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, 62 insertions, 0 deletions

diff --git a/lib/historical_volatility.c b/lib/historical_volatility.c
new file mode 100644
index 0000000..cde0d91
--- /dev/null
+++ b/lib/historical_volatility.c
@@ -0,0 +1,62 @@
+// Historical Volatility
+//
+// historical_volatility
+//
+// Historical volatility of log returns
+//
+// stddev(log(C[t]/C[t-1])) over period
+//
+// Inputs:    1
+// Outputs:   1
+// Constants: 1
+
+
+#include <math.h>
+#include "ts.h"
+
+typedef struct {
+    uint_t k;
+} ts_historical_volatility_params_t;
+
+ts_parameter_t const ts_historical_volatility_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 1, .params_size = sizeof(ts_historical_volatility_params_t) };
+
+uint_t ts_historical_volatility_offset( ts_constant_t const *c )
+{
+	( void )c;
+	return ( c ? ((ts_historical_volatility_params_t *)c)->k > 0 ? ((ts_historical_volatility_params_t *)c)->k - 1 : 0 : 0 );
+}
+
+int ts_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_historical_volatility_p );
+	if ( !c ) return -1;
+    uint_t k = ((ts_historical_volatility_params_t *)c)->k;
+    if (k == 0 || n < k + 1) return -1;
+
+    for ( uint_t d = k - 1; d < n - 1; ++d )
+    {
+        fp_t sum = 0;
+        fp_t sum2 = 0;
+        for ( uint_t j = d - k + 1; j <= d; ++j )
+        {
+            fp_t lr = 0;
+            if ( x[0][j] > 0 && x[0][j + 1] > 0 )
+                lr = log( x[0][j + 1] / x[0][j] );
+            sum += lr;
+            sum2 += lr * lr;
+        }
+        fp_t mean = sum / ( fp_t )k;
+        fp_t variance = sum2 / ( fp_t )k - mean * mean;
+        y[0][d] = variance > 0 ? sqrt( variance ) * sqrt( 252.0 ) : 0;
+    }
+
+    y[0][n - 1] = 0;
+	return 0;
+}
+
+
+