0384f76743
Anti-calibration evidence (smoke ckpts across architecture variants
showed higher reported conviction → MORE negative PnL, -15.6 → -145.2)
traced to a magnitude/direction mismatch in the sizing formula:
conv_ema = EMA(|conviction_signed|) # magnitude smoothing
target_lots = sign(conviction_signed) # INSTANTANEOUS sign
× conv_ema × max_lots # × SMOOTHED magnitude
When per-horizon directions disagree event-to-event, the magnitude EMA
stays high (|x| EMA can't cancel) but the sign flips on noise. Result:
big trades in random direction whenever the 5 horizons disagree.
Replace with a single signed-conviction EMA:
conv_signed_ema = EMA(conviction_signed)
target_lots = sign(conv_signed_ema)
× |conv_signed_ema| × max_lots
Now BOTH sign AND magnitude come from the same smoothed signal. When
directions disagree, signed EMA → 0 collapses size to zero naturally.
Atomic migration across decision_policy.cu (2 kernels), pnl_track.cu
(open-branch reads the SIGNED buffer and takes fabsf for the magnitude-
semantics open_trade_state offsets that composite_exit_check forms ratios
from), and src/sim/mod.rs (renamed device buffers + accessor + 4 launch
sites). No legacy aliases per feedback_no_legacy_aliases; no parallel
buffers per feedback_single_source_of_truth_no_duplicates; α floor 0.4
preserved per pearl_wiener_alpha_floor_for_nonstationary; first-
observation bootstrap preserved per pearl_first_observation_bootstrap.
ml-backtesting lib: 33 passed.
GPU oracle: signed_conviction_ema_collapses_on_sign_flips passes —
observed steady-state shows |signed_ema| ≈ 0.205 (post-fix) producing
|target_lots| = 2 every tail event, vs the pre-fix bug's predicted
|target_lots| = 7 every tail event. Existing
multi_horizon_conviction_cancels_on_disagreement still passes (zero-
cancellation regime is even cleaner under signed EMA).
ml-alpha lib: 33 passed (no regression).
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.