fef5939556
The threshold-tuning smoke at 81decf40f produced n_trades=0 despite
74.6% of decisions having max_conv ≥ 0.30 — the linear-weighted-mean
aggregator in decision_policy_default is structurally dilution-bound
at cold-start (per spec §1).
Q1 stopgap: when sim_variants[i].use_cold_start_stopgap = true, the
harness uploads a max-confidence Strategy bytecode program for that
backtest, routing decisions through decision_policy_program with
OP_AGG_MAX_CONFIDENCE. Existing kernel; zero CUDA changes.
Field additions (atomically across BatchedSimConfig + UniformSimParams
+ ResolvedSimVariant + SweepBase.SimVariant) — every UniformSimParams
literal migrated to include use_cold_start_stopgap: false (default).
The sweep YAML's sim_variants entry sets it to true only for the
validation run; production deployability uses Q2's kernel fix instead.
Sweep YAML (config/ml/sweep_smoke.yaml) flipped to use_cold_start_stopgap=true
at threshold=0.0, cost=0.125 — same anchor as the threshold-tuning
smoke that produced n_trades=0, for direct comparison.
This is a VALIDATION step. Cluster smoke at this commit MUST produce
n_trades > 100 + finite metrics. Q2's kernel CBSW immediately follows
and deletes this entire stopgap atomically (field, harness branch,
YAML setting, every literal).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
410 lines
19 KiB
Rust
410 lines
19 KiB
Rust
//! BacktestHarness — orchestrator wiring ml-alpha's MultiHorizonLoader
|
|
//! and PerceptionTrainer (in inference role) into the LobSimCuda.
|
|
//!
|
|
//! Design: the harness reuses the trainer's loader + forward chain
|
|
//! (PerceptionTrainer::evaluate_batched, accessed via `forward_only`)
|
|
//! so train-vs-deploy skew is structurally impossible. The input struct
|
|
//! (`Mbp10RawInput`), feature-assembly cubin, and full perception
|
|
//! forward are the same code paths used in training. The harness's
|
|
//! only job is to walk the chronological snapshot stream, maintain a
|
|
//! sliding K-window for the recurrent Mamba2 context, drive the sim's
|
|
//! decision loop with the trainer's per-horizon probability output.
|
|
|
|
use anyhow::{Context, Result};
|
|
use ml_alpha::cfc::snap_features::Mbp10RawInput;
|
|
use ml_alpha::data::loader::{
|
|
discover_mbp10_files_sorted, MultiHorizonLoader, MultiHorizonLoaderConfig,
|
|
};
|
|
use ml_alpha::heads::N_HORIZONS;
|
|
use ml_alpha::trainer::perception::PerceptionTrainer;
|
|
use ml_core::device::MlDevice;
|
|
use std::collections::VecDeque;
|
|
use std::path::PathBuf;
|
|
|
|
use crate::sim::LobSimCuda;
|
|
|
|
#[derive(Clone, Debug)]
|
|
pub struct BacktestHarnessConfig {
|
|
pub data_root: PathBuf,
|
|
pub predecoded_dir: PathBuf,
|
|
pub n_parallel: usize,
|
|
pub decision_stride: usize,
|
|
pub target_annual_vol_units: f32,
|
|
pub annualisation_factor: f32,
|
|
pub max_lots: u16,
|
|
/// Max number of inference inputs to consume. 0 = exhaust loader.
|
|
pub max_events: u64,
|
|
/// Per-backtest Strategy compositions. Empty = every cell uses the
|
|
/// hardcoded default policy (WeightedByRealizedSharpe across all 5
|
|
/// horizons). Non-empty: len MUST equal n_parallel; each entry is
|
|
/// flattened to bytecode and uploaded to its backtest slot.
|
|
pub strategies: Vec<crate::policy::Strategy>,
|
|
/// Total submission→fill latency in nanoseconds. 0 = immediate
|
|
/// (legacy path). 100_000_000 = 100ms IBKR + Scaleway baseline.
|
|
/// When non-zero, the orchestrator submits aggressive IOC limits
|
|
/// with `active=2`/`arrival_ts_ns = current + latency_ns` so the
|
|
/// book can move under them during the in-flight window.
