12635bd708
Replaces Phase 4.3's hard V_dq → PPO swap with an adaptive blend
driven by an on-device controller. Per the project's no-tuning
philosophy (pearl_controller_anchors_isv_driven, feedback_adaptive_not_tuned):
V_used[b] = α × V_scalar[b] + (1 − α) × V_dq[b]
where α ∈ [0, 1] is emitted by rl_v_blend_alpha_controller from
the observed V_dq vs V_scalar tracking ratio:
track_ratio = EMA(|V_dq − V_scalar|) / EMA(|V_scalar|)
if track_ratio > 1.5 × TARGET: α ← min(α + 0.01, 1.0)
if track_ratio < TARGET / 1.5: α ← max(α - 0.01, 0.0)
else: hold α
Plus dead-signal guard: if EMA(|V_scalar|) < 1e-4, hold α (no V
signal yet to calibrate against).
Bootstrap on sentinel 0: α = 1.0 (Plan A v2 behavior on first step).
EMAs first-observation bootstrap (no Wiener-α blend on first sample).
Two new kernels:
- rl_v_blend.cu: elementwise blend (~25 LOC). Grid (ceil(B/256),1,1).
- rl_v_blend_alpha_controller.cu: single-block parallel reduction
+ Schulman-bounded controller (~90 LOC). Grid (1,1,1), block (1024,1,1).
Three new ISV slots (585/586/587):
- RL_V_BLEND_ALPHA_INDEX — current α
- RL_V_TRACK_ERR_EMA_INDEX — EMA(|V_dq − V_scalar|)
- RL_V_SCALAR_MAG_EMA_INDEX — EMA(|V_scalar|), dead-signal floor
IntegratedTrainer wiring (~80 LOC):
- 2 new buffers v_blended_d, v_blended_tp1_d
- In step_with_lobsim_gpu_body, after DuelingQHead Adam steps:
1. Launch controller (reads V_scalar at h_t + V_dq at h_t, emits α)
2. Launch blend kernel for s_t → v_blended_d
3. Launch blend kernel for s_tp1 → v_blended_tp1_d
- compute_advantage_return now reads v_blended_d / v_blended_tp1_d
instead of dueling_v_d / dueling_v_tp1_d (Phase 4.3's direct swap)
Both value_head and DuelingQHead still train independently. The blend
just selects which baseline drives PPO advantage per step based on
observed calibration. As V_dq learns to track V_scalar, the controller
gradually shifts α down toward V_dq usage. If V_dq diverges (e.g., late
training entropy spikes producing volatile advantages), controller
raises α back to V_scalar safety.
Phase 4.3 cluster (alpha-rl-qkdm2 @ 25f5ce99b) is still running and
showing dramatic late-training pnl growth (+$20M at step 18132 vs
Plan A v2 peak +$9.3M). Phase 4.4 adds adaptive control on top —
should reduce variance while preserving the architectural benefit.
Validated:
- cargo build --release clean
- integrated_trainer_smoke 1 step passes
- alpha_rl_train --steps 3 --b 128 under compute-sanitizer: 0 errors
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.7 <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.