207fce8778
V7 audit of reward terms R1-R8 against the baseline smoke run
(magnitude_distribution.rs, 3 folds x 20 epochs = 60 HEALTH_DIAG rows):
* R1 step_return: KEEP (base reward, denominator)
* R2 PopArt drift: PENDING (warmup-gated zero at smoke; re-check at L40S)
* R3 CF-flip: KEEP (49-74% contribution, dominates shaping)
* R4 trail_r: KEEP-WITH-CAVEAT (fold-3 dominance, trade-volume gated)
* R5 micro-reward: DELETE (micro_reward_scale=0 in smoke, intended)
* R6 loss-aversion: DELETE (sub-1% in 54/60 epochs, relocate protection)
* R7 segment-patience: ALREADY-REMOVED (stub deleted 83d524f86)
* R8 raw_returns_out: KEEP (load-bearing for Sharpe/MaxDD pipeline)
Cross-term finding: R3 is the dominant reward shaper at smoke scale;
every other shaping term is <=3% of reward magnitude except R4 in fold
3. Confirms Track 1's H4 gradient-starvation diagnosis is compounded
by thin shaping elsewhere in the reward landscape.
Follow-ups flagged for Phase 2:
- stale patience_mult docstring at experience_kernels.cu:1049
- R5 deletion gated on TD-propagation diagnostic (Task #8)
- R6 relocation to Q-target smoothing in C51 Bellman
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.