5d7d4fa3c6
Phase E.4.A Task 4: extend load_snapshots_from_fxcache with `mbp10_dir: Option<&Path>`. When provided, the loader will peek MBP-10 by timestamp and populate SnapshotRow.bid_l[1..10]/ask_l[1..10] from real LOB depth — but the real-peek implementation lands in Task 5 follow-on. This commit: - introduces the parameter (callers pass None) - warns at runtime if mbp10_dir Some until T5 lands - enables downstream wiring of --use-real-depth + --mbp10-dir CLI flags in the smoke / backtest binaries T5 deferred: on ES futures the --real-spread experiment showed 76% of fxcache bars hit the 1-tick floor, so depth-from-MBP-10 likely won't move the needle for ES. Higher-leverage work (Mamba2 wiring) prioritised. T5 implementation reopens as a follow-on if E.4.A gates pass with synthesised depth. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
954 lines
39 KiB
Rust
954 lines
39 KiB
Rust
//! Phase E.3 Task 23 — Composition backtest with cost sweep.
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//!
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//! Trains the Phase E execution-policy DQN (linear Q + Phase 1d.3
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//! alpha-cache + stabilizers) on the first `--train-frac` of the
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//! fxcache, then evaluates the FROZEN policy (no SGD, ε=0 greedy) over
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//! the held-out remainder at multiple transaction costs. Compares
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//! per-cost annualized Sharpe vs the Phase 1d.4 "always-market-when-
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//! confident" baseline (+4.4 frictionless, -4.0 at half-tick).
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//!
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//! Goal per the plan: lift the half-tick Sharpe above 0 — i.e., let
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//! the execution-policy intelligence offset the cost the
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//! threshold-only baseline can't.
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//!
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//! ## What's swept and what's frozen
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//!
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//! Frozen across costs: trained Q-network weights, fill-model
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//! coefficients (from `alpha_fill_coeffs.json`), alpha-logit cache
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//! (from `alpha_logits_cache.bin`). One policy evaluated at multiple
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//! costs.
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//!
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//! Swept: only `ExecutionEnvConfig.cost_per_contract`. The env's
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//! mutable config is updated between cost levels (snapshots stay in
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//! place, cursor re-seeded each episode).
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//!
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//! ## Run
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//!
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//! ```bash
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//! cargo run -p ml --release --example alpha_compose_backtest -- \
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//! --fxcache-path /home/jgrusewski/Work/foxhunt/test_data/feature-cache/9297....fxcache \
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//! --alpha-cache config/ml/alpha_logits_cache.bin \
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//! --fill-coeffs config/ml/alpha_fill_coeffs.json
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//! ```
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use std::fs::File;
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use std::io::Write;
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use std::mem::MaybeUninit;
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use std::path::PathBuf;
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use anyhow::{Context, Result};
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use clap::Parser;
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use cudarc::driver::{CudaContext, DevicePtr, DevicePtrMut};
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use tracing::info;
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// ── Mapped-pinned helpers (mirror gpu_training_guard.rs MappedBuffer) ──
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struct MappedI32 {
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host_ptr: *mut i32,
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dev_ptr: cudarc::driver::sys::CUdeviceptr,
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}
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impl MappedI32 {
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unsafe fn new() -> Result<Self> {
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let flags = cudarc::driver::sys::CU_MEMHOSTALLOC_DEVICEMAP
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| cudarc::driver::sys::CU_MEMHOSTALLOC_PORTABLE;
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let bytes = std::mem::size_of::<i32>();
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let host_ptr = cudarc::driver::result::malloc_host(bytes, flags)
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.map_err(|e| anyhow::anyhow!("mapped i32 alloc: {e}"))?
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as *mut i32;
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std::ptr::write(host_ptr, 0);
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let mut dev_raw = MaybeUninit::uninit();
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cudarc::driver::sys::cuMemHostGetDevicePointer_v2(
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dev_raw.as_mut_ptr(),
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host_ptr as *mut std::ffi::c_void,
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0,
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)
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.result()
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.map_err(|e| anyhow::anyhow!("cuMemHostGetDevicePointer: {e}"))?;
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Ok(Self { host_ptr, dev_ptr: dev_raw.assume_init() })
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}
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fn dev_u64(&self) -> u64 { self.dev_ptr as u64 }
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fn read(&self) -> i32 {
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unsafe { std::ptr::read_volatile(self.host_ptr) }
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}
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}
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impl Drop for MappedI32 {
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fn drop(&mut self) {
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unsafe {
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let _ = cudarc::driver::result::free_host(self.host_ptr as *mut std::ffi::c_void);
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}
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}
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}
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struct MappedF32 {
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host_ptr: *mut f32,
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dev_ptr: cudarc::driver::sys::CUdeviceptr,
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len: usize,
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}
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impl MappedF32 {
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unsafe fn new(len: usize) -> Result<Self> {
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let flags = cudarc::driver::sys::CU_MEMHOSTALLOC_DEVICEMAP
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| cudarc::driver::sys::CU_MEMHOSTALLOC_PORTABLE;
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let bytes = len * std::mem::size_of::<f32>();
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let host_ptr = cudarc::driver::result::malloc_host(bytes, flags)
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.map_err(|e| anyhow::anyhow!("mapped f32 alloc({len}): {e}"))?
