87b52ea946
Tasks 1, 2, 3, 5 landed on main betweena59e7599canda0abc3da3. The remaining Task 4/6/7/8/9 bodies in the plan had accumulated drift: - Task 4: allocated slot 49 (already PLAN_THRESHOLD_INDEX); EMA'd plan_params[0] (kernel compares against readiness). - Task 6: allocated 59/60/61 (now collides with Plan 2 Q-quantile [50..58) and Task 1 reward EMAs [63..69)); required 6 new PS memo fields and included tuned 0.5e-4f penalty. - Task 7: allocated 58/63/64 (63/64 collide with REWARD_TRAIL_EMA / REWARD_MICRO_EMA from Task 1); amplification formula had tuned 2.0× trigger, 0.02 decay, 1.0/2.0 endpoints. - Task 8: referenced trainer helpers that don't exist (sample_state_feature_pair, step_scripted, replay_insert_with_ priority_scale); tuned priority_scale=0.5. - Task 9: used pre-pivot CPU-compute AdaptiveMonitor pattern with tuned 0.9/0.1 EMA rates. This revision: - Adds per-task "Reality reconciliation" sub-header flagging the stale premise being fixed. - Marks landed tasks with ✅ LANDED <SHA> and records actual outcomes (vs. the planned outcomes the original text described). - Rewrites Task 4/6/7/8/9 bodies to use tail-append slot allocation (indices recomputed from ISV_TOTAL_DIM at impl time), GPU kernel producer + read-only AdaptiveMonitor consumer pattern, and ISV-derived adaptive coefficients in place of tuned constants. - Splits Task 6 into 6a/6b/6c with independent PS-slot additions (MIN_PNL / REGIME_SHIFT_BAR / PRE_ENTRY_CONVICTION_EMAs). - Enumerates concrete trainer-helper prereqs for Task 8. - Updates Task 10 metric bands to match actual landed ISV slot names. - Updates exit criteria summary to check off what landed. No code changes.
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.