b8788511ce
experience_kernels.cu line 1916 binned action_mag_per_sample by
actual_mag_core at every step, including trade-close events. But
unified_env_step_core forces `actual_mag = 0 (Quarter)` whenever
actual_dir is Hold/Flat (trade_physics.cuh:772) and trade closes
always land in Hold/Flat state — so every Half/Full close was
attributed to the Quarter bin. close_counts[Half] and close_counts[Full]
were structurally pinned to 0, giving wr_h = wr_f = 0 across all
training runs.
Fix: introduce `seg_mag_bin = is_close ? pre_mag_bin : actual_mag_core`.
At close events bin by pre_mag_bin (the magnitude of the position
being closed); at non-close events keep actual_mag_core (current
realized magnitude). pre_mag_bin is always 0/1/2 at close events
since exiting/reversing requires prev_sign != 0 → pre_trade_position
!= 0 → pre_frac > 0.001 → pre_mag_bin in {0,1,2}.
Smoke verification (5-epoch local): wr_h/wr_f remain 0 because
var_scale (1/(1+sqrt(var_q))) shrinks effective_max_pos to 10-19% of
broker max at smoke maturity → even Long Full target lands at
abs_pos ≈ 0.15 < 0.375 → all positions decode as Quarter; no
Half/Full positions exist for the fix to attribute. This is the
expected structural consequence of the var_scale design (uncertain Q
→ smaller position, conservative). The fix is latent correctness:
in mature L40S 30+ epoch runs where var_q drops and var_scale
grows above 0.375, Half/Full positions become reachable and
wr_h/wr_f will reflect their real realized win rates.
Without the fix, even mature training would show wr_h = wr_f = 0
because of the Hold/Flat → Quarter close convention masking real
per-magnitude win rates. Audit entry updated.
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.