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as *mut f32;
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std::ptr::write_bytes(host_ptr, 0, len);
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let mut dev_raw = MaybeUninit::uninit();
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cudarc::driver::sys::cuMemHostGetDevicePointer_v2(
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dev_raw.as_mut_ptr(),
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host_ptr as *mut std::ffi::c_void,
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0,
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)
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.result()
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.map_err(|e| anyhow::anyhow!("cuMemHostGetDevicePointer: {e}"))?;
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Ok(Self { host_ptr, dev_ptr: dev_raw.assume_init(), len })
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}
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fn dev_u64(&self) -> u64 { self.dev_ptr as u64 }
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fn write(&self, data: &[f32]) {
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debug_assert_eq!(data.len(), self.len);
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unsafe { std::ptr::copy_nonoverlapping(data.as_ptr(), self.host_ptr, self.len); }
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}
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}
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impl Drop for MappedF32 {
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fn drop(&mut self) {
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unsafe {
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let _ = cudarc::driver::result::free_host(self.host_ptr as *mut std::ffi::c_void);
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}
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}
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}
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use ml::cuda_pipeline::alpha_isv_slots::{
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RANDOM_BASELINE_MEAN_INDEX, RANDOM_BASELINE_STD_INDEX,
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};
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use ml::env::action_space::N_ACTIONS;
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use ml::env::execution_env::{
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EpisodeState, ExecutionEnv, ExecutionEnvConfig, ReplayRng, SnapshotRow,
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};
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const STATE_DIM: usize = 10;
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const N_WEIGHTS: usize = N_ACTIONS * STATE_DIM;
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const N_BIASES: usize = N_ACTIONS;
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/// Action allow-list for the pruning experiment. Same shape as the smoke
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/// binary: Q-net is unchanged (9 outputs), only the selector restricts.
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const PRUNED_ACTIONS: [u8; 4] = [0, 1, 4, 7]; // Wait, BuyMarket, SellMarket, FlatMarket
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const FULL_ACTIONS: [u8; 9] = [0, 1, 2, 3, 4, 5, 6, 7, 8];
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#[derive(Debug, Parser)]
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#[command(
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name = "alpha_compose_backtest",
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about = "Phase E.3 Task 23 — composition backtest with cost sweep"
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)]
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struct Cli {
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#[arg(long)]
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fxcache_path: PathBuf,
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#[arg(long, default_value = "config/ml/alpha_fill_coeffs.json")]
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fill_coeffs: PathBuf,
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#[arg(long)]
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alpha_cache: PathBuf,
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/// Train segment fraction. First N% of snapshots used for DQN training,
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/// the rest for evaluation.
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#[arg(long, default_value_t = 0.8)]
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train_frac: f32,
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/// Snapshots to load from the fxcache.
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#[arg(long, default_value_t = 1_500_000)]
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max_snapshots: usize,
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/// Episode horizon in snapshots.
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#[arg(long, default_value_t = 600)]
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horizon: usize,
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/// DQN training episodes (on train segment).
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#[arg(long, default_value_t = 1_000)]
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n_train_episodes: usize,
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/// Frozen-policy evaluation episodes per cost level.
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#[arg(long, default_value_t = 500)]
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n_eval_episodes: usize,
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/// Comma-separated cost grid (price units per contract round-turn).
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/// Phase 1d.4 used [0.0, 0.0625, 0.125, 0.25, 0.50].
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#[arg(long, value_delimiter = ',', default_value = "0.0,0.0625,0.125,0.25,0.5")]
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cost_grid: Vec<f32>,
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/// Comma-separated alpha-confidence threshold grid for the gate at eval.
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/// Direct analogue of Phase 1d.4's `--threshold` sweep — at each
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/// threshold, the policy is forced to Wait when |sigmoid(alpha)−0.5|
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/// < threshold. Threshold 0 = no gate (original Task 23 behaviour).
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#[arg(long, value_delimiter = ',', default_value = "0.0,0.05,0.10,0.15,0.20,0.25")]
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threshold_grid: Vec<f32>,
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#[arg(long, default_value_t = 1)]
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trade_size: i32,
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#[arg(long, default_value_t = 0xCAFEBABE_u64)]
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seed: u64,
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/// Training-time cost (the policy LEARNED against this cost).
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#[arg(long, default_value_t = 0.0625)]
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train_cost: f32,
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/// Training-time alpha-confidence threshold for the gate. Forces
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/// Wait during training when `|sigmoid(alpha)−0.5| < this`, so the
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/// Q-network learns weights for the gated policy class. Default
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/// 0.39 — the equilibrium the controller stabilized to in the
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/// Phase E.2 smoke (alpha_dqn_h600_smoke at ep 200+). Set to 0.0
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/// for the original ungated-training behavior.
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#[arg(long, default_value_t = 0.39)]
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train_threshold: f32,
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/// SGD learning rate during training.
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#[arg(long, default_value_t = 1.0e-4)]
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lr: f32,
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#[arg(long, default_value_t = 0.50)]
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eps_start: f32,
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#[arg(long, default_value_t = 0.05)]
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eps_end: f32,
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#[arg(long, default_value_t = 0.99)]
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gamma: f32,
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#[arg(long, default_value_t = 0.9)]
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alpha_m: f32,
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#[arg(long, default_value_t = 0.03)]
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tau: f32,
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#[arg(long, default_value_t = -1.0)]
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log_clip_min: f32,
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#[arg(long, default_value_t = 1000.0)]
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reward_scale: f32,
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#[arg(long, default_value_t = 10)]
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target_update_every: usize,
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#[arg(long, default_value_t = 1.0)]
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grad_clip: f32,
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/// Restrict action selection to {Wait, BuyMarket, SellMarket, FlatMarket}.
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/// Applies during BOTH training and eval. **FALSIFIED 2026-05-15** —
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/// kept for reproducibility (see `pearl_action_pruning_falsified`).
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#[arg(long, default_value_t = false)]
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pruned_actions: bool,
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/// Use C51 distributional Q-network (Phase E.3 follow-up). Same
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/// semantics as the alpha_dqn_h600_smoke `--c51` flag.
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#[arg(long, default_value_t = false)]
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c51: bool,
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/// C51 atom-support lower bound (normalized reward units).
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#[arg(long, default_value_t = -10.0)]
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c51_vmin: f32,
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/// C51 atom-support upper bound.
