5186701982
Three targeted fixes for v6 smoke (train-x4m96) findings that Phase 8.2
left on the table. v6 cycle-by-cycle showed `win_conc=0.0000` and
`curric_conc=0.0000` pinned across all 9 cycles, and `hindsight_mag`
printed `0.0000` due to format-width rounding.
Fix 1: compute_winner_concentration guard threshold 0.1 → 0.01 × pnl_std
v6 had pnl_std ≈ 1e-5 and val-trade all_mean ≈ 1e-7..1e-6. At 0.1×
the threshold was 1e-6, still above typical all_mean → short-circuit
fired every cycle. At 0.01× (threshold 1e-7) healthy small-but-positive
policies emit a non-zero signal; degenerate-strategy guard preserved.
Fix 2: compute_curriculum_concentration formula
Was: 1 - entropy(weights) / log(n) (entropy-deficit, normalized)
Now: CV(weights) / sqrt(n − 1) (normalized coefficient of variation)
Entropy-deficit is ~weight_std² near uniform. v6's 8 contiguous segments
had per-segment Sharpes in a tight band, so normalized weights stayed
within ~0.5% of 1/n and deficit collapsed to <1e-4 (only cycle 9
reached 0.0001). CV scales linearly with weight_std/weight_mean,
dramatically more sensitive in the near-uniform regime. Cold-start
(uniform) still returns 0; one-hot returns 1.
Fix 3: hindsight_mag display format {:.4} → {:.2e}
pnl_std ≈ 1e-5 on volume bars → hindsight magnitudes are similar scale,
printed as 0.0000 under {:.4} even when the count is non-zero. Matches
pnl_std={:.2e} format already in the log.
Pearls honoured:
- feedback_isv_for_adaptive_bounds: thresholds derived from pnl_std
- pearl_first_observation_bootstrap: uniform/empty inputs → 0.0 sentinel
- pearl_controller_anchors_isv_driven: CV anchor (one-hot = sqrt(n-1))
is structural, not magic
- feedback_no_quickfixes: entropy → CV is a structural reformulation
Verification:
cargo check -p ml --features cuda # clean
Expected v8 smoke (when dispatched):
- win_conc rises to ~1.5..3.0 (top_decile_mean / all_mean ratio)
- curric_conc enters 0.05..0.20 range as segments develop differential
difficulty; grows toward ~0.4 with overfit divergence
- hindsight_mag prints as 5e-6 or similar (real value, not zero)
If these fire, Phase 7 (alpha boost via E6/E7/E8) is finally exercised
end-to-end on volume bars.
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.