|
|
pub latency_ns: u32,
|
|
/// Cold-start Kelly fraction floor — used when isv_kelly is at
|
|
/// sentinel (no closed trades yet). Per
|
|
/// pearl_blend_formulas_must_have_permanent_floor: max(floor, real),
|
|
/// not blend. Default 0.10 = trade conservatively until Kelly state
|
|
/// bootstraps.
|
|
pub kelly_frac_floor: f32,
|
|
/// Cold-start Sharpe weight floor — applied to recent_sharpe when
|
|
/// aggregating signed-sizes across horizons. Lets the cross-horizon
|
|
/// sum produce a non-zero size before recent_sharpe is populated.
|
|
/// Default 0.10 = uniform 1/5 weight per horizon at cold-start.
|
|
pub sharpe_weight_floor: f32,
|
|
/// P4: threshold gate — absolute conviction cutoff. When
|
|
/// `max_h |alpha[h] - 0.5| * 2 < threshold`, the decision kernels
|
|
/// emit noop. Default 0.0 = gate disabled (every signal passes).
|
|
pub threshold: f32,
|
|
/// P4: per-fill cost (price-units / lot / side). Deducted from
|
|
/// pos.realized_pnl and accumulated into total_fees_per_b_d at
|
|
/// every fill. Default 0.0 = no cost (frictionless).
|
|
pub cost_per_lot_per_side: f32,
|
|
/// P6: optional per-backtest variant names. When Some, write_artifacts
|
|
/// writes subdirs as `sim_<name>` instead of `cell_NNNN`. Length MUST
|
|
/// equal n_parallel when set. None preserves the legacy cell_NNNN
|
|
/// naming for non-grid-pack callers (smoke, fixtures).
|
|
pub variant_names: Option<Vec<String>>,
|
|
/// P6: optional pre-built BatchedSimConfig (one entry per backtest =
|
|
/// per variant). When Some, the harness uses this directly instead
|
|
/// of building from_uniform off the scalar fields above. Length MUST
|
|
/// equal n_parallel when set.
|
|
pub sim_config_override: Option<crate::sim::BatchedSimConfig>,
|
|
}
|
|
|
|
pub struct BacktestHarness {
|
|
cfg: BacktestHarnessConfig,
|
|
/// Per-backtest sim parameter arrays. P1 onwards is the source of
|
|
/// truth for sim-side scalar params; cfg's scalar fields are still
|
|
/// used to construct this via BatchedSimConfig::from_uniform at
|
|
/// harness new(), but the run-loop reads sim_config, not cfg.
|
|
sim_config: crate::sim::BatchedSimConfig,
|
|
loader: MultiHorizonLoader,
|
|
/// PerceptionTrainer in inference role — owns the trunk (loaded from
|
|
/// Checkpoint) and the kernel-launch scratches. Forward driven via
|
|
/// `forward_only` at decision-stride boundaries.
|
|
trainer: PerceptionTrainer,
|
|
/// Sliding K-window of recent snapshots for the recurrent forward.
|
|
/// At every decision-stride boundary, when the window has reached
|
|
/// `seq_len` entries, we call `trainer.forward_only(&window)` and
|
|
/// take the last K position's probs.
|
|
snapshot_window: VecDeque<Mbp10RawInput>,
|
|
/// Window capacity = trainer's seq_len, captured at construction.
|
|
seq_len: usize,
|
|
sim: LobSimCuda,
|
|
decision_count: u64,
|
|
event_count: u64,
|
|
/// Per-cell cumulative P&L curve in USD, sampled once per event.
|
|
/// `pnl_curves[b][i]` = realized_pnl USD for backtest b after event i.
|
|
pnl_curves: Vec<Vec<f32>>,
|
|
/// Side-channel log of max_conviction per decision (one value per
|
|
/// stride boundary, NOT per event). Shared across all backtests in
|
|
/// a batched cell because broadcast_alpha gives every backtest the
|
|
/// same probs. Used by the threshold-tuning step to compute p60-p95
|
|
/// absolute values: percentiles of this Vec → calibrated thresholds.
|
|
conviction_log: Vec<f32>,
|
|
}
|
|
|
|
impl BacktestHarness {
|
|
/// Construct with an externally-built MlDevice + PerceptionTrainer.
|
|
/// Caller loads the trainer from a Checkpoint via
|
|
/// `PerceptionTrainer::from_checkpoint`. The harness reads
|
|
/// `trainer.config().seq_len` to size its sliding snapshot window.
