13ce783853
Adds infrastructure for verifying the SP20 Phase 2 Task 2.2-fix
(`is_win_per_env`) producer side at runtime. The consumer-side oracle
test passes; the producer-side wire-up has not been independently
verified end-to-end. Production HEALTH_DIAG observed `wr_ema=0.0000`
across all training epochs even after `64bbbe418` landed, with
`alpha_ema` evolving normally — strongly suggesting either (a) all
profitable trade closes had `hold_time == 0` so the producer write
didn't fire, or (b) the producer write fired but `(segment_return >
0.0f)` evaluated to false in the production data.
Code-inspection audit (this commit, summarized in audit-doc):
- Verified 81 kernel args (launcher) match 81 kernel signature args
for `experience_env_step`. `is_win_per_env` at position 81;
`alpha_per_env` at position 78. cudarc `PushKernelArg<&mut
CudaSlice<T>>` impl pushes `&arg.cu_device_ptr` (stable address
for the lifetime of the chained statement).
- Verified buffer allocation at construction (alloc_zeros), pub(crate)
field, FoldReset entry + dispatch arm, kernel-arg pass at both
experience_env_step and sp20_aggregate_inputs launch sites.
- Verified producer site — both alpha + is_win writes inside the SAME
`if (segment_complete && segment_hold_time > 0.0f)` block at
experience_kernels.cu:3196, both NULL-guarded, race-free.
- Verified consumer site — aggregate kernel reads `is_win_per_env[env]`
+ `alpha_per_env[env]` side-by-side inside `if (tc != 0)`.
- Verified stream ordering — both kernels run on `self.stream` in the
same `for t in 0..timesteps` loop; experience kernel at line 6069,
aggregate at 6577. Stream-implicit producer→consumer ordering.
**No code-level bug surfaced by inspection.** The `is_win_per_env`
write is structurally identical to the `alpha_per_env` write 14
lines earlier in the same gating block.
Files modified:
- `crates/ml/src/cuda_pipeline/gpu_experience_collector.rs` —
`read_is_win_per_env_for_test()` and `read_alpha_per_env_for_test()`
pub fns. Mirror `read_epoch_dsr_state()` pattern: dtoh memcpy via
the collector's stream, returns Vec<i32> / Vec<f32>. Cost-free in
production (only called from tests) and benign for invariants.
- `crates/ml/tests/sp20_is_win_producer_test.rs` — production-path
producer test scaffold. Builds 6-float-per-bar synthetic OHLCV
matching `experience_env_step`'s tgt[0..6] contract, runs
`collect_experiences_gpu`, reads back both buffers, asserts the
three structural invariants:
1. alpha_per_env has at least one non-zero slot (precondition —
the synthetic upward-trend data must produce ≥ 1 trade close).
2. is_win_per_env has at least one slot == 1 (regression-pin for
the Task 2.2-fix producer wire-up).
3. Co-occurrence: every env where alpha > 0 must have is_win == 1
(R_event > 0 ⇒ segment_return > 0 ⇒ is_win = 1; structural
guarantee from the kernel's two writes at the same gate).
Currently `#[ignore]` because the collector's cuBLAS forward chain
requires a real `trainer_params_ptr` (NUM_WEIGHT_TENSORS=163
weights). Activation requires either trainer-params plumbing in the
test scaffold OR moving the test inline as `#[cfg(test)]` in
gpu_experience_collector.rs.
- `docs/dqn-wire-up-audit.md` — audit-doc entry per Invariant 7.
Documents the inspection scope, the unverified producer-side
pre-conditions, and the open-issue framing for the wr_ema=0.0000
discrepancy.
Verification:
- SQLX_OFFLINE=true cargo check -p ml --tests passes
- 9 state_reset_registry tests pass (incl.
sp20_is_win_per_env_registered_fold_reset)
- The new test file compiles cleanly under `--features cuda`
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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.