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#[arg(long, default_value_t = 10.0)]
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c51_vmax: f32,
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/// C51 atom count (canonical 51; kernel caps at 64).
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#[arg(long, default_value_t = 51)]
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c51_n_atoms: usize,
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/// Derive bid/ask from real spread_bps in fxcache instead of fixed
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/// ±0.125-tick. Phase E.3 Path 3 follow-up.
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#[arg(long, default_value_t = false)]
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real_spread: bool,
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#[arg(long, default_value = "config/ml/alpha_compose_backtest.json")]
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out_path: PathBuf,
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}
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struct SmokeRng {
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state: u64,
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}
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impl SmokeRng {
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fn new(seed: u64) -> Self {
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Self { state: seed }
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}
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fn next_u64(&mut self) -> u64 {
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self.state = self.state.wrapping_add(0x9E37_79B9_7F4A_7C15);
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let mut z = self.state;
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z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9);
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z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB);
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z ^ (z >> 31)
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}
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fn next_f32(&mut self) -> f32 {
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((self.next_u64() >> 40) as f32) / ((1u64 << 24) as f32)
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}
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}
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fn epsilon_greedy(q: &[f32], eps: f32, allowed: &[u8], rng: &mut SmokeRng) -> u8 {
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if rng.next_f32() < eps {
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allowed[(rng.next_u64() as usize) % allowed.len()]
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} else {
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let mut best_a: u8 = allowed[0];
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let mut best_v: f32 = q[allowed[0] as usize];
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for &a in &allowed[1..] {
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let v = q[a as usize];
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if v > best_v {
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best_v = v;
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best_a = a;
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}
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}
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best_a
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}
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}
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/// Phase E.3 confidence-gated ε-greedy. If `alpha_confidence < threshold`,
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/// force action=0 (Wait). Otherwise standard ε-greedy over `allowed`.
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fn epsilon_greedy_gated(
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q: &[f32],
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alpha_confidence: f32,
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threshold: f32,
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eps: f32,
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allowed: &[u8],
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rng: &mut SmokeRng,
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) -> u8 {
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if alpha_confidence < threshold {
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return 0;
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}
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epsilon_greedy(q, eps, allowed, rng)
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}
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#[derive(Debug, Clone, serde::Serialize)]
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struct CostBin {
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cost: f32,
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threshold: f32,
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n_episodes: usize,
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mean_reward: f32,
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std_reward: f32,
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sharpe_per_episode: f32,
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/// Sharpe scaled by sqrt(episodes_per_year). Time span derived from
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/// the eval window's mid_price index span × bar_seconds.
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sharpe_annualised: f32,
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win_rate: f32,
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avg_n_trades: f32,
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p05: f32,
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p50: f32,
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p95: f32,
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}
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fn main() -> Result<()> {
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tracing_subscriber::fmt()
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.with_env_filter(
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tracing_subscriber::EnvFilter::try_from_default_env()
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.unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
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)
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.init();
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let cli = Cli::parse();
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info!("Phase E.3 Task 23 — composition backtest starting");
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let allowed_actions: &[u8] = if cli.pruned_actions {
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info!(" ACTION SET: pruned ({} actions: Wait, BuyMarket, SellMarket, FlatMarket)", PRUNED_ACTIONS.len());
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&PRUNED_ACTIONS
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} else {
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info!(" ACTION SET: full ({} actions)", FULL_ACTIONS.len());
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&FULL_ACTIONS
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};
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let ctx = CudaContext::new(0).context("CUDA init")?;
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let stream = ctx.default_stream();
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// --- Load cubins ---
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let lq_module = ctx
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.load_cubin(ml::cuda_pipeline::alpha_kernels::ALPHA_LINEAR_Q_CUBIN.to_vec())
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.context("alpha_linear_q cubin")?;
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let lq_fwd = lq_module.load_function("alpha_linear_q_forward_kernel")?;
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let lq_grad = lq_module.load_function("alpha_linear_q_grad_kernel")?;
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let lq_sgd = lq_module.load_function("alpha_linear_q_sgd_step_kernel")?;
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let lq_clip = lq_module.load_function("alpha_clip_inplace_kernel")?;
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let munch_cubin: Vec<u8> = std::fs::read(concat!(
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env!("OUT_DIR"),
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"/alpha_munchausen_target.cubin"
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))?;
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let munch_module = ctx.load_cubin(munch_cubin)?;
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let munch_kernel =
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munch_module.load_function("alpha_munchausen_target_kernel")?;
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// Phase E.3 follow-up: C51 kernels (always loaded; only used when --c51).
