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, 61 insertions, 0 deletions

diff --git a/lib/annualized_historical_volatility.c b/lib/annualized_historical_volatility.c
new file mode 100644
index 0000000..c0c88cf
--- /dev/null
+++ b/lib/annualized_historical_volatility.c
@@ -0,0 +1,61 @@
+// Annualized Historical Volatility
+//
+// annualized_historical_volatility - ahv
+//
+// y[t] = stddev(log(x[t-k..t])) * sqrt(252)
+//
+// X_n - 1
+// Y_n - 1
+// C_n - 1
+
+#include <math.h>
+#include "ts.h"
+
+typedef struct {
+    uint_t k;
+} ts_annualized_historical_volatility_params_t;
+
+ts_parameter_t const ts_annualized_historical_volatility_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 1, .params_size = sizeof(ts_annualized_historical_volatility_params_t) };
+
+uint_t ts_annualized_historical_volatility_offset( ts_constant_t const *c )
+{
+	( void )c;
+	return ( c ? ((ts_annualized_historical_volatility_params_t *)c)->k : 0 );
+}
+
+int ts_annualized_historical_volatility(
+	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_annualized_historical_volatility_p );
+	if ( !c ) return -1;
+    uint_t k = ((ts_annualized_historical_volatility_params_t *)c)->k;
+    if (k == 0 || n <= k) return -1;
+
+    /* Compute log returns, then stddev, then annualize */
+    for (uint_t i = k; i < n; ++i)
+    {
+        fp_t sum = 0;
+        fp_t sum2 = 0;
+        uint_t count = 0;
+        for (uint_t j = i - k + 1; j <= i; ++j)
+        {
+            if (x[0][j - 1] <= 0 || x[0][j] <= 0) continue;
+            fp_t ret = log(x[0][j] / x[0][j - 1]);
+            sum += ret;
+            sum2 += ret * ret;
+            count++;
+        }
+        if (count == 0) { y[0][i] = 0; continue; }
+        fp_t mean = sum / count;
+        fp_t variance = sum2 / count - mean * mean;
+        if (variance < 0) variance = 0;
+        y[0][i] = sqrt(variance) * sqrt(252.0);
+    }
+	return 0;
+}
+
+
+