9ed882e740
Smoke 1 v2 empirically falsified the D-style labels: 99.99% of K=100 D-labels
were negative on real ES MBP-10 (cost+1.5×MaxDD dominates every typical
100-tick move). Aux head couldn't escape predicting the mean.
CB1 — replace label generator:
- generate_outcome_labels_d → generate_outcome_labels_ab returning
OutcomeLabelsAB { y_prof_{long,short}, y_size_{long,short}, sigma_k,
cost_price_units, pos_fraction }
- y_prof = 1 iff signed_pnl > 2×cost (binary, class-weighted BCE target)
- y_size = signed_pnl / σ_K (σ-normalized regression target,
conditional-masked when y_prof=0)
- σ_K: rolling 1000-bar Welford std of K-step log-returns, floored at
cost/4 per pearl_trade_level_vol_for_stop_distance
- pos_fraction: per-(direction, horizon) positive class fraction for
downstream BCE pos_weight balancing
- 10 unit tests validating sign-correctness, NaN edges, balance,
cost-threshold, sigma-floor, per-horizon independence, error paths
CB2 — atomic caller migration:
- LabeledSequence + LoadedFile: outcome_long/short (2 arrays) → 5 arrays
(prof_long, prof_short, size_long, size_short, sigma_k) + pos_fraction
- Loader splits new generator's outputs + propagates pos_fraction
file-level into each yielded LabeledSequence
- step / step_batched signatures widened to 7 params + pos_fraction
- alpha_train.rs: 4 separate batches + per-snapshot row build for each
- last_pos_fraction stashed on PerceptionTrainer
- aux test fixture (synthetic_aux_outcomes) updated to emit 4 arrays +
pos_fraction matching the constant-up-ramp test signal
- HOLDING PATTERN: step_batched body validates new arrays + stages
prof-binary through the existing D-era Huber kernel so training
produces a real gradient. CB3 rewrites the kernel; CB4 widens head
outputs; CB5 wires BCE+Huber proper loss with class weighting.
cargo check --workspace --all-targets: clean (only pre-existing cudarc
cupti + ml insert_batch unrelated errors).
cargo test -p ml-alpha --lib: 43 passed (15 multi_horizon_labels).
cargo test -p ml-backtesting --lib: 33 passed.
stacked_trainer_aux_supervision RTX 3050: pass (huber=0.0004, dir_acc=1.0,
stop_grad lifted).
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.