4df0867622
Yesterday's fix `trade_context[1] = fminf(0.0f, unrealized_R)` created a structural 12× W/L bias: the agent saw losses fast (closed them), couldn't see wins (rode blindly until max_hold/session). The reproducibility across folds (f0=f1=f2 at step 500) was the SAME exploit pattern in each window, not real edge. This replaces the asymmetric clip with drawdown-from-peak: trade_context[1] = unrealized_R - peak(unrealized_R) (always ≤ 0) The peak tracks per-active-unit and resets on slot activation. Symmetric: fires on losing positions (monotone drawdown from entry) AND on winning-then-retracing positions (drawdown from high-water mark even while still net-positive). Sentinel-zero bootstrap per pearl_first_observation_bootstrap: peak==0 on freshly opened/added/reversed slot; first observation directly replaces peak with unrealized_R. Implementation: - New CudaSlice unit_peak_unrealized_r_d [B×MAX_UNITS], allocated zero - rl_unit_state_update + rl_fused_reward_pipeline write 0.0f sentinel on each OPEN/REVERSE/PYRAMID-ADD activation - rl_trade_context_update reads/updates peak inline, outputs drawdown Local smoke (RTX 3050 Ti, b=128, 500 steps): - avg_hold rose from 17.6 → 26-32 steps (wave-scale-ier) - W/L magnitude ratio is no longer pinned at 12× — varies 0.67-27 across the run, sometimes L>W (genuinely symmetric signal) - No crash, training trajectory healthy 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.