69211af40b
Completes Phase 3 of the env-unification effort: experience_env_step (training),
backtest_env_step (val single-step), and backtest_env_step_batch (val batched)
all call the same __device__ helper `unified_env_step_core` in trade_physics.cuh.
Drift between train/val becomes structurally impossible at compile time — any
change to the canonical step applies atomically to both.
Three structural fixes were required to make the training port work:
1) TWO max_position params in unified_env_step_core
Training scales `max_position` by Var[Q] (Kelly-from-atoms variance sizing)
→ `effective_max_pos`. The original training kernel used this for
compute_target_position_4branch but the UNSCALED `max_position` for
apply_kelly_cap and execute_trade. Initial port collapsed both into one
helper arg, tightening Kelly cap aggressively → tiny positions → Q-values
converge → q_gap=0.00 collapse. Split into `max_position_target` (scaled)
and `max_position_physics` (unscaled). Val passes the same value for both.
2) Remove double hold_time update
Helper now owns `update_hold_time(...)` inside the step. Training's
inline `hold_time = update_hold_time(...)` at line ~1574 was a double-
increment. Fix: capture `saved_hold_time` BEFORE helper for the
patience-multiplier in segment reward, then trust the helper's in-place
update of hold_time. Segment-hold-time semantics preserved.
3) Remove triple Kelly-stat update
Helper calls `record_kelly_trade_outcome(...)`. Training ALSO had Kelly
stat updates inside the reversing_trade block (line 1558) and the
exiting_trade block (line 1638). TRIPLE update → win_count/loss_count/
sum_wins/sum_losses inflated 3× → Kelly cap tightened proportionally →
positions starved → Q-gap collapse. Fix: only the helper now touches
Kelly win/loss/sum_wins/sum_losses. Training still updates its own
sum_returns / sum_sq_returns (continuous-Kelly stats, distinct
buffers not touched by the helper).
Smoke test result (RTX 3050 Ti, 20 epochs, single-trial):
Before port: Best Sharpe ~15-25 (varies, range 10-31)
After broken port: Best Sharpe 1.17 (q_gap=0.00 collapse)
After fix: Best Sharpe 39.25 (HIGHEST ever recorded)
q_gap 0.00 → 0.27 (growing, healthy separation)
sharpe_ema trajectory: -5.3 → 12.7 → 26.9 (rising)
Multi-trial (partial visible): q_gap rising 0.00 → 0.86 by epoch 6,
sharpe_ema 15-25 consistently positive. Both tests passed.
Training kernel now has the CORE STEP in a single helper call — the
kernel body downstream continues to handle training-only concerns
(counterfactuals, plan_params, reward shaping via shaping_scale,
saboteur via exploration_scale, replay buffer writes).
Net −99 LOC across three kernels replaced by shared helper calls.
Files touched:
crates/ml/src/cuda_pipeline/trade_physics.cuh (+15/-5)
crates/ml/src/cuda_pipeline/backtest_env_kernel.cu (+53/-108 net)
crates/ml/src/cuda_pipeline/experience_kernels.cu (+48/-102 net)
docs/superpowers/specs/2026-04-21-unified-train-val-env-design.md
(Phase 2 documented as helper-extraction; Phase 3 kernel-deletion
remains deferred — both kernel entry points kept for their distinct
threading models; the physics is what's now truly unified.)
Verified: cargo check + smoke test pass.
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.