79d15b3196
Phase E.1 Task 13. Same pipeline as the H=600 PASS run (cd5aa3402),
just `--horizon 6000` — the plan's production horizon. Result:
Q_SPREAD_EMA = 35.44 ≥ 0.05 PASS
ACTION_ENTROPY_EMA = 2.00 ≥ 1.099 PASS
RETURN_VS_RANDOM_EMA = +1.003σ ≥ 0.0 PASS
EARLY_Q_MOVEMENT_EMA = 0.130 ≥ 0.01 PASS
Overall: PASS (H=6000 scale-up VIABLE)
H=600 vs H=6000 side-by-side (same DQN, only horizon changed):
H=600 H=6000
rollout_R_mean -18 -236 (13× for 10× horizon — sublinear)
RETURN_VS_RANDOM_EMA +1.043σ +1.003σ (alpha signal generalizes)
Q_SPREAD_EMA 10.92 35.44 (sharper action discrimination)
EARLY_Q_MOVEMENT_EMA 0.099 0.130 (more weight movement per episode)
Overall PASS PASS
The Mamba2 K=6000 alpha-cache (config/ml/alpha_logits_cache.bin) was
directly trained for this horizon, so generalization at H=6000 is the
expected result. Confirmed empirically.
Runtime: 80s for 1000 episodes × 6000 steps = 6M transitions
(~75K transitions/sec, same throughput as H=600).
Milestone E.1 is now CLOSED with all kill criteria PASSING at the
production horizon. Per the plan, this unlocks Milestone E.2 — the
stacker-threshold ISV controller (engagement-rate self-correction).
Reproduction:
cargo run -p ml --release --example alpha_dqn_h600_smoke -- \
--fxcache-path .../9297....fxcache \
--alpha-cache config/ml/alpha_logits_cache.bin \
--horizon 6000 --n-episodes 1000 \
--max-snapshots 1500000
Verdict + per-checkpoint KC trajectory in config/ml/alpha_dqn_h6000_smoke.json.
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.