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let c51_module = ctx
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.load_cubin(ml::cuda_pipeline::alpha_kernels::ALPHA_C51_CUBIN.to_vec())
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.context("alpha_c51 cubin")?;
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let c51_fwd_kernel = c51_module.load_function("alpha_c51_forward_kernel")?;
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let c51_project_kernel = c51_module.load_function("alpha_c51_project_kernel")?;
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let c51_grad_kernel = c51_module.load_function("alpha_c51_grad_kernel")?;
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let c51_thompson_kernel = c51_module.load_function("alpha_c51_thompson_select_kernel")?;
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let c51_n_atoms = cli.c51_n_atoms;
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let c51_v_min = cli.c51_vmin;
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let c51_v_max = cli.c51_vmax;
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let c51_delta_z = (c51_v_max - c51_v_min) / (c51_n_atoms.saturating_sub(1).max(1) as f32);
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if cli.c51 {
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info!(
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" Q-network: C51 ({} atoms, [{:.2}, {:.2}], Δz={:.4})",
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c51_n_atoms, c51_v_min, c51_v_max, c51_delta_z
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||
);
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||
if c51_n_atoms == 0 || c51_n_atoms > 64 {
|
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anyhow::bail!("--c51-n-atoms must be in (0, 64]");
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||
}
|
||
if c51_v_max <= c51_v_min {
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anyhow::bail!("--c51-vmax must exceed --c51-vmin");
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||
}
|
||
} else {
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info!(" Q-network: linear scalar (9 outputs)");
|
||
}
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||
|
||
// --- Load env data ---
|
||
let fill_model = ml::env::loaders::load_fill_model_from_json(&cli.fill_coeffs)?;
|
||
info!("Loaded fill model");
|
||
let alpha_cache = ml::env::loaders::load_alpha_cache(&cli.alpha_cache)?;
|
||
info!("Loaded alpha cache: {} entries", alpha_cache.len());
|
||
let rows = ml::env::loaders::load_snapshots_from_fxcache(
|
||
&cli.fxcache_path,
|
||
cli.max_snapshots,
|
||
Some(&alpha_cache),
|
||
cli.real_spread,
|
||
None, // E.4.A T4: mbp10_dir wiring + --use-real-depth flag lands in T5
|
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)?;
|
||
let n_total = rows.len();
|
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info!("Loaded {} snapshots", n_total);
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||
|
||
let n_train = ((n_total as f32) * cli.train_frac) as usize;
|
||
let n_eval = n_total - n_train;
|
||
if n_train <= cli.horizon || n_eval <= cli.horizon {
|
||
anyhow::bail!(
|
||
"insufficient snapshots: train={}, eval={}, horizon={}",
|
||
n_train,
|
||
n_eval,
|
||
cli.horizon
|
||
);
|
||
}
|
||
info!(
|
||
"Split: train={} bars (cursor 0..{}), eval={} bars (cursor {}..{})",
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||
n_train, n_train, n_eval, n_train, n_total
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||
);
|
||
|
||
let mut env = ExecutionEnv::new(
|
||
ExecutionEnvConfig {
|
||
horizon_snapshots: cli.horizon,
|
||
trade_size_contracts: cli.trade_size,
|
||
cost_per_contract: cli.train_cost,
|
||
},
|
||
fill_model,
|
||
rows,
|
||
cli.seed,
|
||
);
|
||
|
||
// --- Initialize Q-network ---
|
||
let n_weights_eff: usize = if cli.c51 {
|
||
N_ACTIONS * c51_n_atoms * STATE_DIM
|
||
} else {
|
||
N_WEIGHTS
|
||
};
|
||
let n_biases_eff: usize = if cli.c51 { N_ACTIONS * c51_n_atoms } else { N_BIASES };
|
||
let mut rng = SmokeRng::new(cli.seed.wrapping_add(0xDEAD_BEEF));
|
||
let xavier_scale = (2.0_f32 / STATE_DIM as f32).sqrt();
|
||
let w_init: Vec<f32> = (0..n_weights_eff)
|
||
.map(|_| xavier_scale * 2.0 * (rng.next_f32() - 0.5))
|
||
.collect();
|
||
let b_init: Vec<f32> = vec![0.0; n_biases_eff];
|
||
let mut w_dev = stream.clone_htod(&w_init)?;
|
||
let mut b_dev = stream.clone_htod(&b_init)?;
|
||
let mut w_target_dev = stream.clone_htod(&w_init)?;
|
||
let mut b_target_dev = stream.clone_htod(&b_init)?;
|
||
let mut dw_dev = stream.alloc_zeros::<f32>(n_weights_eff)?;
|
||
let mut db_dev = stream.alloc_zeros::<f32>(n_biases_eff)?;
|
||
|
||
let state_dim_i = STATE_DIM as i32;
|
||
let n_act_i = N_ACTIONS as i32;
|
||
let n_atoms_i = c51_n_atoms as i32;
|
||
let mut states_dev = stream.alloc_zeros::<f32>(cli.horizon * STATE_DIM)?;
|
||
let mut next_states_dev = stream.alloc_zeros::<f32>(cli.horizon * STATE_DIM)?;
|
||
let mut actions_dev = stream.alloc_zeros::<i32>(cli.horizon)?;
|
||
let mut rewards_dev = stream.alloc_zeros::<f32>(cli.horizon)?;
|
||
let mut dones_dev = stream.alloc_zeros::<f32>(cli.horizon)?;
|
||
let mut q_current_dev = stream.alloc_zeros::<f32>(cli.horizon * N_ACTIONS)?;
|
||
let mut q_next_dev = stream.alloc_zeros::<f32>(cli.horizon * N_ACTIONS)?;
|
||
let mut target_dev = stream.alloc_zeros::<f32>(cli.horizon)?;
|
||
let mut single_state_dev = stream.alloc_zeros::<f32>(STATE_DIM)?;
|
||
let mut single_q_dev = stream.alloc_zeros::<f32>(N_ACTIONS)?;
|
||
// C51 device buffers (always allocated; cheap).
|
||
let mut probs_current_dev = stream.alloc_zeros::<f32>(cli.horizon * N_ACTIONS * c51_n_atoms)?;
|
||
let mut probs_next_dev = stream.alloc_zeros::<f32>(cli.horizon * N_ACTIONS * c51_n_atoms)?;
|
||
let mut m_dev = stream.alloc_zeros::<f32>(cli.horizon * c51_n_atoms)?;
|
||
let mut single_probs_dev = stream.alloc_zeros::<f32>(N_ACTIONS * c51_n_atoms)?;
|
||
// Mapped-pinned per-step inference buffers (C51 path).
|
||
let state_pinned = unsafe { MappedF32::new(STATE_DIM)? };
|
||
let action_pinned = unsafe { MappedI32::new()? };
|
||
|
||
// ---------------------------------------------------------------
|
||
// Phase 1: TRAIN DQN on train segment.
