fc41440dc9
v1 instrumentation (S1.20) eliminated 3 of 6 hypotheses: apply_fill_to_pos arithmetic is fully clean (avg_px=0 realised=0 realized_pnl=0). But pnl_track open branch still saw vwap_entry=0 (194 times) or > 21M (216 times) at the open transition. v2 adds per-vwap-write-site counters so we can pinpoint which apply_fill branch produced the bad vwap, plus captures the actual last-bad-vwap value and the path id (1..6 = open-flat, scale-in zero/huge, flip-beyond zero/huge, session-gap-saw-stale). The 7th counter (vwap_session_gap_was_bad) fires when the session-gap force-close path sees pos.vwap_entry already in a corrupt state pre-gap. Logged at end of smoke as `nan_counters_v2: zero_flat=N huge_flat=N zero_scale=N huge_scale=N zero_flip=N huge_flip=N session_gap_was_bad=N | b0_last_bad_vwap=X b0_last_bad_path=N`. 19/19 stop_controller CUDA tests pass; 5/5 decision_floor_coldstart pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
426 lines
20 KiB
Rust
426 lines
20 KiB
Rust
//! BacktestHarness — orchestrator wiring ml-alpha's MultiHorizonLoader
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//! and PerceptionTrainer (in inference role) into the LobSimCuda.
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//!
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//! Design: the harness reuses the trainer's loader + forward chain
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//! (PerceptionTrainer::evaluate_batched, accessed via `forward_only`)
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//! so train-vs-deploy skew is structurally impossible. The input struct
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//! (`Mbp10RawInput`), feature-assembly cubin, and full perception
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//! forward are the same code paths used in training. The harness's
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//! only job is to walk the chronological snapshot stream, maintain a
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//! sliding K-window for the recurrent Mamba2 context, drive the sim's
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//! decision loop with the trainer's per-horizon probability output.
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use anyhow::{Context, Result};
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use ml_alpha::cfc::snap_features::Mbp10RawInput;
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use ml_alpha::data::loader::{
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discover_mbp10_files_sorted, MultiHorizonLoader, MultiHorizonLoaderConfig,
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};
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use ml_alpha::heads::N_HORIZONS;
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use ml_alpha::trainer::perception::PerceptionTrainer;
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use ml_core::device::MlDevice;
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use std::collections::VecDeque;
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use std::path::PathBuf;
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use crate::sim::LobSimCuda;
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#[derive(Clone, Debug)]
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pub struct BacktestHarnessConfig {
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pub data_root: PathBuf,
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pub predecoded_dir: PathBuf,
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pub n_parallel: usize,
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pub decision_stride: usize,
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pub target_annual_vol_units: f32,
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pub annualisation_factor: f32,
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pub max_lots: u16,
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/// Max number of inference inputs to consume. 0 = exhaust loader.
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pub max_events: u64,
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/// Per-backtest Strategy compositions. Empty = every cell uses the
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/// hardcoded default policy (WeightedByRealizedSharpe across all 5
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/// horizons). Non-empty: len MUST equal n_parallel; each entry is
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/// flattened to bytecode and uploaded to its backtest slot.
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pub strategies: Vec<crate::policy::Strategy>,
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/// Total submission→fill latency in nanoseconds. 0 = immediate
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/// (legacy path). 100_000_000 = 100ms IBKR + Scaleway baseline.
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/// When non-zero, the orchestrator submits aggressive IOC limits
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/// with `active=2`/`arrival_ts_ns = current + latency_ns` so the
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/// book can move under them during the in-flight window.
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pub latency_ns: u32,
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/// Cold-start Kelly fraction floor — used when isv_kelly is at
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/// sentinel (no closed trades yet). Per
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/// pearl_blend_formulas_must_have_permanent_floor: max(floor, real),
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/// not blend. Default 0.10 = trade conservatively until Kelly state
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/// bootstraps.
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pub kelly_frac_floor: f32,
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/// Cold-start Sharpe weight floor — applied to recent_sharpe when
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/// aggregating signed-sizes across horizons. Lets the cross-horizon
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/// sum produce a non-zero size before recent_sharpe is populated.
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/// Default 0.10 = uniform 1/5 weight per horizon at cold-start.
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pub sharpe_weight_floor: f32,
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/// P4: threshold gate — absolute conviction cutoff. When
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/// `max_h |alpha[h] - 0.5| * 2 < threshold`, the decision kernels
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/// emit noop. Default 0.0 = gate disabled (every signal passes).