|
|
pub fn new(
|
|
cfg: BacktestHarnessConfig,
|
|
dev: &MlDevice,
|
|
trainer: PerceptionTrainer,
|
|
) -> Result<Self> {
|
|
if !cfg.strategies.is_empty() {
|
|
anyhow::ensure!(
|
|
cfg.strategies.len() == cfg.n_parallel,
|
|
"strategies len {} ≠ n_parallel {}",
|
|
cfg.strategies.len(),
|
|
cfg.n_parallel
|
|
);
|
|
}
|
|
let files = discover_mbp10_files_sorted(&cfg.data_root)?;
|
|
let loader_cfg = MultiHorizonLoaderConfig {
|
|
files,
|
|
predecoded_dir: cfg.predecoded_dir.clone(),
|
|
seq_len: 1,
|
|
horizons: [30, 100, 300, 1000, 6000],
|
|
n_max_sequences: 0,
|
|
seed: 0,
|
|
decision_stride: cfg.decision_stride,
|
|
inference_only: true,
|
|
};
|
|
let loader = MultiHorizonLoader::new(&loader_cfg)?;
|
|
|
|
let seq_len = trainer.config().seq_len;
|
|
anyhow::ensure!(
|
|
trainer.config().n_batch == 1,
|
|
"BacktestHarness requires PerceptionTrainer with n_batch=1 (got {})",
|
|
trainer.config().n_batch
|
|
);
|
|
|
|
let mut sim = LobSimCuda::new(cfg.n_parallel, dev)?;
|
|
|
|
// P6: if sim_config_override is provided (sweep runner grid-pack
|
|
// flow), use it directly; otherwise build a uniform broadcast from
|
|
// scalar cfg fields (smoke + fixture flows).
|
|
// Built BEFORE strategy upload so the Q1 stopgap branch can read
|
|
// sim_config.use_cold_start_stopgap[b].
|
|
let sim_config = match cfg.sim_config_override.clone() {
|
|
Some(bc) => {
|
|
anyhow::ensure!(
|
|
bc.n_backtests() == cfg.n_parallel,
|
|
"sim_config_override has {} variants but n_parallel = {}",
|
|
bc.n_backtests(), cfg.n_parallel,
|
|
);
|
|
bc
|
|
}
|
|
None => crate::sim::BatchedSimConfig::from_uniform(
|
|
cfg.n_parallel,
|
|
&crate::sim::UniformSimParams {
|
|
target_annual_vol_units: cfg.target_annual_vol_units,
|
|
annualisation_factor: cfg.annualisation_factor,
|
|
max_lots: cfg.max_lots,
|
|
latency_ns: cfg.latency_ns,
|
|
kelly_frac_floor: cfg.kelly_frac_floor,
|
|
sharpe_weight_floor: cfg.sharpe_weight_floor,
|
|
threshold: cfg.threshold,
|
|
cost_per_lot_per_side: cfg.cost_per_lot_per_side,
|
|
use_cold_start_stopgap: false, // Q1 stopgap defaults off in scalar-cfg path
|
|
},
|
|
),
|
|
};
|
|
|
|
// Q1 cold-start stopgap: when sim_config.use_cold_start_stopgap[b]
|
|
// is true, upload a max-confidence Strategy for that backtest.
|
|
// Routes through decision_policy_program (existing bytecode VM
|
|
// with OP_AGG_MAX_CONFIDENCE), bypassing the linear-weighted-mean
|
|
// dilution in decision_policy_default. Q2's kernel CBSW makes
|
|
// this redundant; the whole branch + field is deleted there.
|
|
let any_stopgap = sim_config.use_cold_start_stopgap.iter().any(|&b| b);
|
|
if any_stopgap {
|
|
let max_conf = crate::policy::Strategy::Ensemble {
|
|
children: (0..crate::policy::N_HORIZONS as u8)
|
|
.map(|h| crate::policy::Strategy::Leaf(crate::policy::StrategyConfig {
|
|
horizon_idx: h,
|
|
sizing_policy: crate::policy::SizingPolicyId::IsvKelly,
|
|
sl_tp_rules: crate::policy::StopRules::default(),
|
|
max_concurrent_lots: cfg.max_lots,
|
|
}))
|
|
.collect(),
|
|
aggregator: crate::policy::EnsembleAggregator::MaxConfidence,
|
|
};
|
|
let prog = max_conf.flatten();
|
|
for (b, &flag) in sim_config.use_cold_start_stopgap.iter().enumerate() {
|
|
if flag {
|
|
sim.upload_program(b, &prog)?;
|
|
}
|
|
}
|
|
}
|
|
|
|
// Caller-provided strategies (legacy path) — only for backtests
|
|
// NOT already set up by the Q1 stopgap above.
