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/bollinger_upper.c | 60 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)
 create mode 100644 lib/bollinger_upper.c

(limited to 'lib/bollinger_upper.c')

diff --git a/lib/bollinger_upper.c b/lib/bollinger_upper.c
new file mode 100644
index 0000000..9294cfb
--- /dev/null
+++ b/lib/bollinger_upper.c
@@ -0,0 +1,60 @@
+// Bollinger Upper Band
+//
+// bollinger_upper - BB upper
+//
+// Upper band: SMA + mult * stddev
+//
+// Inputs:    1
+// Outputs:   1 (Upper Band)
+// Constants: 2 (period, std multiplier)
+
+#include <math.h>
+#include "ts.h"
+
+
+typedef struct {
+    uint_t k;
+    fp_t std_mult;
+} ts_bollinger_upper_params_t;
+
+ts_parameter_t const ts_bollinger_upper_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 2, .params_size = sizeof(ts_bollinger_upper_params_t) };
+
+
+uint_t ts_bollinger_upper_offset( ts_constant_t const *c )
+{
+	( void )c;
+	return ( c ? ((ts_bollinger_upper_params_t *)c)->k - 1 : 0 );
+}
+
+int ts_bollinger_upper(
+	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_bollinger_upper_p );
+	if ( !c ) return -1;
+	uint_t k = ((ts_bollinger_upper_params_t *)c)->k;
+	if (k == 0 || n < k) return -1;
+	fp_t std_mult = ((ts_bollinger_upper_params_t *)c)->std_mult;
+	if (isnan(std_mult) || isinf(std_mult)) return -1;
+
+	for( uint_t i = k - 1; i < n; ++i )
+	{
+		fp_t sum = 0, sum2 = 0;
+		for( uint_t j = 0; j < k; ++j )
+		{
+			fp_t v = x[0][i - k + 1 + j];
+			sum += v;
+			sum2 += v * v;
+		}
+		fp_t mean = sum / k;
+		fp_t variance = sum2 / k - mean * mean;
+		fp_t stddev = variance >= 0 ? sqrt( variance ) : 0;
+		y[0][i] = mean + std_mult * stddev;
+	}
+	return 0;
+}
+
+
+
-- 
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