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pub threshold: f32,
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/// P4: per-fill cost (price-units / lot / side). Deducted from
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/// pos.realized_pnl and accumulated into total_fees_per_b_d at
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/// every fill. Default 0.0 = no cost (frictionless).
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pub cost_per_lot_per_side: f32,
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/// P6: optional per-backtest variant names. When Some, write_artifacts
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/// writes subdirs as `sim_<name>` instead of `cell_NNNN`. Length MUST
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/// equal n_parallel when set. None preserves the legacy cell_NNNN
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/// naming for non-grid-pack callers (smoke, fixtures).
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pub variant_names: Option<Vec<String>>,
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/// P6: optional pre-built BatchedSimConfig (one entry per backtest =
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/// per variant). When Some, the harness uses this directly instead
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/// of building from_uniform off the scalar fields above. Length MUST
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/// equal n_parallel when set.
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pub sim_config_override: Option<crate::sim::BatchedSimConfig>,
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}
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pub struct BacktestHarness {
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cfg: BacktestHarnessConfig,
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/// Per-backtest sim parameter arrays. P1 onwards is the source of
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/// truth for sim-side scalar params; cfg's scalar fields are still
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/// used to construct this via BatchedSimConfig::from_uniform at
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/// harness new(), but the run-loop reads sim_config, not cfg.
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sim_config: crate::sim::BatchedSimConfig,
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loader: MultiHorizonLoader,
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/// PerceptionTrainer in inference role — owns the trunk (loaded from
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/// Checkpoint) and the kernel-launch scratches. Forward driven via
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/// `forward_only` at decision-stride boundaries.
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trainer: PerceptionTrainer,
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/// Sliding K-window of recent snapshots for the recurrent forward.
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/// At every decision-stride boundary, when the window has reached
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/// `seq_len` entries, we call `trainer.forward_only(&window)` and
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/// take the last K position's probs.
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snapshot_window: VecDeque<Mbp10RawInput>,
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/// Window capacity = trainer's seq_len, captured at construction.
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seq_len: usize,
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sim: LobSimCuda,
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decision_count: u64,
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event_count: u64,
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/// Per-cell cumulative P&L curve in USD, sampled once per event.
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/// `pnl_curves[b][i]` = realized_pnl USD for backtest b after event i.
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pnl_curves: Vec<Vec<f32>>,
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/// Side-channel log of max_conviction per decision (one value per
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/// stride boundary, NOT per event). Shared across all backtests in
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/// a batched cell because broadcast_alpha gives every backtest the
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/// same probs. Used by the threshold-tuning step to compute p60-p95
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/// absolute values: percentiles of this Vec → calibrated thresholds.
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conviction_log: Vec<f32>,
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}
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impl BacktestHarness {
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/// Construct with an externally-built MlDevice + PerceptionTrainer.
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/// Caller loads the trainer from a Checkpoint via
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/// `PerceptionTrainer::from_checkpoint`. The harness reads
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/// `trainer.config().seq_len` to size its sliding snapshot window.
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pub fn new(
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cfg: BacktestHarnessConfig,
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dev: &MlDevice,
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trainer: PerceptionTrainer,
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) -> Result<Self> {
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if !cfg.strategies.is_empty() {
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anyhow::ensure!(
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cfg.strategies.len() == cfg.n_parallel,
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"strategies len {} ≠ n_parallel {}",
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cfg.strategies.len(),
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cfg.n_parallel
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);
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}
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let files = discover_mbp10_files_sorted(&cfg.data_root)?;
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let loader_cfg = MultiHorizonLoaderConfig {
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files,
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predecoded_dir: cfg.predecoded_dir.clone(),
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seq_len: 1,
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horizons: [30, 100, 300, 1000, 6000],
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n_max_sequences: 0,
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seed: 0,
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decision_stride: cfg.decision_stride,
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inference_only: true,
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};
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let loader = MultiHorizonLoader::new(&loader_cfg)?;
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let seq_len = trainer.config().seq_len;
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anyhow::ensure!(
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trainer.config().n_batch == 1,
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"BacktestHarness requires PerceptionTrainer with n_batch=1 (got {})",
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trainer.config().n_batch
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);
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let mut sim = LobSimCuda::new(cfg.n_parallel, dev)?;
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// P6: if sim_config_override is provided (sweep runner grid-pack
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// flow), use it directly; otherwise build a uniform broadcast from
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// scalar cfg fields (smoke + fixture flows).