|
|
for (b, strat) in cfg.strategies.iter().enumerate() {
|
|
if !sim_config.use_cold_start_stopgap.get(b).copied().unwrap_or(false) {
|
|
let prog = strat.flatten();
|
|
sim.upload_program(b, &prog)?;
|
|
}
|
|
}
|
|
|
|
let pnl_curves = (0..cfg.n_parallel).map(|_| Vec::with_capacity(1024)).collect();
|
|
Ok(Self {
|
|
cfg,
|
|
sim_config,
|
|
loader,
|
|
trainer,
|
|
snapshot_window: VecDeque::with_capacity(seq_len),
|
|
seq_len,
|
|
sim,
|
|
decision_count: 0,
|
|
event_count: 0,
|
|
pnl_curves,
|
|
// Pre-size for ~2.5M decisions (one full quarter at stride=4).
|
|
// Auto-grows past this; pre-allocation just avoids re-allocs.
|
|
conviction_log: Vec::with_capacity(3_000_000),
|
|
})
|
|
}
|
|
|
|
/// Drive the chronological event stream → trunk inference → sim
|
|
/// decision loop until either the stream is exhausted or
|
|
/// cfg.max_events is reached. See spec §7 orchestrator code.
|
|
pub fn run(&mut self) -> Result<RunStats> {
|
|
let stride = self.cfg.decision_stride.max(1) as u64;
|
|
let mut total_decisions = 0u64;
|
|
// Periodic progress line — without this, multi-million-event
|
|
// runs look identical to a deadlock in pod logs.
|
|
const PROGRESS_EVERY: u64 = 1_000_000;
|
|
let mut next_progress_event = PROGRESS_EVERY;
|
|
let started = std::time::Instant::now();
|
|
|
|
while let Some(raw) = self.loader.next_inference_input()? {
|
|
// Apply snapshot to every backtest's book.
|
|
self.sim.apply_snapshot(&raw.bid_px, &raw.bid_sz, &raw.ask_px, &raw.ask_sz)?;
|
|
|
|
// Process resting orders (in-flight promotion, queue decay,
|
|
// marketability check, stop triggers, OCO). Trade-flow signal
|
|
// comes from the loader's L1-delta tick-rule inference (see
|
|
// ml_alpha::data::loader::infer_signed_trade_flow). Positive =
|
|
// buyer-initiated, negative = seller-initiated.
|
|
self.sim.step_resting_orders(raw.ts_ns, raw.trade_signed_vol)?;
|
|
|
|
// Maintain the sliding K-window for the recurrent forward.
|
|
// Push the current snapshot, evict the oldest if at capacity.
|
|
if self.snapshot_window.len() == self.seq_len {
|
|
self.snapshot_window.pop_front();
|
|
}
|
|
self.snapshot_window.push_back(raw.clone());
|
|
|
|
// At decision-stride boundaries: run forward inference + sim
|
|
// decision. Skip until the window is full (insufficient
|
|
// context for Mamba2's recurrent state).
|
|
if self.event_count % stride == 0 && self.snapshot_window.len() == self.seq_len {
|
|
let window: Vec<Mbp10RawInput> = self.snapshot_window.iter().cloned().collect();
|
|
let probs_all = self.trainer.forward_only(&window)
|
|
.context("trainer.forward_only")?;
|
|
// probs_all is [K * B * N_HORIZONS] with B=1; take the
|
|
// LAST K position's probs as the decision signal.
|
|
let last_probs_start = (self.seq_len - 1) * N_HORIZONS;
|
|
let last_probs: [f32; N_HORIZONS] = probs_all[last_probs_start..]
|
|
.try_into()
|
|
.context("slice last K probs")?;
|
|
// Side-channel: record this decision's max_conviction for
|
|
// the threshold-tuning percentile computation. Doing it
|
|
// BEFORE broadcast/step so the log captures every decision
|
|
// attempt, including those the threshold gate would skip.
