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//use crate::common::validate_inputs;
use crate::common::validate_options;
use crate::common_simd::assets::validate_inputs;
use crate::indicators::dm::{
min_data, multiplier, output_length, IndicatorState, State, INPUTS_WIDTH, OPTIONS_WIDTH,
};
use crate::indicators::simd_indicators::dm_simd::{calc_simd, SimdState};
use crate::indicators::simd_indicators::road_train::{Asset, Driver, PrimeMover};
use crate::types::IndicatorError;
use std::simd::Simd;
/// SIMD driver that advances the Directional Movement (DM) across `N` asset lanes per
/// scheduling epoch.
struct DmDriver {
/// Pre-computed Wilder smoothing multiplier for the given period.
multiplier: f64,
}
impl Driver<State> for DmDriver {
/// Processes one epoch of bars for `N` assets simultaneously using SIMD.
///
/// Reads from `inputs[asset][field]` (high, low), writes to `outputs[asset][output]`,
/// and updates `states[asset]` in place.
fn next_run<const N: usize>(
&mut self,
inputs: Vec<Vec<&[f64]>>,
mut outputs: Vec<Vec<&mut [f64]>>,
mut states: Vec<&mut State>,
_options: Vec<Option<&()>>,
) {
let mut state = SimdState::<N>::new(&states);
let len = inputs[0][0].len();
let multipliers = Simd::splat(self.multiplier);
//collect outputs
let (plus_dm_line_ptr, minus_dm_line_ptr) =
crate::extract_output_ptrs!(outputs, N, plus_dm_line_ptr, minus_dm_line_ptr);
// Optimization 2: Pre-compute all input and output pointers
let (high_ptrs, low_ptrs) = crate::extract_input_ptrs!(inputs, N, high_ptrs, low_ptrs);
// Optimization 3: Simplified main loop with pre-computed offsets
for i in 0..len {
// Get inputs arrays for stocks
let (high, low) = crate::extract_simd_inputs_at_index!(
i,
N,
high @ high_ptrs,
low @ low_ptrs
);
let (plus_dm, minus_dm) = calc_simd(&mut state, high, low, multipliers);
// Store results using pre-computed pointers
crate::write_simd_at_indices!(N, i,
plus_dm_line_ptr => plus_dm,
minus_dm_line_ptr => minus_dm
);
}
// Update states efficiently
state.write_states(&mut states);
}
}
/// Calculates the Directional Movement (DM) for `N` assets simultaneously using SIMD
/// parallelism.
///
/// DM computes the smoothed Plus and Minus Directional Movement (+DM and -DM) over a rolling
/// period. All assets share the same `options`. 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 `[high, low]` for asset `i`.
/// * `options` - Shared options applied to all `N` assets: `[period]`.
/// * `_optional_outputs` - Unused; DM has no optional output lines.
///
/// # Returns
/// `Ok((outputs, states))` where `outputs[i]` contains `[plus_dm, minus_dm]`
/// for asset `i` and `states[i]` is the final [`IndicatorState`] for asset `i`.
/// Returns `Err(IndicatorError)` if any input 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 multiplier = multiplier(period);
let mut road_train = PrimeMover::<N, State>::new();
let mut output_buffers = Vec::with_capacity(N);
for i in 0..N {
let asset_inputs = vec![
inputs[i][0], // high
inputs[i][1], // low
];
let (plus_dm_line, minus_dm_line) = {
let capacity: usize = output_length(inputs[i][0].len(), options);
(
crate::uninit_vec!(f64, capacity),
crate::uninit_vec!(f64, capacity),
)
};
let state = State::init_state(inputs[i][0], inputs[i][1], period);
let mut output_buffer = vec![plus_dm_line, minus_dm_line];
//let adosc_len = output_buffer[0].len();
let mut asset_outputs = Vec::with_capacity(output_buffer.len());
for j in 0..output_buffer.len() {
unsafe {
//let slice_len = output_buffer.len() - starts[j];
// Get a mutable reference to the output buffer for this asset
let output_buffer = &mut output_buffer[j];
asset_outputs.push(std::slice::from_raw_parts_mut(
output_buffer.as_mut_ptr(), //slice from
output_buffer.len(), // slice to
));
}
}
road_train.add_asset(Asset::new(
asset_inputs,
asset_outputs,
i,
period,
0,
state,
None,
));
output_buffers.push(output_buffer);
}
let mut driver = DmDriver { multiplier };
let states_vec = road_train.drive(&mut driver);
let mut states = Vec::with_capacity(N);
for state in states_vec.into_iter() {
states.push(IndicatorState::new(state, multiplier));
}
Ok((output_buffers, states))
}