jgrusewski 629ebd667c feat(ml-alpha): deterministic same-seed training + Tier 1.5 fast-dev-cycle
Two same-seed runs now produce bit-equal eval_summary.json, alpha_rl_train_summary.json,
and diag.jsonl (modulo wall-clock elapsed_s). The 5-phase falsification chain landed:

  Phase 2   PER tree-rebuild: __threadfence is NOT a grid-wide barrier; multiple blocks
            raced across sum-tree levels. Fix: Grid=(1) Block=(1024) + __syncthreads
            in rl_per_tree_rebuild.cu.

  Phase 2.3 cuBLAS GEMM_DFALT + TF32 default-math allowed split-K non-deterministic
            accumulation at 3 sites. New crates/ml-alpha/src/cublas_determinism.rs
            applies CUBLAS_PEDANTIC_MATH via FOXHUNT_DETERMINISTIC env toggle
            (0=TF32 prod, 1=PEDANTIC dev default, 2=DEFAULT_MATH control).

  Phase 2.6 Two bugs surfaced sequentially in the backward kernel chain:
            (1) rl_iqn_tau_cos_features had a multi-block r/w race on prng_state[batch]
                — all N_TAU=32 blocks read seed; only tau_idx==0 wrote back; no
                inter-block barrier. Fix: split into READ-ONLY rl_iqn_tau_cos_features
                + new sibling rl_iqn_advance_prng_state launched on same stream
                (kernel-launch ordering = grid-wide barrier).
            (2) OutcomeHead::new called near_zero_xavier without scoped_init_seed,
                falling back to time+thread-id RNG. Stayed dormant until first done
                event activated non-sentinel labels and divergent weights flowed via
                grad_h_t_outcome into encoder gradient. Fix: add seed param + install
                scoped_init_seed(dqn_seed.wrapping_add(0x0CE0)) guard.

Validation (./scripts/determinism-check.sh --quick, RTX 3050, b=128, 200+50 steps):
  - All 200 rows of checksums.* leaves match (rel-tol 1e-5, abs-tol 1e-7)
  - eval_summary.json, alpha_rl_train_summary.json byte-equal between runs
  - diag.jsonl byte-equal modulo elapsed_s
  - Eval pnl identical run-A vs run-B at seed 42

Pre-fix baseline (Phase 2.5 measurement): same-seed eval pnl spread $450k
($187k vs -$261k). Post-fix: $0 spread.

Speed cost: ~1.5ms/step amortised; ~10-15% slower than TF32 production
(PEDANTIC tax — acceptable in dev, toggle to FOXHUNT_DETERMINISTIC=0 for prod).

Mapped-pinned discipline: all 11 NEW memcpy_dtoh sites in diagnostic dump methods
+ per-step checksum readback use a new pub(crate) helper
read_slice_d_into<T: Copy>(stream, src, dst) — MappedRecordBuffer + raw
memcpy_dtod_async + raw_stream_sync + volatile read. Generic over T (f32, f64,
i32, u32, u8). Satisfies feedback_no_htod_htoh_only_mapped_pinned + hook guard.

Bundled Tier 1.5 fast-dev-cycle infrastructure (spec
docs/superpowers/specs/2026-06-02-fast-dev-cycle.md):
  - scripts/local-mid-smoke.sh        b=128, 2000+500, ~10min on RTX 3050
  - scripts/determinism-check.sh      runs mid-smoke twice, diffs checksums
  - scripts/tier1_5_verdict.py        behavioral kill verdict
  - AdamW checkpoint save/load (crates/ml-alpha/src/trainer/optim.rs)
  - IntegratedTrainer checkpoint save/load (resume from checkpoint)
  - 15 Phase 1 checksum leaves in build_diag_value
  - Env-gated dump methods (FOXHUNT_DETERMINISM_DEBUG_PER/MAMBA2/RL/BACKWARD)
    for future divergence-chasing — never run in production

Documentation:
  - docs/superpowers/specs/2026-06-02-determinism-foundation.md
  - docs/superpowers/specs/2026-06-02-fast-dev-cycle.md
  - docs/superpowers/plans/2026-06-02-determinism-foundation-implementation.md
  - docs/superpowers/notes/2026-06-02-determinism-phase{1,2,2.2,2.5,2.6}-*.md
  - Adjacent specs/plans/notes from the analytical chain that surfaced determinism
    as the load-bearing blocker (eval-summary, eval-boundary, regime-observer,
    multi-head policy, regime-invariance, Phase 3 IQN-complement post-mortem)

Unlocks: every controller / architecture / reward-shaping A/B from this commit
onward attributes outcome differences to the change, not random-init kernel-race
drift cascading through training x eval LOB-sim trajectories. The eval-collapse
investigation (pearl_reward_signal_anti_aligned_with_pnl, multi-head spec,
regime-invariance spec) is now testable with trustworthy verdicts.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-02 17:56:00 +02:00

Foxhunt

Production HFT trading system in Rust.

Architecture

The workspace contains 32 crates organized as follows:

Core Libraries (16)

Crate Purpose
trading_engine Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing
risk VaR, Kelly, circuit breakers, kill switches, compliance
risk-data Risk data types and shared structures
trading-data Trading data types
ml DQN Rainbow, PPO, TFT, Mamba2, ensemble inference
ml-data ML data types and feature definitions
data Market data ingestion and storage
backtesting Replay engine, strategy tester
adaptive-strategy Ensemble execution, microstructure analysis
common Shared types, resilience, error handling
storage S3 and local model storage
model_loader Model serialization and loading
market-data Market data feed handlers
database PostgreSQL access layer (SQLx)
config Configuration management
tli CLI commands and tooling

Services (8)

Service Purpose
backtesting_service gRPC backtesting service
broker_gateway_service FIX routing, broker connectivity
trading_service Core trading operations
ml_training_service Model training orchestration
data_acquisition_service Market data acquisition
trading_agent_service Autonomous trading agents
api_gateway gRPC API gateway with auth
web-gateway Axum REST + WebSocket gateway

Frontend

web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.

Building

# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace

# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib

# Clippy
SQLX_OFFLINE=true cargo clippy --workspace

ML Models

Four production model architectures on Candle v0.9.1 with CUDA:

  • DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
  • PPO -- Proximal Policy Optimization with GAE and LSTM policies
  • TFT -- Temporal Fusion Transformer for multi-horizon forecasting
  • Mamba2 -- State space model for sequence prediction

Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.

Infrastructure

  • Git: Gitea at git.fxhnt.ai (Tailscale-only), Scaleway DEV1-S
  • Observability: OpenTelemetry OTLP (env OTEL_EXPORTER_OTLP_ENDPOINT)
  • Database: PostgreSQL with SQLx offline mode for CI

License

Proprietary. All rights reserved.

Description
No description provided
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