|
||
// ---------------------------------------------------------------
|
||
info!("=== Training phase: {} episodes on train segment ===", cli.n_train_episodes);
|
||
let mut episode_rng = SmokeRng::new(cli.seed.wrapping_add(0xFEED));
|
||
let train_max_start = (n_train.saturating_sub(cli.horizon + 1)).max(1);
|
||
for ep in 0..cli.n_train_episodes {
|
||
let eps = cli.eps_start
|
||
+ (cli.eps_end - cli.eps_start)
|
||
* (ep as f32 / cli.n_train_episodes.max(1) as f32);
|
||
let start_cursor = (episode_rng.next_u64() as usize) % train_max_start;
|
||
let env_seed = episode_rng.next_u64();
|
||
env.reset_at(env_seed, start_cursor);
|
||
let mut state = EpisodeState::new();
|
||
|
||
let mut states_host: Vec<f32> = Vec::with_capacity(cli.horizon * STATE_DIM);
|
||
let mut next_states_host: Vec<f32> = Vec::with_capacity(cli.horizon * STATE_DIM);
|
||
let mut actions_host: Vec<i32> = Vec::with_capacity(cli.horizon);
|
||
let mut rewards_host: Vec<f32> = Vec::with_capacity(cli.horizon);
|
||
let mut dones_host: Vec<f32> = Vec::with_capacity(cli.horizon);
|
||
loop {
|
||
let s_vec = env.state(&state).to_vec();
|
||
let action: u8 = if cli.c51 {
|
||
state_pinned.write(&s_vec);
|
||
{
|
||
let (w_ptr, _g0) = w_dev.device_ptr(&stream);
|
||
let (b_ptr, _g1) = b_dev.device_ptr(&stream);
|
||
let (p_ptr, _g2) = single_probs_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_c51_forward(
|
||
&stream, &c51_fwd_kernel,
|
||
w_ptr, b_ptr, state_pinned.dev_u64(), p_ptr,
|
||
1, state_dim_i, n_act_i, n_atoms_i,
|
||
)?;
|
||
}
|
||
}
|
||
let step_seed = episode_rng.next_u64() as u32;
|
||
{
|
||
let (p_ptr, _g0) = single_probs_dev.device_ptr(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_c51_thompson_select(
|
||
&stream, &c51_thompson_kernel,
|
||
p_ptr,
|
||
state_pinned.dev_u64(),
|
||
cli.train_threshold,
|
||
1,
|
||
state_dim_i,
|
||
c51_v_min, c51_delta_z,
|
||
step_seed,
|
||
action_pinned.dev_u64(),
|
||
1, n_act_i, n_atoms_i,
|
||
)?;
|
||
}
|
||
}
|
||
stream.synchronize()?;
|
||
action_pinned.read() as u8
|
||
} else {
|
||
stream.memcpy_htod(&s_vec, &mut single_state_dev)?;
|
||
{
|
||
let (w_ptr, _g0) = w_dev.device_ptr(&stream);
|
||
let (b_ptr, _g1) = b_dev.device_ptr(&stream);
|
||
let (s_ptr, _g2) = single_state_dev.device_ptr(&stream);
|
||
let (q_ptr, _g3) = single_q_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_linear_q_forward(
|
||
&stream, &lq_fwd, w_ptr, b_ptr, s_ptr, q_ptr,
|
||
1, state_dim_i, n_act_i,
|
||
)?;
|
||
}
|
||
}
|
||
stream.synchronize()?;
|
||
let q_host = stream.clone_dtoh(&single_q_dev)?;
|
||
epsilon_greedy_gated(
|
||
&q_host,
|
||
s_vec[1],
|
||
cli.train_threshold,
|
||
eps,
|
||
allowed_actions,
|
||
&mut episode_rng,
|
||
)
|
||
};
|
||
let (_s_next, reward, done) = env
|
||
.step(action, &mut state)
|
||
.ok_or_else(|| anyhow::anyhow!("step returned None"))?;
|
||
let s_next_vec = env.state(&state).to_vec();
|
||
states_host.extend_from_slice(&s_vec);
|
||
next_states_host.extend_from_slice(&s_next_vec);
|
||
actions_host.push(action as i32);
|
||
rewards_host.push(reward);
|
||
dones_host.push(if done { 1.0 } else { 0.0 });
|
||
if done {
|
||
break;
|
||
}
|
||
}
|
||
let ep_len = actions_host.len() as i32;
|
||
if ep_len < 2 {
|
||
continue;
|
||
}
|
||
// Batched train update (same logic as alpha_dqn_h600_smoke).
