c90553953c
Closes the remaining SP21 Tier 1 hardcoded-constant items.
T1.2 — enrichment fed real metrics, not placeholders:
- was: extract_eval_trades_from_metrics(_, 60000.0, 0.0, 0.5, ...)
with hardcoded trade_count=60000, total_pnl=0.0, win_rate=0.5
- now: reads from self.last_val_metrics: Option<[f32; 14]> populated
by val backtest pass at metrics.rs:868. Layout [2]=win_rate,
[4]=total_trades, [7]=total_pnl. Cold-start fallback (None)
is (0.0, 0.0, 0.0) — preferable to fabricated 60000-trade
signal that biased E2/gamma/ensemble from epoch 0.
- Per feedback_no_todo_fixme + feedback_no_stubs.
T1.4 — backtracking thresholds signal-driven:
- Three hardcoded thresholds in run_backtracking_epoch_end replaced
with sigma = sqrt(ISV[VAL_SHARPE_VAR_EMA_INDEX=351]) derivatives:
a) Save trigger (improvement_rate > 0.01) → > 0.5σ.
The 0.01 fired on every epoch (any tiny change > 0.01);
0.5σ requires a meaningful move (typical sigma O(1-10)).
b) Plateau-detection frozen check (abs(delta) < 0.01) → < 0.5σ.
The 0.01 ~never fired; 0.5σ correctly identifies stagnation.
c) Route acceptance (>= min_improvement_rate=0.1) → >= 1.5σ.
Stricter than save-trigger as designed.
- BacktrackingState::min_improvement_rate field deleted — replaced
by per-call signal-driven computation. Floor 0.5 covers cold-start
before var_ema bootstraps from sentinel per
pearl_blend_formulas_must_have_permanent_floor.
- Per feedback_isv_for_adaptive_bounds + feedback_adaptive_not_tuned.
Affected files:
- crates/ml/src/trainers/dqn/trainer/training_loop.rs:1510-1530
(T1.2 enrichment) + :7458-7530 (T1.4 sigma + 3 threshold sites)
- crates/ml/src/trainers/dqn/trainer/mod.rs:108,142
(T1.4 min_improvement_rate field deletion)
Verification:
- cargo check -p ml --tests: passes (warnings only)
- cargo test -p ml --lib early_stopping: 8/8 pass
Cumulative SP21 Tier 1 status: T1.1a ✓, T1.1b ✓, T1.2 ✓, T1.4 ✓,
T2.3 ✓ — Tier 1 closed. Tier 2 (check_early_stopping(avg_q_value)
deletion + enrichment.rs constants soup) is next.
Plan reference: docs/plans/2026-05-10-sp21-train-eval-coherence-isv-defrost.md
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.