d54b49efc1
Prior C51 bias fix (commit 7a3d88646: ISV-adaptive Boltzmann tau floor)
had no measurable effect on the post-training Hold/Flat collapse —
empirically confirmed in train-bscl2 epoch 2: val_dir_dist
[short=0.135 hold=0.358 long=0.142 flat=0.364], identical to the
pre-fix run [short=0.136 hold=0.348 long=0.148 flat=0.367].
Root cause: once Q-values reflect tx_cost-driven aversion, Boltzmann
correctly samples the biased Q distribution regardless of tau. Tau
adjustments protect cold-start exploration but can't combat learned
preferences. The 2% static eps_dir floor allows only 0.5% random
sampling per direction — too little to break the Q-value lock-in or
generate enough Long/Short experiences for edge discovery.
Fix:
if (ISV available) {
passive_pressure = clamp(0, 1, 1 − ISV[71]/max(ISV[72], 1e-4))
eps_dir = max(eps_dir, 0.5 × passive_pressure)
}
ISV[71] = TRADE_ATTEMPT_RATE_EMA (current Flat→Positioned rate, B.2 producer)
ISV[72] = TRADE_TARGET_RATE (target frozen at epoch 5 from measured EMA)
Feedback semantics:
- attempt_rate >= target → passive_pressure=0 → eps_dir at baseline 0.02
- attempt_rate = 0 (fully passive) → passive_pressure=1 → eps_dir floor=0.5
- intermediate → linear blend
The 0.5 ceiling is a structural blend point (half random / half policy)
— maximum exploration that still preserves directional Q-signal
propagation through the replay buffer. Not a tuned magnitude.
Cold-start safety: ISV[72] is 0 until epoch 5 freeze; with the 1e-4
target floor, passive_pressure clamps to ≈1 immediately, but eps_dir
also has a baseline 0.02 EPS_FLOOR so the formula's max() picks
whichever is larger. Once ISV[72] freezes, the feedback loop activates
properly.
Eval mode unaffected (eps logic gated behind !eval_mode).
Direction branch only — magnitude/order/urgency don't have the
Flat-attractor problem.
ISV-driven, no new ISV slots, no tuned constants per
feedback_isv_for_adaptive_bounds.md and feedback_adaptive_not_tuned.md.
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.