5a29c37cd8
Plumbs new is_win_per_env i32 device-buffer arg through the
sp20_aggregate_inputs kernel signature and Rust launcher, NULL-tolerant
to preserve the legacy `step_ret_per_env > 0` predicate for existing
oracle-test scaffolds. Production caller passes 0 (NULL) in this
commit; commit 2 wires the per-env producer + collector buffer + reset
entry atomically with the kernel-body change that honors the arg.
Adds two new GPU oracle tests in sp20_aggregate_inputs_test:
- wr_ema_uses_segment_pnl_via_is_win_per_env_predicate (FAILS without
fix): step_ret all negative (tx-cost dominated close bars — the
realistic production case), is_win_per_env = [1,1,1,0]. With the
legacy predicate wins_count = 0 ⇒ is_win = 0 (the production bug).
With the fix wins_count = 3 ⇒ is_win = 1.
- null_is_win_per_env_falls_back_to_legacy_step_ret_predicate: pins
the NULL-tolerance contract so existing oracle tests + Phase 1.4
wireup test scaffolds keep working unchanged.
Bug root cause: WR_EMA pinned at 0 across all training epochs in
production (HEALTH_DIAG observed `wr_ema=0.0000` while `alpha_ema`
evolved with real values). The aggregation kernel's win predicate was
`step_ret_per_env[env] > 0.0f` at the close bar, which is the per-bar
mark-to-market `(new_value - prev_equity) / prev_equity`, NOT the
trade's segment-level outcome. At close bars `step_ret = position *
(close_t - close_{t-1}) - tx_cost`; the per-bar tick is small while
tx_cost is fixed → `step_ret < 0` is dominant across closing bars
even for trades that closed profitably overall. Result: wins_count =
0 always, is_win = 0 always, WR_EMA pinned at 0, LOSS_CAP pinned at
-1.0 (cold-start cap, never ramping to -2.0 per spec §4.1).
Fix arrives in the next commit: experience_kernels.cu segment_complete
branch writes `is_win_per_env[i] = (segment_return > 0.0f) ? 1 : 0`
(the segment-level realized-return signed-flag), passed through to
the aggregation kernel which uses the new per-env i32 buffer in place
of the per-bar `sr > 0` predicate when wired.
Per `feedback_no_partial_refactor` the kernel signature change + all
callers (production + 2 test files) migrate atomically in this commit;
the kernel-body behavioral change + producer wire-up + reset registry
+ audit-doc entry land atomically in the immediately following commit.
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.