foxhunt/crates/ml/examples/alpha_compose_backtest.rs

//! Phase E.3 Task 23 — Composition backtest with cost sweep.
//!
//! Trains the Phase E execution-policy DQN (linear Q + Phase 1d.3
//! alpha-cache + stabilizers) on the first `--train-frac` of the
//! fxcache, then evaluates the FROZEN policy (no SGD, ε=0 greedy) over
//! the held-out remainder at multiple transaction costs. Compares
//! per-cost annualized Sharpe vs the Phase 1d.4 "always-market-when-
//! confident" baseline (+4.4 frictionless, -4.0 at half-tick).
//!
//! Goal per the plan: lift the half-tick Sharpe above 0 — i.e., let
//! the execution-policy intelligence offset the cost the
//! threshold-only baseline can't.
//!
//! ## What's swept and what's frozen
//!
//! Frozen across costs: trained Q-network weights, fill-model
//! coefficients (from `alpha_fill_coeffs.json`), alpha-logit cache
//! (from `alpha_logits_cache.bin`). One policy evaluated at multiple
//! costs.
//!
//! Swept: only `ExecutionEnvConfig.cost_per_contract`. The env's
//! mutable config is updated between cost levels (snapshots stay in
//! place, cursor re-seeded each episode).
//!
//! ## Run
//!
//! ```bash
//! cargo run -p ml --release --example alpha_compose_backtest -- \
//!   --fxcache-path /home/jgrusewski/Work/foxhunt/test_data/feature-cache/9297....fxcache \
//!   --alpha-cache  config/ml/alpha_logits_cache.bin \
//!   --fill-coeffs  config/ml/alpha_fill_coeffs.json
//! ```

use std::fs::File;
use std::io::Write;
use std::path::PathBuf;

use anyhow::{Context, Result};
use clap::Parser;
use cudarc::driver::{CudaContext, DevicePtr, DevicePtrMut};
use tracing::info;

use ml::cuda_pipeline::alpha_isv_slots::{
    RANDOM_BASELINE_MEAN_INDEX, RANDOM_BASELINE_STD_INDEX,
};
use ml::env::action_space::N_ACTIONS;
use ml::env::execution_env::{
    EpisodeState, ExecutionEnv, ExecutionEnvConfig, ReplayRng, SnapshotRow,
};

const STATE_DIM: usize = 10;
const N_WEIGHTS: usize = N_ACTIONS * STATE_DIM;
const N_BIASES: usize = N_ACTIONS;

#[derive(Debug, Parser)]
#[command(
    name = "alpha_compose_backtest",
    about = "Phase E.3 Task 23 — composition backtest with cost sweep"
)]
struct Cli {
    #[arg(long)]
    fxcache_path: PathBuf,
    #[arg(long, default_value = "config/ml/alpha_fill_coeffs.json")]
    fill_coeffs: PathBuf,
    #[arg(long)]
    alpha_cache: PathBuf,
    /// Train segment fraction. First N% of snapshots used for DQN training,
    /// the rest for evaluation.
    #[arg(long, default_value_t = 0.8)]
    train_frac: f32,
    /// Snapshots to load from the fxcache.
    #[arg(long, default_value_t = 1_500_000)]
    max_snapshots: usize,
    /// Episode horizon in snapshots.
    #[arg(long, default_value_t = 600)]
    horizon: usize,
    /// DQN training episodes (on train segment).
    #[arg(long, default_value_t = 1_000)]
    n_train_episodes: usize,
    /// Frozen-policy evaluation episodes per cost level.
    #[arg(long, default_value_t = 500)]
    n_eval_episodes: usize,
    /// Comma-separated cost grid (price units per contract round-turn).
    /// Phase 1d.4 used [0.0, 0.0625, 0.125, 0.25, 0.50].
    #[arg(long, value_delimiter = ',', default_value = "0.0,0.0625,0.125,0.25,0.5")]
    cost_grid: Vec<f32>,
    #[arg(long, default_value_t = 1)]
    trade_size: i32,
    #[arg(long, default_value_t = 0xCAFEBABE_u64)]
    seed: u64,
    /// Training-time cost (the policy LEARNED against this cost).
    #[arg(long, default_value_t = 0.0625)]
    train_cost: f32,
    /// SGD learning rate during training.
    #[arg(long, default_value_t = 1.0e-4)]
    lr: f32,
    #[arg(long, default_value_t = 0.50)]
    eps_start: f32,
    #[arg(long, default_value_t = 0.05)]
    eps_end: f32,
    #[arg(long, default_value_t = 0.99)]
    gamma: f32,
    #[arg(long, default_value_t = 0.9)]
    alpha_m: f32,
    #[arg(long, default_value_t = 0.03)]
    tau: f32,
    #[arg(long, default_value_t = -1.0)]
    log_clip_min: f32,
    #[arg(long, default_value_t = 1000.0)]
    reward_scale: f32,
    #[arg(long, default_value_t = 10)]
    target_update_every: usize,
    #[arg(long, default_value_t = 1.0)]
    grad_clip: f32,
    #[arg(long, default_value = "config/ml/alpha_compose_backtest.json")]
    out_path: PathBuf,
}

struct SmokeRng {
    state: u64,
}

impl SmokeRng {
    fn new(seed: u64) -> Self {
        Self { state: seed }
    }
    fn next_u64(&mut self) -> u64 {
        self.state = self.state.wrapping_add(0x9E37_79B9_7F4A_7C15);
        let mut z = self.state;
        z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9);
        z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB);
        z ^ (z >> 31)
    }
    fn next_f32(&mut self) -> f32 {
        ((self.next_u64() >> 40) as f32) / ((1u64 << 24) as f32)
    }
}

fn epsilon_greedy(q: &[f32], eps: f32, rng: &mut SmokeRng) -> u8 {
    if rng.next_f32() < eps {
        (rng.next_u64() % N_ACTIONS as u64) as u8
    } else {
        let mut best_i: usize = 0;
        let mut best_v: f32 = q[0];
        for i in 1..N_ACTIONS {
            if q[i] > best_v {
                best_v = q[i];
                best_i = i;
            }
        }
        best_i as u8
    }
}