|
||
let rewards_norm: Vec<f32> = rewards_host
|
||
.iter()
|
||
.map(|r| r / cli.reward_scale)
|
||
.collect();
|
||
stream.memcpy_htod(&states_host, &mut states_dev)?;
|
||
stream.memcpy_htod(&next_states_host, &mut next_states_dev)?;
|
||
stream.memcpy_htod(&actions_host, &mut actions_dev)?;
|
||
stream.memcpy_htod(&rewards_norm, &mut rewards_dev)?;
|
||
stream.memcpy_htod(&dones_host, &mut dones_dev)?;
|
||
if cli.c51 {
|
||
// C51 forward/project/grad
|
||
{
|
||
let (w_ptr, _g0) = w_dev.device_ptr(&stream);
|
||
let (b_ptr, _g1) = b_dev.device_ptr(&stream);
|
||
let (s_ptr, _g2) = states_dev.device_ptr(&stream);
|
||
let (p_ptr, _g3) = probs_current_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_c51_forward(
|
||
&stream, &c51_fwd_kernel,
|
||
w_ptr, b_ptr, s_ptr, p_ptr,
|
||
ep_len, state_dim_i, n_act_i, n_atoms_i,
|
||
)?;
|
||
}
|
||
}
|
||
{
|
||
let (w_ptr, _g0) = w_target_dev.device_ptr(&stream);
|
||
let (b_ptr, _g1) = b_target_dev.device_ptr(&stream);
|
||
let (s_ptr, _g2) = next_states_dev.device_ptr(&stream);
|
||
let (p_ptr, _g3) = probs_next_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_c51_forward(
|
||
&stream, &c51_fwd_kernel,
|
||
w_ptr, b_ptr, s_ptr, p_ptr,
|
||
ep_len, state_dim_i, n_act_i, n_atoms_i,
|
||
)?;
|
||
}
|
||
}
|
||
{
|
||
let (pn_ptr, _g0) = probs_next_dev.device_ptr(&stream);
|
||
let (r_ptr, _g1) = rewards_dev.device_ptr(&stream);
|
||
let (d_ptr, _g2) = dones_dev.device_ptr(&stream);
|
||
let (m_ptr, _g3) = m_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_c51_project(
|
||
&stream, &c51_project_kernel,
|
||
pn_ptr, r_ptr, d_ptr,
|
||
c51_v_min, c51_v_max, cli.gamma, c51_delta_z,
|
||
m_ptr, ep_len, n_act_i, n_atoms_i,
|
||
)?;
|
||
}
|
||
}
|
||
{
|
||
let (p_ptr, _g0) = probs_current_dev.device_ptr(&stream);
|
||
let (m_ptr, _g1) = m_dev.device_ptr(&stream);
|
||
let (a_ptr, _g2) = actions_dev.device_ptr(&stream);
|
||
let (s_ptr, _g3) = states_dev.device_ptr(&stream);
|
||
let (dw_ptr, _g4) = dw_dev.device_ptr_mut(&stream);
|
||
let (db_ptr, _g5) = db_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_c51_grad(
|
||
&stream, &c51_grad_kernel,
|
||
p_ptr, m_ptr, a_ptr, s_ptr, dw_ptr, db_ptr,
|
||
ep_len, state_dim_i, n_act_i, n_atoms_i,
|
||
1.0 / ep_len as f32,
|
||
)?;
|
||
}
|
||
}
|
||
} else {
|
||
// Linear-Q forward/Munchausen/grad
|
||
{
|
||
let (w_ptr, _g0) = w_dev.device_ptr(&stream);
|
||
let (b_ptr, _g1) = b_dev.device_ptr(&stream);
|
||
let (s_ptr, _g2) = states_dev.device_ptr(&stream);
|
||
let (q_ptr, _g3) = q_current_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_linear_q_forward(
|
||
&stream, &lq_fwd, w_ptr, b_ptr, s_ptr, q_ptr,
|
||
ep_len, state_dim_i, n_act_i,
|
||
)?;
|
||
}
|
||
}
|
||
{
|
||
let (w_ptr, _g0) = w_target_dev.device_ptr(&stream);
|
||
let (b_ptr, _g1) = b_target_dev.device_ptr(&stream);
|
||
let (s_ptr, _g2) = next_states_dev.device_ptr(&stream);
|
||
let (q_ptr, _g3) = q_next_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_linear_q_forward(
|
||
&stream, &lq_fwd, w_ptr, b_ptr, s_ptr, q_ptr,
|
||
ep_len, state_dim_i, n_act_i,
|
||
)?;
|
||
}
|
||
}
|
||
{
|
||
let (qn_ptr, _g0) = q_next_dev.device_ptr(&stream);
|
||
let (qc_ptr, _g1) = q_current_dev.device_ptr(&stream);
|
||
let (a_ptr, _g2) = actions_dev.device_ptr(&stream);
|
||
let (r_ptr, _g3) = rewards_dev.device_ptr(&stream);
|
||
let (d_ptr, _g4) = dones_dev.device_ptr(&stream);
|
||
let (t_ptr, _g5) = target_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_munchausen_target(
|
||
&stream, &munch_kernel,
|
||
qn_ptr, qc_ptr, a_ptr, r_ptr, d_ptr,
|
||
cli.gamma, cli.alpha_m, cli.tau, cli.log_clip_min,
|
||
t_ptr, ep_len, n_act_i,
|
||
)?;
|
||
}
|
||
}
|
||
{
|
||
let (qc_ptr, _g0) = q_current_dev.device_ptr(&stream);
|
||
let (t_ptr, _g1) = target_dev.device_ptr(&stream);
|
||
let (a_ptr, _g2) = actions_dev.device_ptr(&stream);
|
||
let (s_ptr, _g3) = states_dev.device_ptr(&stream);
|
||
let (dw_ptr, _g4) = dw_dev.device_ptr_mut(&stream);
|
||
let (db_ptr, _g5) = db_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_linear_q_grad(
|
||
&stream, &lq_grad,
|
||
qc_ptr, t_ptr, a_ptr, s_ptr, dw_ptr, db_ptr,
|
||
ep_len, state_dim_i, n_act_i,
|
||
1.0 / ep_len as f32,
|
||
)?;
|
||
}
|
||
}
|
||
}
|
||
// Clip + SGD (shared kernels; sizes from n_weights_eff / n_biases_eff)
|
||
{
|
||
let (dw_ptr, _g0) = dw_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_clip_inplace(
|
||
&stream, &lq_clip, dw_ptr, cli.grad_clip, n_weights_eff as i32,
|
||
)?;
|
||
}
|
||
}
|
||
{
|
||
let (db_ptr, _g0) = db_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_clip_inplace(
|
||
&stream, &lq_clip, db_ptr, cli.grad_clip, n_biases_eff as i32,
|
||
)?;
|
||
}
|
||
}
|
||
{
|
||
let (w_ptr, _g0) = w_dev.device_ptr_mut(&stream);
|
||
let (dw_ptr, _g1) = dw_dev.device_ptr(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_linear_q_sgd_step(
|
||
&stream, &lq_sgd, w_ptr, dw_ptr, cli.lr, n_weights_eff as i32,
|
||
)?;
|
||
}
|
||
}
|
||
{
|
||
let (b_ptr, _g0) = b_dev.device_ptr_mut(&stream);
|
||
let (db_ptr, _g1) = db_dev.device_ptr(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_linear_q_sgd_step(
|
||
&stream, &lq_sgd, b_ptr, db_ptr, cli.lr, n_biases_eff as i32,
|
||
)?;
|
||
}
|
||
}
|
||
// Target net hard update
|
||
if (ep + 1) % cli.target_update_every == 0 {
|
||
stream.synchronize()?;
|
||
let w_now = stream.clone_dtoh(&w_dev)?;
|
||
let b_now = stream.clone_dtoh(&b_dev)?;
|
||
stream.memcpy_htod(&w_now, &mut w_target_dev)?;
|
||
stream.memcpy_htod(&b_now, &mut b_target_dev)?;
|
||
}
|
||
if (ep + 1) % 200 == 0 {
|
||
info!(" train ep {}/{}", ep + 1, cli.n_train_episodes);
|
||
}
|
||
}
|
||
info!("Training complete. Frozen policy ready for eval.");
|
||
|
||
// ---------------------------------------------------------------
|
||
// Phase 2: 2D SWEEP — frozen-policy greedy eval per (threshold, cost).
