308c54484e
Per-user direction: every existing mechanism in the DQN system MUST be
explicitly considered for interaction with Thompson, and Thompson itself
MUST be examined for system-specific improvements beyond vanilla.
INTERACTION MATRIX (3 categories, 20 mechanisms):
Category 1 — Compose with Thompson (no change required):
Counterfactual reward, B.2 novelty bonus, PopArt, Saboteur,
Curiosity, NoisyNets/VSN, Distillation, CQL, Polyak target EMA,
HER, PER, Replay warm-start, Multi-fold validation harness.
Category 2 — Trivially adapt to Thompson (one-line changes):
D7/N7 contrarian sign flip (negate the SAMPLE), cosine epsilon
schedule (still applies to mag/ord/urg), per-sample epsilon (IQL
expectile gap), adaptive Boltzmann tau (still applies to mag/ord/urg).
Category 3 — Take precedence over Thompson (hard constraints):
Plan-based action lock (Thompson sample discarded if plan active),
per-magnitude Kelly cap, trail stop, capital floor breach.
Critical insights from the audit:
1. NoisyNets is ALREADY a form of training-time Thompson at the
parameter level. Output-space Thompson stacks on top —
total exploration = parameter-space ⊗ output-space (multiplicative).
2. Curiosity is ORTHOGONAL to Thompson — Thompson explores actions
whose Q is uncertain; curiosity explores states whose dynamics
are uncertain. Both axes desirable; no conflict.
3. Plan lock takes precedence; same as currently with Boltzmann.
OUTSIDE-THE-BOX v2 ENHANCEMENTS (deferred to follow-up specs):
v2.1 Triple-source Thompson (C51 + IQN + Ensemble) — incorporate
the existing ensemble Q-head as 3rd uncertainty source.
v2.2 Persistent Thompson (anti-churn for HFT) — bias sampling
toward current direction, ISV-driven; reduces tx_cost from
Long/Short oscillation across bars.
v2.3 CVaR-aware eval (risk-adjusted deployment) — eval picks
argmax(E[Q] − λ·CVaR_α[Q]); risk-aware decision making for
production with real capital.
v2.4 Information-Directed Sampling (Russo & Van Roy 2014) — picks
action minimizing regret²/info_gain; more efficient than
vanilla Thompson when learning saturates.
v2.5 Hierarchical Thompson on (trade vs no-trade) → (which
direction) — addresses 50/50 structural advantage of no-trade.
v2.6 Composition with curiosity-driven exploration — explicit
coupling beyond reward-side composition.
Each v2 enhancement gets its own spec/plan when prioritised. Vanilla
Thompson is v1; ships first; verified independently.
ALSO FIXED (from earlier self-review):
- Pearl claim softened: only the C51 Hold/Flat bias is directly
attributed; other historic bugs had different mechanisms.
- TFT entry removed from contract table — TFT is a Variable
Selection Network (feature processor), not a Q-head. Replaced
with generic "Future Q-head additions" placeholder.
- Eval direction = argmax E[Q] explicitly flagged as a behavior
change from current Boltzmann-with-tau (val_dir_dist will be
more concentrated than current).
- Phase 0.E budget reduced (1-2 hours, not 1 day) — synthetic
bandit is ~50 lines of Rust, not full RL training loop.
- Phase 0 enumerates existing checkpoints before training new one.
- Architecture diagram parenthesis fixed.
- Conviction implementation note: compute E[Q] once, reuse for
conviction AND eval-mode argmax — no redundant computation.
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.