faa9a73c10
Per project_crt_diag_findings.md — CRT.diag + diag.2 empirically
falsified all three hypotheses about why the model's per-event output
doesn't track horizon-specific dynamics. Labels ARE differentiated per
horizon, AUC=0.66 IS per-event measured, but per-event predictions
behave identically across all 5 horizons (2.5-event mean run length
raw, 3.3-event after aggressive Wiener-α EMA smoothing). h6000
predictions should change every thousands of events, not every 3.3.
Diagnosis: training dynamics issue. The BCE loss provides no signal
pushing toward horizon-coherent predictions. Adjacent-event labels at
horizon K share K-1/K of the forward window so SHOULD produce similar
predictions, but the loss doesn't require this.
Intervention A (this spec, minimum-scope): output smoothness
regularizer
L_smooth[h] = λ[h] × mean_t (p[h](t) - p[h](t-1))²
With horizon-weighted λ (stronger for h6000 than h30): forces h6000
predictions to change slowly while leaving h30 responsive.
Implementation surface:
- multi_horizon_heads.cu backward: extend grad_probs with smoothness
gradient
- perception.rs trainer step: prev_probs_d buffer, per-horizon (p[t]
- p[t-1])² accumulation
- heads.rs: SMOOTHNESS_LAMBDA constant per horizon
- alpha_train.rs: --smoothness-base-lambda CLI flag
Validation gate (CRT.train Gate):
MUST: per-horizon AUC ≥ baseline - 0.02 (no signal destruction)
WIN: h6000 mean_run_len ≥ 100 events; h6000/h30 ratio ≥ 10× (horizons
actually differentiated post-training)
STRETCH: CRT.1 controller smoke shows mean PnL CORRELATED with
conviction (vs anti-correlated in lnfwd)
Future work if intervention A doesn't pass WIN:
B: horizon-conditional output structure (architecture change)
C: curriculum on horizon (multi-day training)
D: different label generation (majority-vote vs single-point)
Status: Design — awaiting user review before plan.
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.