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let sim_config = match cfg.sim_config_override.clone() {
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Some(bc) => {
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anyhow::ensure!(
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bc.n_backtests() == cfg.n_parallel,
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"sim_config_override has {} variants but n_parallel = {}",
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bc.n_backtests(), cfg.n_parallel,
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);
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bc
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}
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None => crate::sim::BatchedSimConfig::from_uniform(
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cfg.n_parallel,
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&crate::sim::UniformSimParams {
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target_annual_vol_units: cfg.target_annual_vol_units,
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annualisation_factor: cfg.annualisation_factor,
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max_lots: cfg.max_lots,
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latency_ns: cfg.latency_ns,
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kelly_frac_floor: cfg.kelly_frac_floor,
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sharpe_weight_floor: cfg.sharpe_weight_floor,
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threshold: cfg.threshold,
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cost_per_lot_per_side: cfg.cost_per_lot_per_side,
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max_hold_ns: 0, // default disabled; set via sim_config_override
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min_reasonable_px: 0.0,
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max_reasonable_px: f32::INFINITY,
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},
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),
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};
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// Upload caller-provided strategies (one program per backtest).
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for (b, strat) in cfg.strategies.iter().enumerate() {
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let prog = strat.flatten();
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sim.upload_program(b, &prog)?;
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}
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// S1.19: upload per-backtest price-range bounds from sim_config.
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sim.upload_price_range(&sim_config.min_reasonable_px, &sim_config.max_reasonable_px)?;
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let pnl_curves = (0..cfg.n_parallel).map(|_| Vec::with_capacity(1024)).collect();
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Ok(Self {
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cfg,
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sim_config,
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loader,
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trainer,
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snapshot_window: VecDeque::with_capacity(seq_len),
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seq_len,
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sim,
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decision_count: 0,
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event_count: 0,
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pnl_curves,
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// Pre-size for ~2.5M decisions (one full quarter at stride=4).
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// Auto-grows past this; pre-allocation just avoids re-allocs.
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conviction_log: Vec::with_capacity(3_000_000),
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})
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}
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/// Drive the chronological event stream → trunk inference → sim
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/// decision loop until either the stream is exhausted or
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/// cfg.max_events is reached. See spec §7 orchestrator code.
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pub fn run(&mut self) -> Result<RunStats> {
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let stride = self.cfg.decision_stride.max(1) as u64;
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let mut total_decisions = 0u64;
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// Periodic progress line — without this, multi-million-event
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// runs look identical to a deadlock in pod logs.
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const PROGRESS_EVERY: u64 = 1_000_000;
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let mut next_progress_event = PROGRESS_EVERY;
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let started = std::time::Instant::now();
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while let Some(raw) = self.loader.next_inference_input()? {
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// Apply snapshot to every backtest's book.
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self.sim.apply_snapshot(&raw.bid_px, &raw.bid_sz, &raw.ask_px, &raw.ask_sz)?;
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// Process resting orders (in-flight promotion, queue decay,
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// marketability check, stop triggers, OCO). Trade-flow signal
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// comes from the loader's L1-delta tick-rule inference (see
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// ml_alpha::data::loader::infer_signed_trade_flow). Positive =
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// buyer-initiated, negative = seller-initiated.
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self.sim.step_resting_orders(raw.ts_ns, raw.trade_signed_vol)?;
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// Maintain the sliding K-window for the recurrent forward.
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// Push the current snapshot, evict the oldest if at capacity.
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if self.snapshot_window.len() == self.seq_len {
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self.snapshot_window.pop_front();
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}
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self.snapshot_window.push_back(raw.clone());
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// At decision-stride boundaries: run forward inference + sim
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// decision. Skip until the window is full (insufficient
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// context for Mamba2's recurrent state).
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if self.event_count % stride == 0 && self.snapshot_window.len() == self.seq_len {
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let window: Vec<Mbp10RawInput> = self.snapshot_window.iter().cloned().collect();
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let probs_all = self.trainer.forward_only(&window)
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.context("trainer.forward_only")?;
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// probs_all is [K * B * N_HORIZONS] with B=1; take the
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// LAST K position's probs as the decision signal.
