4085831452
Symmetric 5-window mean (f1359f3dc) filtered noise but also filtered
exploration. RL Sharpe is plentiful-bad and rare-good in early training,
so the mean always sees the plentiful side — controller locked at 0.3×
LR and the model couldn't escape its initial bad minimum
(train-v82b2: sharpe stuck at −8 to −21 throughout Fold 0, never
peaked positive like prior runs had).
Root fix: the two decisions have different evidence requirements.
* Boost LR: low cost if wrong (clamp caps runaway), high value if
right (kicks out of overfit). Accept weak evidence — ANY of the
last short_window epochs clearly positive fires the boost.
* Dampen LR: high cost if wrong (stuck model), low value if right
(stability we didn't need). Demand strong evidence — MEAN over
long_window must be clearly negative.
Both windows derive from anti_lr_warmup (one knob):
long_window = anti_lr_warmup (full window for sustained-bad)
short_window = anti_lr_warmup / 2 (half window for recent-good)
No new hyperparameter. Asymmetry is structural (max vs mean over
differently-sized windows), not tuned.
Verified: multi-trial smoke 5/5 pass, median_q_gap=2.77, mean_sharpe_ema=12.24.
Compared to symmetric smoothing (2.13 / 11.03) and raw-signal (1.13 / 2.94),
the asymmetric version is the best on all three multi-trial metrics.
Tie-breaking: "good wins" when both signals fire in the same step —
matches the controller's original intent (exploration over dampening)
and our diagnosis (model needs LR headroom to escape bad starting
states).
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.