f9286e938d
Scoping output from the Full=0% investigation (post-Task-2.2 smoke showed
eval_dist [eq=0.580 eh=0.420 ef=0.000] — Quarter+Half recovered,
Full still 0%).
Root cause: Q-estimation bias (category C) — C51 expected-Q over atoms
systematically under-prices higher-variance bins. Full has 4x Quarter's
PnL variance per experience_kernels.cu risk scaling. Bias is already
documented at experience_kernels.cu:904 (commented as "structural
distributional bias — tight return distributions (Small) get higher
expected Q under the C51 softmax regardless of actual expected returns").
Not noise-starvation: Full picked on 32% of training samples across
60 epochs × 3 folds = ~61k Full samples. Ample data.
Proposed Task 2.X = Rank 1 + Rank 2 combined (~90 LOC):
Rank 1: per-bin variance weighting in C51 branch loss — amplify
Full's gradient by sqrt(var_f / mean_var)
Rank 2: Q-spread regularization — lambda * ReLU(target_spread -
Q_spread)^2 penalty preventing degenerate fixpoint
Alternative fixes ranked but not recommended:
Rank 3 per-magnitude reward shaping (~80 LOC) — only if data-disfavor
is confirmed at L40S
Rank 4 magnitude curriculum (~20 LOC) — rejected (noise-starvation ruled out)
Rank 5 state-vector enrichment (~150 LOC) — premature
Prerequisite: ~40 LOC of per-magnitude win-rate + realized-variance
instrumentation to confirm category C vs A at L40S scale.
L40S gating recommendation was:
ef >= 0.15 at L40S → Task 2.X not needed (smoke artefact)
0.05 <= ef < 0.15 → Task 2.X scoped but optional
ef < 0.05 → Task 2.X required
Per feedback_fix_aggressively.md (landed this session): we ship Task 2.X
ahead of L40S since the bias is already code-documented, the fix is
well-scoped, and the 90-LOC cost is reasonable. L40S validation (Task
2.8) will verify the fixed state end-to-end instead of the un-fixed one.
No deletions anywhere per feedback_no_functionality_removal.md.
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.