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let last_probs_start = (self.seq_len - 1) * N_HORIZONS;
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let last_probs: [f32; N_HORIZONS] = probs_all[last_probs_start..]
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.try_into()
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.context("slice last K probs")?;
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// Side-channel: record this decision's max_conviction for
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// the threshold-tuning percentile computation. Doing it
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// BEFORE broadcast/step so the log captures every decision
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// attempt, including those the threshold gate would skip.
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let max_conv = last_probs.iter()
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.map(|p| ((p - 0.5).abs() * 2.0).min(1.0).max(0.0))
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.fold(0.0_f32, f32::max);
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self.conviction_log.push(max_conv);
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self.sim.broadcast_alpha(&last_probs)?;
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self.sim.step_decision_with_latency(raw.ts_ns, &self.sim_config)?;
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self.decision_count += 1;
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total_decisions += 1;
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}
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// Sample per-cell realised P&L (price-units → USD via $50/index-pt).
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for b in 0..self.cfg.n_parallel {
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let pos = self.sim.read_pos(b)?;
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self.pnl_curves[b].push(pos.realized_pnl * 50.0);
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}
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self.event_count += 1;
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if self.event_count >= next_progress_event {
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let elapsed = started.elapsed().as_secs_f32();
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let rate = self.event_count as f32 / elapsed.max(1e-3);
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let trades = self.sim.read_total_trade_count().unwrap_or(0);
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eprintln!(
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"progress: events={} decisions={} trades={} elapsed={:.0}s rate={:.0}ev/s",
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self.event_count, total_decisions, trades, elapsed, rate,
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);
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next_progress_event += PROGRESS_EVERY;
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}
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if self.cfg.max_events > 0 && self.event_count >= self.cfg.max_events {
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break;
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}
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}
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// S1.20: NaN instrumentation summary. Print ONCE per smoke so the
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// cluster log shows which kernel-side arithmetic site produced the
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// residual 97 zero + 85 i32::MAX sentinel trade records.
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match self.sim.read_nan_counters() {
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Ok(counters) => {
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let sum_u32 = |v: &[u32]| -> u64 { v.iter().map(|&x| x as u64).sum() };
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eprintln!(
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"nan_counters: avg_px={} realised={} realized_pnl={} zero_vwap_open={} saturated_vwap_open={} defensive_clamp={}",
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sum_u32(&counters.nan_avg_px),
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sum_u32(&counters.nan_realised),
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sum_u32(&counters.nan_realized_pnl),
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sum_u32(&counters.zero_vwap_at_open),
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sum_u32(&counters.saturated_vwap_at_open),
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sum_u32(&counters.defensive_exit_clamp),
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);
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}
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Err(e) => eprintln!("nan_counters read failed: {e}"),
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}
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// S1.21: per-vwap-write-site counters. Printed after v1 line so both
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// appear together in the smoke log.
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match self.sim.read_nan_counters_v2() {
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Ok(c) => {
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let sum_u32 = |v: &[u32]| -> u64 { v.iter().map(|&x| x as u64).sum() };
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let last_bad_vwap_b0 = c.last_bad_vwap.first().copied().unwrap_or(0.0);
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let last_bad_path_b0 = c.last_bad_path.first().copied().unwrap_or(0);
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eprintln!(
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"nan_counters_v2: zero_flat={} huge_flat={} zero_scale={} huge_scale={} zero_flip={} huge_flip={} session_gap_was_bad={} | b0_last_bad_vwap={} b0_last_bad_path={}",
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sum_u32(&c.vwap_zero_open_flat),
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sum_u32(&c.vwap_huge_open_flat),
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sum_u32(&c.vwap_zero_scale_in),
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sum_u32(&c.vwap_huge_scale_in),
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sum_u32(&c.vwap_zero_flip),
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sum_u32(&c.vwap_huge_flip),
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sum_u32(&c.vwap_session_gap_was_bad),
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last_bad_vwap_b0,
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last_bad_path_b0,
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);
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}
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Err(e) => eprintln!("nan_counters_v2 read failed: {e}"),
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}
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Ok(RunStats {
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events_processed: self.event_count,
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decisions_taken: total_decisions,
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})
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}
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/// After `run()` completes, write per-cell artifacts to
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/// `<out_dir>/cell_<b>/{summary.json,trades.csv,pnl_curve.bin}`.
