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| author | Ethan Werner <mail@ethanwerner.com> | 2026-07-01 09:42:39 +0400 |
|---|---|---|
| committer | Ethan Werner <mail@ethanwerner.com> | 2026-07-01 09:42:39 +0400 |
| commit | 07e18043145f3d91c8ea7ec3ad41b3c905585277 (patch) | |
| tree | 2bdeae46973f4bdfce263679a4008ac768011f28 /lib/band_width.c | |
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
Diffstat (limited to 'lib/band_width.c')
| -rw-r--r-- | lib/band_width.c | 60 |
1 files changed, 60 insertions, 0 deletions
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; +} + + + |