|
|
let max_conv = last_probs.iter()
|
|
.map(|p| ((p - 0.5).abs() * 2.0).min(1.0).max(0.0))
|
|
.fold(0.0_f32, f32::max);
|
|
self.conviction_log.push(max_conv);
|
|
|
|
self.sim.broadcast_alpha(&last_probs)?;
|
|
self.sim.step_decision_with_latency(raw.ts_ns, &self.sim_config)?;
|
|
self.decision_count += 1;
|
|
total_decisions += 1;
|
|
}
|
|
|
|
// Sample per-cell realised P&L (price-units → USD via $50/index-pt).
|
|
for b in 0..self.cfg.n_parallel {
|
|
let pos = self.sim.read_pos(b)?;
|
|
self.pnl_curves[b].push(pos.realized_pnl * 50.0);
|
|
}
|
|
|
|
self.event_count += 1;
|
|
if self.event_count >= next_progress_event {
|
|
let elapsed = started.elapsed().as_secs_f32();
|
|
let rate = self.event_count as f32 / elapsed.max(1e-3);
|
|
eprintln!(
|
|
"progress: events={} decisions={} elapsed={:.0}s rate={:.0}ev/s",
|
|
self.event_count, total_decisions, elapsed, rate,
|
|
);
|
|
next_progress_event += PROGRESS_EVERY;
|
|
}
|
|
if self.cfg.max_events > 0 && self.event_count >= self.cfg.max_events {
|
|
break;
|
|
}
|
|
}
|
|
|
|
Ok(RunStats {
|
|
events_processed: self.event_count,
|
|
decisions_taken: total_decisions,
|
|
})
|
|
}
|
|
|
|
/// After `run()` completes, write per-cell artifacts to
|
|
/// `<out_dir>/cell_<b>/{summary.json,trades.csv,pnl_curve.bin}`.
|
|
pub fn write_artifacts(&self, out_dir: &std::path::Path) -> Result<()> {
|
|
std::fs::create_dir_all(out_dir)
|
|
.with_context(|| format!("create out dir {}", out_dir.display()))?;
|
|
|
|
// Write the threshold-tuning side-channel ONCE per cell (shared
|
|
// across all backtests in a batched cell because broadcast_alpha
|
|
// gives every backtest the same probs). Raw convictions.bin
|
|
// (little-endian f32) + conviction_percentiles.json with the
|
|
// pre-computed p60/p70/p80/p90/p95 values.
|
|
if !self.conviction_log.is_empty() {
|
|
let convictions_path = out_dir.join("convictions.bin");
|
|
let mut bytes = Vec::with_capacity(self.conviction_log.len() * 4);
|
|
for v in &self.conviction_log {
|
|
bytes.extend_from_slice(&v.to_le_bytes());
|
|
}
|
|
std::fs::write(&convictions_path, &bytes)
|
|
.with_context(|| format!("write {}", convictions_path.display()))?;
|
|
|
|
let mut sorted = self.conviction_log.clone();
|
|
sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
|
|
let pct = |q: f32| -> f32 {
|
|
let idx = ((sorted.len() - 1) as f32 * q).round() as usize;
|
|
sorted[idx]
|
|
};
|
|
let pcts = serde_json::json!({
|
|
"n_decisions": sorted.len(),
|
|
"min": sorted.first().copied().unwrap_or(0.0),
|
|
"max": sorted.last().copied().unwrap_or(0.0),
|
|
"mean": sorted.iter().sum::<f32>() / sorted.len() as f32,
|
|
"p10": pct(0.10),
|
|
"p25": pct(0.25),
|
|
"p50": pct(0.50),
|
|
"p60": pct(0.60),
|
|
"p70": pct(0.70),
|
|
"p80": pct(0.80),
|
|
"p90": pct(0.90),
|
|
"p95": pct(0.95),
|
|
"p99": pct(0.99),
|
|
});
|
|
let pcts_path = out_dir.join("conviction_percentiles.json");
|
|
std::fs::write(&pcts_path, serde_json::to_string_pretty(&pcts)?)
|
|
.with_context(|| format!("write {}", pcts_path.display()))?;
|
|
eprintln!(
|
|
"convictions: n={} min={:.4} p60={:.4} p70={:.4} p80={:.4} p90={:.4} p95={:.4} max={:.4}",
|
|
sorted.len(),
|
|
sorted.first().copied().unwrap_or(0.0),
|
|
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,
|
|
}
|