94157a8a69
Smoke after var_scale floor (73fd49f4b) still showed eval Full = 0.000 with
intent = 0.911. Root cause: trade_physics.cuh::unified_env_step_core applied
Kelly cap using ONLY direction conviction (q_dir_gap / q_dir_abs_ref). At
smoke horizon with direction Q-values close (q_s≈q_h≈q_l≈q_f within ~0.07),
direction conviction was ~0.04 → safety = max(0.75, 0.04) = 0.75,
effective_kelly = max(0, 0.75) = 0.75, cap = 0.75 × max_pos × 0.75 =
0.5625 × max_pos → Half bucket pin (abs_pos < 0.75).
The magnitude branch was simultaneously highly confident (q_f / q_h ≈ 1.9,
mag conviction ≈ 0.5) — the policy strongly preferred Full. That signal
never reached the cap.
Thread per-sample magnitude conviction into the cap formula:
- new buffer magnitude_conviction_buf[N*L] (training) parallel to
conviction_buf, and chunked_magnitude_conviction_buf[chunk_len * n_windows]
(eval) parallel to chunked_conviction_buf
- experience_action_select writes (max(q_mag) − min(q_mag)) /
fmaxf(ISV[16], 1e-6); ISV[16] = Q_ABS_REF (magnitude branch's |Q| EMA,
recycled — no new ISV slot)
- scripted_policy_kernel writes 0.0 (no real magnitude preference; helper
composer falls back to direction signal)
- unified_env_step_core takes a parallel magnitude_conviction parameter
- combined: policy_conviction = max(dir_conv, mag_conv); cap formula
becomes safety = max(health_safety, policy_conviction); warmup_floor
uses policy_conviction
- all three env_step kernel call sites migrated together
(backtest_env_step, backtest_env_step_batch, experience_env_step) per
feedback_no_partial_refactor — no mixed state
Per pearl_blend_formulas_must_have_permanent_floor: max() composition,
not multiplicative blend. Per feedback_isv_for_adaptive_bounds: signal
flows from existing ISV[16] = Q_ABS_REF, no tuned constants.
Smoke verification (20-epoch RTX 3050 Ti, magnitude_distribution test):
- pre-fix: [EVAL_DIST] Q=0.586 H=0.414 F=0.000 (intent F=0.911)
- post-fix: [EVAL_DIST] Q=0.618 H=0.153 F=0.229 (intent F=0.911)
Intent unchanged (network already learned Full preference); the Kelly
cap no longer silences it. Both smoke assertions now pass:
ef >= 0.05 (gate) and eh + ef >= 0.30 (Task 2.2 H10).
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.