35db310893
experience_kernels.cu:2788:
float capped_pnl = fminf(base_reward, 10.0f);
^^^^^^^^^^^^^ caps profits, NOT losses
Diagnostic instrumentation in smoke-test-k9drh on commit 774d7552a
captured the asymmetry empirically:
ep1: r_popart min=-9186, max=+10
ep2: r_popart min=-79089, max=+10 (growing)
`base_reward = 2.0f * vol_normalized_return` and
`vol_normalized_return = segment_return / vol_norm` where
`segment_return` has no structural lower bound (signed P&L). The
unilateral `fminf(base_reward, 10.0f)` capped the upper tail only,
so a single large adverse segment_return produced an arbitrarily
negative `capped_pnl` → r_popart → r_weighted →
reward_components[+0] → slot 63 (PopArt input EMA), inflating
C51/IQN/Bellman normalization scale and breaking Q-target
consistency across epochs. Empirical fingerprint matched the
within-fold sharpe degradation observed in smoke-test-gwfn8 on
commit fd24b5383 (10→4 within F0, 9→2.5 within F1).
Spec semantic: reward bounded in [-10, +10]. Fix:
float capped_pnl = fmaxf(-10.0f, fminf(base_reward, 10.0f));
Audit findings (per task §2): all related fminf/fmaxf clamps in
experience_kernels.cu reviewed. Reward modifier chain (3260-3500)
verified bounded once r_popart is bilateral. Other bilateral
clamps already correct (515-517, 2174, 2191, 2227, 3300, 3452,
3729, 3763, 5076, 5210, 5213, 5461, 5535, 6353, 6693, 6694).
Intentional asymmetries verified at 1078, 1082-1085, 2564-2565,
2873, 2949, 4574, 5896 (each documented in the audit doc with
the structural reason the lower side is unbounded).
Latent finding flagged separately (NOT fixed here — feature-side
requires consumer audit per feedback_no_partial_refactor):
plan_isv[PNL_VS_TARGET] at line 850 and plan_isv[PNL_VS_STOP]
at line 853 are upper-clamped at 2.0 but lower-unbounded. These
feed assemble_state as policy features (not reward components),
so out of scope of this reward-chain fix. Mirrored in
backtest_plan_kernel.cu:164,168 (same pattern). Tracking as
follow-up.
Per pearl_bounded_modifier_outputs_require_structural_activation:
spec-bounded values require BILATERAL structural enforcement.
This bug was the asymmetric counterpart to the conviction sigmoid
(which IS correctly bounded structurally).
Diagnostic instrumentation (commit 774d7552a) NOT removed in this
commit — will be removed in a follow-up after the symmetric-cap
smoke validates the fix on L40S.
docs/dqn-wire-up-audit.md updated with Resolution (2026-05-04)
section reflecting root cause, fix, audit follow-through, and the
latent plan_isv finding (per Invariant 7).
Build: SQLX_OFFLINE=true cargo check -p ml --lib — clean.
Test: trainers::dqn::trainer::tests::test_reward_function_price_changes
passes. (PPO test_reward_computation pre-existing failure on
HEAD 774d7552a, unrelated — verified via stash+rerun.)
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.