cb2015ab2f
Restores regime adaptation to the 0.5% trailing stop using ADX (trend
strength, feat idx 40) and CUSUM (directional persistence, feat idx 41).
Previously removed to eliminate train/val asymmetry (val kernel had no
features buffer); this commit wires features to both kernels so the
stop fires on identical thresholds in training and backtest evaluation.
Implementation (V7 methodology, all three steps satisfied):
* Step 1 signal: trending regimes need wider stop to ride trend;
volatile regimes need wider stop to avoid noise exits. Original
formulation from reward v5 (commit 263997ad3).
* Step 2 coverage: no existing mechanism adapts exit *distance* to
regime — Kelly cap scales entry *size*, policy chooses exit *timing*
but not exit *distance*. So this is a genuine gap.
* Step 3 measurement: shared compute_regime_trail_scales helper
guarantees bit-identical scale computation across training and val;
multi-trial smoke (5 trials × 20 epochs) passes 5/5 with
median_q_gap=2.00, Best Sharpe 27.05 in last trial. No regression
vs fixed-width (pre: median_q_gap=2.24; post: 2.00, within variance).
Shared helper (trade_physics.cuh::compute_regime_trail_scales):
trend_scale = min(2.5, 1 + max(ADX - 0.25, 0) * 2.0)
vol_scale = min(2.5, 1 + max(|CUS| - 0.50, 0) * 2.0)
Pass NULL features to fall back to 1.0/1.0 (fixed-width). Thresholds
match existing regime_conditional convention (adx>0.25=Trending).
Kernel-side:
* unified_env_step_core: add vol_scale / trend_scale params; forward
to check_trailing_stop (which already accepted these but callers
passed 1.0/1.0). Updated step-6 docstring.
* experience_env_step (training): computes scales from existing
features+market_dim inputs before calling the helper.
* backtest_env_step + backtest_env_step_batch (val): new `features` +
`market_dim` params plumbed through the single-step and batched
launches.
Rust-side (gpu_backtest_evaluator.rs):
* launch_env_step: pass self.features_buf + feature_dim as i32
(features were already uploaded for the state_gather kernel).
* Batched launcher: same.
Net effect: train/val env kernels now see identical regime-adaptive
trailing stops — no drift, no measurement gap, symmetric physics.
Verified:
cargo check/build clean (release); multi-trial smoke 5/5 pass.
Closes task #25.
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.