543e3c11b9
Val-Flat-collapse fix #3 (task #94, 2026-04-24). Research agent traced the dominant val failure mode: reward = 0.0 for Flat bars produces structural Flat-beats-trading-actions in the argmax — Flat accumulates Q ≈ 0 while CQL pulls trading-action Q's toward pessimistic negative estimates. Result: val argmax picks Flat for ~all states (22 trades/epoch vs 30K+ in training via Boltzmann). Prior comment defended the design: "No signal is correct — the model should not be rewarded or penalized for correctly staying flat when there's no edge." That reasoning was correct in isolation but broken in context: the trading-action branch is penalized by CQL's lower-bound estimator anyway, so Flat's neutral Q becomes the de-facto upper bound on all action Q-values → Flat wins argmax regardless of the policy's actual directional belief. Add a per-bar opportunity cost for Flat bars, proportional to realised volatility (ATR fraction): reward = -shaping_scale * holding_cost_rate * 0.5 * vol_proxy This: - Symmetric to the positioned-bar holding penalty (positioned pays `holding × |pos|`; Flat pays `holding × 0.5 × vol_proxy`). - Vol-scaled: Flat during quiet markets approaches zero cost; Flat during volatile markets has a small negative signal representing missed-move expectation. - Caps vol_proxy at 0.01 (1%) as numerical safety against single-bar vol spikes dominating reward. - Recomputes the ATR locally (the segment-complete branch that normally does this is mutually exclusive with this Flat branch). - Guarded on `features != NULL` and `bar_idx` range for smoke-test compatibility. The `0.5` factor is a numerical-safety symmetric-to-holding-cost bound per the carve-out in `feedback_isv_for_adaptive_bounds.md`, not a tuned hyperparameter. The `holding_cost_rate` itself flows from config (already adaptive via shaping_scale annealing). Not part of the triad yet: - plan_isv train/val parity (task #94 item #2): documented architectural gap requiring val-time plan-state generator; no trivial safe fix. - Atom utilization (task #94 item #4): deep C51 representation work (atom-level entropy regularisation or target-variance engineering); deferred pending tie-break + opp-cost validation. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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.