#[derive(Debug, Clone, serde::Serialize)]
struct CostBin {
    cost: f32,
    n_episodes: usize,
    mean_reward: f32,
    std_reward: f32,
    sharpe_per_episode: f32,
    /// Sharpe scaled by sqrt(episodes_per_year). Time span derived from
    /// the eval window's mid_price index span × bar_seconds.
    sharpe_annualised: f32,
    win_rate: f32,
    p05: f32,
    p50: f32,
    p95: f32,
}

fn main() -> Result<()> {
    tracing_subscriber::fmt()
        .with_env_filter(
            tracing_subscriber::EnvFilter::try_from_default_env()
                .unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
        )
        .init();
    let cli = Cli::parse();
    info!("Phase E.3 Task 23 — composition backtest starting");

    let ctx = CudaContext::new(0).context("CUDA init")?;
    let stream = ctx.default_stream();

    // --- Load cubins ---
    let lq_module = ctx
        .load_cubin(ml::cuda_pipeline::alpha_kernels::ALPHA_LINEAR_Q_CUBIN.to_vec())
        .context("alpha_linear_q cubin")?;
    let lq_fwd = lq_module.load_function("alpha_linear_q_forward_kernel")?;
    let lq_grad = lq_module.load_function("alpha_linear_q_grad_kernel")?;
    let lq_sgd = lq_module.load_function("alpha_linear_q_sgd_step_kernel")?;
    let lq_clip = lq_module.load_function("alpha_clip_inplace_kernel")?;
    let munch_cubin: Vec<u8> = std::fs::read(concat!(
        env!("OUT_DIR"),
        "/alpha_munchausen_target.cubin"
    ))?;
    let munch_module = ctx.load_cubin(munch_cubin)?;
    let munch_kernel =
        munch_module.load_function("alpha_munchausen_target_kernel")?;

    // --- 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),
    )?;
    let n_total = rows.len();
    info!("Loaded {} snapshots", n_total);

    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 {}..{})",
        n_train, n_train, n_eval, n_train, n_total
    );

    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 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)
        .map(|_| xavier_scale * 2.0 * (rng.next_f32() - 0.5))
        .collect();
    let b_init: Vec<f32> = vec![0.0; N_BIASES];
    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)?;
    let mut db_dev = stream.alloc_zeros::<f32>(N_BIASES)?;

    let state_dim_i = STATE_DIM as i32;
    let n_act_i = N_ACTIONS 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)?;

    // ---------------------------------------------------------------
    // 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();
            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 action = epsilon_greedy(&q_host, eps, &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)?;
        {
            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
        {
            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 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 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 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 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: COST SWEEP — frozen-policy greedy eval per cost.
    // ---------------------------------------------------------------
    info!("=== Eval phase: {} episodes per cost × {} costs ===",
          cli.n_eval_episodes, 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());
    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;
        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;
            loop {
                let s_vec = env.state(&state).to_vec();
                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
                let action = epsilon_greedy(&q_host, 0.0, &mut greedy_rng);
                match env.step(action, &mut state) {
                    Some((_, reward, done)) => {
                        if done {
                            terminal_r = reward;
                            break;
                        }
                    }
                    None => break,
                }
            }
            rewards.push(terminal_r);
            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;
        // Annualised Sharpe via per-episode Sharpe × sqrt(episodes/year).
        // Episode duration ≈ horizon × bar_seconds. ES.FUT MBP-10 snapshots
        // at snapshot_interval=50 ≈ 12 sec/bar on average; 600-bar horizon
        // ≈ 7200 s ≈ 2 hours. Episodes per year ≈ 252 × 6.5 × 3600 / 7200
        // ≈ 819. Sharpe_ann ≈ Sharpe_per × sqrt(819) ≈ 28.6.
        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 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,
            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,
            p05: pick(0.05),
            p50: pick(0.50),
            p95: pick(0.95),
        };
        info!(
            "  cost={:>6.4}  mean={:>+9.2}  std={:>8.2}  Sharpe/ep={:+.3}  Sharpe_ann={:+.3}  win_rate={:.3}",
            cost, mean, std, sharpe_per, sharpe_ann, win_rate
        );
        bins.push(bin);
    }

    // --- Print table ---
    info!("");
    info!("=== Phase E.3 cost-sweep table (vs Phase 1d.4 baseline) ===");
    info!(
        "  {:>6}  {:>8}  {:>9}  {:>9}  {:>10}  {:>11}  {:>8}",
        "cost", "n_ep", "mean_R", "std_R", "Sharpe/ep", "Sharpe_ann", "win_rate"
    );
    for b in &bins {
        info!(
            "  {:>6.4}  {:>8}  {:>9.2}  {:>9.2}  {:>10.4}  {:>11.4}  {:>8.3}",
            b.cost, b.n_episodes, b.mean_reward, b.std_reward,
            b.sharpe_per_episode, b.sharpe_annualised, b.win_rate
        );
    }
    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",
        "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,
        "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; 3],
        ask_l: [0.0; 3],
        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(())
}