From 07e18043145f3d91c8ea7ec3ad41b3c905585277 Mon Sep 17 00:00:00 2001
From: Ethan Werner <mail@ethanwerner.com>
Date: Wed, 1 Jul 2026 09:42:39 +0400
Subject: 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
---
 lib/band_width.c | 60 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)
 create mode 100644 lib/band_width.c

(limited to 'lib/band_width.c')

diff --git a/lib/band_width.c b/lib/band_width.c
new file mode 100644
index 0000000..822d835
--- /dev/null
+++ b/lib/band_width.c
@@ -0,0 +1,60 @@
+// Band Width
+//
+// band_width - BW
+//
+// Normalized width of Bollinger Bands: (Upper - Lower) / SMA * 100
+//
+// Inputs:    1
+// Outputs:   1 (Width %)
+// Constants: 2 (period, std multiplier)
+
+#include <math.h>
+#include "ts.h"
+
+typedef struct {
+    uint_t k;
+    fp_t std_mult;
+} ts_band_width_params_t;
+
+ts_parameter_t const ts_band_width_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 2, .params_size = sizeof(ts_band_width_params_t) };
+
+uint_t ts_band_width_offset( ts_constant_t const *c )
+{
+	( void )c;
+	return ( c ? ((ts_band_width_params_t *)c)->k - 1 : 0 );
+}
+
+int ts_band_width(
+	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_band_width_p );
+	if ( !c ) return -1;
+    uint_t k = ((ts_band_width_params_t *)c)->k;
+    fp_t std_mult = ((ts_band_width_params_t *)c)->std_mult;
+    if (k == 0 || n < k) return -1;
+
+    for (uint_t i = k - 1; i < n; ++i)
+    {
+        fp_t sum = 0;
+        fp_t sum2 = 0;
+        for (uint_t j = i - k + 1; j <= i; ++j)
+        {
+            sum += x[0][j];
+            sum2 += x[0][j] * x[0][j];
+        }
+        fp_t mean = sum / k;
+        fp_t variance = sum2 / k - mean * mean;
+        if (variance < 0) variance = 0;
+        fp_t stddev = sqrt(variance);
+        fp_t upper = mean + std_mult * stddev;
+        fp_t lower = mean - std_mult * stddev;
+        y[0][i] = mean != 0 ? (upper - lower) / mean * 100 : 0;
+    }
+	return 0;
+}
+
+
+
-- 
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