|
||
// Direct analogue of Phase 1d.4's threshold × cost table.
|
||
// ---------------------------------------------------------------
|
||
info!("=== Eval phase: {} episodes × {} thresholds × {} costs ===",
|
||
cli.n_eval_episodes, cli.threshold_grid.len(), cli.cost_grid.len());
|
||
let eval_max_start = (n_total - n_train).saturating_sub(cli.horizon + 1).max(1);
|
||
let mut bins: Vec<CostBin> =
|
||
Vec::with_capacity(cli.cost_grid.len() * cli.threshold_grid.len());
|
||
for &threshold in &cli.threshold_grid {
|
||
for &cost in &cli.cost_grid {
|
||
env.config.cost_per_contract = cost;
|
||
let mut rewards: Vec<f32> = Vec::with_capacity(cli.n_eval_episodes);
|
||
let mut win_count = 0_usize;
|
||
let mut trade_count_total: u64 = 0;
|
||
for _ in 0..cli.n_eval_episodes {
|
||
let start_cursor =
|
||
n_train + (episode_rng.next_u64() as usize) % eval_max_start;
|
||
let env_seed = episode_rng.next_u64();
|
||
env.reset_at(env_seed, start_cursor);
|
||
let mut state = EpisodeState::new();
|
||
let mut terminal_r = 0.0_f32;
|
||
let mut ep_n_trades = 0_u32;
|
||
loop {
|
||
let s_vec = env.state(&state).to_vec();
|
||
let action: u8 = if cli.c51 {
|
||
state_pinned.write(&s_vec);
|
||
{
|
||
let (w_ptr, _g0) = w_dev.device_ptr(&stream);
|
||
let (b_ptr, _g1) = b_dev.device_ptr(&stream);
|
||
let (p_ptr, _g2) = single_probs_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_c51_forward(
|
||
&stream, &c51_fwd_kernel,
|
||
w_ptr, b_ptr, state_pinned.dev_u64(), p_ptr,
|
||
1, state_dim_i, n_act_i, n_atoms_i,
|
||
)?;
|
||
}
|
||
}
|
||
let step_seed = episode_rng.next_u64() as u32;
|
||
{
|
||
let (p_ptr, _g0) = single_probs_dev.device_ptr(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_c51_thompson_select(
|
||
&stream, &c51_thompson_kernel,
|
||
p_ptr,
|
||
state_pinned.dev_u64(),
|
||
threshold,
|
||
1,
|
||
state_dim_i,
|
||
c51_v_min, c51_delta_z,
|
||
step_seed,
|
||
action_pinned.dev_u64(),
|
||
1, n_act_i, n_atoms_i,
|
||
)?;
|
||
}
|
||
}
|
||
stream.synchronize()?;
|
||
action_pinned.read() as u8
|
||
} else {
|
||
stream.memcpy_htod(&s_vec, &mut single_state_dev)?;
|
||
{
|
||
let (w_ptr, _g0) = w_dev.device_ptr(&stream);
|
||
let (b_ptr, _g1) = b_dev.device_ptr(&stream);
|
||
let (s_ptr, _g2) = single_state_dev.device_ptr(&stream);
|
||
let (q_ptr, _g3) = single_q_dev.device_ptr_mut(&stream);
|
||
unsafe {
|
||
ml::cuda_pipeline::alpha_kernels::launch_alpha_linear_q_forward(
|
||
&stream, &lq_fwd, w_ptr, b_ptr, s_ptr, q_ptr,
|
||
1, state_dim_i, n_act_i,
|
||
)?;
|
||
}
|
||
}
|
||
stream.synchronize()?;
|
||
let q_host = stream.clone_dtoh(&single_q_dev)?;
|
||
let mut greedy_rng = SmokeRng::new(0); // unused — eps=0
|
||
epsilon_greedy_gated(
|
||
&q_host,
|
||
s_vec[1],
|
||
threshold,
|
||
0.0,
|
||
allowed_actions,
|
||
&mut greedy_rng,
|
||
)
|
||
};
|
||
if action != 0 {
|
||
ep_n_trades += 1;
|
||
}
|
||
match env.step(action, &mut state) {
|
||
Some((_, reward, done)) => {
|
||
if done {
|
||
terminal_r = reward;
|
||
break;
|
||
}
|
||
}
|
||
None => break,
|
||
}
|
||
}
|
||
rewards.push(terminal_r);
|
||
trade_count_total += ep_n_trades as u64;
|
||
if terminal_r > 0.0 {
|
||
win_count += 1;
|
||
}
|
||
}
|
||
let n = rewards.len() as f64;
|
||
let mean = rewards.iter().map(|r| *r as f64).sum::<f64>() / n;
|
||
let var = rewards
|
||
.iter()
|
||
.map(|r| (*r as f64 - mean).powi(2))
|
||
.sum::<f64>()
|
||
/ n;
|
||
let std = var.sqrt().max(1e-6);
|
||
let sharpe_per = (mean / std) as f32;
|
||
let episodes_per_year = 252.0 * 6.5 * 3600.0 / (cli.horizon as f64 * 12.0);
|
||
let sharpe_ann = (sharpe_per as f64 * episodes_per_year.sqrt()) as f32;
