d08ab461db
Phase E Task 5 (code portion; the 5.2M-trade fit run lands separately).
Diagnosis: the plan's draft used pure Poisson NLL with Bernoulli y∈{0,1},
which converges to λ = empirical rate ȳ. But the runtime fill_prob() uses
`p = 1 − exp(−λ)`, so a trained λ=0.4 → predicted p=0.33 → systematic
~30pp under-fill bias on every passive backtest order. Training and
inference must agree on what λ means.
Fix: switch the fitter to the **cloglog (complementary log-log) binary
likelihood**. Per-sample gradient:
∂L/∂β_k = (p − y) · (μ/p) · x_k
where μ = exp(β·x), p = 1 − exp(−μ)
The μ/p factor is the link derivative; p.max(1e-7) handles the μ→0 limit
in f32 (the analytical limit μ/p → 1 is achieved automatically because
both numerator and denominator vanish proportionally).
Recovery test verifies the fix: 1000 deterministic 40% fills, all-zero
features → fitter recovers β_0 ≈ ln(0.5108) ≈ -0.672 (the value at which
1 − exp(−exp(β_0)) = 0.4), within tolerance 0.05. Slope coefficients
stay near zero (features uninformative).
Also adds Serde derives on FillCoeffs / FillFeatures / FillModel for JSON
serialization (downstream when the calibration example lands).
Deferred: the calibration example (Steps 3-7 of the plan task) is held
back until paired with the actual 5M-trade fit run — the plan's example
has a placeholder loop that violates feedback_no_stubs, and the loader
lift from precompute_features.rs deserves a dedicated commit.
4 new tests (7 total in env::fill_model now):
- fit_recovers_baseline_when_features_uninformative
- fit_rejects_empty_and_mismatched_inputs
- coeffs_round_trip_through_json
`cargo test -p ml --lib env::fill_model`: 7 passed.
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.