5d42ab0e98
Connects the per-horizon lambda computed by horizon_ema_and_lambda (landed in37c3a8f4d) to the actual trunk gradient flow. This is the model-behavior change. mhzs7 (3a196382f) hit mean_auc=0.726 but with the long-horizon h6000 stuck at 0.694 — exactly the horizon the auto-horizon-weights formula `min(1, K/h)` over-weights down (h6000 weight = 0.0053). ISV detects "this horizon is hard, give it more trunk-gradient pull" and lambda[h] scales the per-horizon `d_z` contribution into the trunk in heads_bwd. Kernel change (cuda/multi_horizon_heads.cu): multi_horizon_heads_backward_batched(): new arg `const float* __restrict__ lambda` (5 elements, between grad_h_carry and n_batch in arg order). - lambda is broadcast into __shared__ float s_lambda[5] once per block so every thread reads the 5 floats without repeated global loads. - Sentinel: zero buffer (init state, EMA hasn't run yet) is read as 1.0 so the kernel reduces to pre-ISV behavior. After step 1 every entry is clamped to [0.5, 2.0] by horizon_lambda.cu and the > 0 check is always true. - lambda[k] multiplies the `acc += s_lambda[k] * sd_z * w[k]` term that produces grad_h (trunk gradient). - grad_w and grad_b updates are UNCHANGED. The horizon heads keep learning their own weight/bias normally; lambda only biases how much each horizon's error signal leaks into the shared trunk. Per `pearl_adam_normalizes_loss_weights.md`: scaling the effective gradient bypasses Adam's m/sqrt(v) normalization that would otherwise cancel a loss-weight lift. Trainer change (trainer/perception.rs): - heads_bwd_batched launch now passes `&self.lambda_d` between grad_h_carry_d and n_batch_i. lambda_d is refreshed each step by the horizon_ema_and_lambda kernel that ran just after BCE. - Whole chain lives inside the captured CUDA Graph. Validation: - All 7 perception_overfit tests pass. - Synthetic overfit converges marginally FASTER than pre-Phase-3 (final loss 0.0007 → 0.0005). Plausible explanation: in a constant-signal smoke, the per-horizon BCE values are similar enough that lambda mostly stays near 1.0, but the EMA bootstrap on step 1 still produces non-uniform initial lambdas that nudge the trunk toward whichever horizon converges slowest. Either way, no regression. - horizon_ema_and_lambda_track_after_training test still passes with the lambda values now flowing through heads_bwd: ema = [0.50, 0.87, 0.64, 0.49, 0.60] lambda = [0.80, 1.41, 1.03, 0.79, 0.97] → h100 hardest (BCE 0.87) gets 1.41× trunk grad; h300 easiest (BCE 0.49) gets 0.79× — exactly the intended ISV semantics. - 26 lib + 23 integration ml-alpha tests pass. Honors: - feedback_isv_for_adaptive_bounds.md (no hardcoded constants; lambda derives from runtime BCE signal). - pearl_adam_normalizes_loss_weights.md (scaling trunk gradient, not BCE coefficient, to bypass Adam normalization). - pearl_audit_unboundedness_for_implicit_asymmetry.md (lambda is bounded by horizon_lambda.cu's clamp [0.5, 2.0]). - pearl_no_deferrals_for_complementary_fixes.md — Phase 3 wires up the infrastructure from Phase 1+2 in the same logical delivery rather than waiting a CV cycle. Co-Authored-By: Claude Opus 4.7 <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.