|
||
let win_rate = win_count as f32 / cli.n_eval_episodes as f32;
|
||
let avg_n_trades = trade_count_total as f32 / cli.n_eval_episodes as f32;
|
||
let mut sorted = rewards.clone();
|
||
sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
|
||
let pick = |q: f64| -> f32 {
|
||
let idx = (q * (n - 1.0)).round() as usize;
|
||
sorted[idx.min(sorted.len() - 1)]
|
||
};
|
||
let bin = CostBin {
|
||
cost,
|
||
threshold,
|
||
n_episodes: cli.n_eval_episodes,
|
||
mean_reward: mean as f32,
|
||
std_reward: std as f32,
|
||
sharpe_per_episode: sharpe_per,
|
||
sharpe_annualised: sharpe_ann,
|
||
win_rate,
|
||
avg_n_trades,
|
||
p05: pick(0.05),
|
||
p50: pick(0.50),
|
||
p95: pick(0.95),
|
||
};
|
||
info!(
|
||
" τ={:.2} cost={:>6.4} mean={:>+9.2} Sharpe/ep={:+.3} Sharpe_ann={:+.3} win={:.3} trades/ep={:.1}",
|
||
threshold, cost, mean, sharpe_per, sharpe_ann, win_rate, avg_n_trades
|
||
);
|
||
bins.push(bin);
|
||
}
|
||
}
|
||
|
||
// --- Print table ---
|
||
info!("");
|
||
info!("=== Phase E.3 2D sweep (threshold × cost, vs Phase 1d.4 baseline) ===");
|
||
info!(
|
||
" {:>6} {:>6} {:>9} {:>9} {:>10} {:>11} {:>8} {:>10}",
|
||
"τ", "cost", "mean_R", "std_R", "Sharpe/ep", "Sharpe_ann", "win_rate", "trades/ep"
|
||
);
|
||
for b in &bins {
|
||
info!(
|
||
" {:>6.3} {:>6.4} {:>9.2} {:>9.2} {:>10.4} {:>11.4} {:>8.3} {:>10.2}",
|
||
b.threshold, b.cost, b.mean_reward, b.std_reward,
|
||
b.sharpe_per_episode, b.sharpe_annualised, b.win_rate, b.avg_n_trades
|
||
);
|
||
}
|
||
info!("");
|
||
info!("BEST per-cost (max Sharpe_ann across all τ at each cost):");
|
||
for &cost in &cli.cost_grid {
|
||
let best = bins
|
||
.iter()
|
||
.filter(|b| (b.cost - cost).abs() < 1e-6)
|
||
.max_by(|a, b| {
|
||
a.sharpe_annualised
|
||
.partial_cmp(&b.sharpe_annualised)
|
||
.unwrap_or(std::cmp::Ordering::Equal)
|
||
});
|
||
if let Some(b) = best {
|
||
info!(
|
||
" cost={:>6.4} best τ={:.3} Sharpe_ann={:+.3} win={:.3} trades/ep={:.1}",
|
||
b.cost, b.threshold, b.sharpe_annualised, b.win_rate, b.avg_n_trades
|
||
);
|
||
}
|
||
}
|
||
info!("");
|
||
info!("Phase 1d.4 baseline for comparison: +4.4 annualised at cost=0,");
|
||
info!(" -4.0 annualised at cost=0.125.");
|
||
|
||
// --- Save JSON ---
|
||
let json = serde_json::json!({
|
||
"phase": "E.3 Task 23 (2D sweep)",
|
||
"pruned_actions": cli.pruned_actions,
|
||
"n_allowed_actions": allowed_actions.len(),
|
||
"c51": cli.c51,
|
||
"c51_n_atoms": if cli.c51 { c51_n_atoms } else { 0 },
|
||
"c51_vmin": if cli.c51 { c51_v_min } else { 0.0 },
|
||
"c51_vmax": if cli.c51 { c51_v_max } else { 0.0 },
|
||
"horizon": cli.horizon,
|
||
"train_frac": cli.train_frac,
|
||
"n_train_episodes": cli.n_train_episodes,
|
||
"n_eval_episodes": cli.n_eval_episodes,
|
||
"train_cost": cli.train_cost,
|
||
"cost_grid": cli.cost_grid,
|
||
"threshold_grid": cli.threshold_grid,
|
||
"bins": bins,
|
||
});
|
||
let mut f = File::create(&cli.out_path)?;
|
||
write!(f, "{}", serde_json::to_string_pretty(&json)?)?;
|
||
info!("Wrote table to {}", cli.out_path.display());
|
||
|
||
// Touch unused ISV slot constants so they're imported for future expansion.
|
||
let _ = (RANDOM_BASELINE_MEAN_INDEX, RANDOM_BASELINE_STD_INDEX);
|
||
let _: Option<ReplayRng> = None;
|
||
let _ = SnapshotRow {
|
||
mid_price: 0.0,
|
||
bid_l: [0.0; 10],
|
||
ask_l: [0.0; 10],
|
||
alpha_logit: 0.0,
|
||
alpha_confidence: 0.0,
|
||
spread_bps: 0.0,
|
||
l1_imbalance: 0.0,
|
||
ofi_sum_5: 0.0,
|
||
mid_drift_5: 0.0,
|
||
time_since_trade_s: 0.0,
|
||
book_event_rate: 0.0,
|
||
};
|
||
|
||
Ok(())
|
||
}
|