
+
+uint_t ts_choppiness_index_offset( ts_constant_t const *c )
+{
+	( void )c;
+	return ( c ? 2 * ((ts_choppiness_index_params_t *)c)->k : 0 );
+}
+
+int ts_choppiness_index(
+	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_choppiness_index_p );
+	if ( !c ) return -1;
+    uint_t k = ((ts_choppiness_index_params_t *)c)->k;
+    uint_t range_period = 2 * k;
+    if (k == 0 || n < range_period) return -1;
+
+    for ( uint_t i = range_period; i < n; ++i )
+    {
+        fp_t sum_range = 0;
+        for ( uint_t j = i - k + 1; j <= i; ++j )
+        {
+            sum_range += fabs( ts_h( x, j ) - ts_l( x, j ) );
+        }
+
+        fp_t hh = ts_h( x, i - range_period + 1 );
+        fp_t ll = ts_l( x, i - range_period + 1 );
+        for ( uint_t j = i - range_period + 1; j <= i; ++j )
+        {
+            if ( ts_h( x, j ) > hh ) hh = ts_h( x, j );
+            if ( ts_l( x, j ) < ll ) ll = ts_l( x, j );
+        }
+
+        fp_t range = hh - ll;
+        if ( range == 0 || sum_range == 0 || k <= 1 )
+        {
+            y[0][i] = 0;
+        }
+        else
+        {
+            y[0][i] = 100.0 * log( sum_range / range ) / log( ( fp_t )k );
+        }
+    }
+	return 0;
+}
+
+
+