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pub fn write_artifacts(&self, out_dir: &std::path::Path) -> Result<()> {
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std::fs::create_dir_all(out_dir)
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.with_context(|| format!("create out dir {}", out_dir.display()))?;
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// Write the threshold-tuning side-channel ONCE per cell (shared
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// across all backtests in a batched cell because broadcast_alpha
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// gives every backtest the same probs). Raw convictions.bin
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// (little-endian f32) + conviction_percentiles.json with the
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// pre-computed p60/p70/p80/p90/p95 values.
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if !self.conviction_log.is_empty() {
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let convictions_path = out_dir.join("convictions.bin");
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let mut bytes = Vec::with_capacity(self.conviction_log.len() * 4);
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for v in &self.conviction_log {
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bytes.extend_from_slice(&v.to_le_bytes());
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}
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std::fs::write(&convictions_path, &bytes)
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.with_context(|| format!("write {}", convictions_path.display()))?;
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let mut sorted = self.conviction_log.clone();
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sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
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let pct = |q: f32| -> f32 {
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let idx = ((sorted.len() - 1) as f32 * q).round() as usize;
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sorted[idx]
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};
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let pcts = serde_json::json!({
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"n_decisions": sorted.len(),
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"min": sorted.first().copied().unwrap_or(0.0),
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"max": sorted.last().copied().unwrap_or(0.0),
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"mean": sorted.iter().sum::<f32>() / sorted.len() as f32,
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"p10": pct(0.10),
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"p25": pct(0.25),
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"p50": pct(0.50),
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"p60": pct(0.60),
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"p70": pct(0.70),
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"p80": pct(0.80),
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"p90": pct(0.90),
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"p95": pct(0.95),
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"p99": pct(0.99),
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});
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let pcts_path = out_dir.join("conviction_percentiles.json");
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std::fs::write(&pcts_path, serde_json::to_string_pretty(&pcts)?)
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.with_context(|| format!("write {}", pcts_path.display()))?;
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eprintln!(
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"convictions: n={} min={:.4} p60={:.4} p70={:.4} p80={:.4} p90={:.4} p95={:.4} max={:.4}",
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sorted.len(),
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sorted.first().copied().unwrap_or(0.0),
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pct(0.60), pct(0.70), pct(0.80), pct(0.90), pct(0.95),
|
|
sorted.last().copied().unwrap_or(0.0),
|
|
);
|
|
}
|
|
if let Some(names) = self.cfg.variant_names.as_ref() {
|
|
anyhow::ensure!(
|
|
names.len() == self.cfg.n_parallel,
|
|
"variant_names len {} != n_parallel {}",
|
|
names.len(), self.cfg.n_parallel,
|
|
);
|
|
}
|
|
for b in 0..self.cfg.n_parallel {
|
|
// P6: when sweep runner provides variant names, dir = sim_<name>
|
|
// per spec §3.3. Otherwise fall back to legacy cell_NNNN naming.
|
|
let subdir_name = match self.cfg.variant_names.as_ref() {
|
|
Some(names) => format!("sim_{}", names[b]),
|
|
None => format!("cell_{b:04}"),
|
|
};
|
|
let cell_dir = out_dir.join(subdir_name);
|
|
std::fs::create_dir_all(&cell_dir)
|
|
.with_context(|| format!("create cell dir {}", cell_dir.display()))?;
|
|
let records = self.sim.read_trade_records(b)?;
|
|
let curve = &self.pnl_curves[b];
|
|
let summary = crate::artifacts::compute_summary(&records, curve);
|
|
crate::artifacts::write_summary(&cell_dir.join("summary.json"), &summary)?;
|
|
crate::artifacts::write_trades_csv(&cell_dir.join("trades.csv"), &records)?;
|
|
crate::artifacts::write_pnl_curve_bin(&cell_dir.join("pnl_curve.bin"), curve)?;
|
|
}
|
|
Ok(())
|
|
}
|
|
|
|
pub fn sim(&self) -> &LobSimCuda { &self.sim }
|
|
pub fn event_count(&self) -> u64 { self.event_count }
|
|
pub fn decision_count(&self) -> u64 { self.decision_count }
|
|
}
|
|
|
|
#[derive(Clone, Copy, Debug)]
|
|
pub struct RunStats {
|
|
pub events_processed: u64,
|
|
pub decisions_taken: u64,
|
|
}
|