04a6f0dea6
Iterates v2's reward-bias mechanism through v3 (always-fire on tradable),
v4 (cross-branch |Q|-scale fallback), and v5 (structural v_range floor).
Replaces the entire v2 body, not incremental.
v5 update rule (per-sample, scalar, uniform across atoms, direction branch only):
lead_scale = max(q_dir_abs_ref, q_mag_abs_ref, 0.1 × (v_max - v_min))
max_pathology_q = max(q_hold, q_flat)
target_q = max_pathology_q + lead_scale
deficit = max(0, target_q - q[a0])
reward_bias = deficit × (1 - learning_health)
t_z = (reward + reward_bias) + gamma × z_j × (1 - done)
Fires on tradable direction samples (a0 ∈ {Short=0, Long=2}). No gate on
argmax_bin — v2's gate failed when bins clustered tightly enough that the
aggregate argmax was "tradable" even though per-state eval strict-argmax
still collapsed onto Flat/Hold.
Signal stack (all adaptive, no hard-coded knobs):
- isv_signals[17..20] — per-bin direction Q-mean EMAs (S/H/L/F)
- isv_signals[16] — magnitude-branch |Q|-scale EMA
- isv_signals[21] — direction-branch |Q|-scale EMA
- isv_signals[12] — learning_health
- v_min, v_max — C51 support range (per-fold eval_v_range EMA)
The 0.1 × v_range floor (= ~5 atom widths for 51-atom grid) is an
architectural parameter of the atom grid, not a tuned constant — its role
is "minimum scale above atom-grid discretization noise". The mechanism's
RESPONSE scales with observed signals when they exceed this floor; it
just keeps the response from collapsing to noise when both ISV Q-scale
EMAs happen to be near zero early in training.
Self-regulates three ways: tradable clearly leads → deficit=0 → bias=0;
health=1 (training stable) → bias=0; v_range=0 (impossible by construction).
Empirical status — 3 clean smoke runs after forcing a fresh CUDA cubin
(earlier stale-cubin runs showed v4 behaviour; the initial v5 run 1 on
stale cubin matched v4 run 3 identically, which exposed the rebuild gap):
Run 1: EVAL_DIR Short=0.287 Hold=0.000 Long=0.713 Flat=0.000 — Hold+Flat=0 ✓
Run 2: EVAL_DIR Short=0.000 Hold=0.000 Long=1.000 Flat=0.000 — Hold+Flat=0 ✓
Run 3: EVAL_DIR Short=0.000 Hold=0.000 Long=1.000 Flat=0.000 — Hold+Flat=0 ✓
Pre-v5 baseline (committed v2): Hold+Flat ∈ {0.809, 0.872, 0.796} across 3 runs.
The smoke test still fails on magnitude assertions (line 134 eh+ef≥0.30
or line 153 ef≥0.05) because eval magnitude still collapses to Quarter
or Half. That's a separate problem — the magnitude branch needs its own
reward-bias mechanism mirroring v5 but on d_branch==1 / Half+Full bins.
Tracked separately as Task 2.X-ext (internal task #60).
Per feedback_adaptive_not_tuned.md: the mechanism remains signal-driven;
the only scalar constant (0.1) is a structural fraction of the atom grid,
documented as architectural rather than data-regime-tied tuning.
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.