1271d03931
The post-trunk-grows threshold-tuning smoke (81decf40f) produced
n_trades=0 despite the model having a HEALTHY max-conviction
distribution (74.6% of decisions ≥ 0.30, 26.7% ≥ 0.70, full spread
across [0,1]). Diagnosis: the linear-weighted-mean aggregator in
decision_policy_default is structurally dilution-bound at cold-start
— single-horizon strong signals get washed out when uniform-floor
weights produce mean-over-horizons aggregation.
Solution: Conviction-Bootstrapped Sharpe Weighting (CBSW). Hybrid
max-confidence × weighted-sharpe with a per-horizon sigmoid transition
keyed on n_trades_seen vs MIN_TRADES_FOR_VAR_CAP. Cold-start: sig_mag
(decision-time conviction) drives weights AND max-confidence aggregator
fires single-horizon trades. Mature: recent_sharpe (historical) drives
weights AND linear-mean aggregator emphasizes strong-Sharpe horizons.
Permanent floor preserved per pearl_blend_formulas_must_have_permanent_floor.
Mirrors DQN bootstrap pattern (pearl_thompson_for_distributional_action_
selection): when historical estimates are uncertain, use available
signal as the bootstrap. Sig_mag is the ISV signal at decision time;
recent_sharpe is the ISV signal at trade-close time. Transition
self-terminates based on data accumulation, not time constants.
3-tier delivery (one spec, atomic commits):
Q1: Bytecode VM stopgap — upload max-confidence 7-instruction
program per backtest. Validates diagnosis; zero kernel work.
Q2: Kernel CBSW — replace weight + aggregator in both decision
kernels. 5 new regression tests covering cold/mature/transition.
Q3: New memory pearl pearl_conviction_bootstrap_for_kelly_aggregation
capturing the lesson.
Q4: Cross-reference from parallelism spec (deployability sweep
depends on CBSW being live to produce meaningful verdict).
The parallelism work (P1-P6) is fully working — confirmed by the
threshold-tuning smoke completing end-to-end (Succeeded status, 500k
decisions, artifacts written, aggregator parquet emitted). What's
blocked is the deployability VERDICT, because the dilution bug means
all variants would show n_trades=0 regardless of cost/latency/threshold.
CBSW unblocks the verdict.
Awaiting review before transitioning to writing-plans for Q1-Q4
implementation 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.