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//use crate::common::validate_inputs;
use crate::indicators::rocr::{
min_data, output_length, IndicatorState, INPUTS_WIDTH, OPTIONS_WIDTH,
};
use crate::indicators::simd_indicators::road_train::{Asset, Driver, PrimeMover};
use crate::types::IndicatorError;
use crate::{common::validate_options, common_simd::assets::validate_inputs};
use std::simd::Simd;
//use crate::indicators::ad::output_length;
use crate::indicators::simd_indicators::rocr_simd::calc_simd;
/// SIMD driver that advances the Rate of Change Ratio (ROCR) across `N` asset lanes per scheduling epoch.
struct RocrDriver {
period: usize,
}
impl Driver<bool> for RocrDriver {
/// Processes one epoch of bars for `N` assets simultaneously using SIMD.
fn next_run<const N: usize>(
&mut self,
inputs: Vec<Vec<&[f64]>>,
mut outputs: Vec<Vec<&mut [f64]>>,
mut _states: Vec<&mut bool>,
_options: Vec<Option<&()>>,
) {
let len = inputs[0][0].len();
// Optimization 2: Pre-compute all input and output pointers
let output_ptrs = crate::extract_output_ptrs!(outputs, N, output_ptr);
// Optimization 2: Pre-compute all input and output pointers
let input_ptrs = crate::extract_input_ptrs!(inputs, N, real_ptrs);
// Optimization 3: Simplified main loop with pre-computed offsets
for (j, i) in (self.period..len).enumerate() {
let (old_vals, new_vals) = crate::extract_simd_at_indices!(N, input_ptrs,
old_vals @ j,
new_vals @ i
);
let rocr = calc_simd(new_vals, old_vals);
// Store results using pre-computed pointers
crate::write_simd_at_indices!(N, j,
output_ptrs => rocr
);
}
}
}
/// Calculates the Rate of Change Ratio (ROCR) for `N` assets simultaneously using SIMD
/// parallelism.
///
/// ROCR measures the ratio of the current price to the price `period` bars ago.
/// It produces no optional outputs. Uses the [`PrimeMover`] scheduler to batch
/// assets into SIMD-width groups.
///
/// # Arguments
/// * `inputs` - An array of `N` asset input sets; `inputs[i]` is `[&[f64]; INPUTS_WIDTH]`
/// containing `[real]` for asset `i`.
/// * `options` - `[period]` — the look-back period for the ratio calculation.
/// * `_optional_outputs` - Unused; ROCR produces no optional outputs.
///
/// # Returns
/// `Ok((outputs, states))` where `outputs[i][0]` is the ROCR line for asset `i`
/// and `states[i]` is the final [`IndicatorState`] for asset `i`.
/// Returns `Err(IndicatorError)` if any input slice is too short or options are invalid.
pub fn indicator_by_assets<const N: usize>(
inputs: &[&[&[f64]; INPUTS_WIDTH]; N], //stock[ fields [ field [f64] ] ]
options: &[f64; OPTIONS_WIDTH],
_optional_outputs: Option<&[bool]>,
) -> Result<(Vec<Vec<Vec<f64>>>, Vec<IndicatorState>), IndicatorError> {
validate_inputs::<INPUTS_WIDTH>(inputs, min_data(options))?;
validate_options(options)?;
let period = options[0] as usize;
let mut road_train = PrimeMover::<N, bool>::new();
let mut output_buffers: Vec<Vec<Vec<f64>>> = (0..N)
.map(|i| {
vec![{
let capacity = output_length(inputs[i][0].len(), options);
crate::uninit_vec!(f64, capacity)
}]
})
.collect();
for i in 0..N {
let asset_inputs = vec![inputs[i][0]];
unsafe {
// Get a mutable reference to the output buffer for this asset
let output_buffer = &mut output_buffers[i][0];
let asset_outputs = vec![std::slice::from_raw_parts_mut(
output_buffer.as_mut_ptr(),
output_buffer.len(),
)];
road_train.add_asset(Asset::new(
asset_inputs,
asset_outputs,
i,
period,
period,
false,
None,
));
}
}
let mut driver = RocrDriver { period };
road_train.drive(&mut driver);
let mut states = Vec::with_capacity(N);
for i in 0..N {
states.push(IndicatorState::new(
unsafe { inputs.get_unchecked(i).get_unchecked(0) },
period,
));
}
Ok((output_buffers, states))
}
/*pub fn indicator_by_assets_from_state<const N: usize>(
inputs: &[ &[ &[f64]; INPUTS_WIDTH]; N],
states: &mut [IndicatorState; N],
_optional_outputs: Option<&[bool]>,
) -> Result<[Vec<Vec<f64>>; N], IndicatorError>
{
let len = inputs[0][0].len();
// Validate all inputs have same length
for i in 0..N {
if inputs[i][0].len() != len {
return Err(IndicatorError::InvalidInputs);
}
}
// Extract EMAs and multipliers from states
let mut emas = Simd::from_array(std::array::from_fn(|i| states[i].get_ema()));
let multipliers = states[0].get_multipliers();
let multipliers_simd = (Simd::splat(multipliers.0), Simd::splat(multipliers.1));
// Create output arrays and process directly
let mut ema_lines: [Vec<Vec<f64>>; N] = std::array::from_fn(|_| {
vec![crate::uninit_vec!(f64, len)]
});
for i in 0..len {
//let values: [f64; N] = (0..N).map(|j| inputs[j][0][i]).collect::<Vec<_>>().try_into().unwrap();
let values: [f64; N] = std::array::from_fn(|j| inputs[j][0][i]);
let vals = Simd::from_array(values);
emas = calc_simd(vals, emas, multipliers_simd);
let outputs = emas.to_array();
for j in 0..N {
unsafe { *ema_lines[j].get_unchecked_mut(0).get_unchecked_mut(i) = outputs[j] }
}
}
// Update states with final EMA values
let final_emas = emas.to_array();
for i in 0..N {
states[i].set_ema(final_emas[i]);
}
Ok(ema_lines)
}*/