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/random_walk_index.c | 64 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 lib/random_walk_index.c

(limited to 'lib/random_walk_index.c')

diff --git a/lib/random_walk_index.c b/lib/random_walk_index.c
new file mode 100644
index 0000000..b4375e5
--- /dev/null
+++ b/lib/random_walk_index.c
@@ -0,0 +1,64 @@
+// Random Walk Index
+//
+// random_walk_index
+//
+// Random Walk Index
+//
+// 
+//
+// Inputs:    1
+// Outputs:   1
+// Constants: 0
+
+
+#include <math.h>
+#include "ts.h"
+
+typedef struct {
+    uint_t k;
+} ts_random_walk_index_params_t;
+
+ts_parameter_t const ts_random_walk_index_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 1, .params_size = sizeof(ts_random_walk_index_params_t) };
+
+uint_t ts_random_walk_index_offset( ts_constant_t const *c )
+{
+	( void )c;
+	return ( c ? ((ts_random_walk_index_params_t *)c)->k : 0 );
+}
+
+int ts_random_walk_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_random_walk_index_p );
+	if ( !c ) return -1;
+	uint_t k = ((ts_random_walk_index_params_t *)c)->k;
+	if (k == 0 || n < k + 1) return -1;
+
+	fp_t sum_abs = 0;
+	fp_t sum_net = 0;
+
+	for ( uint_t i = 0; i < k; ++i )
+	{
+		fp_t change = x[0][i + 1] - x[0][i];
+		sum_abs += fabs( change );
+		sum_net += change;
+	}
+
+	y[0][k] = sum_net == 0 ? 0 : sum_abs / sum_net;
+
+	for ( uint_t i = k + 1; i < n; ++i )
+	{
+		fp_t change_new = x[0][i] - x[0][i - 1];
+		fp_t change_old = x[0][i - k] - x[0][i - k - 1];
+
+		sum_abs = sum_abs - fabs( change_old ) + fabs( change_new );
+		sum_net = sum_net - change_old + change_new;
+
+		y[0][i] = sum_net == 0 ? 0 : sum_abs / sum_net;
+	}
+	return 0;
+}
+
-- 
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